CN114064751B - Multisource data fusion method in flood simulation prediction - Google Patents
Multisource data fusion method in flood simulation prediction Download PDFInfo
- Publication number
- CN114064751B CN114064751B CN202111321550.XA CN202111321550A CN114064751B CN 114064751 B CN114064751 B CN 114064751B CN 202111321550 A CN202111321550 A CN 202111321550A CN 114064751 B CN114064751 B CN 114064751B
- Authority
- CN
- China
- Prior art keywords
- grid
- grids
- coarse
- data
- fusion
- 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.)
- Active
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 41
- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 14
- 230000004927 fusion Effects 0.000 claims abstract description 79
- 230000007704 transition Effects 0.000 claims abstract description 58
- 238000000034 method Methods 0.000 claims abstract description 32
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000006424 Flood reaction Methods 0.000 description 2
- 230000009467 reduction Effects 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
- 238000001514 detection method Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Probability & Statistics with Applications (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a multisource data fusion method in flood simulation prediction, which belongs to the technical field of flood simulation prediction, fuses coarse grid and fine grid data into a point sequence, and endows fused data with initial elevation according to the coarse grid and fine grid elevation; according to the grid grade corresponding to each coarse grid, carrying out linking treatment on the grid elevation of the transition area of the fusion data; obtaining hydrodynamic parameters corresponding to grids, numbering all grids, and distributing grid numbers adjacent to the grids into grid attributes; and performing error checking processing on the grid fusion data, and performing information correction if information errors are found. The method has the advantages of simple parameters, convenient acquisition, convenience for large-scale flood simulation prediction of part of key high-precision terrains and part of low-precision terrains, and easiness and high efficiency.
Description
Technical Field
The invention belongs to the technical field of flood simulation prediction, and relates to a multisource data fusion method in flood simulation prediction.
Background
In recent years, the rapid urban process causes the flood plain of each main river basin to rapidly gather population and wealth along the two sides of the river, and the risk of flood disasters is increased. Therefore, under the influence of global warming, flood simulation prediction work is carried out, and the method has important theoretical and practical significance for flood risk management, population reduction, economic loss reduction and the like.
The main input data of flood simulation prediction comprises terrain and land cover data, and the data are mainly derived from satellite remote sensing aerial survey and low-altitude unmanned aerial vehicle aerial survey at present. The satellite remote sensing aerial survey can acquire valuable remote sensing data from the satellite remote sensing aerial survey in a short time, has high information acquisition speed and short period, and is less limited by economic cost. However, the resolution and the precision of the remote sensing aerial survey data of the satellite in a large range are low, and meanwhile, the information loss caused by noise is large due to the influence of atmospheric radiation. The aerial survey of the low-altitude unmanned aerial vehicle can well ensure the accuracy of survey data, greatly reduces the error rate in the survey work, ensures the accuracy of the survey data, has very small unmanned aerial vehicle area, has more flexibility, is simpler to operate, can develop the survey work in detail aiming at the survey area, and is more comprehensive in information collection. However, under the conditions of large range and urgent time requirement, the aerial survey of the low-altitude unmanned aerial vehicle is difficult to exert the due advantages, and the measurement cost is high. Flood simulation prediction is generally performed in a relatively large range, the data quality requirement on a local key area is relatively high, the data quality requirement of flood simulation prediction is hardly met by single aerial survey data, water condition guarantee can be provided when large floods are sudden, and unnecessary loss of flood simulation prediction is prevented from being difficult and heavy due to various limitations of data quality and quantity.
In summary, a low-cost and high-efficiency multi-source data fusion technology is urgently needed to meet the dual requirements of flood simulation prediction on data quantity and quality in the flood control emergency and flood control scheduling process.
