Regular grid type frozen soil area ground thermal state distribution estimation method and related device

Abstract

The invention provides a regular grid type permafrost region ground thermal state distribution estimation method and a related device. In the method, an electronic device obtains actually measured temperature information and predicted temperature information of a target frozen soil region, the actually measured temperature information comprises actually measured ground temperatures under various observation scales, and the predicted temperature information comprises predicted ground temperatures under various observation scales; correcting the predicted temperature information through a pre-constructed semi-variation function model to obtain optimized predicted temperature information; fusing the optimized predicted temperature information with the actually measured temperature information to obtain ground temperature information of the target frozen soil region; and generating a ground thermal state distribution diagram of the target frozen soil region according to the ground temperature information of the target frozen soil region. Therefore, the thermal state of the frozen soil can be obtained in a non-destructive manner, and the ground thermal state distribution diagram under the reasonably set monitoring space scale is generated, so that the engineering construction practice and ecological environment evaluation of the frozen soil region are effectively guided.

Description

Technical Field

[0001] The present application relates to the technical field of physical geography and geographic information systems, and in particular to a method for estimating the distribution of ground thermal conditions in a regular grid-type frozen soil zone and related devices. Background Art

[0002] Frozen soil is an important component of the cryosphere, one of the five spheres of the Earth system. Its changes are closely related to the cryosphere, atmosphere, biosphere and pedosphere. Permafrost, seasonal frozen soil and short-term frozen soil areas account for about 50% of the land surface area, of which permafrost accounts for about 22% of the land surface area in the Northern Hemisphere. The soil organic carbon stored in permafrost exceeds the sum of vegetation and atmospheric carbon pools, and is one of the most critical carbon pools in terrestrial ecosystems. Its slight changes will affect the atmosphere, and have an important impact on the sustainable development of human economy and society and the realization of carbon peak and carbon neutrality policies. Ground temperature is a key indicator of energy and water exchange between the atmosphere and the pedosphere, and is also an important driving condition for frozen soil simulation mapping. It plays a decisive role in the stability and dynamic changes of cryosphere-related environmental factors such as climate change and ecohydrology.

[0003] Due to the lack of necessary basic data, traditional permafrost engineering and permafrost thermal status surveys have problems with ecological damage and scale effects of the monitoring system. Summary of the invention

[0004] In order to overcome at least one of the shortcomings of the prior art, the present application provides a method for estimating the ground thermal state distribution in a regular grid-type frozen soil area and a related device, comprising:

[0005] In a first aspect, the present application provides a method for estimating the distribution of ground thermal state in a regular grid-type frozen soil region, the method comprising:

[0006] Acquire measured temperature information and predicted temperature information of the target frozen soil area, wherein the measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at the multiple observation scales;

[0007] Correcting the predicted temperature information by using a pre-constructed semivariogram model to obtain optimized predicted temperature information, wherein the semivariogram model characterizes the spatial variability of ground temperature in the target frozen soil area;

[0008] The optimized predicted temperature information is integrated with the measured temperature information to obtain the ground temperature information of the target frozen soil area;

[0009] A ground thermal state distribution map of the target frozen soil area is generated according to the ground temperature information of the target frozen soil area.

[0010] In a second aspect, the present application provides a regular grid type permafrost region ground thermal state distribution estimation device, the device comprising:

[0011] A temperature acquisition module, used to acquire measured temperature information and predicted temperature information of a target frozen soil area, wherein the measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at the multiple observation scales;

[0012] A temperature optimization module, used for correcting the predicted temperature information through a pre-constructed semivariogram model to obtain optimized predicted temperature information, wherein the semivariogram model characterizes the maximum distance of measurement error, spatial variability and spatial autocorrelation of ground temperature in the target frozen soil area;

[0013] The temperature optimization module is further used to fuse the optimized predicted temperature information with the measured temperature information to obtain the ground temperature information of the target frozen soil area;

[0014] The image drawing module is used to generate a ground thermal state distribution map of the target frozen soil area according to the ground temperature information of the target frozen soil area.

[0015] In a third aspect, the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the method for estimating the distribution of ground thermal conditions in a regular grid-type permafrost area.

[0016] In a fourth aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for estimating the ground thermal state distribution in a regular grid type permafrost area is implemented.

[0017] Compared with the prior art, this application has the following beneficial effects:

[0018] The present application provides a method for estimating the distribution of ground thermal state in a regular grid frozen soil area and a related device. In the method, an electronic device obtains measured temperature information and predicted temperature information of a target frozen soil area, wherein the measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at multiple observation scales; the predicted temperature information is corrected by a pre-constructed semivariogram model to obtain optimized predicted temperature information; wherein the semivariogram model characterizes the measurement error, spatial variability, and spatial autocorrelation of the ground temperature in the target frozen soil area; the optimized predicted temperature information is fused with the measured temperature information to obtain the ground temperature information of the target frozen soil area; and a ground thermal state distribution map of the target frozen soil area is generated according to the ground temperature information of the target frozen soil area. In this way, by using the predicted ground temperature at different scales and the measured ground temperature, the thermal state of the frozen soil can be non-destructively obtained, and a ground thermal state distribution map under a reasonably set monitoring space scale can be generated, thereby effectively guiding the engineering construction practice and ecological environment assessment in the frozen soil area. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic diagram of a flow chart of a method for estimating ground thermal state distribution in a regular grid-type frozen soil region provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of temperature distribution layers in a frozen soil area provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of the relationship between the multi-stage temperature sensor array provided in the embodiment of the present application;

[0023] Figure 4 A schematic diagram of the structure of a device for estimating the ground thermal state distribution in a regular grid-type frozen soil region provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0027] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0028] In the description of the present application, it should be noted that, in addition, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. In addition, the terms "comprise", "include" or any other variants are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0029] Based on the above statement, as introduced in the background technology, traditional permafrost engineering and permafrost drilling temperature detection have problems of ecological damage and scale effect of the monitoring system.

[0030] In this regard, it should be understood that in the relevant technology, geological surveys mainly rely on borehole drilling and temperature chain temperature measurement to obtain the thermal state of permafrost. However, during the entry and drilling of the drill rig, it will cause great damage to the surrounding vegetation and soil, especially in the alpine permafrost areas with harsh climate environments, and these damages are difficult to recover. Frozen soil drilling may even lead to ecological degradation and aggravate the problems of black soil beaching, land sandification and desertification in alpine grasslands. Therefore, how to non-destructively obtain the thermal state of permafrost to guide the practice of permafrost engineering construction and the assessment of the permafrost ecological environment has become one of the major issues that urgently need to be solved in the field of geoengineering and environmental science and technology.

[0031] In addition, when conducting ecological and environmental assessments, freeze-thaw disaster predictions, and frozen soil engineering design and construction operations, whether using frozen soil drilling or self-recording thermometers to monitor the thermal state of frozen soil, the scale effect of the monitoring system will be faced. That is, how should the distance between adjacent monitoring points be set? If the distance between adjacent monitoring points is too far, due to the spatial heterogeneity of geographical elements, insufficient data coverage will inevitably occur; conversely, if the distance between adjacent monitoring points is too close, the spatial autocorrelation is strong, which will lead to a significant increase in monitoring costs and consume more manpower and financial resources. Therefore, how to reasonably set the monitoring spatial scale of permafrost elements has become an issue that must be paid attention to in the current field of physical geography and geographic information systems.

[0032] Based on the discovery of the above technical problems, the inventors have proposed the following technical solutions to solve or improve the above problems through creative work. It should be noted that the defects in the solutions in the above prior art are the results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in the embodiments of the present application for the above problems below should all be the contributions made by the inventors to the present application in the process of invention and creation, and should not be understood as technical contents known to those skilled in the art.

[0033] In view of this, an embodiment of the present application (hereinafter referred to as the present embodiment) provides a method for estimating the ground thermal state distribution in a regular grid-type frozen soil area. Figure 1 As shown, the method includes:

[0034] S1, obtaining the measured temperature information and predicted temperature information of the target frozen soil area.

[0035] The measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at multiple observation scales.

[0036] S2, correcting the predicted temperature information through the pre-built semivariogram model to obtain optimized predicted temperature information.

[0037] Among them, the semivariogram model characterizes the distance of measurement error, spatial variability, and spatial autocorrelation of ground temperature in the target frozen soil area.

[0038] S3, integrating the optimized predicted temperature information with the measured temperature information to obtain the ground temperature information of the target frozen soil area.

[0039] S4, generating a ground thermal state distribution map of the target frozen soil area according to the ground temperature information of the target frozen soil area.

[0040] In this way, by using the predicted ground temperature at different scales and the measured ground temperature, the thermal state of permafrost can be obtained non-destructively, and a ground thermal state distribution map can be generated at a reasonably set monitoring spatial scale, thereby effectively guiding engineering construction practice and ecological environmental assessment.

