Multi-source estimation method for water vapor flux stable isotope of terrestrial ecosystem

Abstract

The invention discloses a terrestrial ecosystem water vapor flux stable isotope multi-source estimation method, which comprises the following steps: collecting monitoring data and environmental data of a preset area, and preprocessing the monitoring data and the environmental data; performing water vapor motion characteristic analysis on the monitoring data according to a stable isotope to obtain division data, and inputting the division data into a stable isotope model to obtain water vapor flux; performing time-varying influence analysis on environmental data according to the water vapor flux to obtain an influence factor, and constructing a terrestrial ecosystem water vapor flux multi-source estimation model according to the influence factor and the monitoring data; and optimizing the terrestrial ecosystem water vapor flux multi-source estimation model, inputting to-be-estimated data into the terrestrial ecosystem water vapor flux multi-source estimation model, and outputting an estimation result. The method not only can improve the multi-source estimation precision of the water vapor flux of the terrestrial ecosystem, but also has good interpretability, and can be directly applied to a water vapor flux multi-source estimation system.

Description

Technical Field

[0001] The invention relates to the field of estimation, and in particular to a multi-source estimation method for stable isotopes of water vapor flux in a terrestrial ecosystem. Background Art

[0002] In current terrestrial ecosystem research, accurate estimation of water vapor flux is crucial for understanding the hydrological cycle, assessing the impact of climate change, and formulating water resource management strategies. Currently, there are a variety of technical methods for estimating water vapor flux, among which stable isotope-based technology has attracted much attention due to its unique tracing ability. However, existing technical solutions mostly rely on a single data source or simplified models, and it is difficult to fully consider the impact of multi-source data fusion and the dynamic changes of environmental factors on water vapor flux estimation.

[0003] Specifically, traditional methods usually use data collected from a single monitoring station combined with empirical formulas for estimation. Although this method is simple and easy to implement, it is limited by the lack of spatial representativeness of the data and the limitations of model assumptions, resulting in limited accuracy of the estimation results and difficulty in reflecting the complex water vapor exchange process of terrestrial ecosystems. In addition, existing technologies often ignore the time-varying effects of environmental factors on water vapor flux and fail to build a multi-source estimation model that can dynamically adapt to environmental changes.

[0004] The present invention proposes a multi-source estimation method for stable isotopes of water vapor flux in terrestrial ecosystems, aiming to overcome the above-mentioned defects of the prior art and improve the accuracy and reliability of water vapor flux estimation. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-source estimation method for stable isotopes of water vapor flux in terrestrial ecosystems.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] Collecting monitoring data and environmental data of a preset area, and preprocessing the monitoring data and the environmental data;

[0009] Performing water vapor movement characteristic analysis on the monitoring data according to stable isotopes to obtain partition data, and inputting the partition data into a stable isotope model to obtain water vapor flux;

[0010] Conducting a time-varying impact analysis on environmental data based on the water vapor flux to obtain an impact factor, and constructing a multi-source estimation model for water vapor flux of a terrestrial ecosystem based on the impact factor and the monitoring data;

[0011] The multi-source estimation model of water vapor flux of terrestrial ecosystem is optimized, the data to be estimated is input into the multi-source estimation model of water vapor flux of terrestrial ecosystem, and the estimation result is output.

[0012] Furthermore, the method of analyzing the water vapor movement characteristics of the monitoring data according to stable isotopes to obtain the divided data includes:

[0013] The monitoring data is coordinate-located according to the stable isotope to obtain the position information, and the position information is sorted according to the time sequence to obtain the time position data;

[0014] Establish the starting point of the movement, take the starting point of the movement as the center, and obtain the maximum and minimum distance of the movement according to the movement capacity represented by evapotranspiration. The expression is:

[0015]

[0016] Among them The horizontal coordinate point of the starting point of the movement at the moment is No. The vertical coordinate point of the starting point of the movement at the moment is No. The time position data in the horizontal direction at the moment is The maximum horizontal evapotranspiration rate is No. Time and The time interval is The coordinate position of the time position data is (g, d), and the maximum vertical evapotranspiration rate is No. The horizontal coordinate position of the time position data is No. The vertical coordinate position of the time position data is

[0017] Calculate the distance vector:

[0018]

[0019] The distance vector of the time position data is Compute the squared regularized distance:

[0020]

[0021] The covariance matrix in the horizontal direction is The covariance matrix in the vertical direction is The transpose of the matrix is ​​T, and the squared normalized distance of the time position data is

[0022] Given a distance threshold, the nearest time position data is selected to be associated with the motion trajectory. When the square regularized distance is less than the distance threshold, the associated time position data conforms to the water vapor motion trajectory, otherwise the time position data is replaced.

