A multi-objective intelligent water control method for tidal river network water gate group

Abstract

The application discloses a kind of tidal river network water gate group multi-objective intelligent water power regulation methods, steps are as follows: S1, obtains tidal river network historical detection data and hydrological data.S2, construct river network flow prediction model, water level prediction model, obtain river network key section hydrodynamic data.S3, based on multi-objective optimization idea, with water power regulation as the core target function, flow prediction model and water level prediction model are nested into improved optimization algorithm.S4, according to the multi-objective demand such as river network flood control, water resources, water environment and water ecology, based on optimization algorithm, the optimal gate opening is solved, and real-time dynamic water gate group joint regulation scheme is generated.The application combines optimization scheduling method and machine learning technology, proposes a tidal river network water gate group multi-objective intelligent water power regulation method, which can generate water gate group joint regulation scheme in real time, significantly enhance the flowability and self-purification capacity of river network water body, and provide important support for the systematic governance and intelligent management of tidal river network water problems.

Description

Technical Field

[0001] The present invention belongs to the fields of hydraulics and river dynamics, and in particular relates to a multi-objective intelligent hydraulic control method for a tidal river network sluice group. Background Art

[0002] Tidal river networks are densely populated, economically developed, and often strategically important. However, these areas are subject to floodwaters from the basin above and high tides below, making drainage difficult and flooding frequent. Their low-lying terrain results in insufficient water resources and a low carrying capacity. Large amounts of water discharged for agricultural, industrial, and domestic use contribute to heavy pollution loads in the river network, leading to significant water ecological and environmental challenges. Therefore, tidal river networks face multiple challenges, including flood control, water resource conservation, and water ecological and environmental issues. Currently, numerous sluice and pump projects are constructed within river networks, and coordinated scheduling of these projects is a key means of ensuring water security. However, the number of coordinated scheduling options is often enormous, making rapid decision-making and optimal selection challenging. Traditional sluice gate scheduling methods in river networks rely on numerical simulation software, which generates rough sluice control operation plans by enumerating different combinations of opening and closing angles for each gate under characteristic boundary conditions. This decision-making approach is time-consuming and labor-intensive, particularly in large river networks, and suffers from low computational efficiency and poor adaptability to real-time hydrological parameters. Therefore, it is urgent to build a multi-objective intelligent hydraulic control method for sluice groups in tidal river networks, generate real-time joint control plans for sluice groups, meet the multi-objective needs of tidal river networks, and provide important support for the systematic governance and intelligent management of river network water problems. Summary of the Invention

[0003] In view of the problems existing in the prior art, the present invention provides a multi-objective intelligent hydraulic control method for a sluice group in a tidal river network, which is an intelligent control method for a sluice group that can reconstruct the spatiotemporal pattern of the hydrodynamics of the river network.

[0004] To solve the above technical problems, the present invention provides the following technical solution: a multi-objective intelligent hydraulic control method for a tidal river network sluice group, comprising the following steps:

[0005] S1. Acquire and pre-process historical detection data and hydrological data of the tidal river network. The historical detection data and hydrological data include gate opening data, tidal water level data, key section flow data, and water level data within the gate;

[0006] S2. Using gate opening data and tidal water level data as input and key section flow data as output, a flow prediction model is constructed and trained. Using gate opening data and tidal water level data as input and gate internal water level data as output, a water level prediction model is constructed and trained.

[0007] S3. Construct a multi-objective function based on the water exchange rate and water level to evaluate the gate opening scheme, and add gate opening constraints; S4. Use a genetic algorithm to solve the multi-objective function, specifically: embed the flow prediction model and the water level prediction model into the genetic algorithm, pass the key section flow data obtained by the flow prediction model under the current gate opening scheme to the genetic algorithm, calculate the objective function value, and the genetic algorithm determines whether the water exchange rate is the largest under the current scheme. If so, the optimal river network sluice opening combination is obtained. Otherwise, the water level prediction model predicts the water level inside the gate under the current scheme, and then compares the water level inside the gate with the tidal water level of the outer river based on the tidal water level of the outer river to obtain the water level difference, return to the optimization algorithm to readjust the gate opening, and then calculate the new key section flow through the flow prediction model, iterate the calculation until the objective function reaches the maximum value, that is, the optimal river network sluice opening combination is obtained.

[0008] Furthermore, the aforementioned step S1 specifically includes the following sub-steps:

[0009] S1.1. Deploy IoT monitoring nodes at pre-determined locations along the tidal river network, including water level sensors, flow monitors, and gate height sensors;

[0010] S1.2. Establish a data transmission network. Use wireless communication technology to build a real-time data transmission network to upload the monitoring data of each device in the monitoring node to the central data platform in real time.

