GIS (Geographic Information System)-based sound environment threshold value quick query method

Abstract

The invention discloses a GIS-based sound environment threshold value rapid query method, and particularly relates to the technical field of environment noise monitoring and intelligent city management. The method comprises the following steps: acquiring meteorological data and topographic data, analyzing the influence of the environment on noise propagation, calculating an actual noise influence range by combining time information and utilizing a dynamic noise propagation model, adjusting a basic noise limit value, and forming a more accurate dynamic threshold value; in combination with real-time noise monitoring data and traffic flow data, a spatial buffer area algorithm is adopted to evaluate a noise exceeding range, complaint frequency weight is introduced to calculate the noise pollution severity of complaint points, and an optimal treatment scheme is intelligently matched; by continuously monitoring the treatment effect, if the noise pollution is not effectively reduced, the treatment strategy is dynamically adjusted, and the noise propagation model is continuously optimized, so that the accuracy of noise prediction and management is improved, a self-adaptive optimization intelligent noise supervision system is constructed, and the scientificity and intelligent level of urban noise treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of environmental noise monitoring and intelligent city management, and particularly relates to a method for quickly querying sound environment thresholds based on GIS. Background Art

[0002] With the acceleration of urbanization, the problem of environmental noise pollution has become increasingly serious, affecting the quality of life of residents and the sustainable development of cities. The existing noise management systems mainly rely on manual complaint handling methods, which require manually checking the sound environment functional areas to which the complaint locations belong and making judgments based on fixed noise thresholds. This method is inefficient and has a high misjudgment rate, and it is difficult to adapt to complex urban environments. In addition, traditional noise monitoring systems often lack comprehensive analysis of factors such as time, space, meteorology, and historical complaint records, and cannot dynamically adjust noise limits, resulting in inaccurate processing results and affecting the effectiveness of supervision and law enforcement. Therefore, there is an urgent need for an intelligent noise management method based on GIS to realize dynamic calculation and intelligent analysis of noise thresholds in order to improve the level of urban noise governance.

[0003] The existing technologies have the following deficiencies:

[0004] The current noise management systems are difficult to effectively cope with the complex geographical impacts of noise propagation. Especially in areas with dense high-rise buildings, due to the influence of factors such as terrain, building reflections, and wind direction, the noise propagation paths are extremely complex, and there may be serious deviations between the actual noise levels and the monitoring data. For example, the acoustic resonance and echo effects formed between high-rise building groups may cause abnormal aggregation of noise in specific areas, resulting in much higher noise pollution at some locations than in other areas, while traditional monitoring systems cannot accurately capture these phenomena. In addition, the existing GIS noise analysis systems lack real-time three-dimensional noise propagation models and cannot be dynamically adjusted in combination with factors such as building heights and terrain undulations, resulting in large errors between noise predictions and actual situations and affecting the scientificity and fairness of law enforcement decisions. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for quickly querying sound environment thresholds based on GIS to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for quickly querying sound environment thresholds based on GIS, including:

[0007] Obtaining noise data through a noise sensor network, mobile monitoring devices or user complaints, parsing the location information of the complaint points by using GIS geocoding technology, and performing real-time matching of the complaint points with the sound environment functional area database to determine the basic noise limit standards of the complaint points;

[0008] Collect meteorological data and terrain data of the complaint points, analyze the impact of the environment on noise propagation, match the corresponding basic noise thresholds according to the time information of the complaint points, and based on the analysis results, use the dynamic noise propagation model to calculate the actual noise impact range of the complaint points, adjust the basic noise limit values to form dynamic thresholds.

[0009] Combine real-time noise monitoring data and traffic flow data, conduct a comprehensive analysis of the current noise level, use the spatial buffer algorithm to determine the range affected by excessive noise, and combine the complaint frequency weight to evaluate the severity of noise pollution at the complaint points and match the optimal noise treatment plan.

[0010] Monitor the implementation effect of noise control measures. If the severity of noise pollution at the complaint points does not decrease, dynamically adjust the treatment plan and continuously update the noise propagation model.