[0023] Calculate the decision value:

[0024]

[0025] The number of associated positioning points is q, the duration of the water vapor movement trajectory is S, and the decision value is D;

[0026] When the decision value is greater than 0.45, a reasonable water vapor movement trajectory is established. If both the square regularized distance and the decision value are not satisfied, the time window is moved back until a reasonable water vapor movement trajectory is established.

[0027] The monitoring data are classified according to the water vapor movement trajectory using similarity, and the classified monitoring data sources and the corresponding water vapor movement trajectories are output as partitioned data.

[0028] Furthermore, the method of inputting the partitioned data into a stable isotope model to obtain water vapor flux comprises:

[0029] The stable isotope model obtains the change in the stable isotope ratio of the target according to the partitioning data, and calculates the vertical water vapor flux according to the change in ratio:

[0030]

[0031] The vertical water vapor flux is The net radiation of the vegetation surface is H, the soil heat flux is Q, the slope of the vapor pressure curve is γ, the average temperature is M, the wind speed is C, the psychrometer constant is χ, and the saturated water vapor pressure is a o , the actual water vapor pressure is a, and the soil moisture function is The vegetation coefficient is The soil water stress coefficient is The amount of vegetation is Q, the Bowen ratio is υ, and the first improvement coefficient is The second improvement factor is The slope of the saturated water vapor pressure changing with temperature is w, the total net radiation is F, the latent heat of vaporization is μ, and the conversion coefficient is The soil coefficient is

[0032] Calculate the horizontal water vapor flux:

[0033]

[0034] The horizontal water vapor flux is The air density is Specific humidity The mass of water vapor is m, and the condensation rate is The water vapor area is S.

[0035] Furthermore, the method of obtaining the impact factor by performing time-varying impact analysis on environmental data according to the water vapor flux includes:

[0036] Sort the environmental data in chronological order and calculate the cumulative membership at different times:

[0037]

[0038] The cumulative membership of the environmental data at time b and time z is The i-th environmental data at the b-th moment is h i (b), the i-th environmental data at the z-th moment is h i (z), the number of types of environmental data is The water vapor flux at time z is The water vapor flux at time z is

[0039] Calculate importance:

[0040]

[0041] The importance of the cth environment is k c , the number of environmental types is N, and the cumulative membership of the cth environment is The control factor is β, and the water vapor flux of the cth environment at the zth time is The water vapor flux of the cth environment at time b is Environmental data i (b) and environmental data h i The correlation degree of (z) is ψ(h i (b),h i (z));

[0042] The environments with importance greater than 0.732 are taken as cluster centers, and the deviation threshold of the environmental data is obtained through the threshold regression model;

[0043] Calculate the similarity between the environment data and the cluster center:

[0044]

[0045] The control coefficient is λ, the sign function is sgn(·), the fixed deviation constant is ε, and the deviation threshold of the i-th environmental data is ζ i , the similarity between the cth environmental data and the cluster center is k c ;

[0046] The environmental data is assigned to the center of the cluster with the largest similarity to obtain the classified data, and the impact factor is calculated based on the classified data:

[0047]

[0048] The impact factor is The system error is σ and the L2 norm is ‖·‖2.

[0049] Furthermore, a method for constructing a multi-source estimation model of water vapor flux of a terrestrial ecosystem based on the influencing factors and the monitoring data includes:

[0050]

[0051] The objective function is The actual value of the water vapor flux is The predicted value of water vapor flux is Actual value and predicted values The loss function is The impact factor is The horizontal water vapor flux is The vertical water vapor flux is

[0052] The multi-source estimation model of water vapor flux in terrestrial ecosystems includes decision tree clustering algorithm, long short-term memory network algorithm, and neural network algorithm;

[0053] The decision tree clustering algorithm classifies data by building a decision tree, and then divides the data points into different clusters according to the branches of the tree to obtain category data;

[0054] The long short-term memory network algorithm extracts the characteristics of category data according to long-term dependencies through a recursive structure and gating mechanism to obtain water vapor evapotranspiration characteristics;

[0055] The neural network algorithm adjusts the horizontal and vertical water vapor fluxes in the objective function according to the water vapor evaporation characteristics of the historical input data, and estimates the multi-source water vapor flux according to the detection data through the adjusted objective function.

