Intelligent water affair management method and system based on digital twinning

Abstract

The invention provides an intelligent water affair management method and system based on digital twinning, and relates to the technical field of water affair management. In order to solve the problems that in the prior art, the influence of water level change on slope stability is not considered, and the water level is not predicted in combination with the slope stability, so that the prediction accuracy is poor, a digital twinborn model is established, reservoir water level information is obtained in real time, and the water level information variation at the current moment is monitored; when the water level information variable quantity at the current moment is larger than a preset change threshold value, the water storage amount and the landform information are obtained; determining the slope stability characteristics of the upstream of the reservoir based on the water level information variable quantity and the landform information at the current moment; predicting the water level variation at the next moment based on the stability characteristics of the upstream slope of the reservoir and the reservoir inflow; and controlling the reservoir to discharge flood according to the water level variation at the next moment. By considering the influence of the water level change on the slope stability and combining the slope stability to predict the water level, the prediction accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water management, and particularly relates to a smart water management method and system based on digital twinning. BACKGROUND

[0002] The smart water management based on digital twinning refers to constructing a digital twinning model highly simulating an entity water system, fusing hydrological data, topographic and geomorphic data, meteorological data and monitoring data of a water area in real time, and dynamically simulating, monitoring and warning, trend forecasting and precise regulation and control of the operation state of the water system by means of three-dimensional visualization, real-time data interaction and intelligent analysis technology.

[0003] The digital twinning technology applied to reservoir management can integrate multi-source data of the reservoir, including water level, reservoir inflow, topography, geomorphology, weather information and the like, simulate and analyze by the constructed digital twinning model, and accurately predict key indicators such as reservoir water level change trend. At present, the prediction method of reservoir water level generally includes: simulating reservoir inflow based on digital twinning scene, combining historical water level, flow and other data to predict water level change, and predicting reservoir water level through the direct correlation between reservoir inflow and water level change, which is used for flood control and other basic management.

[0004] However, the existing technology does not consider the influence of water level change on the slope stability of the upstream watershed of the reservoir, and does not combine the slope stability to predict the water level, resulting in poor prediction accuracy.

[0005] Therefore, it is of great significance to develop a smart water management method and system based on digital twinning for improving the prediction accuracy of reservoir water level. SUMMARY

[0006] In view of the problem that the existing technology does not consider the influence of the slope stability of the upstream watershed of the reservoir on the water level change, and does not combine the slope stability to predict the water level, resulting in poor prediction accuracy, the present application proposes a smart water management method based on digital twinning, which specifically includes the following steps: S1, establishing a digital twinning model by collecting comprehensive data of the reservoir area; S2, acquiring water level information of the reservoir in real time, and monitoring the water level information change amount at the current time; S3, when the water level information change amount at the current time is greater than a preset change threshold, acquiring reservoir inflow and topographic and geomorphic information of the upstream of the reservoir in the digital twinning model; S4, determining the slope stability characteristics of the upstream of the reservoir based on the water level information change amount at the current time and the topographic and geomorphic information of the upstream of the reservoir; S5, predicting the water level change amount at the next time based on the slope stability characteristics of the upstream of the reservoir and the reservoir inflow of the upstream of the reservoir. S6、controlling the reservoir flood discharge according to the water level change of the next time.

[0007] Further, the topographic and geomorphic information of the upstream of the reservoir includes a watershed area, a watershed slope, a watershed length, a slope surface depression depth, and a slope surface protrusion height; in S4, based on the water level information change of the current time and the topographic and geomorphic information of the upstream of the reservoir, the slope stability feature of the upstream of the reservoir is determined, specifically including: determining the slope flatness according to the slope surface depression depth and the slope surface protrusion height; determining the topographic influence degree according to the watershed area, the watershed slope, and the watershed length; determining the slope stability feature of the upstream of the reservoir based on the water level information change of the current time, the slope flatness, and the topographic influence degree.

[0008] Further, determining the slope stability feature of the upstream of the reservoir based on the water level information change of the current time, the slope flatness, and the topographic influence degree includes: determining the slope stability feature of the upstream of the reservoir according to a slope stability feature calculation formula, the slope stability feature calculation formula being:

[0009] wherein, the slope stability feature is, the slope flatness is, the topographic influence degree is, the water level information change of the current time is.

