Multi-source remote sensing data-based civil song spatial distribution analysis method, system and device, and medium

Abstract

The invention provides a civil song spatial distribution analysis method, system and device based on multi-source remote sensing data, and a medium, and the method comprises the steps: S1, collecting civil song spatial data, and extracting point location attributes of the civil song spatial data; s2, acquiring and processing geographical remote sensing data, and constructing an environment feature vector; s3, establishing a mathematical model of the folk song features and geographic elements; and S4, generating a distribution visualization layer, and realizing interactive query and spatial pattern recognition. According to the method, the problems of spatial information missing, geographic factor neglecting, backward technical means and the like in the traditional national music research can be solved.

Description

Technical Field

[0001] This invention relates to the field of cultural geographic information analysis and remote sensing data processing technology, and in particular to a method, system, device and medium for analyzing the spatial distribution of folk songs based on multi-source remote sensing data. Background Technology

[0002] As an important component of intangible cultural heritage, the spatial distribution characteristics of folk music are closely related to the geographical environment. Existing studies mostly employ qualitative descriptions or simple spatial visualization methods, lacking in-depth analysis of the quantitative relationship between the spatial distribution of folk songs and the geographical environment.

[0003] For example, in "Correlation Analysis of Spatial Distribution of Shaanxi Folk Music with Geographical Elements," Li Anqi used GIS technology to visualize the spatial distribution of Shaanxi folk music and preliminarily explored its correlation with geographical elements such as vegetation, topography, and water bodies. However, this method remains at the spatial presentation level, failing to establish a mathematical model for quantitative correlation analysis or to incorporate remote sensing data for environmental feature extraction.

[0004] In terms of patents, existing technologies such as CN202510673991.8 propose a three-dimensional spectrogram coding method based on spatiotemporal data fusion, which constructs a "sound-space" association model through localization and acoustic modeling. However, its focus is on the digitization and visualization of performance behavior, without involving the quantitative analysis of the spatial distribution and geographical environment of folk songs.

[0005] Another patent, CN202510899617.X, proposes a super-resolution method for nighttime light images using multi-source remote sensing data. Although it involves remote sensing data processing, its application is limited to image enhancement and has not been extended to the field of cultural geography analysis.

[0006] In summary, the existing technology has the following problems: (1) Insufficient quantitative analysis methods and lack of ability to model the causal relationship between folk songs and geographical environment; (2) The interdisciplinary integration is not deep enough, and a systematic analytical method for the integration of remote sensing and music geography has not yet been formed; (3) The lack of visualization and interactive analysis capabilities for the spatial distribution of folk songs makes it difficult to support the digital protection and application of cultural heritage. Summary of the Invention

[0007] The purpose of this invention is to address the technical problems raised in the background art by providing a method, system, device, and medium for analyzing the spatial distribution of folk songs based on multi-source remote sensing data, which can solve the problems of missing spatial information, neglect of geographical factors, and outdated technical means in traditional ethnic music research.

[0008] The present invention solves the technical problem by adopting the following technical solution: Firstly, a method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data includes the following steps: Step S1: Collect folk song spatial data and extract its location attributes; Step S2: Acquire and process geographic remote sensing data to construct environmental feature vectors; Step S3: Establish a mathematical model of folk song types and geographical elements; Step S4: Generate a distribution visualization layer to enable interactive querying and spatial pattern recognition.

[0009] Furthermore, in step S1, the method for obtaining the folk song spatial data is as follows: Folk song data was obtained from public cultural databases, music platforms, and field surveys; the location of the folk song data was converted into geographical coordinates to form spatial location data of folk songs.

[0010] Furthermore, in step S1, the method for extracting the spatial data point attributes of folk songs is as follows: Based on audio signal processing technology, time-domain, frequency-domain, and cepstral-domain features of folk song spatial location data are extracted. These features can quantify the artistic style of folk songs. In addition, combined with historical documents, the cultural attributes of the location data are marked, providing multi-dimensional variables for subsequent modeling.

[0011] Furthermore, in step S2, the method for acquiring and preprocessing geographic remote sensing data is as follows: Select remote sensing images that match the distribution area of ​​folk song spatial data, covering key geographical elements such as vegetation, topography, and water bodies; Preprocess the remote sensing data and output an environmental feature raster layer, with each pixel corresponding to a feature vector.

[0012] Furthermore, in step S2, the method for constructing the environmental feature vector is as follows: Remote sensing indices related to cultural ecology are extracted to form a multidimensional feature vector, including vegetation index, water index, and topographic features.

[0013] Furthermore, in step S3, the method for establishing a mathematical model of folk song types and geographical elements is as follows: Feature association analysis: Folk song spatial location data are overlaid with environmental feature vectors, and environmental feature values ​​around each location are extracted through spatial connections; Quantifying the cultural-ecological relationship: Calculating the correlation coefficient between vegetation cover and folk song diversity through regression analysis.

[0014] Furthermore, in step S4, the method for generating a distribution visualization layer and realizing interactive query and spatial pattern recognition is as follows: Based on point density rendering, high-density areas are highlighted with warm colors to form a heat map of folk song distribution. By overlaying NDVI and water system raster data in a semi-transparent manner, the coupling relationship between folk songs and geographical patterns can be intuitively displayed, and environmental elements can be superimposed. Users can click on a location on the map to view details of the folk song and perform a spatial search.

[0015] Secondly, a folk song spatial distribution analysis system based on multi-source remote sensing data includes: The data acquisition module collects folk song spatial data and extracts its location attributes; The environmental feature vector construction module acquires and processes geographic remote sensing data to construct environmental feature vectors. The mathematical model building module establishes mathematical models of folk song types and geographical elements; The interactive query and spatial pattern recognition module generates a distributed visualization layer, enabling interactive querying and spatial pattern recognition.

[0016] Thirdly, a terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the folk song spatial distribution analysis method based on multi-source remote sensing data as described in any one of the preceding claims.

[0017] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the folk song spatial distribution analysis method based on multi-source remote sensing data as described in any of the preceding claims.

[0018] The beneficial effects of this invention are: (1) A geomorphic-musical coupling analysis framework was constructed, realizing the quantitative correlation between geographical environment and folk song form; (2) The introduction of multi-source remote sensing data has improved the accuracy and dimensionality of environmental feature extraction; (3) Provides visualization and interactive analysis capabilities to support multi-dimensional display and query of the spatial distribution of folk songs; (4) Promote the digitization and scientification of ethnic music research, and provide technical support for intangible cultural heritage protection, cultural dissemination, education and teaching; (5) It has cross-regional and cross-cultural promotional value and can be applied to spatial analysis scenarios of other ethnic music or cultural heritage. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention.

[0020] Figure 2 This is a framework diagram of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] refer to Figure 1 This invention provides a method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data. This innovative method integrates geographic information technology, remote sensing data processing, and cultural geography. Its core lies in revealing the ecological basis of cultural phenomena by quantitatively analyzing the correlation between the spatial distribution of folk songs and geographical environmental elements. The method includes the following steps: Step S1: Collect folk song spatial data and extract its location attributes; The method for obtaining folk song spatial data is as follows: Folk song data is obtained from public cultural databases (such as the China Intangible Cultural Heritage Protection Center), music platforms (such as NetEase Cloud Music), and field surveys. For example, metadata (singer's place of origin, region of circulation, dialect characteristics of lyrics, etc.) of folk songs is extracted using web scraping technology. The location of the folk song data is converted into geographical coordinates (such as latitude and longitude) to form spatial location data of folk songs. For example, a Shaanxi folk song can be located in Yulin City, Shaanxi Province, and attributes can be added (such as folk song type "Xintianyou", ethnicity "Han", year of collection, etc.).

[0023] The method for extracting spatial data point attributes of folk songs is as follows: Based on audio signal processing technology, the time domain, frequency domain, and cepstral domain features (such as melody rhythm and timbre spectrum) of folk song spatial location data are extracted. These features can quantify the artistic style of folk songs. In addition, combined with historical documents, the cultural attributes of the location data (such as whether it is an intangible cultural heritage item, the era of popularity, etc.) are marked, providing multi-dimensional variables for subsequent modeling.

[0024] Data is obtained using Python web scraping and then spatialized using GIS software (such as ArcGIS). The output is a point layer (e.g., in Shapefile format) containing latitude and longitude coordinates, folk song features, and cultural attributes.

[0025] Step S2: Acquire and process geographic remote sensing data to construct environmental feature vectors; The methods for acquiring and preprocessing geographic remote sensing data are as follows: Select remote sensing images (such as Landsat series and Gaofen satellite data) that match the distribution area of ​​folk song spatial data, covering key geographic elements such as vegetation, topography, and water bodies; Preprocess the remote sensing data and output an environmental feature raster layer, with each pixel corresponding to a feature vector.

[0026] The method for constructing environmental feature vectors is as follows: Remote sensing indices related to cultural ecology are extracted to form a multidimensional feature vector, including vegetation index, water index, and topographic features.

[0027] Vegetation indices, such as NDVI (Normalized Difference Vegetation Index), reflect the ecological abundance of a region. Water body indices, such as the NDWI (Normalized Difference Water Index), indicate the distribution of water bodies; Topographic features, such as elevation, are extracted from DEM data.

[0028] Step S3: Establish a mathematical model of folk song types and geographical elements; The method for establishing a mathematical model of folk song types and geographical elements is as follows: Feature association analysis: The spatial location data of folk songs are superimposed with environmental feature vectors, and the environmental feature values ​​around each location are extracted through spatial connectivity (such as zonal statistics); for example, the positive correlation between the distribution of folk songs in the Jiangnan water towns and NDWI (water body index), or the correlation between folk songs in Northwest China and slope terrain.

[0029] Quantifying the cultural-ecological relationship: Calculating the correlation coefficient between vegetation cover and folk song diversity through regression analysis.

[0030] The mathematical model's framework is structured as follows: (1) Data layer Remote sensing and Earth big data: Landsat, Sentinel, nighttime light (VIIRS / Luojia1-01), DEM, land cover.

[0031] Folk song data: audio collection, text data, and music score library.

[0032] (2) Feature extraction layer Music signal processing: melody, pitch, rhythm, timbre, dynamics.

[0033] Environmental characteristics: NDVI, LST, DSM, nighttime light intensity, and land use diversity index.

[0034] (3) Analysis layer Spatial analysis: GIS mapping of folk song distribution and spatial statistics.

[0035] Morphology and Machine Learning: Clustering, Pattern Recognition, Correlation Regression, Causal Inference.

[0036] (4) Explanation layer Verify the research hypothesis: Examine the correspondence between environmental factors and musical forms.

[0037] Cultural ecology explanation: revealing the environmental adaptation mechanism of folk songs.

[0038] Step S4: Generate a distribution visualization layer to enable interactive querying and spatial pattern recognition.

[0039] The method for generating a distribution visualization layer and enabling interactive querying and spatial pattern recognition is as follows: Based on point density rendering, high-density areas are highlighted with warm colors (such as red) to form a heat map of folk song distribution; By overlaying NDVI, water system, and other raster data in a semi-transparent manner, the coupling relationship between folk songs and geographical patterns can be intuitively displayed, thus achieving the overlay of environmental elements. When a user clicks on a location on the map, details of the folk song will pop up, allowing them to perform spatial queries (such as audio clips, lyrics, and related geographic indicators).

[0040] Thirty sampling points were obtained from audio literature and field surveys; environmental features were extracted using SRTM and MODIS data; it was found that this type of folk song is mainly concentrated in areas with an altitude of 500-900 meters, an NDVI of 0.6 or higher, and land use dominated by forest and slopes; cluster analysis formed three spatial high-density distribution cores; modeling results showed that NDVI and nighttime lighting had a significant correlation with the distribution of folk songs; the output layer was overlaid with a distribution map of Dong ethnic villages, and the model accuracy was verified to be 87.4%.

[0041] This invention also provides a folk song spatial distribution analysis system based on multi-source remote sensing data, comprising: The data acquisition module collects folk song spatial data and extracts its location attributes; The environmental feature vector construction module acquires and processes geographic remote sensing data to construct environmental feature vectors. The mathematical model building module establishes mathematical models of folk song types and geographical elements; The interactive query and spatial pattern recognition module generates a distributed visualization layer, enabling interactive querying and spatial pattern recognition.

[0042] The present invention also provides a terminal device, the terminal device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the folk song spatial distribution analysis method based on multi-source remote sensing data as described in any one of the above claims.

[0043] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the folk song spatial distribution analysis method based on multi-source remote sensing data as described above.

[0044] This invention constructs a geomorphic-musical coupled analysis framework, realizing a quantitative correlation between the geographical environment and the form of folk songs; it introduces multi-source remote sensing data to improve the accuracy and dimensionality of environmental feature extraction; it provides visualization and interactive analysis capabilities, supporting multi-dimensional display and query of the spatial distribution of folk songs; it promotes the digitalization and scientification of ethnic music research, providing technical support for intangible cultural heritage protection, cultural dissemination, and education; it has cross-regional and cross-cultural promotion value and can be applied to spatial analysis scenarios of other ethnic music or cultural heritage.

[0045] The method of this invention is applicable to: national or regional research on the geographical distribution of folk songs; construction of intangible cultural heritage databases; modeling of the cultural ecology of ethnic settlements; teaching of music, geography, and humanities courses in colleges and universities; and supporting the planning of digital cultural tourism for ethnic cultures.

[0046] The application scenarios and specific use cases of this invention are as follows: Regional folk song research: An analysis of the correlation between the distribution of folk songs and the waterway network in the Yangtze River Delta region reveals that folk songs spread faster in areas with dense water networks. Intangible Cultural Heritage Database: Adding geographical tags to Yunnan folk song intangible cultural heritage projects to enable "map-based" cultural resource retrieval. Cultural Ecology Modeling: Establishing a linear model of grassland folk song distribution and NDVI to predict the impact of climate warming on the transmission of nomadic folk songs. Digital cultural tourism planning: Based on the distribution hotspots of folk songs, design "folk song cultural routes," such as tourist routes connecting high-density areas of Shaanxi folk song. Intangible cultural heritage database construction: Connect the visualization layer to the national intangible cultural heritage platform to achieve "one map" management of folk song data.

[0047] In higher education, in music geography courses, students use interactive maps to analyze the relationship between folk songs and topography (such as the relationship between the wide vocal range of folk songs from mountainous areas and the undulating terrain).

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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 the present invention.

Claims

1. A method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data, characterized in that, Includes the following steps: Step S1: Collect folk song spatial data and extract its location attributes; Step S2: Acquire and process geographic remote sensing data to construct environmental feature vectors; Step S3: Establish a mathematical model of folk song diversity and geographical elements; Step S4: Generate a distribution visualization layer to enable interactive querying and spatial pattern recognition.

2. The method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data according to claim 1, characterized in that, In step S1, the method for obtaining folk song spatial data is as follows: Folk song data was obtained from public cultural databases, music platforms, and field surveys; the location of the folk song data was converted into geographical coordinates to form spatial location data of folk songs.

3. The method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data according to claim 2, characterized in that, In step S1, the method for extracting the spatial data point attributes of folk songs is as follows: Based on audio signal processing technology, time-domain, frequency-domain, and cepstral-domain features of folk song spatial location data are extracted. These features can quantify the artistic style of folk songs. In addition, combined with historical documents, the cultural attributes of the location data are marked, providing multi-dimensional variables for subsequent modeling.

4. The method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data according to claim 3, characterized in that, In step S2, the method for acquiring and preprocessing geographic remote sensing data is as follows: Select remote sensing images that match the distribution area of ​​folk song spatial data, covering key geographical elements such as vegetation, topography, and water bodies; Preprocess the remote sensing data and output an environmental feature raster layer, with each pixel corresponding to a feature vector.

5. The method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data according to claim 4, characterized in that, In step S2, the method for constructing the environmental feature vector is as follows: Remote sensing indices related to cultural ecology are extracted to form a multidimensional feature vector, including vegetation index, water index, and topographic features.

6. The method for spatial distribution analysis of folk songs based on multi-source remote sensing data according to claim 5, characterized in that, In step S3, the method for establishing a mathematical model of folk song types and geographical elements is as follows: Feature association analysis: Folk song spatial location data are overlaid with environmental feature vectors, and environmental feature values ​​around each location are extracted through spatial connections; Quantifying the cultural-ecological relationship: Calculating the correlation coefficient between vegetation cover and folk song diversity through regression analysis.

7. The method for analyzing the spatial distribution of folk songs based on multi-source remote sensing data according to claim 6, characterized in that, In step S4, the method for generating a distribution visualization layer and realizing interactive query and spatial pattern recognition is as follows: Based on point density rendering, high-density areas are highlighted with warm colors to form a heat map of folk song distribution. By overlaying NDVI and water system raster data in a semi-transparent manner, the coupling relationship between folk songs and geographical patterns can be intuitively displayed, and environmental elements can be superimposed. Users can click on a location on the map to view details of the folk song and perform a spatial search.

8. A folk song spatial distribution analysis system based on multi-source remote sensing data, characterized in that, include: The data acquisition module collects folk song spatial data and extracts its location attributes; The environmental feature vector construction module acquires and processes geographic remote sensing data to construct environmental feature vectors. The mathematical model building module establishes mathematical models of folk song types and geographical elements; The interactive query and spatial pattern recognition module generates a distributed visualization layer, enabling interactive querying and spatial pattern recognition.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the folk song spatial distribution analysis method based on multi-source remote sensing data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the folk song spatial distribution analysis method based on multi-source remote sensing data as described in any one of claims 1-7.

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