A method and system for recommending block adjustment measures based on graph neural networks

Abstract

The present application discloses a method and system for recommending block adjustment measures based on graph neural networks, which relates to the technical field of recommending oilfield block adjustment measures. The method includes obtaining historical recommendation data for adjustment measures, constructing an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph; using a BERT pre-trained language model to extract text features corresponding to evaluation indicators, evaluation blocks, and adjustment measures, and inputting them together with the evaluation block-adjustment measure interaction graph into a GCN model to calculate the high-order embedding features of evaluation blocks and adjustment measures; inputting the evaluation block collaboration graph and the adjustment measure co-occurrence graph into a LightGCN model to calculate the collaborative embedding features of evaluation blocks and adjustment measures; obtaining the final embedding features of evaluation blocks and adjustment measures through feature fusion; calculating the predicted recommendation probability score through inner product calculation, and generating a list of recommended adjustment measures. The present application can improve the accuracy of the block adjustment measure recommendation task.

Description

Technical Field

[0001] This application relates to the technical field of oilfield block adjustment measure recommendation, and particularly to a method and system for recommending block adjustment measures based on a graph neural network. Background Art

[0002] As an important chemical fuel and fossil fuel, petroleum occupies a crucial position in the national economy. However, with the continuous exploitation of petroleum resources, the currently discovered main oilfields are gradually entering the late stage of development, resulting in increased oil production difficulty and cost. Improving oil recovery has become the key to the sustainable development of the petroleum industry. In the late stage of oilfield development, a series of oilfield block adjustment measures, such as fracturing, perforation supplementation, and injection conversion, can slow down the rate of reserve reduction and production decline, and improve the economic benefits of the oilfield. However, with the extension of the oilfield development time, various types of dynamic data increase, and there are many internal influencing factors in the reservoir with complex relationships, resulting in a high difficulty in the design and selection of oil well measure adjustment plans, thus reducing the efficiency of the recommendation of block adjustment measures.

[0003] Currently, conventional methods for recommending block adjustment measures mainly rely on the experience of oilfield experts. It takes a large amount of time and cost to formulate a relatively precise and detailed block adjustment measure recommendation plan. Currently, existing methods for recommending block adjustment measures include regression statistical models, decision trees, fuzzy logic theory, knowledge graph-based methods, collaborative filtering-based methods, etc. However, these methods for recommending block adjustment measures only stay at the surface feature extraction and analysis of historical block evaluation data, and cannot capture implicit semantic information and structural relationships. Therefore, the accuracy of the block adjustment measure recommendation task cannot be guaranteed. Based on this, there is an urgent need for a method that can fully mine complex historical block evaluation data and more accurately achieve the block adjustment measure recommendation task to improve the accuracy of the block adjustment measure recommendation task. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for recommending block adjustment measures based on a graph neural network, which can improve the accuracy of the block adjustment measure recommendation task.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In the first aspect, the present application provides a method for recommending block adjustment measures based on a graph neural network. The method for recommending block adjustment measures based on a graph neural network specifically includes the following steps.

[0007] Obtain historical adjustment measure recommendation data; the historical adjustment measure recommendation data includes text data corresponding to evaluation indicators, evaluation blocks, and adjustment measures.

[0008] According to the historical adjustment measure recommendation data, an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph are respectively constructed.

[0009] According to the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph, and the adjustment measure co-occurrence graph, the BERT pre-trained language model is used to extract features from the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures, and the text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures are obtained.

[0010] The text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures and the evaluation block-adjustment measure interaction graph are respectively input into the GCN model to calculate the high-order embedding features of the evaluation blocks and the high-order embedding features of the adjustment measures.

[0011] The evaluation block collaboration graph and the adjustment measure co-occurrence graph are respectively input into the LightGCN model to calculate the collaborative embedding features of the evaluation blocks and the collaborative embedding features of the adjustment measures.

[0012] Feature fusion is respectively performed between the high-order embedding features of the evaluation blocks and the collaborative embedding features of the evaluation blocks, and between the high-order embedding features of the adjustment measures and the collaborative embedding features of the adjustment measures to obtain the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures.

[0013] According to the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures, an inner product calculation is performed to predict the recommended probability score, and a recommended list of adjustment measures for each evaluation block is generated.

[0014] In a second aspect, the present application provides a block adjustment measure recommendation system based on a graph neural network, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the block adjustment measure recommendation method based on the graph neural network.

[0015] According to the specific embodiments provided by the present application, the present application has the following technical effects.

[0016] The present application provides a method and system for recommending block adjustment measures based on graph neural networks, which applies graph neural networks to the specific field and specific scenario of block adjustment measures recommendation. In the process of block adjustment measures recommendation, the BERT pre-trained language model, the GCN model and the LightGCN model are combined, and various graph structures such as the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph and the adjustment measure co-occurrence graph are constructed as inputs of the above models. On the one hand, the BERT pre-trained language model is used to capture the complex semantic features in the text data corresponding to the evaluation indicators, evaluation blocks and adjustment measures, which can capture the rich contextual information around the node text, which is conducive to understanding the position and relationship of the node in the entire text sequence, thereby helping to improve the accuracy of the block adjustment measure recommendation task. On the other hand, using the GCN model and the LightGCN model to respectively learn the embedded representations of evaluation blocks and adjustment measures in graph structures such as the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph, and the adjustment measure co-occurrence graph will help to further obtain richer node features, capture more complex relationships between nodes, and explore the potential associations between evaluation blocks and adjustment measures, thereby obtaining more accurate high-order embedding features, collaborative embedding features, and fused final embedding features of evaluation blocks and adjustment measures, and thus being able to accurately predict the recommendation probability score and generate an accurate and reliable adjustment measure recommendation list, thereby improving the accuracy of the block adjustment measure recommendation task. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is an application environment diagram of a method for recommending block adjustment measures based on a graph neural network provided in one embodiment of the present application.

[0019] Figure 2 A flowchart of a method for recommending block adjustment measures based on a graph neural network is provided in accordance with an embodiment of the present application.

[0020] Figure 3 A schematic diagram of the structure of a block adjustment measure recommendation system based on a graph neural network provided in one embodiment of the present application. DETAILED DESCRIPTION

[0021] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0022] In the related art, there is a method for recommending oilfield stimulation measures based on random forest and gradient boosting decision tree. The method includes the following steps: 1) Collect historical data and divide it into an implementation data set and an implementation effect data set with the moment of stimulation measure implementation as the boundary; 2) Perform data preprocessing on the implementation data set under each stimulation measure in history, including: data cleaning, calculating the recovery ratio, adding stimulation measure effect classification labels, and sampling the training set and test set; 3) Screen out important feature parameters from the training set and the test set respectively; 4) Construct and train an implementation effect classification prediction model; 5) Recommend production wells that can increase production to the stimulation measures. Due to the limitations of the characteristics of offshore oilfields, this method uses big data machine learning algorithms to mine the correlation between parameters in dimensions such as production well formation, wellbore, and implementation design and specific stimulation measures, realizes the recommendation of production wells under effective stimulation measures, ensures the success of the recommended measures to the greatest extent, breaks through the technical difficulties of currently difficult to support multi-dimensional comprehensive analysis to achieve production increase technically, gives reliable and effective recommendation measures to guide the production increase of offshore oilfields, promotes the intelligence of production increase, and ensures the safe, stable, and efficient operation of production wells. There is also a method for automatically recommending oil well stimulation measures in the related art. The method includes: obtaining oil well stimulation measure sample data, digitally converting the text data in the sample data through the One-hot encoding method; performing measure sensitive factor analysis on the sample data to obtain the main sensitive factors; using the main sensitive factors as the recommendation basis, and analyzing the measures taken by historical similar wells and the effects of the measures, and pushing the corresponding optimized measures. The analysis includes a matching stage and a sorting stage; realizing measure recommendation based on the collaborative filtering-based measure recommendation method. This method can more accurately and efficiently select the best production increase plan for common measures such as fracturing, acidification, and perforation and layer modification by fully mining a large amount of measure well monitoring data and combining machine learning algorithms.

[0023] However, although the above-mentioned existing methods for recommending block adjustment measures have achieved certain results in structured data analysis, they rely too much on static historical data and manual feature screening. In addition, there may be a lack of similar historical data, resulting in the failure of recommendations. Moreover, the above-mentioned existing methods for recommending block adjustment measures do not make full use of the attribute information in each block, ignoring complex deep semantic associations and implicit interaction relationships, and have weak generalization in sparse data scenarios.

[0024] A Graph Neural Network (GNN) combines graph data with neural networks to perform end-to-end computations on graph data, capturing complex relationships in the graph data through interactions between nodes. This embodiment aims to provide a method and system for recommending block adjustment measures based on a graph neural network. By combining various graph structures such as an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph with various neural network models such as the BERT pre-trained language model, the GCN (Graph Convolutional Network) model, and the LightGCN (Light Graph Convolutional Network) model, and applying them to the scenario of recommending block adjustment measures, potential associations between evaluation blocks and measures are mined from a large amount of historical adjustment measure recommendation data, thereby improving the accuracy of the block adjustment measure recommendation task and solving the problems existing in the above-mentioned existing block adjustment measure recommendation methods.

[0025] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] The method for recommending block adjustment measures based on a graph neural network provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the historical adjustment measure recommendation data to the server 104. After receiving the historical adjustment measure recommendation data, for the historical adjustment measure recommendation data, the server 104 constructs an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph; uses the BERT pre-trained language model to extract the text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures, and inputs them together with the evaluation block-adjustment measure interaction graph into the GCN model to calculate the high-order embedding features of the evaluation blocks and adjustment measures; inputs the evaluation block collaboration graph and the adjustment measure co-occurrence graph into the LightGCN model to calculate the collaborative embedding features of the evaluation blocks and adjustment measures; obtains the final embedding features of the evaluation blocks and adjustment measures through feature fusion; calculates the predicted recommendation probability score through inner product calculation to generate an adjustment measure recommendation list. The server 104 can feedback the obtained adjustment measure recommendation list to the terminal 102. In addition, in some embodiments, the block adjustment measure recommendation method based on the graph neural network can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform block adjustment measure recommendation processing on the historical adjustment measure recommendation data, or the server 104 can obtain the historical adjustment measure recommendation data from the data storage system and perform block adjustment measure recommendation processing on the historical adjustment measure recommendation data.

[0027] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablets, Internet of Things devices, and portable wearable devices. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0028] In an exemplary embodiment, as Figure 2 shown, a block adjustment measure recommendation method based on the graph neural network is provided. This method is executed by a computer device, and can be specifically executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in it as an example for illustration, it includes the following steps S1 to step S7.

[0029] Step S1, obtain historical adjustment measure recommendation data. Among them, the historical adjustment measure recommendation data includes the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures.

[0030] Step S2: Construct an evaluation block - adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co - occurrence graph respectively according to the historical adjustment measure recommendation data.

[0031] In this embodiment, step S2 constructs an evaluation block - adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co - occurrence graph respectively according to the historical adjustment measure recommendation data, and specifically includes the following steps.

[0032] Step S21: Determine the interaction relationship among evaluation indicators, evaluation blocks, and adjustment measures according to the historical adjustment measure recommendation data.

[0033] Step S22: Construct an evaluation block - adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co - occurrence graph respectively according to the historical adjustment measure recommendation data and the interaction relationship among the evaluation indicators, evaluation blocks, and adjustment measures.

[0034] In this embodiment, step S22 constructs an evaluation block - adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co - occurrence graph respectively according to the historical adjustment measure recommendation data and the interaction relationship among the evaluation indicators, evaluation blocks, and adjustment measures, and specifically includes the following steps.

[0035] Step S221: Define the evaluation block - adjustment measure interaction graph , the evaluation block set and the adjustment measure set are respectively used as the two parts of this evaluation block - adjustment measure interaction graph , then the adjacency matrix of the evaluation block - adjustment measure interaction graph is . When = 1, a connection edge is established between the evaluation block and the adjustment measure .

[0036] Step S222: Define the evaluation block collaboration graph . In this evaluation block collaboration graph , both of the two interacting parts are evaluation block nodes. When establishing the connection edges between each evaluation block node, a content - based similarity calculation method is used, introducing various evaluation indicators of the evaluation blocks, and calculating the cosine similarity among the evaluation indicators of each evaluation block. Then the adjacency matrix of the evaluation block collaboration graph is . When , a connection edge is established between the evaluation block and the evaluation block , is the weight of this connection edge.

[0037] Step S223: Define the co-occurrence graph of adjustment measures , in this co-occurrence graph of adjustment measures , both of the two interacting parts are adjustment measure nodes. When establishing the edges between each adjustment measure node, according to the constructed evaluation block-adjustment measure interaction graph 's edge set, calculate the co-occurrence frequency between two adjustment measure nodes , then the adjacency matrix of the co-occurrence graph of adjustment measures is . When , establish an edge between adjustment measure and adjustment measure , being the weight of this edge.

[0038] Step S3: According to the evaluation block-adjustment measure interaction graph, evaluation block collaboration graph, and co-occurrence graph of adjustment measures, use the BERT pre-trained language model to extract features from the text data corresponding to evaluation indicators, evaluation blocks, and adjustment measures, obtaining the text features corresponding to evaluation indicators, evaluation blocks, and adjustment measures.

[0039] In this embodiment, step S3, according to the evaluation block-adjustment measure interaction graph, evaluation block collaboration graph, and co-occurrence graph of adjustment measures, uses the BERT pre-trained language model to extract features from the text data corresponding to evaluation indicators, evaluation blocks, and adjustment measures, obtaining the text features corresponding to evaluation indicators, evaluation blocks, and adjustment measures, specifically including the following steps.

[0040] Step S31: Set the input sequence , where , = [node type], to are node text characters, .

[0041] Step S32: According to the input vectors of each token in the input sequence , construct the input embedding matrix ; among them, the input vector of each token is represented by the following formula.

[0042] ;

[0043] where represents the token embedding matrix, represents the position embedding matrix, Denote the segment embedding matrix; the constructed input embedding matrix , where is the number of all nodes.

[0044] Step S33: Input the input embedding matrix into the multi-layer Transformer encoder of the BERT pre-trained language model for layer-by-layer processing, and output the text features corresponding to the evaluation metrics, evaluation blocks, and adjustment measures.

[0045] When the multi-layer Transformer encoder performs layer-by-layer processing, the multi-head self-attention is calculated using the following formula.

[0046] ; ;

[0047] where is the attention of the th head, and the self-attention weight matrices of the th head are respectively , , , is the weight matrix, is the input embedding matrix, is function, is the multi-head self-attention, represents the concatenation operation.

[0048] Step S4: Input the text features corresponding to the evaluation metrics, evaluation blocks, and adjustment measures, and the evaluation block-adjustment measure interaction graph into the GCN model respectively, and calculate the high-order embedding features of the evaluation blocks and the high-order embedding features of the adjustment measures.

[0049] In this embodiment, step S4 inputs the text features corresponding to the evaluation metrics, evaluation blocks, and adjustment measures, and the evaluation block-adjustment measure interaction graph into the GCN model respectively, and calculates the high-order embedding features of the evaluation blocks and the high-order embedding features of the adjustment measures, which specifically includes the following steps.

[0050] Step S41: Based on the evaluation block-adjustment measure interaction graph , use the GCN model to perform representation learning on the evaluation blocks and adjustment measures, and obtain the initial feature matrix of the evaluation blocks as according to the evaluation metric embedding; initialize the initial feature matrix of the adjustment measures as , where is the feature dimension of each evaluation block and adjustment measure.

[0051] Step S42: Use the GCN model to update the embedding representations of all nodes through graph convolution at each layer. The embedding update formula for the evaluation block nodes at the th layer is expressed by the following formula.

[0052] ;

[0053] Among them, represents the high-order embedding features of the evaluation block nodes at the th layer, is the normalized adjacency matrix for the interaction between the evaluation block and the adjustment measures, is the embedding representation of the evaluation block after update at the th layer, is the feature transformation matrix at the th layer, (·) is the function.

[0054] The embedding update formula for the adjustment measure nodes at the th layer is expressed by the following formula.

[0055] ;

[0056] Among them, represents the high-order embedding features of the adjustment measure nodes at the th layer, is the transpose of the normalized adjacency matrix for the interaction between the evaluation block and the adjustment measures, is the embedding representation of the adjustment measures after update at the th layer.

[0057] Step S43: After the graph convolution operation of layers, obtain the high-order embedding features of the evaluation block and the high-order embedding features of the adjustment measures.

[0058] Step S5: Input the evaluation block collaboration graph and the adjustment measure co-occurrence graph into the LightGCN model respectively to calculate the collaborative embedding features of the evaluation block and the collaborative embedding features of the adjustment measures.

[0059] In this embodiment, step S5 inputs the evaluation block collaboration graph and the adjustment measure co-occurrence graph into the LightGCN model respectively to calculate the collaborative embedding features of the evaluation block and the collaborative embedding features of the adjustment measures, which specifically includes the following steps.

[0060] Step S51: Based on the evaluation block collaboration graph and the adjustment measure co-occurrence graph , use the LightGCN model to process the evaluation block collaboration graph Co-occurrence graph of adjustment measures , let the initial feature embedding matrix of each evaluation block be , and the initial feature embedding matrix of each adjustment measure node be , where is the feature dimension of the evaluation block and the adjustment measure, then the graph convolution operation of the evaluation block collaboration graph is defined as follows.

[0061] ;

[0062] Among them, represents the embedding representation of the evaluation block, is the normalized adjacency matrix of the evaluation block collaboration graph;

[0063] The graph convolution operation of the adjustment measure co-occurrence graph is defined as follows.

[0064] ;

[0065] Among them, represents the embedding representation of the adjustment measure, is the normalized adjacency matrix of the adjustment measure collaboration graph.

[0066] Step S52. After a single-layer graph convolution operation, obtain the embedding representation of the evaluation block and the embedding representation of the adjustment measure.

[0067] Step S53. Use a linear layer to map each embedding vector in the embedding representation of the evaluation block and the embedding representation of the adjustment measure to dimensions, and obtain the collaborative embedding feature of the evaluation block and the collaborative embedding feature of the adjustment measure.

[0068] Step S6. Perform feature fusion between the high-order embedding features of the evaluation block and the collaborative embedding features of the evaluation block, and between the high-order embedding features of the adjustment measure and the collaborative embedding features of the adjustment measure, respectively, to obtain the final embedding features of the evaluation block and the final embedding features of the adjustment measure.

[0069] In this embodiment, step S6 performs feature fusion between the high-order embedding features of the evaluation block and the collaborative embedding features of the evaluation block, and between the high-order embedding features of the adjustment measure and the collaborative embedding features of the adjustment measure, respectively, to obtain the final embedding features of the evaluation block and the final embedding features of the adjustment measure, which specifically includes the following steps.

[0070] Step S61: Concatenate and fuse the high-order embedded features and co-embedded features of the evaluation block to obtain the final embedded features of the evaluation block, which is represented by the following formula.

[0071] ;

[0072] where, represents the final embedded features of the evaluation block, is the concatenation operation of vectors, represents the high-order embedded features of the evaluation block, represents the co-embedded features of the evaluation block.

[0073] Step S62: Concatenate and fuse the high-order embedded features and co-embedded features of the adjustment measure to obtain the final embedded features of the adjustment measure, which is represented by the following formula.

[0074] ;

[0075] where, represents the final embedded features of the adjustment measure, represents the high-order embedded features of the adjustment measure, represents the co-embedded features of the adjustment measure.

[0076] Step S7: Calculate the inner product based on the final embedded features of the evaluation block and the final embedded features of the adjustment measure, predict the recommended probability score, and generate a recommended list of adjustment measures for each evaluation block.

[0077] In this embodiment, step S7 calculates the inner product based on the final embedded features of the evaluation block and the final embedded features of the adjustment measure, predicts the recommended probability score, and generates a recommended list of adjustment measures for each evaluation block, which specifically includes the following steps.

[0078] Step S71: Calculate the inner product based on the final embedded features of the evaluation block and the final embedded features of the adjustment measure, and predict the recommended probability score of each evaluation block for each adjustment measure, which is represented by the following formula.

[0079] ;

[0080] where, represents the recommended probability score between the evaluation block and the adjustment measure , represents the final embedded features of the evaluation block , represents the final embedded features of the adjustment measure .T Denotes transpose.

[0081] Step S72: According to the recommended probability scores of each evaluation block for each adjustment measure, generate a recommended list of adjustment measures for each evaluation block, which is represented by the following formula.

[0082] ;

[0083] Where, denotes the recommended list of adjustment measures for the evaluation block , denotes selecting the adjustment measures with the highest recommended probability scores, denotes the candidate list of adjustment measures.

[0084] Implement the above steps S1 to S7. By applying the graph neural network to a specific field and specific scenario of block adjustment measure recommendation, during the process of block adjustment measure recommendation, combine the BERT pre-trained language model, GCN model, and LightGCN model, and construct various graph structures such as an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph as the input of the above models. On the one hand, use the BERT pre-trained language model to capture the complex semantic features in the text data corresponding to evaluation indicators, evaluation blocks, and adjustment measures, which can capture the rich context information around the node text, facilitate understanding the position and relationship of the node in the entire text sequence, and thus help improve the accuracy of the block adjustment measure recommendation task. On the other hand, use the GCN model and LightGCN model to learn the embedding representations of evaluation blocks and adjustment measures in graph structures such as the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph, and the adjustment measure co-occurrence graph respectively, which helps to further obtain richer node features, capture more complex inter-node relationships, mine the potential associations between evaluation blocks and adjustment measures, and thus obtain more accurate high-order embedding features, collaborative embedding features, and the finally fused embedding features of evaluation blocks and adjustment measures, and then can accurately predict the recommended probability scores and generate an accurate and reliable recommended list of adjustment measures, improving the accuracy of the block adjustment measure recommendation task.

[0085] In this embodiment, after step S7 calculates the inner product of the final embedding features of the evaluation block and the final embedding features of the adjustment measure to predict the recommended probability scores and generate a recommended list of adjustment measures for each evaluation block, the method for recommending block adjustment measures based on a graph neural network further includes the following steps.

[0086] Using the BPR loss function, maximize the gap between the recommended probability scores of positive and negative samples to improve the ability of the BERT pre-trained language model, the GCN model, and the LightGCN model to rank the adjustment measure recommendation list.

[0087] To make the technical solution of this embodiment clearer, the following will use an example to illustrate the specific implementation process of this embodiment in detail, which specifically includes the following implementation steps.

[0088] Step S1: Collect and organize the historical adjustment measure recommendation data of the evaluation block, obtain the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures, and determine the interaction relationship between the evaluation indicators, evaluation blocks, and adjustment measures.

[0089] Step S2: Based on the historical adjustment measure recommendation data in Step S1, construct an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph, and format the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures to be input.

[0090] The method for constructing the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph, and the adjustment measure co-occurrence graph in this embodiment includes the following steps.

[0091] (1) Define the evaluation block-adjustment measure interaction graph , the evaluation block set and the adjustment measure set are used as the two parts of the evaluation block-adjustment measure interaction graph respectively. Then the adjacency matrix of the evaluation block-adjustment measure interaction graph is . When , an edge is established between the evaluation block and the adjustment measure to form the edge set of the evaluation block-adjustment measure interaction graph.

[0092] (2) Define the evaluation block collaboration graph . In this evaluation block collaboration graph , both of the two interacting parts are evaluation block nodes. When establishing the edges between different evaluation block nodes, consider using a content-based similarity calculation method, introduce the evaluation indicators of the evaluation blocks, and calculate the cosine similarity between the evaluation indicators of each evaluation block. Then the adjacency matrix of the evaluation block collaboration graph is . When , an edge is established between the evaluation block and the evaluation block . is the weight of this connected edge.

[0093] (3) Define the co-occurrence graph of adjustment measures , in this co-occurrence graph of adjustment measures , both of the two interacting parts are adjustment measure nodes. When establishing the connected edges between different adjustment measure nodes, according to the edge set of the evaluation block-adjustment measure interaction graph constructed above, calculate the co-occurrence frequency between adjustment measure pairs , then the adjacency matrix of the co-occurrence graph of adjustment measures is , when , an edge is established between adjustment measure and adjustment measure , is the weight of this connected edge.

[0094] Step S3: On the basis of Step S2, use the BERT pre-trained language model to extract features from the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures, and obtain the text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures.

[0095] The specific method for using the BERT pre-trained language model to extract features from the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures in this embodiment is: on the basis of obtaining the text data of the nodes participating in the interaction, set the input sequence as , where , = [node type], to are the node text characters, .

[0096] The input vector of each token is superimposed by three parts, and the embedding dimension size is , and the calculation formula is as follows.

[0097] ;

[0098] Among them, represents the token embedding matrix, represents the position embedding matrix, represents the segment embedding matrix. Thus, the input embedding matrix is constructed, where is the number of all nodes, and it is input into the layer Transformer encoder of the BERT pre-trained language model for layer-by-layer processing, and the multi-head self-attention is calculated in the following way.

[0099] ; ;

[0100] Among them, is the attention of the th head, and the self-attention weight matrices of the th head respectively have , , . is the weight matrix, is the input embedding matrix, is function, is multi-head self-attention, represents the concatenation operation.

[0101] After concatenating all the self-attention matrices together and multiplying by the weight matrix to obtain the multi-head attention . Finally, the last hidden state corresponding to each input token is output, that is, the text features corresponding to the evaluation index, evaluation block, and adjustment measure that fuse the context information.

[0102] Among them, the input embedding matrix will be input into the multi-layer Transformer encoder of the BERT pre-trained language model to process the input embedding matrix, and then learn the features and dependencies in the sequence. The output of the BERT pre-trained language model in step S3 is the last hidden state corresponding to each input token, that is, the text features corresponding to the evaluation index, evaluation block, and adjustment measure that fuse the context information.

[0103] Step S4: Input the evaluation block-adjustment measure interaction graph obtained in step S2 and the text features corresponding to the evaluation index, evaluation block, and adjustment measure obtained in step S3 into the GCN model to calculate the high-order embedding features of the evaluation block and the adjustment measure.

[0104] For the evaluation block-adjustment measure interaction graph , the GCN model is used for the representation learning of the evaluation block and the adjustment measure. The initial feature matrix of the evaluation block obtained from the evaluation index embedding is ; the initial feature matrix of the initialized adjustment measure is , where is the feature dimension of each evaluation block and adjustment measure. By stacking multiple layers of graph convolution, deeper graph structure information can be captured. Each layer of graph convolution will update the embedding representation of all nodes. The embedding update formula of the evaluation block nodes in the rd layer can be expressed as the following formula.

[0105] ;

[0106] Among them, ​​​​Denote the high-order embedding features of the evaluation block nodes at the layer as the normalized adjacency matrix for the interaction between the evaluation block and the adjustment measures, and the embedded representation of the evaluation block after update at the layer, and the (·) is the function.

[0107] Similarly, the embedding update formula for the adjustment measure nodes at the layer can be expressed as the following formula.

[0108] ;

[0109] where denotes the high-order embedding features of the adjustment measure nodes at the layer, is the transpose of the normalized adjacency matrix for the interaction between the evaluation block and the adjustment measures, and is the embedded representation of the adjustment measures after update at the layer. After the graph convolution operation at the layer, the high-order embedding features

[0110] of the evaluation block and the high-order embedding features

[0111] of the adjustment measures are obtained. These embedded representations capture the high-order graph structure information between the nodes.

[0112]

[0113] The graph convolution operation of the evaluation block co-occurrence graph is defined as follows.

[0112] ;

[0113] where denotes the embedded representation of the evaluation block, To evaluate the normalized adjacency matrix of the block collaboration graph.

[0114] Similarly, the co-occurrence graph of adjustment measures can be obtained. The graph convolution operation is defined as follows.

[0115] ;

[0116] Among them, represents the embedded representation of the adjustment measure, is the normalized adjacency matrix of the adjustment measure co-occurrence graph.

[0117] After a single-layer graph convolution operation, the embedded representation of the evaluation block and the embedded representation of the adjustment measure are obtained. Finally, through the linear layer, each embedded vector in the embedded representation of the evaluation block and the embedded representation of the adjustment measure is mapped to dimensions, obtaining the final representations of the evaluation block collaboration graph and the adjustment measure co-occurrence graph, including the collaborative embedding features of the evaluation block and the collaborative embedding features of the adjustment measure.

[0118] Step S6: Feature fusion is performed on the high-order embedding features of the evaluation block and the adjustment measure obtained in step S4 and the collaborative embedding features of the evaluation block and the adjustment measure obtained in step S5, respectively, to obtain the final embedding features of the evaluation block and the adjustment measure.

[0119] For each node, a concatenation operation is used to fuse the final representations of the evaluation block and the adjustment measure obtained in steps S4 and S5 as shown in the following formula.

[0120] ;

[0121] ;

[0122] Among them, is the concatenation operation of vectors, represents the final embedding feature of the evaluation block, represents the final embedding feature of the adjustment measure, which is used for the subsequent recommendation probability score prediction task.

[0123] Step S7: Inner product calculation is performed according to the final embedding features of the evaluation block and the adjustment measure in step S6, the recommendation probability score is predicted, and a recommendation list of adjustment measures for each evaluation block is generated.

[0124] For any evaluation block and any adjustment measure , its final embedding representation, i.e., the final embedding features, are respectively and , the evaluation block and the adjustment measure The recommended probability score between them can be expressed by the following formula.

[0125] ;

[0126] Among them, represents the recommended probability score between the evaluation block and the adjustment measure , represents the final embedding feature of the evaluation block , represents the final embedding feature of the adjustment measure , T represents transpose.

[0127] This recommended probability score represents the potential interaction tendency between the evaluation block and the adjustment measure . The above is to calculate the recommended probability score of a single evaluation block for a single adjustment measure. According to this method, the recommended probability scores of each evaluation block for all adjustment measures are calculated respectively to obtain the recommended probability score matrix . Specifically, given an evaluation block , its adjustment measure recommendation list can be expressed by the following formula.

[0128] ;

[0129] Among them, represents the adjustment measure recommendation list of the evaluation block , represents selecting the adjustment measures with the highest recommended probability scores, represents the adjustment measure candidate list, represents the evaluation block and the adjustment measure The recommended probability score between them.

[0130] Step S8: Use the BPR loss function to maximize the gap between the recommended probability scores of positive and negative samples to improve the ability of each model, such as the BERT pre-trained language model, GCN model, and LightGCN model, to rank the adjustment measure recommendation list.

[0131] In the model iteration process, the construction of positive and negative samples is a key link in calculating the BPR loss. For positive sample sampling, select the one associated with the evaluation block The adjusted measures that have interacted are used as positive samples, denoted as: . For negative sample sampling, select the evaluation block The adjusted measures that have not interacted are used as negative samples, denoted as: .

[0132] For each evaluation block , given the set of positive sample adjusted measures it has interacted with and the set of negative sample adjusted measures it has not interacted with, the expression of the BPR loss is as follows.

[0133] ;

[0134] Among them, represents the BPR loss, represents the triple in the training data; is the sigmoid function, which is used to map the difference in recommended probability scores to the probability space; represents the evaluation block for the recommended probability score of the sampled negative sample of the adjusted measure, is the regularization term of the model parameters, is for controlling the regularization strength.

[0135] In this embodiment, various graph structures such as the evaluation block-adjusted measure interaction graph, the evaluation block collaboration graph, and the adjusted measure co-occurrence graph are skillfully integrated to improve the recommendation quality and accuracy of the adjusted measures. The BERT pre-trained language model, the GCN model, and the LightGCN model are combined to learn the embedding representations of various graph structures, and the learned embedding representations are fused, enhancing the expression ability of the evaluation block and adjusted measure node features, which helps to obtain a more appropriately sorted adjusted measure recommendation list. This enables the model to be applicable to the problem of oilfield block adjustment measure recommendation, effectively alleviating the sparsity of data interaction, improving the accuracy of block adjustment measure recommendation, and providing a new method for oilfield block adjustment measure recommendation.

[0136] This embodiment fully considers the interaction sparsity of oilfield block adjustment measure data and designs a block adjustment measure recommendation method based on graph neural networks. In order to capture the complex interaction relationship between evaluation blocks and adjustment measures and achieve an efficient block adjustment measure recommendation task, graph neural networks are applied to the field of block adjustment measure recommendation to provide a reference for block adjustment measure recommendation. On the one hand, the BERT pre-trained language model is used to capture complex semantic features, which can capture rich context information around node texts, facilitate understanding the position and relationship of nodes in the entire text sequence, and thus improve the accuracy of the block adjustment measure recommendation task. On the other hand, the GCN model and the LightGCN model are used to learn the embedding representations of evaluation blocks and adjustment measures in graph structures such as evaluation block-adjustment measure interaction graphs, evaluation block collaboration graphs, and adjustment measure co-occurrence graphs, which helps to further obtain richer node features, capture more complex inter-node relationships, and mine the potential associations between evaluation blocks and adjustment measures, so as to obtain higher-order embedding features, collaborative embedding features, and the finally fused embedding features of evaluation blocks and adjustment measures more accurately. Furthermore, the recommendation probability score can be accurately predicted, and an accurate and reliable adjustment measure recommendation list can be generated, improving the accuracy of the block adjustment measure recommendation task.

[0137] In an exemplary embodiment, a block adjustment measure recommendation system based on graph neural networks is provided. The system can be a computer device, which can be a server or a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store historical adjustment measure recommendation data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. The computer program, when executed by the processor, implements a block adjustment measure recommendation method based on graph neural networks.

[0138] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0139] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0140] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0141] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, based on the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on this application.

Claims

1. A method for recommending block adjustment measures based on a graph neural network, characterized in that, The block adjustment measure recommendation method based on graph neural network includes: Obtain historical adjustment measure recommendation data; the historical adjustment measure recommendation data includes evaluation indicators, evaluation blocks, and text data corresponding to adjustment measures; According to the historical adjustment measure recommendation data, construct an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph respectively; According to the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph, and the adjustment measure co-occurrence graph, use the BERT pre-trained language model to extract features from the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures, and obtain the text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures; Input the text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures and the evaluation block-adjustment measure interaction graph into the GCN model respectively to calculate the high-order embedding features of the evaluation blocks and the high-order embedding features of the adjustment measures; Input the evaluation block collaboration graph and the adjustment measure co-occurrence graph into the LightGCN model respectively to calculate the collaborative embedding features of the evaluation blocks and the collaborative embedding features of the adjustment measures; Perform feature fusion between the high-order embedding features of the evaluation blocks and the collaborative embedding features of the evaluation blocks, and between the high-order embedding features of the adjustment measures and the collaborative embedding features of the adjustment measures respectively, to obtain the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures; Perform inner product calculation according to the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures, predict the recommended probability score, and generate an adjustment measure recommendation list for each evaluation block.

2. The method for recommending block adjustment measures based on a graph neural network according to claim 1, wherein According to the historical adjustment measure recommendation data, construct an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph respectively, specifically including: According to the historical adjustment measure recommendation data, determine the interaction relationship between the evaluation indicators, evaluation blocks, and adjustment measures; According to the historical adjustment measure recommendation data and the interaction relationship between the evaluation indicators, evaluation blocks, and adjustment measures, construct an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph respectively.

3. The method for recommending block adjustment measures based on a graph neural network according to claim 2, characterized in that According to the historical adjustment measure recommendation data and the interaction relationship between the evaluation indicators, evaluation blocks, and adjustment measures, construct an evaluation block-adjustment measure interaction graph, an evaluation block collaboration graph, and an adjustment measure co-occurrence graph respectively, specifically including: Define the evaluation block - adjustment measure interaction graph , the set of evaluation blocks and the set of adjustment measures are respectively used as the two parts of this evaluation block - adjustment measure interaction graph , then the adjacency matrix of the evaluation block - adjustment measure interaction graph is , when = 1, establish an edge between the evaluation block and the adjustment measure . Define the evaluation block collaboration graph , in this evaluation block collaboration graph , both of the two parts that interact with each other are evaluation block nodes. When establishing the edges between each evaluation block node, a content-based similarity calculation method is used, introducing various evaluation indicators of the evaluation block, and calculating the cosine similarity between the evaluation indicators of each evaluation block , then the adjacency matrix of the evaluation block collaboration graph is , when , an edge is established between the evaluation block and the evaluation block , is the weight of this edge; Define the co-occurrence graph of adjustment measures , in this co-occurrence graph of adjustment measures , both of the two interacting parts are adjustment measure nodes. When establishing the edges between each adjustment measure node, according to the edge set of the constructed evaluation block-adjustment measure interaction graph , calculate the co-occurrence frequency between two adjustment measure nodes , then the adjacency matrix of the co-occurrence graph of adjustment measures is , when , establish an edge between adjustment measure and adjustment measure , is the weight of this edge.

4. The method for recommending block adjustment measures based on a graph neural network according to claim 1, characterized in that According to the evaluation block-adjustment measure interaction graph, the evaluation block collaboration graph, and the adjustment measure co-occurrence graph, use the BERT pre-trained language model to extract features from the text data corresponding to the evaluation indicators, evaluation blocks, and adjustment measures, specifically including: Set the input sequence , where , = [node type], to are node text characters, ; According to the input sequence and each token in it, an input embedding matrix is constructed; among them, the input vector of each token is expressed as: ​ ； Among them, represents the token embedding matrix, represents the position embedding matrix, represents the segment embedding matrix; the constructed input embedding matrix , where is the number of all nodes; Embed the input into a matrix Input it into the multi-layer Transformer encoder of the BERT pre-trained language model for layer-by-layer processing, and output the text features corresponding to the evaluation metrics, evaluation blocks, and adjustment measures; When the multi-layer Transformer encoder processes layer by layer, use the following formula to calculate the multi-head self-attention: ； ； Among them, is the attention of the th head, and the self-attention weight matrices of the th head respectively have , , . is the weight matrix, is the input embedding matrix, is function, is the multi-head self-attention, represents the concatenation operation.

5. The method for recommending block adjustment measures based on a graph neural network according to claim 1, wherein Input the text features corresponding to the evaluation indicators, evaluation blocks, and adjustment measures and the evaluation block-adjustment measure interaction graph into the GCN model respectively to calculate the high-order embedding features of the evaluation blocks and the high-order embedding features of the adjustment measures, specifically including: Based on the evaluation block-adjustment measure interaction diagram , use the GCN model for the representation learning of evaluation blocks and adjustment measures, and obtain the initial feature matrix of the evaluation block according to the evaluation index embedding as ; Initialize the initial feature matrix of the adjustment measure as , where is the feature dimension of each evaluation block and adjustment measure; Update the embedding representations of all nodes through graph convolution in each layer of the GCN model. The embedding update formula for the evaluation block nodes in the th layer is expressed as: ； Among them, represents the high-order embedding feature of the layer evaluation block node, is the normalized adjacency matrix of the interaction between the evaluation block and the adjustment measure, is the embedding representation of the evaluation block after the layer update, is the feature transformation matrix of the layer, (·) is the function; The embedding update formula of the layer adjustment measure node is expressed as: ； Among them, represents the high-order embedding feature of the layer adjustment measure node, is the transpose of the normalized adjacency matrix for evaluating the interaction between the evaluation block and the adjustment measure, is the embedding representation of the adjustment measure after the layer update; After performing graph convolution operations on the evaluation block and the high-order embedded features of the adjustment measures .

6. The method for recommending block adjustment measures based on a graph neural network according to claim 1, wherein Input the co-occurrence graph of the evaluation blocks and the co-occurrence graph of the adjustment measures into the LightGCN model respectively, and calculate the collaborative embedding features of the evaluation blocks and the collaborative embedding features of the adjustment measures, specifically including: Based on the collaborative graph of evaluation blocks and the co-occurrence graph of adjustment measures , use the LightGCN model to process the collaborative graph of evaluation blocks and the co-occurrence graph of adjustment measures , set the initial feature embedding matrix of each evaluation block as , and the initial feature embedding matrix of each adjustment measure node as , where is the feature dimension of the evaluation block and the adjustment measure, then the graph convolution operation of the collaborative graph of evaluation blocks is defined as: ； Among them, represents the embedding representation of the evaluation block, is the normalized adjacency matrix of the evaluation block collaboration graph; The co-occurrence graph of the adjustment measures The graph convolution operation is defined as: ； Among them, represents the embedded representation of the adjustment measure, is the normalized adjacency matrix of the adjustment measure collaboration graph; After a single-layer graph convolution operation, the embedded representation of the evaluation block is obtained and the embedded representation of the adjustment measures ; Use a linear layer to separately map the embedding representations of the evaluation block and the embedding representations of the adjustment measures for each embedding vector in dimensions to obtain the collaborative embedding features of the evaluation block and the collaborative embedding features of the adjustment measures .

7. The method for recommending block adjustment measures based on a graph neural network according to claim 1, wherein Perform feature fusion between the high-order embedding features of the evaluation blocks and the collaborative embedding features of the evaluation blocks, and between the high-order embedding features of the adjustment measures and the collaborative embedding features of the adjustment measures respectively, to obtain the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures, specifically including: Perform concatenation fusion between the high-order embedding features of the evaluation blocks and the collaborative embedding features of the evaluation blocks to obtain the final embedding features of the evaluation blocks, denoted as: ； Among them, represents the final embedded feature of the evaluation block, is the concatenation operation of vectors, represents the high-order embedded feature of the evaluation block, represents the collaborative embedded feature of the evaluation block; Perform concatenation fusion between the high-order embedding features of the adjustment measures and the collaborative embedding features of the adjustment measures to obtain the final embedding features of the adjustment measures, denoted as: ； Among them, represents the final embedded feature of the adjustment measure, represents the high-order embedded feature of the adjustment measure, represents the collaborative embedded feature of the adjustment measure.

8. The method for recommending block adjustment measures based on a graph neural network according to claim 1, characterized in that Perform inner product calculation based on the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures, predict the recommended probability scores, and generate a recommended list of adjustment measures for each evaluation block, specifically including: Perform inner product calculation based on the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures, and predict the recommended probability scores of each evaluation block for each adjustment measure, denoted as: ； Among them, represents the recommendation probability score between the evaluation block and the adjustment measure, represents the final embedded feature of the evaluation block, represents the final embedded feature of the adjustment measure, T represents transpose; Generate a recommended list of adjustment measures for each evaluation block based on the recommended probability scores of each evaluation block for each adjustment measure, denoted as: ； Among them, represents the recommended list of adjustment measures for the evaluation block , represents selecting the adjustment measures with the highest recommended probability score represents the candidate list of adjustment measures.

9. The method for recommending block adjustment measures based on a graph neural network according to claim 1, wherein After the step of performing inner product calculation based on the final embedding features of the evaluation blocks and the final embedding features of the adjustment measures, predicting the recommended probability scores, and generating a recommended list of adjustment measures for each evaluation block, the block adjustment measure recommendation method based on a graph neural network further includes: Use the BPR loss function to maximize the gap between the recommended probability scores of positive samples and negative samples, so as to improve the ability of the BERT pre-trained language model, the GCN model, and the LightGCN model to sort the recommended list of adjustment measures.

10. A block adjustment measure recommendation system based on a graph neural network, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the block adjustment measure recommendation method based on a graph neural network according to any one of claims 1-9.

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