CN111159810B - Prefabricated component mould transaction platform of assembled building based on BIM technique - Google Patents
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Abstract
The invention discloses an assembled building prefabricated part mould trading platform based on a BIM technology. The platform comprises a database module, a Revit model transmission plug-in, a mold input and inverse modeling module and a model comparison module; in the transaction process of the prefabricated part, a seller uploads a mold model to a transaction platform through a model transmission plug-in of the platform, the platform carries out inverse modeling according to received information to generate a part model corresponding to the mold, and the model is transmitted to a database module; the buyer uploads the component model to the transaction platform through a model transmission plug-in of the platform; and the transaction platform matches and sorts the received buyer component model with the database, and feeds back the priority result to the buyer. Aiming at the condition that the utilization rate of the current prefabricated member mould of the fabricated building is low, the invention establishes a cross-region mould trading cloud platform based on the BIM model, realizes the circulation of the prefabricated member mould in a large range and effectively reduces the cost of the fabricated building.
Description
Technical Field
The invention relates to a transaction cloud platform of an assembly type building prefabricated part template, in particular to a template turnover transaction mode based on a BIM (building information modeling) technology.
Background
With the advance of building industrialization, China is popularizing prefabricated buildings vigorously, but the low prefabrication rate causes the low mold utilization rate and turnover rate of a prefabrication factory, so that the prefabricated cost is always high, and the prefabricated building has no obvious advantages compared with a cast-in-place type and becomes an important factor for hindering the development of the prefabricated building.
The building Information model BIM (building Information modeling) is based on various relevant Information data of a construction engineering project, is used for building a building model, and simulates real Information of a building through digital Information. The method has five characteristics of visualization, coordination, simulation, optimization and graphing.
In order to realize accurate circulation transaction of the prefabricated part template, information management is needed. The BIM technology is a core technology for realizing information management, the parameters, construction progress and other information of a component are placed in a model through 3D modeling, and explosion diagrams can be generated and checked through Revit software, so that real-time sharing of building data is realized. And then, a template information database is built, so that the purposes of information sharing and efficient turnover transaction of the prefabricated part template are achieved.
Disclosure of Invention
The invention aims to build a prefabricated part template trading platform based on the BIM technology aiming at the problem that the existing template of an assembly type building is low in utilization rate, so that the turnover rate of the existing template is improved, and the cost of the assembly type building is reduced.
The technical scheme adopted by the invention is as follows: a transaction platform of prefabricated part moulds of fabricated buildings based on BIM technology comprises a database module, a model input and inverse modeling module and a model comparison module;
the database module is used for storing a prefabricated part three-dimensional model corresponding to a mold uploaded to the cloud platform by a buyer, a mold model uploaded to the cloud platform by a seller and related supplementary information;
the mould input and inverse modeling module is used for enabling a seller to upload a mould model, enabling a platform to generate a component model corresponding to the mould through an inverse modeling technology based on Imageware software, binding the supplementary information and the model after the seller fills in the relevant supplementary information of the mould according to the requirement of the platform, and transmitting the supplementary information and the model to a database together.
And the model comparison module is used for comparing the prefabricated part model uploaded by the buyer with the existing prefabricated part model in the database, carrying out weighting sequencing by combining the relevant supplementary information of the seller mould and providing a priority result for the buyer.
Further, the relevant supplementary information in the database module is node connection, three-view, photo image information of key parts, transaction price, wear degree, seller geographic position and the like of the mold.
Further, the mould input and inverse modeling module firstly models components which can be generated by common moulds on the market through platform workers, binds the moulds and the component models, classifies the models and the moulds bound with the models, and stores the models and the moulds bound with the models into the database module.
When the seller operates, the seller needs to select the type of the mold and then upload the three-dimensional model of the mold. If the mold is the existing mold in the database, the platform extracts the member model bound by the similar mold from the database according to the selected mold type, obtains related parameters from the mold model uploaded by a seller, modifies the corresponding parameters, completes the adjustment of the corresponding prefabricated member model of the mold, finally obtains the prefabricated member model which can be produced by the mold, and completes inverse modeling; if the model is not available in the database, the seller directly uploads the model and stores the model as a new type to the database module. Meanwhile, the seller is required to upload information such as price, quantity, address, three-view photos of the die, photos of key parts, wear degree and the like.
Furthermore, the model comparison module extracts parameters, picture information and the die wear degree uploaded by a seller; then, matching the prefabricated part model uploaded by the buyer with the existing prefabricated part model in the database; and finally, comprehensively weighting and sorting the factors and providing a priority result to the buyer.
Further, the conformity comparison specifically includes: randomly selecting points which have distinct characteristics and can be quickly selected on the same position on the imported prefabricated part model and the existing prefabricated part model in the database as reference points, comparing the size information, the section area, the volume and the node connection of the two models to obtain the similarity ratio, wherein the similarity ratio is that if a certain parameter value of the imported prefabricated part model is alpha, the existing prefabricated part model in the database is beta, and the similarity ratio is beta
And judging whether eta meets the requirement according to the industry specification and the project requirement, thereby quickly obtaining the result whether the model is matched.
Taking two results with high priority pushing level of the same batch and each transaction result in one month as training samples, wherein the sample data comprises the price x of the mold1Degree of matching of members x2Transport distance x3Degree of wear x of die4And (5) waiting for the characteristic values, marking the transaction sample as 'yes', and marking the non-transaction sample as 'no'. The feature values were calculated as follows:
x2: comparing the parameters of the buyer upload model with the parameters of the seller model such as the length, width and height of the component, and comparing the material characteristics such as absorption rate, roughness and the like one by one to obtain the matching degree of the component, wherein the concrete formula is as follows:
x3: taking the distance between a buyer and a seller, wherein the unit is kilometers;
x4: the degree of wear of the die of the seller is taken,in percent;
firstly, preprocessing a sample by using 0-1 standardization, and carrying out unsupervised training on a sparse self-encoder by using all training samples (unlabeled samples and labeled samples) by using a data characteristic value to obtain a deep-level characteristic; then, carrying out supervised training on the sparse self-encoder and the logistic regression classifier by using the labeled samples to obtain supervised deep features; and performing feature compression and dimension reduction on the supervised deep level features, sending the compressed deep level features (with label samples) into a classifier, training, selecting a new test set for testing, and obtaining a final discrimination detection network. And finally, sorting according to the probability that the data is judged to be yes, and providing a priority pushing result for the buyer.
The invention has the beneficial effects that: aiming at the problem of low utilization rate of the existing mold of the assembly type building, the BIM technology and the intelligent algorithm are utilized to automatically match the requirements of buyers and sellers, the circulation rate and the repeated utilization rate of the mold are improved, and the waste of materials and resources is reduced.
The concrete expression is as follows:
1) in the whole process, the computer records and operates, so that the calculation and the matching are automatically carried out, and the error rate is reduced;
2) the building of the BIM model helps the working personnel and the buyers to operate the system more simply and efficiently, so that the time and the human resources are saved;
3) for the data stored in the database, other analysis software can be derived and applied, and can be used for improving the further research of the die.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of mold input and inverse modeling flow;
FIG. 3 is a schematic flow chart of model alignment plate.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
The invention aims to build a prefabricated part template trading platform based on the BIM technology aiming at the problem that the existing template of an assembly type building is low in utilization rate, so that the turnover rate of the existing template is improved, and the cost of the assembly type building is reduced.
As shown in fig. 1, the trading platform for the prefabricated part mould of the fabricated building based on the BIM technology is applied to the turnover trading of the prefabricated part mould of the fabricated building. The platform comprises a database module, a model input and inverse modeling module and a model comparison module:
the database module is used for storing relevant supplementary information such as a prefabricated part three-dimensional model corresponding to a mould uploaded to the cloud platform by a buyer, a mould model uploaded to the cloud platform by a seller, node connection of the mould, a three-dimensional view, photo image information of key parts, transaction price, wear degree, geographical position of the seller and the like;
as shown in fig. 2, the mold input and inverse modeling module firstly models a component which can be generated by a generally common mold in the market through platform staff, binds the mold with the component model, classifies the model and the mold bound with the model, and stores the model and the mold bound with the model into the database module. After the seller uploads the mold model, the platform generates a component model corresponding to the mold through an inverse modeling technology based on Imageware software, and after the seller fills in relevant supplementary information of the mold according to the requirements of the platform, the supplementary information is bound with the mold and is jointly transmitted to a database.
