CN117876053A - Intelligent mapping method and system for engineering cost - Google Patents
Intelligent mapping method and system for engineering cost Download PDFInfo
- Publication number
- CN117876053A CN117876053A CN202410270550.9A CN202410270550A CN117876053A CN 117876053 A CN117876053 A CN 117876053A CN 202410270550 A CN202410270550 A CN 202410270550A CN 117876053 A CN117876053 A CN 117876053A
- Authority
- CN
- China
- Prior art keywords
- result
- building
- mapping
- engineering
- fitting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013507 mapping Methods 0.000 title claims abstract description 109
- 238000000034 method Methods 0.000 title claims abstract description 75
- 239000000463 material Substances 0.000 claims abstract description 56
- 238000013461 design Methods 0.000 claims abstract description 36
- 230000011218 segmentation Effects 0.000 claims abstract description 32
- 238000000605 extraction Methods 0.000 claims abstract description 18
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 238000010276 construction Methods 0.000 claims description 81
- 238000004458 analytical method Methods 0.000 claims description 17
- 238000013139 quantization Methods 0.000 claims description 12
- 238000010606 normalization Methods 0.000 claims description 7
- 238000012098 association analyses Methods 0.000 claims description 6
- 238000010219 correlation analysis Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 abstract description 11
- 230000006870 function Effects 0.000 description 22
- 230000000875 corresponding effect Effects 0.000 description 13
- 230000008569 process Effects 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 11
- 238000011156 evaluation Methods 0.000 description 8
- 238000005259 measurement Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 230000008901 benefit Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 238000013178 mathematical model Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000001816 cooling Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000003339 best practice Methods 0.000 description 1
- 239000004566 building material Substances 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000004567 concrete Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000009429 electrical wiring Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009428 plumbing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- 230000004382 visual function Effects 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides an intelligent mapping method and system for engineering cost, which relate to the technical field of cost prediction, and the method comprises the following steps: obtaining basic design data of a target building, performing functional unit granularity segmentation of the target building, reading newly-increased building engineering entity requirements, inputting the requirements into a concerned prediction model, performing initial granularity building mapping, establishing a BIM model, performing model feature extraction, performing depth granularity building mapping, updating the BIM model, performing building engineering entity layout fitting, and performing engineering cost fitting through fitting results and material data to generate a prediction result. The method mainly solves the problems that the existing method is low in efficiency, high in error rate, huge in time consumption, and poor in accuracy, the reaction to market change is slow, price change cannot be captured timely, and the accuracy is poor. By the intelligent mapping method, quick and accurate valuation of the building engineering is realized, and the working efficiency is improved.
Description
Technical Field
The application relates to the technical field of construction cost prediction, in particular to an intelligent mapping method and system for engineering construction cost.
Background
With the rapid advancement of the urban process, the number and the scale of construction projects are continuously increased, which brings great challenges to the construction cost. In the process of urban treatment, the demands of building projects such as urban infrastructure, houses, business centers and the like are continuously increased, and the projects have the characteristics of short construction period, large investment, complex technology and the like. The conventional engineering cost method cannot meet the market demand. Because the traditional method mainly depends on manual calculation and experience evaluation, engineering cost cannot be rapidly and accurately given, and the rapid response requirement of the market is difficult to meet. In addition, traditional methods lack the ability to process and analyze large amounts of data and cannot provide scientific support for decisions. Meanwhile, as the scale and complexity of the construction engineering increase, more factors are required to be considered in the engineering cost, such as material price fluctuation, labor cost change, construction difficulty and the like. These factors all require accurate calculations and analysis to ensure the accuracy and rationality of the construction cost.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the above technology is found to have at least the following technical problems:
The traditional method excessively relies on manual calculation and evaluation, so that the efficiency is low, the error rate is high, the time consumption is huge, the precision cannot be guaranteed, the reaction to market change is slow, and the price change cannot be captured in time, so that the accuracy of engineering cost is affected.
Disclosure of Invention
The method mainly solves the problems that the existing method is low in efficiency, high in error rate, huge in time consumption, and poor in accuracy, the reaction to market change is slow, price change cannot be captured timely, and the accuracy is poor.
In view of the foregoing, the present application provides an intelligent mapping method and system for construction costs, and in a first aspect, the present application provides an intelligent mapping method for construction costs, the method comprising: basic design data of a target building is obtained, functional unit granularity segmentation of the target building is carried out through the basic design data, and a functional segmentation result is generated; reading the newly added building engineering entity requirement, inputting a concerned prediction model through the building engineering entity requirement and the function segmentation result, and generating a concerned prediction result; generating mapping constraint of mapping according to the attention prediction result, and executing initial granularity building mapping of a target building according to the mapping constraint to generate an initial mapping result; building a BIM model according to the basic design data and the initial mapping result, extracting model features based on the requirements of building engineering entities from the BIM model, and generating regional feature concerns according to the extraction results; performing depth granularity building mapping through the regional feature focus, generating additional mapping results, and updating the BIM based on the additional mapping results; and performing building engineering entity layout fitting based on the updated BIM model, and performing engineering cost fitting through fitting results and material data to generate an engineering cost prediction result.
