Intelligent foundation pit monitoring method and system
By using multi-sensor data fusion and deep learning models, the problems of comprehensiveness and accuracy in foundation pit monitoring were solved, enabling real-time monitoring and risk warning of foundation pit status and providing effective decision support.
Patent Information
- Application Number
- CN202510917215.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-03
- Publication Date
- 2025-10-28
AI Technical Summary
Existing methods and systems for monitoring foundation pits lack comprehensiveness and integration, making it difficult to accurately reflect the overall condition of the foundation pit in real time.
Data is collected using multiple sensors, combined with Kalman filter fusion algorithm and deep learning model, the fusion weights are dynamically adjusted to identify changes in the foundation pit status and potential risks, and early warning thresholds are set and on-site alarms are triggered.
It achieves comprehensive and accurate monitoring of foundation pits, can promptly detect potential risks and issue early warnings, provides decision support, and has good scalability and flexibility.
Smart Images
Figure CN120844633A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of foundation pit monitoring technology, specifically to an intelligent foundation pit monitoring method and system. Background Technology
[0002] A foundation pit refers to the underground space excavated for the construction of the foundation and basement of a building (including structures), and is used in building engineering. Foundation pit monitoring refers to the observation and analysis of changes in the soil and rock properties, support structure displacement, and surrounding environmental conditions during foundation pit excavation and underground engineering construction. This process aims to ensure construction safety and, through real-time monitoring and data analysis, predict the development of deformation and stability after further construction, thereby guiding design and construction and achieving information-based construction.
[0003] While existing technologies facilitate real-time monitoring of foundation pits and ensure construction safety by providing information on pit parameters, most methods and systems only monitor single or a few aspects, lacking comprehensiveness and integration, and failing to accurately reflect the overall condition of the foundation pit in real time. Therefore, this paper proposes an intelligent foundation pit monitoring method and system to address these problems. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides an intelligent foundation pit monitoring method and system, which solves the problem that traditional methods and systems are not comprehensive enough in their monitoring.
[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent foundation pit monitoring method, specifically comprising the following steps: Step 1: Install various types of sensors at different locations in the foundation pit, and perform preliminary processing on the collected raw data; Step 2: The pre-processed data from different sensors are fused using a Kalman filter-based fusion algorithm, taking into account the accuracy and reliability weights of the sensors, to obtain the foundation pit status data. Establish a data fusion model and dynamically adjust the fusion weights based on the correlation and complementarity between different sensor data to adapt to the monitoring needs of different stages and working conditions of the foundation pit. Step 3: Input the fused data into the deep learning model to analyze and predict the state of the foundation pit, identify the change patterns and potential risk characteristics of the foundation pit, and predict the future state and development trend of the foundation pit. Step 4: Based on the analysis results of normal foundation pit data by the artificial intelligence model, standard values are set, and different levels of early warning thresholds are set according to the upper limit gradient of the standard value fluctuation. On-site area division and on-site alarm devices are set up for collection points in different areas. Foundation pit status data is judged, abnormal foundation pit data is processed and suggestions are generated, and the source of abnormal on-site data is traced. On-site alarms are set for the corresponding collection point areas.
[0006] Furthermore, the sensors in step one include displacement sensors, stress sensors, water level sensors, and tilt sensors, which collect data on the displacement, stress, water level, and tilt of the foundation pit accordingly.
[0007] The present invention also discloses an intelligent foundation pit monitoring system, comprising a foundation pit monitoring system, wherein the foundation pit monitoring system includes a data acquisition unit, the data acquisition unit is connected to a data processing unit, the data processing unit is connected to a data fusion unit, the data fusion unit is connected to an intelligent analysis unit, the intelligent analysis unit is connected to an early warning decision unit, and the early warning decision unit is connected to a communication display unit; The data acquisition unit uses multiple sensors to acquire data related to the foundation pit, and the data processing unit collects and processes the data from each sensor.
[0008] Furthermore, the data fusion unit employs a Kalman filter fusion algorithm to fuse data from different types of sensors, establishes a data fusion model, and dynamically adjusts the fusion weights.
[0009] Furthermore, the intelligent analysis unit includes a real-time transmission module, a deep learning module, a historical processing module, and a historical transmission module. The real-time transmission module is connected to the deep learning module, the historical transmission module is connected to the historical processing module, and the historical processing module is connected to the deep learning module. The real-time transmission module is used to transmit real-time fused data; The deep learning module uses a convolutional neural network as a deep learning algorithm to analyze and predict the fused data; The historical processing module is used to select standard data values within the approximate data range in the historical transmission module, to remove data within the numerical approximate range, and to use the retained historical data for training of the deep learning module; The historical transmission module is used to receive historical monitoring data and corresponding foundation pit status information.
[0010] Furthermore, the early warning decision unit includes a threshold setting module, an early warning classification module, an early warning prompting module, and a decision generation module. The threshold setting module is connected to the early warning classification module, the early warning classification module is connected to the early warning prompting module, and the early warning prompting module is connected to the decision generation module.
[0011] Furthermore, the threshold setting module is used to set different levels of early warning thresholds based on the analysis results of the model; The early warning division module is used to divide the data collection points into regions and set up on-site alarm points in each divided region. The early warning module is used to issue early warning signals for foundation pit status data that exceeds the early warning threshold and to provide on-site location alarm prompts based on the area divided by the collection points. The decision generation module is used to generate corresponding handling measures and suggestions based on the early warning level and the specific conditions of the foundation pit.
[0012] Furthermore, the communication display unit includes a communication module and a display module, which are connected to each other. The communication module is used to transmit monitoring data and early warning information to the terminal equipment of the remote monitoring center and relevant management personnel; The display module is used to display real-time status data, early warning information, and decision suggestions of the foundation pit on local and remote terminal devices.
[0013] Beneficial effects This invention provides an intelligent foundation pit monitoring method and system. Compared with existing technologies, it has the following advantages: (1) The intelligent foundation pit monitoring method and system can acquire various parameters of the foundation pit in real time and comprehensively through multi-sensor fusion technology, which overcomes the limitations of single sensor monitoring and improves the accuracy and reliability of monitoring data.
[0014] (2) The intelligent foundation pit monitoring method and system can promptly detect potential risks and abnormalities in the foundation pit by using artificial intelligence technology to analyze and predict the fused data, issue early warning signals in advance, and provide on-site alarm prompts for risk locations, thus providing strong decision support for the construction and operation management of the foundation pit.
[0015] (3) The intelligent foundation pit monitoring method and system have good scalability and flexibility. It can easily add or replace sensor types and adjust monitoring parameters and early warning thresholds according to the characteristics of different foundation pits and monitoring needs. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a system principle block diagram of the present invention; Figure 3 This is a system principle block diagram of the intelligent analysis unit of the present invention; Figure 4 This is a system principle block diagram of the early warning decision unit of the present invention; Figure 5 This is a system principle block diagram of the communication display unit of the present invention.
[0017] In the picture: 1. Excavation pit monitoring system; 2. Data acquisition unit; 3. Data processing unit; 4. Data fusion unit; 5. Intelligent Analysis Unit; 501. Real-time Transmission Module; 502. Deep Learning Module; 503. Historical Processing Module; 504. Historical Transmission Module; 6. Early warning decision-making unit; 601. Threshold setting module; 602. Early warning classification module; 603. Early warning prompt module; 604. Decision generation module; 7. Communication and display unit; 701. Communication module; 702. Display module. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Please see Figure 1-5 This invention provides a technical solution: an intelligent foundation pit monitoring method, specifically including the following steps: Various types of sensors are installed at different locations in the foundation pit, and the raw data collected is preliminarily processed. The sensors include displacement sensors, stress sensors, water level sensors, and tilt sensors, which collect data on the displacement, stress, water level, and tilt of the foundation pit. The pre-processed data from different sensors are fused using a Kalman filter-based fusion algorithm, taking into account the accuracy and reliability weights of the sensors, to obtain the foundation pit status data. Establish a data fusion model and dynamically adjust the fusion weights based on the correlation and complementarity between different sensor data to adapt to the monitoring needs of different stages and working conditions of the foundation pit. The fused data is input into a deep learning model to analyze and predict the state of the foundation pit, identify the change patterns and potential risk characteristics of the foundation pit state, and predict the future state and development trend of the foundation pit. The model uses a convolutional neural network to learn a large amount of historical monitoring data and corresponding foundation pit state information to identify changes in the foundation pit state and potential risks. Standard values are set based on the analysis results of normal foundation pit data by artificial intelligence models. Different levels of early warning thresholds are set according to the upper limit gradient of the standard values. On-site areas are divided into collection points in different regions and on-site alarm devices are set up. Foundation pit status data is judged, abnormal foundation pit data is generated and processing measures and suggestions are provided, and the source of abnormal on-site data is traced. On-site alarms are issued for the collection point areas.
[0020] The present invention also discloses an intelligent foundation pit monitoring system, comprising a foundation pit monitoring system 1 consisting of a data acquisition unit 2, a data processing unit 3, a data fusion unit 4, an intelligent analysis unit 5, an early warning decision unit 6, and a communication display unit 7. The data acquisition unit 2 acquires foundation pit-related data using multiple sensors, and the data processing unit 3 collects and processes the data from each sensor, including but not limited to filtering and calibrating the data, using a moving average filtering algorithm to remove noise from the data, and calibrating the data according to the calibration parameters of the sensors.
[0021] Furthermore, data fusion unit 4 employs a Kalman filter fusion algorithm to fuse data from different types of sensors, establishes a data fusion model, and dynamically adjusts the fusion weights.
[0022] Furthermore, the intelligent analysis unit 5 includes a real-time transmission module 501, a deep learning module 502, a historical processing module 503, and a historical transmission module 504. The real-time transmission module 501 is connected to the deep learning module 502, the historical transmission module 504 is connected to the historical processing module 503, and the historical processing module 503 is connected to the deep learning module 502. The real-time transmission module 501 is used to transmit real-time fused data; The deep learning module 502 uses a convolutional neural network as a deep learning algorithm to analyze and predict the fused data; The historical processing module 503 is used to select standard data values within the approximate data range in the historical transmission module 504, to remove data within the numerical approximate range, and to use the retained historical data for training of the deep learning module 502. The historical transmission module 504 is used to receive historical monitoring data and corresponding foundation pit status information.
[0023] The early warning decision unit 6 includes a threshold setting module 601, an early warning division module 602, an early warning prompt module 603, and a decision generation module 604. The threshold setting module 601 is connected to the early warning division module 602, the early warning division module 602 is connected to the early warning prompt module 603, and the early warning prompt module 603 is connected to the decision generation module 604. The threshold setting module 601 is used to set different levels of early warning thresholds based on the analysis results of the model; The early warning division module 602 is used to divide the data collection points into regions and set up on-site alarm points in each region. The early warning module 603 is used to issue early warning signals for foundation pit status data that exceeds the early warning threshold and to provide on-site location alarm prompts based on the area divided by the collection points; The decision generation module 604 is used to generate corresponding handling measures and suggestions based on the warning level and the specific conditions of the foundation pit.
[0024] The communication display unit 7 includes a communication module 701 and a display module 702, which are connected to each other. The communication module 701 is used to transmit monitoring data and early warning information to the terminal equipment of the remote monitoring center and relevant management personnel; The display module 702 is used to display real-time status data, early warning information, and decision suggestions of the foundation pit on local and remote terminal devices.
[0025] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.
[0026] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A method for monitoring intelligent foundation pits, characterized in that: Specifically, the following steps are included: Step 1: Install various types of sensors at different locations in the foundation pit, and perform preliminary processing on the collected raw data; Step 2: The pre-processed data from different sensors are fused using a Kalman filter-based fusion algorithm, taking into account the accuracy and reliability weights of the sensors, to obtain the foundation pit status data. Establish a data fusion model and dynamically adjust the fusion weights based on the correlation and complementarity between different sensor data to adapt to the monitoring needs of different stages and working conditions of the foundation pit. Step 3: Input the fused data into the deep learning model to analyze and predict the state of the foundation pit, identify the change patterns and potential risk characteristics of the foundation pit, and predict the future state and development trend of the foundation pit. Step 4: Based on the analysis results of normal foundation pit data by the artificial intelligence model, standard values are set, and different levels of early warning thresholds are set according to the upper limit gradient of the standard value fluctuation. On-site area division and on-site alarm devices are set up for collection points in different areas. Foundation pit status data is judged, abnormal foundation pit data is processed and suggestions are generated, and the source of abnormal on-site data is traced. On-site alarms are set for the corresponding collection point areas.
2. The intelligent foundation pit monitoring system according to claim 2, characterized in that: The sensors in step one include displacement sensors, stress sensors, water level sensors, and tilt sensors, which collect data on the displacement, stress, water level, and tilt of the foundation pit.
3. An intelligent foundation pit monitoring system for implementing the intelligent foundation pit monitoring method as described in claim 1, comprising a foundation pit monitoring system (1), characterized in that: The foundation pit monitoring system (1) includes a data acquisition unit (2), which is connected to a data processing unit (3), which is connected to a data fusion unit (4), which is connected to an intelligent analysis unit (5), which is connected to an early warning decision unit (6), and which is connected to a communication display unit (7). The data acquisition unit (2) uses multiple sensors to acquire data related to the foundation pit, and the data processing unit (3) collects and processes the data from each sensor.
4. The intelligent foundation pit monitoring system according to claim 3, characterized in that: The data fusion unit (4) uses the Kalman filter fusion algorithm to fuse data from different types of sensors, establishes a data fusion model, and dynamically adjusts the fusion weights.
5. The intelligent foundation pit monitoring system according to claim 3, characterized in that: The intelligent analysis unit (5) includes a real-time transmission module (501), a deep learning module (502), a historical processing module (503), and a historical transmission module (504). The real-time transmission module (501) and the deep learning module (502) are connected. The historical transmission module (504) and the historical processing module (503) are connected. The historical processing module (503) and the deep learning module (502) are connected. The real-time transmission module (501) is used to transmit real-time fused data; The deep learning module (502) uses a convolutional neural network as a deep learning algorithm to analyze and predict the fused data; The historical processing module (503) is used to select standard data values within the approximate data range in the historical transmission module (504), to remove data within the approximate range of values, and to provide the retained historical data for training of the deep learning module (502); The historical transmission module (504) is used to receive historical monitoring data and corresponding foundation pit status information.
6. The intelligent foundation pit monitoring system according to claim 3, characterized in that: The early warning decision unit (6) includes a threshold setting module (601), an early warning division module (602), an early warning prompt module (603), and a decision generation module (604). The threshold setting module (601) and the early warning division module (602) are connected. The early warning division module (602) and the early warning prompt module (603) are connected. The early warning prompt module (603) and the decision generation module (604) are connected.
7. The intelligent foundation pit monitoring system according to claim 6, characterized in that: The threshold setting module (601) is used to set different levels of early warning thresholds based on the analysis results of the model; The early warning division module (602) is used to divide the data collection points into regions and set up on-site alarm points in each divided region; The early warning module (603) is used to issue early warning signals for foundation pit status data that exceed the early warning threshold and to provide on-site location alarm prompts based on the area divided by the collection points; The decision generation module (604) is used to generate corresponding handling measures and suggestions based on the warning level and the specific situation of the foundation pit.
8. The intelligent foundation pit monitoring system according to claim 3, characterized in that: The communication display unit (7) includes a communication module (701) and a display module (702), which are connected. The communication module (701) is used to transmit monitoring data and early warning information to the terminal equipment of the remote monitoring center and relevant management personnel; The display module (702) is used to display real-time status data, early warning information and decision suggestions of the foundation pit on local and remote terminal devices.