System for automatically detecting compactness of soil body in preloading process

Abstract

The invention discloses an automatic detection system for the compactness of a soil body in a preloading process, and belongs to the technical field of civil engineering and geotechnical engineering detection. The system comprises a sensor module, a data acquisition and transmission module, a data processing module, a display and alarm module and a power supply module. The sensor module is composed of a soil pressure sensor, a settlement sensor and a pore water pressure sensor which are respectively used for collecting soil pressure, settlement volume and pore water pressure data; the data processing module calculates the compactness of the soil body in real time through a multi-parameter fusion calculation model in combination with a soil pressure ratio, settlement correction and pore water pressure correction, and analyzes and predicts a future compactness change trend through a time sequence; and the display and alarm module displays the compactness distribution diagram and the change trend in real time, and triggers an alarm when the compactness distribution diagram and the change trend are abnormal or reach a target value.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering and geotechnical engineering detection technology, and in particular to an automatic detection system for soil density during a preloading process. Background Art

[0002] Preloading is an important method for treating soft soil foundations, widely used in projects such as highways, airport runways, and port terminals. It primarily accelerates soft soil consolidation by adding external loads, reduces post-construction settlement, and thus improves the bearing capacity of the foundation. During the preloading process, the density of the foundation soil is a key parameter for measuring the effectiveness of the preloading process and directly determines the quality of the foundation treatment. However, current methods for measuring soil density during preloading still have many shortcomings, particularly in terms of real-time performance, accuracy, and automation, and technical improvements are urgently needed.

[0003] Traditional soil density testing methods rely primarily on manual sampling and laboratory testing. This method typically involves collecting soil samples from different depths during preloading construction, and then indirectly estimating the density through laboratory tests (such as compression tests and permeability tests). However, this method has the following major issues:

[0004] Inefficient testing: Manual sampling and laboratory testing take a long time and cannot meet the real-time monitoring needs of the construction process. Construction personnel usually have to wait for test results before adjusting the loading plan, which may lead to uneven loading and time delays.

[0005] Result hysteresis: Since sampling and testing are time-consuming, it is impossible to provide real-time feedback on changes in soil density during the loading process. This hysteresis may result in excessive or insufficient loading, affecting the foundation reinforcement effect.

[0006] Insufficient spatial coverage: The density of manual sampling points is limited, making it difficult to fully reflect the overall density distribution of the soil in the loading area. Especially in large-scale loading sites, the distribution of sampling points is limited and uneven, which easily leads to "information blind spots".

[0007] High error risk: There are many manual operation links, which are easily affected by human factors and environmental conditions. The test results may have large errors, especially for complex soft soil or multi-layer soil, where the errors are more significant.

[0008] In recent years, with the development of sensor and information technology, sensor-based soil monitoring methods have gradually emerged. This method uses sensors embedded in the soil to monitor parameters such as soil pressure, settlement, and pore water pressure in real time. Combined with soil mechanics models, it indirectly infers density. Compared with traditional methods, this method has the following advantages:

[0009] Strong real-time performance: The sensor can collect data in real time and quickly feedback the changes in density through the data processing module, so that construction personnel can adjust the loading plan in time.

[0010] Comprehensive spatial coverage: Sensors can be arranged in a grid pattern to achieve all-round monitoring of the entire loading area, avoiding the sparse detection point problem of traditional manual detection.

[0011] High level of automation: The integration of sensors and data processing modules can realize full process automation from data acquisition, processing to display and alarm, reducing manual intervention.

[0012] High precision: Modern sensors have high sensitivity and anti-interference capabilities, which can accurately measure subtle changes in the soil and improve the accuracy of density calculations.

[0013] However, existing sensor-based detection systems still have the following technical difficulties that need to be addressed:

[0014] Insufficient multi-parameter comprehensive analysis: Earth pressure, settlement and pore water pressure are important parameters that affect soil density. Existing systems often only analyze a single parameter and lack a comprehensive evaluation method that integrates multiple parameters.

[0015] Limited dynamic prediction capabilities: During the preloading process, the density of the soil continues to evolve with time and load changes. The existing system lacks the ability to predict future density changes, making it difficult to provide proactive guidance for construction.

[0016] Insufficient environmental adaptability: Preloading is usually carried out in complex outdoor environments. The existing sensor system has limited capabilities in terms of waterproofing, dustproofing, and shockproofing, making it difficult to meet the requirements of long-term stable operation.

[0017] Imperfect alarm function: The alarm mechanism of the existing system is often based on a single threshold and lacks alarm function for regional anomalies, which may cause local problems to be ignored.

[0018] To address the above issues, this paper proposes an automatic soil density detection system during preloading. This system utilizes multiple sensors embedded in the soil (including soil pressure sensors, settlement sensors, and pore water pressure sensors) combined with a multi-parameter fusion algorithm to achieve real-time monitoring, dynamic correction, and trend prediction of soil density. Specific technical features are as follows:

[0019] Multi-parameter fusion calculation model: By collecting soil pressure, settlement and pore water pressure data in real time, a multi-parameter fusion calculation formula is constructed based on the physical model to fully reflect the dynamic changes in soil density.

[0020] Dynamic trend prediction: Using time series analysis methods, combined with current and historical data, predict the density change trend at future moments and provide guidance for construction decisions.

[0021] Real-time display and alarm function: The density distribution map and change curve are displayed through the visual terminal. Combined with threshold judgment and trend analysis, an alarm is triggered when the density is abnormal or reaches the target value.

[0022] Environmental adaptability design: The IP68 protection grade housing design ensures the system can operate stably and long-term in high humidity, water-rich, and corrosive environments.

[0023] The invention can not only solve the limitations of traditional methods in real-time and spatial coverage, but also improve the accuracy and automation level of density monitoring, provide scientific technical support for preloading construction, and has broad engineering application value.

[0024] Preloading is an important method in foundation treatment. It primarily accelerates the consolidation of soft soils by adding external loads, reduces post-construction settlement, and improves the bearing capacity of the foundation. In this process, soil density is a key parameter for measuring the effectiveness of foundation treatment, directly affecting the stability and bearing capacity of the foundation. However, existing technologies for monitoring and assessing soil density still have many shortcomings, particularly in terms of real-time performance, coverage, accuracy, and automation.

[0025] Currently, most soil density testing methods rely on manual sampling and laboratory testing, mainly including standard penetration tests (SPT), compression tests, and other laboratory mechanical tests. These methods require sampling from different depths in the loading area, transporting them to the laboratory for mechanical property testing, and then indirectly inferring the density through calculation. This process is time-consuming, usually taking several hours or even days. As a result, the test results cannot reflect the changes in soil density during the loading process in real time, making it difficult to guide the construction process in a timely manner, increasing the risk of loading construction.

[0026] Traditional manual sampling methods have limited sampling points, especially in large loading areas. Only a small number of discrete sampling points can reflect the soil condition in the loading area. This method makes it difficult to fully understand the overall distribution of density within the loading area. This is especially true when the foundation properties are complex and the soil density varies unevenly. This can easily lead to "blind spots," affecting the accuracy of foundation treatment evaluation.

[0027] The results of manual sampling and laboratory testing are subject to numerous external factors. For example, soil disturbance during sampling can lead to biased test results. Furthermore, differences between laboratory and field conditions can lead to errors in the test results in reflecting the actual soil density. Furthermore, some methods, such as penetration tests, have low accuracy and are unable to accurately reflect subtle changes in density.

[0028] Traditional testing methods require multiple steps, including sampling, transportation, laboratory testing, and analysis. This process consumes significant time and human resources, resulting in low testing efficiency. Furthermore, this method often requires extensive manual work, and repeated operations further increase testing costs. Human factors can also lead to inconsistent data or increased errors.

[0029] Traditional testing methods are mostly static assessments based on single tests, which cannot reflect the dynamic changes in soil density during the loading process. Soil density evolves over time, load, and pore water pressure during loading. However, existing technologies lack dynamic monitoring methods and data processing capabilities, making it difficult to effectively guide load control and unloading operations during construction.

[0030] Soil density is closely related to multiple parameters, such as earth pressure, settlement, and pore water pressure. However, existing technologies typically measure a single parameter individually and infer density based on this single parameter, failing to achieve comprehensive multi-parameter analysis and correction. This approach fails to fully consider the interactions between these parameters, resulting in inaccurate calculations.

[0031] Preloading is often carried out in complex field environments, which can include high humidity, strong corrosiveness, frequent vibration, and other adverse conditions. Existing sensors and detection systems are insufficiently designed for waterproofing, dustproofing, shockproofing, and corrosion resistance, leading to unstable systems or interruptions in monitoring data, making them difficult to meet long-term operational requirements.

[0032] Existing technologies for soil density assessment lack automated alarm functions, particularly anomaly alerts based on real-time data. Some systems only issue alerts when density falls below a fixed threshold, failing to factor in dynamic trends or regional differences. This can easily lead to missed local anomalies or false alarms, reducing the practicality of the alarms.

[0033] In summary, existing soil density detection technologies have significant deficiencies in terms of real-time performance, coverage, accuracy, efficiency, dynamic monitoring capabilities, multi-parameter integration, environmental adaptability, and alarm mechanisms. These issues not only limit the real-time assessment of foundation treatment effectiveness and construction guidance, but can also lead to quality and safety risks during preloading due to delayed or missing monitoring data. Therefore, an automated detection system that can monitor soil density during preloading in real time, comprehensively, accurately, and dynamically is urgently needed to address the shortcomings of existing technologies and provide more scientific and efficient technical support for foundation treatment.

[0034] To this end, we proposed an automatic detection system for soil density during preloading to solve the above problems. Summary of the Invention

[0035] The purpose of the present invention is to solve the above technical problems and provide a method and system for predicting the remaining life of equipment based on an improved convolutional neural network.

[0036] In order to achieve the above purpose, the present invention is implemented as follows:

[0037] A system for automatically detecting soil density during preloading, the system comprising a sensor module composed of the following sensors:

[0038] Soil pressure sensor: used to collect soil pressure data in the loading area i ;

[0039] Settlement sensor: used to collect the settlement amount s in the loading area i ;

[0040] Pore ​​water pressure sensor, used to collect pore water pressure p in the heap loading area i ;

[0041] Data acquisition and transmission module: used to collect the above sensor data and transmit it wirelessly or wiredly;

[0042] Data processing module: Calculate soil density D in real time according to the following formula i :

[0043]

[0044] Where: 理论 =γ·H 堆载 , γ is the soil weight, H 堆载 is the stacking height; k p is the pore water pressure influence coefficient, unitless, ranging from 0.2 to 0.5; p 临界 is the critical value of pore water pressure;

[0045] Display and alarm module: used to display the density distribution curve and change trend in real time, and trigger an alarm when the density is abnormal;

[0046] Power module: Provides power to the system, supports solar panels and backup batteries, and ensures long-term operation in field environments.

[0047] As a preferred technical solution of the present invention, the measuring range of the soil pressure sensor is 0-500kPa, the resolution is 0.1kPa, and the sensor is arranged along the depth of the heaped area. i Satisfies the following formula:

[0048]

[0049] Where: h i is the buried depth of the i-th sensor; n is the number of sensor layout points, with a typical value of 5-10; H 堆载 The stacking height.

[0050] As the preferred technical solution of the present invention, the measurement range of the settlement sensor is 0-500mm, the resolution is 0.1mm, and the settlement data s i The density is corrected by the following formula:

[0051]

[0052] Where: D s is the density value after considering the settlement correction; s i is the settlement at point i.

[0053] As a preferred technical solution of the present invention, the pore water pressure p i The dynamic change of density is further modified by the following formula:

[0054]

[0055] Where: D p is the final corrected density; k p is the pore water pressure influence coefficient, unitless, ranging from 0.2 to 0.5; p i is the real-time monitored pore water pressure; p 临界 is the critical value of pore water pressure.

[0056] As a preferred technical solution of the present invention, the system supports dynamic density prediction. The density value D(t+Δt) at the future time t+Δt is predicted by the following formula:

[0057]

[0058] in: is the rate of change of density over time; α is the adjustment coefficient, which has no unit and ranges from 0.1 to 1; Δt is the time interval.

[0059] As a preferred technical solution of the present invention, the display and alarm module includes:

[0060] Visual display terminal, used to display the density distribution curve and change trend in real time;

[0061] Alarm device, used to alarm when the density reaches the target value D 目标 Or abnormal fluctuations trigger an alarm, where the target density D 目标 The calculation formula is:

[0062] D 目标 =D 标准 (1+β)

[0063] Where: D 标准 is the industry density standard value; β is the adjustment coefficient, ranging from 0.05 to 0.2.

[0064] As a preferred technical solution of the present invention, the sensor modules are arranged in a grid matrix with a horizontal spacing of d h and vertical spacing d v The following conditions are met:

[0065]

[0066] Where: d h d v is the sensor arrangement spacing in the horizontal and vertical directions; n is the number of sensor arrangement points, ranging from 5 to 10.

[0067] As a preferred technical solution of the present invention, the data storage module supports storing data at time intervals of 1 second, with a storage capacity of no less than 1 year, and supports exporting storage files via a USB interface or wirelessly, and the file format is CSV.

[0068] As a preferred technical solution of the present invention, the power module is composed of a solar panel and a 12V lithium battery, wherein the power of the solar panel is not less than 100W, and can support operation for more than 10 days in continuous rainy weather.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention proposes an automatic soil density detection system during the preloading process, which overcomes many shortcomings of existing technologies, is real-time, comprehensive, and efficient, and significantly improves the quality and efficiency of soil density monitoring. Specific beneficial effects are as follows:

[0071] First, the present invention realizes real-time monitoring of soil pressure, settlement and pore water pressure in the loading area through a multi-parameter sensor module. The sensor layout is gridded and layered, covering the entire loading area, and dynamically calculating the soil density through high-precision sensors and multi-parameter fusion algorithms. Compared with traditional manual sampling and laboratory analysis methods, the present invention significantly improves the real-time performance of detection. Construction personnel can adjust loading and unloading operations in a timely manner based on the real-time data feedback from the system, avoiding overloading or underloading due to data lag, and ensuring a more efficient and safe construction process.

[0072] Secondly, the present invention adopts an innovative multi-parameter fusion calculation model, which comprehensively reflects the dynamic changes in soil density through comprehensive analysis of soil pressure ratio, settlement correction and pore water pressure correction. Compared with the traditional single-parameter calculation method, the calculation results of the present invention are more accurate and can effectively avoid misjudgments caused by a single parameter. In addition, the system introduces a dynamic prediction algorithm to predict the density change trend at future moments based on time series analysis, providing forward-looking guidance, further optimizing the preloading construction plan, and improving the scientificity and accuracy of the construction.

[0073] Finally, the present invention has good environmental adaptability and automatic alarm function. The system adopts an IP68 protection grade shell design, which can operate stably for a long time in field environments with high humidity, strong corrosion, high water content and complex vibration, and meet the needs of various complex construction scenarios. At the same time, the display and alarm module can draw the density distribution curve of the loading area in real time, and issue an alarm prompt when the density is abnormal or reaches the target value. The alarm mechanism combines dynamic trend analysis and regional anomaly judgment to avoid the problem of false alarms or missed alarms in traditional alarm systems, and provides construction personnel with more intelligent decision-making support.

[0074] In summary, the present invention solves the shortcomings of existing technologies in real-time performance, accuracy, coverage, and dynamic prediction capabilities through real-time monitoring by sensors, comprehensive calculation of multiple parameters, and intelligent display and alarm, significantly improving the quality control level of preloading construction, and has broad engineering application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 This is a system block diagram of an automatic soil density detection system during preloading;

[0077] Figure 2 The figure shows the composition of the sensor module. DETAILED DESCRIPTION

[0078] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0079] The following is combined with Figure 1 - Figure 2 , the specific implementation methods of the present invention are described in detail.

[0080] Implementation method 1: system basic functions and architecture design;

[0081] The present invention provides an automatic detection system for soil density during preloading. The system comprises a sensor module, a data acquisition and transmission module, a data processing module, a display and alarm module, and a power supply module. The system's functional design is as follows:

[0082] Arrangement and function of sensor modules: Earth pressure sensor: arranged along the depth of the heaped area, measuring the earth pressure σ at different depths in the heaped area i .

[0083] H 堆载 Taking depth = 5m as an example, five sensors are arranged at depths of 1m, 2m, 3m, 4m and 5m to ensure that the soil pressure data covers the entire loading area.

[0084] Settlement sensors: Settlement sensors are arranged in a 2m×2m grid in the plane of the loading area. Each sensor measures the settlement amount s in real time. i .

[0085] Pore ​​water pressure sensor: It is arranged in the same position as the soil pressure sensor and is used to monitor the dynamic changes of pore water pressure during the loading process. i .

[0086] Data acquisition and transmission module Each sensor transmits data to the central data processing unit via wired (optical fiber) or wireless (LoRa) means, with a transmission frequency of once per second.

[0087] The data acquisition unit is equipped with anti-interference function to ensure that the signal is not distorted during long-distance transmission.

[0088] Data processing module: The data processing module calculates the soil density D based on the following formula i :

[0089]

[0090] Where: 理论 =γ·H 堆载 , γ=18kN / m 3 is the soil weight; H 堆载 =5m is the stacking height; k p =0.3,p 临界 =20kPa.

[0091] Display and alarm module: real-time display of density distribution curve, drawing of two-dimensional contour map or three-dimensional surface map.

[0092] If the density D p Below target value D 目标 =0.6, an alarm is triggered. The alarm forms include sound, light prompt, or text message notification to the construction personnel.

[0093] Power supply module: The system uses solar panels (power 100W) combined with 12V lithium batteries to ensure that the system can operate for more than 10 days in continuous rainy weather in the wild.

[0094] Implementation 2: Data processing and formula application examples;

[0095] Soil pressure calculation: The present invention realizes real-time calculation of density through data processing module. The specific calculation example is as follows: the buried depth of a certain sensor position Earth pressure measured by the sensor

[0096] The theoretical earth pressure is calculated as:

[0097] σ 理论 =γ·H 堆载 =18kN / m 3 5m = 90kPa

[0098] The earth pressure ratio is:

[0099]

[0100] Settlement correction: Assume that the settlement amount s measured by the settlement sensor is i =0.15m, loading height H 堆载 =5m. Then the density after settlement correction is:

[0101]

[0102] The pore water pressure sensor measures p i =10kPa, critical pore water pressure p 临界=20kPa, influence coefficient k p =0.3 pore water pressure correction.

[0103] The final corrected density is: 0.85=0.4947, that is, the final density at this position is D p ≈0.495.

[0104] Implementation method 3: dynamic density prediction;

[0105] To predict the density at a certain point in the future, the system uses the following steps:

[0106] Calculate the current time change rate: current density D(t) = 0.495, density at the previous moment D(t-Δt) = 0.490, time interval Δt = 1s.

[0107]

[0108] Predicting future density: Adjustment coefficient α = 0.8, then the predicted density value at the future time t+Δt is:

[0109]

[0110] D(t+Δt)=0.495+0.8·0.005=0.495+0.004=0.499

[0111] The predicted density is D(t+Δt)≈0.499.

[0112] Implementation 4: Sensor grid layout optimization;

[0113] In the loading area A 堆载 =100m 2 In the scenario:

[0114] Layout quantity calculation: horizontal and vertical spacing d h =2m,d v =2m.

[0115] The total number of points n in the grid arrangement satisfies:

[0116]

[0117] Real-time monitoring and display: 25 monitoring points are evenly arranged in the loading area, and soil pressure, settlement and pore water pressure sensors are arranged at each point.

[0118] The data collection frequency is once per second, and all sensor data are calculated to generate a dynamic density surface graph.

[0119] The contour map shows the distribution of density, with high-density areas marked in green and low-density areas marked in red.

[0120] Implementation method 5: Monitoring of heap load in stages;

[0121] During the loading process, a phased monitoring method is adopted: Phase 1: Loading height H 堆载 =3m initial density is D i =0.3.

[0122] The settlement and pore water pressure gradually increase, and the system updates the density distribution map every 30 minutes.

[0123] Stage 2: Loading height H 堆载 =5m Continue to monitor the settlement and pore water pressure, and after density correction D p =0.6, when the density approaches the target value, the construction workers are prompted to stop loading.

[0124] Phase 3: After the loading is completed, the system continuously monitors the dissipation of pore water pressure and generates a density report to evaluate the loading effect.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An automatic soil density detection system during preloading, characterized in that: The system includes: a sensor module, which is composed of the following sensors: Soil pressure sensor: used to collect soil pressure data in the loading area i ; Settlement sensor: used to collect the settlement amount s in the loading area i ; Pore ​​water pressure sensor, used to collect pore water pressure p in the heap loading area i ; Data acquisition and transmission module: used to collect the above sensor data and transmit it wirelessly or wiredly; Data processing module: Calculate soil density D in real time according to the following formula i : Where: 理论 =γ·H 堆载 , γ is the soil weight, H 堆载 is the stacking height; k p is the pore water pressure influence coefficient, unitless, ranging from 0.2 to 0.5; p 临界 is the critical value of pore water pressure; Display and alarm module: used to display the density distribution curve and change trend in real time, and trigger an alarm when the density is abnormal; Power module: Provides power to the system, supports solar panels and backup batteries, and ensures long-term operation in field environments.

2. The automatic soil density detection system during preloading according to claim 1 is characterized in that: The measuring range of the soil pressure sensor is 0-500kPa, the resolution is 0.1kPa, and the sensor is arranged along the depth of the heaped area. i Satisfies the following formula: Where: h i is the buried depth of the i-th sensor; n is the number of sensor layout points, with a typical value of 5-10; H 堆载 The stacking height.

3. The automatic soil density detection system during preloading according to claim 1 or 2, characterized in that: The measurement range of the settlement sensor is 0-500mm, the resolution is 0.1mm, and the settlement data s i The density is corrected by the following formula: Where: D s is the density value after considering the settlement; s i is the settlement at point i.

4. The automatic soil density detection system during preloading according to claim 3 is characterized in that: Pore ​​water pressure p i The dynamic change of density is further modified by the following formula: Where: D p is the final corrected density; k p is the pore water pressure influence coefficient, unitless, ranging from 0.2 to 0.5; p i is the real-time monitored pore water pressure; p 临界 is the critical value of pore water pressure.

5. The automatic soil density detection system during preloading according to claim 4 is characterized in that: The system supports dynamic density prediction. The density value D(t+Δt) at the future time t+Δt is predicted by the following formula: in: is the rate of change of density over time; α is the adjustment coefficient, which has no unit and ranges from 0.1 to 1; Δt is the time interval.

6. The automatic soil density detection system during preloading according to claim 1 is characterized in that: Display and alarm module includes: Visual display terminal, used to display the density distribution curve and change trend in real time; Alarm device, used to alarm when the density reaches the target value D 目标 Or abnormal fluctuations trigger an alarm, where the target density D 目标 The calculation formula is: D 目标 =D 标准 ·(1+b) Where: D 标准 is the industry density standard value; β is the adjustment coefficient, ranging from 0.05 to 0.

2.

7. The automatic soil density detection system during preloading according to claim 1 is characterized in that: The sensor modules are arranged in a grid matrix with a horizontal spacing of d h and vertical spacing d v The following conditions are met: Where: d h d v is the sensor arrangement spacing in the horizontal and vertical directions; n is the number of sensor arrangement points, ranging from 5 to 10.

8. The automatic soil density detection system during preloading according to claim 1 is characterized in that: The data storage module supports storing data at intervals of 1 second, with a storage capacity of no less than 1 year, and supports exporting storage files via a USB interface or wirelessly, with the file format being CSV.

9. The automatic soil density detection system during preloading according to claim 1 is characterized in that: The power module consists of a solar panel and a 12V lithium battery, wherein the power of the solar panel is not less than 100W and can support operation for more than 10 days in continuous rainy weather.

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