[0010] Further, determining the slope flatness according to the slope surface depression depth and the slope surface protrusion height specifically includes: determining the slope flatness according to a flatness calculation formula, the flatness calculation formula being:

[0011] wherein, a is the slope surface depression depth, b is the slope surface protrusion height, the preset weight corresponding to the slope surface depression depth is, the preset weight corresponding to the slope surface protrusion height is.

[0012] Further, determining the topographic influence degree according to the watershed area, the watershed slope, and the watershed length includes: determining the topographic influence degree according to a topographic influence degree calculation formula, the topographic influence degree calculation formula being:

[0013] wherein, S is the watershed area, P is the watershed slope, and L is the watershed length, the preset weight corresponding to the watershed area is, the preset weight corresponding to the watershed slope is. is a preset weight corresponding to the length of the basin.

[0014] Further, the inflow water quantity upstream of the reservoir in the digital twin model is acquired, specifically comprising: acquiring climate information of a geographical location where the reservoir is located and basin water quantity upstream of the reservoir based on the digital twin model; acquiring precipitation information and evaporation information according to the climate information of the geographical location where the reservoir is located; determining the inflow water quantity upstream of the reservoir according to the basin water quantity upstream of the reservoir, the precipitation information and the evaporation information.

[0015] Further, in S5, the inflow water quantity upstream of the reservoir and the slope stability feature upstream of the reservoir are used to predict the water level change quantity at the next moment, specifically comprising: inputting the slope stability feature upstream of the reservoir, the inflow water quantity upstream of the reservoir and a time parameter into a prediction model; and the prediction model outputs the water level change quantity at the next moment.

[0016] Further, the prediction model is:

[0017] wherein, is a slope stability feature, represents the water level change quantity at the next moment, represents the inflow water quantity, and t represents the time length from the current moment to the next moment.

[0018] Further, in S6, the water level change quantity at the next moment is used to control the reservoir to discharge flood, specifically comprising: acquiring the actual water level of the reservoir; obtaining the predicted water level at the next moment according to the actual water level of the reservoir and the water level change quantity at the next moment; comparing the predicted water level with the total capacity of the reservoir, and when the predicted water level exceeds the total capacity of the reservoir, controlling the reservoir to discharge flood.

[0019] The application also provides a smart water management system based on digital twinning, which executes any one of the smart water management methods based on digital twinning, and specifically comprises the following modules: A modeling module is used to establish a digital twin model by collecting comprehensive data of a reservoir area. A collection module is used to acquire water level information of the reservoir in real time and monitor the water level information change quantity at the current moment. A judgment module is connected with the modeling module and the collection module, and is used to acquire the inflow water quantity upstream of the reservoir and topographic and geomorphic information in the digital twin model when the water level information change quantity at the current moment is greater than a preset change threshold. The slope stability calculation module is connected with the acquisition module and the judgment module, and is configured to determine the slope stability characteristics of the upstream of the reservoir based on the current water level information change and the topographic and geomorphic information of the upstream of the reservoir. The prediction module is connected with the slope stability calculation module and the judgment module, and is configured to predict the water level change at the next time based on the slope stability characteristics of the upstream of the reservoir and the inflow of the reservoir. The flood discharge control module is connected with the prediction module, and is configured to control the reservoir flood discharge according to the water level change at the next time.

[0020] Compared with the prior art, the present application has the following advantages: The present application acquires the reservoir water level information in real time by acquiring the comprehensive data of the reservoir area, establishing a digital twin model, and monitoring the current water level information change. When the current water level information change is greater than the preset change threshold, the inflow of the reservoir upstream and the topographic and geomorphic information in the digital twin model are acquired. Based on the current water level information change and the topographic and geomorphic information of the upstream of the reservoir, the slope stability characteristics of the upstream of the reservoir are determined. Based on the slope stability characteristics of the upstream of the reservoir and the inflow, the water level change at the next time is predicted. According to the water level change at the next time, the reservoir flood discharge is controlled. The present application considers the influence of water level change on slope stability and combines slope stability with water level prediction, thereby improving the prediction accuracy. The real-time water level change and the topographic and geomorphic information of the upstream of the reservoir are combined to quantitatively analyze the slope stability characteristics, and the slope stability is coupled with the inflow to predict the water level change at the next time, thereby breaking through the traditional rough prediction mode which only relies on the inflow or historical data. The water level prediction is more in line with the actual situation, the water level prediction accuracy is improved, and the water management accuracy is improved. The present application solves the problem that the influence of water level change on the slope stability of the upstream of the reservoir is not considered in the prior art, and the slope stability is not combined with the water level prediction, resulting in poor prediction accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0022] Figure 1 is a flow chart of a smart water management method based on digital twinning provided by an embodiment of the present application; Figure 2 is a structural schematic diagram of a smart water management system based on digital twinning provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present application.

