Urban acoustic environment comprehensive treatment system based on Internet of Things

Abstract

The invention discloses an urban sound environment comprehensive treatment system based on the Internet of Things, which comprises a noise data monitoring and acquisition module, a noise data calculation and analysis module and a noise grading treatment module, and discloses an urban sound environment comprehensive treatment method. Real-time noise data monitoring and acquisition are carried out on specified urban areas, road sections and monitoring environments, and planning and layout can be carried out in the whole city; transmitting and preprocessing the real-time spectrum data by adopting a digital signal; determining the source of noise pollution on an established noise map by using a spatial positioning method according to the collected noise data through a set limit value; the method comprises the following steps: establishing a noise propagation mathematical model, analyzing a spatial relationship between noise data and a receptor, calculating and predicting noise levels of different areas, and comparing the noise levels with measured values; and different noise distributions are subjected to different-degree grading treatment according to the predicted value and the constructed noise map. According to the method, the performance of real-time noise monitoring and treatment feedback is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban noise control, and in particular to an urban sound environment comprehensive control system based on the Internet of Things. Background Art

[0002] Based on the fact that various environmental noise problems in cities have shown an upward trend in recent years, the existing urban sound environment comprehensive management system for environmental noise often encounters difficulties in measuring background noise during the actual monitoring process. The monitoring personnel cannot make control choices for social life environmental noise according to the actual situation on site, so that the monitoring results cannot reflect the actual impact of noise pollution sources. The sources of social life noise are multifaceted, random and complex, and difficult to supervise. However, due to the mobility and suddenness of "mobile noise sources", it is also difficult to achieve real-time identification and supervision. These sudden noise problems are also nocturnal and sporadic, which makes it difficult to complete the monitoring and environmental supervision of some noise problems in real time, resulting in the prominent problems of difficulty in timely monitoring, difficulty in coverage supervision and delayed supervision. Therefore, it is necessary to design an urban sound environment comprehensive management system based on the Internet of Things to improve the accuracy of noise data and the feedback performance of noise management. Summary of the invention

[0003] The purpose of the present invention is to provide a comprehensive urban sound environment management system based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a comprehensive urban sound environment management method based on the Internet of Things, including real-time noise data monitoring and collection of designated urban areas, road sections and control environments based on fixed and mobile noise monitoring terminals; Use digital signals to transmit and preprocess real-time spectrum data; Based on the collected noise data and the set limit values, the sources of noise pollution are determined on the established noise map using spatial positioning methods; By establishing a mathematical model of noise propagation, analyzing the spatial relationship between noise data and receptors, the noise level in different areas is calculated and predicted, and compared with the measured values; According to the predicted values, different noise distributions are managed in different levels according to the constructed noise map.

[0005] According to the above technical solution, the steps of performing real-time noise data monitoring and collection include: Based on the designated urban area, road section and control environment, determine the location of monitoring points based on fixed noise monitoring terminals according to the type and distribution of noise sources; A fixed noise monitoring terminal is used to collect noise parameters including sound pressure level and frequency in real time. According to the monitoring target route covering the designated urban area, road section and controlled environment, a mobile monitoring terminal equipped with noise monitoring equipment is used to collect noise data in real time, and the location information of the monitoring point is marked by GPS. The fixed noise monitoring terminal includes an automatic environmental noise monitor with integrated video surveillance, and the mobile monitoring terminal includes a plurality of noise monitors with built-in tree-based microphones. The automatic environmental noise monitor with integrated video surveillance continuously and automatically monitors various parameters.

[0006] According to the above technical solution, the method for preprocessing real-time spectrum data includes: The collected noise data is transmitted to the data center or cloud platform in real time. The noise data collected by fixed and mobile monitoring terminals are integrated to form a complete noise monitoring data set. The noise data in the data set is denoised and pre-processed to filter out abnormal values. The amplitude of all collected noise signal data is normalized to the interval of [-1,1]. Pre-emphasis is set to increase the high-frequency part of the noise signal so that the change from low frequency to high frequency band remains uniform and smooth after the noise signal is attenuated through spatial propagation. The pre-processed noise data is further converted into a two-dimensional spectrogram. The time series is iteratively output through a pulse coupled neural network as the feature of noise data classification, and classification is performed according to the corresponding features.

