A method for recommending treatment measures for unfavorable structural planes of arch dam abutments based on a knowledge graph and a terminal device

Abstract

The present invention provides a method and a terminal device for recommending treatment measures for unfavorable structural planes of arch dam abutments based on a knowledge graph, comprising the following steps: constructing an ontology model of the knowledge graph of geological information of unfavorable structural planes of arch dam abutments, extracting geological entity information related to the unfavorable structural planes, and improving the data layer of geological information; constructing an ontology model of the knowledge graph of treatment measures for unfavorable structural planes of arch dam abutments, extracting treatment measure entity information related to the unfavorable structural planes, and improving the data layer of treatment measures; establishing a corresponding relationship between the geological information of the unfavorable structural planes and the treatment measures to form a knowledge graph model of treatment measures for unfavorable structural planes of arch dam abutments; carrying out knowledge graph completion based on an inference method of distributed representation learning; and inputting the geological survey information of the unfavorable structural planes to be processed into the graph to recommend corresponding treatment measures. The present invention performs structured knowledge expression on the geological survey information and the treatment measures for unfavorable structural planes of arch dam abutments, and can quickly adjust the treatment measures for unfavorable structural planes as the geological information is continuously improved.

Description

Technical Field

[0001] The present invention relates to the field of geotechnical engineering, and in particular to a method and terminal equipment for recommending treatment measures for unfavorable structural surfaces of arch dam abutments based on a knowledge graph. Background Art

[0002] Structural surfaces are an important part of the structural characteristics of rock mass, and the engineering mechanical properties of rock mass are often controlled by structural surfaces. Structural surfaces can be divided into faults, dislocation zones, weak zones, joints and fissures according to their absolute scale. Structural surfaces of different scales affect regional mountain stability, engineering rock mass stability, etc. to varying degrees.

[0003] The unfavorable structural surface of the dam abutment refers to the structural surface that exists in the abutment area and has an adverse effect on the stability of the dam. During the construction of high arch dams, unfavorable structural surfaces (faults, weak zones, etc.) will have an adverse effect on the foundation stiffness and stability of the dam abutment, such as the stability of the slider in the abutment resistance body, deformation of the arch end caused by faults and weak zones, and uncoordinated force transmission. In addition, at all stages of the arch dam design, attention should be paid to the stability analysis of the arch dam, especially in terms of the stability of the arch seat of the arch dam. Most safety accidents of arch dams are caused by the instability or excessive deformation of the abutment rock mass, and rarely by stress problems of the arch dam itself. Therefore, it is very important to deal with and analyze the unfavorable structural surfaces of the arch dam abutment.

[0004] In traditional engineering design work, there are often three ways to deal with unfavorable structural surfaces: 1. Based on the experience of designers, preliminary measures are given to deal with unfavorable structural surfaces; 2. Geomechanics model tests are used to simulate the structural characteristics of engineering structures, rock masses, faults, weak zones, etc., and to intuitively track the cracking and damage of the overall structure; 3. Numerical calculation methods are used to simulate the stability of the dam shoulder rock mass and the reinforcement measures based on the finite element method of elastic-plastic mechanics, fracture mechanics and damage mechanics. However, these methods still have the following shortcomings:

[0005] 1. There are many uncertainties in the method based on manual experience. In particular, as the foundation surface is excavated, the geological information may differ from that in the early survey and design stage, resulting in an inability to respond quickly. At the same time, the geological conditions faced by different projects vary greatly, and the experience of designers is difficult to meet such complex and changing situations.

[0006] 2. The geomechanical model test method is limited by the test conditions and cannot conduct a comprehensive and detailed analysis of possible reinforcement measures. It is also limited by the scale effect, which limits the applicability of the test results in actual engineering.

[0007] 3. The results of numerical simulation methods are affected by factors such as boundary condition settings, parameter selection, and long calculation time, and are also difficult to meet the needs of fast real-time response.

[0008] A knowledge graph is a technical method that uses a graph model to describe knowledge and model the association relationships between all things in the world. It uses nodes to represent semantic symbols and edges to represent the relationships between semantics. In the field of library and information science, the knowledge graph is known as knowledge domain visualization or knowledge domain mapping map, which is a series of various graphs showing the development process and structural relationships of knowledge. It uses visualization technology to describe knowledge resources and their carriers, mine, analyze, construct, draw, and display knowledge and their mutual connections.

[0009] Therefore, there is an urgent need for a method for recommending treatment measures for unfavorable structural planes of arch dam abutments based on a knowledge graph, which can perform structured knowledge expression on information such as previous research results, designers' experience, and geological survey data. In engineering design, based on the latest obtained geological survey data, it can quickly give treatment measures for unfavorable structural planes and provide a basis for subsequent calculation and analysis. Summary of the Invention

[0010] The purpose of the present invention is to provide a method for recommending treatment measures for unfavorable structural planes of arch dam abutments and a terminal device based on a knowledge graph, aiming at the deficiencies of the above-mentioned existing technologies.

[0011] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0012] A prediction method for treating unfavorable structural planes of arch dam abutments includes the following steps:

[0013] S1. Construct a knowledge graph ontology model G1 of geological information of unfavorable structural planes of arch dam abutments;

[0014] The expression of the knowledge graph ontology model G1 of geological information of unfavorable structural planes of arch dam abutments is:

[0015] G1 = (C geo , R geo , P geo , I geo );

[0016] Among them, C geo represents geological information-related concepts; R geo represents the spatial position relationship between unfavorable structural planes and abutments; P geo represents the specific attributes of each geological information-related concept; I geo represents the specific instances of geological information;

[0017] S2. Extract geological entity information related to unfavorable structural planes to improve the data layer of the knowledge graph ontology model G1 of geological information;

[0018] S3. Construct a knowledge graph ontology model G2 of treatment measures for unfavorable structural planes of arch dam abutments;

[0019] The expression of the ontology model G2 of the treatment measures for the unfavorable structural planes of the arch dam abutment is as follows:

[0020] G2 = (C rein , R rein , P rein , I rein );

[0021] Among them, C rein represents the concepts related to the treatment measures; R rein represents the corresponding relationship between the treatment measures and the unfavorable structural planes; P rein represents the specific attributes of the concepts related to each treatment measure; I rein represents the specific instances of the treatment measures;

[0022] S4. Extract the treatment measures for the unfavorable structural planes and improve the data layer of the ontology model G2 of the treatment measures;

[0023] S5. Establish a corresponding relationship between the treatment measures and the unfavorable structural planes, add the corresponding treatment measure nodes to the ontology model G1 of the geological information, and couple the ontology model G1 of the geological information and the ontology model G2 of the treatment measures to form an ontology model of the treatment measures for the unfavorable structural planes of the arch dam abutment;

[0024] S6. Use an inference algorithm based on distributed representation learning to improve the entity relationships in the ontology model of the treatment measures for the unfavorable structural planes of the arch dam abutment;

[0025] S7. Before and after the excavation of the arch dam foundation surface, input the geological information of the unfavorable structural planes to be treated into the ontology model of the treatment measures for the unfavorable structural planes of the arch dam abutment to predict the treatment measures matching the geological information.

[0026] The present invention performs a structured knowledge expression on the geological information of the unfavorable structural planes of the arch dam abutment and the corresponding treatment measures, transforms the traditional design method relying on experience into a knowledge-driven design, and the knowledge graph can adjust the treatment measures in real time and quickly as the geological information is continuously improved.

[0027] Further, in step S2, by analyzing the geological survey data in the early stage of the project, the geological entity information related to the unfavorable structural planes is extracted, and the geological entity information with the same meaning is fused through the K-means clustering algorithm, and the fused first entity information is stored in the Neo4j graph database; wherein, the input of the K-means clustering algorithm is all the unfavorable structural planes confirmed by geological exploration, including the spatial position and rock mass parameters of each unfavorable structural plane, etc., and the output is the merged geological entity information (merging the unfavorable structural planes with similar spatial positions and similar rock mass parameters).

[0028] In step S4, by analyzing the design data and the published literature data, the treatment measure entity information related to the unfavorable structural planes is extracted, and through the K-means clustering algorithm, the treatment measure entity information with the same meaning is fused, and the fused second entity information is stored in the Neo4j graph database; wherein, the input of the K-means clustering algorithm is the treatment measures of the unfavorable structural planes extracted from the design data and the published literature data, and the output is the merged treatment measures (that is, merging the treatment measures with different names but actually expressing the same meaning, and expressing them with a unified name).

[0029] Further, in step S6, the TransE algorithm is used to predict and improve the triple information of the knowledge graph of the treatment measures for the unfavorable structural planes of the arch dam abutment. The specific implementation process includes:

[0030] Define a distance function using the TransE algorithm; randomly replace the head entity and the tail entity in the knowledge graph model of the treatment measures for the unfavorable structural planes of the arch dam abutment to generate a damaged triple; add the distance function and the damaged triple to the loss function of the knowledge graph model of the treatment measures for the unfavorable structural planes of the arch dam abutment for knowledge inference training. The training set contains both correct triples (positive samples) and damaged triples (negative samples). These negative samples are generated by randomly replacing the head entity or the tail entity based on the original positive samples. The positive samples and negative samples in the training set are constructed in a ratio of 1:1. The test set only contains positive samples. The data volume of the training set and the test set is divided in a ratio of 80%:20%. In each training iteration, calculate the loss function value of the model (TransE). When the loss function value no longer decreases significantly in multiple consecutive training iterations, it can be considered that the model has converged. A maximum number of iterations, such as 100 times, can also be set. If the model has converged before reaching the maximum number of iterations, the training can be stopped in advance; otherwise, the training will automatically stop when the maximum number of iterations is reached.

[0031] The calculation formula of the distance function is:

[0032]

[0033] Among them, h, r, and t respectively represent the vector representations of the head entity, relation, and tail entity. Indicates the L2 norm;

[0034] The calculation formula for the corrupted triples is:

[0035] S′ (h,l,t) ={(h′, l, t)|h′ ∈ E} ∪ {(h, l, t′)|t′ ∈ E};

[0036] Among them, (h′, l, t) and (h, l, t′) represent that the head entity or the tail entity is replaced by a random entity as a control group, and E represents the entity set;

[0037] The calculation formula for the loss function is:

[0038]

[0039] Among them, (h′, l, t′) represents that the head entity or the tail entity is replaced by a random entity, S represents the uncorrupted triples, γ represents the margin in the loss function, which is equivalent to the margin between a correct triple and an incorrect triple. The larger γ is, the more strict the correction of the word embedding is, [x] + Indicates taking the original value if it is greater than 0, and taking 0 if it is less than 0.

[0040] As an inventive concept, the present invention also provides a terminal device, including:

[0041] One or more processors;

[0042] A memory, on which one or more programs are stored. When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method of the present invention.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] (1) The present invention performs structured knowledge expression on the geological information of the unfavorable structural planes of the arch dam abutment and the corresponding treatment measures, thereby transforming the traditional design method from relying on experience to knowledge-driven design, which can clearly reveal the association and influence between geological information and treatment measures, enabling personnel to more accurately understand and handle the unfavorable structural planes of the arch dam abutment, and providing a more scientific and effective solution for the treatment of the unfavorable structural planes of the arch dam abutment.

[0045] (2) The knowledge graph of treatment measures for unfavorable structural planes of arch dam abutments in the present invention is an intelligent knowledge base, which contains all possible information and treatment measures regarding unfavorable structural planes of arch dam abutments. It can be updated and adjusted in real time as geological information is continuously improved, providing a more comprehensive and scientific solution for the treatment of unfavorable structural planes of arch dam abutments, thereby ensuring the accuracy and effectiveness of treatment measures and guaranteeing the smooth progress of the project and the safety of quality.

[0046] (3) The present invention can be implemented through computer software, featuring high intelligence and automation. It can conveniently acquire and process geological information and automatically generate corresponding treatment measures, not only improving the efficiency and quality of design but also reducing the influence of human factors on design, making the results more objective and fair. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is an example diagram of the knowledge graph of geological information of unfavorable structural planes of arch dam abutments in an embodiment of the present invention;

[0048] Figure 2 It is an example diagram of the knowledge graph of treatment measures for unfavorable structural planes of arch dam abutments in an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0049] Embodiment 1

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0051] As Figure 1 shown, an embodiment of the present invention provides a prediction method for treating unfavorable structural planes of arch dam abutments, including the following steps:

[0052] S1: Construct an ontology model G1 of the knowledge graph of geological information of unfavorable structural planes of arch dam abutments to express geological information in a structured knowledge form.

[0053] The expression of the ontology model G1 of the knowledge graph of geological information of unfavorable structural planes of arch dam abutments is:

[0054] G1 = (C geo , R geo , P geo , I geo );

[0055] Among them, C geo represents concepts, including concepts related to geological information, such as lithology, faults, weak structural planes, etc.; R geoIndicates the spatial position relationship between the adverse structural plane and the dam abutment, including spatial relationship, association relationship, causal relationship, etc.; P geo Indicates attributes, that is, the specific attributes of relevant geological information concepts, such as the grade of lithology, the strike of faults, etc.; I geo Represents a specific instance of geological information.

[0056] In this Embodiment 1, a knowledge graph of geological information of the arch dam abutment is constructed by combining the top-down method with the bottom-up method. That is, the pattern layer defines concept entities and their attributes, hierarchical semantic relationships, constraint rules, etc. from top to bottom, and constructs an accurate and clearly structured concept system architecture. In this Embodiment 1, this system architecture is mainly reflected in characterizing the relationship between the adverse structural plane and the dam abutment through spatial coordinates, coupling the parameters of the adverse structural plane (such as strike, width, etc.) with the rock property parameters (such as strength, rock grade, etc.), and characterizing the factors that may affect subsequent treatment measures through semantic relationship definition.

[0057] S2. By analyzing the geological exploration data in the early stage of the project, extract the geological entity information related to the adverse structural plane, and improve the data layer of the ontology model G1 of the knowledge graph of geological information of the adverse structural plane of the arch dam abutment.

[0058] Such as Figure 1 As shown, the knowledge graph of geological information of the adverse structural plane constructed in this Embodiment 1 focuses on displaying the geological conditions of the arch dam abutment in a structured form. The impact of the adverse structural plane of the dam abutment on the safety and stability of the arch dam is mainly reflected in the strike of the adverse structural plane, the position from the dam abutment, and the rock mass parameters around the adverse structural plane, etc.

[0059] In addition, in this Embodiment 1, the K-means clustering algorithm is used to fuse the geological information entity information with the same meaning, and the fused first entity information is stored in the Neo4j graph database.

[0060] Specifically, the K-means clustering algorithm is a simple and classic distance-based clustering algorithm. It uses distance as an evaluation index of similarity and believes that the closer the distance between two objects, the greater their similarity. This algorithm believes that a cluster is composed of objects with close distances, so the ultimate goal is to obtain compact and independent clusters. In this Embodiment 1, the input of the K-means clustering algorithm is all the adverse structural planes confirmed by geological exploration, including the spatial position of each adverse structural plane and information such as rock mass parameters, and the output is the merged geological entity information (merging the adverse structural planes with similar spatial positions and similar rock mass parameters).

[0061] Specifically, Neo4j is an open-source NoSQL graph database that can convert data into nodes, relationships, and properties. Based on graph theory, it stores data as nodes and relationships and allows for high-performance retrieval and querying of these structures.

[0062] S3. Construct the ontology model G2 of the knowledge graph for the treatment measures of the unfavorable structural planes of the arch dam abutment, and express the treatment measures in structured knowledge.

[0063] The expression of the ontology model G2 of the knowledge graph for the treatment measures of the unfavorable structural planes of the arch dam abutment is:

[0064] G2 = (C rein , R rein , P rein , I rein );

[0065] Among them, C rein represents concepts, including concepts related to treatment measures, such as grouting, excavation and filling replacement, toe pasting, etc.; R rein represents relationships, mainly the corresponding relationships between treatment measures and unfavorable structural planes; P rein represents attributes, that is, the specific attributes of each concept related to treatment measures, such as the length and cross-sectional dimensions of shear-resisting holes; I rein represents specific instances of treatment measures.

[0066] S4. Based on design data and publicly published literature, etc., first extract the entity information of treatment measures related to unfavorable structural planes, that is, what treatment measures are available for unfavorable structural planes, and improve the data layer of the ontology model G2 of the knowledge graph for the treatment measures of the unfavorable structural planes of the arch dam abutment. And through the K-means clustering algorithm, fuse the entity information of treatment measures with the same meaning, and store the fused second entity information into the Neo4j graph database.

[0067] In this Embodiment 1, the input of the K-means clustering algorithm is the treatment measures of unfavorable structural planes extracted from design data and publicly published literature, and the output is the merged treatment measures (that is, merge the treatment measures with different names but actually expressing the same meaning and express them with a unified name).

[0068] S5. Establish the corresponding relationship between treatment measures and unfavorable structural planes, add corresponding treatment measure nodes to the constructed knowledge graph of geological information of unfavorable structural planes, and couple the knowledge graph of geological information and the knowledge graph of treatment measures to form a knowledge graph model for the treatment measures of the unfavorable structural planes of the arch dam abutment, as Figure 2 shown.

[0069] S6: Use an inference algorithm based on distributed representation learning to improve the entity relationships of the constructed knowledge graph model.

[0070] It should be noted that the inference method based on distributed representation learning is to find a mapping function to map the symbolic representation into a vector space for numerical representation, so as to capture the implicit association between entities and relationships.

[0071] In this Embodiment 1, the TransE algorithm, that is, the transfer distance algorithm, is adopted. This algorithm transforms the problem of measuring the rationality of triples in the vectorized knowledge graph into the problem of measuring the distance between the head entity and the tail entity. It regards the entities and relationships in the knowledge graph as two matrices. The entity matrix structure is (ne * d1), where ne represents the number of entities, d1 represents the dimension of each entity vector, and each row in the matrix represents the word vector of an entity; while the relationship matrix structure is (nr * d2), where nr represents the number of relationships, and d2 represents the dimension of each relationship vector. In the TransE algorithm, both entities and relationships are represented as vectors. For a specific relationship (head, relation, tail), the vector representation of the relationship is interpreted as the transfer vector from the vector of the head entity to the vector of the tail entity. By continuously adjusting h, l, and t (the vectors of head, relation, and tail), make (h + l) as equal as possible to t, that is, h + l = t. That is to say, if a certain triple in the knowledge graph holds, its entities and relationships need to satisfy the relationship head + relation = tail.

[0072] Specifically, in this Embodiment 1, the distance function is defined using the TransE algorithm; the head entity in the knowledge graph model for the treatment measures of unfavorable structural planes of arch dam abutments is randomly replaced, and the tail entity generates a damaged triple; the distance function and the damaged triple are added to the loss function in the knowledge graph model for the treatment measures of unfavorable structural planes of arch dam abutments for knowledge inference training.

[0073] Given a triple set S, each triple is represented as (h, l, t), h and t belong to the entity set E, and the corresponding embeddings are (h, l, t), satisfying h + l = t, where h represents the vector representation of the head entity, l represents the vector representation of the relationship, and t represents the vector representation of the tail entity.

[0074] Define the distance function d(h + l, t) to measure the distance between h + l and t:

[0075]

[0076] where 2 represents the L2 norm.

[0077] Meanwhile, Embodiment 1 of the present invention also considers the corrupted triples, that is, those triples that do not exist in the real dataset. Since these triples are incorrect, it is expected that the model can increase the distance between the predicted tail entity vector and the real tail entity vector, so that the loss function value of the corrupted triples is larger.

[0078] The corrupted triples S′ (h,l,t) are calculated as follows:

[0079] S′ (h,l,t) ={(h′,l,t)|h′∈E}∪{(h,l,t′)|t′∈E};

[0080] where (h′,l,t) and (h,l,t′) mean that the head entity or the tail entity is replaced by a random entity as a control group.

[0081] When training the knowledge graph model, since it is desired that the model can correctly predict the correct triple relationship, the original triple loss function should be smaller, while the corrupted triple loss function should be larger.

[0082] Specifically, the TransE algorithm adds the head entity and the relation vector to obtain the predicted vector of the tail entity. If this predicted vector is close to the real tail entity vector, then the triple relationship is correct, that is, the loss function value of the original triple should be smaller.

[0083] Adding the distance function and the corrupted triples to the loss function of the TransE algorithm This loss function is calculated as follows:

[0084]

[0085] where (h′,l,t′) means that the head entity or the tail entity is replaced by a random entity, S represents the uncorrupted triples, γ represents the margin in the loss function, which is equivalent to the margin between a correct triple and an incorrect triple. The larger γ is, the stricter the correction of the word embedding is, and [x] + means taking the original value if it is greater than 0 and taking 0 if it is less than 0.

[0086] Therefore, when training the knowledge graph model, Embodiment 1 of the present invention optimizes the model by minimizing the loss function of the original triples and maximizing the loss function of the corrupted triples. This can make the model more accurately predict the correct triple relationship and avoid predicting incorrect binary relationships.

[0087] Step 7: Before and after the excavation of the arch dam foundation surface, input the geological information of the unfavorable structural planes to be treated into the knowledge graph model for treating the unfavorable structural planes of the arch dam abutment to predict the treatment measures matching the geological information.

[0088] Specifically, before the excavation of the arch dam foundation surface, by inputting the preliminary geological survey information, the knowledge graph of treatment measures for the unfavorable structural planes of the arch dam abutment can be retrieved, queried, and inferred to identify the key unfavorable structural planes that affect the overall stability of the arch dam - foundation, and give preliminary treatment measure suggestions. Designers and geological surveyors can optimize the design scheme of the arch dam foundation surface according to the given suggestions and can repeat this step to continuously adjust the design scheme.

[0089] Specifically, during the excavation of the arch dam foundation surface, with the enrichment of geological survey information, more refined geological information will be stored as new knowledge in the knowledge graph. Similarly, the treatment measures for the unfavorable structural planes will also be updated to match the actual geological conditions in real time.

[0090] Embodiment 2

[0091] Embodiment 2 of the present invention provides a terminal device corresponding to Embodiment 1 above. The terminal device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of the above embodiment.

[0092] The terminal device of this Embodiment 2 includes a memory, a processor, and a computer program stored on the memory; the processor executes the computer program on the memory to implement the steps of the method of the above Embodiment 1.

[0093] In some implementations, the memory can be a high - speed random - access memory (RAM: Random Access Memory), and may also include a non - volatile memory, such as at least one disk memory.

[0094] In other implementations, the processor can be a general - purpose processor of various types such as a central processing unit (CPU), a digital signal processor (DSP), etc., which is not limited here.

[0095] The embodiments of the present invention have been described above in conjunction with the drawings. However, the present invention is not limited to the above - mentioned specific embodiments. The above - mentioned specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention, and these all fall within the protection scope of the present invention.

[0096] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0097] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for recommending treatment measures for unfavorable structural planes of arch dam abutments based on a knowledge graph, characterized in that, It includes the following steps: S1. Construct the ontology model G1 of the geological information knowledge graph for the unfavorable structural planes of the arch dam abutment; The expression of the ontology model G1 of the geological information knowledge graph for the unfavorable structural planes of the arch dam abutment is: G1 = (C geo , R geo , P geo , I geo ); Among them, C geo represents the concept related to geological information; R geo represents the spatial position relationship between the unfavorable structural plane and the dam shoulder; P geo represents the specific attributes of each geological information related concept; I geo represents the specific instance of geological information; Construct the ontology model G2 of the treatment measure knowledge graph for the unfavorable structural planes of the arch dam abutment; The expression of the ontology model G2 of the treatment measure knowledge graph for the unfavorable structural planes of the arch dam abutment is: G2 = (C rein , R rein , P rein , I rein ); Among them, C rein represents the concept related to treatment measures; R rein represents the corresponding relationship between treatment measures and unfavorable structural planes; P rein represents the specific attributes of the concepts related to each treatment measure; I rein represents the specific examples of treatment measures; S2. Extract the geological entity information related to the unfavorable structural planes and improve the data layer of the geological information knowledge graph ontology model G1; Extract the treatment measures for the unfavorable structural planes and improve the data layer of the treatment measure knowledge graph ontology model G2; S3. Establish the corresponding relationship between the treatment measures and the unfavorable structural planes, add the corresponding treatment measure nodes to the geological information knowledge graph ontology model G1 after improving the data layer, couple the geological information knowledge graph ontology model G1 with the added treatment measure nodes and the treatment measure knowledge graph ontology model G2 after improving the data layer to form the treatment measure knowledge graph model for the unfavorable structural planes of the arch dam abutment; S4. Use the reasoning algorithm based on distributed representation learning to improve the entity relationships of the treatment measure knowledge graph model for the unfavorable structural planes of the arch dam abutment; S5. Before and after the excavation of the arch dam foundation surface, input the geological information of the unfavorable structural planes to be treated into the treatment measure knowledge graph model for the unfavorable structural planes of the arch dam abutment after improving the entity relationships to obtain the treatment measures matching the geological information.

2. The method for recommending treatment measures for unfavorable structural planes of arch dam abutments based on a knowledge graph according to claim 1, wherein: In step S2, by analyzing the geological survey data in the early stage of the project, extract the "geological entity and relationship" information related to the unfavorable structural planes, and use the K-means clustering algorithm to fuse the geological entity information with the same meaning. Store the "geological entity and relationship" information obtained after fusion into the Neo4j graph database, and then the geological information knowledge graph ontology model G1 after improving the data layer is obtained; among them, the input of the K-means clustering algorithm is all the unfavorable structural planes confirmed by geological exploration, including the spatial position and rock mass parameter information of each unfavorable structural plane, and the output is the merged geological entity information; In step S2, extract the "treatment measure entity and relationship" information related to the unfavorable structural planes, and use the K-means clustering algorithm to fuse the treatment measure entity information with the same meaning, and store the "treatment measure entity and relationship" information obtained after fusion into the Neo4j graph database, and then the geological information knowledge graph ontology model G2 after improving the data layer is obtained; among them, the input of the K-means clustering algorithm is the extracted treatment measures for the unfavorable structural planes, and the output is the merged treatment measures.

3. The method for recommending treatment measures for unfavorable structural planes of arch dam abutments based on a knowledge graph according to claim 1, wherein: In step S4, use the TransE algorithm to supplement and improve the triple information of the treatment measure knowledge graph for the unfavorable structural planes of the arch dam abutment. The specific implementation process includes: Define the distance function using the TransE algorithm; randomly replace the head entity or the tail entity in the knowledge graph model of the treatment measures for the unfavorable structural planes of the arch dam abutment to generate damaged triples; add the distance function and the damaged triples to the loss function of the knowledge graph model of the treatment measures for the unfavorable structural planes of the arch dam abutment for knowledge inference training; The calculation formula of the distance function is as follows: where h, l, and t represent the vector representations of the head entity, relation, and tail entity respectively, denotes the L2 norm; the expression for the corrupted triple is: S′ (h,l,t) = {(h′, l, t) | h′ ∈ E} ∪ {(h, l, t′) | t′ ∈ E}; Among them, (h′, l, t) and (h, l, t′) represent that the head entity or the tail entity is randomly replaced by a random entity as a control group, and E represents the entity set; The calculation formula of the loss function is: Among them, (h′, l, t′) indicates that the head entity or the tail entity is replaced by a random entity, h + l represents the addition of vector h and vector l. If the triple is correct, then h + l = t, h′ + l represents the addition of vector h′ and vector l, S represents the triple that has not been damaged, γ represents the margin in the loss function, that is, the margin between the correct triple and the incorrect triple, [x] + It means taking the original value when greater than 0 and taking 0 when less than 0.

4. A terminal device, characterized in that, Including: One or more processors; A memory storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the steps of the method according to any one of claims 1 to 3.

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