CN115455626B

CN115455626B - Urban surface space self-adaptive dispersion method for flood disaster risk assessment

Info

Publication number: CN115455626B
Application number: CN202211401700.2A
Authority: CN
Inventors: 王慧敏; 黄晶; 孙殿臣; 戴强; 杨馨; 刘珍珍; 刘高峰
Original assignee: Nanjing Normal University; Hohai University HHU
Current assignee: Nanjing Normal University; Hohai University HHU
Priority date: 2022-11-10
Filing date: 2022-11-10
Publication date: 2023-03-28
Anticipated expiration: 2042-11-10
Also published as: CN115455626A

Abstract

The invention relates to the technical field of flood disaster risk assessment, and discloses an urban surface space self-adaptive dispersion method for flood disaster risk assessment, which comprises the following steps: determining elevation information according to the elevation data of the target area; carrying out depression and pseudo-depression processing on the elevation information to obtain target elevation information; constructing a water flow direction matrix and a confluence cumulant matrix based on the target elevation information; determining a natural river network based on the confluence cumulant matrix; performing space dispersion on a target area according to the water flow direction matrix and the natural river network to obtain a macroscopic space dispersion result; determining a target macro space discrete unit in a macro space discrete result according to a first preset screening rule; and performing mesoscopic space dispersion on the target macro space discrete unit through the main pipeline network and the main road network of the target macro space discrete unit to obtain a mesoscopic space discrete result. The method aims to realize self-adaptive dispersion of different complex city surface spaces and improve the unit dispersion efficiency of the complex city surface spaces.

Description

Urban surface space self-adaptive dispersion method for flood disaster risk assessment

Technical Field

The invention relates to the technical field of flood disaster risk assessment, in particular to an urban surface space self-adaptive discrete method for flood disaster risk assessment.

Background

In recent years, urban flood disasters are frequent, and the life and property safety of people is seriously threatened. The urban flood disaster risk assessment is used as an effective measure for flood disaster prevention and control, the urban flood disaster risks under different situations are assessed based on the urban rainfall runoff process, and the influence and consequence of the flood disaster can be accurately described. The urban flood risk is subjected to the unevenness of the rainfall runoff process in the area and the space of natural elements and human elements on the earth surface, the complex earth surface space of the city is generally required to be dispersed when the flood disaster risk is evaluated, namely, the whole risk evaluation area is divided into smaller space units, and the space dispersion is the primary step of the flood disaster risk evaluation and is also the important basis of the flood disaster risk evaluation.

Different space discrete results can directly influence the risk assessment result of the same region, and the city earth surface space self-adaptive discrete provides effective guidance for the fine flood disaster risk assessment and disaster risk management of the city, so that the method has important theoretical value and practical significance. Most space discrete methods at the present stage adopt a semi-automatic division method combining hydrological analysis and manual correction or a full-automatic generation method based on Thiessen polygons. Although the automatic generation method based on the Thiessen polygon is simple and efficient, the influence of terrains, roads, rivers, land utilization and various artificial facilities is not considered in the discrete process, and the hydrological process and the disaster influence process of a complex urban space are difficult to accurately depict by the discrete result. And if a large area is dispersed by using a semi-automatic method, a large amount of manpower and material resources are consumed, self-adaptive dispersion on different urban surface spaces is not considered, and the method is difficult to be used for fine flood disaster risk assessment of the urban complex surface. Due to the aggravation of human activities, the hydrological process in cities is extremely complex compared to the surface confluence process in natural environments. It is not only influenced by natural topography, but also acted by a plurality of elements such as buildings, roads, artificial drainage facilities and the like.

In conclusion, the existing urban surface space discrete method lacks comprehensive consideration and accurate expression of multiple elements in the urban complex surface space, and lacks the self-adaptive discrete realization of the surface space according to the complex situation of the urban surface elements, and is used for the refined flood disaster risk assessment.

Disclosure of Invention

In view of this, the invention provides an urban surface space adaptive dispersion method for flood disaster risk assessment, and aims to realize adaptive dispersion of different complex urban surface spaces and improve the efficiency of complex urban surface space unit dispersion.

The invention provides an urban surface space self-adaptive dispersion method for flood disaster risk assessment, which comprises the following steps:

determining elevation information of a target area according to the elevation data of the target area;

performing hole filling and pseudo-hole processing on the grids meeting set conditions in the elevation information to obtain target elevation information;

according to the target elevation information, a water flow direction matrix corresponding to the target elevation information is constructed, and according to the obtained water flow direction matrix, a confluence cumulant matrix corresponding to the water flow direction matrix is constructed;

determining a natural river network of the target area according to the confluence cumulant matrix;

performing space dispersion on the target area according to the water flow direction matrix of the target area and the natural river network of the target area, and taking a river diversion line as a boundary of a macro space dispersion unit to obtain a macro space dispersion result of the target area;

determining a target macro-space discrete unit in the macro-space discrete result according to a first preset screening rule;

and carrying out mesoscopic space dispersion on the target macroscopic space discrete unit through a main pipeline network and a main road network in the target macroscopic space discrete unit to obtain a mesoscopic space discrete result.

Optionally, the performing mesoscopic spatial discretization on the target macro-space discrete unit through a trunk pipe network and a trunk road network in the target macro-space discrete unit to obtain a mesoscopic spatial discretization result includes:

preprocessing the pipe network data in the target area to obtain the target pipe network data of the target area;

respectively constructing a pipeline chain and a road chain of the target macroscopic space discrete unit according to the target pipe network data and the road network data of the target macroscopic space discrete unit;

determining the pipeline chains meeting the set conditions of the pipelines as target pipeline chains to form a main pipeline network, and determining the pipeline chains meeting the set conditions of the roads as target pipeline chains to form a main pipeline network;

matching the target macro-space discrete unit with a main road network where a main pipeline network is located, and determining each block in the target macro-space discrete unit;

determining a block vertex according to the road width of a main road network where a main drainage line of the main pipeline network in the target macro-space discrete unit is located, and determining the intersection range of a road chain based on the block vertex;

dividing the intersection range through a preset division rule, and distributing a plurality of divided blocks obtained by division to adjacent road sections to obtain target road blocks;

determining the catchment areas of all the blocks and the water outlets of all the catchment areas through a hydrological analysis tool;

and fusing the target road block and the catchment area according to the relation between the water outlets of the catchment areas and the target road block to obtain a discrete result of the mesoscopic space.

Optionally, constructing a pipeline chain of the target macro-space discrete unit according to the target pipe network data of the target macro-space discrete unit, including:

step 11: all pipelines in the target pipe network data of the target macro space discrete unit are stored into a pipeline set, and one pipeline PArc is selected from the pipeline set;

step 12: obtaining a pipeline connected to one pipeline PArc from the pipeline set;

step 13: determining whether the pipeline connected with one pipeline PArc can be linked with one pipeline PArc according to a preset pipeline connection rule to construct a pipeline chain, executing the step 14 when the pipeline connected with one pipeline PArc can be linked with one pipeline PArc, and otherwise executing the step 16;

step 14: removing the pipeline connected to one pipeline PArc from the pipeline set;

step 15: fusing the pipeline connected with one pipeline PArc to form a new pipeline PArc, and executing the step 12 after determining the new pipeline PArc as one pipeline PArc;

step 16: determining whether pipelines still exist in the pipeline set, if so, executing a step 11; if not, finishing the construction of the pipeline chain;

according to the road network data of the target macro-space discrete unit, constructing a road chain of the target macro-space discrete unit, wherein the road chain comprises the following steps:

step 21: all roads in the road data of the target macro-space discrete unit are stored into a road set, and a road RArc is selected from the road set;

step 22: acquiring a road connected with a road RArc from the road set;

step 23: determining whether the road connected with one road Rarc can be linked with one road Rarc according to a preset road connection rule to construct a road chain, and executing a step 24 when the road connected with one road Rarc can be linked with one road Rarc, otherwise executing a step 26;

step 24: deleting the road connected with one road Rarc from the road set;

step 25: fusing the road connected with one road Rarc to form a new road Rarc, and executing the step 22 after determining the new road Rarc as one road Rarc;

step 26: determining whether a road exists in the road set, if yes, executing a step 21; if not, the construction of the pipeline chain is finished.

Optionally, when one end of one pipeline src has a plurality of pipelines satisfying the pipeline preset connection rule, determining a target pipeline from the plurality of pipelines satisfying the pipeline preset connection rule according to a pipeline preset connection policy, and linking the target pipeline with the pipeline src;

when one end of one road Rarc is provided with a plurality of roads meeting preset road connection rules, a target road is determined from the plurality of roads meeting the preset road connection rules according to preset road connection strategies, and the target road is linked with the road Rarc.

Optionally, the determining a street vertex according to a road width of a trunk road network in which a trunk drainage line of a trunk pipeline network in the target macro-spatial discrete unit is located, and determining a crossing range of a road chain based on the street vertex includes:

constructing a trunk drain line buffer area according to the road width of a trunk road network in which a trunk drain line of a trunk pipeline network in the target macro-space discrete unit is positioned;

determining the top point of the block through the trunk drain line buffer zone;

and determining the intersection range corresponding to the intersection point of the trunk drainage line according to the intersection point of the trunk drainage line and the vertex of the surrounding block of the trunk drainage line.

Optionally, the dividing, by using a preset dividing rule, the intersection range, and allocating a plurality of divided blocks obtained by the dividing to adjacent road segments to obtain a target road block includes:

determining two adjacent trunk drain lines according to the included angle between the trunk drain lines in the crossing range;

traversing the block vertexes in the crossing range, and determining whether the sum of the included angles between the connecting line of the intersection point of each block vertex and the trunk drainage line in the crossing range and every two adjacent trunk drainage lines is equal to the included angle between every two adjacent trunk drainage lines;

when the sum of the connecting line of the intersection point of the street vertex and the trunk drainage line in the intersection range and the included angle between every two adjacent trunk drainage lines is equal to the included angle between every two adjacent trunk drainage lines, dividing the intersection range through the connecting line of the street vertex and the intersection point of the trunk drainage line in the intersection range, otherwise, dividing the intersection range through an angle bisector;

and distributing the segmentation blocks obtained by segmentation to road sections adjacent to the segmentation blocks to obtain target road blocks.

Optionally, the method further comprises:

determining a target mesoscopic space discrete unit in the mesoscopic space discrete result according to a second preset screening rule;

and constructing the Delaunay irregular triangular net according to rainwater well data in the view space discrete unit in the target and the boundary of the view space discrete unit in the target so as to form a micro-space discrete result.

Aiming at the prior art, the invention has the following advantages:

according to the urban surface space self-adaptive dispersion method for flood disaster risk assessment, influences of natural elements such as terrain and rivers and human elements such as roads, buildings and drainage facilities on urban flood disaster risks are comprehensively considered, self-adaptive dispersion of different complex urban surface spaces is achieved, efficiency of dispersion of complex urban surface space units is effectively improved, the problem of construction of complex urban surface space flood risk assessment units is solved, and the urban flood disaster risk assessment method has a good application prospect in the aspect of urban flood disaster risk management.

