Noise treatment prediction and evaluation method and system based on full-chain noise reduction
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-15
- Publication Date
- 2026-08-11
AI Technical Summary
[0004]针对上述问题，本申请提供一种基于全链条降噪的噪声治理预测评估方法及系统，可以解决室外治理措施对高层建筑等特定区域覆盖不足且缺乏针对性末端补救方案的问题，从而实现从源头管控到末端闭环的精准治理
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Figure CN122549641A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of environmental noise prediction and assessment technology, and specifically relates to a noise control prediction and assessment method and system based on whole-chain noise reduction. Background Technology
[0002] Currently, road traffic noise has become one of the major environmental problems in cities, and the industry mainly relies on professional software such as Cadna / A and SoundPLAN for noise simulation. These software programs are usually based on static 3D models and ISO standard acoustic models for simulation, outputting noise distribution maps, which are then adjusted by designers based on experience until the standards are met.
[0003] However, existing technologies have significant shortcomings: First, they lack systematic collaborative design and evaluation of governance measures across the entire "sound source-propagation-reception" chain; second, the model parameters are static, failing to consider the decay of the acoustic performance of road materials over time, resulting in low long-term prediction accuracy; third, they only focus on acoustic effects, making it impossible to simultaneously estimate engineering costs during simulation, thus hindering cost-benefit analysis; and fourth, when public governance measures are insufficient, there is a lack of an automated intelligent matching mechanism for end-point protection measures based on the final indoor noise standard. Summary of the Invention
[0004] To address the aforementioned issues, this application provides a noise control prediction and assessment method and system based on full-chain noise reduction. This method can solve the problems of insufficient coverage of outdoor noise control measures in specific areas such as high-rise buildings and the lack of targeted end-of-pipe remedial solutions, thereby achieving precise noise control from source management to end-of-pipe closed-loop management. The method includes: Collect basic geographic information and sound source data of the target area, and construct a three-dimensional noise field model; Based on the three-dimensional noise field model, in response to the governance instructions, the governance parameters covering the sound source, propagation path and receiver are obtained from the full-chain strategy library, and the acoustic propagation attenuation model and engineering cost model are called to simultaneously calculate the predicted noise value and engineering cost after the implementation of the measures. The predicted noise values are compared with preset sound environment quality standards to generate a list of substandard sensitive points. Based on the principle of the weakest link effect, the system automatically matches and recommends protective measures for the receiving end of substandard buildings. Finally, it outputs a comprehensive treatment plan that includes a noise reduction effect cloud map, a visual map, and a cost estimate list.
[0005] In this embodiment of the application, basic geographic information and sound source data of the target area are collected to construct a three-dimensional noise field model, specifically including: pass Data interfaces or oblique photography techniques are used to acquire geometric information of buildings, road network information, and functional zone division information; Initialize traffic noise source intensity based on real-time monitoring data or standard noise source models; The model calibration mechanism is implemented by accessing automatic noise monitoring stations and video surveillance data, combined with... The system identifies and acquires real-time traffic flow and vehicle type ratios, and then reverse-corrects the ground reflection coefficient and background noise baseline values of the acoustic model to keep the simulation error of the initial model within a preset range.
[0006] In this embodiment of the application, the full-chain strategy library includes: a sound source parameter library, a propagation path parameter library, and a receiver parameter library; The sound source parameter library includes road surface reconstruction material parameters and traffic control strategy parameters; the propagation path parameter library includes sound barrier type, geometric parameters, and material parameters; and the receiving end parameter library includes soundproof window type, glass configuration, and window frame material parameters.
[0007] In this application embodiment, the acoustic propagation attenuation model includes a road surface aging attenuation model, which is used to dynamically predict the road surface noise reduction effect; The calculation expression for the pavement aging degradation model is as follows:
[0008] in, Based on the noise reduction coefficient, For thickness influence coefficient, For road surface thickness, It is a decay function that varies with time. It is zero during the stable period after construction and decreases linearly according to the preset annual decay rate after the stable period.
[0009] In this application embodiment, the acoustic propagation attenuation model includes a traffic control effect prediction model; The calculation expression for the traffic control effect prediction model is as follows:
[0010] in, To predict annual traffic volume, the unit is vehicles per hour, and the traffic volume is predicted for the year or before the project is implemented; The current annual traffic volume is expressed in vehicles per hour, and the traffic volume in the base year or after the project is implemented. The current annual average vehicle speed is expressed in km / h, representing the average vehicle speed in the base year or before the project was implemented. To predict the average annual vehicle speed, in km / h, the average vehicle speed for the predicted year or after the project is implemented; The speed influence coefficient is used to characterize the degree of influence of vehicle speed changes on noise radiation intensity.
[0011] In this embodiment of the application, the acoustic propagation attenuation model includes a sound barrier insertion loss calculation model, which calculates diffraction attenuation based on the acoustic path difference and Fresnel number; The calculation expression for the insertion loss calculation model of the sound barrier is as follows:
[0012] in, For Fresnel numbers, =2δλ, where δ is the path difference and λ is the wavelength of the sound wave.
[0013] In this embodiment of the application, the engineering cost model includes a nonlinear cost model for sound barriers; The calculation expression for the nonlinear cost model of a sound barrier is as follows:
[0014] in, For the length of the barrier, Based on the unit price, For height increment coefficient, This is a correction factor for construction difficulty.
[0015] In this application embodiment, the receiver protection measures based on the short-board effect principle include: Calculate the combined sound insulation of each component. The combined sound insulation of each component The expression is:
[0016] in, For the total area, Let R be the area of each component, and R be the sound insulation. The sound insulation of each component; If the difference in sound insulation between components exceeds a preset threshold, the component with lower sound insulation is identified as the weakest link in sound insulation and is upgraded until the predicted indoor noise value meets the preset standard.
[0017] This application also provides a noise control effect prediction and evaluation system based on full-chain noise reduction measures. The system includes: a data acquisition and modeling module, a full-chain configuration and simulation module, and an evaluation report generation module.
[0018] The data acquisition and modeling module is used to collect basic geographic information and sound source data of the target area and construct a three-dimensional noise field model.
[0019] The full-chain configuration and simulation module is used to respond to governance instructions, obtain governance parameters covering sound sources, propagation paths and receivers from the full-chain strategy library, and call the acoustic propagation attenuation model and engineering cost model to simultaneously calculate the predicted noise value and engineering cost after the implementation of measures.
[0020] The assessment report generation module compares the predicted noise values with the preset sound environment quality standards, generates a list of non-compliant sensitive points, automatically matches and recommends receiving end protection measures for non-compliant buildings based on the principle of the weakest link effect, and finally outputs a comprehensive treatment plan that includes a noise reduction effect cloud map, a visual spectrum, and a cost estimate list.
[0021] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method provided in the above embodiments.
[0022] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 This is a flowchart illustrating a method for predicting and evaluating the effectiveness of noise control based on a full-chain noise reduction approach, as provided in an embodiment of this application.
[0025] Figure 2 This is a schematic diagram of a closed-loop matching logic for a receiver protection measure based on the principle of the weakest link effect, provided in an embodiment of this application.
[0026] Figure 3 This is a block diagram of a noise control effect prediction and evaluation system based on full-chain noise reduction measures, provided in an embodiment of this application. Detailed Implementation
[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0028] Currently, with the acceleration of urbanization and the increasing density of transportation networks, road traffic noise has become the primary source of pollution affecting urban acoustic environment quality and residents' health. Related statistics show that public complaints about traffic noise have remained consistently high. To address this serious challenge, my country has promulgated and implemented the "Noise Pollution Prevention and Control Law of the People's Republic of China," which clearly sets forth the strategic requirements of strengthening noise source control and promoting a shift in governance models from "post-event treatment" to "prevention" and "scientific management."
[0029] Against this backdrop, computer simulation-based environmental noise prediction technology has become a key tool for achieving scientific governance. Existing technologies typically employ specialized noise simulation software such as Cadna / A and SoundPLAN. The technical solutions for these software programs generally follow this approach: First, utilizing a Geographic Information System (GIS)... The simulation process involves several steps: first, constructing a static 3D geographic model using data; then, manually setting sound source parameters, road material properties, and the geometric parameters and locations of sound barriers to establish simulation boundary conditions; finally, the software calculates noise distribution cloud maps of the area and predicts sound levels at specific sensitive points based on internationally recognized acoustic models such as ISO 9613. Designers then use this prediction result and, based on experience, manually identify areas exceeding noise standards. Through repeated trial and error and manual adjustment of mitigation measures (such as the height, location, or road surface type of sound barriers), the simulation results are refined until they meet national environmental noise quality standards.
