CN117473103B - Flood forecasting method, system, storage medium and equipment based on knowledge graph - Google Patents
Flood forecasting method, system, storage medium and equipment based on knowledge graph Download PDFInfo
- Publication number
- CN117473103B CN117473103B CN202311543335.3A CN202311543335A CN117473103B CN 117473103 B CN117473103 B CN 117473103B CN 202311543335 A CN202311543335 A CN 202311543335A CN 117473103 B CN117473103 B CN 117473103B
- Authority
- CN
- China
- Prior art keywords
- nodes
- river
- forecast
- flood
- upstream
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013277 forecasting method Methods 0.000 title claims abstract description 10
- 238000013507 mapping Methods 0.000 claims abstract description 64
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 57
- 238000011144 upstream manufacturing Methods 0.000 claims abstract description 52
- 238000004364 calculation method Methods 0.000 claims abstract description 38
- 238000010586 diagram Methods 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims description 20
- 238000012545 processing Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000012216 screening Methods 0.000 claims description 4
- 230000006798 recombination Effects 0.000 claims 2
- 238000005215 recombination Methods 0.000 claims 2
- 230000006399 behavior Effects 0.000 abstract description 3
- 238000004590 computer program Methods 0.000 description 11
- 238000004891 communication Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 235000017166 Bambusa arundinacea Nutrition 0.000 description 1
- 235000017491 Bambusa tulda Nutrition 0.000 description 1
- 241001330002 Bambuseae Species 0.000 description 1
- 241000237858 Gastropoda Species 0.000 description 1
- 235000015334 Phyllostachys viridis Nutrition 0.000 description 1
- 239000011425 bamboo Substances 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Animal Behavior & Ethology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Alarm Systems (AREA)
Abstract
The invention relates to a flood forecasting method, a flood forecasting system, a flood forecasting storage medium and flood forecasting equipment based on a knowledge graph, which comprise the following steps: digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes; constructing a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations among the river basin, the river reach and the nodes; and carrying out parallel calculation on forecast nodes without dependency in the flood forecast scheme, and carrying out layer-by-layer recursion from upstream nodes to downstream nodes to complete forecast calculation of all objects. The invention can simulate and forecast various behaviors in any complex watershed water flow network, and improves the flood forecasting capability.
Description
Technical Field
The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, a flood forecasting system, a flood forecasting storage medium and flood forecasting equipment based on a knowledge graph.
Background
In flood forecasting business, a Chinese flood forecasting system is mostly adopted as an operation platform at present, and the main problem is that a single-station scheme and a river system scheme can only be constructed manually and cannot be automatically established according to the upstream-downstream relation of a river basin; when flood forecast is carried out on river systems, only station-by-station serial calculation is carried out, and parallel calculation cannot be realized; only the water collection relationship can be considered for the water flow network, and the water diversion relationship cannot be considered.
Internationally comparing representative hydrologic models, such as a SWAT model, can automatically construct river schemes according to DEM, but water diversion relation cannot be considered; the SWMM model can consider the water division relationship, but can not automatically construct a river scheme, and does not realize parallel calculation. The new generation National Water Model (NWM) in the United states automatically generates a water flow network relation based on the high-precision DEM, and also realizes parallel calculation, but the water diversion relation and the manual interaction are not considered.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a flood forecasting method and a flood forecasting system based on a knowledge graph, which can simulate and forecast various behaviors in any complex watershed water flow network and improve the flood forecasting capability.
In order to achieve the above object, according to a first aspect, the present invention adopts the following technical scheme: a flood forecasting method based on knowledge graph, comprising: digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes; constructing a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations among the river basin, the river reach and the nodes; and carrying out parallel calculation on forecast nodes without dependency in the flood forecast scheme, and carrying out layer-by-layer recursion from upstream nodes to downstream nodes to complete forecast calculation of all objects.
Further, the digital mapping of the complex watershed water flow network includes:
Mapping hydrologic stations and trunk and branch flow confluence points in the complex flow domain into confluence points;
The river bifurcation point and the canal water intake are mapped into water diversion points;
Mapping reservoirs with only one outlet and lakes into regulation and storage water collection points;
mapping reservoirs and lakes with two or more outlets into regulation and water diversion points;
mapping river channels and channels into water-conveying river segments, and mapping the yielding units into sub-river domains.
