A water conservancy monitoring system and method based on digital twin

Abstract

The present invention belongs to the technical field of water conservancy monitoring, and relates to a water conservancy monitoring system and method based on digital twins. The present invention constructs a watershed digital model based on SWAT through a watershed digital model generation module; integrates data of various sensors deployed in a target watershed into the watershed digital model through a watershed data integration module; simulates the hydrological dynamics of a target watershed in a watershed digital model based on digital twin technology through a hydrological dynamic simulation module, and predicts areas in the target watershed with water and soil loss risks; reports location information of sub-areas with water and soil loss risks in the target watershed through a risk warning module, and feeds back warning information to corresponding management personnel terminals; and by marking the location information of sub-areas with water and soil loss risks, the warning has higher dynamics, provides targeted guidance for relevant personnel to take prevention and control measures, and improves the efficiency of water and soil conservation and environmental management.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy monitoring, and in particular relates to a water conservancy monitoring system and method based on digital twins. Background Art

[0002] Water conservancy monitoring refers to the process of monitoring and managing the status of water resources and water conservancy facilities using a variety of technical means. With the development of society and the advancement of science and technology, water conservancy monitoring has gradually changed from simple observation and empirical judgment to precise analysis using computer technology, and has developed in the direction of intelligence, integration, and digitization.

[0003] Traditional methods usually rely on manual inspections and simple sensors to detect changes in parameters such as water level and flow, and use GIS technology to combine detection data with geographic location to display monitoring data in a graphical way to help managers understand the hydrological status of a basin or region; however, this method has problems such as low efficiency, poor data real-time performance, and the influence of human factors. With the expansion of the monitoring scope and the improvement of water conservancy monitoring requirements, traditional methods have been unable to meet the needs of modern water conservancy monitoring. In response to this problem, the inventor intends to propose a water conservancy monitoring system and method based on digital twins, using digital twin technology to automatically monitor the hydrological dynamics of the target basin, establish an integrated water conservancy monitoring platform, and effectively solve the problems of untimely warning of hydrological status and low management efficiency of water conservancy monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide a water conservancy monitoring system and method based on digital twins to solve the problems raised in the above background technology.

[0005] In order to achieve the purpose of the present invention, the present invention will be implemented by adopting the following technical solutions.

[0006] A water conservancy monitoring method based on digital twins includes the following steps:

[0007] Step S100: Acquire the topographic data of the target watershed, extract the river GIS layer, and determine the boundary of the target watershed; collect the soil and land use properties in the target watershed, and retrieve the soil and land use GIS layer; acquire meteorological data, which includes the temporal and spatial distribution data of rainfall and temperature information; and construct a watershed digital model based on SWAT;

[0008] Step S200: Capture the location information of each sensor in the target watershed in the watershed digital model and set the corresponding monitoring nodes, sort and number each monitoring node according to the runoff sequence of the target watershed; set the verification node based on the monitoring node, and integrate the data of each sensor deployed in the target watershed into the watershed digital model;

[0009] Step S300: simulating the hydrological dynamics of the target watershed in the watershed digital model based on the digital twin technology, predicting the areas in the target watershed with the risk of soil erosion, and marking the areas with the risk of soil erosion as the first warning areas;

[0010] Step S400: verifying the first warning area according to the prediction result in step S300 and the actual monitoring data, and calculating the hazard index of the first warning area;

[0011] Step S500: Generate a second warning area set according to the hazard index of the first warning area; report the area location information in the second warning area set, and feed back the warning information to the corresponding management personnel terminal.

[0012] As a preferred solution of the present invention, the implementation process of step S100 includes the following steps:

[0013] The watershed digital model constructed based on SWAT in step S100 includes watershed geographic information data, river network, meteorological data and hydrological response units;

[0014] The basin geographic information data includes spatial distribution data of the target basin;

[0015] The river network is used to simulate the river structure of the target basin;

[0016] The meteorological data is combined with a weather model to simulate the watershed response of the target watershed under different weather scenarios;

[0017] The hydrological response unit divides the target watershed into grids, and merges similar grids into one hydrological response unit based on the soil and land use properties within the grids and the slope characteristics in the watershed geographic information data; it is used to improve the computational efficiency of the model and reduce uncertainty in the parameterization process.

