A sensor-based seasonal frozen ground monitoring method and system

Abstract

The application provides a sensor-based seasonal frozen soil monitoring method and system, wherein the method comprises the following steps: dividing a monitored area into a grid, deploying a sensor group in the monitored area, acquiring geographical parameters of the monitored area, and acquiring a topographic influence factor of seasonal frozen soil according to the geographical parameters; acquiring heat flux between sensor monitoring points i and j in the monitored area; calculating a maximum weight matrix, calculating spatial autocorrelation between the sensor monitoring points based on the topographic influence factor, the heat flux and the maximum weight matrix; screening out unnecessary monitoring points according to the spatial autocorrelation, and sorting the monitoring points according to the spatial autocorrelation values, wherein the monitoring points with larger absolute values are sorted in front, and the monitoring points sorted in front are preferentially monitored. The method and the corresponding system can reduce the number of sensors deployed and the maintenance cost, and reduce the operation cost of the whole monitoring system by screening out redundant monitoring points in the seasonal frozen soil monitoring.

Description

TECHNICAL FIELD

[0001] The application provides a sensor-based seasonal frozen soil monitoring method and system, and relates to the technical field of frozen soil monitoring. BACKGROUND

[0002] Frozen soil accounts for about 70% of the land area in the world, and frozen soil accounts for about 53% of the land area in China. Frozen soil is a soil medium extremely sensitive to temperature, and its freezing and thawing is affected by temperature changes during seasonal alternation. The volume of frozen soil inevitably changes. Under the freezing and thawing of frozen soil, rocks in the frozen soil area are damaged, causing buildings to deform, collapse and other hazards. In particular, if water conservancy projects such as dams are damaged, it will cause incalculable losses. Seasonal frozen soil is like the active layer of permafrost, and is always in an alternating state of melting in summer and freezing in winter.

[0003] However, the range of traditional seasonal frozen soil monitoring is usually large, and a large number of sensors are used for point monitoring, which will produce a large amount of data. The direct comparison method with the threshold size is inefficient and prone to false positives, and an innovative method is needed to quickly and effectively detect abnormalities and reduce data processing. SUMMARY

[0004] The application provides a sensor-based seasonal frozen soil monitoring method and system to solve the above-mentioned problems.

[0005] The application provides a sensor-based seasonal frozen soil monitoring method, which comprises the following steps:

[0006] The monitored area is divided into a grid, a sensor group is arranged in the monitored area, geographical parameters of the monitored area are obtained, and a topographic influence factor of seasonal frozen soil is obtained according to the geographical parameters.

[0007] The heat flux between sensor monitoring point i and sensor monitoring point j in the monitored area is obtained.

[0008] The maximum weight matrix is calculated, the spatial autocorrelation between the sensor monitoring points is calculated based on the topographic influence factor, the heat flux and the maximum weight matrix.

[0009] According to the spatial autocorrelation, unnecessary monitoring points are screened out, and the monitoring points are sorted according to the size of the spatial autocorrelation value. The sorting of the absolute value is large, and the monitoring points with high priority are monitored first.

[0010] Further, the monitored area is divided into a grid, a sensor group is arranged in the monitored area, geographical parameters of the monitored area are obtained, and a topographic influence factor of seasonal frozen soil is obtained according to the geographical parameters, comprising:

[0011] The monitored area is divided into a grid, and sensors are arranged at equal intervals in each grid, including temperature sensors, moisture sensors, soil moisture sensors, and strain sensors;

[0012] The elevation, slope, and aspect of the sensor monitoring points are obtained;

[0013] The terrain influence factor between sensor monitoring points is calculated according to the elevation, slope, and aspect of the sensor monitoring points;

[0014]

[0015] wherein T ij represents the terrain influence factor between sensor monitoring points i and j, Δh ij represents the elevation difference between sensor monitoring points i and j, Δs ij represents the slope difference between sensor monitoring points i and j, Δp ij represents the aspect difference between sensor monitoring points i and j, H0 represents the characteristic elevation, S0 represents the characteristic slope, P0 represents the characteristic aspect, k h represents the elevation weight coefficient, k s represents the slope weight coefficient, k p represents the aspect weight coefficient.

[0016] Further, the heat flux between sensor monitoring point i and sensor monitoring point j in the monitored area is obtained, including:

[0017] For each sensor monitoring point, the weighted average thermal conductivity is calculated according to the depth information and the measured data of the soil thermal conductivity sensor, and the weighted average thermal conductivity of sensor monitoring point i is obtained, which reflects the heat conduction performance of the soil at that point,

[0018]

[0019] wherein λ i represents the weighted average thermal conductivity of monitoring point i, represents the soil thermal conductivity of the kth measured soil thermal conductivity sensor of monitoring point i, n i represents the number of sensors measuring soil thermal conductivity of monitoring point i, represents the weight of the kth measured soil thermal conductivity sensor of monitoring point i,

[0020]

[0021] wherein, represents the depth of the kth sensor of monitoring point i,

[0022] According to the weighted average thermal conductivity of adjacent monitoring points, the average thermal conductivity between two points is calculated:

[0023]

[0024] wherein λ ij represents the average thermal conductivity between sensor monitoring points i and j, λ j represents the weighted average thermal conductivity of monitoring point j;

[0025] Based on the average thermal conductivity between sensor monitoring point i and sensor monitoring point j, the heat flux between sensor monitoring point i and sensor monitoring point j is obtained,

[0026]

[0027] wherein q ij represents the heat flux between monitoring points i and j, represents the average temperature gradient between monitoring points i and j, which can be calculated by the central difference method.

[0028] Further, the maximum weight matrix is calculated, and the spatial autocorrelation between sensor monitoring points is calculated based on the terrain influence factor, the heat flux and the maximum weight matrix, including:

[0029] Based on the terrain influence factor and the heat conduction factor, the maximum weight matrix W ij is obtained, ij · (1 + c · |q ij |), wherein c represents a weight factor;

[0030] For each sensor monitoring point i, the correlation between it and the surrounding points is quantified by a correlation model, and specifically, the correlation model is:

[0031]

[0032] wherein x i represents the observation value of the i th sensor, represents the average value of all sensor observation values, s 2 represents the variance of the observation value, W ij represents the weight between the i th point and the j th point in the weight matrix.

