Hole construction safety intelligent supervision method and system based on data analysis

Abstract

This invention belongs to the field of data analysis technology and provides a data-based intelligent monitoring method and system for the safety of pit construction. The method includes acquiring horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit; determining a displacement risk coefficient based on the horizontal displacement data; determining a stress overload coefficient based on the support pile stress data; determining an earth pressure fluctuation coefficient based on the lateral earth pressure data; determining a water pressure change coefficient based on the pore water pressure data; determining a vibration damage coefficient based on the pit perimeter vibration acceleration data; and determining a comprehensive impact index. By collecting data directly related to the ongoing pit construction, the method can accurately capture the disturbance effects of vibration, stress transmission caused by soil excavation, and groundwater level changes caused by precipitation on the completed pit.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, and specifically relates to a data analysis-based intelligent monitoring method and system for pit construction safety. Background Technology

[0002] Construction safety is the core of the smooth progress of the project. The construction process involves complex procedures such as deep foundation pit excavation, high slope excavation and support, tall formwork erection, underground pipeline laying and underground cavern excavation. Safety risks run through the entire construction process, so it is necessary to carry out safety supervision at all times during construction.

[0003] Especially in underground integrated pipe corridors, subway stations, tunnels (caves) and other underground caverns, municipal pipe network renovation projects, pits are necessary spaces for underground structure construction, and pit support is a key technical means to ensure construction safety. Its core objective is to balance water and soil pressure and control water and soil deformation through various support structures to prevent pit collapse and create a safe environment for internal construction.

[0004] While existing pit support technology has formed a relatively mature design and construction system, current regulatory systems mostly focus on pits under construction, such as monitoring excavation progress and displacement deformation, without establishing a specific regulatory mechanism for pits that have already been supported. However, when two pits are close together, such as in the underground cavern complex of a large hydropower station, vibrations generated during the construction of the pit under support, stress transfer caused by soil excavation, groundwater pressure caused by precipitation, and changes in loads transmitted from the upper or surface can all disturb the pits that have already been supported. Even if safety redundancy was considered in the design phase of the supported pits, such external disturbances may still gradually consume the redundancy, leading to the risk of structural loosening and excessive displacement in the supported pits. Most existing technologies only monitor pits that are under construction and support, neglecting to monitor pits that have already been supported. There is no targeted monitoring of supported pits, nor is there a risk correlation analysis between pits under construction and supported pits. This can easily lead to major hidden dangers such as collapse, roof collapse, and water seepage. Summary of the Invention

[0005] To address the problems in the background technology, this invention proposes a data analysis-based intelligent monitoring method and system for pit construction safety.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, this invention proposes an intelligent monitoring method for pit construction safety based on data analysis, comprising: Acquire horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit. Based on the horizontal displacement data, the displacement risk coefficient is determined; Based on the stress data of the support piles, the stress overload coefficient is determined; Based on the lateral earth pressure data, the earth pressure fluctuation coefficient is determined; Based on the pore water pressure data, the water pressure variation coefficient is determined; Based on the vibration acceleration data around the pit, the vibration damage coefficient is determined; Based on the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient, and vibration damage coefficient, a comprehensive impact index is determined, and it is determined whether the comprehensive impact index is greater than a preset first threshold. If so, a risk warning is issued.

[0007] Preferably, the horizontal displacement data includes displacement monitoring values, initial displacement values, cumulative displacement increments, and safety cumulative limits; The displacement risk coefficient is determined based on the horizontal displacement data, and its expression is as follows: In the formula, , This represents the cumulative increment of displacement in the horizontal X-direction. This represents the horizontal displacement value in the X direction at the current moment. This represents the initial displacement value in the horizontal X direction. , This represents the cumulative increment of displacement in the horizontal Y direction. This represents the real-time displacement monitoring value in the horizontal Y direction at time t. This represents the initial displacement value in the horizontal Y direction. For the preset safety cumulative limit, This is the displacement risk coefficient.

[0008] Preferably, the stress data of the support pile includes the monitored stress and the stress limit value of the support pile; The stress overload coefficient is determined based on the stress data of the support piles, and its expression is as follows: In the formula, To monitor stress, For the stress limit of the support pile, This is the stress overload factor.

[0009] Preferably, the lateral earth pressure data includes the average lateral earth pressure over a continuous time period and the absolute deviation of the earth pressure from the average value; Based on the lateral earth pressure data, the earth pressure fluctuation coefficient is determined, and its expression is as follows: , This represents the average lateral earth pressure over a continuous period of time. This represents the absolute deviation of the earth pressure from the average value. To determine the number of earth pressure data collections within the monitoring period, For the first The lateral earth pressure value collected in the second sampling. These are lateral earth pressure monitoring values. This is the earth pressure fluctuation coefficient.

[0010] Preferably, the pore water pressure data includes the absolute change in pore water pressure and the safe change limit of pore water pressure. The water pressure variation coefficient is determined based on the pore water pressure data, and its expression is as follows: In the formula, This refers to the absolute change in pore water pressure within a given time interval. for Real-time monitoring value of pore water pressure at all times for Pore ​​water pressure monitoring values ​​at any given time. The set time interval for water pressure changes. To set safe limits for pore water pressure changes within a set time interval, This is the coefficient of water pressure variation.

[0011] Preferably, the pit perimeter vibration acceleration data includes single vibration energy and vibration damage critical energy; The vibration damage coefficient is determined based on the vibration acceleration data around the pit, and its expression is as follows: In the formula, Energy of a single vibration For vibration velocity, This is the monitoring value of vibration acceleration around the pit. The duration of a single oscillation. For the quality of vibration monitoring equipment, The critical energy for vibration damage. This represents the vibration damage coefficient.

[0012] Preferably, after determining whether the comprehensive impact index is greater than a preset first threshold and issuing a risk warning if so, the method further includes: determining whether the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient, and vibration damage coefficient are greater than preset corresponding thresholds and issuing corresponding warnings if so.

[0013] Preferably, the method further includes: Obtain the horizontal displacement time series dataset; The horizontal displacement time series dataset is input into a pre-trained LSTM model to obtain the displacement change trend; Based on the displacement change trend, it is determined whether the displacement change trend is greater than a preset seventh threshold. If so, an early warning is issued.

[0014] Secondly, this invention proposes an intelligent monitoring system for pit construction safety based on data analysis, comprising: The data acquisition module is used to acquire horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit. The displacement risk coefficient determination module is used to determine the displacement risk coefficient based on the horizontal displacement data. The stress overload coefficient determination module is used to determine the stress overload coefficient based on the stress data of the support pile; The earth pressure fluctuation coefficient determination module is used to determine the earth pressure fluctuation coefficient based on the lateral earth pressure data. A water pressure variation coefficient determination module is used to determine the water pressure variation coefficient based on the pore water pressure data. The vibration damage coefficient determination module is used to determine the vibration damage coefficient based on the vibration acceleration data around the pit. The early warning module is used to determine the comprehensive impact index based on the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient and vibration damage coefficient, and to determine whether the comprehensive impact index is greater than a preset first threshold. If so, a risk warning is issued.

[0015] The beneficial effects of this invention are: 1. The method of the present invention collects horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and vibration acceleration data around the pit of the supported pit. These data are all directly related to the construction of the pit being supported. It can accurately capture the disturbance effects of vibration, stress transmission caused by soil excavation, and groundwater level changes caused by precipitation on the supported pit during the construction of the supported pit, and avoid the limitations of single data monitoring or only focusing on the state of the supported pit itself.

[0016] 2. The method of this invention not only conducts risk warning based solely on the comprehensive impact index, but also independently determines whether any single coefficient is close to or reaches its own safety threshold. This avoids the problem that relying solely on the comprehensive index may mask the hidden dangers of a single coefficient. It can accurately capture the local safety risks that may exist in the supported pits under the disturbance of the pits under construction, further improving the comprehensiveness and timeliness of risk identification and enhancing the regulatory effect.

[0017] 3. The method of the present invention takes into account the gradual change characteristics and time series continuity of the horizontal displacement of the supported pit. It further utilizes the time series attribute of the horizontal displacement to predict the future displacement change trend through the currently collected horizontal displacement data. This can not only avoid ignoring the potential displacement risk caused by the subsequent continuous disturbance of the pit under construction due to the current horizontal displacement data being normal, but also identify the risk of the displacement exceeding the critical value of the supported pit in advance. This provides the construction party with a more sufficient early warning time window to adjust the construction parameters of the pit under construction or take preventive reinforcement measures for the supported pit.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of the intelligent monitoring method for pit construction safety based on data analysis of the present invention is shown; Figure 2 A system framework diagram of the present invention is shown; Figure 3 A schematic diagram of the device structure of the present invention is shown; Figure 4 A schematic diagram of the readable storage medium structure of the present invention is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, a data-driven intelligent monitoring method for pit construction safety includes the following steps: S10. Obtain horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit. Horizontal displacement data is collected by deploying a biaxial laser displacement sensor on the top of the support pile at the midpoint of the long side of the supported pit; support pile stress data is collected by attaching a strain sensor to the middle of the support pile at the same location; lateral earth pressure data is collected by deploying earth pressure sensors on the outside of the support pile and inside the pit (e.g., 1m outside the support pile and at half the depth of the pit); pore water pressure data is collected by deploying a pore water pressure sensor at the same depth next to the earth pressure sensor; and vibration acceleration data around the pit is collected by deploying a vibration acceleration sensor on the ground at the corner of the supported pit.

[0023] Horizontal displacement data and retaining pile stress data belong to the category of pit structure data. Horizontal displacement data refers to the horizontal displacement of the support structure of the supported pit, and is a direct physical quantity reflecting the pit's exposure to lateral soil stress transmission, construction vibration, and other disturbances. Retaining pile stress data represents the internal forces generated within the retaining piles of the supported pit when subjected to soil pressure and external disturbances, directly reflecting the bearing capacity of the support structure.

[0024] Lateral earth pressure data and pore water pressure data belong to soil condition data. Lateral earth pressure data is the horizontal pressure exerted by the soil surrounding the supported pit on the supporting structure, and its magnitude is related to soil type, pit depth, and construction disturbance. Pore water pressure data is the pressure generated by groundwater in the pores of the soil surrounding the supported pit, and is affected by factors such as dewatering of new pits and soil consolidation.

[0025] Vibration acceleration data around a pit is classified as environmental data. It represents the rate of change of velocity of the ground around a supported pit when it vibrates due to the construction of a new pit (such as pile driving or excavation).

[0026] Pit structure data directly reflects the bearing capacity and deformation state of the support system, serving as the core basis for judging pit safety; soil condition data can capture stress and water pressure changes in the surrounding soil, revealing the root cause of structural stress changes; environmental data can indirectly monitor the intensity of external construction disturbances. By integrating pit, soil, and environmental data, a complete risk monitoring chain can be formed, avoiding the limitations of single-dimensional monitoring.

[0027] The method of this invention collects horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and vibration acceleration data around the pit of the supported pit. These data are all directly related to the construction of the pit being supported. The method can accurately capture the disturbance effects of vibration, stress transmission caused by soil excavation, and groundwater level changes caused by precipitation on the supported pit during the construction of the supported pit, thus avoiding the limitations of monitoring a single data or only focusing on the state of the supported pit itself.

[0028] S20. Horizontal displacement data includes displacement monitoring values ​​(horizontal X-direction displacement monitoring value at the current time, horizontal Y-direction real-time displacement monitoring value at time t), initial displacement values ​​(horizontal X-direction initial displacement value, horizontal Y-direction initial displacement value), cumulative displacement increments (horizontal X-direction cumulative displacement increments, horizontal Y-direction cumulative displacement increments) and safety cumulative limits. Determine the displacement risk coefficient based on horizontal displacement data. Its expression is: In the formula, , This represents the cumulative increment of displacement in the horizontal X-direction. This represents the horizontal displacement value in the X direction at the current moment. This represents the initial displacement value in the horizontal X direction. , This represents the cumulative increment of displacement in the horizontal Y direction. This represents the real-time displacement monitoring value in the horizontal Y direction at time t. This represents the initial displacement value in the horizontal Y direction. For pre-set safety cumulative limits; It should be further clarified that the horizontal displacement of the supported pit is not unidirectional, but can occur in any direction within the plane. The disturbance of the supported pit caused by the ongoing construction of the pit may be transmitted along the long side, short side, or any other horizontal direction of the pit. Monitoring only one direction cannot fully capture the displacement risk. Two-dimensional monitoring in both the X and Y directions is required to cover the entire horizontal plane and ensure that no potential displacement hazards are missed. The X direction is parallel to the excavation axis of the new pit, and the Y direction is perpendicular to the excavation axis of the new pit.

[0029] In one specific embodiment, it is assumed that six consecutive X-direction horizontal displacement data points were collected before the construction of the new pit: 2.1, 2.2, 2.0, 2.1, 2.2, and 2.1 (mm), and the average value was taken as the initial displacement value. Calculated After the new pit was constructed, the dual-axis laser displacement sensor collected real-time X-axis displacement data. , The preset size is 10mm. , .

[0030] S30, Support pile stress data includes monitored stress and support pile stress limits; based on the support pile stress data, the stress overload factor is determined. Its expression is: In the formula, To monitor stress, The stress limit of the support pile is determined based on the material strength of the support pile itself.

[0031] S40. Determine the earth pressure fluctuation coefficient based on lateral earth pressure data. Its expression is: , This represents the average lateral earth pressure over a continuous period of time. This represents the absolute deviation of the earth pressure from the average value. To determine the number of earth pressure data collections within the monitoring period, For the first The lateral earth pressure value collected in the second sampling. This represents the lateral earth pressure monitoring value; S50. Based on pore water pressure data, determine the water pressure variation coefficient. Its expression is: In the formula, This refers to the absolute change in pore water pressure within a given time interval. for Real-time monitoring value of pore water pressure at all times for Pore ​​water pressure monitoring values ​​at any given time. The set time interval for water pressure changes. To set safe limits for pore water pressure changes within a set time interval; S60. Determine the vibration damage coefficient based on the pit perimeter vibration acceleration data. Its expression is: In the formula, Energy of a single vibration For vibration velocity, This is the monitoring value of vibration acceleration around the pit. The duration of a single oscillation. For the quality of vibration monitoring equipment, This is the critical energy for vibration damage. S70. Based on the displacement risk coefficient, stress overload coefficient, earth pressure fluctuation coefficient, water pressure variation coefficient, and vibration damage coefficient, determine the comprehensive influence index. It determines whether the comprehensive impact index is greater than the preset first threshold. If so, a risk warning is issued; otherwise (i.e., the comprehensive impact index is less than or equal to the preset first threshold), no risk warning is issued.

[0032] The expression for calculating the comprehensive impact index is: In the formula, , , , , These are the weights of each coefficient, and their sum is 1.

[0033] Furthermore, in monitoring the impact of supported pits, risk cannot be determined solely by whether the comprehensive impact index exceeds the first threshold. Even if the comprehensive index does not exceed the limit, if any of the calculated displacement risk coefficient, stress overload coefficient, earth pressure fluctuation coefficient, water pressure change coefficient, and vibration damage coefficient has a significant problem (such as approaching or reaching its own safety threshold), an early warning must be triggered immediately to avoid safety risks caused by a single coefficient's hidden danger being masked by the comprehensive index. Therefore, after step S70 above, the method of the present invention further includes:

[0034] S80. Determine whether the displacement risk coefficient, stress overload coefficient, earth pressure fluctuation coefficient, water pressure change coefficient, and vibration damage coefficient are greater than the preset corresponding thresholds. If so, issue the corresponding warning. Specifically: Determine whether the displacement risk coefficient is greater than the preset second threshold. If it is, it indicates that the horizontal displacement risk of the supported pit has exceeded the foundation safety line, and a displacement warning needs to be triggered. Determine whether the stress overload coefficient is greater than the third threshold. If it is, it means that the bearing capacity of the support pile has exceeded the design limit, and a stress overload warning is initiated. Determine whether the earth pressure fluctuation coefficient is greater than the fourth threshold. If it is, it indicates that the stress balance of the surrounding soil has been significantly broken, and an earth pressure anomaly warning is triggered. Determine whether the water pressure change coefficient is greater than the fifth threshold. If it is, it reflects that groundwater disturbance has had an adverse effect on soil stability, and an abnormal pore water pressure warning is issued. Determine whether the vibration damage coefficient is greater than the sixth threshold. If it exceeds the limit, it indicates that the cumulative damage of construction vibration to the supported pit has reached a level that requires attention, and a vibration damage warning is triggered.

[0035] The method of this invention not only conducts risk warnings based solely on the comprehensive impact index, but also independently determines whether any single coefficient is close to or reaches its own safety threshold. This avoids the problem that relying solely on the comprehensive index may mask the hidden dangers of a single coefficient. It can accurately capture the local safety risks that may exist in the supported pits under the disturbance of the pits under construction, further improving the comprehensiveness and timeliness of risk identification and enhancing the regulatory effect.

[0036] Furthermore, even if the aforementioned displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient, vibration damage coefficient, and comprehensive impact index do not exceed the preset thresholds, supervision cannot be relaxed. The horizontal displacement of supported pits is affected by soil stress transmission and support structure deformation, and its changes are gradual, exhibiting a clear time-series continuity. Therefore, future displacement trends can be predicted using currently collected horizontal displacement data, enabling early warning of risks and preventing the neglect of potential subsequent disturbance risks due to currently normal data, thus ensuring the long-term safety and stability of the pits.

[0037] The method of the present invention further includes: S901. Obtain the horizontal displacement time series dataset; A horizontal displacement time series dataset refers to an ordered set of data collected continuously at preset time intervals (e.g., every 30 minutes) in a supported pit, including the current moment and past set time periods (e.g., 24 hours).

[0038] S902. Input the horizontal displacement time series dataset into the pre-trained LSTM model to obtain the displacement change trend; The LSTM model architecture consists of a serially connected input layer, hidden layer, and output layer. The hidden layer comprises n LSTM units using the ReLU activation function. In a preferred embodiment, the LSTM unit has two layers. The training method is as follows: 100 sets of displacement monitoring data (this is an example; the actual data volume can be adjusted within the range of 50-200 sets, or expanded as needed) are used as training data. The input layer receives horizontal displacement data from the past 24 time steps (each time step corresponds to a sampling interval of 15 minutes / 30 minutes, etc.). After processing by two LSTM hidden layers, each containing 64 neurons and using the ReLU activation function, the output layer outputs a predicted displacement increment for the next 6 hours via a single neuron. The training process uses mean squared error as the loss function and iterates 500 times using the Adam optimizer (learning rate 0.001, batch size 32; a learning rate decay strategy can be used) (example value, can be adjusted as needed) to complete model training and support subsequent displacement trend prediction. It should be further noted that the LSTM model and its architecture used in this invention are within the scope of conventional architectures in the field, and the specific number of layers of the LSTM unit can also be adjusted according to the actual situation.

[0039] S903. Based on the displacement change trend, determine whether the displacement change trend is greater than the preset seventh threshold. If so, issue an early warning.

[0040] In the above method, , , , , The first through seventh thresholds are all preset by humans.

[0041] The method of this invention addresses the gradual change characteristics and time series continuity of the horizontal displacement of supported pits. It further utilizes the time series attributes of horizontal displacement to predict future displacement trends based on currently collected horizontal displacement data. This not only avoids ignoring potential displacement risks caused by continuous disturbances to the pit under construction due to normal current horizontal displacement data, but also identifies the risk of future displacement exceeding critical values ​​in supported pits in advance. This provides the construction party with a more sufficient time window to adjust the construction parameters of the pit under construction (such as slowing down the excavation speed and optimizing the dewatering plan) or to take preventive reinforcement measures for supported pits.

[0042] Reference Figure 2 As shown, based on the same inventive concept as the above method, this invention also proposes a data analysis-based intelligent monitoring system for pit construction safety, comprising: The data acquisition module 110 is used to acquire horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit. The displacement risk coefficient determination module 120 is used to determine the displacement risk coefficient based on horizontal displacement data. The stress overload coefficient determination module 130 is used to determine the stress overload coefficient based on the stress data of the support piles. Earth pressure fluctuation coefficient determination module 140 is used to determine the earth pressure fluctuation coefficient based on lateral earth pressure data; The water pressure variation coefficient determination module 150 is used to determine the water pressure variation coefficient based on pore water pressure data. The vibration damage coefficient determination module 160 is used to determine the vibration damage coefficient based on the vibration acceleration data around the pit. The early warning module 170 is used to determine the comprehensive impact index based on the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient and vibration damage coefficient, and to determine whether the comprehensive impact index is greater than the preset first threshold. If so, a risk warning is issued.

[0043] Reference Figure 3 As shown, based on the same inventive concept as the above method, the present invention proposes a device including a memory and a processor. The memory stores computer instructions that can be executed on the processor. When the processor executes the computer instructions, it performs the above-mentioned intelligent monitoring method for pit construction safety based on data analysis.

[0044] Reference Figure 4As shown, based on the same inventive concept as the above method, this invention proposes a computer-readable storage medium storing computer instructions. When the computer instructions are executed, the above-mentioned intelligent monitoring method for pit construction safety based on data analysis can be realized.

[0045] Any references to memory, storage, database, or other media used in the embodiments provided in this invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0046] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data analysis-based pit construction safety intelligent supervision method, characterized in that, include: Acquire horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit. Based on the horizontal displacement data, the displacement risk coefficient is determined; Based on the stress data of the support piles, the stress overload coefficient is determined; Based on the lateral earth pressure data, the earth pressure fluctuation coefficient is determined; Based on the pore water pressure data, the water pressure variation coefficient is determined; Based on the vibration acceleration data around the pit, the vibration damage coefficient is determined; Based on the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient, and vibration damage coefficient, a comprehensive impact index is determined, and it is determined whether the comprehensive impact index is greater than a preset first threshold. If so, a risk warning is issued. 2.The data analysis based intelligent supervision method for pit construction safety according to claim 1, characterized in that, The horizontal displacement data includes displacement monitoring values, initial displacement values, cumulative displacement increments, and cumulative safety limits; The displacement risk coefficient is determined based on the horizontal displacement data, and its expression is as follows: In the formula, , This represents the cumulative increment of displacement in the horizontal X-direction. This represents the horizontal displacement value in the X direction at the current moment. This represents the initial displacement value in the horizontal X direction. , This represents the cumulative increment of displacement in the horizontal Y direction. This represents the real-time displacement monitoring value in the horizontal Y direction at time t. This represents the initial displacement value in the horizontal Y direction. For the preset safety cumulative limit, This is the displacement risk coefficient.

3. The intelligent monitoring method for pit construction safety based on data analysis according to claim 1, characterized in that, The stress data for the support piles includes monitored stress and stress limits for the support piles; The stress overload coefficient is determined based on the stress data of the support piles, and its expression is as follows: In the formula, To monitor stress, For the stress limit of the support pile, This is the stress overload factor.

4. The intelligent monitoring method for pit construction safety based on data analysis according to claim 1, characterized in that, The lateral earth pressure data includes the average lateral earth pressure over a continuous period and the absolute deviation of the earth pressure from the average value. Based on the lateral earth pressure data, the earth pressure fluctuation coefficient is determined, and its expression is as follows: , This represents the average lateral earth pressure over a continuous period of time. This represents the absolute deviation of the earth pressure from the average value. To determine the number of earth pressure data collections within the monitoring period, For the first The lateral earth pressure value collected in the second sampling. These are lateral earth pressure monitoring values. This is the earth pressure fluctuation coefficient.

5. The intelligent monitoring method for pit construction safety based on data analysis according to claim 1, characterized in that, The pore water pressure data includes the absolute change in pore water pressure and the safe change limit of pore water pressure. The water pressure variation coefficient is determined based on the pore water pressure data, and its expression is as follows: In the formula, This refers to the absolute change in pore water pressure within a given time interval. for Real-time monitoring value of pore water pressure at all times for Pore ​​water pressure monitoring values ​​at any given time. The set time interval for water pressure changes. To set safe limits for pore water pressure changes within a set time interval, This is the coefficient of water pressure variation.

6. The intelligent monitoring method for pit construction safety based on data analysis according to claim 1, characterized in that, The pit perimeter vibration acceleration data includes single vibration energy and vibration damage critical energy; The vibration damage coefficient is determined based on the vibration acceleration data around the pit, and its expression is as follows: In the formula, Energy of a single vibration For vibration velocity, This is the monitoring value of vibration acceleration around the pit. The duration of a single oscillation. For the quality of vibration monitoring equipment, The critical energy for vibration damage. This represents the vibration damage coefficient.

7. The intelligent monitoring method for pit construction safety based on data analysis according to claim 1, characterized in that, After determining whether the comprehensive impact index is greater than a preset first threshold, and if so, issuing a risk warning, the method further includes: determining whether the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient, and vibration damage coefficient are greater than preset corresponding thresholds, and if so, issuing corresponding warnings.

8. The intelligent monitoring method for pit construction safety based on data analysis according to claim 2, characterized in that, The method further includes: Obtain the horizontal displacement time series dataset; The horizontal displacement time series dataset is input into a pre-trained LSTM model to obtain the displacement change trend; Based on the displacement change trend, it is determined whether the displacement change trend is greater than a preset seventh threshold. If so, an early warning is issued.

9. A data-driven intelligent monitoring system for pit construction safety, characterized in that, include: The data acquisition module is used to acquire horizontal displacement data, support pile stress data, lateral earth pressure data, pore water pressure data, and pit perimeter vibration acceleration data of the supported pit. The displacement risk coefficient determination module is used to determine the displacement risk coefficient based on the horizontal displacement data. The stress overload coefficient determination module is used to determine the stress overload coefficient based on the stress data of the support pile; The earth pressure fluctuation coefficient determination module is used to determine the earth pressure fluctuation coefficient based on the lateral earth pressure data. A water pressure variation coefficient determination module is used to determine the water pressure variation coefficient based on the pore water pressure data. The vibration damage coefficient determination module is used to determine the vibration damage coefficient based on the vibration acceleration data around the pit. The early warning module is used to determine the comprehensive impact index based on the displacement risk coefficient, stress overload coefficient, soil pressure fluctuation coefficient, water pressure change coefficient and vibration damage coefficient, and to determine whether the comprehensive impact index is greater than a preset first threshold. If so, a risk warning is issued.

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