IFC data analysis method and device, equipment, storage medium and program product

Abstract

The invention discloses an IFC data analysis method and device, equipment, a storage medium and a program product, and relates to the technical field of large language models.The IFC data analysis method includes the steps that according to standard documents of IFC data of all versions, a to-be-retrieved database of the IFC data is constructed; performing IFC format conversion on the BIM data to obtain to-be-analyzed IFC data, and performing retrieval in a to-be-retrieved database to obtain a query result; and inputting the to-be-analyzed IFC data and the query result into the large language model for reasoning to obtain analysis content. According to the invention, the IFC data analysis efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of large language models, and in particular to an IFC data parsing method, an IFC data parsing apparatus, an IFC data parsing device, a storage medium, and a computer program product. Background Art

[0002] Existing IFC (Industry Foundation Classes, data exchange standard) data parsing methods include direct parsing and parsing using special software. Among them, direct parsing of IFC data is difficult and requires professional technicians with professional knowledge to read it. In addition, professional technicians must refer to the IFC standard document and find the meaning of the IFC data line by line from the IFC standard document. This is less practical and has low efficiency in IFC data parsing. Using special software to parse IFC data has low flexibility and requires checking the attribute information of the IFC data corresponding to the BIM (Building Information Modeling) model one by one. This is too cumbersome and labor-intensive, and has low efficiency in IFC data parsing. Summary of the invention

[0003] The main purpose of the present application is to provide an IFC data parsing method, an IFC data parsing device, an IFC data parsing equipment, a storage medium and a computer program product, aiming to solve the technical problem of low efficiency of IFC data parsing.

[0004] To achieve the above purpose, the present application proposes an IFC data parsing method, the method comprising: According to the standard documents of each version of IFC data, build a database of IFC data to be retrieved; The IFC data to be parsed obtained by converting the BIM data into the IFC format is searched in the database to be searched to obtain a query result; The IFC data to be parsed and the query result are input into a large language model for reasoning to obtain parsed content.

[0005] In one embodiment, the step of constructing a database of IFC data to be searched according to the standard documents of various versions of IFC data includes: Preprocess the standard documents of each version of IFC data to obtain the target document; The target document is converted into a vector representation, and a to-be-retrieved database of IFC data is constructed according to the target document represented by the vector.

[0006] In one embodiment, the step of inputting the IFC data to be parsed and the query result into a large language model for inference to obtain parsed content includes: The IFC data to be parsed and the query results are spliced ​​with reference to the preset prompt word template to obtain the prompt words for reasoning in the large language model; In the large language model, the IFC data to be parsed is inferred according to the prompt words to obtain parsed content.

[0007] In one embodiment, after the step of inputting the IFC data to be parsed and the query result into a large language model for inference to obtain parsed content, the following steps are included: According to the association relationship between entities in the IFC data to be parsed, determine the entities related to the current entity; The parsed content of the related entity is added to the parsed content of the current entity to obtain parsed content with an associated relationship.

[0008] In one embodiment, after the step of inputting the IFC data to be parsed and the query result into a large language model for inference to obtain parsed content, the following steps are included: Extracting first parsed content from the parsed content; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the same entity to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

[0009] In one embodiment, the IFC data parsing method includes: Extracting a first parsed content from the parsed content with an association relationship, wherein the association relationship is an association relationship between a current entity and an entity related to the current entity; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the entity with the association relationship to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

[0010] In addition, to achieve the above-mentioned purpose, the present application also proposes an IFC data parsing device, the IFC data parsing device comprising: a construction module, used to construct a to-be-retrieved database of IFC data according to standard documents of various versions of IFC data; A retrieval module is used to retrieve the IFC data to be parsed obtained by converting the BIM data into the IFC format, and obtain the query result in the database to be retrieved; The parsing module is used to input the IFC data to be parsed and the query result into a large language model for reasoning to obtain parsed content.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes an IFC data parsing device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the IFC data parsing method described above.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the IFC data parsing method described above are implemented.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the IFC data parsing method as described above are implemented.

