Intelligent deep foundation pit monitoring system based on 5G private network

Abstract

The invention discloses an intelligent deep foundation pit monitoring system based on a 5G private network, and relates to the technical field of deep foundation pit monitoring systems, the intelligent deep foundation pit monitoring system comprises a data acquisition module, a data transmission module, a data processing module and an online monitoring module, the data acquisition module acquires data through a timing and active triggering dual mode, and the data is transmitted to the data processing module through the 5G private network; differential processing, dynamic baseline resolving, a triangulation elevation method and a dynamic early warning algorithm are utilized to calculate the horizontal displacement, the settlement and the water seepage risk score of the foundation pit slope, an online monitoring module analyzes data in real time, and early warning and auxiliary decision making are performed when the data exceed a threshold value. Comprehensive and high-precision real-time monitoring of the deep foundation pit is achieved, safety risks are early warned in time, and the construction safety guarantee level is improved.

Description

Technical Field

[0001] This invention relates to the field of deep foundation pit monitoring system technology, specifically to an intelligent deep foundation pit monitoring system based on a 5G private network. Background Technology

[0002] The limited space and congestion of equipment and personnel in the excavation pit make it difficult to find suitable observation points for long-term, continuous monitoring. This spatial limitation not only affects the accuracy of monitoring but also increases the difficulty and cost of monitoring.

[0003] Deep foundation pit construction is usually carried out 24 hours a day without interruption. However, it is difficult for manual monitoring to maintain full concentration around the clock. Especially when observing at night, the monitoring targets may be limited by factors such as light, resulting in inaccurate or missing monitoring data.

[0004] Traditional foundation pit monitoring generates a large amount of data that needs to be analyzed and processed, but traditional data processing methods are cumbersome and inefficient. This not only increases the workload of monitoring personnel but also makes it difficult to meet the needs of real-time monitoring.

[0005] Some traditional monitoring methods and instruments lack precision and struggle to detect minute deformation changes. This can lead to inaccurate monitoring results, affecting subsequent analysis and decision-making. Particularly in deep foundation pit construction, even minute deformation changes can indicate potential safety risks; therefore, the accuracy of monitoring data is crucial. Summary of the Invention

[0006] This invention addresses the shortcomings of existing technologies by proposing an intelligent deep foundation pit monitoring system based on a 5G private network. This system enables real-time data acquisition, high-speed transmission, precise processing, and dynamic early warning of deep foundation pit data, thus solving the problems of accuracy, efficiency, and safety in traditional monitoring.

[0007] The technical solution to achieve the objective of this invention is as follows:

[0008] The intelligent deep foundation pit monitoring system based on 5G private network includes a data acquisition module, a data transmission module, a data processing module, and an online monitoring module.

[0009] The data acquisition module monitors and collects data from the foundation pit, and transmits the data to the data processing module through a dual-mode collaborative data transmission strategy, which includes a timed acquisition and transmission mode and an active triggering transmission mode.

[0010] The data transmission module is based on 5G private network technology and uses a 5G industrial router to transmit data between the data acquisition and data processing modules. This ensures that the pit monitoring data can be transmitted to the data processing module in real time and quickly for real-time analysis, so that the online monitoring module can make accurate early warnings and decisions.

[0011] The data processing module uses a differential processing method to process the data from the data acquisition module. It calculates the horizontal displacement of the foundation pit slope based on the dynamic baseline solution model, the vertical displacement based on the trigonometric leveling method, and the seepage risk score of the foundation pit based on the dynamic early warning algorithm. The calculated horizontal displacement, vertical displacement, and seepage risk score of the foundation pit slope are then transmitted to the online monitoring module via a 5G private network.

[0012] The online monitoring module analyzes the horizontal and vertical displacement of the foundation pit slope and the seepage risk score of the foundation pit from the data processing module. When any of these three values ​​exceeds the safety threshold, the online monitoring module immediately issues an early warning to the construction personnel, providing them with sufficient time to take countermeasures and prevent potential safety risks.

[0013] Furthermore, the data acquisition module collects real-time data from the foundation pit, including: the slope distance S from the total station to the measurement point, the horizontal angle α from the total station to the measurement point, the vertical angle β from the total station to the measurement point, the total station height i, the measurement point height v, and the current absolute elevation of the groundwater level H. The above data is collected in real time and transmitted to the data processing module through a dual-mode collaborative data transmission strategy, including a timed acquisition and transmission mode and an active trigger transmission mode.

[0014] Furthermore, the data transmission module uses a 5G private network to transmit data between different modules, including data transmission between the data acquisition module and the data processing module, and data transmission between the data processing module and the online monitoring module. The 5G private network can provide data transmission rates of up to 10Gbps, ensuring that the pit monitoring data can be transmitted to the data processing module in real time and quickly. The 5G private network has extensive connectivity and coverage, supporting the access of a large number of sensors and monitoring devices, improving the accuracy and comprehensiveness of monitoring, and can work stably in various complex environments. In addition, the 5G private network has higher security, supporting multiple security protocols and encryption technologies to ensure the security and integrity of data during transmission.

[0015] Furthermore, the data processing module uses a differential processing method to process the data from the data acquisition module. First, it calculates the three-dimensional coordinate difference between the current monitoring point and the initial monitoring point. Then, it calculates the three-dimensional coordinate difference between the current reference point and the initial reference point. Subtracting the two differences gives the net displacement of the current monitoring point.

[0016] Furthermore, based on the dynamic baseline solution model, the current horizontal displacement of the foundation pit slope is calculated. The horizontal displacement of the foundation pit slope at the monitoring point is the change in the coordinates of the current monitoring point compared to the coordinates of the monitoring point in the previous period on the horizontal plane. The calculation method is as follows:

[0017]

[0018] Wherein, S2 represents the slope distance from the current total station to the monitoring point, β2 represents the vertical angle from the current total station to the monitoring point, S1 represents the slope distance from the total station to the monitoring point in the previous cycle, and β1 represents the vertical angle from the total station to the monitoring point in the previous cycle.

[0019] Furthermore, the vertical displacement is calculated based on the trigonometric leveling method. This method uses the slope distance S from the monitoring point and the vertical angle β to calculate the elevation difference based on trigonometric principles, and improves measurement accuracy by eliminating the error between the total station height and the monitoring point height. The vertical displacement at the current monitoring point is calculated based on the slope distance S from the total station to the monitoring point, the vertical angle β from the total station to the monitoring point, the total station height i, the monitoring point height v, and the Earth's radius of curvature R.

[0020]

[0021] The atmospheric refractive index K varies with differences in ambient temperature, air pressure, humidity, and line-of-sight height; therefore, it needs to be calculated based on the actual environment of the current foundation pit.

[0022]

[0023] The above calculation formula is derived based on the relationship between atmospheric refractive index and meteorological elements, where P represents the current air pressure and T represents the current temperature. The vertical temperature gradient is represented by U, which represents the angle of the horizontal line of sight at the current position in the vertical direction, and ke represents the effect of water vapor on the refractive index; the effect of water vapor on the refractive index ke can usually be ignored.

[0024] Furthermore, the seepage risk score of the foundation pit is calculated in real time based on a dynamic early warning algorithm, and the calculation formula is as follows:

[0025]

[0026] Where ε represents the weighting coefficient for the rate of change of groundwater level, η represents the weighting coefficient for the absolute deviation of groundwater level, used to measure the relative importance between the rate of change of groundwater level and the absolute deviation of groundwater level, ΔH represents the current change in groundwater level, Δt represents the time interval between the current monitoring time and the last monitoring time, and H represents the current absolute elevation of groundwater level. safe This indicates the safe water level threshold.

[0027] Furthermore, the online monitoring module analyzes the horizontal and vertical displacements of the foundation pit slope and the seepage risk score from the data processing module, records historical and current data, analyzes the future trends of the horizontal and vertical displacements of the foundation pit slope and the seepage risk score, assists technical personnel in decision-making, improves the safety level of foundation pit construction, and prevents and curbs the occurrence of major accidents.

[0028] Compared with existing technologies, this invention proposes an intelligent deep foundation pit monitoring system based on a 5G private network. It integrates the advantages of 5G communication technology and foundation pit monitoring systems, providing data rates up to 10Gbps. This ensures that foundation pit monitoring data can be transmitted to the online monitoring module in real time and quickly, capturing minute changes in the foundation pit and issuing timely warnings. This provides construction personnel with sufficient time to take countermeasures and prevent potential safety risks. Furthermore, the 5G private network has extensive connectivity and coverage, enabling more comprehensive and detailed monitoring of all aspects of the foundation pit, thereby improving the accuracy and comprehensiveness of monitoring. Attached Figure Description

[0029] Figure 1 This is a module diagram of an intelligent deep foundation pit monitoring system based on a 5G private network.

[0030] Figure 2 This is a 5G private network architecture diagram for the data transmission module.

[0031] Figure 3 A flowchart for calculating the horizontal displacement of the foundation pit slope based on the dynamic baseline solution model;

[0032] Figure 4 A flowchart for calculating vertical displacement based on the trigonometric leveling method;

[0033] Figure 5 A flowchart for calculating the seepage risk score of a foundation pit based on a dynamic early warning algorithm. Detailed Implementation

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0035] like Figure 1 As shown, a specific embodiment of the present invention, an intelligent deep foundation pit monitoring system based on a 5G private network, includes a data acquisition module, a data transmission module, a data processing module, and an online monitoring module;

[0036] The data acquisition module monitors and collects data from the foundation pit, transmitting the data to the data processing module through a dual-mode collaborative data transmission strategy. This strategy includes a timed acquisition and transmission mode and an active triggering transmission mode. The timed acquisition and transmission mode uses the Precision Time Protocol (PTP) to achieve microsecond-level time synchronization and sends the acquired data to the data processing module. The active triggering acquisition mode uses a threshold triggering mode, which is automatically triggered when the monitoring point parameters exceed a preset threshold and sends the acquired data to the data processing module.

[0037] The data transmission module uses a 5G industrial router based on 5G communication technology, which can provide a data transmission rate of up to 10Gbps, ensuring that the pit monitoring data can be transmitted to the data processing module in real time and quickly for real-time analysis, so that the online monitoring module can make accurate early warnings and decisions. At the same time, a firewall is set up between the data acquisition module and the data processing module to ensure the security and integrity of the data transmission process.

[0038] The data processing module uses a differential processing method to process the data from the data acquisition module. It calculates the horizontal displacement of the foundation pit slope based on the dynamic baseline solution model, the vertical displacement based on the trigonometric leveling method, and the seepage risk score of the foundation pit based on the dynamic early warning algorithm. The calculated horizontal displacement, vertical displacement, and seepage risk score of the foundation pit slope are then transmitted to the online monitoring module via a 5G private network.

[0039] The online monitoring module analyzes the horizontal and vertical displacement of the foundation pit slope and the seepage risk score of the foundation pit from the data processing module. When any of these three values ​​exceeds the safety threshold, the online monitoring module immediately issues an early warning to the construction personnel, providing them with sufficient time to take countermeasures and prevent potential safety risks.

[0040] Furthermore, the data acquisition module collects real-time data from the foundation pit. The collected data includes: the slope distance S from the total station to the measurement point, the horizontal angle α from the total station to the measurement point, the vertical angle β from the total station to the measurement point, the total station height i, the measurement point height v, and the current absolute elevation of the groundwater level H. It is important to note that all the above collected data is described as data from the total station to the measurement point, which includes both monitoring points and benchmark points. In the following descriptions, monitoring points and benchmark points are clearly distinguished in specific paragraphs to avoid ambiguity. The above collected data is transmitted to the data processing module in real-time through a dual-mode collaborative data transmission strategy, including a timed acquisition and transmission mode and an active trigger transmission mode.

[0041] Specifically, the timed acquisition and transmission mode uses the IEEE 1588v2 (Precision Time Protocol, PTP) protocol for clock synchronization. This protocol can achieve microsecond-level time synchronization. A specific synchronization period is set, and relevant data is collected at regular intervals in each period and transmitted to the data processing module through the data transmission module. Since the data transmitted in this mode has not triggered the risk threshold, it is all data under normal conditions, which is used to analyze the changing trends of various data in the foundation pit and to provide construction personnel with data trend analysis for future data analysis.

[0042] Specifically, the active triggering transmission mode adopts a threshold triggering mode. When the monitoring point parameters exceed the preset threshold, this mode is automatically triggered, and the currently collected data is sent to the data processing module for specific analysis and calculation. Since the data processing module mainly analyzes three data points: horizontal displacement of the foundation pit slope, vertical displacement, and foundation pit seepage risk score, the active triggering transmission mode sets thresholds for the monitoring point parameters related to the above three data points, including the slope distance S from the total station to the monitoring point, the vertical angle β, and the current absolute elevation of the groundwater level H. When the above three parameters exceed the set thresholds, all data at the current moment is immediately transmitted to the data processing module to calculate the three values ​​of horizontal displacement of the foundation pit slope, vertical displacement, and foundation pit seepage risk score. The above thresholds need to be set specifically due to the different foundation pit conditions under different geographical environments, which will not be elaborated here.

[0043] like Figure 2 As shown, the data transmission module further utilizes a 5G private network to transmit data between different modules, including data transmission between the data acquisition module and the data processing module, and data transmission between the data processing module and the online monitoring module. The 5G private network can provide data transmission rates of up to 10Gbps, ensuring that the pit monitoring data can be transmitted to the data processing module in real time and quickly. The 5G private network has extensive connectivity and coverage, supporting the access of a large number of sensors and monitoring devices, improving the accuracy and comprehensiveness of monitoring, and can work stably in various complex environments. In addition, the 5G private network has higher security, supporting multiple security protocols and encryption technologies to ensure the security and integrity of data during transmission.

[0044] Furthermore, the data processing module uses a differential processing method to process the data from the data acquisition module. Specifically, the differential processing method first calculates the three-dimensional coordinate difference between the current monitoring point and the initial monitoring point, then calculates the three-dimensional coordinate difference between the current reference point and the initial reference point. Subtracting these two differences yields the net displacement of the current monitoring point. More specifically, the three-dimensional coordinates of the measurement point are first calculated based on the polar coordinate system to rectangular coordinate system conversion method.

[0045]

[0046] Based on the above calculation formula, let the three-dimensional coordinates of the monitoring point be (X1, Y1, Z1) and the three-dimensional coordinates of the reference point be (X2, Y2, Z2). Then, the formula for calculating the net displacement of the current monitoring point is as follows:

[0047]

[0048] The principle is that since the same instruments and operating methods are used, and the benchmark points are all buried in stable locations, the benchmark points are considered to be stable. Therefore, this difference is considered to be the result of the influence of external conditions, which is the common error. Since the current monitoring point and the current benchmark point are observed at the same time, it can be considered that the influence of external conditions on the monitoring point and the benchmark point is related. Therefore, after calculating the three-dimensional coordinate error between the current monitoring point and the initial monitoring point, the common error can be eliminated to obtain a more accurate net displacement of the current monitoring point.

[0049] like Figure 3 As shown, the current horizontal displacement of the foundation pit slope is calculated based on the dynamic baseline solution model, and it is analyzed whether the current horizontal displacement of the foundation pit slope is within the safe range. Specifically, the horizontal displacement of the foundation pit slope at the monitoring point is the change in the coordinates of the current monitoring point compared to the coordinates of the monitoring point in the previous period on the horizontal plane, and its calculation method is as follows:

[0050]

[0051] Where S2 represents the slope distance from the current total station to the monitoring point, β2 represents the vertical angle from the current total station to the monitoring point, S1 represents the slope distance from the total station to the monitoring point in the previous cycle, and β1 represents the vertical angle from the total station to the monitoring point in the previous cycle; the horizontal displacement of the foundation pit slope reflects the stability of the current foundation pit support structure and soil state. If the current horizontal displacement ΔA of the foundation pit slope exceeds the set threshold, it indicates that the current soil has deformed. The online monitoring module will immediately issue an alarm to the construction personnel, prompting them to take timely action, such as grouting reinforcement or adding steel supports, to maintain the stability of the foundation pit soil.

[0052] like Figure 4 As shown, the vertical displacement is calculated based on the trigonometric leveling method. This method uses the slope distance and vertical angle of the monitoring point to calculate the height difference based on trigonometric principles, and improves measurement accuracy by eliminating the error between the total station height and the monitoring point height. Furthermore, when using the trigonometric leveling method, it is necessary to eliminate the influence of atmospheric refraction on measurement accuracy. The atmospheric refraction coefficient K varies with ambient temperature, air pressure, humidity, and line-of-sight height; therefore, it needs to be calculated based on the actual environment of the current foundation pit.

[0053]

[0054] The above calculation formula is derived based on the relationship between atmospheric refractive index and meteorological elements, where P represents the current air pressure, T represents the current temperature, dT / dh represents the vertical temperature gradient, U represents the angle of the horizontal line of sight at the current location in the vertical direction, and ke represents the influence of water vapor on the refractive index; the influence of water vapor on the refractive index ke can usually be ignored.

[0055] After calculating the atmospheric refraction coefficient K at the current foundation pit using the above formula, the vertical displacement at the current monitoring point is then calculated based on the slope distance S from the total station to the monitoring point, the vertical angle β from the total station to the monitoring point, the total station height i, the monitoring point height v, and the Earth's radius of curvature R.

[0056]

[0057] Based on the above formula, the data collected by the data acquisition module is used to monitor the current foundation pit in real time. When the vertical displacement exceeds the preset threshold, it indicates that the current foundation pit compression deformation or soil erosion exceeds expectations. The online monitoring module will immediately issue an early warning to the construction personnel, prompting them to carry out settlement control and reduce the accident rate.

[0058] like Figure 5 As shown, a dynamic early warning algorithm is used to calculate the seepage risk score of the foundation pit in real time. This algorithm alerts construction workers by calculating the seepage risk score of the foundation pit. The calculation formula is as follows:

[0059]

[0060] Where ε represents the groundwater level change rate weighting coefficient, η represents the groundwater level absolute deviation weighting coefficient, ΔH represents the current groundwater level change, Δt represents the time interval between the current monitoring time and the last monitoring time, and H represents the current groundwater level absolute elevation. safe This represents the safe water level threshold; the value of ε needs to be determined based on the actual geographical environment of the foundation pit, and its value range is generally 0.5 to 0.7. It should be noted that this value needs to be adjusted according to the weather, such as increasing the weight during the rainy season; the value of η also needs to be determined based on the current actual geographical environment of the foundation pit, and its value range is generally 0.3 to 0.5.

[0061] Based on the above formula, the data acquisition module collects data to monitor the groundwater seepage risk score of the current foundation pit in real time. When the groundwater seepage risk score exceeds the preset threshold, it indicates that the current foundation pit is prone to seepage problems. The online monitoring module will immediately issue an early warning to the construction personnel and prompt them to take action to reduce the accident rate. The preset threshold is determined based on the weather and the actual geographical environment of the foundation pit.

[0062] Furthermore, the online monitoring module analyzes the horizontal and vertical displacements of the foundation pit slope and the seepage risk score from the data processing module, records historical and current data, analyzes the future trends of the horizontal and vertical displacements of the foundation pit slope and the seepage risk score, assists technical personnel in decision-making, improves the safety level of foundation pit construction, and prevents and curbs the occurrence of major accidents.

[0063] This invention discloses an intelligent deep foundation pit monitoring system based on a 5G private network. The system includes a data acquisition module, a data transmission module, a data processing module, and an online monitoring module. The data acquisition module employs a dual-mode collaborative data transmission strategy, including a timed acquisition and transmission mode and an active triggering transmission mode, to collect deep foundation pit data in real time. The data transmission module connects all modules via the 5G private network to ensure data transmission speed, security, and integrity. The data processing module performs differential processing on the collected data and calculates the horizontal displacement of the foundation pit slope based on a dynamic baseline solution model, the vertical displacement based on trigonometric leveling, and the seepage risk score based on a dynamic early warning algorithm. The online monitoring module analyzes the horizontal and vertical displacements of the foundation pit slope and the seepage risk score from the data processing module, records historical and current data, and analyzes future trends.

[0064] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A smart deep foundation pit monitoring system based on a 5G private network, characterized in that, It includes a data acquisition module, a data transmission module, a data processing module, and an online monitoring module; The data acquisition module uses a dual-mode collaborative data transmission strategy, consisting of a timed acquisition and transmission mode and an active trigger transmission mode, to monitor and collect data from the foundation pit and transmit it to the data processing module. The data transmission module is based on 5G private network technology and uses a 5G industrial router to realize the transmission between the data acquisition and data processing modules. The data processing module uses a differential processing method and calculates the horizontal displacement of the foundation pit slope based on the dynamic baseline solution model, the vertical displacement based on the trigonometric leveling method, and the seepage risk score of the foundation pit based on the dynamic early warning algorithm. The calculation results are then transmitted to the online monitoring module via a 5G private network. The online monitoring module analyzes the horizontal and vertical displacement of the foundation pit slope and the seepage risk score of the foundation pit, and issues an early warning when any value exceeds the safety threshold.

2. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, The data acquisition module collects data in real time, including the slope distance from the total station to the measurement point, the horizontal angle, the vertical angle, the total station height, the measurement point height, and the current absolute elevation of the groundwater level. The measurement points include monitoring points and benchmark points.

3. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, The timed data acquisition and transmission mode uses a precise time protocol to achieve microsecond-level time synchronization. It collects data at set intervals and sends it to the data processing module for analyzing data change trends.

4. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, The active triggering mode adopts a threshold triggering method. When the monitoring point parameters exceed the preset threshold, it will be automatically triggered and the currently collected data will be transmitted to the data processing module for analysis and calculation. The trigger threshold is set for parameters related to the horizontal displacement, vertical displacement and seepage risk score of the foundation pit slope.

5. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, The data transmission module uses a 5G private network to transmit data between the data acquisition module and the data processing module, and between the data processing module and the online monitoring module. The 5G private network provides a data transmission rate of 10Gbps.

6. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, The data processing module uses a differential processing method, which first calculates the difference in three-dimensional coordinates between the current monitoring point and the initial monitoring point, then calculates the difference in three-dimensional coordinates between the current reference point and the initial reference point, and obtains the net displacement of the current monitoring point by subtracting the two differences.

7. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, When calculating the horizontal displacement of the foundation pit slope based on the dynamic baseline solution model, the slope distance and vertical angle from the current total station to the monitoring point are compared with the corresponding data of the previous cycle to calculate the change in the coordinates of the current monitoring point and the coordinates of the monitoring point in the previous cycle on the horizontal plane, thereby reflecting the stability of the stress on the foundation pit support structure and the state of the soil.

8. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, When calculating vertical displacement using the trigonometric leveling method, the elevation difference is calculated using the slope distance and vertical angle of the monitoring points to eliminate the error between the total station height and the monitoring point height. The atmospheric refraction coefficient is calculated based on the current foundation pit environment to eliminate the influence of atmospheric refraction. Finally, the vertical displacement is calculated in conjunction with the radius of curvature of the Earth.

9. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, When calculating the seepage risk score of the foundation pit based on the dynamic early warning algorithm, the weight coefficients of the groundwater level change rate, the weight coefficient of the groundwater level deviation, the current groundwater level change, the monitoring time interval, the current groundwater level absolute elevation, and the safe water level threshold are comprehensively considered. The weight coefficients are determined according to the actual geographical environment and weather conditions of the foundation pit.

10. The intelligent deep foundation pit monitoring system based on a 5G private network as described in claim 1, characterized in that, In addition to analyzing and issuing early warnings on the horizontal and vertical displacement of the foundation pit slope and the seepage risk score of the foundation pit in real time, the online monitoring module also records historical and current data and analyzes the future trends of the three.

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