Tubular pile determination method and device in offshore photovoltaic scene, electronic equipment and medium

Abstract

The invention provides a tubular pile determination method and device in an offshore photovoltaic scene, electronic equipment and a computer readable storage medium, and belongs to the field of offshore photovoltaic technologies. The method comprises the following steps: acquiring multi-source data of a target sea area in a current offshore photovoltaic service scene; extracting a first type of key data and a second type of key data from the environment feature data, the economic feature data and the construction feature data; processing the first type of key data through a first network layer to obtain first intermediate data, and processing the second type of key data through a second network layer to obtain second intermediate data; processing the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; and determining a target pipe pile type of a target sea area in the current offshore photovoltaic service scene according to the target intermediate data. According to the method, the target pipe pile type suitable for the target sea area can be quickly and accurately determined.

Description

Technical Field

[0001] The present disclosure relates to the technical field of offshore photovoltaic operations, and in particular to a method for determining pipe piles in an offshore photovoltaic scenario, a device for determining pipe piles in an offshore photovoltaic scenario, an electronic device, and a computer-readable storage medium. Background Art

[0002] Offshore photovoltaics (OPV) is a new clean energy development method that utilizes ocean space to install photovoltaic panels, converting solar energy into electricity. By installing photovoltaic panels offshore and directly converting solar radiation into electricity, this technology helps reduce dependence on fossil fuels, reduces greenhouse gas emissions, and promotes a green transition to the global energy mix. The offshore installation of photovoltaic panels requires first driving piles into the sea to secure the piles. The photovoltaic panels are then hoisted into position on the piles within the sea. However, due to the wide variety of pile types, using different types in the sea can have varying impacts on the installation of photovoltaic panels and the subsequent implementation of the project. Existing technologies typically use a trial-and-error approach or empirically selected pile types. However, this approach not only fails to guarantee the effectiveness of the pile selection but also incurs significant trial-and-error costs.

[0003] Therefore, how to quickly and accurately determine the pipe piles that are suitable for the sea area is a problem that needs to be solved urgently in the existing technology.

[0004] 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

[0005] The present disclosure provides a method for determining pipe piles in an offshore photovoltaic scenario, a device for determining pipe piles in an offshore photovoltaic scenario, an electronic device, and a computer-readable storage medium, thereby at least to a certain extent solving the problem that the existing technology is unable to quickly and accurately determine the appropriate pipe pile type in an offshore photovoltaic scenario.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a method for determining pipe piles in an offshore photovoltaic scenario is provided, comprising: acquiring multi-source data of a target sea area in a current offshore photovoltaic business scenario; the multi-source data including at least environmental characteristic data, economic characteristic data, and construction characteristic data; extracting first-category key data and second-category key data from the environmental characteristic data, economic characteristic data, and construction characteristic data; processing the first-category key data through a first network layer to obtain first intermediate data, and processing the second-category key data through a second network layer to obtain second intermediate data; processing the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; and determining a target pipe pile type for the target sea area in the current offshore photovoltaic business scenario according to the target intermediate data.

[0008] In an exemplary embodiment of the present disclosure, the extracting of the first category of key data and the second category of key data from the environmental characteristic data, the economic characteristic data and the construction characteristic data includes: extracting the soil layer distribution data of the target sea area from the environmental characteristic data, and determining the geological profile image of the target sea area based on the soil layer distribution data; extracting the sea area parameter index data of the target sea area from the environmental characteristic data, and determining the sea area parameter heat map based on the sea area parameter index data; the sea area parameter index data at least includes sea area depth data and wave height data; using the geological profile image of the target sea area and the sea area parameter heat map as the first category of key data; and determining the second category of key data based on the economic characteristic data and the construction characteristic data.

[0009] In an exemplary embodiment of the present disclosure, determining the second category of key data based on the economic characteristic data and the construction characteristic data includes: extracting transportation cost data from the economic characteristic data, and calculating first correlation data of the transportation cost data between different time series; extracting pile driving resistance data from the construction characteristic data, and calculating second correlation data of the pile driving resistance data between different time series; and determining the second category of key data based on the first correlation data and the second correlation data.

[0010] In an exemplary embodiment of the present disclosure, determining the second category key data based on the first correlation data and the second correlation data includes: performing an association calculation on the first correlation data and the second correlation data, and using the calculation result as the second category key data.