[0011] S1.3. Integrate historical monitoring data, including water level, flow, and gate opening records, establish a multi-temporal and spatial scale database, collect tidal water level data predicted by hydrological stations, combine historical trends with real-time monitoring, and generate hydrological data corresponding to historical monitoring data;

[0012] S1.4. Build a data management system and use the big data platform to collect, store and visualize monitoring data, update it in real time, clean and label historical and real-time data, and eliminate outliers.

[0013] Furthermore, in the aforementioned step S2, the traffic prediction model is specifically: a traffic prediction model is constructed based on a time fusion transformer, and the time fusion transformer includes: a gated residual network GRN, a variable selection network, and a multi-layer masked attention mechanism layer; the gated residual network GRN realizes dynamic adjustment of input features through nonlinear transformation; the variable selection network, through the variable selection mechanism, combines the gated residual network to dynamically assign weights to the input features and extract important features; the multi-layer masked attention mechanism layer captures the dependencies between complex time series features, and at the same time enhances the effectiveness of attention through the mask mechanism, and improves the modeling ability of the complex dynamic changes of the tidal river network through multi-layer stacking.

[0014] Furthermore, the aforementioned gated residual network GRN is specifically:

[0015] h1=W1·x+b1

[0016] h2=ReLU(h1)

[0017] g=σ(W g h2+b g )

[0018] y=g·h2+(1-g)x

[0019] Among them, h1 is the linear transformation output of the hidden layer, W1 is the weight matrix input to the hidden layer, b1 is the bias vector input to the hidden layer, h2 is the output of the hidden layer after the activation function, g is the output of the gating mechanism, W g is the weight matrix of the gating mechanism, b g is the bias vector of the gating mechanism; σ and ReLU are activation functions, x is the input feature, and y is the output feature;

[0020] Variable selection network, specifically:

[0021]

[0022] Where z i is the i-th input eigenvalue; It is z i Perform exponential operations to enhance the differences between values; is the original value of the jth feature at time t; It is to flatten multidimensional data into a one-dimensional vector.

[0023] Multi-layer mask attention mechanism layer, specifically:

[0024]

[0025] MultiHead(Q,K,V)=Concat(head1,…,head h )W O

[0026]

[0027] Where Q, K, and V represent query, key, and value vectors, respectively, which are connected by the input X and the weight matrix W. Q ,W K ,W V Sex transformation generation; d k is the key vector dimension, used to scale the dot product result; M is the mask matrix used to mask future time steps or invalid positions; W O is the output weight matrix, is the query weight matrix of the i-th head, is the key weight matrix of the i-th head, The value weight matrix of the i-th head; head i is the i-th attention head, softmax is a normalization operation used to generate attention weights, and Concat is to concatenate the outputs of multiple attention heads into a complete vector.

[0028] Furthermore, the aforementioned water level prediction model is constructed based on the long short-term memory network LSTM, specifically:

[0029] The forget gate determines whether to keep or forget by outputting the value [0,1] of the σ activation function of the formula, as shown below:

[0030] f t =σ(W f ·[h t-1 ,x t +b f ])

[0031] The input gate introduces new information and updates the memory unit as follows:

[0032] i t =σ(W i ·[h t-1 ,x t +b i ])

[0033]

[0034] The current memory unit combines the memory of the previous moment and the current new information as follows:

[0035]

[0036] The output gate determines the output and memory content at the current moment through the formula, as shown below:

[0037] o t =σ(W o ·[h t-1 ,x t +b o ])

[0038] h t =o t *tanh(C t )

[0039] Where σ is the activation function; h t-1 is the output at time t-1; x t is the input at time t; C t is the information memory unit at time t, f t is the forget gate output, Wf is the weight matrix of the forget gate, b t is the bias of the forget gate, i t is the input gate output, W i is the weight matrix of the input gate, b i is the bias of the input gate, is the new memory candidate value, W C is the weight matrix of the new memory candidate value, o t is the output of the output gate, W o is the weight matrix of the output gate, b C is the bias of the new memory candidate value, b o is the bias of the output gate.

[0040] Furthermore, the multi-objective function in the aforementioned step S3 is:

[0041] Z min ≤Z i ≤Z max

[0042] V i =∫Q i

[0043]

[0044] Among them, Z min The lowest water level to meet landscape requirements, in meters; Z max The highest water level to meet flood control needs, unit: m; Q i is the flow rate of the i-th section of the target river, in m 3 / s;V i is the water flow rate of the i-th section of the target river within a certain period of time, in m 3 ; V0 is the total channel storage capacity of the target river section, unit is m 3 ;γ is the water exchange rate;

[0045] The constraints are:

[0046] H(i)∈[0,x i ],i=1,2,3,…,I

[0047] Among them, H(i) is the opening constraint of the i-th gate, 0 means the gate is closed, x i Indicates that the gate is fully open, and I indicates the number of gates.