Disclosure of Invention
The invention aims to overcome the defects that single aerial survey data is difficult to meet the data quality requirement of flood simulation prediction and forecast and the cost is high in the prior art, and provides a multi-source data fusion method in flood simulation prediction.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
a multi-source data fusion method in flood simulation prediction comprises the following steps:
step 1) global low-resolution data and local high-resolution data for flood simulation are obtained, coarse grids and fine grids are established based on the global low-resolution data and the local high-resolution data, and grid grades corresponding to each coarse grid are determined;
step 2) fusing the data of the coarse mesh and the fine mesh, determining a fused data point sequence, and endowing the fused data with initial elevation according to the elevation of the coarse mesh and the fine mesh;
step 3) is based on grid 1:2, selecting a fusion data transition area grid, and carrying out connection processing on the fusion data transition area grid elevation based on the grid grade corresponding to each coarse grid;
step 4) corresponding hydrodynamic parameters are given to each grid, and hydrodynamic parameters of the grids in the fusion data transition area are determined;
and 5) numbering all grids, distributing grid neighbor attributes, performing error checking treatment on the fusion data in the fusion data transition area grids, and performing information correction on error information to obtain multi-source fusion data oriented to flood simulation.
Preferably, the specific operation of step 1) is:
based on coarse grid resolution and fine grid resolution, according to grid 1: and 2, determining the number of grid grades by using a criterion, acquiring a grid resolution sequence, and sleeving the coarse grid and the fine grid according to coordinates so as to determine the grid grade corresponding to each coarse grid.
Preferably, in step 1), the calculation method of the grid class number is as follows:
setting the coarse grid as a 1-level grid, and setting the resolution of the corresponding grid as DX 1 The resolution corresponding to the ith grid is
The last grid resolution in the grid resolution sequence is the value closest to the input fine grid resolution, the number of grid levels is M, and the corresponding grid resolution is DX M 。
Preferably, in step 1), the method for calculating the grid level is as follows:
counting the number of the fine grids corresponding to each coarse grid, wherein if the number of the fine grids corresponding to a certain coarse grid is larger than 1 and does not contain null values, the coarse grid grade is the maximum grade, otherwise, the coarse grid grade is the minimum grade;
querying a grid level maximum of 4 grids directly connected to each coarse grid if the coarse grid level L i Less than the maximum value L of the grid level connected with the grid level nmax The coarse mesh grade is L i ＝L nmax -1；
And performing grid grade calculation on all grids until the difference value of the grid grade values of the two grids connected with each other is not more than 1.
Preferably, the specific operation of step 2) is:
step 2.1) calculating the relative origin coordinates in the coarse mesh,
when the origin of the coarse grid coordinates is (Xc 0, yc 0), then the N ROW Line N COL Relative origin coordinates (X) within a column coarse grid N ,Y N ) Is X N ＝X c0 +(N ROW -1)×DX 1 ，Y N ＝Y c0 +(N COL -1)×DX 1 ；
Step 2.2) at a certain relative origin of coordinates (X) N ,Y N ) Is of grid class L i The number of points in each row and each column of the grid area range isAnd, the gridInner nth row Line n col Point coordinates of columns (X n ,Y n ) Is that
Step 2.3) when the grid level corresponding to the coarse grid is the maximum level L M When the n is in the coarse grid row Line n col Elevation value E of column n According to the nearest 4 fine grids around the line, when the coordinates of the line from the nearest 4 fine grids are (X 1 ,Y 1 )、(X 1 ,Y 2 )、(X 2 ,Y 1 )、(X 2 ,Y 2 ) And the corresponding elevation values are E respectively 11 、E 12 、E 21 、E 22 When in use, then
Step 2.4) when the grid level corresponding to the coarse grid is not the maximum level L M And the elevation values of the coarse grids are initial elevation values corresponding to the coarse grids.