In a second aspect, the present application provides an intelligent mapping system for construction costs, the system comprising: the function segmentation result generation module is used for acquiring basic design data of a target building, performing granularity segmentation on a functional unit of the target building through the basic design data, and generating a function segmentation result; the attention prediction result generation module is used for reading the newly added building engineering entity requirement, inputting an attention prediction model through the building engineering entity requirement and the function segmentation result, and generating an attention prediction result; the initial mapping result generation module is used for generating mapping constraint of mapping according to the attention prediction result, executing initial granularity building mapping of the target building according to the mapping constraint and generating an initial mapping result; the feature extraction module is used for establishing a BIM model according to the basic design data and the initial mapping result, carrying out model feature extraction based on the requirements of building engineering entities on the BIM model, and generating regional feature attention according to the extraction result; the model updating module is used for carrying out depth granularity building mapping through the regional characteristic attention, generating additional mapping results and updating the BIM based on the additional mapping results; the construction cost prediction result generation module is used for executing construction engineering entity layout fitting based on the updated BIM model, and carrying out engineering cost fitting through fitting results and material data to generate an engineering cost prediction result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides an intelligent mapping method and system for engineering cost, which relate to the technical field of cost prediction, and the method comprises the following steps: obtaining basic design data of a target building, performing functional unit granularity segmentation of the target building, reading newly-increased building engineering entity requirements, inputting the requirements into a concerned prediction model, performing initial granularity building mapping, establishing a BIM model, performing model feature extraction, performing depth granularity building mapping, updating the BIM model, performing building engineering entity layout fitting, and performing engineering cost fitting through fitting results and material data to generate a prediction result.
The method mainly solves the problems that the existing method is low in efficiency, high in error rate, huge in time consumption, and poor in accuracy, the reaction to market change is slow, price change cannot be captured timely, and the accuracy is poor. By the intelligent mapping method, quick and accurate valuation of the building engineering is realized, and the working efficiency is improved.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
For a clearer description of the technical solutions of the present application or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described below, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained, without inventive effort, by a person skilled in the art from the drawings provided.
FIG. 1 is a schematic flow diagram of an intelligent mapping method for engineering cost according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for generating regional feature concerns in an intelligent mapping method for engineering costs according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for generating a predicted construction cost result in an intelligent mapping method for construction cost according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an intelligent mapping system for engineering cost according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a function segmentation result generation module 10, a focus prediction result generation module 20, an initial mapping result generation module 30, a feature extraction module 40, a model updating module 50 and a manufacturing cost prediction result generation module 60.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The method mainly solves the problems that the existing method is low in efficiency, high in error rate, huge in time consumption, and poor in accuracy, the reaction to market change is slow, price change cannot be captured timely, and the accuracy is poor. By the intelligent mapping method, quick and accurate valuation of the building engineering is realized, and the working efficiency is improved.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
example 1
An intelligent mapping method for construction costs as shown in fig. 1, the method comprising:
basic design data of a target building is obtained, functional unit granularity segmentation of the target building is carried out through the basic design data, and a functional segmentation result is generated;
Specifically, first, all relevant data about a target building including a design drawing of the building, a construction plan, a construction log, a building specification, building material information, and the like are retrieved by the big data. After all the data is collected, data cleansing is performed to eliminate erroneous and duplicate information, ensuring the accuracy and integrity of the data. Classifying and encoding the data according to certain rules and standards, storing the processed data in a database or other storage media, and determining the functional units to be divided according to the characteristics and the use requirements of the target building. These functional units may include rooms, floors, building groups, etc. Each room has its specific functions and usage requirements, requiring separate evaluations and calculations for its characteristics. For multi-storey or high rise buildings, the division may be performed in units of storeys. Each floor has its specific use needs and characteristics, such as business floors, office floors, residential floors, etc. Independent evaluations and calculations are performed for each floor to help better understand the overall structure and functional layout of the building. For large buildings or complexes, the building may be divided into different building groups, such as residential areas, business areas, office areas, etc. Each building group has specific use requirements and characteristics, and independent evaluation and calculation are needed to identify direct association between each functional unit. For example, the function of use of certain rooms may depend directly on other parts of the building, such as heating and cooling systems, power supply and lighting systems, etc. Understanding these direct correlations helps to accurately estimate the equipment and materials required, and thus more accurately predict construction costs. Indirect associations and dependencies between functional units need to be considered. For example, the function of a certain room may be affected by other rooms or floor layouts, or the function of a certain building group may be affected by the surrounding environment and other buildings. These indirect correlations may lead to fluctuations in construction costs and therefore require sufficient consideration in the evaluation. And performing granularity segmentation on the target building to generate a function segmentation result, and determining a proper partitioning rule according to the use requirements and characteristics of the building. These rules may be determined based on functional relationships of the building, spatial layout, frequency of use, etc. For example, for a commercial building, the division may be based on different commercial areas or floors, and for a residential building, the division may be based on different residential units or floors. And performing granularity segmentation on the target building according to the determined partitioning rule. This may include dividing the building into different areas, floors or rooms, etc. In the granularity segmentation process, functional relationships and dependencies of buildings need to be fully considered to ensure accuracy and rationality of segmentation. And generating a function segmentation result. This may include listing information for each functional unit's name, location, usage function, etc. for subsequent evaluation and calculation. The function segmentation result can clearly reflect the function layout and the use requirement of the building, and provides a reliable basis for the subsequent project cost prediction.
Reading the newly added building engineering entity requirement, inputting a concerned prediction model through the building engineering entity requirement and the function segmentation result, and generating a concerned prediction result;
specifically, newly added construction entity requirements are collected and read, which may come from customers, designers, or other interested parties. These requirements describe in detail the specific requirements and characteristics of the construction project. And taking the building engineering entity requirements and the function segmentation result as input data, and inputting the input data into the attention prediction model. The predictive model of interest may be learned, analyzed, and predicted from the input data. The attention prediction model generates attention prediction results through calculation of an algorithm and the model according to input data. This result may include concerns over the needs of the construction entity, possible problems, optimization suggestions, etc.
