CN118133412A - BIM-based engineering digital delivery model data classification delivery method and system - Google Patents
BIM-based engineering digital delivery model data classification delivery method and system Download PDFInfo
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Abstract
The invention discloses a BIM-based engineering digital delivery model data classification delivery method and system, which relate to the technical field of building engineering and comprise the following steps: collecting data of engineering projects, and automatically classifying and labeling the data; processing and storing data through a cloud computing platform, and integrating engineering project data with a BIM model; automatically generating a delivery list, performing digital delivery information acceptance, and forming a project acceptance report. According to the method, the data are finely classified through the graphic neural network model, and complex building project information can be effectively processed, so that the availability and reliability of project data are improved. And secondly, the edge cloud computing hierarchical model is built, so that the data processing and storage flow are optimized, the data processing is more flexible and efficient, and the timely updating and accurate delivery of the data in different stages are ensured.
Description
Technical Field
The invention relates to the technical field of constructional engineering, in particular to a BIM-based engineering digital delivery model data classification delivery method and system.
Background
The digital delivery is a process of establishing and handing over digital definition aiming at data and information generated in the whole life cycle process of the engineering construction project building by taking the engineering construction project building as a core. The specific content of the digital delivery can comprise BIM digital model information, digital archive information, intelligent chemical engineering systems, hardware and the like. But in general, industry enterprises still lack a sophisticated set of digital delivery solutions to guide delivery of digital achievements.
At present, most domestic engineering construction projects use Word, excel, CAD two-dimensional drawings, pictures, image files and the like as information storage media in the process of each stage of the full life cycle, and implement related data transmission and delivery work according to related processes by means of paper, USB flash disk, CD, mail and the like, and the delivery mode still takes entity files as a leading mode and electronic files as an auxiliary mode. Because of the shortage of construction period, numerous related participants, large quantity of delivered documents, complex auditing flow and easy phenomenon of low delivery quality and efficiency, a large amount of manpower and material resources are consumed in actual operation, a large amount of cost is consumed, and the development and promotion of the whole engineering are difficult to effectively promote. Furthermore, the relevant literature data show: in the current achievements of engineering digital delivery, structured data generally only accounts for 15% -25% of the total amount of information, and the rest of the information is various types of unstructured information. For such unstructured data information, there is generally no effective means to correlate, apply, and share it. It follows that the construction industry is in need of finding an effective digital delivery solution.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: most of the existing data transfer delivery work is mainly entity delivery, the auditing flow is complex, the efficiency is low, and the cost is high.
In order to solve the technical problems, the invention provides the following technical scheme: the BIM-based engineering digital delivery model data classification delivery method comprises the following steps: collecting data of engineering projects, and automatically classifying and labeling the data; processing and storing data through a cloud computing platform, and integrating engineering project data with a BIM model; automatically generating a delivery list, performing digital delivery information acceptance, and forming a project acceptance report.
As a preferable scheme of the BIM-based engineering digital delivery model data classification delivery method, the invention comprises the following steps: the engineering project data comprises BIM model data, environment data, project management data, real-time monitoring data, related historical data and building standard data.
As a preferable scheme of the BIM-based engineering digital delivery model data classification delivery method, the invention comprises the following steps: the automatic classification and labeling of the data of the engineering project comprises extracting the characteristics of each node through a graphic neural network model, converting the characteristics of each node into probability distribution through a Softmax classifier at the last layer of the model, distributing each data point to the category with the highest probability and marking for classified storage, dividing the collected data into five types of building, structure, heating, water supply and drainage and electric data,
；
Wherein,Represents the/>Node in layer/>Features of the data of the project; /(I)Represents the/>Node in layer/>Features of the data of the project; /(I)Representing nodes/>Is a neighbor node of (a); /(I)Representing nodes/>Is a neighbor set of (a); /(I)Represents the/>Weight matrix of layer; -Representing a weight matrix; /(I)Represents the/>A weight matrix of the layer; /(I)Representing nodes/>Pair node/>Is of interest in (2); /(I)Representing weights/>Is a transpose of (2); /(I)Representing nodes/>Features of the data of the project; /(I)Representing nodesFeatures of the data of the project; /(I)Representing a splicing operation; /(I)Representation comprising nodes only/>Is a collection of (3); /(I)Representing nodes/>AndNodes in the intersection; /(I)Representing nodes/>Is a feature of the data of the engineering project.
