Railway BIM model file transmission method based on distributed storage

Abstract

The invention provides a transmission method of a railway building information model BIM model file based on distributed storage. The method comprises the following steps: carrying out lightweight decomposition on a railway BIM model file to obtain a plurality of sub-files, storing each sub-file in each edge server node in a distributed manner by a cloud server, transmitting a sub-file list to a gateway, constructing a BIMdex index model, training the BIMdex index model by utilizing a training data set, deploying the trained BIMdex index model to a local gateway, and storing the trained BIMdex index model in the local gateway. And when the end equipment sends a file request carrying the file identifier to the gateway, the BIMdex index model in the gateway queries the sub-file list according to the file identifier, obtains the corresponding BIMdex address index and returns the BIMdex address index to the end equipment. According to the method, the sub-files of the same model of the railway BIM file data are stored in the multiple physical nodes in a scattered mode, and the load balancing technology is combined, so that the data transmission efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of railway information storage, and particularly relates to a method for transmitting railway BIM (Building Information Modeling) model files based on distributed storage. Background Art

[0002] Due to containing three-dimensional geometric information and attribute data, the volume of a single railway BIM model is huge. At present, the intelligent processing of BIM data in the complex road network topology space of high-speed railways is one of the main directions for the intelligent development of high-speed railway construction and operation and maintenance. The storage strategies for large-volume railway BIM data, as well as the lightweighting and sharing mechanisms of BIM models, are summarized as follows:

[0003] In terms of the storage architecture for large-volume BIM data, in view of the characteristics of large-volume BIM data, technologies such as cloud computing, edge computing, and distributed computing are used to process and store BIM data. For example, in the prior art, a big data storage framework based on BIM was designed on the basis of researching common BIM data standards and Hadoop storage mechanisms. There are also solutions that propose a cloud-based BIM server framework capable of dynamically merging and splitting building models. There are also solutions that develop a cloud computing-based BIM data integration and management platform to support all participating parties to establish, store, manage, and apply BIM data distributively according to their own data requirements, and realize data transfer and sharing. There are also solutions that propose a cloud-based BIMCloud framework to support the collaboration and information sharing of all participating parties in a project. There are also solutions that use graph databases and graph theory to automatically convert the IFC model into a workflow of an attribute graph database by analyzing the IFC model. There are also related studies that propose using a cloud-edge collaboration mode to solve the transmission delay caused by only using a cloud server. For example, in the prior art, a cloud-edge collaborative system is proposed, where lightmap baking is performed in the cloud and the results are integrated with the terminal scene. However, when there is a large amount of real-time data transmission and processing, the collaborative effect between the cloud and the edge is not very ideal. To sum up, the research on using cloud computing, edge computing, distributed technologies, etc. to process and store BIM data is still in the exploratory stage, and related research needs to be further strengthened.

[0004] In the existing BIM models of the prior art, due to their huge data volume, in order to improve the application effect of BIM in stages such as design, construction, and operation and maintenance, and to facilitate the efficient sharing, scheduling, and transmission of BIM data, it is necessary to reduce the volume of the model (such as reducing geometric elements such as points and faces) to achieve the lightweighting of the BIM model.

[0005] A centralized storage solution for railway BIM original files in the prior art includes: after designers complete the design of a BIM model (such as a line or a station), they directly upload it to a cloud server. The cloud server is responsible for storing the model file, which contains the three-dimensional geometric information of all components and the attribute data of the components, and is huge in size. When there is a demand from an end device, the cloud server transfers the corresponding source file to the end device for loading and rendering.

[0006] Another centralized storage solution for railway BIM lightweight files in the prior art includes: before uploading the BIM file to the cloud server, perform offline lightweight processing on the BIM file first. The data volume of the lightweight model is significantly reduced, which is suitable for scenarios where the performance of the end device is relatively limited or the network conditions are restricted. When needed, the end device can quickly load the lightweight model file pulled from the cloud server, thus improving the transmission and loading efficiency.

[0007] Another distributed storage solution for BIM original files in the prior art includes: introducing edge servers between the cloud server and the end device. Based on the mechanism of cloud-edge collaboration, deploy the BIM original files to multiple edge servers in advance according to a certain strategy. When needed, the end device can pull the original files from the corresponding edge servers. These solutions optimize resource allocation and task offloading by storing the original files distributively and performing data access and processing between different nodes.

