CN115358646A - Drainage basin ecological flow early warning analysis method based on geographical knowledge graph - Google Patents
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Abstract
The invention relates to a watershed ecological flow early warning analysis method based on a geographical knowledge graph in the technical field of intelligent water conservancy, which adopts a knowledge graph considering the spatial relationship of geographical objects to model ecological flow early warning, realizes automatic construction of the spatial topological relationship between a monitoring section in a watershed and a water user, a reservoir and a rainfall station through a nine-intersection model algorithm, improves the modeling efficiency, can intuitively know the early warning cause by quantitatively analyzing and visually displaying the cause of an ecological flow early warning event, is convenient for a decision maker to take accurate control measures and is beneficial to keeping the stability of the watershed ecological flow.
Description
Technical Field
The invention relates to the technical field of intelligent water conservancy, in particular to a watershed ecological flow early warning analysis method based on a geographical knowledge map.
Background
The early warning and guarantee scheduling of the drainage basin ecological flow is a key problem in the field of water resource scheduling in recent years, and has important significance for maintaining the biological diversity and ecological stability of the drainage basin. At present, the early warning analysis of the ecological flow of the drainage basin is mainly to analyze the early warning cause of the ecological drainage basin in a subjective layer by comparing the monitoring data and historical data of reservoirs, rainfall and the like when the early warning of the ecological flow occurs. However, the cause of the ecological flow early warning is complex, and the current analysis method mainly depends on the decision experience of a decision maker, so that the analysis and mining of the complex association relationship among various factors of the ecological flow early warning are not sufficient, and the method has certain limitation.
Although some researches introduce the knowledge graph technology into related fields such as hydraulic engineering scheduling, the historical data is mined, and good effects are achieved in the aspects of trend analysis, scheme deduction and the like. However, at present, the use of the knowledge graph in the field of hydraulic engineering scheduling mainly depends on the traditional semantic relationship, and relevant elements of the hydraulic engineering have position attributes, such as rainfall stations, reservoirs and the like, and important spatial relationships exist among the elements, such as the relationship between the rainfall stations and reservoir catchment areas, so that the analysis and modeling of the ecological flow early warning by using the traditional knowledge graph need a lot of manual processing, and are time-consuming and labor-consuming.
Disclosure of Invention
The invention aims to solve the technical problem of providing a watershed ecological flow early warning analysis method based on a geographical knowledge graph, which can automatically generate each relevant node and relation in the geographical knowledge graph by utilizing spatial relation and realize the visualization of ecological flow early warning cause analysis so as to take targeted control measures and improve the control accuracy.
The technical scheme adopted by the invention for solving the technical problems is as follows: a watershed ecological flow early warning analysis method based on a geographical knowledge map comprises the following steps: comprises the steps of (a) preparing a substrate,
s1, designing an ecological flow early warning geographical knowledge map conceptual model;
s2, constructing a node set: extracting the drainage basin ecological flow early warning nodes to form a node set V (a, [ geo ]); wherein a represents the attribute of the node, including the coordinate position, geo represents the entity geographic object represented by the node, including the monitoring section, the rainfall station, the water user and the reservoir;
s3, constructing a spatial topological relation between the catchment area and the rainfall station, the water taking user and the reservoir: automatically calculating the range of the catchment area according to the monitoring section, establishing the spatial topological relation between the catchment area and the rainfall station, the water fetching household and the reservoir in the basin through a nine-intersection model of the following formula, namely calculating the rainfall station, the water fetching household and the reservoir which are related in the catchment area,
wherein topo represents the spatial topological relation between a rainfall station, a water taking user, a reservoir and a catchment area, and A represents a node set V (a, [ geo)]) The attribute of the middle node geo, B represents a catchment area represented by the monitoring section; I.C. A A 、B’ A 、E A Respectively representing the interior, the boundary and the exterior of A, I B 、B’ B 、E B Respectively representing the inside, the boundary and the outside of B, and dim represents a dimension;
s4, constructing a geographical knowledge map: establishing an incidence relation between a monitoring section node and a rainfall station node, a water taking user node and a reservoir node in the catchment area, and accordingly constructing a geographical knowledge map of the watershed ecological flow early warning, wherein the geographical knowledge map is represented as follows:
