CN114819647A - Cascade reservoir flood early warning assessment method and system - Google Patents
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Abstract
The invention discloses a cascade reservoir flood early warning assessment method and a cascade reservoir flood early warning assessment system, wherein the method comprises the following steps: collecting rainfall information of a reservoir area and water regime information of a step reservoir in real time; forecasting future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the cascade reservoir; constructing a unified index system which accords with the cascade reservoir and has direct and indirect influence on the warehousing flood, and determining each index weight; determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to each index weight; and determining the current flood risk level and/or early warning signal of the reservoir according to the observation and/or calculation result of each index and the comprehensive association degree level of the standard cloud matter elements under each flood risk level. By utilizing the method and the system, effective information can be provided for the scheduling decision of the cascade reservoir, so that the safety of a downstream flood control object and a flood control area is guaranteed.
Description
Technical Field
The invention relates to the technical field of water conservancy informatization, in particular to a cascade reservoir flood early warning and assessment method and system.
Background
The reservoir is one of engineering measures widely adopted by flood control departments, and can play a role in reducing and avoiding flood disasters. At present, the existing small and medium-sized reservoirs in China are not only generally serious in aging and overhauling, over-standard in operation and insufficient in management capacity, but also lack of basic information of reservoir operation, lack of perfect forecasting and early warning mechanisms and effective early warning measures. If the flood forecast early warning in the reservoir area fails or is incorrect, disastrous events such as dam overflow, dike burst and dam break can be caused, and engineering facilities such as water supply stations and reservoirs and downstream safety bring important threats, so the flood control safety problem of medium and small-sized reservoirs is one of important subjects in the industry.
Because the quantity of the medium and small-sized reservoirs is large and wide, the difficulty of solving problems by adopting engineering measures is high, and the strengthening of the non-engineering measure construction is one of effective ways, namely, by constructing a medium and small-sized reservoir flood prevention and reduction prediction early warning system, reservoir flood prevention scheduling is carried out, downstream flood prevention objects and flood prevention area safety flood fighting are guaranteed, the reservoir management level and early warning capacity are comprehensively improved, and the purpose of disaster prevention and reduction is achieved.
Disclosure of Invention
The invention provides a cascade reservoir flood early warning assessment method and system, which can accurately and effectively assess the flood early warning level of a designated reservoir and carry out risk early warning.
Therefore, the invention provides the following technical scheme:
in one aspect, the invention provides a cascade reservoir flood early warning and assessment method, which comprises the following steps:
collecting rainfall information of a reservoir area and water regime information of a step reservoir in real time;
forecasting future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the cascade reservoir;
constructing a unified index system which accords with the cascade reservoir and has direct and indirect influence on the warehousing flood, and determining each index weight;
determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to each index weight;
and determining the current flood risk level and/or early warning signal of the reservoir according to the observation and/or calculation result of the index and the comprehensive association degree level of the standard cloud matter elements under each flood risk level.
Optionally, the reservoir rainfall information includes: real-time and short-term forecast information of rainfall intensity of a reservoir area in a certain time scale; the water regime information of the step reservoir comprises: and the real-time information of the water level before the dam and the water level at the tail of the cascade reservoir, the incoming water flow and the outgoing flow.
Optionally, the forecasting of the future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the step reservoir includes:
constructing a water regime forecasting model according to the historical water regime information, the history and the forecast reservoir rainfall information;
and forecasting to obtain the future short-term water regime information of the reservoir by utilizing the water regime forecasting model, the rainfall information and the water regime information of the cascade reservoir.
Optionally, the forecasting the future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the step reservoir further comprises:
and checking whether the future short-term water regime forecast information of the reservoir meets a set requirement, adjusting the parameters of the water regime forecast model under the condition that the future short-term water regime forecast information of the reservoir does not meet the set requirement, and re-forecasting according to the adjusted water regime forecast model to obtain the future short-term water regime information of the reservoir.