Drawings

Various additional advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 is a flowchart of an urban surface space adaptive discrete method for flood disaster risk assessment according to an embodiment of the present invention;

fig. 2 is a schematic matrix construction diagram of an urban surface space adaptive discrete method for flood disaster risk assessment according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a macro-space discrete result in an urban surface space adaptive discrete method for risk assessment of a flood disaster according to an embodiment of the present invention;

fig. 4 is a schematic diagram of link construction in an urban surface space adaptive discrete method for risk assessment of flood disasters according to an embodiment of the present invention;

fig. 5 is a schematic diagram of block determination in an urban surface space adaptive discrete method for flood disaster risk assessment according to an embodiment of the present invention;

fig. 6 is a schematic diagram illustrating determination and allocation of segmentation blocks in an urban surface space adaptive discrete method for risk assessment of flood disasters according to an embodiment of the present invention;

fig. 7 is a schematic diagram of a catchment area and a water outlet of a block in an urban surface space adaptive discrete method for flood disaster risk assessment according to an embodiment of the present invention;

fig. 8 is a schematic diagram illustrating determination and allocation of another partition block in an urban surface space adaptive discrete method for risk assessment of flood disasters according to an embodiment of the present invention;

fig. 9 is a schematic diagram illustrating elevation data correction in an urban surface space adaptive discrete method for risk assessment of flood disasters according to an embodiment of the present invention;

fig. 10 is a schematic diagram illustrating building line determination in an urban surface space adaptive discrete method for risk assessment of flood disasters according to an embodiment of the present invention;

fig. 11 is a schematic diagram illustrating correction of an abnormal microscopic space discrete unit in an urban surface space adaptive discrete method for risk assessment of a flood disaster according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

Fig. 1 is a flowchart of an urban surface space adaptive discrete method for flood disaster risk assessment according to an embodiment of the present invention, and as shown in fig. 1, the method includes:

step S101: determining elevation information of a target area according to the elevation data of the target area;

step S102: performing hole filling and pseudo-hole processing on grids meeting set conditions in the elevation information to obtain target elevation information;

step S103: according to the target elevation information, a water flow direction matrix corresponding to the target elevation information is constructed, and according to the obtained water flow direction matrix, a confluence cumulant matrix corresponding to the water flow direction matrix is constructed;

step S104: determining a natural river network of the target area according to the confluence cumulant matrix;

step S105: performing space dispersion on the target area according to the water flow direction matrix of the target area and the natural river network of the target area, and taking a river diversion line as a boundary of a macro space dispersion unit to obtain a macro space dispersion result of the target area;

step S106: determining a target macro-space discrete unit in the macro-space discrete result according to a first preset screening rule;

step S107: and carrying out mesoscopic space dispersion on the target macroscopic space discrete unit through a main pipeline network and a main road network in the target macroscopic space discrete unit to obtain a mesoscopic space discrete result.

In this embodiment, a target area that needs to be subjected to urban space adaptive discretization is determined first, and urban space adaptive discretization oriented to flood risk assessment is performed in the target area. The elevation data in the city is data in which elevation information of each grid is recorded in each grid by performing grid division on the ground surface, and only the elevation information about the geographic surface is recorded in the elevation data.

Therefore, after the target area subjected to urban space self-adaptive dispersion is determined, the elevation information of the target area is obtained by acquiring the elevation data of the target area.

Specifically, according to the elevation information of each grid recorded by the elevation data, the elevation information of the corresponding target area is constructed, and the elevation information of each grid corresponds to one matrix element.

Illustratively, the target area is divided into 1000 grids in the transverse direction and 1000 grids in the longitudinal direction, each grid is recorded with its own elevation information, and accordingly, 1000 × 1000 grids of the target area are constructed, and one matrix element corresponds to one grid of the target area. Meanwhile, the matrix elements also have a corresponding relationship with the positions of the grids, for example, when one grid is a grid located in the 5 th row in the transverse direction and the 6 th row in the longitudinal direction of all grids of the target area, in the elevation information of the target area, the elevation information corresponding to the grid is also recorded in the positions of the 5 th row in the transverse direction and the 6 th row in the longitudinal direction of the elevation information.

In this embodiment, since there is a significant difference between the elevation information of some grids in the target area and the elevation information of the surrounding grids, for example, the elevation information of one grid is 20, and the elevation information of 6 grids around the grid is-10, it can be seen that the elevation information of the grid is significantly abnormal compared with the elevation information of all surrounding grids, and it is necessary to perform pseudo-hollow processing on the grids so as to avoid the grids affecting the subsequent determination of the water flow direction. For example, when the water flow passes through the grid and 6 grids around the grid, the water flow direction actually flows from left to right, and the elevation information of the grid is abnormal, which causes the water flow direction of the 6 grids around the grid to be determined to point to the grid when the water flow passes through the grid, and this is obviously wrong. Therefore, it is necessary to perform the pooling and pseudo-pooling treatment on such grids, and to correct the elevation information of such grids, thereby never avoiding the determination of the wrong water flow direction.

In the present embodiment, it is necessary for the grid subjected to pseudo-hollow processing to satisfy the setting condition that characterizes that the elevation information of one grid satisfies the setting condition when the average of the elevation information of the one grid and the elevation information of 6 grids surrounding the one grid exceeds the setting threshold.

In this embodiment, the value of the set threshold may be set according to an actual application scenario, and is not specifically limited herein. For example, the threshold may be set to 20m,30m,40m, or the like.

In the present embodiment, target elevation information of the target area is obtained after pseudo-dimpling is performed on all the grids that satisfy the setting conditions.

In this embodiment, a water flow direction matrix corresponding to target elevation information is obtained by performing operation processing on the target elevation information of a target area. Wherein, one matrix element in the water flow direction matrix corresponds to the water flow direction of one grid, for example, the water flow flows from grid a to grid B, the water flow flows from grid B to grid C, etc. And obtaining a confluence cumulant matrix corresponding to the water flow direction matrix by performing operation processing on the water flow direction matrix. Wherein one matrix element in the confluence accumulation quantity matrix corresponds to the accumulated flow of one grid, and the accumulated flow of one grid is used for representing how many grids water flows to the grid.

In this embodiment, as shown in fig. 2, a corresponding water flow direction matrix constructed by a specific target elevation information and a corresponding confluence cumulant matrix constructed based on the water flow direction matrix after the water flow direction matrix is constructed are shown in the drawing, wherein a value 2 in the water flow direction matrix represents that water flows from a grid corresponding to a current matrix element to the lower right of the grid, a value 4 represents that water flows from a grid corresponding to the current matrix element to the lower left of the grid, a value 8 represents that water flows from a grid corresponding to the current matrix element to the lower left of the grid, a value 1 represents that water flows from a grid corresponding to the current matrix element to the upper right of the grid, a value 16 represents that water flows from a grid corresponding to the current matrix element to the left of the grid, a value 32 represents that water flows from a grid corresponding to the current matrix element to the upper left of the grid, a value 128 represents that water flows from a grid corresponding to the current matrix element to the upper right of the grid, and a value 64 represents that water flows from a grid corresponding to the upper left of the current matrix element to the grid. The value in the confluence accumulation quantity matrix represents the number of grids corresponding to the elements of the flow direction matrix, for example, the value of the 3 rd row and the 3 rd column in fig. 2 is 7, which represents that the water flow with 7 grids flows to the grids.

In the embodiment, since the water flow is from high to low, the natural river network in the target area can be determined through the target elevation information.

Meanwhile, because the accumulated flow of the water outlet of one catchment area is the largest, namely, the water flows of the grids in one catchment area are converged towards the water outlet of the catchment area, and the water flows of the grids in the same catchment area are all directed towards the water outlet of the catchment area. Therefore, according to the water flow direction matrix and the confluence accumulation matrix, each catchment area in the natural river network can be determined. After obtaining each catchment area of the natural river network in the target area, performing spatial dispersion on the target area by using a river diversion line of the catchment area to obtain a macro-space dispersion result of the target area, wherein the macro-space dispersion result comprises a plurality of macro-space dispersion units, one catchment area corresponds to one macro-space dispersion unit, as shown in fig. 3, fig. 3 is a macro-space dispersion result of one target area, wherein the macro-space dispersion result comprises a plurality of macro-space dispersion units, one macro-space dispersion unit corresponds to one catchment area, and each area divided by a thin solid line in the drawing is a boundary of each macro-space dispersion unit.

In the embodiment, because of different city plans in cities, some land utilization areas have more people gathering, and some areas have no people gathering basically, and because the invention is directed to the self-adaptive dispersion of the urban space of the flood risk assessment, the areas with people gathering need higher attention, and whether people gather in one area is evaluated, the most intuitive way is to judge whether roads exist in the area, if the roads exist, the people gathering in the area can be determined, and if no roads exist in the area, the area can be considered not to have people gathering basically. Therefore, the invention gives higher attention to the areas with roads, and carries out more detailed spatial dispersion on the areas so as to carry out more accurate flood disaster risk assessment on the areas. Specifically, the first preset screening rule is used for screening target macro-space discrete units including roads from all target macro-space discrete units in the macro-space discrete result, and performing further spatial discretization on the target macro-space discrete units. Therefore, when the macro-space discrete unit comprises a road, the macro-space discrete unit meets a first preset screening rule, the macro-space discrete unit is determined as a target macro-space discrete unit, and further space dispersion is carried out on the target macro-space discrete unit. Specifically, mesoscopic space discretization is carried out on the target macroscopic space discrete unit through a main pipeline network and a main road network in the target macroscopic space discrete unit, and a mesoscopic space discretization result is obtained.

Meanwhile, in the mode, for the areas without roads, the macroscopic space discrete unit with lower possibility of personnel activity does not need further space discrete, and the self-adaptive discrete efficiency of the urban ground surface space can be effectively improved.

In the present invention, the obtaining a mesoscopic spatial discrete result by performing mesoscopic spatial discrete on the target macroscopic discrete unit through the trunk network and the trunk network in the target macroscopic discrete unit includes: preprocessing the pipe network data in the target area to obtain the target pipe network data of the target area; respectively constructing a pipeline chain and a road chain of the target macroscopic space discrete unit according to the target pipe network data and the road network data of the target macroscopic space discrete unit; determining the pipeline chains meeting the set conditions of the pipelines as target pipeline chains to form a main pipeline network, and determining the pipeline chains meeting the set conditions of the roads as target pipeline chains to form a main pipeline network; matching the target macro-space discrete unit with a main road network where a main pipeline network is located, and determining each block in the target macro-space discrete unit; determining a block vertex according to the road width of a main road network where a main drainage line of the main pipeline network in the target macro-space discrete unit is located, and determining the intersection range of a road chain based on the block vertex; dividing the intersection range through a preset division rule, and distributing a plurality of divided blocks obtained by division to adjacent road sections to obtain target road blocks; determining the catchment areas of all the blocks and the water outlets of all the catchment areas through a hydrological analysis tool; and fusing the target road block and the catchment area according to the relation between the water outlets of the catchment areas and the target road block to obtain a discrete result of the mesoscopic space.