[0048] Furthermore, the aforementioned step S4 includes the following sub-steps:

[0049] S4.1. Determine the gate opening range. Set the gate opening range according to the gate settings. Set the gate opening range to [0, x i ], where xi is the maximum opening of the i-th gate, and the first generation of gate opening values ​​is generated within the allowed gate opening range;

[0050] S4.2, let the gate opening measure be N, where N is a natural number between 1 and M, and M is the value of the set algebra. Perform non-dominated sorting on the first generation of gate opening measures, and then perform selection, crossover, and mutation operations to generate the second generation of gate opening measures.

[0051] S4.3, merge the gate opening measure of the Nth generation with the gate opening measure of the N+1th generation, perform non-dominated sorting on the merged gate opening measures, and perform selection, crossover, and mutation operations on the new gate opening measures to generate the gate opening measure of the N+2th generation, where the initial value of N is 1;

[0052] S4.4: Input the N+2 generation gate opening as new input data into the flow prediction model and water level prediction model to predict the flow process of key sections of the river in the basin and the water level process in the gate. Calculate the river flow and water exchange rate based on the objective function. If the water exchange rate has reached the optimal value, output the corresponding gate opening and end. If not, enter S4.5.

[0053] S4.5, determine whether the current algebra is less than M. If so, set N = N + 1 and return to execute S4.3; if not, execute M = 2M, that is, adjust the set algebra to 2M, then set N = N + 1 and return to execute S4.3.

[0054] Compared with the existing technology, the beneficial technical effects of the above technical solutions adopted by the present invention are as follows: First, by integrating Internet of Things technology and machine learning models, a real-time channel is built for hydrological and hydrodynamic data and data platforms, completing data intelligent management, deeply tapping potential, and real-time application. Second, the intelligent optimization algorithm is deeply embedded in the multi-objective function with hydrodynamic regulation as the core, breaking through the bottleneck of traditional scheduling methods that are difficult to respond to the influence of multivariable coupling in real time; third, facing the reciprocating flow problem and water environment pressure unique to tidal river networks, it proposes to optimize and reconstruct the hydrodynamic spatiotemporal pattern, enhance the fluidity and self-purification capacity of water bodies, and provide an intelligent and systematic solution for the governance of water environment problems in tidal river networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Diagram of the intelligent dispatching framework of a sluice group for the spatiotemporal optimization of the hydrodynamics of the constructed river network.

[0056] Figure 2 This is a flow chart for solving the optimal system future gate opening plan using genetic algorithm.

[0057] Figure 3 Build a high-precision hydrological monitoring network map for IoT technology and smart sensing devices.

[0058] Figure 4 This is a schematic diagram of the research area of ​​the Yunnan section of Shanghai's river network.

[0059] Figure 5 This is a flow prediction and error comparison chart for key sections of the river.

[0060] Figure 6 These are the water level prediction and error comparison diagrams inside the gates. In the figure, (a) is the water level prediction and error comparison diagram inside the Dongjiaojing Gate, (b) is the water level prediction and error comparison diagram inside the Xisitang Gate, and (c) is the water level prediction and error comparison diagram inside the Haoqiaogang Gate.

[0061] Figure 7 This is a schematic diagram of the gate opening changes before and after the optimization of the intelligent scheduling method for group gates. In the figure, (a) is the gate opening change diagram of Dongjiaojing Gate before and after optimization, (b) is the gate opening change diagram of Xisitang Gate before and after optimization, and (c) is the gate opening change diagram of Haoqiaogang Gate before and after optimization.

[0062] Figure 8 This diagram shows the results of regulation within the time domain for studying river channel prediction. (a) shows the flow process diagram of the target river section before gate optimization, and (b) shows the flow process diagram of the target river section after gate optimization. DETAILED DESCRIPTION

[0063] In order to better understand the technical content of the present invention, specific embodiments are given and described below with reference to the accompanying drawings.

[0064] Various aspects of the invention are described herein with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. The embodiments of the invention are not limited to those described in the accompanying drawings. It should be understood that the invention is implemented by any of the various concepts and embodiments described above, as well as the concepts and implementations described in detail below, because the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects disclosed herein may be used alone or in any appropriate combination with other aspects disclosed herein. Figure 1 The present invention provides a multi-objective intelligent hydraulic control method for a tidal river network sluice group, comprising the following steps:

[0065] S1. Acquire and pre-process historical detection data and hydrological data of the tidal river network. The historical detection data and hydrological data include gate opening data, tidal water level data, key section flow data, and water level data within the gate;

[0066] S2. Using gate opening data and tidal water level data as input and key section flow data as output, a flow prediction model is constructed and trained. Using gate opening data and tidal water level data as input and gate internal water level data as output, a water level prediction model is constructed and trained.