Optionally, the determining, according to the weight of each index, a comprehensive association degree grade between an observation and/or calculation result of each index and a standard cloud matter element under each flood risk grade includes:
establishing a cloud matter element model for describing randomness and fuzziness of flood causing phenomena;
determining a cloud matter element association function according to the cloud matter element model, wherein the cloud matter element association function comprises: correlation functions between numerical values and cloud object elements, correlation functions between cloud object elements and cloud object elements, and correlation functions between interval numerical values and cloud object elements;
and determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to the cloud matter element association function and each index weight.
Optionally, the method further comprises: displaying the current risk level and/or early warning signal of the reservoir in real time; and/or issue early warning information.
On the other hand, the invention also provides a cascade reservoir flood early warning and evaluation system, which comprises:
the information acquisition module is used for acquiring rainfall information of the reservoir area and water regime information of the cascade reservoir in real time;
the water regime forecasting module is used for forecasting the future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the step reservoir collected by the information collecting module;
the flood comprehensive evaluation index system construction module is used for constructing a unified index system which accords with the cascade reservoir and has direct and indirect influences on the warehousing flood, and determining each index weight;
the flood risk grade membership degree evaluation module is used for determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to each index weight;
and the flood real-time early warning module is used for determining the current flood risk level and/or early warning signals of the reservoir according to the observation and/or calculation results of the indexes and the comprehensive association degree level of the standard cloud matter elements under each flood risk level.
Optionally, the water condition forecasting module comprises:
the modeling unit is used for constructing a water regime forecasting model according to the historical water regime information and the historical and forecasted rainfall information of the reservoir area;
and the water regime forecasting unit is used for forecasting to obtain the future short-term water regime information of the reservoir by utilizing the water regime forecasting model, the rainfall information and the water regime information of the step reservoir.
Optionally, the flood risk level membership evaluation module includes:
the cloud matter element model unit is used for establishing a cloud matter element model for describing randomness and fuzziness of flood causing phenomena;
the cloud matter element association function unit is used for determining a cloud matter element association function according to the cloud matter element model;
and the comprehensive association degree grade unit is used for determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to the cloud matter element association function and each index weight.
Optionally, the system further comprises: the early warning information display module and/or the early warning information release module;
the early warning information display module is used for displaying the current risk level and/or early warning signal of the reservoir in real time;
and the early warning information issuing module is used for issuing early warning information.
According to the cascade reservoir flood early warning assessment method and system, various indexes related to reservoir flood and having influence and restriction relations are comprehensively considered, the risk early warning level of the reservoir flood is quantitatively and comprehensively assessed, the problems of insufficient diagnosis risk and distortion of early warning results caused by the fact that various flood assessment indexes are not easy to couple are solved, effective assessment of the designated reservoir flood early warning level is achieved, high-precision water situation early warning information is provided for cascade reservoir combined optimization scheduling, early warning information is accurately formulated and issued, the occurrence probability of reservoir risk events is reduced, the decision level of a cascade reservoir scheduling system is improved, and the safety of downstream flood control objects and flood control areas is well guaranteed.
Drawings
FIG. 1 is a flow chart of the flood early warning and assessment method for the cascade reservoir of the invention;
fig. 2 is a flowchart of determining a comprehensive association degree between an observation and/or calculation result of each index and a standard cloud matter element under each flood risk level by using a cloud matter element discrimination method in the embodiment of the present invention;
FIG. 3 is a schematic diagram of a cascade reservoir flood warning and evaluating system according to the present invention;
FIG. 4 is a schematic structural diagram of a water condition forecasting module in the cascade reservoir flood early warning and evaluating system according to the present invention;
FIG. 5 is a schematic structural diagram of a flood risk level membership evaluation module in the cascade reservoir flood early warning and assessment system according to the present invention;
fig. 6 is another schematic structural diagram of the cascade reservoir flood early warning and evaluating system of the invention.
Detailed Description
In order to make the technical field of the invention better understand the scheme of the embodiment of the invention, the embodiment of the invention is further described in detail with reference to the drawings and the implementation mode.
