Foundation pit seepage monitoring method and system based on Internet of Things

Abstract

The invention belongs to the technical field of foundation pit seepage monitoring, and provides a foundation pit seepage monitoring method and system based on the Internet of Things, and the method comprises the steps: in a foundation pit seepage process, setting a historical seepage monitoring period, analyzing the change of a phreatic water level in the historical seepage monitoring period, and obtaining a foundation pit seepage rate; the foundation pit seepage rate in the historical seepage monitoring period and the well point dewatering rate around the foundation pit are subjected to rate balance analysis, a rate balance coefficient is obtained, the well point dewatering rate in the current seepage monitoring period is adjusted, the current well point dewatering initial adjustment rate is obtained, it can be ensured that the dewatering rate is always matched with the seepage condition, and the well point dewatering efficiency is improved. And moreover, the initial precipitation adjustment rate of the current well point is obtained in real time through the Internet of Things monitoring system, the precipitation scheme is adjusted in time, the seepage problem can be effectively prevented and controlled, the shutdown frequency and time caused by the seepage problem are reduced, and the construction continuity and progress are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of foundation pit seepage monitoring technology, specifically a foundation pit seepage monitoring method and system based on the Internet of Things. Background Technology

[0002] In the field of construction engineering, foundation pit construction is a fundamental and crucial step in many projects, and the problem of foundation pit seepage has always been an important factor affecting the safety and quality of foundation pit construction. With the acceleration of urbanization, high-rise buildings and underground transportation facilities are constantly emerging, and the excavation depth of foundation pits is increasing day by day. The problem of foundation pit seepage is becoming more and more complex and prominent, making accurate monitoring and effective control of foundation pit seepage an urgent need to ensure the smooth progress of projects.

[0003] In existing technologies, firstly, traditional methods are difficult to adapt to the constantly changing seepage conditions during foundation pit construction. Foundation pit construction is a dynamic process, and changes in geological conditions, construction progress, and the surrounding environment can all lead to changes in the seepage situation. However, traditional monitoring methods often lack flexibility and real-time capability, failing to obtain accurate seepage rate information in a timely manner, and thus making it difficult to ensure that the dewatering rate always matches the seepage situation. This can lead to problems during foundation pit construction, such as insufficient dewatering causing seepage and resulting in foundation pit instability, or excessive dewatering causing settlement of the surrounding soil.

[0004] Secondly, because the current wellpoint dewatering rate cannot be accurately obtained in real time, and the dewatering plan cannot be adjusted promptly based on the seepage situation in the foundation pit, the traditional foundation pit seepage monitoring and control system is often in a reactive state when facing seepage problems. It cannot predict in advance the timing and magnitude of adjustments needed to the dewatering rate. During foundation pit construction, adjustments to the dewatering rate need to be made in real time based on the seepage situation and the risk of settlement, but traditional methods cannot provide timely warning signals to construction personnel, preventing them from making advance preparations for adjustments. Furthermore, due to the lack of precise control methods, it is difficult to obtain dynamic adjustment acceleration, making it impossible to accurately control the wellpoint dewatering rate. This affects the understanding of the development trend of foundation pit seepage and the assessment of its impact on construction progress, easily leading to construction interruptions or delays due to dewatering problems.

[0005] Therefore, the present invention provides a method and system for monitoring seepage in foundation pits based on the Internet of Things. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: Firstly, an IoT-based method for monitoring seepage in foundation pits includes: During the seepage process in the foundation pit, a historical seepage monitoring cycle is set, and the changes in groundwater level within the historical seepage monitoring cycle are analyzed to obtain the seepage rate of the foundation pit. A rate balance analysis was performed on the seepage rate of the foundation pit and the well point dewatering rate around the foundation pit during the historical seepage monitoring period to obtain the rate balance coefficient. The well point dewatering rate during the current seepage monitoring period was then adjusted to obtain the current initial adjustment rate of the well point dewatering. Based on the current initial adjustment rate of wellpoint dewatering, an excessive dewatering simulation is conducted on the unconfined layer in the foundation pit to assess the risk of dewatering settlement. Based on the assessment of precipitation subsidence risk, after adjusting the high-risk precipitation subsidence signal to the low-risk precipitation subsidence signal, the current wellpoint precipitation initial adjustment rate is dynamically monitored and warned, and the current wellpoint precipitation initial adjustment rate is dynamically adjusted.

[0008] Preferably, the method for obtaining the seepage rate in the foundation pit is as follows: The historical seepage monitoring cycle is divided into several historical seepage monitoring nodes. The groundwater level at each historical seepage monitoring node is obtained and sorted according to the time sequence of the groundwater level acquisition to obtain the groundwater time sequence. The difference between the groundwater levels of adjacent historical seepage monitoring nodes within the groundwater time series is calculated, and the absolute value is taken. This absolute value is then compared with the interval between adjacent historical seepage monitoring nodes to calculate the seepage rate of adjacent units. The seepage rate of the foundation pit is obtained by averaging the seepage rates of all adjacent units.

[0009] Preferably, the rate balance coefficient is obtained as follows: Obtain the pumping level at each historical seepage monitoring node, and calculate the difference between the pumping levels at adjacent historical seepage monitoring nodes. Take the absolute value and calculate the ratio with the interval between adjacent historical seepage monitoring nodes to obtain the precipitation rate of adjacent units. Calculate the average of the precipitation rates of all adjacent units to obtain the wellpoint precipitation rate. By combining adjacent historical seepage monitoring nodes within a historical seepage monitoring cycle, multiple unit seepage monitoring periods are obtained; The difference between the seepage rate of adjacent units and the precipitation rate of adjacent units within the same seepage monitoring period is calculated, and the absolute value is taken to obtain the rate difference for the same period. The ratio of this ratio to the corresponding seepage rate of adjacent units is calculated, and the rate difference ratio for the same period is output. The average of all the rate difference ratios for the same period is calculated, and the rate balance coefficient is output.

[0010] Preferably, the initial adjustment rate of the current wellpoint precipitation is obtained as follows: Multiply the current seepage rate in the foundation pit by the rate balance coefficient to obtain the initial rate adjustment of the current wellpoint dewatering. The initial adjustment rate of the current wellpoint dewatering is summed with the current seepage rate in the foundation pit, and the output is the initial adjustment rate of the current wellpoint dewatering.

[0011] Preferably, excessive precipitation simulation is performed on the shallow groundwater layer within the foundation pit, as follows: Based on the actual shape of the foundation pit, several settlement monitoring points are set up to obtain the center point of the foundation pit and the distance from the center point to the edge of the foundation pit, which is used as the center-edge distance. The average of all center-edge distances is calculated to output the radius of the fitted outer circle. Based on the center point of the foundation pit and the radius of the fitted outer circle, a monitoring fitted outer circle is constructed. Half of the radius of the fitted outer circle is taken as the radius of the fitted inner circle. Based on the center point of the foundation pit and the radius of the fitted inner circle, a monitoring fitted inner circle is constructed. The edge lines of the monitoring fitted outer circle and the monitoring fitted inner circle are equally divided to obtain the inner settlement monitoring installation point and the outer settlement monitoring installation point, respectively. Several settlement monitoring points are installed one by one on the inner settlement monitoring installation point, the outer settlement monitoring installation point, and the center point of the foundation pit. The center point of the foundation pit, the internal settlement monitoring installation point, and the external settlement monitoring installation point on a straight line are combined to obtain the internal and external settlement monitoring combination. Within the internal and external settlement monitoring combination, at the precipitation simulation node during the precipitation simulation early warning cycle, the vertical heights corresponding to the settlement monitoring points at the center point of the foundation pit, the internal settlement monitoring installation points, and the external settlement monitoring installation points are obtained as the center monitoring height, the internal monitoring height, and the external monitoring height.

[0012] The preferred assessment process for precipitation subsidence risk is as follows: The differences between the central monitoring height, internal monitoring height, and external monitoring height of adjacent precipitation simulation nodes are calculated, and the absolute values ​​are taken to obtain the differences between the central monitoring height, internal monitoring height, and external monitoring height. The average values ​​are then calculated to output the axial unit settlement value. The average values ​​are then calculated again to obtain the axial settlement monitoring value. At the same precipitation simulation node, the difference between the inner monitoring height and the central monitoring height, and the difference between the outer monitoring height and the central monitoring height are calculated separately. The absolute values ​​are taken to obtain the height difference between the inner and outer sides and the height difference between the inner and outer sides. The average value is then calculated to obtain the radial unit settlement value. The radial settlement monitoring value is obtained by averaging the settlement values ​​of the same node corresponding to all the same precipitation simulation nodes. The axial unit settlement value and the radial settlement monitoring value are summed to obtain the precipitation settlement risk value; If the precipitation subsidence risk value is less than the precipitation subsidence risk threshold, it is displayed as a low-risk precipitation subsidence signal; if the precipitation subsidence risk value is greater than or equal to the precipitation subsidence risk threshold, it is displayed as a high-risk precipitation subsidence signal.

[0013] Preferably, the high-risk precipitation subsidence signal is adjusted to a low-risk precipitation subsidence signal, and the processing procedure is as follows: Compare the speed differences within the same time period and select the largest and smallest speed differences within the same time period. The maximum rate balance coefficient is obtained by calculating the ratio of the maximum simultaneous rate difference to the corresponding seepage rate of the adjacent unit. The minimum rate balance coefficient is obtained by calculating the ratio of the minimum simultaneous rate difference to the corresponding seepage rate of the adjacent unit. The rate balance coefficient range is then constructed. The simulation is repeated based on the rate balance coefficient range until the precipitation subsidence risk value is less than the precipitation subsidence risk threshold, which indicates a low-risk precipitation subsidence signal.

[0014] Preferably, dynamic monitoring and early warning of the current initial adjustment rate of wellpoint precipitation is performed, as follows: After displaying a low-risk precipitation settlement signal, the minimum rate balance coefficient within the rate balance coefficient range is selected and combined with the wellpoint precipitation rate to obtain the minimum balance precipitation rate warning value. The current initial precipitation rate at the wellpoint corresponding to the precipitation subsidence risk threshold is used as the warning value of the maximum equilibrium precipitation rate. A rate dynamic measurement warning model is constructed with the X-axis representing time and the Y-axis representing rate. On the rate dynamic measurement warning model, the warning values ​​of the minimum equilibrium precipitation rate and the maximum equilibrium precipitation rate are marked on the Y-axis, and minimum warning lines and maximum warning lines parallel to the X-axis are drawn respectively.

[0015] Preferably, the initial adjustment rate of the current wellpoint precipitation is dynamically adjusted, as follows: The warning time is obtained by calculating the ratio of the maximum equilibrium precipitation rate warning value or the minimum equilibrium precipitation rate warning value to the warning acceleration. The difference between the maximum equilibrium precipitation rate warning value and the current wellpoint precipitation initial adjustment rate is calculated, and the ratio is calculated with the warning time. The dynamic adjustment acceleration is then calculated, and the ratio is calculated with the warning acceleration to obtain the warning time. The maximum or minimum balanced precipitation rate warning value is calculated by subtracting the current wellpoint precipitation initial adjustment rate from the maximum balanced precipitation rate warning value and then calculating the ratio with the warning time to obtain the dynamic adjustment acceleration.

[0016] Secondly, an Internet of Things (IoT) based foundation pit seepage monitoring system includes: Seepage rate acquisition module: During the seepage process in the foundation pit, a historical seepage monitoring cycle is set, and the changes in seepage level within the historical seepage monitoring cycle are analyzed to obtain the seepage rate of the foundation pit. Preliminary adjustment module for precipitation rate: Performs rate balance analysis on the seepage rate of the foundation pit and the well point precipitation rate around the foundation pit during the historical seepage monitoring period to obtain the rate balance coefficient, and adjusts the well point precipitation rate during the current seepage monitoring period to obtain the current preliminary adjustment rate of well point precipitation. Simulated Settlement Risk Module: Based on the current initial adjustment rate of well point dewatering, excessive dewatering is simulated in the groundwater layer within the foundation pit to assess the risk of settlement due to dewatering. Early warning dynamic adjustment module: Based on the assessment of precipitation subsidence risk, after adjusting the high-risk precipitation subsidence signal to the low-risk precipitation subsidence signal, the module dynamically monitors and issues early warnings for the current wellpoint precipitation initial adjustment rate, and dynamically adjusts the current wellpoint precipitation initial adjustment rate.

[0017] The beneficial effects of this invention are as follows: 1. In the process of foundation pit seepage, this invention sets a historical seepage monitoring cycle, analyzes the changes in groundwater level within the historical seepage monitoring cycle to obtain the foundation pit seepage rate, performs a rate balance analysis on the foundation pit seepage rate within the historical seepage monitoring cycle and the wellpoint dewatering rate around the foundation pit to obtain the rate balance coefficient, and adjusts the wellpoint dewatering rate within the current seepage monitoring cycle to obtain the current initial adjustment rate of the wellpoint dewatering. This can adapt to the constantly changing seepage conditions during foundation pit construction, ensuring that the dewatering rate always matches the seepage conditions, improving the timeliness and accuracy of monitoring and adjustment. Moreover, by acquiring the current initial adjustment rate of the wellpoint dewatering in real time through the Internet of Things monitoring system, the dewatering plan can be adjusted in a timely manner, which can effectively prevent and control the occurrence of seepage problems, reduce the number and duration of downtime caused by seepage problems, and ensure the continuity and progress of construction. 2. This invention simulates and warns of excessive dewatering in the shallow groundwater layer within the foundation pit based on the current initial adjustment rate of wellpoint dewatering, assesses the risk of dewatering settlement, and dynamically monitors and warns of the current initial adjustment rate of wellpoint dewatering based on the assessed risk of settlement, obtaining a dynamic adjustment acceleration. On the one hand, during the foundation pit leakage monitoring process, the dewatering settlement risk value can provide a specific quantitative basis for taking prevention and control measures. By calculating the warning time and dynamic adjustment acceleration, the system can predict in advance the time and magnitude when the dewatering rate needs to be adjusted, and issue warning signals to construction personnel in a timely manner. Moreover, by obtaining the dynamic adjustment acceleration, the wellpoint dewatering rate can be controlled more accurately, reducing the settlement of the surrounding soil caused by uneven or excessively rapid dewatering. On the other hand, it helps construction management personnel to accurately grasp the development trend of foundation pit leakage and the degree of impact on the construction progress, avoiding construction interruption or delay due to dewatering problems. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1This is a flowchart of the steps of a foundation pit seepage monitoring method based on the Internet of Things according to the present invention; Figure 2 This is a flowchart illustrating the judgment process of a foundation pit seepage monitoring method based on the Internet of Things according to the present invention. Figure 3 This is a schematic diagram of an IoT-based foundation pit seepage monitoring system according to the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] Example 1 Please see Figure 1 - Figure 2 As shown in the figure, the method for monitoring seepage in a foundation pit based on the Internet of Things according to an embodiment of the present invention includes the following steps: Step 1: During the seepage process in the foundation pit, set a historical seepage monitoring cycle, analyze the changes in groundwater level within the historical seepage monitoring cycle, and obtain the seepage rate of the foundation pit. In some embodiments, the historical seepage monitoring cycle is equally divided into several historical seepage monitoring nodes, wherein the interval between adjacent historical seepage monitoring nodes is equal. Obtain the groundwater level at each historical seepage monitoring node and sort them according to the time sequence of the groundwater level acquisition to obtain the groundwater time series; The difference between the groundwater levels of adjacent historical seepage monitoring nodes within the groundwater time series is calculated, and the absolute value is taken. This absolute value is then compared with the interval between adjacent historical seepage monitoring nodes to calculate the seepage rate of adjacent units. The seepage rate of the foundation pit is obtained by averaging the seepage rates of all adjacent units. The purpose of obtaining the seepage rate of the foundation pit is that: because if the seepage rate is too fast, there is a risk that the soil particles around the foundation pit may be carried away by the water flow, causing soil loosening and collapse. By obtaining the seepage rate, we can understand the degree of impact of seepage on the stability of the foundation pit in a timely manner. Moreover, different seepage rates have an important impact on the construction progress and construction method selection of the foundation pit. For example, when the seepage rate is large, it may be necessary to adjust the dewatering plan and the construction sequence of the support structure. Based on the calculated seepage rate of the foundation pit and combined with the pre-set safety threshold, the Internet of Things monitoring system can realize real-time dynamic early warning of the seepage situation of the foundation pit, which is conducive to timely detection of potential seepage safety hazards and allows sufficient adjustment and response time to deal with emergencies. Step 2: Perform a rate balance analysis on the seepage rate of the foundation pit and the wellpoint dewatering rate around the foundation pit during the historical seepage monitoring period to obtain the rate balance coefficient. Adjust the wellpoint dewatering rate during the current seepage monitoring period to obtain the current initial adjustment rate of the wellpoint dewatering. In some embodiments, the wellpoint precipitation rate is obtained as follows: Obtain the pumping level at each historical seepage monitoring node, and calculate the difference between the pumping levels at adjacent historical seepage monitoring nodes. Take the absolute value and calculate the ratio with the interval between adjacent historical seepage monitoring nodes to obtain the precipitation rate of adjacent units. The average precipitation rate of all adjacent units is calculated, and the wellpoint precipitation rate is output. If the wellpoint dewatering rate is equal to the pit seepage rate, it means that the pit seepage rate and the wellpoint dewatering rate are in balance during the historical seepage monitoring period, and no adjustment to the wellpoint dewatering rate is required. If the wellpoint dewatering rate is not equal to the pit seepage rate, it indicates that the pit seepage rate and the dewatering rate are not balanced during the historical seepage monitoring period, and the wellpoint dewatering rate needs to be adjusted. The rate balance coefficient is obtained as follows: When the wellpoint dewatering rate and the foundation pit seepage rate are not equal, there are two possible scenarios, as follows: One scenario: If the wellpoint dewatering rate is less than the pit seepage rate, it indicates that the wellpoint dewatering rate was insufficient during the historical seepage monitoring period, lagging behind the pit seepage rate, and is displayed as a dewatering rate lag signal. Another scenario: if the wellpoint dewatering rate is greater than the pit seepage rate, it indicates that the wellpoint dewatering rate was excessive during the historical seepage monitoring period, exceeding the pit seepage rate, and is displayed as an excessive dewatering rate signal. For example, the wellpoint precipitation rate is adjusted for signals that show a lag in precipitation rate. By combining adjacent historical seepage monitoring nodes within a historical seepage monitoring cycle, multiple unit seepage monitoring periods are obtained; The difference between the seepage rate of adjacent units and the precipitation rate of adjacent units within the same seepage monitoring period is obtained by taking the absolute value of the difference in rate during the same period. The ratio of the rate difference during the same period to the seepage rate of the corresponding adjacent unit is calculated, and the rate difference ratio during the same period is output. The average of all the rate differences in the same period is calculated, and the rate balance coefficient is output. It is understandable that the rate balance coefficient is a quantitative indicator obtained by comprehensively analyzing the seepage rate of the foundation pit and the wellpoint dewatering rate within the historical seepage monitoring period. It reflects the degree of balance between the wellpoint dewatering rate and the foundation pit seepage rate within the analyzed historical seepage monitoring period. Specifically, the rate balance coefficient is obtained by calculating the rate difference ratio of the same period and averaging it. This coefficient quantifies the difference between the wellpoint dewatering rate and the foundation pit seepage rate in different periods. The value of the coefficient directly reflects the magnitude of the difference between the two rates. The rate balance coefficient can also indicate to some extent whether the wellpoint dewatering rate is lagging or excessive relative to the foundation pit seepage rate. The process for obtaining the initial adjustment rate of current wellpoint precipitation is as follows: Multiply the current seepage rate in the foundation pit by the rate balance coefficient to obtain the initial rate adjustment of the current well point dewatering. The initial adjustment rate of the current wellpoint dewatering is summed with the current seepage rate in the foundation pit, and the output is the initial adjustment rate of the current wellpoint dewatering. Understandably, the purpose of obtaining the current initial adjustment rate of wellpoint dewatering is as follows: From a data monitoring perspective, the IoT system collects real-time data on groundwater level and pumping level through a large number of sensors, accurately calculates the seepage rate and wellpoint dewatering rate of the foundation pit, and then obtains the rate balance coefficient. Based on this, obtaining the current initial adjustment rate of wellpoint dewatering can more accurately reflect the actual relationship between the current foundation pit seepage and wellpoint dewatering. The foundation pit seepage rate and rate balance coefficient are continuously updated according to real-time monitoring data, thereby adjusting the current initial adjustment rate of wellpoint dewatering in a timely manner. This dynamic adjustment mechanism can adapt to the constantly changing seepage conditions during foundation pit construction. For example, as the excavation depth increases, the seepage path and rate may change, and the system can react quickly to ensure that the dewatering rate always matches the seepage conditions, improving the timeliness and accuracy of monitoring and adjustment. From the perspective of construction progress and safety, excessively rapid seepage in the foundation pit may cause surrounding soil particles to be carried away by the water flow, leading to instability problems such as soil loosening and collapse. By obtaining the current initial adjustment rate of wellpoint dewatering and reasonably controlling the wellpoint dewatering speed, the water level in the foundation pit can be effectively reduced, seepage pressure can be decreased, and soil instability due to seepage can be prevented. Moreover, different seepage rates and dewatering rates have a significant impact on the construction progress of the foundation pit. By obtaining the current initial adjustment rate of wellpoint dewatering in real time through an IoT monitoring system and adjusting the dewatering plan in a timely manner, seepage problems can be effectively prevented and controlled, reducing the number and duration of downtime caused by seepage problems, and ensuring the continuity and progress of construction. The specific solution in this embodiment is as follows: During the seepage process in the foundation pit, a historical seepage monitoring cycle is set, and the changes in the groundwater level within the historical seepage monitoring cycle are analyzed to obtain the seepage rate of the foundation pit. A rate balance analysis is performed on the seepage rate of the foundation pit within the historical seepage monitoring cycle and the wellpoint dewatering rate around the foundation pit to obtain the rate balance coefficient. The wellpoint dewatering rate within the current seepage monitoring cycle is adjusted to obtain the current initial adjustment rate of the wellpoint dewatering. This can adapt to the constantly changing seepage conditions during the foundation pit construction process, ensuring that the dewatering rate always matches the seepage conditions, improving the timeliness and accuracy of monitoring and adjustment. Moreover, by acquiring the current initial adjustment rate of the wellpoint dewatering in real time through the Internet of Things monitoring system, the dewatering plan can be adjusted in a timely manner, which can effectively prevent and control the occurrence of seepage problems, reduce the number and duration of downtime caused by seepage problems, and ensure the continuity and progress of construction.

[0022] Step 3: Based on the current initial adjustment rate of wellpoint dewatering, conduct excessive dewatering simulation on the groundwater layer in the foundation pit to assess the risk of dewatering settlement; In some embodiments, a precipitation simulation warning period is set, and the precipitation simulation warning period is equally divided into several precipitation simulation nodes, wherein the interval between adjacent precipitation simulation nodes is equal. Based on the actual shape of the foundation pit, several settlement monitoring points were set up, and the specific process is as follows: It should be noted that the actual shape of the foundation pit includes linear regular shapes, such as squares, rectangles, linear irregular shapes, and circular shapes; S1. Obtain the center point of the foundation pit and the distance from the center point to the edge of the foundation pit, which is used as the center-edge distance. Calculate the average of all center-edge distances and output the fitted outer circle radius. Based on the center point of the foundation pit and the fitted outer circle radius, construct the monitoring fitted outer circle. S2, take half of the radius of the fitted outer circle as the radius of the fitted inner circle, and construct the monitoring fitted inner circle based on the center point of the foundation pit and the radius of the fitted inner circle; S3, divide the edge lines of the outer and inner circles of the monitoring fitting equally to obtain the inner settlement monitoring installation points and the outer settlement monitoring installation points respectively. Install several settlement monitoring points one by one on the inner settlement monitoring installation points, the outer settlement monitoring installation points, and the center point of the foundation pit. Among them, the outer settlement monitoring installation points on the outer edge line of the monitoring fitting circle and the inner settlement monitoring installation points on the inner edge line of the monitoring fitting circle are all on a straight line with the center point of the foundation pit, and the interval distance between adjacent outer settlement monitoring installation points on the outer edge line of the monitoring fitting circle and adjacent inner settlement monitoring installation points on the inner edge line of the monitoring fitting circle is equal. The center point of the foundation pit, the internal settlement monitoring installation point and the external settlement monitoring installation point on a straight line will be combined to obtain the internal and external settlement monitoring combination; Within the combined internal and external settlement monitoring system, at the rainfall simulation node during the rainfall simulation and early warning cycle, the vertical heights corresponding to the settlement monitoring points at the center of the foundation pit, the internal settlement monitoring installation points, and the external settlement monitoring installation points are obtained as the center monitoring height, internal monitoring height, and external monitoring height. The difference between the center monitoring heights of adjacent precipitation simulation nodes is obtained by taking the absolute value. The difference between the internal monitoring heights corresponding to adjacent precipitation simulation nodes is obtained by taking the absolute value. The difference between the external monitoring heights corresponding to adjacent precipitation simulation nodes is obtained by taking the absolute value. The average of the height difference between the center monitoring, the height difference between the inner monitoring and the height difference between the outer monitoring is calculated to output the axial unit settlement value. The axial settlement monitoring values ​​are calculated by averaging the axial unit settlement monitoring values ​​corresponding to all adjacent precipitation simulation nodes. At the same precipitation simulation node, the difference between the inner monitoring height and the central monitoring height is calculated, and the absolute value is taken to obtain the inner-middle height difference; Similarly, at the same precipitation simulation node, the difference between the external monitoring height and the central monitoring height is calculated, and the absolute value is taken to obtain the height difference between the external and central monitoring heights. The radial unit settlement value is obtained by averaging the height difference between the inner and outer sides. The radial settlement monitoring value is obtained by averaging the settlement values ​​of the same node corresponding to all the same precipitation simulation nodes. The axial unit settlement value and the radial settlement monitoring value are summed to obtain the precipitation settlement risk value; It is understandable that the precipitation settlement risk value represents a quantitative index obtained by combining axial and radial settlement monitoring values. On the one hand, the axial settlement monitoring value reflects the settlement changes at different locations (center, inner circle, and outer circle monitoring points) along the direction from the center to the edge of the foundation pit (axial direction) during the precipitation simulation warning period, reflecting the settlement trend of the foundation pit in the longitudinal depth during precipitation. On the other hand, the radial settlement monitoring value reflects the settlement differences at different radial locations (inner circle and center, outer circle and center) relative to the center point at the same precipitation simulation node, reflecting the uneven settlement of the foundation pit in the horizontal direction. The precipitation settlement risk value obtained by summing the axial unit settlement value and the radial settlement monitoring value thus comprehensively reflects the settlement information of the foundation pit in both axial and radial dimensions, intuitively representing the magnitude of the settlement risk caused by excessive precipitation. Specifically, the purpose of assessing the risk of settlement due to precipitation is: from the perspective of data monitoring, the settlement information of the foundation pit in both axial and radial dimensions intuitively represents the degree of settlement risk caused by excessive precipitation. By integrating and analyzing this massive amount of data, we can more accurately understand the settlement change pattern of the foundation pit during precipitation, discover potential settlement risk points, and avoid the data bias and inaccuracy that may exist in traditional monitoring methods. From the perspective of construction progress and safety, the settlement risk value of dewatering directly reflects the degree of risk of settlement of the foundation pit due to excessive dewatering. By obtaining this risk value, construction personnel can understand the settlement status of the foundation pit in a timely manner, determine whether the dewatering operation has an adverse impact on the foundation pit structure, and use the Internet of Things monitoring system to calculate the settlement risk value of dewatering to assess the degree of impact of dewatering on the surrounding environment, avoid safety accidents caused by blindly rushing the progress, and ensure that the construction progress is carried out under the premise of safety and controllability. The comparison between precipitation subsidence risk value and precipitation subsidence risk threshold is performed as follows: If the precipitation settlement risk value is less than the precipitation settlement risk threshold, it indicates that the risk of settlement of the foundation pit in the longitudinal depth and in the horizontal direction during the simulated precipitation process is low, which is shown as a low-risk precipitation settlement signal. If the precipitation settlement risk value is greater than or equal to the precipitation settlement risk threshold, it indicates that the risk of settlement of the foundation pit in the longitudinal depth and in the horizontal direction during the simulated precipitation process is high, which is a high-risk precipitation settlement signal. Step 4: Based on the assessment of precipitation subsidence risk, after adjusting the high-risk precipitation subsidence signal to the low-risk precipitation subsidence signal, dynamically monitor and warn of the current wellpoint precipitation initial adjustment rate, and dynamically adjust the current wellpoint precipitation initial adjustment rate. In some embodiments, for high-risk precipitation subsidence signals, the rate differences within the same time period are compared, and the maximum and minimum rate differences within the same time period are selected. The maximum rate balance coefficient is obtained by calculating the ratio of the maximum simultaneous rate difference to the corresponding seepage rate of the adjacent unit. The minimum rate balance coefficient is obtained by calculating the ratio of the minimum simultaneous rate difference to the corresponding seepage rate of the adjacent unit. The rate balance coefficient range is then constructed. The simulation is repeated based on the rate balance coefficient range until the precipitation subsidence risk value is less than the precipitation subsidence risk threshold, which is then displayed as a low-risk precipitation subsidence signal. After displaying a low-risk precipitation settlement signal, the minimum rate balance coefficient within the rate balance coefficient range is selected and combined with the wellpoint precipitation rate to obtain the minimum balance precipitation rate warning value. The current initial precipitation rate at the wellpoint corresponding to the precipitation subsidence risk threshold is used as the warning value for the maximum balanced precipitation rate. It should be noted that the method for obtaining the minimum equilibrium precipitation rate warning value is the same as the method for obtaining the current wellpoint precipitation initial adjustment rate; Using the X-axis as time and the Y-axis as rate, a rate dynamic measurement early warning model is constructed. On the rate dynamic measurement early warning model, the minimum equilibrium precipitation rate early warning value and the maximum equilibrium precipitation rate early warning value are marked on the Y-axis, and minimum early warning line and maximum early warning line parallel to the X-axis are drawn respectively. The difference between the current wellpoint precipitation initial adjustment rates of adjacent precipitation simulation nodes is calculated, and the absolute value is taken. This absolute value is then compared with the duration between adjacent precipitation simulation nodes to obtain the adjacent unit acceleration. The largest adjacent unit acceleration is selected as the warning acceleration. For example, the maximum equilibrium precipitation rate warning value or the minimum equilibrium precipitation rate warning value is calculated by ratio to the warning acceleration, and the warning time is output. The maximum or minimum balanced precipitation rate warning value is calculated by subtracting the current wellpoint precipitation initial adjustment rate from the maximum balanced precipitation rate warning value and then calculating the ratio with the warning time to obtain the dynamic adjustment acceleration. The specific scheme of this embodiment is as follows: Based on the current initial adjustment rate of wellpoint dewatering, excessive dewatering simulation and early warning are carried out on the groundwater layer in the foundation pit to assess the risk of dewatering settlement. Based on the assessed risk of dewatering settlement, the current initial adjustment rate of wellpoint dewatering is dynamically monitored and warned, and the dynamic adjustment acceleration is obtained. On the one hand, during the foundation pit leakage monitoring process, the dewatering settlement risk value can provide specific quantitative basis for taking prevention and control measures. By calculating the warning time and dynamic adjustment acceleration, the system can predict in advance the time and magnitude when the dewatering rate needs to be adjusted and issue early warning signals to the construction personnel in a timely manner. Moreover, by obtaining the dynamic adjustment acceleration, the wellpoint dewatering rate can be controlled more accurately, reducing the settlement of the surrounding soil caused by uneven or excessive dewatering. On the other hand, it helps construction management personnel to accurately grasp the development trend of foundation pit leakage and the degree of impact on the construction progress, avoiding construction interruption or delay due to dewatering problems.

[0023] Example 2 Based on the same inventive concept as the IoT-based foundation pit seepage monitoring method in the foregoing embodiments, such as Figure 3 As shown, this application provides an IoT-based foundation pit seepage monitoring system, wherein the system specifically includes: Seepage rate acquisition module: During the seepage process in the foundation pit, a historical seepage monitoring cycle is set, and the changes in seepage level within the historical seepage monitoring cycle are analyzed to obtain the seepage rate of the foundation pit. Preliminary adjustment module for precipitation rate: Performs rate balance analysis on the seepage rate of the foundation pit and the well point precipitation rate around the foundation pit during the historical seepage monitoring period to obtain the rate balance coefficient, and adjusts the well point precipitation rate during the current seepage monitoring period to obtain the current preliminary adjustment rate of well point precipitation. Simulated Settlement Risk Module: Based on the current initial adjustment rate of well point dewatering, excessive dewatering is simulated in the groundwater layer within the foundation pit to assess the risk of settlement due to dewatering. Early warning dynamic adjustment module: Based on the assessment of precipitation subsidence risk, after adjusting the high-risk precipitation subsidence signal to the low-risk precipitation subsidence signal, the module dynamically monitors and issues early warnings for the current wellpoint precipitation initial adjustment rate, and dynamically adjusts the current wellpoint precipitation initial adjustment rate.

[0024] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring seepage in foundation pits based on the Internet of Things, characterized in that: include: During the seepage process in the foundation pit, a historical seepage monitoring cycle is set, and the changes in groundwater level within the historical seepage monitoring cycle are analyzed to obtain the seepage rate of the foundation pit. A rate balance analysis was performed on the seepage rate of the foundation pit and the well point dewatering rate around the foundation pit during the historical seepage monitoring period to obtain the rate balance coefficient. The well point dewatering rate during the current seepage monitoring period was then adjusted to obtain the current initial adjustment rate of the well point dewatering. Based on the current initial adjustment rate of wellpoint dewatering, an excessive dewatering simulation is conducted on the unconfined layer in the foundation pit to assess the risk of dewatering settlement. Based on the assessment of precipitation subsidence risk, after adjusting the high-risk precipitation subsidence signal to the low-risk precipitation subsidence signal, the current wellpoint precipitation initial adjustment rate is dynamically monitored and warned, and the current wellpoint precipitation initial adjustment rate is dynamically adjusted.

2. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The method for obtaining the seepage rate in the foundation pit is as follows: The historical seepage monitoring cycle is divided into several historical seepage monitoring nodes. The groundwater level at each historical seepage monitoring node is obtained and sorted according to the time sequence of the groundwater level acquisition to obtain the groundwater time sequence. The difference between the groundwater levels of adjacent historical seepage monitoring nodes within the groundwater time series is calculated, and the absolute value is taken. This absolute value is then compared with the interval between adjacent historical seepage monitoring nodes to calculate the seepage rate of adjacent units. The seepage rate of the foundation pit is obtained by averaging the seepage rates of all adjacent units.

3. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The rate balance coefficient is obtained as follows: Obtain the pumping level at each historical seepage monitoring node, and calculate the difference between the pumping levels at adjacent historical seepage monitoring nodes. Take the absolute value and calculate the ratio with the interval between adjacent historical seepage monitoring nodes to obtain the precipitation rate of adjacent units. Calculate the average of the precipitation rates of all adjacent units to obtain the wellpoint precipitation rate. By combining adjacent historical seepage monitoring nodes within a historical seepage monitoring cycle, multiple unit seepage monitoring periods are obtained; The difference between the seepage rate of adjacent units and the precipitation rate of adjacent units within the same seepage monitoring period is calculated, and the absolute value is taken to obtain the rate difference for the same period. The ratio of this ratio to the corresponding seepage rate of adjacent units is calculated, and the rate difference ratio for the same period is output. The average of all the rate difference ratios for the same period is calculated, and the rate balance coefficient is output.

4. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The current method for obtaining the initial adjustment rate of wellpoint precipitation is as follows: Multiply the current seepage rate in the foundation pit by the rate balance coefficient to obtain the initial rate adjustment of the current wellpoint dewatering. The initial adjustment rate of the current wellpoint dewatering is summed with the current seepage rate in the foundation pit, and the output is the initial adjustment rate of the current wellpoint dewatering.

5. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The process of simulating excessive precipitation in the shallow groundwater layer within the foundation pit is as follows: Based on the actual shape of the foundation pit, several settlement monitoring points are set up to obtain the center point of the foundation pit and the distance from the center point to the edge of the foundation pit, which is used as the center-edge distance. The average of all center-edge distances is calculated to output the radius of the fitted outer circle. Based on the center point of the foundation pit and the radius of the fitted outer circle, a monitoring fitted outer circle is constructed. Half of the radius of the fitted outer circle is taken as the radius of the fitted inner circle. Based on the center point of the foundation pit and the radius of the fitted inner circle, a monitoring fitted inner circle is constructed. The edge lines of the monitoring fitted outer circle and the monitoring fitted inner circle are equally divided to obtain the inner settlement monitoring installation point and the outer settlement monitoring installation point, respectively. Several settlement monitoring points are installed one by one on the inner settlement monitoring installation point, the outer settlement monitoring installation point, and the center point of the foundation pit. The center point of the foundation pit, the internal settlement monitoring installation point, and the external settlement monitoring installation point on a straight line are combined to obtain the internal and external settlement monitoring combination. Within the internal and external settlement monitoring combination, at the precipitation simulation node during the precipitation simulation early warning cycle, the vertical heights corresponding to the settlement monitoring points at the center point of the foundation pit, the internal settlement monitoring installation points, and the external settlement monitoring installation points are obtained as the center monitoring height, the internal monitoring height, and the external monitoring height.

6. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 5, characterized in that: The assessment process for precipitation subsidence risk is as follows: The differences between the central monitoring height, internal monitoring height, and external monitoring height of adjacent precipitation simulation nodes are calculated, and the absolute values ​​are taken to obtain the differences between the central monitoring height, internal monitoring height, and external monitoring height. The average values ​​are then calculated to output the axial unit settlement value. The average values ​​are then calculated again to obtain the axial settlement monitoring value. At the same precipitation simulation node, the difference between the inner monitoring height and the central monitoring height, and the difference between the outer monitoring height and the central monitoring height are calculated separately. The absolute values ​​are taken to obtain the height difference between the inner and outer sides and the height difference between the inner and outer sides. The average value is then calculated to obtain the radial unit settlement value. The radial settlement monitoring value is obtained by averaging the settlement values ​​of the same node corresponding to all the same precipitation simulation nodes. The axial unit settlement value and the radial settlement monitoring value are summed to obtain the precipitation settlement risk value; If the precipitation subsidence risk value is less than the precipitation subsidence risk threshold, it is displayed as a low-risk precipitation subsidence signal; If the precipitation subsidence risk value is greater than or equal to the precipitation subsidence risk threshold, it is displayed as a high-risk precipitation subsidence signal.

7. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The process of adjusting high-risk precipitation subsidence signals to low-risk precipitation subsidence signals is as follows: Compare the speed differences within the same time period and select the largest and smallest speed differences within the same time period. The maximum rate balance coefficient is obtained by calculating the ratio of the maximum simultaneous rate difference to the corresponding seepage rate of the adjacent unit. The minimum rate balance coefficient is obtained by calculating the ratio of the minimum simultaneous rate difference to the corresponding seepage rate of the adjacent unit. The rate balance coefficient range is then constructed. The simulation is repeated based on the rate balance coefficient range until the precipitation subsidence risk value is less than the precipitation subsidence risk threshold, which indicates a low-risk precipitation subsidence signal.

8. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The process of dynamically monitoring and issuing early warnings for the initial adjustment rate of current wellpoint precipitation is as follows: After displaying a low-risk precipitation settlement signal, the minimum rate balance coefficient within the rate balance coefficient range is selected and combined with the wellpoint precipitation rate to obtain the minimum balance precipitation rate warning value. The current initial precipitation rate at the wellpoint corresponding to the precipitation subsidence risk threshold is used as the warning value of the maximum equilibrium precipitation rate. A rate dynamic measurement warning model is constructed with the X-axis representing time and the Y-axis representing rate. On the rate dynamic measurement warning model, the warning values ​​of the minimum equilibrium precipitation rate and the maximum equilibrium precipitation rate are marked on the Y-axis, and minimum warning lines and maximum warning lines parallel to the X-axis are drawn respectively.

9. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 1, characterized in that: The initial adjustment rate of the current wellpoint precipitation is dynamically adjusted as follows: The warning time is obtained by calculating the ratio of the maximum equilibrium precipitation rate warning value or the minimum equilibrium precipitation rate warning value to the warning acceleration. The difference between the maximum equilibrium precipitation rate warning value and the current wellpoint precipitation initial adjustment rate is calculated, and the ratio is calculated with the warning time. The dynamic adjustment acceleration is then calculated, and the ratio is calculated with the warning acceleration to obtain the warning time. The maximum or minimum balanced precipitation rate warning value is calculated by subtracting the current wellpoint precipitation initial adjustment rate from the maximum balanced precipitation rate warning value and then calculating the ratio with the warning time to obtain the dynamic adjustment acceleration.

10. A foundation pit seepage monitoring system based on the Internet of Things, characterized in that: Includes the following modules: Seepage rate acquisition module: During the seepage process in the foundation pit, a historical seepage monitoring cycle is set, and the changes in seepage level within the historical seepage monitoring cycle are analyzed to obtain the seepage rate of the foundation pit. Preliminary adjustment module for precipitation rate: Performs rate balance analysis on the seepage rate of the foundation pit and the well point precipitation rate around the foundation pit during the historical seepage monitoring period to obtain the rate balance coefficient, and adjusts the well point precipitation rate during the current seepage monitoring period to obtain the current preliminary adjustment rate of well point precipitation. Simulated Settlement Risk Module: Based on the current initial adjustment rate of well point dewatering, excessive dewatering is simulated in the groundwater layer within the foundation pit to assess the risk of settlement due to dewatering. Early warning dynamic adjustment module: Based on the assessment of precipitation subsidence risk, after adjusting the high-risk precipitation subsidence signal to the low-risk precipitation subsidence signal, the module dynamically monitors and issues early warnings for the current wellpoint precipitation initial adjustment rate, and dynamically adjusts the current wellpoint precipitation initial adjustment rate.

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