Intelligent supporting and safety real-time monitoring system and method for deep foundation pit based on internet of things

Abstract

The present application relates to the field of civil engineering and Internet of Things technology, and more particularly to a deep foundation pit intelligent support and safety real-time monitoring system and method based on Internet of Things. The system uses embedded strain gauges to form a full-bridge circuit to monitor the strain of support piles in real time, and uses a sliding window difference method to identify geological micro-change events. Based on the spatial uniformity index and the strain jump feature, the type of soil softening or local collapse is determined, and combined with temperature data, the composite risk is identified. According to the risk type, the airflow cleaning intensity and the heating compensation strategy are dynamically matched to implement geological-driven adaptive adjustment. Through effect verification and self-learning mechanism, parameter optimization and model evolution are realized. The present application provides a deep foundation pit intelligent support and safety real-time monitoring system and method based on Internet of Things, which solves or at least alleviates the problem of response lag and high misjudgment rate of traditional support system.

Description

Technical Field

[0001] This invention relates to the fields of civil engineering and Internet of Things (IoT) technology, and in particular to an IoT-based intelligent support and real-time safety monitoring system and method for deep foundation pits. Background Technology

[0002] Deep foundation pit engineering, as a core component of modern urban underground space development, is widely used in the construction of major infrastructure projects such as high-rise buildings, rail transit, and underground utility tunnels. With the acceleration of urbanization and the development of deeper and more complex underground structures, the uncertainty of the geological environment surrounding deep foundation pits has significantly increased. Safety risks during construction, such as soil instability, excessive deformation of the support structure, and even sudden collapse, are becoming increasingly prominent. Against this backdrop, the reliability of the support system and the real-time and accurate monitoring methods have become key technical elements for ensuring construction safety. Conventional support technologies are mostly designed based on static principles, arranged according to empirical parameters, and often supplemented by discrete manual inspections or environmental sensors at limited locations to indirectly monitor the support conditions. However, this approach is clearly somewhat lagging and limited in its ability to handle dynamic geological disturbances. Despite this, in recent years, numerous patented technologies have explored optimizing the mechanical composition of the support structure to achieve higher physical stability and ease of construction. Essentially, however, this remains a passive form of resistance, lacking proactive perception of the evolving geological conditions and failing to achieve intelligent response, making it difficult to meet the safety monitoring needs of current high-risk, high-precision construction scenarios.

[0003] For example, CN115162335B discloses a U-shaped steel sheet pile movable limiting column structure. Through the synergistic construction of this structure, combined with joint sealing achieved by rubber sealing rings, the overall rigidity and waterproofing of the support system are significantly enhanced, as are the ease of installation and reusability. For support scenarios with conventional homogeneous soil layers, the lateral earth pressure problem is well resolved, demonstrating good engineering applicability and reflecting, to some extent, the phased contribution of structural mechanics optimization to the development of support technology. Another patent, CN116927209B, also proposes a modular system combining unit support folding plates and extrusion contact frames. Through mechanical linkage, it can quickly unfold and adaptively conform, showing good results in loose strata with high sand content and high fluidity. These technical solutions alleviate, to some extent, the long construction cycle and poor adaptability of traditional support structures, promoting the standardization and prefabrication of support systems. However, as deep foundation pit engineering develops towards ultra-deep, irregular shapes, and proximity to existing structures, these technical solutions have insurmountable shortcomings in principle. In particular, existing systems generally handle support functions and condition monitoring functions separately, and monitoring methods are mostly limited to collecting external environmental parameters such as temperature, humidity, and displacement, without establishing a direct causal relationship between the mechanical response of the support structure and the evolution of the geological state.

[0004] This is because, while both soil softening and localized collapse increase the stress on the support structure, they differ fundamentally in their spatial distribution characteristics of development rates and their failure mechanisms. Soil softening is a slow, gradual accumulation of strain, while localized collapse is a sudden, locally concentrated redistribution of stress. If relying solely on preset mechanical response mechanisms or alarm logic based on a single threshold, the system cannot distinguish between different types of geological degradation patterns, easily leading to misjudgments. For example, in low-temperature environments, the softening process caused by the water-bearing phase change of the soil may be misidentified as structural instability, triggering unnecessary high-intensity intervention measures. This not only wastes resources but may also disrupt the original equilibrium state due to excessive cleaning or compensation. Conversely, misjudging localized abrupt changes as ordinary softening may delay critical intervention opportunities, leading to safety accidents. Furthermore, such systems lack a closed-loop verification mechanism for the effects of environmental adaptation and are unable to dynamically optimize control strategies based on historical geological response data, causing the entire support system to operate in an open-loop state, making it difficult to achieve truly intelligent control.

[0005] Against this backdrop, how to construct a closed-loop control mechanism that uses geological structural micro-changes as the core sensing variable, inverts geological evolution types through the strain characteristics of support structures, and thereby achieves adaptive environmental regulation and risk early warning linkage has become a key challenge and an urgent technical problem for those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent support and real-time safety monitoring system and method for deep foundation pits based on the Internet of Things, which can solve or at least alleviate the problems of traditional support systems lacking the ability to actively perceive and intelligently respond to the evolution of geological conditions, making it difficult to meet the safety management and control requirements in high-risk and high-precision construction scenarios, resulting in delayed response and high misjudgment rate.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things, comprising the following steps: S1. Real-time acquisition of strain data of the support structure by resistance strain gauges deployed at the extreme bending moment section of the deep foundation pit support piles. When the rate of change of the main strain direction exceeds the preset threshold during the continuous monitoring period, a geological micro-change event is triggered, and the geological change type is determined based on the strain distribution characteristics. When it is determined that the soil layer is softening and the ambient temperature is lower than the set temperature, a composite risk marker is generated. S2. After receiving the composite risk marker, implement a differentiated adaptation strategy according to the geological type: when it is determined that the soil layer is softening, a cleaning cycle is triggered whenever the cumulative strain increment reaches the preset strain increment threshold. The cleaning time is proportional to the strain increment, and the total compensation is applied in multiple stages; when it is determined that the collapse is local, high-intensity cleaning is started immediately and all compensation is applied at once. S3. Dynamically adjust the monitoring frequency and adaptation cycle according to the rate of geological change; S4. Iteratively optimize the adaptation parameters based on historical risk event data.

[0008] To further realize the present invention, the following technical solutions may be preferred: Preferably, in S1, the strain data is acquired by a configuration in which multiple sets of resistive strain gauges are evenly distributed along the circumference of the support pile at the extreme moment section. Each set includes axial strain gauges and transverse strain gauges, forming a full-bridge circuit. The signal is transmitted to the wireless acquisition node through shielded twisted-pair cable. The wireless acquisition node has a built-in high-precision analog-to-digital converter, and the sampling frequency is a preset frequency. After the data is filtered, it is uploaded to the edge computing server through a wireless protocol.

[0009] Preferably, the determination of the rate of change of the principal strain direction in S1 adopts the sliding window difference method to compensate for the temperature drift of the original strain sequence. The compensation value is calculated based on the thermal expansion compensation coefficient and the ambient temperature difference.

[0010] Preferably, in S2, the cleaning actuator is arranged outside the support pile, each support pile is equipped with multiple nozzles, the solenoid valve operates at a standard voltage, the response time meets the preset requirements, and the pressure resistance level of the air supply pipeline meets the safety requirements.

[0011] Preferably, S2 further includes a geological change synchronous cleaning mechanism: whenever the cumulative strain increment of any key node of the support pile reaches a preset strain increment threshold, a cleaning cycle is triggered, the cleaning duration is proportional to the current strain increment, and a maximum duration limit is set.

[0012] Preferably, the temperature compensation operation in S2 is achieved by adjusting the control signal of the heating element, the adjustment accuracy meets the preset requirements, and the compensation strategy is selected according to the geological type: gradual staged compensation is adopted for soil softening, and one-time full compensation is adopted for local collapse.

[0013] Preferably, the verification of the adaptation effect in S2 includes: in the soil softening scenario, monitoring the trend of the rate of change of the principal strain direction after the adaptation operation; if it decreases significantly compared with before adaptation and the fluctuation amplitude is reduced, the adaptation is considered effective; in the local collapse scenario, comparing the maximum increase of strain jump events; if the subsequent jump value is significantly lower than the previous one, the risk is considered to have been successfully mitigated.

[0014] Preferably, when the energy-saving mode is running in S3, the system reduces the power consumption of the edge computing unit and can quickly restore the full-function mode under sudden disturbances; the frequency of the enhanced monitoring protocol continues until the preset time after the composite risk is resolved.

[0015] Preferably, in S4, the adaptive parameter geological evolution module periodically retrieves historical successful cases with the same geological type and similar initial conditions, extracts the best airflow pressure value, calculates the average value to update the default pressure setting value, and the update process is triggered after accumulating enough new data.

[0016] A smart support and real-time safety monitoring system for deep foundation pits based on the Internet of Things, applicable to the above-mentioned methods, includes: The geological structure micro-change sensing module is used to execute step S1; A geologically driven environmental adaptation execution module is used to execute step S2; The geological environment linkage control module is used to execute step S3; The geological sensing system self-optimization module is used to execute step S4; the geological-driven environmental adaptation execution module includes a composite cavity integrated with a sensor base, the composite cavity having an inner temperature compensation cavity and an outer annular airflow nozzle array, the nozzle axis and the movement direction of the temperature compensation cavity are at a preset angle.

[0017] The beneficial effects of this invention are: This invention constructs a full-chain intelligent support system, encompassing perception, identification, decision-making, execution, verification, and optimization, by using subtle changes in geological structure as the core sensing variable, inverting geological evolution types through strain characteristics of the support structure, and implementing a closed-loop control mechanism that links adaptive environmental adjustment with risk early warning. At the physical layer, it relies on an embedded strain monitoring module; at the logical layer, it establishes risk assessment logic based on strain pattern recognition and composite parameter fusion; at the control layer, it implements a geologically driven dynamic adaptation strategy; and at the system layer, it introduces effect verification and self-learning feedback loops, forming a deep foundation pit safety assurance system with causal correlation, nonlinear response capabilities, and continuous evolutionary characteristics. This represents a technological leap from passive protection to active control, enabling the system to move beyond isolated threshold alarms and instead rely on multi-dimensional feature fusion and closed-loop verification, significantly reducing false alarm rates. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall process of the method of the present invention; Figure 2 This is a schematic diagram of the arrangement of strain gauges on deep foundation pit support piles and the installation of wireless acquisition nodes according to the present invention. Figure 3 This is a flowchart of the strain rate calculation and temperature compensation logic of the present invention; Figure 4This is a schematic diagram of the geological-driven environmental adaptation execution system of the present invention. Detailed Implementation

[0019] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0021] This embodiment discloses an IoT-based intelligent support and real-time safety monitoring system for deep foundation pits. The system relies on the deep integration of multi-source sensor networks, edge computing units, actuators, and a cloud platform to construct a closed-loop control system encompassing perception, identification, decision-making, execution, verification, and optimization. The system uses subtle changes in geological structure as the core sensing variable, inverts geological evolution types through the strain characteristics of the support structure, and drives environmental adaptive adjustment and risk early warning linkage mechanisms accordingly, forming a safety assurance system with causal correlation, nonlinear response capabilities, and continuous evolutionary characteristics. The entire system operates on a layered architecture. The bottom layer consists of edge sensing nodes deployed on the support structure, responsible for raw data acquisition and preliminary filtering; the middle layer is the pit bottom area gateway, performing local aggregation and edge inference; and the top layer is a cloud-based big data center, undertaking model training, cross-project comparison, and global policy distribution tasks.

[0022] The system includes a geological structure micro-change sensing module for sensing and risk identification; a geological-driven environmental adaptation execution module for geological-driven environmental adaptation execution; a geological environment linkage control module for geological environment linkage control; and a geological sensing system self-optimization module for geological sensing system self-optimization. The geological-driven environmental adaptation execution module includes a composite cavity integrated with a sensor base. The composite cavity has an inner temperature compensation cavity and an outer annular airflow nozzle array. The nozzle axis forms a preset angle with the movement direction of the temperature compensation cavity. The modules are interconnected through an industrial-grade communication protocol and are uniformly scheduled by an edge computing node.

[0023] Reference Figure 2 The system's physical layer uses deep foundation pit support piles as the basic carrier. Four sets of resistive strain gauges are evenly distributed circumferentially at the extreme bending moment section of each support pile. Each set contains two axial strain gauges and two transverse strain gauges, forming a full-bridge circuit configuration. The strain gauges are made of nickel-chromium alloy foil with a nominal resistance of 350Ω and a sensitivity coefficient of 2.08±0.5%. The substrate material is a 0.03mm thick polyimide film, exhibiting excellent flexibility and fatigue resistance. The four sets of strain gauges are distributed at 90° intervals on the circumferential surface of the support pile, ensuring omnidirectional sensing capability for changes in the principal strain direction caused by bending moments in any direction. Each set of strain gauges is connected to a wireless acquisition node located at the top of the support pile via shielded twisted-pair cables. This node has a built-in 24-bit Σ-Δ analog-to-digital converter. The signal is processed by a fifth-order Butterworth low-pass filter and then uploaded to the local edge computing server via the LoRaWAN protocol. Example 2

[0024] Based on the system hardware architecture of Embodiment 1, this embodiment discloses a method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things, referring to... Figure 1 The flowchart shown, combined with Figure 4 A hardware-level state transition diagram is provided. The method includes the following steps: In the S1 geological structure micro-change perception and risk identification stage, strain monitoring of the S101 support structure is first performed. When the rate of change of the principal strain direction of any key node of the support pile exceeds 0.05% / minute within five consecutive minutes, the system determines that a geological micro-change event has occurred and generates a trigger signal with a timestamp. This determination logic is implemented based on the sliding window differential method, referring to... Figure 3 The specific process is as follows: Original strain sequence First, temperature drift compensation is performed. The compensation formula is as follows: Where α is the thermal expansion compensation coefficient of the strain gauge, and its value ranges from (11.5-12.3)×10 -6 / ℃, ΔT is the ambient temperature difference recorded by the PT100 temperature sensor embedded in the support pile at the same time; the compensated strain increment sequence is subjected to first-order forward difference operation within a time window of 300 seconds to obtain the instantaneous rate of change sequence dε / dt; if all values ​​in the sequence are greater than 0.05% / minute, the trigger threshold condition is met, and the system immediately starts the S102 geological change type determination process.

[0025] The S102 geological change type determination process first executes the S1021 spatial consistency analysis; among which, a spatial evenness index is defined. ,in The standard deviation of the strain values ​​at all monitoring points at the current moment. The average value is used; when U>0.85 and dε / dt maintains a monotonically increasing trend for no less than 3 minutes, it is judged as soil softening; conversely, if U<0.60 and there is a single monitoring point where the strain jump exceeds three times the standard deviation of the background value, it is judged as local collapse; the standard deviation of the background value is based on the strain fluctuation statistics under static steady state in the past 24 hours, and is updated once an hour. This judgment result serves as the basic input for subsequent risk assessment and implementation strategy selection.

[0026] Next, the S1022 composite risk identification logic will be executed. When the output of S1021 is soil softening, and the soil temperature at a depth of 5 meters is simultaneously acquired at 5°C, the system will activate the composite risk labeling program to generate a composite label containing the geological type code G01 and the temperature level T1. Soil temperature data is collected by digital temperature probes pre-embedded in the periphery of the support structure. The sampling period is 300 seconds by default. The composite risk labeling program will only start after S1012 is triggered. This can avoid misidentification due to simple environmental cooling. This label will directly affect the monitoring frequency adjustment in S3 stage and the compensation strategy selection in S2 stage.

[0027] After determining the geological type, the process moves to the S103 composite risk level assessment stage. S031 will call the corresponding risk index calculation function based on the identified geological change type. For soil softening, a weighted linear combination model will be used. ,in =0.7, =0.3, the normalization factor is set based on the historical maximum value: , For cases of localized collapse, a nonlinear enhancement model is employed. ,in =0.9, =0.1, The maximum strain surge is the percentage of the design yield strain, with a normalization upper limit of 1.0. The outputs of both types of models will be mapped to the 0-100 range to form a unified risk index RI.

[0028] When RI>65, S1032 will be executed to generate a composite early warning information package. This information package is encapsulated in JSON format and includes the early warning level (high), geological feature code (G01 or G02), spatial location coordinates (WGS84 latitude and longitude + depth m), timestamp (UTC+8), risk index value, and recommended intervention intensity level (1-5). It is then pushed to the field command terminal and cloud data center via the MQTT protocol.

[0029] Phase S2 is the geological-driven environmental adaptation execution, the key being the transformation of geological state identification results into precise and controllable physical intervention actions. The S201 adaptation intensity dynamic matching module receives the early warning information packet from S1032, parses the geological feature codes within it, and then calls the preset strategy mapping table. When the code is G01 (soil softening), a medium-intensity cleaning mode is activated, setting the airflow pressure to 0.3 MPa for a duration of 6 seconds. When the code is G02 (local collapse), a high-intensity cleaning mode is activated, setting the airflow pressure to 0.4 MPa for a duration of 8 seconds. The strategy mapping table is stored in the local firmware read-only area and cannot be modified online to prevent malicious tampering of the strategy.

[0030] Cleaning operations are performed on the surface of the support structure to remove deposits. (Refer to...) Figure 4 This system consists of an array of miniature nozzles arranged 1.5 meters above the ground on the outside of the support piles. Each support pile is equipped with 8 nozzles, arranged in a ring. The nozzle orifice diameter is 0.8 mm, and the material is 316L stainless steel. The nozzles are controlled by solenoid valves. The air supply pipe is made of polyetheretherketone (PEEK) material with an outer diameter of 6 mm, a wall thickness of 1 mm, and a pressure resistance of not less than 1.0 MPa. The compressed air source is a mobile screw air compressor with a rated pressure of 0.8 MPa, which is supplied to the system after being treated by a three-stage filter.

[0031] The S202 geological change synchronous cleaning mechanism further refines the time granularity of the cleaning action. Whenever the cumulative strain increment at any support pile node reaches 0.02%, a cleaning cycle will be triggered, regardless of whether a formal warning state is in effect. The cleaning duration τ (in seconds) is set according to a proportional function: Where k = 300s / %. The upper limit for this strain increment is set to 10 seconds. For example, if a strain increment is 0.025%, then... That's 7.5 seconds; if the increase reaches 0.033%, then... The interval will be truncated to 10 seconds. This mechanism ensures that the cleaning frequency is proportional to the rate of geological activity, maintains good coupling between the sensors on the support structure surface and the contact medium, and prevents signal attenuation caused by mud cover.

[0032] The S203 temperature compensation geological adaptation module implements differentiated compensation strategies for different geological deterioration modes. If S1022 determines that the soil layer is softening, then S2031 is executed to adjust the total compensation amount. Divided into three equal phases The interval between each stage is Δt = 120 seconds, forming a gradual compensation curve; if a partial collapse is determined, S2032 is executed, applying the full compensation amount at once. The response delay is no more than 15 seconds. The compensation operation is achieved by adjusting the heating power of the phase change material in the cavity inside the support structure. The heating element is a PTC ceramic heating element, which is controlled by the edge controller according to the duty cycle of the PWM signal output by the strategy selection module.

[0033] The S204 adaptation effect geological verification module is used to confirm the effectiveness of the aforementioned environmental adaptation measures. In the soil softening scenario, S2041 is executed, monitoring the strain change rate trend within 30 minutes after the adaptation operation. If the slope decreases by more than 40% compared to before adaptation, and the fluctuation amplitude decreases to below 60% of the original standard deviation, the adaptation is considered effective. In the local collapse scenario, S2042 is executed, comparing the maximum increase of the two most recent strain jump events. If the second jump value decreases by more than 30% compared to the first, the risk mitigation is considered successful. The slope is calculated using the least squares method to fit the dε / dt sequence within a 10-minute sliding window, and the fluctuation amplitude is expressed as the standard deviation. This verification result will serve as the basis for strategy adjustments in stages S3 and S4.

[0034] Phase S3 is a geological environment linkage control system designed to dynamically adjust system operating parameters to adapt to the rhythm of geological evolution. The S301 geological acceleration adaptation adjustment module monitors the critical state of strain change rate. When the dε / dt of any monitoring point exceeds 0.1% / minute for 10 consecutive seconds, it is determined that the geological acceleration period has begun, and S3011 is immediately executed to shorten the trigger threshold for the next cleaning cycle. In soil softening mode, the rule of triggering cleaning every 0.02% strain is adjusted to triggering every 0.015% strain, improving response sensitivity. In local collapse mode, the cleaning interval threshold remains unchanged, but additional compensation heating operations are allowed, increasing the heating power to 120% of the rated value for 60 seconds.

[0035] S302 Geological Stability Adaptation Degradation is responsible for system energy-saving management. When the strain change rate of all monitoring points is below 0.02% / minute for 10 consecutive minutes and there are no new risk warnings, S3021 is executed to confirm that the geology has entered a stable state. Then, S3022 is executed to switch the system to energy-saving mode: the clean airflow pressure will be reduced from the current value to 0.2MPa, the calibration cycle will be extended from once per hour to once every two hours, the wireless transmission power will be reduced by 30%, and the edge computing unit will switch to low-power standby mode, keeping only the core monitoring channel running. In energy-saving mode, the edge computing unit CPU frequency will be reduced from 1.8GHz to 600MHz, and the memory refresh rate will be reduced by 50%, which can reduce the overall power consumption. This degradation operation can extend the battery life of the device and ensure that it can be restored to full-function mode within 8 seconds under sudden disturbances.

[0036] The S303 geological temperature coupling early warning module will enhance the monitoring density under combined risks. When S1022 outputs the "low temperature softening combined risk" label... When this happens, S3031 will be activated to initiate the enhanced monitoring protocol; S3032 will be executed to increase the sampling frequency of the soil temperature sensor from once every 300 seconds to once every 60 seconds, and at the same time, the thermal response calibration frequency of the support structure compensation cavity will also be increased to the same level. This frequency increase will continue until 60 minutes after the composite risk is eliminated to ensure that sufficient time resolution data can be obtained under low temperature accelerated softening conditions; the calibration process includes a step response test on the PTC heating element, recording the time required for the temperature to rise to 90% of the target value, which is used to update the thermal conduction model parameters.

[0037] The S304 support requirement geological mapping module can dynamically adjust the safety threshold. Based on the adaptation effect verification results provided by S2042, it executes S3041 to recalculate the support force warning threshold. If the verification results show that the adaptation is effective, the original threshold of 80 kPa will be lowered to 72 kPa in the softening scenario to improve the system's sensitivity to minor instability signs. If the verification is ineffective, the original threshold will remain unchanged, and an "adaptation failure" alarm will be sent upstream. This adjustment logic relies on the feedback of actual geological response, rather than static design values, which enhances the reliability of early warning decisions. The adjustment results will be updated synchronously in the structure monitoring model of the cloud platform for cross-project knowledge transfer.

[0038] Phase S4 is the self-optimization phase of the geological sensing system, enabling the system to learn and evolve over a long period. The S401 geological adaptation effect archiving module continuously records the complete process data of each geological event. After each risk event ends, the system automatically packages and generates a structured log entry containing the event ID (UUID format), start and end time (ISO 8601), geological type (G01 / G02 / CT_01), environmental parameter sequence (temperature, humidity, moisture content), execution strategy number (S2012 strategy ID), cleaning and compensation parameters (pressure, duration, PWM duty cycle), strain response curve (CSV format), and effect verification result (Boolean value). For soil softening events determined to be valid by S2041 or S2042, S4012 will be executed to mark it as "progressive risk data" and store it in a dedicated partition. This partition uses SQLite format and is encrypted and stored in the eMMC chip of the edge device.

[0039] The S402 adaptive parameter geological evolution module utilizes historical success cases to optimize future decisions. The system periodically executes S4021, retrieving the three most recent "progressive risk data" records with the same geological type (G01) and similar initial conditions (temperature ±2℃, water content ±5%). It extracts the optimal airflow pressure value P_optimal_i (i=1,2,3) and calculates its arithmetic mean: P_default_new = (P_optimal_1 + P_optimal_2 + P_optimal_3) / 3. The result is then updated to the default pressure setting value of S2012 for the next similar event, replacing the original fixed value of 0.3MPa. This parameter update process is executed monthly or triggered immediately after accumulating three new data points. This ensures that the system parameters evolve synchronously with the slow changes in local geological characteristics. The update log is written to the system audit file, which contains the old value, new value, update time, and data source record ID.

[0040] The S403 geological risk model optimization module reconstructs the underlying prediction logic. Based on long-term accumulated "progressive risk data," it executes S4031, uses Pearson correlation coefficient analysis to explore the statistical correlation between strain change rate and whether an engineering accident will ultimately occur, and constructs a multivariate logistic regression model. ,in =-4.2, =38.5, =-0.31, =0.17, Temperature (°C) The model training dataset contains records of multiple geological events at various construction sites over the past few years, with positive samples (accidents occurring) accounting for 18.3%. When the model input dε / dt>0.08% / min and T<5℃, the output P(accident) = 0.89, which means there is an 89% probability of risk. This probability value will be directly injected into the risk index calculation weight of S1031 as a dynamic parameter tuning factor, allowing the early warning mechanism to be continuously refined with local experience.

[0041] The S404 hardware adaptation module for geological characteristics enables active tuning of physical components. Based on the geological type output by S1022, it executes S4041 to adjust the thermal response characteristics of the bimetallic strip assembly. In soft soil environments, a micro-motor drives an adjusting screw to change the constraint stiffness of the free end of the bimetallic strip, reducing its effective thermal expansion coefficient from the standard value of 14.8 × 10⁻⁶. -6 / ℃ increased to 17.0×10 -6 / ℃, matching the characteristics of slow thermal conductivity and sluggish response in soft soil, while executing S4042, the thickness of the aluminum-silicon coating on the compensation cavity wall is increased by 8μm, from the original 22μm to 30μm, improving the thermal conductivity by about 15% and shortening the temperature response time constant τ from the original 180 seconds to 155 seconds. The coating is prepared using a magnetron sputtering process, with a target purity of 99.99%, a deposition rate of 0.3nm / s, and a film density of ≥95%. This hardware tuning operation is performed once per quarter, or automatically triggered after accumulating more than five similar geological events.

[0042] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things, characterized in that, Includes the following steps: S1. Geological structure micro-change perception and risk identification: Resistance strain gauges deployed at the extreme bending moment section of the deep foundation pit support piles are used to collect strain data of the support structure in real time. When the rate of change of the main strain direction exceeds the preset threshold during the continuous monitoring period, a geological micro-change event is triggered, and the geological change type is determined based on the strain distribution characteristics. When it is determined that the soil layer is softening and the ambient temperature is lower than the set temperature, a composite risk marker is generated. S2, Geologically Driven Environmental Adaptation Execution: After receiving composite risk markers, it executes differentiated adaptation strategies based on geological type: When soil softening is detected, a cleaning cycle is triggered whenever the cumulative strain increment reaches a preset strain increment threshold. The cleaning duration is proportional to the strain increment, and the total compensation is applied in multiple stages. When local collapse is detected, high-intensity cleaning is immediately initiated, and all compensation is applied at once. The cleaning operation is performed on the sediment removal system on the surface of the support structure. The compensation operation is achieved by adjusting the heating power of the phase change material in the cavity inside the support structure. The heating element is a PTC ceramic heating element, which is controlled by the edge controller according to the duty cycle of the PWM signal output by the strategy selection module. S3. Geological environment linkage control, dynamically adjusting the monitoring frequency and adaptation cycle according to the rate of geological change; S4. The geological sensing system self-optimizes by iteratively optimizing the default airflow pressure setting for airflow cleaning based on historical risk event data.

2. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, The strain data acquisition in S1 adopts a configuration in which multiple sets of resistive strain gauges are evenly distributed and installed along the circumferential direction of the support pile at the extreme moment section. Each set includes axial strain gauges and transverse strain gauges, forming a full-bridge circuit. The signal is transmitted to the wireless acquisition node through shielded twisted pair cable. The wireless acquisition node has a built-in high-precision analog-to-digital converter, and the sampling frequency is a preset frequency. After the data is filtered, it is uploaded to the edge computing server through a wireless protocol.

3. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, The determination of the rate of change of the principal strain direction in S1 adopts the sliding window difference method to compensate for the temperature drift of the original strain sequence. The compensation value is calculated based on the thermal expansion compensation coefficient and the ambient temperature difference.

4. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, The cleaning actuator in S2 is arranged on the outside of the support pile. Each support pile is equipped with multiple nozzles. The solenoid valve operates at a standard voltage, the response time meets the preset requirements, and the pressure resistance level of the air supply pipeline meets the safety requirements.

5. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, The S2 also includes a geological change synchronous cleaning mechanism: whenever the cumulative strain increment of any key node of the support pile reaches the preset strain increment threshold, a cleaning cycle is triggered. The cleaning duration is proportional to the current strain increment and has a maximum duration limit.

6. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, The temperature compensation operation in S2 is achieved by adjusting the control signal of the heating element. The adjustment accuracy meets the preset requirements. The compensation strategy is selected according to the geological type: gradual staged compensation is used for soil softening, and one-time full compensation is used for local collapse.

7. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, The verification of the adaptation effect in S2 includes: in the soil softening scenario, monitoring the trend of the rate of change of the principal strain direction after the adaptation operation; if it decreases significantly compared with before adaptation and the fluctuation amplitude is reduced, the adaptation is considered effective; in the local collapse scenario, comparing the maximum increase of strain jump events; if the subsequent jump value is significantly lower than the previous one, the risk is considered to have been successfully mitigated.

8. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, When the S3 is running in energy-saving mode, the system reduces the power consumption of the edge computing unit and can quickly recover to full-function mode under sudden disturbances; the frequency of the enhanced monitoring protocol continues until the preset time after the composite risk is resolved.

9. The method for intelligent support and real-time safety monitoring of deep foundation pits based on the Internet of Things as described in claim 1, characterized in that, In S4, historical successful cases with the same geological type and similar initial conditions are periodically retrieved, the best airflow pressure value is extracted, and the average value is calculated to update the default airflow pressure setting value. The update process is triggered after enough new data has been accumulated.

10. A deep foundation pit intelligent support and real-time safety monitoring system based on the Internet of Things, applicable to the method described in any one of claims 1-9, characterized in that, include: The geological structure micro-change sensing module is used to execute step S1; A geologically driven environmental adaptation execution module is used to execute step S2; The geological environment linkage control module is used to execute step S3; The geological sensing system self-optimization module is used to execute step S4; the geological-driven environmental adaptation execution module includes a composite cavity integrated with a sensor base, the composite cavity having an inner temperature compensation cavity and an outer annular airflow nozzle array, the nozzle axis and the movement direction of the temperature compensation cavity are at a preset angle.

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