[0014] One or more technical solutions proposed in this application have at least the following technical effects: Due to the existing methods of directly parsing IFC data and using specialized software to parse IFC data, the efficiency of IFC data parsing is low. The present application constructs a database of IFC data to be retrieved based on the standard documents of IFC data. During parsing, the parsed content is obtained through retrieval and large language model reasoning. There is no need for professionals to compare standard documents line by line, which reduces the reliance on professional knowledge and improves the efficiency of IFC data parsing. Through retrieval and large language model reasoning, the IFC data to be parsed can be quickly compared and retrieved in the database to be retrieved. The large language model can quickly infer the query results and the data to be parsed, which can reduce the tedious work of checking attribute information one by one, thereby effectively improving the parsing efficiency of IFC data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart of an embodiment of an IFC data parsing method of the present application is provided; Figure 2 A brief flowchart of the IFC data parsing method provided for this application; Figure 3 A flowchart of the IFC data parsing method provided for this application; Figure 4 This is a schematic diagram of the module structure of the IFC data parsing device of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the IFC data parsing method of this application.

[0018] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] In the prior art, directly parsing IFC data means using a text editor to open the IFC data, comparing it with the IFC standard document, reading the IFC data line by line, and finding the corresponding meaning from the standard. Use professional software such as BIMversion to read the IFC data file, and click on the graphics one by one in the software to view the corresponding attribute information. However, directly parsing IFC files is difficult, and requires professional technicians with professional knowledge to read them. It is also inefficient and has poor practicality. The method of reading through BIMversion software is not flexible enough and is too cumbersome. You can only view the attribute information corresponding to the BIM model one by one, which is too cumbersome and labor-intensive. The methods of the prior art make IFC data parsing inefficient.

[0022] There are the following difficulties in combining IFC data analysis with large models: IFC data is described in the EXPRESS language and has a complex format, while large models usually accept standardized data formats. Converting IFC data to a format that large models can process requires complex data analysis and conversion; IFC data contains a large amount of information over the entire life cycle of a construction project, and the data volume is huge. When processing large-scale data, large models require powerful computing resources and efficient algorithms to ensure processing speed and accuracy. Large models need to have the ability to understand knowledge in the construction field in order to accurately process and analyze IFC data.

[0023] The present application provides an IFC data parsing method based on a large model. During parsing, the parsed content is obtained through retrieval and large language model reasoning. There is no need for professionals to compare standard documents line by line, which reduces the dependence on professional knowledge. It can quickly infer the query results and the data to be parsed, and can reduce the tedious work of checking attribute information one by one, thereby effectively improving the parsing efficiency of IFC data.

[0024] It should be noted that the execution subject of this embodiment may be an IFC data analysis device, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a processor, etc. capable of realizing the above functions. The following takes the IFC data analysis device as an example to illustrate this embodiment and the following embodiments.

[0025] Based on this, the present application embodiment provides an IFC data parsing method, referring to Figure 1 , Figure 1 This is a flowchart of an embodiment of the IFC data parsing method of the present application.

[0026] In this embodiment, the IFC data parsing method includes steps S10 to S30: Step S10, constructing a to-be-searched database of IFC data according to standard documents of various versions of IFC data; It should be noted that IFC is a data exchange standard that defines the expression and exchange methods of various types of information in the entire life cycle of a building project (including design, construction, operation and maintenance, etc.). The standard document is a specification of IFC data, which contains detailed definitions of various entities (such as walls, columns, beams and other building components), attributes (such as component size, materials, etc.) and the relationship between them. The standard document is used to understand and process IFC data. For example, multiple versions of IFC data standard documents can be obtained from the IFC standard official website.

[0027] The use of standard documents of various versions of IFC data to construct a database to be searched specifically includes: converting various information in the IFC standard documents (such as text descriptions, data structures, etc.) into vector representations, such as converting standard documents into numerical vectors through an embedding model. The purpose of vectorization is to be able to utilize the characteristics of vector operations, such as efficient similarity calculations (such as cosine similarity, etc.).

[0028] First, build a vector database such as Faiss, Annoy, Elasticsearch's vector search plug-in, Milvus, ChromaDB, etc. to store vectorized data. Store the vector data corresponding to different versions of IFC standard documents in the built vector database for subsequent query and retrieval operations.

[0029] In one embodiment, the database to be searched is a vector database, which stores vectorized information of various versions of IFC data standard documents. The vector representation of various knowledge about IFC data structure, entity, attribute, etc. is equivalent to a knowledge base, which has certain reference value when parsing the IFC data to be parsed.

[0030] In one embodiment, step S10 includes steps A10 to A20: Step A10, preprocessing the standard documents of each version of IFC data to obtain a target document; It should be noted that the standard documents of each version of IFC data are constructed and stored separately. Preprocessing includes data processing, document segmentation and content extraction. Among them, data cleaning includes cleaning the collected IFC standard documents, removing irrelevant information, correcting errors, unifying the format, etc. This includes steps such as removing HTML tags, deleting stop words, word segmentation, and removing special characters; document segmentation includes that the content of the IFC data standard document is long, and due to the input restrictions of the model (such as the number of tokens), it needs to be divided into smaller parts. These parts should maintain semantic integrity as much as possible, such as segmenting by paragraph or chapter; content extraction includes extracting key information from the IFC standard document, such as titles, paragraphs, keywords, data rules, etc., to facilitate subsequent vector generation and storage.

[0031] Step A20: convert the target document into a vector representation, and construct a to-be-retrieved database of IFC data according to the target document represented by the vector.

[0032] It should be noted that the target document obtained by the above preprocessing is converted into a numerical vector using an embedding model, where the embedding model can be a pre-trained model, such as BERT, GPT, etc., or a customized model specific to the task. Using the embedding model can be calling the embedding model API or loading the embedding model into the local environment, sending the above cleaned and segmented target document or target document fragment as input to the embedding model, receiving the vector representation of the target document returned by the embedding model, and finally storing the generated vector in the vector database, that is, building a database to be retrieved for IFC data.

[0033] In this embodiment, preprocessing of each version of the IFC data standard document can improve the quality of the data. Different versions of IFC data may have inconsistent formats or different expressions. Preprocessing can convert these data into a more unified format, making the retrieval results more accurate. The database to be retrieved constructed based on vector representation can more accurately find relevant IFC data by calculating the similarity between the query vector and the target document represented by the vector in the database, which can improve the accuracy of retrieval and the efficiency of parsing IFC data.

[0034] Step S20, converting the BIM data into the IFC format to obtain the IFC data to be parsed, searching the database to be searched, and obtaining the query result; It should be noted that this step is equivalent to data preparation. It is necessary to collect and organize the IFC data to be extracted, confirm that the IFC data can be opened normally, and confirm the IFC version. If the BIM data has not been converted to IFC format, it is necessary to use BIM software to export it to IFC format. BIM, or Building Information Model, is a digital building design and management method. Converting BIM data to IFC format means converting the data generated by BIM software into a format that complies with the IFC standard so that it can be used in other software or systems that support IFC. For example, in Revit software, the building model can be saved as an IFC file through the "Export" function. The IFC data to be parsed contains a lot of building information, but it needs to be parsed to be understood and applied.

[0035] Before retrieving the IFC data to be parsed in the database to be searched, the IFC data to be parsed needs to be converted into a vector representation through an embedded model. Through vectorization, the originally complex IFC data can be converted into a form that is easier for computers to process, thereby preparing for subsequent parsing.

[0036] The vector database has stored the vectorized data of the standard documents of various versions of IFC data; in the vector database, the vectorized IFC data to be parsed is retrieved, and the vector data most similar to the vector data to be retrieved can be found in the vector database by calculating the similarity measure between the vector data to be retrieved and the vector data stored in the database; then the top 100 most similar vector data are used as query results, wherein the query results are the content related to the IFC data to be parsed in the IFC standard document. In addition, there is no specific limitation on how many of the top vector data are taken as query results.

[0037] Step S30: input the IFC data to be parsed and the query result into the large language model for reasoning to obtain parsed content.

[0038] It should be noted that the IFC data to be parsed and the query results are provided as input to the large language model. The large language model will infer the IFC data to be parsed based on its own algorithm and learned knowledge, combined with the reference knowledge provided by the vector database, to obtain the parsed content. For example, the input of the large model can be "input content: IFC data, reference knowledge: query results". It can also be "according to the requirements, based on the content and reference knowledge in the following input, generate an answer that can correctly parse and translate the input content, and the answer must be easy to read and understand." The parsed content obtained can be used for subsequent architectural design, construction management, operation and maintenance and other related work.

[0039] In this embodiment, a database of IFC data to be retrieved is constructed based on the standard documents of IFC data. During parsing, the parsed content is obtained through retrieval and large language model reasoning. There is no need for professionals to compare the standard documents line by line, which reduces the reliance on professional knowledge and improves the efficiency of IFC data parsing. Through retrieval and large language model reasoning, the IFC data to be parsed can be quickly compared and retrieved in the database to be retrieved. The large language model can quickly reason about the query results and the data to be parsed, which can reduce the tedious work of checking the attribute information one by one, thereby effectively improving the parsing efficiency of IFC data.

[0040] In one embodiment, step S30 includes steps B10 to B20: Step B10, splicing the IFC data to be parsed and the query results with reference to a preset prompt word template to obtain prompt words for reasoning in a large language model; It should be noted that the IFC data to be parsed obtained based on the format conversion and the query results retrieved based on the IFC data to be parsed in the vector database are spliced ​​with the "preset prompt word template" to construct "prompt words" suitable for reasoning in the large language model. In the large language model, the prompt word is an instruction or guidance to the large language model, telling the large language model what kind of information to reason, analyze or answer questions based on. By splicing the IFC data and the query results into prompt words according to the template, the large language model can use these data related to the construction field to perform specific reasoning tasks, thereby obtaining results that meet the needs.

[0041] Step B20, inferring the IFC data to be parsed according to the prompt words in the large language model to obtain parsed content.

[0042] It should be noted that the large language model infers the IFC data to be parsed based on the received prompt words, and uses the language knowledge, semantic information, and logical relationships learned during its pre-training process to conduct an in-depth analysis of the IFC data.

[0043] For example, parsing IFC data means translating the IFC data EXPRESS language into an easy-to-read and understand language. Based on the knowledge base, the RAG (Retrieval-augmented Generation) technology is used to parse IFC data. IFC data is text data and can be parsed row by row. Take this row of IFC data as an example "#268678167= IFCWALLSTANDARDCASE('2A_TnvSvT3Ju6Ruoo5s7HB',#268437024,'IfcWall-4',$,$,#268678164,#268678331,$”)”; Then the IFC data to be parsed and the query results are spliced ​​into context according to the template to form an enhanced Prompt (prompt word) for the large language model to answer reasoning. Template example "According to the requirements, based on the content and reference knowledge in the following input, generate an answer that can correctly parse and translate the input content. The answer is required to be easy to read and understand. If the given reference knowledge cannot help answer the question, form an answer based on your own knowledge.". Input the above prompt into the large language model, let the large language model perform reasoning and output, and you can get the parsed content, such as: “#268678167: The globally unique identifier of this node; IFCWALLSTANDARDCASE: instantiated entity type, representing a standard wall; 2A_TnvSvT3Ju6Ruoo5s7HB: ID of the standard wall; #268437024: The #268437024 node in this file. For its specific meaning, see the content of this node. IfcWall-4: Component name; $: indicates an empty value, this position should be a description; $: indicates a null value, this position should be the type; #268678164: The #268678164 node in this file. For its specific meaning, see the content of this node. #268678331: The #268678331 node in this file. For its specific meaning, see the content of this node. $: Indicates an empty value, this position should be a label. ".

[0044] In this embodiment, by splicing the IFC data to be parsed and the query results according to the preset prompt word template, the large language model can be reasoned in a complete information framework, so that the large language model can quickly locate relevant information and perform reasoning, avoiding information fragmentation, thereby improving the accuracy of reasoning, obtaining parsed content more quickly, and thus improving the efficiency of parsing.

[0045] In another embodiment, step S30 includes steps E10 to E20: Step E10, determining entities related to the current entity according to the association relationship between entities in the IFC data to be parsed; It should be noted that in IFC data, entities are abstract representations of various objects in the field of construction engineering. For example, in the IFC data of a construction project, entities can be building components such as walls, columns, beams, etc. in a building, or they can be elements such as personnel and materials in the project. These entities contain various attribute information related to themselves, such as the height, thickness, and material type of the wall. There are various associations between entities, and the associations reflect the actual connections between different objects in a construction project. For example, there may be a connection relationship between a wall entity and a column entity, and the wall may be supported by the column; or there may be a load-bearing relationship between a beam entity and a wall entity, and the beam may bear the weight of the wall.

[0046] Based on the associations between these entities, we can find other entities related to it. For example, if the current entity is a window, based on the associations, we can determine that the entities related to it are walls (because windows are usually installed on walls), rooms (the room where the window is located), etc.

[0047] Step E20, adding the parsed content of the related entity to the parsed content of the current entity to obtain parsed content with an associated relationship.

[0048] It should be noted that, first of all, the related entity and the current entity have their own parsed content; adding the parsed content of the related entity to the parsed content of the current entity means integrating the related entity with the association relationship into the information system of the current entity to obtain the parsed content with the association relationship. The parsed content with the association relationship is more comprehensive and rich, which not only contains the information of the current entity itself, but also contains the information of the impact of other entities associated with the current entity on the current entity.

[0049] For example, since there is an association relationship between nodes in the IFC data, the associated attribute information is associated, and the association can be performed by using keyword matching, association query, etc. After the association, the parsed content with the association relationship is “#268678167: the global unique identifier of the node; IFCWALLSTANDARDCASE: instantiated entity type, representing a standard wall; 2A_TnvSvT3Ju6Ruoo5s7HB: ID of the standard wall; #268437024: This standard wall history was created by the modifier; IfcWall-4: Component name; $: indicates an empty value, this position should be a description; $: indicates a null value, this position should be the type; #268678164: IFCLOCALPLACEMENT(#122,#134); The relative position of the standard wall in the entire building space coordinate system; #268678331: IFCPRODUCTDEFINITIONSHAPE($,$,(#141,#159)); The style, shape, and shape generation method of the standard wall; $: Indicates an empty value, this position should be a label. ".

[0050] In this implementation, related entities are determined based on the association relationship between entities, which can avoid viewing a single entity in isolation, help grasp the layout and functional relationship of the building as a whole, build a more complete spatial relationship network, and improve the completeness of the understanding of the entire IFC data.

[0051] Based on the above embodiment of the present application, in another embodiment of the present application, the same or similar contents as the above embodiment can be referred to the above introduction, and will not be repeated later. After step S30, the IFC data parsing method further includes: Extracting first parsed content from the parsed content; Parsing the IFC data to be parsed in the parsing software to obtain second parsed content; comparing the first parsed content with the second parsed content of the same entity to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

[0052] It should be noted that the first analysis content is extracted from the analysis content obtained by reasoning the large model, and the first analysis content is a part of the results obtained by analyzing the IFC data. The IFC data to be analyzed is analyzed in a special analysis software. In a feasible implementation, the analysis can be automatically performed by a script. The result obtained after the IFC data is analyzed by the analysis software is the second analysis content.

[0053] For the same entity, the first parsing content and the second parsing content are compared. Specifically, the attribute values ​​of the entities can be compared. For example, the length of a beam entity in the first parsing content is 5 meters, while the length of the beam entity in the second parsing content is 4.9 meters. This is the difference in attribute values. According to the comparison results, the IFC data parsing method is optimized. If the comparison results show that there are certain inaccuracies in the first parsing content (for example, there are large deviations in the entity attribute values), the prompt words can be readjusted, the knowledge base can be updated, the vector retrieval model can be replaced, etc. For example, if it is found that the first parsing content is unique in the qualitative analysis of certain entity attributes, and the second parsing content of the parsing software is more accurate in quantitative analysis, then the two can be combined to optimize the parsing method so that it can perform both accurate qualitative analysis and precise quantitative analysis, thereby improving the overall quality of IFC data parsing.

[0054] In this embodiment, the first analysis content is extracted from the analysis content and the second analysis content is obtained by the analysis software, and then compared. The difference in accuracy between the two analysis methods can be found. After optimizing the analysis method according to the difference, the analysis efficiency can be improved.

[0055] In one embodiment, the IFC data parsing method further includes: Extracting a first parsed content from the parsed content with an association relationship, wherein the association relationship is an association relationship between a current entity and an entity related to the current entity; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the entity with the association relationship to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

[0056] It should be noted that the analytical content obtained by large model reasoning can be compared with the analytical content obtained by the analytical software; the analytical content with an associated relationship can also be compared with the analytical content obtained by the analytical software.

[0057] The association relationship is the relationship between the current entity and the related entity. The first analysis content is extracted from the analysis content with the association relationship; then, the IFC data to be analyzed is put into a special analysis software for analysis to obtain the second analysis content; next, for the entities with the association relationship, the first analysis content and the second analysis content are compared, and the relationship between the entities and the attributes of the entities themselves should be considered during the comparison; finally, the IFC data analysis method is optimized based on the comparison results.

[0058] Exemplarily, the incorrectly parsed data is analyzed to find the reasons for the parsing errors, such as the prompt is not accurate enough, the corresponding text is not found in the retrieval, etc.; based on the analysis results, the IFC data parsing method is optimized, the prompt is adjusted, the knowledge base is updated, the vector retrieval model is replaced to increase the retrieval accuracy, etc. The above steps are repeated for multiple experiments to continuously improve the accuracy of IFC data parsing.

[0059] In this embodiment, by extracting the first parsed content from the parsed content with the association relationship and comparing it with the second parsed content obtained by the parsing software, the difference in the entity attribute parsing can be found, and the parsing method can be optimized for this difference, thereby improving the accuracy of the entity attribute parsing. By comparing and optimizing the parsing method, this association relationship can be parsed more accurately, the parsing process can be made more targeted, repetitive parsing work can be reduced, and the efficiency of IFC data parsing can be improved.

[0060] For example, to help understand the brief process of the IFC data parsing method obtained by combining this embodiment with the above embodiments, please refer to Figure 2 , Figure 2 A brief flow diagram of an IFC data parsing method is provided, specifically: the entire IFC data parsing process may include: knowledge base construction, i.e., preprocessing the standard document of IFC data, converting it into a numerical vector, and storing it in a vector library; data preparation, i.e., converting the format of BIM data to obtain IFC data to be parsed, converting the IFC data to be parsed into a numerical vector, searching in a vector database, and obtaining a query result; data parsing, i.e., splicing the IFC data to be parsed and the query result into prompt words, reasoning the IFC data with reference to the knowledge base, and obtaining parsing content; result collation, i.e., adding related entities with an association relationship with the current entity to the parsing content of the current entity; and continuous optimization, i.e., comparing the parsing content with the result obtained by the parsing software, and optimizing the IFC data parsing process according to the comparison result.

[0061] For example, please refer to Figure 3 , Figure 3 A flow chart of an IFC data parsing method is provided, specifically: after vectorizing the standard documents of various versions of IFC data, the documents are stored in a vector database respectively; after vectorizing the IFC data to be parsed, a search query is performed in the vector database to obtain a query result; using the standard documents of various versions as a reference knowledge base, the splicing of the IFC data to be parsed and the query result as a prompt word, the IFC data to be parsed is inferred in a large language model to obtain parsed content; the parsed content obtained by the large model reasoning can be compared with the parsed content obtained by the parsing software to obtain a comparison result, thereby optimizing the IFC data parsing method; the parsed content with an associated relationship can also be compared with the parsed content obtained by the parsing software to obtain a comparison result, thereby optimizing the IFC data parsing method.

[0062] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the IFC data parsing method of the present application. More forms of simple transformations based on this technical concept, such as the interaction and combination of various embodiments, are all within the scope of protection of the present application.

[0063] This application also provides an IFC data parsing device, please refer to Figure 4 , the IFC data parsing device comprises: A construction module 10 is used to construct a database of IFC data to be searched according to standard documents of various versions of IFC data; A search module 20 is used to search the IFC data to be parsed obtained by converting the BIM data into the IFC format in the database to be searched to obtain a query result; The parsing module 30 is used to input the IFC data to be parsed and the query result into a large language model for reasoning to obtain parsed content.

[0064] Optionally, the construction module 10 is further used to pre-process the standard documents of each version of IFC data to obtain a target document; The target document is converted into a vector representation, and a to-be-retrieved database of IFC data is constructed according to the target document represented by the vector.

[0065] Optionally, the parsing module 30 is further used to splice the IFC data to be parsed and the query result with reference to a preset prompt word template to obtain prompt words for reasoning in the large language model; In the large language model, the IFC data to be parsed is inferred according to the prompt words to obtain parsed content.

[0066] Optionally, the parsing module 30 is further configured to determine entities related to the current entity according to association relationships between entities in the IFC data to be parsed; The parsed content of the related entity is added to the parsed content of the current entity to obtain parsed content with an associated relationship.

[0067] Optionally, the parsing module 30 is further configured to extract first parsed content from the parsed content; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the same entity to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

[0068] Optionally, the parsing module 30 is further configured to extract the first parsed content from the parsed content with the association relationship, wherein the association relationship is the association relationship between the current entity and an entity related to the current entity; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the entity with the association relationship to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

[0069] The IFC data parsing device provided by the present application adopts the IFC data parsing method in the above embodiment, which can solve the technical problem of low IFC data parsing efficiency. Compared with the prior art, the beneficial effects of the IFC data parsing device provided by the present application are the same as the beneficial effects of the IFC data parsing method provided by the above embodiment, and other technical features in the IFC data parsing device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0070] The present application provides an IFC data parsing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the IFC data parsing method in the above-mentioned first embodiment.

[0071] Reference below Figure 5, which shows a schematic diagram of the structure of an IFC data parsing device suitable for implementing the embodiment of the present application. The IFC data parsing device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The IFC data parsing device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0072] like Figure 5 As shown, the IFC data parsing device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM 1004, various programs and data required for the operation of the IFC data parsing device are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the IFC data parsing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an IFC data parsing device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0073] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0074] The IFC data parsing device provided by the present application adopts the IFC data parsing method in the above embodiment, which can solve the technical problem of low IFC data parsing efficiency. Compared with the prior art, the beneficial effects of the IFC data parsing device provided by the present application are the same as the beneficial effects of the IFC data parsing method provided by the above embodiment, and the other technical features in the IFC data parsing device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0075] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0076] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0077] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the IFC data parsing method in the above-mentioned embodiment.

[0078] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0079] The computer-readable storage medium may be included in the IFC data parsing device; or may exist independently without being assembled into the IFC data parsing device.

[0080] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the IFC data parsing device, the IFC data parsing device: constructs a to-be-searched database of IFC data according to standard documents of various versions of IFC data; The IFC data to be parsed obtained by converting the BIM data into the IFC format is searched in the database to be searched to obtain a query result; The IFC data to be parsed and the query result are input into a large language model for reasoning to obtain parsed content.

[0081] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0083] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0084] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned IFC data parsing method, and can solve the technical problem of low efficiency of IFC data parsing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the IFC data parsing method provided in the above-mentioned embodiment, and will not be elaborated here.

[0085] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned IFC data parsing method when executed by a processor.

[0086] The computer program product provided by the present application can solve the technical problem of low efficiency of IFC data parsing. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the IFC data parsing method provided by the above embodiment, which will not be repeated here.

[0087] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An IFC data analysis method, characterized in that: The IFC data parsing method comprises: According to the standard documents of each version of IFC data, build a database of IFC data to be retrieved; The IFC data to be parsed obtained by converting the BIM data into the IFC format is searched in the database to be searched to obtain a query result; The IFC data to be parsed and the query result are input into a large language model for reasoning to obtain parsed content.

2. The IFC data analysis method according to claim 1, characterized in that: The step of constructing a database of IFC data to be searched according to the standard documents of various versions of IFC data includes: Preprocess the standard documents of each version of IFC data to obtain the target document; The target document is converted into a vector representation, and a to-be-retrieved database of IFC data is constructed according to the target document represented by the vector.

3. The IFC data analysis method according to claim 1, characterized in that: The step of inputting the IFC data to be parsed and the query result into a large language model for reasoning to obtain parsed content includes: The IFC data to be parsed and the query results are spliced ​​with reference to the preset prompt word template to obtain the prompt words for reasoning in the large language model; In the large language model, the IFC data to be parsed is inferred according to the prompt words to obtain parsed content.

4. The IFC data analysis method according to claim 1, characterized in that: After the step of inputting the IFC data to be parsed and the query result into a large language model for inference to obtain parsed content, the method further comprises: According to the association relationship between entities in the IFC data to be parsed, determine the entities related to the current entity; The parsed content of the related entity is added to the parsed content of the current entity to obtain parsed content with an associated relationship.

5. The IFC data parsing method according to claim 1, characterized in that: After the step of inputting the IFC data to be parsed and the query result into a large language model for inference to obtain parsed content, the method further comprises: Extracting first parsed content from the parsed content; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the same entity to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

6. The IFC data analysis method according to claim 5, characterized in that: The IFC data parsing method comprises: Extracting a first parsed content from the parsed content with an association relationship, wherein the association relationship is an association relationship between a current entity and an entity related to the current entity; Parsing the IFC data to be parsed in the parsing software to obtain second parsing content; Compare the first parsed content and the second parsed content of the entity with the association relationship to obtain a comparison result; According to the comparison result, the IFC data parsing method is optimized.

7. An IFC data analysis device, characterized in that: The IFC data parsing device comprises: A construction module, used for constructing a database of IFC data to be retrieved according to standard documents of various versions of IFC data; A retrieval module is used to retrieve the IFC data to be parsed obtained by converting the BIM data into the IFC format, and obtain the query result in the database to be retrieved; The parsing module is used to input the IFC data to be parsed and the query result into a large language model for reasoning to obtain parsed content.

8. An IFC data analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the IFC data parsing method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the IFC data parsing method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the IFC data parsing method according to any one of claims 1 to 6 are implemented.

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