Reservoir flood early warning relates to the comprehensive problem of judging of multiple evaluation index, and the clear definition of the current evaluation standard has certain difficulty. The specific expression is that uncertainty and ambiguity exist in the selection of indexes and weight determination, which causes that the evaluation results have certain difference to some extent, the presented evaluation state values are different, and even the problem that the evaluation results of different evaluation methods lack consistency occurs. Because the flood phenomenon is extremely complex, all factors are mutually related, and the influence degrees of all factors are different in different flood processes. For example, although the forecast of rainfall distribution in a reservoir area is more and more accurate and the forecast period is longer and longer with the progress of the technology level, if the rainfall in the reservoir area forecasts a highly reliable rainfall result without rain or light rain in the reservoir dispatching process, and the delivery flow of the upstream reservoir is very large due to reasons such as prosperity, the storage flow of the reservoir may be relatively large and the water rise is relatively fast, so that the timeliness and the accuracy of the reservoir flood early warning are challenged.
The existing reservoir flood forecasting and early warning scheme mainly takes single reservoir information as a main part, the achievement of synchronous early warning of a plurality of reservoirs in a full flow area is lacked, the functions of flood control and disaster reduction of a reservoir group and water resource allocation are not fully exerted, and the comprehensive forecasting and early warning technology, mechanism and measure of the reservoir group are lacked.
In order to make up the deficiency of flood control in medium and small watersheds and improve the early warning informatization level of reservoir flood forecast, the invention provides a cascade reservoir flood early warning assessment method and system, which fuse various correlated and restricted meteorological and hydrological factors related to reservoir flood, quantitatively and comprehensively evaluate the early warning level of the reservoir flood, so that the evaluation result is more scientific and reasonable, high-precision water situation early warning information is provided for cascade reservoir combined optimization scheduling, further more decision information is provided for reservoir flood control scheduling according to the water situation early warning information, and the occurrence probability of reservoir risk events is reduced.
As shown in fig. 1, the invention is a flow chart of a flood early warning and evaluation method for a step reservoir, and the method comprises the following steps:
in step S11, rainfall information of the reservoir area and water regime information of the cascade reservoir are collected in real time.
The reservoir rainfall information may include, but is not limited to, real-time and short-term forecast information of reservoir rainfall intensity at a time scale; the time scale may be a unit of day, or a unit of time below a day, which is not limited in the present invention.
The water regime information of the cascade reservoir may include, but is not limited to, real-time information of a pre-dam water level and a post-dam water level of the cascade reservoir, real-time information of an incoming water flow and an outgoing flow, and the like. The warehouse-out flow information can be obtained by calculating water level and flow speed data.
It should be noted that, in a specific application, the information may be acquired and calculated by corresponding devices, for example, rainfall intensity and water level data are automatically monitored by a rain gauge, a water level gauge and other devices, and the monitoring data are transmitted in real time by a hydrologic dedicated remote terminal device and by means of a mobile communication network. Of course, some of the data can also be extracted directly from satellite remote sensing or meteorological radar detection signals provided by meteorological service departments.
Furthermore, related data received in real time and stored in history can be compiled and quality checked.
And step S12, forecasting the future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the step reservoir.
Specifically, a water regime forecasting model can be constructed in advance according to historical water regime information, historical rainfall information and forecast reservoir area rainfall information, then the collected rainfall information and the water regime information of the cascade reservoir are input into the water regime forecasting model, and the future short-term water regime forecasting information of the reservoir is obtained through calculation.
It should be noted that, the water regime forecasting model may adopt a regression model, and the model may be represented as follows:
Y＝a 0 +a 1 ×X 1 +a 2 ×X 2 +…a n ×X n +e；
wherein Y is a water condition variable to be evaluated, such as the flow of incoming water; x 1 ,X 2 ,…,X n The explanatory variables required for evaluating the water regime variables, which may include forecast and historical observed rainfall, historical observed water regime variables, etc., a 0 ,a 1 ,…,a n And e is a model parameter.
For example, for the forecast scenario of incoming water flow, X 1 ,X 2 And X 3 Respectively representing the forecast rainfall at the forecast moment, the historical observed rainfall at the previous moment and the historical inflow water flow data at the previous moment.
The parameter training mode of the water regime forecasting model can be trained by adopting some existing algorithms, and the embodiment of the invention is not limited. The optimal combination value of the model parameters can be obtained through an optimization algorithm, and the future short-term water inflow condition of the reservoir is forecasted under the driving of the newly obtained explained variable data sequence required by the water regime variable estimation.