[0011] In an exemplary embodiment of the present disclosure, the method further includes: generating a simulation image under a preset geological environment based on the geological profile image; taking the geological profile image of the target sea area and the sea area parameter thermal map as the first type of key data, including: taking the geological profile image of the target sea area, the simulation image under the preset geological environment and the sea area parameter thermal map as the first type of key data.

[0012] In an exemplary embodiment of the present disclosure, the processing of the first intermediate data and the second intermediate data through the third network layer to obtain target intermediate data includes: processing the first intermediate data and the second intermediate data through the third network layer to obtain evaluation index data of the target sea area; the evaluation index data includes one or more of bearing capacity index data, cost index data, and construction period index data; determining the target pipe pile type of the target sea area in the current offshore photovoltaic business scenario based on the target intermediate data includes: processing the evaluation index data through a pre-trained pipe pile prediction network to output a pipe pile type probability distribution; determining the target pipe pile type of the target sea area based on the pipe pile type probability distribution.

[0013] In an exemplary embodiment of the present disclosure, the method further includes: collecting sensor data through an Internet of Things sensor configured in the current offshore photovoltaic business scenario; and correcting the environmental characteristic data, the economic characteristic data or the construction characteristic data in real time according to the sensor data.

[0014] According to one aspect of the present disclosure, a device for determining pipe piles in an offshore photovoltaic scenario is provided, comprising: a multi-source data acquisition module for acquiring multi-source data of a target sea area in a current offshore photovoltaic business scenario; the multi-source data at least includes environmental characteristic data, economic characteristic data, and construction characteristic data; a key data extraction module for extracting a first type of key data and a second type of key data from the environmental characteristic data, the economic characteristic data, and the construction characteristic data; an intermediate data acquisition module for processing the first type of key data through a first network layer to obtain first intermediate data, and processing the second type of key data through a second network layer to obtain second intermediate data; an intermediate data processing module for processing the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; and a pipe pile type determination module for determining a target pipe pile type for the target sea area in the current offshore photovoltaic business scenario according to the target intermediate data.

[0015] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above-mentioned methods by executing the executable instructions.

[0016] According to one 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, any one of the above methods is implemented.

[0017] The exemplary embodiments of the present disclosure have the following beneficial effects:

[0018] Acquire multi-source data of a target sea area in a current offshore photovoltaic business scenario; the multi-source data includes at least environmental characteristic data, economic characteristic data, and construction characteristic data; extract first-category key data and second-category key data from the environmental characteristic data, economic characteristic data, and construction characteristic data; process the first-category key data through a first network layer to obtain first intermediate data, and process the second-category key data through a second network layer to obtain second intermediate data; process the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; determine a target pipe pile type for the target sea area in the current offshore photovoltaic business scenario based on the target intermediate data. On the one hand, this exemplary embodiment proposes a new pipe pile determination method. Compared with the prior art of determining pipe piles through trial and error or manual experience, this exemplary embodiment can quickly predict accurate and effective target pipe pile types based on multi-source data of the target sea area, greatly saving trial and error costs and manpower costs, and can ensure the accuracy of pipe pile type determination; on the other hand, this exemplary embodiment can consider the multi-source data of the target sea area, process different key data respectively through different network layers, and determine the target intermediate data based on the obtained intermediate data, and then determine the target pipe pile type of the target sea area. This method of starting from multi-dimensional data, performing data processing and data fusion through multi-level processing steps, and finally determining the target pipe pile type can further ensure the targetedness and applicability of the determined target pipe pile type to the target sea area.

[0019] 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

[0020] 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.

[0021] Figure 1 A schematic diagram schematically illustrates the system architecture of a method for determining pipe piles in an offshore photovoltaic scenario in this exemplary embodiment;

[0022] Figure 2 A flow chart schematically illustrates a method for determining pipe piles in an offshore photovoltaic scenario in this exemplary embodiment;

[0023] Figure 3 A sub-flowchart of a method for determining pipe piles in an offshore photovoltaic scenario in this exemplary embodiment is schematically shown;

[0024] Figure 4 A structural block diagram of a pipe pile determination device in an offshore photovoltaic scenario in this exemplary embodiment is schematically shown;

[0025] Figure 5 An electronic device for implementing the above method in this exemplary embodiment is schematically shown. DETAILED DESCRIPTION

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough 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.

[0027] The exemplary embodiments of the present disclosure first provide a method for determining pipe piles in an offshore photovoltaic scenario, which is applied to an offshore photovoltaic business scenario. The application scenario of the method of this embodiment can be: in an offshore photovoltaic project, determining pipe piles for the sea area in the current offshore photovoltaic business scenario.

[0028] Figure 1 A schematic diagram of the system architecture of the operating environment of this exemplary embodiment is shown. Figure 1 As shown, the system 100 may include a server 110 and a target sea area 120. The server 110 obtains multi-source data from the target sea area 120 and processes it to determine the target pipe pile type of the target sea area 120, and returns the determined target pipe pile type to the user terminal.

[0029] It should be understood that Figure 1 The data of each device shown in is only exemplary. According to actual needs, any number of servers can be set. For example, the server can be a server cluster composed of multiple servers. Based on the above description, the method in this exemplary embodiment can be applied to Figure 1 On the server 110 shown.

[0030] The following is combined with Figure 2 This exemplary embodiment is further described as follows. Figure 2 As shown, the method for determining pipe piles in an offshore photovoltaic scenario may include the following steps S210 to S250:

[0031] Step S210: Acquire multi-source data of a target sea area in a current offshore photovoltaic business scenario; the multi-source data at least includes environmental characteristic data, economic characteristic data, and construction characteristic data.

[0032] Among them, the target sea area refers to the sea area where operations such as piling, hoisting or installation of photovoltaic panels are required in the offshore photovoltaic business scenario. Multi-source data refers to data of different dimensions related to the target sea area obtained from the target sea area. The multi-source data can at least include environmental characteristic data, economic characteristic data, and construction characteristic data. Among them, environmental characteristic data are data related to the target sea area environment, such as sea depth, wave height, wind speed in the target sea area, soil layer distribution, geological structure data, etc.; economic characteristic data refer to economic parameter data that may be involved when operating in the target sea area, such as material cost of different pipes, transportation cost or equipment rental / usage fees, etc.; construction characteristic data refer to corresponding operation parameter data when operating in the target sea area, such as piling equipment parameters, piling resistance record data, construction window period data, etc.

[0033] This exemplary embodiment can configure one or more sensors in the current offshore photovoltaic business scenario, and obtain multi-source data of the target sea area by collecting sensor data through the sensors. It can also be obtained by technical personnel conducting field data surveys in the current offshore photovoltaic business scenario. This disclosure does not make specific limitations on this.

[0034] Step S220 , extracting first-category key data and second-category key data from the environmental characteristic data, the economic characteristic data, and the construction characteristic data.

[0035] This exemplary embodiment can extract the first category of key data and the second category of key data from the environmental characteristic data, the economic characteristic data and the construction characteristic data. Specifically, data can be extracted from the environmental characteristic data, the economic characteristic data and the construction characteristic data respectively, and the extracted data can be divided into the first category of key data and the second category of key data; or the first category of key data can be extracted from the environmental characteristic data, and the second category of key data can be extracted from the economic characteristic data and the construction characteristic data, etc.

[0036] In this exemplary embodiment, the first type of key data may be image data, and the second type of key data may be time-series data. Therefore, image-related data may be extracted from the environmental feature data, and image data may be generated from the extracted data as the first type of key data. Time-related data may be extracted from the economic and construction feature data, and time-series data may be generated as the second type of key data.

[0037] Step S230: Process the first type of key data through the first network layer to obtain first intermediate data, and process the second type of key data through the second network layer to obtain second intermediate data.

[0038] The first network layer may be a CNN (Convolutional Neural Networks) layer, which may process the first type of key data to obtain first intermediate data. The first intermediate data may be the output result of the first network layer. For example, image data related to environmental feature data may be input into the CNN layer for processing to obtain intermediate feature data. The second network layer may be an LSTM (Long Short-Term Memory) layer, which may process the second type of key data to obtain second intermediate data. The second intermediate data may be the output result of the second network layer. For example, time series data related to economic feature data or construction feature data may be input into the LSTM layer for processing to obtain intermediate feature data.

[0039] Step S240: Process the first intermediate data and the second intermediate data through the third network layer to obtain target intermediate data.