[0018] As a preferred solution of the present invention, the implementation process of step S200 includes the following steps:

[0019] The implementation process of step S200 includes the following steps:

[0020] Step S201: Capture the location information of each sensor in the target watershed in the watershed digital model and set the corresponding monitoring nodes, sort and number each monitoring node according to the runoff sequence of the target watershed; obtain the real-time monitoring data collected by each sensor in the target watershed; the runoff sequence indicates the direction of water flow in the watershed after precipitation occurs;

[0021] Step S202: When there are different hydrological response units between any two adjacent monitoring nodes, a verification node is set at the junction of different hydrological response units, and the real-time monitoring data of the verification node is collected through sensors; the monitoring data uploaded by any two adjacent monitoring nodes is recorded as C d and C d+1 ; The monitoring data uploaded by the verification node between any two adjacent monitoring nodes is recorded as H;

[0022] Suppose that the monitoring data uploaded by any two adjacent monitoring nodes and the verification node between them are accumulated n times in a unit time; the average value of the monitoring data uploaded by any two adjacent monitoring nodes in a unit time is generated by a computer, recorded as and ; Generate the average value of the monitoring data uploaded by the verification node between any two adjacent monitoring nodes in unit time, recorded as ;

[0023] Step S203: Verify the real-time monitoring data acquired by each monitoring node based on the verification node; calculate the pass index of the real-time monitoring data acquired by the monitoring node according to the following formula:

[0024] ;

[0025] in, It represents the average value of all monitoring data uploaded by any two adjacent monitoring nodes within a unit time. and They represent the variance of the monitoring data uploaded by any two adjacent monitoring nodes within a unit time; It represents the variance of all monitoring data uploaded by any two adjacent monitoring nodes within a unit time; It represents the variance of the monitoring data uploaded by the verification node between any two adjacent monitoring nodes within a unit time;

[0026] When the passing index U of the real-time monitoring data acquired by the monitoring node is greater than 1, it is determined that the real-time monitoring data of the monitoring node corresponding to the target verification node has passed the verification; the verified real-time monitoring data is integrated into the watershed digital model;

[0027] Among them, when the passing index of the target verification node is lower, it means that the probability of false alarm of the monitoring node corresponding to the target verification node is higher.

[0028] As a preferred solution of the present invention, the implementation process of step S300 includes the following steps:

[0029] The implementation process of step S300 includes the following steps:

[0030] Step S301: generating a target watershed sub-area according to the monitoring area of ​​each monitoring node in the target watershed, and obtaining the target watershed sub-area corresponding to each monitoring node;

[0031] Step S302: Analyze the sub-areas of the target watershed in the watershed digital model based on the digital twin technology, and calculate the risk probability of each sub-area of ​​the target watershed according to the following formula:

[0032] ;

[0033] Where P(z) represents the risk probability of the zth sub-area in the target basin; N total Represents the total number of predictions for the target basin; NUM(A z >T) indicates the number of times the prediction result exceeds the set threshold;

[0034] A z Refers to the simulation prediction results of the z-th sub-area in the target watershed, which is used to represent the soil loss per unit area of ​​the z-th sub-area in the target watershed;

[0035] Step S303: When the risk probability of a sub-region exceeds a preset risk threshold, the sub-region is determined to have a risk of soil erosion, the region with the risk of soil erosion is marked as a first warning region, and a first warning region sequence is generated according to the runoff sequence of the target watershed.

[0036] As a preferred solution of the present invention, the implementation process of step S400 includes the following steps:

[0037] In step S400, the first warning area is verified and the hazard index of the first warning area is calculated by the following formula:

[0038] ;

[0039] Wherein, W(F) represents the hazard index of the Fth first warning area in the first warning area sequence; the hazard index can reflect the risk occurrence probability and impact degree of the first warning area; P(F) represents the risk probability of the Fth first warning area in the first warning area sequence; P(F-1) represents the risk probability of the F-1th first warning area in the first warning area sequence; P(F+1) represents the risk probability of the F+1th first warning area in the first warning area sequence;

[0040] According to the above method, the hazard index of the first warning area can be reflected by comparing the risk probability between the first hazard area and the adjacent areas; when the hazard index of the first warning area is larger, it means that the impact of the first warning area on the downstream is greater, which means that the area may increase the risk of soil erosion in other areas of the target area.

