CN116295198A - Intelligent evaluation method and system for frozen soil active layer thickness - Google Patents
Intelligent evaluation method and system for frozen soil active layer thickness Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, and provides an intelligent evaluation method and system for the thickness of a frozen soil active layer, wherein the method comprises the following steps: acquiring basic information of frozen soil in a preset area; acquiring basic information of the frozen soil area environment; carrying out snow melting prediction according to weather prediction information and snow cover information, obtaining soil water content prediction data, combining frozen soil lithology information and frozen soil structure information, and matching heat conductivity coefficients; obtaining a freezing index or a thawing index through statistics; the freezing index and the heat conductivity coefficient in the freezing period or the thawing index and the heat conductivity coefficient in the thawing period are input into an active layer depth change amount evaluation model, an active layer depth change amount evaluation result is obtained, and the frozen soil active layer thickness prediction data are determined, so that the technical problem of low evaluation accuracy of the frozen soil active layer thickness is solved, the technical effects of comprehensively evaluating the thawing/freezing condition of frozen soil from the surface temperature change and snow/thawing change by changing the soil moisture content into an entry point and improving the evaluation accuracy of the frozen soil active layer thickness are realized.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent evaluation method and system for the thickness of a frozen soil active layer.
Background
Frozen soil is a surface layer which is below 0 ℃ and contains various rocks and soil of ice, the annual average low temperature is less than 0 ℃, and the underlying layer is a movable soil layer for years; frozen soil active layer: the active layer is a soil layer which is covered on permafrost and is melted in summer and frozen in winter; seasonal thickness variations in the frozen earth moving layer have a serious impact on construction and survival development.
At present, the thickness of a frozen soil active layer is estimated in real time, and due to the complexity of analysis of the thickness variation of the active layer, particularly the complexity of snow environment, the prediction estimation of the thickness variation of the active layer is lacked, the key of the development of the dynamic soil construction is determined by the characteristics of the frozen soil foundation, the deformation of the foundation caused by factors such as non-uniformity, large load difference, complex body shape and the like of the frozen soil foundation, and the frozen soil state in the construction and use period of a building is fully considered before the development of a frozen soil distribution area, so that a foundation is provided for ensuring the durability of the building.
In summary, the technical problem of low evaluation accuracy of the thickness of the frozen soil active layer exists in the prior art.
Disclosure of Invention
The application aims to solve the technical problem of low evaluation accuracy of the thickness of the frozen soil active layer in the prior art by providing the intelligent evaluation method and the intelligent evaluation system of the thickness of the frozen soil active layer.
In view of the above problems, the embodiments of the present application provide an intelligent evaluation method and system for the thickness of a frozen soil active layer.
In a first aspect of the disclosure, an intelligent evaluation method for a frozen soil active layer thickness is provided, wherein the method comprises: acquiring basic information of frozen soil in a preset area, wherein the basic information of frozen soil in the preset area comprises the lithology information of the frozen soil and the structural information of the frozen soil; acquiring basic information of a frozen soil area environment, wherein the basic information of the frozen soil area environment comprises snow cover information and weather forecast information; carrying out snow melting prediction according to the weather prediction information and the snow cover information to obtain soil water content prediction data; matching a heat conduction coefficient according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, wherein the heat conduction coefficient comprises a melting period heat conduction coefficient or a freezing period heat conduction coefficient; carrying out parameter statistics according to the weather forecast information to obtain a freezing index or a thawing index; inputting the freezing index and the freezing period heat conductivity coefficient or the melting index and the melting period heat conductivity coefficient into an active layer depth change amount evaluation model to obtain an active layer depth change amount evaluation result; and determining the frozen soil active layer thickness prediction data according to the active layer depth variation evaluation result, wherein the frozen soil active layer thickness prediction data belongs to a first future time zone.
In another aspect of the disclosure, an intelligent evaluation system for a frozen soil active layer thickness is provided, wherein the system comprises: the frozen soil information acquisition module is used for acquiring frozen soil basic information of a preset area, wherein the frozen soil basic information of the preset area comprises frozen soil lithology information and frozen soil structure information; the environment information acquisition module is used for acquiring the basic information of the environment of the frozen soil area, wherein the basic information of the environment of the frozen soil area comprises snow cover information and weather forecast information; the snow melting prediction module is used for performing snow melting prediction according to the weather prediction information and the snow cover information to obtain soil water content prediction data; the thermal conductivity coefficient matching module is used for matching thermal conductivity coefficients according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, wherein the thermal conductivity coefficients comprise melting phase thermal conductivity coefficients or freezing phase thermal conductivity coefficients; the parameter statistics module is used for carrying out parameter statistics according to the weather forecast information to obtain a freezing index or a thawing index; the depth change amount evaluation module is used for inputting the freezing index and the freezing period heat conduction coefficient or the melting index and the melting period heat conduction coefficient into an active layer depth change amount evaluation model to obtain an active layer depth change amount evaluation result; and the prediction data determining module is used for determining the frozen soil active layer thickness prediction data according to the active layer depth variation evaluation result, wherein the frozen soil active layer thickness prediction data belongs to a first future time zone.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
due to the adoption of the method, the basic information of frozen soil in a preset area is acquired; acquiring basic information of the frozen soil area environment; carrying out snow melting prediction according to weather prediction information and snow cover information to obtain soil water content prediction data; matching the heat conductivity coefficient according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data; carrying out parameter statistics according to weather forecast information to obtain a freezing index or a thawing index; the freezing index and the heat conductivity coefficient in the freezing period or the thawing index and the heat conductivity coefficient in the thawing period are input into an active layer depth change amount evaluation model to obtain an active layer depth change amount evaluation result, and the frozen soil active layer thickness prediction data are determined, so that the technical effects of comprehensively evaluating the thawing/freezing condition of frozen soil and improving the evaluation accuracy of the frozen soil active layer thickness by changing the water content of soil into a cut-in point and comprehensively evaluating the thawing/thawing condition of frozen soil from the surface temperature change and snow/thawing snow change are realized.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a possible method for intelligently evaluating the thickness of a frozen soil active layer according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a possible process for generating and obtaining soil water content prediction data in an intelligent evaluation method for frozen soil active layer thickness according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a possible thermal conductivity coefficient matching method in an intelligent evaluation method for a frozen soil active layer thickness according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of an intelligent evaluation system for the thickness of a frozen soil active layer according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a frozen soil information acquisition module 100, an environment information acquisition module 200, a snow melting prediction module 300, a heat conductivity coefficient matching module 400, a parameter statistics module 500, a depth change amount evaluation module 600 and a prediction data determination module 700.
Detailed Description
The embodiment of the application provides an intelligent evaluation method and system for the thickness of a frozen soil active layer, which solve the technical problem of low evaluation precision of the thickness of the frozen soil active layer, realize the technical effect of changing the water content of soil into an entry point, comprehensively evaluating the thawing/freezing condition of the frozen soil from the surface temperature change and snow/snow thawing change, and improve the evaluation precision of the thickness of the frozen soil active layer.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides an intelligent evaluation method for a frozen soil active layer thickness, where the method includes:
s10: acquiring basic information of frozen soil in a preset area, wherein the basic information of frozen soil in the preset area comprises the lithology information of the frozen soil and the structural information of the frozen soil;
s20: acquiring basic information of a frozen soil area environment, wherein the basic information of the frozen soil area environment comprises snow cover information and weather forecast information;
specifically, the thickness of the frozen soil active layer can change due to seasonal temperature changes, the frozen soil basic information of the preset area comprises frozen soil lithology information (commonly, silt and cohesive soil) and frozen soil structure information (commonly, square and strip) and the frozen soil can be randomly sampled in the preset area to obtain the frozen soil basic information of the preset area;
the frozen soil area environment basic information comprises snow cover information (the snow cover information comprises snow thickness and snow amount) and meteorological prediction information (the weather prediction related information in a preset area of a natural circumference in the future comprises related data such as predicted snowfall amount and predicted average daily air temperature) and the meteorological prediction information in the frozen soil area environment basic information can be obtained through networking through longitude and latitude in the preset area; the snow cover information in the basic information of the frozen soil area environment can be acquired through the rain gauge, and data support is provided for subsequent analysis;
S30: carrying out snow melting prediction according to the weather prediction information and the snow cover information to obtain soil water content prediction data;
as shown in fig. 2, step S30 includes the steps of:
s31: acquiring an air temperature prediction sequence according to the weather prediction information, wherein the air temperature prediction sequence belongs to the first future time zone, and the time unit is a day;
s32: inputting the air temperature prediction sequence into a snow melting rate matching table to obtain a snow melting rate sequence;
s33: carrying out snow melting prediction based on the snow cover information according to the snow melting rate sequence to obtain a snow melting amount prediction sequence and a snow melting thickness prediction sequence;
s34: and carrying out frequent item analysis according to the snow melting quantity prediction sequence to obtain the soil water content prediction data.
Specifically, according to the weather prediction information and the snow cover information, performing snow melting prediction to obtain soil water content prediction data, specifically including: arranging the weather forecast information according to the time unit as days to obtain an air temperature forecast sequence, wherein the air temperature forecast sequence belongs to the first future time zone (the first future time zone is shown as 0 time: temperature 2 ℃, 1 time: temperature-1 ℃, 2 time: temperature-3 ℃ and 3 time: temperature-2 ℃); inputting the air temperature prediction sequence into a snow melting rate matching table (the snow is melted when the temperature is above zero, the snow melting rate matching table is obtained by statistics under the condition that the snow is not raining and is not snowing, the temperature is 19 ℃ for a snow melting rate of 0.0274cm/min, the temperature is 24 ℃ for a snow melting rate of 0.0374 cm/min), and correspondingly matching according to the air temperature prediction sequence to obtain a snow melting rate sequence;
Taking the snow cover information as a starting point, and taking the snow melting amount (for example, the average temperature at 11-12 is 19 ℃, the snow melting thickness at 11-12 is 0.0274cm/min multiplied by 60min, if the snow melting thickness at 11-12 in the snow cover information is satisfied, the snow melting amount at 11-12 is=the snow melting thickness at 11-12 multiplied by the snow melting area=the snow melting amount at 11-12) as a variable value, and performing snow melting prediction to obtain a snow melting amount prediction sequence and a snow melting thickness prediction sequence, wherein sequence elements in the snow melting amount prediction sequence and the snow melting thickness prediction sequence are in one-to-one correspondence; and (3) carrying out frequent item analysis according to the snow thawing quantity prediction sequence to obtain the soil water content prediction data (basically, after snow is thawed on the surface layer of the frozen soil region, the surface layer of the frozen soil region can permeate into the frozen soil active layer on the lower layer, if the surface layer of the frozen soil region is not covered with accumulated snow, and under the condition of not rainfall and not snowfall, if the evaporation effect of the soil water content and the water consumption of the frozen soil vegetation are not considered, the soil water content is not changed), and providing data support for the prediction of the frozen soil active layer thickness.
Step S34 includes the steps of:
s341: the basic frozen soil information of the preset area further comprises frozen soil vegetation coverage information, wherein the frozen soil vegetation coverage information comprises vegetation type information and vegetation quantity information;
S342: acquiring the predicted data of the snow melting amount on the ith day according to the predicted sequence of the snow melting amount;
s343: searching by taking the frozen soil lithology information, the frozen soil structure information, the vegetation type information, the vegetation quantity information and the ith snow melting amount prediction data as constraint conditions and taking the soil water absorption as target data to obtain a plurality of soil water absorption recording results, wherein the plurality of soil water absorption recording results comprise a plurality of recording frequency parameters;
s344: screening the soil water absorption recording results according to the plurality of recording frequency parameters to obtain the forecast data of the soil water absorption on the ith day;
s345: carrying out water content calibration on the i-1 th day soil water content calibration result according to the i-th day soil water absorption prediction data to generate an i-th day soil water content calibration result;
s346: and when i belongs to a preset time sequence node of the first future time zone, setting the i-th day soil water content calibration result as the soil water content prediction data.
Specifically, frequent item analysis is performed according to the snow melting amount prediction sequence, and the soil water content prediction data is obtained, specifically including: the frozen soil basic information of the preset area further comprises frozen soil vegetation coverage information, wherein the frozen soil vegetation coverage information comprises vegetation type information (common plants such as alpine grasslands, photinia, arctic orchids, golden phoenix-tail flowers and the like) and vegetation quantity information (the vegetation quantity information can be the area of the alpine grasslands and the plant quantity of the arctic orchids); determining the ith sequence element in the snow melting amount prediction sequence, namely ith day snow melting amount prediction data;
Searching by taking the frozen soil lithology information, the frozen soil structure information, the vegetation type information, the vegetation quantity information and the ith snow melting amount prediction data as constraint conditions and taking the soil water absorption as target data, and screening and obtaining a plurality of soil water absorption record results in a data storage unit of an intelligent evaluation system for the frozen soil active layer thickness, wherein the plurality of soil water absorption record results comprise a plurality of record frequency parameters (a plurality of record frequency parameters, namely the record frequency of the frozen soil lithology historical information, the record frequency of the frozen soil structure historical information, the record frequency of the vegetation type historical information, the record frequency of the vegetation quantity historical information, the record frequency of the historical snow melting amount data and the record frequency of the historical soil water absorption), and the plurality of soil water absorption record results further comprise the frozen soil lithology historical information, the frozen soil structure historical information, the vegetation type historical information, the vegetation quantity historical information, the historical snow melting amount data and the historical soil water absorption;
according to the plurality of recording frequency parameters (generally, the recording frequency of the frozen soil lithology historical information, the recording frequency of the frozen soil structure historical information, the recording frequency of the vegetation type historical information, the recording frequency of the vegetation quantity historical information, the recording frequency of the historical snow melting amount data and the recording frequency of the historical soil water absorption are all larger than each time/time, if the recording frequency of the historical snow melting amount data is 30 min/time, the historical snow melting amount data in even/odd order can be screened), the plurality of recording frequency parameters are limited to each time/time, the soil water absorption recording result is screened, and a soil water absorption recording table (the soil water absorption recording table is in the form of, for example, 0 time frozen soil lithology historical information, 0 time frozen soil structure historical information, 0 time vegetation type historical information, 0 time vegetation quantity historical information, 0 time historical snow melting amount data and 0 time historical soil water absorption; 1, frozen soil lithology historical information at 1, frozen soil structure historical information at 1, vegetation type historical information at 1, vegetation quantity historical information at 1, historical snow melting amount data at 1 and historical soil water absorption at 1), and acquiring i-th day soil water absorption prediction data according to a change rule of recorded data in a soil water absorption record table;
Carrying out water content time-interval calibration on an i-1 day soil water content calibration result according to the i-day soil water absorption prediction data (the current limitation is i-1 day due to the limited credibility of relevant information such as weather prediction information exceeding 24 hours, in short, in order to ensure the precision of the soil water content calibration result, only calibrating the soil water content within 24 hours), and completing the water content time-interval calibration of the i-1 day soil water content calibration result, wherein the water content time-interval calibration result is defined as an i-day soil water content calibration result; and when i belongs to a preset time sequence node (the preset time sequence node: the last time period of the first future time zone, namely 23 to 24 time/0 time), setting the i-th day soil water content calibration result as the soil water content prediction data, and ensuring the precision of the soil water content prediction data.
Step S344 includes the steps of:
s344-1: setting a soil water absorption deviation threshold;
s344-2: performing hierarchical clustering analysis on the soil water absorption recording results according to the soil water absorption deviation threshold value to obtain soil water absorption recording result clustering results, wherein the soil water absorption recording result clustering results comprise characteristic values in the soil water absorption recording result class;
S344-3: classifying and adding the plurality of recording frequency parameters according to the clustering result of the soil water absorption recording result to generate the intra-class frequency of the soil water absorption recording result;
s344-4: and carrying out average analysis on the characteristic values in the soil water absorption recording result class, wherein the frequency in the soil water absorption recording result class meets the intra-class frequency threshold value, and generating the i-th day soil water absorption prediction data.
Specifically, screening the soil water absorption recording result according to the plurality of recording frequency parameters to obtain the i-th day soil water absorption prediction data, which specifically comprises the following steps: setting a soil water absorption deviation threshold (preset parameter index); performing hierarchical clustering analysis (hierarchical clustering analysis: for example, first record data 1 of historical soil water absorption, frequency is 3, second record data 2 of historical soil water absorption, frequency is 2, and intra-class characteristic values after clustering are equal to (3/(3+2) x 1) + (2/(3+2) x 2), and then the whole is divided by 2), so as to obtain a soil water absorption record result clustering result, wherein the soil water absorption record result clustering result comprises intra-class characteristic values of soil water absorption record results, characteristic values of frozen soil lithology historical information, characteristic values of frozen soil structure historical information, characteristic values of vegetation type historical information, characteristic values of vegetation quantity historical information and characteristic values of historical snow melting amount data;
Carrying out classified addition on the plurality of recording frequency parameters according to the clustering result of the soil water absorption recording results to generate the intra-class frequency of the soil water absorption recording results (if the first classified addition is 48, the intra-class frequency of the soil water absorption recording results of the first classification is 30 min/time); based on a variation coefficient method, directly using information contained in the intra-class frequency of the soil water absorption recording result meeting the intra-class frequency threshold (the intra-class frequency threshold set by user definition) as weight, and performing weighted average calculation on the characteristic value in the soil water absorption recording result class by using the variation coefficient method (the variation coefficient method is an objective weighting method), so as to generate the i-th day soil water absorption prediction data and provide data support for evaluating the thickness of the frozen soil activity layer.
S40: matching a heat conduction coefficient according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, wherein the heat conduction coefficient comprises a melting period heat conduction coefficient or a freezing period heat conduction coefficient;
as shown in fig. 3, step S40 includes the steps of:
s41: acquiring a plurality of expert decision modules, wherein information between any two expert decision modules is not communicated, and the expert decision modules have a plurality of decision credibility;
S42: inputting the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data into the expert decision modules to obtain a plurality of heat conductivity coefficient decision results;
s43: and carrying out weighted average analysis according to the decision credibility and the decision results of the heat conduction coefficients to obtain the heat conduction coefficient in the melting period or the heat conduction coefficient in the freezing period, and adding the heat conduction coefficient.
Specifically, according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, the method for matching the heat conductivity coefficient specifically comprises the following steps: acquiring a plurality of expert decision modules (the expert decision modules are constructed for automatic processing when the accumulated data amount of the artificial decision is more after a period of time is continued), wherein the information between any two expert decision modules is not communicated, the expert decision modules have a plurality of decision credibility, and the heat conduction coefficients comprise melting phase heat conduction coefficients (when the daily average air temperature is more than 0 ℃ and the melting phase heat conduction coefficients are directly matched) or freezing phase heat conduction coefficients (when the daily average air temperature is less than 0 ℃ and the freezing phase heat conduction coefficients are directly matched);
the method comprises the steps that data accumulated in an artificial decision stage (the data accumulated in the artificial decision stage comprises frozen soil lithology information, frozen soil structure information, soil water content and a calibration result, the frozen soil lithology information, the frozen soil structure information, the soil water content and the calibration result are stored in an associated mode), the data accumulated in the artificial decision stage are respectively stored in a plurality of address fragments, the address fragments are mutually independent) and serve as a knowledge base, a plurality of expert decision modules (information is not communicated between any two expert decision modules) are constructed, the expert decision modules are in one-to-one correspondence with the address fragments, the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data serve as input data, the input data are input into the expert decision modules, intelligent processing is carried out according to the expert decision modules, and a plurality of heat conductivity coefficient decision results are output; and performing weighted average analysis (weighted average analysis, namely weighted average calculation, is performed, specific operation steps are described and are not repeated here) according to the decision reliability and the decision results of the thermal conductivity coefficients, obtaining the thermal conductivity coefficient in the melting period (namely melting period thermal conductivity coefficient if the daily average air temperature is more than 0 ℃) or the thermal conductivity coefficient in the freezing period (namely melting period thermal conductivity coefficient if the daily average air temperature is less than 0 ℃), and adding the thermal conductivity coefficient in the freezing period to provide data support for performing analysis of the frozen soil state (generally, the frozen soil state is melted in summer and frozen in winter).
S50: carrying out parameter statistics according to the weather forecast information to obtain a freezing index or a thawing index;
step S50 includes the steps of:
s51: acquiring a ground surface temperature prediction sequence according to the weather prediction information, wherein the ground surface temperature prediction information belongs to the first future time zone;
s52: grouping the surface temperature prediction sequences by taking 0 ℃ as a critical value to obtain a positive value temperature prediction sequence and a negative value temperature prediction sequence;
s53: according to the positive temperature prediction sequence, freezing index calculation is carried out, and the freezing index is obtained;
s54: and carrying out freezing index calculation according to the negative temperature prediction sequence to obtain the melting index.
Specifically, parameter statistics is performed according to the weather forecast information to obtain a freezing index or a thawing index, which specifically includes: the ground surface temperature prediction information belongs to the first future time zone, and a ground surface temperature prediction sequence is obtained according to the weather prediction information and the distribution rule of the first future time zone; grouping the surface temperature prediction sequences by taking 0 ℃ as a critical value, dividing the surface temperature prediction sequences above 0 ℃ into positive value temperature prediction sequences, and dividing the surface temperature prediction sequences below 0 ℃ into negative value temperature prediction sequences to obtain positive value temperature prediction sequences and negative value temperature prediction sequences; performing a freezing index calculation (freezing index calculation: freezing index = sum of duration of time of day average air temperature below 0 ℃ and numerical product thereof) according to the positive temperature prediction sequence, obtaining the freezing index; and (3) performing freezing index calculation (freezing index calculation: melting index = sum of duration time of day average air temperature higher than 0 ℃ and numerical product thereof) according to the negative temperature prediction sequence, obtaining the melting index, and providing data support for frozen soil state change evaluation.
S60: inputting the freezing index and the freezing period heat conductivity coefficient or the melting index and the melting period heat conductivity coefficient into an active layer depth change amount evaluation model to obtain an active layer depth change amount evaluation result;
s70: and determining the frozen soil active layer thickness prediction data according to the active layer depth variation evaluation result, wherein the frozen soil active layer thickness prediction data belongs to a first future time zone.
Specifically, an active layer depth variation evaluation model is constructed; and taking the freezing index and the heat conductivity coefficient of the freezing period or the thawing index and the heat conductivity coefficient of the thawing period as input data, inputting an active layer depth change amount evaluation model, obtaining an active layer depth change amount evaluation result, taking the frozen soil structure information as an initial amount, taking the active layer depth change amount evaluation result as a change amount (which can be a positive value or a negative value), accumulating to determine frozen soil active layer thickness prediction data, wherein the frozen soil active layer thickness prediction data belongs to a first future time zone, predicting the frozen soil active layer thickness in advance, and providing technical support for construction and development of a frozen soil area.
Step S60 includes the steps of:
s61: acquiring frozen soil data based on the frozen soil lithology information and the frozen soil structure information, and acquiring active layer freezing record data and active layer melting record data;
S62: the active layer freezing record data comprises freezing index record data, freezing period heat conductivity coefficient record data and first active layer depth change volume record data;
s63: the active layer melting record data comprises melting index record data, melting period heat conductivity coefficient record data and second active layer depth change volume record data;
s64: training a first evaluation unit of the depth variation of the active layer based on a random forest according to the freezing index record data, the freezing period heat conductivity coefficient record data and the first active layer depth variation record data;
s65: training a second evaluation unit of the depth variation of the active layer based on a random forest according to the melting index record data, the melting period heat conductivity coefficient record data and the second active layer depth variation record data;
s66: and merging the first evaluation unit of the depth change of the active layer and the second evaluation unit of the depth change of the active layer to generate the evaluation model of the depth change of the active layer.
Specifically, an active layer depth variation evaluation model is constructed, specifically including: based on the frozen soil lithology information and the frozen soil structure information, frozen soil data acquisition is carried out in a preset area, and active layer freezing record data (data obtained by recording the average surface temperature of more than 0 ℃) and active layer melting record data (data obtained by recording the average surface temperature of less than 0 ℃) are obtained; the active layer freezing record data comprises freezing index record data, freezing period heat conductivity coefficient record data and first active layer depth change volume record data; the active layer melting record data comprises melting index record data, melting period heat conductivity coefficient record data and second active layer depth change volume record data;
Setting the freezing index record data, the freezing period heat conductivity coefficient record data and the first active layer depth change amount record data as root base point characteristics based on a random forest: the freezing indexes record data root point characteristics, namely index characteristics corresponding to a plurality of groups of freezing indexes; the freezing period heat conductivity coefficient records data root point characteristics, namely index characteristics corresponding to a plurality of groups of freezing period heat conductivity coefficients; the first active layer depth variation records data root point characteristics, namely index characteristics corresponding to a plurality of groups of first active layer depth variation; dividing a space through all values of a certain feature, training until a loss function is minimum, determining an optimal dividing point (determining data processing logic of a first evaluation unit through an optimal dividing point limiting area, determining a corresponding output value of the first evaluation unit through the optimal dividing point limiting area), and constructing a plurality of groups of freezing indexes, a plurality of groups of freezing period heat conductivity coefficients and a plurality of groups of first active layer depth change amounts corresponding to the defined root point feature through the root point feature defined by the optimal dividing point after the optimal dividing point is determined (the optimal dividing point limiting area determines the corresponding output value of the first evaluation unit);
Setting the melting index record data, the melting period heat conductivity coefficient record data and the second active layer depth variation record data as root base point characteristics based on random forests, and training an active layer depth variation second evaluation unit (specific operation steps are already described, and repeated description is not performed here); and carrying out multithreading parallel combination on the first evaluation unit of the active layer depth variation and the second evaluation unit of the active layer depth variation (the first evaluation unit of the active layer depth variation and the second evaluation unit of the active layer depth variation are independent processing units), generating an evaluation model of the active layer depth variation, and providing model support for evaluating the variation of the active layer depth.
In summary, the intelligent evaluation method and system for the thickness of the frozen soil active layer provided by the embodiment of the application have the following technical effects:
1. due to the adoption of the method, the basic information of frozen soil in a preset area is acquired; acquiring basic information of the frozen soil area environment; carrying out snow melting prediction according to weather prediction information and snow cover information, obtaining soil water content prediction data, combining frozen soil lithology information and frozen soil structure information, and matching heat conductivity coefficients; obtaining a freezing index or a thawing index through statistics; the method and the system have the technical effects that the technical effects of changing the water content of the soil into the cut-in point, comprehensively evaluating the thawing/freezing condition of the frozen soil from the surface temperature change and the snow/snow thawing change, and improving the evaluation accuracy of the thickness of the frozen soil active layer are realized.
2. Because the set soil water absorption deviation threshold value is adopted, hierarchical clustering analysis is carried out on the soil water absorption record results, the soil water absorption record result clustering results are obtained, and the multiple record frequency parameters are classified and added to generate the intra-class frequency of the soil water absorption record results; and carrying out average analysis on the characteristic values in the soil water absorption recording result class, wherein the frequency in the soil water absorption recording result class meets the intra-class frequency threshold value, generating i day soil water absorption prediction data, and providing data support for evaluating the thickness of the frozen soil activity layer.
Example two
Based on the same inventive concept as the intelligent evaluation method of the thickness of the frozen soil active layer in the foregoing embodiment, as shown in fig. 4, an embodiment of the present application provides an intelligent evaluation system of the thickness of the frozen soil active layer, where the system includes:
the frozen soil information acquisition module 100 is configured to acquire frozen soil basic information of a preset area, where the frozen soil basic information of the preset area includes frozen soil lithology information and frozen soil structure information;
the environment information acquisition module 200 is configured to acquire basic information of a frozen soil area environment, where the basic information of the frozen soil area environment includes snow coverage information and weather prediction information;
The snow melting prediction module 300 is configured to perform snow melting prediction according to the weather prediction information and the snow cover information, and obtain soil water content prediction data;
the thermal conductivity coefficient matching module 400 is configured to match thermal conductivity coefficients according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, where the thermal conductivity coefficients include a melting phase thermal conductivity coefficient or a freezing phase thermal conductivity coefficient;
the parameter statistics module 500 is configured to perform parameter statistics according to the weather prediction information, and obtain a freezing index or a thawing index;
the depth change amount estimation module 600 is configured to input the freezing index and the thermal conductivity coefficient in the freezing period, or the melting index and the thermal conductivity coefficient in the melting period into an active layer depth change amount estimation model, to obtain an active layer depth change amount estimation result;
the prediction data determining module 700 is configured to determine, according to the result of the active layer depth variation evaluation, frozen soil active layer thickness prediction data, where the frozen soil active layer thickness prediction data belongs to a first future time zone.
Further, the system includes:
the air temperature prediction sequence acquisition module is used for acquiring an air temperature prediction sequence according to the weather prediction information, wherein the air temperature prediction sequence belongs to the first future time zone, and the time unit is a day;
The snow melting rate sequence acquisition module is used for inputting the air temperature prediction sequence into a snow melting rate matching table to acquire a snow melting rate sequence;
the snow melting prediction sequence acquisition module is used for carrying out snow melting prediction based on the snow cover information according to the snow melting rate sequence to acquire a snow melting amount prediction sequence and a snow melting thickness prediction sequence;
and the frequent item analysis module is used for carrying out frequent item analysis according to the snow melting amount prediction sequence to acquire the soil water content prediction data.
Further, the system includes:
the frozen soil vegetation coverage data determining module is used for the frozen soil basic information of the preset area and further comprises frozen soil vegetation coverage information, wherein the frozen soil vegetation coverage information comprises vegetation type information and vegetation quantity information;
the snow melting quantity prediction data acquisition module is used for acquiring the i day snow melting quantity prediction data according to the snow melting quantity prediction sequence;
the water absorption recording result acquisition module is used for searching by taking the frozen soil lithology information, the frozen soil structure information, the vegetation type information, the vegetation quantity information and the ith day snow melting amount prediction data as constraint conditions and taking the soil water absorption as target data to acquire a plurality of soil water absorption recording results, wherein the plurality of soil water absorption recording results comprise a plurality of recording frequency parameters;
The water absorption recording result screening module is used for screening the soil water absorption recording results according to the plurality of recording frequency parameters to obtain the i-th day soil water absorption prediction data;
the water content calibration module is used for calibrating the water content of the soil water content calibration result on the i-1 th day according to the i-th day soil water absorption prediction data to generate an i-th day soil water content calibration result;
and the soil water content prediction data confirmation module is used for setting the i-th day soil water content calibration result as the soil water content prediction data when i belongs to a preset time sequence node of the first future time zone.
Further, the system includes:
the water absorption deviation threshold setting module is used for setting a soil water absorption deviation threshold;
the hierarchical clustering analysis module is used for performing hierarchical clustering analysis on the soil water absorption record results according to the soil water absorption deviation threshold value to obtain soil water absorption record result clustering results, wherein the soil water absorption record result clustering results comprise characteristic values in the soil water absorption record result class;
the classifying and adding module is used for classifying and adding the plurality of recording frequency parameters according to the clustering result of the soil water absorption recording result to generate the intra-class frequency of the soil water absorption recording result;
And the average analysis module is used for carrying out average analysis on the characteristic values in the soil water absorption recording result class, wherein the frequency in the soil water absorption recording result class meets the intra-class frequency threshold value, and generating the i-th day soil water absorption prediction data.
Further, the system includes:
the ground surface temperature prediction sequence acquisition module is used for acquiring a ground surface temperature prediction sequence according to the weather prediction information, wherein the ground surface temperature prediction information belongs to the first future time zone;
the sequence grouping module is used for grouping the surface temperature prediction sequences by taking the temperature of 0 ℃ as a critical value to obtain a positive value temperature prediction sequence and a negative value temperature prediction sequence;
the first freezing index calculation module is used for carrying out freezing index calculation according to the positive temperature prediction sequence to obtain the freezing index;
and the second freezing index calculation module is used for carrying out freezing index calculation according to the negative temperature prediction sequence to obtain the melting index.
Further, the system includes:
the frozen soil data acquisition module is used for acquiring frozen soil data based on the frozen soil lithology information and the frozen soil structure information to acquire active layer freezing record data and active layer melting record data;
The first recorded data acquisition module is used for the active layer freezing recorded data including freezing index recorded data, freezing period heat conductivity coefficient recorded data and first active layer depth variation recorded data;
the second recorded data acquisition module is used for enabling the active layer melting recorded data to comprise melting index recorded data, melting period heat conductivity coefficient recorded data and second active layer depth variation recorded data;
the first training module is used for training a first evaluation unit of the depth variation of the active layer based on random forests according to the freezing index record data, the freezing period heat conductivity coefficient record data and the first active layer depth variation record data;
the second training module is used for training a second evaluation unit of the depth variation of the active layer based on random forests according to the melting index recorded data, the melting period heat conductivity coefficient recorded data and the second active layer depth variation recorded data;
and the unit merging module is used for merging the first evaluation unit of the active layer depth variation and the second evaluation unit of the active layer depth variation to generate the active layer depth variation evaluation model.
Further, the system includes:
the decision module acquisition module is used for acquiring a plurality of expert decision modules, wherein information between any two expert decision modules is not communicated, and the expert decision modules have a plurality of decision credibility;
the prediction data input module is used for inputting the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data into the expert decision modules to obtain a plurality of heat conductivity coefficient decision results;
and the heat conductivity coefficient adding module is used for carrying out weighted average analysis according to the decision credibility and the decision results of the plurality of heat conductivity coefficients, obtaining the heat conductivity coefficient in the melting period or the heat conductivity coefficient in the freezing period, and adding the heat conductivity coefficient into the heat conductivity coefficient.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any of the methods to implement embodiments of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. An intelligent evaluation method for the thickness of a frozen soil active layer is characterized by comprising the following steps:
acquiring basic information of frozen soil in a preset area, wherein the basic information of frozen soil in the preset area comprises the lithology information of the frozen soil and the structural information of the frozen soil;
acquiring basic information of a frozen soil area environment, wherein the basic information of the frozen soil area environment comprises snow cover information and weather forecast information;
carrying out snow melting prediction according to the weather prediction information and the snow cover information to obtain soil water content prediction data;
matching a heat conduction coefficient according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, wherein the heat conduction coefficient comprises a melting period heat conduction coefficient or a freezing period heat conduction coefficient;
carrying out parameter statistics according to the weather forecast information to obtain a freezing index or a thawing index;
inputting the freezing index and the freezing period heat conductivity coefficient or the melting index and the melting period heat conductivity coefficient into an active layer depth change amount evaluation model to obtain an active layer depth change amount evaluation result;
and determining the frozen soil active layer thickness prediction data according to the active layer depth variation evaluation result, wherein the frozen soil active layer thickness prediction data belongs to a first future time zone.
2. The method of claim 1, wherein said performing a snow melt prediction based on said weather prediction information and said snow cover information to obtain soil moisture content prediction data comprises:
acquiring an air temperature prediction sequence according to the weather prediction information, wherein the air temperature prediction sequence belongs to the first future time zone, and the time unit is a day;
inputting the air temperature prediction sequence into a snow melting rate matching table to obtain a snow melting rate sequence;
carrying out snow melting prediction based on the snow cover information according to the snow melting rate sequence to obtain a snow melting amount prediction sequence and a snow melting thickness prediction sequence;
and carrying out frequent item analysis according to the snow melting quantity prediction sequence to obtain the soil water content prediction data.
3. The method of claim 2, wherein said obtaining said predicted soil moisture content data from frequent analysis of said predicted sequence of snow melt quantities comprises:
the basic frozen soil information of the preset area further comprises frozen soil vegetation coverage information, wherein the frozen soil vegetation coverage information comprises vegetation type information and vegetation quantity information;
acquiring the predicted data of the snow melting amount on the ith day according to the predicted sequence of the snow melting amount;
Searching by taking the frozen soil lithology information, the frozen soil structure information, the vegetation type information, the vegetation quantity information and the ith snow melting amount prediction data as constraint conditions and taking the soil water absorption as target data to obtain a plurality of soil water absorption recording results, wherein the plurality of soil water absorption recording results comprise a plurality of recording frequency parameters;
screening the soil water absorption recording results according to the plurality of recording frequency parameters to obtain the forecast data of the soil water absorption on the ith day;
carrying out water content calibration on the i-1 th day soil water content calibration result according to the i-th day soil water absorption prediction data to generate an i-th day soil water content calibration result;
and when i belongs to a preset time sequence node of the first future time zone, setting the i-th day soil water content calibration result as the soil water content prediction data.
4. The method of claim 3, wherein said screening said soil water absorption recording according to said plurality of recording frequency parameters to obtain i day soil water absorption prediction data comprises:
setting a soil water absorption deviation threshold;
performing hierarchical clustering analysis on the soil water absorption recording results according to the soil water absorption deviation threshold value to obtain soil water absorption recording result clustering results, wherein the soil water absorption recording result clustering results comprise characteristic values in the soil water absorption recording result class;
Classifying and adding the plurality of recording frequency parameters according to the clustering result of the soil water absorption recording result to generate the intra-class frequency of the soil water absorption recording result;
and carrying out average analysis on the characteristic values in the soil water absorption recording result class, wherein the frequency in the soil water absorption recording result class meets the intra-class frequency threshold value, and generating the i-th day soil water absorption prediction data.
5. The method of claim 1, wherein said performing parameter statistics based on said weather forecast information to obtain a freeze index or a melt index comprises:
acquiring a ground surface temperature prediction sequence according to the weather prediction information, wherein the ground surface temperature prediction information belongs to the first future time zone;
grouping the surface temperature prediction sequences by taking 0 ℃ as a critical value to obtain a positive value temperature prediction sequence and a negative value temperature prediction sequence;
according to the positive temperature prediction sequence, freezing index calculation is carried out, and the freezing index is obtained;
and carrying out freezing index calculation according to the negative temperature prediction sequence to obtain the melting index.
6. The method of claim 1, wherein the inputting the freezing index and the freezing period thermal conductivity, or the thawing index and the thawing period thermal conductivity, into an active layer depth variation assessment model, obtaining an active layer depth variation assessment result, comprises:
Acquiring frozen soil data based on the frozen soil lithology information and the frozen soil structure information, and acquiring active layer freezing record data and active layer melting record data;
the active layer freezing record data comprises freezing index record data, freezing period heat conductivity coefficient record data and first active layer depth change volume record data;
the active layer melting record data comprises melting index record data, melting period heat conductivity coefficient record data and second active layer depth change volume record data;
training a first evaluation unit of the depth variation of the active layer based on a random forest according to the freezing index record data, the freezing period heat conductivity coefficient record data and the first active layer depth variation record data;
training a second evaluation unit of the depth variation of the active layer based on a random forest according to the melting index record data, the melting period heat conductivity coefficient record data and the second active layer depth variation record data;
and merging the first evaluation unit of the depth change of the active layer and the second evaluation unit of the depth change of the active layer to generate the evaluation model of the depth change of the active layer.
7. The method of claim 1, wherein the matching thermal conductivity based on the frozen soil lithology information, the frozen soil structure information, and the soil moisture content prediction data, wherein the thermal conductivity comprises a melting phase thermal conductivity or a freezing phase thermal conductivity, comprises:
Acquiring a plurality of expert decision modules, wherein information between any two expert decision modules is not communicated, and the expert decision modules have a plurality of decision credibility;
inputting the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data into the expert decision modules to obtain a plurality of heat conductivity coefficient decision results;
and carrying out weighted average analysis according to the decision credibility and the decision results of the heat conduction coefficients to obtain the heat conduction coefficient in the melting period or the heat conduction coefficient in the freezing period, and adding the heat conduction coefficient.
8. An intelligent assessment system for the thickness of a frozen earth active layer, which is used for implementing the intelligent assessment method for the thickness of the frozen earth active layer according to any one of claims 1 to 7, and comprises the following steps:
the frozen soil information acquisition module is used for acquiring frozen soil basic information of a preset area, wherein the frozen soil basic information of the preset area comprises frozen soil lithology information and frozen soil structure information;
the environment information acquisition module is used for acquiring the basic information of the environment of the frozen soil area, wherein the basic information of the environment of the frozen soil area comprises snow cover information and weather forecast information;
The snow melting prediction module is used for performing snow melting prediction according to the weather prediction information and the snow cover information to obtain soil water content prediction data;
the thermal conductivity coefficient matching module is used for matching thermal conductivity coefficients according to the frozen soil lithology information, the frozen soil structure information and the soil water content prediction data, wherein the thermal conductivity coefficients comprise melting phase thermal conductivity coefficients or freezing phase thermal conductivity coefficients;
the parameter statistics module is used for carrying out parameter statistics according to the weather forecast information to obtain a freezing index or a thawing index;
the depth change amount evaluation module is used for inputting the freezing index and the freezing period heat conduction coefficient or the melting index and the melting period heat conduction coefficient into an active layer depth change amount evaluation model to obtain an active layer depth change amount evaluation result;
and the prediction data determining module is used for determining the frozen soil active layer thickness prediction data according to the active layer depth variation evaluation result, wherein the frozen soil active layer thickness prediction data belongs to a first future time zone.
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| CN117540132B (en) * | 2024-01-09 | 2024-04-02 | 中国科学院精密测量科学与技术创新研究院 | Permafrost active layer thickness estimation method based on star-earth observation |
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