Method, device and equipment for reducing current force borne by pile foundation

Abstract

The invention provides a method, device and equipment for reducing current force borne by a pile foundation. The method comprises the steps that previous working condition information is loaded to a hydrological prediction model for training; loading preset extreme working condition information to the hydrological prediction model to obtain extreme hydrological information; loading the extreme hydrological information to a meteorological hydrological early warning model for training, and obtaining meteorological information to predict whether an extreme working condition occurs or not; if the extreme working condition occurs, judging whether the pile foundation structure is influenced; if the pile foundation structure is affected, pit scouring information and terrain information are obtained and fed back to the meteorological and hydrological early warning model; through loading previous working condition information to a hydrological prediction model for training, combining preset extreme working condition information to obtain extreme hydrological information, loading the extreme hydrological information to a meteorological and hydrological early warning model for training, and combining real-time meteorological information to predict whether an extreme working condition appears or not. And when an extreme working condition occurs, a protection material is added, pit flushing information and topographic information are fed back to the meteorological and hydrological early warning model, and the resistance of the pile foundation to flow force impact under the extreme working condition is improved.

Description

Technical Field

[0001] This application relates to the field of pile foundation scour analysis technology, and in particular to a method, apparatus and equipment for reducing the fluid forces on pile foundations. Background Technology

[0002] For long-distance cable laying, especially in specific natural environments such as the Yangtze River, where the span is large, it is often necessary to construct underwater power tower foundations to allow cable laying on top of the towers. However, during actual operation, the power tower foundations are continuously eroded by water flow, leading to structural damage, reducing their service life, and creating safety hazards. To address these shortcomings, current methods mostly rely on predicting natural environmental factors and then preparing for prevention and repairs based on the prediction results.

[0003] However, the above prediction methods also have certain shortcomings. This is mainly due to the lack of prediction or coping measures for extreme environments; for example, by acquiring environmental data, including extreme environments, to build a deep relational learning model and adjusting the input according to the actual environmental conditions, the real-time assessment of the state of the pile foundation itself is ignored, which leads to the inability to efficiently reduce the fluid impact received by the pile foundation, resulting in a decrease in the reliability of the pile foundation. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for reducing the fluid forces on pile foundations. It is used to predict possible extreme working conditions based on existing working conditions and adjust the working condition prediction according to the pile foundation structure, thereby ensuring the reliability of pile foundation use.

[0005] In a first aspect, embodiments of this application provide a method for reducing the fluid forces acting on pile foundations, including:

[0006] By loading past working condition information of the pile foundation into the hydrological prediction model for training, the first correlation information between working condition information and hydrological information is obtained;

[0007] The preset extreme working condition information is loaded into the hydrological prediction model, and the corresponding extreme hydrological information under the preset working condition information is obtained through the first correlation information.

[0008] Extreme hydrological information is loaded into the meteorological and hydrological early warning model for training, and meteorological information is acquired in real time to predict whether extreme conditions will occur in the future.

[0009] If extreme working conditions occur, determine whether they will affect the pile foundation structure;

[0010] If it is determined that the impact on the pile foundation structure is to be obtained, information on scour pits and topographical information under the influence of fluid forces is obtained and fed back to the meteorological and hydrological early warning model.

[0011] Secondly, embodiments of this application provide a device for reducing the fluid forces acting on pile foundations, comprising:

[0012] The first association information generation module is used to train the hydrological prediction model by loading the past working condition information of the pile foundation into the hydrological information to obtain the first association information between the working condition information and the hydrological information.

[0013] The extreme hydrological information generation module is used to load preset extreme working condition information into the hydrological prediction model and obtain the corresponding extreme hydrological information under the preset working condition information through the first correlation information.

[0014] The working condition prediction module is used to load extreme hydrological information into the meteorological and hydrological early warning model for training, acquire meteorological information in real time, predict whether extreme working conditions will occur in the future, and if extreme working conditions occur, determine whether they will affect the pile foundation structure.

[0015] The information feedback module is used to determine the impact on the pile foundation structure, obtain information on scour pits and topographical information under the influence of fluid forces, and feed it back to the meteorological and hydrological early warning model.

[0016] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method provided in embodiments of this application.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed in a computer, causes the computer to perform the method provided in embodiments of this application.

[0018] The technical solution provided in this application embodiment trains a hydrological prediction model by loading past working condition information into the model, obtaining corresponding extreme hydrological information by combining it with preset extreme working condition information, and then loading the extreme hydrological information into a meteorological and hydrological early warning model for training. It also combines real-time meteorological information to predict whether extreme working conditions will occur in the future. When extreme working conditions occur, protective materials are added and scour information and terrain information are fed back to the meteorological and hydrological early warning model in real time. This effectively protects the foundation while iterating the model, thereby continuously improving the pile foundation's resistance to the impact of fluid forces under extreme working conditions. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for reducing the fluid force on a pile foundation, provided in an embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating the prediction of extreme working conditions in a method for reducing the fluid force on a pile foundation, as provided in an embodiment of this application.

[0021] Figure 3 This is a flowchart illustrating the reinforcement of a pile foundation structure in a method for reducing the fluid force on a pile foundation, as provided in an embodiment of this application.

[0022] Figure 4 This is a structural block diagram of a device for reducing the fluid force on pile foundations provided in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0024] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Figure 1 This application provides a method for reducing the fluid forces on a pile foundation. The method can be executed by a device for reducing the fluid forces on the pile foundation. The device can be implemented by software and / or hardware and can be configured in an electronic device such as a computer.

[0026] like Figures 1 to 3 As shown, the technical solution provided in this application includes the following steps:

[0027] S10: By loading past operating condition information of the pile foundation into the hydrological prediction model for training, the first correlation information between the operating condition information and the hydrological information is obtained; the extreme hydrological information includes the average flow velocity and water depth under the preset operating condition information. It should be noted that the preset operating condition information includes various extreme operating condition information; for basins like the Yangtze River, extreme operating conditions, such as the occurrence of flood peaks, are usually detected during the upstream formation process. Therefore, it is necessary to record the changes in the average flow velocity and water depth around the pile foundation from the time of detection until the flood peak passes through the pile foundation. By loading past operating condition information into the model for training, the corresponding hydrological information under different operating conditions is clarified. The first correlation information includes, but is not limited to, a one-to-one relationship.

[0028] S20: Load the preset extreme working condition information into the hydrological prediction model, and obtain the corresponding extreme hydrological information under the preset extreme working condition information through the first correlation information; wherein step S20 specifically includes:

[0029] S21: Associate extreme weather information with extreme hydrological information to generate second associated information; it should be noted that the acquisition of extreme weather information can be set at the same time as the preset extreme working condition information, or extracted based on past working condition information;

[0030] S22: Obtain meteorological information in real time and predict whether extreme working conditions will occur in the future through secondary related information. For example, analyze the current weather information or the weather information for the next two or three days to see if extreme hydrological conditions will occur, and thus whether this will lead to extreme working conditions.

[0031] S30: Load extreme hydrological information into the meteorological and hydrological early warning model for training, obtain meteorological information in real time, and predict whether extreme working conditions will occur in the future.

[0032] S40: If extreme working conditions occur, determine whether they affect the pile foundation structure; it should be added here that there are established standards for the identification of extreme working conditions, such as temperature, water flow velocity, and water depth, which all have established data standard values.

[0033] S50: If it is determined that the pile foundation structure is affected, obtain information on scour holes and topographical information under the influence of fluid forces, and feed it back to the meteorological and hydrological early warning model. This needs to be discussed separately depending on whether the pile foundation structure is affected. If the pile foundation structure is not affected, the system will generate corresponding foundation protection information to prompt staff to perform regular inspections and similar operations.

[0034] If the pile foundation structure is affected, corresponding countermeasures need to be taken, including:

[0035] S51: Generate corresponding protection information based on the warning level and the damage information of the pile foundation structure. It should be noted that the warning level is also based on the aforementioned extreme working conditions. For example, when the water flow velocity exceeds 10m / s and the water depth exceeds 10 meters, it is a level one alarm. In addition, the damage information of the pile foundation structure can also be classified according to the preset levels, including but not limited to the classification according to the damaged area or damaged depth of the pile foundation structure.

[0036] Then, based on the corresponding warning level and damage information, corresponding protection information is generated. The protection information includes countermeasures, such as how much protective or reinforcement material needs to be deployed for repairs.

[0037] S52: Apply protective information to reinforce the pile foundation structure. This protective information will be communicated to staff through the system, reducing their preparation time.

[0038] In a preferred embodiment, after reinforcing the pile foundation structure with protective information, the method further includes:

[0039] Based on the information on scour crater and terrain, the amount of protective materials deployed is calculated and recorded. Specifically, the existing scour depth detection module and terrain survey module are used to detect the condition of the pile foundation under reinforcement and its ability to cope with extreme working conditions. This detection is real-time, thereby determining whether additional protective and / or reinforcement materials are needed.

[0040] Furthermore, after the deployment and statistical analysis of protective materials, the process also includes feeding back the deployment amount to the relational learning model. The deployment amount of protective materials, along with information on cratering and terrain, is loaded into the meteorological and hydrological early warning model for training iterations, thereby improving the response speed to extreme conditions and the accurate matching of meteorological and hydrological information.

[0041] The technical solution provided in this application embodiment trains a hydrological prediction model by loading past working condition information into the model, obtaining corresponding extreme hydrological information by combining it with preset extreme working condition information, and then loading the extreme hydrological information into a meteorological and hydrological early warning model for training. It also combines real-time meteorological information to predict whether extreme working conditions will occur in the future. When extreme working conditions occur, protective materials are added and scour information and terrain information are fed back to the meteorological and hydrological early warning model in real time. This effectively protects the foundation while iterating the model, thereby continuously improving the pile foundation's resistance to the impact of fluid forces under extreme working conditions.

[0042] Figure 4 This application provides a device for reducing the fluid force on a pile foundation, comprising:

[0043] The first association information generation module 01 is used to train the hydrological prediction model by loading the past working condition information of the pile foundation into the hydrological prediction model to obtain the first association information between the working condition information and the hydrological information.

[0044] The extreme hydrological information generation module 02 is used to load preset extreme working condition information into the hydrological prediction model and obtain the corresponding extreme hydrological information under the preset working condition information through the first correlation information.

[0045] The working condition prediction module 03 is used to load extreme hydrological information into the meteorological and hydrological early warning model for training, acquire meteorological information in real time, predict whether extreme working conditions will occur in the future, and if extreme working conditions occur, determine whether they will affect the pile foundation structure.

[0046] The information feedback module 04 is used to determine the impact on the pile foundation structure, obtain information on scour pits and topographic information under the influence of fluid forces, and feed it back to the meteorological and hydrological early warning model.

[0047] In one alternative implementation, the extreme hydrological information includes the average flow velocity and water depth under preset extreme operating conditions.

[0048] In one optional implementation, extreme hydrological information is loaded into a meteorological and hydrological early warning model for training to predict whether extreme conditions will occur in the future, including:

[0049] By linking extreme weather information with extreme hydrological information, a second linked information is generated;

[0050] Real-time meteorological information is obtained, and through secondary correlation information, it is predicted whether extreme working conditions will occur in the future.

[0051] In one alternative implementation, if extreme working conditions occur, after determining whether the pile foundation structure is affected, the method further includes:

[0052] If the pile foundation structure is not affected, generate foundation protection information.

[0053] In one optional implementation, before acquiring crater information and terrain information under the influence of fluid dynamics and feeding them back to the secondary deep learning relational learning model, the method further includes:

[0054] Based on the warning level and the damage information of the pile foundation structure, corresponding protection information is generated;

[0055] Protective information is used to reinforce the pile foundation structure.

[0056] In one alternative implementation, after reinforcing the pile foundation structure with protective information, the method further includes:

[0057] Based on the information on the crater and the terrain, the amount of protective materials deployed was determined and tallied.

[0058] In one optional implementation, after dispensing and counting the amount of protective material dispensed, the method further includes:

[0059] Feedback on the amount of protective materials deployed is fed into the meteorological and hydrological early warning model.

[0060] like Figure 5 As shown in the figure, this application provides an electronic device, including a processor 511, a communication interface 512, a memory 513, and a communication bus 514, wherein the processor 511, the communication interface 512, and the memory 513 communicate with each other through the communication bus 514.

[0061] Memory 513 is used to store computer programs;

[0062] In one embodiment of this application, when the processor 511 executes a program stored in the memory 513, it implements the method provided in any of the foregoing method embodiments, including:

[0063] By loading past working condition information of the pile foundation into the hydrological prediction model for training, the first correlation information between working condition information and hydrological information is obtained;

[0064] The preset extreme working condition information is loaded into the hydrological prediction model, and the corresponding extreme hydrological information under the preset working condition information is obtained through the first association information;

[0065] The extreme hydrological information is loaded into the meteorological and hydrological early warning model for training, and the meteorological information is acquired in real time to predict whether extreme working conditions will occur in the future.

[0066] If extreme working conditions occur, determine whether they will affect the pile foundation structure;

[0067] If it is determined that the impact on the pile foundation structure is to be obtained, information on scour pits and topographical information under the influence of fluid force is obtained and fed back to the meteorological and hydrological early warning model.

[0068] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the foregoing method embodiments.

[0069] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0071] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A method for reducing the fluid force on pile foundations, characterized in that, include: By loading past working condition information of the pile foundation into the hydrological prediction model for training, the first correlation information between working condition information and hydrological information is obtained; The preset extreme working condition information is loaded into the hydrological prediction model, and the extreme hydrological information corresponding to the preset extreme working condition information is obtained through the first association information. The extreme hydrological information is loaded into the meteorological and hydrological early warning model for training, and the meteorological information is acquired in real time to predict whether extreme working conditions will occur in the future. If extreme working conditions occur, determine whether they will affect the pile foundation structure; If it is determined that the impact on the pile foundation structure is to be obtained, information on scour pits and topographical information under the influence of fluid force is obtained and fed back to the meteorological and hydrological early warning model.

2. The method according to claim 1, characterized in that, The extreme hydrological information includes the average water flow velocity and water depth under the preset operating conditions.

3. The method according to claim 1, characterized in that, The step of loading the extreme hydrological information into the meteorological and hydrological early warning model for training, acquiring the meteorological information in real time, and predicting whether extreme conditions will occur in the future includes: By associating extreme weather information with the extreme hydrological information, a second association information is generated; The meteorological information is acquired in real time, and the second associated information is used to predict whether extreme working conditions will occur in the future.

4. The method according to claim 1, characterized in that, After determining whether extreme working conditions will affect the pile foundation structure, the following steps are also included: If the pile foundation structure is not affected, generate foundation protection information.

5. The method according to claim 1, characterized in that, Before acquiring the crater information and terrain information under the influence of fluid forces and feeding them back to the secondary deep learning relation learning model, the method further includes: Based on the warning level and the damage information of the pile foundation structure, corresponding protection information is generated; The protective information is used to reinforce the pile foundation structure.

6. The method according to claim 1, characterized in that, After applying the protective information to reinforce the pile foundation structure, the method further includes: Based on the information about the crater and the terrain, the amount of protective materials deployed is determined and counted.

7. The method according to claim 6, characterized in that, After the deployment and statistical analysis of the protective materials deployed, the process also includes: The amount of protective material deployed is fed back to the meteorological and hydrological early warning model.

8. A device for reducing the fluid force on pile foundations, characterized in that, include: The first association information generation module is used to train the hydrological prediction model by loading the past working condition information of the pile foundation into the hydrological information to obtain the first association information between the working condition information and the hydrological information. An extreme hydrological information generation module is used to load preset extreme working condition information into the hydrological prediction model, and obtain the extreme hydrological information corresponding to the preset extreme working condition information through the first association information. The working condition prediction module is used to load the extreme hydrological information into the meteorological and hydrological early warning model for training, acquire the meteorological information in real time, and predict whether extreme working conditions will occur in the future. If extreme working conditions occur, determine whether they will affect the pile foundation structure; The information feedback module is used to determine the impact on the pile foundation structure, obtain information on scour pits and terrain under the influence of fluid forces, and feed it back to the meteorological and hydrological early warning model.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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