CN111815073A

CN111815073A - Grassland biomass prediction method and device, electronic equipment and storage medium

Info

Publication number: CN111815073A
Application number: CN202010785077.XA
Authority: CN
Inventors: 房建东; 赵于东; 王晶
Original assignee: Inner Mongolia University of Technology
Current assignee: Inner Mongolia University of Technology
Priority date: 2020-08-06
Filing date: 2020-08-06
Publication date: 2020-10-23

Abstract

The disclosure relates to a method and a device for predicting grassland biomass, an electronic device and a storage medium. The method comprises acquiring grassland parameter information, wherein the grassland parameter information comprises at least one of soil characteristics, meteorological characteristics, climate characteristics and additional characteristics of a grassland monitoring environment; determining carbon dioxide flux information using the meadow parameter information; and obtaining the grassland biomass grade by using the grassland parameter information and the carbon dioxide flux information according to a set rule. The prediction of the grassland biomass can be simply, conveniently and accurately realized by the embodiment of the disclosure.

Description

Grassland biomass prediction method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of grass industry technologies, and in particular, to a method and an apparatus for predicting grass land biomass, an electronic device, and a storage medium.

Background

Grassland biomass refers to the weight of all organisms per unit area or the total internal energy storage, and at present, the domestic methods for estimating grassland biomass include direct harvesting methods, remote sensing methods, mathematical model prediction methods and the like. Zhang Xuan Gem, etc. on the basis of the sambrook survey, combined with the remote sensing data and meteorological data, regresses the biomass on the grassland vegetation ground and estimates the grassland biomass in the Yili area of Xinjiang. Song dawn researches the relationship between the meadow soil respiration amount and the underground biomass under different grazing strengths and moisture treatments, and the results show that under the environment condition of water shortage, the soil respiration and the community underground biomass increase along with the increase of moisture, and the meadow community underground biomass and the meadow soil respiration have obvious positive correlation. Zhao Xiaoying analyzes the influence of meteorological factors on the biomass on grassland, finds that precipitation is the main factor influencing the biomass on the ground, and has smaller influence of other meteorological factors. The Hu Feilong and the like carry out quantitative research on the aboveground and underground biomass distribution of the inner Mongolia meadow steppe by means of tools such as GIS and the like on the basis of a direct harvesting method to obtain an inner Mongolia biomass carbon horizontal distribution pattern and an underground biomass carbon vertical distribution pattern. The agile sharpness estimates vegetation biomass by building a cubic program set using an improved water cloud model and dual polarization ASAR data. The influence of the synergistic effect of temperature and precipitation on the biomass of the stipa pulata and the distribution of the stipa pulata is researched by the Luxianmi method by adopting an artificial climate box method, and the result shows that the synergistic effect of the water and the temperature obviously influences the change of the biomass of the desert grassland. Seiyichi estimates the terrestrial biomass of the typical grassland of the river basin of Sinogra cassiiolensis by utilizing an artificial neural network and a multivariate linear regression model, and constructs the two models by combining the normalized vegetation index, the terrain variables (the elevation, the slope and the gradient) and the atmospheric corrected reflectivity of the reflection band as candidate input variables.

The foreign grassland biomass estimation method is similar to that in China, Ullah S estimates the grassland biomass by using various vegetation indexes (NDVI, SAVI, TSAVI, REIP, MTCI and frequency band depth analysis parameters) on a regional scale; leis et al used digital photography to estimate grassland biomass; ren proposes a new index (litter-soil adjusted vegetation index, L-SAVI) to estimate arid and semi-arid grassland biomass; quan proposes a method for estimating biomass on the grassland based on a radiation transfer model and estimates using PROSAILH and reflectance data from Landsat 8OLI product.