[0056] Furthermore, the method for optimizing the water vapor flux stable isotope multi-source estimation model comprises:

[0057] The updated bias is expressed as:

[0058]

[0059]

[0060] The bias of the tth iteration is The bias for the t-1th iteration is The learning rate is α, and the gradient of the loss function at the t-1th iteration is ρ t-1 , the first-order moment of the gradient at the tth iteration is y t , the second-order moment of the gradient at the tth iteration is s t , the momentum decay hyperparameter is The scaling decay hyperparameter is The smoothing term is σ, and the bias correction of the first-order moment is The bias correction of the second-order moment is The first-order moment of the gradient at the t-1th iteration is y t-1 , the second-order moment of the gradient at the t-1th iteration is s t-1 ;

[0061] Introduce L2 regularization and update the loss function, the expression is:

[0062]

[0063] The loss function is The L2 regularization coefficient is η, the weight vector of the neural network is ω, the output dimension is U, and the number of input data is The predicted value of the xth output dimension of the bth input data is The actual value of the xth output dimension of the bth input data is Z b,x , the mean square error is

[0064] Batch normalization zero-centers the input of the hidden layer of the neural network and introduces scaling parameters and offset parameters to correct data distribution changes. The expression is:

[0065]

[0066] The estimated input standard deviation vector in the mini-batch v is l v , the zero-centered and normalized vector of the x-th input data is f x , the scaling parameter is θ, the offset parameter is ξ, and the Kronecker product is The output of the xth batch normalization is φ x , the number of input data in a mini-batch is

[0067] Adaptive learning is performed based on the error loss, and the expression is:

[0068]

[0069] The error loss is The hyperparameter is δ, and the iteration is continued until the error loss is less than 0.217, otherwise the bias is updated.

[0070] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0071] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0072] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0073] The beneficial effects of the present invention are:

[0074] The present invention is a multi-source estimation method for stable isotopes of water vapor flux in terrestrial ecosystems. Compared with the prior art, the present invention has the following technical effects:

[0075] The present invention can improve the accuracy of multi-source estimation of stable isotopes of water vapor flux in terrestrial ecosystems through preprocessing, water vapor movement characteristic analysis, water vapor flux calculation, time-varying influence analysis, model construction and model optimization steps, thereby improving the precision of multi-source estimation of stable isotopes of water vapor flux in terrestrial ecosystems, optimizing the multi-source estimation of stable isotopes of water vapor flux in terrestrial ecosystems, greatly saving resources, and improving work efficiency, and can realize automatic estimation of multi-source water vapor flux in terrestrial ecosystems, and perform data fusion and estimation optimization on multi-source estimation of stable isotopes of water vapor flux in terrestrial ecosystems in real time, which is of great significance to multi-source estimation of stable isotopes of water vapor flux in terrestrial ecosystems, can adapt to multi-source estimation of stable isotopes of water vapor flux in terrestrial ecosystems of different standards and multi-source estimation of stable isotopes of water vapor flux in different terrestrial ecosystems, and has certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A flowchart of the steps of a multi-source estimation method for stable isotopes of water vapor flux in terrestrial ecosystems according to the present invention;

[0077] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0078] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0079] The present invention provides a multi-source estimation method for stable isotopes of water vapor flux in a terrestrial ecosystem, comprising the following steps:

[0080] like Figure 1 As shown, in this embodiment, the following steps are included:

[0081] Collecting monitoring data and environmental data of a preset area, and preprocessing the monitoring data and the environmental data;

[0082] In the actual assessment, the monitoring data include rainfall, evaporation, air temperature and humidity, vegetation index, soil moisture and temperature, ecosystem type, vegetation transpiration, water vapor pressure, radiation, saturated water vapor pressure; environmental data include temperature, humidity, wind speed, wind direction, vegetation coverage, topography, soil type, precipitation, weather, evaporation, soil temperature and humidity, and atmospheric circulation patterns;

[0083] Taking XXX wetland as the research object, the water vapor flux in the spring of 2020 was estimated. The actual water vapor flux in the spring of 2020 was 14.329 mm / d;

[0084] Performing water vapor movement characteristic analysis on the monitoring data according to stable isotopes to obtain partition data, and inputting the partition data into a stable isotope model to obtain water vapor flux;

[0085] In the actual evaluation, the water vapor flux in the horizontal direction is 9.259 mm / d, and the water vapor flux in the vertical direction is 5.070 mm / d;

[0086] Conducting a time-varying impact analysis on environmental data based on the water vapor flux to obtain an impact factor, and constructing a multi-source estimation model for water vapor flux of a terrestrial ecosystem based on the impact factor and the monitoring data;

[0087] In the actual evaluation, the impact factor is 0.381;

[0088] Optimizing the multi-source estimation model of water vapor flux of terrestrial ecosystems, inputting the data to be estimated into the multi-source estimation model of water vapor flux of terrestrial ecosystems, and outputting the estimation result;

[0089] In the actual evaluation, the predicted water vapor flux is 14.319 mm / d.

[0090] In this embodiment, the method of analyzing the water vapor movement characteristics of the monitoring data according to stable isotopes to obtain the divided data includes:

[0091] The monitoring data is coordinate-located according to the stable isotope to obtain the position information, and the position information is sorted according to the time sequence to obtain the time position data;

[0092] Establish the starting point of the movement, take the starting point of the movement as the center, and obtain the maximum and minimum distance of the movement according to the movement capacity represented by evapotranspiration. The expression is:

[0093]

[0094] Among them The horizontal coordinate point of the starting point of the movement at the moment is No. The vertical coordinate point of the starting point of the movement at the moment is No. The time position data in the horizontal direction at the moment is The maximum horizontal evapotranspiration rate is No. Time and The time interval is The coordinate position of the time position data is (g, d), and the maximum vertical evapotranspiration rate is No. The horizontal coordinate position of the time position data is No. The vertical coordinate position of the time position data is

[0095] Calculate the distance vector:

[0096]

[0097] The distance vector of the time position data is Compute the squared regularized distance:

[0098]

[0099] The covariance matrix in the horizontal direction is The covariance matrix in the vertical direction is The transpose of the matrix is ​​T, and the squared normalized distance of the time position data is

[0100] Given a distance threshold, the nearest time position data is selected to be associated with the motion trajectory. When the square regularized distance is less than the distance threshold, the associated time position data conforms to the water vapor motion trajectory, otherwise the time position data is replaced.

[0101] Calculate the decision value:

[0102]

[0103] The number of associated positioning points is q, the duration of the water vapor movement trajectory is S, and the decision value is D;

[0104] When the decision value is greater than 0.45, a reasonable water vapor movement trajectory is established. If both the square regularized distance and the decision value are not satisfied, the time window is moved back until a reasonable water vapor movement trajectory is established.

[0105] The monitoring data are classified according to the water vapor movement trajectory using similarity, and the classified monitoring data sources and the corresponding water vapor movement trajectories are output as partitioned data;

[0106] In the actual evaluation, the distance threshold is 0.921.

[0107] In this embodiment, the method of inputting the partitioned data into a stable isotope model to obtain water vapor flux includes:

[0108] The stable isotope model obtains the change in the stable isotope ratio of the target according to the partitioning data, and calculates the vertical water vapor flux according to the change in ratio:

[0109]

[0110] The vertical water vapor flux is The net radiation of the vegetation surface is H, the soil heat flux is Q, the slope of the vapor pressure curve is γ, the average temperature is M, the wind speed is C, the psychrometer constant is χ, and the saturated water vapor pressure is a o , the actual water vapor pressure is a, and the soil moisture function is The vegetation coefficient is The soil water stress coefficient is The amount of vegetation is Q, the Bowen ratio is υ, and the first improvement coefficient is The second improvement factor is The slope of the saturated water vapor pressure changing with temperature is w, the total net radiation is F, the latent heat of vaporization is μ, and the conversion coefficient is The soil coefficient is

[0111] Calculate the horizontal water vapor flux:

[0112]

[0113] The horizontal water vapor flux is The air density is Specific humidity The mass of water vapor is m, and the condensation rate is The water vapor area is S.

[0114] In this embodiment, the method for obtaining the impact factor by performing time-varying impact analysis on environmental data according to the water vapor flux includes:

[0115] Sort the environmental data in chronological order and calculate the cumulative membership at different times:

[0116]

[0117] The cumulative membership of the environmental data at time b and time z is The i-th environmental data at the b-th moment is h i (b), the i-th environmental data at the z-th moment is h i (z), the number of types of environmental data is The water vapor flux at time z is The water vapor flux at time z is

[0118] Calculate importance:

[0119]

[0120] The importance of the cth environment is k c , the number of environmental types is N, and the cumulative membership of the cth environment is The control factor is β, and the water vapor flux of the cth environment at the zth time is The water vapor flux of the cth environment at time b is Environmental data i (b) and environmental data h i The correlation degree of (z) is ψ(h i (b),h i (z));

[0121] The environments with importance greater than 0.732 are taken as cluster centers, and the deviation threshold of the environmental data is obtained through the threshold regression model;

[0122] Calculate the similarity between the environment data and the cluster center:

[0123]

[0124] The control coefficient is λ, the sign function is sgn(·), the fixed deviation constant is ε, and the deviation threshold of the i-th environmental data is ζ i , the similarity between the cth environmental data and the cluster center is k c ;

[0125] The environmental data is assigned to the center of the cluster with the largest similarity to obtain the classified data, and the impact factor is calculated based on the classified data:

[0126]

[0127] The impact factor is The system error is σ and the L2 norm is ‖·‖2.

[0128] In this embodiment, the method for constructing a multi-source estimation model of water vapor flux of a terrestrial ecosystem based on the influencing factors and the monitoring data includes:

[0129]

[0130] The objective function is The actual value of the water vapor flux is The predicted value of water vapor flux is Actual value and predicted values The loss function is The impact factor is The horizontal water vapor flux is The vertical water vapor flux is

[0131] The multi-source estimation model of water vapor flux in terrestrial ecosystems includes decision tree clustering algorithm, long short-term memory network algorithm, and neural network algorithm;

[0132] The decision tree clustering algorithm classifies data by building a decision tree, and then divides the data points into different clusters according to the branches of the tree to obtain category data;

[0133] The long short-term memory network algorithm extracts the characteristics of category data according to long-term dependencies through a recursive structure and gating mechanism to obtain water vapor evapotranspiration characteristics;

[0134] The neural network algorithm adjusts the horizontal and vertical water vapor fluxes in the objective function according to the water vapor evaporation characteristics of the historical input data, and estimates the multi-source water vapor flux according to the detection data through the adjusted objective function.

[0135] In this embodiment, the method for optimizing the water vapor flux stable isotope multi-source estimation model includes:

[0136] The updated bias is expressed as:

[0137]

[0138] The bias of the tth iteration is The bias for the t-1th iteration is The learning rate is α, and the gradient of the loss function at the t-1th iteration is ρ t-1 , the first-order moment of the gradient at the tth iteration is y t , the second-order moment of the gradient at the tth iteration is s t , the momentum decay hyperparameter is The scaling decay hyperparameter is The smoothing term is σ, and the bias correction of the first-order moment is The bias correction of the second-order moment is The first-order moment of the gradient at the t-1th iteration is y t-1 , the second-order moment of the gradient at the t-1th iteration is s t-1 ;

[0139] Introduce L2 regularization and update the loss function, the expression is:

[0140]

[0141] The loss function is The L2 regularization coefficient is η, the weight vector of the neural network is ω, the output dimension is U, and the number of input data is The predicted value of the xth output dimension of the bth input data is The actual value of the xth output dimension of the bth input data is Z b,x , the mean square error is

[0142] Batch normalization zero-centers the input of the hidden layer of the neural network and introduces scaling parameters and offset parameters to correct data distribution changes. The expression is:

[0143]

[0144]

[0145] The estimated input standard deviation vector in the mini-batch v is l v , the zero-centered and normalized vector of the x-th input data is f x , the scaling parameter is θ, the offset parameter is ξ, and the Kronecker product is The output of the xth batch normalization is φ x , the number of input data in a mini-batch is

[0146] Adaptive learning is performed based on the error loss, and the expression is:

[0147]

[0148] The error loss is The hyperparameter is δ, and the iteration is continued until the error loss is less than 0.217, otherwise the bias is updated.

[0149] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0150] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0151] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0152] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a multi-source estimation device for stable isotopes of water vapor flux in terrestrial ecosystems at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned multi-source estimation methods for stable isotopes of water vapor flux in terrestrial ecosystems.

[0153] The above application Figure 1The multi-source estimation method of stable isotopes of water vapor flux in a terrestrial ecosystem disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0154] The electronic device may also perform Figure 1 A multi-source estimation method for stable isotopes of water vapor flux in terrestrial ecosystems was implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0155] An embodiment of the present application also proposes 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 an electronic device including multiple application programs, any one of the aforementioned multi-source estimation methods for stable isotopes of water vapor flux in terrestrial ecosystems is executed.

[0156] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0158] 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.

[0159] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0160] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0161] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0162] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0163] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0164] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-source estimation method for stable isotopes of water vapor flux in terrestrial ecosystems, characterized in that: The following steps are involved: Collecting monitoring data and environmental data of a preset area, and preprocessing the monitoring data and the environmental data; Performing water vapor movement characteristic analysis on the monitoring data according to stable isotopes to obtain partition data, and inputting the partition data into a stable isotope model to obtain water vapor flux; Conducting a time-varying impact analysis on environmental data based on the water vapor flux to obtain an impact factor, and constructing a multi-source estimation model for water vapor flux of a terrestrial ecosystem based on the impact factor and the monitoring data; The multi-source estimation model of water vapor flux of terrestrial ecosystem is optimized, the data to be estimated is input into the multi-source estimation model of water vapor flux of terrestrial ecosystem, and the estimation result is output.

2. The multi-source estimation method of stable isotopes of water vapor flux in terrestrial ecosystems according to claim 1, characterized in that: The method of analyzing the water vapor movement characteristics of the monitoring data according to stable isotopes to obtain the divided data comprises: The monitoring data is coordinate-located according to the stable isotope to obtain the position information, and the position information is sorted according to the time sequence to obtain the time position data; Establish the starting point of the movement, take the starting point of the movement as the center, and obtain the maximum and minimum distance of the movement according to the movement capacity represented by evapotranspiration. The expression is: The horizontal coordinate point of the starting point of the movement at the jth moment is The vertical coordinate point of the starting point of the movement at the jth moment is The time position data of the horizontal direction at the rth moment is: The maximum horizontal evapotranspiration rate is The interval between the rth moment and the jth moment is H rj The coordinate position of the time position data is (g, d), and the maximum vertical evapotranspiration rate is The horizontal coordinate position of the time position data at the rth moment is The vertical coordinate position of the time position data at the rth moment is Calculate the distance vector: The distance vector of the time position data is K rj (g,d), calculate the squared regularized distance: The covariance matrix in the horizontal direction is X r (d), the covariance matrix in the vertical direction is X j (g), the transpose of the matrix is ​​T, and the squared normalized distance of the time position data is B rj (g, d); Given a distance threshold, select the nearest time position data to associate with the motion trajectory. When the square regularized distance is less than the distance threshold, the associated time position data conforms to the water vapor motion trajectory, otherwise the time position data is replaced. Calculate the decision value: The number of associated positioning points is q, the duration of the water vapor movement trajectory is S, and the decision value is D; When the decision value is greater than 0.45, a reasonable water vapor movement trajectory is established. If both the square regularized distance and the decision value are not satisfied, the time window is moved back until a reasonable water vapor movement trajectory is established. The monitoring data are classified according to the water vapor movement trajectory using similarity, and the classified monitoring data sources and the corresponding water vapor movement trajectories are output as partitioned data.

3. The multi-source estimation method of stable isotopes of water vapor flux in terrestrial ecosystems according to claim 1, characterized in that: The method of inputting the partitioned data into a stable isotope model to obtain water vapor flux comprises: The stable isotope model obtains the change in the stable isotope ratio of the target according to the partitioning data, and calculates the vertical water vapor flux according to the change in ratio: The vertical water vapor flux is The net radiation of the vegetation surface is H, the soil heat flux is Q, the slope of the vapor pressure curve is γ, the average temperature is M, the wind speed is C, the psychrometer constant is χ, and the saturated water vapor pressure is a o , the actual water vapor pressure is a, and the soil moisture function is The vegetation coefficient is z1, the soil moisture stress coefficient is z2, the vegetation quantity is Q, the Bowen ratio is υ, and the first improvement coefficient is The second improvement factor is The slope of the saturated water vapor pressure changing with temperature is w, the total net radiation is F, the latent heat of vaporization is μ, and the conversion coefficient is The soil coefficient is z s ; Calculate the horizontal water vapor flux: The horizontal water vapor flux is The air density is Specific humidity The mass of water vapor is m, and the condensation rate is The water vapor area is S.

4. The multi-source estimation method of stable isotopes of water vapor flux in terrestrial ecosystems according to claim 1, characterized in that: The method for obtaining the impact factor by performing time-varying impact analysis on environmental data according to the water vapor flux includes: Sort the environmental data in chronological order and calculate the cumulative membership at different times: The cumulative membership of the environmental data at time b and time z is The i-th environmental data at the b-th moment is h i (b), the i-th environmental data at the z-th moment is h i (z), the number of types of environmental data is n, and the water vapor flux at the zth moment is θ b , the water vapor flux at the zth moment is θ z ; Calculate importance: The importance of the cth environment is k c , the number of environmental types is N, and the cumulative membership of the cth environment is The control factor is β, and the water vapor flux of the cth environment at the zth time is θ c (z), the water vapor flux of the cth environment at time b is θ c (b), environmental data h i (b) and environmental data h i The correlation degree of (z) is ψ(h i (b),h i (z)); The environments with importance greater than 0.732 are taken as cluster centers, and the deviation threshold of the environmental data is obtained through the threshold regression model; Calculate the similarity between the environment data and the cluster center: The control coefficient is λ, the sign function is sgn(·), the fixed deviation constant is ε, and the deviation threshold of the i-th environmental data is ζ i , the similarity between the cth environmental data and the cluster center is k c ; The environmental data is assigned to the center of the cluster with the largest similarity to obtain the classified data, and the impact factor is calculated based on the classified data: The impact factor is The system error is σ and the L2 norm is ‖·‖2.

5. The multi-source estimation method of stable isotopes of water vapor flux in terrestrial ecosystems according to claim 1, characterized in that: The method for constructing a multi-source estimation model of water vapor flux of a terrestrial ecosystem based on the influencing factors and the monitoring data comprises: The objective function is Q, the actual value of water vapor flux is u, and the predicted value of water vapor flux is Actual value and predicted values The loss function is The impact factor is The horizontal water vapor flux is The vertical water vapor flux is The multi-source estimation model of water vapor flux in terrestrial ecosystems includes decision tree clustering algorithm, long short-term memory network algorithm, and neural network algorithm; The decision tree clustering algorithm classifies data by building a decision tree, and then divides the data points into different clusters according to the branches of the tree to obtain category data; The long short-term memory network algorithm extracts the characteristics of category data according to long-term dependencies through a recursive structure and gating mechanism to obtain water vapor evapotranspiration characteristics; The neural network algorithm adjusts the horizontal and vertical water vapor fluxes in the objective function according to the water vapor evaporation characteristics of the historical input data, and estimates the multi-source water vapor flux according to the detection data through the adjusted objective function.

6. The multi-source estimation method of stable isotopes of water vapor flux in terrestrial ecosystems according to claim 1, characterized in that: The method for optimizing the water vapor flux stable isotope multi-source estimation model comprises: The updated bias is expressed as: The bias of the tth iteration is The bias for the t-1th iteration is The learning rate is α, and the gradient of the loss function at the t-1th iteration is ρ t-1 , the first-order moment of the gradient at the tth iteration is y t , the second-order moment of the gradient at the tth iteration is s t , the momentum decay hyperparameter is The scaling decay hyperparameter is The smoothing term is σ, and the bias correction of the first-order moment is The bias correction of the second-order moment is The first-order moment of the gradient at the t-1th iteration is y t-1 , the second-order moment of the gradient at the t-1th iteration is s t-1 ; Introduce L2 regularization and update the loss function, the expression is: The loss function is The L2 regularization coefficient is η, the weight vector of the neural network is ω, the output dimension is U, and the number of input data is The predicted value of the xth output dimension of the bth input data is The actual value of the xth output dimension of the bth input data is Z b,x , the mean square error is Batch normalization zero-centers the input of the hidden layer of the neural network and introduces scaling parameters and offset parameters to correct data distribution changes. The expression is: The estimated input standard deviation vector in the mini-batch v is l v , the zero-centered and normalized vector of the x-th input data is f x , the scaling parameter is θ, the offset parameter is ξ, and the Kronecker product is The output of the xth batch normalization is φ x , the number of input data in a mini-batch is Adaptive learning is performed based on the error loss, and the expression is: The error loss is The hyperparameter is δ, and the iteration is continued until the error loss is less than 0.217, otherwise the bias is updated.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 6.

Images