Further, constructing a flood forecast triplet knowledge graph based on the digitized mapping graph, including:
constructing a flood forecast triplet consisting of a river basin, a river reach and nodes which are smaller than a set threshold area in the river basin according to the high-precision digital elevation model and river water system basic data;
According to the demands of users, the river basin, the river reach and the nodes are screened, and flood forecast triplets with different scales are recombined by utilizing the unchanged yield convergence relationship between the large river basin and the small river basin, so that a river flood forecast scheme is obtained.
Further, the constructed flood forecast scheme comprises upstream and downstream relations among the river basin, the river reach and the nodes so as to determine which objects have or do not have dependency relations.
Further, the flood forecast triplets with different scales are recombined by utilizing the constant yield convergence relation between the big river basin and the small river basin, and the flood forecast triplets comprise:
And determining the concerned node according to the user requirement, sequentially searching the sub-drainage basin and other nodes flowing to the concerned node from the flood forecast triplet, determining whether the node or the sub-drainage basin flows to the other node, if not, stopping, otherwise, continuing searching.
Further, the parallel calculation is performed on the forecast nodes without the dependency relationship in the flood forecast scheme, including:
upstream and downstream analysis is carried out on the forecast nodes through the flood forecast triples, the dependency relationship among different nodes is obtained through reverse traceback analysis, the nodes without the dependency relationship are classified into a node set capable of being calculated in parallel, and the calculation nodes are divided into upstream nodes and downstream nodes;
performing parallel computation on all upstream nodes without dependency, deleting triples corresponding to the upstream nodes with the computation from the list after the computation of the upstream nodes is completed, and re-analyzing the rest triples list to obtain new upstream nodes without dependency to be incorporated into the computation; and recursively layer by layer until calculation is performed to the drainage basin outlet so as to finish forecast calculation of all objects.
In a second aspect, the present invention adopts the following technical scheme: a knowledge-graph-based flood forecasting system, comprising: the first processing module is used for digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes; the second processing module constructs a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations among the river basin, the river reach and the nodes; and the third processing module is used for carrying out parallel calculation on the forecast nodes without the dependency relationship in the flood forecast scheme, and carrying out layer-by-layer recursion from the upstream nodes to the downstream nodes to finish the forecast calculation of all the objects.
Further, constructing a flood forecast triplet knowledge graph based on the digitized mapping graph, including:
constructing a flood forecast triplet consisting of a river basin, a river reach and nodes which are smaller than a set threshold area in the river basin according to the high-precision digital elevation model and river water system basic data;
According to the demands of users, the river basin, the river reach and the nodes are screened, and flood forecast triplets with different scales are recombined by utilizing the unchanged yield convergence relationship between the large river basin and the small river basin, so that a river flood forecast scheme is obtained.
In a third aspect, the present invention adopts the following technical scheme: a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
In a fourth aspect, the present invention adopts the following technical scheme: a computing apparatus, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described above.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the invention can simulate and forecast the behaviors of water production, water collection, water delivery, water diversion, water storage regulation and the like in any complex watershed water flow network, and improves the flood forecasting and forecasting capability.
2. The invention can quickly construct flood forecast schemes of any river system with different scales according to the demands of users, and greatly improves the scheme manufacturing efficiency.
Drawings
FIG. 1 is a flow chart of a flood forecasting method based on a knowledge graph in an embodiment of the invention;
FIG. 2 is a diagram of a digitized mapping of an arbitrarily complex water flow network in accordance with an embodiment of the invention;
FIG. 3 is a digitized map of flood forecast for a river basin above a new cover house of a great river in an embodiment of the invention;
FIG. 4 is a digitized map of flood forecast for river areas above the river Xi County in an embodiment of the invention;
FIG. 5 is a schematic diagram of parallel computation of a first flood forecast for a river basin above a river Xi County in an embodiment of the present invention;
fig. 6 is a schematic diagram of a second flood forecast parallel calculation of a river basin above the river Xi County according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In one embodiment of the invention, a flood forecasting method based on a knowledge graph is provided. In this embodiment, as shown in fig. 1, the method includes the following steps:
1) Digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes;
2) Constructing a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations between river reach and nodes of the river basin;
3) And carrying out parallel calculation on forecast nodes without dependency in the flood forecast scheme, and carrying out layer-by-layer recursion from upstream nodes to downstream nodes to complete forecast calculation of all objects.
In the step 1), the water flow network in the real world is very complex, and digital mapping needs to be performed on the water flow network when the flood forecast system is developed so as to perform flood forecast operation. On the basis of carrying out a large number of early-stage investigation and combining with practical working experience, the invention designs a digitalized mapping assembly (see table 1) of three major 6 minor objects of points, lines and planes, and constructs a 'flood forecast triplet' knowledge graph, namely, a starting object, a stopping object and a directed river reach are adopted to describe the water flow relationship between every two objects in a water flow network, and water is expressed to flow from the starting object to the stopping object through the linked river reach, so that digitalized mapping of any complex water flow network in a natural river basin is realized, as shown in fig. 2 and table 1.
Table 1 digital mapping assembly table for water flow network
In this embodiment, the digital mapping is performed on the complex watershed water flow network, including the following steps:
1.1 Mapping hydrologic stations, trunk and branch flow convergence points and the like in the complex flow domain into water convergence points;
1.2 River bifurcation point, canal intake, etc. are mapped to water diversion points;
1.3 A reservoir, a lake, etc. with only one outlet are mapped to a regulation and water collection point;
1.4 Mapping reservoirs, lakes and the like with two or more outlets into regulation and water diversion points;
1.5 Mapping river channels, channels and the like into water-conveying river segments, and mapping the yielding units into sub-river domains.
Specifically, as shown in fig. 3, taking the river basin above the new cover house hub of the great river of the sea as an example, a digital mapping result of a flood forecasting scheme is given; as shown in fig. 4, taking more than river Xi County as an example, the digitized mapping result of the flood forecasting scheme is given.
In the step 2), a flood forecast triplet knowledge graph is constructed based on the digital mapping graph, and the method comprises the following steps:
2.1 Constructing a flood forecast triplet consisting of river basin sections and nodes with areas smaller than a set threshold value in a flow field according to a high-precision Digital Elevation Model (DEM) and river water system basic data;
according to the high-precision DEM and the set threshold (for example, the set threshold is 25 square kilometers), the watershed above the Hunan river snail bridge hydrologic station (marked I) is divided into a confluence network consisting of A, B, C, D, E, F, G, H, I total 9 nodes, AC, BC, DE, EF, CF, FH, GH, HI total 8 river segments and 8 sub-watershed (1, 2, 3..8), wherein 16 flood forecast triplets are included in total, as shown in table 2.
Table 216 flood forecast triples
2.2 Screening the river basin, the river reach and the nodes according to the user demands, and recombining the river basin, the river reach and the nodes into flood forecast triplets with different scales by utilizing the constant yield and convergence relationship between the large river basin and the small river basin to obtain a river system flood forecast scheme, thereby realizing the rapid construction of the flood forecast scheme according to the user demands.
The flood forecast triplets with different scales are recombined by utilizing the constant yield and confluence relationship between the large drainage basin and the small drainage basin, and the flood forecast triplets are specifically as follows:
And determining the concerned node according to the user requirement, sequentially searching the sub-drainage basin and other nodes flowing to the concerned node from the flood forecast triplet, determining whether the node or the sub-drainage basin flows to the other node, if not, stopping, otherwise, continuing searching.
For example, if the user is only focusing on C, F, I three nodes, the sub-watershed flowing to C, F, I can be found out in turn from the aforementioned 16 flood forecast triples:
(1) The sub-basin objects flowing to C are 1,2 and a node A, B, and as no node or sub-basin flowing into A, B is found, the searched sub-basins 1 and 2 are combined into a sub-basin 1;
(2) The sub-watershed flowing to F is 3,5 and node E, C, because C is the focus point, the upstream tracing is not performed any more, the sub-watershed flowing to E is 4 and node D, and the searched sub-watershed 3, 4 and 5 is combined into 2 as the node or sub-watershed not flowing to D is found out;
(3) The flow direction I is the sub-basin 8 and the node H, the flow direction H is the sub-basins 6, 7 and the node G, F, F is the point of interest, no more trace is made upstream, and the searched sub-basins 6, 7, 8 are merged into the sub-basin 3 until the point is found because there is no node or sub-basin flowing into G. Finally, 5 flood forecast triples with different scales are generated as shown in table 3:
table 35 flood forecast triples of different scales
In this embodiment, in the actual flood forecasting service, the flood forecasting scheme needs to be dynamically adjusted due to external condition changes such as user requirements, site flood forecasting conditions, and hydraulic engineering changes. The embodiment adopts the flood forecast triples to realize the rapid construction of the flood forecast scheme according to the user requirements.
In the step 3), the flood forecasting scheme constructed according to the flood forecasting triplet mode already contains the upstream-downstream relationship among the river basin, the river reach and the nodes, so that the existence or nonexistence of the dependency relationship among the objects can be clearly known, and a parallel computing method can be adopted to perform parallel computing on the objects without the dependency relationship, thereby improving the computing efficiency.
In this embodiment, parallel computation is performed on forecast nodes with no dependency relationship in the flood forecast scheme, including the following steps:
3.1 Upstream and downstream analysis is carried out on the forecast nodes through the flood forecast triples, the dependency relationship among different nodes is obtained through reverse traceback analysis, the nodes without the dependency relationship are classified into a node set capable of being calculated in parallel, namely, the calculation nodes are divided into upstream nodes and downstream nodes according to the topological relationship of the flood forecast triples knowledge graph;
3.2 Parallel computing is carried out on all upstream nodes without dependency, after the upstream node computation is completed, triples corresponding to the upstream nodes with complete computation are deleted from the list, the rest triples list is analyzed again, and new upstream nodes without dependency are acquired and incorporated into the computation; and recursively carrying out layer by layer until calculation reaches the drainage basin outlet, thereby completing forecast calculation of all objects.
Fig. 5 is a schematic diagram of a flood forecasting scheme of a river basin above a river Xi County, according to analysis of a flood forecasting triplet, four nodes of a hillside, a mountain mouth, a south bay and a bamboo pole are obtained as the most upstream nodes, and have no dependency relationship with each other, so that the method can be incorporated into a first batch of parallel calculation flow; after the four nodes are calculated, the formation of the nodes is removed, as shown in fig. 6, and the mountain outlet, the small dragon mountain and the flat bridge become the most upstream nodes at the moment, so that the second batch of parallel calculation flows are incorporated; and so on, only to the last node Xi County.
In summary, compared with the traditional Chinese flood forecasting system, the method disclosed by the invention has the advantages that the scheme construction time is shortened from a few hours to a few minutes, the flood forecasting operation time is shortened from 1-2 hours to less than half an hour, and the flood forecasting scheme compiling efficiency and the flood forecasting operation efficiency are greatly improved as shown in tables 4 and 5.
Table 4 forecast scheme build time versus table
Table 5 flood forecast operation time comparison table
In one embodiment of the present invention, there is provided a flood forecast system based on a knowledge graph, comprising:
The first processing module is used for digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes;
The second processing module constructs a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations among the river basin, the river reach and the nodes;
And the third processing module is used for carrying out parallel calculation on the forecast nodes without the dependency relationship in the flood forecast scheme, and carrying out layer-by-layer recursion from the upstream nodes to the downstream nodes to finish the forecast calculation of all the objects.
In the above embodiment, the digitally mapping the complex watershed water flow network includes:
Mapping hydrologic stations and trunk and branch flow confluence points in the complex flow domain into confluence points;
The river bifurcation point and the canal water intake are mapped into water diversion points;
Mapping reservoirs with only one outlet and lakes into regulation and storage water collection points;
mapping reservoirs and lakes with two or more outlets into regulation and water diversion points;
mapping river channels and channels into water-conveying river segments, and mapping the yielding units into sub-river domains.
In the above embodiment, constructing the flood forecast triplet knowledge graph based on the digitized mapping graph includes:
constructing a flood forecast triplet consisting of a river basin, a river reach and nodes which are smaller than a set threshold area in the river basin according to the high-precision digital elevation model and river water system basic data;
And screening river reach and nodes according to the demands of users, and recombining the river reach and nodes into flood forecast triplets with different scales by utilizing the constant yield and confluence relationship between the large river reach and the small river reach to obtain a river flood forecast scheme.
In the above embodiment, the constructed flood forecast scheme includes upstream and downstream relationships between river reach and nodes, so as to determine which objects have or do not have a dependency relationship.
In the above embodiment, the flood forecast triplets with different scales are recombined by using the constant yield and convergence relationship between the large drainage basin and the small drainage basin, specifically:
And determining the concerned node according to the user requirement, sequentially searching the sub-drainage basin and other nodes flowing to the concerned node from the flood forecast triplet, determining whether the node or the sub-drainage basin flows to the other node, if not, stopping, otherwise, continuing searching.
In the above embodiment, performing parallel computation on the forecast node without the dependency relationship in the flood forecast scheme includes:
upstream and downstream analysis is carried out on the forecast nodes through the flood forecast triples, the dependency relationship among different nodes is obtained through reverse traceback analysis, the nodes without the dependency relationship are classified into a node set capable of being calculated in parallel, and the calculation nodes are divided into upstream nodes and downstream nodes;
Performing parallel computation on all upstream nodes without dependency, deleting triples corresponding to the upstream nodes with the computation from the list after the computation of the upstream nodes is completed, and re-analyzing the rest triples list to obtain new upstream nodes without dependency to be incorporated into the computation; and performing layer-by-layer recursion until calculation reaches a drainage basin outlet, and completing forecast calculation of all objects.
The system provided in this embodiment is used to execute the above method embodiments, and specific flow and details refer to the above embodiments, which are not described herein.
In one embodiment of the present invention, a computing device structure is provided, which may be a terminal, and may include: a processor (processor), a communication interface (Communications Interface), a memory (memory), a display, and an input device. The processor, the communication interface and the memory complete communication with each other through a communication bus. The processor is configured to provide computing and control capabilities. The memory comprises a non-volatile storage medium storing an operating system and a computer program which when executed by the processor implements the method described above; the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, the input device can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computing equipment, and can also be an external keyboard, a touch pad or a mouse and the like. The processor may invoke logic instructions in memory.
Further, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In one embodiment of the present invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the method embodiments described above.
In one embodiment of the present invention, a non-transitory computer readable storage medium storing server instructions that cause a computer to perform the methods provided by the above embodiments is provided.
The foregoing embodiment provides a computer readable storage medium, which has similar principles and technical effects to those of the foregoing method embodiment, and will not be described herein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. The flood forecasting method based on the knowledge graph is characterized by comprising the following steps of:
digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes;
constructing a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations among the river basin, the river reach and the nodes;
Carrying out parallel calculation on forecast nodes without dependency in a flood forecast scheme, and carrying out layer-by-layer recursion from upstream nodes to downstream nodes to complete forecast calculation of all objects;
constructing a flood forecast triplet knowledge graph based on the digitized mapping graph, comprising:
constructing a flood forecast triplet consisting of a river basin, a river reach and nodes which are smaller than a set threshold area in the river basin according to the high-precision digital elevation model and river water system basic data;
screening the river basin, the river reach and the nodes according to the requirements of the users, and recombining the river basin, the river reach and the nodes into flood forecast triplets with different scales by utilizing the unchanged yield convergence relationship between the large river basin and the small river basin to obtain a river system flood forecast scheme;
the constructed flood forecast scheme comprises upstream and downstream relations among the river basin, the river reach and the nodes so as to determine which objects have or do not have dependency relations;
Recombination into flood forecast triples of different scales by utilizing the constant yield convergence relationship between the big river basin and the small river basin, comprising: determining the concerned node according to the user demand, sequentially searching the sub-drainage basin and other nodes flowing to the concerned node from the flood forecast triplet, determining whether the node or the sub-drainage basin flows to the other node, if not, stopping, otherwise, continuously searching;
Parallel computing is carried out on forecast nodes which do not have dependency relations in a flood forecast scheme, and the method comprises the following steps:
upstream and downstream analysis is carried out on the forecast nodes through the flood forecast triples, the dependency relationship among different nodes is obtained through reverse traceback analysis, the nodes without the dependency relationship are classified into a node set capable of being calculated in parallel, and the calculation nodes are divided into upstream nodes and downstream nodes;
performing parallel computation on all upstream nodes without dependency, deleting triples corresponding to the upstream nodes with the computation from the list after the computation of the upstream nodes is completed, and re-analyzing the rest triples list to obtain new upstream nodes without dependency to be incorporated into the computation; and recursively layer by layer until calculation is performed to the drainage basin outlet so as to finish forecast calculation of all objects.
2. The knowledge-based flood forecasting method of claim 1, wherein digitally mapping the complex watershed water flow network comprises:
Mapping hydrologic stations and trunk and branch flow confluence points in the complex flow domain into confluence points;
The river bifurcation point and the canal water intake are mapped into water diversion points;
Mapping reservoirs with only one outlet and lakes into regulation and storage water collection points;
mapping reservoirs and lakes with two or more outlets into regulation and water diversion points;
mapping river channels and channels into water-conveying river segments, and mapping the yielding units into sub-river domains.
3. A knowledge-graph-based flood forecasting system, comprising:
The first processing module is used for digitally mapping the complex watershed water flow network to obtain a digital mapping diagram formed by digital mapping components of points, lines and planes;
The second processing module constructs a flood forecast triplet knowledge graph based on the digital mapping graph to obtain flood forecast schemes of different scales including upstream and downstream relations among the river basin, the river reach and the nodes;
the third processing module performs parallel calculation on forecast nodes without dependency in the flood forecast scheme, and performs layer-by-layer recursion from upstream nodes to downstream nodes to complete forecast calculation of all objects;
in the second processing module, a flood forecast triplet knowledge graph is constructed based on the digital mapping graph, and the method comprises the following steps:
constructing a flood forecast triplet consisting of a river basin, a river reach and nodes which are smaller than a set threshold area in the river basin according to the high-precision digital elevation model and river water system basic data;
screening the river basin, the river reach and the nodes according to the requirements of the users, and recombining the river basin, the river reach and the nodes into flood forecast triplets with different scales by utilizing the unchanged yield convergence relationship between the large river basin and the small river basin to obtain a river system flood forecast scheme;
the constructed flood forecast scheme comprises upstream and downstream relations among the river basin, the river reach and the nodes so as to determine which objects have or do not have dependency relations;
Recombination into flood forecast triples of different scales by utilizing the constant yield convergence relationship between the big river basin and the small river basin, comprising: determining the concerned node according to the user demand, sequentially searching the sub-drainage basin and other nodes flowing to the concerned node from the flood forecast triplet, determining whether the node or the sub-drainage basin flows to the other node, if not, stopping, otherwise, continuously searching;
In the third processing module, parallel computation is performed on forecast nodes which do not have a dependency relationship in the flood forecast scheme, and the method comprises the following steps:
upstream and downstream analysis is carried out on the forecast nodes through the flood forecast triples, the dependency relationship among different nodes is obtained through reverse traceback analysis, the nodes without the dependency relationship are classified into a node set capable of being calculated in parallel, and the calculation nodes are divided into upstream nodes and downstream nodes;
performing parallel computation on all upstream nodes without dependency, deleting triples corresponding to the upstream nodes with the computation from the list after the computation of the upstream nodes is completed, and re-analyzing the rest triples list to obtain new upstream nodes without dependency to be incorporated into the computation; and recursively layer by layer until calculation is performed to the drainage basin outlet so as to finish forecast calculation of all objects.
4. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-2.
5. A computing device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311543335.3A CN117473103B (en) | 2023-11-20 | 2023-11-20 | Flood forecasting method, system, storage medium and equipment based on knowledge graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311543335.3A CN117473103B (en) | 2023-11-20 | 2023-11-20 | Flood forecasting method, system, storage medium and equipment based on knowledge graph |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117473103A CN117473103A (en) | 2024-01-30 |
| CN117473103B true CN117473103B (en) | 2024-07-16 |
Family
ID=89639588
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311543335.3A Active CN117473103B (en) | 2023-11-20 | 2023-11-20 | Flood forecasting method, system, storage medium and equipment based on knowledge graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117473103B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680171A (en) * | 2020-07-08 | 2020-09-18 | 中国水利水电科学研究院 | Method for constructing river flood forecasting scheme based on thematic knowledge map |
| CN114330825A (en) * | 2021-11-25 | 2022-04-12 | 中国电建集团中南勘测设计研究院有限公司 | Integrated method, system, equipment and medium for forecasting and scheduling flood of river system in large watershed |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230161072A1 (en) * | 2021-11-23 | 2023-05-25 | At&T Intellectual Property I, L.P. | Predictive Hydrological Impact Diagnostic System |
| CN115759459B (en) * | 2022-12-01 | 2023-07-28 | 长江水利委员会水文局 | Intelligent forecasting method for drainage basin flood flow set probability of rainwater amorous condition coupling |
| CN116010612A (en) * | 2022-12-09 | 2023-04-25 | 中水珠江规划勘测设计有限公司 | River basin flood control knowledge graph construction method and device and electronic equipment |
-
2023
- 2023-11-20 CN CN202311543335.3A patent/CN117473103B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680171A (en) * | 2020-07-08 | 2020-09-18 | 中国水利水电科学研究院 | Method for constructing river flood forecasting scheme based on thematic knowledge map |
| CN114330825A (en) * | 2021-11-25 | 2022-04-12 | 中国电建集团中南勘测设计研究院有限公司 | Integrated method, system, equipment and medium for forecasting and scheduling flood of river system in large watershed |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117473103A (en) | 2024-01-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tang et al. | Spatio-temporal variation and coupling coordination relationship between urbanisation and habitat quality in the Grand Canal, China | |
| CN110717251B (en) | Distributed hydrological model sub-basin partitioning method considering multiple elements | |
| CN103092572B (en) | The parallel method of distributing numerical control under a kind of cluster environment | |
| CN110597799B (en) | Automatic filling method, system and equipment for missing value of time sequence data | |
| Liang et al. | Revealing spatio-temporal evolution of urban visual environments with street view imagery | |
| CN108614915B (en) | Hydrological model free construction strategy method based on scene driving | |
| Huangpeng et al. | Forecast of the hydropower generation under influence of climate change based on RCPs and Developed Crow Search Optimization Algorithm | |
| Hu et al. | Water quality risk assessment for the Laoguanhe River of China using a stochastic simulation method | |
| CN117236673A (en) | Urban water network multi-scale flood control and drainage combined optimization scheduling method and system | |
| CN110766792A (en) | Grid calculation order coding method based on ArcGIS hydrological analysis tool | |
| Liao et al. | Air quality prediction by integrating mechanism model and machine learning model | |
| Beshir et al. | Trend analysis, past dynamics and future prediction of land use and land cover change in upper Wabe-Shebele river basin | |
| Whiteaker et al. | From a NEXRAD rainfall map to a flood inundation map | |
| CN115496148A (en) | Method and device for evaluating large-scale hydrological situation change of river | |
| CN117473103B (en) | Flood forecasting method, system, storage medium and equipment based on knowledge graph | |
| Wang et al. | A statistical hydrological model for Yangtze river watershed based on stepwise cluster analysis | |
| Zhao et al. | Multiobjective land-use optimization allocation in eucalyptus-introduced regions based on the GMDP–ACO model | |
| CN114610827B (en) | Vergence characteristic quantitative identification method for village and town aggregation | |
| KR20230116440A (en) | Watershed analysis apparatus and method thereof | |
| CN109472072B (en) | Seasonal river and underground water interaction prediction method based on river simulation | |
| Guo et al. | Runoff time series prediction based on hybrid models of two-stage signal decomposition methods and LSTM for the Pearl River in China | |
| CN102831287A (en) | Analog prediction method for urban rainstorm water disaster | |
| Davanlou Tajbakhsh et al. | Hybrid wavelet-M5 modeling in rainfall-runoff process forecast | |
| CN109102558A (en) | A kind of water flow streamline rapid generation based on the non-structural triangulation network | |
| CN114357675B (en) | Method for calculating water outlet flow by replacing SWMM model conveying module |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |