Acoustic environment influence prediction and evaluation method for alternating current transmission line

Abstract

The invention relates to the technical field of sound environment influence prediction, and particularly discloses an AC power transmission line sound environment influence prediction and evaluation method. The method comprises the following steps: determining a plurality of sensitive targets by acquiring project environment data of a target line project; sound source characteristic modeling is carried out on the target line project; planning a plurality of prediction receiving points according to the plurality of sensitive targets, and carrying out noise propagation simulation based on a sound source characteristic model; performing visualization processing on the noise prediction data; comparing and evaluating the noise prediction data; and performing optimization analysis to generate a plurality of engineering optimization suggestions. The method can obtain engineering environment data, determine a plurality of sensitive targets, perform simulation recording of sound source characteristic modeling and noise prediction, fully consider difference of different line structures on noise propagation and actual environmental influence factors according to actual conditions of engineering, determine a plurality of sensitive targets, and perform corresponding planning of prediction receiving points, thereby improving the accuracy of noise prediction. And the prediction precision is effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of acoustic environment impact prediction technology, and particularly relates to a method for predicting and evaluating the acoustic environment impact of AC transmission lines. Background Technology

[0002] The acoustic environment impact of AC transmission lines refers to the noise generated during the operation of AC transmission lines due to corona discharge, conductor vibration, and the operation of electrical equipment, which affects the surrounding environment and human life. This noise is mainly manifested as corona noise, which is usually more obvious under conditions of high humidity, low atmospheric pressure, or rainy and foggy weather. It presents as continuous or intermittent noise and may interfere with quiet areas, residential areas, schools, hospitals, and other sensitive areas.

[0003] The acoustic environment impact not only affects the public's auditory comfort and health, but also the social acceptance and environmental management requirements of power transmission projects. With the increasing demands for ecological and environmental protection, the acoustic environment impact of AC transmission lines has become an important part of the environmental impact assessment of power projects.

[0004] In existing technologies, the prediction and evaluation of the acoustic environment impact of AC transmission lines are usually based on a single line and fixed conductor layout, which ignores the differences in noise propagation caused by different line structures, simplifies the noise propagation path, does not consider actual environmental factors, and does not clearly identify sensitive targets along the line, resulting in insufficient prediction accuracy. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting and evaluating the acoustic environment impact of AC transmission lines, aiming to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for predicting and evaluating the acoustic environmental impact of AC transmission lines, the method specifically includes the following steps: The target line project of the AC transmission line is identified, the engineering environment data of the target line project is obtained, and the engineering environment data is identified to determine multiple sensitive targets; Based on the engineering environment data, the acoustic source characteristics of the target line project are modeled using the terrain diffraction effect and historical meteorological conditions to generate an acoustic source characteristic model. Based on multiple sensitive targets, spatial adaptive optimization is performed using environmental sensitivity and sound propagation path to plan multiple predicted receiving points. Noise propagation simulation is performed based on the sound source characteristic model, and noise prediction data is recorded. The noise prediction data is then visualized. Acquire standard acoustic environment data, compare and evaluate the noise prediction data to determine whether there are any exceedances, and divide multiple acoustic environment impact areas. Multiple acoustic environment impact areas of the target line project are optimized and analyzed, generating multiple engineering optimization suggestions.

[0007] As a further limitation of the technical solution of this embodiment of the invention, the step of determining the target line project of the AC transmission line, obtaining the engineering environment data of the target line project, and identifying multiple sensitive targets by identifying the engineering environment data specifically includes the following steps: Determine the target line project for AC transmission lines; Obtain the basic engineering information of the target line project; Obtain the engineering terrain data of the target route project; Obtain historical meteorological data for the target railway line project; By combining the basic engineering information, the engineering terrain data, and the historical meteorological data, engineering environmental data is obtained; The engineering environment data was identified to determine multiple sensitive targets.

[0008] As a further limitation of the technical solution of this invention, the step of modeling the sound source characteristics of the target line project based on the engineering environment data, utilizing terrain diffraction effect and historical meteorological conditions, and generating a sound source characteristic model specifically includes the following steps: Based on the aforementioned engineering environment data, multiple sound source types were identified; Based on the engineering environment data, obtain the conductor sound source parameters corresponding to multiple sound source types, and determine multiple single sound sources; Based on the aforementioned conductor sound source parameters, an equivalent sound source merging analysis is performed to identify multiple composite sound sources; Based on the engineering environment data, multiple single sound sources and multiple composite sound sources are spatially deployed using terrain diffraction effects and historical meteorological data to construct a sound source characteristic model.

[0009] As a further limitation of the technical solution of this invention, the step of spatially deploying multiple single sound sources and multiple composite sound sources based on the engineering environment data, utilizing terrain diffraction effects and historical meteorological data, and constructing a sound source characteristic model specifically includes the following steps: Based on the engineering environment data, construct a three-dimensional model of the project; Determine the spatial locations of the multiple single sound sources and the multiple composite sound sources; Based on the spatial locations of the multiple sound sources, the multiple single sound sources and multiple composite sound sources are spatially arranged in the three-dimensional engineering model using terrain diffraction effect and historical meteorological data to construct a sound source characteristic model.

[0010] As a further limitation of the technical solution of this invention, the step of spatial adaptive optimization based on environmental sensitivity and sound propagation path to plan multiple predicted receiving points according to multiple sensitive targets, performing noise propagation simulation based on the sound source characteristic model, and recording noise prediction data specifically includes the following steps: Obtain the number of receiving points and the sensitivity ratio; Based on the number of receiving points and the sensitivity ratio, reception planning is carried out inside and outside multiple sensitive targets, and multiple predicted receiving points are selected. Noise propagation simulation is performed based on the sound source characteristic model, and noise reception simulation is performed at multiple predicted receiving points, recording the noise prediction data.

[0011] As a further limitation of the technical solution of this embodiment of the invention, the step of simulating noise propagation based on the sound source characteristic model, simulating noise reception at multiple predicted receiving points, and recording noise prediction data specifically includes the following steps: Determine the propagation and reception paths for multiple predicted receiving points; Select typical forecast weather; Based on the typical predicted weather, sound propagation attenuation is processed. Noise reception simulations were performed at multiple predicted receiving points, and noise prediction data was recorded.

[0012] As a further limitation of the technical solution of this embodiment of the invention, the visualization processing of the noise prediction data specifically includes the following steps: Based on the noise prediction data, a noise level profile is plotted. Based on the noise prediction data, draw a noise contour map; Extract target prediction noise values ​​for multiple sensitive targets from the noise prediction data; The noise level profile, the noise contour map, and the target predicted noise value are visualized.

[0013] As a further limitation of the technical solution of this embodiment of the invention, the steps of obtaining sound environment standard data, comparing and evaluating the noise prediction data, determining whether there is a situation of exceeding the standard, and dividing multiple sound environment impact areas specifically include the following steps: Obtain acoustic environment standard data; Based on the aforementioned acoustic environment standard data, the noise prediction data is compared and evaluated to determine whether there are any exceedances. When there are cases of noise exceeding the standard, multiple levels of noise exceeding the standard are defined; Identify multiple acoustic environment impact areas corresponding to the various degrees of noise exceeding the standard; Warning signs are issued for multiple areas affected by the acoustic environment.

[0014] As a further limitation of the technical solution of this invention, the optimization analysis of multiple acoustic environment impact areas of the target line project and the generation of multiple engineering optimization suggestions specifically include the following steps: Route optimization suggestions for adjusting routes in multiple areas affected by the acoustic environment; Layout optimization suggestions for mitigating sensitivity points of multiple sensitive targets; Recommendations for noise reduction and control during the construction of the target line project; Establish long-term noise monitoring points and conduct real-time monitoring, reporting, and feedback.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention, through obtaining engineering environmental data of a target railway line project, identifies multiple sensitive targets; models the sound source characteristics of the target railway line project; plans multiple predicted receiving points based on the multiple sensitive targets; simulates noise propagation based on the sound source characteristic model; visualizes the noise prediction data; compares and evaluates the noise prediction data; performs optimization analysis; and generates multiple engineering optimization suggestions. It can acquire engineering environmental data, identify multiple sensitive targets, and then perform sound source characteristic modeling and noise prediction simulation recording. It can fully consider the differences in noise propagation caused by different line structures and actual environmental influencing factors according to the actual situation of the project, and effectively improve prediction accuracy by identifying multiple sensitive targets and planning corresponding predicted receiving points. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention.

[0017] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0018] Figure 2 A flowchart of the engineering environment data acquisition and identification process provided in the embodiments of the present invention is shown.

[0019] Figure 3 A flowchart illustrating sound source characteristic modeling is shown in the method provided by an embodiment of the present invention.

[0020] Figure 4A flowchart illustrating the construction of a sound source characteristic model in the method provided by an embodiment of the present invention is shown.

[0021] Figure 5 A flowchart illustrating noise propagation simulation in the method provided by an embodiment of the present invention is shown.

[0022] Figure 6 A flowchart illustrating the recording of noise prediction data in the method provided by an embodiment of the present invention is shown.

[0023] Figure 7 A flowchart illustrating the visualization processing of noise prediction data in the method provided by an embodiment of the present invention is shown.

[0024] Figure 8 A flowchart illustrating the comparative evaluation of noise prediction data in the method provided by an embodiment of the present invention is shown.

[0025] Figure 9 A flowchart illustrating the generation of multiple engineering optimization suggestions in the method provided by an embodiment of the present invention is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0027] Understandably, in existing technologies, the prediction and evaluation of the acoustic environment impact of AC transmission lines are usually based on a single line and fixed conductor layout, ignoring the differences in noise propagation caused by different line structures. Furthermore, the noise propagation path is simplified, the actual environmental impact factors are not considered, and the identification of sensitive targets along the line is unclear, resulting in insufficient prediction accuracy.

[0028] To address the aforementioned issues, this invention identifies the target AC transmission line project, acquires its environmental data, and identifies multiple sensitive targets. Based on the environmental data, it models the sound source characteristics of the target line project, generating a sound source characteristic model. According to the multiple sensitive targets, it plans multiple predicted receiving points, simulates noise propagation based on the sound source characteristic model, and records the noise prediction data. The noise prediction data is then visualized. Sound environment standard data is acquired, and the noise prediction data is compared and evaluated to determine if any exceedances occur, and multiple sound environment impact areas are delineated. Optimization analysis is performed on the multiple sound environment impact areas of the target line project, generating multiple engineering optimization suggestions. This approach, by acquiring environmental data, identifying multiple sensitive targets, and then performing sound source characteristic modeling and noise prediction simulation recording, fully considers the differences in noise propagation caused by different line structures and actual environmental factors, and effectively improves prediction accuracy by identifying multiple sensitive targets and planning corresponding predicted receiving points.

[0029] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0030] Specifically, the method for predicting and evaluating the acoustic environment impact of AC transmission lines includes the following steps: Step S101: Determine the target line project of the AC transmission line, obtain the engineering environment data of the target line project, and identify multiple sensitive targets by analyzing the engineering environment data.

[0031] In this embodiment of the invention, by determining the target AC transmission line project, basic engineering information of the target line project (including project location, voltage level, erection method, conductor arrangement, tower structure type, conductor type and conductor diameter, etc.), engineering terrain data (composed of multiple different elements, including terrain undulation, altitude, slope and surface material, etc.) and historical meteorological data (including historical rainfall data, historical humidity data, historical wind speed data and historical temperature data, etc.) are obtained. By integrating the basic engineering information, engineering terrain data and historical meteorological data, the engineering environmental data of the target line project is obtained. Then, the engineering environmental data is identified to determine multiple sensitive targets (including residential areas, school areas, hospital areas and nature reserves, etc.).

[0032] Understandably, the installation methods include single-circuit, double-circuit, and parallel installations on the same tower; the surface materials include water bodies, bare land, buildings, and vegetation. Furthermore, the identification of sensitive targets can be combined with Geographic Information System (GIS) data and socioeconomic data, such as population density and land use types, to more accurately identify highly sensitive areas.

[0033] Specifically, Figure 2 A flowchart of the engineering environment data acquisition and identification process provided in the embodiments of the present invention is shown.

