Underground powerhouse axis direction selection method adopting knowledge graph and block theory and readable storage medium

Abstract

The invention discloses an underground powerhouse axis direction selection method adopting a knowledge graph and a block theory and a readable storage medium, and the method comprises the steps: S1, constructing an underground powerhouse axis knowledge graph: selecting factors through the axis direction of an underground powerhouse, determining entity object feature indexes, collecting and processing an entity attribute database, and constructing a knowledge graph; and mapping the entity attribute data and the entity relationship to nodes and edges of the knowledge graph, and establishing the knowledge graph. S2, sorting underground powerhouse area survey data, setting sporophores and attribute data of each entity object, and obtaining underground powerhouse axis entity objects and attributes through a knowledge graph; and S3, in combination with a block theory, selecting a proper axis direction of the underground powerhouse. The problems that in the prior art, a traditional arrangement scheme mainly depends on artificial experience and professional knowledge, and a systematic and scientific method is lacked to support the decision making process are solved, and the decision making efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of layout of underground powerhouses in hydropower projects, and specifically is a readable storage medium for a method for selecting the axis direction of underground powerhouses in hydropower projects using knowledge graphs and block theory. Background Art

[0002] During the survey, design and construction stages of hydropower projects, the comparison and selection of hub layout patterns is a very important and critical task. Among them, the selection and determination of the axis direction of the underground powerhouse is very important. After determining the axis direction of the powerhouse, the hub layout pattern is basically determined, which facilitates the overall advancement of subsequent work on the hydropower project.

[0003] At present, the selection of the axis direction of underground powerhouses is basically based on engineering geological conditions, geological structures, and geostress factors, following standard conditions and expert experience, analyzing the hub layout conditions, reducing the impact of geostress and adverse geological structures on the stability of the surrounding rock of the underground powerhouse cavern group, and improving the stability of the surrounding rock of the powerhouse to ensure the safety and efficiency of the project. However, the current method of selecting the axis direction of underground powerhouses mainly relies on manual experience and professional knowledge, has some defects and limitations, lacks systematic and scientific methods to support the decision-making process and efficiency, and has possible problems of subjectivity and uncertainty in decision-making.

[0004] Knowledge graph is a structured graphical model for organizing and representing knowledge. It presents the semantic association of knowledge based on entities (such as people, places, events) and the relationships between entities. By integrating information from different data sources and knowledge fields into a unified semantic network, knowledge graph enables machines to better understand and reason about knowledge relationships, thereby supporting tasks such as natural language processing, data analysis, and intelligent decision-making. The knowledge graph can be used to sort out the appropriate axis direction of the underground powerhouse according to engineering geological conditions, and the block theory can further sort out the axis direction of the underground powerhouse with a higher safety factor. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a readable storage medium for an underground plant axis direction selection method using a knowledge graph and block theory, so as to solve the problems that traditional layout schemes in the prior art mainly rely on manual experience and professional knowledge, lack of systematic and scientific methods to support the decision-making process, and improve decision-making efficiency.

[0006] The present invention is implemented as follows: a method for selecting the axis direction of an underground powerhouse using a knowledge graph and block theory, comprising the following steps:

[0007] S1, construct the knowledge graph of the underground factory axis: adopt the underground factory axis direction selection factors, determine the entity object characteristic indicators, collect and process the entity attribute database, map the entity attribute data and entity relationship to the knowledge graph nodes and edges, and establish the knowledge graph.

[0008] S2, sorting out the underground powerhouse area survey data, setting the sub-entities and attribute data of each entity object, and obtaining the entity objects and attributes of the underground powerhouse axis with the knowledge graph;

[0009] S3. Combine block theory to select appropriate axis direction of underground plant.

[0010] The construction of the underground powerhouse axis direction associated knowledge graph in S1 includes the following steps:

[0011] S1-1, sort out the entities of underground plant axis selection factors, collect entity objects and attribute information, and build a knowledge graph database;

[0012] S1-2, use entity-object transfer rules to establish relationships between entities, combine with the object database, use expert empowerment method to optimize entity relationship weights, and construct a knowledge graph.

[0013] In S1-1, the knowledge graph entities include the maximum principal stress A in the underground plant area, the structural surface B, the fracture structural surface C and the axis D of the underground plant, and the attribute information includes the entity object name, object type and object data.