[0011] Preferably, when the GIS geocoding technology analyzes the location information of the complaint points, it calls the map service GeocodingAPI to convert the text address into longitude and latitude coordinates, and uses a multi-source verification mechanism to improve the analysis accuracy. If the GPS coordinates are missing or the address is incomplete, position correction is performed based on historical complaint record matching and assisted positioning of mobile signal towers or WiFi hotspots.

[0012] Preferably, the matching of the basic noise limit standard queries the type of acoustic environment functional area where the complaint point is located through the GIS system. The types of acoustic environment functional areas include: Class I areas, Class II areas, Class III areas, and Class IV areas, and the day and night noise limit values are matched according to the day and night periods.

[0013] Preferably, the meteorological data includes wind speed, wind direction, temperature, and humidity, and the terrain data includes ground absorption coefficient, building height, and terrain slope. Among them, wind speed and wind direction are used to correct the attenuation of noise propagation, temperature and humidity are used to correct the air absorption effect, and terrain slope and building height are used to analyze the noise reflection, diffraction, and shielding effects. The data are all obtained through the meteorological bureau API, GIS terrain database, or urban building model.

[0014] Preferably, the wind speed v w affects the attenuation of noise in the air, and the correction formula is: where: ΔL w is the attenuation correction value of the wind speed to the noise, c is the speed of sound, θ w is the wind direction angle;

[0015] The formula for the impact of ground materials on noise propagation is: ΔL g =-10log 10 (1-α g ); where: ΔL g is the noise attenuation caused by ground absorption, αg is the ground absorption coefficient;

[0016] Air humidity affects the attenuation of noise in the air. The correction formula is: ΔL H = -γ H ·d; where ΔL H is the attenuation correction value of humidity to noise, d is the noise propagation distance, and γ H is the humidity absorption coefficient;

[0017] The noise propagation loss is calculated comprehensively considering the wind speed, terrain, and humidity correction factors. The expression is: ΔL env = ΔL w + ΔL g + ΔL H ; where: ΔL env is the total noise attenuation caused by environmental factors; The final dynamic noise limit is calculated by combining the basic limit L base with the environmental correction value. The expression is: L dyn = L base + ΔL env ; where: L dyn is the final dynamic noise threshold at the complaint point.

[0018] Preferably, a traffic noise model is used to calculate the contribution of traffic flow to noise: L traffic = L0 + 10log 10 (N v ·v avg ·P h ); where: L0 is the reference traffic noise level, N v is the vehicle traffic volume, v avg is the average vehicle speed, P h is the proportion of heavy vehicles, and L traffic is the contribution value of traffic flow to environmental noise; Combining the real-time noise data and the traffic noise contribution value, the current total noise L total is calculated. The expression is: L total = L real + L traffic ; L real is the noise level, and then it is compared with the dynamic noise threshold L dyn to determine the over-standard situation: ΔL = L total - L dyn ; where: If ΔL > 0, it means the noise exceeds the standard, and the affected range needs to be further analyzed; If ΔL ≤ 0, it means the noise is within the acceptable range and no further treatment is required.

[0019] Preferably, a noise propagation model is used to calculate the affected range of noise exceeding the standard: where: R is the radius of the affected range of noise exceeding the standard, d0 is the reference propagation distance, and ΔL is the amount of noise exceeding the standard;

[0020] A noise impact buffer zone is established within a range of radius R centered on the complaint point. Combining the complaint frequency weight, the severity of noise pollution is evaluated: Suppose that within the past T days, the historical complaint count at a certain location is C T , then the calculation formula for the complaint frequency weight is as follows: Where: W c is the complaint frequency weight, and the noise pollution index I N is defined, and the expression is: I N = ΔL × W c ; If I N is higher than the set threshold, then it is determined as a high-risk area of noise pollution. According to the size of the noise pollution index I N , the system automatically matches the optimal noise control plan.

