CN114399418A - Large-range prospecting boundary calibration layout method considering multiple factors - Google Patents
Large-range prospecting boundary calibration layout method considering multiple factors Download PDFInfo
- Publication number
- CN114399418A CN114399418A CN202210043182.5A CN202210043182A CN114399418A CN 114399418 A CN114399418 A CN 114399418A CN 202210043182 A CN202210043182 A CN 202210043182A CN 114399418 A CN114399418 A CN 114399418A
- Authority
- CN
- China
- Prior art keywords
- factor
- unit grid
- terrain
- unit
- traffic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012417 linear regression Methods 0.000 claims abstract description 17
- 230000000694 effects Effects 0.000 claims description 21
- 238000013507 mapping Methods 0.000 claims description 7
- 230000002093 peripheral effect Effects 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 2
- 230000008685 targeting Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 241000208682 Liquidambar Species 0.000 description 2
- 235000006552 Liquidambar styraciflua Nutrition 0.000 description 2
- 235000013399 edible fruits Nutrition 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012876 topography Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Geometry (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Computer Graphics (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a large-scale surveying and calibrating layout method considering multiple factors, which comprises the following steps: respectively acquiring weights of administrative region factors, terrain factors, traffic factors and building factors by using a multiple linear regression method according to the calibration historical data of the exploration boundary; dividing the boundary lines into equal grids according to a proportional scale to obtain a plurality of unit grids; respectively acquiring a political region factor coefficient, a terrain factor coefficient, a traffic factor coefficient and a building factor coefficient of each unit grid; calculating the scope exploration boundary calibration layout trust degree of each unit grid based on multiple factors according to the weights of the administrative area factor, the terrain factor, the traffic factor and the building factor and the coefficients of the administrative area factor, the terrain factor, the traffic factor and the building factor of each unit grid; and reserving the unit grids with the trust degree larger than the trust degree threshold value, and establishing a large-range exploration boundary calibration layout. The invention can solve the technical problem of low working efficiency of boundary line exploration calibration in the prior art.
Description
Technical Field
The invention relates to the technical field of topographic survey, in particular to a large-scale survey boundary calibration layout method considering multiple factors.
Background
In recent years, in the policy of national space planning, three control lines of an ecological protection red line, a permanent basic farmland and a town development boundary are clearly pointed out, the strictest ecological environment protection system, farmland protection system and land saving system are implemented, and the three control lines are used as the red lines for adjusting the economic structure, planning the industrial development, promoting the urbanization and insurmountable, and tamping the basis of the perpetual development of Chinese nationalities.
In order to ensure the implementation of the policies, the boundary surveying and calibrating work is an important measure for ensuring the accurate landing of the three control lines. For boundary surveying and calibrating work, the specific positions and densities of boundary piles and signboard arrangement are not clearly specified or recommended by related policies and technical standards at present, and different positions and densities are selected, so that the working efficiency of surveying and calibrating work is greatly influenced. Therefore, a scientific, reasonable and practical boundary exploration calibration method is needed to improve the working efficiency of boundary exploration calibration.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a large-scale boundary surveying and calibrating layout method considering multiple factors, and aims to solve the technical problem that the working efficiency of boundary surveying and calibrating in the prior art is low.
The technical scheme adopted by the invention is as follows:
a method for large-scale prospecting calibration layout considering multiple factors is provided, which comprises the following steps:
respectively acquiring weights of administrative region factors, terrain factors, traffic factors and building factors by using a multiple linear regression method according to the calibration historical data of the exploration boundary;
dividing the boundary lines into equal grids according to a proportional scale to obtain a plurality of unit grids;
respectively acquiring a political region factor coefficient, a terrain factor coefficient, a traffic factor coefficient and a building factor coefficient of each unit grid;
calculating the scope exploration boundary calibration layout trust degree of each unit grid based on multiple factors according to the weights of the administrative area factor, the terrain factor, the traffic factor and the building factor and the coefficients of the administrative area factor, the terrain factor, the traffic factor and the building factor of each unit grid;
and reserving the unit grids with the trust degree larger than the trust degree threshold value, and establishing a large-range exploration boundary calibration layout.