[0033] Further, unnecessary monitoring points are screened out according to the spatial autocorrelation, and the monitoring points are sorted according to the size of the spatial autocorrelation value, and the absolute value is larger. The sorting is in front, and the monitoring points in front are monitored preferentially, including:

[0034] If I i is greater than the first preset threshold and |x i -∑ j Wij ·x j | less than the second preset threshold value, the sensor monitoring point is considered as a redundant point, and is removed;

[0035] Otherwise, it is considered that the point is not a redundant point, and the I i absolute value size is sorted, and the I i larger point is preferentially checked to obtain an abnormal point.

[0036] The application provides a seasonal frozen soil monitoring system based on sensors.

[0037] An acquisition terrain influence factor module is configured to divide the monitored area into grids, deploy a sensor group in the monitored area, acquire geographical parameters of the monitored area, and acquire a terrain influence factor of seasonal frozen soil according to the geographical parameters.

[0038] An acquisition heat flux module is configured to acquire a heat flux between sensor monitoring points i and j in the monitored area.

[0039] An acquisition autocorrelation module is configured to calculate a maximum weight matrix, and calculate spatial autocorrelation between sensor monitoring points based on the terrain influence factor, the heat flux and the maximum weight matrix.

[0040] A screening module is configured to screen out unnecessary monitoring points according to the spatial autocorrelation, and sort the monitoring points according to the spatial autocorrelation value, with the absolute value of a larger value being sorted in front.

[0041] Further, the acquisition terrain influence factor module comprises:

[0042] A division module is configured to divide the monitored area into grids, and deploy sensors at equal intervals in each grid, wherein the sensors comprise temperature sensors, moisture sensors, soil humidity sensors and strain sensors.

[0043] An acquisition geographical data module is configured to acquire an altitude, a slope and a slope direction of the sensor monitoring points.

[0044] A calculation terrain influence factor module is configured to calculate a terrain influence factor between the sensor monitoring points according to the altitude, the slope and the slope direction of the sensor monitoring points.

[0045]

[0046] Wherein, T ij represents the terrain influence factor between the sensor monitoring points i and j, Δh ij represents the elevation difference between the sensor monitoring points i and j, Δs ij represents the slope difference between the sensor monitoring points i and j, and Δpij represents the slope difference between sensor monitoring points i and j, H0 represents the characteristic elevation, S0 represents the characteristic slope, P0 represents the characteristic aspect, k h represents the elevation weight coefficient, k s represents the slope weight coefficient, k p represents the aspect weight coefficient.

[0047] Further, the obtaining heat flux module comprises:

[0048] The weighted average thermal conductivity calculation module of the sensor monitoring point i is used to calculate the weighted average thermal conductivity according to the depth information and the measured data of the soil thermal conductivity sensor for each sensor monitoring point, and obtain the weighted average thermal conductivity of the sensor monitoring point i, which reflects the heat conduction performance of the soil at the point,

[0049]

[0050] wherein, λ i represents the weighted average thermal conductivity of the monitoring point i, represents the soil thermal conductivity of the kth measured soil thermal conductivity sensor of the monitoring point i, n i represents the number of sensors measuring the soil thermal conductivity of the monitoring point i, represents the weight of the kth measured soil thermal conductivity sensor of the monitoring point i,

[0051]

[0052] wherein, represents the depth of the kth sensor of the monitoring point i,

[0053] The average thermal conductivity calculation module between two points is used to calculate the average thermal conductivity between two points according to the weighted average thermal conductivities of adjacent monitoring points:

[0054]

[0055] wherein, λ ij represents the average thermal conductivity between sensor monitoring points i and j, λ j represents the weighted average thermal conductivity of the monitoring point j;

[0056] The heat flux calculation module is used to obtain the heat flux between sensor monitoring point i and sensor monitoring point j based on the average thermal conductivity between two points of sensor monitoring point i and sensor monitoring point j,

[0057]

[0058] wherein, q ijrepresents the heat flux between monitoring points i and j, represents the average temperature gradient between monitoring points i and j, which can be calculated by central difference method.

[0059] Further, the obtaining self-correlation module comprises:

[0060] The obtaining maximum weight matrix module is configured to obtain a maximum weight matrix W based on the terrain influence factor and the heat conduction factor. ij = T ij ·(1+c·|q ij |), wherein c represents a weight factor.

[0061] The quantifying correlation module is configured to, for each sensor monitoring point i, quantify the correlation between it and surrounding points by a correlation model, and specifically, the correlation model is:

[0062]

[0063] wherein x i represents an observation value of the i-th sensor, represents the average value of all sensor observation values, s 2 represents the variance of the observation value, W ij represents the weight between the i-th point and the j-th point in the weight matrix.

[0064] Further, the screening module comprises:

[0065] The screening module is configured to, if I i is greater than a first preset threshold and |x i -∑ j W ij ·x j | is less than a second preset threshold, consider that the sensor monitoring point is a redundant point and remove it.

[0066] The priority monitoring module is configured to, otherwise, consider that the point is not a redundant point, sort the points according to the absolute value of I i , and preferentially check the points with greater I i to obtain an abnormal point.

[0067] The beneficial effects of this invention are as follows: Improved monitoring efficiency: By scientifically and rationally dividing areas and deploying sensors, and by optimizing the layout of monitoring points using spatial autocorrelation analysis, monitoring efficiency can be significantly improved, reducing unnecessary resource waste; Enhanced monitoring accuracy: Combining topographic influence factors and heat flux data for spatial autocorrelation analysis can more accurately reflect the spatial distribution and dynamic changes of permafrost conditions, thereby improving monitoring accuracy; Reduced monitoring costs: By screening redundant monitoring points, the number of sensors deployed and maintenance costs can be reduced, lowering the overall operating cost of the monitoring system; Support for decision-making: Real-time and accurate permafrost monitoring data can provide scientific basis for relevant departments and decision-makers, helping them better understand permafrost conditions, formulate effective management and protection measures, and reduce the occurrence and losses of permafrost disasters; By comprehensively utilizing geographic information technology, sensor technology, and data analysis technology, efficient and accurate monitoring of seasonal permafrost conditions is achieved, providing strong support for permafrost research, disaster early warning, and ecological protection. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of a sensor-based seasonal permafrost monitoring method and system according to the present invention. Detailed Implementation

[0069] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0070] Numerous specific details are set forth in the following description to provide a thorough understanding of the invention. The described embodiments are only a part of, and not all, of the 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.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0072] One embodiment of the present invention provides a sensor-based method for monitoring seasonal permafrost, the method comprising:

[0073] The monitored area is divided into grids, sensor groups are deployed in the monitored area, geographical parameters of the monitored area are obtained, and the topographic influence factor of seasonal permafrost is obtained based on the geographical parameters.

