A foundation pit seepage monitoring method and system based on the Internet of Things

Abstract

The present application 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, which comprises the following steps: in the process of foundation pit seepage, a historical seepage monitoring period is set, the change of the water table in the historical seepage monitoring period is analyzed, the foundation pit seepage rate is obtained, the rate balance analysis of the foundation pit seepage rate in the historical seepage monitoring period and the well point dewatering rate around the foundation pit is performed, the rate balance coefficient is obtained, the well point dewatering rate in the current seepage monitoring period is adjusted, and the current well point dewatering initial adjustment rate is obtained, so that the dewatering rate can always be matched with the seepage condition, the timeliness and accuracy of monitoring and adjustment are improved, the current well point dewatering initial adjustment rate is obtained in real time through the Internet of Things monitoring system, the dewatering scheme is adjusted in time, the occurrence of seepage problems can be effectively prevented and controlled, the number and time of shutdowns caused by seepage problems are reduced, and the continuity and progress of construction are ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of foundation pit seepage monitoring, in particular to a foundation pit seepage monitoring method and system based on the Internet of Things. BACKGROUND

[0002] In the field of construction engineering, foundation pit construction is the basis and key link of many projects, and the foundation pit seepage problem has always been an important factor affecting the safety and quality of foundation pit construction. With the acceleration of urbanization, high-rise buildings, underground transportation facilities and other facilities are emerging, the depth of foundation pit excavation is increasing, and the foundation pit seepage problem is becoming more complex and prominent. Precise monitoring and effective control of foundation pit seepage have become an urgent need to ensure the smooth progress of the project.

[0003] In the prior art, first, the traditional method is difficult to adapt to the changing seepage situation in the foundation pit construction process. Foundation pit construction is a dynamic process, and changes in geological conditions, construction progress, and surrounding environment will change the seepage situation of the foundation pit. However, traditional monitoring methods often lack flexibility and real-time performance, and cannot obtain accurate foundation pit seepage rate information in a timely manner, making it difficult to ensure that the dewatering rate always matches the seepage situation. This makes it possible for insufficient dewatering to cause seepage and lead to foundation instability, or excessive dewatering to cause surrounding soil settlement during foundation pit construction.

[0004] Secondly, due to the inability to accurately obtain the current well point dewatering rate in real time, and adjust the dewatering scheme in a timely manner according to the foundation pit seepage situation, the traditional foundation pit seepage monitoring and control system cannot predict the time point and amplitude of the dewatering rate adjustment in advance. In the process of foundation pit construction, the adjustment of the dewatering rate needs to be made in real time according to the seepage situation and dewatering settlement risk, but the traditional method cannot send warning signals to the construction personnel in a timely manner, so that the construction personnel cannot make preparation in advance. At the same time, due to the lack of precise control means, it is difficult to obtain dynamic adjustment acceleration, and it is difficult to more accurately control the well point dewatering rate, thereby affecting the grasp of the development trend of the foundation pit seepage and the evaluation of the impact on the construction progress, which is easy to cause construction interruption or delay due to dewatering problems.

[0005] Therefore, the application provides a foundation pit seepage monitoring method and system based on the Internet of Things. SUMMARY

[0006] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0007] The technical scheme adopted by the application to solve its technical problems is:

[0008] In a first aspect, a foundation pit seepage monitoring method based on the Internet of Things, comprising:

[0009] In the process of foundation pit seepage, a historical seepage monitoring period is set, the change of phreatic level in the historical seepage monitoring period is analyzed, and the seepage rate of the foundation pit is obtained;

[0010] The seepage rate of the foundation pit in the historical seepage monitoring period and the well point dewatering rate around the foundation pit are analyzed, and the rate balance coefficient is obtained. The well point dewatering rate in the current seepage monitoring period is adjusted, and the initial adjustment rate of the current well point dewatering is obtained;

[0011] According to the initial adjustment rate of the current well point dewatering, over-dewatering simulation of the phreatic layer in the foundation pit is carried out, and the dewatering settlement risk is evaluated;

[0012] According to the evaluation of the dewatering settlement risk, the high-risk dewatering settlement signal is adjusted to a low-risk dewatering settlement signal, and the initial adjustment rate of the current well point dewatering is dynamically monitored and warned, and the initial adjustment rate of the current well point dewatering is dynamically adjusted.

[0013] Preferably, the seepage rate of the foundation pit is obtained in the following manner:

[0014] The historical seepage monitoring period is equally divided into a plurality of historical seepage monitoring nodes, the phreatic level at each historical seepage monitoring node is obtained, and the phreatic level is sorted in time sequence, and the phreatic time sequence is obtained;

[0015] The phreatic levels of adjacent historical seepage monitoring nodes in the phreatic time sequence are subtracted, the absolute values are taken, and the interval time between the adjacent historical seepage monitoring nodes is calculated, and the adjacent unit seepage rate is outputted;

[0016] The average value of all adjacent unit seepage rates is calculated, and the seepage rate of the foundation pit is obtained.

[0017] Preferably, the rate balance coefficient is obtained in the following manner:

[0018] The pumping level at each historical seepage monitoring node is obtained, the pumping levels of adjacent historical seepage monitoring nodes are subtracted, the absolute values are taken, and the interval time between the adjacent historical seepage monitoring nodes is calculated, and the adjacent unit dewatering rate is outputted, and the average value of all adjacent unit dewatering rates is calculated, and the well point dewatering rate is outputted;

[0019] The adjacent historical seepage monitoring nodes in the historical seepage monitoring period are combined to obtain a plurality of unit seepage monitoring periods;

[0020] The absolute value of the difference between the adjacent unit seepage rate in the same unit seepage monitoring period and the adjacent unit precipitation rate is obtained as the same period rate difference, and the same period rate difference ratio is output by ratio calculation with the corresponding adjacent unit seepage rate. The rate balance coefficient is output by mean calculation of all the same period rate difference ratios.

[0021] Preferably, the current well point precipitation initial adjustment rate is obtained in the following manner:

[0022] The product of the current foundation seepage rate and the rate balance coefficient is obtained as the current well point precipitation initial adjustment amount;

[0023] The sum of the current well point precipitation initial adjustment amount and the current foundation seepage rate is output as the current well point precipitation initial adjustment rate.

[0024] Preferably, the over-pumping simulation of the phreatic aquifer in the foundation pit is performed in the following manner:

[0025] According to the actual shape of the foundation pit, a plurality of settlement monitoring points are set, the center point of the foundation pit and the distance from the center point of the foundation pit to the edge of the foundation pit are obtained as the center-edge distance, the mean value of all the center-edge distances is calculated, and the fitted outer circle radius is output. The fitted outer circle radius is taken as one-half as the fitted inner circle radius, and the fitted inner circle is constructed based on the center point of the foundation pit and the fitted inner circle radius. 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. A plurality of settlement monitoring points are installed on the inner settlement monitoring installation point, the outer settlement monitoring installation point, and the center point of the foundation pit, respectively.

[0026] The center point of the foundation pit, the inner settlement monitoring installation point, and the outer settlement monitoring installation point on a straight line are combined to obtain the inner-outer settlement monitoring combination. The vertical heights of the center point of the foundation pit, the inner settlement monitoring installation point, and the outer settlement monitoring installation point corresponding to the settlement monitoring points in the inner-outer settlement monitoring combination are obtained as the center monitoring height, the inner monitoring height, and the outer monitoring height, respectively, at the precipitation simulation node in the precipitation simulation early warning period.

[0027] Preferably, the precipitation settlement risk assessment process is as follows:

[0028] The absolute values of the differences between the center monitoring heights, the inner monitoring heights, and the outer monitoring heights corresponding to adjacent precipitation simulation nodes are obtained as the center monitoring height difference, the inner monitoring height difference, and the outer monitoring height difference, respectively, and the mean value calculation is performed to obtain the axial unit settlement value. The mean value calculation is performed again to obtain the axial settlement monitoring value.

[0029] Differences between the inner monitoring height and the center monitoring height, and between the outer monitoring height and the center monitoring height are calculated respectively, and absolute values are taken to obtain the inner height difference and the outer height difference, and mean values are calculated to obtain the radial unit settlement value;

[0030] Mean values of all corresponding same-node settlement values at the same rainfall simulation node are calculated to obtain the radial settlement monitoring value;

[0031] The axial unit settlement value and the radial settlement monitoring value are summed to obtain the rainfall settlement risk value;

[0032] If the rainfall settlement risk value is less than the rainfall settlement risk threshold value, a low-risk rainfall settlement signal is displayed; if the rainfall settlement risk value is greater than or equal to the rainfall settlement risk threshold value, a high-risk rainfall settlement signal is displayed.

[0033] Preferably, the high-risk rainfall settlement signal is adjusted to a low-risk rainfall settlement signal, and the process is as follows:

[0034] The simultaneous period rate differences are compared in size to select the maximum simultaneous period rate difference and the minimum simultaneous period rate difference;

[0035] The maximum simultaneous period rate difference is calculated by ratio with the corresponding adjacent unit seepage rate to output the maximum rate balance coefficient, and the minimum simultaneous period rate difference is calculated by ratio with the corresponding adjacent unit seepage rate to output the minimum rate balance coefficient, thereby constructing the rate balance coefficient interval;

[0036] According to the rate balance coefficient interval, re-simulation is performed until the rainfall settlement risk value is less than the rainfall settlement risk threshold value, and a low-risk rainfall settlement signal is displayed.

[0037] Preferably, the current well point rainfall initial adjustment rate is dynamically monitored and warned, and the process is as follows:

[0038] After the low-risk rainfall settlement signal is displayed, the minimum rate balance coefficient in the rate balance coefficient interval is selected and combined with the well point rainfall rate to obtain the minimum balanced rainfall rate warning value;

[0039] The current well point rainfall initial adjustment rate corresponding to the rainfall settlement risk threshold value is taken as the maximum balanced rainfall rate warning value, and a rate dynamic monitoring warning model is constructed with the X-axis as time and the Y-axis as rate. On the rate dynamic monitoring warning model, the minimum balanced rainfall rate warning value and the maximum balanced rainfall rate warning value are marked on the Y-axis, and the minimum warning line and the maximum warning line parallel to the X-axis are drawn respectively.

[0040] Preferably, the current well point rainfall initial adjustment rate is dynamically adjusted, and the process is as follows:

[0041] The maximum balanced precipitation rate early warning value or the minimum maximum balanced precipitation rate early warning value is subtracted from the current well point precipitation initial adjustment rate, and the early warning time is calculated by ratio calculation to obtain a dynamic adjustment acceleration.

[0042] The maximum balanced precipitation rate early warning value or the minimum maximum balanced precipitation rate early warning value is subtracted from the current well point precipitation initial adjustment rate, and the early warning time is calculated by ratio calculation to obtain a dynamic adjustment acceleration.

[0043] The maximum balanced precipitation rate early warning value or the minimum maximum balanced precipitation rate early warning value is subtracted from the current well point precipitation initial adjustment rate, and the early warning time is calculated by ratio calculation to obtain a dynamic adjustment acceleration.

[0044] In a second aspect, a foundation pit seepage monitoring system based on an Internet of Things comprises:

[0045] The seepage rate acquisition module: during the foundation pit seepage process, a historical seepage monitoring period is set, the seepage level change in the historical seepage monitoring period is analyzed, and the foundation pit seepage rate is obtained.

[0046] The precipitation rate initial adjustment module: the rate balance coefficient is obtained by performing rate balance analysis on the foundation pit seepage rate in the historical seepage monitoring period and the well point precipitation rate around the foundation pit, and the current well point precipitation initial adjustment rate is obtained by adjusting the well point precipitation rate in the current seepage monitoring period.

[0047] The simulated settlement risk module: according to the current well point precipitation initial adjustment rate, over-pumping simulation of the phreatic layer in the foundation pit is performed, and the settlement risk of the precipitation is evaluated.

[0048] The early warning dynamic adjustment module: according to the evaluation of the settlement risk of the precipitation, the current well point precipitation initial adjustment rate is dynamically monitored and warned after the high-risk settlement signal of the precipitation is adjusted to a low-risk settlement signal, and the current well point precipitation initial adjustment rate is dynamically adjusted.

[0049] The beneficial effects of the present application are as follows:

[0050] 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.

[0051] 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

[0052] The invention will now be further described with reference to the accompanying drawings.

[0053] Figure 1 This is a flowchart of the steps of a foundation pit seepage monitoring method based on the Internet of Things according to the present invention;

[0054] 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.

[0055] Figure 3 This is a schematic diagram of an IoT-based foundation pit seepage monitoring system according to the present invention. Detailed Implementation

[0056] 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.

[0057] Example 1

[0058] Referring to Figure 1 - Figure 2 As shown in the drawings, the method for monitoring seepage of a foundation pit based on the Internet of Things comprises the following steps:

[0059] Step 1: During the seepage process of the foundation pit, a historical seepage monitoring period is set, the change of the phreatic level in the historical seepage monitoring period is analyzed, and the seepage rate of the foundation pit is obtained;

[0060] In some embodiments, the historical seepage monitoring period is equally divided into a plurality of historical seepage monitoring nodes, wherein the interval time length between adjacent historical seepage monitoring nodes is equal;

[0061] The phreatic level at each historical seepage monitoring node is obtained, and the phreatic levels are sorted in time sequence, and a phreatic time sequence is obtained;

[0062] The phreatic levels of adjacent historical seepage monitoring nodes in the phreatic time sequence are subtracted, the absolute values are taken, and the interval time length between adjacent historical seepage monitoring nodes is calculated, and the adjacent unit seepage rate is outputted and obtained;

[0063] The seepage rates of all adjacent units are averaged, and the seepage rate of the foundation pit is obtained;

[0064] The purpose of obtaining the seepage rate of the foundation pit is to understand the influence of seepage on the stability of the foundation pit in time, because the seepage rate is too fast, which can cause the soil particles around the foundation pit to be carried away by the water flow, and cause the soil to be loose and collapse, etc. Different seepage rates have important influence on the construction progress and construction method selection of the foundation pit, for example, when the seepage rate is large, the dewatering scheme and the construction sequence of the supporting structure may need to be adjusted;

[0065] According to the calculated seepage rate of the foundation pit, combined with the pre-set safety threshold, the Internet of Things monitoring system can realize real-time dynamic early warning of the seepage of the foundation pit, which is helpful to timely find potential seepage safety hazards and has enough adjustment time to respond to emergencies;

[0066] Step 2: The seepage rate of the foundation pit in the historical seepage monitoring period and the well point dewatering rate around the foundation pit are analyzed, the rate balance coefficient is obtained, the well point dewatering rate in the current seepage monitoring period is adjusted, and the current well point dewatering initial adjustment rate is obtained;

[0067] In some embodiments, the well point dewatering rate is obtained as follows:

[0068] obtaining the pumping level of each historical seepage monitoring node, and performing difference operation on the pumping levels of adjacent historical seepage monitoring nodes, taking absolute value, and performing ratio calculation on the interval duration between adjacent historical seepage monitoring nodes, to output the adjacent unit precipitation rate;

[0069] performing mean value calculation on all adjacent unit precipitation rates, to output the well point precipitation rate;

[0070] if the well point precipitation rate is equal to the foundation pit seepage rate, it is indicated that the foundation pit seepage rate and the well point precipitation rate are balanced in the historical seepage monitoring period, and the well point precipitation rate does not need to be adjusted;

[0071] if the well point precipitation rate is not equal to the foundation pit seepage rate, it is indicated that the foundation pit seepage rate and the well point precipitation rate are not balanced in the historical seepage monitoring period, and the well point precipitation rate needs to be adjusted;

[0072] The rate balance coefficient is obtained in the following manner:

[0073] For the case that the well point precipitation rate is not equal to the foundation pit seepage rate, there are two cases, as follows:

[0074] In one case, the well point precipitation rate is less than the foundation pit seepage rate, which indicates that the well point precipitation rate is insufficient and lags behind the foundation pit seepage rate in the historical seepage monitoring period, showing a precipitation rate lag signal;

[0075] In another case, the well point precipitation rate is greater than the foundation pit seepage rate, which indicates that the well point precipitation rate is excessive and exceeds the foundation pit seepage rate in the historical seepage monitoring period, showing a precipitation rate excess signal;

[0076] For example, for the case that the precipitation rate lag signal is displayed, the well point precipitation rate is adjusted;

[0077] The adjacent historical seepage monitoring nodes in the historical seepage monitoring period are combined to obtain a plurality of unit seepage monitoring time periods;

[0078] The adjacent unit seepage rates and the adjacent unit precipitation rates in the same unit seepage monitoring time period are subjected to difference operation, and the absolute value is taken to obtain the same period rate difference;

[0079] The same period rate difference and the corresponding adjacent unit seepage rate are subjected to ratio calculation, to output the same period rate difference ratio;

[0080] The mean value calculation is performed on all same period rate difference ratios, to output the rate balance coefficient;

[0081] It can be understood that the meaning of the rate balance coefficient is: based on the historical seepage monitoring period, a quantitative index obtained after comprehensive analysis of the foundation pit seepage rate and the well point precipitation rate, reflecting the balance matching degree between the well point precipitation rate and the foundation pit seepage rate in the analyzed historical seepage monitoring period. Specifically, the rate balance coefficient is obtained by calculating the rate difference ratio of the same period and performing mean value processing. The coefficient quantifies the difference between the well point precipitation rate and the foundation pit seepage rate in different periods. The size of the coefficient value directly reflects the size of the difference between the two rates. The rate balance coefficient can also indicate whether the well point precipitation rate lags behind or is excessive relative to the foundation pit seepage rate to some extent.

[0082] The current well point precipitation initial adjustment rate is obtained as follows:

[0083] The current well point precipitation initial adjustment rate is obtained as follows:

[0084] The current well point precipitation initial adjustment rate is obtained as follows:

[0085] It can be understood that the purpose of obtaining the current well point precipitation initial adjustment rate is: from the data monitoring dimension, the Internet of Things system collects large amounts of sensor data in real time to accurately calculate the foundation pit seepage rate and the well point precipitation rate, and then obtains the rate balance coefficient. Based on this, the current well point precipitation initial adjustment rate is obtained, which can more accurately reflect the actual relationship between the current foundation pit seepage and the well point precipitation. According to the real-time monitoring data, the foundation pit seepage rate and the rate balance coefficient are constantly updated, so that the current well point precipitation initial adjustment rate can be adjusted in time. This dynamic adjustment mechanism can adapt to the changing seepage conditions in the foundation pit construction process. For example, as the excavation depth increases, the seepage path and rate may change. The system can quickly respond to ensure that the precipitation rate always matches the seepage conditions, improving the timeliness and accuracy of monitoring and adjustment.

[0086] From the construction progress safety dimension, if the foundation pit seepage is too fast, it may cause the 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 well point precipitation initial adjustment rate, the well point precipitation speed can be reasonably controlled, which can effectively reduce the water level in the foundation pit, reduce the seepage pressure, and prevent the soil from losing stability due to seepage. Different seepage rates and precipitation rates will have an important impact on the construction progress of the foundation pit. By obtaining the current well point precipitation initial adjustment rate in real time through the Internet of Things monitoring system, the precipitation scheme can be adjusted in time to effectively prevent and control the occurrence of seepage problems, reduce the number and time of stoppages caused by seepage problems, and ensure the continuity and progress of construction.

[0087] The specific scheme of the embodiment is: in the seepage process of the foundation pit, a historical seepage monitoring period is set, the change of the phreatic level in the historical seepage monitoring period is analyzed, the seepage rate of the foundation pit is obtained, the rate balance analysis of the seepage rate of the foundation pit in the historical seepage monitoring period and the well point dewatering rate around the foundation pit is performed, the rate balance coefficient is obtained, the well point dewatering rate in the current seepage monitoring period is adjusted, and the current well point dewatering initial adjustment rate is obtained. The seepage condition changes in the construction process of the foundation pit can be adapted, the dewatering rate is always matched with the seepage condition, the timeliness and accuracy of monitoring and adjustment are improved, the current well point dewatering initial adjustment rate is obtained in real time through the Internet of Things monitoring system, the dewatering scheme is adjusted in time, the occurrence of seepage problems can be effectively prevented and controlled, the number and time of shutdown caused by seepage problems are reduced, and the continuity and progress of construction are ensured.

[0088] Step three: according to the current well point dewatering initial adjustment rate, over-dewatering simulation is performed on the phreatic layer in the foundation pit, and the dewatering settlement risk is evaluated;

[0089] In some embodiments, a dewatering simulation early warning period is set, and the dewatering simulation early warning period is equally divided into a plurality of dewatering simulation nodes, wherein the interval time between adjacent dewatering simulation nodes is equal;

[0090] According to the actual shape of the foundation pit, a plurality of settlement monitoring points are set, and the specific process is as follows:

[0091] It should be noted that the actual shape of the foundation pit includes linear regular shape, such as square, rectangle, linear irregular shape, and circular shape;

[0092] S1, obtain the center point of the foundation pit and the distance from the center point of the foundation pit to the edge of the foundation pit as the center edge distance, perform mean value calculation on all the center edge distances, output to obtain the fitting outer circle radius, and construct the monitoring fitting outer circle based on the center point of the foundation pit and the fitting outer circle radius;

[0093] S2, take one half of the fitting outer circle radius as the fitting inner circle radius, and construct the monitoring fitting inner circle based on the center point of the foundation pit and the fitting inner circle radius;

[0094] S3, the edge lines of the monitoring fitting outer circle and the monitoring fitting inner circle are equally divided, respectively obtaining the inner settlement monitoring installation point and the outer settlement monitoring installation point, and a plurality of settlement monitoring points are installed on the inner settlement monitoring installation point, the outer settlement monitoring installation point, and the center point of the foundation pit;

[0095] The outer settlement monitoring installation points on the fitted outer circular edge line and the inner settlement monitoring installation points on the fitted inner circular edge line are both on a straight line with the foundation pit center point, and the interval distances between adjacent outer settlement monitoring installation points on the fitted outer circular edge line and adjacent inner settlement monitoring installation points on the fitted inner circular edge line are both equal.

[0096] The foundation pit center point, the inner settlement monitoring installation point and the outer settlement monitoring installation point on the straight line are combined to obtain a middle inner outer settlement monitoring combination.

[0097] In the middle inner outer settlement monitoring combination, the vertical heights corresponding to the settlement monitoring point at the foundation pit center point, the inner settlement monitoring installation point and the outer settlement monitoring installation point are obtained as the center monitoring height, the inner monitoring height and the outer monitoring height at the settlement simulation node in the settlement simulation early warning period.

[0098] The center monitoring heights corresponding to adjacent settlement simulation nodes are subtracted to obtain the center monitoring height difference.

[0099] The inner monitoring heights corresponding to adjacent settlement simulation nodes are subtracted to obtain the inner monitoring height difference.

[0100] The outer monitoring heights corresponding to adjacent settlement simulation nodes are subtracted to obtain the outer monitoring height difference.

[0101] The center monitoring height difference, the inner monitoring height difference and the outer monitoring height difference are subjected to mean value calculation to output the axial unit settlement value.

[0102] The axial unit settlement monitoring values corresponding to all adjacent settlement simulation nodes are subjected to mean value calculation to obtain the axial settlement monitoring value.

[0103] The inner monitoring height and the center monitoring height are subtracted to obtain the middle inner height difference at the same settlement simulation node.

[0104] Similarly, the outer monitoring height and the center monitoring height are subtracted to obtain the middle outer height difference at the same settlement simulation node.

[0105] The middle inner height difference and the middle outer height difference are subjected to mean value calculation to obtain the radial unit settlement value.

[0106] The same node settlement values corresponding to all the same settlement simulation nodes are subjected to mean value calculation to obtain the radial settlement monitoring value.

[0107] The axial unit settlement value and the radial settlement monitoring value are summed to obtain the settlement risk value.

[0108] It can be understood that the meaning represented by the precipitation settlement risk value is that the quantitative index obtained by integrating the axial settlement monitoring value and the radial settlement monitoring value, on the one hand, reflects the settlement change of different positions (center, inner circle, outer circle monitoring points) along the center to the edge direction (axial direction) of the foundation pit in the precipitation simulation early warning period, embodies the settlement trend of the foundation pit in the longitudinal depth during the precipitation process, on the other hand, the radial settlement monitoring value reflects the settlement difference of different positions (inner circle and center, outer circle and center) relative to the center point of the foundation pit in the same precipitation simulation node, embodies the settlement non-uniformity 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, therefore, comprehensively reflects the settlement information of the foundation pit in the axial and radial two dimensions, and intuitively represents the size degree of the settlement risk of the foundation pit due to excessive precipitation;

[0109] Specifically, the purpose of evaluating the precipitation settlement risk is that from the data monitoring dimension, the settlement information of the foundation pit in the axial and radial two dimensions intuitively represents the size degree of the settlement risk of the foundation pit due to excessive precipitation, through the integration and analysis of these massive data, the settlement change rule of the foundation pit in the precipitation process can be more accurately understood, the potential settlement risk point can be found, and the data one-sidedness and inaccuracy that may exist in the traditional monitoring method can be avoided.

[0110] From the construction progress safety dimension, the precipitation settlement risk value directly reflects the risk degree of the settlement of the foundation pit due to excessive precipitation, through obtaining the risk value, the construction personnel can timely understand the settlement condition of the foundation pit, judge whether the precipitation operation has caused adverse effects on the foundation pit structure, and moreover, the Internet of Things monitoring system can evaluate the influence degree of the precipitation on the surrounding environment by calculating the precipitation settlement risk value, avoid the occurrence of safety accidents due to blind progress, and ensure that the construction progress is carried out under the premise of safety and controllability.

[0111] The comparison between the precipitation settlement risk value and the precipitation settlement risk threshold value is as follows:

[0112] If the precipitation settlement risk value is less than the precipitation settlement risk threshold value, it means that the settlement of the foundation pit in the longitudinal depth and the settlement risk degree in the horizontal direction during the simulated precipitation process is low, and a low-risk precipitation settlement signal is displayed.

[0113] If the precipitation settlement risk value is greater than or equal to the precipitation settlement risk threshold value, it means that the settlement of the foundation pit in the longitudinal depth and the settlement risk degree in the horizontal direction during the simulated precipitation process is high, and a high-risk precipitation settlement signal is displayed.

[0114] Step four: according to the evaluation of the precipitation settlement risk, the high-risk precipitation settlement signal is adjusted to the low-risk precipitation settlement signal, the current well point precipitation initial adjustment rate is dynamically monitored and warned, and the current well point precipitation initial adjustment rate is dynamically adjusted;

[0115] In some embodiments, for a high-risk precipitation settlement signal, the maximum and minimum simultaneous period rate differences are selected by comparing the size of the simultaneous period rate differences;

[0116] The maximum simultaneous period rate difference is calculated by ratio with the corresponding adjacent unit seepage rate, and the maximum rate balance coefficient is output. The minimum simultaneous period rate difference is calculated by ratio with the corresponding adjacent unit seepage rate, and the minimum rate balance coefficient is output, and the rate balance coefficient interval is constructed;

[0117] According to the rate balance coefficient interval, simulation is performed again until the precipitation settlement risk value is less than the precipitation settlement risk threshold, and a low-risk precipitation settlement signal is displayed;

[0118] After displaying the low-risk precipitation settlement signal, the minimum rate balance coefficient in the rate balance coefficient interval is selected and combined with the well point precipitation rate to obtain the minimum balanced precipitation rate warning value;

[0119] The current well point precipitation initial adjustment rate corresponding to the precipitation settlement risk threshold is taken as the maximum balanced precipitation rate warning value;

[0120] It should be noted that the minimum balanced precipitation rate warning value is obtained in the same way as the current well point precipitation initial adjustment rate;

[0121] Taking the X-axis as time and the Y-axis as rate, a rate dynamic measurement warning model is constructed. On the rate dynamic measurement warning model, the minimum balanced precipitation rate warning value and the maximum balanced precipitation rate warning value are marked on the Y-axis, and the minimum warning line and the maximum warning line parallel to the X-axis are drawn respectively;

[0122] The current well point precipitation initial adjustment rate of the adjacent precipitation simulation node is subtracted, the absolute value is taken, and the time interval between the adjacent precipitation simulation nodes is calculated by ratio to obtain the unit adjacent acceleration. The maximum unit adjacent acceleration is selected as the warning acceleration;

[0123] The maximum balanced precipitation rate warning value or the minimum balanced precipitation rate warning value is calculated by ratio with the warning acceleration, and the warning time is output;

[0124] The maximum balanced precipitation rate warning value or the minimum balanced precipitation rate warning value is subtracted from the current well point precipitation initial adjustment rate, and the dynamic adjustment acceleration is calculated by ratio with the warning time;

[0125] The specific scheme of the embodiment is: according to the initial adjustment rate of the current well point dewatering, the over-dewatering simulation early warning of the phreatic layer in the foundation pit is carried out, the dewatering settlement risk is evaluated, and according to the evaluation of the dewatering settlement risk, the dynamic monitoring early warning of the initial adjustment rate of the current well point dewatering is carried out, and the dynamic adjustment acceleration is obtained. On the one hand, in the process of foundation pit leakage monitoring, the dewatering settlement risk value can provide specific quantitative basis for taking prevention and control measures. Through the calculation of the early warning time and the dynamic adjustment acceleration, the system can predict the time point and the amplitude of the dewatering rate that needs to be adjusted in advance, and timely send an early warning signal to the construction personnel. Moreover, by obtaining the dynamic adjustment acceleration, the well point dewatering rate can be more accurately controlled, and the surrounding soil settlement caused by uneven or too fast dewatering can be reduced. On the other hand, it is helpful for the construction management personnel to accurately master the development trend of the foundation pit leakage and the influence degree on the construction progress, so as to avoid the interruption or delay of construction caused by dewatering problems.

[0126] Embodiment 2

[0127] Based on the same inventive concept as the dewatering monitoring method of the foundation pit based on the Internet of Things in the foregoing embodiments, as shown in the following table, the present application provides a dewatering monitoring system of the foundation pit based on the Internet of Things, wherein the system specifically comprises: Figure 3

[0128] The seepage rate acquisition module: in the process of dewatering of the foundation pit, the historical dewatering monitoring period is set, the change of the seepage level in the historical dewatering monitoring period is analyzed, and the dewatering rate of the foundation pit is obtained.

[0129] The initial adjustment module of the dewatering rate: the dewatering rate of the foundation pit in the historical dewatering monitoring period and the well point dewatering rate around the foundation pit are subjected to rate balance analysis, and the rate balance coefficient is obtained. The well point dewatering rate in the current dewatering monitoring period is adjusted, and the initial adjustment rate of the current well point dewatering is obtained.

[0130] The simulation settlement risk module: according to the initial adjustment rate of the current well point dewatering, the over-dewatering simulation of the phreatic layer in the foundation pit is carried out, and the dewatering settlement risk is evaluated.

[0131] The early warning dynamic adjustment module: according to the evaluation of the dewatering settlement risk, the high-risk dewatering settlement signal is adjusted to the low-risk dewatering settlement signal, the initial adjustment rate of the current well point dewatering is dynamically monitored and early warned, and the initial adjustment rate of the current well point dewatering is dynamically adjusted.

[0132] ​The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application 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. 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. 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 of the same period. The ratio of the rate difference of the same period is calculated with the corresponding seepage rate of adjacent units, and the rate difference ratio of the same period is output. The average value of all the rate difference ratios of the same period is calculated to obtain the rate balance coefficient. 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.

2. 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.

3. The method for monitoring seepage in a foundation pit based on the Internet of Things according to claim 2, 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.

4. 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.

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 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.

6. 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 between the maximum or minimum equilibrium precipitation rate warning value and 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 dynamic adjustment acceleration is obtained by subtracting the maximum or minimum equilibrium precipitation rate warning value from the current wellpoint precipitation initial adjustment rate and then calculating the ratio with the warning time.

7. An Internet of Things-based foundation pit seepage monitoring system, which is applied to the monitoring method described in any one of claims 1-6, 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.

Images