[0021] Preferably, after noise control, the system continuously monitors the noise data in this area and calculates the change amount ΔL eff in the noise level before and after the control, and the expression is: ΔL eff = L before - L after ; Where: L before is the noise level before the control, and L after is the noise level after the control, taking the average value within a period of time after the implementation of the control measures;

[0022] To quantify the success degree of noise control, the control effect score S eff is defined as follows: Where: If S eff ≥ 20%, it indicates that the control measures have good effects. If S eff < 20%, it indicates that the control measures are not obvious and the control plan needs to be adjusted dynamically.

[0023] Preferably, the optimized noise propagation model includes: calculating the noise propagation error: E L = L predicted - L actual ; Where: E L is the noise propagation error, L predicted is the noise value predicted by the model, and L actual is the actually measured noise value after the control;

[0024] If the noise propagation error is greater than the pre-set noise propagation error threshold, then the noise propagation model needs to be updated: L new = L predicted + λE L ; Where: L new is the optimized noise prediction value, and λ is the learning rate, which is used to control the model update amplitude.

[0025] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0026] 1. Through technologies such as GIS geographic information system, real-time noise monitoring, and dynamic noise propagation model, the present invention realizes the intelligent query, precise matching, and dynamic adjustment of the sound environment threshold, effectively solving the problems of inaccurate prediction, lagging response, and lack of dynamic adjustment ability in the existing noise management system in complex geographical environments. Compared with the traditional fixed noise limit method, the present invention can combine meteorological data (wind speed, wind direction, humidity, temperature), terrain data (building height, ground absorption coefficient, terrain slope), and traffic flow information to dynamically calculate the actual noise influence range of the complaint point and adjust the noise limit in real time, making noise control more precise and scientific. In addition, the present invention introduces a spatial buffer algorithm and complaint frequency weight analysis to automatically evaluate the severity of noise pollution and intelligently match the optimal treatment plan, thereby improving law enforcement efficiency and optimizing urban noise pollution management.

[0027] 2. The present invention also has the ability to monitor the intelligent treatment effect and adaptively optimize. After the noise treatment measures are implemented, the system can continuously track the noise change trend, calculate the change amount of the noise level before and after treatment. If the noise pollution is not effectively alleviated, the treatment plan will be automatically adjusted, and the noise prediction model will be optimized through noise propagation error analysis, continuously improving the accuracy of noise prediction and the scientific nature of management decisions. Through these technical means, the present invention constructs an automated, intelligent, and precise urban noise management system, which not only improves the efficiency and accuracy of noise complaint handling, but also can identify high-risk areas in advance, provide data support, achieve refined noise control in a smart city, and contribute to the sustainable development of the urban environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0029] Figure 1 It is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] For the embodiments, please refer to Figure 1 As shown, a method for quickly querying the acoustic environment threshold based on GIS in this embodiment includes:

[0032] Obtain noise data through a noise sensor network, mobile monitoring equipment, or user complaints, use GIS geocoding technology to analyze the location information of the complaint points, and perform real-time matching of the complaint points with the acoustic environment functional area database to determine the basic noise limit standard of the complaint points;

[0033] Collect meteorological data and terrain data of the complaint points, analyze the impact of the environment on noise propagation, match the corresponding basic noise thresholds according to the time information of the complaint points, and based on the analysis results, use a dynamic noise propagation model to calculate the actual noise impact range of the complaint points and adjust the basic noise limit to form a dynamic threshold;

[0034] Combine real-time noise monitoring data and traffic flow data to comprehensively analyze the current noise level, use a spatial buffer algorithm to judge the range affected by excessive noise, and combine the complaint frequency weight to evaluate the severity of noise pollution at the complaint points and match the optimal noise treatment plan;

[0035] Monitor the implementation effect of the noise control measures. If the severity of noise pollution at the complaint points has not decreased, dynamically adjust the treatment plan and continuously update the noise propagation model.