[0024] The specific embodiments of the present application will be described below.

[0025] In view of the problems that the influence of water level change on the slope stability of the upstream watershed of the reservoir is not considered in the prior art, and the water level is not predicted in combination with the slope stability, resulting in poor prediction accuracy, the present application establishes a digital twin model to obtain the reservoir water level information in real time, and monitor the water level information change amount at the current time; when the water level information change amount at the current time is greater than a preset change threshold, the reservoir inflow and topographic and geomorphic information are obtained; based on the water level information change amount at the current time and the topographic and geomorphic information, the slope stability characteristics of the upstream of the reservoir are determined; based on the slope stability characteristics of the upstream of the reservoir and the reservoir inflow, the water level change amount at the next time is predicted; and the reservoir flood discharge is controlled according to the water level change amount at the next time. The present application considers the influence of water level change on the slope stability and predicts the water level in combination with the slope stability, thereby improving the prediction accuracy.

[0026] Embodiment 1 The present application provides a smart water management method based on digital twinning, Figure 1 The present application provides a smart water management method based on digital twinning, Figure 1 as shown in the flowchart, the management method specifically includes the following steps: S1, a digital twin model is established by collecting comprehensive data of the reservoir area.

[0027] The comprehensive data of the reservoir area refers to the multi-type data of the reservoir and the upstream basin of the reservoir, including hydrological data, topographic and geomorphic data, geographic and engineering data, etc. The hydrological data refers to the data related to the movement of water and the change of water volume in the reservoir and the upstream basin of the reservoir. The topographic and geomorphic data refers to the data related to the topographic features of the upstream basin of the reservoir, which is the key information reflecting the geographical features of the surface form, slope, and relief of the basin. The geographic data refers to the three-dimensional scene basic data taking the fusion of different precision digital orthophoto maps (DOM), digital elevation models (DEM), and tilt models as the core, which is used to map the topography and geomorphology of the reservoir and the upstream basin. The engineering data is the water conservancy facility data generated according to the civil, water machine, electrical, gold junction, and monitoring professional drawings, which is used to make a high-fidelity BIM model.

[0028] Through sensors, monitoring devices, geographic information systems, etc., the hydrological, topographic, geographic, and engineering data of the reservoir area are obtained. Based on the collected comprehensive data, a three-dimensional scene is constructed by fusing DOM, DEM, tilt model, etc., a high-fidelity BIM model is made in combination with the professional drawings of water conservancy facilities, and a digital twin model covering the reservoir and the upstream basin of the reservoir is built by means of three-dimensional simulation technology, realizing the digital mapping of the topography, hydrology, and facilities of the entity area. Through the collection and integration of the comprehensive data of the reservoir area, it is ensured that the digital twin model can fully reflect the actual state of the reservoir and the basin, providing accurate data support for subsequent water level monitoring, slope stability analysis, water level prediction, etc.

[0029] S2, real-time acquisition of water level information of the reservoir, and monitoring of the water level information change amount at the current time.

[0030] The water level information of the reservoir refers to the water level value of the reservoir at a certain time, which is an index reflecting the water volume of the reservoir. The water level information change amount at the current time refers to the water level change height at the current time compared with the last time, and the interval time between the current time and the last time is the unit time, wherein the unit time is the time reference for measuring the water level change rate. Through the monitoring device, the water level values of the reservoir at different times are dynamically collected at intervals of unit time, and the water level value at the current time is subtracted from the water level value at the last time to obtain the water level information change amount at the current time.

[0031] S3, when the water level information change amount at the current time is greater than the preset change threshold, the inflow water volume and topographic and geomorphic information of the upstream of the reservoir in the digital twin model are acquired.

[0032] The preset change threshold is a critical value of water level change, used to determine whether the water level change is abnormal. The inflow water quantity of the reservoir upstream refers to the final inflow water quantity of the reservoir calculated through the digital twin scene, and the topographic and geomorphic information of the reservoir upstream refers to the topographic feature data of the upstream basin of the reservoir, for example, including the basin area, basin slope, basin length, slope concave depth and slope convex height.

[0033] Specifically, the inflow water quantity of the reservoir upstream in the digital twin model is obtained, including: based on the digital twin model, obtaining the climate information of the geographical location where the reservoir is located, and the basin water quantity of the reservoir upstream; According to the climate information of the geographical location where the reservoir is located, the precipitation information and the evaporation information are obtained; according to the basin water quantity of the reservoir upstream, the precipitation information and the evaporation information, the inflow water quantity of the reservoir upstream is determined.

[0034] The climate information of the geographical location where the reservoir is located refers to the meteorological data of the region where the reservoir is located, mainly including precipitation information and evaporation information. The basin water quantity of the reservoir upstream refers to the total amount of water bodies naturally existing in the upstream basin of the reservoir, such as the water quantity of rivers, lakes, underground water, etc. It is the basic source of inflow water quantity. By adding the basin water quantity and the precipitation, and then subtracting the evaporation, the final inflow water quantity of the reservoir is obtained.

[0035] Considering the influence of the inherent water quantity of the basin and the meteorological factors, the error of estimating the inflow water quantity based on the basin water quantity can be avoided, and the data is more in line with the actual situation. At the same time, the inflow water quantity is the basis for predicting the water level change at the next moment, and the accurate calculation of the inflow water quantity can improve the prediction accuracy of the water level.

[0036] S4, based on the current water level information change quantity and the topographic and geomorphic information of the reservoir upstream, the slope stability characteristics of the reservoir upstream are determined. Wherein, the slope stability characteristics of the reservoir upstream are quantitative indexes reflecting the slope stability of the upstream basin.

[0037] Specifically, based on the current water level information change quantity and the topographic and geomorphic information of the reservoir upstream, the slope stability characteristics of the reservoir upstream are determined, including: determining the slope flatness according to the slope concave depth and the slope convex height; determining the topographic influence degree according to the basin area, the basin slope and the basin length; determining the slope stability characteristics of the reservoir upstream based on the current water level information change quantity, the slope flatness and the topographic influence degree.

[0038] On the basis of the above embodiments, the slope concave depth and the slope convex height are extracted from the topographic and geomorphic information. Wherein, the slope concave depth refers to the vertical depth of the low-lying place of the basin slope, and the slope convex height refers to the vertical height of the raised place of the basin slope. According to the slope concave depth and the slope convex height, the slope flatness is determined, specifically including: The slope flatness is determined according to a flatness calculation formula, the flatness calculation formula is: ; wherein, is the slope flatness, a is the slope concave depth, b is the slope convex height, is the preset weight corresponding to the slope concave depth, is the preset weight corresponding to the slope convex height. for quantifying the influence degree of the slope concave depth on the flatness, for quantifying the influence degree of the slope convex height on the flatness, , The application scene, historical test data and the like can be set according to actual working conditions.

[0039] The quantification index calculated by the slope concave depth, the slope convex height and the corresponding weight reflects the flatness degree of the slope as a whole, converts the abstract slope concave and convex features into specific flatness indexes, so that the micro features of the slope terrain can be quantitatively analyzed, and the basic data for subsequent evaluation of the slope stability is provided.

[0040] On the basis of the above embodiment, the watershed area, the watershed slope and the watershed length are extracted from the topographic and geomorphic information. The watershed area refers to the total area of the upstream watershed of the reservoir, the watershed slope refers to the inclination degree of the watershed surface, and the watershed length refers to the extension length of the watershed. According to the watershed area, the watershed slope and the watershed length, the topographic influence degree is determined, including: The topographic influence degree is determined according to a topographic influence degree calculation formula, the topographic influence degree calculation formula is: ; wherein, is the topographic influence degree, S is the watershed area, P is the watershed slope, L is the watershed length, is the preset weight corresponding to the watershed area, is the preset weight corresponding to the watershed slope, is the preset weight corresponding to the watershed length. for quantifying the influence degree of the watershed area on the topographic influence degree, for quantifying the influence degree of the watershed slope on the topographic influence degree, for quantifying the influence degree of the watershed length on the topographic influence degree. , and The application scene, historical test data and the like can be set according to actual working conditions.

[0041] The quantification index reflecting the influence degree of the slope surface topography is calculated by the basin area, the basin slope and the basin length and corresponding weights, the dispersed topographic parameters such as the basin area, the slope and the length are integrated into the quantification index reflecting the influence degree of the slope surface topography, the comprehensive quantification evaluation of the influence of the topographic characteristics is realized, and a basis is provided for subsequent slope stability analysis.

[0042] On the basis of the above-mentioned embodiments, the slope stability feature of the reservoir upstream is determined based on the current water level information change amount, the slope flatness and the topographic influence degree, and the slope stability feature of the reservoir upstream is determined based on the slope stability feature calculation formula. The slope stability feature of the reservoir upstream is determined according to the slope stability feature calculation formula, and the slope stability feature calculation formula is: ; Among them, D is the slope stability feature, α is the slope flatness, β is the topographic influence degree, ΔH is the current water level information change amount. The slope stability feature influences the water discharge efficiency, when the slope stability is high (i.e. the D value is large), the slope structure is stable, the water flow is less disturbed by the topographic collapse, landslide and the like during the water flow discharge along the slope surface, the water flow can maintain a relatively stable flow state, and the discharge path is unobstructed, so the water discharge efficiency is high. If the slope stability is low (i.e. the D value is small), the slope is prone to collapse, soil accumulation and the like, which may block or change the water flow path, hinder the water discharge, and reduce the discharge efficiency.

[0043] According to the above-mentioned embodiments, according to the calculated slope flatness α, topographic influence degree β and real-time monitored current water level information change amount ΔH, α, β and ΔH are substituted into the slope stability feature calculation formula, and the slope stability feature is calculated. The slope flatness, the topographic influence degree and the water level change are integrated into the same formula, wherein the slope flatness reflects the micro characteristics of the slope, the topographic influence degree reflects the macro characteristics of the basin, and the water level change belongs to the hydrological data, which comprehensively reflects the comprehensive influence of various factors on the slope stability, avoids the one-sidedness of single factor analysis. At the same time, the abstract slope stability is analyzed by specific slope stability feature value, so that the slope risk assessment can be quantitatively analyzed, and the accuracy and operability of the evaluation are improved.

[0044] S5, based on the slope stability feature of the reservoir upstream and the inflow water quantity of the reservoir upstream, predicting the water level change amount of the next moment. Among them, the slope stability feature influences the water discharge efficiency, the inflow water quantity directly determines the water level change, and the slope stability and the inflow water quantity are comprehensively considered, which avoids the error of prediction according to a single factor, and makes the result more in line with the actual water level change.

[0045] Specifically, based on the slope stability characteristics of the upstream of the reservoir and the inflow of the reservoir upstream, the water level change amount at the next moment is predicted, specifically including: inputting the slope stability characteristics of the upstream of the reservoir, the inflow of the reservoir upstream and the time parameter into the prediction model; the prediction model outputs the water level change amount at the next moment. Wherein, the prediction model is: ; Wherein, represents the water level change amount at the next moment, represents the inflow, t represents the time length from the current moment to the next moment. The water level change amount at the next moment refers to the water level change height at the next moment compared with the current moment, and the interval time length t between the next moment and the current moment is a unit time.

[0046] S6, according to the water level change amount at the next moment, control the reservoir to discharge flood.

[0047] On the basis of the above embodiment, the water level change amount at the next moment is calculated according to the prediction model. If is too large, it may lead to water level exceeding the safety threshold, in combination with the maximum discharge capacity of the reservoir, the downstream bearing capacity and the like, the required discharge capacity is determined.

[0048] Specifically, according to the water level change amount at the next moment, the reservoir is controlled to discharge flood, including: obtaining the current actual water level of the reservoir; obtaining the predicted water level at the next moment according to the current actual water level of the reservoir and the water level change amount at the next moment; comparing the predicted water level with the total capacity of the reservoir, and controlling the reservoir to discharge flood when the predicted water level exceeds the total capacity of the reservoir.

[0049] The water level value of the reservoir at different moments is dynamically collected by a monitoring device at a unit time interval, the current actual water level of the reservoir is obtained, the water level change amount at the next moment is calculated by the above prediction model, and the predicted water level at the next moment is obtained by adding the current actual water level of the reservoir and the water level change amount at the next moment. Compare the predicted water level with the total capacity of the reservoir, and control the reservoir to discharge flood when the predicted water level exceeds the total capacity of the reservoir, so that the water level of the reservoir is reduced to the safety range. The overload risk is judged in advance by the predicted water level, and accidents such as dam collapse caused by high water level are avoided, and the operation safety of the reservoir is improved.

[0050] Embodiment 2 The embodiment of the application also provides a smart water management system based on digital twinning. The smart water management system provided by the embodiment of the application executes the smart water management method based on digital twinning in the above embodiment. Figure 2 is a structural schematic diagram of a smart water management system based on digital twinning provided by the embodiment of the application, like Figure 2As shown, the smart water management system comprises: a modeling module 110, configured to establish a digital twin model by collecting comprehensive data of a reservoir area.

[0051] a collection module 120, configured to acquire water level information of the reservoir in real time and monitor a water level information change amount at a current time.

[0052] a judgment module 130, connected with the modeling module 110 and the collection module 120, configured to acquire an inflow water amount and topographic and geomorphic information of an upstream of the reservoir in the digital twin model when the water level information change amount at the current time is greater than a preset change threshold.

[0053] a slope stability calculation module 140, connected with the collection module 120 and the judgment module 130, configured to determine a slope stability feature of the upstream of the reservoir based on the water level information change amount at the current time and the topographic and geomorphic information of the upstream of the reservoir.

[0054] a prediction module 150, connected with the slope stability calculation module 140 and the judgment module 130, configured to predict a water level change amount at a next time based on the slope stability feature of the upstream of the reservoir and the inflow water amount of the upstream of the reservoir.

[0055] a flood discharge control module 160, connected with the prediction module 150, configured to control a reservoir flood discharge according to the water level change amount at the next time.

[0056] In this embodiment, the modeling module 110 collects comprehensive data of a reservoir area and establishes a digital twin model, the collection module 120 acquires water level information of the reservoir in real time and monitors a water level information change amount at a current time. The judgment module 130 judges whether the water level information change amount is greater than a preset threshold, and acquires an inflow water amount and topographic and geomorphic information of an upstream of the reservoir in the digital twin model when the water level information change amount at the current time is greater than a preset change threshold. The slope stability calculation module 140 determines a slope stability feature of the upstream of the reservoir based on the water level information change amount at the current time and the topographic and geomorphic information of the upstream of the reservoir. The prediction module 150 predicts a water level change amount at a next time based on the slope stability feature of the upstream of the reservoir and the inflow water amount of the upstream of the reservoir. The flood discharge control module 160 controls a reservoir flood discharge according to the water level change amount at the next time.

[0057] In this embodiment, the influence of water level change on slope stability is considered, and the water level is predicted in combination with the slope stability, thereby improving the prediction accuracy. In combination with real-time water level change and topographic and geomorphic information of the upstream of the reservoir, the slope stability feature is quantitatively analyzed, the slope stability is coupled with the inflow water amount to predict the water level change at the next time, which breaks through the traditional rough prediction mode that only relies on the inflow water amount or historical data, makes the water level prediction more in line with the actual situation, improves the water level prediction accuracy, and is beneficial to the improvement of water management accuracy.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A smart water management method based on digital twins, characterized in that, include: S1. Establish a digital twin model by collecting comprehensive data on the reservoir area; S2. Obtain real-time water level information of the reservoir and monitor the change in water level information at the current moment; S3. When the change in water level information at the current moment is greater than a preset change threshold, obtain the inflow water and topographic information of the upstream reservoir in the digital twin model. S4. Based on the change in water level at the current moment and the topographic information of the upstream of the reservoir, determine the slope stability characteristics of the upstream of the reservoir; S5. Based on the slope stability characteristics upstream of the reservoir and the inflow of water upstream of the reservoir, predict the water level change at the next moment. S6. Control the reservoir discharge based on the water level change at the next moment.