Further, in a non-limiting embodiment, it may also be checked whether the future short-term water condition forecast information of the reservoir meets set requirements (such as accuracy and/or reliability requirements, etc.), and if the future short-term water condition forecast information of the reservoir does not meet the set requirements, the parameters of the water condition forecast model are adjusted, and the future short-term water condition forecast information of the reservoir is obtained through re-forecasting according to the adjusted water condition forecast model. It should be noted that the accuracy and/or reliability of the forecast regimen can be quantified by statistical evaluation indexes (correlation coefficient, nash efficiency coefficient, and the like).
In step S13, a unified index system that conforms to the cascade reservoir and has direct and indirect effects on the warehousing flood is constructed, and each index weight is determined.
In specific application, hydrological and meteorological factors can be comprehensively considered, a plurality of indexes which have direct and indirect influences on the warehousing flood are selected, and the weight of each index is determined.
In determining the weight of each index, the weight of each index may be determined by an objective weighting method based on a correlation between indexes or a variation coefficient (i.e., a ratio of a standard deviation to an average value) of each index. The objective weighting method is a kind of algorithm, and generally includes principal component analysis method, dispersion and mean square error method, etc.
In the embodiment of the present invention, the index may include, but is not limited to, any one or more of the following: average maximum rainfall, average maximum dam front water level, average maximum reservoir tail water level, average maximum warehousing flow, average maximum ex-warehouse flow of adjacent upstream reservoirs and the like.
In step S14, determining a comprehensive association degree level of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk level according to each index weight.
In the embodiment of the invention, a cloud matter element discrimination method is utilized to convert a plurality of different evaluation factors or indexes into information capable of reflecting the overall flood characteristics of the object to be evaluated. Specifically, a cloud matter element model for describing randomness and fuzziness of flood causing phenomena is established, a cloud matter element association function is determined according to the cloud matter element model, and the comprehensive association degree of observation and/or calculation results of each index and standard cloud matter elements under each flood risk level is determined according to the cloud matter element association function and each index weight. This will be explained in detail below.
First, when building a cloud object model, it is possible to couple ambiguity of flood related index risk definition (i.e., the fact that the boundary is the same) and randomness of occurrence of the warehousing flood event (i.e., the probability of occurrence), and use a unified mathematical expression to describe the phenomenon of double uncertainty of causing flood (i.e., the ambiguity and randomness mentioned above).
The object element theory uses an ordered triple composed of an object name N, a feature c and a quantity value v related to the feature as a basic element (abbreviated as an object element) for describing an object R ═ N, c, v, and then uses a correlation function to describe the relationship between the feature object element and a standard cloud object element.
Specifically, in the scheme of the invention, the characteristic object element corresponds to an observation and/or calculation result of a certain index of the reservoir flood to be evaluated.
If the object N has multiple features, N features c 1 ,c 2 ,...,c n And corresponding magnitude v 1 ,v 2 ,...,v n To describe, then can be expressed as:
expectation for digital features of cloud E x Entropy E n Entropy of H e Three values. Utilizing a cloud model (E) x ,E n ,H e ) Expressing the quantity v of the feature c in the formula (1), the cloud matter element expression based on the normal cloud model can be constructed as follows:
m represents a standard object (referring to a degree of association level), and μ (x) represents a degree of membership of a magnitude x corresponding to a feature c of an object, then M evaluation standard objects can be written as:
the cloud matter element correlation function is used for defining which mode is adopted to calculate the degree that the matter element quantity value meets the requirement. In the embodiment of the present invention, the cloud object correlation function may include, but is not limited to, the following: the correlation function between the numerical value and the cloud object element, the correlation function between the cloud object element and the cloud object element, the correlation function between the interval numerical value and the cloud object element, and the like, and the detailed description of each cloud object element correlation function is described below.