[0036] Fixed noise sensors deployed in various regions of the city (such as roadside monitoring stations, residential area sensors, industrial area sensors, etc.) regularly collect environmental noise data and upload it to the server through Internet of Things (IoT) technology. The noise data collected by the sensors includes information such as noise level (dB), timestamp, coordinate position, background noise, etc.

[0037] Collect noise data through drones, intelligent inspection vehicles, or handheld devices, conduct mobile inspections in key areas of the city, and enhance the monitoring coverage rate. The collected data is synchronized with the data of the fixed sensors to ensure real-time update and supplement the monitoring blind spots.

[0038] User complaint information is collected through environmental protection hotlines, smart city management platforms, noise complaint apps and other channels. Complaint data includes: text address (such as "XX Road XX"), GPS coordinates (if the user enables the positioning function), noise source description (such as "construction site noise" and "too loud music at night in the commercial street") and complaint time (record the specific time to adapt to the day and night limit standards).

[0039] If the user only provides a text address, call the map service Geocoding API (such as Google Maps API, Baidu Maps API) to convert the address into longitude and latitude coordinates (geocoding). During the parsing process, attention should be paid to the accuracy of address parsing to ensure that the street, house number and coordinates match accurately. Use a multi-source verification mechanism (cross-comparison of coordinate information returned by different map APIs) to improve parsing accuracy.

[0040] If the user provides GPS coordinates, they are directly recorded and converted to a unified coordinate system (such as WGS-84, GCJ-02, BD-09). Check whether they fall within the reasonable city range and remove erroneous coordinates (such as drift points). Use a geographic offset correction algorithm to ensure that the coordinate accuracy is consistent with the actual geographic location.

[0041] If the complaint data lacks GPS coordinates or the address is incomplete, we can: match historical complaint records (find similar complaints and infer possible locations), and request the user to supplement the detailed address (such as reconfirming the complaint location) based on auxiliary positioning of surrounding mobile signal towers and WiFi hotspots.

[0042] GIS spatial analysis is used to query the types of acoustic environment functional zones where the complaint points are located, including: Category I areas (residential and convalescent areas, with lower limits at night), Category II areas (commercial and mixed areas, where moderate noise is allowed), Category III areas (industrial areas, with relatively high limits), and Category IV areas (traffic arteries, docks, etc., with the highest noise tolerance). The GIS database is called to automatically retrieve the area to which the complaint point belongs, and obtain the basic day and night noise limits for the area (e.g. 45dB at night and 55dB during the day in residential areas).

[0043] Read the complaint time (e.g. 23:15) to determine whether it falls within the night standard (22:00-6:00 the next day) or the day standard (6:00-22:00). Select the corresponding basic noise limit value according to the time period to ensure that the judgment meets regulatory requirements.

[0044] Using the GIS spatial buffer algorithm, analyze the special circumstances around the complaint points, such as: whether it is close to schools, hospitals (possibly requiring stricter limits), whether it is located in high-rise building clusters (considering the noise reflection effect), whether it is adjacent to industrial areas or traffic arteries (possibly requiring adjusted limits). If there are special influencing factors around the complaint point, dynamically correct the noise standard based on the basic limit to make it more in line with the actual noise environment.

[0045] Combined with the complaint location, time, type of acoustic environment functional area, and surrounding environment, finally determine the basic noise limit applicable to the complaint point. This information is stored in the database and provided to urban managers, environmental protection departments, or automated law enforcement systems for subsequent noise over-limit determination and decision-making on treatment measures.

[0046] The propagation of noise will be affected by various environmental factors such as wind speed, wind direction, temperature, humidity, terrain undulation, and building density. Therefore, the system needs to comprehensively analyze the environment around the complaint point, including:

[0047] Meteorological data: Wind speed: A relatively high wind speed may enhance or weaken the propagation of noise. Wind direction: If the noise propagation direction is consistent with the wind direction, the noise can propagate farther. Temperature: Affects the air density and thus affects the attenuation of sound waves. Humidity: A high-humidity environment can reduce the ability of the air to absorb noise and make the noise propagate farther.

[0048] Terrain data: Ground absorption coefficient: Grasslands and soil absorb noise strongly, while hard ground such as cement and asphalt absorbs less. Building height: High-rise buildings can cause noise reflection and diffraction. Terrain slope: Slopes or mountains may block or reflect noise.

[0049] According to the acoustic environment functional area where the complaint point is located and the complaint time t c , query the basic noise limit L base , for example, if the complaint point is located in a Class 1 acoustic environment functional area (residential area), querying the database gives:

[0050] Wind speed v w Affects the attenuation of noise in the air, and the correction formula is: Where: ΔL w Is the attenuation correction value of the wind speed to the noise (dB), c is the speed of sound (taking 343 m / s), and θ w Is the wind direction angle.

[0051] The formula for the influence of ground materials on noise propagation is: ΔL g =-10log 10 (1 - α g ); Where: ΔL g Is the noise attenuation caused by ground absorption, and αg is the ground absorption coefficient. The larger the value, the stronger the absorption ability.

[0052] Air humidity affects the attenuation of noise in the air. The correction formula is: ΔL H = -γ H ·d; where ΔL H is the attenuation correction value of humidity to noise, d is the noise propagation distance (m), and γ H is the humidity absorption coefficient (dB / m), which can be measured by experiments. For example, when H = 50%, γ H ≈0.002 dB / m; when H = 90%, γ H ≈0.0008 dB / m (the higher the humidity, the lower the noise absorption).

[0053] Comprehensively calculate the noise propagation loss considering the wind speed, terrain, and humidity correction factors. The expression is: ΔL env = ΔL w + ΔL g + ΔL H ; where: ΔL env is the total noise attenuation caused by environmental factors. If ΔL env is negative, it means the noise propagates farther (such as in the case of downwind); if it is positive, it means the noise attenuates faster (such as high humidity, high absorption ground).

[0054] The final dynamic noise limit is calculated by combining the basic limit L base with the environmental correction value. The expression is: L dyn = L base + ΔL env ; where: L dyn is the final dynamic noise threshold (dB) at the complaint point. This value is used to determine whether the noise at the complaint point exceeds the standard and serves as the basis for subsequent decisions.

[0055] Obtain real-time noise data through fixed noise sensors (such as traffic intersections, commercial areas, residential areas) and mobile monitoring devices (such as drones, patrol vehicles) deployed in various regions of the city. The data includes: Noise level: The currently measured noise intensity. Timestamp: The specific time when the noise data is collected. Geographic coordinates: Location information of the noise monitoring point.

[0056] Obtain traffic flow information through intelligent traffic cameras, vehicle detection sensors, GPS floating car data, etc. The data includes: Vehicle volume: The number of vehicles passing through the monitoring point per unit time. Average vehicle speed: The average driving speed of the current road section. Proportion of heavy vehicles: The proportion of high-noise vehicles such as trucks and freight cars.

[0057] Use the traffic noise model to calculate the contribution of traffic flow to noise: L traffic = L0 + 10log10 (N v ·v avg ·P h ); where: L0 is the reference traffic noise level, which can be measured through experiments, N v is the vehicle traffic volume, v avg is the average vehicle speed, and the faster the speed, the greater the noise. P h is the proportion of heavy vehicles, and the noise of heavy vehicles has a greater impact. L traffic is the contribution value of traffic flow to environmental noise and can be used for comprehensive noise analysis.

[0058] Integrate real-time noise data and the traffic noise contribution value to calculate the current total noise level L total , and the expression is: L total = L real + L traffic ; L real is the noise level, and then compare it with the dynamic noise threshold L dyn to determine the over-standard situation: ΔL = L total - L dyn ; where: if ΔL > 0, it means the noise exceeds the standard and the affected range needs to be further analyzed; if ΔL ≤ 0, it means the noise is within the acceptable range and no further treatment is required.

[0059] Use the noise propagation model to calculate the affected range of noise exceeding the standard: where: R is the radius of the affected range of noise exceeding the standard, d0 is the reference propagation distance (usually taken as 10 m), and ΔL is the amount of noise exceeding the standard (dB).

[0060] Generate a GIS buffer: Establish a noise impact buffer with a radius of R centered on the complaint point. Combine with the urban map to analyze the affected buildings (such as schools, hospitals, residential areas) within this range. If R covers sensitive areas, priority should be given to treatment.

[0061] Combine the complaint frequency weight to evaluate the severity of noise pollution: Set that within the past T days, the historical complaint times at a certain location is C T , then the calculation formula for the complaint frequency weight is: where: W c is the complaint frequency weight (times / day).

[0062] Define the noise pollution index I N , and the expression is: I N = ΔL × W c ; if I N is higher than the set threshold, then this area is determined to be a high-risk area of noise pollution.

[0063] According to the noise pollution index I NBased on the size, the system automatically matches the optimal noise control solution:

[0064] When I N < 5, the degree of noise pollution is relatively low. The system records the complaint information but does not take further treatment measures, only for data archiving for future trend analysis.

[0065] When 5 ≤ I N < 15, the system determines that there is a slight noise over - standard in the area and automatically sends a noise warning notice to the responsible party for the noise source (such as merchants, construction sites, traffic management departments) to remind them of rectification. At the same time, the area is marked as a "concerned area" in the GIS system. If the subsequent monitoring data continues to exceed the standard, the treatment measures will be upgraded.

[0066] When 15 ≤ I N < 30, the noise pollution reaches a moderately severe level. The system arranges law enforcement officers to conduct on - site inspections and can deploy temporary noise monitoring equipment to further collect noise data to ensure the basis for subsequent law enforcement. At this time, if the responsible party fails to rectify in time, the system will automatically upgrade to more stringent law enforcement measures.

[0067] When I N ≥ 30, the noise pollution has reached a seriously over - standard state. The system automatically dispatches law enforcement orders, and relevant departments immediately intervene, taking administrative penalties, forced rectification or time - limited noise reduction measures, such as requiring construction sites to stop work, shops to reduce volume, and adjusting traffic control. For long - term high - noise pollution areas, the system will continuously track and recommend implementing long - term noise reduction projects, such as adding sound insulation barriers and optimizing road design, to reduce the noise impact.

[0068] After the implementation of the noise control measures, this method continuously monitors the change in the noise level and evaluates the treatment effect through a feedback mechanism. If the severity of the noise pollution does not decrease, the treatment plan is dynamically adjusted, and the noise propagation model is continuously optimized to improve the accuracy of prediction and decision - making.

[0069] After noise control, the system continuously monitors the noise data in the area and calculates the change amount ΔL of the noise level before and after treatment eff The expression is: ΔL eff =L before -L after ; where: L before is the noise level (dB) before treatment. L after is the noise level (dB) after treatment, which is the average value for a period of time (such as 1 hour, 1 day, 1 week) after the implementation of the treatment measures.

[0070] To quantify the success of noise control, a treatment effect score S is defined eff : where: If Seff ≥20%, indicating that the treatment measures are effective. If S eff <20%, indicating that the treatment measures are not obvious and the treatment plan needs to be adjusted dynamically.

[0071] After the treatment, calculate the noise pollution index again to determine whether the measures still need to be adjusted: I' N = ΔL'×W c ; where: I' N is the noise pollution index after treatment, and ΔL' is the new noise exceedance. If I' N is still greater than the threshold (such as 30), it indicates that the noise pollution has not been effectively alleviated and the treatment plan needs to be upgraded.

[0072] If S eff <20%, it is necessary to analyze the possible reasons for the treatment failure and adjust the plan accordingly, and define the treatment failure factor: where: if F fail >10%, indicating that the noise pollution has increased after treatment, which may be caused by incorrect treatment measures. If 0% ≤ F fail ≤10%, indicating that there is no obvious improvement in the treatment, and it is necessary to increase the treatment intensity or adopt new measures. If F fail <0, indicating that the treatment is effective and no adjustment is required.