When the seller operates, the seller needs to select the type of the mold and then upload the three-dimensional model of the mold. If the mold is the existing mold in the database, the platform extracts the member model bound by the similar mold from the database according to the selected mold type, obtains related parameters from the mold model uploaded by a seller, modifies the corresponding parameters, completes the adjustment of the corresponding prefabricated member model of the mold, finally obtains the prefabricated member model which can be produced by the mold, and completes inverse modeling; if the model is not available in the database, the seller directly uploads the model and stores the model as a new type to the database module. Meanwhile, the seller is required to upload information such as price, quantity, address, three-view photos of the die, photos of key parts, wear degree and the like.
The model comparison module is used for comparing the prefabricated part model uploaded by the buyer with the existing prefabricated part model in the database as shown in fig. 3, and extracting parameters, picture information and the die wear degree uploaded by the seller; then, matching the prefabricated part model uploaded by the buyer with the existing prefabricated part model in the database; if the component type is a basic component type, the size parameters are directly extracted for comparison, if the component type is a special component type, the input keywords are matched with the keywords of the model uploaded by the seller, and finally, the factors are comprehensively weighted and sorted, and then priority results are provided for the buyer.
The conformity comparison is specifically as follows: randomly selecting points which have distinct characteristics and can be quickly selected on the same position on the imported prefabricated part model and the existing prefabricated part model in the database as reference points, comparing the size information, the section area, the volume and the node connection of the two models to obtain the similarity ratio, wherein the similarity ratio is defined by setting a certain parameter value of the imported prefabricated part model as alpha and the existing prefabricated part model in the database as beta, and then setting the similarity ratio as beta
And judging whether eta meets the requirement according to the industry specification and the project requirement, thereby quickly obtaining the result whether the model is matched.
Taking two results with high priority pushing level of the same batch and each transaction result in one month as training samples, wherein the sample data comprises the price x of the mold1Degree of matching of members x2Transport distance x3Degree of wear x of die4And waiting for the characteristic value, marking the transaction sample as yes, and marking the non-transaction sample as no. The eigenvalues take the following values:
x2: uploading buyer and seller model parameters such as length, width and height of component, and material characteristics such as absorptivity and thicknessComparing the characteristics such as roughness and the like one by one to obtain the matching degree of the components;
x3: taking the distance between a buyer and a seller, wherein the unit is kilometers;
x4: taking the abrasion degree of a die of a seller in percentage;
firstly, preprocessing a sample by using 0-1 standardization, and carrying out unsupervised training on a sparse self-encoder by using all training samples (unlabeled samples and labeled samples) by using a data characteristic value to obtain a deep-level characteristic; then, carrying out supervised training on the sparse self-encoder and the logistic regression classifier by using the labeled samples to obtain supervised deep features; and performing feature compression and dimension reduction on the supervised deep level features, sending the compressed deep level features (with label samples) into a classifier, training, selecting a new test set for testing, and obtaining a final discrimination detection network. And finally, sorting according to the probability that the data is judged to be yes, and providing a priority pushing result for the buyer.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.
Claims (3)
1. A transaction platform of prefabricated part moulds of fabricated buildings based on BIM technology is characterized in that the platform comprises a database module, a model input and inverse modeling module and a model comparison module;
the database module is used for storing a prefabricated part three-dimensional model corresponding to a mold uploaded to the cloud platform by a buyer, a mold model uploaded to the cloud platform by a seller and related supplementary information;
the mould input and inverse modeling module is used for enabling a seller to upload a mould model, enabling a platform to generate a component model corresponding to the mould through an inverse modeling technology based on Imageware software, binding the supplementary information and the model after the seller fills in the relevant supplementary information of the mould according to the requirement of the platform, and transmitting the supplementary information and the model to a database together;
the model comparison module is used for comparing the prefabricated part model uploaded by the buyer with the existing prefabricated part model in the database, performing weighted sequencing by combining the relevant supplementary information of the seller mould and providing a priority result for the buyer; the method specifically comprises the following steps: firstly, extracting parameters, picture information and die wear degrees uploaded by a seller; then, matching the prefabricated part model uploaded by the buyer with the existing prefabricated part model in the database; finally, comprehensively weighting and sorting the factors and providing a priority result for the buyer; the conformity comparison is specifically as follows: randomly selecting points which have distinct characteristics and can be quickly selected on the same position on the imported prefabricated part model and the existing prefabricated part model in the database as reference points, comparing the size information, the section area, the volume and the node connection of the two models to obtain the similarity ratio, wherein the similarity ratio is that if a certain parameter value of the imported prefabricated part model is alpha, the existing prefabricated part model in the database is beta, and the similarity ratio is beta
Judging whether eta meets the requirement according to the industry specification and the project requirement, thereby quickly obtaining the result whether the model is matched;
taking two results with high priority pushing level of the same batch and each transaction result in one month as training samples, wherein the sample data comprises the price x of the mold1Degree of matching of members x2Transport distance x3Degree of wear x of die4The deal sample is marked as "yes", and the non-deal sample is marked as "no"; the feature values were calculated as follows:
x2: the length, width and the like of the components of the model parameters uploaded by the buyer and the model parameters of the seller,The material characteristic absorption rate and the roughness characteristic are subjected to one-to-one comparison to obtain the component matching degree, and the concrete formula is as follows:
x3: taking the distance between a buyer and a seller, wherein the unit is kilometers;
x4: taking the abrasion degree of a die of a seller in percentage;
firstly, preprocessing a sample by using 0-1 standardization, and performing unsupervised training on a sparse self-encoder by using all training samples by using a data characteristic value to obtain deep-level characteristics, wherein the training samples comprise unlabeled samples and labeled samples; then, carrying out supervised training on the sparse self-encoder and the logistic regression classifier by using the labeled samples to obtain supervised deep features; carrying out feature compression and dimension reduction on the supervised deep level features, sending the compressed deep level features into a classifier for training, selecting a new test set for testing, and obtaining a final discrimination detection network; and finally, sorting according to the probability that the data is judged to be yes, and providing a priority pushing result for the buyer.
2. The BIM technology-based assembled building prefabricated part mold trading platform according to claim 1, wherein the relevant supplementary information in the database module is node connection, three-view, photo image information of key parts, trading price, wear degree and seller geographical position of the mold.
3. The BIM technology-based assembled building prefabricated part mold trading platform of claim 1, wherein the mold input and inverse modeling module firstly models a component which can be generated by a common mold in the market through a platform worker, binds the mold with a component model, classifies the model and the mold bound with the model, and stores the model and the mold bound with the model into a database module;
when a seller operates, the type of the die needs to be selected firstly, and then the three-dimensional model of the die is uploaded; if the mold is the existing mold in the database, the platform extracts the member model bound by the similar mold from the database according to the selected mold type, obtains related parameters from the mold model uploaded by a seller, modifies the corresponding parameters, completes the adjustment of the corresponding prefabricated member model of the mold, finally obtains the prefabricated member model which can be produced by the mold, and completes inverse modeling; if the model is not the mold model in the database, the seller directly uploads the mold model and stores the mold model as a new type to the database module; meanwhile, the seller is required to upload three-view photos, key part photos and abrasion degree information of the price, the quantity and the address and the die.
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Inventor after: Bao Sheng Inventor after: Kong Youbohong Inventor after: Shen Yong Inventor after: Liu Jingliang Inventor after: Hu Yiping Inventor after: Zhao Zhengye Inventor after: Guan Longhua Inventor after: Lin Fanke Inventor after: Pan Anhao Inventor before: Bao Sheng Inventor before: Kong Youbohong Inventor before: Shen Yong Inventor before: Liu Jingliang Inventor before: Zhao Zhengye Inventor before: Guan Longhua Inventor before: Lin Fanke Inventor before: Pan Anhao |
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Effective date of registration: 20220519 Address after: 310058 Yuhang Tang Road, Xihu District, Hangzhou, Zhejiang 866 Patentee after: ZHEJIANG University Patentee after: Hangzhou Urban Construction Development Group Co., Ltd Address before: 310058 Yuhang Tang Road, Xihu District, Hangzhou, Zhejiang 866 Patentee before: ZHEJIANG University |