[0030] However, the aforementioned existing technical solutions have obvious defects and shortcomings: First, the coordination of governance methods is poor. Existing technologies usually design and evaluate a single link in the "sound source", "propagation path" or "receiving point" in isolation, lacking the ability to coordinate simulation and system optimization of governance measures for the entire "source-path-receiver" chain. This results in insufficient overall coordination of the governance solution and makes it difficult to achieve the best comprehensive noise reduction effect.
[0031] Second, the long-term accuracy of the prediction models is insufficient. Existing models mostly use static parameters and fail to consider dynamic time-varying factors such as the aging and decay of the acoustic properties of low-noise pavement materials over time. This leads to biases in the prediction of the long-term noise reduction effect of the remediation measures, affecting the sustainability of decision-making.
[0032] Third, there is a lack of simultaneous quantitative assessment of cost-effectiveness. Existing software focuses on acoustic effect simulation and cannot be linked with engineering cost models for calculation. It is difficult to assess the economic costs of different treatment solutions in real time and accurately during the design phase, and it is impossible to conduct a scientific cost-effectiveness analysis and trade-off between noise reduction effect and engineering investment, which is not conducive to making the most economically optimal decision within a limited budget.
[0033] Fourth, end-of-pipe treatment lacks an intelligent closed-loop system. When outdoor public treatment measures (such as sound barriers) have limited noise reduction effects on certain sensitive points (such as the upper floors of high-rise buildings), existing technologies lack an automated receiving-end protection measure (such as soundproof windows) matching and recommendation mechanism based on the final indoor noise standard. It still needs to rely on manual experience for remedial design, which is inefficient and lacks accuracy.
[0034] Therefore, there is an urgent need in this field for a technical solution that can overcome the above-mentioned defects, so as to achieve full-chain collaborative simulation, full life cycle accurate prediction and integrated intelligent evaluation of cost-effectiveness of noise control solutions from source to end, thereby improving the scientific, economic and refined level of noise control work.
[0035] To address the aforementioned technical problems, this application proposes a method and system for predicting the flowback volume of coal gas wells, which can easily and efficiently predict the flowback volume based on the characteristics of coal gas reservoirs.
[0036] Figure 1 A flowchart illustrating a noise control effect prediction and evaluation method based on full-chain noise reduction measures provided in this application embodiment is shown below. Figure 1 As shown, the method includes: S1. Collect basic geographic information and sound source data of the target area to construct a three-dimensional noise field model.
[0037] S2. Based on the three-dimensional noise field model, in response to the governance command, the governance parameters covering the sound source, propagation path and receiver are obtained from the full-chain strategy library, and the acoustic propagation attenuation model and engineering cost model are called to simultaneously calculate the predicted noise value and engineering cost after the implementation of the measures.
[0038] S3. Compare the predicted noise value with the preset sound environment quality standard to generate a list of non-compliant sensitive points. Based on the principle of the weakest link effect, automatically match and recommend protective measures for the receiving end of non-compliant buildings. Finally, output a comprehensive treatment plan that includes a noise reduction effect cloud map, a visual map, and a cost estimate list.
[0039] The noise control effect prediction and evaluation method based on the whole chain noise reduction measures provided in this application can solve the problem of collaborative planning caused by the fragmentation of each link in the existing technology by constructing a whole chain governance strategy library covering sound source, propagation path and receiver.
[0040] Furthermore, this application introduces a road acoustic aging attenuation model to dynamically correct the evolution of noise reduction facility performance over time, thereby solving the problem of insufficient long-term prediction accuracy caused by the static model ignoring road aging factors in the prior art.
[0041] Meanwhile, this application combines a nonlinear engineering cost model with an acoustic model for simultaneous calculation to address the pain point of not being able to evaluate the cost-effectiveness of the scheme in real time during the design phase; this application also establishes an automatic matching mechanism for receiving end protection measures based on the principle of the shortest board effect, thereby solving the problem of insufficient coverage of outdoor governance measures for specific areas such as high-rise buildings and the lack of targeted end-of-pipe remedial solutions, thus achieving precise governance from source control to end-of-pipe closed loop.
