Prediction method for accumulated ground temperature at different depths and related device

Abstract

The invention discloses a method for predicting accumulated ground temperature at different depths and a related device, and the method comprises the steps: calculating a dry-wet index sequence through employing the historical air temperature and precipitation of a target region, obtaining ground temperature sequences at different depths in a dry-wet period according to the dry-wet index sequence, and further constructing a simulation model of the accumulated ground temperature at different depths, thereby achieving the prediction of the accumulated ground temperature at different depths. The constructed model is adopted to predict the accumulated ground temperature at different depths, effective parameters can be provided for soil moisture management and ecological environment regulation and control, and the method has important significance on agricultural and ecological protection, disaster prevention and reduction and the like in a target area.

Description

Technical Field

[0001] The invention relates to a prediction method for cumulative ground temperatures at different depths and a related device, belonging to the field of ground temperature prediction. Background Art

[0002] Cumulative ground temperature, as one of the physical properties of soil, affects the soil's water retention capacity and plant growth and development. With climate change and the intensification of human activities, the prediction of cumulative ground temperature at different depths in the future is particularly important. At that time, there was no corresponding prediction method, resulting in a lack of accurate and effective parameter support when conducting soil moisture management and ecological environment regulation. Summary of the invention

[0003] The present invention provides a method for predicting cumulative ground temperatures at different depths and related devices, which solve the problems disclosed in the background technology.

[0004] According to one aspect of the present disclosure, a method for predicting cumulative ground temperatures at different depths is provided, comprising: According to the simulation model of cumulative ground temperature at different depths in the target area, the cumulative ground temperature at different depths in the target area is predicted; Among them, the cumulative geothermal simulation model at different depths in the target area is pre-built, and the pre-building process is: According to the historical temperature and historical precipitation of the target area at different time scales, the dryness and wetness index series of the target area at different time scales are calculated; According to the dry-wet index sequence, determine the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period; According to the dry and wet periods, determine the geothermal sequence at different depths during the dry and wet periods; Based on the dry-wet index sequence, dry-wet characteristics and ground temperature sequence at different depths, determine the response sensitivity of the ground temperature sequence to the dry-wet index sequence, the transmission relationship between different levels of dry-wet periods and ground temperatures at different depths, and the response relationship between the cumulative ground temperature during dry-wet periods and dry-wet characteristics; Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of geothermal in dry and wet periods and different depths, and the response relationship between the cumulative geothermal in dry and wet periods and the dry-wet characteristics, a cumulative geothermal simulation model at different depths in the target area is constructed.

[0005] In some embodiments of the present disclosure, the dryness and wetness index sequence is a monthly dryness and wetness index sequence; the process of calculating the dryness and wetness index sequence is: According to the monthly temperature of the target area at different time scales, the monthly potential evapotranspiration of the target area at different time scales is calculated; Calculate the difference between the monthly precipitation and the corresponding monthly potential evapotranspiration in the target area at different time scales; The difference is normalized and processed to obtain the monthly dryness and wetness index series of the target area at different time scales.

[0006] In some embodiments of the present disclosure, the dry-wet period includes a dry period and a wet period, and the dry-wet characteristics include the duration and severity of the dry period, and the duration and severity of the wet period; According to the dry-wet index sequence, the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period are determined, including: The first wet-dry index greater than or equal to the wet threshold and the second wet-dry index less than or equal to the dry threshold are selected from the wet-dry index sequence; the period corresponding to the continuous first wet-dry index is regarded as the wet period, and the cumulative value of the first wet-dry index during the wet period is regarded as the severity of the wet period; the period corresponding to the continuous second wet-dry index is regarded as the dry period, and the cumulative value of the second wet-dry index during the dry period is regarded as the severity of the dry period.

[0007] In some embodiments of the present disclosure, determining the response sensitivity of a ground temperature sequence to a dryness and wetness index sequence includes: The correlation between the ground temperature series and the dryness and wetness index series is calculated, and the response sensitivity is determined based on the correlation. The closer the correlation is to -B, the more sensitive the negative response is, the closer the correlation value is to B, the more sensitive the positive response is, and the closer the correlation value is to 0, the worse the response sensitivity is. B is the upper limit of the correlation, which is a positive number, and -B is the lower limit of the correlation.

[0008] In some embodiments of the present disclosure, determining the transmission relationship between different levels and different depths of ground temperature during dry and wet periods includes: According to the dry-wet index sequence and the ground temperature sequence at different depths, the time series of different levels of dry-wet periods and the time series of ground temperature at different depths are determined; Based on the time series, the Sankey diagram is used to determine the transmission relationship between different levels and different depths of ground temperature during dry and wet periods.

[0009] In some embodiments of the present disclosure, determining the response relationship between the cumulative ground temperature during dry and wet periods and the dry and wet characteristics includes: Taking the cumulative ground temperature as the dependent variable and the dry-wet characteristics as the independent variables, a linear regression relationship was used to determine the response relationship between the cumulative ground temperature and the dry-wet characteristics during the dry and wet periods.

[0010] In some embodiments of the present disclosure, based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of dry-wet periods and geothermal at different depths, and the response relationship between the cumulative geothermal in the dry-wet period and the dry-wet characteristics, a simulation model of the cumulative geothermal at different depths in the target area is constructed, including: According to the sensitivity of the ground temperature series to the dryness and wetness index series, the optimal time scale is determined; According to the transmission relationship between different levels and different depths of ground temperature during dry and wet periods, the depth at which the model needs to be constructed is determined; According to the response relationship between the cumulative ground temperature in dry and wet periods and the dry and wet characteristics, the positive and negative parameters for constructing the cumulative ground temperature simulation model at different depths are determined; According to the dry-wet characteristics corresponding to the optimal time scale, the depth at which the model needs to be constructed, and the positive and negative parameters for constructing the cumulative geothermal simulation model at different depths, the cumulative geothermal simulation model at different depths in the target area is constructed.

[0011] According to another aspect of the present disclosure, a device for predicting cumulative ground temperatures at different depths is provided, comprising: A prediction module predicts the cumulative ground temperatures at different depths in the target area based on a simulation model of the cumulative ground temperatures at different depths in the target area; Pre-build modules to pre-build the cumulative geothermal simulation models at different depths in the target area; Among them, the pre-built processes are: According to the historical temperature and historical precipitation of the target area at different time scales, the dryness and wetness index series of the target area at different time scales are calculated; According to the dry-wet index sequence, determine the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period; According to the dry and wet periods, determine the geothermal sequence at different depths during the dry and wet periods; Based on the dry-wet index sequence, dry-wet characteristics and ground temperature sequence at different depths, determine the response sensitivity of the ground temperature sequence to the dry-wet index sequence, the transmission relationship between different levels of dry-wet periods and ground temperatures at different depths, and the response relationship between the cumulative ground temperature during dry-wet periods and dry-wet characteristics; Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of geothermal in dry and wet periods and different depths, and the response relationship between the cumulative geothermal in dry and wet periods and the dry-wet characteristics, a cumulative geothermal simulation model at different depths in the target area is constructed.

[0012] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to perform a method for predicting cumulative ground temperatures at different depths.

[0013] According to another aspect of the present disclosure, a computer device is provided, comprising one or more processors and one or more memories, wherein one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a method for predicting cumulative geothermal temperatures at different depths.

[0014] The beneficial effects achieved by the present invention are as follows: the present invention adopts the historical air temperature and precipitation of the target area to calculate the dry-wet index sequence, and obtains the geothermal sequence at different depths in the dry and wet periods according to the dry-wet index sequence, so as to further construct a cumulative geothermal simulation model at different depths. The constructed model is used to realize the cumulative geothermal prediction at different depths, which can provide effective parameters for soil moisture management and ecological environment regulation, and is of great significance to agriculture and ecological protection as well as disaster prevention and mitigation in the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flow chart of the prediction method for cumulative ground temperature at different depths; Figure 2 This is a schematic diagram of the dryness and wetness index sequence; Figure 3 This is a schematic diagram of the sensitivity of the ground temperature series to the dryness and wetness index series; Figure 4 It is a schematic diagram of the transmission relationship between ground temperatures at different levels and depths during dry and wet periods; Figure 5 This is a schematic diagram of the response relationship between the cumulative ground temperature and dry-wet characteristics during dry and wet periods; Figure 6 Flowchart for constructing the simulation model of cumulative geothermal temperature at different depths; Figure 7 This is the evaluation result diagram of the cumulative geothermal simulation model at different depths; Figure 8 Block diagram of the prediction device for the accumulated ground temperature at different depths. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. It is obvious that the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0017] Unless otherwise specified, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0018] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0019] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0020] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0021] It should be noted that similar symbols and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it does not require further discussion in subsequent figures.

[0022] In order to solve the problem of lack of cumulative geothermal prediction methods at different depths, the present disclosure proposes a prediction method and related devices for cumulative geothermal at different depths, specifically, cumulative geothermal prediction is performed by constructing a cumulative geothermal simulation model at different depths.

[0023] Figure 1 This is a schematic diagram of an embodiment of a method for predicting cumulative ground temperatures at different depths disclosed in the present invention. Figure 1 The embodiments may be executed by a terminal (such as a computer).

[0024] like Figure 1 As shown, the steps of the embodiment include predicting the cumulative geothermal at different depths in the target area according to the cumulative geothermal at different depths simulation model of the target area.

[0025] return Figure 1 ,In the embodiment, the cumulative geothermal simulation models at different depths of the target area need to be constructed in advance, and generally all ,models under the required time scale will be constructed.

[0026] The process of building the model can be as follows: 1) Based on the historical temperature and historical precipitation of the target area at different time scales, calculate the dryness and wetness index series of the target area at different time scales.

[0027] It should be noted that the preset time scale can be set according to needs, such as 1 month scale, 2 months scale, 3 months scale, ..., 12 months scale, etc.

[0028] It should be noted that, in order to facilitate statistics and calculations, the temperature (including the maximum temperature, the minimum temperature and the average temperature) and precipitation are all based on monthly data, so the calculated dryness and wetness index sequence is also a monthly dryness and wetness index sequence.

[0029] The monthly temperature and precipitation are both measured data from historical records of the target area. In order to further ensure the accuracy and rationality of the calculation results, long-term historical record data can be used, such as monthly historical data of the target area for more than 30 years.

[0030] In some embodiments, the process of calculating the wetness and dryness index sequence may be as follows: 11) Based on the monthly temperature of the target area at different time scales, calculate the monthly potential evapotranspiration of the target area at different time scales.

[0031] It should be noted that the Standardized Precipitation Evapotranspiration Index (SPEI), which is commonly used at home and abroad to characterize the regional dry and wet characteristics, can be used to calculate the potential evapotranspiration using the Thornthwaite method, which can be expressed as follows: PET i =16.0×(10T i / H) A ; In the formula, PET i is the potential evapotranspiration in the ith month, T i is the average temperature of the ith month (unit: °C); H is the annual heat index, which is the sum of the heat indexes of 12 months, and can be expressed as: ; A is a constant, which can be expressed as: A=6.75×10 -7 ×H 3 -7.71×10 -5 ×H 2 +1.792×10 -2 ×H+0.49.

[0032] When the average temperature of a month is less than or equal to 0℃, the monthly heat index is 0 and the potential evapotranspiration PET=0.

[0033] 12) Calculate the difference between the monthly precipitation and the corresponding monthly potential evapotranspiration in the target area at different time scales, which can be expressed as: D i =P i -PET i ; Where P i is the precipitation in the ith month, D i is the difference.

[0034] 13) The difference is normalized to obtain the monthly dryness and wetness index series of the target area at different time scales.

[0035] Log-logistic analysis can be used to calculate D iThe sequence is fitted and transformed into a standard normal distribution, and D is calculated. i Corresponding SPEI i sequence: ; ; In the formula, p is the cumulative probability, w is the probability weighting matrix, c 0. c 1. c 2. d 1. d 2. d 3 is the constant term, c0=2.515517, c 1=0.802853, c 2=0.010328, d 1=1.432788, d 2=0.189269, d 3=0.00130.

[0036] 2) Based on the wet-dry index sequence, determine the dry and wet periods and the corresponding dry and wet characteristics of the dry and wet periods.

[0037] It should be noted that the wet-dry period includes the dry period and the wet period, and the wet-dry characteristics include the duration and severity of the dry period, and the duration and severity of the wet period. The first wet-dry index greater than or equal to the wet threshold and the second wet-dry index less than or equal to the dry threshold are selected from the wet-dry index sequence; the period corresponding to the continuous first wet-dry index is regarded as the wet period, and the cumulative value of the first wet-dry index in the wet period is regarded as the severity of the wet period; the period corresponding to the continuous second wet-dry index is regarded as the dry period, and the cumulative value of the second wet-dry index in the dry period is regarded as the severity of the dry period. See Figure 2 , the wet threshold is set to 0.5, and the dry threshold is set to -0.5; the difference between each value in the dryness and wetness index sequence and the threshold value -0.5 is calculated. If the difference continues to be negative, the duration of the dry period is from the moment when the negative value appears continuously to the moment when the negative value ends, and the cumulative value of all dryness and wetness indices during the dry period is the severity of the dry period; the difference between each value in the dryness and wetness index sequence and the threshold value 0.5 is calculated. If the difference continues to be positive, the duration of the wet period is from the moment when the positive value appears to the moment when the positive value ends, and the cumulative value of all dryness and wetness indices during the wet period is the severity of the wet period.

[0038] 3) Based on the dry and wet periods, determine the geothermal sequence at different depths during the dry and wet periods.

[0039] It should be noted that the depth depends on the actual situation, for example, it may include 0 cm, 10 cm, 20 cm, 40 cm, 80 cm, 160 cm and 320 cm.

[0040] 4) Based on the wet-dry index sequence, wet-dry characteristics and geothermal sequences at different depths, determine the sensitivity of the geothermal sequence to the wet-dry index sequence, the transmission relationship between geothermal levels at different depths during the wet-dry period, and the response relationship between the cumulative geothermal and wet-dry characteristics during the wet-dry period.

[0041] It should be noted that, in some embodiments, the correlation between the ground temperature sequence and the dryness and wetness index sequence can be calculated, and the response sensitivity can be determined based on the correlation; wherein, the closer the correlation is to -B, the more sensitive the negative response is, the closer the correlation value is to B, the more sensitive the positive response is, and the closer the correlation value is to 0, the worse the response sensitivity is; B is the upper limit of the correlation, which is a positive number, and -B is the lower limit of the correlation.

[0042] Specifically, the Pearson correlation can be calculated, which can be expressed as: ; In the formula, X and Y represent the ground temperature series and the dryness and wetness index series respectively, α and β represent the average values ​​of X and Y respectively, and r represents the correlation coefficient, which is a dimensionless statistical indicator with a value range of -1~1. r<0 means negative correlation, r>0 means positive correlation, and 0 means no correlation. The larger the absolute value of r, the closer the correlation between the two variables and the more sensitive the response.

[0043] The sensitivity of the ground temperature at a certain depth to the SPEI at different time scales can be determined based on the value of r. Figure 3 , a schematic diagram of the correlation results between the ground temperature at 0 cm depth and the wetness and dryness index sequences at different time scales (from SPEI-1 to SPEI-12, which are 1-month scale wetness and dryness index sequences to 12-month scale wetness and dryness index sequences, respectively) was given. Figure 3 The schematic diagram shows that the response of 0 cm to SPEI at different time scales is more sensitive in late spring, summer and early autumn (darker color, larger r value), and is insensitive in late autumn, winter and early spring (lighter color, smaller r value).

[0044] When the correlation value between 0 cm ground temperature and SPEI (SPEI-4) at a 4-month time scale is greater than the correlation with SPEI at other time scales, it indicates that 0 cm ground temperature is most sensitive to SPEI-4, and its response time to dryness and wetness is 4 months.

[0045] It should be noted that the levels of dry and wet periods can be divided according to the dryness and wetness index, as shown in Table 1.

[0046] Table 1 Dry and wet period rating table

[0047] In some embodiments, the time series of different levels in the dry and wet periods and the time series of ground temperatures at different depths can be determined based on the dryness and wetness index sequence and the ground temperature sequence at different depths. Based on the time series, a Sankey diagram can be used to determine the transfer relationship between different levels in the dry and wet periods and ground temperatures at different depths.

[0048] The Sankey diagram is also called the energy flow diagram. It focuses on the energy flow and transfer within the system. Different lines in the Sankey diagram represent different flow flow diversion situations. The width of the line proportionally displays the flow contained in this branch, and the width of the branch node represents the flow size under a specific state. In this embodiment, the Sankey diagram is used to analyze the energy (flow) transfer relationship between different levels and different depths of ground temperature during dry and wet periods. Figure 4 .

[0049] It should be noted that, in some embodiments, the cumulative ground temperature can be used as the dependent variable, and the dry-wet characteristics can be used as the independent variable. A linear regression relationship can be used to determine the response relationship between the cumulative ground temperature and the dry-wet characteristics during the dry and wet periods.

[0050] The response relationship between the cumulative ground temperature and the dry-wet characteristics during the dry and wet periods is fitted through a linear relationship. Through this response relationship, the change pattern of the cumulative ground temperature with the change of the dry-wet characteristics can be determined. When the slope of the linear fitting equation is positive, it means that the two show a positive response, that is, as the dry-wet characteristics increase, the cumulative ground temperature also increases; when the slope of the linear fitting equation is negative, it means that the two show a negative response, that is, as the dry-wet characteristics increase, the cumulative ground temperature decreases. This implementation case gives the response relationship between the cumulative ground temperature and the dry-wet characteristics, see Figure 5 .

[0051] 5) Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of geothermal in dry and wet periods and different depths, and the response relationship between the cumulative geothermal in dry and wet periods and the dry-wet characteristics, a simulation model of cumulative geothermal at different depths in the target area is constructed.

[0052] In some embodiments, the process of constructing the simulation model of cumulative geothermal temperature at different depths in the target area may be: 51) Determine the optimal time scale based on the sensitivity of the geothermal series to the dryness and wetness index series.

[0053] It should be noted that according to the response sensitivity of the ground temperature sequence to the dryness and wetness index sequence, the time for the ground temperature to respond to the dryness and wetness index is determined, and the optimal time scale for identifying the dryness and wetness characteristics is determined according to the response time, so that the corresponding dryness and wetness characteristics can be further obtained, that is, used as samples for building the model.

[0054] 52) Determine the depth at which the model needs to be constructed based on the transmission relationship between different levels and different depths of ground temperature during dry and wet periods.

[0055] It should be noted that the transfer relationship is the correlation between the dry and wet period level and the longitudinal transfer of ground temperature at different depths in the soil. According to the size of the correlation, it can be determined whether to construct a model for the ground temperature at a certain depth.

[0056] 53) According to the response relationship between the cumulative ground temperature in dry and wet periods and the dry and wet characteristics, the positive and negative parameters for constructing the cumulative ground temperature simulation model at different depths are determined.

[0057] A univariate linear response relationship with a response relationship of is adopted, which is a positive relationship or a negative relationship, to provide positive and negative parameter selection for constructing cumulative geothermal simulation models at different depths.

[0058] 54) According to the dry-wet characteristics corresponding to the optimal time scale, the depth at which the model needs to be constructed, and the positive and negative parameters for constructing the cumulative geothermal simulation model at different depths, the cumulative geothermal simulation model at different depths in the target area is constructed.

[0059] Taking the random forest method as an example, the dry-wet characteristics were taken as independent variables, and the cumulative ground temperatures at different depths during the dry and wet periods were taken as dependent variables, and the random forest method was used to construct a simulation model.

[0060] When calling the memory sample data (including dry and wet characteristics, cumulative ground temperature during dry and wet periods) for model construction, the 2 / 3 and 1 / 3 principles are adopted, where 2 / 3 of the sample data is used for model construction (independent training), and the remaining 1 / 3 of the sample data is used for model verification; for the sample data of independent training, a decision tree is constructed according to the characteristics of the sample data; each decision tree is a binary tree, and its generation process adopts a top-down recursive splitting method. Specifically, starting from the root node, the training samples are divided according to the principle of minimizing the impurity of the node to form left child nodes and right child nodes; then, the same splitting rule is continued for each node until the preset stop condition is met; different training sets will generate different decision trees; when n decision trees are completed, the prediction results of the single tree can be integrated.

[0061] Continuously implement the sampling set steps and call different sample data to build and verify the model. Figure 6 A schematic diagram of building a model based on the random forest method is given. The schematic diagram shows the whole process of data sample storage, data call rules, adoption set, decision tree, training integration, verification and satisfactory result evaluation. This is a conventional process and will not be described in detail here.

[0062] In order to ensure the accuracy of the model, its reliability will be verified. That is, according to the constructed model, all the independent variables of the sample data are input into the model, the dependent variables are simulated, and the simulated dependent variables are compared with the actual diagnosed cumulative ground temperature to evaluate the reliability of the constructed simulation model. The evaluation results can be seen in Figure 7 , the figure shows the comparison between the simulated values ​​and the measured values ​​and the evaluation index values ​​of the simulation effect.

[0063] Specifically, the random forest method involves two parameters: the number of trees and nodes. The tree and node parameter values ​​are debugged according to the sample size through trial and error. When the debugged tree and node parameter values ​​reach a satisfactory level in the simulation results of the random forest model, the determination coefficient R is used to verify the satisfaction of the verification effect. 2 , Nash efficiency coefficient Ens and mean deviation percentage PBIAS are used for verification, that is, when the simulation results meet the R 2 When ≥0.60, Ens ≥0.60 and PBIAS ≤20%, the simulation results are satisfactory and the simulation model can be output.

[0064] The above method uses the historical air temperature and precipitation of the target area to calculate the dry-wet index sequence, and obtains the ground temperature sequence at different depths in the dry and wet periods based on the dry-wet index sequence, so as to further construct a cumulative ground temperature simulation model at different depths. The constructed model is used to realize the cumulative ground temperature prediction at different depths, which can provide effective parameters for soil moisture management and ecological environment regulation, and is of great significance to agriculture and ecological protection as well as disaster prevention and mitigation in the target area.

[0065] Figure 8 This is a schematic diagram of an embodiment of a device for predicting cumulative ground temperatures at different depths disclosed in the present invention. Figure 8 The embodiment is a virtual device that can be loaded and executed by a terminal, including a prediction module and a pre-built module.

[0066] The first pose estimation module of an embodiment is configured to.

[0067] The prediction module of the embodiment is configured to predict the cumulative ground temperatures at different depths in the target area according to the cumulative ground temperatures at different depths in the target area simulation model.

[0068] The pre-construction module of the embodiment is configured to pre-construct a cumulative geothermal simulation model at different depths of the target area; Among them, the pre-built processes are: According to the historical temperature and historical precipitation of the target area at different time scales, the dryness and wetness index series of the target area at different time scales are calculated; According to the dry-wet index sequence, determine the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period; According to the dry and wet periods, determine the geothermal sequence at different depths during the dry and wet periods; Based on the dry-wet index sequence, dry-wet characteristics and ground temperature sequence at different depths, determine the response sensitivity of the ground temperature sequence to the dry-wet index sequence, the transmission relationship between different levels of dry-wet periods and ground temperatures at different depths, and the response relationship between the cumulative ground temperature during dry-wet periods and dry-wet characteristics; Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of geothermal in dry and wet periods and different depths, and the response relationship between the cumulative geothermal in dry and wet periods and the dry-wet characteristics, a cumulative geothermal simulation model at different depths in the target area is constructed.

[0069] Similar to the above method, the device uses the historical air temperature and precipitation of the target area to calculate the dry-wet index sequence, and obtains the ground temperature sequence at different depths in the dry and wet periods based on the dry-wet index sequence, thereby further constructing a cumulative ground temperature simulation model at different depths. The constructed model is used to predict the cumulative ground temperature at different depths, which can provide effective parameters for soil moisture management and ecological environment regulation, and is of great significance to agriculture and ecological protection as well as disaster prevention and mitigation in the target area.

[0070] Based on the same technical solution, the present disclosure also relates to a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes a method for predicting cumulative geothermal temperatures at different depths.

[0071] Based on the same technical solution, the present disclosure also relates to a computer device, including one or more processors and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a method for predicting cumulative geothermal temperatures at different depths.

[0072] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for predicting cumulative ground temperature at different depths, characterized in that: include: According to the simulation model of cumulative ground temperature at different depths in the target area, the cumulative ground temperature at different depths in the target area is predicted; Among them, the cumulative geothermal simulation model at different depths in the target area is pre-built, and the pre-building process is: According to the historical temperature and historical precipitation of the target area at different time scales, the dryness and wetness index series of the target area at different time scales are calculated; According to the dry-wet index sequence, determine the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period; According to the dry and wet periods, determine the geothermal sequence at different depths during the dry and wet periods; Based on the dry-wet index sequence, dry-wet characteristics and ground temperature sequence at different depths, determine the response sensitivity of the ground temperature sequence to the dry-wet index sequence, the transmission relationship between different levels of dry-wet periods and ground temperatures at different depths, and the response relationship between the cumulative ground temperature during dry-wet periods and dry-wet characteristics; Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of geothermal in dry and wet periods and different depths, and the response relationship between the cumulative geothermal in dry and wet periods and the dry-wet characteristics, a cumulative geothermal simulation model at different depths in the target area is constructed.

2. The method for predicting cumulative ground temperatures at different depths according to claim 1, characterized in that: The dryness and wetness index series is a monthly dryness and wetness index series; the process of calculating the dryness and wetness index series is: According to the monthly temperature of the target area at different time scales, the monthly potential evapotranspiration of the target area at different time scales is calculated; Calculate the difference between the monthly precipitation and the corresponding monthly potential evapotranspiration in the target area at different time scales; The difference is normalized and processed to obtain the monthly dryness and wetness index series of the target area at different time scales.

3. The method for predicting cumulative ground temperatures at different depths according to claim 1, characterized in that: The dry and wet periods include dry periods and wet periods, and the dry and wet characteristics include the duration and severity of the dry period, and the duration and severity of the wet period; According to the dry-wet index sequence, the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period are determined, including: Filter out a first wetness index greater than or equal to a wetness threshold and a second wetness index less than or equal to a dryness threshold from the wetness index sequence; The period corresponding to the continuous first wet-dry index is regarded as the wet period, and the cumulative value of the first wet-dry index during the wet period is regarded as the severity of the wet period. The period corresponding to the continuous second wet-dry index is regarded as the dry period, and the cumulative value of the second wet-dry index during the dry period is regarded as the severity of the dry period.

4. The method for predicting cumulative ground temperatures at different depths according to claim 1, characterized in that: Determine the sensitivity of the ground temperature series to the dryness and wetness index series, including: The correlation between the ground temperature series and the dryness and wetness index series is calculated, and the response sensitivity is determined based on the correlation. The closer the correlation is to -B, the more sensitive the negative response is, the closer the correlation value is to B, the more sensitive the positive response is, and the closer the correlation value is to 0, the worse the response sensitivity is. B is the upper limit of the correlation, which is a positive number, and -B is the lower limit of the correlation.

5. The method for predicting cumulative ground temperatures at different depths according to claim 1, characterized in that: Determine the transfer relationship between different levels and different depths of ground temperature during dry and wet periods, including: According to the dry-wet index sequence and the ground temperature sequence at different depths, the time series of different levels of dry-wet periods and the time series of ground temperature at different depths are determined; Based on the time series, the Sankey diagram is used to determine the transmission relationship between ground temperatures at different levels and depths during dry and wet periods.

6. The method for predicting cumulative ground temperatures at different depths according to claim 1, characterized in that: Determine the cumulative ground temperature during dry and wet periods and the response relationship between dry and wet characteristics, including: Taking the cumulative ground temperature as the dependent variable and the dry-wet characteristics as the independent variables, a linear regression relationship was used to determine the response relationship between the cumulative ground temperature and the dry-wet characteristics during the dry and wet periods.

7. The method for predicting cumulative ground temperatures at different depths according to claim 1, characterized in that: Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels and different depths of geothermal in the dry-wet period, and the response relationship between the cumulative geothermal in the dry-wet period and the dry-wet characteristics, a simulation model of the cumulative geothermal at different depths in the target area is constructed, including: According to the sensitivity of the ground temperature series to the dryness and wetness index series, the optimal time scale is determined; According to the transmission relationship between different levels and different depths of ground temperature during dry and wet periods, the depth at which the model needs to be constructed is determined; According to the response relationship between the cumulative ground temperature in dry and wet periods and the dry and wet characteristics, the positive and negative parameters for constructing the cumulative ground temperature simulation model at different depths are determined; According to the dry-wet characteristics corresponding to the optimal time scale, the depth at which the model needs to be constructed, and the positive and negative parameters for constructing the cumulative geothermal simulation model at different depths, the cumulative geothermal simulation model at different depths in the target area is constructed.

8. A device for predicting cumulative ground temperatures at different depths, characterized in that: include: A prediction module predicts the cumulative ground temperatures at different depths in the target area based on a simulation model of the cumulative ground temperatures at different depths in the target area; Pre-build modules to pre-build the cumulative geothermal simulation models at different depths in the target area; Among them, the pre-built processes are: According to the historical temperature and historical precipitation of the target area at different time scales, the dryness and wetness index series of the target area at different time scales are calculated; According to the dry-wet index sequence, determine the dry-wet period and the dry-wet characteristics corresponding to the dry-wet period; According to the dry and wet periods, determine the geothermal sequence at different depths during the dry and wet periods; Based on the dry-wet index sequence, dry-wet characteristics and ground temperature sequence at different depths, determine the response sensitivity of the ground temperature sequence to the dry-wet index sequence, the transmission relationship between different levels of dry-wet periods and ground temperatures at different depths, and the response relationship between the cumulative ground temperature during dry-wet periods and dry-wet characteristics; Based on the response sensitivity of the geothermal sequence to the dry-wet index sequence, the transmission relationship between different levels of geothermal in dry and wet periods and different depths, and the response relationship between the cumulative geothermal in dry and wet periods and the dry-wet characteristics, a cumulative geothermal simulation model at different depths in the target area is constructed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes any one of the methods of claims 1 to 7.

10. A computer device, characterized in that: include: One or more processors and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 1 to 7.

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