Hydraulic engineering management and control system and method based on digital twinning

Abstract

The invention discloses a hydraulic engineering management and control system and method based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: firstly collecting and preprocessing channel condition data in real time, then comprehensively processing the data to obtain a sediment deposition factor and a congestion prediction factor, and analyzing whether sediment deposition exists according to the sediment deposition factor and the congestion prediction factor; if yes, whether the channel is congested or not is predicted according to the latter, if congestion is predicted, a congestion early warning is given out, and if no deposition exists or congestion does not exist, the process is ended correspondingly or prediction continues. The method combines flow velocity and flow direction abnormal parameters to form sediment deposition factors, bypasses underwater direct detection bottlenecks by means of digital twin modeling and simulation algorithms, improves sediment deposition judgment accuracy through multi-dimensional analysis, constructs congestion prediction factors based on a standard and actual water flow difference value and the flow direction abnormal parameters, and performs threshold exceeding early warning. The blocking risk is recognized in advance, and time is won for hydraulic engineering maintenance regulation and control.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and specifically to a water conservancy project management and control system and method based on digital twins. Background Art

[0002] In water conservancy projects, channels are regular water transfer channels artificially dug or built with clear cross-sectional shapes and hydraulic gradients. Their core function is to achieve directional transportation, distribution and regulation of water resources. They are widely used in irrigation, water transfer, drainage, power generation and other scenarios. For example, farmland irrigation channels ensure water for crops, large-scale water transfer channels solve cross-basin water resource allocation problems, and drainage channels eliminate waterlogging. They are key infrastructure connecting water sources and water users. Their structural form and scale are designed according to factors such as purpose and terrain, and are of great significance to the efficient use of water resources and flood control and drought relief.

[0003] For example, the invention patent with publication number CN118798002B discloses a digital twin-based water conservancy project management and control system and method. This system utilizes a predictive analysis module to construct formulas for predicting water flow, water flow accumulation location, and equipment pressure resistance performance. This system calculates the water flow Q(t) of the monitored river, the water flow accumulation height P(t), and the actual pressure resistance index C(t) of the water conservancy equipment in real time. The comprehensive calculation and analysis module calculates the first evaluation index Re(t) and compares it with a preset first reliability threshold T1 to provide a preliminary analysis of the reliability of the water conservancy equipment under flood conditions. If the equipment is unreliable under flood conditions, the abnormal warning module will further calculate the second evaluation index Re2(t) based on environmental factors, compare it with the second reliability threshold T2, and generate a corresponding warning level, providing a more detailed reliability assessment to ensure the safety of the water conservancy equipment under various environmental factors.

[0004] However, the above plan does not take into account that when silt accumulates in the channel of a water conservancy project, its water delivery conditions will change significantly. As silt continues to accumulate, the water flow section of the channel gradually shrinks, and the water flow resistance continues to increase, causing the water delivery capacity to gradually decline and the actual water flow rate to decrease accordingly. When the degree of silt accumulation continues to intensify and the water flow section further shrinks, it will eventually lead to complete blockage of the channel, completely losing its water delivery function and no longer being able to transport water resources to the downstream. Summary of the Invention

[0005] Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a water conservancy project management and control system and method based on digital twins, which solves the problem that when siltation occurs in the water conservancy project channel, the water delivery capacity will decrease due to the reduction of the water flow section and the increase of water flow resistance. When the siltation intensifies to a certain extent, the channel will be blocked and lose its water delivery function.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a water conservancy project management and control system and method based on digital twins, including the following specific steps and modules: Step 1: Start real-time collection of channel status data; Step 2: Preprocess the channel status data; Step 3: Comprehensively process the preprocessed channel status data to obtain a siltation factor and a channel congestion prediction factor, and analyze whether there is siltation in the channel according to the siltation factor. If it is analyzed that there is siltation, predict whether the channel is congested according to the channel congestion prediction factor. If it is predicted that the channel is congested, execute step 4. If it is predicted that the channel is not congested, continue to predict whether the channel is congested. If it is analyzed that there is no siltation, end the process; Step 4: Issue a congestion warning and end the process.