[0067] S3. Construct a multi-objective function based on the water exchange rate and water level to evaluate the gate opening scheme, and add gate opening constraints; S4. Use a genetic algorithm to solve the multi-objective function, specifically: embed the flow prediction model and the water level prediction model into the genetic algorithm, pass the key section flow data obtained by the flow prediction model under the current gate opening scheme to the genetic algorithm, calculate the objective function value, and the genetic algorithm determines whether the water exchange rate is the largest under the current scheme. If so, the optimal river network sluice opening combination is obtained. Otherwise, the water level prediction model predicts the water level inside the gate under the current scheme, and then compares the water level inside the gate with the tidal water level of the outer river based on the tidal water level of the outer river to obtain the water level difference, return to the optimization algorithm to readjust the gate opening, and then calculate the new key section flow through the flow prediction model, iterate the calculation until the objective function reaches the maximum value, that is, the optimal river network sluice opening combination is obtained.

[0068] Furthermore, as a preferred embodiment of the present invention, in step S1, the Internet of Things technology and intelligent sensing equipment are used to obtain historical detection data of the tidal river network, including gate opening data, tidal water level data, and key section flow data. The tidal water level data is provided by the hydrological station forecast and can be used as future input conditions. The specific process is as follows: Figure 3 The specific implementation process is as follows:

[0069] like Figure 3 As shown, IoT monitoring nodes are first deployed at key locations in the tidal river network. These nodes include water level sensors, flow monitors, and gate height sensors, monitoring key parameters such as water level, flow, and gate operating status. Wireless communication technologies such as 4G / 5G, LoRa, or NB-IoT enable real-time data transmission from distributed sensing devices. Hydrological data collected by each monitoring node is uploaded to a central data platform via the wireless network, ensuring stable and reliable real-time transmission and providing timely data support for scheduling decisions. Subsequently, historical data is integrated with hydrological forecast information to form a complete data foundation. Real-time data is integrated with historical monitoring records to construct a multi-temporal and spatial-scale database containing information such as water level, flow, and gate opening. Tidal level forecast data provided by hydrological stations is also incorporated. Combined with historical trend analysis and real-time monitoring data, preliminary forecasts of future hydrological conditions are generated, providing a critical basis for subsequent analysis and modeling. Finally, a data management system is established, leveraging a big data platform to comprehensively manage the collected hydrological and hydrodynamic data. This system supports real-time dynamic updates and data visualization, facilitating intuitive analysis and presentation. At the same time, historical and real-time data are cleaned and annotated, and outliers are removed to ensure data reliability and integrity. Big data technologies enable efficient data storage, processing, and analysis, further improving the intelligent level of river network hydrological monitoring and regulation. The input data and model parameter settings used in the prediction model are shown in Table 1 below.

[0070] Table 1

[0071]

[0072]

[0073] Furthermore, as a preferred embodiment of the present invention, the flow prediction model in step S2 is constructed based on the Temporal Fusion Transformers (TFT), and the water level prediction model is constructed based on the Long Short-Term Memory Network (LSTM). Both the Temporal Fusion Transformers (TFT) and the Long Short-Term Memory Network (LSTM) are existing models, among which the Temporal Fusion Transformers is a recently proposed prediction model with unique advantages in non-stationary sequence prediction and feature capture, and is suitable for complex runoff prediction; the Long Short-Term Memory Network is a classic prediction model, suitable for relatively simple water level prediction, saving computing resources. The prediction model in this example is constructed by calling the Temporal Fusion Transformers and the Long Short-Term Memory Network. It should be noted that since the specific calculation and solution process of the relevant modules belongs to the existing technology, this embodiment only lists the model construction principle, and does not list the detailed solution process, as follows:

[0074] The gate opening data and tidal water level data are used as input data. The input data is divided into training set and validation set according to actual needs. To facilitate the subsequent solution of practical problems, the length of the validation set is the length of time that the gate needs to be optimized in the future;

[0075] (1) The flow prediction model is specifically: a flow prediction model is constructed based on a time fusion transformer, which includes: a gated residual network (GRN), a variable selection network, and a multi-layer masked attention mechanism layer; the gated residual network (GRN) realizes dynamic adjustment of input features through nonlinear transformation; the variable selection network, through the variable selection mechanism, combines the gated residual network to dynamically assign weights to input features and extract important features; the multi-layer masked attention mechanism layer captures the dependencies between complex time series features, while enhancing the effectiveness of attention through the mask mechanism, and improving the modeling ability of the model for complex dynamic changes in tidal river networks through multi-layer stacking. Flow prediction model