[0073] According to the magnitude of F fail , adjust the noise treatment measures, and add or optimize the treatment measures on the basis of the original treatment plan, such as strengthening law enforcement, increasing noise barriers, and optimizing traffic flow.

[0074] The adjustment strategies include:

[0075] If F fail >10%, upgrade the treatment measures, such as strengthening law enforcement, adjusting the traffic flow of roads, and increasing noise buffer facilities. If 0% ≤ F fail ≤10%, increase long-term monitoring, such as deploying more noise sensors or optimizing the supervision mechanism. If F fail <0, indicating that the treatment is effective, the current plan can be maintained, and the data can be recorded for optimizing the noise prediction model.

[0076] Optimizing the noise propagation model includes: calculating the noise propagation error: E L = L predicted -L actual ; where: E L is the noise propagation error, L predicted is the noise value predicted by the model, and L actual is the actually measured noise value after treatment.

[0077] If the noise propagation error is greater than a pre-set noise propagation error threshold (e.g., > 3 dB), the noise propagation model needs to be updated: L new = L predicted + λE L ; where: L new is the optimized noise prediction value, λ is the learning rate (usually taken as 0.5 - 1), which is used to control the magnitude of model update. The updated model can be used to improve the future noise prediction accuracy, reduce misjudgment, and improve the treatment efficiency.

[0078] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0079] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0080] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be understood by referring to the context before and after. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0081] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A rapid query method for sound environment thresholds based on GIS, characterized in that: Including: Obtain noise data through a noise sensor network, mobile monitoring devices or user complaints, use GIS geocoding technology to analyze the location information of complaint points, and perform real-time matching between the complaint points and the acoustic environment functional area database to determine the basic noise limit standard for the complaint points; Collect meteorological data and terrain data of the complaint points, analyze the impact of the environment on noise propagation, match the corresponding basic noise thresholds according to the time information of the complaint points, and based on the analysis results, use a dynamic noise propagation model to calculate the actual noise impact range of the complaint points and adjust the basic noise limit to form a dynamic threshold; Combine real-time noise monitoring data and traffic flow data to comprehensively analyze the current noise level, use a spatial buffer algorithm to judge the scope of noise exceeding the standard, and combine the complaint frequency weight to evaluate the severity of noise pollution at the complaint points and match the optimal noise treatment plan; Monitor the implementation effect of noise control measures. If the severity of noise pollution at the complaint points does not decrease, dynamically adjust the treatment plan and continuously update the noise propagation model.

2. The rapid query method for acoustic environment threshold based on GIS according to claim 1, wherein: When the GIS geocoding technology analyzes the location information of complaint points, call the map service Geocoding API to convert the text address into latitude and longitude coordinates, and use a multi-source verification mechanism to improve the parsing accuracy. If GPS coordinates are missing or the address is incomplete, perform location correction based on historical complaint record matching, auxiliary positioning of mobile signal towers or WiFi hotspots.

3. A method for quickly querying the acoustic environment threshold based on GIS according to claim 1, characterized in that: The matching of the basic noise limit standard queries the type of acoustic environment functional area where the complaint point is located through the GIS system. The types of acoustic environment functional areas include: Class I areas, Class II areas, Class III areas and Class IV areas, and match the day and night noise limits according to the day and night periods.

4. A method for quickly querying sound environment thresholds based on GIS according to claim 1, characterized in that: The meteorological data includes wind speed, wind direction, temperature and humidity, and the terrain data includes ground absorption coefficient, building height and terrain slope. Among them, wind speed and wind direction are used to correct noise propagation attenuation, temperature and humidity are used to correct air absorption effects, and terrain slope and building height are used to analyze noise reflection, diffraction and shielding effects. The data are all obtained through the meteorological bureau API, GIS terrain database or urban building model.

