Sampling determination method and device for offshore wind power, program product and equipment

Abstract

The invention relates to the technical field of sampling control, and relates to a sampling determination method and device for offshore wind power, a program product and equipment. The method comprises the steps that an information matching library is acquired, and the information matching library comprises recommendation grade information of candidate sampling schemes under different sea condition categories and recommendation grade information of the candidate sampling schemes under different soil texture categories under different sampling indexes; determining a target sea condition category and a target soil texture category; matching the sampling demand index of the sea area to be sampled and the target sea condition category with an information matching library, determining first target recommendation level information, and determining a first recommendation score according to the first target recommendation level information; matching the sampling demand index and the target soil texture category with an information matching library, determining second target recommendation level information, and determining a second recommendation score according to the second target recommendation level information; and fusing the first recommendation score and the second recommendation score to obtain a target recommendation score, so as to determine a target sampling scheme from the candidate sampling schemes.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of sampling control, and more particularly, to a sampling determination method for offshore wind power, a sampling determination device for offshore wind power, a program product and an apparatus. BACKGROUND

[0002] The sampling of offshore wind power refers to a process of obtaining a rock-soil body (such as a soil sample, a rock sample), a water sample or a sediment sample from a seabed stratum through drilling, in-situ testing and other means during a survey stage of an offshore wind farm. The sampling of offshore wind power is a key link connecting “field survey” and “indoor analysis”. Precise data obtained through sample testing directly determines the rationality of a foundation design of a wind farm, the feasibility of a construction scheme and the safety during an operation period, and is a core support for connecting “geological cognition” and “project landing” of offshore wind power engineering.

[0003] However, a suitable sampling scheme cannot be efficiently selected at present, which to some extent leads to low sampling efficiency.

[0004] It should be noted that the information disclosed in the above background section of the invention is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present disclosure is to provide a sampling determination method for offshore wind power, a sampling determination device for offshore wind power, a computer program product and an electronic apparatus, so as to efficiently determine a suitable sampling scheme for offshore wind power and improve sampling efficiency and quality.

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

[0007] According to one aspect of the present disclosure, a sampling determination method for offshore wind power is provided, comprising: obtaining a pre-established information matching library, the pre-established information matching library comprising recommended level information of a plurality of candidate sampling schemes under different sea state categories under different sampling indicators, and comprising recommended level information of the plurality of candidate sampling schemes under different soil categories; determining a target sea state category according to a sea state feature of a to-be-sampled sea area, and determining a target soil category according to a soil feature of the to-be-sampled sea area; matching the sampling requirement indicators of the to-be-sampled sea area and the target sea state category with the pre-established information matching library to determine first target recommended level information of the plurality of candidate sampling schemes, and respectively determining first recommended scores of the plurality of candidate sampling schemes according to the first target recommended level information; matching the sampling requirement indicators of the to-be-sampled sea area and the target soil category with the pre-established information matching library to determine second target recommended level information of the plurality of candidate sampling schemes, and respectively determining second recommended scores of the plurality of candidate sampling schemes according to the second target recommended level information; for each candidate sampling scheme, fusing the corresponding first recommended score and the second recommended score to obtain a target recommended score, and determining a target sampling scheme from the plurality of candidate sampling schemes according to the target recommended scores of the candidate sampling schemes.

[0008] In an exemplary embodiment of the present disclosure, the pre-established information matching library is obtained by: collecting multi-channel sampling task sample data, the sampling task sample data at least comprising sea state sample data, soil sample data, sampling scheme sample data, and sampling indicator sample data; taking the sampling indicator sample data, the sea state sample data, and the soil sample data as inputs, taking corresponding sampling scheme sample data as outputs, and training a recommendation model based on historical sampling data and expert rule library data, the recommendation model being used to determine a recommended level for the sampling scheme sample; based on the recommendation model, outputting recommended level information of a plurality of candidate sampling schemes under different sea state categories under different sampling indicators, and outputting recommended level information of the plurality of candidate sampling schemes under different soil categories.

[0009] In an exemplary embodiment of the present disclosure, matching the sampling requirement indicators of the to-be-sampled sea area and the target sea state category with the pre-established information matching library to determine first target recommended level information of the plurality of candidate sampling schemes, and respectively determining first recommended scores of the plurality of candidate sampling schemes according to the first target recommended level information, comprises: establishing a corresponding number of multiple processing threads according to the number of sampling requirement indicators; using the multiple processing threads to match the multiple sampling requirement indicators and the target sea state category with the pre-established information matching library in parallel, and determining the first target recommended level information of the plurality of candidate sampling schemes and the corresponding first recommended scores according to the matching results.

[0010] In an example embodiment of the present disclosure, for each processing thread, determining a first recommended score of a candidate sampling scheme comprises: determining a corresponding recommended score processing strategy according to the category of the sampling demand index corresponding to the processing thread; and processing the first target recommended level information corresponding to the processing thread into the first recommended score based on the recommended score processing strategy.

[0011] In an example embodiment of the present disclosure, for each candidate sampling scheme, the corresponding first recommended score and the second recommended score are fused to obtain a target recommended score, and the target sampling scheme is determined from each candidate sampling scheme according to the target recommended score of each candidate sampling scheme, comprising: obtaining environmental feature information of the sea area to be sampled, and determining a sea state weight value and a soil weight value according to the environmental feature information; based on the sea state weight value and the soil weight value, the first recommended score and the second recommended score are weighted and fused to obtain the target recommended score; according to the sea state score threshold, the candidate sampling scheme whose first recommended score is lower than the sea state score threshold is filtered out from each candidate sampling scheme to obtain a first reference sampling scheme, and according to the soil quality score threshold, the candidate sampling scheme whose second recommended score is lower than the soil quality score threshold is filtered out from each candidate sampling scheme to obtain a second reference sampling scheme; and according to the target recommended score, the target sampling scheme is determined from the first reference sampling scheme and the second reference sampling scheme.

[0012] In an example embodiment of the present disclosure, the method further comprises: for each reference sampling scheme in the first reference sampling scheme and the second reference sampling scheme, establishing a multi-dimensional feature vector according to the scheme metadata of the reference sampling scheme, the scheme metadata being obtained by deconstructing the reference sampling scheme; based on the multi-dimensional feature vector corresponding to each reference sampling scheme, clustering processing is performed on each reference sampling scheme to obtain a plurality of clustering clusters; and from the clustering cluster not containing the target sampling scheme, the reference sampling scheme with the highest target recommended score is obtained as a candidate sampling scheme.

[0013] In an example embodiment of the present disclosure, the method further comprises: generating a decision report according to the target sampling scheme and the corresponding target recommended score and the candidate sampling scheme and the corresponding target recommended score.

[0014] According to one aspect of the present disclosure, a sampling determination device for offshore wind power is provided, comprising: an information acquisition module configured to acquire a pre-established information matching library, the pre-established information matching library comprising recommended level information of a plurality of candidate sampling schemes under different sea state categories under different sampling indicators, and comprising recommended level information of the plurality of candidate sampling schemes under different soil categories; a category determination module configured to determine a target sea state category according to a sea state feature of a to-be-sampled sea area, and determine a target soil category according to a soil feature of the to-be-sampled sea area; a first matching module configured to match a sampling demand indicator of the to-be-sampled sea area and the target sea state category with the pre-established information matching library, determine first target recommended level information of the plurality of candidate sampling schemes, and determine a first recommended score of each of the plurality of candidate sampling schemes according to the first target recommended level information; a second matching module configured to match the sampling demand indicator of the to-be-sampled sea area and the target soil category with the pre-established information matching library, determine second target recommended level information of the plurality of candidate sampling schemes, and determine a second recommended score of each of the plurality of candidate sampling schemes according to the second target recommended level information; and a scheme determination module configured to, for each of the candidate sampling schemes, fuse the corresponding first recommended score and the second recommended score to obtain a target recommended score, and determine a target sampling scheme from the candidate sampling schemes according to the target recommended score of each of the candidate sampling schemes.

[0015] According to one aspect of the present disclosure, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the method of any one of the above.

[0016] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the method of any one of the above via execution of the executable instructions.

[0017] The sampling determination method for offshore wind power in the exemplary embodiment of the present disclosure acquires a pre-established information matching library, the pre-established information matching library including recommended level information of a plurality of candidate sampling schemes under different sea state categories under different sampling indicators, and including recommended level information of the plurality of candidate sampling schemes under different soil categories; a target sea state category is determined according to a sea state feature of a to-be-sampled sea area, and a target soil category is determined according to a soil feature of the to-be-sampled sea area; the sampling requirement indicator of the to-be-sampled sea area and the target sea state category are matched with the pre-established information matching library to determine first target recommended level information of the plurality of candidate sampling schemes, and first recommended scores of the plurality of candidate sampling schemes are respectively determined according to the first target recommended level information; the sampling requirement indicator of the to-be-sampled sea area and the target soil category are matched with the pre-established information matching library to determine second target recommended level information of the plurality of candidate sampling schemes, and second recommended scores of the plurality of candidate sampling schemes are respectively determined according to the second target recommended level information; for each candidate sampling scheme, the corresponding first recommended score and the second recommended score are fused to obtain a target recommended score, and a target sampling scheme is determined from the plurality of candidate sampling schemes according to the target recommended scores of the candidate sampling schemes.

[0018] On the one hand, by introducing a pre-established information matching library based on multi-source data, the traditional qualitative decision relying on personal experience is changed into a quantitative and standardized decision based on data and models, which can simultaneously accurately couple the two key environmental constraints of sea state and soil, ensure that the recommended sampling scheme is highly matched with the specific conditions of the to-be-sampled sea area, and thus improve the success rate of sampling operation and the sample quality from the source. On the other hand, by fusing the two parts of recommended scores to obtain the target recommended score, comprehensive evaluation of the sampling scheme is realized, which helps to select the scheme with the highest comprehensive stability, improves the accuracy and efficiency of determining the sampling scheme for offshore wind power, and thus improves the sampling efficiency and quality.

[0019] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will be more apparent from the following detailed description read in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A kind of application environment diagram for sampling determination method for offshore wind power of a kind of exemplary embodiment of the present disclosure is shown.

[0022] Figure 2A flow chart of a sampling determination method for offshore wind power is shown according to an example embodiment of the present disclosure.

[0023] Figure 3 A flow chart of obtaining a pre-established information matching library is shown according to an example embodiment of the present disclosure.

[0024] Figure 4 A schematic diagram of determining processing threads and corresponding processing tasks is shown according to an example embodiment of the present disclosure.

[0025] Figure 5 A flow chart of determining a target sampling scheme is shown according to an example embodiment of the present disclosure.

[0026] Figure 6 A flow chart of obtaining an alternative sampling scheme is shown according to an example embodiment of the present disclosure.

[0027] Figure 7 A composition schematic diagram of a sampling determination apparatus for offshore wind power is shown according to an example embodiment of the present disclosure.

[0028] Figure 8 A block diagram of an electronic device is shown according to an example embodiment of the present disclosure.

[0029] In the drawings, the same or similar reference numerals refer to the same or similar parts. DETAILED DESCRIPTION

[0030] Example embodiments now will be described more fully hereinafter with reference to the accompanying drawings. Example embodiments, however, can be implemented in many different 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 scope of example embodiments to those skilled in the art. Like reference numerals refer to like elements throughout the specification.

[0031] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the techniques described herein can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail in order to avoid obscuring aspects of the disclosure.

[0032] The block diagrams shown in the drawings are merely functional entities and do not necessarily have to correspond to physically independent entities. That is, the functional entities can be implemented in the form of software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0033] The sampling of offshore wind power refers to the process of obtaining rock-soil bodies (such as soil samples, rock samples), water samples or sediment samples from seabed strata through drilling, in-situ testing and other means during the survey stage of an offshore wind farm. The sampling of offshore wind power is a key link connecting "field survey" and "laboratory analysis". Precise data obtained through sample testing directly determines the rationality of the foundation design of the wind farm, the feasibility of the construction scheme and the safety during the operation period, and is the core support for the transition of offshore wind power engineering from "geological cognition" to "engineering landing". However, the current scheme lacks a method for selecting a suitable sampling scheme under different sea conditions and different geological conditions, resulting in low sampling efficiency and affecting the sampling quality to some extent.

[0034] Based on this, the exemplary embodiments of the present disclosure provide a sampling determination method for offshore wind power. By establishing an information matching library, the traditional qualitative decision relying on personal experience is changed into a quantitative and standardized decision based on data and models. The two key environmental constraints of sea conditions and soil quality can be accurately coupled at the same time, ensuring that the recommended sampling scheme is highly matched with the specific conditions of the sea area to be sampled, so as to determine the target sampling scheme by comprehensively considering the recommended scores of the two key environmental constraints, thereby improving the progress efficiency and quality of sampling work while meeting the sampling requirements.

[0035] The sampling determination method for offshore wind power provided by the exemplary embodiments of the present disclosure can be applied in the application environment as shown in Figure 1 The terminal 101 communicates with the server 102 through a network. The data storage system can store data required to be processed by the server 102. The data storage system can be integrated on the server 102, or placed on a cloud or other network server.

[0036] In an exemplary embodiment, the sampling determination method for offshore wind power provided by the exemplary embodiments of the present disclosure can be executed by the server 102, and correspondingly, the sampling determination apparatus for offshore wind power is arranged in the server 102. In this way executed by the server 102, the server 102 can execute the steps in the technical solutions of the exemplary embodiments of the present disclosure in response to a trigger order, which can be sent by a terminal used by a user, or triggered locally by the server in response to some automatic events.

[0037] Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server 102 can execute background tasks.

[0038] Furthermore, in another exemplary embodiment, terminal 101 may also have similar functions to server 102, thereby performing the sampling determination method for offshore wind power provided by the exemplary embodiments of this disclosure.

[0039] The terminal 101 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, IoT device, or portable wearable device. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. The terminal 101 can also be referred to as a mobile terminal, terminal device, mobile device, etc. The exemplary embodiments of this disclosure do not limit the type of terminal 101.

[0040] Furthermore, the technical solutions of the exemplary embodiments of this disclosure can also be executed collaboratively by terminal 101 and server 102. In this collaborative execution method, some steps of the technical solutions provided by the exemplary embodiments of this disclosure are executed by terminal 101, while other steps are executed by server 102. It should be noted that in this collaborative execution method, the steps executed by terminal 101 and server 102 respectively can be dynamically adjusted according to actual circumstances, and no special restrictions are placed on this. Terminal 101 and server 102 can be directly or indirectly connected via wireless communication, and the exemplary embodiments of this disclosure do not impose any special restrictions here.

[0041] like Figure 2 The diagram shown is a flowchart of a sampling determination method for offshore wind power, an exemplary embodiment of this disclosure, with reference to... Figure 2 As shown, the method includes steps S210 to S250: Step S210: Obtain the pre-established information matching library, which includes the recommendation level information of multiple candidate sampling schemes under different sea state categories under different sampling indicators, and the recommendation level information of multiple candidate sampling schemes under different soil categories.

[0042] Step S220: Determine the target sea state category based on the sea state characteristics of the sea area to be sampled, and determine the target soil category based on the soil characteristics of the sea area to be sampled.

[0043] Step S230: matching the sampling demand index and the target sea state category of the sea area to be sampled with the pre-established information matching library, determining first target recommendation level information of a plurality of candidate sampling schemes, and respectively determining first recommendation scores of the plurality of candidate sampling schemes according to the first target recommendation level information.

[0044] Step S240: matching the sampling demand index and the target soil category of the sea area to be sampled with the pre-established information matching library, determining second target recommendation level information of a plurality of candidate sampling schemes, and respectively determining second recommendation scores of the plurality of candidate sampling schemes according to the second target recommendation level information.

[0045] Step S250: for each candidate sampling scheme, fusing the corresponding first recommendation score and the second recommendation score to obtain a target recommendation score, and determining a target sampling scheme from the candidate sampling schemes according to the target recommendation scores of the candidate sampling schemes.

[0046] The sampling determination method for offshore wind power in the exemplary embodiments of the present disclosure can, on the one hand, convert the traditional qualitative decision relying on personal experience into quantitative and standardized decision based on data and models by introducing the pre-established information matching library based on multi-source data, can simultaneously accurately couple the two key environmental constraints of sea state and soil, and can ensure that the recommended sampling scheme is highly matched with the specific conditions of the sea area to be sampled, thereby improving the success rate and sample quality of sampling operation from the source. On the other hand, by fusing the two parts of the recommendation score to obtain the target recommendation score, comprehensive evaluation of the sampling scheme is realized, which helps to select the scheme with the highest comprehensive stability, improves the accuracy and efficiency of determining the sampling scheme of offshore wind power, and thereby improves the sampling efficiency and quality.

[0047] The steps S210 to S250 will be described in more detail below.

[0048] In step S210, a pre-established information matching library is obtained, which includes recommendation level information of a plurality of candidate sampling schemes under different sea state categories under different sampling indexes, and includes recommendation level information of a plurality of candidate sampling schemes under different soil categories.

[0049] In the example embodiments of the present disclosure, the pre-established information matching library is a structured, processed expert knowledge model obtained by processing multi-channel sampling task sample data. The sampling index is a specific requirement of the user for the sampling operation, a quantitative input on the demand side, including sampling efficiency, sampling cost, sampling target substance, sampling depth, etc. The candidate sampling scheme refers to all offshore sampling technical equipment and its operation process that can be selected. For example, the gravity piston sampler uses gravity and piston negative pressure, which is suitable for soft soil on the seabed surface, and the box sampler is used to obtain large-area, undisturbed seabed surface sediments. The example embodiments of the present disclosure can set the candidate sampling scheme according to the actual scene requirements, and this is not specially limited.

[0050] Among them, the sea state category and the soil quality category can be classification labels formed by analysis. For example, the sea state category can be clustered and generated based on parameters such as wave height, flow rate, wind speed, etc., including category 1 (such as wave height <1m, flow rate <0.5 knots, wind speed <4 levels), the operation window of which is generally wide and the device operation is stable. Category 2 (such as wave height <1m, flow rate <0.5 knots, wind speed <4 levels), the device with strong resistance to flow and wave needs to be selected under this category, and so on. The soil quality category can be clustered and generated based on parameters such as cone tip resistance and shear strength, for example, category A is soft clay (very low strength, easy to disturb), special equipment such as fixed piston sampler needs to be used under this category, category B is dense sand (strong permeability, it is difficult to take undisturbed sample), freezing sampling or special liner can be used under this category, and so on.

[0051] The recommendation level information is an applicability evaluation level or score given to each candidate sampling scheme for a specific (sampling index, sea state category, soil quality category) combination, such as a recommendation score.

[0052] In an example embodiment, as shown in Figure 3 The obtaining of the pre-established information matching library can include: Step S310: Collecting multi-channel sampling task sample data, the sampling task sample data at least including sea state sample data, soil quality sample data, sampling scheme sample data, and sampling index sample data.

[0053] The multi-channel sampling task sample data is a structured data set obtained from different sources and recording complete sampling task information. Understandably, a sample is a complete data record of a historical sampling operation. The sea state sample data is marine weather forecast and ship sensor records, including specific values such as wave height, wave period, flow rate, wind speed, etc. The soil sample data is from drilling logs, static sounding tests, laboratory soil tests, etc., including soil types, undrained shear strength, particle size distribution, etc. The sampling scheme sample data is information recording the actual equipment type and operation parameters, and the sampling index sample data is recording the target of this task, such as the target sampling depth, the required sample quality level, the sampling cost, etc. The exemplary embodiments of the present disclosure can obtain sampling task sample data from multiple channels to improve the comprehensiveness of the basic data.

[0054] Step S320: Taking the sampling index sample data, the sea state sample data, and the soil sample data as inputs, taking the corresponding sampling scheme sample data as outputs, and based on the historical sampling data and the expert rule base data, a recommendation model is trained to determine the recommended level for the sampling scheme sample.

[0055] The recommendation model is a trained machine learning model, which can be a decision tree model, a CNN (Convolutional Neural Networks), an RNN (Recurrent Neural Network), etc. network structure. Of course, the recommendation model can also be other heavy models or large language models, and the exemplary embodiments of the present disclosure do not limit this.

[0056] In the training process, the sampling index sample data, the sea state sample data, and the soil sample data are taken as inputs, the corresponding sampling scheme sample data is taken as outputs, and based on the historical sampling data and the expert rule base data, the training process of the model is guided and constrained to adjust the parameters in the model until the number of training iterations is reached, and the recommendation model is obtained.

[0057] Step S330: Based on the recommendation model, the recommended level information of multiple candidate sampling schemes under different sea state categories under different sampling indexes is output, and the recommended level information of multiple candidate sampling schemes under different soil categories is output.

[0058] After the recommendation model is trained, the recommendation model can be used to output the recommended level information of multiple candidate sampling schemes under different sea state categories under different sampling indexes and the recommended level information of multiple candidate sampling schemes under different soil categories.

[0059] The exemplary embodiments of the present disclosure compensate for logical loopholes that may exist in pure human experience by fusing a large amount of historical data (objective laws) and rules (priori knowledge), and the recommendation level obtained by the model makes up for the logical loopholes that may exist in pure human experience, thereby providing an accurate information basis for subsequent determination of a sampling scheme.

[0060] In step S220, a target sea state category is determined according to the sea state characteristics of the sea area to be sampled, and a target soil quality category is determined according to the soil quality characteristics of the sea area to be sampled.

[0061] In the exemplary embodiments of the present disclosure, the sea state characteristics and the soil quality characteristics of the sea area to be sampled are specific parameters that are original, multi-dimensional and continuous, and are obtained by field measurement, sensors, preliminary survey and the like, and describe the environmental conditions of the sea area to be sampled. The sea state characteristics include, but are not limited to, significant wave height (m), mean wave period (s), surface current velocity (m / s), wind speed (m / s) and the like. The soil quality characteristics include, but are not limited to, rock-soil type (such as sandy soil, silt, clay and the like), particle size, water content, density and the like.

[0062] The target sea state category and the target soil quality category are discrete and standardized category labels obtained by mapping the continuous characteristic parameters, and are consistent with the classification system used when the information matching library is constructed.

[0063] In step S230, the sampling requirement indicators of the sea area to be sampled and the target sea state category are matched with the pre-established information matching library, a first target recommendation level information of the plurality of candidate sampling schemes is determined, and a first recommendation score of the plurality of candidate sampling schemes is determined according to the first target recommendation level information.

[0064] In the exemplary embodiments of the present disclosure, the matching here is to quickly locate a subset of data consistent with the current task conditions from the information matching library to determine the first target recommendation level information of the candidate sampling schemes. The first target recommendation level information refers to the recommendation level obtained by each candidate sampling scheme under the premise of ignoring the soil quality conditions and only considering the current sampling requirement indicators and the target sea state category, which reflects the ability and applicability of each scheme to complete the specific sampling requirement under the specific sea state. The first recommendation score is a numerical value converted from the qualitative first target recommendation level information by a quantitative mapping function, and the larger the numerical value, the greater the possibility of the candidate sampling scheme being recommended. Correspondingly, the second target recommendation level information refers to the recommendation level obtained by each candidate sampling scheme under the premise of ignoring the sea state conditions and only considering the current sampling requirement indicators and the target soil quality category, which reflects the ability and applicability of each scheme to complete the specific sampling requirement under the specific soil quality. The second recommendation score is a numerical value converted from the qualitative second target recommendation level information by a quantitative mapping function, and the larger the numerical value, the greater the possibility of the candidate sampling scheme being recommended.

[0065] In an exemplary embodiment, the sampling requirement indicators and target sea state category of the sea area to be sampled are matched with a pre-established information matching database to determine the first target recommendation level information of multiple candidate sampling schemes, and the first recommendation score of each candidate sampling scheme is determined based on the first target recommendation level information, including: First, based on the number of sampling requirement indicators, establish a corresponding number of processing threads.

[0066] Then, multiple processing threads are used to match multiple sampling requirement indicators and target sea state categories with a pre-established information matching database in parallel, and the first target recommendation level information and corresponding first recommendation score of multiple candidate sampling schemes are determined based on the matching results.

[0067] In this context, a processing thread is the smallest unit capable of computational scheduling. In an exemplary embodiment of this disclosure, a processing thread is an independent, concurrently executable task execution flow responsible for the matching process corresponding to the sampling requirement indicators. Multiple processing threads are used for parallel matching, with multiple threads started simultaneously, each thread independently responsible for matching one or more sampling requirement indicators with the information matching database.

[0068] As an example, such as Figure 4 As shown, if the sampling requirement indicators include sampling efficiency and sampling cost, and the target sea state category is determined to be Category 1, and if two sampling requirement indicators need to be processed, the main thread can dynamically create two independent worker threads and assign "sampling requirement indicator + target sea state category" to each thread. This allows each thread to independently handle the matching of one sampling requirement indicator with the information matching database. Within each thread, based on the sampling requirement indicator and the target sea state category, the first target recommendation level information of multiple candidate sampling schemes is determined in the information matching database, and then the first recommendation score is determined based on the first target recommendation level information.

[0069] The exemplary embodiments disclosed herein achieve parallel matching and scoring calculation of different indicators by creating multiple processing threads corresponding to the number of sampling requirement indicators, thereby improving matching processing efficiency and reducing processing time complexity. Furthermore, the evaluation process for each requirement indicator is independent and does not block each other, thus clearly demonstrating the advantages and disadvantages of each candidate sampling scheme across different indicator dimensions.

[0070] Based on the foregoing exemplary embodiments, determining a first recommendation score for a candidate sampling scheme for each processing thread may include: First, determine the corresponding recommendation score processing strategy based on the category of the sampling requirement metric corresponding to the processing thread.

[0071] Then, based on the recommendation score processing strategy, the first target recommendation level information corresponding to the processing thread is processed and converted into a first recommendation score.

[0072] The category of the sampling demand index corresponding to the processing thread can be understood as the category of the sampling demand index processed by the processing thread, i.e., the category of the sampling demand index allocated to the processing thread for processing. For example, sampling efficiency, sampling cost, etc. Since different categories of sampling demand indexes have different ways of converting the target recommendation level information into the recommendation score, the correspondence between different categories of sampling demand indexes and the recommendation score processing strategy can be set in advance. The recommendation score processing strategy defines how to convert the qualitative recommendation level into a recommendation score with specific mathematical meaning. For example, for sampling efficiency, one recommendation score processing strategy is binary judgment. If the recommendation level given by the information matching library is A (e.g., high level), the score is mapped to a high score value (e.g., 100 points), otherwise, if the recommendation level is B (e.g., low level), the score is mapped to a very low score or zero. For sampling cost, if the recommendation level given by the information matching library is 1, the score is mapped to a low score value, otherwise, if the recommendation level is 2, the score is mapped to a high score value.

[0073] According to the exemplary embodiments of the present disclosure, by distinguishing the index categories and applying differentiated strategies, the level processing information can be converted into recommendation scores in different threads in a differentiated manner, so that in the conversion process, the high and low of the recommendation score is related to whether to prioritize the recommendation, thereby improving the accuracy of the recommendation processing.

[0074] In step S240, the sampling demand index and the target soil category of the sea area to be sampled are matched with the pre-established information matching library to determine the second target recommendation level information of the plurality of candidate sampling schemes, and the second recommendation score of each candidate sampling scheme is determined according to the second target recommendation level information. The process is similar to the process of determining the first recommendation score based on the target sea state category, which will not be described here.

[0075] In step S250, for each candidate sampling scheme, the corresponding first recommendation score and second recommendation score are fused to obtain a target recommendation score, and the target sampling scheme is determined from the candidate sampling schemes according to the target recommendation score of each candidate sampling scheme.

[0076] In the exemplary embodiments of the present disclosure, the fusion calculation is a process of combining two-dimensional scores into a comprehensive score, and the target recommendation score can be used for quantifying the comprehensive performance of the scheme and determining the scheme.

[0077] In an exemplary embodiment, as shown in FIG. 2, the target sampling scheme is determined by fusing the first recommendation score and the second recommendation score of each candidate sampling scheme. Figure 5As shown, for each candidate sampling scheme, the corresponding first recommendation score and second recommendation score are fused to obtain a target recommendation score, and the target sampling scheme is determined from the candidate sampling schemes according to the target recommendation scores of the candidate sampling schemes, which can include: Step S510: Obtain the environmental characteristic information of the sea area to be sampled, and determine the sea state weight value and the soil weight value according to the environmental characteristic information.

[0078] The environmental characteristic information is an objective parameter for judging the sea state or soil quality and other factors in the current environment. In addition to the sea state characteristics and soil characteristics, other environmental characteristics can also be included to reflect the actual situation of the current environment of the sea area to be sampled. The sea state weight value and the soil weight value represent the proportion of the first recommendation score (sea state score) and the second recommendation score (soil score) in the fusion calculation, respectively.

[0079] The corresponding relationship between the environmental characteristic information and the weight can be pre-set, and the sea state weight value and the soil weight value can be determined according to the corresponding relationship between the environmental characteristic information and the weight. Alternatively, a pre-trained weight discrimination model can be used to output the sea state weight value and the soil weight value. The weight discrimination model is trained based on environmental characteristic information samples and weight information. The network structure of the model can be a lightweight model such as CCN, RNN, or a large language model, and the exemplary embodiments of the present disclosure do not limit this.

[0080] Step S520: Based on the sea state weight value and the soil weight value, the first recommendation score and the second recommendation score are weighted and fused to obtain the target recommendation score.

[0081] After determining the sea state weight value and the soil weight value, the first recommendation score and the second recommendation score are weighted and fused to obtain the target recommendation score.

[0082] Step S530: According to the sea state score threshold, filter out the candidate sampling scheme whose first recommendation score is lower than the sea state score threshold from the candidate sampling schemes to obtain the first reference sampling scheme, and according to the soil score threshold, filter out the candidate sampling scheme whose second recommendation score is lower than the soil score threshold from the candidate sampling schemes to obtain the second reference sampling scheme.

[0083] The sea state score threshold and the soil score threshold are pre-set score red lines representing the minimum acceptable standard, which can be understood as the bottom line of engineering safety and feasibility. If the score of a scheme in a certain dimension is lower than the threshold, no matter how high the total score is, it means that there is an unacceptable risk or performance defect in that dimension, so the candidate sampling scheme needs to be filtered out to avoid any risk or defect in the scheme, which may lead to subsequent sampling failure.

[0084] Step S540: determining the target sampling scheme from the first reference sampling scheme and the second reference sampling scheme according to the target recommendation score.

[0085] The higher the target recommendation score is, the greater the generalization is. After obtaining the first reference sampling scheme and the second reference sampling scheme, the target sampling scheme can be determined from the first reference sampling scheme and the second reference sampling scheme according to the target recommendation score.

[0086] The exemplary embodiments of the present disclosure double-protect by setting up two independent judgment modes of sea conditions and soil conditions, avoid the recommended risk scheme while obtaining an effective target sampling scheme, and thus improve the sampling efficiency and quality.

[0087] In an exemplary embodiment, in addition to providing the sampling scheme, an alternative sampling scheme can also be provided. Specifically, as shown in Figure 6 including: Step S610: establishing a multi-dimensional feature vector for each reference sampling scheme in the first reference sampling scheme and the second reference sampling scheme according to scheme metadata of the reference sampling scheme, the scheme metadata being obtained by deconstructing the reference sampling scheme.

[0088] The scheme metadata is a parameter describing the essential characteristics of a complete candidate sampling scheme after deconstruction. For example, it includes sampling driving mode, required minimum tonnage of a mother ship, whether to rely on a drilling platform, maximum operating water depth, theoretical sampling length, sampling tube diameter, typical operation cycle, etc. The multi-dimensional feature vector is a vector formed by combining the numerical values of the above scheme metadata.

[0089] Step S620: performing clustering processing on each reference sampling scheme based on the multi-dimensional feature vector corresponding to each reference sampling scheme, to obtain a plurality of clustering clusters.

[0090] The clustering algorithm can automatically divide the schemes with similar spatial positions into groups. The schemes in the same cluster have similar technical paths and resource requirements. The clustering algorithm can use K-means clustering algorithm, etc., and is not limited thereto.

[0091] Step S630: obtaining, from each clustering cluster not containing the target sampling scheme, a reference sampling scheme with the highest target recommendation score as an alternative sampling scheme.

[0092] After obtaining a plurality of clustering clusters, the cluster where the target sampling scheme is located can be identified, such as cluster A. Then, all other schemes in cluster A can be completely excluded, because these schemes are homogenized alternatives of the target scheme A. If scheme A is not feasible, other schemes in cluster A are likely to face the same implementation obstacles. Therefore, the highest target recommendation score can be searched in all other clusters to form an alternative sampling scheme.

[0093] The way of determining the alternative sampling scheme in the example embodiment of the present disclosure can provide a high-quality alternative scheme that is not affected by the same reasons and is certified, and provide more choices for the decision maker, avoiding the same obstacles affecting the implementation of the sampling work.

[0094] Based on the foregoing example embodiment, a decision report can also be generated according to the target sampling scheme and the corresponding target recommendation score and the alternative sampling scheme and the corresponding target recommendation score.

[0095] The decision report is a structured and readable document or a visual interface. In addition to presenting the target sampling scheme and the corresponding recommendation score in the report, the alternative sampling scheme and the target recommendation score are also provided, clearly presenting the recommendation logic and providing a basis for selecting a scheme.

[0096] The sampling determination method for offshore wind power in the example embodiment of the present disclosure can, on the one hand, convert the traditional qualitative decision relying on personal experience into quantitative and standardized decision based on data and models by introducing the pre-established information matching library based on multi-source data, and can simultaneously accurately couple the two key environmental constraints of sea conditions and soil, ensure that the recommended sampling scheme is highly matched with the specific conditions of the sea area to be sampled, and thus improve the success rate and sample quality of the sampling operation from the source. On the other hand, by fusing the recommendation scores of the two parts to obtain the target recommendation score, comprehensive evaluation of the sampling scheme is realized, which helps to select the scheme with the highest comprehensive stability, improves the accuracy and efficiency of determining the sampling scheme for offshore wind power, and thus improves the sampling efficiency and quality.

[0097] In the example embodiment of the present disclosure, a sampling determination apparatus for offshore wind power is also provided. Referring to Figure 7 The sampling determination apparatus 700 for offshore wind power can include an information acquisition module 710, a category determination module 720, a first matching module 730, a second matching module 740, and a scheme determination module 750. Specifically: The information acquisition module 710 is configured to acquire a pre-established information matching library, wherein the pre-established information matching library includes recommendation level information of a plurality of candidate sampling schemes in different sea condition categories under different sampling indicators, and includes recommendation level information of the plurality of candidate sampling schemes in different soil categories. The category determining module 720 is configured to determine a target sea state category according to a sea state feature of the sea area to be sampled and determine a target soil quality category according to a soil quality feature of the sea area to be sampled; the first matching module 730 is configured to match the sampling demand indicators of the sea area to be sampled and the target sea state category with the pre-established information matching library, determine first target recommendation level information of a plurality of candidate sampling schemes, and determine a first recommendation score of each of the plurality of candidate sampling schemes according to the first target recommendation level information; the second matching module 740 is configured to match the sampling demand indicators of the sea area to be sampled and the target soil quality category with the pre-established information matching library, determine second target recommendation level information of the plurality of candidate sampling schemes, and determine a second recommendation score of each of the plurality of candidate sampling schemes according to the second target recommendation level information; and the scheme determining module 750 is configured to, for each candidate sampling scheme, fuse the corresponding first recommendation score and the second recommendation score to obtain a target recommendation score, and determine a target sampling scheme from the plurality of candidate sampling schemes according to the target recommendation scores of the plurality of candidate sampling schemes.

[0098] In an example embodiment of the present disclosure, the information obtaining module 710 is configured to perform: collecting multi-channel sampling task sample data, the sampling task sample data at least including sea state sample data, soil quality sample data, sampling scheme sample data, and sampling indicator sample data; taking the sampling indicator sample data, the sea state sample data, and the soil quality sample data as inputs, taking corresponding sampling scheme sample data as outputs, and training a recommendation model based on historical sampling data and expert rule library data, the recommendation model being used to determine a recommendation level for the sampling scheme sample; based on the recommendation model, outputting recommendation level information of a plurality of candidate sampling schemes in different sea state categories under different sampling indicators, and outputting recommendation level information of the plurality of candidate sampling schemes in different soil quality categories.

[0099] In an example embodiment of the present disclosure, the first matching module 730 is configured to perform: establishing a corresponding number of multiple processing threads according to the number of sampling demand indicators; using the multiple processing threads, matching the multiple sampling demand indicators and the target sea state category with the pre-established information matching library in parallel, and determining first target recommendation level information and corresponding first recommendation scores of the plurality of candidate sampling schemes according to the matching results.

[0100] In an example embodiment of the present disclosure, the first matching module 730 is configured to perform: determining a corresponding recommendation score processing strategy according to the category of the sampling demand indicator corresponding to the processing thread; and based on the recommendation score processing strategy, processing and converting the first target recommendation level information corresponding to the processing thread into the first recommendation score.

[0101] In an example embodiment of the present disclosure, the scheme determining module 750 is configured to perform: obtaining environmental characteristic information of the sea area to be sampled, and determining a sea state weight value and a soil weight value according to the environmental characteristic information; performing weighted fusion calculation on the first recommendation score and the second recommendation score based on the sea state weight value and the soil weight value to obtain a target recommendation score; filtering out, from each candidate sampling scheme, a candidate sampling scheme whose first recommendation score is lower than a sea state score threshold to obtain a first reference sampling scheme according to the sea state score threshold, and filtering out, from each candidate sampling scheme, a candidate sampling scheme whose second recommendation score is lower than a soil score threshold to obtain a second reference sampling scheme according to the soil score threshold; and determining a target sampling scheme from the first reference sampling scheme and the second reference sampling scheme according to the target recommendation score.

[0102] In an example embodiment of the present disclosure, the scheme determining module 750 is configured to perform: establishing a multi-dimensional feature vector for each reference sampling scheme in the first reference sampling scheme and the second reference sampling scheme according to scheme metadata of the reference sampling scheme, the scheme metadata being obtained by deconstructing the reference sampling scheme; performing clustering processing on each reference sampling scheme based on the multi-dimensional feature vector corresponding to the reference sampling scheme to obtain a plurality of clustering clusters; and obtaining, from the clustering clusters that do not contain the target sampling scheme, a reference sampling scheme with the highest target recommendation score as an alternative sampling scheme, respectively.

[0103] In an example embodiment of the present disclosure, the scheme determining module 750 is configured to perform: generating a decision report according to the target sampling scheme and the corresponding target recommendation score, and the alternative sampling scheme and the corresponding target recommendation score.

[0104] Since the detailed content of each functional module of the sampling determination apparatus for offshore wind power of the example embodiment of the present disclosure has been described in the above example embodiment of the sampling determination method for offshore wind power, further description is not repeated here.

[0105] It should be noted that, although several modules or units of the sampling determination apparatus for offshore wind power are mentioned in the above detailed description, such 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 embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into a plurality of modules or units.

[0106] The example embodiments of the present disclosure also provide a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the above-mentioned sampling determination method for offshore wind power.

[0107] In an embodiment, the computer program product can be a tangible product including the computer program, such as a computer-readable storage medium storing the computer program. The computer-readable storage medium can be a storage medium based on electric, magnetic, optical, electromagnetic, infrared, etc. signals, including but not limited to random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory (Flash), mechanical hard disk (HDD), solid-state hard disk (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing the computer program, such as a read-only memory (ROM), a Nand Flash, etc.

[0108] In an embodiment, the computer program product can be an intangible product including the computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, an installation package, etc. digital file storing the computer program.

[0109] The code of the computer program can be written in one or more programming languages. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any kind of network, such as a local area network (LAN), a wide area network (WAN), etc., or the connection can be made to an external computing device (for example, through the Internet using an Internet Service Provider).

[0110] The computer program can be carried or transmitted by an electronic device through electric, magnetic, optical, electromagnetic, infrared, etc. signals. The electronic device can convert the signals carrying the computer program into digital signals, and then run the computer program. When the computer program is running on the electronic device, its code is used to make the electronic device execute (more specifically, can make the processor of the electronic device execute) the method steps of various exemplary embodiments of the present disclosure, such as the steps of the sampling determination method for offshore wind power described above.

[0111] In addition, in the exemplary embodiments of the present disclosure, an electronic device capable of implementing the above method is also provided. Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method or a program product. Therefore, various aspects of the present disclosure can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" here.

[0112] The following refers to Figure 8The electronic device 800 according to such an embodiment of the present disclosure will be described. Figure 8 The electronic device 800 shown is merely one example and should not be taken as limiting the scope of the present disclosure embodiments.

[0113] As shown in Figure 8 The electronic device 800 is in the form of a general computing device. Components of the electronic device 800 can include, but are not limited to, the at least one processing unit 810 described above, the at least one storage unit 820 described above, a bus 830 that connects different system components, including the storage unit 820 and the processing unit 810, a display unit 840.

[0114] The storage unit stores program code that can be executed by the processing unit 810, so that the processing unit 810 performs the steps described in the above "Exemplary Methods" section according to various exemplary embodiments of the present disclosure.

[0115] The storage unit 820 can include a readable medium in the form of volatile storage such as random access memory (RAM) 821 and / or cache memory 822, and can further include non-volatile storage such as read-only memory (ROM) 823.

[0116] The storage unit 820 can also include program / utility 824 having a set of at least one program modules 825, including but not limited to, an operating system, one or more application programs, other program modules, and program data, each or a combination thereof, which can include implementation of a network environment.

[0117] The bus 830 can be representative of one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0118] The electronic device 800 can also communicate with one or more external devices 900 such as a keyboard or pointing device, a Bluetooth device, or a database via I / O interface 850. The communication can be facilitated via an I / O interface 850. The electronic device 800 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the public network, such as the Internet, via network adapter 860. As depicted, the network adapter 860 is in communication with the other modules of the electronic device 800 through the bus 830. It should be appreciated that other hardware and / or software modules can be used in conjunction with the electronic device 800, including but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0119] From the above description of the 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 software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to perform the methods according to the embodiments of the disclosure.

[0120] In addition, the above-described figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-described figures do not indicate or limit the time sequence of the processes. In addition, it is also easy to understand that the processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0121] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. The disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such features that are evident to those skilled in the art to which the disclosure pertains. The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A sampling and determination method for offshore wind power, characterized in that, include: Obtain a pre-established information matching database, which includes recommendation level information of multiple candidate sampling schemes under different sea state categories under different sampling indicators, and recommendation level information of the multiple candidate sampling schemes under different soil types. The target sea state category is determined based on the sea state characteristics of the sea area to be sampled, and the target soil category is determined based on the soil characteristics of the sea area to be sampled. The sampling requirement indicators of the sea area to be sampled and the target sea state category are matched with the pre-established information matching database to determine the first target recommendation level information of the multiple candidate sampling schemes, and the first recommendation score of the multiple candidate sampling schemes is determined according to the first target recommendation level information. The sampling requirement indicators of the sea area to be sampled and the target soil type are matched with the pre-established information matching database to determine the second target recommendation level information of the multiple candidate sampling schemes, and the second recommendation score of the multiple candidate sampling schemes is determined according to the second target recommendation level information. For each candidate sampling scheme, the corresponding first recommendation score and second recommendation score are fused to obtain the target recommendation score, and the target sampling scheme is determined from each candidate sampling scheme based on the target recommendation score of each candidate sampling scheme.

2. The method according to claim 1, characterized in that, The acquisition of the pre-established information matching database includes: Collect sampling task sample data from multiple channels, including at least sea state sample data, soil sample data, sampling plan sample data, and sampling index sample data; Using sampling index sample data, sea state sample data, and soil sample data as inputs, and corresponding sampling scheme sample data as outputs, a recommendation model is trained based on historical sampling data and expert rule base data. The recommendation model is used to determine the recommendation level for the sampling scheme sample. Based on the recommendation model, the recommendation level information of multiple candidate sampling schemes under different sampling indicators under different sea state categories is output, as well as the recommendation level information of the multiple candidate sampling schemes under different soil types is output.

3. The method according to claim 1, characterized in that, The step of matching the sampling requirement indicators of the sea area to be sampled and the target sea state category with the pre-established information matching database to determine the first target recommendation level information of the multiple candidate sampling schemes, and determining the first recommendation score of the multiple candidate sampling schemes based on the first target recommendation level information, includes: Based on the number of sampling requirement indicators, establish a corresponding number of processing threads; Using the multiple processing threads, multiple sampling requirement indicators and the target sea state category are matched in parallel with the pre-established information matching library, and the first target recommendation level information and the corresponding first recommendation score of the multiple candidate sampling schemes are determined based on the matching results.

4. The method according to claim 3, characterized in that, For each of the processing threads, a first recommendation score for the candidate sampling scheme is determined, including: Based on the category of the sampling requirement index corresponding to the processing thread, determine the corresponding recommendation score processing strategy; Based on the recommendation score processing strategy, the first target recommendation level information corresponding to the processing thread is processed and converted into the first recommendation score.

5. The method according to claim 1, characterized in that, For each candidate sampling scheme, the corresponding first recommendation score and second recommendation score are fused to calculate the target recommendation score, and the target sampling scheme is determined from the candidate sampling schemes based on the target recommendation scores of each candidate sampling scheme, including: Obtain environmental characteristic information of the sea area to be sampled, and determine the sea state weight value and soil quality weight value based on the environmental characteristic information; Based on the sea state weight value and soil quality weight value, the first recommendation score and the second recommendation score are weighted and fused to obtain the target recommendation score; Based on the sea state score threshold, candidate sampling schemes with a first recommended score lower than the sea state score threshold are filtered out from each of the candidate sampling schemes to obtain a first reference sampling scheme. Based on the soil quality score threshold, candidate sampling schemes with a second recommended score lower than the soil quality score threshold are filtered out from each of the candidate sampling schemes to obtain a second reference sampling scheme. Based on the target recommendation score, the target sampling scheme is determined from the first reference sampling scheme and the second reference sampling scheme.

6. The method according to claim 5, characterized in that, The method further includes: For each of the first and second reference sampling schemes, a multidimensional feature vector is established based on the scheme metadata of the reference sampling scheme, wherein the scheme metadata is obtained by deconstructing the reference sampling scheme. Based on the multidimensional feature vectors corresponding to each of the reference sampling schemes, clustering is performed on each of the reference sampling schemes to obtain multiple clusters; From the clusters that do not contain the target sampling scheme, obtain the reference sampling scheme with the highest target recommendation score as the alternative sampling scheme.

7. The method according to claim 6, characterized in that, The method further includes: A decision report is generated based on the target sampling scheme and its corresponding target recommendation score, as well as the alternative sampling scheme and its corresponding target recommendation score.

8. A sampling and determination device for offshore wind power, characterized in that, include: The information acquisition module is used to acquire a pre-established information matching library, which includes recommendation level information of multiple candidate sampling schemes under different sea state categories under different sampling indicators, and recommendation level information of the multiple candidate sampling schemes under different soil types. The category determination module is used to determine the target sea state category based on the sea state characteristics of the sea area to be sampled, and to determine the target soil category based on the soil characteristics of the sea area to be sampled. The first matching module is used to match the sampling requirement indicators of the sea area to be sampled and the target sea state category with the pre-established information matching database, determine the first target recommendation level information of the multiple candidate sampling schemes, and determine the first recommendation score of the multiple candidate sampling schemes according to the first target recommendation level information. The second matching module is used to match the sampling requirement indicators of the sea area to be sampled and the target soil type with the pre-established information matching database, determine the second target recommendation level information of the multiple candidate sampling schemes, and determine the second recommendation score of the multiple candidate sampling schemes according to the second target recommendation level information. The scheme determination module is used to calculate the target recommendation score by fusing the corresponding first recommendation score and second recommendation score for each candidate sampling scheme, and to determine the target sampling scheme from each candidate sampling scheme based on the target recommendation score of each candidate sampling scheme.

9. A program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

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