As a preferable scheme of the BIM-based engineering digital delivery model data classification delivery method, the invention comprises the following steps: the cloud computing platform comprises a cloud architecture and an edge end, the cloud architecture is divided into a primary cloud and a secondary cloud, the edge end is divided into a primary edge end and a secondary edge end, and the primary cloud stores all data of the whole project, including BIM model data, real-time monitoring data and history records; the second-level cloud end distributes data in the first-level cloud end to second-level cloud end nodes according to each building, and each second-level cloud end node is responsible for processing and managing data of one building; the primary edge end further subdivides the data of each building, distributes the data to primary edge end nodes according to each layer of the building, and each primary edge end node is responsible for processing the data of one layer; each layer of data is divided into five types of building, structure, heating ventilation, water supply and drainage and electricity by the secondary edge end and is put down to a secondary edge end node, and each secondary edge end node is focused on processing one type of data; computing power, memory capacity, storage space, and bandwidth for each edge node, computing an allocation function based on priority, size, expected processing time, and complexity of the data,
；
Wherein,Representing an assigned weight matrix; /(I)Representing data items/>Is a priority of processing; /(I)Representing data items/>Is a desired processing time for (1); /(I)A sensitivity parameter representing a difference in time and priority; /(I)Representing nodes/>Is used for the calculation of the calculation capacity of (a); Representing nodes/> Is a memory capacity of (a); /(I)Representing nodes/>Is a storage space of (a); /(I)A parameter representing a resource capability; /(I)Representing data items/>Is of a size of (2); /(I)Representing nodes/>Is a bandwidth of (a); /(I)Representing a very small constant; /(I)Representing bandwidth influencing parameters; /(I)Parameters representing the need to adjust quality control; /(I)Representing the rate of increase of the quality control cost; /(I)Representation and data item/>A related quality index; after the secondary edge end completes the task of distribution, the secondary edge end node is utilized to carry out cross verification, if the cross verification results are different, the data are uploaded to an idle secondary cloud end for verification, and the verification results of the secondary cloud end are fed back to the secondary edge end for optimization learning; if the results of the cross verification are the same, judging that the data are correct, uploading the verified data to a first-level edge end, and sending an idle computing signal, wherein the first-level edge end dynamically distributes verification tasks to idle second-level edge ends according to an allocation function; the first-level cloud end is a quick response end and is responsible for processing the data which is verified and confirmed by the second-level cloud end, and the data are integrated and displayed on the monitoring interface in real time; when the verification results among the edge nodes are inconsistent, the inconsistent data are verified by the secondary cloud end through powerful computing resources, the verification results are fed back to the edge end, and if error data are found, the secondary cloud end is responsible for scheduling personnel to process the error data.
As a preferable scheme of the BIM-based engineering digital delivery model data classification delivery method, the invention comprises the following steps: integrating the engineering project data with the BIM model comprises creating the BIM model according to the building design drawing and the specification by using an Autodesk Revit, mapping the collected engineering project data to corresponding parts in the BIM model, integrating environment data into the BIM model, and connecting real-time monitoring data from the site with the BIM model to realize dynamic updating and visualization.
As a preferable scheme of the BIM-based engineering digital delivery model data classification delivery method, the invention comprises the following steps: the digital delivery comprises the steps that a cloud computing platform and a BIM model are combined, whether the responsible type of data can be checked and accepted is confirmed by a secondary edge, if the data reach a checking and accepting standard, the primary edge is uploaded, the delivery is completed, if the data do not reach the checking and accepting standard, the specific condition that the data do not reach the standard is confirmed by combining on-site monitoring, the primary edge is uploaded, and the delivery is not completed; after receiving all the data of the responsible layer, the primary edge end uploads the secondary cloud end if all the data are delivered, displays the layer number corresponding to the primary edge end to finish delivery, and generates a delivery failure report to upload the secondary cloud end after waiting for other data to confirm completion if the data are not delivered; the second-level cloud end receives the data uploaded by the first-level edge end, if all layers of the building corresponding to the second-level cloud end are delivered, an acceptance report of the building corresponding to the second-level cloud end is generated and uploaded to the first-level cloud end, and if the second-level cloud end receives a delivery failure report uploaded by the first-level edge end, a dispatcher processes according to the report and specific conditions; the first-level cloud receives the data uploaded by the second-level cloud, and when all buildings of the project generate acceptance reports, the acceptance reports of all the buildings are integrated to generate project acceptance reports, so that digital delivery is completed.