[0008] The above solutions in the prior art mainly use centralized storage and single-path downloading for the storage of large-scale railway BIM models. For large-scale BIM model original files, it is difficult to load and the utilization efficiency is low, which limits the wide application of BIM models in the whole life cycle. Summary of the Invention

[0009] The present invention provides a transmission method for railway BIM models based on distributed storage and multiplexing to effectively improve the transmission efficiency of railway BIM file data.

[0010] To achieve the above object, the present invention adopts the following technical solutions.

[0011] A transmission method for railway building information model (BIM) model files based on distributed storage includes:

[0012] Perform lightweight decomposition on the railway BIM model files to be deployed to obtain multiple sub-files;

[0013] Upload the sub-files to the cloud server, and the cloud server distributes and stores each sub-file in each edge server node. The cloud server transmits a list of lightweight railway BIM model sub-files to the gateway, and the file list contains the file names, identifiers, and BIMdex address index information of each sub-file;

[0014] The gateway samples the data of the list of lightweight railway BIM model sub-files output by the cloud server, records the storage node location, access frequency, and data size of each sub-file, and organizes the sampling results into a training dataset;

[0015] Construct a BIMdex index model, and use the training dataset to train the BIMdex index model to obtain a trained BIMdex index model;

[0016] Deploy the trained BIMdex index model to the local gateway. When the terminal device sends a file request carrying a file identifier to the gateway, the BIMdex index model in the gateway queries the list of lightweight railway BIM model sub-files according to the file identifier, obtains the BIMdex address index corresponding to the file identifier, and returns the queried BIMdex address index to the terminal device.

[0017] Preferably, the method further includes:

[0018] The cloud server centrally stores BIM folder information F = {f 1 , f 2 , …, f i , …, f M}, and each BIM folder f i contains n sub-BIM files f i = {f i1 , …, f iu , …, f iv , …, f in} of different sizes obtained by lightweight decomposition of a railway BIM model file. The total size v i of each BIM folder is represented as a set V = {v 1 , v 2 , …, v i , …, v M}, and the size of the sub-files in each BIM folder is denoted as v i = {v i1 , v i2 , …, v in};

[0019] After the terminal device obtains the BIM file index, it simultaneously sends requests to different edge server nodes to obtain sub-files f iu ∈ f iFor a request, the corresponding ES node will check whether its own cache contains the required sub-file. If it exists, it will be directly returned; if not, the request will be forwarded to the cloud server. The cloud server will send the file to the end device through this edge server node and cache the corresponding sub-file in the edge server node. The end device will summarize and store the sub-files returned by each edge server.

[0020] Preferably, the method further includes:

[0021] When the end device requests to obtain all sub-files contained in the BIM folder from the gateway, the gateway will check whether the index of the BIM folder has been assigned to the end devices in this area. If not, it will assign the BIMdex address index of each sub-file in the BIM folder to the end devices in this area according to load balancing. The BIMdex address index points to k ES nodes, that is, each sub-file in the BIM folder is distributed and stored in k ES nodes, and each ES node stores f iu ∈f i 。

[0022] Preferably, for building the BIMdex index model and training the BIMdex index model with the training data set to obtain a trained BIMdex index model, it includes:

[0023] Build a BIMdex index model. This BIMdex index model adopts a recursive model index structure. The gateway regularly samples the lightweight railway BIM model sub-file list data output by the cloud server, records the storage node location, access frequency, and data size of each lightweight railway BIM model sub-file, and organizes the sampling results into a training data set;

[0024] Use the training data set to establish a recursive model index through a supervised learning algorithm, and train the BIMdex index model using an incremental training mechanism. The input of the BIMdex index model is the file name of the lightweight railway BIM model sub-file, and the output is the BIMdex address index where the file is stored in each edge server node, so as to obtain a trained BIMdex index model;

[0025] When file migration and node status change occur, re-train and update the BIMdex index model. When the index of the BIMdex index model fails or the prediction fails, the system falls back to the retrieval mechanism based on the traditional tree index.

[0026] As can be seen from the technical solutions provided by the embodiments of the present invention described above, the present invention proposes a method for quickly loading BIM models based on distributed storage and multiplexing technologies to significantly improve data transmission efficiency. By dispersing the sub-files of the same model of railway BIM data among multiple physical nodes and combining load balancing technologies, the present invention improves the fault tolerance and data reliability of the system, effectively solves the latency problem caused by centralized storage, and achieves faster access speeds and higher data throughput.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description, and these will become apparent from the following description or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0029] Figure 1 It is a system architecture diagram of a distributed storage and multiplexing scenario provided for an embodiment of the present invention;

[0030] Figure 2 It is a schematic diagram of the lightweight decomposition of a BIM model provided for an embodiment of the present invention;

[0031] Figure 3 It is a flowchart of distributed storage provided for an embodiment of the present invention;

[0032] Figure 4 It is a flowchart of multiplexing provided for an embodiment of the present invention;

[0033] Figure 5 It is a schematic diagram of a BIMdex index model provided for an embodiment of the present invention;

[0034] Figure 6 It is a processing flowchart of a method for quickly loading a railway BIM model based on distributed storage and multiplexing provided for an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0036] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The phrase "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0037] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as here.

[0038] For the convenience of understanding the embodiments of the present invention, the following will further explain with several specific embodiments in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

[0039] The present invention proposes a distributed storage and method of railway BIM models that introduces multiple edge servers outside the cloud server. The same BIM file is decomposed into multiple small files in a lightweight manner and distributedly stored in each edge node. When the terminal device needs to access the BIM file, it can parallelly pull different sub-files from multiple edge servers and converge them locally to generate a complete file. By designing the BIMdex indexing technology, the process of distributed storage and retrieval of small files is further optimized. BIMdex generates an efficient index structure according to the dynamic access pattern of railway BIM data, greatly reducing the time overhead of index queries, so that the terminal device can quickly locate the target data. This method significantly improves the data transmission efficiency and loading speed, meets the application requirements of railway BIM models in multi-terminal and high-concurrency scenarios, and provides strong support for efficient data access and resource scheduling.

[0040] Under the application of multiplexing technology, the system of the present invention further optimizes the data transmission method. After splitting large files into multiple small files for distributed storage, it can utilize multiple sub-files to be transmitted in parallel through different paths, enabling multiple data sources to be concurrently transmitted to the output channel, significantly improving the transmission efficiency and meeting the high-efficiency transmission requirements of large-scale BIM data. In addition, the present invention designs BIMdex. By modeling the access mode of railway BIM data, it significantly reduces the index query time, effectively improves the retrieval efficiency of small files in a distributed storage environment, enhances the index performance in the scenario of high-concurrency transmission of multiple small files, and provides strong support for loading large-scale railway BIM models.

[0041] In the embodiment of the present invention, for the distributed storage and multiplexing scenarios of railway BIM data, a distributed storage and multiplexing scenario system architecture as shown in Figure 1 is designed. This system architecture includes three levels: cloud server, ES (Edge Server), and end device, and is coordinated and scheduled by the gateway. A multi-level network structure for data transmission and processing is formed to meet the requirements of efficient storage, transmission, and real-time access of massive data in railway engineering projects. The cloud server centrally stores BIM folder information F = {f 1 , f 2 , …, f i , …, f M}. Each BIM folder f i contains n sub-BIM files f i = {f i1 , …, f iu , …, f iv , …, f in} obtained by decomposing and lightweighting a railway BIM model file. That is, each BIM folder f i corresponds to a railway BIM model file to meet different transmission requirements. Figure 2 is a schematic diagram of the lightweight decomposition of a BIM model provided by an embodiment of the present invention; meanwhile, the total size v i of each BIM folder is represented as a set V = {v 1 , v 2 , …, v i , …, v M}. For each BIM folder, the size of the sub-files inside is denoted as v i = {v i1 , v i2 , …, v in}, so that the gateway can allocate resources according to the content size and transmission requirements. The ES is located between the cloud server and the end device. Multiple ESs form a cluster. The cloud server is connected to multiple ESs widely distributed in different geographical locations and transmits data through high-bandwidth backhaul links to ensure the transmission speed and real-time update of the content. The end device requests sub-files from the ES according to the BIM address index returned by the gateway, aggregates and loads each sub-file locally, and finally presents it to the user.

[0042] In the terminal, there is a device set E composed of N end devices, E = {e 1 , e 2 , …, e i , …, e N}., where the end device e i connects several edge server nodes B = {b 1 , b 2 , …, b i , …, b k} within its network coverage area. The cache capacity of each edge node is denoted as C ES . The storages of the cloud server and the end device are CC and CE respectively, and the total file size is S, satisfying C C >> S > C ES >> C E , such a design can ensure that within each time period , the system can transmit data to the end device in parallel from different ES nodes as needed to achieve efficient multiplexing and fast response in the railway engineering scenario.

[0043] In terms of distributed deployment, the embodiment of the present invention disperses the storage and transmission pressure of railway BIM data through a cloud-edge-end architecture. The gateway, as the core of the system, manages all BIM data files, responds to file access requests and performs unified scheduling to ensure fast and efficient content transmission. Figure 3 FIG. is a flowchart of distributed storage provided by the embodiment of the present invention. As shown in Figure 3 -A, when the cloud server uploads and updates a certain BIM folder, the gateway will update the total BIM file list to establish a BIMdex for this folder subsequently.

[0044] Figure 3 -B shows that when an end device in a certain area requests a certain BIM folder, that is, requests all sub-files included in this BIM folder, the gateway will check whether the index of this BIM folder is allocated to the end device in this area. If not, it will allocate the BIMdex address index of each sub-file in this BIM folder to the end device in this area according to load balancing. The BIMdex address index points to k ES nodes, that is, each sub-file in this BIM folder is distributed to k ES nodes for storage, and each ES node stores fiu ∈f i , to reduce the cache pressure of single-edge nodes and disperse traffic.

[0045] The processing flowchart of a transmission method for a railway BIM model based on distributed storage and multiplexing provided by an embodiment of the present invention includes the following processing steps;

[0046] Step S10: First, decompose the railway BIM model file to be deployed into lightweight sub-files (abbreviated as sub-files), reducing the computing and storage resources required for deploying the entire railway BIM model file on resource-constrained terminal devices.

[0047] Step S20: Upload the lightweight railway BIM model sub-files obtained in step S10 to the cloud server, and the cloud server transmits a list of lightweight railway BIM model sub-files to the gateway. The list of lightweight railway BIM model sub-files includes information such as the file name, identifier, BIMdex address index, and data size of each lightweight railway BIM model sub-file. The above BIMdex address index can be the address index of each edge server node. After a railway BIM model is decomposed into lightweight sub-files, each sub-file can correspond to a different BIMdex address index, realizing the distributed storage of each sub-file of a railway BIM model file at different addresses. The list of lightweight railway BIM model sub-files also includes the names and hierarchical structure information of each BIM folder storing the lightweight railway BIM model sub-files. The gateway receives and stores the list of lightweight railway BIM model sub-files sent by the cloud server.

[0048] Step S30: To optimize file indexing and access efficiency, the present invention constructs a BIMdex index model. This BIMdex index model combines machine learning techniques, uses a linear regression model and a neural network, learns and simulates the data distribution to predict the position of data records in a sorted array. Overall, BIMdex adopts a recursive model index structure, gradually narrowing the search range through multi-stage model predictions to improve the efficiency and accuracy of indexing. This structure allows different types of models to be used at different stages to adapt to changes in data distribution.

[0049] Design and manage the access path of lightweight railway BIM model sub-files, generate an efficient index structure for lightweight railway BIM model sub-files, significantly reduce the time overhead of index queries, and enable the terminal device to quickly locate the target data. Specifically, the BIMdex index model realizes the rapid positioning of the split lightweight railway BIM model sub-files and the efficient mapping of storage nodes through sampling the data distribution, handling index updates and failures, and invoking the index.

[0050] Step S31: Data distribution sampling. The gateway regularly samples the data of the lightweight railway BIM model sub-file list output by the cloud server, records the storage node location, access frequency, and data size of each sub-file (i.e., the lightweight railway BIM model sub-file), and organizes the sampling results into a training dataset.

[0051] Step S32: Use the above training dataset to train the BIMdex index model to obtain a trained BIMdex index model.

[0052] Use the above training dataset to establish a recursive model index through a supervised learning algorithm. The input of the BIMdex index model is the file name of the sub-file (i.e., the lightweight railway BIM model sub-file), and the output is the BIMdex address index of the file stored on each edge server node. The BIMdex index model adopts an incremental training mechanism, continuously optimizes the index performance by adding the latest access data in real time, and outputs the BIMdex address index.

[0053] Step S33: Deploy the above-trained BIMdex index model to the local gateway to ensure that index requests can be quickly responded to.

[0054] Step S34: Maintenance of the BIMdex index model. Set trigger conditions (file migration and node status change), retrain and update the BIMdex index model to ensure the accuracy and consistency of the BIMdex index model. When the index fails or the prediction fails, the system will fallback to the retrieval mechanism based on the traditional tree index to ensure the reliability of file access.

[0055] Step S35: Index lookup. When the end device sends a file request carrying a file identifier to the gateway, the BIMdex index model in the gateway queries the above lightweight railway BIM model sub-file list according to the file identifier, obtains the BIMdex address index corresponding to the file identifier, and returns the queried BIMdex address index to the end device. The end device performs a binary search within the maximum prediction error range based on the BIMdex address index returned by the gateway to finally locate the actual storage location of the requested file.

[0056] Step S40: When the end device requests a certain BIM folder, the gateway determines whether the name of the folder exists in the BIM total file list output in s20. If it exists, it determines whether an index is established for each sub-file in the BIM folder. If not, it allocates an index according to the load balance, stores each sub-file in the BIM folder on multiple edge servers with a wide geographical distribution, and at the same time establishes the BIMdex address index obtained in s30 for each sub-file, and returns the BIMdex address index to the end device.

[0057] Step S50: The terminal device determines the BIMdex address index of the sub-files in the requested BIM folder according to the BIMdex index returned in Step S40, requests the sub-files from multiple edge servers. Each edge server checks whether it caches the corresponding sub-file. If not, it pulls from the cloud server and caches locally, and transfers them to the terminal device in parallel from different edge servers. The complete railway BIM model file is generated by summarizing at the terminal device. It realizes the multiplexed transmission of multiple small BIM files to the terminal device. Figure 4 It is a multiplexing flowchart provided by an embodiment of the present invention.

[0058] Step S60: According to the complete railway BIM model file obtained in Step S50, perform model rendering and display, render the complete BIM model file, and display it to the user.

[0059] In terms of multiplexing and cache optimization, the method adopted in the embodiment of the present invention can meet the rapid response requirements of the railway engineering scenario for BIM data. As Figure 1 and Figure 4 shown, the system realizes parallel content download by establishing a decentralized multiplexed transmission path between the cloud, edge, and terminal, thereby significantly reducing the system response time. Specifically, after obtaining the BIM file index, the terminal device can send requests to different ES nodes in parallel to obtain the sub-file f iu ∈f i . The corresponding ES node will check whether its own cache contains the required sub-file. If it exists, it will return directly; if not, it will forward the request to the cloud server. The cloud server sends the file to the terminal device through this ES node and caches the corresponding sub-file in the ES node. Finally, it is summarized and cached at the terminal device.

[0060] To further optimize the file index and access efficiency, the embodiment of the present invention designs a BIMdex index model, which combines machine learning technology to dynamically manage and schedule the distribution and access path of lightweight railway BIM model sub-files. The specific implementation scheme is as follows:

[0061] a. Data distribution sampling:

[0062] The gateway samples the distribution law of lightweight railway BIM model sub-files in the edge server list data, and records the storage node location, access frequency, and data size of each sub-file.

[0063] Organize the sampling results into a training data set to provide a basis for subsequent index model training.

[0064] b. Index model training:

[0065] Using the sampled data, a recursive model index is established through a supervised learning algorithm, as Figure 5 shown.

[0066] The input of the model is the file name of the sub-file, and the output is the address index of the file stored in each edge server node.

[0068] Generally, a neural network model is used as the data fitting model, and gradient descent training is adopted to minimize the prediction error, as Figure 5 (b) shown.

[0070] To adapt to the dynamically changing access patterns, the model adopts an incremental training mechanism to continuously optimize the index performance by adding the latest access data in real time.

[0072] c. Model Deployment:

[0073] Deploy the trained index model to the gateway to ensure that index requests can be quickly responded to.

[0074] d. Index Update and Failure Handling:

[0075] Set the trigger conditions (file migration and node status change), retrain and update the index model to ensure the accuracy and consistency of the index.

[0077] When the index fails or the prediction fails, the system will fallback to the retrieval mechanism based on the traditional tree index to ensure the reliability of file access.

[0079] e. Index Search:

[0080] When the end device sends a file request, the BIMdex module predicts the address of the file storage node based on the file identifier.

[0082] According to the returned prediction result, a binary search is performed within the maximum prediction error range to finally locate the actual storage location of the target file.

[0084] The relevant algorithm is established as shown below.

[0085]

[0086]

[0087]

[0088]

[0089] The processing flow of a fast loading method for a railway BIM model based on distributed storage and multiplexing provided by an embodiment of the present invention is as follows Figure 6 shown, including the following processing procedures: When the terminal device requests a BIM file, the terminal device will first check the local cache; if there is no cache, an index request to the gateway will be initiated. The gateway returns the address index of the ES node where the allocated file is located according to the distributed storage policy, and the terminal device initiates a file request to the corresponding ES node according to this index. Then the terminal device uses the multiplexing technology to obtain the corresponding sub-files from each ES node according to the corresponding index and summarizes them, and finally presents them to the user.

[0090] The objective of the present invention is to reduce the system transmission time and improve the overall speed. An ideal situation is to cache the required files on the terminal device, but the capacity of the terminal device is limited, so the terminal device cannot cache all files. Placing all files on the cloud server will be affected by the network bandwidth. When the uplink bandwidth of the cloud server is fixed, the more devices accessing the cloud server simultaneously, the smaller the bandwidth allocated to each device. Therefore, multiple edge servers are deployed between the cloud server and the terminal device, and multiple lightweight railway BIM model sub-files are distributedly deployed. When the terminal device requests a file, it is multiplexed and transmitted from each edge server to the terminal device, thereby improving the overall speed of the system.

[0091] It can be divided into several stages in terms of time, including the time T from the cloud server to the edge server C―ES and the transmission time T from the edge server to the terminal device ES―E . The transmission time of this solution is denoted as T S , and the time T from the cloud server to the terminal device C―E . In addition, some operations involved in the transmission process are in the millisecond level (such as the time for the terminal device to send a request to the gateway, the gateway processing time, the time for the gateway to request a file index from the cloud server, the time for the cloud server to process the request, the time for the cloud server to return the file index to the gateway, the time for the terminal device to send a request to the edge server, and the time for the edge server to send a request to the cloud server), which can be ignored in the overall calculation. The file loading time T load is not within the scope of consideration of the present invention in order to focus on the time consumption in the main transmission process.

[0092] Considering that there are many devices served by the cloud server and the bandwidth is limited, the uplink bandwidth of the cloud server for a certain request is W C , while the number of devices served by the edge server is relatively small, and the uplink bandwidth of the edge server for a certain request is W B , satisfying W B >W Cand the W of each ES node B is the same. For a certain folder f i , the following are several transmission times:

[0093]

[0094] And the transmission time of a single end device is:

[0095]

[0096] Within a certain period of time, if multiple devices access the BIM file, it can be transformed into n accesses. Then the total time of the system is:

[0097]

[0098] In the present invention, the ES nodes adopt the same configuration. Therefore, when allocating edge servers and establishing BIMdex, the tasks borne by each ES node are approximately the same. So when n→∞, the constant term can be ignored. So:

[0099]

[0100] Obviously it is proved that the transmission speed of the present invention is superior to other methods.

[0101] The present invention verifies the method of pulling lightweight sub-files through an edge server by developing a system based on Vue.js and Electron. Still taking the BIM file of the electromechanical part of a large railway station with a size of 157MB as an example, the 157MB large file is lightweight segmented into 50 small files. The total size of the small files is 91.8MB and they are dispersedly stored on 3 edge servers. The overall download time is only about 3.72 seconds, the rendering time is about 17.28 seconds, and the total loading time is 21 seconds. Compared with the traditional method of storing and transmitting large files in a centralized manner, the download time is significantly improved, reduced from 39.72 seconds to 3.72 seconds, and the speed-up rate is as high as 90.63%. The overall loading speed-up reaches 78.88%. The experimental results verify the efficiency and practicality of the present method. Generally speaking, the embodiments of the present invention successfully achieve the efficient distributed storage and multiplex transmission of railway BIM data, and significantly improve the response ability and loading efficiency in the multi-task concurrent scenario.

[0102] In summary, the distributed storage of the present invention improves the reliability of the system. There is a risk of single-point failure in the traditional centralized storage system in data access and processing. However, through distributed storage, the present invention disperses the sub-file data of the same railway BIM model file among multiple physical nodes, effectively improving the fault tolerance of the system and data reliability. It meets the requirements of railway projects for data stability and persistence.

[0103] Under high-concurrency conditions, the traditional centralized transmission method is prone to transmission bottlenecks. The present invention innovatively introduces multiplexing technology, enabling multiple edge nodes to process data requests in parallel, thus ensuring the rapid transmission of the railway BIM model. Compared with other solutions for distributed storage of multiple files in terms of transmission speed, the present invention achieves the purpose of distributed storage of the same file for accelerated transmission. BIMdex is designed to achieve the rapid loading and accurate retrieval of the railway BIM model. It effectively optimizes the data transmission path, significantly improves the transmission speed and overall throughput, thereby greatly reducing the transmission delay, providing technical support for the rapid loading of railway BIM data on different terminals.

[0104] Those of ordinary skill in the art can understand that the drawings are only schematic diagrams of an embodiment, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0105] From the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0106] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the descriptions in the method embodiments. The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0107] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for transmitting railway building information model (BIM) model files based on distributed storage, characterized in that: include: Decompose the railway BIM model file to be deployed into lightweight form and obtain multiple sub-files; The sub-files are uploaded to a cloud server, and the cloud server stores the sub-files in a distributed manner on each edge server node. The cloud server transmits a list of sub-files of a lightweight railway BIM model to the gateway, where the list of sub-files includes the file name, identifier, and BIMdex address index information of each sub-file; The gateway samples the lightweight railway BIM model sub-file list data output by the cloud server, records the storage node location, access frequency, and data size of each sub-file, and organizes the sampling results into a training data set; Constructing a BIMdex index model, and training the BIMdex index model using the training data set to obtain a trained BIMdex index model; The trained BIMdex index model is deployed locally on the gateway. When the terminal device sends a file request carrying a file identifier to the gateway, the BIMdex index model in the gateway queries the lightweight railway BIM model sub-file list according to the file identifier, obtains the BIMdex address index corresponding to the file identifier, and returns the queried BIMdex address index to the terminal device.

2. The method according to claim 1, characterized in that The method further comprises: The cloud server centrally stores BIM folder information F = {f1,f2,…,f i ,…,f M }, each BIM folder f i Contains n sub-BIM files of different sizes obtained by decomposing a railway BIM model file after lightweighting. i ={f i1 ,…,f iv ,…,f iv ,…,f in }, the total size of each BIM folder v i Represented as a set V = {v1,v2,…,v i ,…,v M }, the size of each sub-file in the BIM folder is recorded as v i = {v i1 ,v i2 ,…,v in }; After the end device obtains the BIM file index, it sends a request to different edge server nodes to obtain the sub-file f in parallel. iu ∈f i When a request is received, the corresponding ES node will check whether its own cache contains the required sub-file. If so, it will be returned directly; if not, the request will be forwarded to the cloud server. The cloud server will send the file to the end device through the edge server node and cache the corresponding sub-file in the edge server node. The end device will aggregate and store the sub-files returned by each edge server.

3. The method according to claim 2, characterized in that The method further comprises: When the end device requests the gateway to obtain all sub-files contained in the BIM folder, the gateway will check whether the index of the BIM folder is allocated to the end device in the area. If not, the gateway will allocate the BIMdex address index of each sub-file in the BIM folder to the end device in the area according to load balancing. The BIMdex address index points to k ES nodes, that is, each sub-file in the BIM folder is distributed to k ES nodes for storage, and each ES node stores f iu ∈f i .

4. The method according to claim 1, characterized in that: The constructing of the BIMdex index model, training the BIMdex index model using the training data set to obtain a trained BIMdex index model, includes: Construct a BIMdex index model that uses a recursive model index structure. The gateway regularly samples the lightweight railway BIM model sub-file list data output by the cloud server, records the storage node location, access frequency, and data size of each lightweight railway BIM model sub-file, and organizes the sampling results into a training data set. A recursive model index is established by using the training data set through a supervised learning algorithm, and the BIMdex index model is trained by using an incremental training mechanism, wherein the input of the BIMdex index model is the file name of the lightweight railway BIM model sub-file, and the output is the BIMdex address index where the file is stored at each edge server node, thereby obtaining a trained BIMdex index model; When file migration and node status change occur, the BIMdex index model is retrained and updated. When the index of the BIMdex index model is invalid or the prediction fails, the system falls back to the retrieval mechanism based on the traditional tree index.

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