Preferably, in step 3), the specific operation of the fusion data transition area grid elevation linking process is as follows:
step 3.1) for not the minimum level L 1 Or maximum level L M Is at the minimum level L 1 Grid (X) p1 ,Y p1 ) And maximum level L M Grid (X) p2 ,Y p2 ) Fusion data transition grid in the middle of the connection line (X) g1 ,Y g1 ) Fusion data transition grid (X) g1 ,Y g1 ) Transition grid elevation E of (2) g1 By the highest level grid elevation E in the same direction p1 And lowest level grid elevation E p2 Obtaining;
when the direction in which the fused data transition grid is located is transverse,when the direction of the fusion data transition grid is vertical, the fusion data transition grid is in the right direction>
Step 3.2) on the basis of step 3.1, for other transition grids (X) than step 3.1 g2 ,Y g2 ) Other transition grid (X) g2 ,Y g2 ) Grid level maxima grid (X r1 ,Y r1 ) And grid class minimum grid (X r2 ,Y r2 ) The elevation of (2) is E respectively r1 And E is r2 Grid level maxima grid (X c1 ,Y c1 ) And grid class minimum grid (X c2 ,Y c2 ) The elevation of (2) is E respectively c1 And E is c2 Then other transition grids (X g2 ,Y g2 ) Transition grid elevation E of (2) g2 Is that
Preferably, in step 4), the method for determining the hydrodynamic parameters of the fusion data transition area grid is as follows:
step 4.1) when the grid grade corresponding to the coarse grid is the maximum grade, fusing the hydrodynamic parameters of the data transition area grid as the hydrodynamic parameters in the coarse grid, and the nth in the coarse grid row Line n col The hydrodynamic parameters of the row are selected from the hydrodynamic parameters of the fine grid closest to the hydrodynamic parameters;
and 4.2) when the grid grade corresponding to the coarse grid is not the maximum grade, the hydrodynamic parameters of the grid of the fusion data transition area are adopted as the hydrodynamic parameters of the corresponding coarse grid.
Preferably, in step 5), the mesh neighbor attribute allocation method specifically includes:
step 5.1) numbering grids from the origin of the coarse grids according to the position relation between the transition region grids of the fusion data and the coarse grids, and sequentially numbering the grids from left to right and from bottom to top, wherein when a plurality of fusion data points exist in the corresponding positions of each coarse grid, the grids are also sequentially numbered according to the left to right and from bottom to top, and then numbering the next coarse grid is started;
step 5.2) sequentially allocating numbers of adjacent grids to each fusion data transition area grid according to the positions of the coarse grids, wherein if a certain coarse grid contains a plurality of fusion data points, the neighbor numbers of the inner fusion grids are numbers of 4 grids directly adjacent to the adjacent coarse grids, the neighbor numbers of the boundary fusion grids are the numbers of the grids nearest to the adjacent coarse grids, when the neighbor coarse grids are the same as or higher than the grids, the number of single-side neighbor grids is 1, and when the neighbor coarse grids are lower than the grids, the number of single-side neighbor grids is 2.
Preferably, the error checking process and the information correction are specifically:
the grid information of each fusion data transition area is subjected to error checking, when the grid elevation is not null, the corresponding hydrodynamic parameter cannot be null, if the corresponding hydrodynamic parameter is null, the grid is defined as a faulty grid, otherwise, the grid is defined as a non-faulty grid;
and carrying out information correction on the wrong grids in all the fusion data, wherein hydrodynamic parameters of the wrong grids are directly connected with hydrodynamic parameters of the grids until the wrong grids do not exist in the fusion data.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a multisource data fusion method in flood simulation prediction, which is implemented according to the following steps: according to the coarse grid resolution and the fine grid resolution, according to grid 1: determining the grid class number and the resolution sequence according to a 2-criterion; the coarse grids and the fine grids are sleeved according to coordinates, and grid grades corresponding to each coarse grid are determined; fusing the coarse grid and fine grid data into a dot sequence, and endowing the fused data with initial elevation according to the coarse grid and fine grid elevation; according to the grid grade corresponding to each coarse grid, carrying out linking treatment on the grid elevation of the transition area of the fusion data; obtaining hydrodynamic parameters corresponding to grids, wherein a nearest neighbor method is adopted for fine grids, and coarse grid parameters are used for other grids; numbering all grids, and distributing grid numbers adjacent to the grids into grid attributes; and performing error checking processing on the grid fusion data, and performing information correction if information errors are found. The method of the invention ensures the double requirements of data quantity and quality in flood simulation prediction. The method has the advantages of simple parameters, convenient acquisition, convenience for large-scale flood simulation prediction in partial critical high-precision topography and partial low-precision topography data areas, and easiness and high efficiency.
Drawings
Fig. 1 is a diagram of a grid 1 to be observed by a multisource data fusion method in flood simulation prediction for distributing a grid of a fusion data transition area: a 2 rule;
fig. 2 is a graph showing the processing effect of the multi-source data fusion method in flood simulation prediction on the mesh numbers of the fusion data transition areas.
Fig. 3 is a diagram showing the effect of the multi-source data fusion method in flood simulation prediction on the fusion process and the processing effect of loess Gao Yuanwang ditch small basin data in northern Shaanxi. (a is full-river basin coarse grid data of the king river basin, b is fine grid data of a king river basin channel part, c is fusion grid data processed by the method, and d is a local enlarged diagram of the fusion grid data processed by the method).
Fig. 4 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures:
example 1
A multi-source data fusion method in flood simulation prediction is shown in fig. 4, and comprises the following steps:
step 1) global low-resolution data and local high-resolution data for flood simulation are obtained, coarse grids and fine grids are established based on the global low-resolution data and the local high-resolution data, and grid grades corresponding to each coarse grid are determined;
step 2) fusing the data of the coarse mesh and the fine mesh, determining a fused data point sequence, and endowing the fused data with initial elevation according to the elevation of the coarse mesh and the fine mesh;
step 3) is based on grid 1:2, selecting a fusion data transition area grid, and carrying out connection processing on the fusion data transition area grid elevation based on the grid grade corresponding to each coarse grid;
step 4) corresponding hydrodynamic parameters are given to each grid, and hydrodynamic parameters of the fusion data are determined; the fine grid adopts a nearest neighbor method, and other grids use coarse grid parameters;
and 5) numbering all grids, distributing four grids adjacent to each grid to grid neighbor attributes, performing error detection processing on the fusion data in the fusion data transition region grids, and performing information correction on error information to obtain multi-source fusion data oriented to flood simulation.
Example 2
The invention relates to a multisource data fusion method in flood simulation prediction, which is implemented according to the following steps as shown in fig. 4:
step 1, determining the number of grid levels and a resolution sequence according to a grid 1:2 criterion according to the resolution of a coarse grid and the resolution of a fine grid; as shown in fig. 1, the method specifically comprises the following steps:
step 1.1, setting a coarse grid as a 1-level grid, wherein the corresponding grid resolution is DX 1 ；
Step 1.2,2 resolution of the grid correspondenceBy analogy, the resolution corresponding to the ith grid is +.>
Step 1.3, the last grid resolution of the grid resolution sequence is the closest value to the input fine grid resolution, the number of grid levels is M, and the corresponding grid resolution is DX M 。
Step 2, sleeving the coarse grids and the fine grids according to coordinates, and determining grid grades corresponding to each coarse grid; the specific calculation method comprises the following steps:
step 2.1, counting the number of the corresponding fine grids in each coarse grid, and if the number of the corresponding fine grids in a certain coarse grid is larger than 1 and does not contain null value, the coarse grid grade is L M Otherwise, grid level is L 1 ；
Step 2.2, based on step 2.1, querying a grid level maximum of 8 grids around each coarse grid if the grid level L i Less than its surrounding grid level maximum L nmax The grid level is L i ＝L nmax -1；
And 2.3, performing grid grade calculation on all grids according to the step 2.2 until the grid grade value difference of the adjacent grids is not more than 1.
Step 3, fusing the data of the coarse grid and the fine grid into a dot sequence on the basis of the steps 1 and 2, and endowing the fused data with initial elevation according to the elevations of the coarse grid and the fine grid; the method comprises the following steps of:
step 3.1, according to the coarse grid origin of coordinates (X c0 ,Y c0 ) Calculating relative origin coordinates in the coarse mesh, and N ROW Line N COL Relative origin coordinates (X) within a column coarse grid N ,Y N ) The calculation method is X N ＝X c0 +(N ROW -1)×DX 1 ,Y N ＝Y c0 +(N COL -1)×DX 1 ；
Step 3.2, at a certain relative origin of coordinates (X N ,Y N ) Is of grid class L i The number of points in each row and each column of the grid area range isThe nth of the grids row Line n col Point coordinates of columns (X n ,Y n ) The calculation method is that
Step 3.3, when the grid level corresponding to the coarse grid is the lowest level L M When the n is in the coarse grid row Line n col Elevation value E of column n According to the nearest 4 fine grids around it, when the coordinates of the nearest 4 fine grids around are (X 1 ,Y 1 )、(X 1 ,Y 2 )、(X 2 ,Y 1 )、(X 2 ,Y 2 ) And the elevation values are E respectively 11 、E 12 、E 21 、E 22 When in use, then
Step 3.4, when the grid level corresponding to the coarse grid is not the lowest level L M And the elevation values of the coarse grids are the elevation values corresponding to the coarse grids.
Step 4, carrying out linking treatment on the grid elevation of the transition area of the fusion data according to the grid grade corresponding to each coarse grid; the specific calculation method comprises the following steps:
step 4.1 for not the highest level L 1 Or the lowest level L M If it is simultaneously with the highest level L 1 Grid (X) p1 ,Y p1 ) And a lowest level L M Grid (X) p2 ,Y p2 ) Fusion data transition grid (X) in the middle of the same direction connection line g1 ,Y g1 ) Its elevation E g1 By the highest level grid elevation E in the same direction p1 And lowest level grid elevation E p2 Obtained when the direction of the transition grid is transverseWhen the direction of the transition grid is vertical +.>
Step 4.2, on the basis of step 4.1, for other transition grids (X g2 ,Y g2 ) Its laterally higher level grid (X r1 ,Y r1 ) And lower level mesh (X r2 ,Y r2 ) The elevation of (2) is E respectively r1 And E is r2 Vertically higher-level grid (X c1 ,Y c1 ) And lower level mesh (X c2 ,Y c2 ) The elevation of (2) is E respectively c1 And E is c2 Then the transition grid elevation E g2 Is that
Step 5, obtaining hydrodynamic parameters corresponding to the grids, wherein the fine grids adopt a nearest neighbor method, and other grids use coarse grid parameters; the method comprises the following steps of:
step 5.1, when the grid level corresponding to the coarse grid is the lowest level L M When the n is in the coarse grid row Line n col The hydrodynamic parameters of the row are taken as the hydrodynamic parameters of the nearest fine grid;
step 5.2, when the grid level corresponding to the coarse grid is not the lowest level L M When the method is used, hydrodynamic parameters corresponding to the coarse meshes are adopted as hydrodynamic parameters.
Step 6, numbering all grids, and distributing the grid numbers adjacent to the grids into grid attributes; the specific calculation method comprises the following steps:
step 6.1, according to the position relation between the transition area grids of the fusion data and the coarse grids, numbering the grids in turn from the origin of the coarse grids according to the principles from left to right and from bottom to top, wherein when a plurality of fusion data points exist in the corresponding positions of each coarse grid, numbering the grids in turn according to the principles from left to right and from bottom to top, and then starting numbering the next coarse grid;
and 6.2, sequentially allocating numbers of adjacent grids to each fusion data transition area grid according to the positions of the coarse grids, wherein if a certain coarse grid contains a plurality of fusion data points, the neighbor numbers of the inner fusion grids are numbers of 4 grids which are directly adjacent to the inner fusion grids, the neighbor numbers of the boundary fusion grids are the numbers of the grids which are most adjacent to the adjacent coarse grids, when the neighbor coarse grids are the same as or higher than the grids, the number of single-side neighbor grids is 1, and when the neighbor coarse grids are lower than the grids, the number of single-side neighbor grids is 2.
Step 7, performing error checking treatment on the grid fusion data, and performing information correction if information errors are found; the method comprises the following steps of:
step 7.1, carrying out error checking on the grid information of each fusion data transition area, when the grid elevation is not null, the corresponding hydrodynamic parameter cannot be null, if the corresponding hydrodynamic parameter is null, defining the grid as a faulty grid, otherwise, defining the grid as an error-free grid;
and 7.2, carrying out information correction on the grids with errors in all the fusion data on the basis of the step 7.1, wherein hydrodynamic parameters of the grids with errors are taken as hydrodynamic parameters of surrounding grids until the grids with errors do not exist in the fusion data.