2. The smart water management method based on digital twins according to claim 1, characterized in that, The topographic information of the upstream area of ​​the reservoir includes the catchment area, catchment slope, catchment length, slope depression depth, and slope convexity height; in step S4, based on the current water level change and the topographic information of the upstream area of ​​the reservoir, the slope stability characteristics of the upstream area of ​​the reservoir are determined, specifically including: The slope smoothness is determined based on the slope depression depth and the slope convex height. The degree of topographic impact is determined based on the watershed area, the watershed slope, and the watershed length; The stability characteristics of the upstream slope of the reservoir are determined based on the change in water level at the current moment, the slope flatness, and the degree of influence of the terrain.

3. The intelligent water management method based on digital twins according to claim 2, characterized in that, The stability characteristics of the upstream slope of the reservoir are determined based on the current water level change, the slope smoothness, and the degree of topographic influence, including: The slope stability characteristics upstream of the reservoir are determined according to the slope stability characteristic calculation formula, which is as follows: in, This is a characteristic of slope stability. For slope flatness, Due to the degree of influence of terrain, This represents the change in water level at the current moment.

4. The intelligent water management method based on digital twins according to claim 3, characterized in that, The slope smoothness is determined based on the slope depression depth and the slope convex height, specifically including: The slope smoothness is determined according to the smoothness calculation formula, which is: Where a is the depth of the slope depression and b is the height of the slope bulge. The preset weights corresponding to the slope depression depth. The preset weights are the heights of the slope bulges.

5. The intelligent water management method based on digital twins according to claim 3, characterized in that, The degree of topographic impact is determined based on the watershed area, the watershed slope, and the watershed length, including: The degree of terrain influence is determined according to the formula for calculating the degree of terrain influence. The formula for calculating the degree of terrain influence is as follows: Where S is the catchment area, P is the catchment slope, and L is the catchment length. The preset weights corresponding to the watershed area, Preset weights corresponding to the watershed slope. The preset weights are the values ​​corresponding to the length of the watershed.

6. The intelligent water management method based on digital twins according to claim 1, characterized in that, Obtaining the inflow of water upstream of the reservoir in the digital twin model specifically includes: Based on the digital twin model, climate information of the reservoir's geographical location and water volume in the upstream basin are obtained. Based on the climate information of the reservoir's geographical location, obtain precipitation and evaporation information; Based on the water volume, precipitation, and evaporation information of the upstream basin of the reservoir, the inflow of water into the upstream of the reservoir is determined.

7. The intelligent water management method based on digital twins according to claim 6, characterized in that, In step S5, based on the slope stability characteristics upstream of the reservoir and the inflow of water upstream of the reservoir, the water level change at the next moment is predicted, specifically including: The slope stability characteristics upstream of the reservoir, the inflow of water upstream of the reservoir, and time parameters are input into the prediction model. The prediction model outputs the water level change at the next moment.

8. The intelligent water management method based on digital twins according to claim 7, characterized in that, The prediction model is as follows: in, This is a characteristic of slope stability. This indicates the change in water level at the next moment. t represents the amount of water entering the reservoir, and t represents the time from the current moment to the next moment.

9. The intelligent water management method based on digital twins according to claim 1, characterized in that, In step S6, controlling the reservoir discharge based on the water level change at the next moment specifically includes: Get the current actual water level of the reservoir; Based on the current actual water level of the reservoir and the water level change at the next moment, the predicted water level at the next moment is obtained; Compare the predicted water level with the total capacity of the reservoir. When the predicted water level exceeds the total capacity of the reservoir, the reservoir will be controlled to release floodwater.

10. A smart water management system based on digital twins, characterized in that, The system is used to execute the smart water management method based on digital twins as described in any one of claims 1-9, and the system specifically includes: The modeling module is used to build a digital twin model by collecting comprehensive data on the reservoir area; The data acquisition module is used to acquire real-time water level information of the reservoir and monitor the change in water level information at the current moment; The judgment module, connected to the modeling module and the acquisition module, is used to acquire the inflow water and topographic information of the upstream of the reservoir in the digital twin model when the change in water level information at the current time is greater than a preset change threshold. The slope stability calculation module, connected to the acquisition module and the judgment module, is used to determine the slope stability characteristics upstream of the reservoir based on the change in water level at the current moment and the topographic information upstream of the reservoir. The prediction module, connected to the slope stability calculation module and the judgment module, is used to predict the water level change at the next moment based on the slope stability characteristics upstream of the reservoir and the inflow of water upstream of the reservoir. The flood discharge control module, connected to the prediction module, is used to control the reservoir's flood discharge based on the water level change at the next moment.

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