Bridge construction and maintenance data middle platform construction method and system based on data lake

Abstract

The application discloses a bridge construction and maintenance data middle platform construction method and system based on a data lake, and the method comprises the following steps: collecting bridge construction and maintenance data, and performing hierarchical storage on the bridge construction and maintenance data, wherein the bridge construction and maintenance data comprises static data of bridge construction and maintenance and dynamic data of bridge construction and maintenance; performing noise reduction and missing value interpolation processing on the bridge construction and maintenance data after hierarchical storage, and extracting component properties and geometric topological relations in the bridge construction and maintenance data after hierarchical storage; generating a BIM model of the bridge according to the processed bridge construction and maintenance data and the component properties and geometric topological relations, and labeling service conditions of the bridge on the BIM model of the bridge, wherein the service conditions comprise bridge component health degrees and bridge component service lives.

Description

Technical Field

[0001] This invention belongs to the field of bridge data platform construction technology, and more specifically, relates to a method and system for constructing a bridge construction and maintenance data platform based on a data lake. Background Technology

[0002] The current management of bridge construction and maintenance data faces the following core issues:

[0003] Data fragmentation: Design BIM models, construction monitoring data, operation and maintenance sensor data, and maintenance documents are stored in different systems, forming data silos;

[0004] Format heterogeneity: Data types cover structured (sensor time series data), semi-structured (JSON logs), unstructured (inspection images, PDF reports), 3D models (BIM / IFC), etc., lacking a unified governance standard;

[0005] Insufficient real-time performance: Traditional databases struggle to support real-time writing and streaming analysis of massive amounts of time-series data (such as vibration sensors with sampling frequencies of tens of thousands per second);

[0006] Lack of intelligent applications: Existing platforms are mostly limited to data storage and simple queries, and do not deeply integrate AI algorithms to support predictive maintenance decisions.

[0007] Existing technologies, solutions based on relational databases or data warehouses suffer from drawbacks such as high storage costs, poor scalability, and weak real-time analysis capabilities. Summary of the Invention

[0008] To address the above technical problems, this invention proposes a method for constructing a bridge construction and maintenance data platform based on a data lake, comprising:

[0009] Bridge construction and maintenance data are collected and stored in layers. The construction and maintenance data includes: static data of bridge construction and operation and maintenance, and dynamic data of bridge construction and operation and maintenance.

[0010] The hierarchical storage of the construction and maintenance data is subjected to noise reduction and missing value imputation processing to extract the component attributes and geometric topological relationships in the hierarchical storage of the construction and maintenance data;

[0011] Based on the processed construction and maintenance data and component attributes and geometric topological relationships, a BIM model of the bridge is generated. The service status of the bridge is marked on the BIM model, including the health of bridge components and the lifespan of bridge components.

[0012] Furthermore, storing the construction and maintenance data in layers includes storing the construction and maintenance data separately according to a hot storage layer and a cold storage layer.

[0013] Furthermore, it also includes: establishing a multi-dimensional index based on bridge component ID, timestamp, and spatial coordinates to query the hierarchically stored construction and maintenance data.

[0014] Furthermore, it also includes: constructing a bridge data lineage map in the construction and maintenance data after hierarchical storage, and recording the data source, processing process and usage chain.

[0015] Set a spatiotemporal region segmentation function to narrow the query range of data points by calculating the spatiotemporal region segmentation value.

[0016] Furthermore, the spatiotemporal region segmentation functions include:

[0017] ,

[0018] in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value corresponding to each target data point.

[0019] Furthermore, the merged bridge construction and maintenance data will be centrally processed. Spatiotemporal region segmentation value of each target data point If it is less than the preset region threshold, then in the [number]th [period]... In the dimension with the first The query center value corresponding to each target data point The area within the preset radius is defined as the hot spot area, and the data points to be queried will be queried within the hot spot area.

[0020] Furthermore, a distance judgment function is set between data points. Within the hotspot area, the distance between the target data point and the data point to be queried is calculated. The target data point with a distance less than the preset distance threshold is taken as the final query result and returned to the user.

[0021] Furthermore, the distance judgment function between data points includes:

[0022] ,

[0023] in, For the first The target data point and the first The distance between the data points to be queried In the first The first dimension Feature values ​​of each target data point In the first The first dimension The feature values ​​of the data points to be queried This is the adjustment factor for the distance judgment function. For the first The timestamp of each target data point For the first The timestamp of the data point to be queried This is an adjustment factor for the time difference. For the first Spatial coordinates of the target data points For the first The spatial coordinates of the data points to be queried This is an adjustment factor for the spatial coordinate difference. In the first The first dimension Feature values ​​of target data points With the The first dimension Feature values ​​of the data points to be queried Adjustment factor for the distance between them.

[0024] This invention also proposes a bridge construction and maintenance data platform construction system based on a data lake, comprising:

[0025] The storage module is used to collect bridge construction and maintenance data and store the construction and maintenance data in layers. The construction and maintenance data includes: static data of bridge construction and operation and maintenance and dynamic data of bridge construction and operation and maintenance.

[0026] The data processing module is used to perform noise reduction and missing value imputation processing on the hierarchically stored construction and maintenance data, and to extract the component attributes and geometric topological relationships in the hierarchically stored construction and maintenance data.

[0027] The middle platform construction module is used to generate a BIM model of the bridge based on the processed construction and maintenance data and component attributes and geometric topological relationships, and to mark the service status of the bridge on the bridge BIM model, wherein the service status includes the health of bridge components and the life of bridge components.

[0028] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0029] The technical solution of this invention can improve the efficiency of bridge construction and maintenance data utilization and provide data support for engineers. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0031] Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0032] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0033] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0034] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0035] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0036] The display screen is used to show the user interface of each application.

[0037] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0038] Example 1

[0039] like Figure 1 As shown, this embodiment proposes a method for constructing a bridge construction and maintenance data platform based on a data lake. The bridge construction and maintenance data includes the bridge's construction and maintenance data, including:

[0040] Step 101: Collect bridge construction and maintenance data, and provide a standardized API interface (RESTful / gRPC) to interface with edge computing devices to achieve low-latency data collection. Store the construction and maintenance data in layers, including: static data of bridge construction and maintenance, and dynamic data of bridge construction and maintenance.

[0041] Static data: BIM model (IFC format), design drawings, material testing reports;

[0042] Dynamic data: Real-time streaming data from GPS displacement monitoring, strain sensors, and vibration accelerometers;

[0043] Specifically, storing the construction and maintenance data in layers includes storing the construction and maintenance data separately according to a hot storage layer and a cold storage layer.

[0044] Hot storage layer: A real-time data pipeline based on Apache Kafka + Apache Parquet, supporting high-concurrency writes and millisecond-level queries;

[0045] Cold storage layer: Object storage based on HDFS+MinIO, for long-term archiving of raw data;

[0046] Specifically, this also includes: establishing a multi-dimensional index based on bridge component ID, timestamp, and spatial coordinates to query the hierarchically stored construction and maintenance data.

[0047] Step 102: Perform noise reduction and missing value imputation processing on the hierarchically stored construction and maintenance data, and extract the component attributes and geometric topological relationships in the hierarchically stored construction and maintenance data;

[0048] For BIM models, an IFC-to-DataLake parser was developed (the core function of the IFC-to-DataLake parser will be to convert IFC format building information model data into a format suitable for storage and analysis, so that it can be stored in the Data Lake and provide data support for subsequent analysis), extracting component attributes and geometric topological relationships;

[0049] For sensor data, a sliding window noise reduction algorithm and a missing value imputation model (based on LSTM prediction) are designed.

[0050] Specifically, it also includes: building a bridge data lineage map in the maintenance data after constructing a hierarchical storage based on Apache Atlas, and recording the data source, processing process and usage chain.

[0051] Step 103: Based on the processed construction and maintenance data and component attributes and geometric topological relationships, generate a BIM model of the bridge, and mark the service status of the bridge on the BIM model of the bridge, wherein the service status includes the health of bridge components and the life of bridge components.

[0052] Service status is identified using YOLOv7 (based on drone images).

[0053] Bridge structural health assessment using Graph Neural Network (based on fused sensor network topology).

[0054] Predicting the remaining lifetime of components using Prophet+XGBoost;

[0055] Set a spatiotemporal region segmentation function to narrow the query range of data points by calculating the spatiotemporal region segmentation value.

[0056] Specifically, spatiotemporal region segmentation functions include:

[0057]

[0058] in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value corresponding to each target data point.

[0059] Hotspot areas: Areas with smaller values ​​indicate that the query point is very close to the query center. Components within this area can be considered key areas of the query and are usually prioritized or highlighted.

[0060] Edge area: A region with a larger value indicates that the query point is far from the query center. Data points in this region are usually not prioritized and can be considered a secondary region of the query.

[0061] Specifically, the merged bridge construction and maintenance data will be centrally processed. Spatiotemporal region segmentation value of each target data point If it is less than the preset region threshold, then in the [number]th [period]... In the dimension with the first The query center value corresponding to each target data point The area within the preset radius is defined as the hot spot area, and the data points to be queried will be queried within the hot spot area.

[0062] The preset radius of the spatiotemporal region is defined based on the query target and query range (e.g., the preset radius is δ). Specifically, the preset radius of the spatiotemporal region can be determined based on the following factors:

[0063] Center value and preset radius:

[0064] Query center value Each query dimension (e.g., time, spatial coordinates, component ID, etc.) has a corresponding center value, which represents the reference point on that dimension.

[0065] Preset radius: This parameter defines the size of the query area. You can set a radius (with radius δ) to determine the specific range of the query, which indicates the desired spatiotemporal proximity of the query to the target component.

[0066] For example, for a bridge support, the preset radius δ represents the spatiotemporal radius that the user is concerned with, that is, all components that are within the preset radius δ in both time and space.

[0067] Specifically, a distance judgment function is set between data points. Within the hotspot area, the distance between the target data point and the data point to be queried is calculated. The target data point with a distance less than the preset threshold is taken as the final query result and returned to the user.

[0068] Specifically, the distance judgment functions between data points include:

[0069] ,

[0070] in, For the first The target data point and the first The distance between the data points to be queried In the first The first dimension Feature values ​​of each target data point In the first The first dimension The feature values ​​of the data points to be queried This is the adjustment factor for the distance judgment function. For the first The timestamp of each target data point For the first The timestamp of the data point to be queried This is an adjustment factor for the time difference. For the first Spatial coordinates of the target data points For the first The spatial coordinates of the data points to be queried This is an adjustment factor for the spatial coordinate difference. In the first The first dimension Feature values ​​of target data points With the The first dimension Feature values ​​of the data points to be queried Adjustment factor for the distance between them.

[0071] Example 2

[0072] like Figure 2 As shown, this embodiment of the invention also provides a bridge construction and maintenance data platform construction system based on a data lake, including:

[0073] The storage module is used to collect bridge construction and maintenance data and store the construction and maintenance data in layers. The construction and maintenance data includes: static data of bridge construction and operation and maintenance and dynamic data of bridge construction and operation and maintenance.

[0074] Specifically, storing the construction and maintenance data in layers includes storing the construction and maintenance data separately according to a hot storage layer and a cold storage layer.

[0075] Specifically, this also includes: establishing a multi-dimensional index based on bridge component ID, timestamp, and spatial coordinates to query the hierarchically stored construction and maintenance data.

[0076] The data processing module is used to perform noise reduction and missing value imputation processing on the hierarchically stored construction and maintenance data, and to extract the component attributes and geometric topological relationships in the hierarchically stored construction and maintenance data.

[0077] Specifically, this also includes: constructing a lineage map of bridge data in the construction and maintenance data after hierarchical storage, and recording the data source, processing process and usage chain.

[0078] The middle platform construction module is used to generate a BIM model of the bridge based on the processed construction and maintenance data and component attributes and geometric topological relationships, and to mark the service status of the bridge on the bridge BIM model, wherein the service status includes the health of bridge components and the life of bridge components.

[0079] Set a spatiotemporal region segmentation function to narrow the query range of data points by calculating the spatiotemporal region segmentation value.

[0080] Specifically, spatiotemporal region segmentation functions include:

[0081]

[0082] in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value corresponding to each target data point.

[0083] Specifically, the merged bridge construction and maintenance data will be centrally processed. Spatiotemporal region segmentation value of each target data point If it is less than the preset region threshold, then in the [number]th [period]... In the dimension with the first The query center value corresponding to each target data point The area within the preset radius is defined as the hot spot area, and the data points to be queried will be queried within the hot spot area.

[0084] Specifically, a distance judgment function is set between data points. Within the hotspot area, the distance between the target data point and the data point to be queried is calculated. The target data point with a distance less than the preset threshold is taken as the final query result and returned to the user.

[0085] Specifically, the distance judgment functions between data points include:

[0086] ,

[0087] in, For the first The target data point and the first The distance between the data points to be queried In the first The first dimension Feature values ​​of each target data point In the first The first dimension The feature values ​​of the data points to be queried This is the adjustment factor for the distance judgment function. For the first The timestamp of each target data point For the first The timestamp of the data point to be queried This is an adjustment factor for the time difference. For the first Spatial coordinates of the target data points For the first The spatial coordinates of the data points to be queried This is an adjustment factor for the spatial coordinate difference. In the first The first dimension Feature values ​​of target data points With the The first dimension Feature values ​​of the data points to be queried Adjustment factor for the distance between them.

[0088] Example 3

[0089] This invention also proposes a storage medium storing multiple instructions for implementing the aforementioned method for constructing a bridge construction and maintenance data platform based on a data lake.

[0090] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0091] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: Step 101, collect bridge construction and maintenance data, and store the construction and maintenance data in layers, wherein the construction and maintenance data includes: static data of bridge construction and maintenance, and dynamic data of bridge construction and maintenance.

[0092] Specifically, storing the construction and maintenance data in layers includes storing the construction and maintenance data separately according to a hot storage layer and a cold storage layer.

[0093] Specifically, this also includes: establishing a multi-dimensional index based on bridge component ID, timestamp, and spatial coordinates to query the hierarchically stored construction and maintenance data.

[0094] Step 102: Perform noise reduction and missing value imputation processing on the hierarchically stored construction and maintenance data, and extract the component attributes and geometric topological relationships in the hierarchically stored construction and maintenance data;

[0095] Specifically, this also includes: constructing a lineage map of bridge data in the construction and maintenance data after hierarchical storage, and recording the data source, processing process and usage chain.

[0096] Step 103: Based on the processed construction and maintenance data and component attributes and geometric topological relationships, generate a BIM model of the bridge, and mark the service status of the bridge on the BIM model of the bridge, wherein the service status includes the health of bridge components and the life of bridge components.

[0097] Set a spatiotemporal region segmentation function to narrow the query range of data points by calculating the spatiotemporal region segmentation value.

[0098] Specifically, spatiotemporal region segmentation functions include:

[0099]

[0100] in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value corresponding to each target data point.

[0101] Specifically, the merged bridge construction and maintenance data will be centrally processed. Spatiotemporal region segmentation value of each target data point If it is less than the preset region threshold, then in the [number]th [period]... In the dimension with the first The query center value corresponding to each target data point The area within the preset radius is defined as the hot spot area, and the data points to be queried will be queried within the hot spot area.

[0102] Specifically, a distance judgment function is set between data points. Within the hotspot area, the distance between the target data point and the data point to be queried is calculated. The target data point with a distance less than the preset threshold is taken as the final query result and returned to the user.

[0103] Specifically, the distance judgment functions between data points include:

[0104] ,

[0105] in, For the first The target data point and the first The distance between the data points to be queried In the first The first dimension Feature values ​​of each target data point In the first The first dimension The feature values ​​of the data points to be queried This is the adjustment factor for the distance judgment function. For the first The timestamp of each target data point For the first The timestamp of the data point to be queried This is an adjustment factor for the time difference. For the first Spatial coordinates of the target data points For the first The spatial coordinates of the data points to be queried This is an adjustment factor for the spatial coordinate difference. In the first The first dimension Feature values ​​of target data points With the The first dimension Feature values ​​of the data points to be queried Adjustment factor for the distance between them.

[0106] Example 4

[0107] This invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the aforementioned method for constructing a bridge construction and maintenance data platform based on a data lake.

[0108] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0109] The storage medium can be used to store software programs and modules, such as the bridge construction and maintenance data platform construction method based on a data lake in this embodiment of the invention. The corresponding program instructions / modules are executed by the processor through running the software programs and modules stored in the storage medium, thereby performing various functional applications and data processing, thus realizing the aforementioned bridge construction and maintenance data platform construction method based on a data lake. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0110] The processor can call the information and application stored in the storage medium through the transmission system to execute the following steps: Step 101, collect the construction and maintenance data of the bridge, and store the construction and maintenance data in layers, wherein the construction and maintenance data includes: static data of bridge construction and operation and maintenance, and dynamic data of bridge construction and operation and maintenance.

[0111] Specifically, storing the construction and maintenance data in layers includes storing the construction and maintenance data separately according to a hot storage layer and a cold storage layer.

[0112] Specifically, this also includes: establishing a multi-dimensional index based on bridge component ID, timestamp, and spatial coordinates to query the hierarchically stored construction and maintenance data.

[0113] Step 102: Perform noise reduction and missing value imputation processing on the hierarchically stored construction and maintenance data, and extract the component attributes and geometric topological relationships in the hierarchically stored construction and maintenance data;

[0114] Specifically, this also includes: constructing a lineage map of bridge data in the construction and maintenance data after hierarchical storage, and recording the data source, processing process and usage chain.

[0115] Step 103: Based on the processed construction and maintenance data and component attributes and geometric topological relationships, generate a BIM model of the bridge, and mark the service status of the bridge on the BIM model of the bridge, wherein the service status includes the health of bridge components and the life of bridge components.

[0116] Set a spatiotemporal region segmentation function to narrow the query range of data points by calculating the spatiotemporal region segmentation value.

[0117] Specifically, spatiotemporal region segmentation functions include:

[0118]

[0119] in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value corresponding to each target data point.

[0120] Specifically, the merged bridge construction and maintenance data will be centrally processed. Spatiotemporal region segmentation value of each target data point If it is less than the preset region threshold, then in the [number]th [period]... In the dimension with the first The query center value corresponding to each target data point The area within the preset radius is defined as the hot spot area, and the data points to be queried will be queried within the hot spot area.

[0121] Specifically, a distance judgment function is set between data points. Within the hotspot area, the distance between the target data point and the data point to be queried is calculated. The target data point with a distance less than the preset threshold is taken as the final query result and returned to the user.

[0122] Specifically, the distance judgment functions between data points include:

[0123] ,

[0124] in, For the first The target data point and the first The distance between the data points to be queried In the first The first dimension Feature values ​​of each target data point In the first The first dimension The feature values ​​of the data points to be queried This is the adjustment factor for the distance judgment function. For the first The timestamp of each target data point For the first The timestamp of the data point to be queried This is an adjustment factor for the time difference. For the first Spatial coordinates of the target data points For the first The spatial coordinates of the data points to be queried This is an adjustment factor for the spatial coordinate difference. In the first The first dimension Feature values ​​of target data points With the The first dimension Feature values ​​of the data points to be queried Adjustment factor for the distance between them.

[0125] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0126] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0131] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for building a data-lake-based bridge maintenance data platform, characterized in that, include: Bridge construction and maintenance data are collected and stored in layers. The construction and maintenance data includes: static data of bridge construction and operation and maintenance, and dynamic data of bridge construction and operation and maintenance. The hierarchical storage of the construction and maintenance data is subjected to noise reduction and missing value imputation processing to extract the component attributes and geometric topological relationships in the hierarchical storage of the construction and maintenance data; Based on the processed construction and maintenance data and component attributes and geometric topological relationships, a BIM model of the bridge is generated. The service status of the bridge is marked on the BIM model of the bridge, wherein the service status includes the health of bridge components and the life of bridge components. Set a spatiotemporal region segmentation function to narrow the query range of the data points to be queried by calculating the spatiotemporal region segmentation value; Spatiotemporal region segmentation functions include: , in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value is the central value corresponding to each target data point. The query center value is the central value corresponding to each dimension of the query, and serves as the reference point for the corresponding dimension. The merged bridge construction and maintenance data will be placed in the first... Spatiotemporal region segmentation value of each target data point If it is less than the preset region threshold, then in the... In the dimension with the first The query center value corresponding to each target data point The area within the preset radius is defined as the hot spot area, and the data points to be queried will be queried within the hot spot area.

2. The method for constructing a bridge construction and maintenance data platform based on a data lake as described in claim 1, characterized in that, The hierarchical storage of the construction and maintenance data includes storing the construction and maintenance data separately according to a hot storage layer and a cold storage layer.

3. The bridge maintenance data platform construction method based on a data lake according to claim 1, characterized in that, Also includes: A multidimensional index is created based on bridge component ID, timestamp, and spatial coordinates to query the construction and maintenance data after hierarchical storage.

4. The bridge maintenance data platform construction method based on a data lake according to claim 1, characterized in that, Also includes: Construct a lineage map of bridge data in the construction and maintenance data after hierarchical storage, and record the data source, processing process and usage chain.

5. The bridge maintenance data platform construction method based on a data lake according to claim 1, characterized in that, Set a distance judgment function between data points. Within the hotspot area, calculate the distance between the target data point and the data point to be queried. Target data points with a distance less than the preset distance threshold are taken as the final query results and returned to the user.

6. The bridge maintenance data platform construction method based on a data lake according to claim 5, characterized in that, The distance judgment functions between data points include: ， in, For the first The target data point and the first The distance between the data points to be queried In the first The first dimension Feature values ​​of each target data point In the first The first dimension The feature values ​​of the data points to be queried This is the adjustment factor for the distance judgment function. For the first The timestamp of each target data point For the first The timestamp of the data point to be queried This is an adjustment factor for the time difference. For the first Spatial coordinates of the target data points For the first The spatial coordinates of the data points to be queried This is an adjustment factor for the spatial coordinate difference. In the first The first dimension Feature values ​​of target data points With the The first dimension Feature values ​​of the data points to be queried Adjustment factor for the distance between them.

7. A bridge maintenance data middle platform construction system based on a data lake, characterized in that, include: The storage module is used to collect bridge construction and maintenance data and store the construction and maintenance data in layers. The construction and maintenance data includes: static data of bridge construction and operation and maintenance and dynamic data of bridge construction and operation and maintenance. The data processing module is used to perform noise reduction and missing value imputation processing on the hierarchically stored construction and maintenance data, and to extract the component attributes and geometric topological relationships in the hierarchically stored construction and maintenance data. The middle platform construction module is used to generate a BIM model of the bridge based on the processed construction and maintenance data and component attributes and geometric topology relationships, and to mark the service status of the bridge on the BIM model of the bridge, wherein the service status includes the health of bridge components and the life of bridge components. Set a spatiotemporal region segmentation function to narrow the query range of the data points to be queried by calculating the spatiotemporal region segmentation value; Spatiotemporal region segmentation functions include: , in, For the integrated bridge construction and maintenance data set, the first The spatiotemporal region segmentation value of each target data point For the number of dimensions, For the first Weights of each dimension In the first The first dimension Feature values ​​of each target data point In the first In the dimension with the first The query center value is the central value corresponding to each target data point. The query center value is the central value corresponding to each dimension of the query, and serves as the reference point for the corresponding dimension. If the spatiotemporal region segmentation value of the first target data point in the fused bridge maintenance data set is less than a preset region threshold, a region corresponding to a query center value of the first target data point in the first dimension and within a preset radius of the query center value is defined as a hot region, and the to-be-queried data point is to be queried in the hot region. ​​​​​

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