Method and device for predicting influence of waves on pile foundation safety, medium and equipment

Abstract

The invention provides a method and device for predicting the influence of waves on the safety of a pile foundation, a medium and equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring wave data at a plurality of historical moments; for a first historical moment farthest from the current moment, taking the wave data corresponding to the first historical moment as the input of a first model unit to perform feature processing so as to obtain a data processing result of the first historical moment; for the ith historical moment in the plurality of historical moments, taking the wave data corresponding to the ith historical moment and the data processing result of the (i-1) th historical moment as the input of the ith model unit to carry out feature processing so as to obtain the data processing result of the ith historical moment; performing data mapping processing on the data processing results of the historical moments to obtain pile foundation safety evaluation data; and the pile foundation safety is predicted according to the pile foundation safety evaluation data. The accuracy of pile foundation safety prediction can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a method, device, medium, and equipment for predicting the impact of waves on pile foundation safety. Background Art

[0002] The pile foundations of offshore photovoltaic systems are exposed to complex marine environments for extended periods of time, and wave action is a key factor affecting their safety. Waves are complex and dynamic, and their interaction with the pile foundations exhibits nonlinear and dynamic characteristics. Current pile foundation safety prediction methods struggle to accurately capture these complex relationships, resulting in inaccurate predictions.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a method and apparatus, medium and equipment for predicting the impact of waves on pile foundation safety, thereby overcoming the problem of inaccurate prediction of pile foundation safety at least to a certain extent.

[0005] According to a first aspect of the present disclosure, a method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario is provided, comprising: obtaining wave data of an offshore area in an offshore photovoltaic scenario at multiple historical moments, the wave data including wave type data, wave height data, wave period data, and wave direction data; for the first historical moment farthest from the current moment among the multiple historical moments, using the wave data corresponding to the first historical moment as the input of a first model unit for feature processing to obtain a data processing result for the first historical moment; for the i-th historical moment among the multiple historical moments, using the wave data corresponding to the i-th historical moment and the data processing result of the i-1-th historical moment as the input of an i-th model unit for feature processing to obtain a data processing result for the i-th historical moment; wherein i is a positive integer greater than 1; performing data mapping processing on the data processing results of each historical moment to obtain pile foundation safety evaluation data; and predicting the pile foundation safety based on the pile foundation safety evaluation data.

[0006] Optionally, obtaining wave data of the offshore area in the offshore photovoltaic scenes at multiple historical moments includes: obtaining original wave data of the offshore area in the offshore photovoltaic scenes at multiple historical moments; and performing maximum and minimum value normalization processing on the original wave data to obtain wave data.

[0007] Optionally, obtaining the original wave data of the offshore area in the offshore photovoltaic scene at multiple historical moments includes: obtaining the original wave data of the offshore area in the offshore photovoltaic scene a predetermined number of historical moments away from the current time; obtaining the meteorological and hydrological data corresponding to each historical moment in the predetermined number of historical moments; if it is determined based on the meteorological and hydrological data that the meteorological and hydrological data of the target historical moment in the predetermined number of historical moments is abnormal, then the original wave data corresponding to the target historical moment is eliminated from the original wave data, and the original wave data corresponding to the most recent historical moment is added.

[0008] Optionally, the prediction method also includes: obtaining multiple sets of training data in advance, each set of training data includes multiple sets of training wave data collected at different times and a set of training pile foundation safety evaluation data corresponding to the multiple sets of training wave data; and using the multiple sets of training data to train each model unit.

[0009] Optionally, the prediction method further includes: obtaining training pile foundation safety evaluation data through a sensing device installed on the experimental pile foundation; wherein the training pile foundation safety evaluation data includes one or more of the pile foundation's stress data, strain data, and displacement data.

[0010] Optionally, predicting pile foundation safety based on pile foundation safety evaluation data includes: normalizing the pile foundation safety evaluation data; and performing feature analysis on the normalized pile foundation safety evaluation data using a trained multi-layer perceptron model to obtain a prediction result of pile foundation safety.

[0011] Optionally, the prediction method further includes: determining a pile foundation rendering type and a text description according to the prediction result of the pile foundation safety; rendering the pile foundation based on the pile foundation rendering type, and displaying the rendering result and the text description.

[0012] According to a second aspect of the present disclosure, a device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario is provided, comprising: a data acquisition module for acquiring wave data of an offshore area in an offshore photovoltaic scenario at multiple historical moments, the wave data including wave type data, wave height data, wave period data, and wave direction data; a first processing module for, for the first historical moment farthest from the current moment among the multiple historical moments, using the wave data corresponding to the first historical moment as input to a first model unit for feature processing to obtain a data processing result for the first historical moment; a second processing module for, for the i-th historical moment among the multiple historical moments, using the wave data corresponding to the i-th historical moment and the data processing result of the i-1-th historical moment as input to an i-th model unit for feature processing to obtain a data processing result for the i-th historical moment, wherein i is a positive integer greater than 1; a third processing module for performing data mapping processing on the data processing results of each historical moment to obtain pile foundation safety evaluation data; and a prediction module for predicting pile foundation safety based on the pile foundation safety evaluation data.

[0013] Optionally, the data acquisition module is used to acquire original wave data of the offshore area in the offshore photovoltaic scene at multiple historical moments; and perform maximum and minimum value normalization processing on the original wave data to obtain wave data.

[0014] Optionally, the data acquisition module is used to obtain original wave data of the offshore area in the offshore photovoltaic scene a predetermined number of historical moments away from the current time; obtain meteorological and hydrological data corresponding to each historical moment in the predetermined number of historical moments; if it is determined based on the meteorological and hydrological data that the meteorological and hydrological data of the target historical moment in the predetermined number of historical moments is abnormal, the original wave data corresponding to the target historical moment is eliminated from the original wave data, and the original wave data corresponding to the most recent historical moment is added.

[0015] Optionally, the prediction device also includes a model training module, which is used to pre-acquire multiple sets of training data, each set of training data includes multiple sets of training wave data collected at different times and a set of training pile foundation safety evaluation data corresponding to the multiple sets of training wave data; and each model unit is trained using the multiple sets of training data.

[0016] Optionally, the model training module is used to obtain training pile foundation safety evaluation data through sensing equipment installed on the experimental pile foundation; wherein the training pile foundation safety evaluation data includes one or more of the pile foundation's stress data, strain data, and displacement data.

[0017] Optionally, the prediction module is used to normalize the pile foundation safety evaluation data; and use a trained multi-layer perceptron model to perform feature analysis on the normalized pile foundation safety evaluation data to obtain a prediction result of the pile foundation safety.

[0018] Optionally, the prediction module is further configured to determine a pile foundation rendering type and a text description according to the prediction result of the pile foundation safety; render the pile foundation based on the pile foundation rendering type, and display the rendering result and the text description.

[0019] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for predicting the impact of waves on pile foundation safety in any of the above-mentioned offshore photovoltaic scenarios is implemented.

[0020] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; the processor is configured to implement a method for predicting the impact of waves on pile foundation safety in any of the above-mentioned offshore photovoltaic scenarios by executing the executable instructions.

[0021] In the technical solutions provided by some embodiments of the present disclosure, wave data at multiple historical moments are analyzed to map out corresponding pile foundation safety evaluation data, and then safety predictions are made based on these evaluation data. By combining this analysis method with historical data, the present disclosure comprehensively considers the influence of wave data including wave type, wave height, wave period, and wave direction, thereby improving the accuracy of pile foundation safety predictions and playing a key role in the safety maintenance of offshore photovoltaic sites. In addition, the present disclosure combines a continuous processing scheme of multiple model units, in which the output of the previous model unit is used as part of the input of the next model unit, thereby improving the accuracy of model processing and further improving the accuracy of prediction results.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0024] Figure 1 The figure schematically shows the processing process of the prediction scheme of the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an exemplary embodiment of the present disclosure.

[0025] Figure 2 A flow chart schematically illustrates a method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an exemplary embodiment of the present disclosure.

[0026] Figure 3 The following schematically illustrates a process diagram for determining pile foundation safety evaluation data according to an embodiment of the present disclosure.

[0027] Figure 4 A block diagram of a device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an exemplary embodiment of the present disclosure is schematically shown.

[0028] Figure 5 A block diagram of a device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to another exemplary embodiment of the present disclosure is schematically shown.

[0029] Figure 6 A block diagram schematically illustrates an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0031] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all steps. For example, some steps may be decomposed, while others may be combined or partially combined. Therefore, the actual order of execution may vary depending on the actual situation. In addition, all terms such as "first," "second," and "third" below are used for distinction purposes only and should not be construed as limitations of this disclosure.

[0033] Figure 1 The following is a schematic diagram showing the processing process of the prediction scheme for the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an exemplary embodiment of the present disclosure. Figure 1 The exemplary prediction scheme for the impact of waves on pile foundation safety in an offshore photovoltaic scenario disclosed herein involves two models. Specifically, wave data from multiple historical moments is input into a first model, which analyzes and processes this wave data to obtain pile foundation safety assessment data. This pile foundation safety assessment data is then input into a second model, which analyzes and processes this data to obtain pile foundation safety prediction results.

[0034] The first model can be a Long Short-Term Memory (LSTM) network, which is composed of multiple model units (LSTM units). Each model unit can contain a cell state and three gating mechanisms, including an input gate, a forget gate, and an output gate. The input gate is used to determine which data in the current input data can enter the cell state. The forget gate is used to determine which data in the cell state at the previous moment can be retained to the current moment. The output gate is used to determine which data in the cell state at the current moment can be output to the next model unit.

[0035] The second model can be a classification model based on machine learning. For example, the second model can be a multi-layer perceptron model. The multi-layer perceptron model can include an input layer, a hidden layer, and an output layer. The input layer can be used to receive multi-dimensional pile foundation safety assessment data. The hidden layer can include multiple neurons, and its activation function is ReLU (Rectified Linear Unit, linear rectification function), which can effectively alleviate the gradient vanishing problem and speed up the model processing speed. The output layer can be used to output the prediction results of each safety level, wherein the Softmax activation function is used to map the output into a probability distribution.

[0036] The following describes a method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario, according to an embodiment of the present disclosure. It should be noted that each step of this prediction method can be performed by an electronic device. This disclosure does not limit the type of electronic device, and can include, for example, a server, personal computer, or mobile terminal.

[0037] Figure 2 The flowchart of the method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an exemplary embodiment of the present disclosure is schematically shown. Figure 2 , the prediction method may include the following steps:

[0038] S20. Acquire wave data of an offshore area in an offshore photovoltaic scenario at multiple historical moments, the wave data including wave type data, wave height data, wave period data, and wave direction data.

[0039] In an exemplary embodiment of the present disclosure, wave data is collected by wave monitoring equipment installed in an offshore area of ​​an offshore photovoltaic scenario. Such wave monitoring equipment includes, for example, a wave height meter and a wave direction meter. The present disclosure does not limit the collection period; for example, collection may be performed once every minute or once every 10 minutes.

[0040] It should be noted that the time interval for data collection differs from the time interval between historical moments in this disclosure. That is, the selection of historical moments may not depend on the data collection period described above. Typically, the time interval between adjacent historical moments is greater than the data collection period described above. In some embodiments of this disclosure, the time interval between adjacent historical moments in multiple historical moments is an integer multiple of the wave data collection period. Furthermore, this disclosure does not impose any restrictions on the number of historical moments that can be selected.

[0041] According to some embodiments of the present disclosure, in order to eliminate dimensional differences and facilitate subsequent data processing, the collected data may be normalized.

[0042] First, the original wave data of the offshore area in the offshore photovoltaic scenario at multiple historical moments can be obtained. Then, the original wave data can be normalized by maximum and minimum values ​​to obtain wave data.

[0043] The selected data may include abnormal data generated by unexpected events, which can affect the accuracy of the final prediction results. To address this issue, an analysis of the situation was conducted, and the results showed that such abnormal data often arises from abnormal meteorological and hydrological conditions. This disclosure also provides a solution for further filtering the raw wave data.

[0044] First, raw wave data for the offshore area of ​​the offshore photovoltaic scenario is obtained for a predetermined number of historical moments from the current time. This predetermined number corresponds to the number of historical moments described above. Next, meteorological and hydrological data corresponding to each of these predetermined number of historical moments is obtained. If the meteorological and hydrological data indicates an anomaly at a target historical moment within the predetermined number of historical moments, where the target historical moment can be any of these historical moments, the raw wave data corresponding to the target historical moment is removed from the raw wave data and supplemented with the raw wave data corresponding to the most recent historical moment.

[0045] In other words, the number of historical moments disclosed in this paper is fixed. Once confirmed, the algorithm must apply this number of data to ensure the consistency of subsequent model processing. Therefore, after removing abnormal data, it is necessary to supplement the original wave data in the nearest time.

[0046] S22. For the first historical moment farthest from the current time among the multiple historical moments, the wave data corresponding to the first historical moment is used as the input of the first model unit for feature processing to obtain the data processing result of the first historical moment.

[0047] S24. For the i-th historical moment among the multiple historical moments, the wave data corresponding to the i-th historical moment and the data processing result of the i-1-th historical moment are used as the input of the i-th model unit for feature processing to obtain the data processing result of the i-th historical moment; wherein i is a positive integer greater than 1.

[0048] S26. Perform data mapping processing on the data processing results at each historical moment to obtain pile foundation safety evaluation data.

[0049] In the exemplary embodiments of the present disclosure, the above-mentioned model units may all be LSTM units. The present disclosure does not limit the specific network structure of the LSTM unit.

[0050] In addition, the pile foundation safety evaluation data may include one or more of stress data, strain data, and displacement data.

[0051] Figure 3 The following schematically illustrates the process of determining pile foundation safety evaluation data according to an embodiment of the present disclosure. Figure 3, use the first model unit to process the wave data of the first historical moment to obtain the data processing result of the first historical moment, then use the second model unit to perform feature processing on the data processing result of the first historical moment and the wave data of the second historical moment to obtain the data processing result of the second historical moment, and so on, until the data processing results of all historical moments are determined. Then, these results can be integrated and data mapped using the fully connected layer to obtain the pile foundation safety evaluation data. For example, when the pile foundation safety evaluation data includes stress data, strain data, and displacement data, the number of neurons in the fully connected layer is 3.

[0052] The disclosed embodiment also provides a method for training the above-mentioned model. Specifically, multiple sets of training data are acquired in advance, and each set of training data includes multiple sets of training wave data collected at different times and a set of training pile foundation safety evaluation data corresponding to these training wave data. Among them, the training pile foundation safety evaluation data can be acquired by the sensor equipment installed on the experimental pile foundation. Similarly, the training pile foundation safety evaluation data may include one or more of the stress data, strain data, and displacement data of the pile foundation. It can be understood that the experimental pile foundation is a pile foundation on which the sensor equipment is specially configured for testing experiments. In order to solve the cost and unnecessary redundancy, not all pile foundations in the scene are experimental pile foundations.

[0053] Next, the training data can be used to train the above model unit. For example, the mean square error can be used as a loss function to measure the relationship between the prediction result and the true value, which is not limited in this disclosure.

[0054] S28. Predict pile foundation safety based on pile foundation safety evaluation data.

[0055] According to some embodiments of the present disclosure, first, the pile foundation safety evaluation data is normalized to map the evaluation data of each dimension to the range [0, 1]. This unifies the representation and helps accelerate the model convergence.

[0056] Next, the trained multi-layer perceptron model can be used to perform feature analysis on the normalized pile foundation safety evaluation data to obtain the prediction results of the pile foundation safety.

[0057] In some embodiments, the multi-layer perceptron model may include an input layer, two hidden layers, and an output layer. Specifically, the input layer may be used to receive multi-dimensional pile foundation safety assessment data, and the number of neurons is consistent with the number of dimensions of the received pile foundation safety assessment data. The first hidden layer may contain 32 neurons, and the second hidden layer may contain 16 neurons. The activation function is ReLU, which can effectively alleviate the gradient vanishing problem and speed up the model processing speed. The output layer may be used to output the prediction results of each safety level, wherein the Softmax activation function is used to map the output into a probability distribution.

[0058] For example, the prediction results of pile foundation safety may include three levels: safe, warning, and dangerous. The corresponding output layer may contain three neurons, which are used to output the probabilities of these three levels respectively, and output the result with the highest probability as the final result.

[0059] For another example, the prediction results of pile foundation safety may include A, B, C, and D, with the danger levels ranging from low to high. In this case, the output layer in the above model may contain 4 neurons, which are used to output the probabilities of these four levels respectively, and the result with the highest probability is output as the final result.

[0060] According to some embodiments of the present disclosure, the electronic device may also determine a pile foundation rendering type and text description based on the prediction results of the pile foundation safety. The pile foundation rendering type may include one or more of an outline rendering type, a color rendering type, and a display mode (static or dynamic) rendering type. The text description refers to a text description summarizing the analysis of the pile foundation safety and may be automatically generated by a computer based on the prediction results. This disclosure does not limit this process.

[0061] Next, the electronic device may render the pile foundation based on the pile foundation rendering type, and display the rendering result and text description, for example, on a display interface of the electronic device.

[0062] It should be noted that although the steps of the method of the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all steps must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0063] Furthermore, this example embodiment also provides a device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario.

[0064] Figure 4The block diagram of the device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an exemplary embodiment of the present disclosure is schematically shown. Figure 4 According to an exemplary embodiment of the present disclosure, the prediction device 4 for the impact of waves on pile foundation safety in an offshore photovoltaic scenario may include a data acquisition module 41, a first processing module 43, a second processing module 45, a third processing module 47 and a prediction module 49.

[0065] Specifically, the data acquisition module 41 can be used to obtain wave data of the offshore area in the offshore photovoltaic scene at multiple historical moments, and the wave data includes wave type data, wave height data, wave period data and wave direction data; the first processing module 43 can be used to target the first historical moment farthest from the current moment among the multiple historical moments, and use the wave data corresponding to the first historical moment as the input of the first model unit for feature processing to obtain the data processing result of the first historical moment; the second processing module 45 can be used to target the i-th historical moment among the multiple historical moments, and use the wave data corresponding to the i-th historical moment and the data processing result of the i-1-th historical moment as the input of the i-th model unit for feature processing to obtain the data processing result of the i-th historical moment; wherein i is a positive integer greater than 1; the third processing module 47 can be used to perform data mapping processing on the data processing results of each historical moment to obtain pile foundation safety evaluation data; the prediction module 49 can be used to predict the pile foundation safety based on the pile foundation safety evaluation data.

[0066] According to an exemplary embodiment of the present disclosure, the data acquisition module 41 can be used to acquire original wave data of the offshore area in the offshore photovoltaic scene at multiple historical moments; and perform maximum and minimum value normalization processing on the original wave data to obtain wave data.

[0067] According to an exemplary embodiment of the present disclosure, the data acquisition module 41 can be used to obtain original wave data of the offshore area in the offshore photovoltaic scene a predetermined number of historical moments away from the current time; obtain meteorological and hydrological data corresponding to each historical moment in the predetermined number of historical moments; if it is determined based on the meteorological and hydrological data that the meteorological and hydrological data of the target historical moment in the predetermined number of historical moments is abnormal, the original wave data corresponding to the target historical moment is eliminated from the original wave data, and the original wave data corresponding to the most recent historical moment is added.

[0068] According to an exemplary embodiment of the present disclosure, referring to Figure 5Compared with the prediction device 4 for the impact of waves on pile foundation safety in offshore photovoltaic scenarios, the prediction device 5 for the impact of waves on pile foundation safety in offshore photovoltaic scenarios can also include a model training module 51. The model training module 51 can be used to pre-acquire multiple sets of training data, each set of training data includes multiple sets of training wave data collected at different times and a set of training pile foundation safety evaluation data corresponding to the multiple sets of training wave data; and each model unit is trained using the multiple sets of training data.

[0069] According to an exemplary embodiment of the present disclosure, the model training module 51 can be used to obtain training pile foundation safety evaluation data through sensing equipment installed on the experimental pile foundation; wherein the training pile foundation safety evaluation data includes one or more of the pile foundation's stress data, strain data, and displacement data.

[0070] According to an exemplary embodiment of the present disclosure, the prediction module 49 can be used to normalize the pile foundation safety evaluation data; use a trained multi-layer perceptron model to perform feature analysis on the normalized pile foundation safety evaluation data to obtain a prediction result of the pile foundation safety.

[0071] According to an exemplary embodiment of the present disclosure, the prediction module 49 may also be used to determine a pile foundation rendering type and text description based on the prediction result of the pile foundation safety; render the pile foundation based on the pile foundation rendering type, and display the rendering result and text description.

[0072] Since the functional modules of the device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to the embodiment of the present disclosure are the same as those in the above-mentioned method embodiment, they will not be described in detail here.

[0073] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present disclosure.

[0074] The program product for implementing the above-mentioned method according to an embodiment of the present disclosure may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0075] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical disk, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0076] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0077] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0078] The program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0079] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0080] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0081] Refer to the following Figure 6 hereinafter, an electronic device 600 according to this embodiment of the present disclosure is described. Figure 6 The electronic device 600 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0082] like Figure 6 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, the aforementioned at least one processing unit 610, the aforementioned at least one storage unit 620, a bus 630 connecting various system components (including storage unit 620 and processing unit 610), and a display unit 640.

[0083] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can perform the steps of the method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to an embodiment of the present disclosure.

[0084] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0085] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0086] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0087] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0088] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0089] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0090] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0091] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing what is disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0092] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario, characterized in that: include: Acquire wave data of an offshore area in an offshore photovoltaic scenario at multiple historical moments, wherein the wave data includes wave type data, wave height data, wave period data, and wave direction data; For a first historical moment farthest from the current time among the multiple historical moments, use the wave data corresponding to the first historical moment as input to the first model unit to perform feature processing to obtain a data processing result for the first historical moment; For an i-th historical moment among the multiple historical moments, using the wave data corresponding to the i-th historical moment and the data processing result of the i-1-th historical moment as inputs of the i-th model unit to perform feature processing to obtain the data processing result of the i-th historical moment; wherein i is a positive integer greater than 1; Perform data mapping on the data processing results at each historical moment to obtain pile foundation safety evaluation data; The pile foundation safety is predicted based on the pile foundation safety evaluation data.

2. The prediction method according to claim 1, characterized in that Obtaining wave data of offshore areas in offshore photovoltaic scenarios at multiple historical moments includes: Obtaining raw wave data of offshore areas in offshore photovoltaic scenarios at multiple historical moments; The original wave data is normalized by maximum and minimum values ​​to obtain the wave data.

3. The prediction method according to claim 2, characterized in that The original wave data of the offshore area in the offshore photovoltaic scenario at multiple historical moments are obtained, including: Obtaining raw wave data of an offshore area in an offshore photovoltaic scenario at a predetermined number of historical moments from the current time; Obtaining meteorological and hydrological data corresponding to each of the predetermined number of historical moments; If it is determined based on the meteorological and hydrological data that the meteorological and hydrological data of the target historical moment among the predetermined number of historical moments are abnormal, the original wave data corresponding to the target historical moment is removed from the original wave data, and the original wave data corresponding to the most recent historical moment is added.

4. The prediction method according to claim 1, wherein: The prediction method further comprises: Acquire multiple sets of training data in advance, each set of training data comprising multiple sets of training wave data collected at different times and a set of training pile foundation safety evaluation data corresponding to the multiple sets of training wave data; Each of the model units is trained using the multiple sets of training data.

5. The prediction method according to claim 4, characterized in that The prediction method further comprises: Acquiring the safety evaluation data of the training pile foundation through a sensor device installed on the experimental pile foundation; The training pile foundation safety evaluation data includes one or more of pile foundation stress data, strain data, and displacement data.

6. The prediction method according to claim 1, characterized in that Predicting the pile foundation safety based on the pile foundation safety evaluation data includes: performing normalization processing on the pile foundation safety evaluation data; The trained multi-layer perceptron model is used to perform feature analysis on the normalized pile foundation safety evaluation data to obtain the prediction results of pile foundation safety.

7. The prediction method according to any one of claims 1 to 6, characterized in that: The prediction method further comprises: Determine the pile foundation rendering type and text description based on the prediction results of pile foundation safety; The pile foundation is rendered based on the pile foundation rendering type, and the rendering result and the text description are displayed.

8. A device for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario, characterized in that: include: A data acquisition module is used to acquire wave data of an offshore area in an offshore photovoltaic scene at multiple historical moments, wherein the wave data includes wave type data, wave height data, wave period data, and wave direction data; a first processing module configured to perform feature processing on a first historical moment farthest from the current moment among the plurality of historical moments, using the wave data corresponding to the first historical moment as input to a first model unit to obtain a data processing result for the first historical moment; a second processing module, configured to perform feature processing on an i-th historical moment among the plurality of historical moments, using the wave data corresponding to the i-th historical moment and the data processing result of the i-1-th historical moment as inputs of the i-th model unit to obtain the data processing result of the i-th historical moment; wherein i is a positive integer greater than 1; The third processing module is used to perform data mapping processing on the data processing results of each historical moment to obtain pile foundation safety evaluation data; The prediction module is used to predict the pile foundation safety according to the pile foundation safety evaluation data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to implement the method for predicting the impact of waves on pile foundation safety in an offshore photovoltaic scenario according to any one of claims 1 to 7 by executing the executable instructions.

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