[0014] The underground powerhouse area has selected the underground powerhouse layout location according to the regional engineering geological conditions. The maximum principal stress A entity is the key entity, the entity object is the maximum principal stress in the underground powerhouse area, and the attribute factor is the direction of the maximum principal stress; the structural surface B entity is the main entity, and the structural surface entity object basically sorts out the small faults and crack-intensive belt structural surfaces with less data. The entity is subdivided into sub-entities according to the number of structural surface groups, and the attribute factor is the direction of the structural surface; the fracture structural surface C entity is a secondary entity, and the entity object includes fracture combination structural surfaces. The entity is subdivided into sub-entities according to the number of fracture structural surface groups, and the attribute factor is the direction of the fracture structural surface; the underground powerhouse axis D entity is the target entity, and the attribute factor is the direction of the underground powerhouse axis.

[0015] The attribute factors of the entity objects A, B, C and D are all quadrant angles or azimuth angles, the object type is the quadrant or azimuth to which the object belongs, the object data is located in NE0~90°, NW270~360°, the object data is the direction interval value set independently, and the entity object attribute value is set independently every 5° or 10° to build an object database.

[0016] There are three types of knowledge graph entity relationships in S1-2, namely AD, BD and CD. The relationship between the maximum principal stress A entity and the plant axis D entity is that the angle between the plant axis and the maximum principal stress direction of the surrounding rock is not greater than 30°. The relationship between the structural surface B entity, the fracture structural surface C entity and the plant axis D entity is that the plant axis has a large angle with the main structural line direction, which is not less than 60°.

[0017] In S1-2, the expert weighting method optimizes the weights, and the experts determine the entity path weights according to the geological conditions and characteristics of the specific application project to optimize the shortest path retrieval algorithm. The experts assign weight values ​​w to the importance of entities A, B, C and the target entity. A 、w B 、w C , does not affect the target entity selected by the overall plant axis direction, determines the sum of the three entity weights as a specific fixed value, sets entity weight values ​​for each sub-entity factor allocation, determines the starting entity according to the entity weight value, optimizes the relationship weight and selects the shortest path algorithm to obtain the target entity D object;

[0018] The formula for optimizing weights by expert empowerment method is as follows:

[0019] w(i,D)=g ij f(i,D)

[0020]

[0021] Among them, w(i,D) is the comprehensive weight between entity object i and target entity object D; g ij It represents the weights of the entity objects and sub-entity objects of the expert-weighted entity objects A, B, and C; f(i,D) represents the relationship weight between entity object i and target entity object D.

[0022] In S2, according to the geological survey results of the project area, the input maximum principal stress, structural surface, and fracture structural surface entity objects are obtained, the sub-entity groups of the structural surface and fracture structural surface are selected, the 5-degree or 10-degree entity object attribute values ​​and entity weight values ​​are set, and the knowledge graph is used to obtain the recommended values ​​of the underground power plant axis direction interval; in S3, according to the underground power plant model data and the survey geological data, the Unwedge program is used to establish the power plant excavation model, the S1 fracture structural surface combination is selected, and according to the physical and mechanical parameters of the rock mass, the underground power plant axis direction value obtained in S2 is analyzed at intervals of 5 degrees or 10 degrees, and the minimum safety factor within the range of each direction of the power plant axis is analyzed, and the power plant axis direction corresponding to the larger value in the minimum safety factor column is taken as the appropriate value.

[0023] A computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the above method.

[0024] A computer device comprises a memory, a processor and a program stored and executable on the memory, wherein the program implements the steps of the above method when executed by the processor.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention can enhance the understanding of designers and decision makers on the key factors of engineering geological conditions by adopting the underground plant layout method based on knowledge graph, and provide accurate and automated decision-making assistance, which can quantitatively select the appropriate axis direction of the underground plant.

[0027] 2. By combining knowledge graph with block theory, the present invention can greatly consider safety risks, improve the identification and management of risks in the axial design stage of underground power plants, and help improve the scientificity, operability and reliability of layout plans, thereby improving the construction quality and engineering benefits of underground power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0029] Figure 2 This is a schematic diagram of the structural surface data of this embodiment.

[0030] Figure 3 This is a schematic diagram of the fracture structure surface data of this embodiment.

[0031] Figure 4 It is a schematic diagram of the safety factor results of the fractured combined block in this embodiment. DETAILED DESCRIPTION

[0032] In order to make the purpose, construction scheme, technical route and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described here are only used to explain the present invention and are not used to limit the present invention.

[0033] The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory of the present invention is as follows: Figure 1 As shown, the method comprises the following steps:

[0034] Step S1, constructing the knowledge graph of the underground factory axis, that is, using the underground factory axis direction selection factors, determining the entity object characteristic indicators, collecting and processing the entity attribute database, mapping the entity attribute data and entity relationships to the knowledge graph nodes and edges, and establishing the knowledge graph.

[0035] Preferably, constructing the knowledge graph associated with the axis direction of the underground powerhouse includes the following steps:

[0036] Step S1-1, sort out the underground plant axis selection factor entities, collect entity objects and attribute information, and build a knowledge graph database.

[0037] Preferably, the knowledge graph entities include the maximum principal stress A in the underground plant area, the structural surface B, the fracture structural surface C and the axis D of the underground plant, and the attribute information includes the entity object name, object type and object data.

[0038] Preferably, the underground powerhouse area has selected the underground powerhouse layout location according to the regional engineering geological conditions. The maximum principal stress entity A is the key entity, the entity object is the maximum principal stress in the underground powerhouse area, and the attribute factor is the maximum principal stress direction. The structural surface entity B is the main entity. The structural surface entity object basically sorts out the small faults and crack-intensive belt structural surfaces with less data. The entity is subdivided into sub-entities according to the number of structural surface groups, and the attribute factor is the direction of the structural surface. The fracture structural surface entity C is a secondary entity. The entity object includes fracture combination structural surfaces. The entity is subdivided into sub-entities according to the number of fracture structural surface groups, and the attribute factor is the direction of the fracture structural surface. The underground powerhouse axis entity D is the target entity, and the attribute factor is the direction of the underground powerhouse axis.

[0039] Preferably, the attribute factors of entity objects A, B, C and D are quadrant angles or azimuth angles, the object type is the quadrant or azimuth to which the object belongs, the object data is located in NE0~90°, NW270~360°, the object data is the direction interval value set independently, and the entity object attribute values ​​are set independently every 5° or 10° to build an object database.

[0040] Step S1-2, use entity-object transfer rules to establish the relationship between entities, combine with the object database, use expert empowerment method to optimize the entity relationship weights, and construct a knowledge graph.

[0041] Preferably, the knowledge graph entity relationship of this method has three relationships: AD, BD, and CD. Among them, the relationship between the maximum principal stress entity A and the plant axis entity D is that the angle between the plant axis direction and the maximum principal stress direction of the surrounding rock should not be greater than 30°. The relationship between the structural surface entities B and C and the plant axis entity D is that the plant axis should have a large angle with the main structural line direction, which should not be less than 60°.

[0042] Preferably, the expert weighting method optimizes the weights, and the experts determine the entity path weights according to the geological conditions and characteristics of the specific application project to optimize the shortest path retrieval algorithm. The experts assign weight values ​​w to the importance of entities A, B, C and the target entity. A 、w B 、w C , does not affect the target entity selected by the overall plant axis direction, determines the sum of the three entity weights as a specific fixed value, sets entity weight values ​​for each sub-entity factor allocation, determines the starting entity according to the entity weight value, optimizes the relationship weight and selects the shortest path algorithm to obtain the target entity D object.

[0043] The formula for optimizing weights by expert empowerment method is as follows:

[0044] w(i,D)=g ij f(i,D)

[0045]

[0046] Among them, w(i,D) is the comprehensive weight between entity object i and target entity object D; g ij It represents the weights of the entity objects and sub-entity objects of the expert-weighted entity objects A, B, and C; f(i,D) represents the relationship weight between entity object i and target entity object D.

[0047] Step S2, combing the underground powerhouse area survey data, setting the sub-entities and attribute data of each entity object, and obtaining the entity object and attributes of the underground powerhouse axis with the knowledge graph. That is, according to the geological survey results of the project area, the input maximum principal stress, structural surface, and fracture structural surface entity objects are obtained, the sub-entity groups of structural surface and fracture structural surface are selected, and the 5-degree or 10-degree entity object attribute values ​​and entity weight values ​​are independently set, and the knowledge graph is used to obtain the recommended value of the underground powerhouse axis direction interval.

[0048] Step S3, combined with block theory, further select the appropriate axis direction of the underground powerhouse. The occurrence, extension length and distribution of the fracture structure surface are highly random, and will be randomly combined and cut to form blocks, which will have a certain safety factor impact on the underground powerhouse area. This step uses the Unwedge program to establish a powerhouse excavation model based on the underground powerhouse model data and survey geological data, selects the fracture structure surface combination of step A above, and analyzes the axis direction of the underground powerhouse obtained in step B at intervals of 5 or 10 degrees according to the physical and mechanical parameters of the rock mass, and analyzes the minimum safety factor within the range of each direction of the powerhouse axis, and takes the powerhouse axis direction corresponding to the larger value in the minimum safety factor column as the appropriate value.

[0049] Through the following examples, combined Figure 1 The technical solution of the present invention is described in detail.

[0050] In this embodiment, the initial planned location of the underground powerhouse hub of a pumped storage power station is located at the tail of the waterway system. The excavation size of the underground powerhouse is 163.5×24.5×54.5m. According to the geological survey of the underground powerhouse area, the maximum principal stress direction is NE58~73°, and the plant area belongs to a medium stress field; the structural surface mainly develops 14 faults and a group of crack-intensive zones, with the strikes mainly NW320~330° and NE10~15° ( Figure 2 ); There are many fracture structural planes, and the fractures are mainly developed in four groups: NW, NNE, NEE and NWW. The fracture dips are mainly steep, and there are a small number of gently dipping fractures in some areas. The main strikes include NW320-350°, NE0-15°, NE80-85°, NW270-280° ( Figure 3 ).

[0051] This embodiment constructs a knowledge graph, autonomously sets the object attribute value to 10° attribute value, sets the entity object to the maximum principal stress entity object A, the structural surface entity objects B1 and B2, the fracture structural surface C, and the entity object weight is g A =5, g B1 =g B2 =2, g C =1, entity relationship weights f(A,D)=0.5, f(B,D)=0.4, f(C,D)=0.1, and knowledge graph is constructed. Input the maximum principal stress entity object A (NE58~73°), the structural surface entity object B1 (strike NW320~330°), B2 (NE10~15°), and the fracture structural surface C (NW320~350°), and obtain the suitable plant axis entity object D with the direction of NE30~40°, NE40~50°, NE50~60°, NE60~70°, NE70~80°, that is, suitable NE30~80°.

[0052] According to the knowledge map, the axis strike interval of the underground powerhouse is suitable, and three groups of fracture combinations are selected considering safety factors: fracture J1: NW320~350°NE<40~65°; fracture J2: NE0~15°SE<50~80°; gentle angle fracture J3: NW330~350°NE<12~30°. The Unwedge program is used to establish the powerhouse excavation model, and its mechanical parameters are selected as: φ=19.3°, C=0t / ㎡, and the unit weight of rock is 2.67t / m 3 , the unit weight of water is 1t / m 3 The factory building axis is selected in the NE30~80° range to calculate the safety factor (such as Figure 4 ), it is found that the minimum safety factor is larger when the axis direction value of the plant is 50-60°, which is more practical in the optimization selection of the axis direction of underground plant.

[0053] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0054] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several algorithm optimizations, improvements and modifications can be made without departing from the principles of the present invention. These optimizations, improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory, characterized in that: The steps include: S1, construct the knowledge graph of the underground factory axis: adopt the underground factory axis direction selection factors, determine the entity object characteristic indicators, collect and process the entity attribute database, map the entity attribute data and entity relationship to the knowledge graph nodes and edges, and establish the knowledge graph. S2, sorting out the underground powerhouse area survey data, setting the sub-entities and attribute data of each entity object, and obtaining the entity objects and attributes of the underground powerhouse axis with the knowledge graph; S3. Combine block theory to select appropriate axis direction of underground plant.

2. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to claim 1 is characterized in that: The construction of the underground powerhouse axis direction associated knowledge graph in S1 includes the following steps: S1-1, sort out the entities of underground plant axis selection factors, collect entity objects and attribute information, and build a knowledge graph database; S1-2, use entity-object transfer rules to establish relationships between entities, combine with the object database, use expert empowerment method to optimize entity relationship weights, and construct a knowledge graph.

3. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to claim 2 is characterized in that: In S1-1, the knowledge graph entities include the maximum principal stress A in the underground plant area, the structural surface B, the fracture structural surface C and the axis D of the underground plant, and the attribute information includes the entity object name, object type and object data.

4. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to claim 3 is characterized in that: The underground powerhouse area has selected the underground powerhouse layout location according to the regional engineering geological conditions. The maximum principal stress A entity is the key entity, the entity object is the maximum principal stress in the underground powerhouse area, and the attribute factor is the direction of the maximum principal stress; the structural surface B entity is the main entity, and the structural surface entity object basically sorts out the small faults and crack-intensive belt structural surfaces with less data. The entity is subdivided into sub-entities according to the number of structural surface groups, and the attribute factor is the direction of the structural surface; the fracture structural surface C entity is a secondary entity, and the entity object includes fracture combination structural surfaces. The entity is subdivided into sub-entities according to the number of fracture structural surface groups, and the attribute factor is the direction of the fracture structural surface; the underground powerhouse axis D entity is the target entity, and the attribute factor is the direction of the underground powerhouse axis.

5. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to claim 4 is characterized in that: The attribute factors of the entity objects A, B, C and D are all quadrant angles or azimuth angles, the object type is the quadrant or azimuth to which the object belongs, the object data is located in NE0~90°, NW270~360°, the object data is the direction interval value set independently, and the entity object attribute value is set independently every 5° or 10° to build an object database.

6. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to any one of claims 3 to 5, characterized in that: There are three types of knowledge graph entity relationships in S1-2, namely AD, BD and CD. The relationship between the maximum principal stress A entity and the plant axis D entity is that the angle between the plant axis and the maximum principal stress direction of the surrounding rock is not greater than 30°. The relationship between the structural surface B entity, the fracture structural surface C entity and the plant axis D entity is that the plant axis has a large angle with the main structural line direction, which is not less than 60°.

7. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to claim 6 is characterized in that: In S1-2, the expert weighting method optimizes the weights, and the experts determine the entity path weights according to the geological conditions and characteristics of the specific application project to optimize the shortest path retrieval algorithm. The experts assign weight values ​​w to the importance of entities A, B, C and the target entity. A 、w B 、w C , does not affect the target entity selected by the overall plant axis direction, determines the sum of the three entity weights as a specific fixed value, sets entity weight values ​​for each sub-entity factor allocation, determines the starting entity according to the entity weight value, optimizes the relationship weight and selects the shortest path algorithm to obtain the target entity D object; The formula for optimizing weights by expert empowerment method is as follows: w(i,D)=g ij ·f(i,D) Among them, w(i,D) is the comprehensive weight between entity object i and target entity object D; g ij It represents the weights of the entity objects and sub-entity objects of the expert-weighted entity objects A, B, and C; f(i,D) represents the relationship weight between entity object i and target entity object D.

8. The method for selecting the axis direction of an underground powerhouse using knowledge graph and block theory according to claim 1 is characterized in that: In S2, according to the geological survey results of the project area, the input maximum principal stress, structural surface, and fracture structural surface entity objects are obtained, the sub-entity groups of the structural surface and fracture structural surface are selected, the 5-degree or 10-degree entity object attribute values ​​and entity weight values ​​are set, and the knowledge graph is used to obtain the recommended values ​​of the underground power plant axis direction interval; in S3, according to the underground power plant model data and the survey geological data, the Unwedge program is used to establish the power plant excavation model, the S1 fracture structural surface combination is selected, and according to the physical and mechanical parameters of the rock mass, the underground power plant axis direction value obtained in S2 is analyzed at intervals of 5 degrees or 10 degrees, and the minimum safety factor within the range of each direction of the power plant axis is analyzed, and the power plant axis direction corresponding to the larger value in the minimum safety factor column is taken as the appropriate value.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 8.

10. A computer device, characterized in that: The computer device comprises a memory, a processor and a program stored and executable on the memory, and the program implements the steps of the method according to any one of claims 1 to 8 when executed by the processor.

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