[0009] Furthermore, the specific method of analyzing whether there is siltation in the channel based on the siltation factor is as follows: setting a siltation threshold, comparing the siltation factor with the siltation threshold, if the siltation factor is greater than the siltation threshold, it indicates that there is siltation, if the siltation factor is less than or equal to the siltation threshold, it indicates that there is no siltation.

[0010] Furthermore, the specific method for obtaining the silt deposition factor is as follows: digital twin modeling is performed on the hydrological information data and the channel structure data, the hydrological information data and the channel structure data are integrated and their operating status is simulated in real time with the help of a simulation algorithm, and standardized processing and comprehensive calculation are performed to obtain the flow velocity abnormality parameters and the flow direction abnormality parameters, and normalization processing is performed according to the flow velocity abnormality parameters and the flow direction abnormality parameters, and comprehensive calculation is performed to obtain the silt deposition factor; JY=ln(SC)-ln(XC); wherein JY represents the silt deposition factor, SC represents the flow velocity abnormality parameter, and is greater than zero, and XC represents the flow direction abnormality parameter, and is greater than zero.

[0011] Furthermore, the specific method for obtaining the flow velocity anomaly parameters is as follows: the flow velocity and flow velocity volatility of the water flow without silt deposition are calculated through experiments, recorded as experimental flow velocity and experimental flow velocity fluctuation value, the actual water flow velocity is calculated by the variance method, and the actual flow velocity fluctuation value is obtained, and the flow velocity anomaly parameters are obtained based on the comprehensive calculation of the actual water flow velocity, experimental flow velocity, actual flow velocity fluctuation value and experimental flow velocity fluctuation value.

[0012] Furthermore, the specific method for obtaining the actual flow velocity fluctuation value is as follows: the actual water flow velocity at each moment is summed up and calculated according to the time series to obtain the total actual water flow velocity, the average is calculated based on the total actual water flow velocity to obtain the actual water flow velocity mean, the difference is calculated between the actual water flow velocity at each moment and the actual water flow velocity mean to obtain the degree of deviation between the actual water flow velocity at each moment and the actual water flow velocity mean, and the square of the degree of deviation between the actual water flow velocity at each moment and the actual water flow velocity mean is calculated, the degree of deviation between the actual water flow velocity at each moment and the actual water flow velocity mean is summed up and recorded as the total deviation, and the average is taken based on the total deviation to obtain the actual flow velocity fluctuation value.

[0013] Furthermore, the specific method for obtaining the flow direction anomaly parameter is as follows: comprehensively calculating the actual buoy position in different time series to obtain the actual water flow movement vector, setting a reference vector, and calculating the actual water flow movement vector and the reference vector by the vector dot product method to obtain the flow direction anomaly parameter; Among them, XC represents the flow direction anomaly parameter, which is a cosine value and is between zero and one. represents the actual water flow movement vector, represents the reference vector, represents the modulus of the actual water flow moving vector, Represents the magnitude of the reference vector.

[0014] Furthermore, the specific method of obtaining the actual water flow movement vector is as follows: suppose the actual buoy position at the j+1th moment is (x1, y1), and the actual buoy position at the jth moment is (x2, y2), so the water flow movement vector from the actual buoy position at the jth moment to the actual buoy position at the j+1th moment is (x1-x2, y1-y2), and since there are several water flow movement vectors in the time series, the actual water flow movement vector is obtained by summing and averaging according to the time series.

[0015] Furthermore, the congestion prediction factor is specifically obtained as follows: set a standard water flow rate, multiply the channel water depth, channel width, and actual water flow velocity according to the water flow rate formula to obtain the actual water flow rate, and perform a comprehensive calculation based on the standard water flow rate, actual water flow rate, and flow direction abnormality parameters to obtain the congestion prediction factor; DY = (BL - SL) × XC; where DY represents the congestion prediction factor, BL represents the standard water flow rate, and SL represents the actual water flow rate.

[0016] Furthermore, the specific method of predicting whether the channel is congested based on the channel congestion prediction factor is as follows: setting a congestion threshold, comparing the congestion prediction factor with the congestion threshold, if the congestion prediction factor is greater than the congestion threshold, it means that the channel is predicted to be congested; if the congestion prediction factor is less than or equal to the congestion threshold, it means that the channel is predicted to be uncongested.