[0034] In a preferred embodiment of the present invention, determining the target AC transmission line project, acquiring the engineering environment data of the target transmission line project, and identifying multiple sensitive targets by analyzing the engineering environment data specifically includes the following steps: Step S1011: Determine the target line project for the AC transmission line; Step S1012: Obtain basic engineering information of the target line project; Step S1013: Obtain the engineering terrain data of the target route project; Step S1014: Obtain historical meteorological data for the target line project; Step S1015: Combine the basic engineering information, the engineering terrain data, and the historical meteorological data to obtain engineering environmental data; Step S1016: Identify the engineering environment data and determine multiple sensitive targets.

[0035] Furthermore, in step S1016, when identifying engineering environment data, machine learning algorithms (such as support vector machines or random forests) can be used to automatically classify and prioritize sensitive targets to improve identification efficiency and accuracy. For example, a model can be trained to identify high-frequency sensitive targets such as residential areas and schools, and the list of sensitive targets can be updated in conjunction with real-time data.

[0036] Furthermore, the method for predicting and evaluating the acoustic environment impact of AC transmission lines also includes the following steps: Step S102: Based on the engineering environment data, the sound source characteristics of the target line project are modeled using terrain diffraction effect and historical meteorological conditions to generate a sound source characteristic model.

[0037] In this embodiment of the invention, based on the number of conductors, their arrangement, and meteorological conditions in the engineering environment data, multiple sound source types are determined for single-circuit, double-circuit, and parallel lines. Then, based on the engineering environment data, the sound power level per unit length of conductor under rainy conditions is estimated using conductor material, diameter, number of splits, voltage level, etc., to obtain conductor sound source parameters corresponding to multiple sound source types and determine multiple single sound sources. For double-circuit and parallel lines, equivalent sound source merging analysis is performed based on the conductor sound source parameters to determine multiple composite sound sources. Subsequently, based on the engineering environment data, a three-dimensional engineering model is constructed, and the spatial locations of multiple single and composite sound sources are determined. Then, according to the spatial locations of multiple sound sources, multiple single and composite sound sources are spatially arranged in the three-dimensional engineering model to construct a sound source characteristic model of the target line project.

[0038] Specifically, when constructing a sound source characteristic model, the calculation of terrain diffraction effects can employ geometric diffraction theory (GTD) or uniform diffraction theory (UTD) to more accurately simulate the diffraction behavior of sound waves at terrain obstacles. The use of historical meteorological data includes statistical analysis of wind speed, temperature gradients, and humidity to determine typical conditions for sound propagation. For example, based on historical meteorological data, sound speed profiles can be calculated, thereby correcting the sound propagation path.

[0039] Specifically, Figure 3 A flowchart illustrating sound source characteristic modeling is shown in the method provided by an embodiment of the present invention.

[0040] In a preferred embodiment of the present invention, the step of modeling the sound source characteristics of the target railway line project based on the engineering environment data, utilizing terrain diffraction effects and historical meteorological conditions, and generating a sound source characteristic model specifically includes the following steps: Step S1021: Based on the engineering environment data, determine multiple sound source types; Step S1022: Based on the engineering environment data, obtain the conductor sound source parameters corresponding to the multiple sound source types, and determine multiple single sound sources; Step S1023: Based on the conductor sound source parameters, perform equivalent sound source merging analysis to determine multiple composite sound sources; Step S1024: Based on the engineering environment data, the multiple single sound sources and multiple composite sound sources are spatially deployed using terrain diffraction effect and historical meteorological data to construct a sound source characteristic model.

[0041] Specifically, Figure 4 A flowchart illustrating the construction of a sound source characteristic model in the method provided by an embodiment of the present invention is shown.

[0042] In a preferred embodiment of the present invention, the step of spatially deploying multiple single sound sources and multiple composite sound sources based on the engineering environment data, utilizing terrain diffraction effects and historical meteorological data, and constructing a sound source characteristic model specifically includes the following steps: Step S10241: Construct a three-dimensional model of the project based on the engineering environment data; Step S10242: Determine the spatial locations of the multiple single sound sources and the multiple composite sound sources; Step S10243: According to the spatial locations of the multiple sound sources, in the engineering three-dimensional model, the multiple single sound sources and the multiple composite sound sources are spatially arranged using terrain diffraction effect and historical meteorological data to construct a sound source characteristic model.

[0043] Specifically, based on the spatial locations of the multiple sound sources, in the engineering three-dimensional model, the multiple single sound sources and multiple composite sound sources are spatially arranged using terrain diffraction effects and historical meteorological data to construct a sound source characteristic model. The specific steps are as follows: The terrain surface features are obtained through the engineering 3D model; a sound wave diffraction attenuation calculation layer is constructed based on the terrain surface features; the sound wave diffraction attenuation calculation layer is used to spatially superimpose the spatial location of the sound source with the engineering terrain data, and at the same time, the sound wave diffraction compensation algorithm is used to compensate and correct the sound source points affected by terrain obstruction in order to obtain the corrected sound source parameter set. Based on hourly wind direction and speed data from historical meteorological data, a sound propagation dominance direction model was established. The propagation path of the modified sound source parameter set was optimized using the sound propagation dominance direction model. The high-frequency component weight was added to the sound source points downstream of the prevailing wind direction, and the low-frequency component was enhanced for the sound source points corresponding to the high incidence of inversion layer. Then, the directional adjustment was performed on the sound source points in the target area with significant sound speed gradient changes according to the seasonal distribution characteristics in historical meteorological data, so as to obtain the meteorological coupled sound source characteristic parameters. The meteorological coupled sound source characteristic parameters are input into the engineering three-dimensional model to perform sound-structure coupling verification, and a sound source-tower spatial relationship matrix is ​​established. Based on the spatial relationship described by the sound source-tower spatial relationship matrix, the displacement of sound source points smaller than the safe distance from the transmission tower is adjusted to obtain a suitable sound source spatial distribution. A sound source interference effect evaluation model is constructed based on the sound source parameters of the conductor. The sound source interference effect evaluation model is used to perform phase interference analysis on the adapted spatial distribution of sound sources. A sound source time-series activation control strategy is adopted to perform time-division activation control on sound source points with spatial spacing less than the critical value. Then, the steady-state noise superposition effect is eliminated by separating the time dimension to generate a sound source characteristic model.

[0044] Furthermore, in the sound source interference effect evaluation model, phase interferometry analysis can employ Fourier transform or wavelet transform to identify the interference patterns between sound sources. The threshold value can be determined based on the ratio of the sound wavelength to the distance between the sound sources; for example, when the distance between the sound sources is less than half a wavelength, time-division activation control can be implemented. Temporal separation can be achieved by introducing random delays or periodic activation to break the coherence of steady-state noise.

[0045] Furthermore, this invention overcomes the technical limitations of traditional sound source modeling, which only performs simple attenuation calculations of terrain influence through terrain sound shadow effect compensation, and achieves dynamic compensation of terrain diffraction effect; this invention solves the problem of disconnect between meteorological elements and sound source characteristics in traditional methods by establishing a meteorological-sound source coupling mechanism and deeply integrating historical meteorological models into the sound source characteristic parameterization process.

[0046] Furthermore, the method for predicting and evaluating the acoustic environment impact of AC transmission lines also includes the following steps: Step S103: Based on the multiple sensitive targets, spatial adaptive optimization is performed using environmental sensitivity and sound propagation path to plan multiple predicted receiving points, noise propagation simulation is performed based on the sound source characteristic model, and noise prediction data is recorded.

[0047] In this embodiment of the invention, by obtaining the number of receiving points and the sensitivity ratio, and then according to the number of receiving points and the sensitivity ratio, receiving planning is carried out inside and outside multiple sensitive targets (with a focus on the periphery of sensitive targets in order to obtain the spatial distribution characteristics of the sound environment influence), multiple predicted receiving points are selected, and noise propagation simulation is performed based on the sound source characteristic model to determine the propagation and receiving paths of multiple predicted receiving points (considering sound propagation effects such as straight-line propagation, reflection, and diffraction), and typical predicted weather conditions (such as rainy weather conditions) are selected. Based on the typical predicted weather conditions, sound propagation attenuation is processed (comprehensively considering the influence of air absorption, surface reflection, vegetation obstruction, building shading, and other factors on the propagation process), and noise receiving simulation is performed at multiple predicted receiving points, and noise prediction data is recorded.

[0048] Specifically, when planning and forecasting receiver points, environmental sensitivity calculations can be weighted by considering factors such as population density, activity type (e.g., residential areas, schools), and time of day (e.g., quiet nighttime hours). Sound propagation path optimization can employ ray tracing or beam tracing methods to simulate sound wave propagation in complex environments. The selection of typical forecast weather conditions can be based on cluster analysis of historical meteorological data to identify the most representative weather scenarios, such as high humidity and low wind speed conditions.

[0049] Specifically, Figure 5 A flowchart illustrating noise propagation simulation in the method provided by an embodiment of the present invention is shown.

[0050] In a preferred embodiment of the present invention, the step of spatial adaptive optimization based on environmental sensitivity and sound propagation path to plan multiple predicted receiving points according to multiple sensitive targets, performing noise propagation simulation based on the sound source characteristic model, and recording noise prediction data specifically includes the following steps: Step S1031: Obtain the number of receiving points and the sensitivity ratio; Step S1032: According to the number of receiving points and the sensitivity ratio, perform reception planning inside and outside multiple sensitive targets, and select multiple predicted receiving points; Step S1033: Based on the sound source characteristic model, noise propagation simulation is performed, noise reception simulation is performed at multiple predicted receiving points, and noise prediction data is recorded.

[0051] Specifically, based on the number of receiving points and the sensitivity ratio, reception planning is performed inside and outside multiple sensitive targets, and multiple predicted receiving points are selected. The specific steps are as follows: Based on the elevation change characteristics in engineering topographic data and the prevailing wind rose diagram in historical meteorological data, a spatial resolution adaptive adjustment model is constructed. The number of receiving points is input into the spatial resolution adaptive adjustment model to generate an initial monitoring grid. The topographic relief threshold is determined based on the initial monitoring grid. Based on the initial monitoring grid, the grid nodes are densified in the topographic areas that exceed the topographic relief threshold, and the grid coverage is expanded in the downstream direction of the prevailing wind to generate a geographic meteorological-guided initial monitoring grid. By identifying the engineering environment data, multiple sensitive targets are determined to obtain a set of sensitive target coordinates. Spatial overlay analysis is performed on the initial monitoring grid of geographic meteorological guidance and the set of sensitive target coordinates to obtain the environmental sensitivity index of each grid unit. Using the environmental sensitivity index, the receiving point density of the grid around the sensitive targets is enhanced by using an inverse distance weighting algorithm. At the same time, the azimuth compensation weighting is applied to the grid in the direction of nighttime sound propagation advantage by using the frequency of inversion layer occurrence in historical meteorological data to generate an optimized grid distribution. The tower coordinates and the perpendicular envelope of the conductor arc are obtained through the engineering 3D model. The reachability of the optimized grid distribution is verified by using the tower coordinates and the perpendicular envelope of the conductor arc, and a candidate set of suitable receiving points is obtained. Using the environmental sensitivity index, an environmental sensitivity-weighted Voronoi diagram spatial segmentation algorithm is adopted to optimize the spatial distribution of each receiver point in the candidate receiver point set. At the same time, the elevation gradient of the traditional planar Voronoi unit is corrected according to the engineering terrain data. Then, based on the seasonal variation characteristics of historical meteorological data, vertical height layer receiver points are added to areas with high inversion layer to obtain a three-dimensional receiver point distribution scheme. Based on a three-dimensional receiver point distribution scheme, receiver planning is performed inside and outside multiple sensitive targets, and multiple predicted receiver points are selected.

[0052] Furthermore, in the environmental sensitivity-weighted Voronoi graph spatial segmentation algorithm, the environmental sensitivity index can be used as a weight for generating Voronoi cells, resulting in smaller cells and more receiver points in highly sensitive areas. Elevation gradient correction can be achieved using digital elevation model (DEM) data to ensure the representativeness of receiver points in the vertical direction. Receiver points in vertically layered areas prone to inversions can be set within the typical height range of the inversion layer, such as 10-100 meters above the ground, to capture the vertical variations in sound propagation.

[0053] Furthermore, this invention integrates abrupt terrain changes and meteorological propagation characteristics into the basic framework of receiver point layout through dynamic grid density division, overcoming the environmental adaptability defects of the traditional uniform distribution method; this invention adopts a dual optimization mechanism of sensitivity weighting and path accessibility correction, so that the distribution of receiver points simultaneously meets the requirements of acoustic monitoring accuracy and engineering feasibility; this invention achieves synergistic optimization of horizontal coverage and vertical gradient monitoring by introducing three-dimensional spatial equalization verification technology.

[0054] Specifically, Figure 6 A flowchart illustrating the recording of noise prediction data in the method provided by an embodiment of the present invention is shown.

[0055] In a preferred embodiment of the present invention, the step of simulating noise propagation based on the sound source characteristic model, simulating noise reception at multiple predicted receiving points, and recording noise prediction data specifically includes the following steps: Step S10331: Determine the propagation and reception paths of the multiple predicted receiving points; Step S10332: Select typical forecast weather; Step S10333: Based on the typical predicted weather, perform sound propagation attenuation processing; Step S10334: Perform noise reception simulation at multiple predicted receiving points and record noise prediction data.

[0056] Specifically, based on the aforementioned typical predicted weather, sound propagation attenuation is processed, and the specific steps are as follows: Atmospheric stability classification indicators are determined using historical meteorological data. Dynamic correlation rules between meteorological parameters and sound wave refraction effects are established based on typical forecast meteorological conditions. Using the dynamic correlation rules and atmospheric stability classification indicators, a bending correction model for the sound wave propagation path caused by temperature and humidity stratification is constructed. The meteorological-sound wave refraction correlation matrix is ​​output through the bending correction model for the sound wave propagation path caused by temperature and humidity stratification. The meteorological-sound wave refraction correlation matrix includes the sound velocity gradient distribution, temperature and humidity stratification data, and air absorption coefficient. By utilizing the sound velocity gradient distribution, a three-dimensional spatial grid system is constructed along the propagation and reception path. The sound wave refraction effect is quantized and allocated to each grid cell in the three-dimensional spatial grid system. Then, a coupled calculation model is constructed by combining engineering terrain data. The air absorption coefficient is dynamically segmented vertically based on temperature and humidity stratification data, and then combined with a coupled calculation model to obtain a terrain-meteorological coupled attenuation field model. The frequency-varying characteristics of the attenuation field are obtained through a terrain-meteorological coupled attenuation field model. Based on the conductor source parameters and the frequency-varying characteristics of the attenuation field, the LMS algorithm is used to generate an error signal by propagating the sound pressure level phase deviation along the receiving path at the receiving point. The characteristic frequency distribution of corona discharge is obtained by using the parameters of the conductor sound source. A frequency band segmentation filter bank is constructed using the characteristic frequency distribution of corona discharge. Based on the error signal, the attenuation compensation coefficient of each frequency band is dynamically iterated using the PSO algorithm through the frequency band segmentation filter bank to generate a frequency-varying correction parameter set. Based on the frequency-varying correction parameter set, the ARIMA time series construction method is used to process the temporal fluctuation characteristics in historical meteorological data, and the temperature compensation function is constructed by combining the Fourier series expansion method. The attenuation field model coupled with topography and meteorology, the frequency-varying correction parameter set, and the temperature compensation function are fused in three dimensions to obtain a three-dimensional fused model; the three-dimensional fused model is then used to process the effects of sound propagation attenuation.

[0057] Furthermore, in processing the effects of sound propagation attenuation, the LMS (Least Mean Square) algorithm is used for adaptive filtering to minimize the error signal, thereby optimizing attenuation compensation. The PSO (Particle Swarm Optimization) algorithm is used to quickly converge to the optimal attenuation compensation coefficients for each frequency band. The ARIMA (Autoregressive Integral Moving Average) model is used to capture the temporal dependence of meteorological data, while Fourier series expansion is used to handle periodic temperature changes. The three-dimensional fusion model can be implemented using the finite element method or the boundary element method to accurately simulate the propagation and attenuation of sound waves in three-dimensional space.

[0058] Furthermore, this invention uses a multi-level processing mechanism to deeply couple meteorological data, terrain data, and sound source characteristics, forming a dynamic attenuation processing system with self-learning capabilities.

[0059] Furthermore, the method for predicting and evaluating the acoustic environment impact of AC transmission lines also includes the following steps: Step S104: Visualize the noise prediction data.

[0060] In this embodiment of the invention, based on noise prediction data, a noise level profile (a noise prediction curve at a conductor height of 1.2m, showing the trend of noise variation with horizontal distance) and a noise contour map (showing the distribution range of different sound levels and determining the spatial boundary of the acoustic environment) are drawn. Furthermore, the target predicted noise values ​​of multiple sensitive targets are extracted from the noise prediction data, and then the noise level profile, noise contour map, and target predicted noise values ​​are visualized.

[0061] Specifically, visualization can be achieved using a Geographic Information System (GIS) platform or specialized acoustic software (such as SoundPlan) to generate interactive maps and charts. Noise contour maps can be generated using Kriging interpolation or inverse distance weighted interpolation to smoothly display noise distribution. Target predicted noise values ​​can be overlaid on the map in tabular or label form for easy and rapid identification of high-risk areas.

[0062] Specifically, Figure 7 A flowchart illustrating the visualization processing of noise prediction data in the method provided by an embodiment of the present invention is shown.

[0063] In a preferred embodiment of the present invention, the visualization processing of the noise prediction data specifically includes the following steps: Step S1041: Based on the noise prediction data, draw a noise level profile. Step S1042: Based on the noise prediction data, draw a noise contour map; Step S1043: Extract target prediction noise values ​​of multiple sensitive targets from the noise prediction data; Step S1044: Visualize the noise level profile, the noise contour map, and the target predicted noise value.

[0064] Furthermore, the method for predicting and evaluating the acoustic environment impact of AC transmission lines also includes the following steps: Step S105: Obtain sound environment standard data, compare and evaluate the noise prediction data, determine whether there are any exceeding the standard, and divide multiple sound environment impact areas.

[0065] In this embodiment of the invention, acoustic environment standard data is acquired, and noise prediction data is compared and evaluated based on the acoustic environment standard data to determine whether there is any exceeding of the standard. When it is determined that there is an exceeding of the standard, multiple noise exceeding levels are divided (including acceptable, slight impact, moderate impact, significant impact, etc.), and multiple acoustic environment impact areas (including acceptable area, slight impact area, moderate impact area, significant impact area, etc.) corresponding to multiple noise exceeding levels are determined. Then, corresponding early warning signs are applied to multiple acoustic environment impact areas (which can be different colors to indicate different levels of regional early warning signs, prompting design units and management departments to pay attention to the environmental risks of the area).

[0066] Specifically, Figure 8 A flowchart illustrating the comparative evaluation of noise prediction data in the method provided by an embodiment of the present invention is shown.

[0067] In a preferred embodiment of the present invention, the steps of acquiring acoustic environment standard data, comparing and evaluating the noise prediction data to determine whether there are any exceedances, and dividing multiple acoustic environment impact areas specifically include the following steps: Step S1051: Obtain acoustic environment standard data; Step S1052: Based on the sound environment standard data, compare and evaluate the noise prediction data to determine whether there is any exceeding of the standard. Step S1053: When there are cases of exceeding the standard, classify the noise exceeding the standard into multiple levels; Step S1054: Determine multiple acoustic environment impact areas corresponding to the multiple noise exceedance levels; Step S1055: Issue early warning labels for multiple areas affected by the acoustic environment.

[0068] Specifically, the following steps are taken to determine the multiple acoustic environment impact areas corresponding to the various levels of noise exceeding the standard: The elevation gradient distribution characteristics and wind speed frequency distribution are obtained by using engineering topographic data and historical meteorological data, respectively; the noise propagation correction coefficient matrix is ​​generated using the elevation gradient distribution characteristics and wind speed frequency distribution; the fixed limit values ​​in the acoustic environment standard data are input into the noise propagation correction coefficient matrix to obtain the dynamic noise threshold spectrum. Based on the dynamic noise threshold spectrum, the excess features of each receiving point in the noise prediction data are extracted; the temperature and humidity conduction coefficients in historical meteorological data are used as environmental impact factors; the surface roughness parameters in engineering topographic data, the excess features of each receiving point, and the environmental impact factors are combined to form a feature dataset. The feature dataset is then subjected to dimensionality reduction and weight calculation by principal component analysis to obtain the environmental sensitivity index. Noise prediction values ​​are obtained from noise prediction data; using the environmental sensitivity index in the sensitivity index model as a constraint, spatial clustering analysis is performed on the set of receivers exceeding the standard in the noise prediction data; using the constrained weighted Delaunay triangulation algorithm, the noise prediction values ​​of adjacent receivers exceeding the standard are spatially interpolated with the environmental sensitivity index based on the environmental sensitivity index to obtain the noise isosurface. The noise isosurface is spatially overlaid with the engineering 3D model to identify the spatial interference region between the noise isosurface and the lowest point perpendicular to the coordinates of the transmission tower and the arc of the conductor. Then, the minimum energy surface optimization algorithm is used to eliminate the influence of sound reflection distortion caused by the transmission tower body to obtain the area affected by the sound environment.

[0069] Furthermore, when delineating areas affected by acoustic environmental impact, a dynamic noise threshold spectrum can be used to adjust noise limits based on different altitudes and wind speeds, making the assessment more consistent with the actual environment. Principal component analysis is used to reduce the dimensionality of feature data and extract key environmental impact factors. A constrained weighted Delaunay triangulation algorithm ensures the generation of smooth noise isosurfaces while considering environmental sensitivity. A minimum energy surface optimization algorithm is used to minimize acoustic reflection errors caused by transmission towers, improving the accuracy of area boundaries.

[0070] Furthermore, this invention overcomes the regional adaptability defects of fixed limit evaluation systems by transforming static noise standards into dynamic threshold spectra; this invention integrates topographic and meteorological elements through an orthogonal weighting algorithm, enabling the regional division results to simultaneously reflect physical propagation characteristics and environmental sensitivity characteristics; this invention achieves adaptive correction of the boundary of the affected area under engineering entity constraints by employing a combined algorithm of constrained weighted Delaunay triangulation and minimum energy surface optimization.

[0071] Furthermore, the method for predicting and evaluating the acoustic environment impact of AC transmission lines also includes the following steps: Step S106: Optimize and analyze multiple acoustic environment impact areas of the target line project to generate multiple project optimization suggestions.

[0072] In this embodiment of the invention, route optimization suggestions are made for multiple areas affected by the sound environment (suggestions such as raising conductors, adjusting the arrangement, and improving conductor structure for areas exceeding the standard to reduce the intensity of the sound source), and layout optimization suggestions are made for mitigating the sensitivity of multiple sensitive targets (setting up barriers, vegetation isolation belts, or optimizing land use layout to reduce the impact of noise). At the same time, noise reduction control suggestions are made for the construction of the target route project (taking measures such as time-limited operation and low-noise design of construction equipment to reduce temporary sound disturbances), and long-term noise monitoring points are established for real-time monitoring, publication, and feedback.

[0073] Specifically, line optimization recommendations can be based on sound source characteristic models and noise propagation simulation results, proposing specific measures for areas exceeding noise standards. For example, increasing the number of conductor splits to reduce corona noise or adjusting tower heights to alter sound propagation paths. Layout optimization recommendations can consider the type and location of sensitive targets, suggesting the installation of sound barriers (such as noise walls) or ecological buffer zones (such as arbor forests). Noise reduction control recommendations can include using low-noise construction equipment and optimizing construction sequences. Long-term noise monitoring points can deploy IoT sensors to achieve real-time data transmission and remote monitoring, and issue early warning information through a cloud platform.

[0074] Specifically, Figure 9 A flowchart illustrating the generation of multiple engineering optimization suggestions in the method provided by an embodiment of the present invention is shown.

[0075] In a preferred embodiment of the present invention, the optimization analysis of multiple acoustic environment impact areas of the target line project and the generation of multiple engineering optimization suggestions specifically include the following steps: Step S1061: Provide route optimization suggestions for multiple areas affected by the acoustic environment; Step S1062: Provide layout optimization suggestions for mitigating the sensitivity of multiple sensitive targets; Step S1063: Noise reduction control recommendations for the construction of the target line project; Step S1064: Establish long-term noise monitoring points and conduct real-time monitoring, reporting, and feedback.

[0076] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting and evaluating the acoustic environmental impact of AC transmission lines, characterized in that, The method specifically includes the following steps: The target line project of the AC transmission line is identified, the engineering environment data of the target line project is obtained, and the engineering environment data is identified to determine multiple sensitive targets; Based on the engineering environment data, the acoustic source characteristics of the target line project are modeled using the terrain diffraction effect and historical meteorological conditions to generate an acoustic source characteristic model. Based on multiple sensitive targets, spatial adaptive optimization is performed using environmental sensitivity and sound propagation path to plan multiple predicted receiving points. Noise propagation simulation is performed based on the sound source characteristic model, and noise prediction data is recorded. The noise prediction data is then visualized. Acquire standard acoustic environment data, compare and evaluate the noise prediction data to determine whether there are any exceedances, and divide multiple acoustic environment impact areas. Multiple acoustic environment impact areas of the target line project are optimized and analyzed, generating multiple engineering optimization suggestions.

2. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 1, characterized in that, The process of identifying the target AC transmission line project, acquiring the project's environmental data, and identifying multiple sensitive targets specifically includes the following steps: Determine the target line project for AC transmission lines; Obtain the basic engineering information of the target line project; Obtain the engineering terrain data of the target route project; Obtain historical meteorological data for the target railway line project; By combining the basic engineering information, the engineering terrain data, and the historical meteorological data, engineering environmental data is obtained; The engineering environment data was identified to determine multiple sensitive targets.

3. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 2, characterized in that, The step of modeling the sound source characteristics of the target railway line project based on the engineering environment data, utilizing terrain diffraction effects and historical meteorological conditions, and generating the sound source characteristic model specifically includes the following steps: Based on the aforementioned engineering environment data, multiple sound source types were identified; Based on the engineering environment data, obtain the conductor sound source parameters corresponding to multiple sound source types, and determine multiple single sound sources; Based on the aforementioned conductor sound source parameters, an equivalent sound source merging analysis is performed to identify multiple composite sound sources; Based on the engineering environment data, multiple single sound sources and multiple composite sound sources are spatially deployed using terrain diffraction effect and historical meteorological data to construct a sound source characteristic model. Specifically, based on the engineering environment data, the spatial deployment of multiple single sound sources and multiple composite sound sources using terrain diffraction effects and historical meteorological data, and the construction of a sound source characteristic model, includes the following steps: Based on the engineering environment data, construct a three-dimensional model of the project; Determine the spatial locations of the multiple single sound sources and the multiple composite sound sources; Based on the spatial locations of the multiple sound sources, the multiple single sound sources and multiple composite sound sources are spatially arranged in the three-dimensional engineering model using terrain diffraction effect and historical meteorological data to construct a sound source characteristic model.

4. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 3, characterized in that, Based on the spatial locations of the multiple sound sources, in the engineering 3D model, the multiple single sound sources and multiple composite sound sources are spatially arranged using terrain diffraction effects and historical meteorological data to construct a sound source characteristic model, specifically including the following steps: The terrain surface features are obtained through the engineering 3D model; a sound wave diffraction attenuation calculation layer is constructed based on the terrain surface features; the sound wave diffraction attenuation calculation layer is used to spatially superimpose the spatial location of the sound source with the engineering terrain data, and at the same time, the sound wave diffraction compensation algorithm is used to compensate and correct the sound source points affected by terrain obstruction in order to obtain the corrected sound source parameter set. Based on hourly wind direction and speed data from historical meteorological data, a model of the dominant direction of sound propagation is established. The propagation path of the modified sound source parameter set is optimized by using the sound propagation direction advantage model. The high-frequency component weight is added to the sound source points downstream of the prevailing wind direction. The low-frequency component is enhanced to the sound source points corresponding to the high occurrence of the inversion layer. Then, the directionality of the sound source points in the target area with significant changes in sound speed gradient is adjusted according to the seasonal distribution characteristics in historical meteorological data to obtain the meteorological coupled sound source characteristic parameters. The meteorological coupled sound source characteristic parameters are input into the engineering three-dimensional model to perform sound-structure coupling verification, and a sound source-tower spatial relationship matrix is ​​established. Based on the spatial relationship described by the sound source-tower spatial relationship matrix, the displacement of sound source points smaller than the safe distance from the transmission tower is adjusted to obtain a suitable sound source spatial distribution. A sound source interference effect evaluation model is constructed based on the sound source parameters of the conductor. The sound source interference effect evaluation model is used to perform phase interference analysis on the adapted spatial distribution of sound sources. A sound source time-series activation control strategy is adopted to perform time-division activation control on sound source points with spatial spacing less than the critical value. Then, the steady-state noise superposition effect is eliminated by separating the time dimension to generate a sound source characteristic model.

5. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 4, characterized in that, The process of spatial adaptive optimization based on environmental sensitivity and sound propagation path to plan multiple predicted receiving points, performing noise propagation simulation based on the sound source characteristic model, and recording noise prediction data specifically includes the following steps: Obtain the number of receiving points and the sensitivity ratio; Based on the number of receiving points and the sensitivity ratio, reception planning is carried out inside and outside multiple sensitive targets, and multiple predicted receiving points are selected. Based on the sound source characteristic model, noise propagation simulation is performed, noise reception simulation is performed at multiple predicted receiving points, and noise prediction data is recorded. The process of simulating noise propagation based on the sound source characteristic model, simulating noise reception at multiple predicted receiving points, and recording noise prediction data specifically includes the following steps: Determine the propagation and reception paths for multiple predicted receiving points; Select typical forecast weather; Based on the typical predicted weather, sound propagation attenuation is processed. Noise reception simulations were performed at multiple predicted receiving points, and noise prediction data was recorded.

6. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 5, characterized in that, Based on the number of receiving points and the sensitivity ratio, reception planning is performed inside and outside multiple sensitive targets, and multiple predicted receiving points are selected. Specifically, this includes the following steps: Based on the elevation change characteristics in engineering topographic data and the prevailing wind rose diagram in historical meteorological data, a spatial resolution adaptive adjustment model is constructed; the number of receiving points is input into the spatial resolution adaptive adjustment model to generate the initial monitoring grid. Determine the terrain undulation threshold based on the initial monitoring grid; Based on the initial monitoring grid, the grid nodes are densified in terrain areas that exceed the terrain undulation threshold, and the grid coverage is expanded in the downstream direction of the prevailing wind to generate a geo-meteorological guided initial monitoring grid. By identifying the engineering environment data, multiple sensitive targets are determined to obtain a set of sensitive target coordinates; Spatial overlay analysis was performed on the initial monitoring grid of the geographic meteorological guidance and the coordinate set of sensitive targets to obtain the environmental sensitivity index of each grid unit; Using the environmental sensitivity index, the inverse distance weighting algorithm is used to enhance the receiver density of the grid around the sensitive target. At the same time, the frequency of temperature inversion in historical meteorological data is used to apply azimuth compensation weighting to the grid in the direction of nighttime sound propagation to generate an optimized grid distribution. The tower coordinates and the perpendicular envelope of the conductor arc are obtained through the engineering 3D model. The accessibility of the optimized grid distribution is verified by using the tower coordinates and the perpendicular envelope of the conductor arc, and a candidate set of suitable receiving points is obtained. Using the environmental sensitivity index, an environmental sensitivity-weighted Voronoi diagram spatial segmentation algorithm is adopted to optimize the spatial distribution of each receiver point in the candidate receiver point set. At the same time, the elevation gradient of the traditional planar Voronoi unit is corrected according to the engineering terrain data. Then, based on the seasonal variation characteristics of historical meteorological data, vertical height layer receiver points are added to areas with high inversion layer to obtain a three-dimensional receiver point distribution scheme. Based on a three-dimensional receiver point distribution scheme, receiver planning is performed inside and outside multiple sensitive targets, and multiple predicted receiver points are selected.

7. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 6, characterized in that, Based on the typical predicted weather, the sound propagation attenuation effect is processed, and the specific steps are as follows: By determining atmospheric stability classification indicators through historical meteorological data, establishing dynamic correlation rules between meteorological parameters and sound wave refraction effects based on typical forecast meteorological conditions, and using dynamic correlation rules and atmospheric stability classification indicators, a bending correction model of sound wave propagation path due to temperature and humidity stratification is constructed. The meteorological-sound refraction correlation matrix is ​​obtained by outputting the bending correction model of the sound wave propagation path through temperature and humidity stratification; the meteorological-sound refraction correlation matrix includes the sound velocity gradient distribution, temperature and humidity stratification data, and air absorption coefficient. By utilizing the sound velocity gradient distribution, a three-dimensional spatial grid system is constructed along the propagation and reception path. The sound wave refraction effect is quantized and allocated to each grid cell in the three-dimensional spatial grid system. Then, a coupled calculation model is constructed by combining engineering terrain data. The air absorption coefficient is dynamically segmented vertically based on temperature and humidity stratification data, and then combined with a coupled calculation model to obtain a terrain-meteorological coupled attenuation field model. The frequency-varying characteristics of the attenuation field are obtained through a terrain-meteorological coupled attenuation field model. Based on the conductor source parameters and the frequency-varying characteristics of the attenuation field, the LMS algorithm is used to generate an error signal by propagating the sound pressure level phase deviation along the receiving path at the receiving point. The characteristic frequency distribution of corona discharge is obtained by using the parameters of the conductor sound source. A frequency band segmentation filter bank is constructed using the characteristic frequency distribution of corona discharge. Based on the error signal, the attenuation compensation coefficient of each frequency band is dynamically iterated using the PSO algorithm through the frequency band segmentation filter bank to generate a frequency-varying correction parameter set. Based on the frequency-varying correction parameter set, the ARIMA time series construction method is used to process the temporal fluctuation characteristics in historical meteorological data, and the temperature compensation function is constructed by combining the Fourier series expansion method. The attenuation field model coupled with topography and meteorology, the frequency-varying correction parameter set, and the temperature compensation function are fused in three dimensions to obtain a three-dimensional fused model; the three-dimensional fused model is then used to process the effects of sound propagation attenuation.

8. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 7, characterized in that, The visualization process for the noise prediction data specifically includes the following steps: Based on the noise prediction data, a noise level profile is plotted. Based on the noise prediction data, draw a noise contour map; Extract target prediction noise values ​​for multiple sensitive targets from the noise prediction data; The noise level profile, the noise contour map, and the target predicted noise value are visualized.

9. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 8, characterized in that, The process of acquiring standard acoustic environment data, comparing and evaluating the noise prediction data to determine whether there are any exceedances, and dividing multiple acoustic environment impact areas specifically includes the following steps: Obtain acoustic environment standard data; Based on the aforementioned acoustic environment standard data, the noise prediction data is compared and evaluated to determine whether there are any exceedances. When there are cases of noise exceeding the standard, multiple levels of noise exceeding the standard are defined; Identify multiple acoustic environment impact areas corresponding to the various degrees of noise exceeding the standard; Warning signs are issued for multiple areas affected by the acoustic environment.

10. The method for predicting and evaluating the acoustic environment impact of AC transmission lines according to claim 9, characterized in that, The optimization analysis of multiple acoustic environment impact areas of the target line project, and the generation of multiple engineering optimization suggestions, specifically includes the following steps: Route optimization suggestions for adjusting routes in multiple areas affected by the acoustic environment; Layout optimization suggestions for mitigating sensitivity points of multiple sensitive targets; Recommendations for noise reduction and control during the construction of the target line project; Establish long-term noise monitoring points and conduct real-time monitoring, reporting, and feedback.

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