[0074] acquiring heat flux between sensor monitoring point i and sensor monitoring point j in the monitored area;

[0075] calculating a most weight matrix, calculating spatial autocorrelation between sensor monitoring points based on the terrain influence factor, the heat flux and the most weight matrix;

[0076] screening out unnecessary monitoring points according to the spatial autocorrelation, and sorting the monitoring points according to the spatial autocorrelation value, the larger the absolute value, the higher the sorting, and the monitoring points with higher sorting are monitored preferentially.

[0077] The working principle and effect of the above technical solution are as follows: first, the monitored area is scientifically and reasonably divided into multiple small areas or grids to ensure that the geographical and climate conditions in each area or grid are relatively consistent, facilitating subsequent data collection and analysis; a sensor group is deployed in each area or grid, which can collect geographical parameters (such as altitude, slope, slope direction, etc.) and related parameters of frozen soil (such as temperature, humidity, deformation, etc.) in real time; according to the collected geographical parameters, a terrain influence factor is calculated using a specific mathematical model or algorithm. This factor reflects the influence of terrain differences on the state of frozen soil (such as freezing depth, melting speed, etc.); by monitoring the temperature difference between the sensor monitoring points and the soil thermal conductivity data, the heat flux between the two points is calculated. Heat flux is a physical quantity that describes the rate and direction of heat transfer in soil, and is crucial for understanding the heat balance and dynamic changes of frozen soil; combined with the terrain influence factor and heat flux data, a most weighted matrix is constructed. This matrix reflects the degree of mutual influence between different monitoring points in space, and the spatial autocorrelation between sensor monitoring points is calculated using a spatial autocorrelation model. Spatial autocorrelation is an index that describes the similarity or correlation between a point and its surrounding points in geographic space; monitoring point optimization and sorting, according to the spatial autocorrelation results, filter out redundant or unnecessary monitoring points to reduce monitoring costs and data redundancy, and sort the remaining monitoring points according to the size of the spatial autocorrelation value. The points with larger absolute values indicate that they deviate from the average value more, i.e., they are more likely to have abnormal conditions, so these points are monitored first to capture changes in the state of frozen soil more accurately. Improving monitoring efficiency, by scientifically and reasonably dividing the area and deploying sensors, and using spatial autocorrelation analysis to optimize the layout of monitoring points, the monitoring efficiency can be significantly improved, and unnecessary resource waste can be reduced; enhancing monitoring accuracy, combined with terrain influence factor and heat flux data for spatial autocorrelation analysis, the spatial distribution and dynamic changes of frozen soil state can be more accurately reflected, thereby improving the accuracy of monitoring; reducing monitoring costs, by filtering out redundant monitoring points, the number of sensors deployed and maintenance costs can be reduced, and the operating costs of the entire monitoring system can be reduced; providing support for decision-making, real-time and accurate frozen soil monitoring data can provide scientific basis for relevant departments and decision-makers, helping them better understand the state of frozen soil and develop effective management and protection measures to reduce the occurrence and loss of frozen soil disasters; by comprehensively using geographic information technology, sensor technology and data analysis technology, efficient and accurate monitoring of seasonal frozen soil state is achieved, providing strong support for frozen soil research, disaster warning and ecological protection.

[0078] In one embodiment of the present application, the monitored area is divided into grids, a sensor group is deployed in the monitored area, geographical parameters of the monitored area are obtained, a terrain influence factor of seasonal frozen soil is obtained according to the geographical parameters, comprising:

[0079] The monitored area is divided into a grid, and sensors are arranged at equal intervals in each grid, wherein the sensors include temperature sensors, moisture sensors, soil humidity sensors and strain sensors;

[0080] The elevation, slope and aspect of the sensor monitoring point are obtained;

[0081] The terrain influence factor between the sensor monitoring points is calculated according to the elevation, slope and aspect of the sensor monitoring point;

[0082]

[0083] wherein T ij represents the terrain influence factor between the two points of sensor monitoring points i and j, Δh ij represents the elevation difference between the two points of sensor monitoring points i and j, Δs ij represents the slope difference between the two points of sensor monitoring points i and j, Δp ij represents the aspect difference between the two points of sensor monitoring points i and j, H0 represents the characteristic elevation, S0 represents the characteristic slope, P0 represents the characteristic aspect, k h represents the elevation weight coefficient, k s represents the slope weight coefficient, k p represents the aspect weight coefficient.

[0084] The working principle and effect of the above technical solution are: based on the influence of terrain factors (elevation, slope and aspect) on the state of seasonal frozen soil, the terrain influence factor between the sensor monitoring points is calculated to evaluate the influence. Specifically, the elevation, slope and aspect data of each sensor monitoring point are obtained, and then the terrain influence factor between any two monitoring points is calculated by using the given formula, which considers the elevation difference, slope difference and aspect difference, and is adjusted by the characteristic value H and the corresponding weight coefficient to reflect the relative importance of different terrain factors on the state of frozen soil. Accurate evaluation of terrain influence: by comprehensively considering the three terrain factors of elevation, slope and aspect, and their relative differences, this scheme can more accurately evaluate the influence of terrain on the state of frozen soil, and provide a more reliable data basis for subsequent frozen soil monitoring; improve the monitoring efficiency, by calculating the terrain influence factor, the monitoring points with consistent terrain influence factors can be identified, thereby optimizing the layout of the monitoring points, reducing unnecessary monitoring points, and improving the monitoring efficiency; support scientific research, the calculation of the terrain influence factor provides quantitative data support for scientific research, which helps to deeply understand the influence mechanism of terrain on the distribution and dynamic change of frozen soil.

[0085] In an embodiment of the present application, the heat flux between the two points of sensor monitoring point i and sensor monitoring point j in the monitored area is obtained, comprising:

[0086] For each sensor monitoring point, the weighted average thermal conductivity is calculated according to the depth information and the measured data of the soil thermal conductivity sensor, and the weighted average thermal conductivity of the sensor monitoring point i is obtained, which reflects the heat conduction performance of the soil at the point,

[0087]

[0088] wherein λ i represents the weighted average thermal conductivity of the monitoring point i, represents the soil thermal conductivity of the kth measuring soil thermal conductivity sensor of the monitoring point i, n i represents the number of sensors measuring the soil thermal conductivity of the monitoring point i, represents the weight of the kth measuring soil thermal conductivity sensor of the monitoring point i,

[0089]

[0090] wherein, represents the depth of the kth sensor of the monitoring point i, represents the depth of the k+1th sensor of the monitoring point i;

[0091] According to the weighted average thermal conductivities of adjacent monitoring points, the average thermal conductivity between the two points is calculated:

[0092]

[0093] wherein λ ij represents the average thermal conductivity between the sensor monitoring points i and j, λ j represents the weighted average thermal conductivity of the monitoring point j;

[0094] Based on the average thermal conductivity between the sensor monitoring point i and the sensor monitoring point j, the heat flux between the sensor monitoring point i and the sensor monitoring point j is obtained,

[0095]

[0096] wherein q ij represents the heat flux between the monitoring points i and j, represents the average temperature gradient between the monitoring points i and j, which can be calculated by the central difference method.

[0097]

[0098] wherein, represents the temperature measured by the kth sensor of the monitoring point i, represents the temperature measured by the k+1th sensor of the monitoring point i, n j represents the number of sensors measuring the soil thermal conductivity of the monitoring point j.

[0099] The working principle and effect of the above technical solution are: the above technical solution evaluates the heat flux between different monitoring points based on the measurement and calculation of soil thermal conductivity (i.e. thermal conductivity); first, for each sensor monitoring point, the weighted average thermal conductivity is calculated according to the depth information and the measurement data of the soil thermal conductivity sensor, which reflects the thermal conductivity of the soil at this point; then, the average thermal conductivity between two points is calculated according to the weighted average thermal conductivity of adjacent monitoring points. Finally, based on the average thermal conductivity and the average temperature gradient between the two points, the heat flux between the two points is calculated. Accurate evaluation of thermal conductivity: by calculating the weighted average thermal conductivity, the thermal conductivity of soil at different depths can be more accurately reflected, providing accurate basic data for subsequent heat flux calculation; improve the accuracy of heat flux calculation: using the average thermal conductivity and average temperature gradient of adjacent monitoring points to calculate the heat flux, considering the change of soil thermal conductivity and the influence of temperature gradient, improving the accuracy of heat flux calculation; support soil thermal condition research: this scheme provides quantitative data support for the research of soil thermal condition, which helps to understand the soil thermal conduction mechanism and its influence on environmental change. The weighted average thermal conductivity formula calculates the weighted average thermal conductivity of the monitoring point by considering the thermal conductivity measurement value of the sensor at different depths and its corresponding weight (proportional to the depth difference), which can more accurately reflect the thermal conductivity of the entire soil profile; it can consider the thermal conductivity of soil at different depths comprehensively, avoid the error caused by single measurement value, and improve the accuracy and representativeness of thermal conductivity calculation; the average thermal conductivity formula between two points simply takes the average of the weighted average thermal conductivity of two adjacent monitoring points as the average thermal conductivity between the two points. This design is convenient for calculation and can reflect the average level of soil thermal conductivity between the two points. The calculation is simple and intuitive, and the average thermal conductivity between the two points can be quickly obtained, which provides convenience for subsequent heat flux calculation; the heat flux formula is based on the heat conduction law, and the average thermal conductivity and the average temperature gradient are used to calculate the heat flux. This design can accurately reflect the heat transfer in soil, has clear physical meaning, and the calculation is accurate, which can be directly used to evaluate the heat transfer in soil and provide strong support for the research of soil thermal condition, and can deeply obtain the similarities and differences between seasonal frozen soil thermal conductivity.

[0100] In an embodiment of the present application, the maximum weight matrix is calculated, the spatial autocorrelation between sensor monitoring points is calculated based on the terrain influence factor, the heat flux and the maximum weight matrix, which comprises:

[0101] The maximum weight matrix is obtained based on the terrain influence factor and the heat conduction factor, W ij = T ij ·(1+c·|q ij |), wherein c represents a weight factor;

[0102] For each sensor monitoring point i, its correlation with surrounding points is quantified by a correlation model, specifically, the correlation model is:

[0103]

[0104] where x i represents the observation value of the i-th sensor, represents the average value of all sensor observation values, s 2 represents the variance of the observation value, W ij represents the weight between the i-th point and the j-th point in the weight matrix.

[0105] The working principle and effect of the above technical solution are: first, based on the terrain influence factor and the heat flux, the minimum weight matrix between the sensor monitoring points is calculated, the terrain influence factor considers the influence of altitude, slope, slope direction and other factors on the relationship between monitoring points; the heat flux reflects the heat exchange between monitoring points due to heat conduction, c is a weight factor, used to adjust the relative importance of heat flux in weight calculation, the weight matrix W_ij obtained in this way considers both topographic factors and the influence of heat conduction, so as to more comprehensively describe the mutual relationship between monitoring points; for each sensor monitoring point i, the correlation between it and the surrounding points is quantified by a correlation model, the correlation model measures the correlation between the monitoring point and the surrounding points by calculating the difference between the weighted average of the observation values of each monitoring point and all other monitoring points, the weight matrix plays a key role here, it adjusts the correlation strength between different monitoring points according to the terrain and heat conduction factors. By considering various factors, this technical solution can more comprehensively describe the mutual relationship between monitoring points by integrating terrain influence factors, heat conduction factors and sensor observation data, improve the accuracy and reliability of data analysis; enhance data interpretation, by quantifying the correlation between monitoring points, this technical solution provides a powerful tool for data analysis, which helps to reveal the potential interaction and correlation between monitoring points, and enhances the interpretability of data; support decision making, based on the weight matrix and correlation quantization results obtained by calculation, decision makers can more scientifically evaluate the importance of different monitoring points and their mutual relationship, so as to make more reasonable monitoring strategies and decisions. The weight matrix formula combines terrain influence factors and heat conduction factors, and calculates the weight between monitoring points through product and weighted summation, which considers the basic influence of terrain on the relationship between monitoring points, and integrates the adjustment effect of heat conduction on the relationship strength; this design makes the weight matrix more accurately reflect the actual relationship strength between monitoring points, improves the accuracy and practicality of data analysis. At the same time, by adjusting the weight factor c, the relative importance of terrain and heat conduction factors in weight calculation can be flexibly balanced; the correlation quantization model is based on the concept of Z-score (standardized score) in statistics, which quantifies the correlation by calculating the difference between the weighted average of the observation values of each monitoring point and all other monitoring points. The weight matrix plays a role in adjusting the contribution of different monitoring point observation values to the correlation calculation; this design makes the correlation quantization model more accurately reflect the actual correlation strength between monitoring points, avoiding the misleading caused by single observation value. At the same time, by introducing the weight matrix, the model can more flexibly adapt to the complex relationship between different monitoring points, improving the flexibility and applicability of data analysis.

[0106] One embodiment of the application filters out unnecessary monitoring points according to spatial autocorrelation, and ranks the monitoring points according to the size of the spatial autocorrelation value, with the absolute value of a larger value being ranked first, and the monitoring points ranked first being monitored preferentially, comprising:

[0107] If I i is greater than a first preset threshold and |x i -∑ j W ij ·x j | is less than a second preset threshold, the sensor monitoring point is considered to be a redundant point and is removed.

[0108] Otherwise, the point is considered not to be a redundant point, and is ranked according to the absolute value of I i , with the point with a larger I i being checked preferentially to obtain an abnormal point.

[0109] The working principle and effects of the above technical solution are as follows: whether a sensor monitoring point is a redundant point is determined based on two conditions, and an abnormal point is further processed; the redundant point determination is as follows: first, the correlation quantization value I_i of each sensor monitoring point is calculated, which reflects the correlation strength of the monitoring point with surrounding points; then, whether I i is greater than a first preset threshold is determined, if yes, it is indicated that the correlation of the monitoring point with surrounding points is very strong, and the monitoring point is possibly redundant; meanwhile, the difference between the observation value of the monitoring point and the weighted average observation value of surrounding points, |x i -∑ j W ij ·x j | is calculated, and whether it is less than a second preset threshold is determined, if yes, it is indicated that the observation value of the monitoring point is very close to the observation value of surrounding points, which further supports the determination that the monitoring point is redundant, if the above two conditions are both satisfied, the sensor monitoring point is considered to be a redundant point and is removed; for the monitoring points not satisfying the redundant point determination condition, the monitoring points are ranked according to the absolute value of I iThe larger points are less related to the surrounding points, but the observation value difference may be larger, and it is more likely to be an abnormal point. Further checking and processing is performed on these points, such as manual review, data cleaning or marking as an abnormal point. The data quality is improved, and the redundant points are removed to reduce the repeated information in the data set, improve the quality and representativeness of the data. This helps subsequent data analysis and modeling work, making the results more accurate and reliable; optimize the monitoring network, after removing the redundant points, the layout of the sensor monitoring network can be optimized, and unnecessary monitoring points can be reduced, thereby saving resources and costs. At the same time, the remaining monitoring points are more critical and effective, and can more comprehensively reflect the situation of the monitoring area; enhance the ability of abnormal point detection: by preferentially checking the points with larger I_i, abnormal points can be more effectively identified and processed, which helps to discover the melting of seasonal frozen soil in a timely manner and improves the accuracy and reliability of the data; by setting a threshold and sorting mechanism, fast screening and classification processing of monitoring points is realized, which greatly improves the efficiency of data processing and reduces the time and cost of manual intervention; by comprehensively considering the correlation and observation value difference of the monitoring points, redundant points and abnormal points are effectively identified and processed, improving the quality and processing efficiency of the data, and at the same time, it also optimizes the layout of the monitoring network, providing strong support for subsequent monitoring data analysis and application of seasonal frozen soil.

[0110] In one embodiment of the present application, a sensor-based seasonal frozen soil monitoring system comprises:

[0111] An acquisition terrain influence factor module is used to divide the monitored area into a grid, deploy a sensor group in the monitored area, acquire geographic parameters of the monitored area, and acquire terrain influence factors of seasonal frozen soil according to the geographic parameters;

[0112] An acquisition heat flux module is used to acquire heat flux between sensor monitoring points i and sensor monitoring points j in the monitored area;

[0113] An acquisition autocorrelation module is used to calculate a maximum weight matrix, and calculate spatial autocorrelation between sensor monitoring points based on the terrain influence factors, the heat flux and the maximum weight matrix;

[0114] A screening module is used to screen out unnecessary monitoring points according to the spatial autocorrelation, and sort the monitoring points according to the size of the spatial autocorrelation value, with larger absolute values sorted in front, and the monitoring points sorted in front being preferentially monitored.

[0115] The working principle and effect of the above technical solution are: first, the monitored area is scientifically and reasonably divided into multiple small areas or grids to ensure that the geographical and climate conditions in each area or grid are relatively consistent, facilitating subsequent data collection and analysis; a sensor group is deployed inside each area or grid, which can collect geographical parameters (such as altitude, slope, slope direction, etc.) and related parameters of frozen soil (such as temperature, humidity, deformation, etc.) in real time; according to the collected geographical parameters, a terrain influence factor is calculated using a specific mathematical model or algorithm. This factor reflects the degree of influence of topographic differences on the state of frozen soil (such as freezing depth, melting speed, etc.); by monitoring the temperature difference between the sensor points and the soil thermal conductivity data, the heat flux between the two points is calculated. Heat flux is a physical quantity that describes the rate and direction of heat transfer in the soil, and is crucial for understanding the heat balance and dynamic changes of frozen soil; combined with the terrain influence factor and heat flux data, a most weighted matrix is constructed. This matrix reflects the degree of mutual influence between different monitoring points in space, and the spatial autocorrelation between sensor monitoring points is calculated using a spatial autocorrelation model. Spatial autocorrelation is an index that describes the similarity or correlation between a point and its surrounding points in geographic space; monitoring point optimization and sorting, according to the spatial autocorrelation results, filter out redundant or unnecessary monitoring points to reduce monitoring costs and data redundancy, and sort the remaining monitoring points according to the size of the spatial autocorrelation value. The points with larger absolute values indicate that they deviate from the average value more, i.e., they are more likely to have abnormal conditions, so these points are monitored first to capture changes in the state of frozen soil more accurately. Improve monitoring efficiency, by scientifically and reasonably dividing the area and deploying sensors, and using spatial autocorrelation analysis to optimize the layout of monitoring points, the monitoring efficiency can be significantly improved, and unnecessary resource waste can be reduced; enhance monitoring accuracy, combined with terrain influence factor and heat flux data for spatial autocorrelation analysis, the spatial distribution and dynamic changes of frozen soil state can be more accurately reflected, thereby improving the accuracy of monitoring; reduce monitoring costs, by filtering out redundant monitoring points, the number of sensors deployed and maintenance costs can be reduced, and the operating cost of the entire monitoring system can be reduced; provide support for decision-making, real-time and accurate frozen soil monitoring data can provide scientific basis for relevant departments and decision-makers, helping them better understand the state of frozen soil and develop effective management and protection measures to reduce the occurrence and loss of frozen soil disasters; by comprehensively using geographic information technology, sensor technology and data analysis technology, efficient and accurate monitoring of seasonal frozen soil state is achieved, providing strong support for frozen soil research, disaster warning and ecological protection.

[0116] In an embodiment of the present application, the terrain influence factor acquisition module comprises:

[0117] The division module is used for dividing the monitored area into a grid, and deploying sensors in each grid at equal intervals, wherein the sensors include a temperature sensor, a moisture sensor, a soil humidity sensor and a strain sensor.

[0118] The geographic data acquisition module is used for acquiring the elevation, slope and aspect of the sensor monitoring point.

[0119] The terrain influence factor calculation module is used for calculating the terrain influence factor between the sensor monitoring points according to the elevation, slope and aspect of the sensor monitoring points.

[0120]

[0121] Wherein, T ij represents the terrain influence factor between the two points of sensor monitoring points i and j, Δh ij represents the elevation difference between the two points of sensor monitoring points i and j, Δs ij represents the slope difference between the two points of sensor monitoring points i and j, Δp ij represents the aspect difference between the two points of sensor monitoring points i and j, H0 represents the characteristic elevation, S0 represents the characteristic slope, P0 represents the characteristic aspect, k h represents the elevation weight coefficient, k s represents the slope weight coefficient, k p represents the aspect weight coefficient.

[0122] The working principle and effect of the above technical solution are: based on the influence of terrain factors (elevation, slope and aspect) on seasonal frozen soil state, the terrain influence factor between the sensor monitoring points is calculated to evaluate the influence. Specifically, the elevation, slope and aspect data of each sensor monitoring point are acquired, and then the terrain influence factor between any two monitoring points is calculated by using the given formula, which considers the elevation difference, slope difference and aspect difference, and is adjusted by the characteristic value H and the corresponding weight coefficient to reflect the relative importance of different terrain factors on the frozen soil state. Accurate evaluation of terrain influence: by comprehensively considering the three terrain factors of elevation, slope and aspect, and their relative differences, this scheme can more accurately evaluate the influence of terrain on frozen soil state, and provide a more reliable data basis for subsequent frozen soil monitoring; improve the monitoring efficiency, by calculating the terrain influence factor, the monitoring points with consistent terrain influence factors can be identified, thereby optimizing the layout of the monitoring points, reducing unnecessary monitoring points, and improving the monitoring efficiency; support scientific research, the calculation of the terrain influence factor provides quantitative data support for scientific research, which helps to deeply understand the influence mechanism of terrain on frozen soil distribution and dynamic change.

[0123] In an embodiment of the present application, the heat flux acquisition module comprises:

[0124] A weighted average thermal conductivity calculation module is configured to calculate a weighted average thermal conductivity of each sensor monitoring point according to the depth information and the measured data of the soil thermal conductivity sensor, and obtain the weighted average thermal conductivity of the sensor monitoring point i, which reflects the heat conduction performance of the soil at the point,

[0125]

[0126] wherein λ i represents the weighted average thermal conductivity of the monitoring point i, represents the soil thermal conductivity of the kth sensor for measuring the soil thermal conductivity of the monitoring point i, n i represents the number of sensors for measuring the soil thermal conductivity of the monitoring point i, represents the weight of the kth sensor for measuring the soil thermal conductivity of the monitoring point i,

[0127]

[0128] wherein, represents the depth of the kth sensor of the monitoring point i,

[0129] An average thermal conductivity calculation module is configured to calculate the average thermal conductivity between two points according to the weighted average thermal conductivities of adjacent monitoring points:

[0130]

[0131] wherein λ ij represents the average thermal conductivity between the sensor monitoring points i and j, λ j represents the weighted average thermal conductivity of the monitoring point j;

[0132] A heat flux calculation module is configured to obtain the heat flux between the sensor monitoring point i and the sensor monitoring point j based on the average thermal conductivity between the two points,

[0133]

[0134] wherein q ij represents the heat flux between the monitoring points i and j, represents the average temperature gradient between the monitoring points i and j, which can be calculated by the central difference method.

[0135] In an embodiment of the present application, the correlation acquisition module comprises:

[0136] A maximum weight matrix acquisition module is configured to obtain a maximum weight matrix W ij = T ij• (1 + c • |q ij where c represents a weight factor;

[0137] a correlation quantification module, configured to quantify the correlation of each sensor monitoring point i with surrounding points by a correlation model, in particular, the correlation model is:

[0138]

[0139] where x i represents an observation value of the i-th sensor, represents an average value of all sensor observation values, s 2 represents a variance of the observation value, W ij represents a weight between the i-th point and the j-th point in the weight matrix.

[0140] The observation value of the i-th sensor includes: temperature of seasonal frozen soil, soil moisture, soil humidity sensor and strain data.

[0141] The working principle and effect of the above technical solution are as follows: the above technical solution is based on the measurement and calculation of soil thermal conductivity (i.e. thermal conductivity) to evaluate the heat flux between different monitoring points; first, for each sensor monitoring point, the weighted average thermal conductivity is calculated according to the depth information and the measurement data of the soil thermal conductivity sensor, which reflects the thermal conductivity of the soil at this point; then, the average thermal conductivity between two points is calculated according to the weighted average thermal conductivity of adjacent monitoring points. Finally, based on the average thermal conductivity and the average temperature gradient between the two points, the heat flux between the two points is calculated. Accurate evaluation of thermal conductivity: by calculating the weighted average thermal conductivity, the thermal conductivity of soil at different depths can be more accurately reflected, providing accurate basic data for subsequent heat flux calculation; improve the accuracy of heat flux calculation: using the average thermal conductivity and average temperature gradient of adjacent monitoring points to calculate the heat flux, considering the change of soil thermal conductivity and the influence of temperature gradient, improving the accuracy of heat flux calculation; support soil thermal condition research: this scheme provides quantitative data support for soil thermal condition research, which helps to understand the soil thermal conduction mechanism and its influence on environmental change. The weighted average thermal conductivity formula calculates the weighted average thermal conductivity of the monitoring point by considering the thermal conductivity measurement value of the sensor at different depths and its corresponding weight (proportional to the depth difference), which can more accurately reflect the thermal conductivity of the entire soil profile; it can consider the thermal conductivity of soil at different depths comprehensively, avoid the error caused by single measurement value, and improve the accuracy and representativeness of thermal conductivity calculation; the average thermal conductivity formula between two points simply takes the average of the weighted average thermal conductivity of two adjacent monitoring points as the average thermal conductivity between the two points. This design is convenient for calculation and can reflect the average level of soil thermal conductivity between the two points. The calculation is simple and intuitive, and the average thermal conductivity between the two points can be quickly obtained, which provides convenience for subsequent heat flux calculation; the heat flux formula is based on the heat conduction law, and the average thermal conductivity and the average temperature gradient are used to calculate the heat flux. This design can accurately reflect the heat transfer in soil, has clear physical meaning, and the calculation is accurate, which can be directly used to evaluate the heat transfer in soil and provide strong support for the research of soil thermal condition, and can deeply obtain the similarities and differences between seasonal frozen soil thermal conductivity.

[0142] In an embodiment of the present application, the screening module comprises:

[0143] The screening module is used to determine whether the sensor monitoring point is redundant if I i Greater than the first preset threshold and |x i -∑ j W ij ·x j Less than the second preset threshold, it is considered that the sensor monitoring point is a redundant point and is removed;

[0144] The priority monitoring module, otherwise, it is considered that the point is not a redundant point, and Ii The absolute value size is sorted, and I i larger points are checked first to get outliers.

[0145] The working principle and effect of the above technical solution are as follows: first, the minimum weight matrix between the sensor monitoring points is calculated based on the terrain influence factor and the heat flux, the terrain influence factor considers the influence of altitude, slope, slope direction and other factors on the relationship between the monitoring points; the heat flux reflects the heat exchange between the monitoring points due to heat conduction, c is a weight factor for adjusting the relative importance of the heat flux in weight calculation, and the weight matrix W_ij obtained in this way considers both the terrain factor and the influence of heat conduction, so as to more comprehensively describe the mutual relationship between the monitoring points; for each sensor monitoring point i, the correlation between it and the surrounding points is quantified by a correlation model, which measures the correlation between the monitoring point and the surrounding points by calculating the difference between the weighted average of the observation values of each monitoring point and all other monitoring points, and the weight matrix plays a key role here, which adjusts the correlation strength between different monitoring points according to the terrain and heat conduction factors. By considering various factors, this technical solution integrates terrain influence factors, heat conduction factors and sensor observation data to more comprehensively describe the mutual relationship between monitoring points, improving the accuracy and reliability of data analysis; enhancing data interpretation, by quantifying the correlation between monitoring points, this technical solution provides a powerful tool for data analysis, which helps to reveal the potential interaction and correlation between monitoring points, enhancing the interpretability of data; supporting decision making, based on the calculated weight matrix and correlation quantification results, decision makers can more scientifically evaluate the importance of different monitoring points and their mutual relationship, so as to make more reasonable monitoring strategies and decisions. The weight matrix formula combines terrain influence factors and heat conduction factors to calculate the weight between monitoring points through product and weighted summation, which considers both the basic influence of terrain on the relationship between monitoring points and the adjustment of relationship strength by heat conduction; this design makes the weight matrix more accurately reflect the actual relationship strength between monitoring points, improving the accuracy and practicality of data analysis. At the same time, by adjusting the weight factor c, the relative importance of terrain and heat conduction factors in weight calculation can be flexibly balanced; the correlation quantification model is based on the concept of Z-score (standardized score) in statistics, which quantifies the correlation by calculating the difference between the weighted average of the observation values of each monitoring point and all other monitoring points. The weight matrix plays a role in adjusting the contribution of different monitoring point observation values to the correlation calculation; this design makes the correlation quantification model more accurately reflect the actual correlation strength between monitoring points, avoiding the misleading caused by single observation value. At the same time, by introducing the weight matrix, the model can more flexibly adapt to the complex relationship between different monitoring points, improving the flexibility and applicability of data analysis.

[0146] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A sensor-based seasonal frozen ground monitoring method, characterized by, The method comprises: dividing the monitored area into a grid, deploying a sensor group in the monitored area, obtaining geographical parameters of the monitored area, and obtaining a topographic influence factor of the seasonal frozen soil according to the geographical parameters; obtaining heat flux between sensor monitoring points i and j in the monitored area; calculating a maximum weight matrix, and calculating spatial autocorrelation between sensor monitoring points based on the topographic influence factor, the heat flux and the maximum weight matrix; screening out unnecessary monitoring points according to the spatial autocorrelation, and sorting the monitoring points according to the size of the spatial autocorrelation value, with the absolute value being large being sorted in front, and the monitoring points in front being monitored preferentially; According to the elevation difference between the two sensor monitoring points i and j, the slope difference between the two sensor monitoring points i and j, the aspect difference between the two sensor monitoring points i and j, the feature elevation, the feature slope, the feature aspect, the elevation weight coefficient, the slope weight coefficient and the aspect weight coefficient, the terrain influence factor between the two sensor monitoring points i and j is calculated , indicates the terrain influence factor between the two sensor monitoring points i and j; wherein the heat flux between sensor monitoring points i and j in the monitored area comprises: For each sensor monitoring point, according to the depth information and the measured data of the soil thermal conductivity sensor, a weighted average thermal conductivity is calculated to obtain a weighted average thermal conductivity of the sensor monitoring point i, which reflects the heat conduction performance of the soil at the point, wherein, represents the weighted average thermal conductivity of the monitoring point i, represents the soil thermal conductivity of the kth soil thermal conductivity measuring sensor of the monitoring point i, represents the number of soil thermal conductivity measuring sensors of the monitoring point i, represents the weight of the kth soil thermal conductivity measuring sensor of the monitoring point i, wherein, represents the depth of the kth sensor of the monitoring point i, ; The average thermal conductivity between two points is calculated according to the weighted average thermal conductivity of the adjacent monitoring points: wherein, represents the average thermal conductivity between sensor monitoring points i and j, represents the weighted average thermal conductivity of monitoring point j; obtaining the heat flux between sensor monitoring points i and j based on the average thermal conductivity between the two points, where, represents the heat flux between monitoring points i and j, represents the average temperature gradient between monitoring points i and j, calculated using central difference method; wherein the calculation of the maximum weight matrix and the calculation of the spatial autocorrelation between sensor monitoring points based on the topographic influence factor, the heat flux and the maximum weight matrix comprise: obtaining a most weight matrix based on a terrain influence factor and a heat conduction factor, wherein c represents a weight factor; for each sensor monitoring point i, quantifying the correlation with surrounding points through a correlation model, and specifically, the correlation model is: wherein, represents the observation value of the i-th sensor, represents the average of all sensor observation values, represents the variance of the observation value, represents the weight between the i-th point and the j-th point in the weight matrix.

2. The sensor-based monitoring method of seasonal frozen ground according to claim 1, characterized in that, dividing the monitored area into a grid, deploying a sensor group in the monitored area, obtaining geographical parameters of the monitored area, and obtaining a topographic influence factor of the seasonal frozen soil according to the geographical parameters, comprising: dividing the monitored area into a grid, deploying sensors in each grid at equal intervals, the sensors comprising: temperature sensors, moisture sensors, soil humidity sensors and strain sensors; obtaining the altitude, slope and aspect of the sensor monitoring points; calculating the topographic influence factor between sensor monitoring points according to the altitude, slope and aspect of the sensor monitoring points; wherein, represents a terrain influence factor between sensor monitoring points i and j, represents an elevation difference between sensor monitoring points i and j, represents a slope difference between sensor monitoring points i and j, represents an aspect difference between sensor monitoring points i and j, represents a characteristic elevation, represents a characteristic slope, represents a characteristic aspect, represents an elevation weight coefficient, represents a slope weight coefficient, represents an aspect weight coefficient.

3. The sensor-based seasonal frozen ground monitoring method according to claim 1, characterized in that, screening out unnecessary monitoring points according to the spatial autocorrelation, and sorting the monitoring points according to the size of the spatial autocorrelation value, with the absolute value being large being sorted in front, and the monitoring points in front being monitored preferentially, comprising: If greater than a first preset threshold and less than a second preset threshold, the sensor monitoring point is considered as a redundant point and is removed. Otherwise, the point is not considered as a redundant point, and is sorted by the absolute value of , and the point with larger value is checked first to obtain the outlier. ​ 4. A sensor-based seasonal frozen ground monitoring system, characterized by, The system comprises: a topographic influence factor acquisition module for dividing the monitored area into a grid, deploying a sensor group in the monitored area, obtaining geographical parameters of the monitored area, and obtaining a topographic influence factor of the seasonal frozen soil according to the geographical parameters; a heat flux acquisition module for obtaining heat flux between sensor monitoring points i and j in the monitored area; an autocorrelation acquisition module for calculating a maximum weight matrix, and calculating spatial autocorrelation between sensor monitoring points based on the topographic influence factor, the heat flux and the maximum weight matrix; a screening module for screening out unnecessary monitoring points according to the spatial autocorrelation, and sorting the monitoring points according to the size of the spatial autocorrelation value, with the absolute value being large being sorted in front, and the monitoring points in front being monitored preferentially; According to the elevation difference between the two sensor monitoring points i and j, the slope difference between the two sensor monitoring points i and j, the aspect difference between the two sensor monitoring points i and j, the feature elevation, the feature slope, the feature aspect, the elevation weight coefficient, the slope weight coefficient and the aspect weight coefficient, the terrain influence factor between the two sensor monitoring points i and j is calculated , represents the terrain influence factor between the two sensor monitoring points i and j; The heat flux acquisition module comprises: The weighted average thermal conductivity of the sensor monitoring point i is calculated by the weighted average thermal conductivity calculation module, which calculates the weighted average thermal conductivity according to the depth information and the measured data of the soil thermal conductivity sensor for each sensor monitoring point, and obtains the weighted average thermal conductivity of the sensor monitoring point i, which reflects the heat conduction performance of the soil at the point, wherein, represents the weighted average thermal conductivity of the monitoring point i, represents the soil thermal conductivity of the kth soil thermal conductivity measuring sensor of the monitoring point i, represents the number of soil thermal conductivity measuring sensors of the monitoring point i, represents the weight of the kth soil thermal conductivity measuring sensor of the monitoring point i, wherein, represents the depth of the kth sensor of the monitoring point i, ; A module to calculate the average thermal conductivity between two points, for calculating the average thermal conductivity between two points from the weighted average thermal conductivity of the adjacent monitoring points: wherein, represents the average thermal conductivity between sensor monitoring points i and j, represents the weighted average thermal conductivity of monitoring point j; a heat flux calculation module for obtaining the heat flux between sensor monitoring points i and j based on the average thermal conductivity between the two points, where, represents the heat flux between monitoring points i and j, represents the average temperature gradient between monitoring points i and j, calculated using central difference method; wherein the autocorrelation acquisition module comprises: The acquisition module is configured to acquire the most weight matrix based on the terrain influence factor and the heat conduction factor, wherein c represents a weight factor. The quantifying correlation module is configured to quantify, for each sensor monitoring point i, a correlation with surrounding points by a correlation model, and the correlation model is specifically: wherein, represents the observation value of the i-th sensor, represents the average of all sensor observation values, represents the variance of the observation value, represents the weight between the i-th point and the j-th point in the weight matrix.

5. The sensor-based seasonal frozen ground monitoring system according to claim 4, wherein, The terrain influence factor acquisition module comprises: The dividing module is configured to divide the monitored area into grids, and to arrange sensors, including temperature sensors, moisture sensors, soil humidity sensors and strain sensors, in each grid at equal intervals; The geographic data acquisition module is configured to acquire an altitude, a slope and a slope direction of the sensor monitoring point; The terrain influence factor calculation module is configured to calculate a terrain influence factor between the sensor monitoring points according to the altitude, the slope and the slope direction of the sensor monitoring point; wherein, represents a terrain impact factor between sensor monitoring points i and j, represents an elevation difference between sensor monitoring points i and j, represents a slope difference between sensor monitoring points i and j, represents an aspect difference between sensor monitoring points i and j, represents a characteristic elevation, represents a characteristic slope, represents a characteristic aspect, represents an elevation weight coefficient, represents a slope weight coefficient, represents an aspect weight coefficient.

6. The sensor-based seasonal frozen ground monitoring system according to claim 4, wherein, The screening module comprises: The screening module is configured to greater than a first preset threshold and less than a second preset threshold, the sensor monitoring point is considered as a redundant point and is removed. The priority monitoring module, otherwise, consider that the point is not redundant point, according to The absolute value size of the sorting, priority check Large point, to get the abnormal point.

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