5. A method for quickly querying the acoustic environment threshold based on GIS according to claim 4, characterized in that: Wind speed v w Affects the attenuation of noise in the air, and the correction formula is: Where: ΔL w Is the attenuation correction value of wind speed to noise, c is the speed of sound, θ w Is the wind direction angle; The formula for the influence of floor materials on noise propagation is: ΔL g =-10log 10 (1-α g ); where: ΔL g is the noise attenuation caused by floor absorption, and α g is the floor absorption coefficient; Air humidity affects the attenuation of noise in the air, and the correction formula is: ΔL H = -γ H ·d; where, ΔL H is the attenuation correction value of humidity to noise, d is the noise propagation distance, and γ H is the humidity absorption coefficient; The noise propagation loss is comprehensively calculated by considering the wind speed, terrain, and humidity correction factors. The expression is: ΔL env = ΔL w + ΔL g + ΔL H ; where: ΔL env is the total noise attenuation caused by environmental factors; The final dynamic noise limit is calculated by combining the basic limit L base with the environmental correction value. The expression is: L dyn = L base + ΔL env ; where: L dyn is the final dynamic noise threshold at the complaint point.

6. A method for quickly querying the acoustic environment threshold based on GIS according to claim 5, characterized in that: Calculate the contribution of traffic flow to noise using a traffic noise model: L traffic = L0 + 10 log 10 (N v ·v avg ·P h ); where: L0 is the reference traffic noise level, N v is the vehicle volume, v avg is the average vehicle speed, P h is the proportion of heavy vehicles, and L traffic is the contribution value of traffic flow to environmental noise; Combine real-time noise data and the traffic noise contribution value to calculate the current total noise level L total , and the expression is: L total = L real + L traffic ; L real is the noise level, and then compare it with the dynamic noise threshold L dyn to determine the over-standard situation: ΔL = L total - L dyn ; where: if ΔL > 0, it means the noise exceeds the standard and further analysis of the affected range is required; if ΔL ≤ 0, it means the noise is within the acceptable range and no further treatment is needed.

7. A method for quickly querying the sound environment threshold based on GIS according to claim 6, characterized in that: Calculate the influence range of noise exceeding the standard using the noise propagation model: Where: R is the influence radius of noise exceeding the standard, d0 is the reference propagation distance, and ΔL is the amount of noise exceeding the standard; A noise impact buffer zone is established within a range of radius R centered on the complaint point. Combining the complaint frequency weight, the severity of noise pollution is evaluated. It is assumed that within the past T days, the historical complaint count at a certain location is C T , then the calculation formula for the complaint frequency weight is: where: W c is the complaint frequency weight, and the noise pollution index I N is defined, and its expression is: I N = ΔL × W c ; if I N is higher than the set threshold, then it is determined as a high-risk area of noise pollution. According to the magnitude of the noise pollution index I N , the system automatically matches the optimal noise control plan.

8. A method for quickly querying the sound environment threshold based on GIS according to claim 7, characterized in that: After noise treatment, the system continuously monitors the noise data in this area and calculates the change amount ΔL of the noise level before and after treatment eff , the expression is: ΔL eff = L before - L after ; Where: L before is the noise level before treatment, and L after is the noise level after treatment, which is the average value within a certain period after the implementation of the treatment measures; To quantify the success of noise control, a control effect score S is defined eff : Where: If S eff ≥ 20%, it indicates that the control measures are effective. If S eff < 20%, it indicates that the control measures are not obvious and the control plan needs to be adjusted dynamically.

9. A method for quickly querying sound environment thresholds based on GIS according to claim 8, characterized in that: The optimized noise propagation model includes: calculating the noise propagation error: E L = L predicted - L actual ; where: E L is the noise propagation error, L predicted is the noise value predicted by the model, and L actual is the actually measured noise value after treatment; If the noise propagation error is greater than a pre-set noise propagation error threshold, the noise propagation model needs to be updated: L new = L predicted + λE L ; where: L new is the optimized noise prediction value, and λ is the learning rate, which is used to control the magnitude of model update.

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