wherein G represents a geographical knowledge graph, E (r, [ topo ]) represents a set of edges in the graph, namely, relationships between nodes, and r represents attributes of the relationships between nodes;
s5, early warning cause analysis: when early warning occurs, adding early warning information and current state generation nodes of water users, reservoirs and rainfall stations into a geographical knowledge graph; obtaining current state data and historical state data of water users, reservoirs and rainfall stations when early warning occurs, and calculating to obtain variation D of water amounts taken by the water users, reservoir delivery flow and rainfall of the rainfall stations when early warning occurs according to the following formula when And changing dynamics DI when ，
D when =C-H
DI when =D when /H*100%
Wherein C is current state data when early warning occurs, and H is historical state data;
determining the size of the current state node of the water user, the reservoir and the rainfall station according to the C value and the DI value when The value determines the thickness of a relation connecting line between the current state node and the early warning node of the water user, the reservoir and the rainfall station so as to realize the visualization of early warning cause;
s6, early warning management and control: according to the size of the node and the thickness of the connecting line determined in the S5, taking corresponding control measures for the corresponding early warning control object, and adding the control measure generation node into the map;
s7, management and control evaluation: and evaluating the effect of the control measures.
Further, the control measures comprise reducing the water taking amount of water taken by a water user or/and increasing the reservoir outlet flow.
Further, the method for evaluating the effect of the tube control measure comprises the following steps,
s71, adding the current state generation node of the node implementing the management and control measure at the end of the early warning period into a map;
S72acquiring current state data of the node implementing the control measure at the end of the early warning period, and calculating to obtain the current data variable quantity D of the node implementing the control measure at the end of the early warning period according to the following formula after And changing dynamics DI after By DI after Value and-DI when /（DI when + 1) value magnitude relation judges the effect of the control measure,
D after =F-C’
DI after =D after /C’*100%
and F is the current state data of the node implementing the management and control measure when the early warning period is ended, and C' is the current state data C when the early warning of the node implementing the management and control measure occurs.
Compared with the prior art, the invention has the advantages that: the method can realize the automatic establishment of the spatial topological relation between the monitoring section and the rainfall station, the water taking user and the reservoir, does not need manual association, saves manpower and improves the modeling efficiency; meanwhile, the method can carry out quantitative analysis and visual expression on all relevant factors causing the early warning event, provides visual data support for implementing management and control measures, is convenient for rapidly and accurately taking the management and control measures, improves the accuracy of the management and control measures, and can carry out objective evaluation on the effect of the management and control measures.
Drawings
FIG. 1 is a schematic diagram of a geographic knowledge graph conceptual model of the present application.
Fig. 2 is a schematic diagram of the setting position of an ecological flow early warning monitoring section of a hanjiang river basin.
Fig. 3 is a schematic view of visualized display of an early warning cause of an Ankang monitoring section of the Hanjiang river basin.
Fig. 4 is a schematic diagram showing the state and the dynamic degree of change of the water quantity taken by part of water taking users when the safety and health monitoring section of the Hanjiang river basin gives an early warning.
Fig. 5 is a schematic view of effect evaluation of management and control measures implemented on one of the water users after an early warning of an safety and health monitoring section of a Hanjiang river basin occurs.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawing, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present application, it should be noted that, for the terms of orientation, such as "central", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., it indicates that the orientation and positional relationship shown in the drawings are based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be construed as limiting the specific scope of protection of the present application. The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The river basin ecological flow early warning analysis method based on the geographic knowledge map comprises the following seven steps S1 to S7.
S1, designing an ecological flow early warning geographical knowledge map conceptual model.
S2, constructing a node set: extracting the drainage basin ecological flow early warning nodes to form a node set V (a, [ geo ]); wherein a represents the attribute of the node, including the coordinate position, geo represents the geographical object of the entity represented by the node, including monitoring section, rainfall station, fetching water user, reservoir.
