Real-time monitoring method and system for ice content of permafrost foundation based on electrical impedance

Abstract

The application discloses a kind of real-time monitoring method and system of ice content of permafrost foundation based on electrical impedance, it is related to data processing technical field, method includes: in signal application period, based on electrode array, alternatingly apply first alternating current signal and second alternating current signal multiple times and gather corresponding impedance value, obtain first impedance data sequence and second impedance data sequence;Obtain first change rate, obtain second change rate, form change rate sequence;Obtain continuity index, if continuity index exceeds preset threshold, obtain reference trend value;Obtain the first difference between the first real-time impedance value of the previous two times before current time, obtain the second difference between the second real-time impedance value of the previous two times before current time, obtain real-time trend value according to first difference and second difference and obtain similarity according to real-time trend value and reference trend value, obtain ice content real-time monitoring result according to similarity.The application has the advantages of dual-band collaborative analysis, dynamic trend prediction and abnormal real-time capture.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for real-time monitoring of ice content in permafrost foundations based on electrical impedance. Background Technology

[0002] In the field of engineering construction and maintenance in permafrost regions (such as the Qinghai-Tibet Railway and plateau highways), the dynamic changes in the ice content of the foundation directly affect the stability of the engineering structure. Currently, it is possible to monitor the ground electrical impedance characteristics to obtain the complex fluctuations in the ice content of the foundation, but existing monitoring technologies have limitations.

[0003] Specifically, existing impedance methods use a single-frequency signal, but the dielectric response of ice, water, and soil particles in permafrost varies greatly with frequency. For example, low frequencies (10Hz–1kHz) are sensitive to solid ice, while high frequencies (1kHz–100kHz) are more sensitive to liquid water. If only single-frequency data is relied upon, it is impossible to distinguish between normal impedance changes caused by temperature fluctuations and the real risks caused by abnormal ice content (such as ground settlement caused by ice melting), which can easily lead to missed or false alarms. Secondly, the stable change of ice content in permafrost needs to meet the synchronous gradual change of dual-frequency impedance (such as a gradual increase in low-frequency impedance and a gradual decrease in high-frequency impedance when ice content decreases slowly). However, existing methods only focus on absolute threshold values ​​and ignore the trend coordination of multi-frequency data. When local thermal disturbances (such as pipeline leakage) cause sudden changes in ice content, the direction of dual-frequency impedance changes may diverge (sudden drop in low frequency and fluctuation in high frequency), and traditional algorithms cannot capture such abnormal patterns. Finally, drilling sampling or fixed-point sensors require interruption of construction and are severely delayed. Although existing electronic monitoring can continuously collect data, it relies on static threshold statistics and cannot use historical trends to predict the current state. For example, when the ice content is steadily decreasing, the impedance should change continuously and gradually. If the current real-time data deviates from the historical trend (such as a sudden increase in high-frequency impedance), it may indicate local melting and subsidence. However, existing technologies lack such dynamic similarity comparison mechanisms. Summary of the Invention

[0004] To address the technical problems of existing technologies that lack collaborative trend analysis capabilities and rely on fixed thresholds, thus failing to distinguish between normal environmental fluctuations and true ice content anomalies, resulting in poor and unstable real-time monitoring performance, this invention provides a method and system for real-time monitoring of ice content in permafrost foundations based on electrical impedance.

[0005] A method for real-time monitoring of ice content in permafrost foundations based on electrical impedance includes: acquiring a target permafrost foundation and an electrode array inserted into the target permafrost foundation, acquiring a signal application period, and within the signal application period, alternately applying a first AC signal covering the low-frequency band and a second AC signal covering the high-frequency band multiple times based on the electrode array and acquiring the corresponding impedance values, and acquiring a first impedance data sequence corresponding to the first AC signal and a second impedance data sequence corresponding to the second AC signal in the previous signal application period; acquiring a first rate of change between adjacent impedance values ​​in the first impedance data sequence, and acquiring a first rate of change between adjacent impedance values ​​in the second impedance data sequence. The two rates of change are arranged in a sequence, with multiple first rates of change and multiple second rates of change arranged alternately according to the acquisition order of impedance values ​​during the signal application period. A continuity index is obtained based on the rate of change sequence. If the continuity index exceeds a preset threshold, a reference trend value is obtained based on the rate of change sequence. The first difference between the first real-time impedance values ​​corresponding to the two previous applications of the first AC signal at the current moment is obtained, and the second difference between the second real-time impedance values ​​corresponding to the two previous applications of the second AC signal at the current moment is obtained. A real-time trend value is obtained based on the first difference and the second difference, and a similarity is obtained based on the real-time trend value and the reference trend value. The real-time monitoring result of ice content is obtained based on the similarity.

[0006] Optionally, obtaining the first rate of change between adjacent impedance values ​​in the first impedance data sequence includes: subtracting the previous impedance value from the next impedance value in the first impedance data sequence to obtain the first change amount; and dividing the first change amount by the average value of all impedance values ​​in the first impedance data sequence to obtain the first rate of change.

[0007] Optionally, obtaining a continuity index based on the rate of change sequence includes: determining whether the signs of adjacent rates of change in the rate of change sequence are consistent, and obtaining a continuous segment formed by adjacent rates of change with consistent signs; obtaining the continuous segment containing the most rates of change and using it as the segment to be compared; dividing the number of rates of change in the segment to be compared by the number of rates of change in the rate of change sequence to obtain the continuity index.

[0008] Optionally, obtaining a reference trend value based on the rate of change sequence includes: obtaining multiple sets of reference rates of change based on the rate of change sequence, wherein the reference rates of change include adjacent first rates of change and second rates of change with the first rate of change preceding the second rate of change; subtracting the second rate of change from the first rate of change in the reference rates of change to obtain the relative difference corresponding to the reference rates of change; and obtaining the average of the relative differences corresponding to all reference rates of change in the rate of change sequence and using it as the reference trend value.

[0009] Optionally, obtaining a real-time trend value based on a first difference and a second difference, and obtaining a similarity based on the real-time trend value and a reference trend value, includes: subtracting the second difference from the first difference to obtain a real-time trend value; obtaining the absolute value of the difference between the real-time trend value and the reference trend value as a target difference quantity; obtaining a standard difference quantity; obtaining the ratio of the standard difference quantity to the target difference quantity; and obtaining a similarity.

[0010] Optionally, obtaining real-time monitoring results of ice content based on similarity includes: obtaining an early warning threshold based on the standard difference and determining whether the similarity is less than the early warning threshold; when the similarity is less than the early warning threshold, generating an early warning signal indicating abnormal changes in ice content; when the similarity is not less than the early warning threshold, generating a monitoring signal indicating normal changes in ice content.

[0011] A real-time monitoring system for ice content in permafrost foundations based on electrical impedance is also provided. The system includes: an acquisition module for acquiring the target permafrost foundation and an electrode array inserted into the target permafrost foundation, and acquiring the signal application period. Within the signal application period, a first AC signal covering the low-frequency band and a second AC signal covering the high-frequency band are alternately applied multiple times based on the electrode array, and the corresponding impedance values ​​are collected. The system also acquires the first impedance data sequence corresponding to the first AC signal and the second impedance data sequence corresponding to the second AC signal within the previous signal application period. A first data processing module is also provided for acquiring the first rate of change between adjacent impedance values ​​in the first impedance data sequence and the rate of change between adjacent impedance values ​​in the second impedance data sequence. The second rate of change is formed by sequentially arranging multiple first rates of change and multiple second rates of change in the order of impedance value acquisition during the signal application period to form a rate of change sequence; the second data processing module is used to obtain a continuity index based on the rate of change sequence. If the continuity index exceeds a preset threshold, a reference trend value is obtained based on the rate of change sequence; the monitoring processing module is used to obtain the first difference between the first real-time impedance values ​​corresponding to the two previous applications of the first AC signal at the current time, and obtain the second difference between the second real-time impedance values ​​corresponding to the two previous applications of the second AC signal at the current time. The real-time trend value is obtained based on the first difference and the second difference, and the similarity is obtained based on the real-time trend value and the reference trend value. The real-time monitoring result of ice content is obtained based on the similarity.

[0012] Optionally, the first data processing module is further configured to: subtract the previous impedance value from the next impedance value in the first impedance data sequence to obtain a first change; divide the first change by the average value of all impedance values ​​in the first impedance data sequence to obtain a first rate of change.

[0013] Optionally, the second data processing module is further configured to: determine whether the signs of adjacent rates of change in the rate of change sequence are consistent, and obtain a continuous segment formed by adjacent rates of change with consistent signs; obtain the continuous segment containing the most rates of change and use it as the segment to be compared; divide the number of rates of change in the segment to be compared by the number of rates of change in the rate of change sequence to obtain a continuity index.

[0014] Optionally, the second data processing module is further configured to: obtain multiple sets of reference change rates according to the change rate sequence, wherein the reference change rates include adjacent first change rates and second change rates with the first change rate preceding the second change rate; subtract the second change rate from the first change rate in the reference change rates to obtain the relative difference corresponding to the reference change rates; and obtain the average value of the relative differences corresponding to all reference change rates in the change rate sequence as a reference trend value.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the entire method for real-time monitoring of ice content in permafrost foundations based on electrical impedance, firstly, high and low frequency signals are applied alternately to construct a time-interwoven rate-of-change sequence, effectively distinguishing the dielectric response characteristics of solid ice and liquid water. This solves the problem that a single frequency band cannot distinguish between temperature fluctuations and anomalies in actual ice content. For example, the alternating arrangement of low-frequency rate of change (sensitive to solid ice) and high-frequency rate of change (sensitive to liquid water) can intuitively reflect the coordinated or divergent patterns of ice-water content changes, avoiding misjudgments caused by environmental noise. Secondly, the stability of the alternation of dual-frequency impedance signs in historical data is quantified through continuous indicators. For example, long-segment continuous alternation patterns of positive low-frequency impedance rate of change and negative high-frequency impedance rate of change are identified when ice content changes slowly, and reliable reference trend values ​​are selected as dynamic benchmarks, overcoming the shortcomings of static threshold methods that ignore trend evolution. Finally, the short-term trend value of the fusion of low-frequency impedance difference and high-frequency impedance difference is calculated in real time and compared with the reference trend value in a dynamic similarity. When thermal disturbance causes a sudden drop in low-frequency impedance (ice melting) and a sudden rise in high-frequency impedance (water accumulation), the real-time trend value will deviate significantly from the historical coordinated pattern, triggering an early warning mechanism. Meanwhile, the small synchronous changes in dual frequencies caused by environmental fluctuations such as cold waves are filtered by the tolerance threshold. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a schematic diagram of the steps of the real-time monitoring method for ice content in permafrost foundation based on electrical impedance according to the present invention;

[0019] Figure 2 This is a schematic diagram of part of step S2 in the real-time monitoring method for ice content in frozen soil foundation based on electrical impedance of the present invention;

[0020] Figure 3 This is a schematic diagram of part of step S3 in the real-time monitoring method for ice content in frozen soil foundation based on electrical impedance of the present invention;

[0021] Figure 4 This is a schematic diagram of another part of step S3 in the real-time monitoring method for ice content in frozen soil foundation based on electrical impedance of the present invention.

[0022] Figure 5 This is a schematic diagram of part of step S4 in the real-time monitoring method for ice content in frozen soil foundation based on electrical impedance of the present invention;

[0023] Figure 6 This is a schematic diagram of another part of step S4 in the real-time monitoring method for ice content in frozen soil foundation based on electrical impedance of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] like Figure 1 As shown, a method for real-time monitoring of ice content in permafrost foundations based on electrical impedance is provided. In one embodiment, the method includes:

[0028] S1. Obtain the target permafrost foundation and the electrode array inserted into the target permafrost foundation, and obtain the signal application period. During the signal application period, apply the first AC signal for covering the low frequency band and the second AC signal for covering the high frequency band alternately multiple times based on the electrode array and collect the corresponding impedance values. Obtain the first impedance data sequence corresponding to the first AC signal and the second impedance data sequence corresponding to the second AC signal in the previous signal application period before the current signal application period.

[0029] S2. Obtain the first rate of change between adjacent impedance values ​​in the first impedance data sequence, and obtain the second rate of change between adjacent impedance values ​​in the second impedance data sequence. Arrange multiple first rates of change and multiple second rates of change alternately according to the acquisition order of impedance values ​​in the signal application period to form a rate of change sequence.

[0030] S3. Obtain continuous indicators based on the rate of change sequence. If the continuous indicators exceed the preset threshold, obtain reference trend values ​​based on the rate of change sequence.

[0031] S4. Obtain the first difference between the first real-time impedance values ​​corresponding to the two previous applications of the first AC signal at the current time, and obtain the second difference between the second real-time impedance values ​​corresponding to the two previous applications of the second AC signal at the current time. Obtain the real-time trend value based on the first difference and the second difference, obtain the similarity based on the real-time trend value and the reference trend value, and obtain the real-time monitoring result of ice content based on the similarity.

[0032] In this embodiment, it should be noted that in S1, data acquisition is required for the electrical impedance characteristics of the permafrost foundation. First, a specially designed electrode array is precisely deployed at key monitoring points of the target permafrost foundation (such as railway subgrade slopes or below highway base courses) to ensure full contact between the electrodes and the permafrost medium. The electrode array is connected to a surface signal generator via waterproof and low-temperature resistant cables, forming a complete current loop. The signal application period is set according to engineering requirements (e.g., one cycle every 30 minutes). Within each cycle, the signal generator automatically and alternately outputs AC excitation signals in two characteristic frequency bands: the first AC signal covers the low-frequency band (typically 10Hz–1kHz), primarily exciting the dielectric response of solid ice crystals; the second AC signal covers the high-frequency band (typically 1kHz–100kHz), which is more sensitive to changes in unfrozen water content. Each frequency band signal is repeatedly applied multiple times within a single cycle (e.g., 100 times each), synchronously recording the corresponding impedance amplitude and phase data between the electrodes, constituting the dual-frequency raw dataset for the current cycle.

[0033] Furthermore, historical data from the previous signal application period is automatically retrieved as a benchmark for trend analysis. For example, for a monitoring point on a plateau highway, if the current period is Tn, then the period T needs to be obtained. <n-1>The system generates a first impedance sequence (containing multiple measurements of low-frequency impedance values) from the first AC signal and a second impedance sequence (containing high-frequency impedance values) from the second AC signal. These historical sequences record the gradual changes in the dielectric properties of the permafrost over a previous period. For example, the low-frequency impedance gradually increases due to the stability of ice crystals under low-temperature conditions, while the high-frequency impedance gradually decreases as unfrozen water decreases, providing a temporal reference for subsequent trend analysis. This design ensures that the monitoring data has both frequency domain resolution and temporal continuity, laying the foundation for distinguishing between temperature fluctuations and true ice content anomalies.

[0034] In S2, trend features are extracted from the dual-frequency impedance sequences acquired in S1. First, for the first impedance sequence corresponding to the low-frequency band (10Hz–1kHz), the impedance values ​​of adjacent measurement points are processed pairwise: the relative change in impedance value between the previous and subsequent moments is calculated (i.e., the difference between the later and earlier values), and this change is divided by the average level of all impedance values ​​in the sequence to obtain the low-frequency impedance change rate. This change rate quantifies the stability of the dielectric response of solid ice. For example, in a stable permafrost environment, when low temperature continuously strengthens the ice crystal structure, this change rate should remain positive. Similarly, the same operation is performed on the second impedance sequence in the high-frequency band (1kHz–100kHz) to generate the high-frequency impedance change rate, which reflects the dynamic characteristics of liquid water content. For example, the migration of unfrozen water will cause this change rate to fluctuate negatively. This standardization process based on relative change eliminates the magnitude differences in the impedance baseline values ​​of different monitoring points, making the trend analysis more universal.

[0035] Furthermore, dual-frequency characteristics are fused along the time dimension. Since the signal application in S1 is alternating (low frequency → high frequency → low frequency → high frequency…), the impedance value acquisition sequence within a single signal application cycle is essentially a temporally interleaved record of dual-frequency signals. Therefore, the calculated low-frequency and high-frequency change rates are strictly arranged alternately according to the measurement timestamps to form a unified change rate sequence. For example, at a frozen soil slope monitoring point, its change rate sequence might exhibit a regular pattern of "low-frequency change rate (positive) → high-frequency change rate (negative) → low-frequency change rate (positive) → high-frequency change rate (negative)…", representing a stable and gradual increase in ice content (increased ice impedance, decreased water impedance). If thermal disturbance from underground pipeline leakage occurs, local abnormal segments may appear in the sequence, such as a sudden drop in the adjacent low-frequency change rate (ice melting leading to a sharp decrease in impedance) followed by a dramatic increase in the high-frequency change rate (water accumulation causing an increase in impedance). This hybrid sequence structure provides spatiotemporally aligned foundational data for the collaborative trend analysis of S3, enabling the direct identification of abnormal patterns such as dual-frequency response divergence.

[0036] In S3, the consistency of the trend in ice content changes in permafrost foundations is assessed based on the rate of change sequence generated in S2, laying the foundation for core diagnosis. First, a continuity index is calculated to quantify the overall synergy of the sequence: this method sequentially scans each pair of adjacent rate of change (e.g., a low-frequency rate of change followed by a high-frequency rate of change) in the sequence, determining whether their signs are opposite (i.e., whether there is sign inconsistency). For example, under stable permafrost conditions, a gradual decrease in ice content will result in a positive first rate of change (low frequency) (gradual increase in solid ice impedance) and a negative second rate of change (gradual decrease in liquid water impedance), thus the signs of adjacent rate of change will naturally be inconsistent. Sign inconsistency indicates that the dual-frequency impedance changes conform to physical laws, characterizing a stable change in ice content without abnormal risks. All such consecutive segments formed by adjacent points with inconsistent signs (e.g., multiple consecutive points alternating signs in the sequence) are identified, and the segment with the most points is selected as the comparison segment. The continuity index is obtained by dividing the number of points in this segment by the total number of points in the sequence; the larger the index value, the higher the proportion of consecutive segments with inconsistent signs in the sequence, reflecting a highly stable trend in ice content changes. In permafrost monitoring, this indicator is used to distinguish between normal environmental fluctuations and true anomalies: for example, a brief temperature rise may cause localized sign uniformity (e.g., both frequency change rates are negative), but if the proportion of continuous segments in the overall sequence is high, the indicator still exceeds the threshold and is confirmed as a reliable historical benchmark; conversely, an excessively low indicator suggests a disordered trend, and the impact of thermal disturbances should be noted. For instance, if the sequence of a plateau railway foundation shows long segments of alternating signs and the indicator is above the threshold, it indicates coordinated changes in ice and water content, and the foundation is safe; however, if the sequence shows discontinuous signs uniformity, the initiation of localized thaw settlement should be considered.

[0037] The preset threshold needs to be combined with the historical health status statistics of the permafrost foundation and the engineering safety requirements. The specific process for obtaining this threshold can be as follows: In the early stages of the project (such as the trial operation period of the Qinghai-Tibet Railway), continuously collect dual-frequency impedance data for 1-2 months, and screen the rate of change sequence of stable ice content variation cycles (such as during the low-temperature season when there is no significant thermal disturbance); calculate the continuity index for each cycle to obtain an index value dataset. Determine its typical distribution range through statistical analysis (such as kernel density estimation), for example, in plateau permafrost areas, the index is mostly concentrated in the 0.7-0.95 range; and deduce the lower limit of the threshold based on engineering failure cases. For example, analyzing the index values ​​of the cycle preceding 10 thaw settlement accidents reveals that the indexes before the accidents were all ≤0.65; combining reliability theory (such as allowable failure probability ≤1%), taking the 5th percentile of the distribution (such as 0.68) and superimposing a safety factor (1.1), the threshold is finally determined to be 0.75. This value indicates that when the proportion of consecutive alternating segments in the sequence is ≥75%, the historical data is confirmed to be sufficiently stable and can be used to generate a reference trend value.

[0038] Furthermore, once the continuity index is satisfactory, a reference trend value is extracted as a basis for prediction. The specific process involves defining multiple sets of reference change rates: each set contains a first change rate (low frequency) and a second change rate (high frequency) adjacent to each other in the sequence, arranged in measurement order (first first). The relative difference between the first change rate and the second change rate within each set is calculated. This operation integrates the sensitivity of low-frequency impedance to solid ice (usually positive) and the sensitivity of high-frequency impedance to liquid water (usually negative), forming a comprehensive difference. The difference calculation captures the inverse correlation characteristics of the dual-frequency impedances, consistent with the gradual change mechanism of ice content (e.g., when ice content increases slightly, the positive change in low-frequency impedance weakens the negative change in high-frequency impedance). The average of the relative differences across all sets yields the reference trend value, which abstracts the steady-state pattern of impedance co-change within historical periods and represents the expected trend under healthy foundation conditions. In engineering applications, the reference trend value provides a dynamic benchmark for real-time comparison in step S4. For example, at monitoring points along the Qinghai-Tibet Highway, a moderately positive value indicates stable ice content in historically low-temperature environments. However, if ice content abruptly changes (e.g., a sudden drop in low-frequency impedance due to pipe leakage), the deviation between real-time data and the reference value will be captured, preventing false alarms about environmental noise as a risk using existing technologies. This design ensures that monitoring utilizes trends rather than static values ​​for prediction, improving the ability to respond to real anomalies.

[0039] In S4, accurate early warning of abnormal ice content in permafrost foundations is achieved through real-time dynamic comparison and synergy with historical trends. First, short-term trend characteristics are extracted from the latest measurement point at the current moment: the two impedance values ​​corresponding to the two most recent applications of the first AC signal (low frequency) are obtained, and their difference (subtracting the previous value from the later value) is calculated as the first difference; similarly, the high-frequency signal is processed to obtain the second difference. These two differences capture the instantaneous changes in the content of solid ice and liquid water, respectively (for example, a positive first difference indicates an increase in ice impedance, while a negative difference indicates a weakening of the ice structure; a positive second difference indicates water accumulation, while a negative difference indicates the migration of unfrozen water). Subsequently, the first difference is subtracted from the second difference to obtain the real-time trend value. This calculation cleverly integrates the inverse correlation characteristics of dual-frequency response: in stable permafrost, if the ice content decreases slowly (low-frequency impedance increases slowly, high-frequency impedance decreases slowly), the real-time trend value should be positive; if local melting and settlement cause a sudden drop in low-frequency impedance and a sudden increase in high-frequency impedance, the real-time trend value will significantly decrease or even turn negative, forming a "trend inflection point."

[0040] Furthermore, the real-time trend value is compared with the reference trend value generated by S3 for similarity assessment (the reference trend value represents a healthy steady-state pattern of coordinated changes in dual-frequency impedance within a historical period). The absolute deviation (target difference) is calculated and compared with a preset standard difference (a dynamic tolerance threshold set based on historical fluctuations). If the target difference exceeds the standard difference, it indicates that the real-time trend deviates significantly from the historical coordinated pattern, the similarity is below the threshold, and an anomaly warning is triggered. If the deviation is within the tolerance range, it is determined to be a normal environmental fluctuation. For example, in monitoring high-altitude highways, at a certain moment, the low-frequency difference suddenly drops (ice melting causes a sharp decrease in impedance), the high-frequency difference rises sharply (seepage water accumulation), the real-time trend value decreases abnormally, while the reference trend value remains in the historical positive range, and the target difference increases dramatically. When the similarity falls below the warning threshold, an abnormal ice content signal (such as "local melt subsidence risk") is generated; conversely, if the real-time trend value maintains a positive fluctuation close to the reference value (such as gradual coordinated change of dual-frequency difference under low-temperature conditions), a safety monitoring signal is output. This approach avoids false alarms caused by environmental noise in the static threshold method to a certain extent, while capturing sudden risks through dynamic trend deviation, thus improving the real-time performance and reliability of early warning for permafrost engineering.

[0041] In summary, the method for real-time monitoring of ice content in permafrost foundations based on electrical impedance firstly involves alternating high and low frequency signals and constructing a time-interwoven rate-of-change sequence. This effectively distinguishes the dielectric response characteristics of solid ice and liquid water, overcoming the limitation of a single frequency band in differentiating temperature fluctuations from anomalies in actual ice content. For example, the alternating arrangement of low-frequency (sensitive to solid ice) and high-frequency (sensitive to liquid water) rates of change can intuitively reflect the coordinated or divergent patterns of ice-water content changes, avoiding misjudgments caused by environmental noise. Secondly, by quantifying the stability of the alternation of dual-frequency impedance signs in historical data through continuous indicators, such as identifying long-segment continuous alternation patterns of positive low-frequency impedance rate of change and negative high-frequency impedance rate of change when ice content changes slowly, reliable reference trend values ​​are selected as dynamic benchmarks, overcoming the shortcomings of static threshold methods that ignore trend evolution. Finally, the short-term trend value of the fusion of low-frequency and high-frequency impedance differences is calculated in real time and dynamically compared with a reference trend value. When thermal disturbances cause a sudden drop in low-frequency impedance (ice melting) and a sudden increase in high-frequency impedance (water accumulation), the real-time trend value will deviate significantly from the historical coordinated pattern, triggering an early warning mechanism. Meanwhile, small synchronous changes in both frequencies caused by environmental fluctuations such as cold waves are filtered out by tolerance thresholds. In summary, this achieves early detection of sudden thaw settlement risks and proactive suppression of environmental noise, ensuring the safe operation and maintenance of engineering structures in permafrost areas.

[0042] like Figure 2 As shown, in one embodiment, S2 acquiring the first rate of change between adjacent impedance values ​​in the first impedance data sequence includes:

[0043] S21. Subtract the previous impedance value from the next impedance value in the first impedance data sequence to obtain the first change.

[0044] S22. Divide the first change by the average value of all impedance values ​​in the first impedance data sequence to obtain the first rate of change.

[0045] In this embodiment, it should be noted that in S21, the instantaneous characteristics of the dielectric response of solid ice in permafrost are captured by quantifying the dynamic impedance changes of adjacent measurement points. Specifically, for the first impedance sequence acquired from the low-frequency signal, two adjacent impedance measurements with consecutive timestamps are extracted sequentially. The algebraic difference between the impedance value at the next moment and the impedance value at the previous moment is calculated, i.e., "subtracting the previous value from the subsequent value," to generate the first change. This operation is essentially a first-order differential calculation, which can eliminate the influence of the absolute deviation of the measurement base value and directly reflect the short-term fluctuation direction of the dielectric properties of solid ice. For example, at the monitoring point on the Qinghai-Tibet Railway, when the ice content in the permafrost increases slightly due to low temperature, the densification of the ice crystal structure leads to a continuous increase in adjacent impedance values, at which point the first change is positive. If a sudden heat source attack occurs (such as a ruptured underground pipeline), the partial melting of the ice crystals causes a sharp drop in impedance, and the change becomes negative. It is worth noting that this design can effectively isolate the influence of slow fluctuations in ambient temperature—although slow temperature changes cause overall impedance drift, the difference between adjacent points is slight, while sudden changes in ice content will produce significant changes.

[0046] In S22, the first change value generated in S21 is normalized to eliminate the magnitude deviation caused by differences in monitoring points. The core of this process is to divide the first change value by the average level of the sequence (i.e., all low-frequency impedance values ​​within the current signal application period) to obtain the standardized first rate of change. The normalization denominator uses the sequence mean instead of a fixed threshold, which can adapt to the characteristics of different permafrost regions (e.g., high-ice-content areas have a larger impedance base, while low-ice-content areas have a smaller base), making the rate of change comparable across different points. For example, at a monitoring point on a plateau highway, if the sequence mean is in a high-impedance state (reflecting historical ice crystal stability), a single change value needs to be relatively large to trigger a significant rate of change; conversely, in melting risk areas, the impedance mean is lower, and the same change value will generate a higher rate of change, naturally amplifying the abnormal signal. This processing enables subsequent trend analysis to identify both subtle ice loss in high-altitude and cold regions (such as the risk of slow changes with a daily average change rate of 0.5%) and sensitively capture abrupt changes in melting zones (such as a single ice melt event with a change rate of -8%), providing interference-resistant standardized input for the calculation of S3 continuity indicators.

[0047] It should also be noted that the method for obtaining the second rate of change is the same as the method for obtaining the first rate of change, except that the object of data processing changes from the first impedance data sequence to the second impedance data sequence.

[0048] like Figure 3 As shown, in one embodiment, S3 obtains the continuity index based on the rate of change sequence, including:

[0049] S31. Determine whether the signs of adjacent rates of change in the rate of change sequence are consistent, and obtain the continuous segment formed by adjacent rates of change with inconsistent signs.

[0050] S32. Obtain the continuous segment containing the most rates of change and use it as the segment to be compared.

[0051] S33. Divide the number of rates of change in the segment to be compared by the number of rates of change in the rate of change sequence to obtain the continuity index.

[0052] In this embodiment, it should be noted that in S31, the stability of the ice content change in permafrost is assessed by analyzing the sign coordination of adjacent rate changes in the rate change sequence. The time-interleaved rate change sequence generated in S2 (composed of alternating low-frequency and high-frequency rate changes) is scanned, and the signs of each pair of adjacent rate changes (i.e., the current low-frequency rate and the immediately following high-frequency rate) are sequentially determined to be opposite. Opposite signs indicate that the dual-frequency impedance response conforms to the physical laws of permafrost phase transition—an increase in low-frequency impedance (solid ice stability) corresponds to a decrease in high-frequency impedance (liquid water migration), characterizing a stable change in ice content. For example, in stable permafrost layers, the sequence will exhibit an alternating sign pattern of "positive (low frequency) → negative (high frequency) → positive → negative…"; if adjacent pairs with the same sign appear (such as "positive → positive" or "negative → negative"), it may indicate that local thermal disturbances have caused an imbalance in the ice-water response. This step provides a primary signal for identifying trend disturbances.

[0053] In S32, based on the sign analysis results of S31, continuous segments with inconsistent signs (i.e., opposite signs) are extracted. The entire rate of change sequence is scanned, all adjacent rate of change pairs that satisfy the opposite sign condition are marked, and multiple adjacent pairs that consecutively satisfy this condition are merged into continuous segments.

[0054] For example, consider a local segment of a frozen soil slope monitoring sequence. Segment A: low frequency (+) → high frequency (-) → low frequency (+) → high frequency (-) (four consecutive alternating sign points); Segment B: low frequency (-) → high frequency (+) (two consecutive alternating sign points). The longest continuous segment is selected as the comparison segment (segment A in this example). This design captures the longest stable period of the frozen soil state, avoiding interference from short-term environmental disturbances (such as individual identical signs caused by a single temperature fluctuation) on trend assessment, and providing a reliable basis for continuity indicators.

[0055] In S33, a continuity index is calculated to quantify the stability of the trend. The number of rates of change contained in the comparison segment (selected in S32) is divided by the total number of rates of change in the entire sequence. For example, if the comparison segment contains 40 rates of change (symbols alternating 40 times consecutively), and the total number of rates of change in the sequence is 50, then the continuity index is 0.8; if the comparison segment contains only 15 rates of change, and the total number is 50, the index is 0.3. An index exceeding a preset threshold (e.g., 0.6) indicates that the main body of the sequence conforms to the dual-frequency synergistic law, and the ice content changes smoothly; an index that is too low suggests a large number of symbol synchronization anomalies in the sequence, requiring vigilance against the risk of foundation instability. This index is a key threshold for screening usable historical data, ensuring that the reference trend value is generated only based on reliable steady-state conditions.

[0056] like Figure 4 As shown, in one embodiment, S3 obtains the reference trend value based on the rate of change sequence, including:

[0057] S34. Obtain multiple sets of reference change rates according to the change rate sequence, wherein the reference change rates include adjacent first change rates and second change rates, with the first change rate preceding the second;

[0058] S35. Subtract the second rate of change from the first rate of change in the reference rate of change and obtain the relative difference corresponding to the reference rate of change.

[0059] S36. Obtain the average of the relative differences corresponding to all reference rates of change in the rate of change sequence and use it as the reference trend value.

[0060] In this embodiment, it should be noted that in S34, under the premise that the continuity index is qualified, multiple sets of reference rates of change are extracted from the rate of change sequence. Each set contains an adjacent first rate of change (low frequency) and a second rate of change (high frequency), arranged in the order of measurement time (low frequency first, high frequency next). For example, set 1: first low frequency rate of change (positive) → first high frequency rate of change (negative); set 2: second low frequency rate of change (positive) → second high frequency rate of change (negative). Each set of data represents the instantaneous synergistic relationship between the response of solid ice and liquid water within a measurement cycle, essentially reflecting the coupling mechanism of the dielectric properties of permafrost on a microscopic time scale.

[0061] In S35, the relative difference of each set of reference rates of change is calculated: the first rate of change (low frequency) minus the second rate of change (high frequency). In a steady state: the difference between the low-frequency positive value (increased ice impedance) and the high-frequency negative value (decreased water impedance) increases significantly (e.g., positive 3 minus negative 2 equals positive 5). In an abnormal state: if ice melting leads to a low-frequency negative value (e.g., negative 4) while water accumulation leads to a high-frequency positive value (e.g., positive 3), the difference decreases sharply (negative 4 minus positive 3 equals negative 7). This difference amplifies the inverse characteristic changes in the ice-water response, highlighting the difference between gradual and abrupt changes in ice content, providing a highly sensitive input unit for the reference trend value.

[0062] In S36, the average of the relative differences (generated in S35) of all reference change rate groups is taken to obtain the reference trend value. This value abstracts the steady-state pattern of the coordinated change of permafrost impedance within historical cycles: in low-temperature environments, most differences are high positive values ​​(e.g., average +4.2), reflecting the continuous enhancement of ice crystals and the reduction of unfrozen water; in the early stage of thaw settlement, if there is local thermal disturbance, the differences of some groups may drop sharply (e.g., a group difference of -7), but the average value still remains positive (e.g., +0.5); while global thaw settlement causes the average value to turn negative.

[0063] By using the trend value as a dynamic benchmark input in step S4, it becomes possible to distinguish between "short-term mean fluctuations caused by cold waves" (the mean difference value slightly decreases from +4.1 to +3.9) and "trend reversals caused by pipeline leakage" (from +4.2 to -1.5) in the monitoring of plateau railways, thereby improving the accuracy of early warning.

[0064] like Figure 5 As shown, in one embodiment, S4 obtains a real-time trend value based on a first difference and a second difference, and obtains a similarity based on the real-time trend value and a reference trend value, including:

[0065] S41. Divide the first difference by the average of all impedance values ​​in the first impedance data sequence to obtain the first value to be processed, and divide the second difference by the average of all impedance values ​​in the second impedance data sequence to obtain the second value to be processed. Subtract the second value to be processed from the first value to be processed to obtain the real-time trend value.

[0066] S42. Obtain the absolute value of the difference between the real-time trend value and the reference trend value and use it as the target difference quantity;

[0067] S43. Obtain the standard difference, the ratio of the standard difference to the target difference, and the similarity.

[0068] In this embodiment, it should be noted that in S41, the real-time trend value is constructed by integrating the short-term impedance variation characteristics of dual-frequency signals to create a core indicator reflecting the immediate state of the permafrost foundation. Specifically, the operation is as follows: The impedance values ​​of the two previous low-frequency signals (10Hz–1kHz) are acquired at the current moment. The algebraic difference between the previous and subsequent values ​​is calculated as the first difference—this value quantifies the short-term direction of the dielectric response of solid ice (positive for ice crystal enhancement, negative for ice structure weakening). Simultaneously, the second difference of the impedance values ​​of the two previous high-frequency signals (1kHz–100kHz) is acquired, reflecting the abrupt change in liquid water content (positive for water accumulation, negative for unfrozen water migration). Then, this difference is divided by the average level of the corresponding sequence (i.e., all low-frequency impedance values ​​in the previous period) to eliminate the influence of impedance base value fluctuations caused by regional differences in permafrost, obtaining the first value to be processed. Simultaneously, the same operation (original difference divided by the average of the sequence in the previous period) is performed on the high-frequency signals (1kHz–100kHz) to generate the second value to be processed. Finally, the first value to be processed is subtracted from the second value to obtain the real-time trend value.

[0069] For example, in high-temperature degradation zones, the infiltration of unfrozen water leads to a lower low-frequency impedance base and a higher high-frequency impedance base. If the original difference is used directly (e.g., low-frequency difference -5, high-frequency difference +8), environmental interference will be amplified. After standardization, the low-frequency value can be converted to -0.15 (weakening the absolute deviation), and the high-frequency value can be converted to +0.12 (suppressing the magnitude advantage). The final real-time trend value is -0.27, which not only preserves the reverse characteristics of the ice-water response, but also ensures the horizontal comparability of data from different regions, providing input parameters with clear physical meaning and noise resistance for subsequent similarity analysis.

[0070] In S42, the deviation between the real-time trend value and the historical reference trend value is evaluated. The target difference is defined as the absolute difference between the real-time trend value and the reference trend value generated in S3. Its physical meaning is to eliminate interference in the positive and negative directions and directly quantify the instantaneous deviation magnitude of trend coordination. The reference trend value represents the dual-frequency impedance coordination mode under historical healthy conditions (such as long-term stability around +4), while the target difference captures the degree of separation between the current state and the historical baseline: normal fluctuations, such as a cold wave causing the real-time trend value to drop slightly to +3 while the reference value is +4, result in a target difference of 1; true anomalies, such as thermal disturbances causing the real-time value to drop to -5 while the reference value remains at +4, result in a target difference that expands to 9. This design addresses the shortcomings of the traditional absolute value threshold method—for example, when the overall ambient temperature rises, the absolute impedance value generally decreases, easily triggering false alarms. However, the target difference, because it only focuses on relative trend deviation, can effectively filter out such interference.

[0071] In S43, an abnormal risk level is determined through a dynamic tolerance mechanism. The standard deviation is a fluctuation tolerance threshold based on historical health data statistics (e.g., set according to the maximum fluctuation amplitude of the target deviation over the past 10 periods), representing the tolerable range of normal environmental disturbances. Similarity is defined as the ratio of the standard deviation to the target deviation: if the target deviation is ≤ the standard deviation, the similarity is ≥ 1, and it is judged as noise fluctuation (e.g., a similarity of 1.2 caused by short-term temperature differences); if the target deviation is > the standard deviation, the similarity is < 1 and decreases with the degree of deviation (e.g., when the target deviation is 3 times the standard deviation, the similarity = 0.33).

[0072] This ratio design adaptively matches the sensitivity to sudden anomalies with historical stability. In long-term stable permafrost areas (where the standard deviation is set relatively small), a slight deviation will trigger an alarm; in areas with large fluctuations (where the standard deviation is relaxed), a significant deviation is required to trigger an alarm, thus avoiding frequent false alarms.

[0073] The standard deviation (SD) is an adaptive tolerance threshold characterizing the fluctuation range of historical health data. Its acquisition process involves two steps. First, within a verified stable period (i.e., when the S3 continuity index consistently meets the target), the target deviation (the absolute deviation between the real-time trend value and the reference trend value) for each period is recorded. The maximum fluctuation range of these target deviations is then calculated—and the 95th percentile value is taken as the initial SD. For example, for a highway monitoring point during a stable period, the recorded target deviation dataset is {0.1, 0.3, 0.5, 0.8, 1.0, 1.2}. After sorting in ascending order, the 95th percentile (i.e., the value at the 95th position) is taken. Due to the small data volume, linear interpolation is used for calculation: 6 * 0.95 = 5.7. The value corresponding to the 5.7th data point is approximately 1.15. Therefore, the initial SD is set to 1.15. Secondly, when a new historical period is verified by S3 (continuity index > threshold), the new target difference is included in the dataset. A sliding window (such as the most recent 20 periods) is used to count the standard deviation of the data set (the calculation formula is Bessel's correction). If the current standard deviation is more than 20% larger than the initial value, the standard deviation is increased proportionally; if the standard deviation is reduced to less than 80% of the initial value, the tolerance threshold is tightened simultaneously.

[0074] like Figure 6 As shown, in one embodiment, S4 obtains the real-time monitoring result of ice content based on similarity, including:

[0075] S44. Obtain the warning threshold based on the standard difference and determine whether the similarity is less than the warning threshold;

[0076] S45. When the similarity is less than the warning threshold, a warning signal is generated to indicate abnormal changes in ice content.

[0077] S46. When the similarity is not less than the warning threshold, a monitoring signal representing a normal change in ice content is generated.

[0078] In this embodiment, it should be noted that in S44, similarity is converted into an operable early warning threshold based on the engineering risk level. The early warning threshold is typically set to a critical value close to 1 (e.g., 0.8), meaning that real-time trend values ​​are allowed to gradually change within the historical fluctuation range (similarity > 0.8), but exceeding this threshold indicates a risk of loss of control. The threshold setting can be dynamically adjusted in conjunction with geological characteristics: in high-temperature degradation areas, the threshold is stringent (e.g., 0.9) to detect the onset of thaw settlement early; in highly stable permafrost areas, the threshold is lenient (e.g., 0.7) to filter seasonal fluctuations. For example, in monitoring high-altitude oil pipelines, when the initial similarity of leakage drops from 0.95 to 0.75 (below the early warning threshold of 0.8), the early warning process is initiated.

[0079] The early warning threshold is a dynamically set critical value for engineering safety, determined by analyzing the similarity distribution characteristics under historical health conditions. Specifically, it can be obtained by continuously collecting similarity data over multiple cycles (at least 10 signal application cycles) during the initial operation phase (e.g., the stabilization period of permafrost). These data correspond to normal operating conditions without abnormal alarms. The distribution pattern of similarity during this phase is statistically analyzed, with a focus on calculating its lower limit boundary value. For example, in monitoring high-altitude railways, similarity under stable low-temperature environments is mainly distributed between 0.9 and 1.5. The lowest tolerance boundary within this distribution range (e.g., the 5th percentile value of 0.85) is taken as the baseline early warning threshold. Subsequently, when a new continuous safety period is added, the similarity distribution interval is recalculated, and the early warning threshold is dynamically adjusted to match changes in the geological environment (e.g., the threshold for permafrost degradation zones is lowered from 0.85 to 0.75 to improve sensitivity). In addition, weighted correction is made in conjunction with the importance coefficient of the engineering structure: more stringent thresholds are used for key locations such as railway subgrades (e.g., benchmark value × 0.95), while those for ordinary slopes are relaxed (benchmark value × 1.05) to ensure that the early warning strategy is strictly matched with the engineering risk level.

[0080] In S45, when the similarity falls below the warning threshold, an abnormal ice content warning signal is generated. This signal needs to distinguish the abnormal mode: ice melt-dominated type, with a negative low-frequency difference and a near-zero high-frequency difference (similarity 0.4), triggering an "ice loss" warning; water intrusion type, with a significantly positive high-frequency difference (similarity 0.3), generating a "groundwater seepage" alarm. The warning signal is pushed in real time through the engineering monitoring platform. For example, when the Qinghai-Tibet Railway Operation and Maintenance Center receives the location information of "localized subsidence risk on the K203+550 slope," it guides personnel to conduct on-site verification and handling.

[0081] In S46, when the similarity is greater than or equal to the warning threshold, a monitoring signal indicating normal changes in ice content is output. This signal includes two states: a stable state, with a similarity ≥ 1 (real-time trend value within historical tolerance), indicating "no abnormalities in the foundation"; and a transitional state, with a similarity ∈ [threshold, 1) (e.g., 0.85), indicating "continuous monitoring of environmental fluctuations." For example, during a cold wave, a safety signal is continuously output with a trend annotation: "Dual-frequency difference gradually changes, consistent with low-temperature slow-freezing mode," avoiding misjudgment and interference from the maintenance team. The entire process forms a closed loop of "dynamic comparison - graded response - precise decision-making," improving the timeliness of risk identification by more than 48 hours compared to traditional methods.

[0082] A real-time monitoring system for ice content in permafrost foundations based on electrical impedance is also provided. The system includes:

[0083] The acquisition module is used to acquire the target permafrost foundation and the electrode array inserted into the target permafrost foundation, and to acquire the signal application period. Within the signal application period, a first AC signal for covering the low frequency band and a second AC signal for covering the high frequency band are alternately applied multiple times based on the electrode array and the corresponding impedance values ​​are collected. The module also acquires the first impedance data sequence corresponding to the first AC signal and the second impedance data sequence corresponding to the second AC signal in the previous signal application period.

[0084] The first data processing module is used to obtain the first rate of change between adjacent impedance values ​​in the first impedance data sequence and the second rate of change between adjacent impedance values ​​in the second impedance data sequence, and to sequentially and alternately arrange multiple first rates of change and multiple second rates of change according to the acquisition order of impedance values ​​in the signal application period to form a rate of change sequence.

[0085] The second data processing module is used to obtain continuous indicators based on the rate of change sequence. If the continuous indicators exceed a preset threshold, a reference trend value is obtained based on the rate of change sequence.

[0086] The monitoring and processing module is used to obtain the first difference between the first real-time impedance values ​​corresponding to the two previous applications of the first AC signal at the current time, and to obtain the second difference between the second real-time impedance values ​​corresponding to the two previous applications of the second AC signal at the current time. Based on the first difference and the second difference, the module obtains the real-time trend value and the similarity based on the real-time trend value and the reference trend value, and obtains the real-time monitoring result of ice content based on the similarity.

[0087] In one embodiment, the first data processing module is further configured to: subtract the previous impedance value from the next impedance value of adjacent impedance values ​​in the first impedance data sequence to obtain a first change; divide the first change by the average value of all impedance values ​​in the first impedance data sequence to obtain a first rate of change.

[0088] In one embodiment, the second data processing module is further configured to: determine whether the signs of adjacent rates of change in the rate of change sequence are consistent, and obtain a continuous segment formed by adjacent rates of change with consistent signs; obtain the continuous segment containing the most rates of change and use it as the segment to be compared; divide the number of rates of change in the segment to be compared by the number of rates of change in the rate of change sequence to obtain a continuity index.

[0089] In one embodiment, the second data processing module is further configured to: obtain multiple sets of reference change rates according to the change rate sequence, wherein the reference change rates include adjacent first change rates and second change rates with the first change rate preceding the second change rate; subtract the second change rate from the first change rate in the reference change rates to obtain the relative difference corresponding to the reference change rates; and obtain the average value of the relative differences corresponding to all reference change rates in the change rate sequence and use it as a reference trend value.

[0090] In this embodiment, it should be noted that the specific operation method of the above-mentioned real-time monitoring system for ice content in permafrost foundation based on electrical impedance has been described in detail in the embodiments of the real-time monitoring method for ice content in permafrost foundation based on electrical impedance, and will not be elaborated here.

[0091] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0092] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0093] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A real-time monitoring method of ice content in a frozen ground foundation based on electrical impedance, characterized in that, The method comprises the following steps: obtaining a target permafrost foundation and an electrode array inserted into the target permafrost foundation, and obtaining a signal application period, in which a first alternating current signal for covering a low frequency band and a second alternating current signal for covering a high frequency band are alternately applied based on the electrode array multiple times and corresponding impedance values are collected in the signal application period, and obtaining a first impedance data sequence corresponding to the first alternating current signal and a second impedance data sequence corresponding to the second alternating current signal in a signal application period before a current signal application period; obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; obtaining a continuity index according to the change rate sequence, if the continuity index exceeds a preset threshold, obtaining a reference trend value according to the change rate sequence; obtaining a first difference value between first real-time impedance values corresponding to the first alternating current signal applied twice before the current time, and obtaining a second difference value between second real-time impedance values corresponding to the second alternating current signal applied twice before the current time, obtaining a real-time trend value according to the first difference value and the second difference value, obtaining a similarity according to the real-time trend value and the reference trend value, and obtaining an ice content real-time monitoring result according to the similarity.

2. The real-time resistive impedance-based monitoring method of ice content in a permafrost foundation according to claim 1, characterized in that, The method comprises the following steps: obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; 3. The real-time resistive impedance-based monitoring method of ice content in a permafrost foundation according to claim 1, characterized in that, obtaining a continuity index according to the change rate sequence, if the continuity index exceeds a preset threshold, obtaining a reference trend value according to the change rate sequence; obtaining a first difference value between first real-time impedance values corresponding to the first alternating current signal applied twice before the current time, and obtaining a second difference value between second real-time impedance values corresponding to the second alternating current signal applied twice before the current time, obtaining a real-time trend value according to the first difference value and the second difference value, obtaining a similarity according to the real-time trend value and the reference trend value, and obtaining an ice content real-time monitoring result according to the similarity. The method comprises the following steps: obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; 4. The real-time resistive impedance-based monitoring method of ice content in a permafrost foundation according to claim 1, characterized in that, obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; obtaining a continuity index according to the change rate sequence, if the continuity index exceeds a preset threshold, obtaining a reference trend value according to the change rate sequence; obtaining a first difference value between first real-time impedance values corresponding to the first alternating current signal applied twice before the current time, and obtaining a second difference value between second real-time impedance values corresponding to the second alternating current signal applied twice before the current time, obtaining a real-time trend value according to the first difference value and the second difference value, obtaining a similarity according to the real-time trend value and the reference trend value, and obtaining an ice content real-time monitoring result according to the similarity. The method comprises the following steps:

5. The real-time resistive impedance-based monitoring method of ice content in a permafrost foundation according to claim 1, characterized in that, obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; obtaining a first change rate between adjacent impedance values in the first impedance data sequence, and obtaining a second change rate between adjacent impedance values in the second impedance data sequence, sequentially arranging the plurality of first change rates and the plurality of second change rates in turn according to the collection sequence of the impedance values in the signal application period to form a change rate sequence; obtaining a continuity index according to the change rate sequence, if the continuity index exceeds a preset threshold, obtaining a reference trend value according to the change rate sequence; obtaining a first difference value between first real-time impedance values corresponding to the first alternating current signal applied twice before the current time, and obtaining a second difference value between second real-time impedance values corresponding to the second alternating current signal applied twice before the current time, obtaining a real-time trend value according to the first difference value and the second difference value, obtaining a similarity according to the real-time trend value and the reference trend value, and obtaining an ice content real-time monitoring result according to the similarity.

6. The electrical impedance-based real-time monitoring method of ice content of a permafrost foundation according to claim 5, characterized in that, ​ ​ ​ When the similarity is not less than the warning threshold, a monitoring signal is generated to indicate that the change in ice content is normal.

7. A real-time monitoring system for ice content of a permafrost foundation based on electrical impedance, characterized in that, The system includes: The acquisition module is used to acquire the target permafrost foundation and the electrode array inserted into the target permafrost foundation, and to acquire the signal application period. Within the signal application period, a first AC signal for covering the low frequency band and a second AC signal for covering the high frequency band are alternately applied multiple times based on the electrode array and the corresponding impedance values ​​are collected. The module also acquires the first impedance data sequence corresponding to the first AC signal and the second impedance data sequence corresponding to the second AC signal in the previous signal application period. The first data processing module is used to obtain the first rate of change between adjacent impedance values ​​in the first impedance data sequence and the second rate of change between adjacent impedance values ​​in the second impedance data sequence, and to sequentially and alternately arrange multiple first rates of change and multiple second rates of change according to the acquisition order of impedance values ​​in the signal application period to form a rate of change sequence. The second data processing module is used to obtain continuous indicators based on the rate of change sequence. If the continuous indicators exceed a preset threshold, a reference trend value is obtained based on the rate of change sequence. The monitoring and processing module is used to obtain the first difference between the first real-time impedance values ​​corresponding to the two previous applications of the first AC signal at the current time, and to obtain the second difference between the second real-time impedance values ​​corresponding to the two previous applications of the second AC signal at the current time. Based on the first difference and the second difference, the module obtains the real-time trend value and the similarity based on the real-time trend value and the reference trend value, and obtains the real-time monitoring result of ice content based on the similarity.

8. The electrical impedance-based real-time monitoring system for ice content of a ground base according to claim 7, wherein, The first data processing module is also used for: The first change is obtained by subtracting the previous impedance value from the next adjacent impedance value in the first impedance data sequence. The first change is divided by the average of all impedance values ​​in the first impedance data sequence to obtain the first rate of change.

9. The electrical impedance-based real-time monitoring system for ice content of a ground base according to claim 7, wherein, The second data processing module is also used for: Determine whether the signs of adjacent rates of change in the rate of change sequence are consistent, and obtain the continuous segment formed by adjacent rates of change with consistent signs; Obtain the continuous segment containing the highest number of change rates and use it as the comparison segment; The continuity index is obtained by dividing the number of rates of change in the segment to be compared by the number of rates of change in the rate of change sequence.

10. The electrical impedance-based real-time monitoring system for ice content of a ground base according to claim 7, wherein, The second data processing module is also used for: Multiple sets of reference change rates are obtained based on the change rate sequence, wherein the reference change rates include adjacent first change rates and second change rates, with the first change rate preceding the second. Subtract the second rate of change from the first rate of change in the reference rate of change to obtain the relative difference corresponding to the reference rate of change. Obtain the average of the relative differences corresponding to all reference rates of change in the rate of change sequence and use it as the reference trend value.

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