Construction site living quarter arrangement method based on knowledge graph and intelligent algorithm

Abstract

The invention discloses a construction site living quarter arrangement method based on a knowledge graph and an intelligent algorithm, and relates to the technical field of model application. According to the method, automatic optimization design is carried out by establishing the knowledge graph and using an intelligent algorithm, design optimization can be efficiently and accurately carried out according to user requirements, the design quality is improved, manual intervention is saved, full-process automation from the design requirements to automatic generation of the BIM model is achieved, manual intervention is not needed, and the method is suitable for large-scale popularization and application. According to the method, complicated steps and potential errors in the design process are reduced, better customized design is achieved, optimization design can be conducted according to specific space types and area requirements, personalized design better meeting actual requirements can be achieved, the reasonability and practicability of the design are improved, design optimization is guided through comprehensive evaluation standards, and the design efficiency is improved. And the design result not only meets the space requirement, but also can meet the optimal balance of comprehensive factors such as environment, functions and the like.

Description

Technical Field

[0001] The present invention relates to the field of model application technology, and specifically to a method for arranging living areas of construction sites based on knowledge graphs and intelligent algorithms. Background Art

[0002] The layout of the construction site includes the layout of the construction area and the layout of the living area.

[0003] Among them, the layout of the living area needs to take into account the functional zoning, traffic flow and other requirements of the living area.

[0004] The current design process mainly relies on designers to make manual adjustments based on their design experience and design cases, which results in a large amount of time wasted and makes it impossible to quickly generate optimized layout plans.

[0005] At the same time, in terms of construction site layout, there are currently more methods for arranging construction areas, but fewer methods for arranging living areas.

[0006] Therefore, a new solution to the above problems needs to be proposed. Summary of the invention

[0007] The purpose of the present invention is to provide a method for arranging living areas in construction sites based on knowledge graphs and intelligent algorithms to solve the technical problems raised in the background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm, comprising at least the following steps:

[0009] S1: Construction and extraction of knowledge graph for the layout of living areas in construction sites. The user's design requirements are obtained through the established knowledge graph, and the specific space types and areas required are obtained, and the comprehensive evaluation criteria for the design are obtained;

[0010] S2: Establish an intelligent algorithm for the layout of the living area of ​​the construction site, take the space type and its area as the design conditions for the optimization design, and the comprehensive evaluation criteria as the goal of the optimization design, and use the intelligent algorithm for automatic optimization design;

[0011] S3: Based on the optimization design results, the BIM model of the living area of ​​the construction site is automatically obtained through the Dynamo software in the Revit software.

[0012] Furthermore, the construction and extraction of the knowledge graph of the living area layout of the construction site at least includes data collection and processing, knowledge graph construction, and extraction of design conditions and design objectives.

[0013] Furthermore, the data collection and processing includes at least the following steps:

[0014] First, sort out the current relevant specifications for the layout of living areas on construction sites, as well as previous design documents;

[0015] Normalize the collected text-based specifications and design documents;

[0016] When the text is standardized, remove the content that is not related to the layout of the living area of ​​the construction site;

[0017] Finally, the continuous text is divided into paragraphs.

[0018] Furthermore, the knowledge graph construction includes at least the following steps:

[0019] First, the entity recognition of the living area layout of the construction site is carried out according to the word frequency-inverse design specification frequency, and then the importance of the words in the design specification is evaluated, as shown in formulas (1)-(3):

[0020] (1)

[0021] (2)

[0022] (3)

[0023] Among them, TF is the term frequency, IDF is the inverse design frequency, is the number of times word t appears in design specification d, is the sum of the occurrences of all words in design specification d, N is the total number of design specifications, and df(t) is the number of design specifications containing word t;

[0024] Then, the dependency relationship is analyzed according to formula (4), and the semantic relationship between the verbs and related entities in the sentence is determined according to formula (5);

[0025] (4)

[0026] (5)

[0027] Among them, entity represents entity, relation represents relationship, Count represents statistical number, S is subject, V is verb, O is object, F is feature function, and w is weight vector;

[0028] Finally, the entity is converted into a node in the graph, and then relationship edges are established between corresponding entities based on the identified relationships.

[0029] Furthermore, the extraction of the design conditions and design objectives includes at least the following steps:

[0030] Based on the input design requirements, use the graph database query language (Cypher for Neo4j) to query each space type and its corresponding area, and use it as the design condition;

[0031] At the same time, the graph database is used to query the relevant specification requirements, and then the weighted method is used as the final optimization goal, as shown in formula (6):

[0032] Score (6)

[0033] Among them, w represents different weights, Indicates the score of the nth target.

[0034] Furthermore, the intelligent algorithm for arranging the living area of ​​the construction site is a two-stage dynamic intelligent algorithm;

[0035] First, a dynamic genetic algorithm is used for global optimization, and then a hill climbing algorithm is used for local optimization to finally obtain the optimal solution;

[0036] The dynamic genetic algorithm includes dynamic population adjustment, dynamic crossover probability and dynamic mutation probability;

[0037] The population dynamic adjustment is shown in formula (7):

[0038] (7)

[0039] in, is the population individual of generation t, is the diversity control parameter, is a newly generated set of individuals;

[0040] The dynamic crossover probability and dynamic mutation probability are shown in equations (8) and (9):

[0041] (8)

[0042] (9)

[0043] in, and is the crossover and mutation probability of the tth generation, and is the initial crossover and mutation probability, is a measure of the degree of environmental change, i.e., the number of iterations; and Tuning parameters set by the user.

[0044] Furthermore, the application of the intelligent algorithm for the layout of the living area of ​​the construction site includes at least the following steps:

[0045] First, we need to mathematically model the problem of arranging the living area of ​​the construction site;

[0046] Then, the variables are parameterized;

[0047] Finally, an intelligent algorithm for the layout of the living area at the construction site is used for calculation.

[0048] Furthermore, the mathematical modeling determines the following mathematical modeling process according to the index of the design conditions and design goals by the knowledge graph:

[0049] S2.1: Building facility location constraints. All building facilities must be located within the living area and within the range that the tower crane cannot reach, as shown in formula (10):

[0050] (10)

[0051] in, represents the location of the i-th building facility, Indicates the living area, Indicates the range that the tower crane can reach;

[0052] S2.2: The regulations for dormitories in living areas need to be of a specified type. This is because different construction units often use a few fixed types of dormitories, as shown in formula (11):

[0053] (11)

[0054] in, is the type of the jth dormitory, T is the set of allowed dormitory types;

[0055] S2.3: The capacity of dormitories and office areas needs to meet the requirements of the design specifications;

[0056] S2.4: Each building needs to satisfy the adjacent constraints of previous design cases;

[0057] S2.5: The streamlines of the living area of ​​the entire construction site need to meet the requirements of fire prevention, that is, the fire trucks can arrive and extinguish the fire smoothly, as shown in formula (12):

[0058] (12)

[0059] Where x represents the location of any building facility in the living area, P(x) represents the path from the fire truck entrance to location x, and Set lives Represents the collection of all living quarters building facilities;

[0060] Formula (12) shows that for each building facility location in the living area , there exists a path P(x) that can reach it.

[0061] Furthermore, the parameterization at least includes parameterization of terrain and parameterization of buildings, and the parameterization of terrain is to obtain the position of each building functional area;

[0062] The parameterization of the terrain comprises at least the following steps:

[0063] First, the area within the building red line is divided into Rasterize and obtain grid points ;

[0064] At the same time, all grid points are numbered, so that each position i corresponds to a code ;

[0065] The parameterization of the building comprises at least the following steps:

[0066] First, the minimum rectangular enclosing rectangle is constructed according to the building planes of different functional areas, and the center of the building area plane is used as the anchor point of the building plane. The position of this anchor point is a parameter.

[0067] The angle at which the plane is rotated around the anchor point As another parameter;

[0068] The step size of the rotation angle is 45 degrees;

[0069] Finally, for building area i, positioning is achieved through two parameters, namely .

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] 1. The present invention can efficiently and accurately optimize the design according to user needs by establishing a knowledge graph and using intelligent algorithms for automatic optimization design, thereby improving the design quality and saving manual intervention. The method of the present invention realizes the automation of the entire process from design requirements to automatic generation of BIM models without manual intervention, thus reducing the cumbersome steps and potential errors in the design process.

[0072] 2. The present invention has better customized design and can be optimized according to specific space types and area requirements. It can achieve personalized design that is more in line with actual needs, improve the rationality and practicality of the design, and guide the design optimization through comprehensive evaluation standards to ensure that the design results not only meet the space requirements, but also meet the optimal balance of comprehensive factors such as environment and function.

[0073] 3. Based on the optimization design results, the present invention realizes the automatic generation of the BIM model of the living area of ​​the construction site through Revit and Dynamo, thereby improving the efficiency of the connection between design and construction. The technology of intelligent optimization design and automatic generation of BIM models can greatly improve the design efficiency, while reducing human errors and ensuring design accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0075] Figure 1 This is a schematic diagram of the architecture of the method of the present invention;

[0076] Figure 2 It is a parameterized schematic diagram of the present invention;

[0077] Figure 3 Schematic diagram of the two-stage dynamic intelligent algorithm of the present invention. DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0079] The input of the present invention is the design requirements of the living and office area, and the final output obtained is the BIM model of the living area of ​​the construction site.

[0080] See also Figure 1 , a method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm, comprising at least the following steps:

[0081] S1: Construction and extraction of knowledge graph for the layout of living areas in construction sites. The user's design requirements are obtained through the established knowledge graph, and the specific space types and areas required are obtained, and the comprehensive evaluation criteria for the design are obtained;

[0082] S2: Establish an intelligent algorithm for the layout of the living area of ​​the construction site, take the space type and its area as the design conditions for the optimization design, and the comprehensive evaluation criteria as the goal of the optimization design, and use the intelligent algorithm for automatic optimization design;

[0083] S3: Based on the optimization design results, the BIM model of the living area of ​​the construction site is automatically obtained through the Dynamo software in the Revit software.

[0084] The construction and extraction of the knowledge graph of the layout of the living area of ​​the construction site includes at least data collection and processing, knowledge graph construction, and the extraction of design conditions and design objectives.

[0085] The collection and processing of data includes at least the following steps:

[0086] First, sort out the current relevant specifications for the layout of living areas on construction sites, as well as previous design documents;

[0087] Standardize the collected relevant specifications and design documents in text format, such as standardizing terminology, such as standardizing "living area design", "living area layout" and other similar expressions into "construction site living area layout";

[0088] When the text is standardized, remove the content that is not related to the layout of the living area of ​​the construction site;

[0089] Finally, the continuous text is divided into paragraphs.

[0090] The construction of knowledge graph includes at least the following steps:

[0091] First, we perform entity recognition of the layout of the living area of ​​the construction site based on the term frequency-inverse design specification frequency (TF-IDF), and then evaluate the importance of words, such as "office location", in the design specification, as shown in equations (1)-(3):

[0092] (1)

[0093] (2)

[0094] (3)

[0095] Among them, TF is the term frequency, IDF is the inverse design frequency, is the number of times word t appears in design specification d, is the sum of the occurrences of all words in design specification d, N is the total number of design specifications, and df(t) is the number of design specifications containing word t;

[0096] Then, the dependency relationship is analyzed according to formula (4), and the semantic relationship between the verbs and related entities in the sentence is determined according to formula (5);

[0097] (4)

[0098] (5)

[0099] Among them, entity represents entity, relation represents relationship, Count represents statistical number, S is subject, V is verb, O is object, F is feature function, and w is weight vector;

[0100] Finally, the entity is converted into a node in the graph, and then relationship edges are established between corresponding entities based on the identified relationships.

[0101] The extraction of design conditions and design goals includes at least the following steps:

[0102] Based on the input design requirements, use the graph database query language (Cypher for Neo4j) to query each space type and its corresponding area, and use it as the design condition;

[0103] At the same time, the graph database is used to query the relevant specification requirements, and then the weighted method is used as the final optimization goal, as shown in formula (6):

[0104] Score (6)

[0105] Among them, w represents different weights, Indicates the score of the nth target.

[0106] See also Figure 3 ,The intelligent algorithm for arranging the living area of ​​the construction site is a two-stage dynamic intelligent algorithm;

[0107] First, a dynamic genetic algorithm is used for global optimization, and then a hill climbing algorithm is used for local optimization to finally obtain the optimal solution;

[0108] Dynamic genetic algorithm includes dynamic population adjustment, dynamic crossover probability and dynamic mutation probability;

[0109] The population dynamics adjustment is shown in formula (7):

[0110] (7)

[0111] in, is the population individual of generation t, is the diversity control parameter, is a newly generated set of individuals;

[0112] The dynamic crossover probability and dynamic mutation probability are shown in equations (8) and (9):

[0113] (8)

[0114] (9)

[0115] in, and is the crossover and mutation probability of the tth generation, and are the initial crossover and mutation probabilities, is a measure of the degree of environmental change, i.e., the number of iterations; and Tuning parameters set by the user.

[0116] The application of intelligent algorithms for the layout of living areas on construction sites includes at least the following steps:

[0117] First, we need to mathematically model the problem of arranging the living area of ​​the construction site;

[0118] Then, the variables are parameterized;

[0119] Finally, an intelligent algorithm for the layout of the living area at the construction site is used for calculation.

[0120] Mathematical modeling According to the index of design conditions and design goals in the knowledge graph, the following mathematical modeling process is determined:

[0121] S2.1: Building facility location constraints. All building facilities (such as workers' accommodation area, conference room, etc.) must be within the living area and within the range that the tower crane cannot reach, as shown in formula (10):

[0122] (10)

[0123] in, represents the location of the i-th building facility, Indicates the living area, Indicates the range that the tower crane can reach;

[0124] S2.2: The regulations for dormitories in living areas need to be of a specified type. This is because different construction units often use a few fixed types of dormitories, as shown in formula (11):

[0125] (11)

[0126] in, is the type of the jth dormitory, T is the set of allowed dormitory types;

[0127] S2.3: The capacity of dormitories and office areas needs to meet the requirements of the design specifications;

[0128] S2.4: Each building needs to meet the adjacent constraints of previous design cases, for example, the canteen should be as close to the dormitory area as possible, the bathroom should be as far away from the kitchen as possible, and the meeting room should be as close to the main entrance as possible;

[0129] S2.5: The streamlines of the living area of ​​the entire construction site need to meet the requirements of fire prevention, that is, the fire trucks can arrive and extinguish the fire smoothly, as shown in formula (12):

[0130] (12)

[0131] Where x represents the location of any building facility in the living area, P(x) represents the path from the fire truck entrance to location x, and Set lives Represents the collection of all living quarters building facilities;

[0132] Formula (12) shows that for each building facility location in the living area , there exists a path P(x) that can reach it.

[0133] Parameterization at least includes parameterization of terrain and parameterization of buildings. Parameterization of terrain is to obtain the location of each functional area of ​​the building;

[0134] Parameterizing a terrain involves at least the following steps:

[0135] First, the area within the building red line is divided into Rasterize and obtain grid points ;like Figure 2 As shown;

[0136] At the same time, all grid points are numbered, so that each position i corresponds to a code ;

[0137] Parametric design of a building consists of at least the following steps:

[0138] First, construct the minimum rectangular enclosing rectangle of different functional areas according to the building plane, such as Figure 2 As shown. The center of the building area plane is used as the anchor point of the building plane, and the position of this anchor point is a parameter;

[0139] The angle at which the plane is rotated around the anchor point As another parameter;

[0140] The step size of the rotation angle is 45 degrees;

[0141] Finally, for building area i, positioning is achieved through two parameters, namely .

[0142] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm, characterized in that: At least the following steps are included: S1: Construction and extraction of knowledge graph for the layout of living areas in construction sites. The user's design requirements are obtained through the established knowledge graph, and the specific space types and areas required are obtained, and the comprehensive evaluation criteria for the design are obtained; S2: Establish an intelligent algorithm for the layout of the living area of ​​the construction site, take the space type and its area as the design conditions for the optimization design, and the comprehensive evaluation criteria as the goal of the optimization design, and use the intelligent algorithm for automatic optimization design; S3: Based on the optimization design results, the BIM model of the living area of ​​the construction site is automatically obtained through the Dynamo software in the Revit software.

2. According to claim 1, a method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm is characterized by: The construction and extraction of the knowledge graph of the living area layout of the construction site at least includes data collection and processing, knowledge graph construction, and extraction of design conditions and design goals.

3. According to claim 2, a method for arranging living areas in a construction site based on knowledge graph and intelligent algorithm is characterized in that: The data collection and processing includes at least the following steps: First, sort out the current relevant specifications for the layout of living areas on construction sites, as well as previous design documents; Normalize the collected text-based specifications and design documents; When the text is standardized, remove the content that is not related to the layout of the living area of ​​the construction site; Finally, the continuous text is divided into paragraphs.

4. According to claim 3, a method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm is characterized in that: The knowledge graph construction includes at least the following steps: First, the entity recognition of the living area layout of the construction site is carried out according to the word frequency-inverse design specification frequency, and then the importance of the words in the design specification is evaluated, as shown in formulas (1)-(3): （1） （2） （3） Among them, TF is the term frequency, IDF is the inverse design frequency, is the number of times word t appears in design specification d, is the sum of the occurrences of all words in design specification d, N is the total number of design specifications, and df(t) is the number of design specifications containing word t; Then, the dependency relationship is analyzed according to formula (4), and the semantic relationship between the verbs and related entities in the sentence is determined according to formula (5); （4） （5） Among them, entity represents entity, relation represents relationship, Count represents statistical number, S is subject, V is verb, O is object, F is feature function, and w is weight vector; Finally, the entity is converted into a node in the graph, and then relationship edges are established between corresponding entities based on the identified relationships.

5. According to claim 4, a method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm is characterized in that: The extraction of the design conditions and design objectives comprises at least the following steps: Based on the input design requirements, use the graph database query language (Cypher for Neo4j) to query each space type and its corresponding area, and use it as the design condition; At the same time, the graph database is used to query the relevant specification requirements, and then the weighted method is used as the final optimization goal, as shown in formula (6): Score (6) Among them, w represents different weights, Indicates the score of the nth target.

6. According to claim 1, a method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm is characterized by: The intelligent algorithm for arranging the living area of ​​the construction site is a two-stage dynamic intelligent algorithm; First, a dynamic genetic algorithm is used for global optimization, and then a hill climbing algorithm is used for local optimization to finally obtain the optimal solution; The dynamic genetic algorithm includes dynamic population adjustment, dynamic crossover probability and dynamic mutation probability; The population dynamic adjustment is shown in formula (7): （7） in, is the population individual of generation t, is the diversity control parameter, is a newly generated set of individuals; The dynamic crossover probability and dynamic mutation probability are shown in equations (8) and (9): （8） （9） in, and is the crossover and mutation probability of the tth generation, and is the initial crossover and mutation probability, is a measure of the degree of environmental change, i.e., the number of iterations; and Tuning parameters set by the user.

7. The method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm according to claim 6 is characterized by: The application of the intelligent algorithm for the layout of the living area of ​​the construction site includes at least the following steps: First, we need to mathematically model the problem of arranging the living area of ​​the construction site; Then, the variables are parameterized; Finally, an intelligent algorithm for the layout of the living area at the construction site is used for calculation.

8. The method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm according to claim 7 is characterized by: The mathematical modeling determines the following mathematical modeling process based on the index of the design conditions and design goals by the knowledge graph: S2.1: Building facility location constraints. All building facilities must be located within the living area and within the range that the tower crane cannot reach, as shown in formula (10): （10） in, represents the location of the i-th building facility, Indicates the living area, Indicates the range that the tower crane can reach; S2.2: The regulations for dormitories in living areas need to be of a specified type. This is because different construction units often use a few fixed types of dormitories, as shown in formula (11): （11） in, is the type of the jth dormitory, T is the set of allowed dormitory types; S2.3: The capacity of dormitories and office areas needs to meet the requirements of the design specifications; S2.4: Each building needs to satisfy the adjacent constraints of previous design cases; S2.5: The flow lines of the living area of ​​the entire construction site need to meet the requirements of fire prevention, that is, the fire trucks can arrive and extinguish the fire smoothly, as shown in formula (12): （12） Where x represents the location of any building facility in the living area, P(x) represents the path from the fire truck entrance to location x, and Set lives Represents the collection of all living quarters building facilities; Formula (12) shows that for each building facility location in the living area , there exists a path P(x) that can reach it.

9. The method for arranging living areas in construction sites based on knowledge graph and intelligent algorithm according to claim 8 is characterized in that: The parameterization at least includes parameterization of terrain and parameterization of buildings, and the parameterization of terrain is to obtain the position of each building functional area; The parameterization of the terrain comprises at least the following steps: First, the area within the building red line is divided into Rasterize and obtain grid points At the same time, all grid points are numbered, so that each position i corresponds to a code ; The parameterization of the building comprises at least the following steps: First, the minimum rectangular enclosing rectangle is constructed according to the building planes of different functional areas, and the center of the building area plane is used as the anchor point of the building plane. The position of this anchor point is a parameter. The angle at which the plane is rotated around the anchor point As another parameter; The step size of the rotation angle is 45 degrees; Finally, for building area i, positioning is achieved through two parameters, namely .

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