S3, constructing a spatial topological relation between the catchment area and the rainfall station, the water taking user and the reservoir: the method comprises the steps of automatically calculating the range of a catchment area according to the position of a monitored section, namely performing confluence accumulation calculation and river network classification through a DEM (DEM is an abbreviation of a Digital Elevation Model), so as to obtain the range of the catchment area corresponding to the monitored section, wherein the DEM technology belongs to the prior art and is not developed in detail, and the spatial topological relation between the catchment area and a rainfall station, a user and a reservoir in a drainage basin is established through a nine-intersection Model, namely the rainfall station, the user and the reservoir related to the catchment area in the drainage basin are calculated,
wherein topo represents the spatial topological relation between a rainfall station, a water taking user, a reservoir and a catchment area, and A represents a node set V (a, [ geo)]) The attribute of the middle node geo, B represents a catchment area represented by the monitoring section; i is A 、B’ A 、E A Respectively representing the interior, the boundary and the exterior of A, I B 、B ， B 、E B Respectively, the inside of B, the boundary of the catchment area, and the outside of the catchment area, and dim is an abbreviation of dimension, and refers to dimension. The nine-intersection model belongs to the prior art, and the meanings of relevant parameters are not detailed here.
S4, constructing a geographical knowledge map: establishing an incidence relation between a monitoring section node and a rainfall station node, a water user taking node and a reservoir node in a catchment area, namely connecting the monitoring section node with the rainfall station node, the water user taking node and the reservoir node by arrow lines, and accordingly establishing a geographical knowledge map of basin ecological flow early warning, wherein the geographical knowledge map is represented as follows:
where G represents a geographical knowledge graph, E (r, [ topo ]) represents a set of edges in the graph, i.e., relationships between nodes, and r represents an attribute of a relationship between nodes.
S5, early warning cause analysis: when early warning occurs, adding early warning information and current state generation nodes of water users, reservoirs and rainfall stations into a geographical knowledge graph;
obtaining current state data and historical state data of water users, reservoirs and rainfall stations when early warning occurs, and calculating to obtain variation D of water amounts taken by the water users, reservoir delivery flow and rainfall of the rainfall stations when early warning occurs according to the following formula when And changing dynamics DI when ，
D when =C-H
DI when =D when /H*100%
Wherein, C is the current state data when the early warning occurs, H is the historical state data, and the historical state data can adopt historical synchronization data or be defined as historical state data at other moments as required;
determining the sizes of the nodes of the water user, the reservoir and the rainfall station according to the C value, wherein the larger the C value is, the larger the area of the node is; according to DI when The value is determined by the thickness of a relation connecting line between the current state node and the early warning node when early warning of a user, a reservoir and a rainfall station node occurs, so that the visualization of early warning causes is realized, and of course, it needs to be explained that the size proportion of the node area is not necessarily proportional to the size of the C value, so that visual distinction is only convenient to be carried out visually, nodes larger than a certain value can be displayed by adopting nodes with larger areas according to needs, monitoring personnel are convenient to remind, and the thickness of the connecting line is also the same.
It should be noted that, to those skilled in the art, such a relationship should be known: the reason for the early warning event is that the water intake quantity is increased for the water intake user, the flow of the reservoir out of the reservoir is reduced for the reservoir, and the rainfall is reduced for the rainfall station. Therefore, when the cause analysis is carried out when the early warning event occurs, for the water taking user, only the water taking user with the increased water taking amount is analyzed, namely the C value is greater than the H value, and the corresponding DI value when Is positive number, DI when The larger the value of the early warning node is, the thicker the connection between the current state node of the water user and the early warning node is; for reservoirs, only reservoirs with reduced discharge from the reservoir are analyzed, i.e. C is less than H, DI when Is negative number, DI when The smaller the value of the early warning node is, the thicker the connection between the current state node of the reservoir and the early warning node is; shape of rainfallThe state nodes are similar to the current state nodes of the reservoir in analysis.
S6, early warning management and control: and taking corresponding control measures for the corresponding early warning control objects according to the node size and the connection line thickness determined in the step S5, and adding the control measure generation nodes into the map.
S7, management and control evaluation: and evaluating the effect of the control measures.
In this embodiment, the control measures include reducing the water consumption of the corresponding water consumer, increasing the flow of the corresponding reservoir, or taking both the control measures. In the process of taking control measures, for water users, the users with large water taking amount and large change dynamic degree are generally controlled, namely, the parts of the water users with the largest water taking amount increment are controlled, and for the reservoirs, the reservoirs with large delivery flow and large change dynamic degree are generally controlled, namely, the parts of the reservoirs with the largest delivery flow of the reservoirs are controlled. However, no control measures are generally taken for water users and reservoirs with small current state quantity and small dynamic change degree, because the measures have little effect on improving the ecological flow of the drainage basin and consume more control cost. Of course, the specific management and control measures need to be comprehensively considered by a decision maker according to local economic conditions, ecological environment conditions and other factors, and the management and control measures provided herein are only an application example, and provide reference for the decision maker to take the management and control measures so as to improve the accuracy of the management and control measures.
In the present embodiment, the method for evaluating the effect of the management control measure includes,
s71, adding the current state generation node of the node implementing the management and control measure at the end of the early warning period into a map;
s72, obtaining the current state data of the node implementing the management and control measures when the early warning period is ended, and calculating the current data variable quantity D of the node implementing the management and control measures when the early warning period is ended according to the following formula after And changing the dynamics DI after By DI after Value and-DI when /（DI when The size relation of the value + 1) judges the effect of the control measure, if DI is adopted after The more value ofProximity to-DI when /（DI when + 1), that is, the closer the current data of the node implementing the management and control measure at the end of the early warning period is to the historical data of the corresponding node before the early warning event occurs, the better the management and control measure effect is,
D after =F-C’
DI after =D after /C’*100%
and F is the current state data of the node implementing the management and control measure when the early warning period is ended, and C' is the current state data C of the node implementing the management and control measure when the early warning occurs.
It should be noted that the evaluation of the effect of the management and control measures includes evaluation of the water users or the reservoir management and control measures, or both, that is, the obtaining of the current state data of the node implementing the management and control measures at the end of the early warning period in step S72 includes obtaining the current water consumption of the water users implementing the management and control measures at the end of the early warning period, or obtaining the delivery flow of the reservoir implementing the management and control measures at the end of the early warning period, or obtaining both of them.
The designing of the conceptual model of the geographical knowledge graph in S1 includes defining nodes, relationships, and attributes in the geographical knowledge graph, specifically, the nodes, relationships, and attributes in the geographical knowledge graph are shown in the following tables, and the geographical knowledge graph conceptual model constructed thereby is shown in fig. 1.