[0076] Gated residual network GRN, specifically:

[0077] h1=W1·x+b1

[0078] h2=ReLU(h1)

[0079] g=σ(W g h2+b g)

[0080] y=g·h2+(1-g)x

[0081] Among them, h1 is the linear transformation output of the hidden layer, W1 is the weight matrix input to the hidden layer, b1 is the bias vector input to the hidden layer, h2 is the output of the hidden layer after the activation function, g is the output of the gating mechanism, W g is the weight matrix of the gating mechanism, b g is the bias vector of the gating mechanism; σ and ReLU are activation functions, x is the input feature, and y is the output feature;

[0082] Variable selection network, specifically:

[0083]

[0084]

[0085]

[0086] Where h i is the i-th input eigenvalue; It is z i Perform exponential operations to enhance the differences between values; is the original value of the jth feature at time t; It is to flatten multidimensional data into a one-dimensional vector.

[0087] Multi-layer mask attention mechanism layer, specifically:

[0088]

[0089] MultiHead(Q,K,V)=Concat(head1,…,head h )W O

[0090]

[0091] Where Q, K, and V represent query, key, and value vectors, respectively, which are connected by the input X and the weight matrix W. Q ,W K ,W V Sex transformation generation; d k is the key vector dimension, used to scale the dot product result; M is the mask matrix used to mask future time steps or invalid positions; W O is the output weight matrix, is the query weight matrix of the i-th head, is the key weight matrix of the i-th head, The value weight matrix of the i-th head; headi is the i-th attention head, softmax is a normalization operation used to generate attention weights, and concat is to concatenate the outputs of multiple attention heads into a complete vector. The main parameters of the model are shown in Table 2 below:

[0092] Table 2

[0093]

[0094] (2) Build a water level prediction model based on the long short-term memory network LSTM, specifically:

[0095] The forget gate determines whether to keep or forget by outputting the value [0,1] of the σ activation function of the formula, as shown below:

[0096] f t =σ(W f ·[h t-1 ,x t +b f ])

[0097] The input gate introduces new information and updates the memory unit as follows:

[0098] i t =σ(W i ·[h t-1 ,x t +b i ])

[0099]

[0100] The current memory unit combines the memory of the previous moment and the current new information as follows:

[0101]

[0102] The output gate determines the output and memory content at the current moment through the formula, as shown below:

[0103] o t =σ(W o ·[h t-1 ,x t +b o ])

[0104] h t =o t *tanh(C t )

[0105] Where σ is the activation function; h t-1 is the output at time t-1; x t is the input at time t; C t is the information memory unit at time t.t is the forget gate output, W f is the weight matrix of the forget gate, b f is the bias of the forget gate, i t is the input gate output, W i is the weight matrix of the input gate, b i is the bias of the input gate, is the new memory candidate value, W C is the weight matrix of the new memory candidate value, o t is the output of the output gate, W o is the weight matrix of the output gate, b C is the bias of the new memory candidate value, b o is the bias of the output gate. The main parameter settings of the LSTM model are shown in Table 3 below:

[0106] Table 3

[0107]

[0108]

[0109] Furthermore, as a preferred embodiment of the present invention, in S3, the primary goal is to improve the hydrodynamics of the river network, and the objective function constructed by evaluating the gate opening scheme based on the water exchange rate and water level parameters is:

[0110] Z min ≤Z i ≤Z max

[0111] V i =∫Q i

[0112]

[0113] Where Z min The lowest water level to meet landscape requirements, m; Z max The highest water level to meet flood control needs, m; Q i is the flow rate of the i-th section of the target river, m 3 / s;V i is the water flow rate of the i-th section of the target river within a certain period of time, m 3 ; V0 is the total channel storage capacity of the target river section, m 3 ;γ is the water exchange rate;

[0114] Constraints:

[0115] H(i)∈[0,x i ],i=1,2,3,…,I

[0116] Where H(i) is the opening constraint of the i-th gate, 0 means the gate is closed, x i Indicates that the gate is fully open. Since each gate is dispersed in space and the riverbed elevation is different, the maximum opening height of each gate is not exactly the same and needs to be adapted to local conditions. I represents the number of gates.

[0117] It should be noted that in order to construct the objective function, it is necessary to obtain the future river section flow, and an accurate runoff prediction model is needed to ensure the accuracy of the objective function. Therefore, a time fusion transformer is used to predict the river section flow; the river water level outside the gate can be obtained through the tide forecast station, but the river network water level inside the gate cannot be known in advance, and it is difficult to open and close the gate by the water level difference. Therefore, a long short-term memory network is used to predict the water level inside the gate, and then compare it with the water level outside the gate to obtain the water level difference to control the gate opening and closing time.

[0118] Furthermore, as a preferred embodiment of the present invention, step S4 is implemented specifically using a genetic algorithm (GA), such as Figure 2 As shown in the flowchart, the steps for using genetic algorithm to solve the optimal future gate opening plan for the river network are as follows:

[0119] S4.1, determine the gate opening range, according to the gate setting corresponding to the gate opening value range, set the gate opening range to [0, x i ], where x i is the maximum opening of the i-th gate, and the first generation of gate opening values ​​is generated within the allowed gate opening range;

[0120] S4.2, let the gate opening measure be N, where N is a natural number between 1 and M, and M is the value of the set algebra. Perform non-dominated sorting on the first generation of gate opening measures, and then perform selection, crossover, and mutation operations to generate the second generation of gate opening measures.

[0121] S4.3, merge the gate opening measure of the Nth generation with the gate opening measure of the N+1th generation, perform non-dominated sorting on the merged gate opening measures, and perform selection, crossover, and mutation operations on the new gate opening measures to generate the gate opening measure of the N+2th generation, where the initial value of N is 1;

[0122] S4.4: Input the N+2 generation gate opening as new input data into the flow prediction model and water level prediction model to predict the flow process at key sections of the river in the basin and the water level process inside the gate. Calculate the river water exchange rate based on the objective function. If the river water exchange rate has reached the optimal value and cannot be improved any further, output the corresponding gate opening and end. If not, proceed to S4.5.

[0123] S4.5, determine whether the current generation is less than the set generation M. If so, set N=N+1 and return to execute S43; if not, execute M=2M, that is, adjust the set generation to 2M, then set N=N+1 and return to execute S43.

[0124] It should be noted that when those skilled in the art know that genetic algorithms are used to solve the optimal gate control quantity, they should know the specific operating methods of non-dominated sorting, selection, crossover, mutation and fitness calculation, which will not be described in detail here.

[0125] The following is a further description of the method of the present invention using the example of the Shanghai River Network’s Southern Section. The total area of ​​the Southern Section is 173.37 km 2 There are 69 rivers in the area with a total length of 159.51 km. The water system in this area is relatively complex. The main rivers have three longitudinal river sections (Nansitang-Shajing Port, Xisitang-Yujingpu, Dongjiaojing-Pengyuepu) and one transverse river section (Zoumatang). The main drainage gates and water diversion gates are as follows: Figure 4 As shown in the mark. In this embodiment, the input data of the tidal river network prediction model adopts the main diversion and drainage gate opening data, tidal water level data and key section flow data of Zoumatang River from January 1 to June 22, 2022. Among them, the data from 0:00 on January 1 to 0:00 on June 21, 2022 is used for prediction model training, and the data from 0:00 on June 21 to 12:00 on June 21 is used for prediction model verification. The error of the water level prediction results of the verification data set is generally small, with the maximum error controlled within 20cm, 65.28% of the errors controlled within 5cm, and 95.83% of the errors controlled within 10cm. The flow prediction determination coefficient R 2 The model has good prediction performance, showing a high level of prediction for the future flow trend and peak and valley values, and can be used for river network regulation calculation and analysis. Figure 5 and Figure 6 As shown, Figure 6 (a) is the water level prediction and error comparison diagram inside Dongjiaojing Gate, (b) is the water level prediction and error comparison diagram inside Xisitang Gate, and (c) is the water level prediction and error comparison diagram inside Haoqiaogang Gate.

[0126] This example considers a sunny scenario, i.e., a rainless period, to predict the flow and water level of an urban river network and determine the gate opening and closing process. The prediction timeframe is set to 12 hours, and a prediction model is used to predict the water level process within the gates and the flow process at key sections of the river during the prediction timeframe.

[0127] In the constructed multi-objective intelligent hydraulic control method for tidal river network sluice groups, the genetic algorithm, flow prediction model and water level prediction model are coupled to form a system model; the gate opening data and the water level data outside the gate at the current moment and the past moment are the system input values, and the predicted river network gate opening and the water level inside the gate in the time domain are the future output values ​​of the system. Through the constructed objective function and constraint conditions, the genetic algorithm is used to solve the predicted gate opening in the time domain, in which the value of the algebra M is set to 100. In this embodiment, the time step of the gate pump control is 0.5h. Of course, it can be adjusted to 1h, 1.5h, etc. according to the requirements of accuracy. By solving, the gate opening in the predicted time domain can be obtained as follows Figure 7 As shown in the figure, (a) is the gate opening change diagram of Dongjiaojing Gate before and after optimization, (b) is the gate opening change diagram of Xisitang Gate before and after optimization, and (c) is the gate opening change diagram of Haoqiao Port Gate before and after optimization. Comparison of flow process before and after optimization of key sections of the river Figure 8 As shown in the figure, (a) is the flow process diagram of the target river section before gate optimization, and (b) is the flow process diagram of the target river section after gate optimization. The selected target river section has a serious reciprocating flow phenomenon from 15:30 on June 21 to 3:00 on June 22, 2022, with a forward flow of 5893.2m 3 , reverse flow 25687.8m 3 , total flow rate 19794.6m 3 After the gate opening was optimized by the group gate intelligent scheduling model, the forward flow of the target river section was reduced to 753.08m 3 , a decrease of 87.22%, and the reverse flow was reduced to 24262.4m 3 , the decrease is 5.55%, but the total flow rate is 23509.3m 3 , an increase of 18.77% compared to before the gate was optimized. By optimizing the gate with an intelligent model, the water exchange rate was increased from 16.1% to 19.1%, and the water exchange time was reduced from 6.2 days to 5.2 days, providing technical support for water environment problems such as sewage and waterlogging that require rapid water exchange. Although the present invention has been described above with a preferred embodiment, it is not intended to limit the present invention. Those with ordinary knowledge in the technical field to which the present invention belongs can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on what is defined in the claims.

Claims

1. A multi-objective intelligent hydraulic control method for a tidal river network sluice group, characterized in that: The following steps are involved: S1. Acquire and pre-process historical detection data and hydrological data of the tidal river network. The historical detection data and hydrological data include gate opening data, tidal water level data, key section flow data, and water level data within the gate; S2. Using gate opening data and tidal water level data as input and key section flow data as output, a flow prediction model is constructed and trained. Using gate opening data and tidal water level data as input and gate internal water level data as output, a water level prediction model is constructed and trained. Specifically, the traffic prediction model is constructed based on a time fusion transformer, which includes a gated residual network (GRN), a variable selection network, and a multi-layer masked attention mechanism layer. The gated residual network (GRN) dynamically adjusts input features through nonlinear transformations. The variable selection network dynamically weights input features and extracts important features through a variable selection mechanism combined with a gated residual network. The multi-layer masked attention mechanism layer captures the dependencies between complex time series features, while enhancing the effectiveness of attention through a masking mechanism. Furthermore, the model's ability to model complex dynamic changes in tidal river networks is enhanced through multi-layer stacking. Build a water level prediction model based on the long short-term memory network LSTM; S3, construct a multi-objective function based on the water exchange rate and water level to evaluate the gate opening scheme, and add gate opening constraints; S4. Use genetic algorithm to solve multi-objective functions, specifically: embed the flow prediction model and water level prediction model into the genetic algorithm, pass the key section flow data obtained by the flow prediction model under the current gate opening scheme to the genetic algorithm, calculate the objective function value, and the genetic algorithm determines whether the water exchange rate is the largest under the current scheme. If so, the optimal river network sluice opening combination is obtained. Otherwise, the water level prediction model predicts the water level inside the gate under the current scheme, and then compares the water level inside the gate with the tidal water level of the external river to obtain the water level difference based on the external river tidal water level. Return to the optimization algorithm to readjust the gate opening, and then calculate the new key section flow through the flow prediction model. Iterate the calculation until the objective function reaches the maximum value, that is, the optimal river network sluice opening combination is obtained.

2. The multi-objective intelligent hydraulic control method for a tidal river network sluice group according to claim 1 is characterized in that: Step S1 specifically includes the following sub-steps: S1.

1. Deploy IoT monitoring nodes at pre-determined locations along the tidal river network, including water level sensors, flow monitors, and gate height sensors; S1.

2. Establish a data transmission network. Use wireless communication technology to build a real-time data transmission network to upload the monitoring data of each device in the monitoring node to the central data platform in real time. S1.

3. Integrate historical monitoring data, including water level, flow, and gate opening records, establish a multi-temporal and spatial scale database, collect tidal water level data predicted by hydrological stations, combine historical trends with real-time monitoring, and generate hydrological data corresponding to historical monitoring data; S1.

4. Build a data management system and use the big data platform to collect, store and visualize monitoring data, update it in real time, clean and label historical and real-time data, and eliminate outliers.