Furthermore, the sum of the weight coefficients of 4 factors, namely the administrative region factor, the terrain factor, the traffic factor and the construction factor is 1.
Further, acquiring a political region factor coefficient of each unit grid, specifically as follows:
dividing the types of the political regions into a central city region, a main city new region and other districts;
obtaining factor coefficients of the administrative regions of the unit grids by using a multiple linear regression method according to the administrative regions and the survey boundary calibration historical data of the unit grids; and the element grids of different administrative regions have different administrative region factor coefficients and the sum is 1.
Further, acquiring a terrain factor coefficient of each unit grid, specifically as follows:
dividing the terrain into flat land, hilly land, mountain land and high mountain land according to the gradient;
obtaining a terrain factor coefficient of each unit grid by using a multivariate linear regression method according to the terrain category and the survey boundary calibration historical data of the unit grid; the terrain factor coefficients of the unit grids of different terrain categories are different and the sum is 1.
Further, the traffic factor coefficient of each cell grid is obtained as follows:
determining the accessibility of the unit grid road according to the distance between the unit grid and the peripheral road: when a road passes through the unit grid range, the accessibility of the unit grid road is excellent; when a road passes through the unit grid within the range of the peripheral distance threshold, the road accessibility of the unit grid is good; when no road passes through the unit grid within the peripheral distance threshold range, the road accessibility of the unit grid is poor;
obtaining traffic factor coefficients of each unit grid by using a multivariate linear regression method according to road accessibility and survey boundary calibration historical data of the unit grids; the unit grids with different road accessibility have different traffic factor coefficients and the sum is 1.
Further, obtaining the building factor coefficient of each unit grid, specifically as follows:
determining the artificial activity frequency degree of the unit grid according to the floor area of the residential points in the unit grid: when the floor area accumulation area of the residential points in the periphery of the unit grid is smaller than a first area threshold value, the frequency degree of artificial activities is weak; when the floor area accumulation area of the residential points in the periphery of the unit grid is greater than or equal to the first area threshold value and less than or equal to the second area threshold value, the frequency degree of artificial activities is medium; when the accumulated floor area of the residential points in the periphery of the unit grid is larger than a second floor area threshold, the frequency degree of artificial activities is high;
obtaining the building factor coefficient of each unit grid by using a multivariate linear regression method according to the frequency degree of artificial activities in the unit grid and the calibration historical data of the survey boundary; the unit grids are unit grids with different activity frequency degrees, and the building factor coefficients are different and sum to 1.
Further, the calculation method of the calibration layout confidence level S of each unit grid based on the multi-factor range survey is as follows:
Si＝λ1·ai+λ2·bi+λ3·ci+λ4·di
in the above formula, λ1Is a political region factor weight, lambda2As a terrain factor weight, λ3As a weight of the traffic factor, λ4As a weight of the construction factor, aiIs the factor coefficient of the administrative district, biIs the terrain factor coefficient, ciIs the traffic factor coefficient, diFor the building factor coefficient, i represents a certain cell grid.
Further, a weight equipartition method is adopted to set a confidence threshold, and the confidence threshold is 0.5.
According to the technical scheme, the beneficial technical effects of the invention are as follows:
1. through the technical scheme who adopts this embodiment, the typical characteristic of sweetgum fruit road passageway nature, the frequent degree of artificial activity, administrative area functional attribute, topography and landform has been considered, makes the interior calibration overall arrangement of reconnaissance have scientificity, rationality, practicality, maneuverability, has effectively promoted the work efficiency of reconnaissance calibration.
