Early warning method and system for determining high-pressure triggering instability threshold value through supergravity test

Abstract

The invention relates to the technical field of landfill safety monitoring, and particularly discloses an early warning method and system for determining a high-pressure triggering instability threshold value through a supergravity test. And obtaining a model configuration parameter set containing a centrifugal scale proportion and a material ratio, and constructing a physical model in the centrifugal machine to restore the prototype high stress field. Then, under the supergravity environment, instability is induced through bottom gas injection, pressure and displacement data of the whole process are synchronously collected in a high-frequency mode to determine a critical air pressure ratio threshold value, finally, risk comparison judgment is conducted on the critical air pressure ratio threshold value and the air pressure ratio obtained through field real-time monitoring, and therefore accurate early warning of the instability risk is achieved.

Description

Technical Field

[0001] This application relates to the field of landfill safety monitoring technology, and more specifically, to an early warning method and system for determining the high-pressure-triggered instability threshold using a hypergravity test. Background Technology

[0002] Municipal solid waste landfills, as a long-standing and still important method of urban solid waste disposal, are receiving increasing attention for their long-term safe operation. During the long degradation and stabilization process of organic matter within landfills, large amounts of landfill gas and leachate are generated. In some high-water-level or humid landfills, due to the high concentration of organic matter and surfactants in the leachate, landfill gas easily combines with the liquid during transport, forming foam flows. The gas resistance effect generated by these foam flows leads to a significant accumulation of deep pore gas pressure, exceeding the pore liquid pressure, thereby drastically reducing the effective stress of the landfill.

[0003] Traditional landfill stability monitoring methods typically focus on surface displacement observation or groundwater level monitoring. However, surface displacement signals often exhibit significant lag; by the time obvious displacement is detected, a penetrating sliding surface has often already formed within the landfill, making early warning difficult. Furthermore, existing warning indicators are mostly based on critical water levels, neglecting the dominant role of high pressure in triggering landslides and lacking quantitative threshold criteria for high-pressure-triggered instability. In laboratory research, conventional constant gravity environment test models, limited by their geometric dimensions, exhibit effective stress at the bottom that is far lower than the high-stress conditions at the depths of actual landfills, leading to significant discrepancies between simulated gas-liquid transport characteristics and failure patterns and actual engineering sites. Moreover, due to the high complexity and heterogeneity of the landfill internal environment, single pressure monitoring values ​​are insufficient to meet the warning needs at different depths and locations, resulting in technical bottlenecks such as missed or frequent false alarms in existing warning mechanisms. Therefore, how to accurately recreate the prototype stress field using hypergravity technology and precisely determine the quantitative threshold for high-pressure-triggered instability has become a critical issue urgently needing to be addressed to improve landfill disaster prevention and mitigation capabilities. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. This application provides an early warning method and system for determining the high-pressure-triggered instability threshold in hypergravity experiments.

[0005] The technical solution of this application is as follows: According to one aspect of this application, a method for early warning of high-pressure-triggered instability threshold determination in hypergravity experiments is provided, comprising: S1: Perform statistical analysis and physical model scaling estimation on the acquired raw data stream from the field survey to obtain a set of model configuration parameters including centrifugal scaling ratio, artificial material ratio, and simulated liquid formulation; S2: Based on the model configuration parameter set, a physical model is built inside the centrifuge and centrifugal acceleration loading is performed to build a hypergravity test environment system; S3: In the hypergravity testing environment system, the experimental values ​​of pore air pressure, overlying soil pressure and slope displacement inside the model are monitored throughout the entire process by bottom air injection and synchronous high-frequency acquisition to obtain the instability process dataset. S4: Determine the critical pressure ratio threshold based on the instability process dataset; S5: Based on the field air pressure and field earth pressure values ​​in the field monitoring data stream obtained from the target landfill, determine the real-time field air pressure ratio that reflects the actual working conditions of the current landfill. S6: Compare the real-time air pressure ratio with the critical air pressure ratio threshold to determine whether a high-pressure-triggered instability warning signal is generated.

[0006] According to another aspect of this application, an early warning system for determining the high-pressure-triggered instability threshold in hypergravity experiments is provided, comprising: The model parameter configuration module is used to perform statistical analysis and physical model scaling estimation on the acquired raw data stream from the field survey to obtain a set of model configuration parameters including centrifugal scaling ratio, artificial material ratio and simulated liquid formula. The hypergravity physics modeling module is used to build a physical model inside a centrifuge and perform centrifugal acceleration loading to construct a hypergravity testing environment system based on the model configuration parameter set. The instability process monitoring module is used to monitor the experimental values ​​of pore air pressure, overlying soil pressure, and slope displacement inside the model in a hypergravity testing environment system through bottom air injection and synchronous high-frequency acquisition to obtain the instability process dataset. The critical pressure ratio identification module is used to determine the critical pressure ratio threshold based on the instability process dataset. The on-site working condition sensing module is used to determine the real-time on-site air pressure ratio, which reflects the actual working condition of the current landfill, based on the on-site air pressure and on-site earth pressure values ​​in the on-site monitoring data stream obtained from the target landfill. The instability risk warning and discrimination module is used to compare the real-time air pressure ratio with the critical air pressure ratio threshold to determine whether a warning signal for high pressure triggering instability is generated.

[0007] Compared with existing technologies, this application provides an early warning method and system for determining the high-pressure-triggered instability threshold using hypergravity experiments. On the one hand, by introducing hypergravity centrifuge simulation technology, this application effectively overcomes the defects in traditional constant gravity model tests where the gas-liquid transport characteristics and soil failure patterns are distorted due to excessively low stress levels, ensuring that the acquired instability process data can truly reflect the high confining pressure conditions at the depth of the landfill. On the other hand, this application abandons the single absolute pressure threshold or the lagging displacement threshold, and instead uses the critical pressure ratio, a dimensionless parameter reflecting the essence of effective stress, as the early warning indicator. This eliminates the influence of differences in monitoring depth, significantly improves the universality and accuracy of the early warning, and realizes a technological leap from reactive disaster relief to proactive disaster prevention. Attached Figure Description

[0008] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0009] Figure 1 This is a flowchart of a method for determining a high-pressure-triggered instability threshold in a hypergravity experiment according to an embodiment of this application; Figure 2 This is a schematic diagram of the physical model setup according to an embodiment of this application; Figure 3 Here are schematic diagrams and physical images of the pore pressure sensor used according to embodiments of this application; Figure 4 The diagram below shows the testing process of the hypergravity model according to an embodiment of this application, including three stages: (a) centrifugal loading, (b) liquid injection stabilization, and (c) gas injection induction. Figure 5 These are pressure evolution curves monitored during the liquid injection and gas injection processes according to embodiments of this application, wherein (a) is the pore hydraulic pressure development curve and (b) is the pore gas pressure development curve; Figure 6 The slope displacement development curve during the water and air injection process according to the embodiments of this application; Figure 7 The slope crest displacement development curve during water and air injection according to the embodiments of this application; Figure 8 This is a vector diagram of the PIV displacement at the moment of instability and a schematic diagram of the measured slip surface according to an embodiment of this application; Figure 9 The shear strength variation curve considering the combined effects of pore hydraulic pressure and pore gas pressure during the test according to the embodiments of this application; Figure 10 This is a block diagram of an early warning system for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to an embodiment of this application. Detailed Implementation

[0010] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0011] Figure 1 This is a flowchart illustrating a method for determining a high-pressure-triggered instability threshold in a hypergravity experiment according to an embodiment of this application. Figure 1 As shown, the early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to an embodiment of this application includes: S1: Perform statistical analysis and physical model scaling estimation on the acquired raw data stream from the field survey to obtain a set of model configuration parameters including centrifugal scaling ratio, artificial material ratio, and simulated liquid formulation. The raw data stream from the field survey includes: macroscopic geometric dimension data, solid phase geotechnical property data, and liquid phase physicochemical rheological data. The macroscopic geometric dimension data includes the total height of the prototype pile, the characteristic depth of the target instability area, and the slope of the on-site slope. The solid phase geotechnical property data includes the particle size distribution curve of the prototype waste soil borehole, the dry density of the prototype waste soil, and the percentage of organic matter content. The liquid phase physicochemical rheological data includes the foaming volume index of the on-site leachate and the foam half-life of the on-site leachate.

[0012] It is understandable that landfill bodies are highly stress-dependent. Key mechanical and physical parameters such as shear strength, porosity, and permeability coefficient of landfill soil change nonlinearly with increasing stress levels. Traditional constant gravity model tests cannot reproduce the high-stress environment of deep landfill bodies, resulting in serious deviations in gas-liquid transport patterns and instability characteristics. Therefore, it is necessary to construct a physical model under strict similarity criteria using hypergravity centrifugation simulation technology to ensure that the instability threshold obtained in the laboratory can be accurately mapped to the engineering site.

[0013] In one example of this application, step S1 includes: calculating and extracting features of the centrifugation simulation scale ratio based on the macroscopic geometric dimension data in the original data stream of the site survey and combined with the centrifuge load capacity to obtain the centrifugation scale ratio and physicochemical feature sub-data stream; fitting the mass fraction of the basic components of peat, quartz sand and kaolin to obtain the artificial material ratio and liquid property data package based on the dry density and organic matter content percentage of the prototype waste soil in the physicochemical feature sub-data stream; establishing a concentration mapping relationship based on the foaming volume index and foam half-life of the site leachate in the liquid property data package and back-calculating the surfactant mass concentration to determine the simulation liquid formula; and performing structured verification and encapsulation of the centrifugation scale ratio, artificial material ratio and simulation liquid formula to obtain the model configuration parameter set.

[0014] First, based on the macroscopic geometric dimension data in the original data stream from the on-site investigation and combined with the centrifuge load capacity, the scale ratio of the centrifuge simulation is calculated and features are extracted to obtain the centrifuge scale ratio and physicochemical feature sub-data streams. Specifically, it is necessary to analyze the total height of the prototype stack. and the characteristic depth of the target instability region And combined with the effective maximum height of the centrifuge model box and the maximum load gravitational acceleration capacity of the centrifuge Calculate the centrifugal scaling ratio that satisfies the boundary effect constraints based on the geometric similarity criterion. Centrifugal scaling ratio The determination of [a] requires the following conditions to be met: and In the formula, The centrifugal scaling ratio represents the multiple of the model's gravitational acceleration relative to normal gravitational acceleration. This refers to the centrifuge's maximum load capacity under gravitational acceleration. This is the standard gravitational acceleration; This represents the maximum effective internal height of the centrifuge model box; The boundary effect safety factor is typically set to a value of [value missing]. ; The total height of the target landfill prototype.

[0015] Subsequently, based on the prototype waste soil dry density in the physicochemical feature sub-data stream... and percentage of organic matter content The mass fractions of the basic components—peat moss, quartz sand, and kaolin—were fitted to obtain the artificial material mix and liquid property data package. Since landfill waste contains a large amount of degradation residue, this step adjusts the ratio of skeletal quartz sand (with fiber reinforcement and high compressibility) to fit the mechanical properties of natural landfill soil, generating the artificial material mix. The calculation formula is as follows: In the formula, The proportions represent the mass ratios of peat moss, quartz sand, and kaolin, respectively. The empirical mapping function can be obtained through indoor geotechnical tests. Specifically, different proportions of peat moss-quartz sand-kaolin mixtures are prepared in advance, and their compression modulus, shear strength and permeability coefficient are measured. Comparative tests are conducted with natural waste soil with target dry density and organic matter content, and the proportion with the highest mechanical property similarity is selected as the mapping result. The average dry density of the prototype waste soil; The percentage of organic matter content in the original waste soil.

[0016] Next, based on the in-situ leachate foaming volume index in the liquid property data package... and the half-life of on-site leachate foam Establish a concentration mapping relationship and back-calculate the surfactant mass concentration. To determine the formulation of the simulated liquid, the gas resistance effect generated by foam in porous media is the core mechanism leading to high-pressure accumulation. By selecting surfactants such as sodium dodecyl sulfate (SDS) and utilizing the foaming characteristic inversion function, it is ensured that the foam generated by the simulated liquid in the model pores has the same pressure-stabilizing capability as in the field. The calculation formula is as follows: In the formula, This refers to the mass concentration of the surfactant in the simulated solution formulation; This is the inversion function for foaming properties; The foaming volume index is the volume index of the leachate sample from the site. The foam half-life of the leachate sample was determined. Finally, the centrifugation scaling ratio, artificial material proportions, and simulated liquid formulation were structurally verified and encapsulated to obtain the model configuration parameter set.

[0017] In one specific embodiment, when the height of the prototype landfill pile is 20 meters, the centrifugal acceleration is set to 66.7g based on centrifuge performance and similarity criteria, thus determining the centrifugal scaling ratio to be 66.7. Regarding material preparation, referring to the characteristics of the on-site landfill soil, peat moss, quartz sand, and kaolin are mixed in a 4:4:1 mass ratio. The resulting model solid waste closely resembles the actual landfill waste in terms of bulk density, compressibility, permeability, and strength characteristics. In terms of simulated liquid preparation, to simulate the foaming characteristics of real leachate, 0.08% SDS surfactant is added to pure water. This ensures that the foam composite index of the generated simulated leachate is within the measured range of the on-site leachate, thereby ensuring that subsequent experiments can accurately reproduce the high-pressure disaster process induced by foam flow. Through the combination and encapsulation of the above parameters, a complete model configuration parameter set is formed, providing accurate input criteria for the subsequent construction of the hypergravity testing environment system.

[0018] S2: Based on the model configuration parameter set, a physical model is constructed within a centrifuge, and centrifugal acceleration is applied to build a hypergravity testing environment system. It should be understood that because the mechanical parameters of landfill soil, such as shear strength, porosity, and permeability, are highly dependent on stress levels, a model only a few tens of centimeters high under a 1g gravity field cannot accurately represent the self-weight stress field of an actual landfill tens of meters deep. Therefore, a centrifugal force field N times the gravitational acceleration must be generated using a centrifuge to enable the scaled-down model (height of...) to... This generates self-weight stress consistent with the prototype, thereby ensuring the accurate reproduction of gas-liquid transport laws and instability failure mechanisms. The core concepts involved in this step include the pre-embedded sensor model box, which is a physical entity in which sensing elements are embedded according to preset coordinates during the filling process; and the liquid level stable hypergravity field, which refers to the stable equilibrium state of seepage and hydrostatic pressure distribution established inside the model by liquid injection under centrifugal high overload environment.

[0019] In one example of this application, step S2 includes: constructing a pre-embedded sensor model box based on the artificial material ratio, centrifugal scaling ratio, and simulated liquid formula extracted from the model configuration parameter set; installing a microporous aeration network and a liquid injection pipeline at the reserved interface of the pre-embedded sensor model box and connecting it to an external control device to construct the assembled model system; calculating the target angular velocity according to the centrifugal scaling ratio, driving the assembled model system to be loaded in stages to the target rotation speed and injecting simulated leachate to obtain a liquid level-stable hypergravity field; and performing system state verification and ready-to-encapsulate on the liquid level-stable hypergravity field to obtain a hypergravity testing environment system.

[0020] Specifically, firstly, based on the artificial material ratios, centrifugal scaling ratios, and simulated liquid formulations extracted from the model configuration parameter set, a pre-embedded sensor model box is constructed. During this process, a mixture is prepared according to the artificial material ratios, and the thickness of the model's layered filling and the sensor's embedding depth coordinates are calculated based on the centrifugal scaling ratio. For example... Figure 2 As shown in (a), the model box is designed as a landfill profile structure, with a geomembrane laid at the bottom, and is constructed according to... Figure 2 As shown in (c), a microporous aeration network is pre-installed 4 cm above the bottom. During the model filling process, as... Figure 2 As shown in (b), sensor arrays are installed at key locations (such as the toe of the slope and the middle of the slope) at a distance of 8 cm or more from the bottom. To accurately distinguish between gas and liquid phase pressures, this embodiment employs the following... Figure 3 The specially designed sensor shown: Figure 3 (a) shows a pressure sensor with a semi-permeable membrane, which uses a hydrophobic and breathable membrane to isolate the liquid in order to measure pure gas pressure; Figure 3 (b) shows a tensiometer with a clay plate for measuring pore fluid pressure under unsaturated conditions. Subsequently, a microporous aeration network and liquid injection lines are installed at the pre-reserved interface of the pre-embedded sensor model box, and external control equipment is connected to construct the assembled model system. This includes installing a microporous aeration network at the bottom of the model box to simulate deep biochemical gas production, installing liquid injection lines on the side walls or bottom to regulate water levels, leading all sensor cables and pipes to the centrifuge slip ring interface, sealing the model box boundaries to prevent fluid leakage at high g values, and connecting an external high-pressure gas source controller and a precision peristaltic pump.

[0021] Next, the target angular velocity is calculated based on the centrifugal scaling ratio. The assembled model system is then driven to be loaded to the target rotational speed in stages, and simulated leachate is injected to obtain a stable hypergravity field. For example... Figure 4 As shown, the process is divided into three stages: First, as Figure 4 As shown in (a), centrifugal loading was performed, with the centrifuge sequentially accelerating to the target speed (e.g., 66.7 g) and held steady to complete model sedimentation; subsequently, as shown in (a), centrifugal loading was performed to complete model sedimentation. Figure 4 As shown in (b), while maintaining rotation under a high gravitational field, simulated leachate is injected through the side supply chamber until the liquid level reaches a preset height (e.g., 15 cm) and a stable hydrostatic pressure field is established; finally, as shown in (b), simulated leachate is injected through the side supply chamber until the liquid level reaches a preset height (e.g., 15 cm) and a stable hydrostatic pressure field is established; Figure 4 As shown in (c), the gas injection induction stage begins, where gas is injected through the bottom piping network to trigger instability. A simulated leachate containing surfactants is prepared using a simulated liquid formulation, and the target angular velocity of the centrifuge is calculated according to the following formula: In the formula, The target angular velocity of the centrifuge, in units of This parameter determines how fast the centrifuge arm rotates; The scale is centrifugal, representing the multiple of the simulated gravitational field relative to Earth's normal gravitational field. Let the standard gravitational acceleration be a constant. ; This represents the effective rotation radius of the centrifuge arm, in meters (m). Start the centrifuge and apply loads in stages until the target rotation speed is reached, thus placing the model in... In a hypergravity field, simulated leachate is injected via a peristaltic pump while maintaining centrifugal rotation until the readings of the pore pressure sensor group and the soil pressure sensor group stabilize and the liquid level reaches a preset initial height. Finally, the system state is verified and ready for packaging in a hypergravity field with a stable liquid level to obtain the hypergravity testing environment system. This includes baseline calibration of all sensors, confirming that the readings under pure liquid level load are consistent with the theoretical hydrostatic pressure, eliminating sensor faults, and confirming that the sampling frequency of the data acquisition system is set to a high-frequency mode (e.g., greater than or equal to 100Hz) to capture instantaneous damage.

[0022] In one specific embodiment, the prototype landfill was 20 meters high, the model box was designed to be 0.3 meters high and 0.7 meters wide, and the centrifugal scaling ratio was determined to be 66.7 (i.e., 66.7g) based on similarity criteria. During model construction, a geomembrane was first laid at the bottom of the model box to simulate a weak interface. Then, artificially synthesized solid waste, prepared with peat moss, quartz sand, and kaolin in a 4:4:1 mass ratio, was evenly spread in layers of 0.05 meters thickness and compacted to the designed bulk density (e.g., 9kN / m³). An air injection network was laid 4 cm above the bottom, and pore pressure sensors, tensiometers, and earth pressure cells were embedded 8 cm above the bottom. After the model was filled to a height of 30 cm, the right side was cut into a 1:1.5 slope. During the centrifugal loading stage, the centrifuge was sequentially accelerated to 20g, 40g, and 50g, finally stabilizing at 66.7g. Each acceleration stage was maintained for approximately 10 minutes to ensure settlement stability. Once the acceleration stabilized at 66.7g (around the 35th minute), a simulated leachate containing 0.08% sodium dodecyl sulfate (SDS) was injected through the left-side liquid supply chamber until the liquid level stabilized at a height of 15cm around the 60th minute. The sensor readings showed that a stable hydrostatic pressure field had been established, thus completing the construction of the hypergravity test environment system and preparing for the subsequent high-pressure induced instability test.

[0023] S3: In the hypergravity testing environment system, the experimental values ​​of pore air pressure, overlying soil pressure, and slope displacement within the model are monitored throughout the entire process by bottom gas injection and synchronous high-frequency acquisition to obtain a dataset of the instability process. It should be understood that the triggering mechanism of landfill instability is closely related to the evolution of the liquid-gas two-phase flow within the landfill, especially in humid landfills. When the injected gas mixes with leachate containing surfactants, foam is generated within the pores. The presence of foam significantly reduces gas-phase permeability, leading to pore air pressure accumulation and the formation of a high-pressure zone higher than the pore hydraulic pressure. Since the instability process is usually sudden and the failure timescale is extremely short, traditional low-frequency monitoring methods cannot capture the instantaneous pressure surge and displacement abrupt change during a landslide. Therefore, it is necessary to induce failure through bottom constant-pressure gas injection, supplemented by high-frequency synchronous acquisition technology, to obtain complete time-series data containing critical failure characteristics, thus providing a high-fidelity data foundation for the accurate calculation of subsequent thresholds.

[0024] In one example of this application, step S3 includes: performing micro-flow gas injection and foam evolution induction on the hypergravity testing environment system to obtain a dynamic loading system; using a data acquisition system to synchronously read sensor values ​​and camera images in the dynamic loading system at a fixed sampling frequency, and performing pressure change rate calculation and displacement detection to obtain the original time series data packet; determining the full-field displacement vector based on the original time series data, and locking the instability moment based on the instantaneous velocity threshold and extracting effective time window data to obtain the instability process dataset.

[0025] Specifically, firstly, a dynamic loading system is obtained by micro-flow gas injection and foam evolution induction in the hypergravity testing environment system. The dynamic loading system refers to the mechanical state entity in which the pore pressure distribution inside the model dynamically evolves over time after a gas phase pressure boundary is applied to the ready hypergravity testing system. In the hypergravity field maintained by high-speed centrifuge rotation, a high-pressure gas source is controlled to initiate the micro-flow injection mode using a stepped pressurization scheme. The initial injection rate is typically set to 10% of the critical seepage velocity. As the gas enters the pores of the model containing surfactants, the surface tension instability at the gas-liquid interface causes microbubbles to aggregate and form a continuous foam flow. Because the liquid film in the foam liquid significantly increases the gas phase kinematic viscosity, it reduces the relative permeability coefficient of the gas phase and increases the gas phase transport resistance, causing the model to enter the dynamic loading system state before critical instability.

[0026] Subsequently, the data acquisition system synchronously reads sensor values ​​and camera images from the dynamic loading system at a fixed sampling frequency, and performs pressure change rate calculation and displacement detection to obtain the raw time-series data packets. Sampling frequency The settings must meet the requirement of capturing the moment of destruction, and the calculation method is as follows: in, The sampling frequency of the data acquisition system is expressed in Hertz (Hz). The shortest timescale for the anticipated instability and failure process is expressed in seconds (s). During the data acquisition process, the time series data of each measuring point within the model are read synchronously. The experimental values ​​of pore air pressure and the corresponding overlying soil pressure at the above location were obtained simultaneously by a high-speed camera, triggering the acquisition of slope displacement image streams.

[0027] Finally, the total displacement vector is determined based on the original time-series data, and the instability moment is locked based on the instantaneous velocity threshold. Data within the effective time window is extracted to obtain the instability process dataset. The particle image velocimetry (PIV) algorithm is applied to the acquired slope displacement image stream to calculate the total displacement vector field of the model. The instability moment is determined based on the instantaneous velocity of the slope feature points, using the following formula: in, The instability moment refers to the exact point in time when the model transitions from a stable state to an unstable and destructive state. For the feature points of the model slope in The instantaneous displacement-velocity vector at a given moment; A preset instability velocity threshold is set, determined based on slope displacement image streams acquired by the data acquisition system (analyzed using Particle Image Velocity (PIV) technology). For example, in a landslide case, the monitored maximum daily slip rate reached 1.3 m / d (1.3 m / day), which is considered a typical value indicating severe instability of the landslide. This instability moment... Based on this, all pressure and displacement data within the preceding time window are backtracked and then packaged into an instability process dataset after noise removal.

[0028] In one specific embodiment, under a hypergravity test environment with a centrifugal acceleration of 66.7g, when the liquid level stabilizes at 15cm, gas is injected at a constant pressure of 200kPa through the bottom gas injection network. Figure 5 The pressure evolution curve shown records the characteristics of abrupt pressure changes during this process: Figure 5 (a) shows that the pore hydraulic pressure fluctuated after gas injection but remained at a low level (approximately 61.3 kPa to 75.6 kPa); while Figure 5 (b) clearly shows that around the 6th minute after the start of gas injection (i.e., the 71st minute of the entire test), the experimental value of pore gas pressure rapidly climbed to a peak range of 83.0 kPa to 100.8 kPa, which was significantly higher than the pore hydraulic pressure of 61.3 kPa to 75.6 kPa during the same period.

[0029] Based on the test results of the macroscopic displacement of the model, Figure 6This is the slope displacement development curve during the water and air injection process. Figure 7 This is the slope crest displacement development curve during water and air injection. Figure 6 and Figure 7 It can be seen that during the initial "water injection" and stable operation phases, the displacement changes were relatively gradual; however, after "air injection," due to the rapid accumulation of high pore pressure at the bottom and the combined effect of water pressure, when the system reached the critical threshold and "began to slip," the slope displacement exhibited sharp oscillations (e.g., Figure 6 As shown, it fluctuates drastically between -21.7 mm and 13.3 mm), while the slope crest displacement rapidly subsides to approximately 8.3 mm or more (as shown). Figure 7 (As shown).

[0030] Moreover, the deformation inside the model is equally intense. Figure 8 This is a PIV displacement vector diagram acquired using a high-speed camera. From... Figure 8 The displacement vectors in the data clearly show that the magnitude of the downward displacement vector exceeds 30 mm, and a "measured slip surface" that runs from the deep part to the foot of the slope is clearly formed in the slope body, which fully proves that the high pore air pressure induces deep sliding instability.

[0031] To further verify the instability process from a mechanical mechanism perspective, Figure 9 The shear strength variation curves considering the combined effects of pore hydraulic pressure and pore gas pressure during the experiment are shown. From Figure 9 It can be seen that the shear strength of the measuring points was partially reduced during the water injection stage; however, during the gas injection stage, especially at the critical moment of slippage, the effective stress dropped sharply due to the supporting effect of high gas pressure. The shear strength of measuring points 1 and 2 both experienced a precipitous drop, with the lowest strength value approaching the failure envelope. Based on this, the system integrates the abrupt changes in displacement and strength, accurately pinpoints the moment of instability, and extracts the pressure and displacement data within that time window to form a complete dataset of the instability process. This provides precise physical evidence for subsequently determining the critical pressure ratio threshold.

[0032] S4: Determine the critical air pressure ratio threshold based on the instability process dataset. It is understandable that due to safety restrictions at landfill sites, destructive testing cannot be used to determine the critical value, while relying solely on numerical simulation is easily limited by the accuracy of the constitutive model. Therefore, it is necessary to realistically reproduce the failure critical point under the "high stress + foam flow" condition using a centrifugal model with high gravity, thereby obtaining the true critical air pressure ratio, i.e., the ratio of pore air pressure to overlying soil pressure. Since the shear strength of landfill soil is highly dependent on stress levels, and the air resistance effect generated by foam leads to the accumulation of deep air pressure, the critical air pressure ratio established in this step, as a core early warning indicator, can eliminate the industry problem of difficulty in unifying thresholds due to differences in absolute pressure values ​​at different monitoring depths.

[0033] In one example of this application, step S4 includes: parsing the instability process dataset to locate the instability moment, backtracking the limit equilibrium state data of the previous sampling point, extracting the experimental values ​​of pore air pressure and overlying soil pressure of all measuring points at that moment to obtain a transient pressure data matrix; traversing the transient pressure data matrix, performing dimensionless ratio calculation of pore air pressure to overlying total stress for each measuring point to obtain a discrete critical ratio set; and determining the critical air pressure ratio threshold based on the discrete critical ratio set.

[0034] Specifically, firstly, the instability process dataset is analyzed to pinpoint the moment of instability. The process involves tracing back to the previous valid sampling point to capture the ultimate equilibrium state just before the failure occurs. At this transient moment, all buried measuring points are extracted. pore pressure experimental values Compared with the experimental value of overlying soil pressure The transient pressure data matrix is ​​then obtained. Subsequently, the transient pressure data matrix is ​​traversed, and for each measuring point, the dimensionless ratio of pore pressure to total overlying stress is calculated to obtain the discrete critical ratio set. The calculation formula is as follows: In the formula, For measuring points The local critical pressure ratio at that location; Measurement points at the moment of instability Experimental value of pore air pressure at the location; This represents the experimental value of the total overburden pressure at that point in time.

[0035] Finally, a critical pressure ratio threshold is determined based on a discrete critical ratio set. In one embodiment of this application, statistical analysis is performed on the discrete critical ratio set, and a globally uniform scalar threshold is constructed using the lower quartile or minimum value interval in statistics (i.e., using the same interval for the entire landfill, for example, 0.74~0.84). However, while this implementation method is simple to operate, it may overlook the spatial heterogeneity of landfill slope stability. That is, the critical pressure ratio for maintaining stability in the slope toe region is often extremely low, while the deeper regions can withstand higher pore pressure ratios due to the high confining pressure effect. This may result in the global threshold being insufficiently safe at the slope toe, while being overly conservative in the deeper regions.

[0036] In practical engineering scenarios, the toe region of landfill slopes is typically an area of ​​concentrated shear stress and minimal lateral constraint, resulting in an extremely low critical air pressure ratio for stability, making it highly susceptible to failure under low pressure. Conversely, the interior or deeper regions of the landfill, due to the significant confining pressure provided by the overlying soil, exhibit increased shear strength of the soil skeleton, often capable of withstanding higher pore air pressure ratios without shear failure. The original scheme mixed and statistically analyzed data from all spatially located measuring points to generate a single threshold. This threshold might be too aggressive for high-risk areas like the toe (unsafe enough, with a risk of underreporting), while being too conservative for deep, high-confining-pressure areas (frequent false alarms, impacting operational efficiency). Furthermore, the original scheme lost the spatial coordinate dimension information of the sensor data, failing to establish a functional mapping relationship between the critical threshold and geometric location (such as relative depth and distance from the free surface), thus hindering differentiated management of different risk areas.

[0037] This improved mechanism introduces a spatial adaptive sensitivity field construction scheme, upgrading a single static threshold to a dynamic threshold surface that varies with spatial location. More specifically, in another embodiment of this application, the critical pressure ratio threshold is determined based on a discrete critical ratio set, including: calculating the normalized airspace distance index for the absolute coordinates of each measuring point to obtain a spatial feature enhancement dataset containing a binary pair of the normalized airspace distance index and the discrete critical ratio set; determining an adaptive threshold function parameter set based on the spatial feature enhancement dataset; and performing a global discretization mapping on the field monitoring grid coordinate set based on the adaptive threshold function parameter set to obtain a spatial adaptive threshold field as the critical pressure ratio threshold.

[0038] Specifically, firstly, the normalized airspace distance index is calculated for the absolute coordinates of each measuring point to obtain a spatial feature enhancement dataset containing the normalized airspace distance index and a discrete critical ratio set binary. This aims to address the model generality issue caused by the differences in absolute size of landfills of different sizes, and to make the model scale-invariant through dimensionless processing. During execution, the absolute coordinates of each measuring point are traversed, and the normalized airspace distance index is introduced. This method quantifies the degree of danger of a measuring point relative to the exposed surface of the slope. It uses the Euclidean distance from the measuring point to the slope toe, the total height of the landfill, and the vertical burial depth of the measuring point to calculate the risk using specific physical formulas. The exponential enhancement term is introduced in this process to characterize the nonlinear enhancement of the resistance to failure of deep soil due to confining pressure (i.e., the tolerance of deep soil to air pressure increases exponentially with depth). Finally, the calculated results are compared with the original critical ratio. Binding, generating includes The spatial feature enhancement dataset of the binary tuples ensures that subsequent modeling can correctly identify the essential differences in physical mechanisms between the foot of the slope and the deep part.

[0039] Normalized distance index The calculation process is as follows: in, Let be the normalized free distance exponent (dimensionless) for measuring point i. The smaller the value, the higher the overall risk of airborne danger at that point (such as being extremely close to the toe of a slope or in a shallow, unconfined area). Let be the Euclidean distance from measuring point i to the slope toe; This represents the total height of the landfill slope; Let i be the vertical burial depth of measuring point i; This is the depth attenuation factor, used to adjust the contribution of the confining pressure effect to the enhanced ability to resist damage; it is usually set to 1.0.

[0040] Secondly, based on the spatial feature enhancement dataset, the parameter set of the adaptive threshold function is determined. Since simple statistical elimination methods cannot handle the non-stationary nature of data distribution changes with spatial location, a regression model must be used to capture this trend in order to establish a mathematical mapping relationship between the critical pressure ratio threshold and spatial location.

[0041] Specifically, based on the aforementioned augmented dataset, a location-dependent quantile regression model is constructed. The objective function is set to find a smooth curve. This ensures that the model closely follows the lower edge of the data point (corresponding to the most dangerous operating condition). During this process, weighted quantile regression (WQR) is used to estimate the parameters of the lower bound model, and the coefficients of the quadratic envelope function are calculated. , , This constitutes the parameter set of the adaptive threshold function. Specifically, a risk weighting factor is introduced. , making The smaller the value (the closer to the foot of the slope), the greater the weight. This means that the algorithm is forced to be extremely precise and conservative in high-risk areas such as the foot of the slope, while allowing a certain degree of fitting tolerance in the inner regions. This achieves the mathematical goal of strictly guarding the foot of the slope and relaxing the fit in the deeper scientific areas, thus enabling a refined modeling approach.

[0042] This step is described as follows: in, , , Let be the coefficients of the quadratic envelope function to be solved; The quantile loss function, 0.05 (i.e., 5% quantile) is used to extract the lower envelope; Let i be the local critical pressure ratio at measuring point i; Risk weighting factor; To prevent tiny constants with a denominator of zero.

[0043] Finally, based on the adaptive threshold function parameter set, the coordinate set of the field monitoring grid is discretized globally to obtain a spatial adaptive threshold field as the critical pressure ratio threshold. This aims to transform the continuous mathematical model into a discretized monitoring indicator that can be executed on the engineering site, giving each sensor its own dedicated alarm threshold, thereby significantly improving the spatial resolution and reliability of the early warning system. During execution, each coordinate point j in the actual field monitoring grid is converted into a corresponding normalized feature. The function parameters calculated using the previous steps , , Calculate the personalized threshold for that specific location. .

[0044] To ensure the generated thresholds have high statistical confidence (e.g., 95%) and prevent misjudgments, a safety gradient correction term, including a model uncertainty deduction term, is introduced. This reflects the statistical concept of a prediction interval and prevents excessive threshold jumps between adjacent measurement points. Simultaneously, a global minimum physical limit value is set as a hard constraint to prevent unreasonably low values ​​from being generated during model extrapolation. Ultimately, a spatially adaptive threshold field map covering the entire field is generated, providing accurate, point-to-point early warning criteria for the on-site monitoring system. This process is represented as follows: in, The critical air pressure ratio threshold is the value at the field measurement point j. This is the global minimum physical limit (the theoretical lower limit determined by the soil mechanics properties); This is a deduction term for model uncertainty, where This is the confidence coefficient (e.g., 1.96). This represents the standard deviation of the residuals in the regression model.

[0045] This technical solution introduces a spatial adaptive sensitivity field construction technique to address the applicability issues of traditional single-threshold early warning methods in complex geotechnical engineering scenarios, constructing a safe, reliable, and efficient spatial adaptive early warning system. Its technical effectiveness lies in achieving refined control over different risk areas of the landfill: in highly sensitive areas such as the toe of the slope, a highly conservative low threshold is generated through high-weighted lower envelope fitting, effectively preventing missed alarms due to sudden instability caused by local stress concentration; while in deep high confining pressure areas, the threshold restrictions are scientifically relaxed by introducing a depth attenuation factor and location-dependent regression, significantly reducing the false alarm rate caused by the natural accumulation of deep air pressure. Ultimately, this achieves the optimal balance between safety and efficiency by maximizing the load-bearing potential of the landfill structure while ensuring its structural safety and avoiding unnecessary shutdowns or excessive engineering interventions.

[0046] S5: Based on the field air pressure and soil pressure values ​​obtained from the field monitoring data stream of the target landfill, determine the real-time field air pressure ratio that reflects the actual working conditions of the current landfill. It is understandable that the internal working conditions of landfills are extremely complex and dynamic, especially in high-water-level humid landfills. The large amount of gas generated by the biochemical degradation of organic matter will create excess pore pressure in the deep layers of the landfill due to the obstruction effect of leachate and foam, leading to a reduction in effective stress. Since destructive testing to determine critical values ​​cannot be performed on-site, and surface displacement monitoring often has significant lag, it is necessary to acquire real-time air pressure and soil pressure data from deep measuring points, converting the absolute pressure values ​​into a dimensionless ratio reflecting the degree of approach to the instability criticality. This eliminates the absolute numerical differences caused by different monitoring depths and provides a standardized data basis for subsequent comparison with thresholds calibrated by hypergravity tests.

[0047] In one example of this application, step S5 includes: based on the on-site engineering implementation instructions and monitoring point planning, drilling and installation are carried out at the target landfill to construct a deployed monitoring network; the deployed monitoring network is activated to perform multi-physical quantity streaming acquisition and filtering and noise reduction processing to obtain a field physical quantity data stream containing on-site air pressure values ​​and on-site soil pressure values; the time frame data of the field physical quantity data stream is read, and dimensionless calculation is performed using the air pressure and soil pressure data at the same time to obtain the real-time on-site air pressure ratio.

[0048] Specifically, firstly, based on the on-site engineering implementation instructions and monitoring point planning, boreholes are drilled and installed at the pre-set key layers of the target landfill to construct a deployed monitoring network. During this process, referring to the three-dimensional coordinates obtained from the monitoring point planning, industrial-grade air-permeable and water-permeable pore pressure gauges and earth pressure cells are installed to characteristic depths such as the potential sliding surface depth. Subsequently, the deployed monitoring network is activated for multi-physical quantity streaming acquisition, and filtering and denoising are performed to obtain a data stream of on-site physical quantities including on-site air pressure and on-site earth pressure values. The system aggregates data through an industrial IoT gateway, acquiring the raw signals from each node at a set minute-level sampling rate, and applying a Kalman filter algorithm to denoise the raw signals, generating synchronous and clean time-series data. Finally, the time frame data of the on-site physical quantity data stream is read, and dimensionless calculations are performed using the air pressure and earth pressure data at the same moment to obtain the real-time on-site air pressure ratio. This calculation process subscribes to the latest time frame data through an online calculation module and generates a continuous real-time on-site air pressure ratio data stream according to a preset algorithm. The calculation formula is as follows: In the above formula, For a moment The real-time air pressure ratio at the site is used to characterize the dimensionless stress state at the monitoring point of the target landfill. For a moment The collected and filtered on-site air pressure values ​​are in kilopascals (kPa). For a moment The collected and filtered on-site earth pressure values ​​are in kilopascals (kPa).

[0049] In one specific embodiment, during stability monitoring at a landfill with high food waste content, a sensor array deployed at a depth of 20 meters in the slip risk zone collected real-time on-site air pressure values ​​of 180 kPa to 220 kPa at that depth, while the concurrent liquid pressure was only 100 kPa to 120 kPa. By processing data from the same time frame, if the on-site soil pressure reported by the sensors at this time is 187 kPa, the real-time on-site air pressure ratio calculated using the aforementioned formula is approximately 0.96 to 1.17. This real-time ratio reflects that the pore air pressure under this condition has approached or even exceeded the self-weight stress of the overlying stockpile, suggesting that the effective stress has dropped to a critical level, thus providing direct on-site evidence of the operating conditions for generating an early warning signal.

[0050] S6: A risk comparison is performed between the real-time on-site air pressure ratio and the critical air pressure ratio threshold to determine whether a high-pressure-triggered instability warning signal is generated. It is understandable that landfill instability is typically triggered by deep high-pressure zones, and the failure process is sudden. Considering only hydraulic influence would overestimate the safety factor of landfill slopes. Since destructive testing cannot be conducted on-site and surface displacement monitoring exhibits significant lag, dynamically comparing the critical ratio calibrated by hypergravity testing with real-time on-site monitoring data allows for a precise mapping from "physical mechanism" to "engineering state," thereby outputting a reliable warning signal in the early stages before the formation of the sliding surface.

[0051] In one example of this application, step S6 includes: reading the real-time air pressure ratio and critical air pressure ratio threshold at the site, performing risk index comparison calculation in combination with a preset safety redundancy coefficient to obtain a dynamic risk index reflecting the degree of approach to the instability criticality; inputting the dynamic risk index into a multi-level classifier for logical discrimination, dividing the area into safe zone, warning zone and high-risk instability zone according to the numerical range from low to high to obtain risk assessment metadata; parsing the risk assessment metadata, and combining geographical information to synthesize and distribute alarm information when the risk level reaches the warning standard to output a warning signal.

[0052] Specifically, firstly, a dynamic risk index comparison calculation process is performed. This process takes into account the critical pressure ratio threshold (Threshold_Critical_Ratio) and the real-time field pressure ratio data stream (DataStream_Field_Ratio), and reads the current time... Real-time air pressure ratio at the site And the lower safety limit value in the critical pressure ratio threshold determined by the hypergravity experiment. And set the engineering safety redundancy factor (Coeff_SafetyMargin). (Typically taken as 1.05 to 1.20) to compensate for the nonlinear error between the model and the field, thereby calculating the dynamic risk index. The calculation formula is as follows: In the formula, For a moment The dynamic risk index, the closer the value is to or greater than 1.0, the higher the risk of instability; This refers to the real-time air pressure ratio at the site. This is the safety redundancy factor; This is the conservative lower limit value in the critical pressure ratio threshold.

[0053] Subsequently, the hierarchical discrimination and decision-making logic execution process is performed. This process takes the previously generated dynamic risk index (Index_DynamicRisk) as input and inputs it into a preset multi-level classifier for logical discrimination: if The system determines it as a "safe zone"; if The system identifies it as a "restricted zone"; if If the risk level is found to be high, it is classified as a "high-risk instability zone." Based on this judgment logic, risk assessment metadata containing a risk level code and an estimated probability of instability is generated. Finally, the process of synthesizing and distributing early warning signals is executed. This process takes the risk assessment metadata as input. When the risk level is found to reach the "alert" standard or above, it immediately combines the geographical information of the current monitoring node to synthesize a structured early warning signal containing the specific alarm location, risk level, and recommended exhaust operation suggestions, and pushes it to the field management terminal through the industrial interface.

[0054] In one specific embodiment, during the monitoring of a high-volume food waste landfill, the system collects and calculates the real-time on-site air pressure ratio. The critical pressure ratio threshold previously determined through centrifugation experiments in hypergravity... The range is 0.74 to 0.84, and the conservative lower limit is taken. The value is 0.74. If a safety redundancy factor is set... The value is 1.0. Substituting this value into the formula, the dynamic risk index is calculated. The value is approximately 1.297. Since this index is significantly greater than 1.0, the decision logic identifies it as a high-risk instability zone. The system then synthesizes and issues an early warning signal containing the coordinates of the monitoring point and a recommendation to immediately perform deep venting and decompression. This early warning method based on the air pressure / soil pressure ratio not only eliminates the influence of monitoring depth but also solves the problem of ambiguity in early warning of high-pressure instability in landfills by utilizing precise thresholds obtained from hypergravity experiments.

[0055] In summary, the early warning method for determining the high-pressure-triggered instability threshold using a hypergravity test according to the embodiments of this application is explained. First, based on rigorous statistical analysis and physical model scaling design using geometric, geotechnical, and rheological data from on-site investigation, a test environment system highly consistent with the deep stress state of the actual landfill is constructed using centrifuge hypergravity loading technology. Under this high-fidelity environment, instability is induced in the model through bottom air injection, and the pore air pressure and overlying soil pressure at the moment of instability are captured using high-frequency synchronous acquisition technology, thereby calculating the dimensionless critical air pressure ratio threshold. Finally, this critical "high air pressure - soil pressure" ratio calibrated by the hypergravity test is used as the core criterion and dynamically compared with the air pressure ratio obtained by the on-site real-time monitoring system, thereby achieving real-time identification of instability risk.

[0056] Figure 10This is a block diagram of an early warning system for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to an embodiment of this application. Figure 10 As shown, the early warning system 600 for determining the high-pressure-triggered instability threshold in a hypergravity test according to an embodiment of this application includes: a model parameter configuration module 610, used to perform statistical analysis and physical model scaling estimation on the acquired raw data stream from the field survey to obtain a model configuration parameter set including centrifugation scaling ratio, artificial material ratio, and simulated liquid formula; a hypergravity physical modeling module 620, used to construct a physical model in a centrifuge based on the model configuration parameter set and perform centrifugal acceleration loading to construct a hypergravity test environment system; and an instability process monitoring module 630, used to monitor the hypergravity test environment system by bottom air injection and synchronous high-frequency sampling. The system integrates full-process monitoring of pore air pressure experimental values, overlying soil pressure experimental values, and slope displacement data within the model to obtain an instability process dataset; a critical air pressure ratio identification module 640 is used to determine the critical air pressure ratio threshold based on the instability process dataset; a field condition perception module 650 is used to determine the real-time field air pressure ratio reflecting the actual working conditions of the current landfill based on the field air pressure and field soil pressure values ​​in the field monitoring data stream obtained from the target landfill; and an instability risk early warning discrimination module 660 is used to compare and discriminate the real-time field air pressure ratio with the critical air pressure ratio threshold to determine whether a high-pressure-triggered instability early warning signal should be generated.

[0057] It should be noted that the early warning system for determining the high-pressure triggered instability threshold by hypergravity experiment in this application embodiment is similar in principle to the aforementioned early warning method for determining the high-pressure triggered instability threshold by hypergravity experiment. Therefore, the implementation process, implementation principle, and beneficial effects of the early warning system for determining the high-pressure triggered instability threshold by hypergravity experiment can all be found in the description of the implementation process, implementation principle, and beneficial effects of the aforementioned method, and repeated details will not be repeated.

[0058] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for early warning of high-pressure-triggered instability threshold determined by hypergravity experiments, characterized in that, include: S1: Perform statistical analysis and physical model scaling estimation on the acquired raw data stream from the field survey to obtain a set of model configuration parameters including centrifugal scaling ratio, artificial material ratio, and simulated liquid formulation; S2: Based on the model configuration parameter set, a physical model is built inside the centrifuge and centrifugal acceleration loading is performed to build a hypergravity test environment system; S3: In the hypergravity testing environment system, the experimental values ​​of pore air pressure, overlying soil pressure and slope displacement inside the model are monitored throughout the entire process by bottom air injection and synchronous high-frequency acquisition to obtain the instability process dataset. S4: Determine the critical pressure ratio threshold based on the instability process dataset; S5: Based on the field air pressure and field earth pressure values ​​in the field monitoring data stream obtained from the target landfill, determine the real-time field air pressure ratio that reflects the actual working conditions of the current landfill. S6: Compare the real-time air pressure ratio with the critical air pressure ratio threshold to determine whether a high-pressure-triggered instability warning signal is generated.

2. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 1, characterized in that, The raw data stream from the on-site investigation includes: macroscopic geometric dimension data, solid phase geotechnical property data, and liquid phase physicochemical rheological data. Among them, the macroscopic geometric dimension data includes the total height of the prototype pile, the characteristic depth of the target instability area, and the slope of the on-site slope; the solid phase geotechnical property data includes the particle size distribution curve of the prototype waste soil borehole, the dry density of the prototype waste soil, and the percentage of organic matter content; and the liquid phase physicochemical rheological data includes the foaming volume index of the on-site leachate and the foam half-life of the on-site leachate.

3. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 2, characterized in that, Step S1 includes: Based on the macroscopic geometric dimension data in the original data stream of the on-site survey and combined with the centrifuge load capacity, the centrifuge simulation scale ratio is calculated and features are extracted to obtain the centrifuge scale ratio and physicochemical feature sub-data stream. Based on the dry density and percentage of organic matter content of the prototype waste soil in the physicochemical feature sub-data stream, the mass fraction of the basic components of peat, quartz sand and kaolin is fitted to obtain the artificial material ratio and liquid property data package. Based on the foaming volume index and foam half-life of the field leachate in the liquid property data package, a concentration mapping relationship is established and the surfactant mass concentration is calculated to determine the simulated liquid formulation. The centrifugation scaling ratio, artificial material ratio, and simulated liquid formulation were structured and validated and encapsulated to obtain the model configuration parameter set.

4. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 1, characterized in that, Step S2 includes: Based on the artificial material ratio, centrifugal scaling ratio and simulated liquid formula extracted from the model configuration parameter set, a pre-embedded sensor model box is constructed. At the reserved interface of the pre-embedded sensor model box, install the microporous aeration network and liquid injection pipeline and connect the external control equipment to build the assembled model system; Calculate the target angular velocity based on the centrifugal scaling ratio, drive the assembled model system to be loaded to the target speed in stages and inject simulated leachate to obtain a stable hypergravity field; A system state verification and ready-to-use encapsulation are performed on a stable liquid level hypergravity field to obtain a hypergravity testing environment system.

5. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 1, characterized in that, Step S3 includes: A dynamic loading system was obtained by micro-flow gas injection and foam evolution induction in a hypergravity testing environment system; The data acquisition system synchronously reads sensor values ​​and camera images from the dynamic loading system at a fixed sampling frequency, and performs pressure change rate calculation and displacement detection to obtain raw time-series data packets. The full-field displacement vector is determined based on the original time series data, and the instability moment is locked based on the instantaneous velocity threshold and the effective time window data is extracted to obtain the instability process dataset.

6. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 1, characterized in that, Step S4 includes: The instability process dataset is analyzed to locate the instability moment. The limit equilibrium state data of the previous sampling point is traced back, and the experimental values ​​of pore air pressure and overlying soil pressure of all measuring points at that moment are extracted to obtain the transient pressure data matrix. The transient pressure data matrix is ​​traversed, and the dimensionless ratio of pore pressure to overlying total stress is calculated for each measuring point to obtain the discrete critical ratio set. The critical pressure ratio threshold is determined based on the discrete critical ratio set.

7. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 1, characterized in that, Step S5 includes: Based on the on-site engineering implementation instructions and monitoring point planning, drilling and installation operations were carried out at the target landfill to construct the deployed monitoring network; The deployed monitoring network is activated to perform multi-physical quantity streaming acquisition and filtered and denoised to obtain a field physical quantity data stream containing field air pressure and field earth pressure values; Read the time frame data of the on-site physical quantity data stream, and perform dimensionless calculations using the air pressure and soil pressure data at the same moment to obtain the real-time air pressure ratio on site.

8. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 1, characterized in that, Step S6 includes: The real-time air pressure ratio and critical air pressure ratio threshold are read, and the risk index is compared and calculated in combination with the preset safety redundancy coefficient to obtain a dynamic risk index that reflects the degree of approach to the instability criticality. The dynamic risk index is input into a multi-level classifier for logical discrimination. Based on the numerical range, the safe zone, warning zone and high-risk instability zone are divided from low to high to obtain risk assessment metadata. The system analyzes risk assessment metadata and, when the risk level reaches the warning standard, combines geographic information to synthesize and distribute alarm information to output early warning signals.

9. The early warning method for determining the high-pressure-triggered instability threshold in a hypergravity experiment according to claim 6, characterized in that, Based on the discrete critical ratio set, the critical pressure ratio threshold is determined, including: The normalized airspace distance index is calculated for the absolute coordinates of each measuring point to obtain a spatial feature enhancement dataset containing the normalized airspace distance index and the discrete critical ratio set binary. Determine the parameter set of the adaptive threshold function based on the spatial feature enhancement dataset; Based on the adaptive threshold function parameter set, the field monitoring grid coordinate set is discretized and mapped globally to obtain a spatial adaptive threshold field as the critical pressure ratio threshold.

10. An early warning system for determining the high-pressure-triggered instability threshold in a hypergravity experiment, characterized in that, include: The model parameter configuration module is used to perform statistical analysis and physical model scaling estimation on the acquired raw data stream from the field survey to obtain a set of model configuration parameters including centrifugal scaling ratio, artificial material ratio and simulated liquid formula. The hypergravity physics modeling module is used to build a physical model inside a centrifuge and perform centrifugal acceleration loading to construct a hypergravity testing environment system based on the model configuration parameter set. The instability process monitoring module is used to monitor the experimental values ​​of pore air pressure, overlying soil pressure, and slope displacement inside the model in a hypergravity testing environment system through bottom air injection and synchronous high-frequency acquisition to obtain the instability process dataset. The critical pressure ratio identification module is used to determine the critical pressure ratio threshold based on the instability process dataset. The on-site working condition sensing module is used to determine the real-time on-site air pressure ratio, which reflects the actual working condition of the current landfill, based on the on-site air pressure and on-site earth pressure values ​​in the on-site monitoring data stream obtained from the target landfill. The instability risk warning and discrimination module is used to compare the real-time air pressure ratio with the critical air pressure ratio threshold to determine whether a warning signal for high pressure triggering instability is generated.

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