Early Warning node (Warning): the time and the level of a certain early warning are recorded and are associated with the monitoring section, and the method is the core of the geographical knowledge map. And setting blue early warning, orange early warning and red early warning according to 120%, 100% and 80% of the ecological base flow. The early warning node comprises the following fields:
table 1 early warning node contains fields
Take water status node (User _ State): recording the water consumption of the water user, and dividing the water consumption into an early warning time state and an early warning period end state. The nodes are dynamically generated along with the early warning nodes, and when the early warning occurs, the state of the water user related to the early warning occurrence monitoring section is only taken. The take water status node contains the following fields:
TABLE 2 Water State node containing field
Rainfall State node (Precipitation _ State): and recording rainfall when early warning occurs, and reflecting the current soil wetting degree. The nodes are dynamically generated along with the early warning nodes, and when the early warning occurs, the states of the rainfall stations in the catchment areas where the monitoring sections are located are only taken. The rainfall state node comprises the following fields:
TABLE 3 rainfall State node contains fields
Reservoir status node (Reservoir _ State): and recording information such as water level, water storage capacity, delivery flow and the like of the reservoir, and dividing the information into an early warning state and an early warning period end state. The nodes are dynamically generated along with the early warning nodes, and when the early warning occurs, only the state of the reservoir related to the early warning occurrence monitoring section is taken. The reservoir status nodes contain the following fields:
TABLE 4 reservoir status node contains fields
Taking water management and control measure Node (Node: user _ Governed): and recording the water taking and controlling measures adopted after the early warning occurs, and associating the water taking and controlling measures with related water taking households. The node of the water taking management and control measure comprises the following fields:
TABLE 5 Water taking and control measure node contains fields
Reservoir management and control measure node (Reservoir _ Governed): and recording reservoir management and control measures taken after early warning occurs, and associating the reservoir management and control measures with the related reservoirs. The reservoir management and control measure node comprises the following fields:
TABLE 6 reservoir management and control measure node containing field
Monitoring section node (Station): and monitoring the section of the reservoir with early warning. The monitoring section node comprises the following fields:
TABLE 7 monitoring section node containing field
Fetch water consumer node (User): recording the user information. The fetch water consumer node contains the following fields:
table 8 access water user node containing field
Reservoir Node (Node: reservoir): and recording reservoir information. The reservoir nodes contain the following fields:
TABLE 9 reservoir node Containment fields
Rainfall station node (Precipitation): and recording rainfall station information. The rainfall station node contains the following fields:
table 10 rainfall station node contains fields
Early warning (time) -water usage status relationship (where): the starting node: user _ State, termination node: and (5) turning. And recording the change values of the water taking state and the historical state of a certain water taking user when the early warning occurs. The early warning (time) -water taking state relationship comprises the following fields:
TABLE 11 early warning (TIME) -WATER-TAKING STATE RELATIONS INCLUDING FIELD
Early warning (time) -reservoir status relationship (where): the starting node: reservier _ State, termination node: and (5) Warning. And recording the change value of the state and the historical state of a certain reservoir when early warning occurs. The early warning (time) -reservoir state relationship contains the following fields:
TABLE 12 early-warning (hour) -reservoir state relation containing field
Early warning (time) -rainfall state relationship (when): the starting node: precipitation _ State, termination node: and (5) Warning. And recording the change value of the state and the historical state of a certain rainfall station when early warning occurs. The early warning (time) -rainfall state relationship comprises the following fields:
TABLE 13 early warning (hour) -rainfall state relationship containing field
Early warning (after) -water usage status relationship (after): the starting node: user _ State, termination node: and (5) Warning. And recording the water taking state of a certain user and the change value of the state during early warning when the early warning period is ended. The early warning (post) -water status relationship comprises the following fields:
TABLE 14 early warning (after) -water-taking status relationship containing field
Early warning (post) -reservoir status relationship (after): the starting node: reservier _ State, termination node: and (5) Warning. And recording the state of a certain reservoir and the change value of the state during early warning when the early warning period is ended. The early warning (post) -reservoir status relationship contains the following fields:
TABLE 15 PRE-ALARM (REAR) -RESERVOIR RELATIONS CONTAINING FIELD
Early warning (after) -rainfall state relationship (after): the starting node: precipitation _ State, termination node: and (5) Warning. And recording the change value of the state during recording and early warning of a certain rainfall station when the early warning period is ended. The early warning (post) -rainfall state relationship contains the following fields:
TABLE 16 early warning (after) -rainfall state relationship containing fields
Reservoir-profile relationship (in _ control _ of): the starting node: reservoir, termination node: and (4) standing. And associating the reservoir with the monitoring section. The reservoir-monitoring section relation comprises the following fields:
TABLE 17 reservoir-monitoring section relation-containing field
The user-profile relationship (in _ score _ of): the starting node: user, termination node: and (4) standing. And associating the water user with the monitoring section. The relation of the water user and the monitoring section comprises the following fields:
TABLE 18 Water user-monitoring section relation containing field
Rainfall station-profile relationship (in _ rating _ of): the starting node: precipitation, termination node: and (4) standing. And associating the rainfall station with the monitoring section. The rainfall station-monitoring section relation comprises the following fields:
table 19 rainfall station-monitoring section relation containing field
| Name of field | Description of field | Type of field | Remarks for note |
| topo | Spatial topological relation | Boolean[] |
The invention is further described below by taking the hanjiang river basin ecological flow early warning system as an example.