2. By considering a large-range boundary surveying and layout method with multiple factors, a set of complete boundary surveying and layout method is established, a referential technical mode is provided for the boundary surveying and layout work of different types of control lines, and the embedding cost of the boundary piles or the signboard at the later stage is effectively reduced; the overall technical route of the surveying and calibrating is optimized, and the management cost is effectively saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flowchart of a method for a large-scale, scoped calibration layout that accounts for multiple factors, according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of the present invention for dividing boundaries into equal grids on a scale;
FIG. 3 is a schematic diagram of the terrain category distribution of all the unit grids according to the embodiment of the present invention;
FIG. 4 is a diagram illustrating a mapping layout result obtained according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Examples
The embodiment provides a large-scale surveying and calibrating layout method considering multiple factors, as shown in fig. 1, which includes the following steps:
s1, respectively obtaining the weights of the administrative region factor, the terrain factor, the traffic factor and the building factor
In this embodiment, the multifactor includes: administrative factors, terrain factors, traffic factors, and construction factors. According to the calibration historical data of the exploration boundary, weights of administrative region factors, terrain factors, traffic factors and building factors are obtained by using a multiple linear regression method; the sum of the weight coefficients occupied by the factors is 1,
namely: lambda [ alpha ]1+λ2+λ3+λ4＝1
The mapping calibration historical data comprises positions where the boundary piles and the signboard are arranged, and the positions where the boundary piles and the signboard are arranged are respectively corresponding to political region factors, terrain factors, traffic factors and building factors. The weight of each factor can be obtained by a multiple linear regression method.
In a specific embodiment, for example, the obtained political region factor weight λ10.3, terrain factor weight λ20.3, traffic factor weight λ30.2, building factor weight λ4＝0.2。
S2, dividing the boundary into equal grids according to the scale to obtain a plurality of unit grids
In a specific embodiment, in the software ArcGIS, the boundary lines are subjected to equal grid division according to a proportional scale to obtain a plurality of unit grids, and each unit grid is taken as a minimum calculation unit. Preferably, the scale is set to 1: 2000, can obtain better division effect.
S3, respectively obtaining the administrative region factor coefficient, the terrain factor coefficient, the traffic factor coefficient and the building factor coefficient of each unit grid
S3-1, obtaining factor coefficients of administrative regions of each unit grid according to administrative regions of the unit grids
In the embodiment, the political region types are divided into a central urban region, a main urban new region and other sub-regions; and classifying the unit grids according to the administrative regions to which the unit grids belong, and obtaining factor coefficients of the administrative regions of the unit grids by using a multiple linear regression method according to calibration historical data of an exploration boundary. And the element grids of different administrative regions have different administrative region factor coefficients and the sum is 1.
In a specific embodiment, the following are exemplified: unit grid administrative region factor coefficient value a of central urban area1＝0.6、a2＝0.29、a3＝0.11；a1+a2+a3＝1。
S3-2, obtaining the terrain factor coefficient of each unit grid according to the terrain gradient of the unit grid
Determining the terrain category according to the terrain slope, dividing the terrain category into flat land, hilly land, mountain land and high mountain land, and obtaining the terrain factor coefficient of each unit grid by using a multivariate linear regression method according to the terrain category and the survey boundary calibration historical data of the unit grid; the terrain factor coefficients of the unit grids of different terrain categories are different and the sum is 1.
In a specific embodiment, the following are exemplified: the flat ground is the area with most slopes below 2 degrees, and the terrain factor coefficient of the unit grid is b10.32, the hilly land is the area with most slopes between 2 degrees and 6 degrees, and the terrain factor coefficient of the unit grid is b20.26, the mountain land is the area with most slopes between 6 degrees and 25 degrees, and the terrain factor coefficient of the unit grid is b3The mountain land is the area with most gradient over 25 degrees, and the terrain factor coefficient of the unit grid is b4＝0.2；b1+b2+b3+b4＝1。
S3-3, obtaining the traffic factor coefficient of each cell grid according to the distance between the cell grid and the surrounding roads
In the embodiment, determining the road accessibility of the unit grid according to the distance between the unit grid and the surrounding roads, and obtaining the traffic factor coefficient of each unit grid by using a multiple linear regression method according to the road accessibility of the unit grid and the calibration historical data of the exploration boundary; the unit grids with different road accessibility have different traffic factor coefficients and the sum is 1.
In a specific embodiment, the following are exemplified: when the road passes through the cell grid range, the road accessibility of the cell grid is excellent, and the traffic factor coefficient is c10.6; when the unit grid has a road passing through the periphery of the unit grid within the range of 0-50 m, the road accessibility of the unit grid is good, and the traffic factor coefficient is c20.4; when no road access exists in the cell grids and within the peripheral 50 m range, the road access performance of the cell grids is poor, and the traffic factor coefficient is c3＝0；c1+c2+c3＝1。
In the above example, 50 meters is the distance threshold.
S3-4, obtaining the building factor coefficient of each unit grid according to the size of the floor area of the residential points in the unit grid
In the embodiment, the artificial activity frequency degree of the unit grid is determined according to the floor area of the residential points in the unit grid, and the building factor coefficient of each unit grid is obtained by using a multiple linear regression method according to the artificial activity frequency degree and the calibration historical data of the exploration boundary in the unit grid; the unit grids are unit grids with different activity frequency degrees, and the building factor coefficients are different and sum to 1.
In a specific embodiment, the following are exemplified: when the accumulated floor area of residential points in the unit grid range is less than 2000 square meters, the frequency of artificial activities is low, and the building factor coefficient is d10; when the occupied area of residential sites is less than 5000 square meters within the grid range and less than 2000 square meters, the artificial activity frequency is medium, and the building factor coefficient is d20.4; when the occupied area of the residential points is more than or equal to 5000 square meters within the grid range, the artificial movement frequency is high, and the building factor coefficient is d3＝0.6；d1+d2+d3＝1。
In the above example, 2000 square meters is the first area threshold and 5000 square meters is the second area threshold.
S4, calculating the scope exploration boundary calibration layout credibility of each unit grid based on multiple factors according to the administrative region factor, the terrain factor, the traffic factor and the weight of the building factor and the coefficients of the administrative region factor, the terrain factor, the traffic factor and the building factor of each unit grid
The calculation method of the scope-mapping calibration layout credibility S is as follows:
Si＝λ1·ai+λ2·bi+λ3·ci+λ4·di
in the above formula, λ1Is a political region factor weight, lambda2As a terrain factor weight, λ3As a weight of the traffic factor, λ4As a weight of the construction factor, aiIs the factor coefficient of the administrative district, biIs the terrain factor coefficient, ciIs the traffic factor coefficient, diFor the building factor coefficient, i represents a certain cell grid.
S5, reserving the cell grids with the trust degree larger than the trust degree threshold value, and establishing a large-scale exploration boundary calibration layout
The larger the confidence threshold value is set, the fewer the reserved unit grids are; the smaller the density of boundary piles and signboards required to be set during boundary calibration, the higher the working efficiency of the boundary calibration layout. However, the arrangement density of the boundary piles and the signboard is low, and the accuracy of the calibration layout of the surveying boundary can be influenced to a certain extent. In this embodiment, a weight averaging method is adopted to set the confidence threshold.
In a particular embodiment, a confidence threshold S is setMWhen S is 0.5i＞SMThen, the unit grid is reserved and used as a candidate area for setting boundary piles and signboard; and calculating all unit grids, combining all the obtained candidate areas, and establishing a large-range exploration boundary calibration layout.
Fig. 2-4 are schematic diagrams of a process when performing the mapping and definition calibration by using the technical solution of the present embodiment, and a final mapping and calibration layout result.
Through the technical scheme who adopts this embodiment, the typical characteristic of sweetgum fruit road passageway nature, the frequent degree of artificial activity, administrative area functional attribute, topography and landform has been considered, makes the interior calibration overall arrangement of reconnaissance have scientificity, rationality, practicality, maneuverability, has effectively promoted the work efficiency of reconnaissance calibration.
By considering a large-range boundary surveying and layout method with multiple factors, a set of complete boundary surveying and layout method is established, a referential technical mode is provided for the boundary surveying and layout work of different types of control lines, and the embedding cost of the boundary piles or the signboard at the later stage is effectively reduced; the overall technical route of the surveying and calibrating is optimized, and the management cost is effectively saved.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A method for large-scale world-mapping calibration layout with multi-factor consideration, comprising:
respectively acquiring weights of administrative region factors, terrain factors, traffic factors and building factors by using a multiple linear regression method according to the calibration historical data of the exploration boundary;
dividing the boundary lines into equal grids according to a proportional scale to obtain a plurality of unit grids;
respectively acquiring a political region factor coefficient, a terrain factor coefficient, a traffic factor coefficient and a building factor coefficient of each unit grid;
calculating the scope exploration boundary calibration layout trust degree of each unit grid based on multiple factors according to the weights of the administrative area factor, the terrain factor, the traffic factor and the building factor and the coefficients of the administrative area factor, the terrain factor, the traffic factor and the building factor of each unit grid;
and reserving the unit grids with the trust degree larger than the trust degree threshold value, and establishing a large-range exploration boundary calibration layout.
2. The multi-factor-considered large-scale surveying and mapping method according to claim 1, wherein the sum of the weight coefficients of 4 factors, i.e., the administrative area factor, the terrain factor, the traffic factor, and the construction factor, is 1.
3. The multi-factor-considered large-scale surveying and calibrating layout method according to claim 1, wherein the political region factor coefficients of each unit grid are obtained as follows:
dividing the types of the political regions into a central city region, a main city new region and other districts;
obtaining factor coefficients of the administrative regions of the unit grids by using a multiple linear regression method according to the administrative regions and the survey boundary calibration historical data of the unit grids; and the element grids of different administrative regions have different administrative region factor coefficients and the sum is 1.
4. The method of claim 1, wherein the terrain factor coefficients of each unit grid are obtained as follows:
dividing the terrain into flat land, hilly land, mountain land and high mountain land according to the gradient;
obtaining a terrain factor coefficient of each unit grid by using a multivariate linear regression method according to the terrain category and the survey boundary calibration historical data of the unit grid; the terrain factor coefficients of the unit grids of different terrain categories are different and the sum is 1.
5. The multi-factor-considered large-scale world calibration layout method according to claim 1, wherein the traffic factor coefficients of each cell grid are obtained as follows:
determining the accessibility of the unit grid road according to the distance between the unit grid and the peripheral road: when a road passes through the unit grid range, the accessibility of the unit grid road is excellent; when a road passes through the unit grid within the range of the peripheral distance threshold, the road accessibility of the unit grid is good; when no road passes through the unit grid within the peripheral distance threshold range, the road accessibility of the unit grid is poor;
obtaining traffic factor coefficients of each unit grid by using a multivariate linear regression method according to road accessibility and survey boundary calibration historical data of the unit grids; the unit grids with different road accessibility have different traffic factor coefficients and the sum is 1.
6. The multi-factor-aware large-scale world calibration layout method according to claim 1, wherein the building factor coefficients of each unit grid are obtained as follows:
determining the artificial activity frequency degree of the unit grid according to the floor area of the residential points in the unit grid: when the floor area accumulation area of the residential points in the periphery of the unit grid is smaller than a first area threshold value, the frequency degree of artificial activities is weak; when the floor area accumulation area of the residential points in the periphery of the unit grid is greater than or equal to the first area threshold value and less than or equal to the second area threshold value, the frequency degree of artificial activities is medium; when the accumulated floor area of the residential points in the periphery of the unit grid is larger than a second floor area threshold, the frequency degree of artificial activities is high;
obtaining the building factor coefficient of each unit grid by using a multivariate linear regression method according to the frequency degree of artificial activities in the unit grid and the calibration historical data of the survey boundary; the unit grids are unit grids with different activity frequency degrees, and the building factor coefficients are different and sum to 1.
7. The multi-factor-aware dimension-mapping-scale layout method of claim 1, wherein the confidence level S of each unit grid based on the multi-factor-aware dimension-mapping-scale layout is calculated as follows:
Si＝λ1·ai+λ2·bi+λ3·ci+λ4·di
in the above formula, λ1Is a political region factor weight, lambda2As a terrain factor weight, λ3As a weight of the traffic factor, λ4As a weight of the construction factor, aiIs the factor coefficient of the administrative district, biIs the terrain factor coefficient, ciIs the traffic factor coefficient, diFor the building factor coefficient, i represents a certain cell grid.
8. The multifactor-aware world wide scale targeting layout method of claim 1, wherein a weight averaging method is used to set the confidence threshold, which is 0.5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210043182.5A CN114399418A (en) | 2022-01-14 | 2022-01-14 | Large-range prospecting boundary calibration layout method considering multiple factors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210043182.5A CN114399418A (en) | 2022-01-14 | 2022-01-14 | Large-range prospecting boundary calibration layout method considering multiple factors |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114399418A true CN114399418A (en) | 2022-04-26 |
Family
ID=81231127
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210043182.5A Pending CN114399418A (en) | 2022-01-14 | 2022-01-14 | Large-range prospecting boundary calibration layout method considering multiple factors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114399418A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116337012A (en) * | 2023-03-20 | 2023-06-27 | 重庆地质矿产研究院 | Surface mine boundary pile position layout optimization method based on oblique photogrammetry technology |
-
2022
- 2022-01-14 CN CN202210043182.5A patent/CN114399418A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116337012A (en) * | 2023-03-20 | 2023-06-27 | 重庆地质矿产研究院 | Surface mine boundary pile position layout optimization method based on oblique photogrammetry technology |
| CN116337012B (en) * | 2023-03-20 | 2023-10-20 | 重庆地质矿产研究院 | Surface mine boundary pile position layout optimization method based on oblique photogrammetry technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jin et al. | Designing a spatial pattern to rebalance the orientation of development and protection in Wuhan | |
| Gong et al. | Land suitability evaluation for development using a matter-element model: a case study in Zengcheng, Guangzhou, China | |
| CN109359350B (en) | Wind power plant road intelligent design method for optimizing fine construction cost | |
| CN104050323B (en) | High altitude mountainous area fuzzy multi-criteria wind power plant site selection method | |
| Mougiakou et al. | Urban green space network evaluation and planning: Optimizing accessibility based on connectivity and raster gis analysis | |
| CN102360458A (en) | Fuzzy evaluation method for selection of immigrant settlement areas | |
| CN107872808A (en) | A kind of WLAN sites prediction analysis method and device | |
| CN112528516B (en) | Watershed water environment management method coupling land utilization type and climate change | |
| Xu et al. | Urban planning construction land standard and its revision based on climate and topography in China | |
| Ramachandra et al. | Land use land cover dynamics in a rapidly urbanising landscape | |
| CN113240257A (en) | Territorial space partitioning method and device based on minimum cumulative resistance model | |
| CN114399418A (en) | Large-range prospecting boundary calibration layout method considering multiple factors | |
| Matsuo et al. | Analysis of spatial and temporal distribution patterns of temperatures in urban and rural areas: Making urban environmental climate maps for supporting urban environmental planning and management in Hiroshima | |
| CN114881466A (en) | Multi-source data-based population space partition fitting method | |
| Liu et al. | Detection of wind corridors based on “Climatopes”: a study in central Ji’nan | |
| Chu et al. | An example of ecological wisdom in historical settlement: The wind environment of Huazhai village in Taiwan | |
| CN111274700A (en) | Habitat network construction method based on vector data | |
| CN114782235A (en) | Construction method for ecological safety analysis | |
| CN114528672A (en) | Urban hydrological station network layout method and system based on 3S technology | |
| CN115759883A (en) | Ecological management partitioning method based on network group characteristics | |
| CN107247747A (en) | A kind of regional nature ecogeography national conditions quality comprehensive Measurement Method | |
| CN116776403A (en) | Three-dimensional simulation system, equipment and terminal for homeland space planning | |
| Li et al. | Spatial distribution characteristics and optimum layout of urban and rural areas in different geomorphological types | |
| CN113657776A (en) | Natural protection site marking site selection method and system | |
| CN108502198B (en) | Fixed-wing unmanned plane city landing point choosing method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |