Self-adaptive feature optimization soil salinity estimation method and device

Abstract

The invention discloses a soil salinity estimation method and device based on adaptive feature optimization. The method comprises the following steps: removing cloud and a water body in Landsat-9 image waveband data obtained in real time by adopting quality evaluation data, so as to obtain preprocessed Landsat-9 image waveband data; extracting a real-time feature element set corresponding to a preset feature element set from the preprocessed Landsat-9 image waveband data; respectively inputting the real-time feature element set into a pre-constructed random forest soil salinity estimation model and a Light GBM soil salinity estimation model, and outputting a first soil salinity estimation result and a second soil salinity estimation result; and inputting the first soil salinity estimation result and the second soil salinity estimation result into a pre-constructed soil salinity calculation formula containing a temperature regulation factor to obtain a final soil salinity estimation result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil salinity estimation, and more particularly, to a self-adaptive feature optimization soil salinity estimation method and device. BACKGROUND

[0002] Traditional soil salinization extraction methods require field investigation and laboratory testing to obtain relevant information on salinization. In contrast, satellite remote sensing observation can monitor soil salinity in a large area at a lower cost. With the development of computer technology, the construction of soil salinization remote sensing estimation models has gradually shifted from traditional statistical methods to machine learning algorithms. Although machine learning algorithms have obvious advantages in non-linear modeling and automatic feature extraction, small sample size machine learning soil salinization remote sensing monitoring often cannot fully represent the complexity of the entire region. The model may only remember the specific noise of limited data during training, resulting in insufficient generalization ability on unknown data and overfitting problems. Small sample data are more difficult to ensure the robustness of the model when facing noise, further reducing the accuracy of the model and the accuracy of soil salinity estimation. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a self-adaptive feature optimization soil salinity estimation method and device.

[0004] According to one aspect of the present application, a self-adaptive feature optimization soil salinity estimation method is provided, comprising:

[0005] The quality evaluation data is used to remove clouds and water bodies in the real-time acquired Landsat-9 image band data to obtain pre-processed Landsat-9 image band data;

[0006] The real-time feature element set corresponding to the pre-set feature element set is extracted from the pre-processed Landsat-9 image band data;

[0007] The real-time feature element set is input into the pre-constructed random forest soil salinity estimation model and the LightGBM soil salinity estimation model, respectively, to output a first soil salinity estimation result and a second soil salinity estimation result;

[0008] According to the first soil salinity estimation result, the second soil salinity estimation result, the site temperature data, and the pre-constructed soil salinity calculation formula containing the temperature adjustment factor, the final soil salinity estimation result is calculated.

[0009] According to another aspect of the present application, a self-adaptive feature optimization soil salinity estimation device is provided, comprising:

[0010] The removing module is used for removing clouds and water bodies in the real-time acquired Landsat-9 image band data by using the quality evaluation data, so as to obtain the preprocessed Landsat-9 image band data.

[0011] The extracting module is used for extracting a real-time feature element set corresponding to a pre-set feature element set from the preprocessed Landsat-9 image band data.

[0012] The estimating module is used for inputting the real-time feature element set into a pre-constructed random forest soil salt estimation model and a LightGBM soil salt estimation model respectively, and outputting a first soil salt estimation result and a second soil salt estimation result.

[0013] The calculating module is used for calculating a final soil salt estimation result according to the first soil salt estimation result, the second soil salt estimation result, site temperature data and a pre-constructed soil salt calculation formula containing a temperature adjustment factor.

[0014] According to another aspect of the present application, a computer readable storage medium is provided, which stores a computer program for executing the method according to any one of the above aspects of the present application.

[0015] According to another aspect of the present application, an electronic device is provided, which comprises a processor, a memory for storing executable instructions of the processor, and the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of the above aspects of the present application.

[0016] Therefore, the present application provides a weather factor adjusted adaptive feature optimized soil salt estimation method: the feature importance sorting of random forest (RF) and LightGBM (LGBM) is alternately used, the double model importance cross-validation and fusion are performed by combining the algorithm advantages of the two, the feature subset is dynamically screened and the model hyperparameters are simultaneously optimized, and the soil salt estimation is performed. The estimation accuracy of the soil salt is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] The exemplary embodiments of the present application can be more completely understood by referring to the following drawings:

[0018] Figure 1 is a flowchart of the adaptive feature optimized soil salt estimation method provided by an exemplary embodiment of the present application;

[0019] Figure 2 is a structural schematic diagram of the adaptive feature optimized soil salt estimation device provided by an exemplary embodiment of the present application;

[0020] Figure 3 is a structure of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0021] Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that these described embodiments are merely exemplary of the present application and should not be considered limiting the scope of the present application. Therefore, the disclosure of these exemplary embodiments is intended to be illustrative, and not to be limiting of the scope of the present application.

[0022] It should be noted that the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments are not limiting of the scope of the application unless otherwise specifically stated.

[0023] Those skilled in the art can understand that the terms "first", "second" and the like in the embodiments of the present application are only used to distinguish different steps, devices or modules and the like, neither represent any specific technical meaning nor indicate their logical order.

[0024] It should also be understood that "a plurality of" in the embodiments of the present application can mean two or more, and "at least one" can mean one, two or more.

[0025] It should also be understood that for any component, data or structure mentioned in the embodiments of the present application, unless specifically limited or unless the context clearly indicates otherwise, it can be understood as one or more.

[0026] In addition, the term "and / or" in the present application is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0027] It should also be understood that the description of the embodiments of the present application focuses on the differences between the embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, will not be repeated.

[0028] At the same time, it should be understood that, for the convenience of description, the size of each part shown in the drawings is not drawn according to the actual proportion relationship.

[0029] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting of the application or its use.

[0030] Techniques, methods, and apparatus known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered as part of the specification, where appropriate.

[0031] It should be noted that like reference numerals and characters refer to like items throughout the attached drawings and alternative embodiments thereof. Note that not all of the depicted components can be required, and that some components can take different physical forms. Some components can be implemented in one or more processing circuits (e.g., an ASIC). Unless otherwise specified, a "first" processing circuit can be the same as or different from a "second" processing circuit.

[0032] Embodiments of the application can be applied to terminal devices, computer systems, servers, and other electronic devices, which can operate with many other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations that can be suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems, and the like.

[0033] Terminal devices, computer systems, servers, and other electronic devices can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like, which perform particular tasks or implement particular abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in local or remote computer system storage media including storage devices.

[0034] Exemplary method

[0035] Figure 1 is a flowchart of an adaptive feature preferred soil salinity estimation method provided by an exemplary embodiment of the application. The embodiment can be applied to electronic devices, such as Figure 1 As shown in FIG. 1, the adaptive feature preferred soil salinity estimation method 100 includes the following steps:

[0036] Step 101, using quality assessment data to remove clouds and water bodies in the real-time acquired Landsat-9 image band data, to obtain pre-processed Landsat-9 image band data;

[0037] Step 102, extracting a real-time feature element set corresponding to a pre-set feature element set from the pre-processed Landsat-9 image band data;

[0038] Step 103, inputting the real-time feature element set into a pre-constructed random forest soil salinity estimation model and a LightGBM soil salinity estimation model, respectively, to output a first soil salinity estimation result and a second soil salinity estimation result;

[0039] Step 104, according to the first soil salt estimation result, the second soil salt estimation result, the site temperature data and the pre-constructed soil salt calculation formula containing the temperature adjustment factor, the final soil salt estimation result is calculated.

[0040] Specifically, in view of the technical problems existing in the background art, the present application proposes a soil salt estimation method based on adaptive optimal feature selection (AOFS): by alternately using the feature importance ranking of random forest (RF) and LightGBM (LGBM), combining the algorithm advantages of the two, cross-validation and fusion of double model importance are carried out, the feature subset is dynamically selected and the model hyperparameters are optimized simultaneously, and the soil salt estimation is carried out. The specific implementation is as follows:

[0041] 1. Landsat-9 image band data and quality assessment data (Quality Assessment, QA), using QA data to remove clouds and water bodies, extracting and calculating 37 feature element sets F={f1, f2…f i …f 36 ,f 37}, combined with the measured data (including latitude and longitude information and conductivity data) of field investigation and laboratory detection, using the measured latitude and longitude and time information to match Landsat-9 data for data labeling, and matching the temperature data of the space-time adjacent weather station (used for calculating the temperature adjustment factor), obtaining the matching sample set, wherein the conductivity data is used as the true value, and the sample set is divided into training set and validation set according to the ratio of 7:3.

[0042] f1=ρ R

[0043] f2=ρ G

[0044] f3=ρ B

[0045] f4=ρ NIR

[0046] f5=ρ SWIR1

[0047] f6=ρ SWIR2

[0048] f7=ρ SWIR1 -ρ SWIR2

[0049]

[0050]

[0051]

[0052] f 25 =ρ R ×ρ NIR ×100

[0053]

[0054] In the formula, ρ R ρ G ρ B ρ NIR ρ SWIR1 ρ SWIR2 The reflectances are red, green, blue, near-infrared, short-wave infrared 1, and short-wave infrared 2, respectively.

[0055] 2. Feature importance ranking is performed using Random Forest (RF) and LightGBM (LGBM). The feature selection strategy is adaptively adjusted based on model performance. The initial feature set is f = F. The RF and LGBM models... and The initial root mean square error of the validation set of the corresponding model

[0056] (1) Using the training set, network search is used to simultaneously optimize the hyperparameters of RF and LGBM to obtain the model RF(θ). RF ) and LGBM(θ LGBM ), and obtain RMSE using the validation set. RF and RMSE LGBM ,like but Jump to step (2); if Model If unchanged, proceed to step (2);

[0057] (2) If the number of feature set f is not greater than 5, jump to (4); if the number of feature set f is greater than 5, jump to (3).

[0058] (3) Obtain the model corresponding to the feature set f. Feature importance ranking, importance of RF and LGBM RF (f i ) and I LGBM (f i Cross-fusion yields the importance of fusion I fused (f i ), sort by fusion importance and remove the last feature, i.e. F', update f = F', and return to (1).

[0059] Dynamic weight fusion of element importance:

[0060] I fused (f i )=γ×I RF (f i )+(1-γ)×I LGBM (f i )

[0061] Where γ is the model weight factor, determined by the performance of RF and LGBM models, the better the model performance, the higher the weight. According to the validation set RMSE, γ is calculated as

[0062]

[0063] (4) Output the optimal model and the corresponding final iteration feature element set f, output the final weight factor γ * :

[0064]

[0065] 3、Feature elements f, Landsat-9 all input data, using model Estimate soil salinity and Combined with γ * , the simulated soil salinity is calculated as

[0066]

[0067] 4、According to the simulated soil salinity SS f , add the temperature adjustment factor δ, and use the least squares method to fit a and b in the linear relationship:

[0068] SS=a×δ×SS f +b

[0069]

[0070] Where δ is the temperature adjustment factor; T is the temperature data of the matching spatiotemporal adjacent weather station, T m is the average temperature of all matching points. SS is the true result. a and b are the fitting parameters in the linear relationship.

[0071] 5、Landsat-9 image band data and quality assessment data (Quality Assessment, QA), using QA data to remove clouds and water bodies, and obtain ρ R , ρ G , ρ B , ρ NIR , ρSWIR1 , p SWIR2 The two-dimensional feature elements in the final obtained feature element set are calculated according to The two-dimensional simulated soil salt content is estimated, and the final two-dimensional soil salt content result is calculated according to the linear relationship in step 4.

[0072] The final calculation formula is:

[0073]

[0074] Therefore, the application provides a soil salt content estimation method with adaptive feature optimization and meteorological factor adjustment: the feature importance ranking of random forest (RF) and LightGBM (LGBM) is alternately used, the algorithm advantages of the two are combined to perform double-model importance cross-validation and fusion, the feature subset is dynamically screened and the model hyperparameters are simultaneously optimized, and the soil salt content is estimated. The estimation accuracy of the soil salt content is greatly improved.

[0075] Exemplary apparatus

[0076] Figure 2 is a structural schematic diagram of an adaptive feature optimization soil salt content estimation device provided by an exemplary embodiment of the application. As Figure 2 shown, the device 200 includes:

[0077] The removal module 210 is configured to remove clouds and water bodies in the real-time acquired Landsat-9 image band data by using the quality evaluation data, to obtain preprocessed Landsat-9 image band data.

[0078] The extraction module 220 is configured to extract a real-time feature element set corresponding to a pre-set feature element set from the preprocessed Landsat-9 image band data.

[0079] The estimation module 230 is configured to input the real-time feature element set into a pre-constructed random forest soil salt content estimation model and a LightGBM soil salt content estimation model respectively, and output a first soil salt content estimation result and a second soil salt content estimation result.

[0080] The calculation module 240 is configured to calculate a final soil salt content estimation result according to the first soil salt content estimation result, the second soil salt content estimation result, site temperature data, and a pre-constructed soil salt content calculation formula containing a temperature adjustment factor.

[0081] Exemplary electronic device

[0082] Figure 3 is a structure of an electronic device provided by an exemplary embodiment of the application. As Figure 3As shown, the electronic device 30 includes one or more processors 31 and a memory 32.

[0083] The processor 31 can be a central processing unit (CPU) or other form of processing unit having data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0084] The memory 32 can include one or more computer program products that can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory, for example, can include random access memory (RAM), cache memory, and / or the like. The non-volatile memory, for example, can include read only memory (ROM), hard disk, flash memory, and / or the like. One or more computer program instructions can be stored on the computer-readable storage media, which the processor 31 can run to implement the methods of the software programs of the various embodiments of the present application described above and / or other desired functions. In one example, the electronic device can further include an input device 33 and an output device 34, which are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0085] In addition, the input device 33 can include, for example, a keyboard, a mouse, and / or the like.

[0086] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and / or the like.

[0087] Of course, in order to simplify, Figure 3 Only some of the components of the electronic device related to the present application are shown in FIG. 1, and components such as buses, input / output interfaces, and the like are omitted. In addition, the electronic device can include any other appropriate components according to the specific application.

[0088] Exemplary computer program product and computer readable storage medium

[0089] In addition to the methods and devices described above, embodiments of the present application can also be a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present application described in the above "Exemplary Methods" section of the specification.

[0090] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0091] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0092] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0093] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0095] The block diagrams of the devices, systems, apparatuses, systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations be as shown in the block diagrams. As will be recognized by one of ordinary skill in the art, the devices, systems, apparatuses, systems can be connected, arranged, configured in any manner. Words such as "including," "containing," "comprising," and the like are to be construed in an inclusive fashion, indicating open-ended duration, and are intended to be equivalent to "including, but not limited to." As used herein, the terms "or" and "and" shall each be construed as the term "and / or" unless expressly indicated otherwise. As used herein, the term "such as" shall be construed as the phrase "such as but not limited to."

[0096] The methods and systems of the present application can be implemented in a number of ways. For example, the methods and systems of the present application can be implemented using software, hardware, firmware, or any combination of software, hardware, and firmware. The above described order of steps for the methods is merely illustrative, and the steps of the methods of the present application are not limited to the order described above unless otherwise specifically stated. Furthermore, in some embodiments, the present application can also be implemented as a program recorded on a recording medium, which includes machine readable instructions for implementing the methods according to the present application. Thus, the present application also covers a recording medium storing a program for executing the methods according to the present application.

[0097] It is also to be noted that in the systems, apparatuses, and methods of the present application, various components or steps can be decomposed and / or recombined. Such decompositions and / or re-combinations are to be considered as equivalent to the present application. The above description of the disclosed aspects is given for illustrative purposes and is not intended to limit the aspects of the application in any way. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0098] The above description has been given for the purpose of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to forms disclosed herein. Although several example aspects and embodiments have been discussed above, those of ordinary skill in the art will appreciate a variety of modifications, alternatives, permutations, additions, and sub-combinations of the aspects and embodiments disclosed herein.

Claims

1. A method of adaptive feature-preferred soil salinity estimation, characterized by, The method comprises the following steps: The cloud and water body in the real-time acquired Landsat-9 image band data are removed by using the quality evaluation data to obtain preprocessed Landsat-9 image band data; An initial feature element set F and a real conductivity as a matching sample set are extracted from the preprocessed historical Landsat-9 image band data; An adaptive feature screening strategy is adopted to sort the feature importance of the random forest model and the LightGBM model based on the matching sample set and iteratively train to obtain the final feature element set, the random forest soil salinity estimation model, the LightGBM soil salinity estimation model and the model weight factor. The initial feature element set comprises:

2. The method of claim 1, wherein, An adaptive feature screening strategy is adopted to sort the feature importance of the random forest model and the LightGBM model based on the matching sample set and iteratively train to obtain the final feature element set, the random forest soil salinity estimation model, the LightGBM soil salinity estimation model and the model weight factor, comprising: Step 1: dividing the matching sample set into a training set and a validation set; Step 4: if the number of feature element sets f is not greater than 5, jump to step 6, if the number of feature element sets f is greater than 5, jump to step 5; The calculation expression of the fusion importance is:

3. The method of claim 2, wherein, Wherein, γ is the model weight factor, and its calculation expression is: f1 = p R f2 = p G f3 = p B f4 = p NIR f5 = p SWIR1 f6 = p SWIR2 f7 = p SWIR1 - p SWIR2 f 25 = p R x p NIR x 100 In the formula, p R , p G , p B , p NIR , p SWIR1 , p SWIR2 are reflectances of red, green, blue, near-infrared, short-wave infrared 1, and short-wave infrared 2, respectively.

4. The method of claim 2, wherein, Further comprising: A temperature adjustment factor δ is added to the simulated soil salinity to perform linear simulation to obtain a soil salinity calculation formula. Step 2: Initialize Random Forest model and LightGBM model and initialize the set of feature elements f = F, the initial root mean square error for the validation set of the model Step 3: Using the training set, search for the optimal hyperparameters of the random forest model RF and the LightGBM model LGBM using the network to obtain the models RF(θ RF ) and LGBM(θ LGBM ), and use the validation set to obtain the root mean square errors RMSE RF and RMSE LGBM of the two models, if , let Jump to step 4; if , the model is unchanged, and jump to step 4; The expression of the temperature adjustment factor δ is: Step 5: Importance I of Random Forest Model RF and LightGBM Model LGBM RF (f i ) and I LGBM (f i ), cross fusion to get fusion importance I fused (f i ), remove the last feature according to the fusion importance, get the updated feature element set F', update f=F', return to step 1; Step 6: Output the optimal random forest soil salinity estimation model LightGBM soil salinity estimation model Final random forest root mean square error Final LightGBM root mean square error Final set of feature elements f and final model weight factor g of iteration * .

5. The method of claim 4, wherein, The soil salinity calculation formula is: I fused (f i )=γ×I RF (f i )+(1-γ)×I LGBM (f i ) In the formula, SS is the final estimated soil salinity, and the real result is used in linear simulation. where f i is the set of feature elements for the i-th iteration.

6. The method of claim 4, wherein, The method comprises the following steps: Input the corresponding important feature elements of the matching sample set into the random forest soil salinity estimation model LightGBM soil salinity estimation model Obtain the random forest soil salinity estimation result of the sample and the LightGBM soil salinity estimation result of the sample According to and Computational simulation of soil salinity SS f ; The cloud and water body in the real-time acquired Landsat-9 image band data are removed by using the quality evaluation data to obtain preprocessed Landsat-9 image band data; 7. The method of claim 6, wherein, The simulated soil salinity SS f The calculation expression is: An initial feature element set F and a real conductivity as a matching sample set are extracted from the preprocessed historical Landsat-9 image band data; An adaptive feature screening strategy is adopted to sort the feature importance of the random forest model and the LightGBM model based on the matching sample set and iteratively train to obtain the final feature element set, the random forest soil salinity estimation model, the LightGBM soil salinity estimation model and the model weight factor. SS = a x δ x SS f + b ​ T is the temperature data of the matched spatio-temporally proximate weather stations, T m is the mean of all matched point temperatures, and a and b are the fitting parameters in the linear relationship.

8. An apparatus for adaptive feature-preferred soil salinity estimation, comprising: ​ ​ ​ An estimation module is configured to input the real-time feature element set into a pre-constructed random forest soil salt estimation model and a LightGBM soil salt estimation model respectively, and output a first soil salt estimation result and a second soil salt estimation result; A calculation module is configured to calculate a final soil salt estimation result according to the first soil salt estimation result, the second soil salt estimation result, site temperature data, and a pre-constructed soil salt calculation formula containing a temperature adjustment factor.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used for executing the method in any one of claims 1-7.

10. An electronic device, comprising: The electronic device comprises: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method in any one of claims 1-7.

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