The Chinese river ecological drainage basin is respectively provided with monitoring sections in Ankang, huangjiahong harbor and Huangzhuang, the schematic diagram of the setting positions of the monitoring sections is shown in figure 2, and the early warning setting data of each monitoring section is shown in the following table:
TABLE 20 hanjiang ecological flow monitoring section and early warning setup
Taking an 'health' monitoring section as an example, the catchment area range is calculated according to the position of the monitoring section, and 56 water users, 3 reservoirs and 579 rainfall stations which are associated with the monitoring section are obtained according to the spatial relationship.
Blue early warning events occur on 19-day-safety-recovery monitoring sections in 2021, 1 month and 19 months, and because the factors causing the early warning events can be one or more of the water taking amount of water users, the delivery flow of a reservoir and the rainfall, various possible factors are analyzed respectively:
the method comprises the steps of analyzing the water taking condition, inquiring the water taking condition of the water taking user associated with the early warning in a map on the same day, mainly analyzing the water taking users with daily average water taking amount larger than 10000 cubic meters in practice, of course, adjusting the limit according to needs, analyzing the water consumption variation and the variation dynamics of the current water taking amount and the history of the main water taking users in the same period, and finding out the partial users with the most increased water consumption.
And (4) reservoir condition analysis, wherein the water level, the water storage capacity and the ex-warehouse flow of the early warning on the current day are inquired in a map, the variation and the variation dynamics of the ex-warehouse flow of each reservoir and the historical synchronization are analyzed, and the part of the reservoir with the most reduced ex-warehouse flow is found out.
And (4) rainfall condition analysis, namely inquiring the current rainfall condition of the rainfall station associated with the early warning in a map, calculating the rainfall variation and the change dynamics of each station in the same period as the history, and analyzing whether the early warning is caused by the reason of rainfall reduction.
According to the analysis, the sizes of the partial nodes and the connecting lines with the most prominent changes in all factors are displayed, as shown in fig. 3, the partial nodes in the same factor can be displayed, as shown in fig. 4, the water taking states of the three water taking users when early warning occurs are displayed, and then the water taking users and/or reservoirs with the most changes are subjected to management and control measures, so that a decision maker can perform the management and control measures more intuitively and accurately.
After the early warning period is finished, the effect of early warning management and control measures is evaluated, and the method specifically comprises the following steps:
and (3) evaluating the effect of the water taking management and control measures: in the map, aiming at the water taking and controlling measure target water taking user in the early warning period, comparing the water taking variable quantity and the change dynamic degree when the early warning occurs and the early warning period is finished, performing feedback evaluation on the management and control effect and the ecological flow recovery effect, and if the early warning management and control measure is implemented, changing the water taking quantity of the water taking userThe change dynamics of the water quantity taken by the water user when the dynamic degree and the early warning occur satisfies the following requirements: DI after The closer to-DI the value of when /（DI when The value of + 1) proves that the better the effect of the control measures is, i.e. the more obvious the effect of the control measures taken by the user is. As shown in FIGS. 4 and 5, taking tap water of Hanzhong city, china, for example, C = C' =58333, DI when =60%，F=36294，DI after = 38%, H was calculated to be 36458,the value of H was very close to the value of F, while-DI when /（DI when + 1) value of-37.5%, DI after Is also very close to-DI when /（DI when The value of + 1) proves that the effect of the control measures for the water intake users is very ideal.
Evaluating reservoir management and control measures: in the map, the variation and the variation dynamics of the ex-warehouse flow of the target reservoir implementing the management and control measures during the early warning period are compared with the variation and the variation dynamics of the ex-warehouse flow at the early warning occurrence time and the early warning period end time, and the feedback evaluation is performed on the management and control effect and the ecological flow recovery effect, which are similar to the water taking management and control evaluation and are not detailed here.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (3)
1. A drainage basin ecological flow early warning analysis method based on a geographic knowledge map is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s1, designing an ecological flow early warning geographical knowledge map conceptual model;
s2, constructing a node set: extracting the drainage basin ecological flow early warning nodes to form a node set V (a, [ geo ]); wherein a represents the attribute of the node, including the coordinate position, geo represents the geographical object of the entity represented by the node, including the monitoring section, the rainfall station, the water fetching user and the reservoir;
s3, constructing a spatial topological relation between the catchment area and the rainfall station, the water taking user and the reservoir: automatically calculating the range of the catchment area according to the monitoring section, establishing the spatial topological relation between the catchment area and the rainfall station, the water intake user and the reservoir in the drainage basin through a nine-intersection model of the following formula, namely calculating the rainfall station, the water intake user and the reservoir which are related in the catchment area,
wherein topo represents the spatial topological relation between a rainfall station, a water taking user, a reservoir and a catchment area, and A represents a node set V (a, [ geo)]) The attribute of the middle node geo, B represents a catchment area represented by the monitoring section; i is A 、B’ A 、E A Respectively representing the interior, boundary and exterior of A, I B 、B’ B 、E B Respectively representing the inside, the boundary and the outside of B, and dim represents a dimension;
s4, constructing a geographical knowledge map: establishing an incidence relation between a monitoring section node and a rainfall station node, a water taking user node and a reservoir node in the catchment area, and accordingly constructing a geographical knowledge map of the drainage basin ecological flow early warning, wherein the geographical knowledge map is represented as follows:
wherein G represents a geographical knowledge graph, E (r, [ topo ]) represents a set of edges in the graph, i.e. relationships between nodes, and r represents an attribute of the relationship between the nodes;
s5, early warning cause analysis: when early warning occurs, adding early warning information and current state generation nodes of water users, reservoirs and rainfall stations into a geographical knowledge graph; obtaining current state data and historical state data of water users, reservoirs and rainfall stations when early warning occurs, and calculating to obtain variation D of water amounts taken by the water users, reservoir delivery flow and rainfall of the rainfall stations when early warning occurs according to the following formula when And changing the dynamics DI when ，
D when =C-H
DI when =D when /H*100%
Wherein C is current state data when early warning occurs, and H is historical state data;
determining the sizes of the current state nodes of the water user, the reservoir and the rainfall station according to the C value and determining the sizes of the current state nodes according to the DI value when The value determines the thickness of a relation connecting line between the current state node and the early warning node of the water user, the reservoir and the rainfall station so as to realize the visualization of early warning cause;
s6, early warning management and control: according to the node size and the connection line thickness determined in the S5, corresponding control measures are taken for the corresponding early warning control object, and control measure generation nodes are added into the map;
s7, management and control evaluation: and evaluating the effect of the control measures.
2. The watershed ecological flow early warning analysis method based on the geographical knowledge graph as claimed in claim 1, wherein the watershed ecological flow early warning analysis method comprises the following steps:
the management and control measures comprise reducing the water taking amount of water users or/and increasing the outlet flow of the reservoir.
3. The watershed ecological flow early warning analysis method based on the geographical knowledge graph as claimed in claim 2, wherein the geographical knowledge graph comprises the following steps:
the method for evaluating the effect of the control measures comprises the following steps,
s71, adding the current state generation node of the node implementing the management and control measure at the end of the early warning period into a map;
s72, obtaining the current state data of the node implementing the management and control measures when the early warning period is ended, and calculating the current data variable quantity D of the node implementing the management and control measures when the early warning period is ended according to the following formula after And changing dynamics DI after By DI after Value and-DI when /（DI when + 1) value magnitude relation judges the effect of the control measure,
D after =F-C’
DI after =D after /C’*100%
and F is the current state data of the node implementing the management and control measure when the early warning period is ended, and C' is the current state data C when the early warning of the node implementing the management and control measure occurs.
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