As a preferable scheme of the BIM-based engineering digital delivery model data classification delivery method, the invention comprises the following steps: the project acceptance report further comprises an electronic file transfer list for providing the delivery information during the transfer according to the delivery form and the schedule of the digital delivery, wherein the transfer list comprises file names, formats, descriptions, modification dates and version information, and finally the delivery information acceptance is executed according to the delivery object list of the data, the documents, the three-dimensional model, the hardware equipment and the digital platform.
The invention also aims to provide a BIM-based engineering digital delivery model data classification delivery system which comprises a data acquisition module, a cloud processing module and a data classification module, wherein the data acquisition module acquires engineering projects and BIM model data, performs normalization processing on the data, divides the data into five types of building, structure, heating ventilation, water supply and drainage and electricity through a neural network model, and transmits the data to the cloud processing module; the cloud processing module refines project data to each layer of each type of processing through matching of a cloud end and an edge end, integrates engineering project data with a BIM model and performs visual display; and the acceptance module is used for executing information handover according to the delivery form and the schedule of the digital delivery, carrying out acceptance through edge cloud combination, processing the found problems and generating a project acceptance report.
In a third aspect, the present invention also provides a computing device comprising: a memory and a processor;
The memory is configured to store computer-executable instructions that, when executed by the processor, implement the steps of the BIM-based engineering digital delivery model data sort delivery method.
In a fourth aspect, the present invention also provides a computer readable storage medium storing computer executable instructions that when executed by a processor implement the steps of the BIM-based engineering digital delivery model data sort delivery method.
The invention has the beneficial effects that: according to the method, the data are finely classified through the graphic neural network model, and complex building project information can be effectively processed, so that the availability and reliability of project data are improved. And secondly, the cloud computing platform is used, particularly the cloud end and the edge end are combined, so that the data processing and storage flow are optimized, and the data processing is more flexible and efficient. This hierarchical approach ensures timely updates and accurate delivery of data at different stages. In addition, the BIM model and the real-time data are integrated, dynamic updating and visualization are provided, transparency and real-time monitoring capability of project management are enhanced, management quality and delivery efficiency of the whole building project are improved, consumption of time and cost is reduced, and efficient completion and quality assurance of the project are ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall flow chart of a BIM-based engineering digital delivery model data classification delivery method according to one embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a BIM-based engineering digital delivery model data classification delivery method including:
s1: and collecting data of engineering projects, and automatically classifying and labeling the data.
Further, BIM models are created and edited on a computer using BIM software such as AutodeskRevit, archiCAD. And a 3D scanner and laser scanning equipment are used for carrying out field measurement in combination with field monitoring, so that accurate geometric information of a real building or a construction site is obtained.
The collected data includes BIM model data: geometric data, material properties, and component information; environmental data: geographic Information System (GIS) data and climate data; project management data: schedule data and resource allocation; real-time monitoring data, historical project data, and regulations and standards.
Further, constructing a deep learning model based on a graph neural network for analyzing visual data of the BIM model, simultaneously combining with an attention mechanism, increasing the resolving power of the model to different types of data, extracting the characteristics of each node through the graph neural network model, converting the characteristics of each node into probability distribution through a Softmax classifier at the last layer of the model, distributing each data point to the category with the highest probability and marking for classified storage, dividing the acquired data into five types of building, structure, heating ventilation, water supply and drainage and electric data,
；
Wherein,Represents the/>Node in layer/>Features of the data of the project; /(I)Represents the/>Node in layer/>Features of the data of the project; /(I)Representing nodes/>Is a neighbor node of (a); /(I)Representing nodes/>Is a neighbor set of (a); /(I)Represents the/>Weight matrix of layer; -Representing a weight matrix; /(I)Represents the/>A weight matrix of the layer; /(I)Representing nodes/>Pair node/>Is of interest in (2); /(I)Representing weights/>Is a transpose of (2); /(I)Representing nodes/>Features of the data of the project; /(I)Representing nodesFeatures of the data of the project; /(I)Representing a splicing operation; /(I)Representation comprising nodes only/>Is a collection of (3); /(I)Representing nodes/>AndNodes in the intersection; /(I)Representing nodes/>Is a feature of the data of the engineering project.
Historical BIM model data is used as training data, and during the training process, the model learns to identify and classify features in the image through input data. Optimizing network weights using a back propagation algorithm and a gradient descent method, helping the model to gradually reduce prediction errors during training, calculating differences between model outputs and actual labels using a cross entropy loss function,
；
Wherein,A value representing a loss function, i.e., the loss of the model over a single training sample; /(I)A vector representing a real tag; /(I)Representing the output vector of the model prediction.
Building data is mainly building construction and interior building, including building walls, building columns, stairs, ceilings, interior component decorations, and the like. The structural data are mainly related parameters of foundation foundations, concrete structures, steel-wood structures and various structures. The heating and ventilation data are mainly cold and heat source equipment, liquid conveying equipment, air treatment and conveying equipment and the like. The water supply and drainage data mainly relate to water supply, drainage, fire protection and the like in the building. The electrical data mainly comprises related data such as strong current, weak current, special electric appliances, cables and the like.
It should be noted that BIM data generally contains a large amount of association information, such as connection and dependency relationships between individual building elements, on which GNNs can directly learn to capture complex relationships between data. In addition, the invention considers that the data is combined with the BIM model, and the graph neural network is naturally suitable for processing the data with the graph structure, so that the GNN can effectively process and analyze the structured data in the BIM model.
The main purpose of this step is to classify data, and the data sources in the building are overlapped and complex, so attention mechanisms are added, so that GNNs can further identify the relationship between different building elements, such as the interaction between walls and floors or between a water supply and drainage system and an electrical system, and the hierarchical structure captures local and global data features, thereby being beneficial to understanding and classifying complex geometric structures and related attributes in a BIM model.
S2: and processing and storing the data through the cloud computing platform, and integrating the engineering project data with the BIM model.
Further, a suitable cloud service provider, such as AmazonWebServices (AWS) or MicrosoftAzure, is selected according to project requirements, and a Virtual Private Cloud (VPC) environment is established on the selected cloud platform, so that data security and isolation are ensured.
And migrating the classified data, the original BIM model data, the real-time monitoring data and other related original data to a cloud platform, and integrating the data with different sources and formats into a unified data warehouse by utilizing a data integration tool provided by the cloud platform.
The cloud computing and edge computing hierarchical model is established, so that the efficiency and accuracy of data processing can be effectively improved, and the method is particularly suitable for large-scale and complex building projects. The hierarchical model comprises a cloud architecture and an edge end, wherein the cloud architecture is divided into a primary cloud and a secondary cloud, the edge end is divided into a primary edge end and a secondary edge end, and the primary cloud stores all data of the whole project, including BIM model data, real-time monitoring data, historical records and the like; the second-level cloud end distributes data in the first-level cloud end to second-level cloud end nodes according to each building, and each second-level cloud end node is responsible for processing and managing data of one building.
The first-level edge end further subdivides the data of each building, distributes the data to first-level edge end nodes according to each layer of the building, and each first-level edge end node is responsible for processing the data of one layer; the secondary edge end divides each layer of data into five types of building, structure, heating ventilation, water supply and drainage and electricity, and the data are delivered to secondary edge end nodes, and each secondary edge end node is focused on processing one type of data.
Computing power, memory capacity, storage space, and bandwidth for each edge node, computing an allocation function based on priority, size, expected processing time, and complexity of the data,
；
Wherein,Representing an assigned weight matrix; /(I)Representing data items/>Is a priority of processing; /(I)Representing data items/>Is a desired processing time for (1); /(I)A sensitivity parameter representing a difference in time and priority; /(I)Representing nodes/>Is used for the calculation of the calculation capacity of (a); Representing nodes/> Is a memory capacity of (a); /(I)Representing nodes/>Is a storage space of (a); /(I)A parameter representing a resource capability; /(I)Representing data items/>Is of a size of (2); /(I)Representing nodes/>Is a bandwidth of (a); /(I)Representing a very small constant; /(I)Representing bandwidth influencing parameters; /(I)Parameters representing the need to adjust quality control; /(I)Representing the rate of increase of the quality control cost; /(I)Representation and data item/>Related quality index.
By passing throughAnd/>Providing a non-linear response characteristic allows the allocation function to react more sensitively to the priority of the data and the quality control requirements, and to get a higher score for urgent tasks even if their processing time is somewhat longer.
By passing throughAnd/>A smoother and more flexible relationship is established between computing resources and data requirements, even for nodes with less resources, which may be assigned to tasks as long as they are not fully saturated.
By resource capability indexThe computing capacity, the memory and the storage space of the nodes have larger influence in the score calculation, so that the nodes with more abundant resources can process larger or more complex data, and the nodes with fewer resources can process smaller or simple tasks.
Index of introduced bandwidth impactThe function is allowed to have stronger adaptability to the change of bandwidth, so that reasonable consideration of data transmission time in allocation decision is ensured, and the exponential growth of quality control cost/>The task allocation method ensures that tasks with high quality requirements (more cross validation possibly required) do not occupy excessive resources when tasks are allocated, and prevents the system from being excessively concentrated on a few high-cost tasks.
The edge cloud node can be adjusted according to real-time data and historical performance through the allocation function, the system is allowed to be self-optimized, the efficiency of resource allocation is continuously improved along with the time, and in the environment with continuous change of multitasking and conditions, the complex function can better handle various situations, not only simple resource allocation problems, but also emergency situations, resource bottlenecks and quality control problems.
After the secondary edge end completes the task of distribution, the secondary edge end node is utilized to carry out cross verification, if the cross verification results are different, the data are uploaded to an idle secondary cloud end for verification, and the verification results of the secondary cloud end are fed back to the secondary edge end for optimization learning; if the results of the cross verification are the same, judging that the data are correct, uploading the verified data to a first-level edge end, sending an idle computing signal, and dynamically distributing the verification task to an idle second-level edge end by the first-level edge end according to the distribution function.
The first-level cloud end is a quick response end and is responsible for processing the data which is verified and confirmed by the second-level cloud end, and the data are integrated and displayed on the monitoring interface in real time; when the verification results among the edge nodes are inconsistent, the inconsistent data are verified by the secondary cloud end through powerful computing resources, the verification results are fed back to the edge end, and if error data are found, the secondary cloud end is responsible for scheduling personnel to process the error data.
And carrying out processes such as data cleaning, format conversion, real-time analysis and the like on each edge node, periodically feeding back a processing result and update to the corresponding upper cloud or edge, ensuring the consistency and instantaneity of data, simultaneously implementing strict data security measures including encryption, access control and security monitoring on each level of cloud and edge nodes, and periodically backing up key data, thereby ensuring quick recovery when faults occur.
Through the hierarchical cloud computing and edge computing structure, efficient dispersion of data processing is achieved, the load of a central cloud is reduced, meanwhile, the speed and response time of data processing are improved, compared with a traditional centralized data processing model, the method is more suitable for large-scale building projects with large data quantity and high real-time requirements, and the advantages of modern computing technology are fully utilized, so that the data management efficiency and effect of the whole project are improved.
Further, integrating engineering project data with the BIM model, creating the BIM model according to a building design drawing and a specification by using an Autodesk Revit, mapping the collected engineering project data to corresponding parts in the BIM model, integrating environment data into the model, connecting real-time monitoring data from a site with the BIM model to realize dynamic updating and visualization, and integrating a Geographic Information System (GIS) and the environment data into the model on the basis of the dynamic updating and visualization to reflect the surrounding environment and geographic features.
And Asana is selected as project management software so as to facilitate task allocation, progress tracking and resource management, and the project management software and the cloud computing platform are subjected to data integration, so that BIM model data, real-time monitoring data and other related data can be directly used in the project management software.
By combining project management software and artificial intelligence, not only is automation and efficiency of project management improved, but also decision support based on data driving is provided.
S3: automatically generating a delivery list, performing digital delivery information acceptance, and forming a project acceptance report.
Further, in terms of project data delivery, the data is effectively managed and processed by utilizing a hierarchical architecture of cloud computing and edge computing, and accuracy and integrity of the data are ensured.
The digital delivery comprises the steps that a cloud computing platform and a BIM model are combined, whether the responsible type of data can be checked and accepted is confirmed by a secondary edge, if the data reach a checking standard, the primary edge is uploaded, the delivery is completed, if the data do not reach the checking standard, the specific condition that the data do not reach the standard is confirmed by combining on-site monitoring, the primary edge is uploaded, and the delivery is not completed; edge computation allows data processing and verification to be performed near the data source, which can reduce data transmission time and improve data processing efficiency.
After receiving all the data of the responsible layer, the primary edge end uploads the secondary cloud end if all the data are delivered, displays the layer number corresponding to the primary edge end to finish delivery, and generates a delivery failure report to upload the secondary cloud end after waiting for other data to confirm completion if the data are not delivered; considering the mutual correlation influence of all components in the building, when the data errors are detected, other data of the layer may have errors, so that a report of delivery failure is generated after all data of the layer such as the primary edge end are confirmed to be finished, and all error data of the layer are reported in a unified way.
The second-level cloud end receives the data uploaded by the first-level edge end, if all layers of the building corresponding to the second-level cloud end are delivered, an acceptance report of the building corresponding to the second-level cloud end is generated and uploaded to the first-level cloud end, and if the second-level cloud end receives a delivery failure report uploaded by the first-level edge end, a dispatcher processes according to the report and specific conditions; because the cloud has necessary computing resources to process a large amount of data, comprehensive analysis of the data and the BIM model is performed in the secondary cloud efficiently and quickly, and more complex data analysis and report generation can be realized.
The first-level cloud receives the data uploaded by the second-level cloud, and when all buildings of the project generate acceptance reports, the acceptance reports of all the buildings are integrated to generate project acceptance reports, so that digital delivery is completed.
Furthermore, when the project acceptance report is generated by the edge cloud computing hierarchical model, information handover is required to be executed according to the delivery form and the schedule of digital delivery, an electronic file handover list of the delivery information is provided during the handover, the handover list comprises file names, formats, descriptions, modification dates and version information, and finally, the delivery information acceptance is executed according to the data, documents, three-dimensional models, hardware equipment and the delivery object list of the digital platform.
On the other hand, the embodiment also provides a BIM-based engineering digital delivery model data classification delivery system, which comprises:
The data acquisition module acquires data of engineering projects and the BIM model, performs normalization processing on the data, classifies the data into five types of buildings, structures, heating ventilation, water supply and drainage and electricity through the neural network model, and transmits the data to the cloud processing module; the cloud processing module refines project data to each layer of each type of processing through matching of a cloud end and an edge end, integrates engineering project data with a BIM model and performs visual display; and the acceptance module is used for executing information handover according to the delivery form and the schedule of the digital delivery, carrying out acceptance through edge cloud combination, processing the found problems and generating a project acceptance report.
The data acquisition module acquires fine field data through the laser scanner and the field camera, and deploys internet of things (IoT) equipment at the same time, so that wider and real-time data acquisition is realized.
In the acceptance module, the acceptance of the delivery bill of the data, the document, the three-dimensional model, the hardware equipment and the digital platform is automatically performed through an intelligent algorithm, and the system can automatically identify the delivery items which do not reach the standard or are missing by contrasting with the predefined acceptance standard.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 2
In the following, for one embodiment of the present invention, a BIM-based engineering digital delivery model data classification delivery method is provided, and in order to verify the beneficial effects of the present invention, experiments are performed through simulation data of a certain item.
The method is used for standardizing and classifying the original data by combining the historical data of a certain layer of building, dividing the original data into five types of building, structure, heating ventilation, water supply and drainage and electric, and selecting part of important data as shown in the following table.
Table 1 building and construction data sheet
Table 2 heating, ventilation, water supply and drainage and electric data table
It should be noted that the contents in the table are only a part of the classified data, and it can be seen from the table that the method of the invention can accurately distinguish the data types and further provide the description and the important parameters of the related data by combining with the BIM model.
The method can provide highly accurate material and structural information, so that the design and construction are more accurate, errors and reworks in the construction process are reduced, meanwhile, the structural performance analysis is more accurate, the safety and durability of the building are guaranteed, the BIM model is combined for heating ventilation, water supply and drainage and the design of an electrical system, the energy efficiency of the building and the efficient operation of a water source system are guaranteed, meanwhile, the design of a safe and reliable electrical system is facilitated, and the accident risk is reduced.
Furthermore, the invention has obvious advantages in the aspects of data processing and finished delivery by establishing a hierarchical model of double-layer edge cloud computing, can save a great deal of time compared with manual confirmation and filling of acceptance reports, reduces the probability of human participation and can reduce acceptance errors, and the related data compared with the prior method in the embodiment are shown in the following table.
Table 3 delivery efficiency vs. table
The automated method of the present invention presents significant advantages in terms of construction project data processing and acceptance reporting. The method can remarkably reduce the time required for data processing and report generation from 15 hours and 8 hours to 5 hours and 2 hours respectively, which not only improves the efficiency, but also lightens the workload of staff.
For a construction unit, the digital delivery of engineering construction projects is implemented, so that the chain for information data transmission is greatly shortened, errors in the information transmission process are reduced, meanwhile, the association and intercommunication of data information are realized, the checking workload is effectively reduced, the compactness and the integrity of the engagement of each stage of the engineering are improved, the accuracy and the efficiency of project construction are promoted, the project period is shortened, and the cost is saved; for the five-party responsibility main body, the digital delivery of engineering construction projects is implemented, a unified online data information convergence platform is provided, related information of the projects can be traced and inquired at any time, the responsibility main body is positioned, the internal consumption is reduced, the cooperation is facilitated, and the working efficiency is greatly improved.
The method can promote the implementation of intelligent operation and maintenance in the intelligent construction field, lead the construction of the intelligent operation and maintenance engineering with the result, complete digital delivery of the building BIM model, intelligent hardware and corresponding systems, promote the connection between the operation and maintenance main body and the engineering main body, and promote the overall efficiency level of intelligent operation and maintenance management of the building.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The BIM-based engineering digital delivery model data classification delivery method is characterized by comprising the following steps of:
Collecting data of engineering projects, and automatically classifying and labeling the data;
Processing and storing data through a cloud computing platform, and integrating engineering project data with a BIM model;
Automatically generating a delivery list, performing digital delivery information acceptance, and forming a project acceptance report.
2. The BIM-based engineering digital delivery model data sort delivery method of claim 1, wherein: the engineering project data comprises BIM model data, environment data, project management data, real-time monitoring data, related historical data and building standard data.
3. The BIM-based engineering digital delivery model data classification delivery method of claim 2, wherein: the automatic classification and labeling of the data of the engineering project comprises extracting the characteristics of each node through a graphic neural network model, converting the characteristics of each node into probability distribution through a Softmax classifier at the last layer of the model, distributing each data point to the category with the highest probability and marking for classified storage, dividing the collected data into five types of building, structure, heating, water supply and drainage and electric data,
；
Wherein,Represents the/>Node in layer/>Features of the data of the project; /(I)Represents the/>Node in layer/>Features of the data of the project; /(I)Representing nodes/>Is a neighbor node of (a); /(I)Representing nodes/>Is a neighbor set of (a); /(I)Represents the/>Weight matrix of layer; -Representing a weight matrix; /(I)Represents the/>A weight matrix of the layer; /(I)Representing nodes/>Pair node/>Is of interest in (2); /(I)Representing weights/>Is a transpose of (2); /(I)Representing nodes/>Features of the data of the project; /(I)Representing nodes/>Features of the data of the project; /(I)Representing a splicing operation; /(I)Representation comprising nodes only/>Is a collection of (3); /(I)Representing nodes/>And/>Nodes in the intersection; /(I)Representing nodes/>Is a feature of the data of the engineering project.
4. The BIM-based engineering digital delivery model data sort delivery method of claim 3, wherein: the cloud computing platform comprises a cloud architecture and an edge end, the cloud architecture is divided into a primary cloud and a secondary cloud, the edge end is divided into a primary edge end and a secondary edge end, and the primary cloud stores all data of the whole project, including BIM model data, real-time monitoring data and history records;
The second-level cloud end distributes data in the first-level cloud end to second-level cloud end nodes according to each building, and each second-level cloud end node is responsible for processing and managing data of one building;
the primary edge end further subdivides the data of each building, distributes the data to primary edge end nodes according to each layer of the building, and each primary edge end node is responsible for processing the data of one layer;
Each layer of data is divided into five types of building, structure, heating ventilation, water supply and drainage and electricity by the secondary edge end and is put down to a secondary edge end node, and each secondary edge end node is focused on processing one type of data;
Computing power, memory capacity, storage space, and bandwidth for each edge node, computing an allocation function based on priority, size, expected processing time, and complexity of the data,
；
Wherein,Representing an assigned weight matrix; /(I)Representing data items/>Is a priority of processing; /(I)Representing data items/>Is a desired processing time for (1); /(I)A sensitivity parameter representing a difference in time and priority; /(I)Representing nodes/>Is used for the calculation of the calculation capacity of (a); /(I)Representing nodes/>Is a memory capacity of (a); /(I)Representing nodes/>Is a storage space of (a); /(I)A parameter representing a resource capability; /(I)Representing data items/>Is of a size of (2); /(I)Representing nodes/>Is a bandwidth of (a); /(I)Representing a very small constant; /(I)Representing bandwidth influencing parameters; /(I)Parameters representing the need to adjust quality control; /(I)Representing the rate of increase of the quality control cost; /(I)Representation and data item/>A related quality index;
after the secondary edge end completes the task of distribution, the secondary edge end node is utilized to carry out cross verification, if the cross verification results are different, the data are uploaded to an idle secondary cloud end for verification, and the verification results of the secondary cloud end are fed back to the secondary edge end for optimization learning; if the results of the cross verification are the same, judging that the data are correct, uploading the verified data to a first-level edge end, and sending an idle computing signal, wherein the first-level edge end dynamically distributes verification tasks to idle second-level edge ends according to an allocation function;
The first-level cloud end is a quick response end and is responsible for processing the data which is verified and confirmed by the second-level cloud end, and the data are integrated and displayed on the monitoring interface in real time; when the verification results among the edge nodes are inconsistent, the inconsistent data are verified by the secondary cloud end through powerful computing resources, the verification results are fed back to the edge end, and if error data are found, the secondary cloud end is responsible for scheduling personnel to process the error data.
5. The BIM-based engineering digital delivery model data classification delivery method of claim 4, wherein: integrating the engineering project data with the BIM model comprises creating the BIM model according to the building design drawing and the specification by using an Autodesk Revit, mapping the collected engineering project data to corresponding parts in the BIM model, integrating environment data into the BIM model, and connecting real-time monitoring data from the site with the BIM model to realize dynamic updating and visualization.
6. The BIM-based engineering digital delivery model data classification delivery method of claim 5, wherein: the digital delivery comprises the steps that a cloud computing platform and a BIM model are combined, whether the responsible type of data can be checked and accepted is confirmed by a secondary edge, if the data reach a checking and accepting standard, the primary edge is uploaded, the delivery is completed, if the data do not reach the checking and accepting standard, the specific condition that the data do not reach the standard is confirmed by combining on-site monitoring, the primary edge is uploaded, and the delivery is not completed;
after receiving all the data of the responsible layer, the primary edge end uploads the secondary cloud end if all the data are delivered, displays the layer number corresponding to the primary edge end to finish delivery, and generates a delivery failure report to upload the secondary cloud end after waiting for other data to confirm completion if the data are not delivered;
the second-level cloud end receives the data uploaded by the first-level edge end, if all layers of the building corresponding to the second-level cloud end are delivered, an acceptance report of the building corresponding to the second-level cloud end is generated and uploaded to the first-level cloud end, and if the second-level cloud end receives a delivery failure report uploaded by the first-level edge end, a dispatcher processes according to the report and specific conditions;
the first-level cloud receives the data uploaded by the second-level cloud, and when all buildings of the project generate acceptance reports, the acceptance reports of all the buildings are integrated to generate project acceptance reports, so that digital delivery is completed.
7. The BIM-based engineering digital delivery model data classification delivery method of claim 6, wherein: the project acceptance report further comprises an electronic file transfer list for providing the delivery information during the transfer according to the delivery form and the schedule of the digital delivery, wherein the transfer list comprises file names, formats, descriptions, modification dates and version information, and finally the delivery information acceptance is executed according to the delivery object list of the data, the documents, the three-dimensional model, the hardware equipment and the digital platform.
8. A BIM-based engineering digital delivery model data classification delivery system is characterized by comprising,
The data acquisition module acquires data of engineering projects and the BIM model, performs normalization processing on the data, classifies the data into five types of buildings, structures, heating ventilation, water supply and drainage and electricity through the neural network model, and transmits the data to the cloud processing module;
the cloud processing module refines project data to each layer of each type of processing through matching of a cloud end and an edge end, integrates engineering project data with a BIM model and performs visual display;
And the acceptance module is used for executing information handover according to the delivery form and the schedule of the digital delivery, carrying out acceptance through edge cloud combination, processing the found problems and generating a project acceptance report.
9. A computing device, comprising: a memory and a processor;
The memory is for storing computer executable instructions, the processor being for executing the computer executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the method of any one of claims 1 to 7.
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| CN112597579A (en) * | 2020-12-25 | 2021-04-02 | 江苏燕宁工程科技集团有限公司 | Engineering project digital delivery method and system based on BIM |
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