Generating mapping constraint of mapping according to the attention prediction result, and executing initial granularity building mapping of a target building according to the mapping constraint to generate an initial mapping result;
specifically, mapping constraints are generated according to the prediction result of interest, and initial granularity building mapping of the target building is performed accordingly, so that accuracy and efficiency of mapping work can be ensured. First, mapping constraints are generated based on a prediction of interest. The prediction of interest reflects information such as building engineering entity requirements and regional feature interest, which is used to generate mapping constraints. These constraints define the scope, accuracy, focus, etc. of the mapping effort to ensure that the mapping effort can meet the needs of the subsequent construction project. Next, an initial granular building mapping of the target building is performed according to the mapping constraints. A process for making detailed measurements of a target building using corresponding tools and techniques. By using high-precision measuring equipment, accurate size, position and form measurement is carried out on each functional unit of the building, and an initial mapping result of the building is obtained. This initial mapping result includes detailed data for each functional unit, such as the size, shape, door and window position, etc. of the room. The data provides basic data for subsequent building engineering design and construction, and is helpful for ensuring the accuracy and reliability of engineering. In addition, the initial mapping result can be checked and corrected through comparison and analysis with the attention prediction result. Thus, the accuracy and the reliability of the mapping result can be ensured, and a solid data base is provided for the subsequent project cost prediction and the construction project implementation.
Building a BIM model according to the basic design data and the initial mapping result, extracting model features based on the requirements of building engineering entities from the BIM model, and generating regional feature concerns according to the extraction results;
specifically, first, the underlying design data provides the necessary information and basis for building the BIM model. These data, including information on building geometry, dimensions, materials, equipment, etc., are the basis for constructing the BIM model. By inputting these data into the BIM software, a three-dimensional model corresponding to the actual building can be built. Secondly, after building a BIM model, model feature extraction based on the requirements of the building engineering entity is carried out. And analyzing and processing the BIM model, and extracting characteristic information related to the requirements of the building engineering entity. For example, information such as structural form, material type, equipment configuration and the like of a building can be extracted, and the information has important significance for subsequent engineering design and construction. Based on the extracted feature information, regional feature concerns may be further generated. Regional feature concerns refer to concerns and analyses of specific regions or sites in the BIM model that may have a significant impact on the implementation of the construction project. For example, special attention and analysis is required for critical parts of the load-bearing structure, insulation system, electrical wiring, etc. of the building. Through the attention and analysis of the characteristics of the areas, the requirements and the characteristics of the building engineering can be better understood, and basis and guidance are provided for subsequent design and construction.
Performing depth granularity building mapping through the regional feature focus, generating additional mapping results, and updating the BIM based on the additional mapping results;
specifically, according to the attention points of regional characteristic attention, the range of depth granularity building mapping is determined. This range may include a particular area, floor, room, or the like. Ensuring the rationality and feasibility of the mapping range. And (3) according to the determined mapping range, a detailed depth granularity building mapping plan is formulated. This plan should include the measurement equipment, measurement methods, and accuracy requirements used, etc., to ensure the accuracy and reliability of the mapping results. And (5) starting actual measurement work according to the established depth granularity building mapping plan. During the mapping process, relevant measurement standards and specifications should be strictly adhered to, and the accuracy and reliability of measurement data are ensured. Meanwhile, any abnormal situation is recorded and processed in time, and the influence on the mapping result is avoided. After deep granularity building mapping is completed, the collected data is processed and analyzed to generate additional mapping results. This result should include detailed measurement data and information for various aspects of the target building, which can be used as the basis data and reference for updates to the BIM model. And updating the original BIM model according to the additional mapping result. This update process may include modifying the appearance, internal structure, equipment configuration, etc. of the building to ensure that the BIM model remains consistent with the actual situation. Meanwhile, more accurate data support is provided for related building engineering design and construction.
And performing building engineering entity layout fitting based on the updated BIM model, and performing engineering cost fitting through fitting results and material data to generate an engineering cost prediction result.
Specifically, on the basis of the updated BIM model, building engineering entity layout fitting can be performed, and engineering cost fitting is performed through fitting results and material data, so that an engineering cost prediction result is generated. And simulating the layout process of the building engineering entity according to the updated BIM model. Through the visual function of the BIM model, the rationality of entity layout is checked, and each part can be ensured to be matched together correctly. If a conflict or problem is found, adjustments and optimizations are made until the requirements are met. Data of various materials required for construction engineering are collected, including kinds, specifications, quantity, price and the like of the materials. The accuracy and the integrity of material data are ensured, and a foundation is provided for subsequent engineering cost fitting. Based on the fitting result and the material data, fitting is performed using an appropriate engineering cost calculation method. And estimating and predicting the cost of each part by considering factors such as the scale, complexity, construction difficulty and the like of the building engineering. And the rationality and the accuracy of the fitting result are ensured. And generating a project cost prediction result of the building project according to the fitting result. This result may include total cost, cost details of the parts, etc., providing basis for subsequent decision making and budgeting.
Further, as shown in fig. 2, in the method of the present application, the extracting model features based on requirements of building engineering entities from the BIM model, generating regional feature concerns according to the extracting results, further includes:
extracting in-model features of the BIM model, and establishing a feature set, wherein the feature set is provided with a position mark;
performing feature association analysis of a feature set based on the building engineering entity requirement to generate a feature association result;
performing position association analysis on building engineering entity requirements through the position identification to generate a position association result;
and carrying out normalization treatment on the feature association result and the position association result, and generating the regional feature attention through the treatment result.