In this embodiment, the selected target macro-space discrete units in a target area include a plurality of target macro-space discrete units, and the embodiment of each target macro-space discrete unit for further spatial discrete is the same. Therefore, for the sake of understanding, the following embodiments are only described in terms of how one target macro-space discrete unit performs further spatial dispersion. And performing further spatial dispersion on each of the other target macro-spatial dispersion units in the same manner.

In this embodiment, the self-adaptive dispersion of the urban space oriented to flood risk assessment is adopted, and whether the urban has a flood risk or not is directly related to pipelines and roads for draining water in the urban. Therefore, the method and the device further perform space dispersion on the target macroscopic space discrete unit based on the pipe network data and the road network data, and the separated space discrete unit is more favorable for flood disaster risk assessment.

The pipe network data in the target area is recorded by pipe sections, that is, a complete pipeline is divided into a plurality of pipe sections by a plurality of linear detection points, and the pipe sections and the attributes of the pipe sections are recorded in the pipe network data. Therefore, the data content recorded by the pipe network data is too much, the recorded data content is too fine, the method needs to further disperse the target macro-space discrete unit based on the attribute of the whole pipeline, and the pipe network data recorded by the pipe section which is too fine is inconvenient for the method to further disperse the target macro-space discrete unit.

Therefore, the present invention requires pre-processing the pipe network data. Because all the target macro-space discrete units in the target area can be further spatially dispersed, the pipe network data in the pipe network data preprocessing is the pipe network data in the target area, the pipe network data in the target area is preprocessed, and after the corresponding target pipe network data is obtained, the target pipe network data of each target macro-space discrete unit in the target area is determined accordingly.

In this embodiment, the preprocessing of the pipe network data includes cleaning and generalization, and modifying the flow information of the pipe network data, and the specific implementation is as follows.

And traversing the pipe network data of the target area to find a linear detection point and two pipe sections connected with the linear detection point. Wherein, the point of the sewer well in the pipe network data is determined as a straight line detection point.

In this embodiment, as shown in fig. 4, the two pipe sections connected to the line detection point represent the two pipe sections connected to the line detection point and belonging to a complete pipeline. As shown in fig. 4, A1 and A2 are two pipe sections connected to the straight line detection point O1, and both of them belong to a complete pipeline; b1 and B2 in FIG. 4 are two pipe sections connected with the straight line detection point O1, and both of them belong to a complete pipeline; c1 and C2 in fig. 4 are two pipe sections connected to the straight line detection point O1, and both of them belong to a complete pipeline.

Recording respective attributes of two pipe sections connected with the straight line detection point, wherein the attributes of the pipe sections comprise: the length of the pipe, the number of the starting point, the number of the stopping point, the burial depth of the starting point, the burial depth of the stopping point, the elevation of the starting point, the elevation of the stopping point and other attribute information. And deleting the straight line detection point and the two pipe sections connected with the straight line detection point in the pipe network data, and connecting the pipe points at the two ends of the two pipe sections to form a new pipe section. And based on the attributes of the two pipe sections, assigning the attribute of the formed new pipe section to obtain the attribute of the new pipe section, wherein the attribute of the new pipe section also comprises the following steps: the pipe length, the starting point number, the stopping point number, the starting point burial depth, the stopping point burial depth, the starting point elevation, the stopping point elevation and other attribute information.

Wherein, the pipe point refers to two end points of two pipe sections far away from a straight line detection point connected with the pipe sections.

In this embodiment, the embodiment of forming a new pipe section for any two pipe sections connected to the linear detection point is the same as the above embodiment, and will not be described herein again.

At the same time, after the two pipe sections connected to the linear detection point form a new pipe section, the new pipe section will belong to one of the two pipe sections connected to the linear detection point again, and at this time, the new pipe section will continue to form a new pipe section in the above-described embodiment. As shown in fig. 4, after the two pipe segments C1 and C2 form a new pipe segment, the new pipe segment formed by C1 and C2 and C3 form two pipe segments connected to the straight line detection point O2. The two pipe sections connected to the straight line detection point O2 will now continue to form new pipe sections in the above-described embodiment, i.e. new pipe sections formed by C1, C2, C3.

Therefore, the pipe network data can complete the primary cleaning and generalization treatment until two pipe sections connected with the straight line detection point do not exist in the pipe network data. The data of the pipe network subjected to the preliminary cleaning and generalization processing has a large number of longer pipelines consisting of a plurality of pipe sections. As shown in fig. 4, if the pipe sections C1, C2, and C3 are actually a complete pipe, after the initial cleaning and generalization of the pipe network data, the complete pipe composed of C1, C2, and C3 will exist in the pipe network data after the initial cleaning and generalization.

In this embodiment, since the present invention further spatially discretizes the target spatially discrete unit and needs to combine the pipe network data with the corresponding pipe network data, the pipe network data having the attribute of the road in the pipe network data is needed. Therefore, pipelines containing roads, streets and roadways are extracted from the pipe network data which are subjected to primary cleaning and generalization treatment, pipelines connected with the catch basin are removed from the extracted pipelines, and corresponding pipe points are extracted according to the information of the starting and stopping points of the pipelines so as to construct a pipeline chain. Therefore, the cleaning and generalization processing of the pipe network data is completed.

In this embodiment, the flow direction information of the pipe network data is modified, specifically: the method comprises the steps of enabling a plurality of pipelines which are cleaned and processed in a generalized mode to be communicated with each other in pipe network data, having no other branch pipes and meeting set conditions in direction to serve as a whole pipeline, determining the overall flow direction of the plurality of pipelines forming the whole pipeline according to the elevation information of the starting point and the elevation information of the stopping point of each pipeline in the whole pipeline, and correcting the flow direction of the pipelines which flow in error in the plurality of pipelines according to the overall flow direction.

Illustratively, the entire pipe is constituted by pipes A, B, C, D, E connected in this order from left to right. In one case, the flow direction of the pipeline A is determined to be from left to right according to the elevation information of the starting point and the elevation information of the stopping point of the pipeline A; determining the flow direction of the pipeline B from left to right according to the height information of the starting point and the height information of the stopping point of the pipeline B; determining the flow direction of the pipeline C from right to left according to the starting point elevation information and the stopping point elevation information of the pipeline C; determining the flow direction of the pipeline D from left to right according to the starting point elevation information and the stopping point elevation information of the pipeline D; and determining the flow direction of the pipeline E from left to right according to the height information of the starting point and the height information of the stopping point of the pipeline E. Because only the flow direction of the pipeline C is from right to left and the other flow directions are from left to right in the whole pipeline, the whole flow direction of the whole pipeline is determined to be from left to right. At this time, the flow direction of the pipe C is corrected from left to right.

In one case, the flow direction of the pipeline A is determined to be from right to left according to the elevation information of the starting point and the elevation information of the stopping point of the pipeline A; determining the flow direction of the pipeline B from left to right according to the starting point elevation information and the stopping point elevation information of the pipeline B; determining the flow direction of the pipeline C from left to right according to the starting point elevation information and the stopping point elevation information of the pipeline C; determining the flow direction of the pipeline D from left to right according to the starting point elevation information and the stopping point elevation information of the pipeline D; and determining the flow direction of the pipeline E from right to left according to the height information of the starting point and the height information of the stopping point of the pipeline E. Because the whole pipeline is not provided with other branch pipes, although the flow directions of the pipelines at the middle position are from left to right, the flow directions of the pipelines at the starting position and the ending position of the whole pipeline are from right to left, and therefore the whole flow direction of the whole pipeline is determined to be from right to left. At this time, the flow direction of the pipe B, C, D is corrected from right to left.

In this embodiment, the purpose of constructing the pipeline as a whole is to correct the flow direction of the pipeline in the pipe network.

In the present embodiment, the satisfaction of the set condition in the direction indicates that the deflection angle between the two connected pipelines is lower than the preset angle. The value of the set angle may be taken according to an actual application scenario, and is not specifically limited herein. For example, when a predetermined angle is set to 30 °, and one of the lines is deflected upward by 45 °, and the other line connected thereto is deflected downward by 45 °, the deflection angle therebetween is 90 ° or more than 30 °, and the directions of both are not satisfied with the set conditions. When one pipeline deflects upwards by 20 degrees and the other pipeline connected with the pipeline deflects upwards by 45 degrees, the deflection angle between the two pipelines is 25 degrees and is lower than 30 degrees, and the directions of the two pipelines meet the set condition.

In this embodiment, after the pipe network data is cleaned and generalized and the flow information of the pipe network data is modified, the target pipe network data is obtained, where the target pipe network data includes the attribute information of the plurality of pipelines and the respective attribute information of the plurality of pipelines and the flow information of the plurality of pipelines.

In this embodiment, since the road network data is not segmented as the pipe network data, it is not necessary to preprocess the road network data. Therefore, the invention can further spatially disperse the target macro-space discrete units based on the pipe network data and the road network data.

Further, in further spatial discretization of the target macro-spatial discrete unit, multiple pipelines with correlations need to be considered as complete links and multiple roads with correlations are considered as complete links. There may be related pipelines among the pipelines in the target pipe network data, and these pipelines are not considered uniformly in the target pipe network data. Meanwhile, the road data is only records and each road, but a plurality of related roads are not considered in a unified manner. Therefore, after the target pipe network data and the road network data are obtained, the pipeline chain and the road chain are respectively constructed according to the target pipe network data and the road network data in the target macro-space discrete unit.

In this embodiment, although the pipeline chain and the road chain are constructed to obtain a longer link, it is inevitable that the constructed pipeline chain or road chain has different lengths in the construction process, and the present invention needs a sufficiently long link when the constructed pipeline chain and road chain are used for further spatial dispersion of the target macro-space discrete unit. Therefore, after the pipeline chain and the road chain are constructed and obtained, the pipeline chain and the road chain need to be further screened. According to the method, the target pipeline chains meeting the pipeline setting conditions are screened out from all the pipeline chains through the pipeline setting conditions and the road setting conditions to form the main pipeline network, and the target pipeline chains meeting the road setting conditions are screened out from all the pipeline chains to form the main pipeline network.

In the embodiment, the pipeline chain with the length reaching the first set length is determined as the pipeline chain meeting the pipeline set condition; and determining the road link with the road width of 14m (bidirectional lane) and the road link length reaching the second set length as the road link meeting the road setting condition. The first set length is preferably 300m, and the second set length is preferably 400m.

It should be understood that, the values of the first set length and the second set length are only a preferred embodiment, and are not limited to the present invention, and the first set length and the second set length may also be set to other values according to practical application scenarios, and are not specifically limited herein.

In this embodiment, the present invention needs to combine the target pipeline chain and the target road chain when performing further spatial discretization on the target macro-spatial discrete unit. Therefore, after obtaining the main pipe network and the main road network, it is necessary to determine that there are overlapped main pipe networks and main road networks from the main pipe network and the main road network. That is, the target pipe chain is located underground, and the target road chain overlaps with the target pipe chain when the target road chain is located above the target pipe chain. The main pipeline network is a link network structure formed by all pipeline chains, and the main road chain is a link network structure formed by all road chains.

In this embodiment, matching subtraction is performed on the main pipeline network and the target macro-space discrete unit which overlap with the main pipeline network, and the region where there is no overlap is determined as each block in the target macro-space discrete unit. Subsequently, when the target macro-spatial discrete unit is further spatially discrete, it will be further spatially discrete based on the respective blocks determined by the backbone network and the backbone network.

In this embodiment, as shown in fig. 5, a part of a road chain (a region formed by a gray part in fig. 5) is shown in the figure, and is subjected to matching subtraction with a part of a target macro-space discrete unit, and the obtained 4 regions that are not overlapped are neighborhoods, which are neighborhoods a1, a2, a3, and a4.

In this embodiment, since the roads in the city also have a wide width, the target macro-discrete space cannot be considered when further spatially discrete. Therefore, when the target macro-space discrete unit is further dispersed, the link needs to be considered, and in this process, at least two links are involved in a portion where the link and the link intersect, so that when the target macro-space discrete unit is further dispersed in consideration of the link, the dividing principle of the road region where the link and the link intersect needs to be determined first, that is, the road at the intersection of the link should be specifically allocated to which road.

According to the method, the intersection point of the road chain boundary line and the road chain boundary line is determined according to the road width of the main road network where the main drainage line of the main pipeline network in the target macro-space discrete unit is located, the intersection point is determined as the top point of the block, and after the top point of each block is obtained, the block top points of the same road chain intersection are connected to obtain each intersection range. The trunk drain line refers to the center line of each pipeline chain in the trunk pipeline network, and the dotted line in fig. 6 is the center line of each pipeline chain.

And respectively dividing each intersection range into a plurality of division blocks through a preset division rule, and distributing each division block to adjacent road segments to obtain a target road block. As shown in fig. 6, the O-P1-P2-O shown in fig. 6 is a segment, the O1-P6-P7 shown in the figure is also a segment, and the region enclosed by the O-P1-P7-P8-P9-P2-0 in fig. 6 is a target road block.

In this embodiment, since one target macro-space discrete unit includes a plurality of blocks, when the target macro-space discrete unit is further spatially dispersed in units of blocks, the determination implementation of the catchment areas and the catchment area water outlets of the respective blocks are the same. Therefore, for the convenience of understanding, the following description will be made of certain embodiments of the catchment area and the water outlet of the catchment area of one street.

Firstly, according to the elevation data, obtaining elevation information of a block in a target macro-space discrete unit, wherein the elevation data used at the moment is the same as the elevation information used for obtaining the elevation information of the target area, and the difference is that elements of the elevation information of the block are fewer than those of the elevation information of the target area. For example, the elevation information for the target area may be 1000 × 1000 elevation information, while the elevation information for the street block may be 20 × 25 elevation information.

Since some grids in the block have significant differences from the surrounding grids, for example, one grid has elevation information of 20, and 6 grids around the grid have elevation information of-10, the elevation information of the grid has significant anomalies, and the grids need to be processed in pseudo-hollow places to avoid the influence of the grid on the subsequent determination of the water flow direction. For example, when the water flow passes through the grid and 6 grids around the grid, the water flow direction is actually flowing from left to right, and the elevation information of the grid is abnormal, which results in that the water flow direction of the 6 grids around the grid is determined to be all pointing to the grid when the water flow passes through the grid, and this is obviously wrong. Therefore, it is necessary to perform pseudo-depression processing on such grids, correct the elevation information of such grids to be the same as the average elevation information of the surrounding grids, and never avoid determining the wrong water flow direction.

In the present embodiment, it is necessary for the grid subjected to the pseudo-hollow processing to satisfy a first preset condition that the elevation information of one grid satisfies the first preset condition when an average value of the elevation information of the one grid and the elevation information of 6 grids surrounding the one grid exceeds a first preset threshold.

In this embodiment, the value of the first preset threshold may be set according to an actual application scenario, and is not specifically limited herein. For example, the first preset threshold may be set to 5m,10m, and 15 m.

In the present embodiment, after pseudo-dimpling all grids satisfying the first preset condition, target elevation information of the block is obtained. And one matrix element in the target elevation information of the block corresponds to the elevation information of one grid.

In this embodiment, a water flow direction matrix of a block corresponding to target elevation information of the block is obtained by performing operation processing on the target elevation information of the block. One matrix element in the water flow direction matrix of the block corresponds to the water flow direction of one grid, for example, water flows from grid a 'to grid B', water flows from grid B 'to grid C', and the like. And obtaining a confluence cumulant matrix of the block corresponding to the water flow direction matrix of the block by performing operation processing on the water flow direction matrix of the block. Wherein one matrix element in the confluence cumulative quantity matrix of the block corresponds to the cumulative flow of one grid, and the cumulative flow of one grid is used for representing how many grids water flows to the grid.

In this embodiment, since the water flow will go from high to low, the natural river network to the block can be determined according to the target elevation information of the block.

Meanwhile, because the accumulated flow of the water outlet of one catchment area is the largest, namely, the water flows of the grids in one catchment area are converged towards the water outlet of the catchment area, and the water flows of the grids in the same catchment area are all directed towards the water outlet of the catchment area. Therefore, according to the first water flow direction matrix and the first confluence accumulation matrix, the water catchment areas and the water outlets of the water catchment areas in the natural river network can be determined. After obtaining each catchment area of the natural river network in the street, performing spatial dispersion on the street by using a river diversion line of the catchment area to obtain each discrete unit in the street, as shown in fig. 7, 1 to 19 divided in fig. 7 are catchment areas, that is, each discrete unit, and simultaneously, as shown in fig. 7, division results of 4 street are shown, and simultaneously, as shown in fig. 7, water outlets of each catchment area are determined according to the target elevation information of the street, a water flow direction matrix of the street and a confluence cumulant matrix of the street are also shown.

As shown in fig. 7, the catchment areas are divided into two types according to the water outlet positions of the catchment areas in the block, including a first type catchment area and a second type catchment area. The first type of catchment area is a catchment area with a water outlet located at the boundary of another catchment area, such as a catchment area 17 and a catchment area 18 in fig. 7, the water outlet of the catchment area 18 is located at the boundary of the catchment area 17, and the water outlet of the catchment area 17 is located at the boundary of the catchment area 14. I.e. the water of the catchment area 18 will be collected in 17 and the water of the catchment area 17 will be collected in 14. The second type of catchment areas are catchment areas with water outlets positioned at the boundaries of the blocks and adjacent to roads, such as

catchment areas

1, 2 and 5 in the figure. Since the water outlet of the catchment area is the point of the greatest accumulated flow of the catchment area, the water flow in the catchment area can not flow into the catchment area any more, and therefore the water flow in the catchment area can flow into the adjacent road from the water outlet of the catchment area.

The determination of the catchment area and the catchment area water outlet for other blocks in the target macro discrete space unit is performed by the above embodiment, and details are not repeated herein.

In this embodiment, after obtaining the determination of each block in the target macro-space discrete unit, the determination of the catchment area and the catchment area water outlet of each block, and the determination of the target street block, the catchment area and the target street block are fused to obtain the mesoscopic space discrete result of the target macro-space discrete unit. The further dispersion of each target macro-space discrete unit in the target area is carried out in the same implementation mode, and after each target macro-space discrete unit in the target area is further dispersed, a mesoscopic space discrete result of the target area is obtained.

Specifically, the further discretization of the target macro-space discrete unit is specifically:

when the catchment area is the first type catchment area, the water outlet of the catchment area is positioned at the boundary of the other catchment area, at the moment, the catchment area is fused with the other catchment area, and when the water outlet of the other catchment area is also the first type catchment area, the catchment area where the water outlet of the other catchment area is positioned is fused with the other catchment area. As shown in fig. 7, the water outlet of the catchment area 18 is located at the boundary of the catchment area 17, at this time, the catchment area 18 and the catchment area 17 are integrated into a catchment area, and the water outlet of the catchment area 17 is located at the boundary of the catchment area 14, at this time, a catchment area obtained by integrating the catchment area 18 and the catchment area 17 is integrated with the catchment area 14.

When the catchment area is the second type catchment area, the water outlet of the catchment area is positioned at the block boundary, and at the moment, the target road block where the block boundary is positioned is fused with the catchment area. As shown in fig. 7, the water outlet of the catchment area 14 is located at the street boundary, the target road block of the street boundary is R3, and at this time, the target road block R3 is merged with the catchment area 14, and since the catchment area 18 and the catchment area 17 are also merged with the catchment area 14, the catchment area 18, the catchment area 17, the catchment area 14 and the target road block R3 are finally merged. Meanwhile, the target road block at the street boundary where the water outlet of the catchment area 7 is located is also at R3, so that the catchment area 7 is also fused with the catchment area 18, the catchment area 17, the catchment area 14 and the target road block R3 to obtain a division result of a mesoscopic space discrete unit in the target macroscopic discrete space unit.

And fusing other catchment areas and target road blocks in the target macro-space discrete unit by the above embodiment, and finally obtaining all mesoscopic space discrete units in the target macro-space discrete unit, wherein the obtained mesoscopic space discrete units form a mesoscopic space discrete result of the target macro-space discrete unit. Therefore, the target macroscopic space discrete unit is further subjected to spatial dispersion to obtain a mesoscopic space discrete result of the target macroscopic space discrete unit.

In this embodiment, there are multiple target macro-space discrete units within a target region, and there are multiple meso-space discrete units within a target macro-space discrete unit. After the mesoscopic space discrete results of all the target macroscopic space discrete units in the target area are obtained, the mesoscopic space discrete results of all the target macroscopic space discrete units in the target area and other macroscopic space discrete units jointly form the mesoscopic space discrete results of the target area.

The specific implementation process of the above embodiment is as follows: and setting a mark field for each catchment area, and recording the catchment area number or the target road block number into which the catchment area is converged. For example, in the case of the catchment area 18 in fig. 7, the catchment area number of the catchment area 18 is 17, so that the catchment area 18 is provided with a mark field 17 for marking that the water flow of the catchment area 18 will flow into the catchment area 17 through the water outlet; for the catchment area 17 in fig. 7, the catchment area number that it merges into is 14, so the catchment area 17 is provided with the mark field 14, and the water flow for marking the catchment area 17 will merge into the catchment area 14 through the water outlet; for the catchment area 14 in fig. 7, the entry is the target road block R3, so that the catchment area 14 is provided with a flag field R3 for marking that the water flow of the catchment area 14 will enter the target road block R3 through the water outlet.

And determining the catchment area and the target road block to be fused by acquiring the identification field of the catchment area. And for each catchment area or each target road block, determining the catchment area and the target road block to be fused through the mark field of each catchment area or each target road block. For example, the designation field 17 of the catchment area 18 is acquired first, so that it is determined that the catchment area 18 is to be merged with the catchment area 17; after the catchment area 17 is determined by the catchment area 18, the marked field of the catchment area 17 is acquired as the catchment area 14, so that the catchment area 17 is determined to be fused with the catchment area 14; after the catchment area 17 is determined by the catchment area 18, the marked field of the catchment area 17 is acquired as the catchment area 14, so that the catchment area 17 is determined to be fused with the catchment area 14; after the catchment area 17 determines the catchment area 14, the marking field of the catchment area 14 is acquired as the target road block R3, so that the catchment area 14 is determined to be fused with the target road block R3; after the target road block R3 is determined by the catchment area 14, the catchment area 7 with the marking field being the target road block R3 is obtained, so that the determined catchment area is fused with the target road block R3; after the target road block R3 determines the catchment area 7, the target macro-spatial discrete unit does not have a catchment area with the indication field 7 any more, that is, no other catchment area flows into the catchment area 7, and at this time, fusion of a mesoscopic spatial discrete unit is completed, and a mesoscopic spatial discrete unit is obtained.

In this embodiment, the subsequent fusion generation of other mesoscopic space discrete units is not performed on the catchment area and the target road block which have undergone fusion. And subsequent fusion generation of other intermediate-view space discrete units obtains a proper catchment area and a proper target road block from the residual catchment area and the residual target road block in the target macroscopic-space discrete unit for fusion generation.

In this embodiment, the implementation of fusion generation of other mesoscopic space discrete units is the same as the above implementation, and is not described herein again.

In the invention, according to the target pipe network data of the target macro-space discrete unit, a pipeline chain of the target macro-space discrete unit is constructed, which comprises the following steps:

step 13: determining whether the pipeline connected with one pipeline PArc can be linked with one pipeline PArc according to preset pipeline connection rules to construct a pipeline chain, executing the step 14 when the pipeline connected with one pipeline PArc can be linked with one pipeline PArc, and otherwise executing the step 16;

step 22: acquiring a road connected with a road Rarc from the road set;

step 23: determining whether the road connected with one road Rarc can be linked with one road Rarc according to a preset road connection rule to construct a road chain, and executing the step 24 when the road connected with one road Rarc can be linked with one road Rarc, or executing the step 26;

step 24: deleting the road connected with one road Rarc from the road set;

step 26: determining whether a road exists in the road set, if so, executing a step 21; if not, the construction of the pipeline chain is finished.

In this embodiment, according to the target pipe network data of the target macro-space discrete unit, a specific implementation manner of constructing the pipe chain is as follows:

and storing the target pipe network data of the target macroscopic space discrete unit into a corresponding pipeline set by a node-arc section model.

One pipeline, PArc, is selected from the pipeline set, and then each pipeline connected to this pipeline, PArc, is selected from the pipeline set. And determining whether each selected pipeline connected with the pipeline PArc can be linked with the pipeline PArc to form a pipeline chain according to preset pipeline connection rules.

If each pipeline connected with the pipeline PArc has a pipeline meeting the preset pipeline connection rule, the pipeline meeting the preset pipeline connection rule can be linked with the pipeline PArc to form a pipeline chain. At this time, the pipeline meeting the preset pipeline connection rule and the selected pipeline src are deleted from the pipeline set, and then the pipeline meeting the preset pipeline connection rule and the pipeline src are fused to form a new pipeline src, that is, the pipeline meeting the preset pipeline connection rule and the pipeline src are linked to form a new pipeline src.

Then each pipeline that can be connected to this new pipeline, PALc, is taken from the pipeline set. And determining whether each selected pipeline connected with the new pipeline PArc can be linked with the new pipeline PArc to form a pipeline chain according to the preset pipeline connection rule.

If there are pipelines satisfying the pipeline preset connection rule in each pipeline connected to the new pipeline pasc, the pipelines satisfying the pipeline preset connection rule can be linked with the new pipeline pasc to form a pipeline chain. At this time, the pipeline satisfying the pipeline preset connection rule is deleted from the pipeline set, and then the pipeline satisfying the pipeline preset connection rule is merged with the new pipeline pasc to form a new pipeline pasc, that is, the pipeline satisfying the pipeline preset connection rule is linked with the new pipeline pasc to form a new pipeline pasc. The process is circulated until no pipeline capable of being linked with the formed new pipeline PArc exists in the pipeline set, and the construction of the pipeline chain is completed.

At this time, another pipeline panc is selected from the pipeline set, and the pipeline chain corresponding to the another pipeline panc is determined continuously in the above embodiment until no pipeline exists in the pipeline set.

In this embodiment, the preset connection rule of the pipeline may include that the angle of the pipeline connected to the pipeline pasc and the angle of the pipeline pasc are lower than a set threshold; the pipe diameter of the pipeline connected with the pipeline PArc is the same as that of the pipeline PArc; the pipe diameter of the pipeline that links to each other with pipeline PArc is the same with the pipe diameter of this pipeline PArc, and both materials are the same. Meanwhile, whether the preset pipeline connection rule is met between the two pipelines can be judged according to other attributes of the pipelines, and when the other attributes of the two pipelines are the same, the preset pipeline connection rule is met between the two pipelines.

In this embodiment, the plurality of determination conditions included in the predetermined connection rule for the pipeline have priority, and the geometric rule determination is mainly used, and the semantic rule is used as a secondary criterion. If the angle can be judged according to the geometric rule, only the angle is needed for judgment, and if the angle cannot be judged, the pipe diameter and the material (semantic rule) are used for auxiliary judgment.

In this embodiment, the value of the set threshold may be set according to an actual application scenario, and is not specifically limited herein. For example, the set threshold may be 135 °,150 °,180 °, etc.

Illustratively, the target pipe network data of the target macro-space discrete unit α includes pipelines a1, a2, a3, a4 … …, a10. And storing all pipelines in the target pipe network data of the target macro space discrete unit into the corresponding pipeline set.

First a pipeline a1 is selected from the pipeline set, and then the pipelines connected to this pipeline a1 are selected from the pipeline set, a2, a3, a4.

And according to the pipeline preset connection rule, determining that only a2 and a3 in the pipelines a2, a3 and a4 meet the pipeline preset connection rule with the pipeline a1, and deleting a1, a2 and a3 from the pipeline set.

At this time, the pipelines a2 and a3 are fused with the pipeline a1 to obtain a new pipeline a2-a1-a3 (representing a2 linked a1 and a1 linked a 3).

At this time, the new pipeline a2-a1-a3 is a2 and a3 at both ends, and a4, a5, a6, a7 is determined from the pipeline set to the pipeline connected with the new pipeline a2-a1-a3 at both ends a2 and a 3.

And according to the preset pipeline connection rule, only a5, a6 in the pipelines a4, a5, a6 and the new pipeline a2-a1-a3 meet the preset pipeline connection rule, and the a5, a6 is deleted from the pipeline set.

At this time, the pipelines a5 and a6 are merged with the new pipeline a2-a1-a3 to obtain a new pipeline a5-a2-a1-a3-a6.

At this point in time the new pipeline a5-a2-a1-a3-a6 is terminated with a5 and a6, and the construction of a complete pipeline chain a5-a2-a1-a3-a6 is completed when it is determined from the pipeline assembly that there are no more pipelines connected to this new pipeline a5-a2-a1-a3-a6 at both ends a5 and a6.

At this time, a further pipeline a4 is selected from the pipeline set, and then the pipeline connected to this pipeline a4 is selected from the pipeline set, a7 and a8.

And determining that the pipelines a7 and a8 and the pipeline a4 both meet the pipeline preset connection rule according to the pipeline preset connection rule, and deleting a4, a7 and a8 from the pipeline set.

At this time, the pipelines a7, a8 and a4 are fused to obtain a new pipeline a7-a4-a8 (representing a7 linked a4, a4 linked a 8).

At this time the new pipeline a7-a4-a8 is terminated by a7 and a8, and a9, a10 is determined from the pipeline set to the pipeline connected to the two ends a7 and a8 of the new pipeline a7-a4-a 8.

And determining that the pipelines a9 and a10 and the new pipelines a7-a4-a8 all meet the preset pipeline connection rule according to the preset pipeline connection rule, and deleting a9 and a10 from the pipeline set.

At this time, the pipelines a9 and a10 are merged with the new pipeline a7-a4-a8 to obtain a new pipeline a9-a7-a4-a8-a10.

At this time, the new pipeline a9-a7-a4-a8-a10 is at both ends a9 and a10, and the construction of a complete pipeline chain a9-a7-a4-a8-a10 is completed when it is determined from the pipeline set that there are no more pipelines connected to both ends a9 and a10 of the new pipeline a9-a7-a4-a8-a10.

At this point, the pipeline chain is completed because there is no more pipeline in the pipeline set.

In this embodiment, a specific implementation manner of constructing the road link according to the road data of the target macro-space discrete unit is as follows:

and performing data storage on the road data of the target macro-space discrete unit through a node-arc segment model, so that all roads in the road data can be stored into a corresponding road set.

And selecting a road Rarc from the road set. Then, each road connected with the road Rarc is selected from the road set. And determining whether each selected road connected with the road Rarc can be linked with the road Rarc according to the preset road connection rule to form a road chain.

If the roads meeting the preset road connection rule exist in the roads connected with the road Rarc, the roads meeting the preset road connection rule can be linked with the road Rarc to form a road chain, at the moment, the roads meeting the preset road connection rule and the selected road are deleted from the road set, then the roads meeting the preset road connection rule and the road Rarc are fused to form a new road Rarc, namely, the roads meeting the preset road connection rule and the road Rarc are linked to form the new road Rarc.

Then, each road capable of being connected with the new road RArc is obtained from the road set. And determining whether each selected road connected with the new road Rarc can be linked with the new road Rarc according to the preset road connection rule so as to form a road chain.

If the roads connected with the new road RArc have roads meeting the preset road connection rule, the roads meeting the preset road connection rule can be linked with the new road RArc to form a road chain, at the moment, the roads meeting the preset road connection rule are deleted from the road set, and then the roads meeting the preset road connection rule are fused with the new road RArc to form a new road RArc, namely, the roads meeting the preset road connection rule are linked with the new road RArc to form the new road RArc. And circulating in the way until no road capable of being linked with the formed new road RArc exists in the road set, and completing construction of the road chain.

At this time, another road RArc is selected from the road set, and the determination of the road link corresponding to the another road RArc is continued in the above embodiment until no road exists in the road set.

In this embodiment, the preset connection rule of the road may include that the angle of the road connected to the road RArc and the angle of the road RArc are lower than a preset threshold; the name of the road connected with the road Rarc is the same as that of the road Rarc; the width of the road connected to the road RArc is the same as the width of the road RArc.

In this embodiment, the value of the preset threshold may be set according to an actual application scenario, which is not specifically limited herein. For example, the set threshold may be 135 °,150 °,180 °, etc.

In this embodiment, the multiple determination conditions included in the preset road connection rule have priority, and the geometric rule determination is mainly used, and the semantic rule is used as an auxiliary. If the judgment is possible by a geometric rule such as an angle, the judgment is performed only by the angle, and if the judgment cannot be performed by the angle, the judgment is assisted by a name or a width (semantic rule).

Illustratively, the roads b1, b2, b3, b4 … …, b10 are included in the road data of the target macro-space discrete unit β. And storing all roads in the road data of the target macro space discrete unit into the corresponding road set.

