Method and device for searching similar building information model and medium

Abstract

The invention discloses a method and device for searching similar building information models and a medium, and the method comprises the steps: firstly obtaining a first feature value of a first building information model, and then obtaining multiple groups of first comparison feature information of the model according to the first feature value; then, multiple groups of historical comparison feature information are extracted from a building information model feature library, and the feature library comprises historical comparison feature information of multiple historical building information models; after the first comparison feature information and the historical comparison feature information are obtained, a feature difference result between the first comparison feature information and the historical comparison feature information can be obtained by comparing the first comparison feature information and the historical comparison feature information. According to the difference results, a historical building information model corresponding to the historical comparison feature information can be determined in a building information model feature library. According to the embodiment of the invention, the feature information of the building information model is automatically and efficiently extracted and processed, so that the searching efficiency and accuracy are improved, and the management of the building information model component family library is optimized.

Description

Technical Field

[0001] The embodiments of the present application relate to but are not limited to the field of building information model technology, and in particular to a method, device and medium for searching similar building information models. Background Art

[0002] With the rapid development of Building Information Modeling (BIM) technology, how to efficiently manage and utilize these models has become an urgent problem to be solved. Especially in application scenarios such as comparison and screening of architectural design plans, quickly and accurately identifying and screening other models similar to the target BIM model is crucial to optimizing resource allocation. However, the traditional processing method often takes the method of comparing the target BIM model with all other BIM models one by one. This process is not only time-consuming and labor-intensive, but also highly dependent on manual visual judgment, which is very likely to lead to omissions and misjudgments, greatly limiting the search efficiency and accuracy. In addition, in the management of BIM component family libraries, duplicate BIM models will take up a lot of storage space, and will also bring inconvenience to subsequent model retrieval and application. At present, the management of most BIM component family libraries relies on manual elimination of duplicate or similar models, which is not only inefficient but also prone to omissions.

[0003] Therefore, there is an urgent need for a method that can automatically and accurately identify similar BIM models to improve the search efficiency and accuracy, as well as the management efficiency and accuracy of the BIM component family library. Summary of the invention

[0004] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0005] The embodiment of the present application provides a method for searching similar building information models, which not only improves the search efficiency and accuracy, but also optimizes the management of the building information model component family library.

[0006] In a first aspect, an embodiment of the present application provides a method for searching for similar building information models, comprising: Obtaining a first eigenvalue of a first building information model; According to the first characteristic value, obtaining multiple groups of first comparison characteristic information of a first building information model; Extracting multiple sets of historical comparison feature information from a building information model feature library, wherein the building information model feature library includes the historical comparison feature information of multiple historical building information models; Obtaining a feature difference result according to the first comparison feature information and the historical comparison feature information; According to the feature difference result, the historical building information model corresponding to the historical comparison feature information is determined in the building information model feature library.

[0007] In combination with the first aspect, in an embodiment of the present application, obtaining a first characteristic value of a first building information model includes: obtaining a first building information model, the first building information model including multiple first components; obtaining a first information set of the first components according to the first building information model; sorting the multiple first components according to the first information set to obtain first model characteristic information of the first building information model, the first model characteristic information including information of multiple dimensions of the multiple first components; obtaining a first characteristic value of the first building information model according to the first model characteristic information.

[0008] In combination with the first aspect, in an embodiment of the present application, the first model feature information includes type information, position information, material information and vertex information of multiple first components; the first feature value of the first building information model is obtained according to the first model feature information, including: assigning a first weight to the type information, assigning a second weight to the position information, assigning a third weight to the material information, and assigning a fourth weight to the vertex information; according to a preset function, the type information, the position information, the material information and the vertex information of the first component are respectively calculated to obtain a mapping value set, the mapping value set including a first mapping value of the type information, a second mapping value of the position information, a third mapping value of the material information and a fourth mapping value of the vertex information; according to the first weight, the second weight, the third weight and the fourth weight, the mapping value set is weightedly combined and calculated to obtain the first feature value of the first building information model.

[0009] In combination with the first aspect, in an embodiment of the present application, obtaining multiple groups of first comparison feature information of the first building information model based on the first feature value includes: segmenting the first feature value to obtain multiple key feature identifiers; obtaining key feature identifier values ​​based on the key feature identifiers; obtaining multiple groups of first comparison feature information based on multiple key feature identifiers and multiple key feature identifier values.

[0010] In combination with the first aspect, in an embodiment of the present application, the building information model feature library is obtained according to the following steps: constructing an initial model feature library; obtaining multiple second building information models, the second building information models including multiple second components; obtaining a second information set of the second components according to the second building information models, the second information set including information of different dimensions of the components; sorting the multiple second components according to the second information set to obtain second model feature information of the second building information model, the model feature information including information of different dimensions of the second components; obtaining a second eigenvalue of the second building information model according to the model feature information; segmenting the second eigenvalue to obtain multiple second eigenvalues; obtaining multiple groups of second comparison feature information of the second building information model according to the second eigenvalue; obtaining a key identifier of the second building information model according to the multiple groups of the second comparison feature information; storing the key identifier, the second eigenvalue and the multiple groups of the second comparison feature information in the initial model feature library to obtain a building information model feature library.