[0042] In this embodiment of the application, basic geographic information and sound source data of the target area are collected to construct a three-dimensional noise field model, specifically including the following steps: S10, Through Data interfaces or oblique photography techniques are used to acquire geometric information of buildings, road network information, and functional area division information.
[0043] S20. Initialize traffic noise source intensity based on real-time monitoring data or standard noise source model.
[0044] Preferably, the standard noise source model can be CNOSSOS-EU (Common Noise Assessment Methods in Europe) or Rizzo Model (Advanced Railway Noise Prediction Model).
[0045] S30, Implement the model calibration mechanism by accessing automatic noise monitoring station and video surveillance data, combined with... The system identifies and acquires real-time traffic flow and vehicle type ratios, and then reverse-corrects the ground reflection coefficient and background noise baseline values of the acoustic model to keep the simulation error of the initial model within a preset range.
[0046] Specifically, when constructing the three-dimensional noise field model, this application implements a model calibration mechanism. Specifically, the system accesses video surveillance data from automatic noise monitoring stations deployed within the area and from high-point towers. Then, real-time traffic flow and vehicle type ratios are obtained through video AI recognition, and combined with measured sound level data from the monitoring stations, the ground reflection coefficient and background noise baseline values of the acoustic model are corrected in reverse. This ensures that the simulation error of the initial model is controlled within a preset range (e.g., ±3dB), thereby providing an accurate benchmark for subsequent prediction of the treatment effect.
[0047] Furthermore, in the above embodiments, the full-chain strategy library includes: a sound source parameter library, a propagation path parameter library, and a receiver parameter library. The sound source parameter library includes road surface modification material parameters and traffic control strategy parameters; the propagation path parameter library includes sound barrier type, geometric parameters, and material parameters; and the receiver parameter library includes soundproof window type, glass configuration, and window frame material parameters.
[0048] Specifically, in some embodiments, road surface reconstruction material parameters include porous asphalt, rubber asphalt, and cement concrete. Traffic control strategy parameters include policies such as traffic restrictions, speed limits, and no-honking. Sound barrier types include upright, micro-arc, and fully enclosed types. Window frame material parameters include materials such as insulated glass, vacuum glass, and ventilated soundproof windows.
[0049] Based on the various parameters provided in the above embodiments, this application uses the acoustic propagation attenuation model and the engineering cost model to perform multi-dimensional simulation calculations. Specifically, in this application's embodiments, the acoustic propagation attenuation model includes a road surface aging attenuation model, a traffic control effect prediction model, and a sound barrier insertion loss calculation model.
[0050] This application performs dynamic prediction of the road surface modification effect at the sound source end. Compared with the existing technology, traditional prediction only considers the initial noise reduction value, while this application introduces the time dimension.
[0051] Taking the user's choice of porous asphalt pavement as an example, this application first obtains the basic noise reduction coefficient. (For example, 4dB-6dB), and then the road surface aging degradation model is invoked.
[0052] Specifically, a pavement aging attenuation model is used to dynamically predict the pavement noise reduction effect. The calculation expression for the pavement aging attenuation model is as follows:
[0053] in, Based on the noise reduction coefficient, For thickness influence coefficient, For road surface thickness, This is a decay function that varies over time, being zero during the stable period after construction and decreasing linearly at a preset annual decay rate afterward. For example, in a specific example, the decay function... Before it was built The noise reduction effect remains unchanged over years (e.g., 5 years). =0; after that period, the annual decay rate is used. (e.g., a linear decrease from 1.2% to 2.5%), i.e. = .
[0054] At the same time, consider the road surface thickness The effect on the sound absorption frequency band is addressed by introducing a thickness correction term. .
[0055] This application predicts the effectiveness of traffic control at the sound source end, based on traffic flow. and vehicle speed The logarithmic relationship is calculated separately for large vehicles, medium vehicles, and small vehicles: Specifically, the calculation expression for the traffic control effect prediction model is as follows:
[0056] in, To predict annual traffic volume, the unit is vehicles per hour, and the traffic volume is predicted for the year or before the project is implemented; The current annual traffic volume is expressed in vehicles per hour, and the traffic volume in the base year or after the project is implemented. The current annual average vehicle speed is expressed in km / h, representing the average vehicle speed in the base year or before the project was implemented. To predict the average annual vehicle speed, in km / h, the average vehicle speed for the predicted year or after the project is implemented; The speed influence coefficient is used to characterize the degree of influence of vehicle speed changes on noise radiation intensity.
[0057] The value of K is between 10 and 20. For example, in some embodiments, K can be 10, 12, 15, 18, or 20.
[0058] Under low-speed conditions (<48km / h), engine noise dominates, and the relationship between vehicle speed and noise no longer follows a simple logarithmic linear relationship. Therefore, a fixed K value cannot be used directly; instead, a measured model must be used instead. For example, noise data at different low speeds (e.g., 20km / h, 30km / h, 40km / h) can be collected on-site through monitoring. Based on the measured noise data, a nonlinear regression model of "vehicle speed-source strength" is established. This model is used to directly calculate the source strength, replacing the original formula. item.
[0059] Under high-speed conditions (>48km / h), directly refer to the recommended values in the "Technical Guidelines for Environmental Impact Assessment". Generally, K is taken as 15 for mixed traffic flow, and 18~20 for highway conditions.
[0060] This application performs a dual calculation of the sound barrier's effectiveness and cost at the propagation path end.
[0061] In acoustics, the path difference δ = ST + TR between the sound source S, the top of the barrier T, and the receiving point R is calculated. SR. Calculate the Fresnel number N=2δλ, and then use the Maekawa formula or its modified form to calculate the diffraction attenuation.
[0062] Specifically, the sound barrier insertion loss calculation model calculates diffraction attenuation based on the acoustic path difference and Fresnel number. The calculation expression for the sound barrier insertion loss model is as follows:
[0063] in, For Fresnel numbers, =2δλ, where δ is the path difference and λ is the wavelength of the sound wave.
[0064] Based on the above embodiments, in this application embodiment, the engineering cost model includes a nonlinear cost model for sound barriers.
[0065] The calculation expression for the nonlinear cost model of the sound barrier is as follows:
[0066] in, For the length of the barrier, Based on the unit price, For height increment coefficient, This is a correction factor for construction difficulty.
[0067] The nonlinear cost model constructed in this application reflects the trend of increasing altitude. The increase in cost is not linear, but rather exponential or higher-order due to the influence of wind pressure resistance structural requirements. (The coefficients are reflected), while also taking into account the difficulty of construction. Adjustments to costs (such as those related to slope and roadbed modification requirements).
[0068] In this embodiment of the application, the system compares the predicted noise value with the preset sound environment quality standard to generate a list of non-compliant sensitive points.
[0069] The system automatically iterates through all sensitive points in the calculation area (such as 1 meter in front of a residential building window) and determines whether the noise level exceeds the standard during the day / night according to the "Environmental Noise Quality Standard" (GB3096). If the predicted value... If so, the building, its specific floors, and the amount exceeding the standard (e.g., exceeding the standard by 3.5 dB) are added to the list. Based on the list of non-compliant sensitive points, the receiver protection measures are automatically matched using the principle of the weakest link effect.
[0070] It should be noted that the above Used to characterize the limits of sound environment quality standards.
[0071] Determine whether daytime / nighttime standards are exceeded based on the "Environmental Noise Quality Standard" (GB3096). Specific applications are as follows: The system automatically matches the corresponding standard limit based on the functional area category (such as category 0, category 1, category 2, category 3, category 4a, category 4b) where the sensitive point is located.
[0072] For example, the daytime limit for Class 1 functional areas (residential and educational areas) is 55 dB, and the nighttime limit is 45 dB.
[0073] If the predicted value If a point is identified as a non-compliant sensitive point, it will be included in the list and trigger recommendations for end-point protection measures.
[0074] For any non-compliant sensitive points on the list, the system will proceed to a "last-mile" calculation process. At this point, the overall sound insulation level needs to be calculated. This application introduces the "weakest link" logic, which states that the overall sound insulation performance is limited by the component with the worst sound insulation (such as the window frame or sealing strip), rather than the glass itself.
[0075] In this application embodiment, the receiver protection measures based on the short-board effect principle include: Calculate the combined sound insulation of each component. The combined sound insulation of each component The calculation expression is:
[0076] in, For the total area, Let R be the area of each component, and R be the sound insulation. The sound insulation of each component; If the difference in sound insulation between components exceeds a preset threshold, the component with lower sound insulation will be identified as a weak point in sound insulation and will be upgraded until the predicted indoor noise value meets the preset standard.