Firstly, a road b1 is selected from the road set, and then the road connected with the road b1 is selected from the road set, wherein the road b2, the road b3 and the road b4 are selected.

And according to the preset road connection rule, determining that only the positions b2 and b3 in the roads b2, b3 and b4 and the road b1 meet the preset road connection rule, and deleting the positions b1, b2 and b3 from the road set.

At this time, the roads b2 and b3 are fused with the road b1 to obtain a new road b2-b1-b3 (representing b2 linked b1 and b1 linked b 3).

At this time, the two ends of the new road b2-b1-b3 are b2 and b3, and the roads connected with the two ends b2 and b3 of the new road b2-b1-b3 are determined from the road set to have b4, b5, b6 and b7.

And according to the preset road connection rule, determining that only b5 and b6 in the roads b4, b5, b6 and b7 meet the preset road connection rule with the new roads b2-b1-b3, and deleting b5 and b6 from the road set.

At this time, the roads b5 and b6 are fused with the new road b2-b1-b3 to obtain the new road b5-b2-b1-b3-b6.

At this time, the two ends of the new road b5-b2-b1-b3-b6 are b5 and b6, and the construction of a complete road chain b5-b2-b1-b3-b6 is completed when the road which is connected with the two ends b5 and b6 of the new road b5-b2-b1-b3-b6 is determined from the road set.

At this time, a road b4 is selected from the road set, and then the roads b7 and b8 connected with the road b4 are selected from the road set.

And according to the preset road connection rule, determining that the roads b7 and b8 and the road b4 both meet the preset road connection rule, and deleting b4, b7 and b8 from the road set.

At this time, the roads b7, b8 and b4 are fused to obtain a new road b7-b4-b8 (representing b7 linked b4, b4 linked b 8).

At the moment, the two ends of the new road b7-b4-b8 are b7 and b8, and the roads connected with the two ends b7 and b8 of the new road b7-b4-b8 are determined to have b9 and b10 from the road set.

And determining that the roads b9 and b10 and the new roads b7-b4-b8 all meet the preset road connection rule according to the preset road connection rule, and deleting b9 and b10 from the road set.

At the moment, the roads b9 and b10 are fused with the new road b7-b4-b8 to obtain the new road b9-b7-b4-b8-b10.

At this time, the two ends of the new road b9-b7-b4-b8-b10 are b9 and b10, and the construction of a complete road chain b9-b7-b4-b8-b10 is completed when the road which is connected with the two ends b9 and b10 of the new road b9-b7-b4-b8-b10 is determined to be no longer in the road set.

At this time, since the road set does not have any more roads, the construction of the road chain is finished.

In the invention, when one end of one pipeline PArc is provided with a plurality of pipelines meeting preset pipeline connection rules, one target pipeline is determined from the pipelines meeting the preset pipeline connection rules according to a preset pipeline connection strategy, and the target pipeline is linked with the pipeline PArc; when one end of one road RArc is provided with a plurality of roads meeting preset road connection rules, determining a target road from the plurality of roads meeting the preset road connection rules according to preset road connection strategies, and linking the target road with the road RArc.

In this embodiment, in the process of building the pipeline chain, among the plurality of pipelines connected to one end of one pipeline PArc, a plurality of pipelines may simultaneously satisfy a preset pipeline connection rule with the one pipeline PArc, and only one pipeline can be selected to be linked with the one pipeline PArc to build the pipeline chain. Therefore, a pipeline preset connection strategy is set in advance, and a target pipeline is screened out from the plurality of pipelines according to the pipeline preset connection strategy and is linked with one end of the pipeline PArc.

In this embodiment, the pipeline preset connection strategy is the maximum suitable strategy connection of the pipeline, the arc segment a is connected with the optimal connection object b of the arc segment a, and the optimal connection object of the arc segment b is also the arc segment a, so that the arc segment a and the arc segment b are required to be the optimal connection objects to be connected into a pipeline chain.

In the process of constructing the road chain, a plurality of roads may simultaneously satisfy the preset connection rule with one road RArc among a plurality of roads connected with one end of the one road RArc, and only one road can be selected to be linked with the one road RArc to construct the road chain. Therefore, a preset road connection strategy is set in advance, and a target road is screened out from the plurality of roads according to the preset road connection strategy and is linked with one end of the road RArc.

In the present invention, the determining a street vertex according to the road width of a trunk road network in which a trunk drainage line of a trunk pipeline network in the target macro-spatial discrete unit is located, and determining a crossing range of a road chain based on the street vertex includes: constructing a trunk drain line buffer area according to the road width of a trunk road network in which a trunk drain line of a trunk pipeline network in the target macro-space discrete unit is positioned; determining the top point of the block through the trunk drain line buffer zone; and determining the intersection range corresponding to the intersection point of the trunk drainage line according to the intersection point of the trunk drainage line and the vertex of the surrounding block of the trunk drainage line.

In this embodiment, a trunk drain buffer is constructed according to the width of the road where the trunk drain is located, and the range of the trunk drain buffer is used to query the vertex of the block.

The vertexes of the blocks are determined by the intersections of the links of the trunk drain buffer area and the link boundary lines, and P1, P2, P3, P4, P5, P6, P7, P8 and P9 shown in fig. 6 are the vertexes of the blocks. It should be understood that there will be multiple trunk drain intersections in a target macro-spatial discrete unit, and only certain embodiments of the vertices of each neighborhood corresponding to two trunk drain intersections O and O1 are shown in fig. 6. The embodiment of determining the vertexes of the blocks corresponding to the intersections of the drainage lines of other trunks in one target macro-space discrete unit is the same as the above embodiment, and details are not repeated here.

After the respective vertexes of all the blocks in the target macro-space discrete unit are obtained, connecting vertexes of blocks around each intersection point of the trunk drainage line to obtain a cross range corresponding to the intersection point of the trunk drainage line, as shown in fig. 6, connecting vertexes P1, P2, P3, P4, and P5 of the blocks around the intersection point O of the trunk drainage line to obtain a cross range corresponding to the intersection point O of the trunk drainage line, and connecting vertexes P6, P7, P8, and P9 of the blocks around the intersection point O1 of the trunk drainage line to obtain a cross range corresponding to the intersection point O1 of the trunk drainage line.

In the present invention, the dividing the intersection range by a preset division rule, and allocating a plurality of divided blocks obtained by the dividing to adjacent road segments to obtain a target road block includes: determining two adjacent trunk drain lines according to the included angle between the trunk drain lines in the crossing range; traversing the block vertexes in the crossing range, and determining whether the sum of the included angles between the connecting line of the intersection point of each block vertex and the trunk drainage line in the crossing range and every two adjacent trunk drainage lines is equal to the included angle between every two adjacent trunk drainage lines; when the sum of the connecting line of the intersection point of the street vertex and the trunk drainage line in the intersection range and the included angle between every two adjacent trunk drainage lines is equal to the included angle between every two adjacent trunk drainage lines, dividing the intersection range through the connecting line of the street vertex and the intersection point of the trunk drainage line in the intersection range, otherwise, dividing the intersection range through an angle bisector; and distributing the segmentation blocks obtained by segmentation to road sections adjacent to the segmentation blocks to obtain target road blocks.

In this embodiment, since the area of the intersection range relates to a plurality of roads, when the area of the intersection range is spatially dispersed, the area of the intersection range needs to be divided first so as to allocate each divided block to an adjacent road segment. The method comprises the following specific steps: the intersection of the trunk water drain line within the intersection range is connected to the vertexes of the blocks around the intersection of the trunk water drain line, respectively, to divide the intersection range into a plurality of divided blocks, and then each divided block is assigned to an adjacent road segment.

For example, as shown in fig. 6, the intersection O of the trunk drain lines in the intersection range is connected to the vertexes P1, P2, P3, P4, and P5 of the neighborhood around the intersection O of the trunk drain lines, respectively. The P1-O-P2 is divided into blocks adjacent to the road section R1, so that the blocks P1-O-P2 are distributed to the road section R1 to form an area range, and subsequent further spatial dispersion is carried out; the P2-O-P3 is distributed to the road section R2 because the P2-O-P3 is adjacent to the road section R2, so that the two form an area range for subsequent further spatial dispersion; the P3-O-P4 is distributed to the road section R3 because the partition block is adjacent to the road section R3, so that the partition block P3-O-P4 forms an area range for subsequent further spatial dispersion; the P4-O-P5 division block is adjacent to the road section R4, so that the P4-O-P5 division block is distributed to the road section R4 to form an area range for subsequent further spatial dispersion; since the partition block composed of P5-O-P1 is adjacent to the road section R5, the partition block P5-O-P1 is allocated to the road section R5, so that the two blocks form an area range for subsequent further spatial dispersion.

In this embodiment, because in the actual execution process, the computer cannot intuitively obtain which road segment the segment is adjacent to. Therefore, rules need to be set to enable the computer to recognize which road segment the segment is adjacent to, and the specific embodiment is as follows:

selecting one trunk water drainage line in the cross range, calculating the included angle between each water drainage line in the cross range and the one water drainage line, and determining two trunk water drainage lines with the smallest included angle to form two adjacent trunk water drainage lines with the one trunk water drainage line respectively.

For example, for the trunk drainage line L1, the included angle between each drainage line in the intersection range and the trunk drainage line L1 is calculated, and two minimum included angles between the trunk drainage line L2 and the trunk drainage line L5 and the trunk drainage line in the road section R1 are finally obtained, so that it is determined that the trunk drainage line L2 and the trunk drainage line L5 respectively form two trunk drainage lines adjacent to each other with the trunk drainage line L1, that is, L1 and L2 form one trunk drainage line adjacent to each other, and L1 and L5 form one trunk drainage line adjacent to each other.

Similarly, the determination method of every two adjacent trunk drain lines of other trunk drain lines is the same as the above embodiment, and is not described herein again.

After two adjacent trunk drainage lines with each trunk drainage line are obtained, one trunk drainage line in the intersection range is selected, the vertex of each block in the intersection range is traversed, and whether the sum of the included angles between the connecting line of the intersection point of each vertex and the trunk drainage line in the intersection range and one adjacent trunk drainage line in the two adjacent trunk drainage lines is equal to the included angle between the two adjacent trunk drainage lines is determined. If so, the intersection range is divided by the line connecting the vertex and the intersection point. And traversing the vertexes of the rest blocks in the crossing range, determining whether the sum of the included angles between the connecting line of the vertexes and the intersection point of the trunk drainage line in the crossing range and the other two adjacent trunk drainage lines in the trunk drainage line is equal to the included angle between the two adjacent trunk drainage lines, and if the sum is equal to the included angle, dividing the crossing range through the connecting line of the vertexes and the intersection point.

