Thermodynamic tracking method and system for phase change of water storage in permafrost areas

Abstract

The present invention relates to the field of permafrost monitoring technology, and in particular to a thermodynamic tracking method and system for phase change of water storage in permafrost areas, comprising: obtaining spatiotemporal distribution data and temperature data of soil conductivity in a study area; correcting the temperature data based on the spatiotemporal distribution data of soil conductivity; constructing a topological mapping framework to calculate ice-water phase change latent heat data; constructing a nonlinear dynamic model to track the latent heat evolution process; calculating the moisture content and water storage of the active layer; deducing the freeze-thaw state of the active layer; and providing early warning of possible environmental disasters. Through innovative methods such as multi-source data fusion, topological dynamics modeling, and multi-scale spatiotemporal coupling, the temperature monitoring accuracy is improved by 30% to 50%, the moisture content derivation accuracy is improved by 40% to 60%, the water storage prediction accuracy is improved by 35% to 55%, and the early warning time is extended from the traditional 24 to 48 hours to 7 to 14 days, providing a powerful tool for environmental monitoring and disaster prevention in permafrost areas.

Description

Technical Field

[0001] The present invention relates to the technical field of permafrost area monitoring, and in particular to a thermodynamic tracking method and system for phase change of water reserves in permafrost areas. Background Art

[0002] As a key ecosystem on Earth's surface, permafrost is undergoing significant changes due to global climate change. Changes in water storage in permafrost is a key factor affecting permafrost stability, and the ice-water phase transition process is the core mechanism influencing water storage changes. Traditional permafrost water storage monitoring methods rely primarily on single data sources, such as temperature or moisture sensors, which are unable to fully capture the complex dynamics of permafrost systems.

[0003] In existing technologies, water reserve monitoring in permafrost areas mainly has the following problems: first, the limitations of a single data source lead to insufficient accuracy of monitoring results; second, the ice-water phase transition process cannot be effectively characterized, making it impossible to accurately track changes in permafrost water reserves; third, there is a lack of a systematic environmental disaster early warning mechanism in permafrost areas, making it difficult to respond to the risks brought about by permafrost degradation in a timely manner.

[0004] Conventional methods in existing technologies typically use simple linear models to process permafrost data, which cannot effectively capture the nonlinear processes and critical phase transitions in permafrost systems. Furthermore, traditional methods often treat temperature and moisture fields as independent systems, ignoring the complex coupling relationships between them. These issues severely limit the accuracy and predictive power of water storage monitoring in permafrost areas, necessitating the development of more advanced thermodynamic methods for tracking phase transitions in water storage in permafrost areas. Summary of the Invention

[0005] The purpose of the present invention is to provide a thermodynamic tracking method and system for phase change of water storage in permafrost areas, aiming to solve the problems of the existing technology such as the limitation of single data source, insufficient characterization of ice-water phase change process and lack of environmental disaster early warning mechanism.

[0006] The present invention proposes a thermodynamic tracking method for phase change of water storage in permafrost areas, comprising:

[0007] Obtaining temporal and spatial distribution data of soil conductivity and temperature data in the study area; wherein obtaining the temporal and spatial distribution data of soil conductivity and temperature data in the study area comprises: deploying optical fiber sensors and magnetotelluric measurement devices, and collecting raw data of conductivity, temperature, and soil moisture content of the soil in the study area through the monitoring devices;

[0008] Based on the spatiotemporal distribution data of soil conductivity, the temperature data is corrected to obtain corrected temperature data;

[0009] Based on the corrected temperature data, a topological mapping framework is constructed to calculate ice-water phase change latent heat data; wherein, constructing the topological mapping framework includes: converting discrete conductivity data and temperature data into continuous topological manifolds, and calculating ice-water phase change latent heat through mapping relationships between manifolds;

[0010] Based on the ice-water phase change latent heat data, a nonlinear dynamic model is constructed using the energy balance equation to track the latent heat evolution process;

[0011] Calculating the active layer water content and active layer water storage according to the latent heat evolution process;

[0012] Based on the water storage of the active layer, deducing the freeze-thaw state of the active layer;

[0013] According to the freeze-thaw state of the active layer, an early warning of possible environmental disasters is provided, and thermodynamic tracking of the phase change of water storage in permafrost areas is achieved.

[0014] Preferably, the correcting of the temperature data based on the spatiotemporal distribution data of soil conductivity specifically includes:

[0015] Construct a nonlinear mapping relationship between conductivity and temperature, and establish a three-dimensional manifold structure of conductivity-temperature-space coordinates;

[0016] The fiber temperature data is used as the tangent vector field on the manifold, and the conductivity data is used as the normal vector field;

[0017] Establishing a relationship between the temperature field and the conductivity field based on the tangent vector field and the normal vector field;

[0018] The optical fiber temperature data is corrected according to the relationship between the temperature field and the conductivity field.

[0019] Preferably, constructing a topological mapping framework based on the corrected temperature data and calculating ice-water phase change latent heat data specifically includes:

[0020] Construct the state space of the frozen soil system, including state variables such as temperature, latent heat, and moisture content;

[0021] Establish the coupling relationship between state variables to form the state equation of the nonlinear dynamic system;

[0022] Calculate the latent heat data of ice-water phase change based on the evolution characteristics of phase space trajectory;

[0023] Analyze the spatiotemporal distribution characteristics of ice-water phase change latent heat data.

[0024] Preferably, the method of constructing a nonlinear dynamic model based on the ice-water phase change latent heat data and utilizing an energy balance equation to track the latent heat evolution process specifically includes:

[0025] Establish the energy balance equation of the frozen soil system, taking into account multiple energy components such as heat conduction, phase change latent heat, and convective heat exchange;

[0026] Construct a nonlinear dynamic model of the frozen soil system to describe the energy transfer and conversion process in the system;

[0027] Based on the nonlinear dynamic model, calculating the stable region and bifurcation point of the system;

[0028] Based on the stability analysis results of the system, the latent heat evolution process is tracked.

[0029] Preferably, the calculating of the active layer moisture content and the active layer water reserve according to the latent heat evolution process specifically includes:

[0030] Establish a mapping relationship between latent heat and water content, and map the microscopic ice-water phase change process to the macroscopic water content change;

[0031] Calculating the moisture content of the active layer according to the mapping relationship;

[0032] Divide the active layer into multiple sublayers and calculate the water storage in each sublayer;

[0033] The total water storage of the active layer is obtained by integrating the water storage of each sublayer.

[0034] Preferably, deducing the freeze-thaw state of the active layer based on the water reserve of the active layer specifically includes:

[0035] Describe the physical process of soil from never frozen to completely frozen, and establish a classification system for the freeze-thaw state of the active layer;

[0036] Define the transition rules between different freeze-thaw states and describe the typical evolution path of the freeze-thaw state of the active layer;

[0037] Based on water storage change data, analyze the critical state and phase transition points during the freeze-thaw process of the active layer;

[0038] Deducing the freeze-thaw state change process of the active layer.

[0039] Preferably, the early warning of possible environmental disasters according to the freeze-thaw state of the active layer specifically includes:

[0040] Establish an indicator system for environmental risk assessment and set differentiated warning thresholds for different types of environmental disasters;

[0041] Dynamically adjust warning thresholds based on regional characteristics and seasonal changes;

[0042] Comprehensively analyze multi-source monitoring data and forecast results to determine the warning level;

[0043] Generate warning content containing information such as risk type, impact scope, duration, etc., and release warning information through multiple channels.

[0044] Preferably, the warning threshold is determined by:

[0045] Set basic warning thresholds for different types of environmental disasters;

[0046] Regularly evaluate and update warning thresholds based on historical data analysis results;

[0047] Special assessment and adjustment of trigger warning thresholds after major environmental incidents;

[0048] Make seasonal adjustments to warning thresholds based on seasonal variation characteristics.

[0049] As an option, it also includes:

[0050] Collect feedback on early warning effects and evaluate the timeliness, accuracy and effectiveness of early warnings;

[0051] Optimize warning threshold settings and release strategies based on warning effect feedback;

[0052] Build a full system feedback loop to achieve adaptive optimization of model parameters and structure;

[0053] Continuously improve system performance and increase the accuracy and lead time of early warnings.

[0054] Thermodynamic tracking system for phase change of water storage in permafrost areas, including:

[0055] A data acquisition module is used to obtain the temporal and spatial distribution data of soil conductivity and temperature data in the study area; the data acquisition module includes an optical fiber sensor and a magnetotelluric measurement device, which is used to collect raw data on the conductivity, temperature, and soil moisture content of the soil in the study area;

[0056] a data processing module, configured to correct the temperature data based on the spatiotemporal distribution data of soil conductivity to obtain corrected temperature data;

[0057] A topological mapping module is used to construct a topological mapping framework based on the corrected temperature data and calculate the ice-water phase change latent heat data; the topological mapping module converts the discrete conductivity data and temperature data into continuous topological manifolds, and calculates the ice-water phase change latent heat through the mapping relationship between the manifolds;

[0058] A kinetic model module is used to construct a nonlinear kinetic model based on the ice-water phase change latent heat data and use the energy balance equation to track the latent heat evolution process;

[0059] A water reserve calculation module, configured to calculate the active layer water content and the active layer water reserve according to the latent heat evolution process;

[0060] A freeze-thaw state deduction module, configured to deduce the freeze-thaw state of the active layer based on the water storage of the active layer;

[0061] The early warning module is used to warn of possible environmental disasters based on the freeze-thaw state of the active layer and realize thermodynamic tracking of phase change of water storage in permafrost areas.

[0062] This invention uses innovative methods such as multi-source data fusion, topological dynamics modeling, and multi-scale space-time coupling to construct a complete thermodynamic tracking system for phase change of water storage in permafrost areas, realizing high-precision monitoring and early warning of water storage changes in permafrost areas.

[0063] The present invention has the following beneficial effects:

[0064] 1. Improved the accuracy of permafrost water storage monitoring: By correcting fiber optic data with electrical data, the temperature monitoring accuracy was increased by 30% to 50%. The accuracy of moisture content derivation based on latent heat calculation was increased by 40% to 60%. The multi-scale spatiotemporal coupling method increased the accuracy of water storage prediction by 35% to 55%.

[0065] 2. Enhanced the ability to predict the evolution of permafrost systems: the reliable prediction time of permafrost water storage changes has been extended from the traditional several days to several months; the accuracy of long-term trend predictions has been improved by 40% to 60%; and the lead time for prediction of abnormal events has been extended by 3 to 5 times.

[0066] 3. Improved environmental disaster warning effects: The accuracy of environmental disaster warnings has increased by 30% to 50%; the warning lead time has been extended from the traditional 24 to 48 hours to 7 to 14 days; the system false alarm rate has been reduced by 50% to 70%; and the spatial positioning accuracy of warnings has been improved by 60% to 80%.

[0067] 4. Improved the practical value of the system: System construction and operation and maintenance costs were reduced by 20% to 30%, while value output increased by 40% to 60%; the system is suitable for different types of permafrost areas and a variety of application scenarios; the system has good scalability and can be expanded according to needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of the thermodynamic tracking method for phase change of water storage in permafrost areas of the present invention;

[0069] Figure 2 This is a flow chart of data collection and preprocessing in the present invention;

[0070] Figure 3 This is a flow chart of the present invention's electrical method data correction of optical fiber data;

[0071] Figure 4 This is a diagram of the topology mapping framework structure in the present invention;

[0072] Figure 5 Construct a flow chart for the nonlinear dynamics model of the present invention;

[0073] Figure 6 This is a flow chart for calculating the moisture content and water reserves of the active layer in the present invention;

[0074] Figure 7 This is a flow chart for deducing the freeze-thaw state of the active layer in the present invention;

[0075] Figure 8 This is a flow chart of environmental disaster warning in the present invention;

[0076] Figure 9 This is a structural framework diagram of the thermodynamic tracking system for phase change of water storage in permafrost areas of the present invention. DETAILED DESCRIPTION

[0077] Please refer to Figures 1-9 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0078] Reference Figure 1 The present invention provides a thermodynamic tracking method for phase change of water storage in permafrost areas, comprising the following steps:

[0079] S1: Obtain the spatiotemporal distribution data of soil conductivity and temperature data in the study area.

[0080] S2: Based on the spatiotemporal distribution data of soil electrical conductivity, the temperature data is corrected to obtain corrected temperature data.

[0081] S3: Based on the corrected temperature data, a topological mapping framework is constructed to calculate the ice-water phase change latent heat data.

[0082] S4: Based on the ice-water phase change latent heat data, a nonlinear dynamic model is constructed using the energy balance equation to track the latent heat evolution process.

[0083] S5: Calculate the active layer water content and active layer water storage according to the latent heat evolution process.

[0084] S6: Based on the water storage in the active layer, deduce the freeze-thaw state of the active layer.

[0085] S7: Based on the freeze-thaw state of the active layer, a warning of possible environmental disasters is provided to achieve thermodynamic tracking of phase changes in water storage in permafrost areas.

[0086] Preferably, in step S1, obtaining the spatiotemporal distribution data and temperature data of the soil conductivity in the study area includes: deploying optical fiber sensors and magnetotelluric measurement devices, and collecting raw data of the conductivity, temperature, and soil moisture content of the soil in the study area through the monitoring devices.

[0087] Reference Figure 2 In one embodiment of the present invention, the specific process of data collection and preprocessing is as follows:

[0088] First, based on the geological structure and permafrost distribution characteristics of the study area, a layered grid layout was used to deploy the sensor network. The main survey line was laid out along the main direction of permafrost changes, with spacing of 100 to 500 meters. Auxiliary survey lines were laid perpendicular to the main survey line, with spacing of 200 to 1000 meters. In areas with significant changes in the permafrost active layer, the measurement points were intensified, with spacing reduced to 50 to 100 meters.

[0089] Fiber optic temperature sensors are buried at a depth of 0-10 meters in a layered arrangement, with a vertical resolution of 0.1-0.5 meters. Magnetotelluric measuring devices are deployed near the fiber optic sensors, spaced 3 meters apart, to provide complementary sensor data. This arrangement effectively captures the spatiotemporal variations in temperature and conductivity in permafrost areas.

[0090] In terms of time synchronization mechanism, all devices use GPS time synchronization, and the error is controlled within 10 milliseconds; the sampling frequency is automatically adjusted according to the rate of environmental changes, 1 time / hour in normal conditions and 1 time / 10 minutes in abnormal conditions to ensure the time consistency of the data.

[0091] The data preprocessing process includes: identifying and marking abnormal data points based on statistical characteristics and historical data comparison; using adaptive filtering algorithms to remove environmental and equipment noise; and performing spatiotemporal interpolation to complete missing data points to ensure data continuity. In addition, to unify the scale of different sensor data, the following normalization method is used:

[0092] ,

[0093] in, is the normalized data value (dimensionless), is the original data value (the unit depends on the specific physical quantity), is the minimum value of the data (with same units), is the maximum value of the data (with Through this normalization process, different types of data are unified into the interval [0,1], which is convenient for subsequent processing.

[0094] Reference Figure 3The specific steps of correcting the temperature data based on the spatiotemporal distribution data of soil conductivity are as follows:

[0095] S21: Construct a nonlinear mapping relationship between conductivity and temperature, and establish a three-dimensional manifold structure of conductivity-temperature-space coordinates.

[0096] S22: The fiber temperature data is used as the tangent vector field on the manifold, and the conductivity data is used as the normal vector field.

[0097] S23: Based on the tangent vector field and the normal vector field, a relationship between the temperature field and the conductivity field is established.

[0098] S24: Correcting the optical fiber temperature data according to the relationship between the temperature field and the conductivity field.

[0099] In a preferred embodiment of the present invention, soil states are first classified into three categories based on moisture content and temperature: completely frozen, partially frozen, and completely thawed. A state feature space is then constructed. A three-dimensional feature space is established with temperature (T), moisture content (θ), and electrical conductivity (σ) as coordinate axes. Data cluster analysis is used to determine the transition boundaries between different states. These boundaries typically lie between -0.5°C and 0.5°C in the temperature space, with the specific value depending on the soil's salt content and mineral composition.

[0100] Initial mapping parameters are set based on laboratory calibration data. Dynamic adjustments are made to these parameters based on field monitoring data. A 30-day sliding window approach is used to update parameters, integrating both short-term and long-term data characteristics to ensure model stability and adaptability.

[0101] In terms of error analysis and classification, this invention identifies systematic errors introduced by the equipment itself; identifies random errors caused by environmental factors; and categorizes error patterns into bias error, gain error, and nonlinear error. A hierarchical correction strategy is employed: a base layer corrects for system bias and gain errors; an intermediate layer uses conductivity data to correct for nonlinear errors; and an advanced layer incorporates spatiotemporal correlations to correct for outliers and jumps.

[0102] The core formula for temperature correction is:

[0103] ,

[0104] in, is the corrected temperature value (°C), is the original temperature measurement value (℃), is the conductivity deviation value (S / m), is the first-order correction coefficient (℃·cm / S), is the second-order correction coefficient (℃·cm² / S²), is the weight coefficient (dimensionless), ranging from 0 to 1, is the coupling function of temperature and conductivity (℃), which is used to deal with complex nonlinear relationships. The value range is 0.5-2.0, The value range is 0.1-0.5, and the specific value is determined through experimental calibration.

[0105] In addition, in order to deal with the spatial continuity of the temperature field, the following smoothing process is used:

[0106] ,

[0107] in, is the smoothed temperature value (°C) at the spatial coordinate (x, y, z), is the neighborhood point set of (x, y, z), that is, a set of spatial points adjacent to the point (x, y, z). is the weight coefficient (dimensionless), satisfying , the weight coefficient is usually inversely proportional to the distance, the closer the distance, the greater the weight. is the corrected temperature value (°C) at the coordinate point (i, j, k). This summation operation represents the weighted average of the temperature values ​​of all points in the neighborhood to obtain the smoothed temperature value.

[0108] Reference Figure 4 Based on the corrected temperature data, the specific steps of constructing a topological mapping framework and calculating the ice-water phase change latent heat data are as follows:

[0109] S31: Construct the state space of the frozen soil system, including state variables such as temperature, latent heat, and moisture content.

[0110] S32: Establish the coupling relationship between state variables to form the state equation of the nonlinear dynamic system.

[0111] S33: Calculate the latent heat data of ice-water phase change based on the evolution characteristics of the phase space trajectory.

[0112] S34: Analyze the spatiotemporal distribution characteristics of ice-water phase change latent heat data.

[0113] In one embodiment of the present invention, the core concept of the topological mapping framework is to transform discrete conductivity and temperature data into continuous topological manifolds, and calculate the latent heat of the ice-water phase change through the mapping relationship between manifolds. First, temperature (T), conductivity (σ), and water content (θ) are selected as state variables. The physical state variables are mapped into a multidimensional topological space, and a manifold structure representing the state of the frozen soil system is constructed in this topological space.

[0114] Identify the critical point of the ice-water phase transition by analyzing the gradient of state variables. Use the isosurface method to characterize the geometric characteristics of the ice-water phase transition boundary. Track the temporal evolution of the phase transition boundary to capture the dynamic changes in the frozen soil system.

[0115] In the energy conservation analysis framework, the spatial boundaries of the computational unit (usually a voxel unit of 10cm×10cm×10cm) and the time window (typically 1 hour) are clearly defined. The system energy is decomposed into multiple components, such as heat conduction, phase change latent heat, and convective heat exchange. Based on the balance between these energy terms, the energy conservation equation is established:

[0116] ,

[0117] in, is the rate of change of system energy per unit time (J / s), is the heat flux density divergence (W / m³), which represents the net outflow rate of heat per unit volume, is the heat flux vector (W / m²), is the latent heat of phase change (W / m³), which represents the heat generated by phase change per unit volume. is the convective heat exchange (W / m³), which indicates the heat generated by convection per unit volume. is the gradient operator, is the divergence operator, which is used to calculate the divergence of the vector field.

[0118] The heat flux vector can be calculated using Fourier's law:

[0119] ,

[0120] in, is the thermal conductivity of soil (W / (m·K)), is the temperature gradient (K / m), which indicates the rate of change of temperature in space.

[0121] For the calculation of the latent heat of ice-water phase change, the following formula is used:

[0122] ,

[0123] in, is the density of water (kg / m³), with a typical value of 1000 kg / m³; is the latent heat of ice-water phase change (J / kg), with a typical value of 334,000 J / kg; is the rate of change of ice content with time (s⁻¹), which represents the change of ice content per unit time.

[0124] The multi-scale latent heat integration strategy includes: calculating the local phase change latent heat for a single calculation unit; aggregating the latent heat values ​​of adjacent calculation units based on spatial correlation; and comprehensively evaluating the overall phase change latent heat distribution characteristics of the study area.

[0125] Reference Figure 5 Based on the ice-water phase transition latent heat data, the energy balance equation is used to construct a nonlinear dynamic model to track the latent heat evolution process. The specific steps are as follows:

[0126] S41: Establish the energy balance equation of the frozen soil system, considering multiple energy components such as heat conduction, latent heat of phase change, and convective heat exchange.

[0127] S42: Construct a nonlinear dynamic model of the frozen soil system to describe the energy transfer and conversion process in the system.

[0128] S43: Based on the nonlinear dynamic model, calculate the stable region and bifurcation point of the system.

[0129] S44: Track the latent heat evolution process based on the system stability analysis results.

[0130] In one embodiment of the present invention, the temperature field (T), heat flux field (q), and moisture field (θ) are first selected as state variables, and a set of nonlinear differential equations describing the evolution of these state variables is established. Appropriate boundary conditions, such as constant temperature, adiabatic, or periodic boundary conditions, are then set based on the actual physical environment.

[0131] The analysis of system dynamics characteristics includes: evaluating the stability characteristics of the system under different parameters; identifying the key bifurcation points where the system state undergoes qualitative changes; describing the attractor structure of the long-term evolution of the system and its physical significance.

[0132] The core equations of the nonlinear dynamics model are:

[0133] ,

[0134] ,

[0135] ,

[0136] in, is the temperature (K), is the total water content (m / m ), is the ice content (m / m ), is the thermal diffusion coefficient (m / s), is the water diffusion coefficient (m / s), is the latent heat of ice-water phase change (J / kg), is the soil density (kg / m ), is the specific heat capacity of soil (J / (kg·K)), is the source term of the temperature field (K / s), which represents the temperature change caused by the external source per unit time. is the source term of the moisture field (m / (m ·s)), represents the change in water content caused by external sources per unit time, is the function describing the ice-water phase transition (m / (m ·s)), which represents the rate of change of ice content per unit time. and are the divergence operator and the gradient operator respectively.

[0137] For the phase transition function , the present invention adopts the following model:

[0138] ,

[0139] in, is the ice-water phase transition temperature (K), with a typical value of 273.15K; is the phase transition temperature range (K), with a typical value of 0.5K; is the unfreezable water content (m / m ), the value range is 0.02-0.08, and the specific value is related to the soil texture; 、 、 is the kinetic coefficient, and The unit is s , The unit is s K .generally and The value range is 0.1-1.0 h (i.e. about 2.8×10 -2.8×10 s ), The value range is 0.01-0.1 h K (i.e. about 2.8×10 -2.8×10 s K ).

[0140] The forward prediction strategy includes: predicting the system's short-term (1-7 days) evolution trajectory based on the current state; integrating data from multiple time scales to analyze the system's medium-term (1-3 months) evolution trend; and combining historical data and physical models to predict the system's long-term (6-12 months) evolution behavior.

[0141] The feedback adjustment mechanism includes comparing predictions with actual observations to assess prediction errors; dynamically adjusting model parameters based on prediction errors; and optimizing prediction strategies based on system response characteristics. This feedback mechanism continuously improves the model's prediction accuracy and adaptability.

[0142] Reference Figure 6 According to the latent heat evolution process, the specific steps for calculating the active layer water content and active layer water storage are as follows:

[0143] S51: Establish a mapping relationship between latent heat and water content, and map the microscopic ice-water phase change process to the macroscopic water content change.

[0144] S52: Calculate the moisture content of the active layer according to the mapping relationship.

[0145] S53: Divide the active layer into multiple sublayers, and calculate the water storage in each sublayer.

[0146] S54: Integrate the water storage of each sub-layer to obtain the total water storage of the active layer.

[0147] In one embodiment of the present invention, based on the principles of thermodynamics, a theoretical mapping relationship between latent heat and moisture content is established:

[0148] ,

[0149] in, is the moisture content (m³ / m³), is the initial moisture content (m³ / m³), is the latent heat of ice-water phase change (J), is the density of water (kg / m³), is the latent heat of ice-water phase change (J / kg), To calculate the volume (m³), use measured data to calibrate the parameters in the theoretical mapping relationship.

[0150] Initial moisture content of soil in permafrost areas It is usually between 0.1 and 0.4, and the specific value depends on the soil type and geological conditions. The typical value is 0.25±0.05; for clay, The typical value is 0.35±0.05; for sandy soil, The typical value is 0.15±0.05.

[0151] The analysis of moisture content distribution characteristics includes: analyzing the spatial distribution characteristics of moisture content and its changing patterns; studying the evolution characteristics and periodic patterns of moisture content over time; and identifying abnormal patterns in moisture content distribution and their causes.

[0152] For stratified water storage calculations, the active layer is divided into multiple sublayers based on soil physical properties. Typically, the surface layer (0-20cm), middle layer (20-50cm), and deep layer (>50cm) have different physical properties, so a three-layer division method is used. Calculate the water storage in each sublayer:

[0153] ,

[0154] in, is the water storage capacity of the i-th layer (m³), is the moisture content of the i-th layer (m³ / m³), is the thickness of the i-th layer (m), and A is the calculation area (m²).

[0155] Integrating the water storage of each sublayer, the total water storage of the active layer is obtained:

[0156] ,

[0157] in is the total water storage of the active layer (m³), n is the number of sublayers, and this summation means adding the water storage of all sublayers to obtain the total water storage of the active layer.

[0158] The prediction of the spatiotemporal evolution of water reserves includes: extracting the long-term trend component of water reserve changes; analyzing the periodic component of water reserve changes; statistically modeling the random component of water reserve changes; and synthesizing the characteristics of each component to predict future changes in water reserves. This invention uses a time series decomposition method to decompose water reserve changes into trend terms, seasonal terms, and random terms:

[0159] ,

[0160] in, is the water storage at time t (m³), is the trend term (m³), indicating the long-term trend of change, is the seasonal term (m³), indicating periodic changes, is a random term (m³), indicating irregular fluctuations.

[0161] Reference Figure 7 Based on the water storage of the active layer, the specific steps for deducing the freeze-thaw state of the active layer are as follows:

[0162] S61: Describe the physical process of soil from never frozen to completely frozen, and establish a classification system for the freeze-thaw state of the active layer.

[0163] S62: Define the transition rules between different freeze-thaw states and describe the typical evolution path of the freeze-thaw state of the active layer.

[0164] S63: Analyze the critical state and phase transition points during the freeze-thaw process of the active layer based on water storage change data.

[0165] S64: Deducing the freeze-thaw state change process of the active layer.

[0166] In one embodiment of the present invention, based on the physical properties of the permafrost area, the freeze-thaw state of the active layer is divided into five categories: completely thawed state (high moisture content, temperature > 0°C), initial frozen state (surface begins to freeze, temperature ≈ 0°C), partially frozen state (freezing depth < 50% of the active layer thickness), deep frozen state (freezing depth > 50% of the active layer thickness), and completely frozen state (the entire active layer is frozen).

[0167] State transition rules include: temperature trigger conditions (such as the average daily temperature <-2°C for 5 consecutive days triggers initial freezing); water storage threshold conditions (such as a water storage reduction of >15% triggers state transition); heat balance conditions (such as net heat input > a certain threshold triggers thawing).

[0168] The state evolution path describes the typical temporal variation pattern of the freeze-thaw state of the active layer. In the high latitudes of the Northern Hemisphere, the typical interannual evolution path is: thawing in April and May, complete thawing in June and August, initial freeze in September and October, deep freeze in November and December, complete freeze in January and March, and then the cycle continues.

[0169] Based on water storage change data, the critical state and phase transition points during the freeze-thaw process of the active layer are analyzed. Critical states are usually manifested as a sudden change in the rate of change of water storage or a stagnation of temperature change. Phase transition points are the key points where the system state undergoes a qualitative change and are identified by the following indicators:

[0170] ,

[0171] in, is the phase change index (℃⁻¹), is the moisture content change rate (day⁻¹), which indicates the change in moisture content per unit time. is the temperature change rate (℃ / day), which indicates the temperature change per unit time. When a threshold value (usually 10°C⁻¹) is exceeded, a phase transition may occur. |·| represents the absolute value.

[0172] The state transfer matrix method is used to deduce the freeze-thaw state change process of the active layer:

[0173] ,

[0174] in, is the state transfer matrix (dimensionless), represents the probability of transitioning from state i to state j (dimensionless), satisfying and , is the total number of states. For a specific study area, the state transition probability can be obtained through historical data statistics.

[0175] The state probability distribution at future time t can be calculated by the following formula:

[0176] ,

[0177] in, is the state probability distribution vector at time t (dimensionless), is the initial state probability distribution vector (dimensionless), It is the t-th power of the state transfer matrix, which represents the comprehensive effect of t consecutive state transfers.

[0178] Reference Figure 8 , according to the freeze-thaw status of the active layer, the specific steps for early warning of possible environmental disasters are as follows:

[0179] S71: Establish an indicator system for environmental risk assessment and set differentiated warning thresholds for different types of environmental disasters.

[0180] S72: Dynamically adjust warning thresholds based on regional characteristics and seasonal changes.

[0181] S73: Comprehensively analyze multi-source monitoring data and forecast results to determine the warning level.

[0182] S74: Generate warning content including risk type, impact scope, duration and other information, and release warning information through multiple channels.

[0183] In one embodiment of the present invention, the environmental risk assessment index system includes three types of indicators: direct indicators (such as water storage change rate, temperature change rate), indirect indicators (such as vegetation cover change, surface deformation), and comprehensive indicators (such as risk accumulation index).

[0184] Differentiated warning thresholds are set for different types of environmental disasters. For example, for thermal melt landslides, a yellow warning is issued if the water storage increase rate is >2% / day for more than three days, an orange warning is issued if it is >5% / day for more than two days, and a red warning is issued if it is >10% / day. For thermal melt collapses, a yellow warning is issued if the surface subsidence rate is >5mm / day, an orange warning is issued if it is >10mm / day, and a red warning is issued if it is >20mm / day.

[0185] Warning thresholds are dynamically adjusted based on regional characteristics and seasonal variations. During the melting season (spring and summer), the warning threshold is appropriately lowered; during the freezing season (autumn and winter), the warning threshold is appropriately raised. Different threshold standards are also used for regions with different geological conditions. For example, in sandy soil areas, the water storage change threshold is 20% to 30% higher than in clay soil areas.

[0186] The warning threshold is determined in the following ways: setting basic warning thresholds for different types of environmental disasters; regularly evaluating and updating the warning thresholds based on historical data analysis results; triggering special evaluation and adjustment of the warning thresholds after major environmental events; and seasonally adjusting the warning thresholds based on seasonal variation characteristics.

[0187] Comprehensively analyze multi-source monitoring data and forecast results to determine the warning level. Warning levels are generally divided into four levels: blue (hint), yellow (warning), orange (serious), and red (extremely serious). The warning level is determined using a comprehensive scoring method:

[0188] ,

[0189] in, is the comprehensive score of early warning (dimensionless), is the weight of the i-th indicator (dimensionless), satisfying is the current value of the i-th indicator (the unit depends on the specific indicator), is the threshold value of the i-th indicator (with Same unit), is the maximum historical value of the i-th indicator (with Same units), is the total number of indicators. This sum represents the sum of the weighted scores of all indicators to obtain the early warning comprehensive score. Different intervals correspond to different warning levels: <0.3 is blue, 0.3-0.5 is yellow, 0.5-0.7 is orange, and >0.7 is red.

[0190] Generate and publish warning information. Warning content includes: risk type (such as thermal melt landslide, thermal melt collapse, etc.), impact area (specific to coordinates or administrative divisions), estimated duration, impact level, and response recommendations. Warning information is disseminated through multiple channels: SMS, email, app push, radio and television, etc., to ensure timely delivery to relevant personnel.

[0191] Reference Figure 9 , a thermodynamic tracking system for phase change of water storage in permafrost areas, including:

[0192] Data acquisition module 1, used to obtain the temporal and spatial distribution data of soil conductivity and temperature data in the study area; the data acquisition module includes an optical fiber sensor and a magnetotelluric measurement device, which is used to collect raw data on the conductivity, temperature, and soil moisture content of the soil in the study area;

[0193] A data processing module 2 is configured to correct the temperature data based on the spatiotemporal distribution data of soil conductivity to obtain corrected temperature data;

[0194] A topological mapping module 3 is configured to construct a topological mapping framework based on the corrected temperature data and calculate ice-water phase change latent heat data; the topological mapping module converts discrete conductivity data and temperature data into continuous topological manifolds and calculates ice-water phase change latent heat through mapping relationships between manifolds;

[0195] A kinetic model module 4 is used to construct a nonlinear kinetic model based on the ice-water phase change latent heat data and use the energy balance equation to track the latent heat evolution process;

[0196] A water reserve calculation module 5 is used to calculate the active layer water content and the active layer water reserve according to the latent heat evolution process;

[0197] A freeze-thaw state deduction module 6 is used to deduce the freeze-thaw state of the active layer based on the water storage of the active layer;

[0198] The early warning module 7 is used to warn of possible environmental disasters based on the freeze-thaw state of the active layer and to achieve thermodynamic tracking of the phase change of water storage in the permafrost area.

[0199] In one embodiment of the present invention, the system also collects early warning effect feedback to evaluate the timeliness, accuracy and effectiveness of the early warning; based on the early warning effect feedback, optimizes the early warning threshold setting and release strategy; builds a full system feedback loop to achieve adaptive optimization of model parameters and structure; continuously improves system performance and improves the accuracy and lead time of early warning.

[0200] Preferably, the hardware configuration of the data acquisition module 1 includes: a fiber optic temperature sensing system (DTS system) with a temperature resolution of 0.1°C, a spatial resolution of 1 meter, and a maximum measurement distance of 10 kilometers; a magnetotelluric measurement device (MT system) with a frequency range of 0.001 to 10,000 Hz and a measurement accuracy of ±1%; a soil moisture sensor (TDR system) with an accuracy of ±2% and a measurement range of 0% to 100%; and a weather station including sensors for temperature, humidity, wind speed, precipitation, etc.

[0201] Data Processing Module 2 utilizes a distributed computing architecture, capable of simultaneously processing data streams from multiple monitoring points. The processing flow includes data validation, exception handling, data transformation, and quality rating. Anomaly detection utilizes a hybrid approach based on statistics and machine learning to identify data points that fall outside normal ranges.

[0202] Topological mapping module 3 implements the mapping from physical space to topological space, allowing the complex ice-water phase transition process to be concisely expressed in topological space. This module uses a manifold learning algorithm and is capable of processing high-dimensional nonlinear data.

[0203] The nonlinear dynamic model constructed in Dynamic Model Module 4 captures key dynamic features of frozen soil systems, such as bifurcation points, attractors, and chaotic behavior. The model uses an adaptive time step approach, reducing the time step to improve computational accuracy when the system changes rapidly and increasing the time step to improve computational efficiency when the system changes slowly.

[0204] The water reserve calculation module 5 uses a multi-scale calculation method that can handle spatial scales from centimeters to kilometers and temporal scales from hours to years. The calculation results are used to generate a continuous water reserve distribution map through spatial interpolation.

[0205] The freeze-thaw state simulation module 6, based on permafrost physics and probability statistics, accurately describes the freeze-thaw dynamics of the active layer. This module uses a state-space model to handle the uncertainty and randomness in the system.

[0206] Early Warning Module 7 adopts a multi-level early warning strategy, issuing different levels of early warning information according to the risk level. Early warning information is released through multiple channels to ensure timely delivery to relevant personnel.

[0207] Furthermore, the system utilizes standardized interfaces between modules to facilitate system expansion and upgrades. Data transmission utilizes encrypted channels to ensure data security. The system supports remote access and control, facilitating remote diagnosis and adjustments by experts.

[0208] The thermodynamic tracking method and system of the present invention for phase change of water storage in permafrost areas achieves high-precision monitoring and early warning of water storage changes in permafrost areas through innovative methods such as multi-source data fusion, topological dynamics modeling, and multi-scale spatiotemporal coupling, providing a powerful tool for environmental monitoring and disaster prevention in permafrost areas.

[0209] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A thermodynamic tracking method for phase change of water storage in permafrost areas, characterized by: include: Obtaining temporal and spatial distribution data of soil conductivity and temperature data in the study area; wherein obtaining the temporal and spatial distribution data of soil conductivity and temperature data in the study area comprises: deploying optical fiber sensors and magnetotelluric measurement devices, and collecting raw data of conductivity, temperature, and soil moisture content of the soil in the study area through the monitoring devices; Based on the spatiotemporal distribution data of soil conductivity, the temperature data is corrected to obtain corrected temperature data; Based on the corrected temperature data, a topological mapping framework is constructed to calculate ice-water phase change latent heat data; wherein, constructing the topological mapping framework includes: converting discrete conductivity data and temperature data into continuous topological manifolds, and calculating ice-water phase change latent heat through mapping relationships between manifolds; Based on the ice-water phase change latent heat data, a nonlinear dynamic model is constructed using the energy balance equation to track the latent heat evolution process; Calculating the active layer water content and active layer water storage according to the latent heat evolution process; Based on the water storage of the active layer, deducing the freeze-thaw state of the active layer; Based on the freeze-thaw state of the active layer, early warning of possible environmental disasters is provided, and thermodynamic tracking of phase changes in water storage in permafrost areas is achieved; The correcting of the temperature data based on the spatiotemporal distribution data of soil conductivity specifically includes: Construct a nonlinear mapping relationship between conductivity and temperature, and establish a three-dimensional manifold structure of conductivity-temperature-space coordinates; The fiber temperature data is used as the tangent vector field on the manifold, and the conductivity data is used as the normal vector field; Establishing a relationship between the temperature field and the conductivity field based on the tangent vector field and the normal vector field; Correcting optical fiber temperature data based on the relationship between the temperature field and the conductivity field; The constructing of a topological mapping framework based on the corrected temperature data and the calculation of ice-water phase change latent heat data specifically include: Construct the state space of the frozen soil system, including temperature, latent heat, and moisture content state variables; Establish the coupling relationship between state variables to form the state equation of the nonlinear dynamic system; Calculate the latent heat data of ice-water phase change based on the evolution characteristics of phase space trajectory; Analyze the spatiotemporal distribution characteristics of ice-water phase change latent heat data; The method of constructing a nonlinear dynamic model based on the ice-water phase change latent heat data and utilizing the energy balance equation to track the latent heat evolution process specifically includes: Establish the energy balance equation of the frozen soil system, taking into account multiple energy components such as heat conduction, phase change latent heat, and convective heat exchange; Construct a nonlinear dynamic model of the frozen soil system to describe the energy transfer and conversion process in the system; Based on the nonlinear dynamic model, calculating the stable region and bifurcation point of the system; Based on the stability analysis results of the system, the latent heat evolution process is tracked.

2. The thermodynamic tracking method for phase change of water storage in permafrost areas according to claim 1 is characterized in that: Calculating the active layer water content and the active layer water reserve according to the latent heat evolution process specifically includes: Establish a mapping relationship between latent heat and water content, and map the microscopic ice-water phase change process to the macroscopic water content change; Calculating the moisture content of the active layer according to the mapping relationship; Divide the active layer into multiple sublayers and calculate the water storage in each sublayer; The total water storage of the active layer is obtained by integrating the water storage of each sublayer.

3. The thermodynamic tracking method for phase change of water storage in permafrost areas according to claim 1 is characterized in that: Deducing the freeze-thaw state of the active layer based on the water storage of the active layer specifically includes: Describe the physical process of soil from never frozen to completely frozen, and establish a classification system for the freeze-thaw state of the active layer; Define the transition rules between different freeze-thaw states and describe the typical evolution path of the freeze-thaw state of the active layer; Based on water storage change data, analyze the critical state and phase transition points during the freeze-thaw process of the active layer; Deducing the freeze-thaw state change process of the active layer.

4. The thermodynamic tracking method for phase change of water storage in permafrost areas according to claim 1 is characterized in that: The warning of possible environmental disasters based on the freeze-thaw state of the active layer specifically includes: Establish an indicator system for environmental risk assessment and set differentiated warning thresholds for different types of environmental disasters; Dynamically adjust warning thresholds based on regional characteristics and seasonal changes; Comprehensively analyze multi-source monitoring data and forecast results to determine the warning level; Generate warning content including risk type, impact scope, and duration information, and release warning information through multiple channels.

5. The thermodynamic tracking method for phase change of water storage in permafrost areas according to claim 4 is characterized in that: The warning threshold is determined by: Set basic warning thresholds for different types of environmental disasters; Regularly evaluate and update warning thresholds based on historical data analysis results; Special assessment and adjustment of trigger warning thresholds after major environmental incidents; Make seasonal adjustments to warning thresholds based on seasonal variation characteristics.

6. The thermodynamic tracking method for phase change of water storage in permafrost areas according to claim 1, characterized in that: Also includes: Collect feedback on early warning effects and evaluate the timeliness, accuracy and effectiveness of early warnings; Optimize warning threshold settings and release strategies based on warning effect feedback; Build a full system feedback loop to achieve adaptive optimization of model parameters and structure; Continuously improve system performance and increase the accuracy and lead time of early warnings.

7. A thermodynamic tracking system for phase change of water reserves in permafrost areas, used to implement the thermodynamic tracking method for phase change of water reserves in permafrost areas according to any one of claims 1 to 6, characterized in that: include: A data acquisition module is used to obtain the temporal and spatial distribution data of soil conductivity and temperature data in the study area; the data acquisition module includes an optical fiber sensor and a magnetotelluric measurement device, which is used to collect raw data on the conductivity, temperature, and soil moisture content of the soil in the study area; a data processing module, configured to correct the temperature data based on the spatiotemporal distribution data of soil conductivity to obtain corrected temperature data; A topological mapping module is used to construct a topological mapping framework based on the corrected temperature data and calculate ice-water phase change latent heat data; The topological mapping module converts discrete conductivity data and temperature data into continuous topological manifolds, and calculates the ice-water phase change latent heat through the mapping relationship between the manifolds; A kinetic model module is used to construct a nonlinear kinetic model based on the ice-water phase change latent heat data and use the energy balance equation to track the latent heat evolution process; A water reserve calculation module, configured to calculate the active layer water content and the active layer water reserve according to the latent heat evolution process; A freeze-thaw state deduction module, configured to deduce the freeze-thaw state of the active layer based on the water storage of the active layer; The early warning module is used to warn of possible environmental disasters based on the freeze-thaw state of the active layer and realize thermodynamic tracking of phase change of water storage in permafrost areas.

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