[0041] For the regular grid type frozen soil area ground thermal state distribution estimation method provided in this embodiment, the electronic device implementing the method may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a desktop computer, and a server, etc. The server may be a single server or a server group. The server group may be centralized or distributed (for example, the server may be a distributed system). In some embodiments, the server may be local or remote relative to the user terminal. In some embodiments, the server may be implemented on a cloud platform; as an example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (Community Cloud), a distributed cloud, an inter-cloud (Inter-Cloud), a multi-cloud (Multi-Cloud), etc., or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.

[0042] To make the solution provided by this embodiment clearer, the following uses a server as an electronic device to implement the method. Figure 1 Each step in the flowchart is described in detail. However, it should be understood that the operations of the flowchart may not be implemented in order, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart, or remove one or more operations from the flowchart, under the guidance of the content of this application. Therefore, continue to refer to Figure 1 , the method comprising:

[0043] S1, obtaining the measured temperature information and predicted temperature information of the target frozen soil area.

[0044] The measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at multiple observation scales.

[0045] In this embodiment, the target frozen soil area refers to an area where the thermal state of the ground is unknown. First, a pre-trained temperature prediction model is used to generate predicted temperature information at multiple observation scales based on vegetation information, soil information, climate information, and terrain information. However, since the prediction model may have certain errors, it is necessary to correct it through the measured temperature information obtained by the multi-stage temperature sensor array. Therefore, as an optional implementation, step S1 may include:

[0046] S1-1, obtain vegetation information, soil information, climate information and terrain information of the target permafrost area.

[0047] S1-2 processes vegetation information, soil information, climate information and terrain information through a pre-trained temperature prediction model to obtain the predicted temperature information of the target permafrost area.

[0048] The following is an explanation of the training method of the above temperature prediction model. In order to train the temperature prediction model, it is necessary to go through the stages of data collection, data preprocessing, feature extraction and dimensionality reduction, and model training to ensure that the model can accurately predict the ground temperature in the frozen soil area.

[0049] Before explaining the training method of the above temperature prediction model, it should be noted that the ground temperature in this embodiment refers to the temperature at a specific depth. Figure 2 As shown, the temperature distribution layers in the frozen soil zone include the boundary layer, land surface, ground surface, active layer, and permafrost layer. Among them, the detection position of the ground temperature is located at a depth of 0-10 cm of the vegetation root system, and near-ground air temperature and land surface temperature observations are arranged according to the differences in altitude and terrain. However, the current methods based on MODIS thermal infrared land surface temperature calculation or using it to drive frozen soil simulation mapping, the thermal infrared land surface temperature that can be detected is the temperature of the vegetation or snow canopy. These studies do not consider the complex thermal effects of vegetation or snow, so there is an essential difference between the thermal infrared land surface temperature and the ground temperature. The ground temperature in this embodiment is the temperature at a depth of 0-10 cm of the vegetation root system, which is the real upper boundary condition for empirical or physical process frozen soil simulation mapping.

[0050] In addition, the above-mentioned multiple observation scales show a law of gradual transition. Therefore, training samples also need to be collected at multiple observation scales in order to reveal the scale effect of ground temperature. In this regard, a multi-level temperature sensor array is deployed in the target permafrost area. The lower arrays in the multi-level temperature sensor array are distributed in the grids of the upper array. The lower arrays and the upper arrays include the same number and distribution of temperature sensors.

[0051] In this way, data collection at different observation scales is achieved by deploying multi-level temperature sensor arrays in the target permafrost area. These sensor arrays are distributed according to a certain pattern to ensure that data collection at each scale can complement and support each other. For example, the upper array may cover a wider range, while the lower array performs more detailed measurements within the grid of the upper array. Therefore, the hierarchical layout of sensors can not only improve the richness and accuracy of data, but also help identify temperature change patterns and anomalies at different scales, temperature measurement errors, and spatial autocorrelation distances.

[0052] (1) In the data collection stage, in order to better reveal the scale effect of ground temperature, a regular grid is deployed in the target frozen soil area for monitoring. Figure 3 As shown in the figure, for the target permafrost area, the scheme adopts progressive multi-scale regular grids for sensor deployment, that is, three grid units of different scales, 10m×10m, 100m×100m and 1000m×1000m, are set in the study area, and temperature sensors are evenly distributed inside each grid unit. Small-scale (10m×10m) grids are used to capture microenvironmental changes and are suitable for studying the thermal state characteristics of local permafrost; medium-scale (100m×100m) grids are used for long-term trend analysis and are suitable for thermal state monitoring in large-scale permafrost areas; large-scale (1000m×1000m) grids are used for regional-level spatial distribution estimation, providing data support for large-scale permafrost modeling.

[0053] Secondly, within each regular grid unit, the layout of sensors follows the principle of hierarchical nesting. That is, the sensors of the lower grid are distributed in the sub-units of the upper grid, and maintain the same number and distribution pattern. For example, within a 100m×100m grid, temperature sensors are evenly laid out in some 10m×10m sub-grids to ensure the comparability and consistency of multi-scale data. This nested sensor layout method can not only improve the spatial resolution of the data, but also improve the prediction accuracy by fusing data of different scales.

[0054] In addition, since the near-surface air temperature is less affected by turbulence and surface cover, only 1-2 near-surface air temperature and land surface temperature observations are deployed in each 100m×100m and 1000m×1000m site. Additional near-surface air temperature and land surface temperature observations are only considered when the terrain is highly undulating (the altitude difference exceeds 50m).

[0055] At the same time, we use remote sensing images, ground observations, drone mapping, weather station monitoring, and historical data collection to obtain vegetation information, soil information, climate information, and terrain information for each grid node (also known as an observation point). The following is a detailed description of how to obtain this information:

[0056] Vegetation information is mainly obtained through ground sample surveys, satellite remote sensing images and drone multispectral mapping. Vegetation indices (NDVI, EVI) are calculated using satellite remote sensing data such as Landsat and MODIS to assess vegetation coverage and health. In addition, high-resolution vegetation indices are obtained through drone hyperspectral imaging, and ground sample surveys are combined to measure vegetation height, aboveground biomass and other information to refine the impact of vegetation on surface temperature.

[0057] Soil information is obtained through a combination of ground sampling, laboratory analysis and remote sensing inversion. The ground survey uses root drilling sampling to determine key parameters such as soil texture, moisture content, organic matter content, and dry bulk density. Laboratory analysis further determines soil thermophysical properties such as thermal conductivity and thermal diffusivity. In addition, thermal infrared remote sensing and microwave remote sensing are used to extract soil moisture information, and spatial interpolation is performed through geostatistical analysis to compensate for the limitations of observation points.

[0058] Climate information can be collected through ground weather station monitoring, long-term meteorological data and reanalysis data. Specifically, automatic weather stations can be deployed in the study area to monitor key meteorological parameters such as air temperature, precipitation, wind speed and direction, and relative humidity in real time. At the same time, global meteorological reanalysis data such as ERA5-Land and GLDAS are collected to provide long-term climate background information, and they are integrated with measured data to improve the spatial and temporal resolution of the data.

[0059] Topographic information is mainly obtained through digital elevation models (DEM), drone mapping and ground RTK measurement. Basic terrain factors such as altitude, slope and aspect are extracted through global DEM data such as SRTM and ASTERGDEM. In addition, for key research areas, drone LiDAR scanning is used to provide high-precision micro-topography information, and RTK (real-time kinematic positioning) and CORS (continuously operating reference station) technology are combined for precise measurement to analyze the impact of local terrain on ground temperature.

[0060] (2) In the data preprocessing stage, a series of data cleaning and standardization measures were taken in this example to improve data quality and consistency. First, missing data were filled by removing outliers and interpolating methods to reduce data noise. Then, time series alignment was performed for data of different time scales (such as ground monitoring data at the hourly level, while satellite remote sensing data is usually only at the daily level) to ensure data matching. In the spatial dimension, point data and raster data were unified through resampling methods to make them consistent with regular grid-type observation data to ensure the effectiveness of subsequent model training.

[0061] (3) In the feature extraction and dimensionality reduction stage, in order to improve the generalization ability of the model, this example uses principal component analysis (PCA) to reduce the dimension of high-dimensional environmental variables (such as multiple meteorological, soil and vegetation indicators) and extract the key factors that mainly affect the change of ground temperature. At the same time, new feature variables are constructed, such as the interaction term between vegetation index and temperature, and the combined factor of slope and soil moisture, to enhance the prediction ability of the model. In addition, correlation analysis and SHAP (Shapley Additive Explanations) method are used to evaluate the contribution of each feature to the ground temperature prediction, and the most representative variables are selected to reduce redundant information and improve calculation efficiency.