[0017] Furthermore, there are a data acquisition module, a data preprocessing module, a central computing and processing module, and an early warning module; the data acquisition module is used to start real-time collection of channel status data; the data preprocessing module is used to preprocess the channel status data; the central computing and processing module is used to comprehensively process the preprocessed channel status data to obtain a silt deposition factor and a channel congestion prediction factor, and analyze whether there is silt deposition in the channel according to the silt deposition factor. If it is analyzed that there is silt deposition, whether the channel is congested is predicted according to the channel congestion prediction factor. If it is predicted that the channel is congested, the early warning module is executed. If it is predicted that the channel is not congested, the prediction of whether the channel is congested is continued. If it is analyzed that there is no silt deposition, the process is terminated; the early warning module is used to issue a congestion warning and terminate the process.

[0018] Beneficial effects

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] 1. By combining abnormal flow velocity parameters with abnormal flow direction parameters to form a sedimentation factor, the problem of complex underwater environment and difficulty in directly detecting sedimentation can be circumvented. The sedimentation status can be indirectly reflected with the help of abnormal changes in water flow velocity and flow direction. This method uses digital twin modeling to integrate hydrological and channel structure data, simulates the operating status in real time through simulation algorithms, and then obtains relevant parameters through standardization and comprehensive calculation. This not only avoids the technical bottleneck of direct sediment detection, but also improves the accuracy of sedimentation judgment through collaborative analysis of multi-dimensional parameters, providing a more reliable basis for channel condition analysis.

[0021] 2. A congestion prediction factor is constructed based on standard water flow, actual water flow, and flow direction anomaly parameters, which can achieve forward-looking prediction of channel congestion. This method first calculates the actual water flow through the water flow formula, and then comprehensively evaluates the congestion risk by combining the difference between the standard water flow and the flow direction anomaly parameters. When the congestion prediction factor exceeds the threshold, an early warning is issued in a timely manner. This prediction mechanism can identify risks in advance before the channel is blocked, buying time for the maintenance and regulation of water conservancy projects, avoiding the decline in channel water delivery capacity or even complete blockage due to increased siltation, and ensuring the stability and reliability of the channel's water delivery function.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This invention: a flow chart of a water conservancy project management and control method based on digital twins.

[0024] Figure 2 This is the present invention: a structural diagram of a water conservancy project management and control system based on digital twins. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with 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 making creative efforts are within the scope of protection of the present invention.

[0026] It should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include," "comprise," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0027] Example 1

[0028] like Figure 1 As shown, the embodiment of the present invention provides a water conservancy project management and control method based on digital twins, which includes the following specific steps:

[0029] Step one: start real-time collection of channel condition data, the channel condition data includes hydrological information data and channel structure data, the hydrological information data includes the actual water flow speed obtained by the water flow speed sensor and the actual float position obtained by the float type flow speed and direction instrument, because the thrust when the water flow moves will push the movement of the float type flow speed and direction instrument, the change of the actual float position reflects the moving direction of the water flow, and the coordinates of the actual float position are two-dimensional, because the float moves on the water surface and cannot move underwater, therefore the movement dimension of the float is two-dimensional rather than three-dimensional, wherein the existence of the sediment increases the resistance of the water flow, so that the water flow energy loss is intensified, and then the actual water flow speed is reduced, so that the influence of the sediment deposition is reflected by the actual water flow speed, in addition, the irregular shape, size difference and interaction with the water flow of the sediment particles break the uniformity of the water flow, so that the water flow is subjected to asymmetric force in the transverse and longitudinal directions, thereby changing the moving direction of the water flow, so that the influence of the sediment deposition is reflected by the moving direction of the water flow;

[0030] And the reason why the sediment deposition is not directly detected is that the underwater environment is complex, the water body is turbid or the terrain is variable, and the movement and deposition process of the sediment particles change dynamically, so that the existing detection technology cannot directly and accurately detect the sediment deposition;

[0031] The channel structure data includes the channel water depth obtained by the pressure type liquid level sensor and the channel width obtained by measurement, which provides conditions for subsequent water flow calculation, and the change of the water flow is used to reflect the channel blockage.

[0032] Step two: data cleaning processing is performed on the hydrological information data and the channel structure data to remove redundant values and improve the data quality of the hydrological information data and the channel structure data.

[0033] Step 3: Conduct digital twin modeling on the cleaned hydrological information data and channel structure data, that is, by building a digital model mapped to the channel physical entity, integrating the hydrological information data and channel structure data and using simulation algorithms to simulate their operating status in real time, to achieve full life cycle monitoring and control of the channel physical entity, and predict the sediment congestion in the channel, and standardize the cleaned hydrological information data and channel structure data, which helps to eliminate the dimensional difference between the hydrological information data and the channel structure data, and convert the values ​​of different orders of magnitude into a unified numerical range, and perform comprehensive calculations to obtain the abnormal flow velocity parameters and abnormal flow direction parameters. Due to the presence of sediment in the channel, The friction and mutual collision between sediment particles and water flow will consume water flow energy, resulting in the attenuation of water flow velocity; and sediment deposition will make the bottom or slope of the channel rough, and even form sediment bodies, forcing the water flow to bypass obstacles or induce local eddies, thereby changing the direction of water flow. Therefore, normalization processing is performed based on the velocity anomaly parameters and the flow direction anomaly parameters to further eliminate the dimensional difference between the velocity anomaly parameters and the flow direction anomaly parameters, and convert the values ​​of different orders of magnitude into a unified numerical range. After comprehensive calculation, the sediment deposition factor is obtained. The sediment deposition factor obtained by combining the velocity anomaly parameters and the flow direction anomaly parameters jointly reflects the impact of sediment deposition on water flow, thereby improving the accuracy of sediment deposition analysis in the channel.

[0034] Therefore, according to the sedimentation factor, whether there is sedimentation in the channel is analyzed. If the analysis shows that there is sedimentation, whether the channel is congested is predicted. If the channel is predicted to be congested, step four is executed. If the channel is predicted to be not congested, the channel is continued to be predicted. If the analysis shows that there is no sedimentation, the process ends.

[0035] The specific method of analyzing whether there is sedimentation in the channel based on the sedimentation factor is as follows:

[0036] A sedimentation threshold is set through experiments as a standard for judging whether there is sedimentation in the channel. The sedimentation factor is compared with the sedimentation threshold. If the sedimentation factor is greater than the sedimentation threshold, it means that sedimentation exists. If the sedimentation factor is less than or equal to the sedimentation threshold, it means that there is no sedimentation.

[0037] The specific method for obtaining the sediment deposition factor is as follows:

[0038] JY=ln(SC)-ln(XC);

[0039] Among them, JY represents the sedimentation factor, which reflects whether there is sedimentation in the channel; SC represents the velocity anomaly parameter, which is greater than zero, reflecting whether the water velocity in the channel is abnormal; XC represents the flow direction anomaly parameter, which is greater than zero, reflecting whether the water direction in the channel is abnormal.

[0040] The specific method for obtaining the abnormal flow velocity parameters is as follows:

[0041] The flow velocity and flow velocity fluctuation of the water without sediment deposition are calculated through experiments and recorded as experimental flow velocity and experimental flow velocity fluctuation value. The experimental flow velocity is used as the standard of the water flow velocity without sediment deposition. The experimental flow velocity fluctuation value represents the change and fluctuation of the water flow velocity when there is no sediment deposition in the channel. The actual water flow velocity is calculated by the variance method to obtain the actual flow velocity fluctuation value. The flow velocity anomaly parameter is obtained based on the comprehensive calculation of the actual water flow velocity, the experimental flow velocity, the actual flow velocity fluctuation value and the experimental flow velocity fluctuation value.

[0042] SC = (YS - SD) × (SB - YB);

[0043] Among them, SC represents the velocity anomaly parameter, and is greater than zero, reflecting whether the water velocity in the channel is abnormal. YS represents the experimental velocity, which indicates the water velocity in the channel without sediment deposition. SD represents the actual water velocity, which reflects the actual water velocity in the channel. When there is sediment deposition in the channel, the sediment interferes with the actual water velocity, causing the actual water velocity to decrease, thereby causing the difference between the experimental flow velocity and the actual water velocity to be greater than zero. The greater the difference between the experimental flow velocity and the actual water velocity, the greater the velocity anomaly parameter. SB represents the actual flow velocity fluctuation value, which indicates the fluctuation of the water velocity in the channel. The actual water flow velocity changes, YB represents the experimental flow velocity fluctuation value, which indicates the water flow velocity change when there is no silt accumulation in the channel. When there is silt accumulation in the channel, the silt also affects the volatility of the water flow velocity change. The silt accumulation causes the water section to be irregular, and the resistance of the silt to the water flow changes dynamically, resulting in the actual flow velocity fluctuation disorder, causing the actual flow velocity fluctuation value to be greater than the experimental flow velocity fluctuation value. Therefore, the difference between the actual flow velocity fluctuation value and the experimental flow velocity fluctuation value is greater than zero, and the greater the difference between the actual flow velocity fluctuation value and the experimental flow velocity fluctuation value, the greater the velocity abnormality parameter.

[0044] The specific method of obtaining the actual flow rate fluctuation value is as follows:

[0045] The actual water flow velocity at each moment is summed up according to the time series to obtain the total amount of the actual water flow velocity. The average of the total amount of the actual water flow velocity is taken to obtain the mean actual water flow velocity, which is used as the standard for the fluctuation of the actual water flow velocity around the mean actual water flow velocity. Therefore, the actual water flow velocity at each moment is subtracted from the mean actual water flow velocity to obtain the degree of deviation between the actual water flow velocity at each moment and the mean actual water flow velocity. The square of the degree of deviation between the actual water flow velocity at each moment and the mean actual water flow velocity is calculated to ignore the positive and negative differences and simplify the calculation without affecting the calculation accuracy. The degree of deviation between the actual water flow velocity at each moment and the mean actual water flow velocity is summed up and recorded as the total deviation. The average of the total deviation is taken to obtain the actual flow velocity fluctuation value.

[0046]

[0047] Among them, SB represents the actual flow velocity fluctuation value, reflecting the actual water flow velocity fluctuation in the channel, T represents the time series acting on the actual water flow velocity, and SB i represents the actual flow velocity fluctuation value of the i-th value, Indicates the total amount of actual water flow velocity, represents the actual mean water velocity, Indicates the degree of deviation between the actual water flow velocity at each moment and the mean value of the actual water flow velocity. It means that the deviation degree between the actual water flow velocity at each moment and the mean of the actual water flow velocity is calculated by taking the square, ignoring the positive and negative differences. Indicates the total amount of deviation.

[0048] The specific method for obtaining flow direction abnormal parameters is as follows:

[0049] The actual buoy positions in different time series are comprehensively calculated to obtain the actual water flow movement vector. A reference vector is set, which is the average movement vector of the water flow in different time series without sediment interference. The actual water flow movement vector and the reference vector are calculated using the vector dot product method to obtain the flow direction anomaly parameter.

[0050]

[0051] Among them, XC represents the flow direction abnormality parameter, which is a cosine value between zero and one, reflecting whether the flow direction of water in the channel is abnormal. represents the actual water flow movement vector, represents the reference vector, represents the modulus of the actual water flow moving vector, It represents the modulus of the reference vector. If the actual water flow movement vector is more in the same direction as the reference vector, the flow direction anomaly parameter is closer to one. If the actual water flow movement vector is more out of the same direction as the reference vector, the flow direction anomaly parameter is closer to zero.

[0052] The specific method of obtaining the actual water flow movement vector is as follows:

[0053] Suppose the actual buoy position at the j+1th moment is (x1, y1), and the actual buoy position at the jth moment is (x2, y2). Therefore, the water flow movement vector from the actual buoy position at the jth moment to the actual buoy position at the j+1th moment is (x1-x2, y1-y2). Since there are several water flow movement vectors in the time series, the actual water flow movement vector is obtained by summing and averaging according to the time series.

[0054] The difference between Example 2 and Example 1 is that:

[0055] The specific method for predicting whether a channel is congested is as follows:

[0056] Set the standard water flow, that is, the water flow in the channel when there is no sediment. According to the water flow formula, the product of the channel water depth, channel width and actual water flow velocity is calculated to obtain the actual water flow. Based on the standard water flow, actual water flow and flow direction abnormality parameters, a comprehensive calculation is performed to obtain the congestion prediction factor.