Specifically, using the functions of the BIM software, various features are extracted from the existing BIM model. These features may include specific parts of the building, equipment, piping, construction details, etc. Ensuring that the extracted features are complete and accurate for subsequent analysis. The extracted features are consolidated into a feature set. Each feature has a unique location identifier in the collection that can be used to represent the specific location of the feature in the building. The feature set should be comprehensive and exhaustive so as to be able to reflect various attributes and requirements of the building. And analyzing the relation between each feature in the feature set and the requirements of the building engineering entity. For example, a particular building feature may suggest a particular functional need or method of construction. For example, features that the water outlet toilet bowl, the entity is a sewer pipe, the two entities need to be connected, for example, the circuit, the entity is a water pipe, the entity is irrelevant in nature, but they need to be separated far apart, so that the combination position and the non-forward correlation feature are determined together. Through such correlation analysis, it is possible to identify which features are associated with a particular construction requirement and to understand the degree of association thereof. For example, the characteristics of a particular location may have a significant impact on the functional layout of a building, equipment configuration, and the like. The relationship between the location of these features and the requirements of the building engineering entity is analyzed. For example, the location of certain features may affect the functional layout of a building, such as the location of particular pipes, lines, or equipment may affect the layout or use functions of a room. Also, the location of certain features may affect the configuration and installation of the device, such as space requirements for large devices or weight bearing constraints for particular areas. Correlations and dependencies between features can also be analyzed. For example, the location of certain features may be closely related to the location of other features, such as the location of heating and cooling systems may interact. Understanding these relationships may help us to better plan and design construction projects. This step relates the features to their physical location in the building, helping to more fully understand the needs and features of the building project. And carrying out normalization processing on the feature association result and the position association result. Normalization can eliminate the effects of different data types and dimensions, making the results more comparable and operable. Through normalization processing, which features and positions are most relevant to the requirements of building engineering entities can be better identified, and powerful support is provided for subsequent decisions. Based on the normalization result, features and positions highly related to the requirements of the building engineering entity are identified, and the features and positions are called regional feature attention. Through the steps, the data in the BIM model can be deeply mined, the characteristics and the positions closely related to the requirements of the building engineering entities are identified, and key decision support is provided for project design, construction and management.
Further, the method of the present application further comprises:
establishing an experience database, wherein the experience database is an intra-building distributed position experience database constructed by taking building engineering entity requirements as matching characteristics;
when the position correlation analysis is carried out, the position identification of the feature set is input into the experience database, and a probability experience matching result of the experience database is generated;
and carrying out position association conversion on the probability experience matching result to obtain the position association result.
In particular, the construction of the empirical database requires building engineering entity requirements as matching features. The data in the database should be closely related to the requirements of the actual construction project. For example, the database may contain empirical data in terms of spatial layout, equipment configuration, construction difficulties, etc. for different types of buildings. These data may provide valuable references to new construction projects, helping design teams to better understand the requirements and characteristics of the project. First, input data is strictly checked and verified. New experience data is updated and supplemented in time to continuously expand and enrich the content of the database. Meanwhile, in order to ensure timeliness of data, the database needs to be updated and maintained regularly so as to provide up-to-date information and data. In addition, for ease of use and management, the empirical database should be designed to be easy to query and retrieve. The user can screen and match according to different characteristics of the building engineering entity requirements, and related experience data can be quickly found. Meanwhile, the database should provide a certain analysis function to help users to deeply mine and process data so as to better understand the requirements and characteristics of the construction engineering. In performing the location correlation analysis, the location identity in the feature set is entered into an empirical database. These location identifications are used to identify specific locations of features in the building. By entering location identifications, the empirical data of the building engineering entity needs associated with these locations in the empirical database can be queried. Location identification is key information used to identify a specific location of a feature in a building. These identifications may include coordinates, relative positions, directions, etc. that together constitute the exact position of the feature in space. These location identifications play an important role in performing the location correlation analysis, as they can provide specific location information of the features in the building. And the experience database returns probability experience matching results of the requirements of the building engineering entities related to the positions according to the input position identifications. These results may provide information about factors, problems, or best practices that may need to be considered for a particular location. The probabilistic empirical match results returned by the empirical database contain empirical data related to the entered location identity. Such data may include information related to design decisions, construction difficulties, problem solutions, etc. in the past construction engineering regarding such locations. These probabilistic empirical matching results are then converted into location correlation results. Associating it with the building engineering entity requirements. For example, if empirical data indicates that a location is prone to construction problems, the location correlation results should reflect this information and indicate the corresponding construction needs. The translated location correlation results can more specifically reflect the needs of a particular portion or area of a building. Through the steps, a final position association result can be obtained. These results integrate the feature set, the experience database, and the information required by the building engineering entity, providing important references and support for the design and construction of the building. The final position association result should accurately reflect the relationship between the building engineering entity requirements and the distributed positions in the building, which is helpful to improve the efficiency and benefit of the project.
Further, the method of the present application further comprises:
synchronously inputting the feature set into the experience database to obtain the feature association result;
extracting non-forward correlation features in the feature correlation results;
and compensating the probability experience matching result by the non-forward correlation characteristic, and obtaining the position correlation result according to the compensation result.