[0062] (4) In the model training stage, this example uses XGBoost (extreme gradient boosting decision tree) as the core temperature prediction model. It should be understood that XGBoost was chosen because the model can effectively process high-dimensional data, use nonlinear relationships to improve prediction accuracy, and has the ability to automatically handle missing values ​​and reduce the risk of overfitting through regularization. The training process first divides the data set into 75% training set and 25% test set to ensure the generalization ability of the model. Subsequently, the model hyperparameters, including the maximum depth of the tree, learning rate, and subsampling rate, are initialized, and error minimization optimization is performed on the training data.

[0063] The above-deployed sensor array can not only be used to collect data for model training, but also can continue to collect measured temperature information after model training is completed, so as to correct the predicted temperature information of other locations in the target frozen soil area. Therefore, the server can also obtain the measured temperature information of the target frozen soil area through the multi-level temperature sensor array deployed in the target frozen soil area, wherein the lower-level arrays in the multi-level temperature sensor array are distributed in the grids in the upper-level array, and the lower-level arrays and the upper-level arrays include the same number and distribution pattern of temperature sensors.

[0064] Based on the above description of step S1, Figure 1 Step S2 in the description is as follows:

[0065] S2, correcting the predicted temperature information through the pre-built semivariogram model to obtain optimized predicted temperature information.

[0066] Among them, the semivariogram model characterizes the measurement error, spatial variability, and distance of spatial autocorrelation of ground temperature in the target frozen soil area. The first thing to understand here is that the semivariogram model is a tool in geostatistics that is used to describe the autocorrelation of spatial data, that is, how the similarity between data values ​​changes with the increase of spatial distance. The model calculates the semivariance between data points with different spacings, draws a semivariogram curve, and fits it using a mathematical model (such as an exponential model, a Gaussian model, or a spherical model). The semivariogram model is widely used in spatial interpolation (such as Kriging interpolation) and error correction, and can be used to optimize the prediction results of environmental variables (such as temperature, humidity, soil moisture, etc.) and improve the spatial consistency and accuracy of the data.

[0067] Therefore, in Figure 1 Before step S2, the server also performs spatial variation analysis based on the measured temperature information to obtain a semivariogram curve; and fits a semivariogram model based on the semivariogram curve.

[0068] In the implementation details, the server can first perform data normality and trend effect analysis on the measured temperature information to evaluate the distribution characteristics of the measured temperature information obtained and its overall change trend in space. Among them, data normality analysis takes into account that when performing spatial statistical modeling (such as Kriging interpolation), many geostatistical methods require data to satisfy a normal distribution. If the data deviates from the normal distribution, it may affect the accuracy of spatial interpolation. Therefore, it is first necessary to verify whether the measured temperature information conforms to the normal distribution. The trend effect analysis is used to indicate whether there is a systematic change pattern in the ground temperature data in space. For example, whether the ground temperature shows an upward or downward trend with changes in latitude, longitude, altitude or other geographical factors. If there is an obvious trend effect in the data, directly using spatial interpolation methods such as Kriging interpolation may cause errors, so the trend needs to be modeled and removed before interpolation.

[0069] Then, the spatial variation analysis of the target permafrost area is carried out to determine the variation pattern of ground temperature at different spatial scales. Since the ground temperature in permafrost areas is affected by a variety of environmental factors (such as altitude, soil moisture, vegetation coverage, etc.), it often presents a non-uniform distribution in space. Therefore, it is necessary to use the semivariogram as a tool to measure the spatial autocorrelation distance of temperature. The core idea is to calculate the temperature difference between any two observation points, and thereby reveal the correlation of temperature at different spatial distances. The calculation formula of the semivariogram is as follows:

[0070]

[0071] Where y(h) represents the semivariogram, N(h) represents the number of observation points with a distance of h, and Z(x i) and Z(x i +h) represents the ground temperature at a position distance h. Therefore, by calculating the semivariance values ​​at different distances h, the semivariogram curve can be obtained to analyze the spatial autocorrelation of the ground temperature. In this embodiment, in order to further analyze whether the spatial heterogeneity has directionality, the semivariogram is calculated by setting different angles (0°, 45°, 90°, 135°).

[0072] Next, the server needs to select a suitable mathematical model for fitting based on the calculated semivariogram curve, such as an exponential model, a spherical model, or a Gaussian model, to build a semivariogram model. The spatial distribution and correlation of ground temperatures at different grid points are studied. For example, using an exponential model to build:

[0073]

[0074] In the formula, a represents the range, which indicates the maximum distance of spatial autocorrelation. When this distance is exceeded, the correlation between temperature data tends to disappear. C represents the structural variance, C 0 is the nugget value, which indicates the semivariogram value when the distance is zero, reflecting the measurement error or temperature fluctuation at the microscopic scale; (C+C 0 ) is the base value, which indicates the maximum value of the semivariogram function and reflects the overall variation of ground temperature. / (C+C 0 ) indicates the degree of spatial correlation of system variables. <25%, 25-75%, and >75% indicate that the spatial correlation of ground temperature is weak, medium, and strong, respectively. This embodiment performs parameter fitting on the model based on the least squares method, and uses cross-validation and residual analysis to verify and optimize the model. Therefore, the key parameters of the semivariogram model can be used to evaluate the variability of measurement errors and measurement values, and at the same time obtain the maximum distance of its spatial autocorrelation, providing a basis for setting the distance between observation points in the future.

[0075] Based on the obtained semivariogram model, the server can use the model to correct the predicted temperature information. In this regard, it should be understood that since the predicted temperature information is usually predicted by machine learning models such as XGBoost based on environmental variables (such as vegetation, soil, climate, terrain, etc.), there may be certain deviations. Therefore, the predicted temperature information corrected by the semivariogram model can more accurately reflect the real spatial distribution characteristics of the ground temperature.

[0076] Based on the above description of step S3, Figure 1 Steps S3 and S4 in FIG. 1 are described as follows:

[0077] S3, integrating the optimized predicted temperature information with the measured temperature information to obtain the ground temperature information of the target frozen soil area.

[0078] In this regard, it can be understood that what the grid measures is the ground temperature data of a small range in the target frozen soil area, that is, local, discrete, high-precision observation point data collected by sensors arranged in a regular grid. These data have high temporal resolution (such as once every half an hour), but have limited coverage and are limited to the area where the sensors are arranged. The predicted surface temperature information is large-scale, that is, based on remote sensing data (such as MODIS, Landsat thermal infrared images) and machine learning models (XGBoost), the ground temperature of the entire permafrost area is inferred. Therefore, the present embodiment can match and fuse the optimized predicted temperature information with the measured temperature information on a spatial and temporal scale to improve the accuracy and completeness of the ground temperature estimation.

[0079] S4, generating a ground thermal state distribution map of the target frozen soil area according to the ground temperature information of the target frozen soil area.

[0080] In this embodiment, the server can perform spatial interpolation based on the spatial autocorrelation information of the ground temperature in the target frozen soil area and the ground temperature information of the target frozen soil area to obtain the temperature distribution information of the target frozen soil area; based on the temperature distribution information, generate a ground thermal state distribution map of the target frozen soil area.

[0081] It can be understood that when the server generates the ground thermal state distribution map of the target frozen soil area, it first needs to use the spatial autocorrelation analysis method to process the ground temperature information of the target frozen soil area to determine the spatial correlation and change trend of the ground temperature. Since the core of spatial autocorrelation analysis is to judge the similarity of ground temperature between adjacent areas, that is, the degree of aggregation or dispersion of temperature data in space, it can reveal the distribution characteristics of ground temperature at different spatial scales and provide theoretical support for subsequent interpolation calculations.

[0082] After obtaining the spatial autocorrelation information, the next step is to interpolate the ground temperature of the target frozen ground area based on this information, that is, to spatially supplement and optimize the temperature data through interpolation methods to improve the accuracy of temperature distribution. In this process, the server can use the Kriging interpolation method, which is based on the semivariogram model and uses the temperature values ​​of known measuring points to infer the temperature values ​​of unknown measuring points. Kriging interpolation can not only take into account spatial autocorrelation, but also finely model the spatial variability of temperature data through range, nugget value and sill value, so as to obtain more accurate temperature distribution information.

[0083] After completing the interpolation calculation, the temperature distribution information of the target permafrost area is obtained. The server can further use the geographic information system (GIS) to visualize the data and finally generate a ground thermal state distribution map of the target permafrost area. In the GIS environment, different spatial mapping methods can be used, such as isotherm drawing, heat map or graded color filling, to make the spatial distribution characteristics of ground temperature more intuitive. At the same time, combined with environmental factors such as vegetation, soil, and topography, the relationship between temperature distribution and environmental factors can be further analyzed, providing a scientific basis for ecological environment monitoring, engineering construction, and climate change research in permafrost areas.

[0084] In this way, the method provided by this implementation can provide high-precision prediction and spatial distribution mapping for the ground thermal state in permafrost areas, improving the accuracy and spatial consistency of temperature estimation. In addition, through multi-scale data fusion, the integrity and reliability of temperature monitoring are enhanced, while reducing the damage to the ecological environment caused by traditional temperature measurement methods.

[0085] Based on the same inventive concept as the method for estimating the ground thermal state distribution in a regular grid type frozen soil area provided in this embodiment, this embodiment also provides a device for estimating the ground thermal state distribution in a regular grid type frozen soil area, the device comprising at least one software function module that can be stored in a memory or solidified in an electronic device in the form of software. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function module and computer program included in the device. Please refer to Figure 4 , functionally speaking, the device may include:

[0086] The temperature acquisition module 11 is used to obtain the measured temperature information and the predicted temperature information of the target frozen soil area, wherein the measured temperature information includes the measured ground temperature under multiple observation scales, and the predicted temperature information includes the predicted ground temperature under multiple observation scales;

[0087] The temperature optimization module 12 is used to correct the predicted temperature information through a pre-constructed semivariogram model to obtain optimized predicted temperature information, wherein the semivariogram model characterizes the maximum distance of the measurement error, spatial variability and spatial autocorrelation of the ground temperature in the target frozen soil area;

[0088] The temperature optimization module 12 is also used to merge the optimized predicted temperature information with the measured temperature information to obtain the ground temperature information of the target frozen soil area;

[0089] The image drawing module 13 is used to generate a ground thermal state distribution map of the target frozen soil area according to the ground temperature information of the target frozen soil area.

[0090] In this embodiment, the temperature acquisition module 11 is used to implement Figure 1 Step S1 in the temperature optimization module 12 is used to achieve Figure 1 Steps S2 and S3 in the image drawing module 13 are used to implement Figure 1 Therefore, for the detailed description of the above modules, please refer to the specific implementation methods of the corresponding steps.

[0091] In addition, since the method for estimating the distribution of ground thermal state in a regular grid type permafrost area provided in this embodiment has the same inventive concept, the device for estimating the distribution of ground thermal state in a regular grid type permafrost area can also implement other steps or sub-steps of the method through the above-mentioned modules.

[0092] Optionally, the temperature acquisition module 11 is further specifically used for:

[0093] The measured temperature information of the target permafrost area is obtained through a multi-level temperature sensor array deployed in the target permafrost area, wherein the lower array in the multi-level temperature sensor array is distributed in the grid in the upper array, and the lower array and the upper array include the same number of temperature sensors and have the same distribution pattern.

[0094] Optionally, the temperature acquisition module 11 is further specifically used for:

[0095] Obtain vegetation information, soil information, climate information and terrain information in the target permafrost area;

[0096] The vegetation information, soil information, climate information and terrain information are processed through the pre-trained temperature prediction model to obtain the predicted temperature information of the target permafrost area.

[0097] Optionally, before correcting the predicted temperature information by using the pre-built semivariogram model, the temperature optimization module 12 is further used to:

[0098] According to the measured temperature information, the spatial variation analysis is carried out to obtain the semivariogram curve;

[0099] According to the semivariogram curve, the semivariogram model is fitted.

[0100] Optionally, the image drawing module 13 is further specifically used for:

[0101] Performing spatial interpolation based on the spatial autocorrelation information of the ground temperature in the target frozen soil area and the ground temperature information in the target frozen soil area to obtain the temperature distribution information of the target frozen soil area;

[0102] Based on the temperature distribution information, a ground thermal state distribution map of the target permafrost area is generated.

[0103] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0104] It should also be understood that if the above implementation is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0105] Therefore, this embodiment further provides a storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for estimating the ground thermal state distribution in a regular grid frozen soil area provided in this embodiment is implemented. The storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0106] This embodiment provides an electronic device for implementing a method for estimating the ground thermal state distribution in a regular grid-type frozen soil region. Figure 5 As shown, the electronic device may include a processor 22 and a memory 21. In addition, the memory 21 stores a computer program, and the processor implements the method for estimating the ground thermal state distribution in a regular grid-type frozen soil area provided in this embodiment by reading and executing the computer program corresponding to the above implementation manner in the memory 21.

[0107] Continue to see Figure 5 The electronic device further includes a communication unit 23. The memory 21, the processor 22 and the communication unit 23 are electrically connected to each other directly or indirectly through a system bus 24 to achieve data transmission or interaction.

[0108] The memory 21 may be an information recording device based on any electronic, magnetic, optical or other physical principle, used to record execution instructions, data, etc. In some embodiments, the memory 21 may be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0109] In some embodiments, the volatile memory may be a random access memory (RAM); in some embodiments, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a flash memory, etc.; in some embodiments, the storage drive may be a disk drive, a solid-state drive, any type of storage disk (such as a CD, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0110] The communication unit 23 is used to send and receive data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, through which one or more components of the service request processing system may be connected to the network to exchange data and / or information.

[0111] The processor 22 may be an integrated circuit chip having a signal processing capability, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), or a microprocessor, or any combination thereof.

[0112] Understandably, Figure 5 The structure shown is for illustration only. The electronic device 100 may also have Figure 5 More or fewer components than shown, or with Figure 5 Different configurations are shown. Figure 5 The components shown may be implemented in hardware, software or a combination thereof.

[0113] It should be understood that the apparatus and method disclosed in the above-mentioned embodiments can also be implemented in other ways. The apparatus embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for estimating the distribution of ground thermal state in a regular grid frozen soil area, characterized in that: The method comprises: Acquire measured temperature information and predicted temperature information of the target frozen soil area, wherein the measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at the multiple observation scales; Correcting the predicted temperature information by a pre-constructed semivariogram model to obtain optimized predicted temperature information, wherein the semivariogram model characterizes the maximum distance of measurement error, spatial variability, and spatial autocorrelation of ground temperature in the target frozen soil area; The optimized predicted temperature information is integrated with the measured temperature information to obtain the ground temperature information of the target frozen soil area; A ground thermal state distribution map of the target frozen soil area is generated according to the ground temperature information of the target frozen soil area.

2. The method for estimating the ground thermal state distribution in a regular grid frozen soil area according to claim 1, characterized in that: The multiple observation scales show a law of gradual transition.

3. The method for estimating the ground thermal state distribution in a regular grid frozen soil area according to claim 2, characterized in that: Obtain the measured temperature information of the target frozen soil area, including: The measured temperature information of the target frozen soil area is obtained through a multi-level temperature sensor array deployed in the target frozen soil area, wherein the lower-level array in the multi-level temperature sensor array is distributed in the grid in the upper-level array, and the lower-level array and the upper-level array include the same number of temperature sensors and have the same distribution pattern.

4. The method for estimating the ground thermal state distribution in a regular grid frozen soil region according to claim 1, characterized in that: Obtain the predicted temperature information of the target frozen ground area, including: Acquiring vegetation information, soil information, climate information and terrain information of the target frozen soil area; The vegetation information, soil information, climate information and terrain information are processed by a pre-trained temperature prediction model to obtain predicted temperature information of the target frozen soil area.

5. The method for estimating the ground thermal state distribution in a regular grid frozen soil region according to claim 4, characterized in that: The temperature prediction model is obtained by training the XGboost model through sample data.

6. The method for estimating the ground thermal state distribution in a regular grid frozen soil region according to claim 1, characterized in that: Before correcting the predicted temperature information by using a pre-built semivariogram model, the method further includes: Perform spatial variation analysis based on the measured temperature information to obtain a semivariogram curve; The semivariogram model is fitted according to the semivariogram curve.

7. The method for estimating the ground thermal state distribution in a regular grid frozen soil region according to claim 1, characterized in that: Generating a ground thermal state distribution map of the target frozen soil area according to the ground temperature information of the target frozen soil area includes: Performing spatial interpolation based on the spatial autocorrelation information of the ground temperature in the target frozen soil area and the ground temperature information in the target frozen soil area to obtain the temperature distribution information of the target frozen soil area; A ground thermal state distribution map of the target frozen soil area is generated according to the temperature distribution information.

8. A regular grid type ground thermal state distribution estimation device in permafrost areas, characterized in that: The device comprises: A temperature acquisition module, used to acquire measured temperature information and predicted temperature information of a target frozen soil area, wherein the measured temperature information includes measured ground temperatures at multiple observation scales, and the predicted temperature information includes predicted ground temperatures at the multiple observation scales; A temperature optimization module, used for correcting the predicted temperature information through a pre-constructed semivariogram model to obtain optimized predicted temperature information, wherein the semivariogram model characterizes the maximum distance of measurement error, spatial variability and spatial autocorrelation of ground temperature in the target frozen soil area; The temperature optimization module is further used to fuse the optimized predicted temperature information with the measured temperature information to obtain the ground temperature information of the target frozen soil area; The image drawing module is used to generate a ground thermal state distribution map of the target frozen soil area according to the ground temperature information of the target frozen soil area.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for estimating the ground thermal state distribution in a regular grid-type frozen soil area according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for estimating the ground thermal state distribution in a regular grid-type frozen soil area as described in any one of claims 1-7 is implemented.

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