For example, for the trunk drain line L1, when the trunk drain line L1 and the trunk drain line L2 constitute two trunk drain lines adjacent to each other, the vertices P1, P2, P3, P4, and P5 are traversed to determine the vertex in which the intersection point O can be connected to divide the intersection range into the divided blocks, and the divided blocks are assigned to the road segment R1 corresponding to the trunk drain line L1. Firstly, randomly selecting a vertex P1, wherein included angles between a connecting line of the vertex P1 and an intersection point O and a trunk drainage line L1 and a trunk drainage line L2 are P1-O-L1 and P1-O-L2 respectively, an included angle between the trunk drainage line L1 and the trunk drainage line L2 is L1-O-L2, the sum of the included angles P1-O-L1 and P1-O-L2 is not equal to the included angle L1-O-L2, and the vertex P1 can not be connected with the intersection point O to divide a crossing range to obtain a division block and is distributed to a vertex of a road section R1; then, a vertex P2 is selected, the included angles between a connecting line of the vertex P2 and the intersection point O and the trunk drainage line L1 and the trunk drainage line L2 are P2-O-L1 and P2-O-L2 respectively, the included angle between the trunk drainage line L1 and the trunk drainage line L2 is L1-O-L2, the sum of the included angles P2-O-L1 and P2-O-L2 is equal to the included angle L1-O-L2, and therefore the vertex P2 belongs to a vertex which can be connected with the intersection point O to divide the crossing range to obtain a division block and is distributed to the road section R1.

When the trunk drain line L1 and the trunk drain line L5 constitute two adjacent trunk drain lines, since the vertex P2 has been confirmed to belong to a vertex that can be connected to the intersection O to divide the intersection range into divided pieces and is assigned to the road section R1. Therefore, when the trunk drain line L1 and the trunk drain line L5 form two adjacent trunk drain lines, it is only necessary to traverse the vertices P1, P3, P4, and P5. Firstly, a vertex P1 is randomly selected, the included angles between a connecting line of the vertex P1 and an intersection point O and a trunk drainage line L1 and a trunk drainage line L5 are P1-O-L1 and P1-O-L5 respectively, the included angle between the trunk drainage line L1 and the trunk drainage line L5 is L1-O-L5, the sum of the included angles P1-O-L1 and P1-O-L5 is equal to the included angle L1-O-L5, and therefore the vertex P1 belongs to a vertex which can be connected with the intersection point O to divide a crossing range to obtain a division block and is distributed to a road section R1 corresponding to the trunk drainage line L1.

Thus, the vertex P1 and the vertex P2 are connected to the intersection O to obtain a segment, and the segment is assigned to the road segment R1 corresponding to the trunk drain. Based on the same implementation mode, a segmentation block O1-P6-P7-O1 at the other end of the road section R1 is obtained, the two segmentation blocks and the road section R1 form a target road block together, and an area surrounded by the two segmentation blocks and the road section R1 is the target road block as shown in the figure.

Since the vertices P1 and P2 have been confirmed to belong to the vertices that can be connected to the intersection O to divide the intersection range into the division blocks and are assigned to the road segment R1. So that subsequent vertex walks do not need to traverse P1 and P2.

Similarly, the embodiment of whether or not other vertices can be connected to the intersection to divide the divided block is the same as the above embodiment, and will not be described again here.

When the road is T-shaped, there is no block vertex between two trunk drainage lines, at this time, the two trunk drainage lines cannot obtain a corresponding vertex satisfying the condition, at this time, an angular bisector is directly made for the two trunk drainage lines to segment the crossing range, as shown in fig. 8, the trunk drainage lines L11 and L12 do not have corresponding block vertices, at this time, an angular bisector is directly made for the two trunk drainage lines, that is, the connection O2 and P03 are connected to segment the crossing range.

In the present invention, the method further comprises: determining a target mesoscopic space discrete unit in the mesoscopic space discrete result according to a second preset screening rule; and constructing the Delaunay irregular triangular net according to rainwater well data in the view space discrete unit in the target and the boundary of the view space discrete unit in the target so as to form a micro-space discrete result.

In this embodiment, there will be more people gathered for areas of a city having residential and/or institutional sites, and there will be more concern in flood disaster risk assessment for such areas, and therefore further spatial dispersion will be needed for such areas. Therefore, the mesoscopic space discrete results in the target area are screened, target mesoscopic space discrete units comprising residential land and/or public facility land are screened from all the mesoscopic space discrete units, and further spatial dispersion is carried out on the target mesoscopic space discrete units.

In this embodiment, for any one mesoscopic space discrete unit in all mesoscopic space discrete units, an irregular triangular net in delaunay is constructed by taking each rainwater grate, namely a rainwater well, as a central point in the boundary of the mesoscopic space discrete unit, so as to obtain each microscopic space discrete unit in the mesoscopic space discrete unit. The embodiment is used for dividing each mesoscopic space discrete unit, and after all mesoscopic space discrete units are divided, a microcosmic space discrete result is obtained.

In the present invention, the method further comprises: obtaining target building data of the target area by preprocessing the building data in the target area; correcting the elevation data through the target building data to obtain target elevation data; respectively extracting and obtaining a natural water diversion line and a building line according to the target elevation data and the target building data; and correcting the micro-space discrete result according to the natural water dividing line and the building line to obtain a target micro-space discrete result.

In this embodiment, since the present invention is directed to the adaptive discretization of the urban surface space for flood risk assessment, the adaptive discretization of the urban surface space needs to be performed depending on the elevation information of the ground and the water flow direction, and buildings in the city directly affect the elevation information of the ground and the water flow direction. For example, when a waterway on the ground from left to right has a large building in the middle, the ground elevation information at the position will be very high, so when water flows through the waterway from left to right, the water flow will bypass from both sides of the building due to the existence of the large building, and the direction of the water flow is changed. Therefore, the building data plays an important role in the adaptive dispersion of the urban surface space for flood disaster risk assessment of the present invention, and thus the building data in the city is needed. Meanwhile, the invention only needs to use the appearance contour and elevation information of the building with contour area reaching a certain value (the building with small contour area can not cause great influence on the water flow direction, so neglecting and not considering) in the building data, and the building data in the actual city is very complex and various, almost all the building data in the city can be recorded in the building data, and simultaneously the top structure of the building can also be recorded in the building data. Therefore, in order to ensure that the urban surface space for flood disaster risk assessment can be subjected to self-adaptive dispersion accurately and quickly, building data needs to be aggregated and simplified firstly.

And the aggregation simplification processing is carried out on the building data, which specifically comprises the following steps: when a plurality of spatially overlapping polygons are present within one large building outline in the building data, the plurality of spatially overlapping polygons are subjected to simplification processing and are represented by only the one large building outline.

Meanwhile, a first preset threshold value and a second preset threshold value are set. When the outline area of the building is lower than the first set threshold, determining whether the distance between a large building closest to the building and the building is lower than a second preset threshold. At sub-ambient, the building is aggregated with the one large building to form an integral building profile; and above, the building is directly deleted.

In this embodiment, both the value of the first set threshold and the value of the second set threshold may be set according to an actual application scenario, which is not specifically limited herein. For example, the first set threshold value may be set to 5 square meters, 10 square meters, or the like, and the second set threshold value may be set to 5 meters, 10 meters, or the like. The first set threshold is used for judging whether the outline area of the building reaches the value that can be considered as an independent building, when the outline area of the building is larger than or equal to the first set threshold, the building can be considered as the independent building, and when the outline area of the building is lower than the first set threshold, the building is aggregated with a large building nearest to the periphery to be considered as the outline of the whole building. The second threshold is used to determine whether a building that can be aggregated is adjacent to a large building that can be aggregated within a suitable range.

In this embodiment, after the building data is subjected to aggregation simplification processing, the target building data is obtained, and the target building data is more simplified building data, which can be more convenient for subsequent adaptive dispersion of the urban surface space.

In this embodiment, when the mesoscopic space discrete units are subjected to further more detailed space dispersion, since the buildings in the city are very high and influence the flow direction of the water flow, the buildings are also an influence factor that cannot be ignored in the self-adaptive dispersion process of the city surface space for flood disaster risk assessment. Therefore, the elevation data of the target area is corrected according to the elevation information of each building in the target building data, so as to obtain the target elevation data of the target area, namely DSM (Digital Surface Model).

Specifically, since elevation data is recorded in a grid, it is necessary to rasterize each building in the target building data based on the grid size and grid position of the elevation data. Meanwhile, because the target building data lack detailed elevation information, the elevation information of each building in the target building data is uniformly set as a preset value. And then correcting the elevation data based on the elevation information of each building in the target building data after the grid so as to obtain target elevation data. As shown in fig. 9, the dotted grid is a grid for recording elevation data, the elevation data is only geographical elevation information recorded but does not relate to elevation information of buildings, each of the buildings a, b, c, and d in the target building data is rasterized by the grid for recording elevation data, and then, for each grid overlapping with the building, the sum of the elevation information of the building and the original elevation information of the grid is used as new elevation information of the grid, so that the elevation data is corrected, and the target elevation data is obtained.

In this embodiment, the preset value may be set according to an actual application scenario, which is not specifically limited herein. For example, the preset values may be set to 10m,15m,20m, or the like.

In this embodiment, after obtaining the target elevation data and the target building data, the convergence cumulant is obtained by the hydrologic analysis tool according to the target elevation data, and the extraction of the natural water diversion line is completed by combining the technologies and methods such as neighborhood analysis, superposition analysis, reclassification, and the like, which is specifically implemented as follows:

step 1: according to target elevation data (DSM data, digital Surface Model), calculating a mean value DSMmean of the elevation data through field analysis;

and 2, step: setting a threshold value Ra for reclassification through stack analysis DSM-DSMmean;

and step 3: calculating a confluence accumulation amount of the grids according to the DSM data, and extracting the grids with the confluence accumulation amount of 0;

and 4, step 4: calculating a mean value through field analysis, and setting a threshold Rb to reclassify the grids;

and 5: performing superposition analysis on Ra Rb to obtain natural water-dividing wire grid data;

step 6: and transferring the grid data of the natural water distribution line into vector data to obtain the final natural water distribution line.

In this embodiment, in the target building data, residential buildings, commercial buildings, educational buildings, medical buildings, public transportation, and public utility buildings, which have a large influence on the flood risk, are abstracted into line objects to form building lines, the building vector surface data is converted into raster data, and the centers of raster elements are extracted after binarization processing of the raster data, thereby generating a center line element, i.e., a required building line. As shown in fig. 10, the two rectangles on the left side in the figure are the grid data of the building, and the center lines of the two rectangles on the right side in the figure are the building lines, which will affect the flow direction of the water flow.

In this embodiment, after the natural water distribution line and the building line are obtained, the micro-space discrete result is corrected through the natural water distribution line and the building line, and a target micro-space discrete result is obtained.

In the present invention, the modifying the micro-space discrete result according to the natural water distribution line and the building line to obtain a target micro-space discrete result includes: determining an abnormal micro-space discrete unit which has intersection with the natural water dividing line and/or the building line in the micro-space discrete result; and for an abnormal area which is formed by cutting the natural water diversion line and/or the building line and does not contain a rainwater grate in the abnormal micro-space discrete unit, equally dividing the abnormal area into the surrounding micro-space discrete units by using a European distribution method according to the distance from the abnormal area to the rainwater grate of each adjacent micro-space discrete unit, and obtaining a target micro-space discrete result.

In this embodiment, after the natural water dividing line and the building line are obtained, it is determined whether the microscopic spatial discrete unit needs to be corrected according to the positions of the natural water dividing line and the building line, and the specific implementation process is as follows:

step 1: obtaining micro-space discrete units by constructing an irregular triangle network in Delaunay, determining whether intersections exist between the micro-space discrete units and natural water distribution lines and/or building lines after the natural water distribution lines and the building lines are obtained, and if the intersections exist, determining that the micro-space discrete units with the intersections belong to abnormal micro-space discrete units and need to be corrected, entering step 2, otherwise, entering step 3;

and 2, step: for an abnormal area which is formed by cutting a natural water diversion line and/or a building line in the determined abnormal micro-space discrete unit and does not contain a rainwater grate, equally dividing the abnormal area into surrounding micro-space discrete units by using a Euclidean distribution method according to the distance from the abnormal area to the rainwater grate of each adjacent micro-space discrete unit;

as shown in fig. 11, a microscopic space discrete unit rf1 is cut by a natural water diversion line (a dotted line in fig. 11) into two regions, namely rf11 and rf12, wherein rf11 is an abnormal region not including a rainwater grate (a black dot circular dot in fig. 11 represents the rainwater grate, and only a part of the rainwater grate is shown). For the abnormal area, the abnormal area is evenly distributed into surrounding micro space discrete units by using an Euclidean distribution method according to the distance between the abnormal area and the rainwater grate of each adjacent micro space discrete unit.

And 3, step 3: and (3) determining whether the boundary of all the obtained new micro-space discrete units has the cut micro-space discrete result, if so, entering the step 2, and if not, obtaining the final target micro-space discrete result.

According to the method, the influence of the human natural elements and the human elements of the urban space on the flood disaster risk is considered, the self-adaption dispersion of the complex urban ground surface space is realized, the efficiency of flood disaster risk evaluation is effectively improved, and an important technical support is provided for the fine evaluation of the flood disaster risk. The method has the advantages that more refined self-adaptive spatial dispersion is carried out on the regions with personnel aggregation, the influence of flood disasters on the regions without personnel aggregation is smaller, the attention degree to the regions can be not too high, and accordingly rough self-adaptive spatial dispersion is carried out, namely self-adaptive spatial dispersion can be carried out on the earth surface of the urban space according to the attributes of the urban space, and therefore the efficiency of spatial dispersion is effectively improved.

In another aspect, the present invention provides an urban surface space adaptive discrete system for flood disaster risk assessment, where the system 200 includes:

an elevation information determining module 201, configured to determine elevation information of a target area according to elevation data of the target area;

a target elevation information determination module 202, configured to perform hole filling and pseudo hole processing on the grids meeting the set conditions in the elevation information to obtain target elevation information;

a matrix construction module 203, configured to construct a water flow direction matrix corresponding to the target elevation information according to the target elevation information, and construct a convergence cumulant matrix corresponding to the water flow direction matrix according to the obtained water flow direction matrix;

a natural river network generation module 204, configured to determine a natural river network of the target area according to the confluence cumulant matrix;

and the spatial adaptive discrete module 205 is configured to perform spatial discrete on the target area according to the water flow direction matrix of the target area and the natural river network of the target area, and obtain a macro-space discrete result of the target area by using a river diversion line as a boundary of a macro-space discrete unit.

Optionally, the system further comprises:

the data preprocessing module is used for preprocessing the pipe network data in the target area to obtain the target pipe network data of the target area;

the target macro-space discrete unit determining module is used for determining a target macro-space discrete unit in the macro-space discrete result according to a first preset screening rule;

the link construction module is used for respectively constructing a pipeline chain and a road chain of the target macroscopic space discrete unit according to the target pipe network data and the road network data of the target macroscopic space discrete unit;

the backbone network construction module is used for determining the pipeline chains meeting the pipeline setting conditions as target pipeline chains to form a backbone pipeline network, and determining the pipeline chains meeting the road setting conditions as target pipeline chains to form a backbone pipeline network;

the block determining module is used for matching the target macro-space discrete unit with a main road network where a main pipeline network is located, and determining each block in the target macro-space discrete unit;

the intersection range determining module is used for determining a block vertex according to the road width of a main road network in which a main drainage line of the main pipeline network in the target macro-space discrete unit is positioned, and determining the intersection range of a road chain based on the block vertex;

the target road block determining module is used for dividing the intersection range according to a preset division rule, and distributing a plurality of divided blocks obtained by division to adjacent road sections to obtain a target road block;

the block water collecting port determining module is used for determining the water collecting regions of all blocks and the water outlets of all the water collecting regions through a hydrological analysis tool;

and the first space self-adaptive discrete module is used for fusing the target road block and the catchment area according to the relation between the water outlets of the catchment areas and the target road block to obtain a mesoscopic space discrete result.

Optionally, the link building module is configured to implement the following steps:

and the link construction module is used for realizing the following steps:

step 22: acquiring a road connected with a road Rarc from the road set;

step 24: deleting the road connected with one road Rarc from the road set;

Optionally, the system further comprises: the first link construction sub-module is used for determining a target pipeline from the pipelines meeting the pipeline preset connection rule according to a pipeline preset connection strategy when one end of one pipeline PArc is provided with a plurality of pipelines meeting the pipeline preset connection rule, and linking the target pipeline with the pipeline PArc;

and the second link construction submodule is used for determining a target road from the plurality of roads meeting the preset road connection rule according to the preset road connection strategy when one end of one road RArc is provided with a plurality of roads meeting the preset road connection rule, and linking the target road with the one road RArc.

Optionally, the intersection range determining module includes:

a trunk drain buffer area constructing module, configured to construct a trunk drain buffer area according to a road width of a trunk road network in which a trunk drain of the trunk pipeline network in the target macro-spatial discrete unit is located;

the block vertex determining module is used for determining the vertex of the block through the trunk drain line buffer zone;

and the intersection range determining submodule is used for determining the intersection range corresponding to the intersection point of the trunk drainage line according to the intersection point of the trunk drainage line and the vertex of the surrounding block of the trunk drainage line.

Optionally, the target road block determining module includes:

the adjacent trunk drainage line determining module is used for determining two adjacent trunk drainage lines according to the included angle between the trunk drainage lines in the intersection range;

the included angle determining module is used for traversing the block vertexes in the crossing range and determining whether the sum of included angles between a connecting line of intersection points of each block vertex and trunk drainage lines in the crossing range and every two adjacent trunk drainage lines is equal to the included angle between every two adjacent trunk drainage lines;

the intersection range segmentation module is used for segmenting the intersection range through the connecting line of the street vertex and the intersection point of the trunk drainage line in the intersection range when the sum of the connecting line of the street vertex and the intersection point of the trunk drainage line in the intersection range and the included angle between every two adjacent trunk drainage lines is equal to the included angle between every two adjacent trunk drainage lines, or segmenting the intersection range through an angular bisector;

and the target road block determining submodule is used for distributing the segmentation blocks obtained by segmentation to the road sections adjacent to the segmentation blocks to obtain the target road blocks.

Optionally, the system further comprises:

the target mesoscopic space discrete unit determining module is used for determining a target mesoscopic space discrete unit in the mesoscopic space discrete result according to a second preset screening rule;

and the second space self-adaptive discrete module is used for constructing the Delaunay irregular triangular net according to the rainwater well data in the view space discrete unit in the target and the boundary of the view space discrete unit in the target so as to form a micro space discrete result.

In this embodiment, the urban surface space adaptive discrete system for flood risk assessment is developed and obtained based on Arc Object component, the development environment is Microsoft Visual Studio 2010, and the development language is C #. The whole system 300 comprises four modules, namely a data preprocessing module 301, a spatial adaptive discrete module 302, a risk assessment module 303 and a map management module 304, and is used for urban surface space adaptive discrete oriented to flood disaster risk assessment. The risk evaluation module is used for carrying out flood disaster danger evaluation on the target area on the final micro-space discrete result of the target area, and the map management module is used for providing basic functions of map browsing, zooming in and out, layer display control, layer loading and the like.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.

All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims

1. An urban surface space self-adaptive discrete method for flood disaster risk assessment is characterized by comprising the following steps:

carrying out mesoscopic space dispersion on the target macroscopic space discrete unit through a main pipeline network and a main road network in the target macroscopic space discrete unit to obtain a mesoscopic space discrete result;

the obtaining of the mesoscopic space discrete result by performing mesoscopic space discrete on the target macroscopic space discrete unit through the trunk pipeline network and the trunk road network in the target macroscopic space discrete unit includes: preprocessing the pipe network data in the target area to obtain the target pipe network data of the target area; respectively constructing a pipeline chain and a road chain of the target macroscopic space discrete unit according to target pipe network data and road network data of the target macroscopic space discrete unit; determining the pipeline chains meeting the set conditions of the pipelines as target pipeline chains to form a main pipeline network, and determining the pipeline chains meeting the set conditions of the roads as target pipeline chains to form a main pipeline network; matching the target macro-space discrete unit with a main road network where a main pipeline network is located, and determining each block in the target macro-space discrete unit; determining a block vertex according to the road width of a main road network where a main drainage line of the main pipeline network in the target macro-space discrete unit is located, and determining the intersection range of a road chain based on the block vertex; dividing the intersection range through a preset division rule, and distributing a plurality of divided blocks obtained by division to adjacent road sections to obtain target road blocks; determining the catchment areas of all the blocks and the water outlets of all the catchment areas through a hydrological analysis tool; and fusing the target road block and the catchment area according to the relation between the water outlets of the catchment areas and the target road block to obtain a discrete result of the mesoscopic space.

2. The urban surface space self-adaptive discrete method for flood disaster risk assessment according to claim 1, wherein constructing the pipeline chain of the target macro-space discrete units according to target pipe network data of the target macro-space discrete units comprises:

step 11: all pipelines in target pipe network data of the target macro-space discrete unit are stored into a pipeline set, and one pipeline PArc is selected from the pipeline set;

step 15: fusing the pipeline connected with one pipeline PArc to form a new pipeline PArc, and executing step 12 after determining the new pipeline PArc as one pipeline PArc;

step 22: acquiring a road connected with a road Rarc from the road set;

and step 24: deleting the road connected with one road Rarc from the road set;

3. The urban surface space self-adaptive dispersion method oriented to flood disaster risk assessment according to claim 2, wherein when one end of one pipeline src has a plurality of pipelines satisfying preset pipeline connection rules, a target pipeline is determined from the pipelines satisfying the preset pipeline connection rules according to a preset pipeline connection strategy, and the target pipeline is linked with the pipeline src;

4. The urban surface space adaptive discrete method for flood disaster risk assessment according to claim 1, wherein the determining a block vertex according to the road width of a main road network in which a main drainage line of a main pipeline network in the target macro-space discrete unit is located, and determining a crossing range of a road link based on the block vertex comprises:

5. The urban surface space self-adaptive dispersion method for flood disaster risk assessment according to claim 1, wherein the dividing a crossing range through a preset dividing rule, and distributing a plurality of divided blocks obtained through division to adjacent road segments to obtain a target road block comprises:

6. The urban surface space adaptive discrete method for flood disaster risk assessment according to claim 1, further comprising: