Mountain village function evaluation method based on space-time big data

Abstract

The invention discloses a mountainous region and rural region function evaluation method based on space-time big data. The method comprises the following steps: 1) obtaining mountainous region and rural region function evaluation reference data; 2) carrying out fusion processing on the mountain country function evaluation reference data to obtain a country space basic geographic information graph; and 3) carrying out rural production-life-ecological space identification on the rural space basic geographic information graph, and then carrying out mountain rural function evaluation to obtain a rural multifunctional evaluation index. According to the mountain village function evaluation method based on the space-time big data, the multi-source geographic information of the village is analyzed based on the space-time big data, the data, technology and village development requirements can be deeply fused, and the scientificity and efficiency of village planning, management and decision making are remarkably improved. According to the invention, based on three functional indexes of rural space living, employment and communication, the rural multifunctional development is comprehensively evaluated, the actual condition of the rural can be comprehensively reflected, and the rural planning is guided, so that the rural revitalization is promoted.

Description

Technical Field

[0001] The present invention relates to the field of national land space planning, and in particular to a mountain village function assessment method based on spatiotemporal big data. Background Art

[0002] Rural areas are a complex of natural, social, and economic features, encompassing a complex mix of humanistic, social, economic, and resource elements. They not only meet the production and living needs of rural residents but also fulfill a range of diverse functions, including supplying agricultural products to urban residents, providing recreational spaces, and maintaining ecological balance. Multifunctional rural development is not only a theoretical perspective for exploring new paths for diversified rural development but also a key paradigm guiding the transformation and upgrading of agriculture and rural areas and the integrated development of urban and rural areas.

[0003] Assessing the level of rural development has become increasingly important, involving both the collection of rural natural environment and socioeconomic data and the processing, evaluation, and analysis of this data. To achieve this, some existing techniques first establish expected indicators for rural development, then collect natural environment and socioeconomic data from rural areas through field surveys. These indicators are then compared uniformly across all villages, with villages deemed to have met the indicators. However, this assessment method fails to accurately categorize villages, resulting in indicators that are unlikely to be applicable to all types of villages, and the assessment accuracy deviates significantly from actual conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating the function of mountainous rural areas based on spatiotemporal big data, comprising the following steps:

[0005] 1) Obtain reference data for mountainous rural functional assessment.

[0006] 2) The reference data of mountainous rural function assessment are integrated and processed to obtain the basic geographical information map of rural space.

[0007] 3) Identify the rural production-living-ecological space based on the basic geographic information map of rural space, and then conduct a functional assessment of mountainous villages to obtain a rural multifunctional assessment index.

[0008] Furthermore, the reference data for mountainous rural function assessment includes spatiotemporal big data.

[0009] The spatiotemporal big data includes POI data covering typical mountainous villages, mobile phone signaling data, remote sensing image data, drone aerial survey image data, measured current terrain cadastral data, DEM data, rural real estate cadastral survey data, geographical national conditions monitoring data, land survey data, land use status data, agricultural land classification data, cultivated land quality classification data, basic farmland data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration data, and rural housing real estate registration multi-source vector data.

[0010] Furthermore, the reference data for mountainous rural function assessment also includes departmental statistical data.

[0011] The departmental statistical data mentioned above include district and county statistical yearbooks, rural economic statistical yearbooks, township national economic development plans, village and town land consolidation plans, rural construction land reclamation, rural property rights registration, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, cultivated land quality grades, agricultural land classification data, basic farmland construction plans, soil census data, meteorological data, land use change data and statistical ledgers over the years, economic census data, population census data, village annual work summaries, village situation introductions, village-level plans, and reporting materials.

[0012] Furthermore, the steps of fusing the mountainous rural function assessment reference data to obtain a rural spatial basic geographic information map are as follows:

[0013] 2.1) Obtain the current topographic map of the village and capture real-time village image data.

[0014] 2.2) Perform orthophoto processing on real-time rural image data and integrate and correct it with the current rural topographic map to establish a basic survey base map containing the geographic information spatial data of typical mountain villages.

[0015] 2.3) Convert the spatial data in the mountainous rural function assessment reference data to the basic survey base map to obtain a survey base map containing spatial data.

[0016] 2.4) Convert the non-spatial data in the mountainous rural function assessment reference data into a survey base map containing spatial data to obtain a basic rural spatial geographic information map.

[0017] Furthermore, the real-time rural image data includes remote sensing image data and drone aerial survey image data.

[0018] The current rural topographic map includes measured current topographic cadastral data and current land use data.

[0019] Furthermore, the method of converting spatial data to a basic survey base map includes coordinate correction, projection transformation, and base map registration.

[0020] Furthermore, the spatial data includes POI data, mobile phone signaling data, rural real estate cadastral survey data, geographical conditions monitoring data, land survey data, basic farmland data, ecological protection red line data, and geological survey data.

[0021] The non-spatial data include township national economic development plans, village and town land consolidation plans, rural construction land reclamation plans, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, and village-level plans.

[0022] Furthermore, the method for converting non-spatial data into a survey base map containing spatial data is as follows:

[0023] The seamless integration method of geospatial data and non-spatial data overlays non-spatial data onto the survey base map containing spatial data through spatialization methods.

[0024] Furthermore, the rural multifunctional evaluation index is as follows:

[0025]

[0026] Where i represents time, ω i Represents the rural multifunctional evaluation index. i Represents the residential function index. β i represents the employment function index. γ i Represents the communication function index.

[0027] Furthermore, the residential function index α i , employment function index β i , social function index γ i As shown below:

[0028]

[0029] In the formula, i represents time, j represents status, among which j=1 represents working day, j=2 represents weekend, and j=3 represents holiday. represents the mean population during the daytime on weekdays. represents the product of the mean population during the day and evening hours on weekdays.

[0030] The average daytime population Average population at night The product of the mean daytime and nighttime populations As shown below:

[0031]

[0032] Where m and n represent the total duration of night and day respectively. e and d represent the start time of night and day respectively. ij 、AM ij They represent the population size at the i-th moment in the j-th state at night and during the day respectively.

[0033] The technical benefits of this invention are undeniable. This invention proposes a method for assessing the functions of mountainous rural areas based on spatiotemporal big data. This method analyzes multi-source rural geographic information based on spatiotemporal big data, enabling a deep integration of data, technology, and rural development needs, significantly improving the scientific nature and efficiency of rural planning, management, and decision-making. Based on the three major functional indices of rural space—residence, employment, and communication—this method comprehensively evaluates the multifunctional development of rural areas, comprehensively reflecting the actual conditions of rural areas, guiding the development of rural planning, and thus promoting rural revitalization. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all should be included in the scope of protection of the present invention.

[0036] Example 1:

[0037] See also Figure 1 ,The mountain rural function assessment method based on spatiotemporal big data includes the following steps:

[0038] 1) Obtain reference data for mountainous rural functional assessment.

[0039] 2) The reference data of mountainous rural function assessment are integrated and processed to obtain the basic geographical information map of rural space.

[0040] 3) Identify the rural production-living-ecological space based on the basic geographic information map of rural space, and then conduct a functional assessment of mountainous villages to obtain a rural multifunctional assessment index.

[0041] Example 2:

[0042] The main technical content of the mountainous rural function evaluation method based on spatiotemporal big data is shown in Example 1. Furthermore, the mountainous rural function evaluation reference data includes spatiotemporal big data.

[0043] The spatiotemporal big data includes POI data covering typical mountainous villages, mobile phone signaling data, remote sensing image data, drone aerial survey image data, measured current terrain cadastral data, DEM data, rural real estate cadastral survey data, geographical national conditions monitoring data, land survey data, land use status data, agricultural land classification data, cultivated land quality classification data, basic farmland data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration data, and rural housing real estate registration multi-source vector data.

[0044] Example 3:

[0045] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data can be found in any one of Examples 1 to 2. Furthermore, the mountainous rural function assessment reference data also includes departmental statistical data.

[0046] The departmental statistical data mentioned above include district and county statistical yearbooks, rural economic statistical yearbooks, township national economic development plans, village and town land consolidation plans, rural construction land reclamation, rural property rights registration, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, cultivated land quality grades, agricultural land classification data, basic farmland construction plans, soil census data, meteorological data, land use change data and statistical ledgers over the years, economic census data, population census data, village annual work summaries, village situation introductions, village-level plans, and reporting materials.

[0047] Example 4:

[0048] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data is shown in any one of Examples 1 to 3. Furthermore, the steps of fusing the mountainous rural function assessment reference data to obtain a rural spatial basic geographic information map are as follows:

[0049] 2.1) Obtain the current topographic map of the village and capture real-time village image data.

[0050] 2.2) Perform orthophoto processing on real-time rural image data and integrate and correct it with the current rural topographic map to establish a basic survey base map containing the geographic information spatial data of typical mountain villages.

[0051] 2.3) Convert the spatial data in the mountainous rural function assessment reference data to the basic survey base map to obtain a survey base map containing spatial data.

[0052] 2.4) Convert the non-spatial data in the mountainous rural function assessment reference data into a survey base map containing spatial data to obtain a basic rural spatial geographic information map.

[0053] Example 5:

[0054] The main technical content of the mountain village function assessment method based on spatiotemporal big data can be found in any one of Examples 1 to 4. Furthermore, the real-time village image data includes remote sensing image data and drone aerial survey image data.

[0055] The current rural topographic map includes measured current topographic cadastral data and current land use data.

[0056] Example 6:

[0057] The main technical content of the mountain village function assessment method based on spatiotemporal big data can be found in any one of Examples 1 to 5. Furthermore, the method of converting spatial data into a basic survey base map includes coordinate correction, projection transformation, and base map registration.

[0058] Example 7:

[0059] A method for evaluating the functions of mountainous villages based on spatiotemporal big data, the main technical content of which can be found in any one of Examples 1 to 6. Furthermore, the spatial data includes POI data, mobile phone signaling data, rural real estate cadastral survey data, geographic national conditions monitoring data, land survey data, basic farmland data, ecological protection red line data, and geological survey data.

[0060] The non-spatial data include township national economic development plans, village and town land consolidation plans, rural construction land reclamation plans, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, and village-level plans.

[0061] Example 8:

[0062] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data is shown in any one of Examples 1 to 7. Furthermore, the method for converting non-spatial data into a survey base map containing spatial data is as follows:

[0063] The seamless integration method of geospatial data and non-spatial data overlays non-spatial data onto the survey base map containing spatial data through spatialization methods.

[0064] Example 9:

[0065] The mountainous rural function evaluation method based on spatiotemporal big data, the main technical content of which is shown in any one of Examples 1 to 8, further, the rural multifunctional evaluation index is as follows:

[0066]

[0067] Where i represents time, ω iRepresents the rural multifunctional evaluation index. i Represents the residential function index. β i represents the employment function index. γ i Represents the communication function index.

[0068] Example 10:

[0069] The mountainous rural function evaluation method based on spatiotemporal big data, the main technical content of which is shown in any one of Examples 1 to 9, further, the residential function index α i , employment function index β i , social function index γ i As shown below:

[0070]

[0071] In the formula, i represents time, j represents status, among which j=1 represents working day, j=2 represents weekend, and j=3 represents holiday. represents the mean population during the daytime on weekdays. represents the product of the mean population during the day and evening hours on weekdays.

[0072] The average daytime population Average population at night The product of the mean daytime and nighttime populations As shown below:

[0073]

[0074] Where m and n represent the total duration of night and day respectively. e and d represent the start time of night and day respectively. ij 、AM ij They represent the population size at the i-th moment in the j-th state at night and during the day respectively.

[0075] Example 11:

[0076] See also Figure 1 ,The mountain rural function assessment method based on spatiotemporal big data includes the following steps:

[0077] 1) Obtain reference data for mountainous rural functional assessment.

[0078] 2) The reference data of mountainous rural function assessment are integrated and processed to obtain the rural spatial basic geographic information map, and a rural spatial basic geographic information database is constructed based on the rural spatial basic geographic information map.

[0079] 3) Identify the rural production-living-ecological space of the rural spatial basic geographic information map in the rural spatial basic geographic information database, and then carry out the mountain village function assessment to obtain the rural multifunctional assessment index, so as to intuitively reflect the advantageous functions and shortcomings of the village, and carry out targeted village planning to maximize the advantages, make up for the shortcomings, and achieve favorable rural development.

[0080] The identification method is to determine the delineation of production, living and ecological space through the classification of land use types.

[0081] Example 12:

[0082] The mountainous rural function evaluation method based on spatiotemporal big data, the main technical content is shown in Example 11. Furthermore, the mountainous rural function evaluation reference data includes spatiotemporal big data.

[0083] The spatiotemporal big data includes point of interest (POI) data covering typical mountainous villages, mobile phone signaling data, high-resolution remote sensing image data, drone aerial survey image data, measured large-scale current terrain cadastral data, digital elevation model (DEM) data (1:2000), rural real estate cadastral survey data, geographical national conditions monitoring data, the third national land survey data, land use status data, agricultural land classification data, cultivated land quality classification data, high-standard basic farmland data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration data, and rural housing real estate registration multi-source vector data.

[0084] Example 13:

[0085] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data can be found in any one of Examples 11 to 12. Furthermore, the mountainous rural function assessment reference data also includes departmental statistical data.

[0086] The departmental statistical data mentioned above include district and county statistical yearbooks, rural economic statistical yearbooks, township national economic development plans, village and town land consolidation plans, rural construction land reclamation, rural property rights registration, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, cultivated land quality grades, agricultural land classification data, high-standard basic farmland construction plans, soil census data, meteorological data, land use change data and statistical ledgers over the years, economic census data, population census data, village annual work summaries, village situation introductions, village-level plans, and reporting materials.

[0087] Example 14:

[0088] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data is shown in any one of Examples 11 to 13. Furthermore, the steps of fusing the mountainous rural function assessment reference data to obtain a rural spatial basic geographic information map are as follows:

[0089] 2.1) Obtain the current topographic map of the village and capture real-time village image data.

[0090] 2.2) Perform orthophoto processing on real-time rural image data and integrate and correct it with the current rural topographic map to establish a basic survey base map containing the geographic information spatial data of typical mountain villages.

[0091] Orthophoto processing is performed on high-resolution remote sensing images / UAV low-altitude aerial survey images, and fusion correction is performed with a 1:2000 large-scale current topographic map. Through visual interpretation and field verification, a basic survey base map of geographic information spatial data of typical mountainous villages is established.

[0092] 2.3) Convert the spatial data in the mountainous rural function assessment reference data to the basic survey base map to obtain a survey base map containing spatial data.

[0093] 2.4) Convert the non-spatial data in the mountainous rural function assessment reference data into a survey base map containing spatial data to obtain a basic rural spatial geographic information map.

[0094] Example 15:

[0095] The main technical content of the mountain village function assessment method based on spatiotemporal big data can be found in any one of Examples 11 to 14. Furthermore, the real-time village image data includes remote sensing image data and drone aerial survey image data.

[0096] The current rural topographic map includes measured current topographic cadastral data and current land use data.

[0097] Example 16:

[0098] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data can be found in any one of Examples 11 to 15. Furthermore, the method of converting spatial data into a basic survey base map includes coordinate correction, projection transformation, and base map registration.

[0099] Example 17:

[0100] A method for evaluating the functions of mountainous villages based on spatiotemporal big data, the main technical content of which can be found in any one of Examples 11 to 16. Furthermore, the spatial data includes POI data, mobile phone signaling data, rural real estate cadastral survey data, geographic national conditions monitoring data, the third national land survey data, basic farmland data, ecological protection red line data, and geological survey data.

[0101] The non-spatial data include township national economic development plans, village and town land consolidation plans, rural construction land reclamation plans, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, and village-level plans.

[0102] Example 18:

[0103] The main technical content of the mountainous rural function assessment method based on spatiotemporal big data is shown in any one of Examples 11 to 17. Furthermore, the method for converting non-spatial data into a survey base map containing spatial data is as follows:

[0104] The seamless integration method of geospatial data and non-spatial data overlays non-spatial data onto the survey base map containing spatial data through spatialization methods.

[0105] Example 19:

[0106] The mountainous rural function evaluation method based on spatiotemporal big data, the main technical content of which is shown in any one of Examples 11 to 18, further, the rural multifunctional evaluation index is as follows:

[0107]

[0108] Where i represents time, i=0,1,2,...,23,ω i Represents the rural multifunctional evaluation index. i Represents the residential function index. β i represents the employment function index. γ i Represents the communication function index.

[0109] Example 20:

[0110] The mountainous rural function evaluation method based on spatiotemporal big data, the main technical content of which is shown in any one of Examples 11 to 19, further, the residential function index α i , employment function index β i , social function index γ i As shown below:

[0111]

[0112]

[0113] In the formula, i represents time, j represents status, among which j=1 represents working day, j=2 represents weekend, and j=3 represents holiday. represents the mean population during the daytime on weekdays. represents the product of the mean population during the day and evening hours on weekdays.

[0114] The average daytime population Average population at night The product of the mean daytime and nighttime populations As shown below:

[0115]

[0116] Where m and n represent the total duration of night and day respectively. e and d represent the start time of night and day respectively. ij 、AM ij They represent the population size at the i-th moment in the j-th state at night and during the day respectively.

[0117] Example 21:

[0118] See also Figure 1 ,The mountain rural function assessment method based on spatiotemporal big data includes the following steps:

[0119] Based on the actual situation of the research subjects and literature, data collection work was carried out. The specific data included:

[0120] Spatiotemporal big data, including POI (Point of Interest) data covering typical mountainous villages, mobile phone signaling data, high-resolution remote sensing image data, UAV aerial survey image data, measured large-scale current topographic cadastral data (1:2000), DEM data, rural real estate cadastral survey data, geographical national conditions monitoring data, the third national land survey data, land use status data, agricultural land classification data, cultivated land quality classification data, high-standard basic farmland data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration, and rural house real estate registration multi-source vector data. Departmental statistical data, including obtaining district and county statistical yearbooks, rural economic statistical yearbooks, township national economic development plans, village and town land consolidation plans, rural construction land reclamation, rural property rights registration, village and town industrial planning, ecological protection plans, forestry planning, village and town planning, rural planning, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, cultivated land quality grades, agricultural land classification data, high-standard basic farmland construction plans, soil census data, meteorological data, land use change data and statistical ledgers over the years, economic census data, population census data, village annual work summaries, village situation introductions, village-level plans, and reporting materials.

[0121] Based on the collected data, carry out fusion processing and analysis of rural multi-source geographic information data.

[0122] Spatial data processing: orthophoto processing of high-resolution remote sensing images / UAV low-altitude aerial survey images, fusion correction with 1:2000 large-scale current topographic maps, establishment of a basic survey base map of geographic information spatial data of typical mountainous villages through visual interpretation and field verification, and the use of coordinate correction, projection transformation, base map registration and other methods to uniformly convert POI (Point of Interest) data, mobile phone signaling data, rural real estate cadastral surveys, geographic national conditions monitoring, land surveys, ecological red lines, basic farmland, geological disasters and other spatial data to the basic survey base map.

[0123] Non-spatial data processing integrates the seamless integration technology of geographic spatial data and non-spatial data, and superimposes non-spatial attribute data such as land consolidation planning, rural construction land reclamation, village and town planning, agricultural development planning and other related special planning on the basic survey base map through spatial methods to establish a rural spatial basic geographic information database.

[0124] Based on the rural space basic geographic information database, accurate identification of rural production, living and ecological space is carried out.

[0125] Carry out functional assessment of mountainous villages.

[0126] Construct three functional indexes of rural space residence, employment and communication.

[0127]

[0128] Where, α i is the residential function index, β i is the employment function index, γ i It is the social function index. i=0,1,2,...,23, represents 24 hours a day; j=1,2,3, represents weekdays, weekends and holidays respectively; PM ij and AM ij Represents the population size at various times during the night and day, respectively. Based on general patterns of work-residence behavior, daytime hours d to d+n can take values ​​between 7:00 AM and 5:00 PM, while evening hours e to e+m can take values ​​between 6:00 PM and 6:00 AM the following day.

[0129] A comprehensive evaluation method is used to construct the rural multifunctional index, and the specific details are as follows.

[0130]

[0131] Where, ω i is the rural multifunctional index, α i is the residential function index, β i is the employment function index, γ i It is the social function index.

[0132] Example 22:

[0133] See also Figure 1 ,The mountain rural function assessment method based on spatiotemporal big data includes the following steps:

[0134] S1, select research subjects and collect relevant data;

[0135] S2, carry out fusion processing and analysis of rural multi-source geographic information data.

[0136] S3. Carry out functional assessment of mountain villages.

[0137] S11, collect spatiotemporal big data, specifically including: obtaining POI (Point of Interest) data covering typical mountainous villages, mobile phone signaling data, high-resolution remote sensing image data, UAV aerial survey image data, measured large-scale current topographic cadastral data (1:2000), DEM data, rural real estate cadastral survey data, geographical national conditions monitoring data, the third national land survey data, land use status data, agricultural land classification data, cultivated land quality classification data, high-standard basic farmland data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration, and rural house real estate registration multi-source vector data. Departmental statistical data, including obtaining district and county statistical yearbooks, rural economic statistical yearbooks, township national economic development plans, village and town land consolidation plans, rural construction land reclamation, rural property rights registration, village and town industrial planning, ecological protection plans, forestry planning, village and town planning, rural planning, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, cultivated land quality grades, agricultural land classification data, high-standard basic farmland construction plans, soil census data, meteorological data, land use change data and statistical ledgers over the years, economic census data, population census data, village annual work summaries, village situation introductions, village-level plans, and reporting materials.

[0138] Step S2 includes the following steps:

[0139] S21, spatial data processing, performs orthophoto processing on high-resolution remote sensing images / UAV low-altitude aerial survey images, integrates and corrects them with a 1:2000 large-scale current topographic map, and establishes a basic survey base map of geographic information spatial data for typical mountainous villages through visual interpretation and field verification. Using coordinate correction, projection transformation, base map registration and other methods, POI (Point of Interest) data, mobile phone signaling data, rural real estate cadastral surveys, geographic national conditions monitoring, land surveys, ecological red lines, basic farmland, geological disasters and other spatial data are uniformly converted to the basic survey base map.

[0140] S22, non-spatial data processing, integrates the seamless integration technology of geographic spatial data and non-spatial data, and superimposes non-spatial attribute data such as land consolidation planning, rural construction land reclamation, village and town planning, agricultural development planning and other related special planning on the basic survey base map through spatial methods to establish a rural spatial basic geographic information database.

[0141] Step S3 includes the following steps:

[0142] S31, construct three functional indexes of rural space: residence, employment and communication.

[0143]

[0144] Where, α i is the residential function index, β i is the employment function index, γ i It is the social function index. i=0,1,2,...,23, represents 24 hours a day; j=1,2,3, represents weekdays, weekends and holidays respectively; PM ij and AM ij Represents the population size at various times during the night and day, respectively. Based on general patterns of work-residence behavior, daytime hours d to d+n can take values ​​between 7:00 AM and 5:00 PM, while evening hours e to e+m can take values ​​between 6:00 PM and 6:00 AM the following day.

[0145] S32, a comprehensive evaluation method is used to construct a rural multifunctional index.

[0146]

[0147] Where, ω i is the rural multifunctional index, α i is the residential function index, β i is the employment function index, γ i It is the social function index.

Claims

1. A mountainous rural function assessment method based on spatiotemporal big data is characterized by: The following steps are involved: 1) Obtain reference data for mountainous rural functional assessment. 2) Fusing and processing the reference data of mountainous rural functional assessment to obtain a basic rural spatial geographic information map; 3) Identify the rural production-living-ecological space based on the basic geographic information map of rural space, and then conduct a functional assessment of mountainous villages to obtain a rural multifunctional assessment index.

2. The mountainous rural function assessment method based on spatiotemporal big data according to claim 1 is characterized in that: The reference data for mountainous rural function assessment includes spatiotemporal big data; The spatiotemporal big data includes POI data covering typical mountainous villages, mobile phone signaling data, remote sensing image data, drone aerial survey image data, measured current terrain cadastral data, DEM data, rural real estate cadastral survey data, geographical national conditions monitoring data, land survey data, land use status data, agricultural land classification data, cultivated land quality classification data, basic farmland data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration data, and rural housing real estate registration multi-source vector data.

3. The mountainous rural function assessment method based on spatiotemporal big data according to claim 1 is characterized in that: The reference data for the evaluation of mountainous rural functions also include departmental statistical data; The departmental statistical data mentioned above include district and county statistical yearbooks, rural economic statistical yearbooks, township national economic development plans, village and town land consolidation plans, rural construction land reclamation, rural property rights registration, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, cultivated land quality grades, agricultural land classification data, basic farmland construction plans, soil census data, meteorological data, land use change data and statistical ledgers over the years, economic census data, population census data, village annual work summaries, village situation introductions, village-level plans, and reporting materials.

4. The mountainous rural function assessment method based on spatiotemporal big data according to claim 1 is characterized in that: The steps of fusing the mountainous rural function assessment reference data to obtain the rural spatial basic geographic information map are as follows: 2.1) Obtain current rural topographic maps and capture real-time rural image data; 2.2) Perform orthophoto processing on real-time rural image data and integrate and correct it with the current rural topographic map to establish a basic survey base map containing the geographic information spatial data of typical mountain villages; 2.3) Convert the spatial data in the mountainous rural function assessment reference data to the basic survey base map to obtain a survey base map containing spatial data; 2.4) Convert the non-spatial data in the mountainous rural function assessment reference data into a survey base map containing spatial data to obtain a basic rural spatial geographic information map.

5. The mountainous rural function assessment method based on spatiotemporal big data according to claim 4 is characterized in that: The real-time rural image data includes remote sensing image data and drone aerial survey image data; The current rural topographic map includes measured current topographic cadastral data and current land use data.

6. The mountainous rural function assessment method based on spatiotemporal big data according to claim 4 is characterized in that: The method for converting spatial data into a basic survey base map includes coordinate correction, projection transformation, and base map registration.

7. The mountainous rural function assessment method based on spatiotemporal big data according to claim 4 is characterized in that: The spatial data includes POI data, mobile phone signaling data, rural real estate cadastral survey data, geographical conditions monitoring data, land survey data, basic farmland data, ecological protection red line data, and geological survey data; The non-spatial data include township national economic development plans, village and town land consolidation plans, rural construction land reclamation plans, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, and village-level plans.

8. The mountainous rural function assessment method based on spatiotemporal big data according to claim 4 is characterized in that: The method for converting non-spatial data to a survey base map containing spatial data is as follows: The seamless integration method of geospatial data and non-spatial data overlays non-spatial data onto the survey base map containing spatial data through spatialization methods.

9. The mountainous rural function assessment method based on spatiotemporal big data according to claim 1 is characterized in that: The rural multifunctional assessment index is as follows: Where i represents time, ω i represents the rural multifunctional evaluation index; α i represents the residential function index; β i represents the employment function index; γ i Represents the communication function index.

10. The mountainous rural function assessment method based on spatiotemporal big data according to claim 9 is characterized in that: The residential function index α i , employment function index β i , social function index γ i As shown below: Where i represents time, j represents state, j=1 represents working day, j=2 represents weekend, and j=3 represents holiday; represents the mean population during the daytime on weekdays; represents the product of the mean population during the day and at night on weekdays; The average daytime population Average population at night The product of the mean daytime and nighttime populations As shown below: Where m and n represent the total duration of night and day respectively; e and d represent the starting time of night and day respectively; PM ij 、AM ij They represent the population size at the i-th moment in the j-th state at night and during the day respectively.

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