[0041] Furthermore, the implementation process of generating the second warning area set according to the hazard index of the first warning area in step S500 is:

[0042] When the hazard index of any first warning area exceeds the preset warning upper limit, the first warning area is marked as a second warning area; the marks of each sub-area of ​​the target basin are checked one by one, and the location information of the sub-area marked as the second warning area is added to the second warning area set;

[0043] When there are x adjacent first warning areas exceeding the preset warning upper limit in the first warning area sequence, the location information of the next target basin sub-area corresponding to the last adjacent second warning area is added to the second warning area set according to the runoff order.

[0044] A water conservancy monitoring system based on digital twins, the system comprising: a watershed digital model generation module, a watershed data integration module, a hydrological dynamic simulation module and a risk warning module;

[0045] The watershed digital model generation module constructs a watershed digital model based on SWAT;

[0046] The watershed data integration module is used to integrate the data of each sensor deployed in the target watershed into the watershed digital model;

[0047] The hydrological dynamic simulation module simulates the hydrological dynamics of the target basin in the basin digital model based on the digital twin technology, and predicts the areas in the target basin with the risk of soil erosion;

[0048] The risk warning module is used to report the location information of the sub-areas with soil and water loss risks in the target watershed according to the output results of the hydrological dynamic simulation module, and feed back the warning information to the corresponding management personnel terminal.

[0049] Furthermore, the watershed data integration module includes a data acquisition unit, a data verification unit and a data integration unit;

[0050] The data acquisition unit is used to acquire real-time monitoring data collected by each sensor in the target watershed;

[0051] The data verification unit verifies the real-time monitoring data acquired by each monitoring node based on the verification node;

[0052] The data integration unit integrates the verified real-time monitoring data into the watershed digital model based on the verification result of the data verification unit.

[0053] Furthermore, the hydrological dynamic simulation module includes a hydrological dynamic update unit and a watershed risk prediction unit;

[0054] The hydrological dynamics updating unit is used to update the hydrological dynamics of each sub-area of ​​the target watershed in the watershed digital model according to the output of the watershed data integration module;

[0055] The watershed risk prediction unit is used to predict whether there is a risk of soil erosion in each sub-area of ​​the target watershed.

[0056] Further, the risk warning module includes a risk location marking unit and a warning feedback unit;

[0057] The risk location marking unit is used to integrate the soil and water loss risks of each sub-area of ​​the target watershed according to the output of the hydrological dynamic simulation module, and obtain the location information of the area where the soil and water loss risk exists; the warning feedback unit is used to feed back the location information of the area where the soil and water loss risk exists to the corresponding management personnel terminal, and remind the relevant personnel to deal with it before the risk occurs.

[0058] Beneficial effects: (1) The present invention constructs a digital model of the watershed based on digital twin technology and SWAT, and monitors the target watershed in real time through monitoring nodes and verification nodes. It integrates multi-source data to dynamically update the digital model of the watershed, provides more comprehensive and accurate data support, and realizes real-time simulation of water conservancy monitoring.

[0059] (2) The present invention predicts the risk of soil erosion in the target basin by simulating hydrological dynamics, and marks the location information of the sub-areas with soil erosion risk by calculating the risk probability and hazard index of each sub-area in the target basin, so that the early warning has higher dynamics and provides targeted guidance for relevant personnel to take preventive measures.

[0060] (3) The present invention can help relevant personnel supervise the target watershed and improve the efficiency of soil and water conservation and environmental management by accurately predicting and locating the location where risks occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a structural schematic diagram of a water conservancy monitoring system based on digital twins of the present invention;

[0062] Figure 2 It is a flow chart of a water conservancy monitoring method based on digital twins of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present invention will be clearly and completely described below in combination with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] As an embodiment of the present invention, Figure 1 As shown, a water conservancy monitoring system based on digital twin is provided, including: a watershed digital model generation module, a watershed data integration module, a hydrological dynamic simulation module and a risk warning module;

[0065] Among them, the watershed digital model generation module builds the watershed digital model based on SWAT;

[0066] The watershed data integration module is used to integrate the data of each sensor deployed in the target watershed into the watershed digital model; it includes a data acquisition unit, a data verification unit and a data integration unit;

[0067] A data acquisition unit, used to acquire real-time monitoring data collected by various sensors in the target watershed;

[0068] A data verification unit verifies the real-time monitoring data obtained by each monitoring node based on the verification node;

[0069] The data integration unit integrates the verified real-time monitoring data into the digital model of the watershed based on the verification results of the data verification unit.

[0070] The hydrological dynamic simulation module simulates the hydrological dynamics of the target basin in the basin digital model based on the digital twin technology, and predicts the areas with soil and water loss risks in the target basin; it includes a hydrological dynamic update unit and a basin risk prediction unit;

[0071] The hydrological dynamics updating unit is used to update the hydrological dynamics of each sub-area of ​​the target watershed in the watershed digital model according to the output of the watershed data integration module;

[0072] The watershed risk prediction unit is used to predict whether there is a risk of soil erosion in each sub-area of ​​the target watershed.

[0073] The risk warning module is used to report the location information of the sub-areas with soil and water loss risks in the target basin according to the output results of the hydrological dynamic simulation module, and feed back the warning information to the corresponding management personnel terminal; it includes a risk location marking unit and a warning feedback unit;

[0074] The risk location marking unit is used to integrate the soil and water loss risks of each sub-region of the target basin according to the output of the hydrological dynamic simulation module, and obtain the location information of the region with soil and water loss risks;

[0075] The alarm feedback unit is used to feed back the location information of the area with soil and water loss risks to the corresponding management personnel terminal, reminding the relevant personnel to deal with the risks before they occur.

[0076] As an embodiment of the present invention, Figure 2As shown, a water conservancy monitoring method based on digital twin is provided, comprising the following steps:

[0077] Step S100: Acquire the topographic data of the target watershed, extract the river GIS layer, and determine the boundary of the target watershed; collect the soil and land use properties in the target watershed, and retrieve the soil and land use GIS layer; acquire meteorological data, which includes the temporal and spatial distribution data of rainfall and temperature information; and construct a watershed digital model based on SWAT;

[0078] The watershed digital model constructed based on SWAT in step S100 includes watershed geographic information data, river network, meteorological data and hydrological response units;

[0079] The basin geographic information data includes spatial distribution data of the target basin;

[0080] The river network is used to simulate the river structure of the target basin;

[0081] The meteorological data is combined with a weather model to simulate the watershed response of the target watershed under different weather scenarios;

[0082] The hydrological response unit divides the target watershed into grids, and merges similar grids into one hydrological response unit based on the soil and land use properties within the grids and the slope characteristics in the watershed geographic information data; it is used to improve the computational efficiency of the model and reduce uncertainty in the parameterization process.

[0083] Step S200: Capture the location information of each sensor in the target watershed in the watershed digital model and set the corresponding monitoring nodes, sort and number each monitoring node according to the runoff sequence of the target watershed; set the verification node based on the monitoring node, and integrate the data of each sensor deployed in the target watershed into the watershed digital model;

[0084] The implementation process of step S200 includes the following steps:

[0085] Step S201: Capture the location information of each sensor in the target watershed in the watershed digital model and set the corresponding monitoring nodes, sort and number each monitoring node according to the runoff sequence of the target watershed; obtain the real-time monitoring data collected by each sensor in the target watershed; the runoff sequence indicates the direction of water flow in the watershed after precipitation occurs;

[0086] Step S202: When there are different hydrological response units between any two adjacent monitoring nodes, a verification node is set at the junction of different hydrological response units, and the real-time monitoring data of the verification node is collected through sensors; the monitoring data uploaded by any two adjacent monitoring nodes is recorded as C d and C d+1; The monitoring data uploaded by the verification node between any two adjacent monitoring nodes is recorded as H;

[0087] Suppose that the monitoring data uploaded by any two adjacent monitoring nodes and the verification node between them are accumulated n times in a unit time; the average value of the monitoring data uploaded by any two adjacent monitoring nodes in a unit time is generated by a computer, recorded as and ; Generate the average value of the monitoring data uploaded by the verification node between any two adjacent monitoring nodes in unit time, recorded as ;

[0088] Step S203: Verify the real-time monitoring data acquired by each monitoring node based on the verification node; calculate the pass index of the real-time monitoring data acquired by the monitoring node according to the following formula:

[0089] ;

[0090] in, It represents the average value of all monitoring data uploaded by any two adjacent monitoring nodes within a unit time. and They represent the variance of the monitoring data uploaded by any two adjacent monitoring nodes within a unit time; It represents the variance of all monitoring data uploaded by any two adjacent monitoring nodes within a unit time; It represents the variance of the monitoring data uploaded by the verification node between any two adjacent monitoring nodes within a unit time;

[0091] When the passing index U of the real-time monitoring data acquired by the monitoring node is greater than 1, it is determined that the real-time monitoring data of the monitoring node corresponding to the target verification node has passed the verification; the verified real-time monitoring data is integrated into the watershed digital model;

[0092] Among them, when the passing index of the target verification node is lower, it means that the probability of false alarm of the monitoring node corresponding to the target verification node is higher.

[0093] Step S300: simulating the hydrological dynamics of the target watershed in the watershed digital model based on the digital twin technology, predicting the areas in the target watershed with the risk of soil erosion, and marking the areas with the risk of soil erosion as the first warning areas;

[0094] The implementation process of step S300 includes the following steps:

[0095] Step S301: generating a target watershed sub-area according to the monitoring area of ​​each monitoring node in the target watershed, and obtaining the target watershed sub-area corresponding to each monitoring node;

[0096] Step S302: Analyze the sub-areas of the target watershed in the watershed digital model based on the digital twin technology, and calculate the risk probability of each sub-area of ​​the target watershed according to the following formula:

[0097] ;

[0098] Where P(z) represents the risk probability of the zth sub-area in the target basin; N total Represents the total number of predictions for the target basin; NUM(A z >T) indicates the number of times the prediction result exceeds the set threshold;

[0099] A z Refers to the simulation prediction result of the z-th sub-area in the target watershed, which is used to represent the soil loss per unit area of ​​the z-th sub-area in the target watershed; illustratively, the soil loss per unit area is calculated by the CSLE formula, and when used, it is locally adjusted according to the parameters in the watershed digital model;

[0100] Step S303: When the risk probability of a sub-region exceeds a preset risk threshold, the sub-region is determined to have a risk of soil erosion, the region with the risk of soil erosion is marked as a first warning region, and a first warning region sequence is generated according to the runoff sequence of the target watershed.

[0101] Step S400: verifying the first warning area according to the prediction result in step S300 and the actual monitoring data, and calculating the hazard index of the first warning area;

[0102] In step S400, the first warning area is verified and the hazard index of the first warning area is calculated, which is achieved by the following formula:

[0103] ;

[0104] Wherein, W(F) represents the hazard index of the Fth first warning area in the first warning area sequence; the hazard index can reflect the risk occurrence probability and impact degree of the first warning area; P(F) represents the risk probability of the Fth first warning area in the first warning area sequence; P(F-1) represents the risk probability of the F-1th first warning area in the first warning area sequence; P(F+1) represents the risk probability of the F+1th first warning area in the first warning area sequence;

[0105] For example, W(3)=0.7-0.6 / 0.6-0.55=2;

[0106] Determining that the hazard index of the third first warning area in the first warning area sequence exceeds a preset warning upper limit, marking the first warning area as a second warning area;

[0107] According to the above method, the hazard index of the first warning area can be reflected by comparing the risk probability between the first hazard area and the adjacent areas; when the hazard index of the first warning area is larger, it means that the impact of the first warning area on the downstream is greater, which means that the area may increase the risk of soil erosion in other areas of the target area.

[0108] Step S500: Generate a second warning area set according to the hazard index of the first warning area; report the area location information in the second warning area set, and feed back the warning information to the corresponding management personnel terminal.

[0109] The implementation process of generating the second warning area set according to the hazard index of the first warning area in step S500 is as follows:

[0110] When the hazard index of any first warning area exceeds the preset warning upper limit, the first warning area is marked as a second warning area; the marks of each sub-area of ​​the target basin are checked one by one, and the location information of the sub-area marked as the second warning area is added to the second warning area set;

[0111] When there are three adjacent first warning areas exceeding the preset warning upper limit in the first warning area sequence, the location information of the next target basin sub-area corresponding to the last adjacent second warning area is added to the second warning area set according to the runoff order.

[0112] The technical solution of the present invention is described in detail above in conjunction with the embodiments / drawings, but the present invention is not limited to the above technical solution. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. A water conservancy monitoring method based on digital twins, characterized in that: The following steps are involved: Step S100: Acquire the terrain data of the target watershed, extract the river GIS layer, and determine the boundary of the target watershed; Collect soil and land use attributes in the target watershed and retrieve soil and land use GIS layers; Acquiring meteorological data, wherein the meteorological data includes temporal and spatial distribution data of rainfall and temperature information; Construct a digital model of the watershed based on SWAT; Step S200: Capture the location information of each sensor in the target watershed in the watershed digital model and set the corresponding monitoring nodes, sort and number each monitoring node according to the runoff sequence of the target watershed; set the verification node based on the monitoring node, and integrate the data of each sensor deployed in the target watershed into the watershed digital model; Step S300: simulating the hydrological dynamics of the target watershed in the watershed digital model based on the digital twin technology, predicting the areas in the target watershed with the risk of soil erosion, and marking the areas with the risk of soil erosion as the first warning areas; Step S400: verifying the first warning area according to the prediction result in step S300 and the actual monitoring data, and calculating the hazard index of the first warning area; Step S500: generating a second warning area set according to the hazard index of the first warning area; reporting the area location information in the second warning area set, and feeding back the warning information to the corresponding management personnel terminal; The implementation process of step S200 includes the following steps: Step S201: Capture the location information of each sensor in the target watershed in the watershed digital model and set the corresponding monitoring nodes, sort and number each monitoring node according to the runoff sequence of the target watershed; obtain the real-time monitoring data collected by each sensor in the target watershed; Step S202: When there are different hydrological response units between any two adjacent monitoring nodes, a verification node is set at the junction of different hydrological response units, and real-time monitoring data of the verification node is collected through sensors; Suppose that the monitoring data uploaded by any two adjacent monitoring nodes and the verification node between them are accumulated n times in a unit time; the average value of the monitoring data uploaded by any two adjacent monitoring nodes in a unit time is generated by a computer, recorded as and ; Generate the average value of the monitoring data uploaded by the verification node between any two adjacent monitoring nodes in unit time, recorded as ; Step S203: verifying the real-time monitoring data acquired by each monitoring node based on the verification node; The pass index of the real-time monitoring data obtained by the monitoring node is calculated according to the following formula: ； in, It represents the average value of all monitoring data uploaded by any two adjacent monitoring nodes within a unit time. and They represent the variance of the monitoring data uploaded by any two adjacent monitoring nodes within a unit time; It represents the variance of all monitoring data uploaded by any two adjacent monitoring nodes within a unit time; It represents the variance of the monitoring data uploaded by the verification node between any two adjacent monitoring nodes within a unit time; When the passing index U of the real-time monitoring data acquired by the monitoring node is greater than 1, it is determined that the real-time monitoring data of the monitoring node corresponding to the target verification node has passed the verification; the verified real-time monitoring data is integrated into the watershed digital model; The implementation process of step S300 includes the following steps: Step S301: generating a target watershed sub-area according to the monitoring area of ​​each monitoring node in the target watershed, and obtaining the target watershed sub-area corresponding to each monitoring node; Step S302: Analyze the sub-areas of the target watershed in the watershed digital model based on the digital twin technology, and calculate the risk probability of each sub-area of ​​the target watershed according to the following formula: ； Where P(z) represents the risk probability of the zth sub-area in the target basin; N total Represents the total number of predictions for the target basin; NUM(A z >T) indicates the number of times the prediction result exceeds the set threshold; A z Refers to the simulation prediction results of the z-th sub-area in the target watershed, which is used to represent the soil loss per unit area of ​​the z-th sub-area in the target watershed; Step S303: when the risk probability of a sub-region exceeds a preset risk threshold, the sub-region is determined to have a risk of soil and water loss, the region with the risk of soil and water loss is marked as a first warning region, and a first warning region sequence is generated according to the runoff sequence of the target watershed; In step S400, the first warning area is verified and the hazard index of the first warning area is calculated by the following formula: ； Wherein, W(F) represents the hazard index of the Fth first warning area in the first warning area sequence; P(F) represents the risk probability of the Fth first warning area in the first warning area sequence; P(F-1) represents the risk probability of the F-1th first warning area in the first warning area sequence; P(F+1) represents the risk probability of the F+1th first warning area in the first warning area sequence; The implementation process of generating the second warning area set according to the hazard index of the first warning area in step S500 is as follows: When the hazard index of any first warning area exceeds the preset warning upper limit, the first warning area is marked as a second warning area; the marks of each sub-area of ​​the target basin are checked one by one, and the location information of the sub-area marked as the second warning area is added to the second warning area set; When there are x adjacent first warning areas exceeding the preset warning upper limit in the first warning area sequence, the location information of the next target basin sub-area corresponding to the last adjacent second warning area is added to the second warning area set according to the runoff order.

2. A water conservancy monitoring method based on digital twin according to claim 1, characterized in that: The watershed digital model constructed based on SWAT in step S100 includes watershed geographic information data, river network, meteorological data and hydrological response units; The basin geographic information data includes spatial distribution data of the target basin; The river network is used to simulate the river structure of the target basin; The meteorological data is combined with a weather model to simulate the watershed response of the target watershed under different weather scenarios; The hydrological response unit divides the target watershed into grids, and merges similar grids into one hydrological response unit based on the soil and land use attributes within the grids and the slope characteristics in the watershed geographic information data.

3. A water conservancy monitoring system based on digital twins, executing a water conservancy monitoring method based on digital twins as described in any one of claims 1-2, characterized in that: The system includes: a watershed digital model generation module, a watershed data integration module, a hydrological dynamic simulation module and a risk warning module; The watershed digital model generation module constructs a watershed digital model based on SWAT; The watershed data integration module is used to integrate the data of each sensor deployed in the target watershed into the watershed digital model; The hydrological dynamic simulation module simulates the hydrological dynamics of the target basin in the basin digital model based on the digital twin technology, and predicts the areas in the target basin with the risk of soil erosion; The risk warning module is used to report the location information of the sub-areas with soil and water loss risks in the target watershed according to the output results of the hydrological dynamic simulation module, and feed back the warning information to the corresponding management personnel terminal.

4. According to claim 3, a water conservancy monitoring system based on digital twins is characterized in that: The watershed data integration module includes a data acquisition unit, a data verification unit and a data integration unit; The data acquisition unit is used to acquire real-time monitoring data collected by each sensor in the target watershed; The data verification unit verifies the real-time monitoring data acquired by each monitoring node based on the verification node; The data integration unit integrates the verified real-time monitoring data into the watershed digital model based on the verification result of the data verification unit.

5. The water conservancy monitoring system based on digital twin according to claim 3 is characterized by: The hydrological dynamic simulation module includes a hydrological dynamic update unit and a watershed risk prediction unit; The hydrological dynamics updating unit is used to update the hydrological dynamics of each sub-area of ​​the target watershed in the watershed digital model according to the output of the watershed data integration module; The watershed risk prediction unit is used to predict whether there is a risk of soil erosion in each sub-area of ​​the target watershed.

6. The water conservancy monitoring system based on digital twin according to claim 3 is characterized by: The risk warning module includes a risk position marking unit and a warning feedback unit; The risk location marking unit is used to integrate the soil and water loss risks of each sub-region of the target basin according to the output of the hydrological dynamic simulation module, and obtain the location information of the region with soil and water loss risks; The warning feedback unit is used to feed back the location information of the area with soil and water loss risks to the corresponding management personnel terminal.

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