[0057] DY=(BL-SL)×XC;

[0058] Among them, DY is the congestion prediction factor, which reflects the congestion of sediment in the channel, BL is the standard water flow, and SL is the actual water flow. The larger the difference between the standard water flow and the actual water flow, the more congested the channel is, that is, the more sediment congestion there is, the smaller the actual water flow is. The more sediment congestion there is, the stronger the obstruction to the water flow is, so the more water vortex or water flow deflection there is, that is, the larger the flow direction anomaly parameter is, the larger the congestion prediction factor is.

[0059] A congestion threshold is set through experiments as a criterion for judging whether the channel is congested. The congestion prediction factor is compared with the congestion threshold. If the congestion prediction factor is greater than the congestion threshold, it means that the channel is predicted to be congested. If the congestion prediction factor is less than or equal to the congestion threshold, it means that the channel is predicted to be uncongested.

[0060] Step 4: Issue a congestion warning and end the process.

[0061] like Figure 2 Shown: A water conservancy project management and control system based on digital twins, including:

[0062] Data collection module: used to start collecting channel status data in real time;

[0063] Data preprocessing module: used to preprocess channel status data;

[0064] Central computing and processing module: used to comprehensively process the pre-processed channel condition data to obtain the sedimentation factor and the channel congestion prediction factor. The channel is analyzed for sedimentation based on the sedimentation factor. If sedimentation is found, the channel is predicted to be congested based on the channel congestion prediction factor. If congestion is predicted, the early warning module is executed. If the channel is not congested, the prediction of channel congestion is continued. If no sedimentation is found, the process ends.

[0065] Early warning module: used to issue a blockage warning and end the process when the system determines that there is a risk of channel blockage. When the central computing and processing module analyzes the existence of channel siltation based on the sedimentation factor and predicts channel congestion based on the channel congestion prediction factor, the early warning module will be triggered;

[0066] The early warning module will issue a congestion warning in a variety of ways, such as emitting obvious sound and light signals through the sound and light alarm device so that relevant staff can detect it in time; at the same time, it will send early warning notifications containing detailed information such as the location and severity of the congestion risk to the mobile terminals of water conservancy project managers, computers in the monitoring center and other equipment to ensure that the information can be conveyed quickly and accurately. At the same time as issuing the congestion warning, the early warning module will end the current monitoring and analysis process according to the preset process, and provide a clear start signal for subsequent channel dredging, maintenance and other work, so that relevant personnel can respond quickly and take effective measures to deal with the channel congestion problem, so as to avoid further aggravation of the congestion and cause greater impact on the normal operation of the water conservancy project.

[0067] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A water conservancy project management and control method based on digital twins, characterized by: The specific steps include: Step 1: Start collecting channel status data in real time; Step 2: Preprocess the channel status data; Step 3: Comprehensively process the pre-processed channel condition data to obtain a sedimentation factor and a channel congestion prediction factor. Analyze whether the channel has sedimentation based on the sedimentation factor. If sedimentation is found, predict whether the channel is congested based on the channel congestion prediction factor. If congestion is found, proceed to step 4. If no congestion is found, continue to predict whether the channel is congested. If no sedimentation is found, end the process. Step 4: Issue a congestion warning and end the process.

2. A water conservancy project management and control method based on digital twin according to claim 1, characterized in that: The specific method of analyzing whether there is sedimentation in the channel based on the sedimentation factor is as follows: Set a sedimentation threshold and compare the sedimentation factor with the sedimentation threshold. If the sedimentation factor is greater than the sedimentation threshold, it means that sedimentation exists. If the sedimentation factor is less than or equal to the sedimentation threshold, it means that there is no sedimentation.

3. A water conservancy project management and control method based on digital twin according to claim 2, characterized in that: The specific method of obtaining the sediment deposition factor is as follows: Conduct digital twin modeling of hydrological information data and channel structure data, integrate them, and simulate their operating status in real time using simulation algorithms. Standardize and comprehensively calculate the abnormal velocity and direction parameters, and then normalize and comprehensively calculate the abnormal velocity and direction parameters to obtain the sediment deposition factor. JY=ln(SC)-ln(XC); Among them, JY represents the sediment deposition factor, SC represents the velocity anomaly parameter and is greater than zero, and XC represents the flow direction anomaly parameter and is greater than zero.

4. A water conservancy project management and control method based on digital twin according to claim 3, characterized in that: The specific method for obtaining the abnormal flow velocity parameter is as follows: The flow velocity and flow velocity fluctuation of water without silt deposition are calculated through experiments and recorded as experimental flow velocity and experimental flow velocity fluctuation value. The actual water flow velocity is calculated by variance method to obtain the actual flow velocity fluctuation value. The flow velocity anomaly parameter is obtained based on the comprehensive calculation of actual water flow velocity, experimental flow velocity, actual flow velocity fluctuation value and experimental flow velocity fluctuation value.

5. A water conservancy project management and control method based on digital twin according to claim 4, characterized in that: The specific method for obtaining the actual flow rate fluctuation value is as follows: According to the time series, the actual water flow velocity at each moment is summed up and calculated to obtain the total actual water flow velocity. The average of the total actual water flow velocity is calculated to obtain the mean actual water flow velocity. The difference between the actual water flow velocity at each moment and the mean actual water flow velocity is calculated to obtain the degree of deviation between the actual water flow velocity at each moment and the mean actual water flow velocity. The square of the degree of deviation between the actual water flow velocity at each moment and the mean actual water flow velocity is calculated. The degree of deviation between the actual water flow velocity at each moment and the mean actual water flow velocity is summed up and recorded as the total deviation. The average of the total deviation is taken to obtain the actual flow velocity fluctuation value.

6. The water conservancy project management and control method based on digital twin according to claim 3 is characterized by: The specific method for obtaining the flow direction abnormality parameter is as follows: The actual buoy position in different time series is comprehensively calculated to obtain the actual water flow movement vector, and the reference vector is set. The actual water flow movement vector and the reference vector are calculated by the vector dot product method to obtain the flow direction anomaly parameter; Among them, XC represents the flow direction anomaly parameter, which is a cosine value and is between zero and one. represents the actual water flow movement vector, represents the reference vector, represents the modulus of the actual water flow moving vector, Represents the magnitude of the reference vector.

7. A water conservancy project management and control method based on digital twin according to claim 6, characterized in that: The specific method of obtaining the actual water flow movement vector is as follows: Suppose the actual buoy position at the j+1th moment is (x1, y1), and the actual buoy position at the jth moment is (x2, y2). Therefore, the water flow movement vector from the actual buoy position at the jth moment to the actual buoy position at the j+1th moment is (x1-x2, y1-y2). Since there are several water flow movement vectors in the time series, the actual water flow movement vector is obtained by summing and averaging according to the time series.

8. The water conservancy project management and control method based on digital twin according to claim 1 is characterized by: The specific method of obtaining the congestion prediction factor is as follows: Set the standard water flow rate, calculate the product of channel depth, channel width and actual water velocity according to the water flow formula to obtain the actual water flow rate, and perform a comprehensive calculation based on the standard water flow rate, actual water flow rate and flow direction abnormality parameters to obtain the congestion prediction factor; DY=(BL-SL)×XC; Where DY represents the congestion prediction factor, BL represents the standard water flow, and SL represents the actual water flow.

9. The water conservancy project management and control method based on digital twin according to claim 1, characterized in that: The specific method of predicting whether a channel is congested based on the channel congestion prediction factor is as follows: A congestion threshold is set and the congestion prediction factor is compared with the congestion threshold. If the congestion prediction factor is greater than the congestion threshold, it indicates that the channel is predicted to be congested. If the congestion prediction factor is less than or equal to the congestion threshold, it indicates that the channel is predicted to be uncongested.

10. A water conservancy project management and control system based on digital twins, used to implement a water conservancy project management and control method based on digital twins according to any one of claims 1 to 9, characterized in that: The system includes: a data acquisition module, a data pre-processing module, a central computing and processing module, and an early warning module; The data collection module is used to start collecting channel status data in real time; The data preprocessing module is used to preprocess the channel status data; The central computing and processing module is used to comprehensively process the pre-processed channel condition data to obtain a sedimentation factor and a channel congestion prediction factor, analyze whether the channel has sedimentation based on the sedimentation factor, and if sedimentation is found to be present, predict whether the channel is congested based on the channel congestion prediction factor. If channel congestion is predicted, execute the early warning module. If the channel is not congested, continue to predict whether the channel is congested. If the analysis shows that there is no sedimentation, end the process. The warning module is used to issue a congestion warning and end the process.

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