In particular, by entering the feature set and location identity into an experience database, experience data and information related to these features can be retrieved. The empirical database will match and analyze based on the feature set to obtain feature correlation results related to the feature set. These results reflect the relationship between the features and the physical needs of the construction project and provide guidance and reference for subsequent design and construction. The feature association results may include information related to design decisions, construction difficulties, problem solutions, etc. of the features in the past construction engineering. Through analysis of the feature correlation results, potential problems and challenges in construction engineering can also be found. In the feature association results, features that are not directly related or negatively affected by the requirements of the building engineering entity are identified and extracted. These features are referred to as non-forward correlation features. Extracting non-positively correlated features helps to better understand which features may adversely affect the construction project, for consideration and adjustment in subsequent designs and constructions. And compensating the probability experience matching result returned by the experience database based on the extracted non-forward correlation characteristic. The empirical matching results are adjusted or revised to account for the effects of the non-forward correlation characteristics. By compensation, more accurate and reliable position correlation results can be obtained, as they already take into account features that may have negative effects. And generating a final position association result according to the compensated probability experience matching result. These results, which integrate the feature set, the empirical database, and the consideration of non-forward-associated features, provide more complete and accurate location-associated information.
Further, the method of the present application further comprises:
carrying out layout scheme configuration of building engineering entities on the updated BIM model, and establishing a corresponding fitting result according to the configuration result of each scheme;
obtaining layout requirements of users, carrying out fitting optimization on all fitting results through the layout requirements, and determining an optimizing fitting result;
and finishing engineering cost fitting by using the optimizing fitting result to generate an engineering cost prediction result.
Specifically, the configuration of the building engineering entity layout scheme is performed based on the updated BIM model. Including detailed planning of the layout and configuration of various parts, equipment, plumbing, etc. of the building. The rationality and feasibility of the scheme configuration are ensured to meet the functional requirements and construction requirements of the building engineering. For each protocol configuration, the appropriate fitting method is used to evaluate its corresponding results. This may include an assessment of the economics of the solution, technical feasibility, environmental impact, etc. Statistical methods and techniques may be used to ensure accuracy and reliability of the results. This may include regression analysis, decision tree analysis, support vector machine, and the like. By the method, the advantages and disadvantages of each scheme configuration can be accurately reflected, and powerful support is provided for subsequent decision making and selection. The fitting result should accurately reflect the advantages and disadvantages of each scheme configuration for subsequent decision making and selection. Through communication and communication with users, specific requirements and expectations of the users on the layout of the constructional engineering entities are known. Including layout, functional, cost considerations, etc. And optimizing all the established fitting results according to the layout requirements of the users. Including comparing, balancing and selecting different scheme configurations. And determining one or more optimal scheme configurations meeting the requirements of users through fitting optimization. In the fitting optimizing process, a best fitting result is determined as the final scheme configuration. This result should take into account a combination of aspects of user demand, economy, technical feasibility, etc. The optimizing fitting result is the basis of the subsequent engineering cost fitting, and the rationality and the accuracy of the optimizing fitting result are ensured. And carrying out fitting calculation of engineering cost based on the determined optimizing fitting result. The total cost is predicted and estimated by considering the scale, complexity, materials, labor and other cost factors of the building engineering. The accuracy and the integrity of engineering cost fitting are ensured so as to provide reliable basis for subsequent budget formulation and decision making. And generating a final project cost prediction result based on the project cost fitting result. This result should include information about the total cost, cost details of the parts, etc.
Further, as shown in fig. 3, the method of the present application further includes:
carrying out material demand analysis according to the optimizing fitting result and the building engineering entity demand, and establishing a material demand quantization table;
calling market material data according to the material demand quantization table, and predicting trend of the market material data according to building dates;
and fitting the construction cost according to the trend prediction result and the material demand quantization table to generate a construction cost prediction result.
Specifically, the type and specification of the desired material is determined. This may include concrete, steel, glass, wood, etc., each of which may have different specifications and grades. Based on the design and construction plan of the construction project, the specific required amount of each material is calculated. Including the needs of the various parts and floors of the building, and the loss rate during construction. And arranging information such as the determined material types, specifications, the determined quantity and the like into a table form. The table may include column headings, material names, specifications, numbers, etc. for ease of viewing and comparison. And acquiring corresponding material data from the market according to the material demand quantization table, wherein the corresponding material data comprises information such as price, supply quantity, quality and the like of the material. Market material prices, supply amounts, etc. data over a period of time are collected. Such data may be obtained through market research, industry reports, professional websites, and the like. It is critical to ensure accuracy, integrity and timeliness of the data. Based on the collected historical market data, an appropriate predictive model is selected, such as a time series analysis (e.g., ARIMA model, exponential smoothing, etc.) or a regression analysis (e.g., linear regression, polynomial regression, etc.). These models can make trend predictions based on time series characteristics of material prices and supply. And establishing a prediction model based on the historical market data by using the selected prediction model. The method comprises the steps of cleaning, processing and converting data to meet the input requirements of a model. And using the established prediction model to predict the market material price and supply quantity in a future period of time. This may provide an estimate of future market conditions, helping the decision maker to formulate a corresponding strategy. And analyzing the prediction result to know the material price and supply trend in a future period of time. This helps to evaluate possible variations in material costs and formulate corresponding countermeasures. Since market conditions are constantly changing, it is necessary to update and adjust the predictive model periodically to maintain the accuracy and validity of the predicted results. The trend prediction results and the data in the material demand quantization table are collated into one data table, including the predicted price, supply amount, and the like of each material. And selecting proper fitting methods, such as linear regression, multiple regression and the like, so as to establish a mathematical model between engineering cost and factors such as material price, supply quantity and the like. And fitting the overall well-organized data by using a selected fitting method to generate a corresponding mathematical model. This may be accomplished by statistical software or programming language. And evaluating the fitted model, and checking the fitting effect and the prediction accuracy of the fitted model. This can be done by calculating indices of the model for coefficients, R-square values, residuals, etc. Based on the evaluation result, if the model fitting effect is good and the prediction accuracy is high, the model can be applied to predict the construction cost in a future period of time. And calculating the project cost prediction result in a future period by using the established mathematical model according to the trend prediction result and the material demand quantization table.
Further, the method of the present application further comprises:
carrying out engineering quantity analysis according to the optimizing fitting result to generate engineering quantity data;
and performing quota matching of the engineering quantity according to the engineering quantity data, and performing engineering cost fitting result compensation according to a quota matching result.
Specifically, first, each part of the construction project is analyzed in detail according to the result of the optimizing fit. This includes one-by-one accounting of the work amounts of various professions such as building structure, equipment installation, finishing work, etc. This step may be performed using specialized engineering quantity calculation software or tools. Including AutoCAD software: engineering quantity calculation including length, area, volume and the like can be conveniently carried out by using AutoCAD. Various geometric parameters can be rapidly acquired through measuring tools and object characteristics of AutoCAD. Generating engineering quantity data: after the engineering quantity analysis is completed, the obtained professional engineering quantity data are arranged into a form of a table or a database, so that the accuracy and the integrity of the data are ensured. Such data may include individual specialized amounts of material, workload, man-hours, and the like. And performing quota matching based on the generated engineering quantity data. Quota matching is to find corresponding quota criteria (such as manual, material, mechanical, etc. quota) based on engineering quantity data to determine the cost of each project. After the quota matching is performed, the preliminary engineering cost fitting result can be compensated according to the quota matching result. Due to factors such as market price fluctuation, material replacement and the like, the actual cost may deviate from the initial fitting result of the engineering cost. The actual cost can be predicted more accurately through quota matching, so that the preliminary engineering cost fitting result is compensated. Due to the complexity and dynamics of construction engineering, the actual amount of engineering and costs may vary with factors such as construction progress, design changes, etc. Therefore, the engineering quantity data and the corresponding quota matching result need to be updated and adjusted regularly to ensure the accuracy and reliability of engineering cost prediction. The detailed engineering quantity analysis can be performed based on the optimizing fitting result, and the preliminary engineering cost fitting result is compensated through quota matching. This process helps to more accurately predict the actual cost of construction engineering, providing powerful support for project decisions.
Example two
Based on the same inventive concept as the intelligent mapping method for construction costs of the previous embodiments, as shown in fig. 4, the present application provides an intelligent mapping system for construction costs, the system comprising:
the function segmentation result generation module 10 is used for acquiring basic design data of a target building, performing granularity segmentation of a functional unit of the target building through the basic design data, and generating a function segmentation result;
the attention prediction result generation module 20 is configured to read a newly added building engineering entity requirement, input an attention prediction model through the building engineering entity requirement and the function segmentation result, and generate an attention prediction result;
an initial mapping result generation module 30, where the initial mapping result generation module 30 is configured to generate a mapping constraint of mapping according to the attention prediction result, and perform initial granularity building mapping of the target building according to the mapping constraint, and generate an initial mapping result;
the feature extraction module 40 is configured to establish a BIM model according to the basic design data and the initial mapping result, perform model feature extraction based on the requirements of the building engineering entity on the BIM model, and generate regional feature attention according to the extraction result;
The model updating module 50 is configured to perform depth granularity building mapping through the regional feature focus, generate additional mapping results, and update the BIM model based on the additional mapping results;
the construction cost prediction result generating module 60, wherein the construction cost prediction result generating module 60 performs construction engineering entity layout fitting based on the updated BIM model, and performs engineering cost fitting through fitting results and material data to generate an engineering cost prediction result.
Further, the system further comprises:
the regional feature concern generation module is used for extracting the intra-model features of the BIM model and establishing a feature set, wherein the feature set is provided with a position mark; performing feature association analysis of a feature set based on the building engineering entity requirement to generate a feature association result; performing position association analysis on building engineering entity requirements through the position identification to generate a position association result; and carrying out normalization treatment on the feature association result and the position association result, and generating the regional feature attention through the treatment result.
Further, the system further comprises:
the position association result acquisition module is used for establishing an experience database, wherein the experience database is an intra-building distributed position experience database constructed by taking building engineering entity requirements as matching characteristics; when the position correlation analysis is carried out, the position identification of the feature set is input into the experience database, and a probability experience matching result of the experience database is generated; and carrying out position association conversion on the probability experience matching result to obtain the position association result.
Further, the system further comprises:
the result compensation module is used for synchronously inputting the feature set into the experience database so as to obtain the feature association result; extracting non-forward correlation features in the feature correlation results; and compensating the probability experience matching result by the non-forward correlation characteristic, and obtaining the position correlation result according to the compensation result.
Further, the system further comprises:
the construction cost prediction result generation module is used for carrying out construction engineering entity layout scheme configuration on the updated BIM model, and establishing a corresponding fitting result according to each scheme configuration result; obtaining layout requirements of users, carrying out fitting optimization on all fitting results through the layout requirements, and determining an optimizing fitting result; and finishing engineering cost fitting by using the optimizing fitting result to generate an engineering cost prediction result.
Further, the system further comprises:
the construction cost fitting module is used for carrying out material demand analysis according to the optimizing fitting result and the building engineering entity demand and establishing a material demand quantization table; calling market material data according to the material demand quantization table, and predicting trend of the market material data according to building dates; and fitting the construction cost according to the trend prediction result and the material demand quantization table to generate a construction cost prediction result.
Further, the system further comprises:
the fitting result compensation module is used for carrying out engineering quantity analysis according to the optimizing fitting result to generate engineering quantity data; and performing quota matching of the engineering quantity according to the engineering quantity data, and performing engineering cost fitting result compensation according to a quota matching result.
The foregoing detailed description of an intelligent mapping method for construction costs will be clear to those skilled in the art, and the intelligent mapping system for construction costs in this embodiment is described more simply for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An intelligent mapping method for engineering cost, the method comprising:
basic design data of a target building is obtained, functional unit granularity segmentation of the target building is carried out through the basic design data, and a functional segmentation result is generated;
reading the newly added building engineering entity requirement, inputting a concerned prediction model through the building engineering entity requirement and the function segmentation result, and generating a concerned prediction result;
generating mapping constraint of mapping according to the attention prediction result, and executing initial granularity building mapping of a target building according to the mapping constraint to generate an initial mapping result;
building a BIM model according to the basic design data and the initial mapping result, extracting model features based on the requirements of building engineering entities from the BIM model, and generating regional feature concerns according to the extraction results;
performing depth granularity building mapping through the regional feature focus, generating additional mapping results, and updating the BIM based on the additional mapping results;
and performing building engineering entity layout fitting based on the updated BIM model, and performing engineering cost fitting through fitting results and material data to generate an engineering cost prediction result.
2. The method of claim 1, wherein the performing model feature extraction on the BIM model based on requirements of a building engineering entity, generating regional feature concerns according to extraction results, further comprises:
extracting in-model features of the BIM model, and establishing a feature set, wherein the feature set is provided with a position mark;
performing feature association analysis of a feature set based on the building engineering entity requirement to generate a feature association result;
performing position association analysis on building engineering entity requirements through the position identification to generate a position association result;
and carrying out normalization treatment on the feature association result and the position association result, and generating the regional feature attention through the treatment result.
3. The method of claim 2, wherein the method further comprises:
establishing an experience database, wherein the experience database is an intra-building distributed position experience database constructed by taking building engineering entity requirements as matching characteristics;
when the position correlation analysis is carried out, the position identification of the feature set is input into the experience database, and a probability experience matching result of the experience database is generated;
And carrying out position association conversion on the probability experience matching result to obtain the position association result.
4. A method as claimed in claim 3, wherein the method further comprises:
synchronously inputting the feature set into the experience database to obtain the feature association result;
extracting non-forward correlation features in the feature correlation results;
and compensating the probability experience matching result by the non-forward correlation characteristic, and obtaining the position correlation result according to the compensation result.
5. The method of claim 1, wherein the method further comprises:
carrying out layout scheme configuration of building engineering entities on the updated BIM model, and establishing a corresponding fitting result according to the configuration result of each scheme;
obtaining layout requirements of users, carrying out fitting optimization on all fitting results through the layout requirements, and determining an optimizing fitting result;
and finishing engineering cost fitting by using the optimizing fitting result to generate an engineering cost prediction result.
6. The method of claim 5, wherein the method further comprises:
carrying out material demand analysis according to the optimizing fitting result and the building engineering entity demand, and establishing a material demand quantization table;
Calling market material data according to the material demand quantization table, and predicting trend of the market material data according to building dates;
and fitting the construction cost according to the trend prediction result and the material demand quantization table to generate a construction cost prediction result.
7. The method of claim 6, wherein the method further comprises:
carrying out engineering quantity analysis according to the optimizing fitting result to generate engineering quantity data;
and performing quota matching of the engineering quantity according to the engineering quantity data, and performing engineering cost fitting result compensation according to a quota matching result.
8. An intelligent mapping system for engineering costs, the system comprising:
the function segmentation result generation module is used for acquiring basic design data of a target building, performing granularity segmentation on a functional unit of the target building through the basic design data, and generating a function segmentation result;
the attention prediction result generation module is used for reading the newly added building engineering entity requirement, inputting an attention prediction model through the building engineering entity requirement and the function segmentation result, and generating an attention prediction result;
The initial mapping result generation module is used for generating mapping constraint of mapping according to the attention prediction result, executing initial granularity building mapping of the target building according to the mapping constraint and generating an initial mapping result;
the feature extraction module is used for establishing a BIM model according to the basic design data and the initial mapping result, carrying out model feature extraction based on the requirements of building engineering entities on the BIM model, and generating regional feature attention according to the extraction result;
the model updating module is used for carrying out depth granularity building mapping through the regional characteristic attention, generating additional mapping results and updating the BIM based on the additional mapping results;
the construction cost prediction result generation module is used for executing construction engineering entity layout fitting based on the updated BIM model, and carrying out engineering cost fitting through fitting results and material data to generate an engineering cost prediction result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410270550.9A CN117876053B (en) | 2024-03-11 | 2024-03-11 | Intelligent mapping method and system for engineering cost |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410270550.9A CN117876053B (en) | 2024-03-11 | 2024-03-11 | Intelligent mapping method and system for engineering cost |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117876053A true CN117876053A (en) | 2024-04-12 |
| CN117876053B CN117876053B (en) | 2024-05-28 |
Family
ID=90579529
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410270550.9A Active CN117876053B (en) | 2024-03-11 | 2024-03-11 | Intelligent mapping method and system for engineering cost |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117876053B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096134A (en) * | 2016-06-13 | 2016-11-09 | 北京航空航天大学 | A kind of Structural Metallic Fatigue fail-safe analysis based on damage mechanics and Optimization Design |
| CN109558845A (en) * | 2018-11-30 | 2019-04-02 | 合肥工业大学 | A kind of mcg-signals intelligence fitting optimization method based on ant group algorithm |
| CN110909450A (en) * | 2019-10-22 | 2020-03-24 | 国网冀北电力有限公司经济技术研究院 | Intelligent design cost integrated acquisition method for power transmission line |
| CN113919028A (en) * | 2021-10-13 | 2022-01-11 | 赵亮 | Engineering cost evaluation management system based on big data analysis |
| CN114445149A (en) * | 2022-02-08 | 2022-05-06 | 正茂日升工程咨询有限公司 | BIM-based engineering cost method and system |
| KR20220064061A (en) * | 2020-11-11 | 2022-05-18 | 주식회사 연우테크놀러지 | Intergrated managing system based on the BIM and method therefor |
| CN114638658A (en) * | 2022-05-07 | 2022-06-17 | 广东飞腾工程咨询有限公司 | Engineering cost method and system based on BIM technical data sharing |
| CN115983897A (en) * | 2022-12-29 | 2023-04-18 | 广州祥辉工程技术有限公司 | Engineering quantity measuring method, device, equipment and storage medium |
| CN116843399A (en) * | 2023-07-27 | 2023-10-03 | 天栋建设管理有限公司 | Construction cost analysis method and system based on balance state |
| WO2024027437A1 (en) * | 2022-08-03 | 2024-02-08 | 南京工业大学 | Component parameter library-based method for integrated collaboration of prefabricated building design and construction |
-
2024
- 2024-03-11 CN CN202410270550.9A patent/CN117876053B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096134A (en) * | 2016-06-13 | 2016-11-09 | 北京航空航天大学 | A kind of Structural Metallic Fatigue fail-safe analysis based on damage mechanics and Optimization Design |
| CN109558845A (en) * | 2018-11-30 | 2019-04-02 | 合肥工业大学 | A kind of mcg-signals intelligence fitting optimization method based on ant group algorithm |
| CN110909450A (en) * | 2019-10-22 | 2020-03-24 | 国网冀北电力有限公司经济技术研究院 | Intelligent design cost integrated acquisition method for power transmission line |
| KR20220064061A (en) * | 2020-11-11 | 2022-05-18 | 주식회사 연우테크놀러지 | Intergrated managing system based on the BIM and method therefor |
| CN113919028A (en) * | 2021-10-13 | 2022-01-11 | 赵亮 | Engineering cost evaluation management system based on big data analysis |
| CN114445149A (en) * | 2022-02-08 | 2022-05-06 | 正茂日升工程咨询有限公司 | BIM-based engineering cost method and system |
| CN114638658A (en) * | 2022-05-07 | 2022-06-17 | 广东飞腾工程咨询有限公司 | Engineering cost method and system based on BIM technical data sharing |
| WO2024027437A1 (en) * | 2022-08-03 | 2024-02-08 | 南京工业大学 | Component parameter library-based method for integrated collaboration of prefabricated building design and construction |
| CN115983897A (en) * | 2022-12-29 | 2023-04-18 | 广州祥辉工程技术有限公司 | Engineering quantity measuring method, device, equipment and storage medium |
| CN116843399A (en) * | 2023-07-27 | 2023-10-03 | 天栋建设管理有限公司 | Construction cost analysis method and system based on balance state |
Non-Patent Citations (2)
| Title |
|---|
| 李兰芳;: "BIM技术在建筑工程造价管理中的应用", 科技视界, no. 21, 25 July 2018 (2018-07-25) * |
| 李奋龙;房静;: "BIM技术在工程造价管理中的应用及效益分析", 价值工程, no. 24, 28 August 2016 (2016-08-28) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117876053B (en) | 2024-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kim et al. | Combining case-based reasoning with genetic algorithm optimization for preliminary cost estimation in construction industry | |
| Lee et al. | BIM-based preliminary estimation method considering the life cycle cost for decision-making in the early design phase | |
| CN106779859A (en) | A kind of real-time Method for Sales Forecast method of mobile terminal product | |
| KR101167848B1 (en) | Method on Labor Management Process of Construction Including Small Space or Element Information | |
| Di Bari et al. | Step-by-step implementation of BIM-LCA: A case study analysis associating defined construction phases with their respective environmental impacts | |
| Jrade et al. | Computer-integrated system for estimating the costs of building projects | |
| Tas et al. | A building cost estimation model based on cost significant work packages | |
| Di Giuda et al. | BIM and Post-occupancy evaluations for building management system: Weaknesses and opportunities | |
| CN114036452B (en) | Yield evaluation method applied to discrete production line | |
| Rathnasiri et al. | Green building information modelling technology adoption for existing buildings in Sri Lanka. Facilities management perspective | |
| Zhang et al. | BIM-based automated design of drainage systems for panelized residential buildings | |
| CN117876053B (en) | Intelligent mapping method and system for engineering cost | |
| Mayer et al. | Data granularity for life cycle modelling at an urban scale | |
| CN116843399B (en) | Construction cost analysis method and system based on balance state | |
| KR101835551B1 (en) | Method and system for estimating 3D BIM-based repair and replacement cost | |
| CN112330030A (en) | System and method for predicting requirements of expansion materials | |
| Valentini et al. | Comparison between traditional and digital preliminary cost-estimating approaches | |
| Zainon et al. | Developing a framework for life cycle assessment of construction materials through building information modelling (BIM) | |
| Idowu et al. | Web-based application for predesign cost planning of vertical building envelopes | |
| KR20110002637A (en) | Cbr-based cost prediction system and model for pre-design and schematic design phase of public multi-housing construction projects | |
| Cho et al. | Application of OLAP information model to parametric cost estimate and BIM | |
| KR20100026953A (en) | Storage medium storing finish work item, management system for managing finish work item and design method of finish work item | |
| Dou et al. | A review of information technology application in reducing carbon emission: From buildings to tunnels | |
| Di Bella | Application of BIM Model Checking in building acoustic design | |
| CN117196224B (en) | Intelligent repair management method and system for ancient building |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |