Tunnel construction risk dynamic assessment and safety management and control method

Abstract

The invention provides a tunnel construction risk dynamic assessment and safety management and control method. The method comprises the following steps: constructing a quantum heuristic neural network model to perform multi-dimensional feature extraction on tunnel construction micro-seismic signals; according to the method, the quantum heuristic neural network model is constructed, multi-dimensional feature extraction is carried out on the tunnel construction micro-seismic signals by using the superposition state characteristics of quantum bits, and compared with a traditional signal processing method, key features in the micro-seismic signals can be captured more quickly and accurately, a high-precision data basis is provided for subsequent risk assessment, and the method is suitable for large-scale popularization and application. The method greatly improves the reliability and effectiveness of data, carries out distributed storage on microseismic signal collection data and construction behavior parameters in combination with a block chain technology, effectively guarantees the authenticity and safety of the data by using the decentralization and non-tampering characteristics of the block chain technology, deeply mines the non-linear association relationship between the data, and improves the accuracy and reliability of the data. A comprehensive and accurate risk assessment basic database is formed, and rich and reliable data support is provided for risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering construction, and in particular to a method for dynamic risk assessment and safety management of tunnel construction. Background Art

[0002] At a time when transportation infrastructure construction is booming, tunnel engineering, as a key underground space development project, faces many risks such as landslides, water gushing, and rock bursts due to its complex construction environment and changeable geological conditions. Construction safety accidents occur frequently, seriously threatening the lives of construction workers and the progress of project construction.

[0003] Currently, risk assessment and safety management for tunnel construction rely heavily on traditional methods. For risk assessment, assessment methods based on empirical formulas and static models are primarily used. These methods struggle to fully capture the complex and ever-changing risk factors involved in tunnel construction. Furthermore, they lack the accuracy to extract key data such as microseismic signals, making them incapable of adapting to the dynamic nature of tunnel construction. This results in low accuracy and timeliness in risk assessment results. Regarding data management, traditional data storage methods are susceptible to tampering and lack security, making it difficult to ensure data authenticity and reliability. Furthermore, their ability to mine nonlinear relationships between microseismic signal data and construction behavior parameters is limited, making it difficult to provide comprehensive data support for risk assessment. Furthermore, during data collection, conventional sensors are susceptible to interference in complex environments, making data collection quality difficult to guarantee, impacting subsequent analysis and decision-making. For safety management, traditional methods lack effective visualization and simulation tools, making it difficult for construction personnel to intuitively understand potential risks and optimization solutions. Furthermore, risk predictions are often based on simple qualitative analysis and lack quantitative assessment of risk uncertainty, making scientific and accurate management and control difficult. Therefore, a dynamic risk assessment and safety management method for tunnel construction is proposed. Summary of the Invention

[0004] In view of this, the embodiments of the present invention hope to provide a method for dynamic risk assessment and safety management of tunnel construction to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] The technical solution of the embodiment of the present invention is implemented as follows: A method for dynamic risk assessment and safety management of tunnel construction, comprising the following steps:

[0006] Construct a quantum-inspired neural network model to extract multi-dimensional features from microseismic signals during tunnel construction;

[0007] Combined with blockchain technology, microseismic signal acquisition data and construction behavior parameters are distributedly stored and nonlinear correlations are mined to form a basic database for risk assessment;

[0008] Use flexible sensor arrays and dynamic anti-interference algorithms to optimize data acquisition quality;

[0009] Use digital twin technology to build a virtual model of tunnel construction and map real-time data;

[0010] Simulate and conduct deductions in virtual models based on deep learning, and display the results through AR devices;

[0011] Deploy edge computing nodes for data collection, processing, and collaborative verification;

[0012] The cloud management platform generates control instructions based on edge node information, using time series analysis and Monte Carlo simulation for risk prediction and quantitative analysis to form a closed-loop system.

[0013] In some embodiments, the construction of a quantum-inspired neural network model utilizes the superposition state characteristics of quantum bits to extract features from microseismic signals, providing a high-precision data basis for subsequent risk assessment.

[0014] In the above embodiment: the blockchain technology is combined to perform data storage and association mining, and the decentralized and tamper-proof characteristics of the blockchain are used to ensure data authenticity and security, and form a basic database for risk assessment.

[0015] In the above embodiment: the flexible sensor array and dynamic anti-interference algorithm are used to optimize data acquisition quality, wherein the flexible sensor array can autonomously adjust the sensing sensitivity and acquisition frequency according to environmental changes, and the dynamic anti-interference algorithm optimizes interference identification and removal strategies based on reinforcement learning.

[0016] In the above embodiment: the digital twin technology is used to construct a virtual model of tunnel construction, and the microseismic signals and construction behavior parameters collected in real time are synchronously mapped to the virtual model, providing a visualization platform for risk assessment and control.

[0017] In the above embodiment: the simulation and deduction based on deep learning is performed in a virtual model, construction risks are predicted and construction behavior optimization plans are generated through deep learning algorithms, and the plans are presented to construction personnel through AR devices.

[0018] In the above embodiment: the edge computing nodes are deployed to perform data collection, processing and collaborative verification, multiple edge computing nodes are deployed at the construction site, construction behavior is preliminarily analyzed through distributed machine learning algorithms, and abnormal information is intelligently and collaboratively verified between edge nodes.

[0019] In the above embodiment: the cloud management platform generates control instructions based on the edge node information, and the cloud management platform conducts a comprehensive analysis of the key information uploaded by the edge computing nodes, and generates scientific and reasonable control instructions based on the risk assessment results.

[0020] In the above embodiment: the risk prediction and quantitative analysis are performed using time series analysis and Monte Carlo simulation, the time variation trends of microseismic signals and construction behavior parameters are analyzed by the time series analysis algorithm, and the uncertainty of construction risks is quantitatively analyzed by the Monte Carlo simulation method.

[0021] The embodiment of the present invention adopts the above technical solution, which has the following advantages:

[0022] 1. This invention constructs a quantum-inspired neural network model and uses the superposition state characteristics of quantum bits to extract multi-dimensional features from microseismic signals during tunnel construction. Compared with traditional signal processing methods, it can capture key features in microseismic signals more quickly and accurately, providing a high-precision data foundation for subsequent risk assessment, greatly improving the reliability and effectiveness of the data. In combination with blockchain technology, microseismic signal acquisition data and construction behavior parameters are distributedly stored, and its decentralized and tamper-proof characteristics are utilized to effectively ensure the authenticity and security of the data. At the same time, it deeply mines the nonlinear correlation between data to form a comprehensive and accurate risk assessment basic database, providing rich and reliable data support for risk assessment.

[0023] 2. The present invention adopts a flexible sensor array and a dynamic anti-interference algorithm. The flexible sensor array can autonomously adjust the sensing sensitivity and acquisition frequency according to environmental changes, and can better adapt to the complex and changeable tunnel construction environment. The dynamic anti-interference algorithm optimizes the interference identification and removal strategy based on reinforcement learning, significantly improving the quality and stability of data acquisition, reducing data errors and interference factors, and providing a high-quality data source for risk assessment and management.

[0024] 3. The present invention uses digital twin technology to construct a virtual model of tunnel construction and synchronously maps the real-time collected data to the virtual model, providing an intuitive and visual platform for risk assessment. Based on deep learning, simulation and deduction are carried out in the virtual model, which can accurately predict construction risks and generate scientific and reasonable construction behavior optimization plans. At the same time, time series analysis and Monte Carlo simulation are used for risk prediction and quantitative analysis. The time change trend of the data is grasped through the time series analysis algorithm, and the uncertainty of construction risks is quantitatively evaluated with the help of the Monte Carlo simulation method. Compared with traditional qualitative or simple quantitative analysis methods, the risk assessment is more scientific, comprehensive and reliable.

[0025] 4. The present invention deploys edge computing nodes to collect and process data and conduct collaborative verification. Multiple edge computing nodes preliminarily analyze construction behaviors through distributed machine learning algorithms, and nodes intelligently and collaboratively verify abnormal information, thereby achieving rapid data processing and timely discovery of abnormal situations. The cloud management platform generates scientific and reasonable control instructions based on edge node information and risk assessment results, forming a closed-loop system from data collection, analysis, risk assessment to control instruction generation, realizing dynamic assessment and intelligent safety management of tunnel construction risks, effectively improving the safety and management efficiency of tunnel construction, and reducing the probability of construction accidents.

[0026] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a flow chart of a method for dynamic risk assessment and safety management of tunnel construction according to the present invention. DETAILED DESCRIPTION

[0029] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0030] It should be noted that the terms "first," "second," "symmetrical," "array," etc. are used only to distinguish descriptions from positional descriptions and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, limitations on features such as "first" and "symmetrical" may explicitly or implicitly include one or more of these features; similarly, when the number of certain features is not limited in the form of words such as "two" or "three," it should be noted that these features also explicitly or implicitly include one or more of the number of features.

[0031] In the present invention, unless otherwise expressly specified or limited, terms such as "installation," "connection," and "fixation" should be understood broadly; for example, they may refer to fixed connection, detachable connection, or integral molding; they may refer to mechanical connection, direct connection, welding, or indirect connection through an intermediate medium; they may refer to internal communication between two components or interaction between two components. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on the specification and drawings in conjunction with specific circumstances.

[0032] Some technical problems, main solutions, minor technical problems and their solutions.

[0033] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamic risk assessment and safety management of tunnel construction, comprising the following steps:

[0035] Construct a quantum-inspired neural network model to extract multi-dimensional features from microseismic signals during tunnel construction;

[0036] Combined with blockchain technology, microseismic signal acquisition data and construction behavior parameters are distributedly stored and nonlinear correlations are mined to form a basic database for risk assessment;

[0037] Use flexible sensor arrays and dynamic anti-interference algorithms to optimize data acquisition quality;

[0038] Use digital twin technology to build a virtual model of tunnel construction and map real-time data;

[0039] Simulate and conduct deductions in virtual models based on deep learning, and display the results through AR devices;

[0040] Deploy edge computing nodes for data collection, processing, and collaborative verification;

[0041] The cloud management platform generates control instructions based on edge node information, using time series analysis and Monte Carlo simulation for risk prediction and quantitative analysis to form a closed-loop system.

[0042] Specifically, the construction of the quantum-inspired neural network model utilizes the superposition state characteristics of quantum bits to extract features from microseismic signals, providing a high-precision data basis for subsequent risk assessment.

[0043] Specifically, the method combines blockchain technology for data storage and relationship mining, utilizes the decentralized and tamper-proof characteristics of blockchain to ensure data authenticity and security, and forms a basic database for risk assessment.

[0044] Specifically, the flexible sensor array and dynamic anti-interference algorithm are used to optimize data acquisition quality, wherein the flexible sensor array can autonomously adjust the sensing sensitivity and acquisition frequency according to environmental changes, and the dynamic anti-interference algorithm optimizes interference identification and removal strategies based on reinforcement learning.

[0045] Specifically, the digital twin technology is used to construct a virtual model of tunnel construction, and the real-time collected microseismic signals and construction behavior parameters are synchronously mapped to the virtual model, providing a visualization platform for risk assessment and control.

[0046] Specifically, the method is based on deep learning and simulates and deduces in a virtual model, predicts construction risks and generates construction behavior optimization plans through deep learning algorithms, and presents the plans to construction personnel through AR devices.

[0047] Specifically, the edge computing nodes are deployed to perform data collection, processing and collaborative verification. Multiple edge computing nodes are deployed at the construction site, and construction behavior is preliminarily analyzed through distributed machine learning algorithms. Abnormal information is intelligently and collaboratively verified between edge nodes.

[0048] Specifically, the cloud management platform generates control instructions based on edge node information. The cloud management platform conducts a comprehensive analysis of key information uploaded by edge computing nodes and generates scientific and reasonable control instructions based on risk assessment results.

[0049] Specifically, the risk prediction and quantitative analysis are performed using time series analysis and Monte Carlo simulation. The time variation trends of microseismic signals and construction behavior parameters are analyzed by time series analysis algorithm, and the uncertainty of construction risks is quantitatively analyzed by Monte Carlo simulation method.

[0050] In this embodiment, the specific present invention works as follows: first, a flexible sensor array is used in combination with a dynamic anti-interference algorithm to adaptively adjust the sensing sensitivity and acquisition frequency to optimize the data acquisition quality of microseismic signals and construction behavior parameters; then, a quantum-inspired neural network model is used to extract multi-dimensional features of the microseismic signals, and at the same time, blockchain technology is used to distribute and store the collected data, and the nonlinear correlation between the data is mined to form a basic database for risk assessment; then, edge computing nodes are deployed at the construction site, and a distributed machine learning algorithm is used to preliminarily analyze the construction behavior, and abnormal information is intelligently and collaboratively verified between nodes; then, digital twin technology is used to construct a virtual model of tunnel construction, and real-time data is synchronously mapped therein, and deep learning is used to simulate and deduce in the virtual model to predict construction risks and generate optimization plans; finally, the cloud management platform comprehensively analyzes the key information uploaded by the edge computing nodes, and combines the risk assessment results obtained by time series analysis and Monte Carlo simulation to generate scientific and reasonable control instructions, completing a closed loop from data collection, analysis, risk assessment to control instruction generation, and realizing dynamic assessment and safety control of tunnel construction risks.

[0051] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various modifications and substitutions within the technical scope disclosed in the present invention, and such modifications and substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for dynamic risk assessment and safety management of tunnel construction, characterized in that: The following steps are involved: Construct a quantum-inspired neural network model to extract multi-dimensional features from microseismic signals during tunnel construction; Combined with blockchain technology, microseismic signal acquisition data and construction behavior parameters are distributedly stored and nonlinear correlations are mined to form a basic database for risk assessment; Use flexible sensor arrays and dynamic anti-interference algorithms to optimize data acquisition quality; Use digital twin technology to build a virtual model of tunnel construction and map real-time data; Simulate and conduct deductions in virtual models based on deep learning, and display the results through AR devices; Deploy edge computing nodes for data collection, processing, and collaborative verification; The cloud management platform generates control instructions based on edge node information, using time series analysis and Monte Carlo simulation for risk prediction and quantitative analysis to form a closed-loop system.

2. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The quantum-inspired neural network model is constructed to extract features of microseismic signals using the superposition state characteristics of quantum bits, providing a high-precision data basis for subsequent risk assessment.

3. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The above-mentioned combination of blockchain technology for data storage and association mining utilizes the decentralized and tamper-proof characteristics of blockchain to ensure data authenticity and security, and forms a basic database for risk assessment.

4. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The flexible sensor array and dynamic anti-interference algorithm are used to optimize data acquisition quality, wherein the flexible sensor array can autonomously adjust the sensing sensitivity and acquisition frequency according to environmental changes, and the dynamic anti-interference algorithm optimizes interference identification and removal strategies based on reinforcement learning.

5. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The digital twin technology is used to construct a virtual model of tunnel construction, and the real-time collected microseismic signals and construction behavior parameters are synchronously mapped to the virtual model, providing a visualization platform for risk assessment and control.

6. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The method is based on deep learning and simulation in a virtual model. The construction risks are predicted and construction behavior optimization plans are generated through deep learning algorithms, and the plans are presented to construction personnel through AR devices.

7. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The edge computing nodes are deployed to collect and process data and conduct collaborative verification. Multiple edge computing nodes are deployed at the construction site. Construction behavior is preliminarily analyzed through a distributed machine learning algorithm, and abnormal information is intelligently and collaboratively verified between edge nodes.

8. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The cloud management platform generates control instructions based on edge node information. The cloud management platform conducts a comprehensive analysis of key information uploaded by edge computing nodes and generates scientific and reasonable control instructions based on risk assessment results.

9. A tunnel construction risk dynamic assessment and safety management method according to claim 1, characterized in that: The risk prediction and quantitative analysis are carried out by using time series analysis and Monte Carlo simulation. The temporal variation trends of microseismic signals and construction behavior parameters are analyzed by time series analysis algorithm, and the uncertainty of construction risks is quantitatively analyzed by Monte Carlo simulation method.

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