A structured rule description method and system for building code index query

Abstract

This invention relates to a structured rule description method and system for querying building code indicators. The method includes: acquiring multiple building BIM models of the project to be queried and their corresponding SNL rules; extracting entity data from the multiple building BIM models and determining the corresponding entity relationships to construct a multiple building semantic model of the project to be queried; parsing and acquiring the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtaining the SNL for querying building indicators, and determining the SPARQL query statement for building indicators; performing a query in the multiple building semantic model using the SPARQL query statement for building indicators to obtain the building code indicators of the multiple building BIM models of the project to be queried; parsing the conclusion clause of the SNL for querying building indicators, determining whether the conclusion clause contains calculations, and outputting the corresponding building code indicator as the target building code indicator based on the determination result. This invention can be widely applied in the field of automatic detection.

Description

Technical Field

[0001] This invention relates to the field of automatic detection, and in particular to a structured rule description method and system for querying building code indicators. Background Technology

[0002] With the rapid development and widespread application of Building Information Modeling (BIM), the automated review of building codes has become a hot topic in related industries. Existing technology discloses a complex building code compliance detection device combining semantic and geometric calculations; however, this device can only perform compliance checks on building codes. The statistics and review of various building-related indicators are key focuses of building planning and permit application reviews. The output of these reviews is usually specific indicator values, rather than "correct" or "incorrect" compliance results. To acquire and statistically analyze various building-related indicators, effective data organization and extraction methods are needed. Furthermore, since building planning indicators typically adopt local standards, their definitions are related to factors such as the project's geographical location and type. Therefore, scalable methods for describing and defining building indicators are also crucial for achieving efficient building indicator extraction and querying.

[0003] Currently, some companies have developed automated review tools for building planning and approval applications based on Building Information Modeling (BIM), which include functions for statistical analysis and export of building planning and approval indicators. These tools utilize a Revit-assisted modeling plugin to mark different types of components, such as green spaces and parking spaces. The tools also use pre-defined forms to statistically analyze and generate reports on relevant indicators; the forms are in the format shown below. Figure 1 As shown, this tool uses a combination of assisted modeling and pre-built statistical algorithms to statistically analyze common building planning indicators. However, because its statistical methods are not visible to the user, users cannot query target indicators by customizing statistical rules. Furthermore, categorizing components by tags adds extra modeling work for the user.

[0004] Currently, many automated code review tools are similar to those mentioned above, exhibiting high customization and a lack of scalability. The BIMChecker model inspection tool developed by the School of Software at Tsinghua University addresses this issue. This tool uses Structured Natural Language (SNL) to define and describe review rules, and then processes them using a corresponding review engine to review custom rules, thereby enabling its expansion to different fields and types of building codes. The tool generates corresponding semantic models from BIM models to check semantic information. However, because it only supports generating semantic models of single buildings, it can only perform code reviews within a single building and cannot support larger-scale building-related code reviews, such as building planning and permit reviews for multiple building models. Summary of the Invention

[0005] To address the aforementioned issues, the purpose of this invention is to provide a structured rule description method and system for querying building code indicators, capable of reviewing building planning applications for multiple building models.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, it provides a structured rule description method for querying building code indicators, comprising:

[0007] Obtain the multiple building BIM models of the project to be queried and their corresponding SNL rules;

[0008] Extract entity data from multiple building BIM models, determine the corresponding entity relationships, and construct a multi-building semantic model for the project to be queried.

[0009] Parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtain the SNL for building index query, and determine the SPARQL query statement for building index.

[0010] In the multi-building semantic model, the SPARQL query statement oriented towards building indicators is used to query and obtain the building code indicators of the multi-building BIM model of the project to be queried.

[0011] Parse the conclusion clause of the SNL query for building indexes, determine whether the conclusion clause contains calculations, and output the corresponding building code index as the target building code index based on the determination result.

[0012] Furthermore, it also includes:

[0013] The target building code indicators obtained from the statistics are stored in the format of "indicator name-entity name-indicator value".

[0014] Further, the step of extracting entity data from multiple building BIM models, determining corresponding entity relationships, and constructing a multi-building semantic model for the project to be queried includes:

[0015] The system retrieves attribute information from the multi-building BIM model of the project to be queried and stores it as an entity in the semantic model. The multi-building BIM model includes several individual building models and a site model.

[0016] Based on a macro-to-micro approach, entity data of the multi-building BIM model of the project to be queried is extracted layer by layer, and the corresponding entity relationships are determined to construct a multi-building semantic model of the project to be queried.

[0017] Furthermore, the step of extracting entity data from the multi-building BIM model of the project to be queried layer by layer based on a macro-to-micro approach, determining the corresponding entity relationships, and constructing a multi-building semantic model of the project to be queried includes:

[0018] Extract the individual building model entities and site model entities from the multi-building BIM model of the project to be queried, and store them in the corresponding entity containment relationship;

[0019] Extract and organize the entities in the individual building models of the multi-building BIM model of the project to be queried, and store them in the corresponding semantic model structure;

[0020] After traversing all components in the multi-building BIM model of the project to be queried, the multi-building semantic model is completed.

[0021] Further, the process of parsing and obtaining the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtaining the SNL for building index queries, and determining the SPARQL query statement for building indicators includes:

[0022] The SNL syntax parsing framework is used to parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, and to obtain the SNL for querying building indicators.

[0023] Based on the SNL for building metric queries, obtain the SPARQL query statement for building metrics.

[0024] Furthermore, the step of obtaining a SPARQL query statement based on the SNL for building metrics includes:

[0025] Identify the target attributes of the multiple building BIM models of the project to be queried using identifiers;

[0026] The target attributes of the multi-building BIM model of the project to be queried are identified by the attribute mapping configuration.

[0027] Skip the generation of the SPARQL clause corresponding to the conclusion clause of the SNL query for building indicators, and place the identifiers of the corresponding target attributes into the SELECT clause according to the corresponding order in the SNL to obtain the SPARQL query statement for building indicators.

[0028] Furthermore, the step of parsing the conclusion clause of the SNL query for building indexes, determining whether the conclusion clause contains calculations, and outputting the corresponding building code index as the target building code index based on the determination result includes:

[0029] The SNL syntax parsing framework is used to parse the conclusion clause of SNL queries for building indicators and determine whether the conclusion clause contains calculations.

[0030] If calculations are involved, the attribute information from the queried building code indicators will be backfilled into the corresponding attributes of SNL in order, the corresponding attribute values ​​will be calculated, and the calculation results will be output as the target building code indicator.

[0031] If no calculations are involved, the building code indicators obtained from the query will be directly output as the target building code indicators.

[0032] Secondly, a structured rule description system for querying building code indicators is provided, including:

[0033] The input module is used to input the multiple building BIM models of the project to be queried and their corresponding SNL rules;

[0034] The semantic model building module is used to extract entity data from multiple building BIM models, determine the corresponding entity relationships, and build a multi-building semantic model for the project to be queried.

[0035] The SNL extension module is used to parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtain the SNL for building index queries, and determine the SPARQL query statement for building indexes.

[0036] The query module is used to perform queries in the multi-building semantic model using SPARQL query statements oriented towards building indicators, and obtain the building code indicators of the multi-building BIM model of the project to be queried.

[0037] The conclusion clause parsing module is used to parse the conclusion clause of SNL for building index queries, determine whether the conclusion clause contains calculations, and output the corresponding building code index as the target building code index based on the judgment result.

[0038] Thirdly, a processing device is provided, including computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the above-mentioned structured rule description method for querying building code indicators.

[0039] Fourthly, a computer-readable storage medium is provided, wherein computer program instructions are stored on the computer-readable storage medium, and the computer program instructions, when executed by a processor, are used to implement the steps corresponding to the above-mentioned structured rule description method for querying building code indicators.

[0040] The present invention has the following advantages due to the adoption of the above technical solutions:

[0041] 1. The present invention constructs a semantic model of the multi-building BIM model of the project. By extending the structured natural language SNL for engineering domain inspection, it realizes the description of building indicators such as green space ratio, plot ratio, and building area. At the same time, through the corresponding SPARQL query statement generation method, it realizes the query of the above-mentioned rule-described indicators.

[0042] 2. This invention can solve the problem of lack of scalability in existing building planning and application review tools, and make it convenient for users to use custom rules to describe and query building indicators.

[0043] In summary, this invention can be widely applied in the field of automatic detection. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0045] Figure 1 This is a schematic diagram of a CBIM indicator form configuration window in the prior art provided by an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of a method flow provided in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the structure of a multi-building semantic model provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of an identifier-based index query extension SNL example provided in an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of a configuration-based index query extension SNL provided in an embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of a multi-building BIM model of a project provided in an embodiment of the present invention;

[0051] Figure 7 This is a schematic diagram of a portion of the SNL rules for a certain project provided in an embodiment of the present invention;

[0052] Figure 8 This is a schematic diagram of the query results for a certain item provided in an embodiment of the present invention. Detailed Implementation

[0053] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0054] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0055] Building planning and permitting is a key application area currently focused on in the automated review of building codes. The requirements for such reviews are typically related to factors such as the project's geographical location and area. The review process requires statistical analysis of relevant indicator values ​​by reviewers to determine compliance, rather than directly using rule-based compliance checks. Structured Natural Language (SNL) supports qualitative rule description methods, describing the relationships between components or the conditions that component attributes should meet, outputting a pass or fail result. However, this language does not support the description of rules for querying building indicators, thus failing to meet the needs of building planning and permitting reviews. The structured rule description method and system for querying building code indicators provided in this invention can be applied to the review of building planning and permitting and other building indicator-related codes, automatically detecting building code indicators in Building Information Modeling (BIM).

[0056] Example 1

[0057] like Figure 2 As shown, this embodiment provides a structured rule description method for querying building code indicators, including the following steps:

[0058] 1) Input the multi-building BIM model of the project to be queried and its corresponding SNL rules.

[0059] Specifically, a multi-building BIM model includes several individual building models and a site model.

[0060] 2) Obtain the attribute information from the multi-building BIM model of the project to be queried, and store it as an entity in the semantic model.

[0061] Specifically, the attribute information includes area entities such as project area, total building area, floor building area, green area, and underground building area.

[0062] More specifically, this attribute information refers to the attribute values ​​that the inspection engine determines an entity should possess based on its type. These attribute values ​​can be achieved by directly adding attribute fields within the entity (e.g., adding an area attribute to a green space entity), or by creating a new entity (e.g., creating a "Floor Building Area" entity with the "Area" attribute). The advantage of the latter is that it further decouples attribute data, allowing users to filter suitable entities for calculation and statistics in a customized way, rather than directly exposing the already calculated, larger-granularity attribute values ​​to the user (e.g., directly summing the building areas of each floor of a building to obtain the total building area).

[0063] 3) such as Figure 3As shown, based on a macro-to-micro approach, entity data of the multi-building BIM model of the project to be queried is extracted layer by layer, and the corresponding entity relationships are determined. This constructs a semantic model of the multi-building BIM model of the project to be queried, namely, a multi-building semantic model. Specifically:

[0064] 3.1) Extract the individual building model entities and site model entities from the multi-building BIM model of the project to be queried, and store them in the corresponding entity containment relationship.

[0065] Specifically, since a project with multiple building BIM models includes several models, in order to avoid conflicts in the numbers corresponding to entities extracted from each model and affecting the uniqueness of entity data, the "uniqueness" paradigm of "model number: entity number" is adopted to number the entities extracted from the model.

[0066] Specifically, the entity types of the multi-building BIM model of the project to be queried include individual building models and site models. Among them, the site model is the main body of the project and is the carrier for linking individual building models. Individual building models include floor entities, wall entities, door entities, and window entities, etc. Floor entities include spatial entities and independent components within the floor. Spatial entities include components contained in the space and spatial boundary components. The site model includes area entities, building floor entities, green space entities, parking space entities, and building set-line entities, etc.

[0067] Specifically, four types of entity relationships are proposed for entities and their attribute data, including the hasSubType inclusion relationship between the multi-building BIM model of the project to be queried and regional entities; the hasSubType inclusion relationship between a region and individual building model entities, building floor entities, and sub-regions (a component type used to model site models, usually used to model entities such as green spaces and roads); the isContaining inclusion relationship between a region and components such as building boundary line entities; and the hasSubType inclusion relationship between individual building model entities and building floor entities, as shown in Table 1 below:

[0068] Table 1: Entity Relationships in Multi-Building Models

[0069]

[0070] It should be noted that both isContaining and hasSubType contain mean "containment", but their actual meanings are different: isContaining mainly refers to "spatial containment of components", such as a project containing building boundaries, or a room containing doors; hasSubType mainly refers to "spatial containment of spaces", such as a project containing individual buildings, or an area containing rooms.

[0071] 3.2) Extract and organize the entities in the individual building models of the multi-building BIM model of the project to be queried, and store them in the corresponding semantic model structure.

[0072] 3.3) After traversing all components in the multi-building BIM model of the project to be queried, the semantic model of the multi-building BIM model is constructed. The specific components contained in the project of the multi-building BIM model of the project to be queried can be based on the components in the specific situation. For example, if the multi-building BIM model of the project to be queried does not have green space, then the corresponding entity cannot be extracted and the query cannot be performed.

[0073] 4) Using the SNL syntax parsing framework, the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried is parsed and obtained, and the SNL for querying building indicators is obtained, specifically:

[0074] 4.1) Parse the syntax structure of the SNL rule corresponding to the multiple building BIM models of the project to be queried, and locate the conclusion clause (i.e. the clause corresponding to keywords such as "then" or "all").

[0075] 4.2) Check if the conclusion clause of the positioning contains an "=" sign. This symbol is a necessary identifier for SNLs that are oriented towards building index queries. If it contains an "=" sign, proceed to step 4.3); otherwise, it is not an SNL that is oriented towards building index queries and the regular review process needs to be performed.

[0076] 4.3) Check whether the conclusion clause of the location contains the identifier "?" or the name of the attribute to be queried in the attribute mapping configuration. If it does, the SNL for building index query is obtained; otherwise, it is not an SNL for building index query and the regular review process needs to be performed.

[0077] 5) Based on the SNL for querying building metrics, obtain the SPARQL (SPARQL Protocol and RDF Query Language) query statement for building metrics, specifically:

[0078] 5.1) Use the identifier "?" to identify the target attribute of the multi-building BIM model of the project to be queried, such as... Figure 4 As shown.

[0079] 5.2) Identify the target attributes of the multiple building BIM models of the project to be queried through attribute mapping configuration, such as... Figure 5 As shown.

[0080] 5.3) Skip the generation of the SPARQL clause corresponding to the conclusion clause of the SNL for building index query, and place the identifiers of the corresponding target attributes from left to right into the SELECT clause according to the corresponding order in the SNL to obtain the SPARQL query statement for building index.

[0081] 6) In the multi-building semantic model, use the SPARQL query statement oriented towards building indicators to obtain the building code indicators of the multi-building BIM model of the project to be queried, including the correspondence between target entities and attribute information.

[0082] Specifically, this step yields the correspondence between target entities and attribute information, such as the total building area of ​​a building. However, whether it is a target entity for review needs to be determined through the following steps.

[0083] 7) Using the SNL syntax parsing framework, the conclusion clause of the SNL for building index queries is parsed to determine whether the conclusion clause contains calculations. If it does, the queried attribute information is backfilled into the corresponding attribute of the SNL in left-to-right order, the corresponding attribute value is calculated by inorder calculation, and the calculation result is output as the target building code index. If it does not contain calculations, the queried building code index is directly output as the target building code index.

[0084] Specifically, the calculation results are a multi-attribute comprehensive index, including plot ratio and green space ratio.

[0085] Specifically, the purpose of calculating and obtaining the corresponding attribute values ​​is to process building code indicators that involve multiple attribute calculations, such as the plot ratio. This attribute is usually obtained by dividing the total building area of ​​the project by the planned land area of ​​the project. The entity obtained in step 6) should be multiple buildings and a planned land area, as well as the total building area attribute and the planned land area attribute of each building. These attributes are calculated using the corresponding calculation formulas, and the results are used as the target building code indicators.

[0086] 8) Calculate the target building code indicators and store them in the format of "indicator name-entity name-indicator value".

[0087] The structured rule description method for querying building code indicators of the present invention will be described in detail below through specific embodiments:

[0088] like Figure 6 As shown, the project comprises four individual buildings and four green spaces. Each building has eight floors, and some of the regular SNLs are as follows: Figure 7 As shown, the final query results are as follows: Figure 8As shown, since the project does not have an underground section, there is no query value for the underground building area. As for building indicators such as plot ratio and green space ratio, which are calculated from multiple attributes, the results are consistent with the results obtained by using the corresponding attributes for calculation.

[0089] Example 2

[0090] This embodiment provides a structured rule description system for querying building code indicators, including:

[0091] The input module is used to input the multiple building BIM models of the project to be queried and their corresponding SNL rules.

[0092] The semantic model building module is used to extract entity data from multiple building BIM models, determine the corresponding entity relationships, and build a multi-building semantic model for the project to be queried.

[0093] The SNL extension module is used to parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtain the SNL for building index queries, and determine the SPARQL query statement for building indexes.

[0094] The query module is used to perform queries in the multi-building semantic model using SPARQL query statements oriented towards building indicators, and obtain the building code indicators of the multi-building BIM model of the project to be queried.

[0095] The conclusion clause parsing module is used to parse the conclusion clause of SNL for building index queries, determine whether the conclusion clause contains calculations, and output the corresponding building code index as the target building code index based on the judgment result.

[0096] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0097] Example 3

[0098] This embodiment provides a processing device corresponding to the structured rule description method for querying building code indicators provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.

[0099] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores computer programs that can run on the processing device. When the processing device runs the computer programs, it executes the structured rule description method for querying building code indicators provided in Embodiment 1.

[0100] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0101] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0102] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] Those skilled in the art will understand that the structure of the above-described computing device is only a partial structure related to the solution of this application and does not constitute a limitation on the computing device to which the solution of this application is applied. A specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0104] Example 4

[0105] This embodiment provides a computer program product corresponding to the structured rule description method for querying building code indicators provided in Embodiment 1. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the structured rule description method for querying building code indicators as described in Embodiment 1 are loaded.

[0106] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0107] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] The above embodiments are only used to illustrate the present invention. The structure, connection method and manufacturing process of each component can be varied. All equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A structured rule description method for querying building code indicators, characterized in that, include: Obtain the multiple building BIM models of the project to be queried and their corresponding SNL rules; Extract entity data from multiple building BIM models, determine the corresponding entity relationships, and construct a multi-building semantic model for the project to be queried. Parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtain the SNL for building index query, and determine the SPARQL query statement for building index. In the multi-building semantic model, the SPARQL query statement oriented towards building indicators is used to query and obtain the building code indicators of the multi-building BIM model of the project to be queried. The SNL syntax parsing framework is used to parse the conclusion clause of SNL queries for building indexes. It determines whether the conclusion clause contains calculations and outputs the corresponding building code index as the target building code index based on the result. If calculations are included, the queried attribute information is backfilled into the corresponding attributes of the SNL in left-to-right order, and the corresponding attribute values ​​are calculated using inorder calculus. The calculation result is then output as the target building code index. If no calculations are included, the queried building code index is directly output as the target building code index. The process of extracting entity data from multiple building BIM models, determining corresponding entity relationships, and constructing a multi-building semantic model for the project to be queried includes: The system retrieves attribute information from the multi-building BIM model of the project to be queried and stores it as an entity in the semantic model. The multi-building BIM model includes several individual building models and a site model. Based on a macro-to-micro approach, entity data of the multi-building BIM model of the project to be queried is extracted layer by layer, and the corresponding entity relationships are determined to construct a multi-building semantic model of the project to be queried. The process of parsing and obtaining the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtaining the SNL for building index queries, and determining the SPARQL query statement for building indexes includes: The SNL syntax parsing framework is used to parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, and to obtain the SNL for querying building indicators. Based on the SNL for querying building metrics, the SPARQL query statement for querying building metrics is obtained, including: Identify the target attributes of the multiple building BIM models of the project to be queried using identifiers; The target attributes of the multi-building BIM model of the project to be queried are identified by the attribute mapping configuration. Skip the generation of the SPARQL clause corresponding to the conclusion clause of the SNL query for building indicators, and place the identifiers of the corresponding target attributes into the SELECT clause according to the corresponding order in the SNL to obtain the SPARQL query statement for building indicators.

2. The structured rule description method for querying building code indicators as described in claim 1, characterized in that, Also includes: The target building code indicators obtained from the statistics are stored in the format of "indicator name-entity name-indicator value".

3. The structured rule description method for querying building code indicators as described in claim 1, characterized in that, The process involves extracting entity data from the multi-building BIM model of the project to be queried layer by layer, based on a macro-to-micro approach, determining the corresponding entity relationships, and constructing a multi-building semantic model of the project to be queried, including: Extract the individual building model entities and site model entities from the multi-building BIM model of the project to be queried, and store them in the corresponding entity containment relationship; Extract and organize the entities in the individual building models of the multi-building BIM model of the project to be queried, and store them in the corresponding semantic model structure; After traversing all components in the multi-building BIM model of the project to be queried, the multi-building semantic model is completed.

4. A structured rule description system for querying building code indicators, characterized in that, include: The input module is used to input the multiple building BIM models of the project to be queried and their corresponding SNL rules; The semantic model building module is used to extract entity data from multiple building BIM models, determine the corresponding entity relationships, and build a multi-building semantic model for the project to be queried. The SNL extension module is used to parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtain the SNL for building index queries, and determine the SPARQL query statement for building indexes. The query module is used to perform queries in the multi-building semantic model using SPARQL query statements oriented towards building indicators, and obtain the building code indicators of the multi-building BIM model of the project to be queried. The conclusion clause parsing module uses the SNL syntax parsing framework to parse the conclusion clause of SNL queries for building indicators. It determines whether the conclusion clause contains calculations and outputs the corresponding building code indicator as the target building code indicator based on the result. If calculations are included, the queried attribute information is backfilled into the corresponding attributes of the SNL in left-to-right order, and the corresponding attribute values ​​are calculated using inorder calculus. The calculation result is then output as the target building code indicator. If no calculations are included, the queried building code indicator is directly output as the target building code indicator. The process of extracting entity data from multiple building BIM models, determining corresponding entity relationships, and constructing a multi-building semantic model for the project to be queried includes: The system retrieves attribute information from the multi-building BIM model of the project to be queried and stores it as an entity in the semantic model. The multi-building BIM model includes several individual building models and a site model. Based on a macro-to-micro approach, entity data of the multi-building BIM model of the project to be queried is extracted layer by layer, and the corresponding entity relationships are determined to construct a multi-building semantic model of the project to be queried. The process of parsing and obtaining the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, obtaining the SNL for building index queries, and determining the SPARQL query statement for building indexes includes: The SNL syntax parsing framework is used to parse and obtain the syntax structure of the SNL rules corresponding to the multiple building BIM models of the project to be queried, and to obtain the SNL for querying building indicators. Based on the SNL for querying building metrics, the SPARQL query statement for querying building metrics is obtained, including: Identify the target attributes of the multiple building BIM models of the project to be queried using identifiers; The target attributes of the multi-building BIM model of the project to be queried are identified by the attribute mapping configuration. Skip the generation of the SPARQL clause corresponding to the conclusion clause of the SNL query for building indicators, and place the identifiers of the corresponding target attributes into the SELECT clause according to the corresponding order in the SNL to obtain the SPARQL query statement for building indicators.

5. A processing device, characterized in that, It includes computer program instructions, wherein when the computer program instructions are executed by a processing device, they are used to implement the steps corresponding to the structured rule description method for querying building code indicators as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, wherein when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the structured rule description method for querying building code indicators as described in any one of claims 1-3.

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