1) Correlation function between numerical value and cloud matter element
If the evaluation index is the determined value x, the evaluation index can be regarded as a cloud droplet. Substituting the value into a normal cloud model, and calculating the association degree of the value x and the normal cloud model as follows:
2) correlation function between cloud object elements
If the evaluation index data is the object characteristics expressed by the normal cloud model, 99.74% of cloud droplets in the normal cloud fall (E) x -3E' n ,E x +3E' n ) When the interval is regarded as a set, the shared part and the non-shared part of the two clouds are represented by N and M, and the association degree of the two clouds is:
3) correlation function between interval numerical value and cloud matter element
The interval numerical value is converted into cloud representation, and then the cloud representation is calculated by using a cloud-cloud association degree calculation method. In which the interval value is regarded as a double-constrained index c min ,c max ]Then, the interval number can be converted into a normal cloud model, and then the calculation is performed by using the formula (6). Wherein the expected value parameter of a normal cloud can be expressed as:
E x ＝(c min +c max )/2 (6)
E n ＝(c max -c min )/2.355 (7)
and H e The degree of fuzziness according to flood risk classification can be specifically determined by combining with industry standard standards, expert experience or related research results.
By using the indexes determined in the above step S13, the standard cloud object can be calculated according to the above formulas (2) to (7).
Determining the comprehensive association degree of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk level refers to determining the association degree of each evaluation level index and each flood-related index (including various meteorological and hydrological elements) of the selected typical reservoir to be evaluated.
Specifically, according to the standard cloud matter element parameters of the indexes obtained by the previous calculation, the relevance degree of a certain characteristic c of the object o to be evaluated (namely the selected typical reservoir) is mu (x), and the relevance degree of each index related to the warehouse flood risk of the object o to be evaluated can be obtained; and then according to the weight vector omega of each index, calculating to obtain a plurality of feature association degrees of the level j, namely the comprehensive association degree is as follows:
according to the maximum membership principle, the comprehensive relevance grade of the object to be evaluated is as follows:
that is, the highest one of the plurality of integrated relevance degrees calculated by the above formula (8) is set as the integrated relevance degree level of the object to be evaluated.
And calculating the comprehensive association degree and the comprehensive association degree grade of the reservoir to be evaluated according to the formulas (8) to (9) by using the standard cloud matter element parameters of the evaluation indexes obtained by calculation.
Based on the above manner, in the embodiment of the present invention, a flowchart of determining the comprehensive association degree between the observation and/or calculation result of each index and the standard cloud matter element at each flood risk level by using a cloud matter element identification method is shown in fig. 2, and includes the following steps:
and step S21, determining standard cloud matter elements of flood related indexes under various flood risk levels.
Step S22, defining and establishing a correlation function.
And step S23, calculating the association degree between the object element characteristics to be evaluated and each evaluation grade standard cloud, and evaluating the comprehensive association degree grade according to the maximum membership degree principle.
The object element characteristics of the object to be evaluated refer to observation or calculation results of each index.
With continued reference to fig. 1, in step S15, the current flood risk level and/or warning signal of the reservoir is determined according to the comprehensive association degree between the observation and/or calculation result of the index and the standard cloud matter elements under each flood risk level.
Specifically, the importance judgment and comparison of each index can be performed through expert experience, the hierarchical corresponding relation of the comprehensive relevance grade, the flood risk grade and the early warning signal is constructed, and the real-time hierarchical early warning release mode of the warehousing flood and the decision support for flood control scheduling are realized.
For example, the hierarchical correspondence may be, but is not limited to, the following table 1.
TABLE 1
In view of the fact that when a cloud matter element evaluation method is used in the early warning process of reservoir flood, the upper limit of the flood risk level interval is an indefinite value, the value taking problem may be disputed, and the cloud matter element model algorithm requires the upper limit and the lower limit of the interval to be definite values, so when the flood risk cloud matter element model is used for risk level judgment, the risks can be judged independently and firstly.
Further, in another non-limiting embodiment of the method of the present invention, the flood risk level and/or the warning signal information corresponding to the comprehensive association level of the object to be assessed may be displayed in real time. Furthermore, rainfall, water level and runoff data acquired and forecasted in real time can be combined to be visually displayed in the forms of schematic diagrams, reports, bar charts and the like.
Further, in another non-limiting embodiment of the method of the present invention, the method may further issue early warning information, for example, the flood risk level and/or the early warning signal information may be issued to a user receiving end of a site broadcasting station, a large screen of a dispatching center, a WeChat or short message manner, so that relevant personnel can receive the early warning information in time, and thus make a decision and respond in time.
Correspondingly, the invention also provides a cascade reservoir flood early warning and evaluation system, and as shown in fig. 3, the cascade reservoir flood early warning and evaluation system is a structural schematic diagram of the cascade reservoir flood early warning and evaluation system.
The cascade reservoir flood early warning and evaluating system comprises the following modules:
the information acquisition module 101 is used for acquiring rainfall information of the reservoir area and water regime information of the cascade reservoir in real time;
the water regime forecasting module 102 is used for forecasting future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the step reservoir, which are acquired by the information acquisition module 101;
a flood comprehensive assessment index system construction module 103, configured to construct a unified index system that conforms to the cascade reservoir and has direct and indirect effects on the warehousing flood, and determine each index weight;
the flood risk level membership degree evaluation module 104 is used for determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk level according to each index weight;
and the flood real-time early warning module 105 is used for determining the current flood risk level and/or early warning signal of the reservoir according to the observation and/or calculation result of each index and the comprehensive association degree level of the standard cloud matter elements under each flood risk level.
The information acquisition module 101 may include the following units:
the system comprises a storage area rainfall information acquisition unit, a storage area rainfall information acquisition unit and a storage area rainfall information acquisition unit, wherein the storage area rainfall information acquisition unit is used for acquiring real-time and short-term forecast information and the like of storage area rainfall intensity in a certain time scale in real time;
and the reservoir water regime information acquisition unit is used for acquiring the real-time information of the water level before the dam and the water level after the dam, the incoming water flow, the real-time information of the delivery flow and the like of the cascade reservoir in real time.
It should be noted that the rainfall information collection unit in the reservoir area may specifically collect rainfall intensity information through a rain gauge, or directly obtain rainfall data extracted from satellite remote sensing or meteorological radar detection signals.
The reservoir water regime information acquisition unit can specifically utilize water level meters arranged in front of a reservoir dam and at the tail of the reservoir dam to automatically acquire water level information in front of the dam and the water level information at the tail of the reservoir dam, and obtain the inflow flow and the outflow flow information through certain calculation.
As shown in fig. 4, the water condition forecasting module 102 may specifically include: a modeling unit 121 and a water condition forecasting unit 122. In another non-limiting embodiment, the water condition forecasting module 102 may further include: the forecast effect evaluating unit 123. Wherein:
the modeling unit 121 is used for constructing a water regime forecasting model according to the historical water regime information and the historical and forecasted rainfall information of the reservoir area;
the water regime forecasting unit 122 is used for forecasting to obtain future short-term water regime information of the reservoir by utilizing the water regime forecasting model, the rainfall information and the water regime information of the step reservoir;
the forecasting effect evaluation unit 123 is configured to check whether the future short-term water condition forecasting information of the reservoir, which is forecasted by the water condition forecasting unit 122, meets a set requirement, and trigger the modeling unit 121 to adjust a parameter of the water condition forecasting model if the future short-term water condition forecasting information of the reservoir does not meet the set requirement. Accordingly, the water regime forecasting unit 122 re-forecasts the future short-term water regime information of the reservoir according to the adjusted water regime forecasting model, so that the forecasting result meets the precision and/or reliability requirement.
In the system of the present invention, the flood comprehensive assessment index system building module 103 may comprehensively consider the hydrometeorology factors, select a plurality of indexes that have direct and indirect influences on the warehousing flood, and determine the weights of the indexes, so that the subsequent flood risk level membership degree evaluation module 104 may determine the comprehensive association degree between the observation and/or calculation results of the indexes and the standard cloud matter elements at each flood risk level.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a flood risk level membership evaluation module in the cascade reservoir flood early warning evaluation system of the present invention.
In this example, the flood risk level membership evaluation module 104 includes a cloud object model unit 141, a cloud object association function unit 142, and a comprehensive association level unit 143. Wherein:
the cloud matter element model unit 141 is used for establishing a cloud matter element model for describing randomness and fuzziness of flood causing phenomena;
the cloud object correlation function unit 142 is configured to determine a cloud object correlation function according to the cloud object model;
the comprehensive association degree grade unit 143 is configured to determine, according to the cloud matter element association function and each index weight, a comprehensive association degree grade between an observation and/or calculation result of the index and a standard cloud matter element under each flood risk grade.
The cloud object element association function may include, but is not limited to, the following: a correlation function between the numerical values and the cloud object elements, a correlation function between the interval numerical values and the cloud object elements, and the like. The specific process of determining the comprehensive association degree between the observation and/or calculation result of each index and the standard cloud matter element under each flood risk level by the comprehensive association degree level unit 143 according to the cloud matter element model and the cloud matter element association function may be referred to the description in the foregoing embodiment of the method of the present invention, and is not described herein again.
Referring to fig. 6, fig. 6 is another schematic structural diagram of the flood early warning and evaluating system for a step reservoir according to the present invention.
The difference with the embodiment shown in fig. 3 is that in this embodiment the system further comprises: the early warning information display module 601 and/or the early warning information release module 602. Wherein:
the early warning information display module 601 is used for displaying the current risk level and/or early warning signal of the reservoir in real time; further, the early warning information display module 601 may also combine rainfall, water level, and runoff data collected and forecasted in real time to visually display in the form of schematic diagrams, reports, histograms, and the like.
The early warning information issuing module 602 is configured to issue early warning information, for example, to issue the flood risk level and/or the early warning signal information to a user receiving end of a field broadcasting station, a large screen of a dispatching center, a WeChat of an appointed person, or a short message.
According to the cascade reservoir flood early warning assessment method and system, the topological structure of a reservoir group is considered, the warehousing and ex-warehouse flow of upstream and downstream reservoirs is coordinated comprehensively, the comprehensive relevance grade of an object to be evaluated is judged by adopting a comprehensive index evaluation system in the cloud matter element judgment process according to rainfall and water regime information of a reservoir area and based on a cloud matter element judgment method, and compared with the existing early warning result based on a single index (such as the most frequently adopted warehousing flow), the real-time performance of early warning and the accuracy of the early warning result can be improved. In addition, the cloud model has the unique advantage of processing double uncertainties, the ambiguity of the concept of reservoir flood related index grade boundary is depicted by normal cloud, and the randomness of the evaluation data is used, so that the multi-index fuzzy quantification and comprehensive early warning functions of the warehousing flood are realized.
Compared with the prior art, the flood risk level early-warned by the scheme can support a flood control dispatching target required by the operation of the reservoir, the judgment information is provided by fully utilizing various hydrological meteorological elements related to the flood, the current risk level of the reservoir is timely and accurately determined, effective flood control measures can be made, the decision level of a cascade reservoir dispatching system is improved, and the safety of a downstream flood control object and a flood control area is guaranteed.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Furthermore, the above-described system embodiments are merely illustrative, wherein modules and units illustrated as separate components may or may not be physically separate, i.e., may be located on one network element, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The present invention has been described in detail with reference to the embodiments, and the description of the embodiments is provided to facilitate the understanding of the method and apparatus of the present invention, and is intended to be a part of the embodiments of the present invention rather than the whole embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without making any creative effort shall fall within the protection scope of the present invention, and the content of the present specification shall not be construed as limiting the present invention. Therefore, any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A cascade reservoir flood early warning assessment method is characterized by comprising the following steps:
collecting rainfall information of a reservoir area and water regime information of a step reservoir in real time;
forecasting future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the cascade reservoir;
constructing a unified index system which accords with the cascade reservoir and has direct and indirect influence on the warehousing flood, and determining each index weight;
determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to each index weight;
and determining the current flood risk level and/or early warning signal of the reservoir according to the observation and/or calculation result of the index and the comprehensive association degree level of the standard cloud matter elements under each flood risk level.
2. The cascade reservoir flood warning assessment method according to claim 1, wherein the reservoir rainfall information comprises: real-time and short-term forecast information of rainfall intensity of a reservoir area in a certain time scale; the water regime information of the step reservoir comprises: and the real-time information of the water level before the dam and the water level at the tail of the cascade reservoir, the incoming water flow and the outgoing flow.