[0040] The third network layer may be a network layer for fusing the first intermediate data and the second intermediate data. For example, the third network layer may be a Transformer (embedding) layer. In this exemplary embodiment, the first intermediate data and the second intermediate data may be fused through the third network layer to obtain target intermediate data. For example, the first intermediate data and the second intermediate data may be in vector or matrix form. The third network layer may merge or concatenate the first intermediate data and the second intermediate data to obtain matrix data including the first intermediate data and the second intermediate data, i.e., target intermediate data. In this exemplary embodiment, the target intermediate data may be comprehensive feature data that fuses image features and time series features. Based on the target intermediate data, the type of pipe piles may be predicted and selected more comprehensively and accurately.

[0041] Step S250: determining the target pipe pile type of the target sea area in the current offshore photovoltaic business scenario according to the target intermediate data.

[0042] In offshore photovoltaic business scenarios, various types of pipe piles can be included, such as high-strength prestressed concrete pipe piles, bamboo piles, steel strand piles, steel pipe piles, prefabricated high-strength concrete thin-walled steel pipe columns, etc. Different pipe piles have different effective prestressing stress, strength grade, bearing capacity, adaptability, etc. Selecting pipe piles suitable for the sea area can ensure the stability of photovoltaic panel installation and ensure that material costs can be fully utilized without being wasted.

[0043] After determining the target intermediate data, this exemplary embodiment can determine the target pipe pile type for the target sea area in the current offshore photovoltaic business scenario based on the target intermediate data. For example, a pre-trained neural network model can be used to process the target intermediate data, and the predicted probabilities of different pipe pile types can be input to determine the pipe pile type with the highest predicted probability as the target pipe pile type; or the pipe pile type classification result can be directly output to give the target pipe pile type suitable for the current target sea area.

[0044] Based on the above description, in this exemplary embodiment, multi-source data of the target sea area in the current offshore photovoltaic business scenario is obtained; the multi-source data includes at least environmental characteristic data, economic characteristic data, and construction characteristic data; the first type of key data and the second type of key data are extracted from the environmental characteristic data, the economic characteristic data, and the construction characteristic data; the first type of key data is processed through the first network layer to obtain first intermediate data, and the second type of key data is processed through the second network layer to obtain second intermediate data; the first intermediate data and the second intermediate data are processed through the third network layer to obtain target intermediate data; and the target pipe pile type of the target sea area in the current offshore photovoltaic business scenario is determined according to the target intermediate data. On the one hand, this exemplary embodiment proposes a new pipe pile determination method. Compared with the prior art of determining pipe piles through trial and error or manual experience, this exemplary embodiment can quickly predict accurate and effective target pipe pile types based on multi-source data of the target sea area, greatly saving trial and error costs and manpower costs, and can ensure the accuracy of pipe pile type determination; on the other hand, this exemplary embodiment can consider the multi-source data of the target sea area, process different key data respectively through different network layers, and determine the target intermediate data based on the obtained intermediate data, and then determine the target pipe pile type of the target sea area. This method of starting from multi-dimensional data, performing data processing and data fusion through multi-level processing steps, and finally determining the target pipe pile type can further ensure the targetedness and applicability of the determined target pipe pile type to the target sea area.

[0045] In an exemplary embodiment, Figure 3 As shown, the extraction of the first type of key data and the second type of key data from the environmental characteristic data, the economic characteristic data, and the construction characteristic data may include the following steps:

[0046] Step S310: Extract the soil layer distribution data of the target sea area from the environmental characteristic data, and determine the geological profile image of the target sea area according to the soil layer distribution data;

[0047] Step S320: Extract the sea area parameter index data of the target sea area from the environmental characteristic data, and determine the sea area parameter heat map according to the sea area parameter index data; the sea area parameter index data at least includes sea area depth data and wave height data;

[0048] Step S330: Take the geological profile image and the sea area parameter heat map of the target sea area as the first type of key data;

[0049] Step S340: Determine the second type of key data according to the economic characteristic data and the construction characteristic data.

[0050] In this exemplary embodiment, the environmental characteristic data may include various data, such as soil layer distribution data, sea area depth data, wave height data, and so on.

[0051] In this exemplary embodiment, the soil layer distribution data can be extracted from the environmental characteristic data first. The soil layer distribution data refers to the data used to reflect the spatial distribution of soil layers. In this exemplary embodiment, the SPT (standard penetration test) value can be determined by conducting a standard penetration test on the target sea area, and the SPT value is used as the soil layer distribution data. For example, the SPT values of multiple drilling points in the target sea area can be obtained. According to actual needs, the collected SPT values can also be subjected to outlier removal, or spatial interpolation of discrete SPT values can be performed to generate continuous soil layer distribution data, etc. After determining the soil layer distribution data, a geological modeling tool can be used to map the layer distribution data into a layered geological model. For example, when SPT ≤ 4, it is determined as a silt layer and represented by blue; when 4 < SPT ≤ 15, it is determined as a sand layer and represented by yellow; when SPT > 15, it is determined as a clay layer or a rock layer and represented by gray, etc. Based on the layered geological model, the geological profile image, such as a horizontal profile diagram, can be further determined. According to actual needs, the boundaries and depth information of different soil layers can also be marked in the image.

[0052] This exemplary embodiment can also extract sea depth data and wave height data from environmental feature data. For example, the target sea area is first divided into grids of a preset size (such as 100*100), and then the corresponding sea depth data and wave height data are obtained from each grid. If there is a missing area, the adjacent data can be used to perform interpolation calculations to obtain the relevant data. Then, a sea parameter heat map can be determined based on the sea parameter index data. For example, an algorithm tool is used to load the grid distribution of the target sea area and the corresponding grid data to generate a depth heat map and a wave height heat map respectively. Further, a sea parameter heat map can be generated based on the depth heat map and the wave height heat map. For example, the depth heat map and the wave height heat map can be directly used as the sea parameter heat map; or a composite heat map can be generated by superimposing the depth heat map and the wave height heat map. The composite heat map can also add the boundary and key coordinate point information of the target sea area as needed. The regional points in the composite heat map can correspond to the sea depth data and wave height data of the area. Then, the composite heat map can be used as the sea parameter heat map, etc.

[0053] Finally, the geological profile image and the sea area parameter heat map of the target sea area are used as the first type of key data. For example, this exemplary embodiment can integrate the geological profile image and the sea area parameter heat map into a multi-channel image tensor. For example, channel 1 can be a geological profile grayscale image to reflect the soil layer distribution, channel 2 can be a depth heat map, channel 3 can be a wave height heat map, etc., and information such as coordinate range, scale or data acquisition timestamp can be added as needed.

[0054] In addition, considering that the economic characteristic data and the construction characteristic data may be affected by time and may have large differences at different times, this exemplary embodiment may also determine the second type of key data based on the economic characteristic data and the construction characteristic data.

[0055] Specifically, in an exemplary embodiment, the second type of key data determined based on the economic characteristic data and the construction characteristic data may include:

[0056] Extracting transportation cost data from economic characteristic data and calculating first correlation data between transportation cost data in different time series;

[0057] Extracting pile driving resistance data from construction characteristic data and calculating second correlation data between pile driving resistance data in different time series;

[0058] The second type of key data is determined based on the first correlation data and the second correlation data.

[0059] When determining the second type of key data, transportation cost data can be extracted from the economic characteristic data, and the first correlation data of the transportation cost data between different time series can be calculated. For example, multiple transportation cost time series data can be constructed based on the transportation cost data of different time series, and then the first correlation data between the multiple transportation cost time series data can be calculated. Specifically, the transportation cost time series data can be calculated through a multi-head attention mechanism to obtain the first correlation data between different time series of the transportation cost data.

[0060] Taking into account that the pile driving resistance may produce a curve change with the pile driving depth, this exemplary embodiment can also extract the pile driving resistance data from the construction feature data when determining the second type of key data. For example, a plurality of pile driving resistance time series data are constructed based on the pile driving resistance data of different time series. Then, based on the plurality of pile driving resistance time series data, the second correlation data between the different time series of the pile driving resistance data are calculated.

[0061] After obtaining the first correlation data and the second correlation data, the second type of key data can be obtained by associating the first correlation data and the second correlation data.

[0062] In an exemplary embodiment, determining the second type of key data based on the first correlation data and the second correlation data may include:

[0063] The first correlation data and the second correlation data are correlated and calculated, and the calculation result is used as the second type of key data.

[0064] This exemplary embodiment can associate the first correlation data and the second correlation data through the spatiotemporal attention association module, and use the calculation result as the second type of key data, for example, by determining the second type of key data Attention total :

[0065] Attention total =[Attention1,Attention2] T

[0066] Here, Attention1 represents the first correlation data, and Attention2 represents the second correlation data.

[0067] In an exemplary embodiment, the method for determining pipe piles in the above offshore photovoltaic scenario may further include:

[0068] Generate simulation images under preset geological environments based on geological profile images;

[0069] The geological profile image and thermal map of sea area parameters of the target sea area are taken as the first type of key data, which may include:

[0070] The geological profile image of the target sea area, the simulation image under the preset geological environment and the thermal map of the sea area parameters are taken as the first type of key data.

[0071] To ensure accurate pile type determination, this exemplary embodiment can perform data enhancement on the geological profile image to generate a simulated image under a preset geological environment. For example, a geological profile image of the seafloor scour morphology under a preset level (e.g., the strongest level) of typhoon load can be generated. The first type of key data is then determined based on the existing geological profile image, the data-enhanced simulated image under the preset geological environment, and the thermal image of the sea area parameters.

[0072] In an exemplary embodiment, the processing of the first intermediate data and the second intermediate data through the third network layer to obtain the target intermediate data may include:

[0073] Processing the first intermediate data and the second intermediate data through a third network layer to obtain evaluation index data of the target sea area; the evaluation index data includes one or more of bearing capacity index data, cost index data, and construction period index data;

[0074] The target pile type for the target sea area in the current offshore photovoltaic business scenario is determined based on the target intermediate data, including:

[0075] The evaluation index data is processed through the pre-trained pipe pile prediction network to output the probability distribution of pipe pile types;

[0076] The target pipe pile type in the target sea area is determined according to the probability distribution of pipe pile types.

[0077] This exemplary embodiment can process the first intermediate data and the second intermediate data through the third network layer, for example, concatenating the two types of data and inputting them into a fully connected layer for feature fusion, and then outputting evaluation index data of the target sea area. The evaluation index data can be index data used to evaluate the state of the target sea area. For example, this exemplary embodiment can process the fused feature data of the first intermediate data and the second intermediate data to obtain one or more of the following: bearing capacity index data, cost index data, and construction period index data of the target sea area. Among them, the bearing capacity index data can be used to reflect the bearing capacity of the target sea area for pipe piles, such as the maximum number of pipe piles accommodated, the density of pipe piles, etc. The cost index data can be the cost data of pile driving operations in the target sea area, and the construction period index data can be the construction period data of pile driving operations in the target sea area, such as the construction period of a preset number of pipe piles, or the average construction period of each pipe pile, etc.

[0078] Then, the evaluation index data of the target sea area can be input into the pre-trained pipe pile prediction network for processing, and the probability distribution of the pipe pile type can be output. For example, the bearing capacity index data, cost index data and construction period index data are converted into three-dimensional vectors, and the three-dimensional vectors are input into the pre-trained pipe pile prediction network to obtain the probability distribution corresponding to different pipe pile types. Finally, the target pipe pile type of the target sea area can be determined according to the probability distribution of the pipe pile type. For example, the pipe pile type with the largest probability can be used as the target pipe pile type.

[0079] In an exemplary embodiment, the method for determining pipe piles in the above offshore photovoltaic scenario may further include:

[0080] Collect sensor data through IoT sensors configured in current offshore photovoltaic business scenarios;

[0081] Environmental characteristic data, economic characteristic data or construction characteristic data are corrected in real time based on the sensor data.

[0082] This exemplary embodiment can deploy Internet of Things (IoT) sensors (such as submarine pressure sensors, depth sensors, and wave radars) in offshore photovoltaic business scenarios to collect real-time sea depth and wave height data. If the collected sensor data deviates from a preset value (such as an initial value) by more than a threshold, such as a depth change of ±10%, a data update is triggered to correct environmental, economic, or construction characteristic data. Furthermore, sensor data can be recollected, and geological profile images, sea area parameter heat maps, and other data can be regenerated based on the recollected or corrected data, and recommended pile types can be updated.

[0083] The exemplary embodiments of the present disclosure also provide a device for determining pipe piles in an offshore photovoltaic scenario. Figure 4 The pipe pile determination device 400 in the offshore photovoltaic scenario may include a multi-source data acquisition module 410, which is used to acquire multi-source data of a target sea area in the current offshore photovoltaic business scenario; the multi-source data includes at least environmental characteristic data, economic characteristic data, and construction characteristic data; a key data extraction module 420, which is used to extract first-category key data and second-category key data from the environmental characteristic data, economic characteristic data, and construction characteristic data; an intermediate data acquisition module 430, which is used to process the first-category key data through a first network layer to obtain first intermediate data, and to process the second-category key data through a second network layer to obtain second intermediate data; an intermediate data processing module 440, which is used to process the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; and a pipe pile type determination module 450, which is used to determine a target pipe pile type of the target sea area in the current offshore photovoltaic business scenario based on the target intermediate data.

[0084] In an exemplary embodiment, the key data extraction module 420 includes: a first image determination unit, used to extract soil layer distribution data of the target sea area from the environmental characteristic data, and determine the geological profile image of the target sea area based on the soil layer distribution data; a second image determination unit, used to extract sea area parameter index data of the target sea area from the environmental characteristic data, and determine the sea area parameter heat map based on the sea area parameter index data; the sea area parameter index data at least includes sea area depth data and wave height data; the first data determination unit, used to use the geological profile image and the sea area parameter heat map of the target sea area as the first type of key data; the second data determination unit, used to determine the second type of key data based on the economic characteristic data and the construction characteristic data.

[0085] In an exemplary embodiment, the second data determination unit includes: a first correlation data calculation subunit, used to extract transportation cost data from the economic characteristic data, and calculate first correlation data of the transportation cost data between different time series; a second correlation data calculation subunit, used to extract pile driving resistance data from the construction characteristic data, and calculate second correlation data of the pile driving resistance data between different time series; a second type of key data determination subunit, used to determine the second type of key data based on the first correlation data and the second correlation data.

[0086] In an exemplary embodiment, the second-category key data determination subunit is configured to perform correlation calculation on the first correlation data and the second correlation data, and use the calculation result as the second-category key data.

[0087] In an exemplary embodiment, the pipe pile determination device in an offshore photovoltaic scene also includes: a simulation image generation unit, which is used to generate a simulation image under a preset geological environment based on a geological profile image; and a first data determination unit, which is used to use the geological profile image of the target sea area, the simulation image under the preset geological environment, and the sea area parameter thermal map as the first type of key data.

[0088] In an exemplary embodiment, the intermediate data processing module 440 includes: an evaluation index data acquisition unit, which is used to process the first intermediate data and the second intermediate data through the third network layer to obtain evaluation index data of the target sea area; the evaluation index data includes one or more of bearing capacity index data, cost index data, and construction period index data; the pipe pile type determination module 450 includes: a target pipe pile type determination unit, which is used to process the evaluation index data through a pre-trained pipe pile prediction network and output a probability distribution of the pipe pile type; and determine the target pipe pile type of the target sea area according to the pipe pile type probability distribution.

[0089] In an exemplary embodiment, the pipe pile determination device in the offshore photovoltaic scenario also includes: a sensor data acquisition module, which is used to collect sensor data through the Internet of Things sensors configured in the current offshore photovoltaic business scenario; and to correct environmental characteristic data, economic characteristic data or construction characteristic data in real time according to the sensor data.

[0090] The specific details of each module / unit in the above device have been described in detail in the embodiments of the method part. For undisclosed details, please refer to the embodiments of the method part, so they will not be repeated here.

[0091] Exemplary embodiments of the present disclosure also provide an electronic device capable of implementing the above method.

[0092] 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."

[0093] Refer to the following Figure 5 5. The electronic device 500 according to this exemplary embodiment of the present disclosure is described below. Figure 5 The electronic device 500 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0094] like Figure 5 As shown, electronic device 500 is implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, the aforementioned at least one processing unit 510, the aforementioned at least one storage unit 520, a bus 530 connecting various system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0095] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps described in the "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 510 can execute Figure 1-2 Steps shown, etc.

[0096] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522 , and may further include a read-only memory unit (ROM) 523 .

[0097] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 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.

[0098] Bus 530 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.

[0099] The electronic device 500 can also communicate with one or more external devices 600 (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 500, and / or any device that enables the electronic device 500 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 550. Furthermore, the electronic device 500 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 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. 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 500, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0100] 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 exemplary embodiment of the present disclosure.

[0101] The exemplary embodiments of the present disclosure further provide a computer-readable storage medium having stored thereon a program product capable of implementing the methods described above in 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 that, when executed on a terminal device, causes the terminal device to execute the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure.

[0102] The exemplary embodiments of the present disclosure further provide a program product for implementing the above-mentioned method, which can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can 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 can 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.

[0103] 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 fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to exemplary 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.

[0109] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0110] 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 may 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 determining pipe piles in an offshore photovoltaic scenario, characterized in that: include: Acquire multi-source data of a target sea area in a current offshore photovoltaic business scenario; the multi-source data includes at least environmental characteristic data, economic characteristic data, and construction characteristic data; extracting first-category key data and second-category key data from the environmental characteristic data, the economic characteristic data, and the construction characteristic data; Processing the first type of key data through a first network layer to obtain first intermediate data, and processing the second type of key data through a second network layer to obtain second intermediate data; Processing the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; The target pipe pile type of the target sea area in the current offshore photovoltaic business scenario is determined according to the target intermediate data.

2. The method according to claim 1, characterized in that The extracting of the first type of key data and the second type of key data from the environmental characteristic data, the economic characteristic data and the construction characteristic data includes: Extracting soil layer distribution data of the target sea area from the environmental characteristic data, and determining a geological profile image of the target sea area based on the soil layer distribution data; Extracting sea area parameter index data of the target sea area from the environmental characteristic data, and determining a sea area parameter heat map according to the sea area parameter index data; the sea area parameter index data at least includes sea area depth data and wave height data; Using the geological profile image of the target sea area and the thermal map of the sea area parameters as the first type of key data; The second type of key data is determined based on the economic characteristic data and the construction characteristic data.

3. The method according to claim 2, characterized in that The determining of the second type of key data based on the economic characteristic data and the construction characteristic data includes: Extracting transportation cost data from economic characteristic data and calculating first correlation data between transportation cost data in different time series; extracting pile driving resistance data from the construction characteristic data, and calculating second correlation data between the pile driving resistance data in different time series; The second type of key data is determined based on the first correlation data and the second correlation data.

4. The method according to claim 3, characterized in that The determining the second type of key data according to the first correlation data and the second correlation data includes: Perform an association calculation on the first correlation data and the second correlation data, and use the calculation result as the second type of key data.

5. The method according to claim 2, characterized in that The method further comprises: Based on the geological profile image, generating a simulation image under a preset geological environment; The using the geological profile image of the target sea area and the sea area parameter thermal map as the first type of key data includes: The geological profile image of the target sea area, the simulation image under the preset geological environment and the thermal map of the sea area parameters are used as the first type of key data.

6. The method according to claim 2, characterized in that The processing of the first intermediate data and the second intermediate data through the third network layer to obtain target intermediate data includes: Processing the first intermediate data and the second intermediate data through a third network layer to obtain evaluation index data of the target sea area; the evaluation index data includes one or more of bearing capacity index data, cost index data, and construction period index data; The determining, according to the target intermediate data, the target pipe pile type of the target sea area in the current offshore photovoltaic business scenario includes: Processing the evaluation index data through a pre-trained pipe pile prediction network to output a probability distribution of pipe pile types; The target pipe pile type of the target sea area is determined according to the pipe pile type probability distribution.

7. The method according to claim 1, characterized in that The method further comprises: Collect sensor data through IoT sensors configured in the current offshore photovoltaic business scenario; The environmental characteristic data, the economic characteristic data or the construction characteristic data are corrected in real time according to the sensing data.

8. A device for determining pipe piles in an offshore photovoltaic scenario, characterized in that: include: A multi-source data acquisition module is used to acquire multi-source data of the target sea area in the current offshore photovoltaic business scenario; the multi-source data includes at least environmental characteristic data, economic characteristic data, and construction characteristic data; A key data extraction module, configured to extract first-category key data and second-category key data from the environmental characteristic data, the economic characteristic data, and the construction characteristic data; an intermediate data obtaining module, configured to process the first type of key data through a first network layer to obtain first intermediate data, and to process the second type of key data through a second network layer to obtain second intermediate data; an intermediate data processing module, configured to process the first intermediate data and the second intermediate data through a third network layer to obtain target intermediate data; The pipe pile type determination module is used to determine the target pipe pile type of the target sea area in the current offshore photovoltaic business scenario according to the target intermediate data.

9. 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 perform the method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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