[0007] According to the above technical scheme, the classification according to the corresponding features specifically includes: constructing a PCNN model of noise data, simulating the pulse emission of the biological visual system, performing nonlinear processing on the input time series data, using the preprocessed noise time series data as the input of the PCNN model, and outputting feature data related to the time series through iterative operations of the PCNN model, wherein the feature data includes pulse emission mode, pulse frequency, and pulse intensity, reflecting the intrinsic characteristics and classification information of the noise data based on the feature data, constructing a classification model based on a neural network according to the extracted feature data, training the classification model using the feature data and noise data, defining a loss function and an optimization algorithm to adjust the model parameters and minimize the classification error, using the trained classification model to classify the new noise data according to the features output by the PCNN, and outputting the classification results.

[0008] According to the above technical solution, the source of noise pollution is determined by: using GIS technology to superimpose noise data with designated urban areas, road sections and control environments, generating noise contour maps based on the superimposed data using inverse distance weighted interpolation IDW, associating noise data with specific areas, road sections and control environments to generate noise maps, marking the location and type of noise sources on the map, using noise data as dependent variables, using defined time period data, road network data and environmental data as independent variables, establishing a noise fitting model based on a neural network, and fitting the noise data through the learning and prediction capabilities of the neural network; On the noise map, the noise levels of designated urban areas, road sections and controlled environments are evaluated according to the set noise limits, and noise pollution areas that exceed the limits are identified. The specific sources of noise pollution are determined by utilizing an array of sound sensors, and the time difference or phase difference of the sound signals reaching each sensor is further measured. Combined with the geographic information data of the designated urban areas, road sections and controlled environments, the specific location of the noise source is located, and the location of the noise source and the degree of pollution are marked.

[0009] According to the above technical solution, the noise propagation mathematical model is established, the spatial relationship between noise data and receptors is analyzed, and the noise level in different areas is calculated and predicted, which includes: A mathematical model of noise propagation is established based on designated urban areas, road sections and control environments, and noise data, environmental data and specific location data of receptors are input into the model. The specific location data of the receptors is based on the location of the noise source marked on the noise map. The noise propagation mathematical model simulates the noise propagation process in the environment based on the input noise source data, environmental data and receptor data; The noise propagation mathematical model predicts the noise level in different areas and time periods based on the input data, and calculates the parameters of the sound pressure level, sound intensity level or equivalent continuous sound level at each receiving point; Identify the prediction results to obtain the spatial relationship between noise sources and receptors and the main paths of noise propagation; The noise level area and time period for the receptor are evaluated based on the noise peak and noise distribution range in the prediction results.

[0010] According to the above technical solution, the different levels of hierarchical management include: According to the constructed noise map, when the noise pollution level is judged to be serious, the corresponding noise area is classified as the priority control area for noise control and management. The priority weight matrix of the most serious noise pollution area is determined based on the set noise standard value. The sound pressure level, soundscape quality, and visual perception data are selected as evaluation indicators to construct a weight system. According to the importance of each evaluation indicator, the weight of each indicator is determined through expert scoring and hierarchical analysis method. The noise data of each area is substituted into the weight system, and the priority index of each area is calculated. The priority is divided according to the size of the priority index, and noise control feedback is provided to the designated indoor and outdoor areas according to the priority. The specific method of providing feedback on noise control for designated indoor and outdoor areas according to priority is as follows: outdoor noise is treated through multiple directional speakers, and indoor noise is treated by building an acoustic shed at a specific location using ultra-microporous panels. The operation process of outdoor directional speakers is as follows: the sound of the sound unit is concentrated in front of the directional speaker equipment, so that the sound pressure value is lower as it is closer to the two sides of the directional speaker equipment, forming an effect in which the sound pressure value gradually decreases and tends to the environmental protection value. The directional sound technology is integrated with the digital guided environmental protection speaker array to realize that the sound emitted by the directional speaker equipment is concentrated in a fixed area and propagated in different areas. When it exceeds the range, the sound is greatly attenuated to below the environmental protection decibel level, and the indoor The operation process of the sound science booth is as follows: the ultra-microporous aluminum honeycomb sound-absorbing panel is composed of a day-type keel and an ultra-microporous aluminum honeycomb sound-absorbing panel. Through the peak-type design, the ultra-microporous aluminum honeycomb sound-absorbing panel is designed to a certain inclination angle to increase the sound-absorbing surface to absorb noise. When the sound wave propagates to the ultra-microporous aluminum honeycomb sound-absorbing panel, the sound wave enters the air layer behind the panel through the micropores. When the frequency of the sound wave matches the inherent frequency of the air layer system, resonance occurs. The resonance is used to cause a large amount of sound wave energy to be consumed in the air layer for sound absorption, ensuring good sound absorption and noise reduction while reflecting part of the noise until the noise dissipates in the peak-type sound science soundproof booth and will not propagate outside the peak-type sound science soundproof booth to cause noise pollution to the surrounding environment.

[0011] According to the above technical solution, the urban sound environment comprehensive management system includes: Noise data monitoring and collection module, used to monitor and collect real-time noise data in designated urban areas, road sections and control environments; Noise data calculation and analysis module, used to perform numerical calculation and comprehensive analysis on the collected noise data; The noise classification and control module is used to carry out different levels of classification and control of different noise distributions according to the predicted values ​​and the constructed noise map.

[0012] According to the above technical solution, the noise data monitoring and acquisition module includes: Intelligent online monitoring instrument, which is responsible for real-time data collection and monitoring of urban noise distributed indoors and outdoors; A noise data preprocessing module, used to transmit and preprocess real-time spectrum data using digital signals; A noise data feature classification module is used to classify noise data according to corresponding features; The noise data calculation and analysis module includes: A noise map generation module is used to associate noise data with specific areas, road sections and control environments to generate noise maps; A noise fitting model building module is used to use noise data as a dependent variable and to build a noise fitting model based on a neural network using defined independent variables; The noise level prediction module is used to predict the noise levels in different areas and time periods.

[0013] According to the above technical solution, the noise classification control module includes: Outdoor noise control terminal, used to control outdoor noise through multiple different noise processing devices; The outdoor noise control terminal also includes: directional speakers, sound barriers and sound science sheds, modularly assembled soundproof walls, and sound-absorbing and anti-dizziness panels, among which: directional speakers are used to directional propagate the received noise and control the propagation direction and range of the sound; sound barriers and sound science sheds are used to establish acoustic barriers between the received noise source and the sound receiving point; modularly assembled soundproof walls are used to isolate noise by assembling a complete soundproof wall composed of multiple prefabricated sound insulation structures; sound-absorbing and anti-dizziness panels are used to absorb noise and prevent glare using panels with sound-absorbing holes or sound-absorbing material layers; Indoor noise control terminal, used to control indoor noise through multiple different noise processing devices; The indoor noise control terminal also includes: sound-absorbing slats, wall sound-absorbing panels, soundproof doors, soundproof windows, sound-absorbing curtains, and rotatable sound-absorbing bodies; among them: the sound-absorbing slats are used to absorb indoor noise using sound-absorbing materials; the wall sound-absorbing panels are used to absorb the noise sound wave energy in the air through the porous structure of the material itself, and convert it into heat energy or other forms of energy dissipation; the soundproof doors and soundproof windows are used to block the noise in the designated place through doors and windows composed of soundproof materials; the rotatable sound-absorbing body is used to change the exposure degree of the sound-absorbing surface according to the noise control needs through the sound-absorbing material and the adjustable rotating structure; The noise control feedback module is used to provide noise control feedback based on noise control management rules.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, through the use of fixed and mobile noise monitoring terminals, monitors and collects real-time noise data for designated urban areas, road sections and control environments, can monitor noise in designated areas around the clock and continuously, ensure the real-time and continuity of data, and use digital signals to transmit and preprocess real-time spectrum data, thereby improving the accuracy and reliability of noise data; according to the collected noise data, the source of noise pollution is determined on the established noise map by using the set limit value and the spatial positioning method; by establishing a noise propagation mathematical model, the spatial relationship between noise data and receptors is analyzed, the noise levels of different areas are calculated and predicted, and compared with the measured values; according to the predicted values, different noise distributions are hierarchically governed according to the constructed noise map, and the source of noise pollution is accurately determined on the noise map by the set limit value and the spatial positioning method, which is helpful to quickly locate the problem area and the noise source, while improving the prediction accuracy, and by coordinating with the territorial authorities, actively guiding residents to use directional audio to reduce the impact of dancers' dancing and fitness activities on other residents. At the same time, the smart dance floor system plans to adopt big data and Internet + operation methods, integrating multiple functions such as "smart dance floor", "party building", "grassroots convenience services", etc., to help solve the problem of square dancing disturbing the public, while promoting "popular science education" and "smart city sound environment and civilization construction". BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of the steps of a comprehensive urban acoustic environment management method based on the Internet of Things provided in Embodiment 1 of the present invention; Figure 2 A schematic diagram of the module composition of a comprehensive urban acoustic environment management system based on the Internet of Things provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: This embodiment can be applied to the scenario of comprehensive urban sound management. The method can be executed by a comprehensive urban sound environment management system based on the Internet of Things provided in this embodiment. The method specifically includes the following steps: Based on fixed and mobile noise monitoring terminals, real-time noise data monitoring and collection are carried out for designated urban areas, road sections and control environments; Use digital signals to transmit and preprocess real-time spectrum data; Based on the collected noise data and the set limit values, the sources of noise pollution are determined on the established noise map using spatial positioning methods; By establishing a mathematical model of noise propagation, analyzing the spatial relationship between noise data and receptors, the noise level in different areas is calculated and predicted, and compared with the measured values; According to the predicted values, different noise distributions are managed in different levels according to the constructed noise map.

[0018] In an embodiment of the present invention, based on the designated urban area, road section and control environment, the location of the monitoring point based on the fixed noise monitoring terminal is determined according to the type and distribution of the noise source; the fixed noise monitoring terminal is used to collect noise parameters including sound pressure level and frequency in real time, and according to the monitoring target route covering the designated urban area, road section and control environment, the mobile monitoring terminal equipped with the noise monitoring equipment is used to collect noise data in real time, and the location information of the monitoring point is marked by GPS.

[0019] Exemplarily, the fixed noise monitoring terminal includes an automatic environmental noise monitor with integrated video surveillance, and the mobile monitoring terminal includes a plurality of noise monitors with built-in electret microphones. The automatic environmental noise monitor with integrated video surveillance continuously and automatically monitors various parameters, specifically including real-time online monitoring of parameters such as particulate matter concentration, noise, TSP, PM10, PM2.5, temperature, humidity, air pressure, wind speed, wind direction, and video surveillance. The plurality of noise monitors with built-in electret microphones receive sound signals through electret microphones, and have built-in electret films that are sensitive to sound. When sound waves cause the film to vibrate, the distance between the film and the back electrode is changed, the capacity of the capacitor is changed, and a voltage change corresponding to the sound signal is generated. The weak voltage signal generated by the electret microphone is amplified by an amplifier and input into an attenuator to adjust the signal strength according to a set signal strength threshold to prevent damage to the equipment or signal distortion caused by excessive signals. The analog signal is converted into a digital signal using an AD collector after weighted processing according to the set sensitivity of the human ear to sounds of different frequencies, and the processed digital signal is output.

[0020] Exemplarily, the collected noise data is transmitted to a data center or cloud platform in real time, the noise data collected by fixed and mobile monitoring terminals are integrated to form a complete noise monitoring data set, the noise data in the data set is denoised, and pre-processing is performed to filter out abnormal values. The amplitude of all collected noise signal data is normalized to the interval of [-1,1], and pre-emphasis is set to increase the high-frequency part of the noise signal so that the change from low frequency to high frequency band remains uniform and smooth after the noise signal is attenuated through spatial propagation. The pre-processed noise data is further converted into a two-dimensional spectrogram, and the time series is iteratively output through a pulse coupled neural network as a feature for noise data classification, and classification is performed according to the corresponding features.

[0021] Exemplarily, classification according to corresponding features specifically includes: constructing a PCNN model of noise data, simulating the pulse emission of a biological visual system, performing nonlinear processing on the input time series data, using the preprocessed noise time series data as the input of the PCNN model, and outputting feature data related to the time series through iterative operations of the PCNN model, wherein the feature data includes pulse emission mode, pulse frequency, and pulse intensity, reflecting the intrinsic characteristics and classification information of the noise data based on the feature data, constructing a classification model based on a neural network according to the extracted feature data, training the classification model using the feature data and noise data, defining a loss function and an optimization algorithm to adjust model parameters and minimize classification errors, using the trained classification model to classify new noise data according to the features output by the PCNN, and outputting the classification results.

[0022] In an embodiment of the present invention, GIS technology is used to superimpose noise data with designated urban areas, road sections and control environments. Based on the superimposed data, an inverse distance weighted interpolation (IDW) is used to generate a noise contour map. The noise data is associated with specific areas, road sections and control environments to generate a noise map. The location and type of noise sources are marked on the map. The noise data is used as a dependent variable, and defined time period data, road network data and environmental data are used as independent variables to establish a noise fitting model based on a neural network. The noise data is fitted through the learning and prediction capabilities of the neural network to improve the accuracy and reliability of the data.

[0023] Exemplarily, on the noise map, the noise levels of designated urban areas, road sections and controlled environments are evaluated according to the set noise limits. The specific sources of noise pollution are determined by utilizing an array of sound sensors, the location of each monitoring device is obtained, and the real-time monitoring factors including noise, particulate matter, meteorological parameters, and video collected by the various sensors of the equipment are marked. The noise pollution areas exceeding the limits are identified, and the time difference or phase difference of the sound signals reaching each sensor is further measured. Combined with the geographic information data of the designated urban areas, road sections and controlled environments, the specific locations of the noise sources are located, and the locations of the noise sources and the degree of pollution are marked.

[0024] In this embodiment, a noise propagation mathematical model is established based on designated urban areas, road sections and controlled environments, and noise data, environmental data and specific location data of receptors are input into the model. The specific location data of the receptors is based on the location of the noise source marked on the noise map. The noise propagation mathematical model simulates the propagation process of noise in the environment based on the input noise source data, environmental data and receptor data. The noise propagation mathematical model predicts the noise levels in different areas and time periods based on the input data, calculates the parameters of the sound pressure level, sound intensity level or equivalent continuous sound level of each receiving point, identifies the predicted results, obtains the spatial relationship between the noise source and the receptor and the main path of noise propagation, and evaluates the noise level area and time period for the receptor based on the noise peak and noise distribution range in the predicted results.

[0025] Exemplarily, according to the constructed noise map, when the degree of noise pollution is judged to be serious, the area corresponding to the noise is designated as the priority control area for noise management and control, and the priority weight matrix of the most serious noise pollution area is determined based on the set noise standard value. Sound pressure level, soundscape quality, and visual perception data are selected as evaluation indicators to construct a weight system. Then, according to the importance of each evaluation indicator, the weight of each indicator is determined through expert scoring and hierarchical analysis method. The noise data of each area is substituted into the weight system, and the priority index of each area is calculated. The priority is divided according to the size of the priority index, and noise management feedback is provided to the designated indoor and outdoor areas according to the priority.

[0026] Exemplary, the method of providing feedback on noise control for designated indoor and outdoor areas according to priority is as follows: outdoor noise is treated through multiple directional speakers, and indoor noise is treated by building a sound science shed at a specific location using ultra-microporous panels. The operation process of outdoor directional speakers is as follows: the sound of the sound unit is concentrated in front of the directional speaker equipment, so that the sound pressure value is lower as it is closer to the two sides of the directional speaker equipment, forming an effect in which the sound pressure value gradually decreases and tends to the environmental protection value. The directional sound technology is integrated with the digital guided environmental protection speaker array to realize that the sound emitted by the directional speaker equipment is concentrated in a fixed area and propagated in different areas. When it exceeds the range, the sound is greatly attenuated to below the environmental protection decibel. The operation process of the indoor sound science shed is as follows: the ultra-microporous aluminum honeycomb sound-absorbing panel is composed of a Japanese keel and an ultra-microporous aluminum honeycomb sound-absorbing panel. Through the peak-type design, the ultra-microporous aluminum honeycomb sound-absorbing panel is designed to a certain inclination angle to increase the sound-absorbing surface to absorb noise. When the sound wave propagates to the ultra-microporous aluminum honeycomb sound-absorbing panel, the sound wave enters the air layer behind the panel through the micropores. When the frequency of the sound wave matches the inherent frequency of the air layer system, resonance occurs. The resonance is used to cause a large amount of sound wave energy to be consumed in the air layer for sound absorption, ensuring good sound absorption and noise reduction while also reflecting part of the noise until the noise dissipates in the peak-type sound science soundproof booth and does not propagate outside the peak-type sound science soundproof booth to cause noise pollution to the surrounding environment. Embodiment 2: Embodiment 2 of the present invention provides a comprehensive urban acoustic environment management system based on the Internet of Things. Figure 2 A schematic diagram of the module composition of a comprehensive urban acoustic environment management system based on the Internet of Things provided in the second embodiment of the present invention is shown in FIG. Figure 2 As shown, the system includes: Noise data monitoring and collection module, used to monitor and collect real-time noise data in designated urban areas, road sections and control environments; Noise data calculation and analysis module, used to perform numerical calculation and comprehensive analysis on the collected noise data; The noise classification and control module is used to carry out different levels of classification and control of different noise distributions according to the predicted values ​​and the constructed noise map.

