Deep foundation pit construction safety risk pre-control method and system based on control theory and dual-system theory

Abstract

The invention discloses a deep foundation pit construction safety risk pre-control method and system based on a control theory and a dual-system theory, and the method comprises the steps: carrying out the risk recognition and risk monitoring of deep foundation pit construction safety based on the expansion of a sensor, determining a risk point, carrying out the pre-control discrimination and pre-control rating of the recognized and monitored deep foundation pit construction safety risk, and carrying out the early warning of the deep foundation pit construction safety risk. And finally, performing pre-control decision making and pre-control execution on the pre-control risk in the step 2 based on an actuator and DST expansion. According to the method, the source risk and the state risk are systematically identified and monitored, potential risks can be timely found and prevented, the operation stability of a project is improved, potential safety hazards can be timely identified and coped with by monitoring various state risks, and the risk risk is effectively prevented. Safety of constructors and the surrounding environment is ensured, and a positive effect on cost control of a project is achieved.

Description

Technical Field

[0001] The invention belongs to the field of deep foundation pit construction risk management, and specifically relates to a deep foundation pit construction safety risk pre-control method and system based on cybernetics and dual system theory. Background Art

[0002] Control theory, also known as cybernetic, was originally used to study the stability and control problems of mechanical systems. With the development of science and technology, control theory has gradually expanded to industrial control systems, automation systems, transportation and other fields. At the same time, control theory has also been proven to provide effective guidance for the safety management of complex systems ( & Rollenhagen, 2014). In control theory, the control loop of a system is described as a closed-loop structure consisting of sensors, controllers, and actuators. Sensors are used to measure dynamically changing systems, while controllers determine the operation of actuators based on the difference between the sensor measurement results and the control standards, and ultimately the actuators adjust the state of the system. At the same time, the system usually performs multiple tasks in parallel in a closed-loop structure, which means that feedback loops will appear on each task performed. These feedback loops will form a hierarchical structure based on the degree of influence on the system (Powers, 1973), and this structure is precisely the core that can help improve the performance of safety risk prevention and control of deep foundation pit projects. The introduction of control theory ideas will enable real-time monitoring and adjustment of the construction status of deep foundation pits, so that the status of deep foundation pits can continuously obtain feedback from safety risk prevention and control management under changing environmental conditions, and maintain the steady progress of safe construction of deep foundation pits.

[0003] The dual system view originated from William Jame (1996). After being discussed and demonstrated by many scholars, it was finally summarized into the commonly used cognitive psychology theory - Dual System Theory (DST). DST believes that human behavior decisions are driven by two relatively independent cognitive systems, namely: System 1 and System 2. System 1 is called the intuitive heuristic system. This system relies on intuition and experience to deal with problems, can automatically provide solutions, and can make efficient decisions on the problems faced. System 2 is called the rational analytical processing system. This system relies on further processing of information to deal with problems. It has the functions of reflection, control and inhibition, and spends more resources to deal with problems during the process. Often, the decisions processed by System 2 can optimize the decision results of System 1 or supplement the decisions that System 1 cannot complete. The dual system theory provides a theoretical basis for explaining personal safety behavior, and it is also worth learning from for safety management decision-making models. Relying solely on monitoring and early warning of engineering risks does not meet the needs of active safety risk pre-control management in deep foundation pit construction.

[0004] Deep foundation pit excavation construction has always been a key focus of the construction industry due to its high risk. At present, some technologies have introduced DST to conduct safety risk pre-control management of deep foundation pits based on control theory; for example, patent CN110414785A discloses a risk identification method for deep foundation pit excavation construction, which quantifies the risks in deep foundation pit excavation construction so as to take timely response measures according to the risk level. For example, patent CN112115529B discloses a risk assessment method for ultra-deep foundation pit construction, which solves the technical problem that the risk assessment is far from the actual risk due to the amplification effect of the risk increasing with the depth not being considered.

[0005] However, the above prior art still has the following deficiencies:

[0006] First, the risk factors are not fully integrated. Although the above methods have identified and evaluated the risk factors in deep foundation pit construction, they have failed to reasonably integrate the causal factors that lead to accidents into the risk management system, and have failed to extract effective accident prevention measures in practice. This has led to a disconnect between risk management and actual construction safety, and it is impossible to achieve comprehensive control of risks.

[0007] Secondly, there is a lack of clear guidance on safety risk management. Current safety supervision mainly relies on the combination of government regulations and corporate safety practices, but there is a lack of clear guidelines for safety risk management within deep foundation pit projects. This makes it difficult for laws and regulations to fully consider the uniqueness of each project and reduces the guiding value for on-site managers.

[0008] In addition, there is an over-reliance on subjective judgment. Risk source identification, risk rating and risk control decisions still rely mainly on the experience and subjective judgment of safety managers or supervisors. Due to differences in the professional level of managers, lack of experience or human errors may aggravate safety hazards, and in serious cases may further cause safety accidents.

[0009] Finally, it is difficult to achieve purely automated risk management. Due to the complexity and unpredictability of deep foundation pit construction, it is challenging to fully cover all potential risks before the project begins. The dynamic changes at the construction site mean that it is impossible to predict and plan all possible risk scenarios in advance, and purely automated risk management methods are less feasible in practical applications.

[0010] In summary, the existing technologies have some defects in risk management of deep foundation pit construction, such as insufficient integration, weak guidance, difficulty in active prevention, and over-reliance on subjective judgment. In order to ensure the safety and smooth completion of deep foundation pit construction, a semi-automatic active safety risk pre-control method that balances monitoring data and expert experience is urgently needed. Summary of the invention

[0011] Purpose of the invention: The purpose of the present invention is to solve the deficiencies in the prior art and to provide a method and system for pre-controlling safety risks in deep foundation pit construction based on cybernetics and dual-system theory. In view of the current problems faced by safety risk management of deep foundation pit projects, combined with the needs of deep integration of information technology and production safety, the traditional safety management mode that mainly relies on post-recovery is transformed into an active and spontaneous pre-safety management mode to ensure the safety of deep foundation pit construction, continuity of construction period and smooth completion.

[0012] Technical solution: A method for pre-controlling safety risks in deep foundation pit construction based on cybernetics and dual-system theory of the present invention comprises the following steps:

[0013] Step 1: Based on the expansion of sensors, risk identification Se-RI and risk monitoring Se-RM are carried out for deep foundation pit construction safety to identify risk points;

[0014] Step 1.1, risk identification Se-RI process, first divide the safety risks of deep foundation pit construction into root risk RR and state risk SR; when identifying the root risk RR, the root causes that may lead to a series of state risks or accidents are excavated, including the operation data of construction personnel, the operating conditions of mechanical equipment, and environmental monitoring indicators; when identifying the state risk SR, the current physical state of the deep foundation pit is focused on, including pit displacement, crack development and water level changes; after completing the identification of root risk and state risk (i.e., completing risk identification Se-RI), use text mining (Text Mining) and pattern recognition (Pattern Recognition) to extract key risk signals from the case library to identify and classify the safety risks of deep foundation pit construction;

[0015] Step 1.2, Risk Monitoring During the Se-RM process, firstly, the identified root risk RR is monitored through checklist inspection and monitoring cameras, and the identified state risk SR is monitored through sensors; then the obtained monitoring data is transmitted to the central monitoring system in real time through wireless or wired networks, and the central monitoring system performs preliminary screening and preprocessing; then, the multi-source heterogeneous risk source data preprocessing mechanism (Multi-source Heterogeneous Risk Source Data Preprocessing Mechanism) of deep foundation pit construction is used to preprocess multivariate heterogeneous data such as manual inspection data, mechanical sensor data, and geological environment monitoring data to form a structured risk monitoring data set;

[0016] Step 2: Based on the controller expansion, the deep foundation pit construction safety risks identified and monitored in step 1 are pre-controlled and judged Co-PJ and pre-controlled and rated Co-PR;

[0017] Step 2.1: When pre-controlling and identifying Co-PJ, the initial warning standard thresholds are first set based on real-time monitoring data through the dynamic feedback mechanism on risk early warning standards (DFM-REWS) for deep foundation pit construction. basic Perform dynamic feedback adjustment. If the current stage risk monitored in step 1 exceeds the warning threshold after dynamic adjustment, the pre-control rating Co-PR will be automatically initiated;

[0018] Step 2.2: Pre-control rating Co-PR process: first rate the risk points according to severity, classify and prioritize various risks through a hierarchical rating mechanism, and use decision trees and risk matrices to guide the formulation of risk management strategies;

[0019] Step 3: Based on the actuator and DST extension, make pre-control decision Ac-PD and pre-control execution Ac-PE for the pre-control risk in step 2;

[0020] Step 3.1, when making pre-control decisions Ac-PD, develop an intuitive heuristic execution system IHES (Intuitive Heuristic Execution System) and an analytical processing execution system APES (Analytical Processing Execution System) based on DST. IHES simulates human intuitive judgment to quickly screen and apply existing risk control strategies. The built-in heuristic rules and algorithms of IHES enable IHES to call effective risk assessment and decision-making in the safety risk countermeasures database; when faced with risk points that are not recorded in the database, APES will intervene in the risk decision-making process. APES adopts a slow system approach to analyze the newly identified risk points in detail, clarify specific pre-control strategies and measures, and specify the departments and personnel involved in the follow-up. APES is responsible for updating the risk pre-control decision library to ensure that all pre-control measures are based on the latest practical experience;

[0021] Step 3.2: When Ac-PE is executed during pre-control, the pre-control decision output by Ac-PD will be converted into specific actions, and execution orders will be automatically issued to relevant departments and personnel to ensure that risk prevention measures are effectively implemented.

[0022] Furthermore, the deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory is characterized in that the method of dynamically adjusting the risk warning threshold in step 2.1 is as follows:

[0023] Set the initial warning standard thresholds basic Together with the standardized monitoring data, it is continuously input into the deep foundation pit construction risk early warning standard dynamic feedback mechanism DFM-REWS for prediction fitting;

[0024] Calculate the deviation between real-time data and predicted values ​​using data-driven models t , then, DFM-REWS evaluates the current performance P t , if the current performance P t Target performance not achieved target , the difference ΔP between the two will be calculated t =P target -P t , and finally according to ΔP t The result of the feedback adjustment parameter β is used to dynamically update the warning threshold Threshold st+1 =Threshold st +β×ΔP t ;

[0025] Among them, et is obtained by comparing the actual monitoring value y t With the model f(x t θ t ) is determined by the expected value of the prediction, x t represents the input variable for real-time monitoring, θ t is the parameter of the preset prediction model, which is calculated by t The influence of P t , Threshold st is the risk warning threshold at the current stage, and Threshold st+1 is the updated threshold.

[0026] Further, the details of the preliminary screening and preprocessing by the central monitoring system in step 1.2 are as follows:

[0027] Data Validation is the process of checking the collected raw data to identify and eliminate errors or outliers in data collection, such as readings outside the expected range or data points that are not physically possible;

[0028] Data format standardization is to convert data from various sources into a unified format to ensure data compatibility and consistency in the subsequent processing stage. This includes unified timestamp format, data encoding, and measurement units; missing data handling is to identify missing data and apply appropriate techniques to fill or interpolate to prevent information discontinuity in subsequent analysis.

[0029] Through screening and preprocessing, we prepare for subsequent in-depth data analysis and structuring, ensuring that the data has been cleaned and standardized before entering the multi-source heterogeneous risk source data preprocessing mechanism, providing a reliable foundation for accurate risk assessment and management.

[0030] Furthermore, in the step 2.2 of pre-control rating Co-PR, the collected root risk and status risk data are firstly analyzed, and the fuzzy logic algorithm is used to determine the nature and severity of the risk. Then, the risk data is sent to the dynamic risk rating model, which sorts and classifies the risks according to the preset rating standards and thresholds to ensure that the highest priority risks are responded to in a timely manner. The specific steps are as follows:

[0031] First, define the membership function evaluation indicators: Probability (P) and Severity (S). Both indicators are divided into three fuzzy sets: low, medium, and high, and the membership function is defined as follows:

[0032] For the probability of occurrence P:

[0033] Low (R 1* ):

[0034] Middle (R 2* ):

[0035] High (R 3* ):

[0036] For hazard level S:

[0037] Low (R *1 ):

[0038] Middle (R *2 ):

[0039] High (R *3 ):

[0040] Next, define the risk prevention level rules, combine the fuzzy sets of occurrence probability and hazard degree, and define the following rules to evaluate the final risk prevention level:

[0041] Rule 1: If the risk level classification result is R 11 , R 12 , R 21 , then the risk control level is 1 (L1); Rule 2: If the risk level classification result is R 13 , R 22 , R 31 , then the risk control level is 2 (L21); Rule 3: If the risk level classification result is R 23 , R 32 , then the risk control level is 3 (L3);

[0042] Rule 4: If the risk level classification result is R 33 , then the risk control level is 4 (L4);

[0043] Finally, reasoning and defuzzification are performed, using the Mamdani reasoning method for fuzzy reasoning; for each rule, the output risk level is also expressed using fuzzy sets. Defuzzification uses the centroid method to calculate the final value of the risk level;

[0044] In addition, a performance feedback mechanism is set up in Co-PR to ensure that the rating model can be continuously optimized based on new risk monitoring data and historical response effects.

[0045] The present invention also discloses a system for realizing a deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory, including a deep foundation pit construction safety risk identification and monitoring module based on sensor expansion, a deep foundation pit construction safety risk pre-control discrimination and rating module based on controller expansion, and a deep foundation pit construction safety risk pre-control decision-making and execution module based on actuator and DST expansion;

[0046] The sensor-based deep foundation pit construction safety risk identification and monitoring module is provided with a risk identification (Risk Identification, Se-RI) submodule and a risk monitoring (Risk Monitoring, Se-RM) submodule; the risk identification submodule Se-RI identifies the deep foundation pit construction safety risk and divides it into root risk (Root Risk, RR) and status risk (Status Risk, SR); the risk monitoring submodule Se-RM monitors the identified risks, and the monitoring data is transmitted to the central monitoring system in real time, which is screened and preprocessed by the central monitoring system to finally form a structured risk monitoring data set;

[0047] The controller-based extended deep foundation pit construction safety risk pre-control judgment and rating module includes a pre-control judgment submodule Co-PJ (Pre-control Judgment) and a pre-control rating submodule Co-PR (Pre-control Rating); the pre-control judgment submodule Co-PJ dynamically adjusts the initial threshold based on real-time monitoring data through the deep foundation pit construction risk early warning standard dynamic feedback mechanism DFM-REWS, and automatically starts the pre-control rating submodule Co-PR if the risk at the current stage exceeds the adjusted threshold; the pre-control rating submodule Co-PR rates risk points according to severity, classifies and prioritizes various risks through a hierarchical rating mechanism, and uses a decision tree and risk matrix to guide the formulation of risk management strategies;

[0048] The deep foundation pit construction safety risk pre-control decision and execution module based on the actuator and DST expansion includes a pre-control decision sub-module Ac-PD (Pre-control Decision) and a pre-control execution sub-module Ac-PE (Pre-control Execution); in the pre-control decision sub-module Ac-PD, an intuitive heuristic execution system IHES and an analysis and processing execution system APES are developed based on DST. IHES simulates human intuitive judgment to quickly screen and apply existing risk control strategies. When faced with risk points not recorded in the database, APES will intervene in the risk decision-making process; in the pre-control execution sub-module Ac-PE, the pre-control decision output by Ac-PD will be converted into specific actions, and execution orders will be automatically issued to relevant departments and personnel to ensure that risk prevention measures are effectively implemented.

[0049] Beneficial effects: The present invention can significantly improve the scientificity and systematicity of risk management, thereby effectively avoiding the above-mentioned problems. Through the implementation of this method, risk identification and monitoring are more comprehensive and systematic, pre-control judgment and rating are more dynamic and accurate, pre-control decision-making and execution are more timely and efficient, and ultimately achieve dynamic management and semi-automatic control of deep foundation pit construction risks. Compared with the prior art, the present invention has the following advantages:

[0050] 1. Significantly improved risk management efficiency: Through a semi-automated active safety risk management method based on monitoring data and expert experience, the risk management response time is reduced from several hours to several minutes, and the management efficiency is greatly improved, making real-time monitoring and immediate response to risks possible. 2. Obvious economic benefits: According to preliminary implementation data, the present invention can reduce the occurrence of risk accidents by more than 15%, and correspondingly reduce the direct and indirect costs caused by risk accidents. The project cost savings are expected to reach 5% of the total cost, which is significantly lower than traditional risk management methods.

[0051] 3. Significant social effects: More accurate risk management methods improve the safety of construction sites, reduce work-related accidents, thereby reducing social insurance and medical expenses, and increasing public confidence in construction safety.

[0052] In addition, compared with traditional methods, the technical and operational innovations of the present invention make risk management no longer rely on the experience and subjective judgment of individual managers, but through a systematic and data-driven approach, thereby achieving higher safety management standards and lower risk probability. Therefore, the technical effect of the present invention has significant social significance and economic value, and is particularly suitable for deep foundation pit construction projects with high risks and high technical requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the entire pre-control method of the present invention;

[0054] Figure 2 This is a diagram for identifying the root risk and status risk of deep foundation pit construction according to the present invention;

[0055] Figure 3 It is a schematic diagram of the preprocessing mechanism of multi-source heterogeneous risk source data in deep foundation pit construction in the present invention;

[0056] Figure 4 It is a schematic diagram of the dynamic feedback mechanism of the deep foundation pit construction risk early warning standard in the present invention;

[0057] Figure 5 This is a schematic diagram of the operation logic of the deep foundation pit construction safety risk pre-control rating function of the present invention;

[0058] Figure 6 It is a logical diagram of the deep foundation pit construction safety risk pre-control decision-making based on the dual system theory in the present invention;

[0059] Figure 7 It is a schematic diagram of the overall framework of the present invention;

[0060] Figure 8 It is a schematic diagram of the implementation process of the embodiment;

[0061] Fig. 9 Schematic diagram of surface subsidence warning rate in the embodiment. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is described in detail below, but the protection scope of the present invention is not limited to the embodiments.

[0063] Deep foundation pit construction has always been a key focus area in the construction industry due to its complexity and high risk. Although existing risk management methods have been improved, it is often difficult to achieve active risk management due to problems such as insufficient integration of risk factors, unclear safety management guidance, and over-reliance on subjective judgment, resulting in high accident rates and risk management costs. These problems not only extend project duration and increase costs, but may also cause major building quality and personal safety issues, resulting in unpredictable economic losses.

[0064] To solve the existing technical problems, such as Figure 1 and Figure 7 As shown, the deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory of the present invention comprises the following steps:

[0065] Step 1: Based on the expansion of sensors, risk identification Se-RI and risk monitoring Se-RM are carried out for deep foundation pit construction safety to identify risk points;

[0066] Step 1.1, risk identification Se-RI process, first divide the safety risks of deep foundation pit construction into root risk RR and state risk SR; when identifying the root risk RR, the root causes that may lead to a series of state risks or accidents are excavated, including the operation data of construction personnel, the operating conditions of mechanical equipment, and environmental monitoring indicators; when identifying the state risk SR, the current physical state of the deep foundation pit is focused on, including pit displacement, crack development and water level changes; after completing the identification of root risk and state risk (after completing the risk identification Se-RI), use text mining and pattern recognition to extract key risk signals from the case library to identify and classify the safety risks of deep foundation pit construction;

[0067] Step 1.2, Risk Monitoring During the Se-RM process, the identified root risk RR is first monitored through inventory inspection and monitoring cameras, and the identified state risk SR is monitored through sensors; then the obtained monitoring data is transmitted to the central monitoring system in real time through wireless or wired networks, and the central monitoring system performs preliminary screening and preprocessing; then, the artificial detection data, mechanical sensor data, and geological environment monitoring data are preprocessed through the multi-source heterogeneous risk source data preprocessing mechanism of deep foundation pit construction to form a structured risk monitoring data set;

[0068] Step 2: Based on the controller expansion, the deep foundation pit construction safety risks identified and monitored in step 1 are pre-controlled and judged Co-PJ and pre-controlled and rated Co-PR;

[0069] Step 2.1: When pre-controlling and identifying Co-PJ, the initial warning standard thresholds Thresholds are first set through the deep foundation pit construction risk warning standard dynamic feedback mechanism DFM-REWS basic Perform dynamic feedback adjustment. If the current stage risk monitored in step 1 exceeds the warning threshold after dynamic adjustment, the pre-control rating Co-PR will be automatically initiated;

[0070] Step 2.2: Pre-control rating Co-PR process, first rate the risk points according to severity, classify and prioritize various risks through a hierarchical rating mechanism, and use decision trees and risk matrices to guide the formulation of risk management strategies;

[0071] Step 3: Based on the actuator and DST extension, make pre-control decision Ac-PD and pre-control execution Ac-PE for the pre-control risk in step 2;

[0072] Step 3.1: When making pre-control decisions Ac-PD, develop an intuitive heuristic execution system IHES and an analytical processing execution system APES based on DST. IHES simulates human intuitive judgment to quickly screen and apply existing risk control strategies. When faced with risk points that are not recorded in the database, APES will intervene in the risk decision-making process, analyze the newly identified risk points in detail, clarify specific pre-control strategies and measures, and specify the departments and personnel involved in the follow-up. APES is responsible for updating the risk pre-control decision library to ensure that all pre-control measures are based on the latest practical experience.

[0073] Step 3.2: When Ac-PE is executed during pre-control, the pre-control decision output by Ac-PD will be converted into specific actions, and execution orders will be automatically issued to relevant departments and personnel to ensure that risk prevention measures are effectively implemented, such as Figure 6 shown.

[0074] like Figure 2 As shown, in this embodiment, the safety risks of deep foundation pit construction obtained after risk identification Se-RI are identified and classified. For the root risk RR, it is distinguished one by one according to the pre-control elements and pre-control objects, and people, machines, materials, methods and environment, etc. are obtained. For example, the equipment status of the submersible pump includes its maintenance status, etc. The submersible pump is divided into mud preparation pumps, etc. For the status risk SR, it is distinguished one by one according to the pre-control range and pre-control objects, and settlement risks (surface settlement, pile top settlement), deformation risks (lateral deformation of soil, etc.), displacement risks and others are obtained; finally, the risk range, risk point, risk frequency, risk warning indicators, etc. are obtained.

[0075] like Figure 3 As shown, in the risk monitoring Se-RM process of this embodiment, different strategies are used to complete risk data monitoring and collection for the root risk RR and state risk SR. Root risk RR monitoring is mainly implemented through checklist inspections and surveillance cameras. The inspection checklist based on the Safety Risk Evolution Mechanism of deep foundation pit construction can directly evaluate the safety status of the construction site and promptly identify potential dangers caused by improper operation of construction personnel, equipment failure or changes in environmental factors; surveillance cameras provide continuous visual monitoring and provide continuous risk behavior and environmental changes. State risk SR monitoring relies on high-precision sensor technology, such as displacement sensors, crack monitors, and water level sensors, to track the physical state of deep foundation pits in real time and ensure continuous monitoring of these key indicators.

[0076] Here, the multivariate heterogeneous data obtained above are finally preprocessed to form a structured risk monitoring data set, which includes text information, coordinate positioning, static images, dynamic videos, and continuous numerical data.

[0077] The method of dynamically adjusting the risk warning threshold in step 2.1 of this embodiment is as follows: Figure 4 As shown, the initial warning standard threshold of the monitoring data at the current time t is input, and a prediction fit is performed. The loss deviation of the prediction fitting result is calculated, and performance evaluation and performance deviation calculation are performed. Then, the warning threshold is dynamically adjusted; the details are as follows:

[0078] Set the initial warning thresholds basic Together with the standardized monitoring data, it is continuously input into the deep foundation pit construction risk early warning standard dynamic feedback mechanism DFM-REWS for prediction fitting;

[0079] Calculate the deviation between real-time monitoring data and risk prediction value using data-driven model t , then, DFM-REWS evaluates the current performance P t , if the current performance P t Target performance not achieved target , the difference ΔP between the two will be calculated t =P target -P t , and finally according to ΔP t The result of the feedback adjustment parameter β is used to dynamically update the warning threshold Threshold st+1 =Threshold st +β×ΔP t ;

[0080] Among them, e t By comparing the actual monitoring value y t With the model f(x t θ t ) is determined by the expected value of the prediction, x t Represents the monitoring data at the current time t, θ t is the parameter of the preset prediction model, which is calculated by t The influence of P t , Threshold st is the risk warning threshold at the current stage, and Threshold st+1 is the updated threshold.

[0081] In step 2.2 of this embodiment, the pre-control rating Co-PR first performs a preliminary analysis on the collected root risk and status risk data, and uses a fuzzy logic algorithm to determine the nature and severity of the risk. Then, the risk data is fed into a dynamic risk rating model, which sorts and classifies the risks according to preset rating standards and thresholds to ensure that the highest priority risks receive a timely response, such as Figure 5 As shown, the specific steps are as follows:

[0082] First, define the membership function evaluation indicators: probability of occurrence P and severity of harm S. Both indicators are divided into three fuzzy sets: low, medium, and high, and the membership function form is defined as follows:

[0083] For the probability of occurrence P:

[0084] Low (R 1* ):

[0085] Middle (R 2* ):

[0086] High (R 3* ):

[0087] For hazard level S:

[0088] Low (R *1 ):

[0089] Middle (R *2 ):

[0090] High (R *3 ):

[0091] Next, define the risk prevention level rules, combine the fuzzy sets of occurrence probability and hazard degree, and define the following rules to evaluate the final risk prevention level:

[0092] Rule 1: If the risk level classification result is R 11 , R 12 , R 21 , then the risk control level is 1 (L1);

[0093] Rule 2: If the risk level classification result is R 13 , R 22 , R 31 , then the risk control level is 2 (L21);

[0094] Rule 3: If the risk level classification result is R 23 , R 32, then the risk control level is 3 (L3); Rule 4: If the risk level classification result is R 33 , then the risk control level is 4 (L4);

[0095] Finally, reasoning and defuzzification are performed, using the Mamdani reasoning method for fuzzy reasoning; for each rule, the output risk level is also expressed using fuzzy sets. Defuzzification uses the centroid method to calculate the final value of the risk level.

[0096] like Figure 7 As shown, the deep foundation pit construction safety risk pre-control system based on cybernetics and dual-system theory of this embodiment includes a deep foundation pit construction safety risk identification and monitoring module based on sensor expansion, a deep foundation pit construction safety risk pre-control judgment and rating module based on controller expansion, and a deep foundation pit construction safety risk pre-control decision-making and execution module based on actuator and DST expansion. The deep foundation pit construction safety risk identification and monitoring module based on sensor expansion is provided with a risk identification Se-RI submodule and a risk monitoring Se-RM submodule; the risk identification submodule Se-RI identifies the deep foundation pit construction safety risk and divides it into root risk RR and state risk SR; the risk monitoring submodule Se-RM monitors the identified risks, and the monitoring data is transmitted to the central monitoring system in real time, which is screened and preprocessed by the central monitoring system to form a structured risk monitoring data set.

[0097] The controller-expanded deep foundation pit construction safety risk pre-control identification and rating module includes a pre-control identification submodule Co-PJ and a pre-control rating submodule Co-PR; the pre-control identification submodule Co-PJ dynamically adjusts the initial threshold based on real-time monitoring data through the deep foundation pit construction risk early warning standard dynamic feedback mechanism DFM-REWS. If the risk in the current stage exceeds the adjusted threshold, the pre-control rating submodule Co-PR will be automatically started; the pre-control rating submodule Co-PR rates risk points according to severity, classifies and prioritizes various risks through a hierarchical rating mechanism, and uses decision trees and risk matrices to guide the formulation of risk management strategies.

[0098] The deep foundation pit construction safety risk pre-control decision-making and execution module based on the actuator and DST expansion includes the pre-control decision sub-module Ac-PD and the pre-control execution sub-module Ac-PE; in the pre-control decision sub-module Ac-PD, the intuitive heuristic execution system IHES and the analysis and processing execution system APES are developed based on DST. IHES simulates human intuitive judgment to quickly screen and apply existing risk control strategies. When faced with risk points not recorded in the database, APES will intervene in the risk decision-making process; in the pre-control execution sub-module Ac-PE, the pre-control decisions output by Ac-PD will be converted into specific actions, and execution orders will be automatically issued to relevant departments and personnel to ensure that risk prevention measures are effectively implemented.

[0099] Example 1

[0100] As shown in Figure 8, based on the technical solution of the present invention, safety risk pre-control is performed on a deep foundation pit construction site. The specific process is as follows:

[0101] Step 1: Set up the data basis for risk prevention. At the initial stage of risk prevention, first establish the environment and data basis for deep foundation pit construction risk management, including the establishment of the initial risk database and relevant risk assessment standards.

[0102] Step 2: Conduct preliminary risk identification and real-time monitoring based on the set risk prevention and control objectives. The risk identification and monitoring module is used to preliminarily screen and classify possible risks. This process includes real-time monitoring of the construction site and analysis of historical data to determine the key risk factors that may affect construction safety and efficiency.

[0103] Step 3: Evaluate the controllability and risk level of identified risks. The initially identified risks are imported into the pre-control identification and rating module for a more in-depth evaluation to determine the controllability and specific risk level of these risks.

[0104] Step 4: Execute risk prevention decisions and response strategies. Based on the controllability and level of the risk, the prevention decision and execution module determines whether to execute the existing risk prevention decisions and response strategies, or to further review and discuss the risk points for which there are no risk prevention strategies.

[0105] Step 5: Monitor the effectiveness of risk prevention measures and make feedback adjustments. After the risk prevention measures are implemented, continue to monitor their effectiveness, and adjust the risk prevention standard thresholds and store new risk prevention countermeasures through feedback loop learning according to actual conditions.

[0106] Example 2

[0107] A tunnel construction project JLT covering a deep foundation pit project was used as a pilot to implement the invention technology scheme. The foundation pit length of the main tunnel project is about 1.1km, 21-41m wide, with a maximum excavation depth of 22m. The excavation depth of the local intersection with the rail transit is 26m. The foundation pit design is divided into 22 sub-pits, and parallel flow operations are organized. The following takes the deep foundation pit project of the B02 structural block of a certain area A sub-pit of the tunnel as an example.

[0108] Table 1 Overview of deep foundation pit

[0109]

[0110] according to Figure 2 The risk identification classification shown in the figure identifies and extracts the root risks that may be encountered during the construction process of this example. These risks are sorted out from five aspects: personnel, machinery, materials, methods and environment to form a root risk table. The table covers the parent root risk code, parent root risk, root risk code, root risk, root risk manifestation code and specific manifestation of root risk.

[0111] Table 2 lists some possible root risks identified before the start of construction of this project.

[0112] Table 2 Example of root risk list

[0113]

[0114]

[0115] The identification of state risk will be sorted out from four aspects: settlement, deformation, displacement and other states to form a state risk table. This table clearly defines the state risk code, state risk, state risk object code and state risk object. In the risk identification at this stage, it is more critical to use a variety of methods in combination to identify the root risks that cause state risks as much as possible and establish association rules between them.

[0116] Table 3 lists the possible status risks identified before the start of the case construction and their relationship with the root risks.

[0117] Table 3 Example of status risk list

[0118]

[0119]

[0120] Table 4 shows the monitoring object, monitoring range, measurement point section and spacing of typical condition risk monitoring equipment of example part A-B02. The setting points and monitoring status of the condition risk equipment can be viewed in real time in the Se-RM submodule. The Se-RM submodule will continuously observe and record risks throughout the deep foundation pit construction process based on the root risk table, condition risk table and monitoring equipment.

[0121] Table 4A-B06 Example of Condition Risk Monitoring Equipment

[0122]

[0123] When a risk situation that requires pre-control occurs, the deep foundation pit construction safety risk pre-control identification and rating module "Co-PJ&Co-PR" based on the controller extension and the deep foundation pit construction safety risk pre-control decision-making and execution module "Ac-PD&Ac-PE" based on the actuator and DST extension will be called.

[0124] The following will take "the risk of surface subsidence around A-B06 exceeds the warning value" as an example to describe in detail how this embodiment performs pre-control for specific risks. In the "Co-PJ&Co-PR" module, the warning standards for surface subsidence status risks and risk pre-control ratings under various scenarios are shown in Table 5. One day, when monitoring the surface subsidence around A-B06, it was found that the settlement value reached 3.1mm and the cumulative settlement value reached 33.7mm (the environmental protection level of the structural block is level I, depth H=21.8m). The risk monitoring situation this time met the warning standard, and the pre-control rating was L2. The Co-PR submodule generates a risk warning sheet coded as "20210405-SE-SU-YJ-01" based on the rating results. In the safety risk pre-control countermeasures database, automatic screening is performed based on the key search term "SE-SU-YJ" in the risk warning sheet code.

[0125] The screening results show that the following key preventive measures are in place for this risk situation:

[0126] 1. Stop earth excavation immediately.

[0127] 2. Contact the design and supervision units to find out the main causes of foundation pit deformation according to the association rules with specific root risks

[0128] 3. Take measures to control deformation, such as increasing the support axial force or increasing the number of supports. If necessary, water injection should be adopted to offset the surrounding soil pressure.

[0129] 4. Redefine the inspection list and strengthen inspection efforts on relevant root risks.

[0130] Since the corresponding pre-control countermeasures already exist in the safety risk pre-control countermeasures database, the Ac-PD submodule directly generates a risk pre-control form numbered "YKD20210415-001" with a status of "pending execution". The pre-control form will directly push specific pre-control countermeasures to relevant departments through the Ac-PE submodule (this risk pre-control form will be pushed to the Engineering Department, Quality Department, and Safety Department). After each department implements the pre-control countermeasures, the status of the pre-control form will be updated to "executed". So far, this method has successfully completed the pre-control operation of "the risk of surface settlement around the A-B06 foundation pit exceeds the warning value".

[0131] Table 5 Warning standards and risk control levels for surface settlement around deep foundation pits

[0132]

[0133] Example 3

[0134] In order to verify the management benefits of the technical solution of the present invention, the operation data of the implementation case for one year was analyzed. Based on this method, a total of 196 root risk points and 21 state risk points were identified, and 556 risk prevention orders were completed. 207 prevention countermeasures were pre-stored in the risk prevention decision library, and 34 new ones were added during the deep foundation pit construction process. Next, the management effect of this method will be demonstrated by taking "surface settlement around the foundation pit" as an example.

[0135] The monthly surface subsidence risk warning rate calculated based on the monitoring frequency and the number of warnings is shown in Figure 6. Risk, defect to accident is an evolutionary path. Timely and appropriate preventive measures for state risks can greatly reduce the possibility of further evolution of risks. The preventive measures taken for the root risk of the state risk can effectively reduce the probability of the state risk being out of control next time. Therefore, the risk warning rate can well reflect the effect of this method in reducing the probability of risk occurrence during the construction process. Fig. 9 As can be seen from the trend line in the figure, the risk out of control ratio is generally on a downward trend.

[0136] Table 6 Key indicators for risk control of surface settlement around foundation pit

[0137]

[0138] In general, the present invention first helps to timely discover and prevent potential risks through systematic identification and monitoring of root risks and state risks, thereby improving the operational stability of the project and bringing higher controllability and predictability to the project. Secondly, as a data-driven management method, the inspection of root risks and monitoring of state risks provide the project management team with instant and objective project status information. This data-based decision support ensures that the management team can make decisions based on facts and data, rather than relying solely on experience or intuition, thereby improving the accuracy and credibility of decisions. Furthermore, the present invention is crucial to improving project safety. By monitoring various state risks, such as settlement, offset and deformation of foundation pits, potential safety hazards can be identified and responded to in a timely manner to ensure the safety of construction personnel and the surrounding environment. This not only helps to prevent accidents, but also mitigates their impact when accidents occur, thereby greatly improving the overall safety level of the project. Finally, the present invention also plays a positive role in the cost control of the project. According to preliminary calculations, the application of this method in the implementation case saves about 10% of the total cost. By reducing the occurrence of accidents and unexpected events, the resulting additional costs and project delays can be avoided, ensuring the economic benefits and sustainability of the project.

Claims

1. A safety risk pre-control method for deep foundation pit construction based on cybernetics and dual system theory, characterized in that: The following steps are involved: Step 1: Based on the expansion of sensors, risk identification Se-RI and risk monitoring Se-RM are carried out for deep foundation pit construction safety to identify risk points; Step 1.1, risk identification Se-RI process, first divide the safety risks of deep foundation pit construction into root risk RR and state risk SR; when identifying the root risk RR, the root causes that may lead to a series of state risks or accidents are excavated, including the operation data of construction personnel, the operating conditions of mechanical equipment, and environmental monitoring indicators; when identifying the state risk SR, the current physical state of the deep foundation pit is focused on, including pit displacement, crack development and water level changes; after completing the identification of root risk and state risk (after completing the risk identification Se-RI), use text mining and pattern recognition to extract key risk signals from the case library to identify and classify the safety risks of deep foundation pit construction; Step 1.2: Risk Monitoring During the Se-RM process, firstly, the identified root risk RR is monitored through inventory inspection and surveillance cameras, and the identified state risk SR is monitored through sensors; The obtained monitoring data is then transmitted to the central monitoring system in real time via wireless or wired networks, and the central monitoring system performs preliminary screening and preprocessing. Then, the artificial detection data, mechanical sensor data, and geological environment monitoring data are preprocessed through the multi-source heterogeneous risk source data preprocessing mechanism of deep foundation pit construction to form a structured risk monitoring data set. Step 2: Based on the controller expansion, the deep foundation pit construction safety risks identified and monitored in step 1 are pre-controlled and judged Co-PJ and pre-controlled and rated Co-PR; Step 2.1: When pre-controlling and identifying Co-PJ, the initial warning standard thresholds Thresholds are first set through the deep foundation pit construction risk warning standard dynamic feedback mechanism DFM-REWS basic Perform dynamic feedback adjustment. If the current stage risk monitored in step 1 exceeds the warning threshold after dynamic adjustment, the pre-control rating Co-PR will be automatically initiated; Step 2.2: Pre-control rating Co-PR process, first rate the risk points according to severity, classify and prioritize various risks through a hierarchical rating mechanism, and use decision trees and risk matrices to guide the formulation of risk management strategies; Step 3: Based on the actuator and DST extension, make pre-control decision Ac-PD and pre-control execution Ac-PE for the pre-control risk in step 2; Step 3.1, when making pre-control decisions Ac-PD, develop an intuitive heuristic execution system IHES and an analytical processing execution system APES based on DST, and use IHES to simulate human intuitive judgment to quickly screen and apply existing risk control strategies; When faced with risk points that are not recorded in the database, APES will intervene in the risk decision-making process, analyze the newly identified risk points in detail, clarify specific prevention and control strategies and measures, and designate the departments and personnel involved in the follow-up. APES is also responsible for updating the risk prevention and control decision database to ensure that all prevention and control measures are based on the latest practical experience; Step 3.2: When Ac-PE is executed during pre-control, the pre-control decision output by Ac-PD will be converted into specific actions, and execution orders will be automatically issued to relevant departments and personnel to ensure that risk prevention measures are effectively implemented.

2. The deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory according to claim 1 is characterized in that: The detailed method of the central monitoring system for preliminary screening and preprocessing in step 1.2 includes data verification, data format table conversion and missing data processing; Data validation refers to checking the collected raw data, identifying and eliminating errors or outliers in data collection; data format standardization refers to converting data from various sources into a unified format to ensure data compatibility and consistency in subsequent processing stages; missing data processing refers to identifying missing data and applying appropriate techniques to fill or interpolate.

3. The deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory according to claim 1 is characterized in that: The method of dynamically adjusting the risk warning threshold in step 2.1 is as follows: Set the initial warning standard thresholds basic Together with the monitoring data, it is continuously input into the dynamic feedback mechanism DFM-REWS for deep foundation pit construction risk early warning standard for prediction fitting; Calculate the deviation between real-time monitoring data and predicted risk value using data-driven models t , then, DFM-REWS evaluates the current performance P t , if the current performance P t Target performance not achieved target , the difference ΔP between the two will be calculated t =P target -P t , and finally according to ΔP t The result of the feedback adjustment parameter β is used to dynamically update the warning threshold Threshold st+1 =Threshold st +β×ΔP t ; Among them, e t By comparing the actual monitoring value y t With the model f(x t θ t ) is determined by the expected value of the prediction, x t represents the input variable for real-time monitoring, θ t is the parameter of the preset prediction model, which is calculated by t The influence of P t , Threshold st is the risk warning threshold at the current stage, and Threshold st+1 is the updated threshold.

4. The deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory according to claim 1 is characterized in that: In the step 2.2 of pre-control rating Co-PR, the collected root risk and status risk data are first analyzed, and the fuzzy logic algorithm is used to determine the nature and severity of the risk. Then, the risk data is sent to the dynamic risk rating model, which sorts and classifies the risks according to the preset rating standards and thresholds to ensure that the highest priority risks are responded to in a timely manner. The specific steps are as follows: First, the membership function evaluation indicators are defined: the probability of occurrence P and the degree of harm S. Both indicators are divided into three fuzzy sets: low, medium and high, and the membership function form is defined as follows: For the probability of occurrence P: Low (R 1* ): Middle (R 2* ): High (R 3* ): For hazard level S: Low (R *1 ): Middle (R *2 ): High (R *3 ): Next, define the risk prevention level rules, combine the fuzzy sets of occurrence probability and hazard degree, and define the following rules to evaluate the final risk prevention level: Rule 1: If the risk level classification result is R 11 , R 12 , R 21 , then the risk control level is 1 (L1); Rule 2: If the risk level classification result is R 13 , R 22 , R 31 , then the risk control level is 2 (L21); Rule 3: If the risk level classification result is R 23 , R 32 , then the risk control level is 3 (L3); Rule 4: If the risk level classification result is R 33 , then the risk control level is 4 (L4); Finally, reasoning and defuzzification are performed, and fuzzy reasoning is performed using the Mamdani reasoning method; For each rule, the output risk level is also expressed using fuzzy sets. Defuzzification uses the centroid method to calculate the final value of the risk level.

5. A system for implementing the deep foundation pit construction safety risk pre-control method based on cybernetics and dual system theory as described in any one of claims 1 to 4, characterized in that: It includes a deep foundation pit construction safety risk identification and monitoring module based on sensor expansion, a deep foundation pit construction safety risk pre-control judgment and rating module based on controller expansion, and a deep foundation pit construction safety risk pre-control decision-making and execution module based on actuator and DST expansion; The sensor-based deep foundation pit construction safety risk identification and monitoring module is provided with a risk identification Se-RI submodule and a risk monitoring Se-RM submodule; The risk identification submodule Se-RI identifies the safety risks of deep foundation pit construction and divides them into root risk RR and state risk SR; The risk monitoring submodule Se-RM monitors the identified risks, and the monitoring data is transmitted to the central monitoring system in real time, which screens and pre-processes them to form a structured risk monitoring data set. The controller-based extended deep foundation pit construction safety risk pre-control discrimination and rating module includes a pre-control discrimination sub-module Co-PJ and a pre-control rating sub-module Co-PR; The pre-control discrimination submodule Co-PJ uses the deep foundation pit construction risk early warning standard dynamic feedback mechanism DFM-REWS to dynamically adjust the initial threshold based on real-time monitoring data. If the risk at the current stage exceeds the adjusted threshold, the pre-control rating submodule Co-PR will be automatically started; The pre-control rating submodule Co-PR rates risk points according to severity, classifies and prioritizes various risks through a hierarchical rating mechanism, and uses decision trees and risk matrices to guide the formulation of risk management strategies; The deep foundation pit construction safety risk pre-control decision and execution module based on actuator and DST expansion includes a pre-control decision sub-module Ac-PD and a pre-control execution sub-module Ac-PE; in the pre-control decision sub-module Ac-PD, an intuitive heuristic execution system IHES and an analysis and processing execution system APES are developed based on DST. IHES simulates human intuitive judgment to quickly screen and apply existing risk control strategies. When faced with risk points not recorded in the database, APES will intervene in the risk decision-making process; In the pre-control execution submodule Ac-PE, the pre-control decision output by Ac-PD will be converted into specific actions, and execution orders will be automatically issued to relevant departments and personnel to ensure that risk prevention measures are effectively implemented.

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