3. The cascade reservoir flood early warning and assessment method according to claim 1, wherein forecasting future short-term reservoir water regime information according to reservoir rainfall information and cascade reservoir water regime information comprises:
constructing a water regime forecasting model according to the historical water regime information, the history and the forecast reservoir rainfall information;
and forecasting to obtain the future short-term water regime information of the reservoir by utilizing the water regime forecasting model, the rainfall information and the water regime information of the cascade reservoir.
4. The cascade reservoir flood early warning and assessment method according to claim 3, wherein forecasting future short-term reservoir water regime information according to the reservoir rainfall information and the water regime information of the cascade reservoir further comprises:
and checking whether the future short-term water regime forecast information of the reservoir meets a set requirement, adjusting the parameters of the water regime forecast model under the condition that the future short-term water regime forecast information of the reservoir does not meet the set requirement, and forecasting again according to the adjusted water regime forecast model to obtain the future short-term water regime information of the reservoir.
5. The cascade reservoir flood early warning and assessment method according to claim 1, wherein the determining of the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to each index weight comprises:
establishing a cloud matter element model for describing randomness and fuzziness of a flood causing phenomenon;
determining a cloud matter element association function according to the cloud matter element model, wherein the cloud matter element association function comprises: correlation functions between numerical values and cloud object elements, correlation functions between cloud object elements and cloud object elements, and correlation functions between interval numerical values and cloud object elements;
and determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to the cloud matter element association function and each index weight.
6. The cascade reservoir flood warning assessment method according to any one of claims 1 to 5, further comprising:
displaying the current risk level and/or early warning signal of the reservoir in real time; and/or issue early warning information.
7. A cascade reservoir flood early warning evaluation system, characterized in that the system comprises:
the information acquisition module is used for acquiring rainfall information of the reservoir area and water regime information of the cascade reservoir in real time;
the water regime forecasting module is used for forecasting the future short-term water regime information of the reservoir according to the rainfall information of the reservoir area and the water regime information of the step reservoir collected by the information collecting module;
the flood comprehensive evaluation index system construction module is used for constructing a unified index system which accords with the cascade reservoir and has direct and indirect influences on the warehousing flood, and determining each index weight;
the flood risk level membership degree evaluation module is used for determining the comprehensive association degree level of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk level according to the weight of each index;
and the flood real-time early warning module is used for determining the current flood risk level and/or early warning signals of the reservoir according to the observation and/or calculation results of the indexes and the comprehensive association degree level of the standard cloud matter elements under each flood risk level.
8. The cascade reservoir flood warning and assessment system according to claim 7, wherein said regimen forecasting module comprises:
the modeling unit is used for constructing a water regime forecasting model according to the historical water regime information and the historical and forecasted rainfall information of the reservoir area;
and the water regime forecasting unit is used for forecasting to obtain the future short-term water regime information of the reservoir by utilizing the water regime forecasting model, the rainfall information and the water regime information of the step reservoir.
9. The cascade reservoir flood early warning and assessment system according to claim 7, wherein said flood risk level membership degree evaluation module comprises:
the cloud matter element model unit is used for establishing a cloud matter element model for describing randomness and fuzziness of flood causing phenomena;
the cloud matter element association function unit is used for determining a cloud matter element association function according to the cloud matter element model;
and the comprehensive association degree grade unit is used for determining the comprehensive association degree grade of the observation and/or calculation result of each index and the standard cloud matter element under each flood risk grade according to the cloud matter element association function and each index weight.
10. The cascade reservoir flood warning assessment system according to any one of claims 7 to 9, further comprising: the early warning information display module and/or the early warning information release module;
the early warning information display module is used for displaying the current risk level and/or early warning signal of the reservoir in real time;
the early warning information issuing module is used for issuing early warning information.
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| CN107578134A (en) * | 2017-09-12 | 2018-01-12 | 西安理工大学 | A kind of the upper reaches of the Yellow River step reservoir Flood Control Dispatch method for considering early warning |
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| CN107578134A (en) * | 2017-09-12 | 2018-01-12 | 西安理工大学 | A kind of the upper reaches of the Yellow River step reservoir Flood Control Dispatch method for considering early warning |
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