Most of the methods require mathematical prediction models to be established according to remote sensing data and meteorological observation data. The direct harvesting method has the most accurate prediction result, but simultaneously causes damage to plant growing communities and has larger consumption of manpower and material resources; the mathematical model prediction method is a method which does not destroy the ecology of the grassland community and can simply estimate the biomass, but the accuracy of model estimation is difficult to ensure; the remote sensing estimation method needs high cost, is suitable for long-term supervision and management of a large-range grassland, and cannot well meet the requirements of local regional and real-time services.

Disclosure of Invention

The present disclosure provides a grassland biomass prediction method and apparatus, an electronic device, and a storage medium, which can simply, conveniently, and inexpensively realize the prediction of grassland biomass without establishing a mathematical prediction model corresponding to remote sensing data and meteorological observation data.

According to an aspect of the present disclosure, there is provided a method for predicting grassy biomass, which includes:

acquiring grassland parameter information, wherein the grassland parameter information comprises at least one of soil characteristics, meteorological characteristics, climate characteristics and additional characteristics of a grassland monitoring environment;

determining carbon dioxide flux information using the meadow parameter information;

and obtaining the grassland biological quantity grade by using the grassland parameter information and the carbon dioxide flux information according to a set rule.

In some possible embodiments, the determining carbon dioxide flux information using the meadow parameter information comprises:

determining a prediction model according to the acquired grassland parameter information or model selection information, wherein the prediction model at least comprises: a regression analysis model and a neural network model;

obtaining the carbon dioxide flux information according to the prediction model, wherein the carbon dioxide flux information comprises: at least one of carbon dioxide flux and carbon dioxide flux level.

In some possible embodiments, the determining a prediction model according to the acquired grassland parameter information includes:

determining a prediction model as a regression analysis model if the meadow parameter information includes additional features and climate features;

and determining the prediction model as a neural network model under the condition that the grassland parameter information comprises soil characteristics and meteorological characteristics.

In some possible embodiments, the obtaining the carbon dioxide flux information according to the prediction model includes:

obtaining the carbon dioxide flux by using the regression analysis model;

and obtaining the carbon dioxide flux grade by utilizing the neural network model.

In some possible embodiments, the obtaining the grassland biomass rating according to the set rule by using the grassland parameter information and the carbon dioxide flux information comprises:

performing fuzzification processing on the grassland parameter information to correspondingly obtain a first fuzzy set corresponding to the grassland parameter information;

determining a second fuzzy set corresponding to the carbon dioxide flux information based on the carbon dioxide flux information;

and determining grassland biomass grades corresponding to the first fuzzy set and the second fuzzy set by using a set rule.

In some possible embodiments, the performing fuzzification processing on the grassland parameter information to obtain a first fuzzy set corresponding to the grassland parameter information includes:

processing the grassland parameter information by using a membership function to obtain a corresponding grassland parameter fuzzy value;

and determining a first fuzzy set corresponding to the soil fuzzy value based on the corresponding range interval of the grassland parameter fuzzy value.

In some possible embodiments, the determining, based on the carbon dioxide flux information, the second fuzzy set corresponding to the carbon dioxide flux information includes at least one of:

processing the carbon dioxide flux in the carbon dioxide flux information by using a membership function to obtain a corresponding carbon dioxide flux fuzzy value, and determining the second fuzzy set by using a range interval corresponding to the carbon dioxide fuzzy value;

and determining the second fuzzy set based on a range interval corresponding to the carbon dioxide flux level in the carbon dioxide flux information.

According to a second aspect of the present disclosure, there is provided a prediction apparatus of grassy biomass, comprising:

the system comprises an information acquisition module, a data processing module and a data processing module, wherein the information acquisition module is used for acquiring grassland parameter information which comprises at least one of soil characteristics, meteorological characteristics, climate characteristics and additional characteristics of a grassland monitoring environment;

a determination module for determining carbon dioxide flux information using the meadow parameter information;

and the prediction module is used for obtaining the grassland biomass grade by utilizing the grassland parameter information and the carbon dioxide flux information according to a set rule.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of the first aspects.

According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of any one of the first aspects

In the embodiment of the disclosure, the obtained grassland parameter information is used for determining the carbon dioxide flux information, the carbon dioxide flux information is used as a main change factor influencing the biomass, and the grassland biomass is predicted according to the set rule by combining the grassland parameter information, so that the real-time monitoring and the grade evaluation of the grassland biomass are realized. The method and the device do not need to carry out mathematical modeling on the remote sensing data and the meteorological observation data, can reduce the labor cost, can predict the grassland biomass according to the collected information in real time, can be used locally, and can meet the requirements of local regionality and real-time business. Be favorable to the managers can be according to the aassessment result, formulate grazing and forage grass and reap the scheme, rationally use the meadow resource, enclose when the meadow biomass is few and forbid pasturing, the release grazing when the meadow biomass is many.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 shows a flow chart of a method of predicting grassy biomass according to an embodiment of the present disclosure;

FIG. 2 shows a block diagram of a grassland biomass prediction system, according to an embodiment of the present disclosure;

FIG. 3 shows a functional block diagram of a method of prediction of grass biomass in accordance with an embodiment of the present disclosure;

FIG. 4 shows a block diagram of a neural network model in accordance with an embodiment of the present disclosure;

FIG. 5 shows a flow diagram of training a neural network in a method of predicting grass biomass according to an embodiment of the present disclosure;

FIG. 6 is a flowchart illustrating step S30 of a method for predicting grassy biomass according to an embodiment of the present disclosure;

FIG. 7 shows a flow chart of a fuzzy legend process to derive grassy biomass ratings according to an embodiment of the present disclosure;

FIG. 8 shows a block diagram of a prediction apparatus for grass biomass in accordance with an embodiment of the present disclosure;

FIG. 9 shows a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure;

fig. 10 shows a block diagram of another electronic device 1900 according to an embodiment of the disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

The disclosed embodiment provides a prediction method of grassland biomass, the main execution body of the method can be an information processing device, for example, the method can be executed by a terminal device or a server or other processing devices, wherein the terminal device can be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, and the like. The server may include a local server and a cloud server, and in some possible implementations, the method may be implemented by a processor invoking computer readable instructions stored in a memory.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted.

Fig. 1 shows a flowchart of a method for predicting grassy biomass according to an embodiment of the present disclosure, wherein the method for predicting grassy biomass may include:

s10: acquiring grassland parameter information, wherein the grassland parameter information comprises at least one of soil characteristics, meteorological characteristics, climate characteristics and additional characteristics of a grassland monitoring environment;

in some possible embodiments, an information acquisition module is arranged in a grassland monitoring environment needing to predict grassland biomass, and is used for acquiring grassland parameter information. The information acquisition module can comprise a sensor and an information transmission module, the sensor is used for acquiring various information in the grassland monitoring environment, such as temperature, humidity, light intensity, wind speed, air pressure, gas concentration and the like of soil and air, the information transmission module is used for receiving input information, such as weather conditions, growth period (growing period) types and the like, the input can be information which is set by a user, and the information can be information which is requested to be acquired by being associated with a related meteorological office server. That is to say, the information acquisition module can directly gather meadow parameter information through the sensor, also can acquire the various information of input through the information transmission module, and this disclosed embodiment can be according to the type of required meadow parameter information, confirms the mode of obtaining information, and this disclosure does not do specifically limit to this.

In some possible embodiments, the turf parameter information may include at least one of soil characteristics, meteorological characteristics, climate characteristics, and additional characteristics. In the disclosed embodiment, the soil characteristics may include soil humidity and soil temperature, and the meteorological characteristics may include average air temperature, average surface temperature, average ground temperature of 5cm, average wind speed, weather conditions, and pasture growth period type. The climate characteristics may include: temperature (air temperature), air pressure, additional features may include carbon dioxide concentration. The weather conditions include sunny days, cloudy days, light rain, rain shower and the like, and the type of the pasture growth period (growing period) can include a green turning period, a flowering period, a yellow withering period and the like. The foregoing is merely an illustration of embodiments of the present disclosure and is not to be construed as a specific limitation thereof.