[0077] For example, in a specific example, when The number of terms to sum in the sound insulation formula, (Two components: glass and window frame). If the glass's sound insulation... Sound insulation of window frame If the difference exceeds a preset threshold, the window frame is identified as a weak point, and the window frame material is upgraded until the indoor predicted noise value meets the standard.
[0078] Preferably, the sound insulation of the glass How to obtain: Based on parameters such as glass type (single-layer / double-layer / hollow / vacuum), thickness, and air layer thickness, the data is retrieved from a pre-set material acoustics database.
[0079] Window frame sound insulation How to obtain: Based on parameters such as window frame material (ordinary aluminum alloy / thermal break aluminum / PVC, etc.), cross-sectional structure, and sealing performance, the parameters are retrieved from a pre-set profile acoustic database.
[0080] Of course, the noise control effect prediction and evaluation system based on the whole chain of noise reduction measures provided in this application can automatically simulate different configurations.
[0081] For example, a standard aluminum alloy frame with single-pane glass versus a thermally broken aluminum frame with double-pane insulated glass. If calculations reveal... Very high but The low sound insulation level results in insufficient overall sound insulation, and the system will automatically recommend upgrading the window frame material until... = - To meet indoor noise standards, for example, the standard for indoor noise at night is 30 dB.
[0082] Finally, the system outputs a report that includes source modification parameters, sound barrier construction plans, a list of non-compliant households requiring soundproof window renovation, and a total cost-benefit analysis report.
[0083] The following is a detailed explanation of the specific calculation logic at both the sound source and propagation ends of the noise control effect prediction and evaluation system based on full-chain noise reduction measures provided in this application, using a specific example.
[0084] In practical applications, the system first receives management instructions from users for specific road sections, such as converting the road surface to rubber asphalt and implementing nighttime traffic control.
[0085] Specifically, when predicting the effects of road reconstruction, the system no longer uses only a single noise reduction value, but instead obtains a basic noise reduction coefficient based on the road material type. and thickness influence coefficient Combined with the road surface thickness input by the user and predicted years Call the formula Calculations are performed. Among them, the attenuation function... Operates according to pre-set rules: during the stabilization period after completion. The noise reduction effect remains unchanged within the current period; after that period, it will decrease at the set annual attenuation rate. A linear decrease is performed to obtain the dynamic noise reduction effect throughout the entire life cycle.
[0086] Furthermore, when predicting the effects of traffic control, the system obtains traffic flow data before and after the control measures are implemented. and vehicle speed Changes in data, using formulas The reduction in sound source intensity is calculated, where the velocity influence coefficient K can be nonlinearly corrected based on measured data in the low-speed range to ensure prediction accuracy. In the calculation... The system not only relies on static statistical data but also supports dynamic input based on machine vision. Based on historical observation data from the tower-connected cameras, the system analyzes the proportion of large trucks on a given road segment during specific time periods (e.g., 10:00 PM to 6:00 AM). If the system identifies the segment as frequently used by heavy-duty vehicles, it automatically increases the weight of the sound power level of large vehicles in the prediction model. This ensures that the predicted noise reduction of traffic restriction measures better reflects actual road conditions and avoids prediction bias caused by incorrect estimations of vehicle type composition.
[0087] Furthermore, during the sound barrier mitigation simulation, the system calculates the sound path difference and insertion loss based on the barrier path drawn by the user and the set height and material parameters. Simultaneously, the system incorporates a nonlinear cost model. Cost estimation was performed, and the model considered the exponential increase in the cost of wind-resistant structures due to the increase in barrier height (from...). (reflected by coefficients) and the difficulty of the construction environment (by...) (The coefficients are reflected), thus achieving a dual evaluation from acoustic effects to engineering economics.
[0088] Figure 2 This document presents a schematic diagram of closed-loop matching logic for receiver protection measures based on the short-board effect principle, as provided in an embodiment of this application. A specific embodiment will now be used to explain the closed-loop matching logic process of the receiver protection measures based on the short-board effect principle of this application. Figure 2 As shown: After addressing the source and propagation path issues described in the above embodiments, the system still detects some sensitive buildings, such as the upper floors of high-rise residential buildings. When the predicted noise level exceeds a preset standard, the system automatically initiates a fallback calculation process.