[0027] In some embodiments of the present invention, the noise data monitoring and collection module includes: Intelligent online monitoring instrument, which is responsible for real-time data collection and monitoring of urban noise distributed indoors and outdoors; A noise data preprocessing module, used to transmit and preprocess real-time spectrum data using digital signals; The noise data feature classification module is used to classify the noise data according to the corresponding features.

[0028] In some embodiments of the present invention, the noise data calculation and analysis module includes: A noise map generation module is used to associate noise data with specific areas, road sections and control environments to generate noise maps; A noise fitting model building module is used to use noise data as a dependent variable and to build a noise fitting model based on a neural network using defined independent variables; The noise level prediction module is used to predict the noise levels in different areas and time periods.

[0029] In some embodiments of the present invention, the noise classification control module includes: Outdoor noise control terminal, used to control outdoor noise through multiple different noise processing devices; The outdoor noise control terminal also includes: directional speakers, sound barriers and sound science sheds, modularly assembled soundproof walls, and sound-absorbing and anti-dizziness panels, among which: directional speakers are used to directional propagate the received noise and control the propagation direction and range of the sound; sound barriers and sound science sheds are used to establish acoustic barriers between the received noise source and the sound receiving point; modularly assembled soundproof walls are used to isolate noise by assembling a complete soundproof wall composed of multiple prefabricated sound insulation structures; sound-absorbing and anti-dizziness panels are used to absorb noise and prevent glare using panels with sound-absorbing holes or sound-absorbing material layers; Indoor noise control terminal, used to control indoor noise through multiple different noise processing devices; The indoor noise control terminal also includes: sound-absorbing slats, wall sound-absorbing panels, soundproof doors, soundproof windows, sound-absorbing curtains, and rotatable sound-absorbing bodies; among them: the sound-absorbing slats are used to absorb indoor noise using sound-absorbing materials; the wall sound-absorbing panels are used to absorb the noise sound wave energy in the air through the porous structure of the material itself, and convert it into heat energy or other forms of energy dissipation; the soundproof doors and soundproof windows are used to block the noise in the designated place through doors and windows composed of soundproof materials; the rotatable sound-absorbing body is used to change the exposure degree of the sound-absorbing surface according to the noise control needs through the sound-absorbing material and the adjustable rotating structure; The noise control feedback module is used to provide noise control feedback based on noise control management rules.

[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0031] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A comprehensive management method for urban acoustic environment based on the Internet of Things, characterized by: The method comprises: Based on fixed and mobile noise monitoring terminals, real-time noise data monitoring and collection are carried out for designated urban areas, road sections and control environments; Use digital signals to transmit and preprocess real-time spectrum data; Based on the collected noise data and the set limits, the sources of noise pollution are determined on the established noise map using spatial positioning methods; By establishing a mathematical model of noise propagation, analyzing the spatial relationship between noise data and receptors, the noise level in different areas is calculated and predicted, and compared with the measured values; According to the predicted values, different noise distributions are managed in different levels according to the constructed noise map.

2. According to claim 1, a comprehensive urban acoustic environment management method based on the Internet of Things is characterized by: The steps of performing real-time noise data monitoring and collection include: Based on the designated urban area, road section and control environment, determine the location of monitoring points based on fixed noise monitoring terminals according to the type and distribution of noise sources; The fixed noise monitoring terminal includes an automatic environmental noise monitoring instrument with integrated video surveillance, and the mobile monitoring terminal includes a plurality of noise monitors with built-in tree-based microphones, wherein the automatic environmental noise monitoring instrument with integrated video surveillance continuously and automatically monitors various parameters; Fixed noise monitoring terminals are used to collect noise parameters including sound pressure level and frequency in real time. Based on the monitoring target routes covering designated urban areas, road sections and controlled environments, mobile monitoring terminals equipped with noise monitoring equipment are used to collect noise data in real time, and the location information of the monitoring points is marked by GPS.

3. The method for comprehensive management of urban acoustic environment based on the Internet of Things according to claim 2 is characterized in that: The method for preprocessing real-time spectrum data comprises: The collected noise data is transmitted to the data center or cloud platform in real time. The noise data collected by fixed and mobile monitoring terminals are integrated to form a complete noise monitoring data set. The noise data in the data set is denoised and pre-processed to filter out abnormal values. The amplitude of all collected noise signal data is normalized to the interval of [-1,1]. Pre-emphasis is set to increase the high-frequency part of the noise signal so that the change from low frequency to high frequency band remains uniform and smooth after the noise signal is attenuated through spatial propagation. The pre-processed noise data is further converted into a two-dimensional spectrogram. The time series is iteratively output through a pulse coupled neural network as the feature of noise data classification, and classification is performed according to the corresponding features.

4. The method for comprehensive management of urban acoustic environment based on the Internet of Things according to claim 3 is characterized in that: The classification according to the corresponding features specifically includes: constructing a PCNN model of noise data, simulating the pulse emission of a biological visual system, performing nonlinear processing on the input time series data, using the preprocessed noise time series data as the input of the PCNN model, and outputting feature data related to the time series through iterative operations of the PCNN model, wherein the feature data includes a pulse emission pattern, a pulse frequency, and a pulse intensity, reflecting the intrinsic characteristics and classification information of the noise data based on the feature data, constructing a classification model based on a neural network according to the extracted feature data, training the classification model using the feature data and the noise data, defining a loss function and an optimization algorithm to adjust the model parameters and minimize the classification error, using the trained classification model to classify the new noise data according to the features output by the PCNN, and outputting the classification results.

5. The method for comprehensive management of urban acoustic environment based on the Internet of Things according to claim 4 is characterized in that: The method of determining the source of noise pollution includes: using GIS technology to superimpose noise data with designated urban areas, road sections and control environments, generating noise contour maps using inverse distance weighted interpolation (IDW) based on the superimposed data, associating noise data with specific areas, road sections and control environments to generate noise maps, marking the locations and types of noise sources on the maps, using noise data as dependent variables, using defined time period data, road network data and environmental data as independent variables, establishing a noise fitting model based on a neural network, and fitting the noise data through the learning and prediction capabilities of the neural network; On the noise map, the noise levels of designated urban areas, road sections and controlled environments are evaluated according to the set noise limits. The specific sources of noise pollution are determined by using an array of sound sensors to obtain the location of each monitoring device, mark the real-time monitoring factors collected by the various sensors on the equipment, including noise, particulate matter, meteorological parameters, and video, identify noise pollution areas that exceed the limit, further measure the time difference or phase difference of the sound signal reaching each sensor, and combine the geographic information data of the designated urban areas, road sections and controlled environments to locate the specific location of the noise source, and mark the location of the noise source and the degree of pollution.

6. The method for comprehensive management of urban acoustic environment based on the Internet of Things according to claim 5 is characterized in that: The noise propagation mathematical model is established, the spatial relationship between noise data and receptors is analyzed, and the noise level in different areas is calculated and predicted, including: A noise propagation mathematical model is established based on designated urban areas, road sections and control environments, and noise data, environmental data and specific location data of receptors are input into the model. The specific location data of the receptors is based on the location of the noise source marked on the noise map. The noise propagation mathematical model simulates the noise propagation process in the environment based on the input noise source data, environmental data and receptor data; The noise propagation mathematical model predicts the noise level in different areas and time periods based on the input data, and calculates the parameters of the sound pressure level, sound intensity level or equivalent continuous sound level at each receiving point; Identify the prediction results to obtain the spatial relationship between noise sources and receptors and the main paths of noise propagation; The noise level area and time period for the receptor are evaluated based on the noise peak and noise distribution range in the prediction results.

7. The method for comprehensive management of urban acoustic environment based on the Internet of Things according to claim 6 is characterized in that: The different levels of hierarchical governance include: According to the constructed noise map, when the noise pollution level is judged to be serious, the corresponding noise area is classified as the priority control area for noise control and management. The priority weight matrix of the most serious noise pollution area is determined based on the set noise standard value. The sound pressure level, soundscape quality, and visual perception data are selected as evaluation indicators to construct a weight system. According to the importance of each evaluation indicator, the weight of each indicator is determined through expert scoring and hierarchical analysis method. The noise data of each area is substituted into the weight system to calculate the priority index of each area. According to the size of the priority index, noise control feedback is provided to the designated indoor and outdoor areas according to priority. The method of providing feedback on noise control for designated indoor and outdoor areas according to priority is as follows: outdoor noise is treated through multiple directional speakers, and indoor noise is treated by building an acoustic shed at a specific location using ultra-microporous panels. The operation process of outdoor directional speakers is as follows: the sound of the sound unit is concentrated in front of the directional speaker equipment, so that the sound pressure value closer to the two sides of the directional speaker equipment is lower, and the sound pressure value is reduced to an environmentally friendly value. The directional sound technology is integrated with a digital oriented environmentally friendly speaker array to achieve that the sound emitted by the directional speaker equipment is concentrated in a fixed area and propagated in different areas. When the range is exceeded, the sound is greatly attenuated to below the environmentally friendly decibel level. The operation process of the indoor sound science shed is as follows: the ultra-microporous aluminum honeycomb sound-absorbing panel is composed of a day-type keel and an ultra-microporous aluminum honeycomb sound-absorbing panel. Through the peak-type design, the ultra-microporous aluminum honeycomb sound-absorbing panel is designed to a certain inclination angle to increase the sound-absorbing surface to absorb noise. When the sound wave propagates to the ultra-microporous aluminum honeycomb sound-absorbing panel, the sound wave enters the air layer behind the panel through the micropores. When the frequency of the sound wave matches the inherent frequency of the air layer system, resonance occurs. The resonance causes the sound wave energy to be consumed in large quantities in the air layer for sound absorption, ensuring good sound absorption and noise reduction while reflecting part of the noise until the noise dissipates in the peak-type sound science soundproof booth and does not propagate outside the peak-type sound science soundproof booth to cause noise pollution to the surrounding environment.

8. A comprehensive urban sound environment management system based on the Internet of Things, comprising: The noise data monitoring and collection module, the noise data calculation and analysis module and the noise classification and control module are characterized in that: the noise data monitoring and collection module is used to perform real-time noise data monitoring and collection on designated urban areas, road sections and control environments; the noise data calculation and analysis module is used to perform set numerical calculations and comprehensive analysis on the collected noise data; the noise classification and control module is used to perform different degrees of classification and control on different noise distributions according to the predicted values ​​and the constructed noise map.

9. The urban sound environment comprehensive management system based on the Internet of Things according to claim 8 is characterized by: The noise data monitoring and acquisition module includes: an intelligent online monitor, a noise data preprocessing module and a noise data feature classification module. The intelligent online monitor is responsible for real-time data collection and monitoring of urban noise distributed indoors and outdoors; the noise data preprocessing module is used to use digital signals to transmit and preprocess real-time spectrum data; the noise data feature classification module is used to classify noise data according to corresponding features; The noise data calculation and analysis module includes: a noise map generation module, a noise fitting model establishment module, and a noise level prediction module. The noise map generation module is used to associate noise data with specific areas, road sections, and control environments to generate a noise map; the noise fitting model establishment module is used to use noise data as a dependent variable and to establish a noise fitting model based on a neural network using defined independent variables; and the noise level prediction module is used to predict the noise levels in different areas and different time periods.

10. The urban sound environment comprehensive management system based on the Internet of Things according to claim 9 is characterized by: The noise classification control module includes: the noise classification control module includes: an outdoor noise control terminal, an indoor noise control terminal and a noise control feedback module, the outdoor noise control terminal is used to control outdoor noise through multiple different noise processing devices; The outdoor noise control terminal also includes: directional sound, sound barrier and sound science shed, modularly assembled soundproof wall, and sound-absorbing anti-vertigo board, wherein: directional sound is used to directional propagate the received noise and control the propagation direction and range of the sound; the sound barrier and sound science shed are used to establish an acoustic barrier between the received noise source and the sound receiving point; the modularly assembled soundproof wall is used to isolate noise by assembling a complete soundproof wall composed of multiple prefabricated sound insulation structures; the sound-absorbing anti-vertigo board is used to absorb noise and prevent glare by using a board with sound-absorbing holes or a sound-absorbing material layer; The indoor noise control terminal is used to control indoor noise through a plurality of different noise processing devices; The indoor noise control terminal also includes: sound-absorbing slats, wall sound-absorbing panels, soundproof doors, soundproof windows, sound-absorbing curtains, and rotatable sound-absorbing bodies; wherein: the sound-absorbing slats are used to absorb indoor noise using sound-absorbing materials; the wall sound-absorbing panels are used to absorb noise sound wave energy in the air through the porous structure of the material itself, and convert it into heat energy or other forms of energy loss; the soundproof doors and soundproof windows are used to block noise in designated places through doors and windows composed of soundproof materials; the rotatable sound-absorbing body is used to change the exposure degree of the sound-absorbing surface according to the noise control requirements through the sound-absorbing material and the adjustable rotating structure; The noise control feedback module is used to provide noise control feedback based on noise management rules.

Images