Grid 1:2 criteria refers to that at most 1 high-level grid can only be adjacent to 2 low-level grids on interfaces where grids of different levels are adjacent, wherein the grid level is higher as the grid level value is smaller.
In recent years, a plurality of times of large-scale storm mountain floods occur in loess plateau areas, wang Maogou small watershed in loess hilly and gully areas are selected to perform 30m resolution data and 5m resolution data multisource data fusion, and as shown in fig. 3 (a) and 3 (b), 30m resolution point data and 5m resolution point data of the small gully of the king gully are respectively obtained. As shown in fig. 3 (c) and fig. 3 (d), before the multi-source data fusion method in flood simulation prediction is applied, part of the obtained high-precision data cannot be used for hydrodynamic simulation of the whole river basin, and after the method is applied, as shown in fig. 2, part of the high-precision data and low-precision data are fused for hydrodynamic simulation of the whole river basin, so that basic data support is provided for river basin flood simulation.
Note that, grid 1: the 2 criteria refers to that at most, 1 high-level grid can only be adjacent to 2 low-level grids on interfaces where grids of different levels are adjacent, and the grid level is higher as the grid level value is smaller.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (7)
1. A multi-source data fusion method in flood simulation prediction is characterized by comprising the following steps:
step 1) global low-resolution data and local high-resolution data for flood simulation are obtained, coarse grids and fine grids are established based on the global low-resolution data and the local high-resolution data, and grid grades corresponding to each coarse grid are determined;
step 2) fusing the data of the coarse mesh and the fine mesh, determining a fused data point sequence, and endowing the fused data with initial elevation according to the elevation of the coarse mesh and the fine mesh, wherein the initial elevation specifically comprises the following steps:
step 2.1) calculating the relative origin coordinates in the coarse mesh,
when the origin of the coarse grid coordinates is (Xc 0, yc 0), then the N ROW Line N COL Relative origin coordinates (X) within a column coarse grid N ,Y N ) Is X N ＝X c0 +(N ROW -1)×DX 1 ，Y N ＝Y c0 +(N COL -1)×DX 1 ；
Step 2.2) at a certain relative origin of coordinates (X) N ,Y N ) Is of grid class L i The number of points in each row and each column of the grid area range isThe nth of the grids row Line n col Point coordinates of columns (X n ,Y n ) Is that
Step 2.3) when the grid level corresponding to the coarse grid is the maximum level L M When the n is in the coarse grid row Line n col Elevation value E of column n According to its surroundings mostThe near 4 fine cells are determined when the coordinates of the line from the nearest 4 fine cells are (X 1 ,Y 1 )、(X 1 ,Y 2 )、(X 2 ,Y 1 )、(X 2 ,Y 2 ) And the corresponding elevation values are E respectively 11 、E 12 、E 21 、E 22 When in use, then
Step 2.4) when the grid level corresponding to the coarse grid is not the maximum level L M When the height values of the coarse grids are the initial height values corresponding to the coarse grids;
step 3) is based on grid 1:2, selecting a fusion data transition area grid, and carrying out connection processing on the fusion data transition area grid elevation based on the grid grade corresponding to each coarse grid;
the specific operation of carrying out the linking treatment on the grid elevation of the fusion data transition area is as follows:
step 3.1) for not the minimum level L 1 Or maximum level L M Is at the minimum level L 1 Grid (X) p1 ,Y p1 ) And maximum level L M Grid (X) p2 ,Y p2 ) Fusion data transition grid in the middle of the connection line (X) g1 ,Y g1 ) Fusion data transition grid (X) g1 ,Y g1 ) Transition grid elevation E of (2) g1 By the highest level grid elevation E in the same direction p1 And lowest level grid elevation E p2 Obtaining;
when the direction in which the fused data transition grid is located is transverse,when the direction of the fusion data transition grid is vertical, the fusion data transition grid is in the right direction>
Step 3.2) on the basis of step 3.1, for other transition grids (X) than step 3.1 g2 ,Y g2 ) Other transition grid (X) g2 ,Y g2 ) Grid level maxima grid (X r1 ,Y r1 ) And grid class minimum grid (X r2 ,Y r2 ) The elevation of (2) is E respectively r1 And E is r2 Grid level maxima grid (X c1 ,Y c1 ) And grid class minimum grid (X c2 ,Y c2 ) The elevation of (2) is E respectively c1 And E is c2 Then other transition grids (X g2 ,Y g2 ) Transition grid elevation E of (2) g2 Is that
Step 4) corresponding hydrodynamic parameters are given to each grid, and hydrodynamic parameters of the grids in the fusion data transition area are determined;
and 5) numbering all grids, distributing grid neighbor attributes, performing error checking treatment on the fusion data in the fusion data transition area grids, and performing information correction on error information to obtain multi-source fusion data oriented to flood simulation.
2. The method for multi-source data fusion in flood simulation prediction according to claim 1, wherein the specific operation of step 1) is as follows:
based on coarse grid resolution and fine grid resolution, according to grid 1: and 2, determining the number of grid grades by using a criterion, acquiring a grid resolution sequence, and sleeving the coarse grid and the fine grid according to coordinates so as to determine the grid grade corresponding to each coarse grid.
3. The method for multi-source data fusion in flood simulation prediction according to claim 2, wherein in step 1), the calculation method of the grid class number is as follows:
setting upThe coarse grid is a 1-level grid, and the resolution of the corresponding grid is DX 1 The resolution corresponding to the ith grid is
The last grid resolution in the grid resolution sequence is the value closest to the input fine grid resolution, the number of grid levels is M, and the corresponding grid resolution is DX M 。
4. The method for multi-source data fusion in flood simulation prediction according to claim 2, wherein in step 1), the method for calculating the grid level is as follows:
counting the number of the fine grids corresponding to each coarse grid, wherein if the number of the fine grids corresponding to a certain coarse grid is larger than 1 and does not contain null values, the coarse grid grade is the maximum grade, otherwise, the coarse grid grade is the minimum grade;
querying a grid level maximum of 4 grids directly connected to each coarse grid if the coarse grid level L i Less than the maximum value L of the grid level connected with the grid level nmax The coarse mesh grade is L i ＝L nmax -1；
And performing grid grade calculation on all grids until the difference value of the grid grade values of the two grids connected with each other is not more than 1.
5. The method for multi-source data fusion in flood simulation prediction according to claim 1, wherein in the step 4), the method for determining the hydrodynamic parameters of the mesh of the fusion data transition area is as follows:
step 4.1) when the grid grade corresponding to the coarse grid is the maximum grade, fusing the hydrodynamic parameters of the data transition area grid as the hydrodynamic parameters in the coarse grid, and the nth in the coarse grid row Line n col The hydrodynamic parameters of the row are selected from the hydrodynamic parameters of the fine grid closest to the hydrodynamic parameters;
and 4.2) when the grid grade corresponding to the coarse grid is not the maximum grade, the hydrodynamic parameters of the grid of the fusion data transition area are adopted as the hydrodynamic parameters of the corresponding coarse grid.
6. The method for multi-source data fusion in flood simulation prediction according to claim 1, wherein in step 5), the mesh neighbor attribute allocation method specifically comprises:
step 5.1) numbering grids from the origin of the coarse grids according to the position relation between the transition region grids of the fusion data and the coarse grids, and sequentially numbering the grids from left to right and from bottom to top, wherein when a plurality of fusion data points exist in the corresponding positions of each coarse grid, the grids are also sequentially numbered according to the left to right and from bottom to top, and then numbering the next coarse grid is started;
step 5.2) sequentially allocating numbers of adjacent grids to each fusion data transition area grid according to the positions of the coarse grids, wherein if a certain coarse grid contains a plurality of fusion data points, the neighbor numbers of the inner fusion grids are numbers of 4 grids directly adjacent to the adjacent coarse grids, the neighbor numbers of the boundary fusion grids are the numbers of the grids nearest to the adjacent coarse grids, when the neighbor coarse grids are the same as or higher than the grids, the number of single-side neighbor grids is 1, and when the neighbor coarse grids are lower than the grids, the number of single-side neighbor grids is 2.
