Water conservancy project monitoring method and system based on BIM

Abstract

The invention provides a BIM-based water conservancy project monitoring method and system, and solves the problems of single water conservancy project monitoring mode, poor data collaboration and the like, and the method comprises the steps: constructing a corresponding BIM region model based on a three-dimensional remote sensing image corresponding to a current project region; data sensors are arranged in different water conservancy areas to be monitored in the engineering area to obtain monitoring simulation parameters corresponding to all the areas, and the collected monitoring simulation parameters are fused and synchronized to the BIM area model through a data sensing network; a data processing model is constructed to perform dynamic monitoring processing on the detection simulation parameters, in the processing process, different water conservancy areas to be monitored are divided according to different risk level thresholds, and the processed simulation parameters are synchronously and dynamically mapped into the BIM area model to perform dynamic classification identification on the different water conservancy areas to be monitored in the BIM area model; and dynamically updating the monitoring simulation data based on an online learning algorithm strategy so as to continuously optimize the BIM model.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy monitoring, and in particular to a monitoring method and system for water conservancy projects based on BIM. Background Art

[0002] With the continuous improvement of economic development level, my country's construction industry continues to develop, and the society's requirements for the design, construction and operation of construction projects are becoming increasingly stringent. In order to complete the project more efficiently, with higher standards and higher quality, the relevant construction units must introduce advanced technologies and change the original relatively backward means. The research on BIM technology has broken the spatial limitations of conventional two-dimensional design. It has impacted the engineering design industry with its efficient collaborative working methods, vivid design methods, and real-time shared knowledge management model, providing a better communication platform for design, construction and operation management units, and laying a technical foundation for the digital development of engineering. Due to its obvious advantages, BIM has been widely used in current construction projects.

[0003] Water conservancy projects are very different from construction projects. They need to be considered from a global perspective and designed systematically and comprehensively. They have characteristics and difficulties such as large scale, complex working conditions, complex technology, long construction period, and the need for people from various professions to work together. The corresponding water conservancy protection monitoring is also a top priority in water conservancy projects, but the existing means of early warning are single, and monitoring data is difficult to dynamically feedback for monitoring.

[0004] To this end, we propose a BIM-based monitoring method and system for water conservancy projects to solve the above problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art. The present invention proposes a monitoring method and system for water conservancy projects based on BIM to solve the above problems, and effectively solves the problem of increased construction costs caused by the lack of intuitive drawings and inaccurate structural calculations in two-dimensional design. The current technical level achieved by the project research is superior to similar services in the market. At the same time, the research on BIM technology in water conservancy projects is still in its infancy, and its market prospects are very broad, with important social and economic benefits.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a monitoring method for water conservancy projects based on BIM, comprising the following steps:

[0007] S1. Construct the corresponding BIM area model based on the three-dimensional remote sensing image corresponding to the current project area;

[0008] S2. Arrange data sensors in different water conservancy areas to be monitored in the project area to obtain monitoring simulation parameters corresponding to each area, fuse the collected monitoring simulation parameters and synchronize them to the BIM regional model through the data sensor network;

[0009] S3. Construct a data processing model to dynamically monitor and process the detection simulation parameters. The processing process divides different water conservancy areas to be monitored according to different risk level thresholds, and dynamically maps the processed simulation parameters to the BIM regional model to dynamically classify and identify different water conservancy areas to be monitored in the BIM regional model.

[0010] S4. Dynamically update the monitoring simulation data based on the online learning algorithm strategy to continuously optimize the BIM model.

[0011] Furthermore, the BIM regional model construction steps in step S1 are specifically as follows:

[0012] The cloud acquires three-dimensional remote sensing images and processes the image data into three-dimensional coordinates to obtain corresponding point cloud data;

[0013] All point cloud data are distributed according to the distribution distance between adjacent data in the point cloud data to obtain the local plane density index of each point cloud data in the initial state of regional distribution;

[0014] Based on the local plane density index of each point cloud data in the initial state, a dynamic splitting clustering algorithm is used to obtain the regional plane clustering degree. Finally, the current project area is downsampled according to the regional plane clustering degree to construct the corresponding BIM regional model.

[0015] Furthermore, the data processing model processing steps in step S3 are specifically as follows:

[0016] Each monitoring simulation parameter is point-fused with its corresponding monitoring area location information, and the fused location point data is mapped.

[0017] Through kernel density estimation, all location point data are converted into raster data and used as sample data. Then, the sample data is simulated and trained through the random forest algorithm to obtain the probability model corresponding to the distribution of multiple water conservancy areas to be monitored and synchronize the probability distribution results to the BIM regional model.

[0018] The specific steps of kernel density estimation are as follows:

[0019] For each point in the point set containing all the location point data, a small block containing a predefined pattern is added to the grid cells around the point. The whole process is a local map algebra operation based on a random sample X1,X2...X of size n. n, for the conventional kernel density estimate f of the univariate density f h (x),

[0020]

[0021] Where h is the water level, f h (x) is the kernel density estimate calculated exactly at position x under h, xX i It is from point x to point X i The distance between them, K is the kernel function.

[0022] Furthermore, the data sensors in step S1 include temperature sensors, water pressure sensors, vibration sensors and displacement sensors, so as to collect multi-dimensional parameters of each water conservancy area to be monitored during the construction process in real time.

[0023] Furthermore, the online learning algorithm in step S4 is an Adam optimization algorithm to adaptively adjust the monitoring simulation parameters.

[0024] A monitoring system according to the above-mentioned BIM-based monitoring method for water conservancy projects comprises:

[0025] The acquisition module uses a variety of sensor devices to collect multi-dimensional data from different monitoring water conservancy areas;

[0026] The BIM module obtains the plane clustering degree corresponding to different water conservancy areas to be detected based on the processing of the three-dimensional remote sensing image set, and constructs the corresponding BIM regional model based on the sampling results of the engineering area;

[0027] The data processing module has a built-in kernel density algorithm to convert the location point data corresponding to all monitoring simulation data into raster data, and use it as sample data. The sample data is then simulated and trained through the random forest algorithm to obtain the probability model corresponding to the distribution of multiple water conservancy areas to be monitored and synchronize the probability distribution results to the BIM regional model;

[0028] The output module integrates the detection simulation data obtained by various sensors, divides them into different risk level thresholds, and synchronizes them to the BIM area model of the corresponding area for monitoring personnel to make judgments.

[0029] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for monitoring a water conservancy project based on BIM are implemented.

[0030] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, is used to implement the steps of the above-mentioned method for monitoring a water conservancy project based on BIM.

[0031] Compared with the prior art, the beneficial effects of the present invention include: by introducing BIM technology in the design of hydraulic structures, visual modeling can be achieved, allowing the internal structure of the project to be more intuitively displayed in three-dimensional space, providing convenience for project layout, structural arrangement, scheme comparison, design review, etc. At the same time, BIM technology can be used to realize various analysis functions such as spatial analysis, mechanical analysis and environmental analysis, providing new methods and means for design optimization, thereby accelerating the progress of engineering projects and effectively reducing construction costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the accompanying drawings, the same reference numerals are used to refer to the same components. Among them:

[0033] Figure 1 The schematic diagram shows the overall method flow chart according to one embodiment of the present invention. DETAILED DESCRIPTION

[0034] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0035] According to one embodiment of the present invention, Figure 1 Shown.

[0036] As for the overall method process, in this embodiment, the monitoring method for water conservancy projects based on BIM includes the following steps:

[0037] S1. Construct the corresponding BIM area model based on the three-dimensional remote sensing image corresponding to the current project area;

[0038] S2. Arrange data sensors in different water conservancy areas to be monitored in the project area to obtain monitoring simulation parameters corresponding to each area, fuse the collected monitoring simulation parameters and synchronize them to the BIM regional model through the data sensor network;

[0039] S3. Construct a data processing model to dynamically monitor and process the detection simulation parameters. The processing process divides different water conservancy areas to be monitored according to different risk level thresholds, and dynamically maps the processed simulation parameters to the BIM regional model to dynamically classify and identify different water conservancy areas to be monitored in the BIM regional model.

[0040] S4. Dynamically update the monitoring simulation data based on the online learning algorithm strategy to continuously optimize the BIM model.

[0041] By introducing BIM technology in the water conservancy area to be monitored, visual modeling can be achieved, allowing the internal structure of the area to be more intuitively displayed in three-dimensional space, providing convenience for project layout, structural arrangement, scheme comparison, design review, etc. At the same time, BIM technology can be used to realize various analysis functions such as spatial analysis, water level warning analysis and environmental analysis, which greatly improves the data interaction and coordination in existing water conservancy monitoring methods, and the warning results are clearer and more definite.

[0042] Furthermore, the BIM regional model construction steps in step S1 are specifically as follows:

[0043] The cloud acquires three-dimensional remote sensing images and processes the image data into three-dimensional coordinates to obtain corresponding point cloud data;

[0044] All point cloud data are distributed according to the distribution distance between adjacent data in the point cloud data to obtain the local plane density index of each point cloud data in the initial state of regional distribution;

[0045] Based on the local plane density index of each point cloud data in the initial state, a dynamic splitting clustering algorithm is used to obtain the regional plane clustering degree. Finally, the current project area is downsampled according to the regional plane clustering degree to construct the corresponding BIM regional model.

[0046] Furthermore, the data processing model processing steps in step S3 are specifically as follows:

[0047] Each monitoring simulation parameter is point-fused with its corresponding monitoring area location information, and the fused location point data is mapped.

[0048] Through kernel density estimation, all location point data are converted into raster data and used as sample data. Then, the sample data is simulated and trained through the random forest algorithm to obtain the probability model corresponding to the distribution of multiple water conservancy areas to be monitored and synchronize the probability distribution results to the BIM regional model.

[0049] The specific steps of kernel density estimation are as follows:

[0050] For each point in the point set containing all the location point data, a small block containing a predefined pattern is added to the grid cells around the point. The whole process is a local map algebra operation based on a random sample X1,X2...X of size n. n , for the conventional kernel density estimate f of the univariate density f h (x),

[0051]

[0052] Where h is the water level, f h (x) is the kernel density estimate calculated exactly at position x under h, xX i It is from point x to point X i The distance between them, K is the kernel function.

[0053] Through the above data and the feedback of the location point information in the BIM regional model, it is possible to conduct dynamic data monitoring of each water conservancy area to be monitored in the form of indicators based on various types of sensor data combined with different risk level thresholds, and determine the early warning level of different areas. Exemplarily, based on the water level drop early warning risk indicator, if the water level in the current monitored water conservancy area exceeds the preset risk indicator, a corresponding multi-level early warning response measure is configured. The multi-level early warning response measure includes the current water level data value, the corresponding early warning level feedback and the corresponding response measures. It helps to improve the pertinence of the corresponding monitoring risks in different monitoring areas. At the same time, the multi-level early warning response method can also help improve the safety and efficiency of the project and avoid or reduce possible losses.

[0054] Specifically, in this embodiment, the data sensors in step S1 include temperature sensors, water pressure sensors, vibration sensors and displacement sensors to collect multi-dimensional parameters of each water conservancy area to be monitored during construction in real time. The online learning algorithm in step S4 is the Adam optimization algorithm to adaptively adjust the monitoring simulation parameters.

[0055] Similarly, a monitoring system constructed according to the above-mentioned BIM-based water conservancy project monitoring method is also within the protection scope of the present invention. Specifically, the system includes:

[0056] The acquisition module uses a variety of sensor devices to collect multi-dimensional data from different monitoring water conservancy areas;

[0057] The BIM module obtains the plane clustering degree corresponding to different water conservancy areas to be detected based on the processing of the three-dimensional remote sensing image set, and constructs the corresponding BIM regional model based on the sampling results of the engineering area;

[0058] The data processing module has a built-in kernel density algorithm to convert the location point data corresponding to all monitoring simulation data into raster data, and use it as sample data. The sample data is then simulated and trained through the random forest algorithm to obtain the probability model corresponding to the distribution of multiple water conservancy areas to be monitored and synchronize the probability distribution results to the BIM regional model;

[0059] The output module integrates the detection simulation data obtained by various sensors, divides them into different risk level thresholds, and synchronizes them to the BIM area model of the corresponding area for monitoring personnel to make judgments.

[0060] In addition, the above-mentioned method steps and system can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0061] The technical scope of the present invention is not limited to the contents in the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. The monitoring method of water conservancy projects based on BIM is characterized by: The steps include: S1. Construct the corresponding BIM area model based on the three-dimensional remote sensing image corresponding to the current project area; S2. Arrange data sensors in different water conservancy areas to be monitored in the project area to obtain monitoring simulation parameters corresponding to each area, fuse the collected monitoring simulation parameters and synchronize them to the BIM regional model through the data sensor network; S3. Construct a data processing model to dynamically monitor and process the detection simulation parameters. The processing process divides different water conservancy areas to be monitored according to different risk level thresholds, and dynamically maps the processed simulation parameters to the BIM regional model to dynamically classify and identify different water conservancy areas to be monitored in the BIM regional model. S4. Dynamically update the monitoring simulation data based on the online learning algorithm strategy to continuously optimize the BIM model.

2. The method for monitoring water conservancy projects based on BIM according to claim 1, characterized in that: The steps of constructing the BIM regional model in step S1 are as follows: The cloud acquires three-dimensional remote sensing images and processes the image data into three-dimensional coordinates to obtain corresponding point cloud data; All point cloud data are distributed according to the distribution distance between adjacent data in the point cloud data to obtain the local plane density index of each point cloud data in the initial state of regional distribution; Based on the local plane density index of each point cloud data in the initial state, a dynamic splitting clustering algorithm is used to obtain the regional plane clustering degree. Finally, the current project area is downsampled according to the regional plane clustering degree to construct the corresponding BIM regional model.

3. The method for monitoring water conservancy projects based on BIM according to claim 1, characterized in that: The data processing model processing steps in step S3 are specifically as follows: Each monitoring simulation parameter is point-fused with its corresponding monitoring area location information, and the fused location point data is mapped. Through kernel density estimation, all location point data are converted into raster data and used as sample data. Then, the sample data is simulated and trained through the random forest algorithm to obtain the probability model corresponding to the distribution of multiple water conservancy areas to be monitored and synchronize the probability distribution results to the BIM regional model. The specific steps of kernel density estimation are as follows: For each point in the point set containing all the location point data, a small block containing a predefined pattern is added to the grid cells around the point. The whole process is a local map algebra operation based on a random sample X1,X2...X of size n. n , for the conventional kernel density estimate f of the univariate density f h (x), Where h is the water level, f h (x) is the kernel density estimate calculated exactly at position x under h, xX i It is from point x to point X i The distance between them, K is the kernel function.

4. The water conservation monitoring optimization method based on model feature encryption technology according to claim 3 is characterized in that: The data sensors in step S1 include temperature sensors, water pressure sensors, vibration sensors and displacement sensors, so as to collect multi-dimensional parameters of each water conservancy area to be monitored during the construction process in real time.

5. The water conservation monitoring optimization method based on model feature encryption technology according to claim 4 is characterized in that: The online learning algorithm in step S4 is the Adam optimization algorithm, which is used to adaptively adjust the monitoring simulation parameters.

6. A monitoring system for a water conservancy project based on BIM according to any one of claims 1 to 5, characterized in that: include: The acquisition module uses a variety of sensor devices to collect multi-dimensional data from different monitoring water conservancy areas; The BIM module obtains the plane clustering degree corresponding to different water conservancy areas to be detected based on the processing of the three-dimensional remote sensing image set, and constructs the corresponding BIM regional model based on the sampling results of the engineering area; The data processing module has a built-in kernel density algorithm to convert the location point data corresponding to all monitoring simulation data into raster data, and use it as sample data. The sample data is then simulated and trained through the random forest algorithm to obtain the probability model corresponding to the distribution of multiple water conservancy areas to be monitored and synchronize the probability distribution results to the BIM regional model; The output module integrates the detection simulation data obtained by various sensors, divides them into different risk level thresholds, and synchronizes them to the BIM area model of the corresponding area for monitoring personnel to make judgments.

7. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that : When the processor executes the program, the steps of the method for monitoring water conservancy projects based on BIM as described in any one of claims 1 to 5 are implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the method for monitoring water conservancy projects based on BIM as described in any one of claims 1 to 5 above.

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