[0089] Specifically, the system first generates a list of substandard buildings, identifying the floors exceeding the standard and their decibel levels. Then, based on the principle of the weakest link effect, the system optimizes and matches sound insulation window solutions. When calculating the combined sound insulation, the system uses a formula... Taking into account the glass body With window frame profiles Its sound insulation performance.
[0090] During this process, the system executes the bottleneck judgment logic: if the calculation finds... ( For example, a preset threshold. When the sound insulation performance of the glass is far superior to that of the window frame, the system determines that the window frame is the weakest link in sound insulation and automatically prioritizes upgrading the window frame material. For example, upgrading from ordinary aluminum alloy to thermally broken aluminum or PVC, rather than simply increasing the glass thickness. Through iterative calculations, the system continues to refine the system until the predicted indoor noise level is reached. To meet the sound environment quality standards, the system ultimately outputs a comprehensive treatment plan and its total cost, including source modification, sound barrier construction, and soundproof window renovation.
[0091] Figure 3 A module block diagram of a noise control effect prediction and evaluation system based on full-chain noise reduction measures provided in this application embodiment is shown below. Figure 3 As shown, the system includes: a data acquisition and modeling module, a full-chain configuration and simulation module, and an evaluation report generation module.
[0092] The data acquisition and modeling module is used to collect basic geographic information and sound source data of the target area and construct a three-dimensional noise field model.
[0093] The full-chain configuration and simulation module is used to respond to governance instructions, obtain governance parameters covering sound sources, propagation paths and receivers from the full-chain strategy library, and call the acoustic propagation attenuation model and engineering cost model to simultaneously calculate the predicted noise value and engineering cost after the implementation of measures.
[0094] The assessment report generation module compares the predicted noise values with the preset sound environment quality standards, generates a list of non-compliant sensitive points, automatically matches and recommends receiving end protection measures for non-compliant buildings based on the principle of the weakest link effect, and finally outputs a comprehensive treatment plan that includes a noise reduction effect cloud map, a visual spectrum, and a cost estimate list.
[0095] In some embodiments, the data acquisition and modeling module also includes Map data, oblique photogrammetry model, student source database, and cost rule library.
[0096] The end-to-end configuration and simulation module also includes an end-to-end strategy configuration module and a simulation calculation module. The end-to-end strategy configuration module includes a source governance parameter library, a propagation path parameter library, and a receiver parameter library. The simulation calculation module includes a source strength calculation unit, a propagation attenuation unit, and an engineering cost calculation unit.
[0097] The assessment report generation module includes a noise heat map, a list of non-compliant sensitive points, and a project cost estimate.
[0098] The noise control effect prediction and evaluation system based on full-chain noise reduction measures provided in this application can solve the problem of collaborative planning caused by the fragmentation of each link in the existing technology by constructing a full-chain governance strategy library covering sound source, propagation path and receiving end.
[0099] Furthermore, this application introduces a road acoustic aging attenuation model to dynamically correct the evolution of noise reduction facility performance over time, thereby solving the problem of insufficient long-term prediction accuracy caused by the static model ignoring road aging factors in the prior art.
[0100] Meanwhile, this application combines a nonlinear engineering cost model with an acoustic model for simultaneous calculation to address the pain point of not being able to evaluate the cost-effectiveness of the scheme in real time during the design phase; this application also establishes an automatic matching mechanism for receiving end protection measures based on the principle of the shortest board effect, thereby solving the problem of insufficient coverage of outdoor governance measures for specific areas such as high-rise buildings and the lack of targeted end-of-pipe remedial solutions, thus achieving precise governance from source control to end-of-pipe closed loop.
[0101] Based on the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described in the above embodiments.
[0102] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0103] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A noise control prediction and assessment method based on whole-chain noise reduction, characterized in that, The method includes: Collect basic geographic information and sound source data of the target area, and construct a three-dimensional noise field model; Based on the three-dimensional noise field model, in response to the governance command, the governance parameters covering the sound source, propagation path and receiver are obtained from the full-chain strategy library, and the acoustic propagation attenuation model and engineering cost model are called to simultaneously calculate the predicted noise value and engineering cost after the implementation of the measures. The predicted noise value is compared with the preset sound environment quality standard to generate a list of substandard sensitive points. Based on the principle of the weakest link effect, the system automatically matches and recommends protective measures for the receiving end of the substandard buildings. Finally, a comprehensive treatment plan is output, which includes a noise reduction effect cloud map, a visual spectrum, and a cost estimate list.
2. The method according to claim 1, characterized in that, Collect basic geographic information and sound source data of the target area, and construct a three-dimensional noise field model, specifically including: pass Data interfaces or oblique photography techniques are used to acquire geometric information of buildings, road network information, and functional zone division information; Initialize traffic noise source intensity based on real-time monitoring data or standard noise source models; The model calibration mechanism is implemented by accessing automatic noise monitoring stations and video surveillance data, combined with... The system identifies and acquires real-time traffic flow and vehicle type ratios, and then reverse-corrects the ground reflection coefficient and background noise baseline values of the acoustic model to keep the simulation error of the initial model within a preset range.
3. The method according to claim 1, characterized in that, The full-chain strategy library includes: a source parameter library, a propagation path parameter library, and a receiver parameter library; The sound source parameter library includes road surface reconstruction material parameters and traffic control strategy parameters; the propagation path parameter library includes sound barrier type, geometric parameters, and material parameters; and the receiving end parameter library includes soundproof window type, glass configuration, and window frame material parameters.
4. The method according to claim 1, characterized in that, The acoustic propagation attenuation model includes a road surface aging attenuation model, which is used to dynamically predict the road surface noise reduction effect. The calculation expression for the road surface aging and attenuation model is as follows: in, Based on the noise reduction coefficient, For thickness influence coefficient, For road surface thickness, It is a decay function that varies with time. It is zero during the stable period after construction and decreases linearly according to the preset annual decay rate after the stable period.
5. The method according to claim 1, characterized in that, The acoustic propagation attenuation model includes a traffic control effect prediction model; The calculation expression for the traffic control effect prediction model is as follows: in, To predict annual traffic volume, the unit is vehicles per hour, and the traffic volume is predicted for the year or before the project is implemented; The current annual traffic volume is expressed in vehicles per hour, and the traffic volume in the base year or after the project is implemented. The current annual average vehicle speed is expressed in km / h, representing the average vehicle speed in the base year or before the project was implemented. To predict the average annual vehicle speed, in km / h, the average vehicle speed for the predicted year or after the project is implemented; The speed influence coefficient is used to characterize the degree of influence of vehicle speed changes on noise radiation intensity.
6. The method according to claim 1, characterized in that, The acoustic propagation attenuation model includes a sound barrier insertion loss calculation model, which calculates diffraction attenuation based on the acoustic path difference and Fresnel number. The calculation expression for the sound barrier insertion loss calculation model is as follows: in, For Fresnel numbers, =2δλ, where δ is the path difference and λ is the wavelength of the sound wave.
7. The method according to claim 1, characterized in that, The engineering cost model includes a nonlinear cost model for sound barriers; The calculation expression for the nonlinear cost model of the sound barrier is as follows: in, For the length of the barrier, Based on the unit price, For height increment coefficient, This is a correction factor for construction difficulty.
8. The method according to claim 1, characterized in that, The matching of receiver protection measures based on the principle of the weakest link effect includes: Calculate the combined sound insulation of each component. The combined sound insulation of the components The calculation expression is: in, For the total area, Let R be the area of each component, and R be the sound insulation. The sound insulation of each component; If the difference in sound insulation between components exceeds a preset threshold, the component with lower sound insulation is identified as the weakest link in sound insulation, and the component is upgraded until the indoor predicted noise value meets the preset standard.
9. A noise control prediction and assessment system based on whole-chain noise reduction, characterized in that, The system includes: The data acquisition and modeling module is used to collect basic geographic information and sound source data of the target area and construct a three-dimensional noise field model. The full-chain configuration and simulation module is used to respond to governance instructions, obtain governance parameters covering sound sources, propagation paths and receivers from the full-chain strategy library, and call the acoustic propagation attenuation model and engineering cost model to simultaneously calculate the predicted noise value and engineering cost after the implementation of measures. The assessment report generation module compares the predicted noise values with preset acoustic environment quality standards, generates a list of non-compliant sensitive points, automatically matches and recommends receiving end protection measures for non-compliant buildings based on the principle of the weakest link effect, and finally outputs a comprehensive treatment plan including a noise reduction effect cloud map, a visual spectrum, and a cost estimate list.
10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 8.