[0011] In combination with the first aspect, in an embodiment of the present application, obtaining a feature difference result based on the first comparison feature information and the historical comparison feature information includes: extracting a first feature identification value of the first comparison feature information and a second feature identification value of the historical comparison feature information; obtaining an error value between the first comparison feature information and the historical comparison feature information based on the first feature identification value and the second feature identification value; and obtaining a feature difference result based on the error value and a preset error threshold.

[0012] In combination with the first aspect, in an embodiment of the present application, determining the historical building information model corresponding to the historical comparison feature information in the building information model feature library based on the feature difference result includes: when the feature difference result characterizes that the first building information model and the historical building information model are similar, determining the historical building information model in the building information model feature library based on multiple groups of the historical comparison feature information.

[0013] In combination with the first aspect, in an embodiment of the present application, determining the historical building information model in the building information model feature library includes: searching for historical feature values ​​corresponding to multiple groups of historical comparison feature information; obtaining a target model key feature identifier based on the historical feature values; and determining the historical building information model based on the target model key feature identifier.

[0014] On the other hand, an embodiment of the present application further provides an electronic device, including: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the method for searching similar building information models as described above is implemented.

[0015] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program executable by a processor. When the computer program executable by the processor is executed by the processor, it is used to implement the method for searching similar building information models as described above.

[0016] A method for searching similar building information models provided by an embodiment of the present application comprises the following steps: first, obtaining a first characteristic value of a first building information model. Subsequently, according to the characteristic value, further obtaining multiple sets of first comparison feature information. Next, extracting multiple sets of historical comparison feature information from a building information model feature library, wherein the feature library contains historical comparison feature information of multiple historical building information models. After obtaining the first comparison feature information and the historical comparison feature information, by comparing the first comparison feature information with the historical comparison feature information, the feature difference result between them can be obtained. According to these difference results, the historical building information model corresponding to the historical comparison feature information can be determined in the building information model feature library, that is, the historical building information model similar to the first building information model can be located in the building information model feature library. The embodiment of the present application avoids the cumbersome and time-consuming one-by-one comparison process in the traditional method by automatically and efficiently extracting and processing the feature information of the building information model, and significantly reduces the burden of manual operation. At the same time, other models similar to the target building information model can be screened out more accurately, thereby effectively reducing the possibility of omission and misjudgment. In addition, this embodiment can also efficiently clean up duplicate or similar models in the building information model component family library, further reducing the difficulty and cost of manually removing duplicate models, and significantly improving the accuracy and efficiency of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a method for searching similar building information models provided by an embodiment of the present application; Figure 2 The embodiment of this application provides Figure 1 Specific flow chart of step 110; Figure 3 The embodiment of this application provides Figure 2 Specific flow chart of step 240; Figure 4 The embodiment of this application provides Figure 1 Specific flow chart of step 120; Figure 5 The embodiment of this application provides Figure 1Specific flow chart of step 140; Figure 6 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] It should be noted that, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order from that in the flowchart. The terms "first", "second", etc. in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions that the present invention can be implemented, so they have no technical substantive significance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope of the technical content disclosed by the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of narration, and are not used to limit the scope of the implementation of the present invention. The change or adjustment of the relative relationship should also be regarded as the scope of the implementation of the present invention without substantially changing the technical content.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0021] With the rapid development of Building Information Modeling (BIM) technology, its application in architectural design, construction, operation and maintenance is becoming more and more extensive. As a digital expression that integrates the information of the entire life cycle of a construction project, the BIM model not only contains the geometric information of the building, but also covers a variety of attribute information such as material, cost, and progress. However, with the rapid growth of the number of BIM models, how to efficiently manage and use these models has become an urgent problem to be solved. Especially in application scenarios such as comparison and screening of architectural design plans, quickly and accurately identifying and screening out other models similar to the target BIM model plays a vital role in improving design efficiency and optimizing resource allocation. However, the traditional processing method often adopts the method of comparing the target BIM model with all other BIM models one by one. This process is not only time-consuming and labor-intensive, but also highly dependent on manual visual judgment, which is very likely to lead to omissions and misjudgments, greatly limiting the search efficiency and accuracy. In addition, in the management of the BIM component family library, duplicate BIM models will take up a lot of storage space, and will also bring inconvenience to subsequent model retrieval and application. At present, the management of most BIM component family libraries relies on manual elimination of duplicate models, which is not only inefficient but also prone to omissions.

[0022] In summary, the existing BIM model management methods have many deficiencies in dealing with large-scale BIM model management and similarity judgment. There is an urgent need for a method that can automatically and accurately identify similar BIM models to improve the search efficiency and accuracy, as well as the management efficiency and accuracy of the BIM component family library.

[0023] In view of this, the embodiment of the present application provides a method for searching similar building information models, an electronic device and a computer-readable storage medium. First, a first feature value of a first building information model is obtained. Subsequently, multiple groups of first comparison feature information are further obtained according to this feature value. Next, multiple groups of historical comparison feature information are extracted from the building information model feature library, wherein the feature library contains historical comparison feature information of multiple historical building information models. After obtaining the first comparison feature information and the historical comparison feature information, the feature difference results between them can be obtained by comparing the first comparison feature information with the historical comparison feature information. According to these difference results, the historical building information model corresponding to the historical comparison feature information can be determined in the building information model feature library, that is, the historical building information model similar to the first building information model can be located in the building information model feature library. The embodiment of the present application avoids the cumbersome and time-consuming one-by-one comparison process in the traditional method by automatically and efficiently extracting and processing the feature information of the BIM model, and significantly reduces the burden of manual operation. At the same time, through feature information matching and difference analysis, other models similar to the target BIM model can be more accurately screened out, thereby effectively reducing the possibility of omission and misjudgment. In addition, this embodiment can efficiently clean up duplicate or similar models in the BIM component family library, further reducing the difficulty and cost of manually removing duplicate models, and significantly improving the accuracy and efficiency of management.

[0024] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0025] like Figure 1 As shown, Figure 1 1 is a flow chart of a method for searching similar building information models provided by an embodiment of the present application. The method for searching similar building information models includes but is not limited to steps 110 to 150.

[0026] Step 110: Obtain a first eigenvalue of a first building information model; Step 120: obtaining a plurality of groups of first comparison feature information of the first building information model according to the first feature value; Step 130: extracting multiple groups of historical comparison feature information from a building information model feature library, where the building information model feature library includes historical comparison feature information of multiple historical building information models; Step 140: Obtaining a feature difference result according to the first comparison feature information and the historical comparison feature information; Step 150: According to the feature difference result, determine the historical building information model corresponding to the historical comparison feature information in the building information model feature library.

[0027] Steps 110 to 150 are described in detail below.

[0028] In a feasible embodiment, Building Information Modeling (BIM) is a method and technology for information management using models and digital technology throughout the life cycle of construction project assets. Building Information Modeling is based on a three-dimensional model and presents each component and system of a construction project in an accurate three-dimensional form to ensure the accuracy of design and construction. Building Information Modeling contains a variety of components, which are the basic units in Building Information Modeling. They can be building components such as walls, doors and windows, floor slabs, roofs, etc., or structural components such as beams and columns, or electromechanical components such as electrical, water supply and drainage, HVAC, etc. These components are presented in three-dimensional form in the model and have clear geometric dimensions and shapes. Each BIM component has a specific function, such as walls for dividing spaces, doors and windows for ventilation and lighting, and beams and columns for supporting structures. These functions are defined and described in the model through the attributes and parameters of the components.

[0029] In a feasible embodiment, the characteristic value of the building information model refers to a quantitative or qualitative indicator that can summarize and describe the key attributes and characteristics of each element (such as components, etc.) in the BIM model. These characteristic values ​​are usually extracted from the BIM model and used to represent different aspects of the model, including but not limited to the following aspects: type information, vertex information (such as the number of vertices, vertex positions, etc.), material information (such as color, processing technology, etc.), location information (such as coordinates, elevation, direction, etc.) or any other parameters that can distinguish different components. Figure 2 As shown, the specific process of obtaining the first characteristic value of the first building information model includes but is not limited to steps 210 to 240.

[0030] Step 210: Acquire a first building information model, wherein the building information model includes a plurality of first components; Step 220: extracting a first information set of a first component based on the first building information model; Step 230: sorting all first components according to the first information set to obtain first model feature information of the first building information model, wherein the first model feature information includes information of multiple dimensions of multiple first components; Step 240: Obtain a first characteristic value of the first building information model according to the first model characteristic information.

[0031] In a feasible embodiment, the multiple dimensions of the first component include dimensions such as type, material and position. In the process of obtaining the first information set of the first component according to the first building information model, the first building information model can be comprehensively traversed and identified first, the type information of each component can be obtained and distinguished, and then the key information of each component, including its position information, material information and vertex information, can be extracted, and then the type information, position information, material information and vertex information obtained can be comprehensively processed and integrated to generate a complete information set (i.e., the first information set) of each component. It should be noted that the type information of the BIM component (such as the first component) refers to the type or category of the component. In the building information model, the types of components are various, covering multiple aspects such as architecture, structure, and equipment. For example, building components such as walls, doors and windows, floor slabs, and roofs are the basic elements that constitute a building. Structural components such as beams, columns, foundations, and floor slabs constitute the skeleton of the building and support the entire building. Electromechanical components such as distribution cabinets, switches, sockets, lamps, pipes, valves, pumps, etc. are components of electrical, water supply and drainage, HVAC and other systems in the building. Decorative components such as floors, ceilings, and wall decoration materials (ceramic tiles, wallpaper, paint, etc.) can be used to improve the comfort of using buildings. In addition, the location information of BIM components refers to the spatial position of the components in the building information model. Through the location information, the specific position of the components in the building can be determined. For example, the location information of wall components may include the starting and ending coordinates of the wall, the thickness and height of the wall, etc. The location information of beam components may include the starting and ending coordinates of the beam, the cross-sectional dimensions and span of the beam, etc. The location information of door and window components may include the specific position of the door and window on the wall (such as the distance from the edge of the wall), the size and opening direction of the door and window, etc. In addition, the material information of BIM components refers to the material used by the component. Different components may use different materials. For example: wall components may use materials such as concrete, bricks, and gypsum boards. Beam and column components may use materials such as steel, wood, or concrete. Door and window components may use materials such as wood, aluminum alloy, and plastic steel. The vertex information of BIM components refers to the key points of the component's geometric shape. Vertex information can describe the geometric shape and size of the component. For example, in a wall component, vertex information may include the coordinates of the four corner points of the wall. In a beam component, vertex information may include the coordinates of the starting and ending points of the beam, as well as the coordinates of key points on the beam (such as the bending point or the point of variable section of the beam). In a door and window component, vertex information may include the coordinates of the four corner points of the door and window, as well as the coordinates of key points in the opening direction of the door and window. It can be seen that the type information, location information, material information and vertex information of a BIM component together constitute a complete information set of the component.

[0032] In a feasible embodiment, after obtaining the first information set, the first components can be further sorted according to the first information set to obtain the first model feature information of the first building information model. In this process, the components can be grouped according to the type information to obtain multiple component type groups; then, the multiple component type groups are sorted according to the position information, material information and vertex information to obtain multiple feature information groups; then, the overall feature information of the model (i.e., the first model feature information) is obtained according to the multiple feature information groups. Specifically, by traversing all components in the BIM model and grouping them according to their type information (such as walls, doors and windows, beams, columns, etc.), components with similar properties and functions are classified together to facilitate subsequent processing and analysis. Through grouping, multiple component type groups can be obtained, each group contains a class of components with the same or similar types. After obtaining the component type group, the components in each group are sorted. It is intended to arrange components with similar or related characteristics together according to feature information such as component position information, material information and vertex information to form feature information groups. It should be noted that the sorting process can be performed based on different rules, such as sorting by the position coordinates of the components, or sorting by the material type of the components, etc. Through the sorting process, multiple feature information groups can be obtained, each of which contains a group of components with similar or related features. Furthermore, after obtaining multiple feature information groups, the overall feature information of the model can be obtained by integrating and extracting the component information in each group.

[0033] In a feasible embodiment, the first model feature information includes type information, position information, material information and vertex information of the plurality of first components. Figure 3 As shown, the specific process of obtaining the first characteristic value of the first building information model according to the first model characteristic information may include but is not limited to steps 310 to 330.

[0034] Step 310: assigning a first weight to the type information, a second weight to the position information, a third weight to the material information, and a fourth weight to the vertex information; Step 320: Calculate the type information, position information, material information and vertex information of the first component according to a preset function to obtain a mapping value set, where the mapping value set includes a first mapping value of the type information, a second mapping value of the position information, a third mapping value of the material information and a fourth mapping value of the vertex information; Step 330: Perform a weighted combination calculation on the mapping value set according to the first weight, the second weight, the third weight and the fourth weight to obtain a first eigenvalue of the first building information model.

[0035] In a feasible embodiment, in step 310, in the process of assigning different weights to each component in the first model feature information, flexible allocation can be performed according to actual needs. For example, if the requirement for component location information is high, it can be assigned a relatively high weight; on the contrary, if the importance of component type information is relatively low, its corresponding weight will be lowered accordingly. This embodiment of the present application is not limited to this.

[0036] In a feasible embodiment, in step 320, in the process of calculating the type information of the component using a preset function to obtain the first mapping value, a preset hash function can be selected. The function of this function is to perform a hash operation on the type information of the component to generate a hash value. This hash value is usually a binary string, in which each bit is either 0 or 1. This string is the first mapping value. Taking actual operation as an example, assuming that the type information of a component is "wooden door", the string is hashed using a preset hash function. The hash function converts the string "wooden door" into a binary string of a fixed length, such as "1100". This binary string is the first mapping value of the "wooden door" type information. It should be noted that the process of calculating the position information, material information and vertex information of the component using a preset function is similar to the process of processing the component type information using a preset function. In view of the high consistency of the two in logic and operation, in order to avoid repetition, the former will not be elaborated in detail here.

[0037] In a feasible embodiment, in the process of performing weighted combination calculation on the mapping value set according to the first weight, the second weight, the third weight and the fourth weight to obtain the first eigenvalue of the first building information model, weighted processing is performed on each bit of each mapping value (specifically expressed as a hash value) in the mapping value set. The specific operation is: when a bit of the hash value is 1, it is positively multiplied by the corresponding weight; and when the bit is 0, it is negatively multiplied by the corresponding weight. It should be noted that although the product of 0 and any number is still 0 in actual operation, from a logical level, this is equivalent to the contribution of the bit to the final eigenvalue being regarded as negative (or regarded as no contribution). Then, the bits in the same position in all feature parts are summed up. This means that the weighted values ​​of all hash values ​​at the first bit are added to obtain the sum of the first bit; then, the same summing operation is performed on the second bit, the third bit, etc. in turn until all bits are processed. Finally, each bit of the eigenvalue is determined according to the result of the summation. If the sum of a certain bit is greater than 0, the final result of the bit is set to 1; if the sum is less than 0 (or less than or equal to a preset threshold according to a specific implementation method), the final result of the bit is set to 0. Through this series of calculation steps, the characteristic value of the entire first building information model can be obtained. The characteristic value is presented in the form of a binary string and can accurately reflect the key characteristic information of the first building information model.

[0038] The following is an explanation of the calculation of the first building information model (BIM) characteristic value using a specific example.

[0039] Assume that there is a BIM model. In the weight distribution, the first weight of type information is 0.5, the second weight of position information is 0.3, the third weight of material information is 0.1, and the fourth weight of vertex information is 0.1. The BIM model component information includes: component A (for example, a wall): type information is concrete wall, position information is (X, Y, Z), material information is concrete, and vertex information includes four vertices. According to the preset function, four mapping values ​​(hash values) of component A can be obtained. Assume that the first mapping of type information is 1010, the second mapping value of position information is 0110, the third mapping value of material information is 1100, and the fourth mapping value of vertex information is 0011. Next, each bit of each hash value is weighted, including: for hash value 1010 (type information): first bit: 1×0.5=0.5, second bit: 0×0.5=0 (logically regarded as no contribution), third bit: 1×0.5=0.5, fourth bit: 0×0.5=0 (logically regarded as no contribution). For hash value 0110 (position information): first bit: 0×0.3=0 (logically regarded as no contribution), second bit: 1×0.3=0.3, third bit: 1×0.3=0.3, fourth bit: 0×0.3=0 (logically regarded as no contribution). For hash value 1100 (material information): first bit: 1×0.1=0.1, second bit: 1×0.1=0.1, third bit: 0×0.1=0 (logically regarded as no contribution), fourth bit: 0×0.1=0 (logically regarded as no contribution). For hash value 0011 (vertex information): first bit: 0×0.1=0 (logically regarded as no contribution), second bit: 0×0.1=0 (logically regarded as no contribution), third bit: 1×0.1=0.1, fourth bit: 1×0.1=0.1. Subsequently, the weighted values ​​of all hash values ​​at the same position are summed, where the sum of the first position is: 0.5 (type) + 0 (position) + 0.1 (material) + 0 (vertex) = 0.6, the sum of the second position is: 0 (type) + 0.3 (position) + 0.1 (material) + 0 (vertex) = 0.4, the sum of the third position is: 0.5 (type) + 0.3 (position) + 0 (material) + 0.1 (vertex) = 0.9, and the sum of the fourth position is: 0 (type) + 0 (position) + 0 (material) + 0.1 (vertex) = 0.1. Assuming that a threshold is set (0.5 in this case), if the sum of a certain position is greater than the threshold, the final result of the position is set to 1; if the sum is less than or equal to the threshold, it is set to 0. For example, the first digit is 0.6, which is greater than 0.5, and the final result of this digit is 1. The second digit is 0.4, which is less than 0.5, and the final result of this digit is 0. The third digit is 0.9, which is greater than 0.5, and the final result of this digit is 1. The fourth digit is 0.1, which is less than 0.5, and the final result of this digit is 0. Through the above steps, it can be concluded that the characteristic value of the BIM model is the binary string "1010".

[0040] In a feasible embodiment, multiple groups of first comparison feature information of the first building information model can be further obtained according to the first feature value of the first building information model. Figure 4 As shown, the specific process may include but is not limited to steps 410 to 430.

[0041] Step 410: Segment the first feature value to obtain multiple key feature identifiers; Step 420: Obtain a key feature identification value according to the key feature identification; Step 430: Obtain multiple groups of first comparison feature information according to multiple key feature identifiers and multiple key feature identifier values.

[0042] In a feasible embodiment, due to the huge information content of the entire building information model component, the generated model eigenvalue may be quite lengthy. In order to optimize the subsequent comparison and retrieval efficiency, the eigenvalue of the model can be divided into N parts for processing. In each part, there is a key feature identifier, which can be represented by the number of bits in each part. For example, assuming that the first eigenvalue is 128 bits, it can be divided into four parts by taking the number of bits of the eigenvalue modulo 4, where the first part contains the first 32 bits of the first eigenvalue, the second part contains the next 32 bits, the third part contains 32 bits from the 65th to the 96th bit, and the fourth part contains the last 32 bits of the first eigenvalue. For the first part, its core feature identifier is the first 32 bits of the eigenvalue contained in the part.

[0043] In a feasible embodiment, the corresponding key feature identification value can be determined according to the number of bits of each key feature identification. Further, multiple sets of first comparison feature information can be generated using multiple key feature identifications and their corresponding identification values. Taking the 24-bit first feature value as an example, if it is divided in a modulo 4 manner according to the number of bits of the feature value, the following four parts of key feature identification (expressed in binary strings) can be obtained: the first part is "101001", the second part is "101101", the third part is "111000", and the fourth part is "110010". When determining the key feature identification value of the first part, it is necessary to calculate the sum of the number of binary bits "1" in the key feature identification of the remaining three parts (i.e., the second part, the third part and the fourth part). Specifically, the second part has 4 "1", the third part has 3 "1", and the fourth part also has 3 "1", so the key feature identification value of the first part is the sum of the number of "1" in these three parts, i.e., 10. Similarly, the key feature identification values ​​of the second part, the third part and the fourth part can be calculated. The key feature identification value of the second part is the sum of the number of "1" in the other three parts (i.e., the first part, the third part, and the fourth part), which is calculated to be 9; the key feature identification value of the third part is the sum of the number of "1" in the other three parts (i.e., the first part, the second part, and the fourth part), which is also 10; the key feature identification value of the fourth part is the sum of the number of "1" in the other three parts (i.e., the first part, the second part, and the third part), which is also 10. Finally, the following multiple groups of first comparison feature information can be obtained: the key feature identification of the first part is "101001", and the key feature identification value is 10; the key feature identification of the second part is "101101", and the key feature identification value is 9; the key feature identification of the third part is "111000", and the key feature identification value is 10; the key feature identification of the fourth part is "110010", and the key feature identification value is 10. It is worth noting that the number of bits of the feature value (24 bits) and the number of bits of the key feature identification (6 bits in the example, but in fact it should be determined according to the specific situation) in the above description are only examples and may be different in actual applications. In addition, the calculation method of the key feature identification value may also be adjusted depending on the application scenario.

[0044] In a feasible embodiment, the overall feature value of the building information model can also be directly regarded as its key feature identifier, and at the same time, the unique identifier of the model (such as the model ID) is used as the key feature identifier value. The two are combined to form the overall comparison feature value. However, when dealing with the scene of massive building information models, it is inevitable to encounter the problem of repeated feature values. In order to effectively solve this problem, the embodiment of the present application provides a strategy: when encountering multiple models with the same feature value, they are not directly overwritten or discarded, but the IDs of these models are spliced ​​through a special separator identifier to form a composite string containing multiple model IDs as the key feature identifier value corresponding to the feature value. This method not only ensures the uniqueness of each feature value in the comparison feature value, but also utilizes the repeatability of the feature value to effectively associate and aggregate multiple related models. In this way, in the subsequent query and retrieval process, it is possible to more efficiently locate a set of models with the same or similar feature values, and then further analyze and process according to actual needs.

[0045] In a feasible embodiment, after obtaining multiple sets of first comparison feature information of the first building information model, multiple sets of historical comparison feature information can be retrieved and extracted from the building information model feature library to compare with the first comparison feature information, so as to determine whether there is a model similar to the first building information model in the feature library. The construction process of the building information model feature library is as follows: First, an initial model feature library is constructed. Then, multiple second building information models are collected, and these models are composed of multiple second components. For each second building information model, a second information set of multiple second components is extracted, and the set covers detailed information of these components in different dimensions. Then, the second components are sorted according to the second information set, and then the second model feature information of the second building information model is generated. These information also contain detailed information of the second components in different dimensions. Based on these model feature information, the second eigenvalue of the second building information model can be further calculated. In order to analyze more carefully, the second eigenvalue is then segmented to obtain multiple segmented second eigenvalues. Then, according to these segmented second eigenvalues, multiple sets of second comparison feature information of the second building information model are extracted. On this basis, the key identifier of the second building information model is determined according to the multiple groups of second comparison feature information. Finally, this key identifier, the segmented second eigenvalues ​​and the multiple groups of second comparison feature information are stored together in the initial model feature library, thereby completing the construction of the building information model feature library. It should be noted that in the process of obtaining the second eigenvalue of the second building information model, the steps are highly similar to the process of obtaining the first eigenvalue of the first building information model; similarly, the process of collecting multiple groups of second comparison feature information of the second building information model and the process of collecting multiple groups of first comparison feature information of the first building information model also show a high degree of similarity. In view of the fact that the specific process of obtaining the first eigenvalue of the first building information model and its multiple groups of first comparison feature information has been elaborated in detail in the previous embodiment, in order to avoid repetition and redundancy of content, it will not be described in detail here.

[0046] In a feasible embodiment, after retrieving and extracting multiple sets of historical comparison feature information from the building information model feature library, feature difference results can be further obtained based on multiple sets of first comparison feature information and multiple sets of historical comparison feature information. Figure 5 As shown, the specific process may include but is not limited to steps 510 to 530.

[0047] Step 510: extracting a first feature identification value of the first comparison feature information and a second feature identification value of the historical comparison feature information; Step 520: Obtain an error value between the first comparison feature information and the historical comparison feature information according to the first feature identification value and the second feature identification value; Step 530: Obtain a feature difference result according to the error value and a preset error threshold.

[0048] In a feasible embodiment, among multiple groups of first comparison feature information, the key feature identifier of each group of first comparison feature information can be used to determine the historical comparison feature information that matches it, and further compare the first feature identifier value (i.e., the key feature identifier value) of the first comparison feature information with the second feature identifier value of the historical comparison feature information, and calculate the error value between the two. This error can be obtained by calculating the Euclidean distance, Manhattan distance or other similarity measurement method between the two key feature identifier value vectors.

[0049] In a feasible embodiment, based on the comparison between the error value and the preset error threshold, the result of the feature difference can be derived, and the historical building information model matching the current feature value can be determined in the building information model feature library. Specifically, when the feature difference result shows that the first building information model and a certain historical building information model are highly similar (that is, when the error value is within the preset error threshold range), multiple sets of historical comparison feature information can be used as a retrieval basis to conduct an in-depth search in the building information model feature library to find the historical building information model that is closest to the current model feature. It should be noted that the preset error threshold can be adjusted in combination with actual conditions or machine learning to improve the accuracy of searching for similar building information models.

[0050] In a feasible embodiment, in the process of determining the historical building information model in the building information model feature library, the following steps are followed: first, the historical feature values ​​corresponding to multiple groups of historical comparison feature information are retrieved and obtained; then, based on the historical feature values, the key feature identifier of the target model is confirmed, that is, the complete feature value of the historical building information model similar to the first building information model. Based on this feature value, the model ID of the historical building information model that is most similar to the first building information model can be found, thereby completing the determination of the historical building information model.

[0051] The following is an example to illustrate the process of finding similar building information models.

[0052] Suppose there is a building information model with the following components: columns (type: circular column, position: (10,10,10), material: concrete, number of vertices: 4, vertex positions: [(10,10,10), (11,10,10), (11,11,10), (10,11,10)]); beams (type: rectangular beam, position: (12,12,12), material: steel, number of vertices: 6, vertex positions: [(12,12,12), (13,12,12), (13,13,12), (13,13,13), (12,13,13), (12,12,13)]); walls (type: solid wall, position: (14,14,14), material: brick, number of vertices: 8, vertex positions: [(14,14,14), (15,14,14), (15,15,14), (15,15,15), (14,15,15), (14,14,15), (13,14,15), (13,14,14)]). Based on this information, the component features are extracted and sorted to form model feature information, and different weights are assigned (for example, the position weight is 0.5, the type weight is 0.2, the material weight is 0.15, the vertex number weight is 0.1, and the vertex position weight is 0.05). Next, a hash value is calculated for each feature part and identified with a binary string. For example, assume that the hash value of the column is "10101101", the hash value of the beam is "11010010", and the hash value of the wall is "01101111". Then, each bit of the hash value is weighted to form the final building information model feature value. Assume that the final building information model feature value is a 128-bit binary string. According to the modulo 4 method of the number of bits of the eigenvalue, it is divided into 4 parts, namely: Part 1 (32 bits): as the first key feature identifier, the number of 1s in the second, third, and fourth parts of the first key feature identifier value; Part 2 (32 bits): as the second key feature identifier, the second key feature identifier value is the number of 1s in the first, third, and fourth parts; Part 3 (32 bits): as the third key feature identifier, the third key feature identifier value is the number of 1s in the first, second, and fourth parts; Part 4 (32 bits): as the fourth key feature identifier, the fourth key feature identifier value is the number of 1s in the first, second, and third parts. These 4 groups of key feature identifiers and key feature identifier values ​​are stored in 4 comparison buckets (which can be regarded as four groups of comparison feature information) respectively. At the same time, the entire building information model eigenvalue (128 bits) is used as the model key feature identifier, and the model unique mark (such as model id: "BIM001") is used as the model key feature identifier value to form an index bucket (which can be regarded as the feature information of the entire model). Suppose there is a new building information model that needs to be compared. First, follow the same steps to extract its eigenvalues ​​and divide it into 4 parts.Then, search for a target building information model that is similar to the model key feature identification value of the current building information model in different comparison buckets. For example, assume that in comparison bucket 1 (i.e., the first set of comparison feature information), the error between the model key feature identification value of the current building information model and the model key feature identification value of a target building information model is within the allowable threshold (for example, the error is less than 5%). At this point, it can be considered that the current building information model is similar to the target building information model. Next, find the complete feature value of the target building information model through the index bucket, and reversely restore its component features. Finally, find the corresponding model unique identifier (such as model id: "BIM002") in the index bucket to confirm which is the target building information model.

[0053] Reference Figure 6 The embodiment of the present application further discloses an electronic device, the electronic device 600 comprising: at least one processor 601; At least one memory 602, used to store at least one program; When at least one program is executed by at least one processor 601, the method for searching similar building information models as described in the previous embodiment is implemented.

[0054] The embodiment of the present application further discloses a computer-readable storage medium, which stores a computer program executable by a processor. When the computer program executable by the processor is executed by the processor, it is used to implement the method for searching similar building information models as described in the previous embodiment.

[0055] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for finding similar building information models, characterized in that: include: Obtaining a first eigenvalue of a first building information model; According to the first characteristic value, obtaining multiple groups of first comparison characteristic information of a first building information model; Extracting multiple sets of historical comparison feature information from a building information model feature library, wherein the building information model feature library includes the historical comparison feature information of multiple historical building information models; Obtaining a feature difference result according to the first comparison feature information and the historical comparison feature information; According to the feature difference result, the historical building information model corresponding to the historical comparison feature information is determined in the building information model feature library.

2. The method for searching similar building information models according to claim 1, characterized in that: The obtaining of the first characteristic value of the first building information model comprises: Acquire a first building information model, wherein the first building information model includes a plurality of first components; Obtaining a first information set of the first component according to the first building information model; Sorting the plurality of first components according to the first information set to obtain first model feature information of the first building information model, wherein the first model feature information includes information of multiple dimensions of the plurality of first components; A first characteristic value of the first building information model is obtained according to the first model characteristic information.

3. The method for searching similar building information models according to claim 2, characterized in that: The first model feature information includes type information, position information, material information and vertex information of a plurality of first components; and obtaining a first feature value of the first building information model according to the first model feature information includes: Assigning a first weight to the type information, a second weight to the position information, a third weight to the material information, and a fourth weight to the vertex information; Calculate the type information, the position information, the material information and the vertex information of the first component according to a preset function to obtain a mapping value set, wherein the mapping value set includes a first mapping value of the type information, a second mapping value of the position information, a third mapping value of the material information and a fourth mapping value of the vertex information; The mapping value set is weightedly combined and calculated according to the first weight, the second weight, the third weight and the fourth weight to obtain a first eigenvalue of the first building information model.

4. The method for searching similar building information models according to claim 1, characterized in that: The step of obtaining a plurality of sets of first comparison feature information of a first building information model according to the first feature value includes: Segmenting the first feature value to obtain a plurality of key feature identifiers; According to the key feature identifier, a key feature identifier value is obtained; A plurality of groups of first comparison feature information are obtained according to the plurality of key feature identifiers and the plurality of key feature identifier values.

5. The method for searching similar building information models according to claim 1, characterized in that: The building information model feature library is obtained according to the following steps: Construct an initial model feature library; Acquire a plurality of second building information models, wherein the second building information models include a plurality of second components; obtaining a second information set of the second component according to the second building information model, wherein the second information set includes information of different dimensions of the component; Sorting the plurality of second components according to the second information set to obtain second model feature information of the second building information model, wherein the model feature information includes information of different dimensions of the second components; Obtaining a second characteristic value of the second building information model according to the model characteristic information; Performing segmentation processing on the second eigenvalue to obtain a plurality of second eigenvalues; obtaining a plurality of groups of second comparison feature information of the second building information model according to the second feature value; Obtaining a key identifier of the second building information model according to the plurality of sets of the second comparison feature information; The key identifier, the second feature value and multiple groups of the second comparison feature information are stored in the initial model feature library to obtain a building information model feature library.

6. The method for searching similar building information models according to claim 1, characterized in that: The obtaining of a feature difference result according to the first comparison feature information and the historical comparison feature information includes: Extracting a first feature identification value of the first comparison feature information and a second feature identification value of the historical comparison feature information; Obtaining an error value between the first comparison feature information and the historical comparison feature information according to the first feature identification value and the second feature identification value; A feature difference result is obtained according to the error value and a preset error threshold.

7. The method for searching similar building information models according to claim 1, characterized in that: Determining the historical building information model corresponding to the historical comparison feature information in the building information model feature library according to the feature difference result includes: When the feature difference result indicates that the first building information model and the historical building information model are similar, the historical building information model is determined in the building information model feature library according to multiple groups of the historical comparison feature information.

8. The method for searching similar building information models according to claim 7, characterized in that: Determining the historical building information model in the building information model feature library includes: Searching for historical feature values ​​corresponding to multiple groups of the historical comparison feature information; Obtaining a target model key feature identifier according to the historical feature value; The historical building information model is determined according to the key feature identifier of the target model.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the method for searching similar building information models according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program executable by a processor is stored therein, and when the computer program executable by the processor is executed by the processor, it is used to implement the method for searching similar building information models as described in any one of claims 1 to 8.

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