Advanced prediction and early warning system for key risk factors of oversized deep foundation pit

Abstract

The invention relates to the technical field of risk early warning, and discloses an advanced prediction and early warning system for key risk factors of an oversized deep foundation pit, which realizes advanced prediction and early warning for the key risk factors in the construction process of the oversized deep foundation pit through the following modules which are mutually associated and cooperatively work. According to the advanced prediction and early warning system for the key risk factors of the oversized deep foundation pit, the construction safety management level is remarkably improved by integrating advanced technologies. The system carries out deep analysis and prediction on key risk factors in the construction process by utilizing machine learning and deep learning algorithms and combining multiple models such as time sequence analysis, grey prediction and a neural network model.

Description

Technical Field

[0001] The present invention relates to the technical field of risk warning, and in particular to an advance prediction and warning system for key risk factors of a super-large deep foundation pit. Background Art

[0002] With the continuous advancement of urbanization, urban construction land is becoming increasingly scarce, and the development and utilization of plots around existing buildings is increasing. Deep foundation pit projects, especially those with excavation depths exceeding certain standards (such as more than 5 meters or even deeper), are considered to be more dangerous sub-projects because they involve multiple complex links such as earth excavation, support, and precipitation. Such projects are not only difficult to construct, but also pose a serious threat to the surrounding environment and the lives of construction workers.

[0003] The traditional deep foundation pit monitoring method mainly relies on manual detection, which has many disadvantages. For example, in harsh environments and remote transportation conditions, the monitoring cost is high, and many scenarios are not conducive to operations, and personnel safety is threatened. In addition, manual monitoring is greatly affected by human factors, and single-time monitoring lacks continuity and systematicness, making it difficult to timely discover and push early warning information of accidents and disasters. The processing results are cumbersome and inefficient, lacking in innovation, and cannot meet the needs of modern engineering safety management. To this end, the present invention provides an advance prediction and early warning system for key risk factors of ultra-large deep foundation pits. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an advance prediction and early warning system for key risk factors of ultra-large deep foundation pits. The system considers the correlation of multiple data sources, combines field experience and risk classification standards, and conducts a more comprehensive assessment of the safety status of the foundation pit.

[0005] The present invention provides the following technical solutions: an advance prediction and early warning system for key risk factors of super-large deep foundation pits, which realizes advance prediction and early warning of key risk factors in the construction process of super-large deep foundation pits through the following interrelated and collaborative modules;

[0006] a. Data preprocessing module:

[0007] Used to construct a three-dimensional model of a deep foundation pit to provide a basis for subsequent analysis; import multi-dimensional data such as construction progress, geological conditions, and environmental parameters, and perform preprocessing, such as data cleaning, denoising, and normalization, to ensure data accuracy and consistency; and set warning thresholds based on this to provide a basis for subsequent warning mechanisms;

[0008] B. Intelligent prediction and analysis module:

[0009] Based on machine learning or deep learning algorithms, use time series analysis, grey prediction, neural network models, support vector machines or random forest models to conduct in-depth analysis of preprocessed data; predict the changing trends of key risk factors (such as foundation pit deformation, groundwater level changes), and evaluate their potential risks, and compare the prediction results with the warning thresholds in the data preprocessing module to provide data support for subsequent warning mechanisms;

[0010] c. Real-time monitoring and data transmission module:

[0011] Through sensor networks and wireless communication technology, various monitoring data during deep foundation pit construction are collected and transmitted in real time; the accuracy and timeliness of the data are ensured, providing the latest data support for the intelligent prediction and analysis module; the real-time monitoring data is compared with the prediction data to verify the accuracy of the prediction model;

[0012] d. Multi-level warning and response module:

[0013] According to the results of the intelligent prediction and analysis module, a multi-level early warning mechanism is set up. When the monitoring data reaches or exceeds the early warning threshold, early warning information is issued in a timely manner through platform alarms, SMS notifications, and on-site sound and light alarms, and corresponding emergency response measures are initiated, such as adjusting the construction plan and strengthening monitoring to reduce risks;

[0014] e. Remote monitoring and management module:

[0015] It provides the function of remotely viewing and managing engineering projects, so that users can grasp the construction progress and risk status anytime and anywhere, set the parameters of structures and sensors, ensure the accuracy and reliability of monitoring data, and finally archive, query, store and manage the data of each measuring point of the foundation pit, providing data support for subsequent analysis and early warning;

[0016] f. Cloud platform and big data processing module:

[0017] Use the cloud platform to conduct statistical analysis on the received monitoring data, draw deformation curves, conduct prediction analysis, and generate reports; connect with third-party or government information platforms to achieve data sharing and improve the utilization value of data; provide data processing and analysis support for other modules, such as intelligent prediction and analysis modules, and multi-level early warning and response modules;

[0018] g. User interface and interaction module:

[0019] Design an intuitive and easy-to-use user interface to facilitate user operation and management of the system. Provide real-time data display, alarm information prompts, parameter settings and early warning management functions, so that users can grasp the system status and risk status at any time; support the integration of virtual reality (VR) and augmented reality (AR) technology to achieve visual display of on-site risks and improve users' risk perception ability;

[0020] h. Knowledge base and case study module:

[0021] Establish a knowledge base related to deep foundation pit construction risks, including specifications, typical cases, and expert opinions, and provide keyword-based search and case study functions to assist users in decision-making and risk management; combine with the intelligent prediction analysis module to optimize the prediction model using historical cases and expert opinions;

[0022] i. Multi-source data fusion and analysis module:

[0023] Integrate multi-source data such as meteorological data, geological exploration data, and traffic flow data, and use data fusion technology to improve the accuracy and reliability of risk prediction; provide more comprehensive data support for the intelligent prediction and analysis module, and improve the accuracy and generalization ability of the prediction model;

[0024] j. Adaptive learning and optimization module:

[0025] Based on new monitoring data and user feedback, adaptive learning is carried out to continuously optimize the prediction model and early warning mechanism, improve the intelligence level and prediction accuracy of the system, and work together with other modules (such as intelligent prediction analysis module, multi-level early warning and response module, etc.) to achieve continuous improvement and optimization of the system;

[0026] k. Environmental protection and sustainability assessment module:

[0027] Assess the impact of deep foundation pit construction on the surrounding environment, including noise, vibration, air and water pollution, provide sustainability recommendations, and promote green construction and sustainable development; combine with other modules (such as intelligent prediction analysis module, multi-level early warning and response module) to achieve environmental protection and sustainable development goals during the construction process.

[0028] Preferably, the data preprocessing module further comprises:

[0029] Data cleaning unit, used to remove outliers and noise to ensure data cleanliness;

[0030] Data denoising unit, using wavelet transform or filter technology to reduce random fluctuations in data;

[0031] Data normalization unit, through maximum and minimum value normalization or Z score normalization, so that the data has a uniform scale;

[0032] An outlier detection unit, which uses statistical tests or machine learning methods to identify and process outliers in the data;

[0033] Data fusion technology combines data from different sources to improve data integrity and consistency.

[0034] Preferably, the machine learning or deep learning algorithm in the intelligent prediction analysis module includes:

[0035] Parameter optimization algorithms, which automatically adjust model parameters to improve prediction accuracy, using gradient descent or genetic algorithms;

[0036] Feature selection techniques, which select the most influential features through correlation analysis or recursive feature elimination;

[0037] Model ensemble methods, such as random forests or gradient boosting machines, improve model stability and accuracy;

[0038] Online learning mechanism allows the model to be dynamically updated and optimized based on real-time data.

[0039] Preferably, the multi-level warning and response module further includes:

[0040] Fault diagnosis algorithm, using pattern recognition technology to quickly locate and identify system faults;

[0041] Self-healing mechanism, which automatically performs fault recovery operations, such as restarting services or reconfiguring resources;

[0042] Emergency response strategy: initiate corresponding emergency response plan according to the warning level;

[0043] Feedback learning system to optimize warning thresholds and response strategies based on actual warning effects.

[0044] Preferably, the time series analysis model in the intelligent prediction analysis module specifically adopts an autoregressive moving average (ARMA) model, and the formula is as follows: Where Xt is the value of the time series at time t, μ is the mean, φ i is the autoregressive coefficient, θ j is the moving average coefficient, τ t It's white noise.

[0045] Preferably, the grey prediction model in the intelligent prediction analysis module specifically adopts the GM (1,1) model, and the formula is as follows: Where a and b are model parameters and Xt is the value of the time series at time t.

[0046] Preferably, the neural network model in the intelligent prediction and analysis module specifically adopts a back propagation neural network (BPNN), and the formula is as follows: Y=f(W·X+b), wherein Y is the output layer, W is the weight matrix, X is the input layer, b is the bias vector, and f is the activation function.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) The system integrates advanced technologies to significantly improve the level of construction safety management. The system uses machine learning and deep learning algorithms, combined with time series analysis, grey prediction, neural network models and other models, to conduct in-depth analysis and prediction of key risk factors in the construction process. The application of these technologies improves the accuracy of predictions, enabling managers to identify and respond to potential risks in a timely manner, thereby effectively preventing and reducing construction accidents and ensuring personnel safety and project quality.

[0049] (2) The system's real-time monitoring and data transmission module ensures the timeliness and accuracy of construction site data, while the multi-level warning and response module quickly issues warnings when the monitoring data reaches or exceeds the warning threshold, prompting emergency response measures. The remote monitoring and management module further enhances management efficiency, allowing project managers to grasp project progress and risk status anytime and anywhere, improving construction efficiency and decision-making response speed. In addition, the cloud platform and big data processing module not only optimize the data processing process, but also enhance supervision efficiency and decision-making support by sharing data with third parties or government information platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits detailed descriptions of known functions and known components to avoid unnecessary confusion of the concepts of the present invention.

[0052] See also Figure 1 , an advance prediction and early warning system for key risk factors of super-large deep foundation pits, which realizes advance prediction and early warning of key risk factors in the construction process of super-large deep foundation pits through the following interrelated and collaborative modules;

[0053] a. Data preprocessing module:

[0054] Used to construct a three-dimensional model of a deep foundation pit to provide a basis for subsequent analysis; import multi-dimensional data such as construction progress, geological conditions, environmental parameters, and perform preprocessing, such as data cleaning, denoising, and normalization, to ensure data accuracy and consistency; and set early warning thresholds on this basis to provide a basis for subsequent early warning mechanisms.

[0055] The data preprocessing module further comprises:

[0056] Data cleaning unit, used to remove outliers and noise to ensure data cleanliness;

[0057] Data denoising unit, using wavelet transform or filter technology to reduce random fluctuations in data;

[0058] Data normalization unit, through maximum and minimum value normalization or Z score normalization, so that the data has a uniform scale;

[0059] An outlier detection unit, which uses statistical tests or machine learning methods to identify and process outliers in the data;

[0060] Data fusion technology combines data from different sources to improve data integrity and consistency.

[0061] B. Intelligent prediction and analysis module:

[0062] Based on machine learning or deep learning algorithms, use time series analysis, grey prediction, neural network models, support vector machines or random forest models to conduct in-depth analysis of preprocessed data; predict the changing trends of key risk factors (such as foundation pit deformation, groundwater level changes), and evaluate their potential risks, and compare the prediction results with the warning thresholds in the data preprocessing module to provide data support for subsequent warning mechanisms;

[0063] The machine learning or deep learning algorithms in the intelligent prediction analysis module include:

[0064] Parameter optimization algorithms, which automatically adjust model parameters to improve prediction accuracy, using gradient descent or genetic algorithms;

[0065] Feature selection techniques, which select the most influential features through correlation analysis or recursive feature elimination;

[0066] Model ensemble methods, such as random forests or gradient boosting machines, improve model stability and accuracy;

[0067] Online learning mechanism allows the model to be dynamically updated and optimized based on real-time data.

[0068] c. Real-time monitoring and data transmission module:

[0069] Through sensor networks and wireless communication technologies, various monitoring data during deep foundation pit construction are collected and transmitted in real time; the accuracy and timeliness of the data are ensured to provide the latest data support for the intelligent prediction and analysis module; the real-time monitoring data is compared with the prediction data to verify the accuracy of the prediction model.

[0070] d. Multi-level warning and response module:

[0071] Based on the results of the intelligent prediction and analysis module, a multi-level early warning mechanism is set up. When the monitoring data reaches or exceeds the early warning threshold, early warning information is issued in a timely manner through platform alarms, SMS notifications, and on-site sound and light alarms, and corresponding emergency response measures are initiated, such as adjusting the construction plan and strengthening monitoring to reduce risks.

[0072] The multi-level warning and response module further includes:

[0073] Fault diagnosis algorithm, using pattern recognition technology to quickly locate and identify system faults;

[0074] Self-healing mechanism, which automatically performs fault recovery operations, such as restarting services or reconfiguring resources;

[0075] Emergency response strategy: initiate corresponding emergency response plan according to the warning level;

[0076] Feedback learning system to optimize warning thresholds and response strategies based on actual warning effects.

[0077] The time series analysis model in the intelligent prediction analysis module specifically adopts an autoregressive moving average (ARMA) model, and the formula is as follows: Where Xt is the value of the time series at time t, μ is the mean, φ i is the autoregressive coefficient, θ j is the moving average coefficient, τ t It's white noise.

[0078] The grey prediction model in the intelligent prediction analysis module specifically adopts the GM (1,1) model, and the formula is as follows: Where a and b are model parameters and Xt is the value of the time series at time t.

[0079] The neural network model in the intelligent prediction and analysis module specifically adopts a back propagation neural network (BPNN), and the formula is as follows: Y=f(W·X+b), where Y is the output layer, W is the weight matrix, X is the input layer, b is the bias vector, and f is the activation function.

[0080] e. Remote monitoring and management module:

[0081] It provides the function of remotely viewing and managing engineering projects, allowing users to grasp the construction progress and risk status anytime and anywhere, and set structure and sensor parameters to ensure the accuracy and reliability of monitoring data. Finally, it archives, queries, stores and manages the data of each measuring point in the foundation pit to provide data support for subsequent analysis and early warning.

[0082] f. Cloud platform and big data processing module:

[0083] Use the cloud platform to conduct statistical analysis on the received monitoring data, draw deformation curves, conduct predictive analysis, and generate reports; connect with third-party or government information platforms to achieve data sharing and improve the utilization value of data; provide data processing and analysis support for other modules, such as intelligent predictive analysis modules and multi-level early warning and response modules.

[0084] g. User interface and interaction module:

[0085] An intuitive and easy-to-use user interface is designed to facilitate user operation and management of the system. It provides real-time data display, alarm information prompts, parameter settings and early warning management functions, allowing users to grasp the system status and risk status at any time; it supports the integration of virtual reality (VR) and augmented reality (AR) technologies to achieve visual display of on-site risks and improve users' risk perception capabilities.

[0086] h. Knowledge base and case study module:

[0087] Establish a knowledge base related to deep foundation pit construction risks, including specifications and standards, typical cases, and expert opinions, and provide keyword-based search and case study functions to assist users in decision-making and risk management; combine with the intelligent prediction and analysis module to optimize the prediction model using historical cases and expert opinions.

[0088] i. Multi-source data fusion and analysis module:

[0089] Integrate multi-source data such as meteorological data, geological exploration data, and traffic flow data, and use data fusion technology to improve the accuracy and reliability of risk prediction; provide more comprehensive data support for the intelligent prediction and analysis module, and improve the accuracy and generalization ability of the prediction model.

[0090] j. Adaptive learning and optimization module:

[0091] Based on new monitoring data and user feedback, adaptive learning is carried out to continuously optimize the prediction model and early warning mechanism, improve the intelligence level and prediction accuracy of the system, and work together with other modules (such as intelligent prediction and analysis module, multi-level early warning and response module, etc.) to achieve continuous improvement and optimization of the system.

[0092] k. Environmental protection and sustainability assessment module:

[0093] Assess the impact of deep foundation pit construction on the surrounding environment, including noise, vibration, air and water pollution, provide sustainability recommendations, and promote green construction and sustainable development; combine with other modules (such as intelligent prediction analysis module, multi-level early warning and response module) to achieve environmental protection and sustainable development goals during the construction process.

[0094] The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. An advance prediction and early warning system for key risk factors of ultra-large deep foundation pits, characterized by: The system uses the following interrelated and collaborative modules to achieve advanced prediction and early warning of key risk factors in the construction process of ultra-large deep foundation pits; a. Data preprocessing module: Used to construct a three-dimensional model of a deep foundation pit to provide a basis for subsequent analysis; import multi-dimensional data such as construction progress, geological conditions, and environmental parameters, and perform preprocessing, such as data cleaning, denoising, and normalization, to ensure data accuracy and consistency; and set warning thresholds based on this to provide a basis for subsequent warning mechanisms; B. Intelligent prediction and analysis module: Based on machine learning or deep learning algorithms, use time series analysis, grey prediction, neural network models, support vector machines or random forest models to conduct in-depth analysis of preprocessed data; predict the changing trends of key risk factors (such as foundation pit deformation, groundwater level changes), and evaluate their potential risks, and compare the prediction results with the warning thresholds in the data preprocessing module to provide data support for subsequent warning mechanisms; c. Real-time monitoring and data transmission module: Through sensor networks and wireless communication technology, various monitoring data during deep foundation pit construction are collected and transmitted in real time; the accuracy and timeliness of the data are ensured, providing the latest data support for the intelligent prediction and analysis module; the real-time monitoring data is compared with the prediction data to verify the accuracy of the prediction model; d. Multi-level warning and response module: According to the results of the intelligent prediction and analysis module, a multi-level early warning mechanism is set up. When the monitoring data reaches or exceeds the early warning threshold, early warning information is issued in a timely manner through platform alarms, SMS notifications, and on-site sound and light alarms, and corresponding emergency response measures are initiated, such as adjusting the construction plan and strengthening monitoring to reduce risks; e. Remote monitoring and management module: It provides the function of remotely viewing and managing engineering projects, so that users can grasp the construction progress and risk status anytime and anywhere, set the parameters of structures and sensors, ensure the accuracy and reliability of monitoring data, and finally archive, query, store and manage the data of each measuring point of the foundation pit, providing data support for subsequent analysis and early warning; f. Cloud platform and big data processing module: Use the cloud platform to conduct statistical analysis on the received monitoring data, draw deformation curves, conduct prediction analysis, and generate reports; connect with third-party or government information platforms to achieve data sharing and improve the utilization value of data; provide data processing and analysis support for other modules, such as intelligent prediction and analysis modules, and multi-level early warning and response modules; g. User interface and interaction module: Design an intuitive and easy-to-use user interface to facilitate user operation and management of the system. Provide real-time data display, alarm information prompts, parameter settings and early warning management functions, so that users can grasp the system status and risk status at any time; support the integration of virtual reality (VR) and augmented reality (AR) technology to achieve visual display of on-site risks and improve users' risk perception ability; h. Knowledge base and case study module: Establish a knowledge base related to deep foundation pit construction risks, including specifications, typical cases, and expert opinions, and provide keyword-based search and case study functions to assist users in decision-making and risk management; combine with the intelligent prediction analysis module to optimize the prediction model using historical cases and expert opinions; i. Multi-source data fusion and analysis module: Integrate multi-source data such as meteorological data, geological exploration data, and traffic flow data, and use data fusion technology to improve the accuracy and reliability of risk prediction; provide more comprehensive data support for the intelligent prediction and analysis module, and improve the accuracy and generalization ability of the prediction model; j. Adaptive learning and optimization module: Based on new monitoring data and user feedback, adaptive learning is carried out to continuously optimize the prediction model and early warning mechanism, improve the intelligence level and prediction accuracy of the system, and work together with other modules (such as intelligent prediction analysis module, multi-level early warning and response module, etc.) to achieve continuous improvement and optimization of the system; k. Environmental protection and sustainability assessment module: Assess the impact of deep foundation pit construction on the surrounding environment, including noise, vibration, air and water pollution, provide sustainability recommendations, and promote green construction and sustainable development; combine with other modules (such as intelligent prediction analysis module, multi-level early warning and response module) to achieve environmental protection and sustainable development goals during the construction process.

2. The system for advanced prediction and early warning of key risk factors of ultra-large deep foundation pits according to claim 1 is characterized in that: The data preprocessing module further comprises: Data cleaning unit, used to remove outliers and noise to ensure data cleanliness; Data denoising unit, using wavelet transform or filter technology to reduce random fluctuations in data; Data normalization unit, through maximum and minimum value normalization or Z score normalization, so that the data has a uniform scale; An outlier detection unit, which uses statistical tests or machine learning methods to identify and process outliers in the data; Data fusion technology combines data from different sources to improve data integrity and consistency.

3. The system for advanced prediction and early warning of key risk factors of ultra-large deep foundation pits according to claim 1 is characterized in that: The machine learning or deep learning algorithms in the intelligent prediction analysis module include: Parameter optimization algorithms, which automatically adjust model parameters to improve prediction accuracy, using gradient descent or genetic algorithms; Feature selection techniques, which select the most influential features through correlation analysis or recursive feature elimination; Model ensemble methods, such as random forests or gradient boosting machines, improve model stability and accuracy; Online learning mechanism allows the model to be dynamically updated and optimized based on real-time data.

4. The system for advanced prediction and early warning of key risk factors of ultra-large deep foundation pits according to claim 1 is characterized in that: The multi-level warning and response module further includes: Fault diagnosis algorithm, using pattern recognition technology to quickly locate and identify system faults; Self-healing mechanism, which automatically performs fault recovery operations, such as restarting services or reconfiguring resources; Emergency response strategy: initiate corresponding emergency response plan according to the warning level; Feedback learning system to optimize warning thresholds and response strategies based on actual warning effects.

5. The system for advanced prediction and early warning of key risk factors of ultra-large deep foundation pits according to claim 1 is characterized by: The time series analysis model in the intelligent prediction analysis module specifically adopts an autoregressive moving average (ARMA) model, and the formula is as follows: Where Xt is the value of the time series at time t, μ is the mean, φ i is the autoregressive coefficient, θ j is the moving average coefficient, τ t It's white noise.

6. The system for advanced prediction and early warning of key risk factors of ultra-large deep foundation pits according to claim 1 is characterized by: The grey prediction model in the intelligent prediction analysis module specifically adopts the GM (1,1) model, and the formula is as follows: Where a and b are model parameters and Xt is the value of the time series at time t.

7. The system for advanced prediction and early warning of key risk factors of ultra-large deep foundation pits according to claim 1 is characterized by: The neural network model in the intelligent prediction and analysis module specifically adopts a back propagation neural network (BPNN), and the formula is as follows: Y=f(W·X+b), where Y is the output layer, W is the weight matrix, X is the input layer, b is the bias vector, and f is the activation function.

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