Building intelligent design specification provision digital model generation method

Abstract

The invention relates to the technical field of building model construction, and discloses a building intelligent design standard provision digital model generation method, which comprises the following steps: pulling an original provision set from a pre-constructed standard provision database according to a generation instruction, each original provision of the original provision set being in a text form, and each original provision being in a text form; the structural articles are in a tetrad form and are composed of entity articles and confidence coefficient true values, a standard article original model is obtained, the original article set in the text form is input into the standard article original model to execute confidence coefficient judgment, a confidence coefficient judgment value is obtained, and the confidence coefficient true values are determined according to the confidence coefficient judgment value. And according to a difference value between the confidence coefficient true value and the confidence coefficient judgment value, adjusting model parameters until the difference value between the confidence coefficient true value and the confidence coefficient judgment value is smaller than a preset threshold value, and obtaining a standard provision standard model. The invention mainly aims to efficiently generate the standard article model under the condition of ensuring accurate judgment of confidence.

Description

Technical Field

[0001] The invention relates to a method for generating a digital model of building intelligent design specification clauses, and belongs to the technical field of building model construction. Background Art

[0002] The field of architectural design involves a large number of complex regulations. Traditional manual processing methods are inefficient and prone to errors. By digitizing the regulations, the regulations can be automatically parsed, stored, and applied, improving design efficiency and ensuring that the design meets the requirements of the regulations. The digital model can convert text-based regulations into structured data, which is convenient for computer processing and analysis, and provides reliable technical support for intelligent design.

[0003] However, the current digital model generation technology for regulatory provisions has certain drawbacks. When processing provisions, existing models often have difficulty accurately capturing the semantic and structural information of the provisions, resulting in inaccurate confidence judgments and affecting the reliability of model output. In addition, there is a lack of systematic methods for adjusting and optimizing model parameters. Therefore, how to ensure efficient generation of digital models under accurate confidence judgments is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a method, device and computer-readable storage medium for generating a digital model of building intelligent design specification clauses, the main purpose of which is to ensure accurate confidence judgment and efficiently generate a specification clause model.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for generating a digital model of building intelligent design specification clauses, comprising: Receiving a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and pulling an original clause set from a pre-built specification clause database according to the generation instruction, wherein each original clause of the original clause set is in text form; Obtain the structural clause of each original clause in text form, wherein the structural clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; Obtaining the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes, in order from data input to data output, a BERT layer, an LSTM layer, a GNN layer, and a confidence determination layer; Inputting the original clause set in text form into the original model of the standard clause to perform confidence determination and obtain a confidence determination value; According to the difference between the true confidence value and the confidence judgment value, the model parameters of the BERT layer, LSTM layer, and GNN layer are adjusted until the difference between the true confidence value and the confidence judgment value is less than a preset threshold. The model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the regulatory text database, and the regulatory text standard model is obtained at the same time.

[0006] Optionally, according to the generating instruction, an original article set is pulled from a pre-built standard article database, wherein each original article in the original article set is in text form, including: Parsing the generation instruction to obtain the building information of the digital model of the specification clause before the generation, wherein the building information includes the building type, building time, building scale, building style and material; Using the building information as a first search condition, searching in a regulatory article database to obtain a first article collection; Determine whether the size of the first set of articles is larger than the size of data generated by the pre-set digital model of regulatory articles; If the size of the first set of articles is not greater than the size of data generated by the digital model of the standard articles, the construction time in the building information is removed to obtain the second search condition, and the second search condition is used to perform a search again in the standard article database to obtain the second set of articles; Determine whether the size of the second set of articles is larger than the size of data generated by the digital model of regulatory articles; If the size of the second set of articles is not greater than the size of data generated by the digital model of the standard articles, a generation failure instruction of the digital model of the standard articles is directly obtained, and the generation failure instruction is sent to the port of the generation instruction of the digital model of the standard articles; If the scale of the first or second article collection is larger than the data scale generated by the digital model of the standard articles, the first or second article collection shall be the original article collection.

[0007] Optionally, the structural clause is expressed as: , in, Indicates The structural provisions of the original provisions in the form of article texts, Indicates The substantive provisions of the original article, Indicates The header entity of the original article, Indicates The end entity of the original article, Represents the entity relationship between the head entity and the tail entity, Indicates The true value of the confidence of the entity clause corresponding to the original clause.

[0008] Optionally, the confidence determination layer includes a confidence calculation function and a confidence determination function, wherein the confidence determination function is: , in, Represents the difference between the confidence judgment value and the true confidence value, Indicates the original article The original text in the form of the article is input into the BERT layer, LSTM layer, and GNN layer of the standard article original model to obtain the entity article and the first The textual difference between the substantive clauses of the original clauses, Indicates The confidence value of the original article.

[0009] Optionally, the step of inputting the original set of textual articles into the original model of the standard article to perform confidence determination and obtain the confidence determination value includes: Each original article in the original article set in text form is input into the original model of the standard article in sequence, and the confidence judgment value calculation process of each original article is as follows: Use the BERT layer to perform word encoding on the original text to obtain the original text in vectorized form; The original clauses in vectorized form are imported into the LSTM layer, and the original clauses in vectorized form are corrected by using the LSTM to obtain the corrected original clauses; Use the GNN layer to transform the corrected original clauses into the original clauses in structured form; The confidence level is calculated using the confidence level determination layer to obtain the confidence level determination value. .

[0010] Optionally, the using the confidence determination layer to calculate the confidence of the original clause in structured form to obtain the confidence determination value includes: The original structured clause is transformed into a single-dimensional vector to obtain a single-dimensional clause, where the representation of the single-dimensional clause is: , in, Indicates The single-dimensional article of the original article, Indicates The first entity after the original article is transformed by the GNN layer, and the first entity after the single-dimensional vector transformation is performed vector value, Indicates The entity relationship of the original article after the GNN layer transformation, the first vector value, Indicates The tail entity of the original article after the GNN layer transformation, the first vector value; Use the confidence calculation function to calculate the confidence of the single-dimensional clause and obtain the confidence judgment value .

[0011] Optionally, the confidence calculation function is used to calculate the confidence of the single-dimensional clause to obtain a confidence judgment value , including the confidence value calculated according to the following formula : , in, yes The weight value of yes The weight value of yes The weight value of .

[0012] Optionally, the The weight value of is negative. and The weight value of is a positive number.

[0013] Optionally, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are adjusted according to the difference between the true confidence value and the confidence judgment value until the difference between the true confidence value and the confidence judgment value is less than a preset threshold, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are stored in the standard text database, and the standard text standard model is obtained at the same time, including: Using the confidence judgment function, the difference between the true confidence value and the confidence judgment value is calculated; Determine the relationship between the difference between the true confidence value and the confidence determination value and a preset threshold value; When the difference between the true confidence value and the confidence judgment value is greater than a preset threshold, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are adjusted, where the model parameters include the number of attention heads of the BERT layer, the number of hidden units and sequence length of the LSTM layer, and the number of stacking layers and aggregation function of the GNN layer; When the difference between the true confidence value and the confidence judgment value is less than or equal to a preset threshold, the adjusted model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the standard article database, and when the model parameters of the BERT layer, LSTM layer, and GNN layer are successfully stored in the standard article database, the standard article standard model automatic generation program is started, wherein the standard article standard model automatic generation program is embedded with a standard article generation template; The standard model of regulatory clauses is automatically generated by using the program, the model parameters of the stored BERT layer, LSTM layer, and GNN layer are extracted from the regulatory clause database, and the regulatory clause generation template is combined to obtain the standard model of regulatory clauses.

[0014] To achieve the above object, the present invention also provides a digital model generation system for building intelligent design specification clauses, characterized in that the system comprises: A clause extraction module is used to receive a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and according to the generation instruction, extract an original clause set from a pre-built specification clause database, wherein each original clause of the original clause set is in text form; A structure clause acquisition module is used to obtain the structure clause of each original clause in text form, wherein the structure clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; The confidence judgment module is used to obtain the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes a BERT layer, an LSTM layer, a GNN layer and a confidence judgment layer in the order from data input to output, and the original clause set in text form is input into the original model of the regulatory clauses to perform confidence judgment and obtain a confidence judgment value; The standard model completion module is used to adjust the model parameters of the BERT layer, LSTM layer, and GNN layer according to the difference between the true confidence value and the confidence judgment value, until the difference between the true confidence value and the confidence judgment value is less than a preset threshold, and then store the model parameters of the BERT layer, LSTM layer, and GNN layer in the specification clause database, and obtain the standard model of the specification clause at the same time.

[0015] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned method for generating a digital model of building intelligent design specification clauses.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for generating a digital model of building intelligent design specification clauses.

[0017] Compared with the problems described in the background technology, the present invention clarifies the model structure of each level. Specifically, the BERT layer, LSTM layer, and GNN layer have clear division of labor, and comprehensively parse the clauses from the dimensions of semantics, sequence, and structured information, respectively, to ensure the accuracy of confidence judgment. At the same time, the present invention drives parameter optimization through the difference between the true value of confidence and the judgment value, and combines threshold control to achieve rapid convergence, taking into account both accuracy and efficiency. In addition, through clause database support, structured processing, automatic generation, and templated output, full automation from data input to model output is achieved, significantly improving generation efficiency. Therefore, the digital model generation method, system, electronic device, and computer-readable storage medium of the building intelligent design specification clause proposed in the present invention can ensure the efficient generation of the specification clause model under accurate confidence judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a process for generating a digital model of building intelligent design specification clauses provided in an embodiment of the present invention; Figure 2 A functional module diagram of a digital model generation system for building intelligent design specification clauses provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for generating a digital model of building intelligent design specification clauses provided in an embodiment of the present invention.

[0019] Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] The embodiment of the present application provides a method for generating a digital model of the provisions of the building intelligent design specification. The execution subject of the method for generating a digital model of the provisions of the building intelligent design specification includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for generating a digital model of the provisions of the building intelligent design specification can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Embodiment 1: Reference Figure 1 FIG. 1 is a flow chart of a method for generating a digital model of a building intelligent design specification clause provided by an embodiment of the present invention. In this embodiment, the method for generating a digital model of a building intelligent design specification clause includes: S1. Receive a generation instruction for a digital model of standard clauses, wherein the digital model of standard clauses is applied to the field of architectural design. According to the generation instruction, pull an original clause set from a pre-built standard clause database, wherein each original clause in the original clause set is in text form.

[0024] It should be explained that the generation instruction refers to an instruction for realizing a digital model of standard clauses, and in the embodiment of the present invention, the digital model of standard clauses is the standard model of standard clauses. Specifically, the standard model of standard clauses is obtained by adjusting the model parameters of the internal BERT layer, LSTM layer, and GNN layer of the original model of the standard clauses. Furthermore, the field of architectural design described in the embodiment of the present invention includes architectural planning, design, construction, and management, and covers functions such as function, aesthetics, structure, environment, and sustainability.

[0025] Generally speaking, the instructions for generating digital models of regulatory provisions are usually issued by personnel or departments in the field of architectural design, and use pre-built digital models of regulatory provisions to generate APPs or mini-programs. For example, Xiao Zhang is the head of the architectural planning department and is currently in charge of the construction work of a certain commercial district. In order to improve the construction efficiency of the commercial district, Xiao Zhang wants to quickly generate a set of regulatory provisions for the commercial district based on the digital model of regulatory provisions. Therefore, Xiao Zhang needs to generate the corresponding digital model of regulatory provisions first, so he initiates the generation instruction.

[0026] Furthermore, according to the generation instruction, an original set of clauses is pulled from a pre-built database of standard clauses, wherein each original clause of the original set of clauses is in text form, including: Parsing the generation instruction to obtain the building information of the digital model of the specification clause before the generation, wherein the building information includes the building type, building time, building scale, building style and material; Using the building information as a first search condition, searching in a regulatory article database to obtain a first article collection; Determine whether the size of the first set of articles is larger than the size of data generated by the pre-set digital model of regulatory articles; If the size of the first set of articles is not greater than the size of data generated by the digital model of the standard articles, the construction time in the building information is removed to obtain the second search condition, and the second search condition is used to perform a search again in the standard article database to obtain the second set of articles; Determine whether the size of the second set of articles is larger than the size of data generated by the digital model of regulatory articles; If the size of the second set of articles is not greater than the size of data generated by the digital model of the standard articles, a generation failure instruction of the digital model of the standard articles is directly obtained, and the generation failure instruction is sent to the port of the generation instruction of the digital model of the standard articles; If the scale of the first or second article collection is larger than the data scale generated by the digital model of the standard articles, the first or second article collection shall be the original article collection.

[0027] Specifically, the building type refers to the purpose of the building, such as residential and commercial; the construction time reflects the historical background and technical level; the building scale involves the size and capacity of the building. This information jointly determines the design, function and value of the building, and also determines the conditions for pulling the original set of clauses from the standard clause database. For example, the commercial area that Xiao Zhang is responsible for has a building type of commercial, a construction time of 2024, and a building scale that can accommodate at least 5,000 people, etc. Therefore, these building information is used as the first search condition, and a search is performed in the standard clause database to obtain the first set of clauses.

[0028] It should be emphasized that the regulatory clause database has pre-collected historical regulatory clauses in the field of architectural design in previous time periods. Therefore, when Xiao Zhang uses the above-mentioned building information as the first search condition, he will retrieve the regulatory clauses that meet the requirements in previous time periods, which are the first set of clauses.

[0029] However, it should be noted that the digital models of the regulatory clauses described in the embodiments of the present invention are all deep learning models, and their generation process is obtained by adjusting the model parameters of the internal BERT layer, LSTM layer, and GNN layer of the original model of the regulatory clauses. The parameter adjustment of the BERT layer, LSTM layer, and GNN layer requires a large amount of data scale, and experimental analysis shows that the data scale is at least 500. That is, when the first set of articles has less than 500 articles, it is necessary to eliminate the retrieval condition of the construction time of 2024 and perform a second search, so as to meet the requirement that the scale of the original set of articles retrieved in the end is greater than 500.

[0030] S2. Obtain the structural clauses of each original clause in text form, wherein the structural clauses are in the form of four-tuples and consist of entity clauses and true confidence values.

[0031] It should be explained that the main purpose of the structure clause is to display the original text clause in the form of a quadruple, so as to provide adjustment direction for adjusting the model parameters of the BERT layer, LSTM layer, and GNN layer. It should also be emphasized that there are many ways to generate structure clauses, including but not limited to using graph neural networks, large models, and other methods. In detail, the expression form of the structure clause is: , in, Indicates The structural provisions of the original provisions in the form of article texts, Indicates The substantive provisions of the original article, Indicates The header entity of the original article, Indicates The end entity of the original article, Represents the entity relationship between the head entity and the tail entity, Indicates The true value of the confidence of the entity clause corresponding to the original clause.

[0032] Furthermore, there are two ways to express entity relationships, namely ← and →, where ← means that the tail entity depends on the head entity, and → means that the head entity depends on the tail entity. For example, the original text of Article 25 in the original article set is: The load-bearing capacity of each floor of a three-story circular shopping mall is not less than 500 tons. It is recommended to use a reinforced concrete frame structure and use high-strength steel as the main material. Then the expression of its structural clause is: , It should be explained that the true confidence value indicates the strength of the entity relationship between the head entity and the tail entity (from a mathematical perspective: also known as the probability value), such as The true confidence value is , which means that when the building type is a 3-story circular shopping mall, there is a 92% probability that it will need to rely on reinforced concrete frame structure and high-strength steel.

[0033] S3. Obtain the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes a BERT layer, a LSTM layer, a GNN layer and a confidence determination layer in the order from data input to output.

[0034] It should be explained that the structure of the original model of the specification clauses described in the embodiment of the present invention has been fixed in advance, and includes a BERT layer, an LSTM layer, a GNN layer and a confidence determination layer in the order from input to output of data, wherein the confidence determination layer includes a confidence calculation function and a confidence determination function, wherein the confidence determination function is: , in, Represents the difference between the confidence judgment value and the true confidence value, Indicates the original article The original text in the form of the article is input into the BERT layer, LSTM layer, and GNN layer of the standard article original model to obtain the entity article and the first The textual difference between the substantive clauses of the original clauses, Indicates The confidence value of the original article. The entity provisions in the regulatory provisions database are ,If the original text of Article 25 is input into the original model of the standard, the entity clause obtained after the BERT layer, LSTM layer, and GNN layer is (3-story commercial plaza, →, reinforced concrete), then by comparing the text difference between the two entity clauses, we can calculate , and then calculate the difference between the confidence judgment value and the true confidence value.

[0035] In addition, the BERT (Bidirectional Encoder Representations from Transformers) layer is based on the Transformer architecture and captures the contextual information of the original text articles through a bidirectional attention mechanism, thereby achieving the purpose of word vector conversion; the LSTM (Long Short-Term Memory) layer is a special recurrent neural network (RNN) that processes sequence data through memory units and is good at capturing the long-term dependencies of the original text articles, thereby further optimizing the accuracy of word vectorization; the GNN (Graph Neural Network) layer is used to process graph structured data and updates the node representation by aggregating neighbor node information, thereby converting the original text articles into entity articles.

[0036] S4. Input the original set of clauses in text form into the original model of the standard clauses to perform confidence judgment and obtain a confidence judgment value.

[0037] It should be explained that the embodiment of the present invention needs to input each original article in the original article set into the standard article original model to perform confidence determination and obtain a confidence determination value. For example, if the original article set has 600 articles, the 600 original articles need to be input into the standard article original model in sequence. In detail, the inputting of the original article set in text form into the standard article original model to perform confidence determination and obtain a confidence determination value includes: Each original article in the original article set in text form is input into the original model of the standard article in sequence, and the confidence judgment value calculation process of each original article is as follows: Use the BERT layer to perform word encoding on the original text to obtain the original text in vectorized form; The original clauses in vectorized form are imported into the LSTM layer, and the original clauses in vectorized form are corrected by using the LSTM to obtain the corrected original clauses; Use the GNN layer to transform the corrected original clauses into the original clauses in structured form; The confidence level is calculated using the confidence level determination layer to obtain the confidence level determination value. .

[0038] It should be emphasized that how BERT and LSTM convert the original text form into the original word vector form is a public technology. Similarly, using the GNN layer to convert the modified original article into the original structured form is also a public technology, and the embodiments of the present invention will not be repeated here.

[0039] In addition, the original clause of the structured form is expressed as follows: ,in, Indicates The original text in structured form, Indicates The head entity of the original text after being transformed by the GNN layer, Indicates The tail entity of the original article after transformation by the GNN layer, Indicates The entity relationships of the original articles after transformation by the GNN layer.

[0040] Furthermore, the confidence determination layer is used to calculate the confidence of the original clause in structured form to obtain the confidence determination value, including: The original structured clause is transformed into a single-dimensional vector to obtain a single-dimensional clause, where the representation of the single-dimensional clause is: , in, Indicates The single-dimensional article of the original article, Indicates The first entity after the original article is transformed by the GNN layer, and the first entity after the single-dimensional vector transformation is performed vector value, Indicates The entity relationship of the original article after the GNN layer transformation, the first vector value, Indicates The tail entity of the original article after the GNN layer transformation, the first vector value; Use the confidence calculation function to calculate the confidence of the single-dimensional clause and obtain the confidence judgment value .

[0041] It should be explained that converting the original structured text into a single-dimensional vector can simplify complex text into a unified format, which is convenient for the calculation and comparison of subsequent confidence judgment values, because the single-dimensional vector can quickly identify the similarities and differences between texts and improve processing efficiency. In other words, the semantics and features of the single-dimensional vector are compressed into a numerical form, and the confidence judgment value is directly calculated.

[0042] Specifically, the confidence calculation function is used to calculate the confidence of the single-dimensional clause to obtain the confidence judgment value , including the confidence value calculated according to the following formula : , in, yes The weight value of yes The weight value of yes The weight value of .

[0043] As can be seen from the above, when the original set of textual articles is input into the original model of standard articles, it passes through the BERT layer, LSTM layer and GNN layer in sequence to obtain the original article in structured form. , and secondly, the original text in structured form Perform a single-dimensional vector conversion to obtain a single-dimensional clause Finally, the confidence of the single-dimensional clause is calculated by the confidence calculation function to obtain the confidence judgment value .

[0044] It should be noted that the embodiment of the present invention sets The weight value of is negative. and The weight value of is a positive number.

[0045] S5. According to the difference between the true confidence value and the confidence judgment value, adjust the model parameters of the BERT layer, LSTM layer, and GNN layer until the difference between the true confidence value and the confidence judgment value is less than a preset threshold value, store the model parameters of the BERT layer, LSTM layer, and GNN layer in the regulatory text database, and obtain the regulatory text standard model at the same time.

[0046] In detail, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are adjusted according to the difference between the true confidence value and the confidence judgment value, until the difference between the true confidence value and the confidence judgment value is less than a preset threshold, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are stored in the regulatory text database, and the regulatory text standard model is obtained at the same time, including: Using the confidence judgment function, the difference between the true confidence value and the confidence judgment value is calculated; Determine the relationship between the difference between the true confidence value and the confidence determination value and a preset threshold value; When the difference between the true confidence value and the confidence judgment value is greater than a preset threshold, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are adjusted, where the model parameters include the number of attention heads of the BERT layer, the number of hidden units and sequence length of the LSTM layer, and the number of stacking layers and aggregation function of the GNN layer; When the difference between the true confidence value and the confidence judgment value is less than or equal to a preset threshold, the adjusted model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the standard article database, and when the model parameters of the BERT layer, LSTM layer, and GNN layer are successfully stored in the standard article database, the standard article standard model automatic generation program is started, wherein the standard article standard model automatic generation program is embedded with a standard article generation template; The standard model of regulatory clauses is automatically generated by using the program, the model parameters of the stored BERT layer, LSTM layer, and GNN layer are extracted from the regulatory clause database, and the regulatory clause generation template is combined to obtain the standard model of regulatory clauses.

[0047] It should be explained that the difference between the true confidence value and the judgment value of the model output is calculated through the confidence judgment function and compared with the preset threshold. If the difference is greater than the threshold, the model parameters of the BERT layer, LSTM layer and GNN layer are adjusted, including the number of attention heads of the BERT layer, the number of hidden units and sequence length of the LSTM layer, and the number of stacking layers and aggregation function of the GNN layer; if the difference is less than or equal to the threshold, the adjusted parameters are stored in the standard text database. When the model parameters are successfully stored, the standard text model automatic generation program is started. The program extracts the stored model parameters of the BERT layer, LSTM layer and GNN layer from the standard text database, and automatically generates the standard text model in combination with the embedded standard text generation template. This process iteratively optimizes the model parameters to ensure that the model output meets the preset confidence requirements, and finally generates a standardized standard text model.

[0048] Compared with the problems described in the background technology, the present invention clarifies the model structure of each level. Specifically, the BERT layer, LSTM layer, and GNN layer have clear division of labor, and comprehensively parse the clauses from the dimensions of semantics, sequence, and structured information, respectively, to ensure the accuracy of confidence judgment. At the same time, the present invention drives parameter optimization through the difference between the true value of confidence and the judgment value, and combines threshold control to achieve rapid convergence, taking into account both accuracy and efficiency. In addition, through clause database support, structured processing, automatic generation, and templated output, full automation from data input to model output is achieved, significantly improving generation efficiency. Therefore, the digital model generation method, system, electronic device, and computer-readable storage medium of the building intelligent design specification clause proposed in the present invention can ensure the efficient generation of the specification clause model under accurate confidence judgment.

[0049] Embodiment 2: like Figure 2 As shown, it is a functional module diagram of a digital model generation system for building intelligent design specification clauses provided by an embodiment of the present invention. The digital model generation system 100 for building intelligent design specification clauses of the present invention can be installed in an electronic device. According to the functions implemented, the digital model generation system 100 for building intelligent design specification clauses may include a clause pulling module 101, a structural clause acquisition module 102, a confidence determination module 103 and a standard model completion module 104. The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in a memory of an electronic device; The clause extraction module 101 is used to receive a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and according to the generation instruction, extract an original clause set from a pre-built specification clause database, wherein each original clause of the original clause set is in text form; The structure clause acquisition 102 is used to acquire the structure clause of each original clause in text form, wherein the structure clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; The confidence determination module 103 is used to obtain the original model of the standard article, wherein the original model of the standard article includes a BERT layer, an LSTM layer, a GNN layer and a confidence determination layer in the order from data input to output, and the original article set in text form is input into the original model of the standard article to perform confidence determination and obtain a confidence determination value; The standard model completion module 104 is used to adjust the model parameters of the BERT layer, LSTM layer, and GNN layer according to the difference between the true confidence value and the confidence judgment value, until the difference between the true confidence value and the confidence judgment value is less than a preset threshold value, and store the model parameters of the BERT layer, LSTM layer, and GNN layer in the standard text database, and simultaneously obtain the standard model of the standard text.

[0050] In detail, each module in the photovoltaic panel positioning modeling system 100 based on drone image measurement in the embodiment of the present invention is used in the same manner as described above. Figure 1 The photovoltaic panel positioning modeling method based on drone image measurement described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0051] Embodiment 3: like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for realizing a method for generating a digital model of building intelligent design specification clauses provided by an embodiment of the present invention.

[0052] The electronic device 1 may include a processor 10, a memory 11, a bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for generating a digital model of building intelligent design specification clauses.

[0053] The memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the digital model generation program of the building intelligent design specification clauses, but also be used to temporarily store data that has been output or is to be output.

[0054] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a program for generating digital models of building intelligent design specification clauses, etc.), and calls data stored in the memory 11, so as to execute various functions of the electronic device 1 and process data.

[0055] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0056] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0057] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0058] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0059] Optionally, the electronic device 1 may further include a user interface, which may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0060] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0061] The digital model generation program for building intelligent design specification clauses stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: Receiving a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and pulling an original clause set from a pre-built specification clause database according to the generation instruction, wherein each original clause of the original clause set is in text form; Obtain the structural clause of each original clause in text form, wherein the structural clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; Obtaining the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes, in order from data input to data output, a BERT layer, an LSTM layer, a GNN layer, and a confidence determination layer; Inputting the original clause set in text form into the original model of the standard clause to perform confidence determination and obtain a confidence determination value; According to the difference between the true confidence value and the confidence judgment value, the model parameters of the BERT layer, LSTM layer, and GNN layer are adjusted until the difference between the true confidence value and the confidence judgment value is less than a preset threshold. The model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the regulatory text database, and the regulatory text standard model is obtained at the same time.

[0062] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0063] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0064] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement: Receiving a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and pulling an original clause set from a pre-built specification clause database according to the generation instruction, wherein each original clause of the original clause set is in text form; Obtain the structural clause of each original clause in text form, wherein the structural clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; Obtaining the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes, in order from data input to data output, a BERT layer, an LSTM layer, a GNN layer, and a confidence determination layer; Inputting the original clause set in text form into the original model of the standard clause to perform confidence determination and obtain a confidence determination value; According to the difference between the true confidence value and the confidence judgment value, the model parameters of the BERT layer, LSTM layer, and GNN layer are adjusted until the difference between the true confidence value and the confidence judgment value is less than a preset threshold. The model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the regulatory text database, and the regulatory text standard model is obtained at the same time.

[0065] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0066] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0067] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for generating a digital model of building intelligent design specification clauses, characterized in that: The method comprises: Receiving a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and pulling an original clause set from a pre-built specification clause database according to the generation instruction, wherein each original clause of the original clause set is in text form; Obtain the structural clause of each original clause in text form, wherein the structural clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; Obtaining the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes, in order from data input to data output, a BERT layer, an LSTM layer, a GNN layer, and a confidence determination layer; Inputting the original clause set in text form into the original model of the standard clause to perform confidence determination and obtain a confidence determination value; According to the difference between the true confidence value and the confidence judgment value, the model parameters of the BERT layer, LSTM layer, and GNN layer are adjusted until the difference between the true confidence value and the confidence judgment value is less than a preset threshold. The model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the regulatory text database, and the regulatory text standard model is obtained at the same time.

2. The method for generating a digital model of building intelligent design specification clauses according to claim 1, characterized in that: According to the generation instruction, an original set of clauses is pulled from a pre-built standard clause database, wherein each original clause of the original set of clauses is in text form, including: Parsing the generation instruction to obtain the building information of the digital model of the specification clause before the generation, wherein the building information includes the building type, building time, building scale, building style and material; Using the building information as a first search condition, searching in a regulatory article database to obtain a first article collection; Determine whether the size of the first set of articles is larger than the size of data generated by the pre-set digital model of regulatory articles; If the size of the first set of articles is not greater than the size of data generated by the digital model of the standard articles, the construction time in the building information is removed to obtain the second search condition, and the second search condition is used to perform a search again in the standard article database to obtain the second set of articles; Determine whether the size of the second set of articles is larger than the size of data generated by the digital model of regulatory articles; If the size of the second set of articles is not greater than the size of data generated by the digital model of the standard articles, a generation failure instruction of the digital model of the standard articles is directly obtained, and the generation failure instruction is sent to the port of the generation instruction of the digital model of the standard articles; If the scale of the first or second article collection is larger than the data scale generated by the digital model of the standard articles, the first or second article collection shall be the original article collection.

3. The method for generating a digital model of building intelligent design specification clauses according to claim 2, characterized in that: The structural clause is expressed as follows: ， in, Indicates The structural provisions of the original provisions in the form of article texts, Indicates The substantive provisions of the original article, Indicates The header entity of the original article, Indicates The end entity of the original article, Represents the entity relationship between the head entity and the tail entity, Indicates The true value of the confidence of the entity clause corresponding to the original clause.

4. The method for generating a digital model of building intelligent design specification clauses according to claim 3, characterized in that: The confidence determination layer includes a confidence calculation function and a confidence determination function, wherein the confidence determination function is: ， in, Represents the difference between the confidence judgment value and the true confidence value, Indicates the original article The original text in the form of the article is input into the BERT layer, LSTM layer, and GNN layer of the standard article original model to obtain the entity article and the first The textual difference between the substantive clauses of the original clauses, Indicates The confidence value of the original article.

5. The method for generating a digital model of building intelligent design specification clauses according to claim 4, characterized in that: The step of inputting the original set of textual articles into the original model of the standard articles to perform confidence determination and obtain the confidence determination value includes: Each original article in the original article set in text form is input into the original model of the standard article in sequence, and the confidence judgment value calculation process of each original article is as follows: Use the BERT layer to perform word encoding on the original text to obtain the original text in vectorized form; The original clauses in vectorized form are imported into the LSTM layer, and the original clauses in vectorized form are corrected by using the LSTM to obtain the corrected original clauses; Use the GNN layer to transform the corrected original clauses into the original clauses in structured form; The confidence level is calculated using the confidence level determination layer to obtain the confidence level determination value. .

6. The method for generating a digital model of building intelligent design specification clauses according to claim 5, characterized in that: The using the confidence determination layer to calculate the confidence of the original clause in structured form to obtain the confidence determination value includes: The original structured clause is transformed into a single-dimensional vector to obtain a single-dimensional clause, where the representation of the single-dimensional clause is: ， in, Indicates The single-dimensional article of the original article, Indicates The first entity after the original article is transformed by the GNN layer, and the first entity after the single-dimensional vector transformation is performed vector value, Indicates The entity relationship of the original article after the GNN layer transformation, the first vector value, Indicates The tail entity of the original article after the GNN layer transformation, the first vector value; Use the confidence calculation function to calculate the confidence of the single-dimensional clause and obtain the confidence judgment value .

7. The method for generating a digital model of building intelligent design specification clauses according to claim 6, characterized in that: The confidence calculation function is used to calculate the confidence of the single-dimensional clause to obtain the confidence judgment value , including the confidence value calculated according to the following formula : ， in, yes The weight value of yes The weight value of yes The weight value of .

8. The method for generating a digital model of building intelligent design specification clauses according to claim 7, characterized in that: Said The weight value of is negative. and The weight value of is a positive number.

9. The method for generating a digital model of building intelligent design specification clauses according to claim 8, characterized in that: The method adjusts the model parameters of the BERT layer, the LSTM layer, and the GNN layer according to the difference between the true confidence value and the confidence judgment value, until the difference between the true confidence value and the confidence judgment value is less than a preset threshold, stores the model parameters of the BERT layer, the LSTM layer, and the GNN layer in the standard text database, and simultaneously obtains the standard text model, including: Using the confidence judgment function, the difference between the true confidence value and the confidence judgment value is calculated; Determine the relationship between the difference between the true confidence value and the confidence determination value and a preset threshold value; When the difference between the true confidence value and the confidence judgment value is greater than a preset threshold, the model parameters of the BERT layer, the LSTM layer, and the GNN layer are adjusted, where the model parameters include the number of attention heads of the BERT layer, the number of hidden units and sequence length of the LSTM layer, and the number of stacking layers and aggregation function of the GNN layer; When the difference between the true confidence value and the confidence judgment value is less than or equal to a preset threshold, the adjusted model parameters of the BERT layer, LSTM layer, and GNN layer are stored in the standard article database, and when the model parameters of the BERT layer, LSTM layer, and GNN layer are successfully stored in the standard article database, the standard article standard model automatic generation program is started, wherein the standard article standard model automatic generation program is embedded with a standard article generation template; The standard model of regulatory clauses is automatically generated by using the program, the model parameters of the stored BERT layer, LSTM layer, and GNN layer are extracted from the regulatory clause database, and the regulatory clause generation template is combined to obtain the standard model of regulatory clauses.

10. A digital model generation system for building intelligent design specification clauses, characterized in that: The system comprises: A clause extraction module is used to receive a generation instruction of a digital model of a specification clause, wherein the digital model of the specification clause is applied to the field of architectural design, and according to the generation instruction, extract an original clause set from a pre-built specification clause database, wherein each original clause of the original clause set is in text form; A structure clause acquisition module is used to obtain the structure clause of each original clause in text form, wherein the structure clause is in the form of a four-tuple and consists of an entity clause and a true confidence value; The confidence judgment module is used to obtain the original model of the regulatory clauses, wherein the original model of the regulatory clauses includes a BERT layer, an LSTM layer, a GNN layer and a confidence judgment layer in the order from data input to output, and the original clause set in text form is input into the original model of the regulatory clauses to perform confidence judgment and obtain a confidence judgment value; The standard model completion module is used to adjust the model parameters of the BERT layer, LSTM layer, and GNN layer according to the difference between the true confidence value and the confidence judgment value, until the difference between the true confidence value and the confidence judgment value is less than a preset threshold, and then store the model parameters of the BERT layer, LSTM layer, and GNN layer in the specification clause database, and obtain the standard model of the specification clause at the same time.

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