Dynamic risk monitoring method for ship lock deep foundation pit construction

Abstract

The invention discloses a risk dynamic monitoring method for ship lock deep foundation pit construction, and the method comprises the steps: collecting data of different regions of a ship lock deep foundation pit construction site in real time through a sensor, and obtaining monitoring data related to the ship lock deep foundation pit construction; identifying and processing the monitoring data, and establishing an evaluation index system of the ship lock deep foundation pit construction risk; an intelligent prediction model is constructed through the evaluation index system, and dynamic risk evaluation in the ship lock deep foundation pit construction process is carried out; and carrying out construction risk judgment according to the risk assessment result, and outputting risk prompt information by utilizing risk prompt equipment if a risk is judged to exist. Real-time management and control of the construction site are achieved, various dangerous sources existing in the construction site are found in time, the site construction sequence is reasonably arranged, and safety management of the ship lock deep foundation pit is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of risk monitoring, and in particular to a method for dynamic risk monitoring of deep foundation pit construction of a ship lock. Background Art

[0002] The construction of deep foundation pit excavation is a dynamic process. The most prominent problems are the deformation of the structure, the tilt of the support, the change of axial force, the rise and fall of the groundwater level and the surrounding environment, which are related to the overall stability of the deep foundation pit. If during the construction phase, the monitoring points of a certain support structure are not arranged in place, the early warning values are not set in a standardized manner, or the monitoring and early warning are not carried out in accordance with technical specifications and monitoring procedures, it will cause deformation of the deep foundation pit at the least, and even cause casualties and property losses at the worst.

[0003] As for the construction of deep foundation pit of ship lock, due to the particularity of its construction environment, there are more challenges in the construction process. At present, accidents in the construction of deep foundation pit of ship lock occur frequently, resulting in heavy economic losses. The monitoring and early warning technology is backward, and it is impossible to carry out key detection of factors that may cause danger in the construction of deep foundation pit of ship lock in real time, and adopt intelligent monitoring methods to predict possible accident hazards, so as to prevent them in advance, improve risk sensitivity, and realize the whole process monitoring of the foundation pit body and the surrounding external environment during the construction of deep foundation pit of ship lock. Summary of the invention

[0004] The purpose of the present invention is to provide a risk dynamic monitoring method for deep foundation pit construction of ship lock, so as to solve the above problems existing in the prior art.

[0005] The specific application is as follows:

[0006] like Figure 1 As shown, the present application provides a method for dynamic risk monitoring of deep foundation pit construction of ship lock, which is characterized by comprising the following steps:

[0007] Step 1: Use sensors to collect data from different areas of the ship lock deep foundation pit construction site in real time to obtain monitoring data related to the ship lock deep foundation pit construction;

[0008] Step 2: Identify and process the monitoring data to establish an assessment index system for the construction risk of the deep foundation pit of the ship lock;

[0009] Step 3: construct an intelligent prediction model through the evaluation index system to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock;

[0010] Step 4: Conduct construction risk assessment based on the risk assessment results. If a risk is determined to exist, output risk warning information using a risk warning device.

[0011] Step 1: Collect data from different areas of the construction site of the deep foundation pit of the ship lock in real time through sensors, and obtain the monitoring data related to the construction of the deep foundation pit of the ship lock, including: According to the construction site conditions of the deep foundation pit of the ship lock, arrange sensors, place sensors on the foundation pit body and the surrounding of the construction site during the construction of the deep foundation pit of the ship lock, collect the basic data indicators during the construction process, the indicators causing structural deformation, and the external environment data of the construction site of the deep foundation pit of the ship lock, and interact with various sensors on site through a wireless transmission network to construct a wireless sensor network to realize the real-time transmission of the data of the construction site of the deep foundation pit of the ship lock.

[0012] Step 2: Identify and process the monitoring data, and establish an evaluation index system for the construction risk of the deep foundation pit of the ship lock, including:

[0013] Standardize the monitoring data of the construction of the deep foundation pit of the ship lock. For the monitoring indicators, grade the construction risk of the deep foundation pit of the ship lock according to the risk breakdown structure, establish a weight analysis model in combination with the fuzzy analytic hierarchy process, assign corresponding weights, and complete the construction of the evaluation index system during the construction of the deep foundation pit of the ship lock.

[0014] Step 3: Construct an intelligent prediction model through the evaluation index system, and conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock, including:

[0015] According to the index data in the evaluation index system, obtain the variable indicators during the construction of the deep foundation pit of the ship lock in a time series manner; construct an intelligent prediction model through the variable indicators, predict the variable indicators during the construction of the deep foundation pit of the ship lock, and obtain the predicted values of the variable indicators during the construction of the deep foundation pit of the ship lock; combine the predicted values of the variable indicators during the construction of the deep foundation pit of the ship lock and the remaining monitoring indicators to construct a membership function, and conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock.

[0016] The obtaining of the variable indicators during the construction of the deep foundation pit of the ship lock in a time series manner according to the index data in the evaluation index system, constructing an intelligent prediction model through the variable indicators, predicting the variable indicators during the construction of the deep foundation pit of the ship lock, and obtaining the predicted values of the variable indicators during the construction of the deep foundation pit of the ship lock includes:

[0017] Construct a data set X for the variable indicators during the construction of the deep foundation pit of the ship lock, and form a time series X = {x1, x2... x p}; Take X = {x1, x2... x p} as the training sample, construct an intelligent prediction model, and predict the changes in the indicators during the construction of the deep foundation pit of the ship lock in the next q days; obtain the set of predicted values X' = {x p+1 , x p+2 ... x p+q} of the variable indicators during the construction of the deep foundation pit of the ship lock in the next q days.

[0018] The input-output pairs of the intelligent prediction model are (x, x'), where x ∈ X, x' ∈ X', and the corresponding regression function is:

[0019] x′ = f(x) + ε

[0020] Among them, x represents the variable index during the construction of the lock deep foundation pit, x' represents the predicted value of the variable index during the construction of the lock deep foundation pit, and ε represents the noise value that follows a normal distribution, representing the deviation between the true value and the predicted value;

[0021] f(x) is a machine learning algorithm based on similarity, and the function distribution is defined by setting a stochastic process for the construction process of the lock deep foundation pit in the next q days:

[0022]

[0023] Among them, RD() represents the stochastic process, E represents the mathematical expectation, σ 2 represents the standard deviation of the noise value, φ represents the custom parameter, x p represents the input containing p variable indices, and x' q represents the predicted value of the variable index during the construction of the lock deep foundation pit in the next q days.

[0024] Combining the predicted value of the variable index during the construction of the lock deep foundation pit and other monitoring indices, constructing a membership function, and conducting dynamic risk assessment during the construction process of the lock deep foundation pit includes:

[0025] Establishing the mapping between qualitative and quantitative of the index data, and setting the membership function of a single risk index for the entire lock deep foundation pit construction system in combination with the predicted value of the variable index.

[0026] The establishment of the mapping between qualitative and quantitative of the index data, and setting the membership function of a single risk index for the entire lock deep foundation pit construction system in combination with the predicted value of the variable index includes:

[0027] Calculating the characteristic values of different risk levels, characterizing the fuzziness and randomness of the monitoring data of the lock deep foundation pit construction, and constructing the membership function of the five-level lock deep foundation pit construction under different risk levels in the next q days in combination with the predicted value of the variable index.

[0028] The membership function is:

[0029]

[0030] Among them, represents the membership function of the k-th index characteristic value x' kq at the g-th risk level in the next q days; x' kqIndicates the index characteristic values during the construction of the lock deep foundation pit. Indicates the expectation of the k-th index characteristic value in the future q days under the g-th risk level. Indicates the entropy of the k-th index characteristic value on the q-th day in the future under the g-th risk level, H e Indicates the hyper entropy, Rand() represents a random number from 0 to 1, k = 1, 2... m, g = 1, 2... 5.

[0031] Step 4: Conduct a risk judgment on the construction based on the risk assessment result. If a risk is judged to exist, use the risk warning device to output risk warning information, including:

[0032] The risk warning device displays real-time data and predicted data, compares the current real-time data with the predicted data, and enables a hierarchical early warning function during the monitoring process. Different early warning levels are distinguished by different colors.

[0033] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0034] The present invention discloses a risk dynamic monitoring method for the construction of a lock deep foundation pit. By arranging sensors, the monitoring data during the construction of the lock deep foundation pit is collected in real time, realizing the whole-process monitoring of the foundation pit main body and the surrounding external environment during the construction of the lock deep foundation pit; focusing on monitoring the risk factors causing the construction of the lock deep foundation pit, using an intelligent prediction model to predict the index data of displacement and settlement during the construction of the lock deep foundation pit, taking preventive measures in advance for the risks that may cause dangerous accidents, and improving the risk sensitivity; setting up a dynamic risk assessment system for the construction of the lock deep foundation pit, conducting dynamic risk assessment during the construction of the lock deep foundation pit, and visually displaying the construction monitoring data and risk warning information, realizing the real-time control of the construction site, timely discovering various hazard sources existing at the construction site, reasonably arranging the on-site construction sequence, and optimizing the safety management of the lock deep foundation pit. Brief Description of the Drawings

[0035] Figure 1 is a flow chart of a risk dynamic monitoring method for the construction of a lock deep foundation pit provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of a risk dynamic monitoring and assessment index system for the construction of a lock deep foundation pit provided by an embodiment of the present invention. Detailed Embodiments

[0037] The following will describe the present invention in detail with reference to the drawings.

[0038] Embodiment 1

[0039] As Figure 1As shown in the figure, the present application provides a method for dynamically monitoring the risks of a deep foundation pit construction of a ship lock, including the following steps:

[0040] Step 1: Collect data from different areas of the construction site of the deep foundation pit of the ship lock in real time through sensors, and obtain the monitoring data related to the construction of the deep foundation pit of the ship lock;

[0041] Step 2: Identify and process the monitoring data, and establish an evaluation index system for the risks of the deep foundation pit construction of the ship lock;

[0042] Step 3: Construct an intelligent prediction model through the evaluation index system to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock;

[0043] Step 4: Make a risk judgment on the construction according to the risk assessment result. If it is judged that there is a risk, use the risk warning device to output a risk warning message.

[0044] The above-mentioned Step 1: Collect data from different areas of the construction site of the deep foundation pit of the ship lock in real time through sensors, and obtain the monitoring data related to the construction of the deep foundation pit of the ship lock, including: According to the construction site conditions of the deep foundation pit of the ship lock, arrange sensors, place sensors on the foundation pit body and the surrounding area of the construction site during the construction of the deep foundation pit of the ship lock, collect the basic data indexes during the construction process, the indexes causing structural deformation, and the external environment data of the construction site of the deep foundation pit of the ship lock, and interact with various sensors on site through a wireless transmission network to construct a wireless sensor network to realize the real-time transmission of the data of the construction site of the deep foundation pit of the ship lock;

[0045] Specifically, the monitoring data related to the construction of the deep foundation pit of the ship lock includes the basic data during the construction process: the ship lock grade, the water head height of the ship lock, the foundation pit depth, the terrain slope rate, the formation lithology, and the excavation body structure; the indexes causing structural deformation include: the monitoring of cracks, pressure, displacement, inclination, settlement, strain, stress, and axial force. Therefore, the data collected from the foundation pit body includes: the horizontal displacement at the top of the slope, the vertical displacement at the top of the slope, the deep horizontal displacement, the support axial force, and the groundwater level; the collection of the surrounding environment of the construction site includes: the displacement of the surrounding buildings, the settlement of the surrounding strata, the displacement of the surrounding ship locks, the displacement of the surrounding pipelines, the air quality, the water level, the rainfall, the wind force, the temperature, and the humidity;

[0046] There are various types of sensors. The sensors are selected according to the actual acquisition needs. Specifically, displacement sensors are applied to measure horizontal and vertical displacements, and are used to measure the horizontal and vertical displacements at the top of the slope, the displacements of surrounding buildings, and the displacements of surrounding ship locks; inclinometers are used to observe the inclination changes of the wall and soil structure for measuring deep horizontal displacements; load cells are used to measure the changes and distributions of axial forces to master the stress state, change trends and processes, and strain gauges are used to monitor the stress and strain of the retaining structure for measuring the support axial force; piezometers are used to monitor the looseness and movement of the soil caused by external soil disturbance for measuring the groundwater level; settlement inclinometers are used to measure the deep settlement of the soil behind the wall to judge the stability state of the wall for measuring the settlement of the surrounding strata; the sensors are buried at the bottom or inside of the components before or during the construction of the deep foundation pit of the ship lock to sense the change state of the components in real time, and to monitor the stress and deformation of the surrounding environment and the foundation pit body of the deep foundation pit during the construction process in real time;

[0047] The layout positions, spacings, monitoring purposes, and monitoring frequencies of the sensors are determined by comprehensively considering the construction conditions of the ship lock construction site and the monitoring requirements of the monitoring objects; they comply with the construction standards and specifications such as the "Technical Code for Building Foundation Pit Support" (JGJ120 - 2012), the "Construction Specification for Ship Lock Engineering" (JTS218 - 2014), and the "Technical Standard for Building Foundation Pit Engineering Monitoring" (GB50497 - 2019);

[0048] More specifically, in this embodiment, the construction project of the deep foundation pit of the ship lock is arranged at the position of the reserved three - line ship lock, aligned with the center of the navigation hole of the 2nd - span dam - top bridge on the bank side of the first and second - line ship locks. It is necessary to carry out the excavation of earth and rock in the range of the three - line ship lock foundation pit, mainly including part of the upstream and downstream approach channel ranges (main and auxiliary navigation walls, intake walls), upper and lower lock heads, and lock chambers; the axis of the three - line ship lock is 114m away from the axis of the second - line ship lock, parallel to the axes of the first and second - line ship locks. The upstream side line of the intake wall of the three - line ship lock is flush with the upstream side line of the upper lock head of the first and second - line ship locks, and is about 20m downstream of the hub dam - top bridge; the top of the river - side slope of the main foundation pit of the ship lock is 5m away from the building of the ship lock control center. The upstream approach channel foundation pit contains Pier 50 of Huangqiao Avenue. The bank - side of the main foundation pit of the ship lock is connected to Xiaoxiang North Road and the flood control dike; during the process of arranging the sensors, considering the impact on surrounding buildings, the acquisition of surrounding environment data is emphasized.

[0049] The second step: identifying and processing the monitoring data and establishing an evaluation index system for the construction risks of the deep foundation pit of the ship lock includes: standardizing the monitoring data of the construction of the deep foundation pit of the ship lock, rating the construction risks of the deep foundation pit of the ship lock according to the risk breakdown structure for the monitoring indicators, establishing a weight analysis model by combining the fuzzy analytic hierarchy process, assigning corresponding weights, and completing the construction of the evaluation index system during the construction process of the deep foundation pit of the ship lock;

[0050] Such asFigure 2 As shown, according to the actual construction needs, the risks are decomposed from four aspects: construction conditions, foundation pit body, surrounding environment, and climate conditions, which are used as the basic elements of the fuzzy analytic hierarchy process to construct an evaluation index system. The first layer is construction conditions, foundation pit body, surrounding environment, and climate conditions. The second layer includes the lock grade, lock water head height, foundation pit depth, terrain slope rate, formation lithology, and excavation body structure under construction conditions; the horizontal displacement at the top of the slope, vertical displacement at the top of the slope, deep horizontal displacement, support axial force, and groundwater level under the foundation pit body; the displacement of surrounding buildings, settlement of surrounding strata, displacement of surrounding locks, and displacement of surrounding pipelines under the surrounding environment; air quality, water level, rainfall, wind force, temperature, and humidity under climate conditions. The third layer is the change amount and change rate of the horizontal displacement at the top of the slope, vertical displacement at the top of the slope, deep horizontal displacement, support axial force, groundwater level, displacement of surrounding buildings, settlement of surrounding strata, displacement of surrounding locks, and displacement of surrounding pipelines;

[0051] According to industry specifications and national standards, the early warning strategies are formulated as: no risk, low risk, medium risk, high risk, and extremely high risk; calculate the risk indexes at all levels, and evaluate the risk levels according to the risk level division;

[0052] Combined with the fuzzy analytic hierarchy process, a weight analysis model is established to compare the relative importance of risks at the same level and branch risks pairwise, and the comparison results a of the relative importance of risks ij are combined into a fuzzy judgment matrix A = (a ij ) m*n , and a fuzzy consistent matrix is formed. Calculate the maximum eigenvector λ ij of the judgment matrix A = (a m*n ), and rank the importance of evaluation indexes through the obtained eigenvector; in order to facilitate the hierarchical evaluation of various influencing factors, normalize the initial weights of evaluation factors to obtain the weight coefficients of each index. Among them, the geometric mean method is used to calculate the weight coefficients: max

[0053] For each row i, calculate the product of its elements: For each M i , take the nth root and calculate the geometric mean: Normalize the geometric mean to obtain the weight: The normalized relative weight coefficient W = (ω1, ω2,... ω n ) T , calculate the maximum eigenvector λ max of the judgment matrix A, Calculate the consistency test index

[0054] ​Step 3: Construct an intelligent prediction model through the evaluation index system to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock, including: according to the index data in the evaluation index system, obtain the variable indexes during the construction of the deep foundation pit of the ship lock in the form of time series;

[0055] Construct an intelligent prediction model through the variable indexes to predict the variable indexes during the construction of the deep foundation pit of the ship lock, and obtain the predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock;

[0056] Combine the predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock and the remaining monitoring indexes to construct a membership function to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock;

[0057] The obtaining of the variable indexes during the construction of the deep foundation pit of the ship lock in the form of time series according to the index data in the evaluation index system, constructing an intelligent prediction model through the variable indexes to predict the variable indexes during the construction of the deep foundation pit of the ship lock, and obtaining the predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock includes:

[0058] Construct a data set X from the variable indexes during the construction of the deep foundation pit of the ship lock, forming a time series X = {x1, x2... x p}; Take X = {x1, x2... x p} as the training sample, construct an intelligent prediction model, and predict the changes in the indexes during the construction of the deep foundation pit of the ship lock in the next q days; obtain the set of predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock in the next q days X' = {x p+1 , x p+2 ... x p+q};

[0059] Specifically, during the construction of the deep foundation pit of the ship lock, due to the suddenness of accidents, the variable indexes represent the monitoring data with certain regular changes in the monitoring data over time, manifested as the index data of displacement and settlement. The variable indexes in this engineering project are: horizontal displacement at the top of the slope, vertical displacement at the top of the slope, deep horizontal displacement, support axial force, groundwater level, displacement of surrounding buildings, settlement of surrounding strata, displacement of surrounding ship locks, displacement of surrounding pipelines; predicting the variable indexes can obtain the future risk level during construction, predict the construction risk, realize the dynamic monitoring of construction risk, and better guide the on-site construction;

[0060] Taking X = {x1, x2... x p} as the training sample, constructing an intelligent prediction model, and predicting the changes in the indexes during the construction of the deep foundation pit of the ship lock in the next q days includes:

[0061] The input-output pair of the intelligent prediction model is (x, x'), where x ∈ X and x' ∈ x'. The corresponding regression function is:

[0062] x′ = f(x) + ε

[0063] where x represents the variable index during the construction of the ship lock deep foundation pit, x' represents the predicted value of the variable index during the construction of the ship lock deep foundation pit, and ε represents the noise value that follows a normal distribution, representing the deviation between the true value and the predicted value;

[0064] f(x) is a machine learning algorithm based on similarity. For the construction process of the ship lock deep foundation pit in the next q days, the function distribution is defined by setting a stochastic process:

[0065]

[0066] where RD() represents the stochastic process, E represents the mathematical expectation, σ 2 represents the standard deviation of the noise value, φ represents a custom parameter, x p represents the input containing p variable indices, and x' q represents the predicted value of the variable index during the construction of the ship lock deep foundation pit in the next q days;

[0067] Combining the predicted value of the variable index during the construction of the ship lock deep foundation pit and other monitoring indices, constructing a membership function, and performing dynamic risk assessment during the construction process of the ship lock deep foundation pit includes: establishing the mapping between qualitative and quantitative of the index data, and setting the membership function of a single risk index for the entire ship lock deep foundation pit construction system in combination with the predicted value of the variable index.

[0068] The establishment of the mapping between qualitative and quantitative of the index data, and setting the membership function of a single risk index for the entire ship lock deep foundation pit construction system in combination with the predicted value of the variable index includes:

[0069] Calculating the characteristic values of different risk levels, characterizing the fuzziness and randomness of the monitoring data of the ship lock deep foundation pit construction, and constructing the membership function of the five-level ship lock deep foundation pit construction under different risk levels on the qth day in the future in combination with the predicted value of the variable index;

[0070] Specifically, the characteristic value calculation formula is:

[0071]

[0072] where EX is the expectation value, C max 、C min represent the maximum and minimum values of the digital characteristic interval of the risk level, EN is the entropy, representing the fuzziness and uncertainty of the qualitative concept, H e is the hyperentropy, representing the measure of the uncertainty of the entropy, and h represents a constant; through the expectation Ex , entropy E n and hyper-entropy H e Determine the membership functions for different risk levels of each index.

[0073] The membership function is as follows:

[0074]

[0075] where represents the membership function of the eigenvalue x' of the k-th index in the future q days at the g-th risk level, and x' kq represents the eigenvalue of the index in the construction of the deep foundation pit of the ship lock, kq represents the expectation of the eigenvalue of the k-th index in the future q days at the g-th risk level, represents the entropy of the eigenvalue of the k-th index in the future q days at the g-th risk level, H represents the hyper-entropy, Rand() represents a random number between 0 and 1, k = 1, 2... m, g = 1, 2... 5; e

[0076] In step four: perform risk judgment on the construction according to the risk assessment result. If it is judged that there is a risk, use the risk warning device to output risk warning information, including: the risk warning device displays real-time data and predicted data, compares the current real-time data with the predicted data, and enables a hierarchical early warning function during the monitoring process. Different early warning levels are distinguished by different colors.

[0077] Specifically, establish a unique mapping relationship within the 3D model through sensors, monitoring points, and monitoring data, so that by clicking on a certain monitoring point in the 3D model, the real-time monitoring status of the monitoring point can be popped up, and the collected monitoring data can be intuitively displayed in the form of a table. Green indicates no risk, blue indicates low risk, yellow indicates medium risk, orange indicates high risk, and red indicates extremely high risk. When the monitoring data exceeds the early warning threshold, start the early warning program to remind the management to pay close attention to the early warning location.

[0078] In the specification provided here, a large number of specific details are described. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies are not shown in detail so as not to obscure the understanding of this specification.

[0079] ​In addition, those skilled in the art will be able to understand that although some embodiments herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments is meant to be within the scope of the present invention and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

Claims

1. A dynamic risk monitoring method for the construction of a deep foundation pit of a ship lock, characterized in that, It includes the following steps: Step 1: Collect data from different areas of the construction site of the deep foundation pit of the ship lock in real time through sensors, and obtain the monitoring data related to the construction of the deep foundation pit of the ship lock; Step 2: Identify and process the monitoring data, and establish an evaluation index system for the construction risk of the deep foundation pit of the ship lock; Step 3: Construct an intelligent prediction model through the evaluation index system to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock; Step 4: Make a risk judgment on the construction according to the risk assessment result. If it is judged that there is a risk, use the risk warning device to output a risk warning message.

2. The risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 1, characterized in that The said Step 1: Collect data from different areas of the construction site of the deep foundation pit of the ship lock in real time through sensors, and obtain the monitoring data related to the construction of the deep foundation pit of the ship lock includes: According to the construction site conditions of the deep foundation pit of the ship lock, arrange sensors, place sensors on the foundation pit body of the deep foundation pit of the ship lock and the surrounding area of the construction site, collect the basic data indexes during the construction process, the indexes causing structural deformation, and the external environment data of the construction site of the deep foundation pit of the ship lock, and interact with various sensors on site through a wireless transmission network to construct a wireless sensor network to realize the real-time transmission of the data of the construction site of the deep foundation pit of the ship lock.

3. The risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 1, characterized in that The said Step 2: Identify and process the monitoring data, and establish an evaluation index system for the construction risk of the deep foundation pit of the ship lock includes: Standardize the monitoring data of the construction of the deep foundation pit of the ship lock. For the monitoring indexes, grade the construction risk of the deep foundation pit of the ship lock according to the risk breakdown structure, establish a weight analysis model in combination with the fuzzy analytic hierarchy process, assign corresponding weights, and complete the construction of the evaluation index system during the construction of the deep foundation pit of the ship lock.

4. The risk dynamic monitoring method for the construction of a ship lock deep foundation pit according to claim 1, characterized in that The said Step 3: Construct an intelligent prediction model through the evaluation index system to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock includes: According to the index data in the evaluation index system, obtain the variable indexes during the construction of the deep foundation pit of the ship lock in a time series manner; Construct an intelligent prediction model through the variable indexes, predict the variable indexes during the construction of the deep foundation pit of the ship lock, and obtain the predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock; Combine the predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock and the remaining monitoring indexes to construct a membership function to conduct dynamic risk assessment during the construction of the deep foundation pit of the ship lock.

5. The risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 4, characterized in that The said obtaining the variable indexes during the construction of the deep foundation pit of the ship lock in a time series manner according to the index data in the evaluation index system, constructing an intelligent prediction model through the variable indexes, predicting the variable indexes during the construction of the deep foundation pit of the ship lock, and obtaining the predicted values of the variable indexes during the construction of the deep foundation pit of the ship lock includes: Construct a data set X with variable indicators during the construction process of the deep foundation pit of the ship lock, and form a time series X = {x1, x2... x p} that contains p variable indicators; Let \(X = \{x_1, x_2, \ldots, x\) p \} be used as training samples to construct an intelligent prediction model for predicting the changes in indicators during the construction of the deep foundation pit of the ship lock in the next \(q\) days. Obtain the predicted value set X' = {x p+1 , x p+2 ... x p+q} for the variable indicators during the construction of the deep foundation pit of the shiplock in the next q days.

6. The dynamic risk monitoring method for the construction of the deep foundation pit of a ship lock according to claim 5, wherein: The input-output pair of the intelligent prediction model is (x, x'), x ∈ X, x' ∈ X', and the corresponding regression function is: x' = f(x) + ε Wherein, x represents the variable index during the construction of the deep foundation pit of the ship lock, x' represents the predicted value of the variable index during the construction of the deep foundation pit of the ship lock, and ε represents the noise value subject to a normal distribution, representing the deviation between the true value and the predicted value; f(x) is a similarity-based machine learning algorithm. For the construction process of the lock deep foundation pit in the next q days, the function distribution is defined by setting a stochastic process: where RD() represents a random process, E represents the mathematical expectation, σ 2 represents the standard deviation of the noise value, φ represents a custom parameter, x p represents an input containing p variable indicators, x' q represents the predicted value of the variable indicators in the construction of the deep foundation pit of the ship lock in the next q days.

7. A risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 4, characterized in that, Combining the predicted values of the variable indicators and other monitoring indicators in the construction of the lock deep foundation pit, constructing a membership function, and performing dynamic risk assessment during the construction process of the lock deep foundation pit includes: Establishing the mapping between qualitative and quantitative index data, and setting the membership function of a single risk index for the entire lock deep foundation pit construction system in combination with the predicted values of the variable indicators.

8. A risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 7, characterized in that, The establishment of the mapping between qualitative and quantitative index data, and setting the membership function of a single risk index for the entire lock deep foundation pit construction system in combination with the predicted values of the variable indicators includes: Calculating the characteristic values of different risk levels to characterize the fuzziness and randomness of the monitoring data of the lock deep foundation pit construction, and constructing the membership function of the five-level lock deep foundation pit construction at different risk levels on the qth day in the future in combination with the predicted values of the variable indicators.

9. A risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 8, characterized in that, The membership function is: Among them, represents the eigenvalue x' of the k-th index in the next q days kq of the membership function under the g-th risk level, x' kq represents the eigenvalue of the index in the construction of the deep foundation pit of the ship lock represents the expectation of the eigenvalue of the k-th index in the next q days under the g-th risk level represents the entropy of the eigenvalue of the k-th index in the next q days under the g-th risk level, H e represents the hyperentropy, Rand() represents a random number from 0 to 1, k = 1, 2... m, g = 1, 2... 5 10. A risk dynamic monitoring method for the construction of a deep foundation pit of a ship lock according to claim 1, characterized in that, Step four: Making a risk judgment on the construction according to the risk assessment result. If a risk is judged to exist, using the risk warning device to output a risk warning message includes: The risk warning device displays real-time data and predicted data, compares the current real-time data with the predicted data, and enables a hierarchical early warning function during the monitoring process. Different early warning levels are distinguished by different colors.

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