3. The multi-objective intelligent hydraulic control method for a tidal river network sluice group according to claim 2 is characterized in that: Gated residual network GRN, specifically: h1=W1·x+b1 h2=ReLU(h1) g=σ(W g ·h2+b g ) y=g·h2+(1-g)x Among them, h1 is the linear transformation output of the hidden layer, W1 is the weight matrix input to the hidden layer, b1 is the bias vector input to the hidden layer, h2 is the output of the hidden layer after the activation function, g is the output of the gating mechanism, W g is the weight matrix of the gating mechanism, b g is the bias vector of the gating mechanism; σ and ReLU are activation functions, x is the input feature, and y is the output feature; Variable selection network, specifically: Where z i is the i-th input eigenvalue; It is z i Perform exponential operations to enhance the differences between values; is the original value of the jth feature at time t; It is to flatten multidimensional data into a one-dimensional vector; Multi-layer mask attention mechanism layer, specifically: MultiHead(Q,K,V)=Concat(head1,…,head h )W O Where Q, K, and V represent query, key, and value vectors, respectively, which are connected by the input X and the weight matrix W. Q ,W K ,W V Sex transformation generation; d k is the key vector dimension, used to scale the dot product result; M is the mask matrix used to mask future time steps or invalid positions; W O is the output weight matrix, is the query weight matrix of the i-th head, is the key weight matrix of the i-th head, The value weight matrix of the i-th head; head i is the i-th attention head, softmax is a normalization operation used to generate attention weights, and Concat is to concatenate the outputs of multiple attention heads into a complete vector.

4. The multi-objective intelligent hydraulic control method for a tidal river network sluice group according to claim 1 is characterized in that: In step S2, a water level prediction model is constructed based on the long short-term memory network (LSTM), specifically: The forget gate determines whether to keep or forget by outputting the value [0,1] of the σ activation function of the formula, as shown below: f t =σ(W f ·[h t-1 ,x t +b f ]) The input gate introduces new information and updates the memory unit as follows: i t =σ(W i ·[h t-1 ,x t +b i ]) The current memory unit combines the memory of the previous moment and the current new information as follows: The output gate determines the output and memory content at the current moment through the formula, as shown below: the t =σ(W o ·[h t-1 ,x t +b o ]) h t =o t *fishy(C) t ) Where σ is the activation function; h t-1 is the output at time t-1; x t is the input at time t; C t is the information memory unit at time t, f t is the forget gate output, W f is the weight matrix of the forget gate, b f is the bias of the forget gate, i t is the input gate output, W i is the weight matrix of the input gate, b i is the bias of the input gate, is the new memory candidate value, W C is the weight matrix of the new memory candidate value, o t is the output of the output gate, W o is the weight matrix of the output gate, b C is the bias of the new memory candidate value, b o is the bias of the output gate.

5. The multi-objective intelligent hydraulic control method for a tidal river network sluice group according to claim 1 is characterized in that: The multi-objective function in step S3 is: WITH min ≤Z i ≤Z max In i =∫Q i Among them, Z min The lowest water level to meet landscape requirements, in meters; Z max The highest water level to meet flood control needs, unit: m; Q i is the flow rate of the i-th section of the target river, in m 3 / s;V i is the water flow rate of the i-th section of the target river within a certain period of time, in m 3 ; V0 is the total channel storage capacity of the target river section, unit is m 3 ;γ is the water exchange rate; The constraints are: H(i)∈[0,x i ],i=1,2,3,…,I Among them, H(i) is the opening constraint of the i-th gate, 0 means the gate is closed, x i Indicates that the gate is fully open, and I indicates the number of gates.

6. The multi-objective intelligent hydraulic control method for a tidal river network sluice group according to claim 1 is characterized in that: Step S4 includes the following sub-steps: S4.

1. Determine the gate opening range. Set the gate opening range according to the gate settings. Set the gate opening range to [0, x i ], where x i is the maximum opening of the i-th gate, and the first generation of gate opening values ​​is generated within the allowed gate opening range; S4.2, let the gate opening measure be N, where N is a natural number between 1 and M, and M is the value of the set algebra. Perform non-dominated sorting on the first generation of gate opening measures, and then perform selection, crossover, and mutation operations to generate the second generation of gate opening measures. S4.3, merge the gate opening measure of the Nth generation with the gate opening measure of the N+1th generation, perform non-dominated sorting on the merged gate opening measures, and perform selection, crossover, and mutation operations on the new gate opening measures to generate the gate opening measure of the N+2th generation, where the initial value of N is 1; S4.4: Input the gate opening of the N+2 generation as new input data into the flow prediction model and the water level prediction model. The flow process at key sections of the river within the basin and the water level process within the gate are predicted. The river flow volume and water exchange rate are calculated based on the objective function. If the water exchange rate has reached the optimal value, the corresponding gate opening is output and the process ends. If not, proceed to S4.

5. S4.5, determine whether the current algebra is less than M. If so, set N = N + 1 and return to execute S4.3; if not, execute M = 2M, that is, adjust the set algebra to 2M, then set N = N + 1 and return to execute S4.3.

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