7. The method for multi-source data fusion in flood simulation prediction according to claim 1, wherein the error-checking processing and the information correction are specifically as follows:
the grid information of each fusion data transition area is subjected to error checking, when the grid elevation is not null, the corresponding hydrodynamic parameter cannot be null, if the corresponding hydrodynamic parameter is null, the grid is defined as a faulty grid, otherwise, the grid is defined as a non-faulty grid;
and carrying out information correction on the wrong grids in all the fusion data, wherein hydrodynamic parameters of the wrong grids are directly connected with hydrodynamic parameters of the grids until the wrong grids do not exist in the fusion data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111321550.XA CN114064751B (en) | 2021-11-09 | 2021-11-09 | Multisource data fusion method in flood simulation prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111321550.XA CN114064751B (en) | 2021-11-09 | 2021-11-09 | Multisource data fusion method in flood simulation prediction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114064751A CN114064751A (en) | 2022-02-18 |
| CN114064751B true CN114064751B (en) | 2023-09-19 |
Family
ID=80273961
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111321550.XA Active CN114064751B (en) | 2021-11-09 | 2021-11-09 | Multisource data fusion method in flood simulation prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114064751B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111651885A (en) * | 2020-06-03 | 2020-09-11 | 南昌工程学院 | Intelligent sponge urban flood forecasting method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2436312C (en) * | 2003-08-01 | 2011-04-05 | Perry Peterson | Close-packed, uniformly adjacent, multiresolutional, overlapping spatial data ordering |
-
2021
- 2021-11-09 CN CN202111321550.XA patent/CN114064751B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111651885A (en) * | 2020-06-03 | 2020-09-11 | 南昌工程学院 | Intelligent sponge urban flood forecasting method |
Non-Patent Citations (1)
| Title |
|---|
| GIS技术支持下的洪水模型建模;万洪涛,周成虎,万庆,汪承义;地理研究(04);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114064751A (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102915227B (en) | Parallel method for large-area drainage basin extraction | |
| CN108804805B (en) | Method for automatically extracting multiple river basin exit points | |
| CN101882320B (en) | Method for digital restoration of urban spatial pattern | |
| CN111243091B (en) | Massive DEM pyramid slice parallel construction method based on distributed system | |
| CN106981092B (en) | Priority-Flood-based internal flow domain extraction method | |
| CN101050961A (en) | Method for fine correcting satellite remote sensing image geometry based on topographic line | |
| Zhao et al. | Analysis of Jiangsu tidal flats reclamation from 1974 to 2012 using remote sensing | |
| CN111563135A (en) | GM (1,3) model urban ground subsidence prediction method combining terrain factor and neural network | |
| CN112926468B (en) | Tidal flat elevation automatic extraction method | |
| CN112017282B (en) | Digital elevation model-oriented arbitrary section water collecting area boundary and river network extraction method | |
| CN107886444A (en) | A kind of distribution Runoff calculation method in region of no relief under urbanization | |
| CN114064751B (en) | Multisource data fusion method in flood simulation prediction | |
| CN104392113A (en) | Method for estimating wind speed of cold air wind on offshore sea surface | |
| CN117496377A (en) | Method for monitoring estuary wetland connectivity based on unmanned aerial vehicle | |
| CN113379828B (en) | Slope length extraction method fusing surface morphological characteristics | |
| JP5337995B2 (en) | Watershed extraction program and watershed extraction device | |
| CN112184900B (en) | Method, device and storage medium for determining elevation data | |
| CN111899336A (en) | Method for obtaining minimum tide surface model of unknown sea area theory | |
| CN110955742A (en) | Method for analyzing arrangement area of rainwater inlet of booster station terrace based on DEM | |
| CN103049583A (en) | Conversion method and conversion interface of land monitoring data | |
| CN103268522A (en) | Runoff algorithm | |
| CN114972991B (en) | Automatic recognition method and system for collapsing sentry | |
| CN115544789B (en) | River valley plain extraction method based on digital elevation model and gradient cost distance | |
| CN116778332B (en) | DEM-based river coastal easy-inundation range identification method and system | |
| CN111447549B (en) | Non-uniform UWB positioning error set network construction method and positioning error modeling 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |