Gate opening degree and flow dynamic coupling prediction method based on AI driving

Abstract

The invention discloses a gate opening flow dynamic coupling prediction method based on AI driving, and relates to the field of water conservancy projects, and the method comprises the steps: obtaining a conversion function; establishing a settlement simulation model, a water flow extrusion model and a water flow blocking model; taking a conventional opening subarea exposed by opening of the opening control gate as a target opening subarea, and dividing the target opening subarea into a first target opening subarea and a second target opening subarea based on the water surface of the downstream of the gate; the water flow velocity of the characteristic opening subarea is predicted and serves as the characteristic velocity, and the water flow velocity of the target opening subarea is calculated and serves as the target velocity; and obtaining a prediction function of the gate opening area and the gate opening flow. The method comprises the following steps: forming a conversion function, establishing a settlement simulation model, establishing a water flow extrusion model, establishing a water flow blocking model, classifying target opening subareas, obtaining a prediction function, and opening a gate to the required size according to a prediction result to generate the required flow.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy projects, and in particular to an AI-driven gate opening and flow dynamic coupling prediction method. Background Art

[0002] A gate is a mechanism used to control water flow and is widely used in water conservancy projects. Gates can be categorized by their structural form, including flat gates, radial gates, and sliding gates. The most common type is the flat gate, whose opening can be controlled manually or electrically. The size of the gate opening plays a crucial role in controlling water flow. To precisely control flow, it's necessary to control the area of ​​the gate opening.

[0003] When the gate is open, the water flow rate is not affected by a single factor. It will be affected by the obstruction of downstream water flow, the gravity of the part above the gate, and the squeezing of the water flow blocked by the gate. Since the water flow at different positions where the gate is opened is affected differently by the above factors, it is difficult to predict the overall flow rate. Summary of the Invention

[0004] In order to solve the above technical problems, an AI-driven gate opening flow dynamic coupling prediction method is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] AI-driven gate opening and flow dynamic coupling prediction method includes:

[0007] Establishing a three-dimensional coordinate system for the opening environment where the opening control gate is located, obtaining the water flow in the opening environment where the opening control gate is located, obtaining the water flow velocity upstream of the gate as a first velocity, and obtaining the water flow velocity downstream of the gate as a second velocity;

[0008] The water flow surface located upstream and in contact with the opening control gate is evenly divided into blocks to obtain at least one opening partition, the opening partition located at the bottom of the gate is used as the characteristic opening partition, the remaining opening partitions are used as regular opening partitions, and a conversion function between the regular opening partitions and the characteristic opening partitions is obtained;

[0009] Establish a sedimentation simulation model, a water flow extrusion model, and a water flow obstruction model;

[0010] The normal opening zone exposed by the opening of the opening control gate is used as the target opening zone, and the target opening zone is divided into a first target opening zone and a second target opening zone based on the water surface downstream of the gate;

[0011] Based on the established model, the water flow velocity of the characteristic opening partition is predicted as the characteristic velocity, and based on the conversion function, the water flow velocity of the target opening partition is calculated as the target velocity;

[0012] Based on the characteristic speed and target speed, the prediction function of gate opening area and gate opening flow is obtained.

[0013] Preferably, the step of uniformly dividing the water flow surface upstream of the opening control gate into blocks to obtain at least one opening partition comprises the following steps:

[0014] The contact surface of the water flow located upstream and in contact with the opening control gate is used as the characteristic surface;

[0015] The characteristic surface is segmented using at least one equally spaced horizontal line to obtain at least one opening partition.

[0016] Preferably, the acquisition of the conversion function between the conventional opening partition and the characteristic opening partition comprises the following steps:

[0017] Obtaining the area of ​​the characteristic surface located above the conventional opening partition as the first area, and obtaining the area of ​​the characteristic surface located above the characteristic opening partition as the second area;

[0018] Divide the first area by the second area to obtain a conversion factor;

[0019] The coordinates of the center of the conventional opening partition are paired with the conversion coefficient and fitted to obtain a conversion function, wherein the coordinates of the center of the conventional opening partition are independent variables and the conversion coefficient is the dependent variable.

[0020] Preferably, the establishment of the convection sedimentation simulation model comprises the following steps:

[0021] Obtain the distance range from the water surface to the bottom upstream of the gate, divide the distance range from the water surface to the bottom upstream of the gate into equal intervals, and obtain at least one identification point;

[0022] A hole is opened in a column filled with water. The distance from the hole to the liquid surface in the column is the value at the identification point. The column is placed on the ground.

[0023] Obtain the distance from the opening to the bottom of the column, obtain the point where the water flow at the opening falls on the ground, and calculate the water flow velocity at the opening as the representative velocity;

[0024] The values ​​at the identified points are paired with the characteristic velocity and fitted to obtain the sedimentation fitting function.

[0025] Preferably, the establishment of the water flow extrusion model comprises the following steps:

[0026] Obtain the area of ​​the characteristic surface, obtain the gate opening area, compare the area of ​​the characteristic surface with the gate opening area, and obtain a proportional coefficient;

[0027] A water flow extrusion function y=kx is formed, where x is the water flow velocity before passing through the gate, k is the proportional coefficient, and y is the water flow velocity after passing through the gate.

[0028] Preferably, the establishing of the water flow obstruction model comprises the following steps:

[0029] Obtain a water flow velocity range upstream of the gate, divide the water flow velocity range upstream of the gate into equal intervals, and obtain at least one upper test point;

[0030] Obtaining a water flow velocity range downstream of the gate, dividing the water flow velocity range downstream of the gate into equal intervals, and obtaining at least one lower test point;

[0031] Randomly combine the upper test point and the lower test point to form at least one test combination, and delete the test combination where the upper test point is smaller than the lower test point;

[0032] A test box is formed, wherein water flows to the left in the test box, and the test box is divided into a first part and a second part, wherein the first part is located to the left of the second part;

[0033] The first part of the water flow is generated by the first pump, and the water flow speed of the first part is equal to the value of the lower test point; the second part of the water flow is generated by the second pump, and the water flow speed of the second part is equal to the value of the upper test point;

[0034] The water flow of the second part is dyed, and the velocity of the water flow of the first part and the water flow of the second part when they meet is measured based on the dyed water flow, which is used as the measured velocity;

[0035] The measured speed is divided by the value of the above test point to obtain the hindering effect coefficient;

[0036] The test combination and the obstruction effect coefficient are paired and fitted to obtain the water flow obstruction function, where the upper test point and the lower test point in the test combination are independent variables and the obstruction effect coefficient is the dependent variable.

[0037] Preferably, dividing the target opening zone into a first target opening zone and a second target opening zone based on the water surface downstream of the gate comprises the following steps:

[0038] The target opening zone below the water surface downstream of the gate is taken as the first target opening zone, and the target opening zone above the water surface downstream of the gate is taken as the second target opening zone.

[0039] Preferably, the method of predicting the water flow velocity of the characteristic opening partition based on the established model includes the following steps:

[0040] Obtain the distance from the center of the characteristic opening partition to the water surface upstream of the gate as the measurable distance;

[0041] Substituting the measurable distance into the sedimentation fitting function, the first promotion velocity is obtained;

[0042] The first promotion speed is superimposed on the first speed to obtain a first comprehensive speed;

[0043] Substitute the first integrated velocity into the water extrusion function to obtain the first gate exit velocity;

[0044] Substitute the first exit speed and the second speed into the water flow resistance function to obtain the actual resistance coefficient;

[0045] The first gate velocity is multiplied by the actual resistance coefficient to obtain the water flow velocity in the characteristic opening partition.

[0046] Preferably, the step of calculating the water flow velocity of the target opening partition based on the conversion function comprises the following steps:

[0047] Substitute the coordinates of the center of the target opening partition into the conversion function to obtain the actual conversion coefficient;

[0048] The actual conversion factor is multiplied by the first promotion speed to obtain the second promotion speed;

[0049] The second promotion speed is superimposed on the first speed to obtain a second comprehensive speed;

[0050] Substitute the second integrated velocity into the water extrusion function to obtain the second gate exit velocity;

[0051] When the target opening partition is the first target opening partition, the second gate exit speed and the second speed are substituted into the water flow resistance function to obtain the target resistance coefficient. The water flow speed of the target opening partition is equal to the product of the second gate exit speed and the target resistance coefficient.

[0052] When the target opening partition is the second target opening partition, the water flow velocity in the target opening partition is equal to the second gate exit velocity.

[0053] Preferably, obtaining a prediction function of gate opening area and gate opening flow based on the characteristic speed and target speed comprises the following steps:

[0054] Accumulating the areas of the characteristic opening partition and at least one target opening partition to obtain the gate opening area;

[0055] The characteristic flow rate is obtained by multiplying the area of ​​the characteristic opening partition by the characteristic speed, and the target flow rate is obtained by multiplying the area of ​​the target opening partition by the target speed;

[0056] Accumulate the characteristic flow and at least one target flow to obtain the gate opening flow;

[0057] The gate opening area and gate opening flow are paired and fitted to obtain the prediction function of the gate opening area and gate opening flow.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] By forming a conversion function, establishing a sedimentation simulation model, a water flow extrusion model, a water flow obstruction model, classifying the target opening zones and obtaining a prediction function, the influence of downstream water flow obstruction, the gravity of the part above the gate and the extrusion effect of the water flow part blocked by the gate can be taken into account according to the area of ​​the gate opening. Then, combining their influences, the influence on the water flow velocity upstream of the gate can be gradually calculated, so that the flow rate generated under different gate opening areas can be predicted, and then according to the prediction results, the gate can be opened to the required size to generate the required flow rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Schematic diagram of the flow chart of the AI-driven gate opening flow dynamic coupling prediction method of the present invention;

[0061] Figure 2 A schematic flow chart of the present invention for uniformly dividing the upstream water flow surface in contact with the opening control gate to obtain at least one opening partition;

[0062] Figure 3 Schematic diagram of the process of obtaining the conversion function of the conventional opening partition and the characteristic opening partition of the present invention;

[0063] Figure 4 A schematic diagram of a process for establishing a convection sedimentation simulation model according to the present invention;

[0064] Figure 5 A schematic diagram of a process for establishing a water extrusion model of the present invention;

[0065] Figure 6 A schematic diagram of a flow chart for establishing a water flow obstruction model according to the present invention;

[0066] Figure 7 This is a schematic flow chart of predicting the water flow velocity of characteristic opening partitions based on the established model of the present invention;

[0067] Figure 8 A schematic diagram of a flow chart of calculating the water flow velocity of a target opening partition based on a conversion function of the present invention;

[0068] Figure 9 The figure is a flow chart of the present invention for obtaining a prediction function of the gate opening area and the gate opening flow rate based on the characteristic speed and the target speed. DETAILED DESCRIPTION

[0069] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0070] Reference Figure 1 As shown in the figure, the AI-driven gate opening flow dynamic coupling prediction method includes:

[0071] Establishing a three-dimensional coordinate system for the opening environment where the opening control gate is located, obtaining the water flow in the opening environment where the opening control gate is located, obtaining the water flow velocity upstream of the gate as a first velocity, and obtaining the water flow velocity downstream of the gate as a second velocity;

[0072] The water flow surface located upstream and in contact with the opening control gate is evenly divided into blocks to obtain at least one opening partition, the opening partition located at the bottom of the gate is used as the characteristic opening partition, the remaining opening partitions are used as regular opening partitions, and a conversion function between the regular opening partitions and the characteristic opening partitions is obtained;

[0073] Establish a sedimentation simulation model, a water flow extrusion model, and a water flow obstruction model;

[0074] The normal opening zone exposed by the opening of the opening control gate is used as the target opening zone, and the target opening zone is divided into a first target opening zone and a second target opening zone based on the water surface downstream of the gate;

[0075] Based on the established model, the water flow velocity of the characteristic opening partition is predicted as the characteristic velocity, and based on the conversion function, the water flow velocity of the target opening partition is calculated as the target velocity;

[0076] Based on the characteristic speed and target speed, the prediction function of gate opening area and gate opening flow is obtained.

[0077] The opening of the gate refers to the distance from the bottom of the gate to the downstream water surface. In this scheme, when the gate leaves the water bottom, it will generate flow. The water surface upstream of the gate is usually higher than the water surface downstream of the gate due to the effect of the gate. When the gate is opened, the area of ​​the gate opening will be smaller than the cross-sectional area of ​​the water flow. Therefore, the water flow velocity out of the gate opening will maintain the water flow velocity upstream of the gate, that is, the first velocity. However, since the part of the gate upstream that is higher than the gate opening will have a sedimentation effect, which is similar to the effect of squeezing, it will cause the water flow at the gate outlet to be faster. Therefore, it is necessary to characterize the impact model of this effect on the velocity. This is a longitudinal effect. At the same time, when the water flow comes to the gate, since the gate opening is smaller than the water flow cross-sectional area, The water flow beside the gate opening will produce a squeezing effect and cause the water flow to be faster. Therefore, it is also necessary to characterize the impact model of this effect on the speed. In addition, when the bottom of the gate is not higher than the downstream water surface and when the bottom of the gate is higher than the downstream water surface, the flow generated in these two situations is also different in analysis, because the part above the downstream water surface will not be hindered by the downstream water flow. This is because generally speaking, the downstream water flow speed must be lower than the speed of opening the gate, but the part below the downstream water surface will be hindered by the downstream water flow, so the speed of this part needs to be predicted separately. All the influences mentioned above will be simulated by corresponding models in the subsequent period, and then predicted based on the model.

[0078] The process of uniformly dividing the upstream water flow surface in contact with the opening control gate into at least one opening partition comprises the following steps:

[0079] The contact surface of the water flow located upstream and in contact with the opening control gate is used as the characteristic surface;

[0080] The characteristic surface is segmented using at least one equally spaced horizontal line to obtain at least one opening partition.

[0081] When the distance between adjacent horizontal lines is very close, the opening partition is very flat. Therefore, the conditions within the opening partition can be considered the same and can be analyzed as a whole. According to the requirements of prediction accuracy, when the accuracy requirement is higher, the distance between adjacent horizontal lines can be further reduced to meet the accuracy requirement.

[0082] Both sides of the gate are in contact with the water flow, but the flow rate is determined by the upstream water flow. Therefore, the surface that is in contact with the side of the gate and located upstream of the gate is used as the feature surface.

[0083] Obtaining the conversion function between conventional opening partitions and characteristic opening partitions includes the following steps:

[0084] Obtaining the area of ​​the characteristic surface located above the conventional opening partition as the first area, and obtaining the area of ​​the characteristic surface located above the characteristic opening partition as the second area;

[0085] Divide the first area by the second area to obtain a conversion factor;

[0086] The coordinates of the center of the conventional opening partition are paired with the conversion coefficient and fitted to obtain a conversion function, wherein the coordinates of the center of the conventional opening partition are independent variables and the conversion coefficient is the dependent variable.

[0087] When performing calculations, the influence of water gravity on flow velocity is different. For convenience, only the influence of gravity on velocity of characteristic opening partitions is calculated. For other conventional opening partitions, the relationship between the influence of gravity on their velocity and the influence of gravity on velocity of characteristic opening partitions is determined based on the proportional relationship of height. Therefore, the influence of gravity on velocity of characteristic opening partitions can be directly derived.

[0088] The establishment of the convective sedimentation simulation model includes the following steps:

[0089] Obtain the distance range from the water surface to the bottom upstream of the gate, divide the distance range from the water surface to the bottom upstream of the gate into equal intervals, and obtain at least one identification point;

[0090] A hole is opened in a column filled with water. The distance from the hole to the liquid surface in the column is the value at the identification point. The column is placed on the ground.

[0091] Obtain the distance from the opening to the bottom of the column, obtain the point where the water flow at the opening falls on the ground, and calculate the water flow velocity at the opening as the representative velocity;

[0092] The values ​​at the identified points are paired with the characteristic velocity and fitted to obtain the sedimentation fitting function.

[0093] When establishing a convection sedimentation simulation model, since the conversion function is formed in advance, it is sufficient to ensure that the domain of the sedimentation fitting function includes the distance from the characteristic opening partition to the water surface, and the distance from the characteristic opening partition to the water surface is the range of the upstream water depth, without the need to include the values ​​of all identification points. In this way, during the test, the identification points are measured from large to small. When the identification point is initially less than the minimum value of the upstream water depth, the measurement can be stopped. This can reduce the time of the modeling experiment. In actual use, for underwater depths that are not in the domain of the sedimentation fitting function, the conversion function can be used for calculation.

[0094] The establishment of the water extrusion model includes the following steps:

[0095] Obtain the area of ​​the characteristic surface, obtain the gate opening area, compare the area of ​​the characteristic surface with the gate opening area, and obtain a proportional coefficient;

[0096] A water flow extrusion function y=kx is formed, where x is the water flow velocity before passing through the gate, k is the proportional coefficient, and y is the water flow velocity after passing through the gate.

[0097] It is easy to know that the smaller the gate opening area, the faster the speed passing through the gate, that is, the two are inversely proportional. When the gate is fully opened, it can be equal to the cross-sectional area of ​​the water flow. Therefore, according to this proportional relationship, a water flow extrusion function can be formed.

[0098] Building a water flow obstruction model involves the following steps:

[0099] Obtain a water flow velocity range upstream of the gate, divide the water flow velocity range upstream of the gate into equal intervals, and obtain at least one upper test point;

[0100] Obtaining a water flow velocity range downstream of the gate, dividing the water flow velocity range downstream of the gate into equal intervals, and obtaining at least one lower test point;

[0101] Randomly combine the upper test point and the lower test point to form at least one test combination, and delete the test combination where the upper test point is smaller than the lower test point;

[0102] A test box is formed, wherein water flows to the left in the test box, and the test box is divided into a first part and a second part, wherein the first part is located to the left of the second part;

[0103] The first part of the water flow is generated by the first pump, and the water flow speed of the first part is equal to the value of the lower test point; the second part of the water flow is generated by the second pump, and the water flow speed of the second part is equal to the value of the upper test point;

[0104] The water flow of the second part is dyed, and the velocity of the water flow of the first part and the water flow of the second part when they meet is measured based on the dyed water flow, which is used as the measured velocity;

[0105] The measured speed is divided by the value of the above test point to obtain the hindering effect coefficient;

[0106] The test combination and the obstruction effect coefficient are paired and fitted to obtain the water flow obstruction function, where the upper test point and the lower test point in the test combination are independent variables and the obstruction effect coefficient is the dependent variable.

[0107] Since the downstream water flow velocity is less than the gate opening speed, it is necessary to analyze the impact of the downstream water flow velocity on the obstruction of the water when the gate is opened, so as to obtain the effect of water flow obstruction under different conditions.

[0108] Dividing the target opening zone into a first target opening zone and a second target opening zone based on the water surface downstream of the gate comprises the following steps:

[0109] The target opening zone below the water surface downstream of the gate is taken as the first target opening zone, and the target opening zone above the water surface downstream of the gate is taken as the second target opening zone.

[0110] The first target opening partition is below the water surface downstream of the gate. Therefore, when it passes through the gate, it will be blocked by the downstream water flow. The second target opening partition is above the water surface downstream of the gate. Therefore, when it passes through the gate, it will not be blocked by the downstream water flow. These two situations need to be classified and processed, otherwise, there will be errors in the calculated flow rate.

[0111] Based on the established model, the water flow velocity of the characteristic opening partition is predicted by the following steps:

[0112] Obtain the distance from the center of the characteristic opening partition to the water surface upstream of the gate as the measurable distance;

[0113] Substituting the measurable distance into the sedimentation fitting function, the first promotion velocity is obtained;

[0114] The first promotion speed is superimposed on the first speed to obtain a first comprehensive speed;

[0115] Substitute the first integrated velocity into the water extrusion function to obtain the first gate exit velocity;

[0116] Substitute the first exit speed and the second speed into the water flow resistance function to obtain the actual resistance coefficient;

[0117] The first gate velocity is multiplied by the actual resistance coefficient to obtain the water flow velocity in the characteristic opening partition.

[0118] Since the characteristic opening partition is located at the bottom of the gate, it will inevitably generate flow when the gate is opened. First, when it reaches the gate at the first speed, due to the effect of upper gravity, a superimposed speed will be generated, namely the first promotion speed. When passing through the gate, the two will be squeezed and accelerated synchronously. Since the characteristic opening partition is located at the bottom of the gate, it will be blocked by the downstream water flow after passing through the gate. Therefore, it is necessary to calculate its speed after being blocked.

[0119] Based on the conversion function, the water flow velocity of the target opening partition is calculated, which includes the following steps:

[0120] Substitute the coordinates of the center of the target opening partition into the conversion function to obtain the actual conversion coefficient;

[0121] The actual conversion factor is multiplied by the first promotion speed to obtain the second promotion speed;

[0122] The second promotion speed is superimposed on the first speed to obtain a second comprehensive speed;

[0123] Substitute the second integrated velocity into the water extrusion function to obtain the second gate exit velocity;

[0124] When the target opening partition is the first target opening partition, the second gate exit speed and the second speed are substituted into the water flow resistance function to obtain the target resistance coefficient. The water flow speed of the target opening partition is equal to the product of the second gate exit speed and the target resistance coefficient.

[0125] When the target opening partition is the second target opening partition, the water flow speed in the target opening partition is equal to the second gate exit speed.

[0126] The calculation principle here is consistent with the above situation. However, in order to save time, the testing process of some identification points was omitted when establishing the settlement fitting function. Therefore, its domain may not include the depth of the target opening partition. Therefore, it is necessary to use a conversion function to calculate the velocity caused by gravity. When analyzing the target opening partition after passing through the gate, a classified discussion is required.

[0127] Based on the characteristic speed and target speed, the prediction function of the gate opening area and the gate opening flow is obtained, which includes the following steps:

[0128] Accumulating the areas of the characteristic opening partition and at least one target opening partition to obtain the gate opening area;

[0129] The characteristic flow rate is obtained by multiplying the area of ​​the characteristic opening partition by the characteristic speed, and the target flow rate is obtained by multiplying the area of ​​the target opening partition by the target speed;

[0130] Accumulate the characteristic flow and at least one target flow to obtain the gate opening flow;

[0131] The gate opening area and gate opening flow are paired and fitted to obtain the prediction function of the gate opening area and gate opening flow.

[0132] Since the prediction function is obtained, when the gate opening flow is determined, the gate opening area can be obtained by inversely solving the prediction function, and then the height to which the gate is raised can be determined, so that the flow can be accurately controlled.

[0133] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned AI-driven gate opening flow dynamic coupling prediction method is executed.

[0134] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0135] To sum up, the advantages of the present invention are: by forming a conversion function, establishing a sedimentation simulation model, establishing a water flow extrusion model, establishing a water flow obstruction model, classifying the target opening partitions and obtaining a prediction function, the influence of downstream water flow obstruction, the gravity of the part above the gate and the extrusion influence of the water flow part blocked by the gate can be taken into consideration according to the area of ​​the gate opening, and then their influence can be combined to gradually calculate their influence on the water flow velocity upstream of the gate, so as to predict the flow rate generated under different gate opening areas, and then according to the prediction results, the gate can be opened to the required size to generate the required flow rate.

[0136] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. The AI-driven gate opening flow dynamic coupling prediction method is characterized by: include: Establishing a three-dimensional coordinate system for the opening environment where the opening control gate is located, obtaining the water flow in the opening environment where the opening control gate is located, obtaining the water flow velocity upstream of the gate as a first velocity, and obtaining the water flow velocity downstream of the gate as a second velocity; The water flow surface located upstream and in contact with the opening control gate is evenly divided into blocks to obtain at least one opening partition, the opening partition located at the bottom of the gate is used as the characteristic opening partition, the remaining opening partitions are used as regular opening partitions, and a conversion function between the regular opening partitions and the characteristic opening partitions is obtained; Establish a sedimentation simulation model, a water flow extrusion model, and a water flow obstruction model; The normal opening zone exposed by the opening of the opening control gate is used as the target opening zone, and the target opening zone is divided into a first target opening zone and a second target opening zone based on the water surface downstream of the gate; Based on the established model, the water flow velocity of the characteristic opening partition is predicted as the characteristic velocity, and based on the conversion function, the water flow velocity of the target opening partition is calculated as the target velocity; Based on the characteristic speed and target speed, the prediction function of gate opening area and gate opening flow is obtained.

2. The AI-driven gate opening flow dynamic coupling prediction method according to claim 1 is characterized in that: The water flow surface located upstream and in contact with the opening control gate is evenly divided into blocks to obtain at least one opening partition. The following steps are involved: The contact surface of the water flow located upstream and in contact with the opening control gate is used as the characteristic surface; The characteristic surface is segmented using at least one equally spaced horizontal line to obtain at least one opening partition.

3. The AI-driven gate opening flow dynamic coupling prediction method according to claim 2 is characterized in that: The method of obtaining the conversion function between the conventional opening partition and the characteristic opening partition comprises the following steps: Obtaining the area of ​​the characteristic surface located above the conventional opening partition as the first area, and obtaining the area of ​​the characteristic surface located above the characteristic opening partition as the second area; Divide the first area by the second area to obtain a conversion factor; The coordinates of the center of the conventional opening partition are paired with the conversion coefficient and fitted to obtain a conversion function, wherein the coordinates of the center of the conventional opening partition are independent variables and the conversion coefficient is the dependent variable.

4. The AI-driven gate opening flow dynamic coupling prediction method according to claim 3 is characterized in that: The described establishment of convection sedimentation simulation model comprises the following steps: Obtain the distance range from the water surface to the bottom upstream of the gate, divide the distance range from the water surface to the bottom upstream of the gate into equal intervals, and obtain at least one identification point; A hole is opened in a column filled with water. The distance from the hole to the liquid surface in the column is the value at the identification point. The column is placed on the ground. Obtain the distance from the opening to the bottom of the column, obtain the point where the water flow at the opening falls on the ground, and calculate the water flow velocity at the opening as the representative velocity; The values ​​at the identified points are paired with the characteristic velocity and fitted to obtain the sedimentation fitting function.

5. The AI-driven gate opening flow dynamic coupling prediction method according to claim 4 is characterized in that: Described setting up water flow extrusion model comprises the following steps: Obtain the area of ​​the characteristic surface, obtain the gate opening area, compare the area of ​​the characteristic surface with the gate opening area, and obtain a proportional coefficient; A water flow extrusion function y=kx is formed, where x is the water flow velocity before passing through the gate, k is the proportional coefficient, and y is the water flow velocity after passing through the gate.

6. The AI-driven gate opening flow dynamic coupling prediction method according to claim 5 is characterized in that: The water flow obstruction model is established and comprises the following steps: Obtain a water flow velocity range upstream of the gate, divide the water flow velocity range upstream of the gate into equal intervals, and obtain at least one upper test point; Obtaining a water flow velocity range downstream of the gate, dividing the water flow velocity range downstream of the gate into equal intervals, and obtaining at least one lower test point; Randomly combine the upper test point and the lower test point to form at least one test combination, and delete the test combination where the upper test point is smaller than the lower test point; A test box is formed, wherein water flows to the left in the test box, and the test box is divided into a first part and a second part, wherein the first part is located to the left of the second part; The first part of the water flow is generated by the first pump, and the water flow speed of the first part is equal to the value of the lower test point; the second part of the water flow is generated by the second pump, and the water flow speed of the second part is equal to the value of the upper test point; The water flow of the second part is dyed, and the velocity of the water flow of the first part and the water flow of the second part when they meet is measured based on the dyed water flow, which is used as the measured velocity; The measured speed is divided by the value of the above test point to obtain the hindering effect coefficient; The test combination and the obstruction effect coefficient are paired and fitted to obtain the water flow obstruction function, where the upper test point and the lower test point in the test combination are independent variables and the obstruction effect coefficient is the dependent variable.

7. The AI-driven gate opening flow dynamic coupling prediction method according to claim 6 is characterized in that: The step of dividing the target opening zone into a first target opening zone and a second target opening zone based on the water surface downstream of the gate comprises the following steps: The target opening zone below the water surface downstream of the gate is taken as the first target opening zone, and the target opening zone above the water surface downstream of the gate is taken as the second target opening zone.

8. The AI-driven gate opening flow dynamic coupling prediction method according to claim 7 is characterized in that: The method of predicting the water flow velocity of the characteristic opening partition based on the established model includes the following steps: Obtain the distance from the center of the characteristic opening partition to the water surface upstream of the gate as the measurable distance; Substituting the measurable distance into the sedimentation fitting function, the first promotion velocity is obtained; The first promotion speed is superimposed on the first speed to obtain a first comprehensive speed; Substitute the first integrated velocity into the water extrusion function to obtain the first gate exit velocity; Substitute the first exit speed and the second speed into the water flow resistance function to obtain the actual resistance coefficient; The first gate velocity is multiplied by the actual resistance coefficient to obtain the water flow velocity in the characteristic opening partition.

9. The AI-driven gate opening flow dynamic coupling prediction method according to claim 8 is characterized in that: The method of calculating the water flow velocity of the target opening partition based on the conversion function includes the following steps: Substitute the coordinates of the center of the target opening partition into the conversion function to obtain the actual conversion coefficient; The actual conversion factor is multiplied by the first promotion speed to obtain the second promotion speed; The second promotion speed is superimposed on the first speed to obtain a second comprehensive speed; Substitute the second integrated velocity into the water extrusion function to obtain the second gate exit velocity; When the target opening partition is the first target opening partition, the second gate exit speed and the second speed are substituted into the water flow resistance function to obtain the target resistance coefficient. The water flow speed of the target opening partition is equal to the product of the second gate exit speed and the target resistance coefficient. When the target opening partition is the second target opening partition, the water flow velocity in the target opening partition is equal to the second gate exit velocity.

10. The AI-driven gate opening flow dynamic coupling prediction method according to claim 9 is characterized in that: The method of obtaining a prediction function of gate opening area and gate opening flow based on the characteristic speed and the target speed comprises the following steps: Accumulating the areas of the characteristic opening partition and at least one target opening partition to obtain the gate opening area; The characteristic flow rate is obtained by multiplying the area of ​​the characteristic opening partition by the characteristic speed, and the target flow rate is obtained by multiplying the area of ​​the target opening partition by the target speed; Accumulate the characteristic flow and at least one target flow to obtain the gate opening flow; The gate opening area and gate opening flow are paired and fitted to obtain the prediction function of the gate opening area and gate opening flow.

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