Steel cofferdam opening dynamic scheduling method and system

Abstract

The application is suitable for the technical field of steel cofferdams, and provides a steel cofferdam opening dynamic scheduling method and system. The steel cofferdam opening dynamic scheduling method provided by the application collects sea wave data of instant waves, opening rate data of the steel cofferdam and width data of a wave-encountering surface, so as to calculate a wave force reduction coefficient. When the wave force reduction coefficient exceeds a preset threshold value, an opening rate adjustment instruction is sent to adjust the opening rate on the outer wall of the steel cofferdam, so as to realize optimal opening rate adjustment for eliminating wave impact.

Description

Technical Field

[0001] This application belongs to the field of steel cofferdam technology, and in particular relates to a method and system for dynamic scheduling of openings in steel cofferdams. Background Technology

[0002] In bridge foundation construction, cofferdams are commonly used to prevent water and soil from entering the structure. Drainage, foundation excavation, and structural construction can be carried out within the cofferdam. For areas with deep water and high flow velocity, such as rivers or oceans, steel sheet pile cofferdams and double-layer thin-walled steel cofferdams are often used. In complex hydrological environments, the surface of steel cofferdams is subjected to complex random wave forces. Scholars both domestically and internationally have conducted extensive research on the wave forces on large steel cofferdams and their dynamic response under wave loads.

[0003] In existing technologies, steel cofferdams are generally used to resist the impact of wave forces. Current steel cofferdams typically rely on CFD (Computational Fluid Dynamics) simulations to design the perforations in their outer walls to withstand wave impact. However, in real-world applications, waves under strong typhoon conditions exhibit various wave impact patterns due to different wave modes. Different perforation ratios on the outer wall of the steel cofferdam achieve varying wave damping effects. Traditional static perforated structures are ill-suited to handle the diverse wave force impacts under strong typhoon conditions, lacking sufficient safety and reliability, flexibility, and reusability. Summary of the Invention

[0004] This application provides a method and system for dynamic scheduling of openings in steel cofferdams, which can solve the problems of traditional static hole structures being unable to cope with various wave force impacts under strong typhoon conditions, lacking safety and reliability, lacking flexibility, and having low reusability.

[0005] In a first aspect, this application provides a method for dynamic scheduling of openings in a steel cofferdam, comprising the following steps:

[0006] Collect ocean wave data, including wave velocity, wave wavelength, wave height, and water depth;

[0007] Collect data on the opening ratio of the outer wall of the steel cofferdam and the width of the wave-facing surface;

[0008] Based on sea wave data, orifice ratio data, and wave-facing width data, the wave force reduction factor is calculated using the following optimal parameter calculation formula:

[0009]

[0010] Where k represents the wave force reduction factor, b represents the width of the wave-facing face, α represents the opening ratio, d represents the water depth, and L represents the wave wavelength. H represents the relative water depth, and H represents the wave height. Indicates a steep wave;

[0011] The opening ratio on the outer wall of the steel cofferdam is adjusted according to the wave force reduction coefficient.

[0012] Optionally, the opening ratio on the outer wall of the steel cofferdam can be adjusted according to the wave force reduction coefficient, specifically including:

[0013] When the wave force reduction factor k ≤ 0.95, the opening ratio remains unchanged;

[0014] When the wave force reduction factor k > 0.95, reduce the opening ratio.

[0015] Optionally, when the wave force reduction factor k > 0.95, the opening ratio α is reduced by 0.001 until the wave force reduction factor k ≤ 0.95.

[0016] Optionally, the wave force reduction factor can be calculated using the POA-SVR prediction model, which is used to predict the optimal parameters between the wave force reduction factor and the wave front width, opening ratio, relative water depth, and wave steepness.

[0017] Optionally, the prediction process of the POA-SVR prediction model includes:

[0018] The dataset is divided into training and validation sets, with the opening rate data of the outer wall of the steel cofferdam used as sample data. The training and validation sets are then normalized for training.

[0019] By selecting a kernel function and modifying the parameters of the Gaussian kernel function in the traditional SVR model, the optimal parameter fitness is achieved by fusing the Gaussian regression kernel and the quadratic rational kernel, while ensuring the effectiveness of the global search.

[0020] The penalty factor of the SVR model is optimized by using the POA algorithm. The parameters of the POA algorithm are initialized and the fitness function is set. By initializing the peacock flock, flight and display simulation, competition and selection, the penalty factor and kernel function bandwidth of the SVR model are finally obtained through continuous iteration.

[0021] The model is trained using the training set data to obtain the POA-SVR prediction model.

[0022] The POA-SVR model is validated using the input validation set data, and the final predicted opening ratio is output by inverse normalization. The accuracy of the POA-SVR prediction model in predicting the opening ratio is verified by calculating the deviation between the actual life and the predicted life.

[0023] The optimal regression function is output, and the prediction results are compared with the validation set data. The accuracy is greater than 95%, and the calculation formula of the wave force reduction coefficient of the optimal parameter is obtained.

[0024] Secondly, this application also provides a dynamic scheduling system for steel cofferdam openings, applicable to any of the above-mentioned methods for dynamic scheduling of steel cofferdam openings. The dynamic scheduling system for steel cofferdam openings includes:

[0025] The steel cofferdam module includes an opening outer wall, a sliding cover plate, and a drive assembly. The opening outer wall has multiple circular holes. The sliding cover plate is slidably mounted on the opening outer wall and has multiple through holes. The drive assembly is mounted on the opening outer wall and is connected to the sliding cover plate. The drive assembly is used to drive the sliding cover plate to slide relative to the opening outer wall to adjust the overlap between the through holes and the circular holes.

[0026] The data acquisition module is used to collect sea wave data, open area ratio data of the outer wall of the steel cofferdam, and wave front width data. Sea wave data includes wave velocity, wave wavelength, wave height, and water depth; and

[0027] The processing module is used to calculate the wave force reduction coefficient based on sea wave data, opening ratio data, and wave-facing width data, and to send an opening ratio adjustment command to the drive component based on the wave force reduction coefficient. This causes the drive component to drive the sliding cover plate to slide on the outer wall of the opening, adjusting the overlap between the circular hole and the through hole to achieve optimal opening ratio adjustment for mitigating wave impact.

[0028] Optionally, the data acquisition module includes:

[0029] Doppler current meter, used to collect wave velocity data;

[0030] Nearshore remote wave instrument is used to collect wave wavelength and wave height;

[0031] Sonar depth sounders are used to collect water depth data; and

[0032] The visual image acquisition instrument is used to collect data on the opening ratio of the outer wall of the steel cofferdam and the width of the wave-facing surface.

[0033] The above-mentioned solution in this application has the following beneficial effects:

[0034] The dynamic scheduling method for steel cofferdam openings provided in this application collects real-time wave data from the sea area, opening ratio data of the steel cofferdam, and width data of the wave-facing face to calculate the wave force reduction coefficient. When the wave force reduction coefficient exceeds a preset threshold, an opening ratio adjustment command is sent to adjust the opening ratio on the outer wall of the steel cofferdam, so as to achieve the optimal opening ratio adjustment for dissipating wave impact.

[0035] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart of a method for dynamic scheduling of steel cofferdam openings provided in an embodiment of this application;

[0038] Figure 2 A flowchart of the POA-SVR prediction model algorithm provided in an embodiment of this application;

[0039] Figure 3 A flowchart for calculating the wave force reduction factor provided in one embodiment of this application;

[0040] Figure 4 This is a schematic diagram of a steel cofferdam module structure provided in one embodiment of this application.

[0041] [Explanation of Labels in the Attached Image]

[0042] 100. Steel cofferdam module;

[0043] 110. Outer wall of the opening; 111. Circular hole; 120. Sliding cover plate; 121. Through hole; 130. Drive assembly. Detailed Implementation

[0044] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0045] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0046] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0047] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0048] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0049] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0050] The following describes the method and system for dynamic scheduling of steel cofferdam openings provided in this application by way of specific embodiments.

[0051] like Figure 1 As shown in the embodiment of this application, the method for dynamic scheduling of steel cofferdam openings includes the following steps:

[0052] Step 11. Collect ocean wave data, including wave velocity, wave wavelength, wave height, and water depth.

[0053] Specifically, in the embodiments of this application, the instantaneous waves formed in the target sea area can be detected by related equipment such as Doppler current meters, near-shore remote wave meters, and sonar depth sounders to obtain data such as wave velocity, wave wavelength, wave height, and water depth of the instantaneous waves, and then these data are transmitted in real time.

[0054] For example, the placement of Doppler current meters, near-shore remote wave meters, and sonar depth sounders depends on the specific sea conditions. Generally, they are evenly distributed at 50m intervals in the sea area 30m-200m in front of the steel cofferdam, uniformly surrounding the outer sea area of ​​the steel cofferdam. Communication is achieved via RS485 bus, transmitting current wave data to the host computer in real time via underwater fiber optic cable.

[0055] Step 12. Collect data on the opening ratio and wave-facing width of the outer wall of the steel cofferdam.

[0056] Specifically, in the embodiments of this application, image data of the outer wall of the steel cofferdam can be collected by a visual image acquisition device or other related equipment, and the image data can be processed to obtain the opening ratio data and wave-facing width data of the outer wall of the steel cofferdam.

[0057] Step 13. Calculate the wave force reduction factor based on the sea wave data, opening ratio data, and wave-facing width data:

[0058] The formula for calculating the optimal parameters is as follows:

[0059]

[0060] Where k represents the wave force reduction factor, b represents the width of the wave-facing face, α represents the opening ratio, d represents the water depth, and L represents the wave wavelength. H represents the relative water depth, and H represents the wave height. It indicates that the wave is steep.

[0061] Specifically, in the embodiments of this application, the collected sea wave data, opening ratio data, and wave-facing width data are used to calculate the instantaneous wave force reduction coefficient using the aforementioned calculation formula. This patent uses the wave force reduction coefficient to represent the impact of changes in various parameters on wave attenuation; the smaller the reduction coefficient, the better the wave attenuation effect of the outer wall opening cofferdam.

[0062] Step 14. Adjust the opening ratio on the outer wall of the steel cofferdam according to the wave force reduction coefficient.

[0063] Specifically, in the embodiments of this application, the opening ratio on the outer wall of the steel cofferdam is adjusted according to the calculated wave force reduction coefficient. When the wave force reduction coefficient is within a preset threshold, there is no need to adjust the opening ratio on the outer wall of the steel cofferdam; when the wave force reduction coefficient exceeds the preset threshold, the host computer sends an opening ratio adjustment command to adjust the opening ratio on the outer wall of the steel cofferdam, and then re-submits the opening ratio data into the above calculation formula. If the calculated wave force reduction coefficient still exceeds the preset threshold, the opening ratio on the outer wall of the steel cofferdam is adjusted until the wave force reduction coefficient is within the preset threshold, so as to achieve the optimal opening ratio adjustment for dissipating wave impact.

[0064] The dynamic scheduling method for steel cofferdam openings provided in this application collects real-time wave data from the sea area, opening ratio data of the steel cofferdam, and width data of the wave-facing face to calculate the wave force reduction coefficient. When the wave force reduction coefficient exceeds a preset threshold, an opening ratio adjustment command is sent to adjust the opening ratio on the outer wall of the steel cofferdam, so as to achieve the optimal opening ratio adjustment for dissipating wave impact.

[0065] In one embodiment, such as Figure 3 As shown, step 14. Adjust the opening ratio on the outer wall of the steel cofferdam according to the wave force reduction coefficient, specifically including: when the wave force reduction coefficient k≤0.95, the opening ratio remains unchanged; when the wave force reduction coefficient k>0.95, the opening ratio is reduced.

[0066] In the above embodiments, a wave force reduction coefficient k ≤ 0.95 is a preset threshold. When the wave force reduction coefficient calculated using real-time wave data, the opening ratio data of the steel cofferdam, and the width data of the wave-facing face is within this preset threshold, the opening ratio on the outer wall of the steel cofferdam can achieve the optimal wave-damping effect. When the wave force reduction coefficient exceeds this preset threshold, an opening ratio adjustment command is sent to reduce the opening ratio of the steel cofferdam until the wave force reduction coefficient meets the preset threshold.

[0067] In one specific embodiment, such as Figure 3 As shown, when the wave force reduction factor k > 0.95, the opening ratio α decreases by 0.001 until the wave force reduction factor k ≤ 0.95.

[0068] In the above embodiment, when the wave force reduction coefficient k>0.95, the opening ratio on the outer wall of the steel cofferdam is reduced by 0.001 each time by sending an opening ratio adjustment command until the wave force reduction coefficient reaches the preset threshold.

[0069] The wave force reduction factor is generally expressed as: k = F / F0, where F is the horizontal wave force of a double-walled steel cofferdam with external openings under wave action, and F0 is the horizontal wave force of a steel cofferdam without openings calculated by the standard formula under the same wave parameters and structural dimensions. The smaller the reduction factor, the better the wave dissipation effect of the cofferdam with external openings. The original formula for calculating the wave force reduction factor is:

[0070]

[0071] Where, d i It is the i-th component of vector D = Ax + b, c i It is the i-th component of vector C. Matrices A, x, b, and C are respectively...

[0072]

[0073]

[0074]

[0075]

[0076] Where x1, x2, x3, and x4 represent, respectively, the width d of the wave-facing surface of the steel cofferdam, the opening ratio α of the steel cofferdam, and the wave steepness. and relative water depth

[0077] In one embodiment, such as Figure 2 As shown, the formula for calculating the wave force reduction coefficient in step 13, which uses the POA-SVR prediction model to predict the optimal parameters between the wave force reduction coefficient and the wave front width, opening ratio, relative water depth, and wave steepness.

[0078] In one specific embodiment, the prediction process of the POA-SVR prediction model based on the above-mentioned original wave force reduction coefficient calculation formula specifically includes:

[0079] i. Divide the dataset by using the opening rate data of the outer wall of the steel cofferdam as sample data to divide it into training set data and validation set data, and normalize the training set data and validation set data for training.

[0080] Specifically, the data from the openings in the steel cofferdam were used as sample data and divided into two sets: 70% for the training set and 30% for the validation set, and then normalized for training. The training sample set can be represented as follows:

[0081] T = {(x1,y1),(x2,y2),…,(x M ,y M )}

[0082] Where (x) i ,y i ) represents the i-th sample, y i It is x i The response value.

[0083] ii. Select a kernel function. Modify the parameters of the Gaussian kernel function in the traditional SVR model. By fusing the Gaussian regression kernel and the quadratic rational kernel, the optimal parameter fitness can be achieved while ensuring the effectiveness of the global search.

[0084] Specifically, the kernel function is modified by altering the parameters of the Gaussian kernel function in the traditional SVR model, thus broadening its scope and preventing it from getting trapped in local optima. The original objective function of SVR is O(x):

[0085] O(x)=ω T x+b

[0086] Where ω and b are the model parameters. Due to the allowance for a certain range of bias and the need to minimize the loss, the support vector regression model can be written as follows after adding slack variables:

[0087]

[0088]

[0089] Where C is the penalty factor, ε is the insensitive loss variable, and ξ i and Let be the slack variable of the function, and st be the constraint condition.

[0090] Introducing the Lagrange multiplier μ i ≥0, α i For all inequalities ≥ 0, the SVR model can be expressed as a Lagrange function using the Lagrange multiplier method. The optimal parameters of the function can then be obtained by finding the extrema of this equation. The Lagrange function can be expressed as:

[0091]

[0092] Based on the above formula, let For ω,b,ξ, Since the partial derivative is 0, we can obtain:

[0093]

[0094] Where μ i , α i All of these are Lagrange multipliers. After transforming them into a dual problem for solution, the original SVR problem can be expressed as:

[0095]

[0096]

[0097] Based on this, we substitute the KKT constraints:

[0098]

[0099] The original SVR equation can then be simplified to:

[0100]

[0101] in The kernel function determines the performance of SVR regression. However, the previous Gaussian regression kernel had the drawback of having an excessively small regression domain. By fusing the Gaussian regression kernel and the quadratic rational kernel, the optimal parameter fitness can be achieved while ensuring the effectiveness of the global search.

[0102] Wherein, kernel function:

[0103]

[0104] Where C represents the penalty factor and σ represents the kernel function bandwidth.

[0105] iii. Optimize the penalty factor of the SVR model using the POA algorithm. Initialize the parameters of the POA algorithm, set the fitness function, and obtain the penalty factor C and kernel function bandwidth σ of the optimized SVR model through continuous iteration by initializing the peacock flock, flight and display simulation, competition and selection.

[0106] Specifically, the parameters are set as follows:

[0107] 1. Peacock's location:

[0108] Location of the dominant male peacock:

[0109] Locations of other male peacocks:

[0110] X ri =2·rand(1,3)-1,i=1,2

[0111] in:

[0112] t represents the current iteration number. This indicates the position of the i-th male peacock in the t-th iteration;

[0113] X ri (i = 1, 2, 3) is a random vector with values ​​between [0, 1], which is mainly related to the dimension;

[0114] ||X ri ||(i=1,2,3) represents X ri The parameter modulus corresponding to (i = 1, 2, 3);

[0115] r i (i = 1, 2) represents a random number between [0, 1], used to determine the position update method for the male peacock. That is, if r i If the probability value (i = 1, 2, 3) is less than the following probability value, the male peacock will rotate; otherwise, it will remain stationary and its position will not change.

[0116] R t Used to define the radius of rotation, representing the radius of rotation of the male peacock in the t-th iteration.

[0117]

[0118] R s0=C v ·(ub-lb)

[0119] In the formula:

[0120] T represents the maximum number of iterations, R s0 It is the initial radius of rotation, C v The rotation factor for male peacocks is 0.2 by default. ub and lb are the upper and lower bounds of the problem, respectively, and their specific values ​​need to be determined based on the problem.

[0121] 2. Approach behavior of female peacocks

[0122]

[0123]

[0124] in, This represents the position of the i-th female peacock in the t-th iteration, and similarly... r represents the position of the i-th male peacock in the t-th iteration; r5 is a random number between [0, 1]; θ0 and θ1 take values ​​of 0.1 and 1 respectively, t max This represents the maximum number of iterations.

[0125] 3. Peacock chick search behavior

[0126]

[0127] in, It is the position of the i-th point at time t, and similarly... It represents the position of the i-th point at time t+1; α is the step size coefficient, determined by the modulus of the original problem, typically taken as 1; H is the transition function (a function with a value of 1 when x is greater than or equal to 1, and 0 otherwise), p a The probability of abandoning that point; Dot product; and These are two possible points arbitrarily selected within the domain at time t.

[0128] The location update method for each peacock chick is designed as follows:

[0129]

[0130]

[0131] in, This represents the position of the i-th peacock cub when the number of iterations is t. Similarly; and This represents the position vector of the male peacock chosen by the i-th peacock chick at time t. r8 is a random number between [0, 1] used to achieve equal probability selection. α and β are two coefficient factors that dynamically change with the number of iterations, defined as follows:

[0132]

[0133]

[0134] Where α0 = 0.9, α1 = 0.4, and β0 = 0.1, β1 = 1, t and t max The meaning is the same as above.

[0135] 4. Interactions among male peacocks

[0136]

[0137]

[0138] Among them, X ri It is a D-dimensional random vector, the calculation process of which has been listed above; r 7+i For different random numbers in the range [0, 1], the symbol * defines the inner product between two vectors.

[0139] By substituting the optimal penalty factor C and kernel function bandwidth σ into the above steps, the optimal values ​​of penalty factor C and kernel function bandwidth σ can be obtained.

[0140] iv. Train the model using the training set data to obtain the POA-SVR prediction model.

[0141] v. Input validation set data to validate the POA-SVR model, and output the final opening ratio prediction result by inverse normalization. The accuracy of the opening ratio prediction of the POA-SVR prediction model is verified by calculating the deviation between the actual lifetime and the predicted lifetime.

[0142] vi. Output the optimal regression function, compare the prediction results with the validation set data, and if the accuracy is greater than 95%, derive the formula for calculating the wave force reduction coefficient of the optimal parameters:

[0143]

[0144] Where k represents the wave force reduction factor, b represents the width of the wave-facing face, α represents the opening ratio, d represents the water depth, and L represents the wave wavelength. H represents the relative water depth, and H represents the wave height. It indicates that the wave is steep.

[0145] This application also provides a dynamic scheduling system for steel cofferdam openings, applicable to the dynamic scheduling method for steel cofferdam openings described in any of the above embodiments. The dynamic scheduling system for steel cofferdam openings includes: a steel cofferdam module, a data acquisition module, and a processing module; as shown below. Figure 4 As shown, the steel cofferdam module includes an open outer wall, a sliding cover plate, and a drive assembly. The open outer wall has multiple circular holes, and the sliding cover plate is slidably mounted on it. The sliding cover plate has multiple through holes. The drive assembly is mounted on the open outer wall and connected to the sliding cover plate. The drive assembly drives the sliding cover plate to slide relative to the open outer wall to adjust the overlap between the through holes and the circular holes. A data acquisition module collects sea wave data, open area ratio data of the steel cofferdam outer wall, and wave-facing width data. The sea wave data includes wave velocity, wave wavelength, wave height, and water depth. A processing module calculates the wave force reduction coefficient based on the sea wave data, open area ratio data, and wave-facing width data. It then sends an open area ratio adjustment command to the drive assembly based on the wave force reduction coefficient, causing the drive assembly to drive the sliding cover plate to slide on the open outer wall, adjusting the overlap between the circular holes and the through holes to achieve optimal open area ratio adjustment for mitigating wave impact.

[0146] In the above embodiments, the data acquisition module collects sea wave data, opening ratio data of the outer wall of the steel cofferdam, and wave-facing width data, and transmits them to the processing module. The processing module calculates the wave force reduction coefficient based on the sea wave data, opening ratio data, and wave-facing width data, and sends an opening ratio adjustment command to the drive component based on the wave force reduction coefficient. This causes the drive component to drive the sliding cover plate to slide on the outer wall of the opening, adjusting the overlap between the circular hole and the through hole, so as to achieve the optimal opening ratio adjustment to dissipate wave impact.

[0147] Specifically, the drive component can be a hydraulic cylinder.

[0148] In one embodiment, the data acquisition module includes: a Doppler current meter for acquiring wave velocity; a nearshore remote wave meter for acquiring wave wavelength and wave height; a sonar depth sounder for acquiring water depth; and a visual image acquisition device for acquiring data on the opening ratio and wave-facing width of the outer wall of the steel cofferdam.

[0149] Specifically, the Doppler current meter, nearshore remote wave meter, and sonar depth sounder use sonar detection methods to collect real-time wave data of the current sea area. Their respective deployment positions depend on the specific sea area conditions. Generally, they are evenly distributed 50m apart in the sea area 30m-200m in front of the steel cofferdam, evenly surrounding the sea area outside the steel cofferdam. They communicate using an RS485 bus and transmit the current sea area wave data to the processing module in real time via underwater optical cable.

[0150] Specifically, the processing module can be a host computer. The host computer can receive the collected sea wave data, the opening ratio data of the outer wall of the steel cofferdam, and the width data of the wave-facing face, and calculate the wave force reduction coefficient of the instantaneous sea waves. When the wave force reduction coefficient is within the preset threshold, there is no need to adjust the opening ratio on the outer wall of the steel cofferdam. When the wave force reduction coefficient exceeds the preset threshold, the host computer sends an opening ratio adjustment command and uploads all data to the cloud server to adjust the opening ratio on the outer wall of the steel cofferdam. Then, the opening ratio data is re-entered into the above calculation formula. If the calculated wave force reduction coefficient still exceeds the preset threshold, the opening ratio on the outer wall of the steel cofferdam is adjusted until the wave force reduction coefficient is within the preset threshold, so as to achieve the optimal opening ratio adjustment for dissipating wave impact.

[0151] In one embodiment, the dynamic scheduling system for steel cofferdam openings further includes a monitoring and control module. This module receives data from the processing module, enabling on-site monitoring personnel to monitor the data in real time. When on-site monitoring personnel detect a malfunction in the processing module's instructions, they can directly transmit an opening rate adjustment command to the driving components of the steel cofferdam module. The monitoring and control module has higher command authority than the processing module, providing dual protection for the safety of the steel cofferdam module.

[0152] Specifically, the monitoring and control module can be a common unit.

[0153] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for dynamic scheduling of openings in a steel cofferdam, characterized in that, Includes the following steps: Collect ocean wave data, which includes wave velocity, wave wavelength, wave height, and water depth; Collect data on the opening ratio of the outer wall of the steel cofferdam and the width of the wave-facing surface; The wave force reduction factor is calculated using the following optimal parameter calculation formula based on the sea wave data, the orifice ratio data, and the wave front width data: in, This represents the wave force reduction factor. This indicates the width of the wave-facing surface. Indicates the open area ratio. Indicates water depth. Indicates the wave wavelength. Indicates relative water depth. Represents the wave height, Indicates a steep wave; Adjusting the opening ratio on the outer wall of the steel cofferdam according to the wave force reduction coefficient specifically includes: When the wave force reduction coefficient At that time, the aperture ratio remains unchanged; When the wave force reduction coefficient At that time, the aperture ratio Decrease by 0.001 until the wave force reduction factor is reached. .

2. The method for dynamic scheduling of steel cofferdam openings according to claim 1, characterized in that, The formula for calculating the wave force reduction coefficient using the POA-SVR prediction model to predict the optimal parameters between the wave force reduction coefficient and the wave front width, the opening ratio, the relative water depth, and the wave steepness.

3. The method for dynamic scheduling of steel cofferdam openings according to claim 2, characterized in that, The prediction process of the POA-SVR prediction model includes: The dataset is divided into training set data and validation set data, with the opening rate data of the outer wall of the steel cofferdam as sample data. The training set data and the validation set data are then normalized for training. By selecting a kernel function and modifying the parameters of the Gaussian kernel function in the traditional SVR model, the optimal parameter fitness is achieved by fusing the Gaussian regression kernel and the quadratic rational kernel, while ensuring the effectiveness of the global search. The penalty factor of the SVR model is optimized using the POA algorithm. The parameters of the POA algorithm are initialized, the fitness function is set, and the penalty factor and kernel function bandwidth of the SVR model are obtained through continuous iteration by initializing peacock groups, flight and display simulations, competition and selection. The model is trained using the training set data to obtain the POA-SVR prediction model; The POA-SVR model is validated by inputting the validation set data, and the final predicted result of the opening ratio is output by inverse normalization. The prediction accuracy of the opening ratio of the POA-SVR prediction model is verified by calculating the deviation between the actual lifetime and the predicted lifetime. The optimal regression function is output, and the prediction results are compared with the validation set data. The accuracy is greater than 95%, and the calculation formula of the wave force reduction coefficient of the optimal parameters is obtained.

4. A dynamic scheduling system for openings in steel cofferdams, applicable to the dynamic scheduling method for openings in steel cofferdams as described in any one of claims 1-3, characterized in that, The dynamic scheduling system for opening in the steel cofferdam includes: A steel cofferdam module includes an open outer wall, a sliding cover plate, and a driving assembly. The open outer wall has multiple circular holes. The sliding cover plate is slidably disposed on the open outer wall and has multiple through holes. The driving assembly is disposed on the open outer wall and drivenly connected to the sliding cover plate. The driving assembly is used to drive the sliding cover plate to slide relative to the open outer wall to adjust the overlap between the through holes and the circular holes. The data acquisition module is used to collect sea wave data, open area ratio data of the outer wall of the steel cofferdam, and wave-facing width data. The sea wave data includes wave velocity, wave wavelength, wave height, and water depth. The processing module is used to calculate the wave force reduction coefficient based on the sea wave data, the opening ratio data, and the wave-facing width data, and to send an opening ratio adjustment command to the drive component based on the wave force reduction coefficient, so that the drive component drives the sliding cover to slide on the outer wall of the opening, adjusting the degree of overlap between the circular hole and the through hole, so as to achieve the optimal opening ratio adjustment to dissipate wave impact.

5. The dynamic scheduling system for opening in steel cofferdams according to claim 4, characterized in that, The data acquisition module includes: A Doppler current meter is used to collect the wave velocity. A nearshore remote wave instrument is used to collect the wave wavelength and wave height. A sonar depth sounder is used to acquire the water depth; and A visual image acquisition device is used to collect data on the opening ratio and wave-facing width of the outer wall of the steel cofferdam.

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