In some possible embodiments, a static box may be provided on the lawn, which may be used to place the information acquisition module. In particular, at least one sensor may be placed, and the skilled person may select and place the appropriate sensor in the static box at the appropriate location according to the grass parameter information required. For example, the light sensor for collecting light intensity, the sensor for obtaining air temperature, the wind speed sensor, the concentration sensor for measuring carbon dioxide concentration can be placed on the outer surface of the static box, the sensor for measuring soil humidity and temperature can be arranged inside soil, the sensor for measuring earth surface temperature can be arranged on the earth surface, and the sensor for measuring 5cm average earth temperature can be arranged in the grassland at the position 5cm away from the earth surface. In addition, the average value of the grassland information may be an average value of information collected within a preset time, the preset time may be one day, and the time interval for collecting information may be half an hour, which is not specifically limited by the present disclosure.

S20: determining carbon dioxide flux information using the meadow parameter information;

in some possible embodiments, the resulting grass parameter information may be analyzed to obtain carbon dioxide flux information for the grass, the carbon dioxide flux information including carbon dioxide flux and/or carbon dioxide flux ratings. Carbon dioxide flux information is a variable describing the respiration of grassland plants, directly affecting grassland biomass changes.

In some possible implementations, embodiments of the present disclosure may determine carbon dioxide flux information through different prediction modules, such as a neural network model may be utilized to predict carbon dioxide flux levels, or carbon dioxide flux values may also be determined through a regression analysis model.

S30: and obtaining the grassland biomass grade by using the grassland parameter information and the carbon dioxide flux information according to a set rule.

In some possible implementations, embodiments of the present disclosure may perform fuzzy processing on the grassland parameter information, and determine the grassland biomass level using the fuzzy processed result and the set rule. Reasonably master the grassland biomass grade, and is beneficial to protecting the carbon-oxygen balance of grasslands and the balance of grassland and livestock.

The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

FIG. 2 shows a block diagram of a grassland biomass prediction system according to an embodiment of the present disclosure. The system comprises a basic data layer, an internet of things network layer and a data application processing layer. Wherein the basic data layer is composed of sensor nodes, each node comprises a plurality of sensor components, each node can be used for measuring at least one kind of grassland biological information, and each node can comprise: temperature sensor, carbon dioxide concentration sensor, soil temperature and humidity sensor, wind speed sensor, optical sensor, baroceptor etc. this disclosure does not limit this specifically. The sensor assembly may be disposed in a static bin or other location, as the present disclosure is not particularly limited thereto. The basic data layer can further comprise an information transmission module for receiving input information, and the sensor assembly and the information transmission module can transmit the corresponding grassland parameter information to an internet of things network layer (internet of things gateway) through an integrated communication interface (data transmission module) under the condition that the corresponding grassland parameter information is received. The network layer of the internet of things can receive the grassland parameter information through Bluetooth or other communication modes. The internet of things network layer can be integrated with low-power consumption remote internet of things technologies such as GPRS and LoRa, and wired or wireless transmission of data is achieved. Interaction between a basic data layer and a data application processing layer can be established through an internet of things network layer, grassland parameter information is transmitted to the data application processing layer, the data application processing layer can finish aggregation and analysis processing of basic data such as grassland parameter information, data preprocessing is firstly finished, a corresponding carbon dioxide flux calculation method is selected according to requirements, and finally carbon dioxide flux and soil temperature and humidity are input into a fuzzy reasoning model to obtain a grassland biomass prediction value (the process is specifically explained in the following embodiment).

FIG. 3 shows a functional block diagram of a method of prediction of grass biomass in accordance with an embodiment of the present disclosure. Wherein various grass parameter information, such as soil characteristics, climate characteristics, meteorological characteristics, and additional characteristics, may first be obtained using the sensor assembly. The resulting grass parameter information is then used to determine carbon dioxide flux information.

Specifically, in this embodiment of the present disclosure, the determining carbon dioxide flux information by using the grassland parameter information includes: determining a prediction model according to the acquired grassland parameter information or model selection information, wherein the prediction model at least comprises: a regression analysis model and a neural network model; obtaining the carbon dioxide flux information according to the prediction model, wherein the carbon dioxide flux information comprises: at least one of carbon dioxide flux and carbon dioxide flux level.

In the embodiment of the disclosure, different information acquisition devices can be configured according to specific environmental conditions to obtain different types of grassland parameter information, and correspondingly, according to the obtained grassland parameter information, a prediction model for determining the carbon dioxide flux information can be further determined. The embodiments of the present disclosure may determine the prediction model as a regression analysis model under the condition that the grassland parameter information includes additional features and climate features; and determining the prediction model as a neural network model also in case the meadow parameter information comprises soil characteristics and meteorological characteristics. Alternatively, the transmitted model selection information may be received, and the selected prediction model may be determined based on the model selection information. The model selection information may be information input by a user, including information used to determine whether the predictive model is a regression analysis model or a neural network model.

In some possible embodiments, in combination with the grassland parameter information obtained by monitoring the environment, the data required by the determined prediction model can be extracted from the grassland parameter information and input into the corresponding prediction model. For example, when the prediction model is determined to be a regression analysis model, the carbon dioxide concentration, the air pressure, and the temperature data in the grassland parameter information may be input to the regression analysis model, and when the prediction model is determined to be a neural network model, the light intensity, the average air temperature, the average surface temperature, the average ground temperature of 5cm, the average wind speed, the weather condition, the type of grass growth, and the like in the grassland parameter information may be input to the neural network model. Carbon dioxide flux information may then be obtained from the selected predictive model. The carbon dioxide flux can be obtained using the regression analysis model; and obtaining the carbon dioxide flux grade by utilizing the neural network model.

Specifically, the regression analysis model may obtain the carbon dioxide flux using a first method, where the expression of the first method is:

f is carbon dioxide gas emission flux, the F value is positive and indicates that the grassland ecosystem emits more carbon dioxide gas, the F value is negative and indicates that the grassland ecosystem absorbs more carbon dioxide gas, H is the box height of the static box, m is the mass of the carbon dioxide gas, and t is time; rho₀Is the density of carbon dioxide gas in the standard state, T₀And P₀The thermodynamic temperature (273.15K) and pressure (1.013X 10) of air in the standard state are respectively⁵Pa); t and P are respectively the thermodynamic temperature at the time of sampling and the air pressure at the sampling point, dC_t(dt) represents the rate of change in carbon dioxide concentration, C_tIs the volumetric specific concentration of carbon dioxide gas at time t.

The verification process is as follows:

the carbon dioxide flux is defined as the amount of material passing through a unit area of carbon dioxide per unit time, and the carbon dioxide flux calculation formula is as follows:

wherein, F is the carbon dioxide gas flux of discharging, and the F value is positive, and it is many to express the carbon dioxide gas that grassland ecosystem discharged, and the F value is negative, and it is many to express the carbon dioxide gas that grassland ecosystem absorbed, and A is the area of static case, and m is the carbon dioxide gas quality, and t is the time, according to the relation of gas quality and density, equation (1) can be changed into:

where V is the volume of the static tank, ρ 'is the density of carbon dioxide gas in the sampled state (ρ' ═ nM/V '), n is the amount of carbon dioxide, M is the molar mass of carbon dioxide, V' is the volume in the sampled state, C_tThe volume mixing ratio concentration of the carbon dioxide gas at the time t is shown.

According to the gas state equation, the standard state of the gas to be measured comprises the following components: p₀V₀＝nRT₀And ρ₀＝nM/V₀In the sampling case, PV' ═ nRT, we can substitute:

the volume in the sampling state can thus be deduced as:

substituting ρ '═ nM/V' and equation (4) into equation (2) yields the static tank carbon dioxide flux calculated as:

where ρ is₀Is the density of carbon dioxide gas in the standard state, T₀And P₀Respectively at atmospheric thermodynamic temperature (273.15K) and atmospheric pressure (1.013X 10)⁵Pa); t and P are respectively the thermodynamic temperature during sampling and the air pressure at the sampling point.

According to the relation V between the static tank area and the tank height, which is HA, the carbon dioxide flux calculation formula is as follows:

with the above configuration, the carbon dioxide flux F can be obtained in the first manner using the regression analysis model.

In addition, the neural network model can predict the carbon dioxide flux level according to the input grassland parameter information.

The carbon dioxide flux of the grassland is closely related to factors such as the growth period of the pasture, weather conditions, light and effective radiation coefficients, average air temperature, average surface temperature, average low temperature of 5cm, average wind speed and the like. Illustratively, table 1 gives the input parameters for the neural network, including 7 environmental factors, and the output is the emission level of the carbon dioxide flux from the grass, as shown in table 2.

TABLE 1 BP neural network input data description

TABLE 2 carbon dioxide flux rating Scale

In particular, fig. 4 shows a block diagram of a neural network model according to an embodiment of the present disclosure. The neural network model comprises an input layer, a hidden layer and an output layer. Wherein x is (x)₁,x₂,...,x₇) As input layer vectors, x₁Can be thatWind speed, x₂Is the air temperature (average air temperature), x₃Is the soil temperature, x₄Is the intensity of light, x₅Is the surface temperature, x₆For weather conditions, x₇Is a type of pasture in the growing period. The above is merely exemplary, and other soil characteristics or meteorological characteristics may be input into the neural network model. w is a_ijIs the connection weight between the input layer and the hidden layer, t_jkThe connection weight between the hidden layer and the output layer; y ═ y₁,y₂,…,y₇) As output layer vector, representing carbon dioxide flux level, y₁To y₇Respectively representing output levels of 1-7. In the prediction process, y can be expressed as an output vector, wherein the y value corresponding to the prediction grade is 1, and the rest is 0, namely the grade corresponding to the y value with the value of 1 in the output result can be determined as the carbon dioxide flux grade, wherein y is the grade of the carbon dioxide flux₁To y₇Gradually increases in level. Here, the number of parameters of the input layer x and the number of parameters of the output layer y are not limited to 7, and may be set as needed.

Next, a training process of the neural network model will be explained. FIG. 5 shows a flow chart of training a neural network in a method for predicting grass biomass according to an embodiment of the present disclosure. The weight and the threshold may be initialized first, and the initialized weight and threshold may be generated using a random function and the initialization result may be input to the hidden layer. The learning samples X are input to the input layer, where the grass parameter information needed by the neural network model to perform the prediction may be included, such as X (X) as described above₁,x₂,...,x₇). The bank layer carries out forward propagation on the input sample to obtain the output of the hidden layer

And then output by using the output layer

Wherein, theta₁And theta₂The thresholds, representing the hidden layer and the output layer, respectively, may be initially randomly set, e.g.,

θ₁＝[-1.7427 -3.8237 4.8534 -0.3563 -1.4512 -1.5686 -2.7993]^T，

θ₂＝[-0.0075]。

obtaining a prediction error by using a real result t and a prediction result d corresponding to the sample X

k denotes a connection number k between the bank layer and the output layer as 1,2,3,4,5,6, 7.

When the prediction error is smaller than the set error expected value E_{Expectation of}This indicates that the model training is complete, and the model can be used to predict the carbon dioxide flux level. When the prediction error is greater than or equal to E_{Expectation of}Under the condition of (1), the accuracy of the representation model is not enough, the errors of the hidden layer and the input layer can be further obtained at the moment, the weight is adjusted by utilizing the set learning rate, and the training is carried out again until the prediction error is smaller than the set error expected value E_{Expectation of}。

By means of the method, the neural network model can be trained, and the trained network model can predict the carbon dioxide flux level.

In the case of obtaining carbon dioxide flux information, the grassland biomass rating may be predicted in combination with the grassland parameter information and the carbon dioxide flux information.

Fig. 6 is a flowchart illustrating a step S30 in a method for predicting grass biomass according to an embodiment of the present disclosure, and fig. 7 is a flowchart illustrating a process of obtaining a fuzzy legend of grass biomass rating according to an embodiment of the present disclosure. Wherein, according to setting rules, the grassland biomass grade is obtained by using the grassland parameter information and the carbon dioxide flux information, and the method comprises the following steps:

s31: performing fuzzification processing on the grassland parameter information to correspondingly obtain a first fuzzy set corresponding to the grassland parameter information;

s32: determining a second fuzzy set corresponding to the carbon dioxide flux information based on the carbon dioxide flux information;

s33: and determining grassland biomass grades corresponding to the first fuzzy set and the second fuzzy set by using a set rule.

In some possible implementations, step S31 in the embodiments of the present disclosure may be executed simultaneously or separately, and the present disclosure does not specifically limit this.

Determining the updating succession of the grassland plants by the climate factors; environmental temperature changes affect photosynthesis of plants, which in turn affects energy flow in the grassland ecosystem; the soil humidity meets the transpiration effect of the plants, the soil humidity is high, the plant stomata are large, the plant growth efficiency is high, the plant growth efficiency is maximum in a range with proper temperature values and humidity values, and when the temperature values and the humidity values are too large, the plant growth is slow; the carbon dioxide flux represents the respiration condition of plants, generally, the organic carbon of soil and biomass are in positive correlation, and the change of grassland biomass can be influenced by the temperature, the humidity and the carbon dioxide flux of soil. In the embodiment of the disclosure, the average daily temperature, the soil humidity and the carbon dioxide flux in the grassland parameter information can be selected as the input of the fuzzy inference system, and the biomass grade can be used as the output of the fuzzy inference system.

Specifically, in step S31, the grassland parameter information is processed by using a membership function to obtain a corresponding grassland parameter fuzzy value; and determining a first fuzzy set corresponding to the soil fuzzy value based on the corresponding range interval of the grassland parameter fuzzy value. The field parameter information that is subjected to the fuzzy processing may include soil characteristics such as soil moisture and soil temperature, among others. The membership function of the embodiment of the present disclosure may be at least one of a gaussian membership function, a triangular membership function, and a trapezoidal membership function.

The formula of the triangular membership function is shown as follows:

wherein a, b and c are constants and represent boundary values of the trigonometric membership function, and x represents an input parameter.

The formula of the trapezoidal membership function is shown in formula (8):

and a, b, c and d are constants and represent abscissa values of the boundaries of the membership functions, and compared with the triangular membership functions, large flat areas are arranged at the tops of the trapezoidal membership functions, so that the method is suitable for the condition of small variation range.

The formula of the Gaussian-type membership function is shown in formula (9):

wherein c is the mean value of the Gaussian distribution, and σ is the standard deviation of the Gaussian distribution, and all values are constant values. Compared with a triangular membership function and a trapezoidal membership function, the Gaussian membership function has a definite peak, can better shield the probability part with lower membership at the bottom, and is suitable for the gradual change process of the input parameters.

In one example, the gaussian membership function may be selected for deblurring in the embodiments of the present disclosure, but is not a specific limitation of the present disclosure. And respectively inputting the soil humidity and the soil temperature into the Gaussian membership function to obtain a fuzzy value corresponding to the soil humidity and a fuzzy value corresponding to the soil temperature. The embodiment of the present disclosure may further set a plurality of fuzzy sets for each parameter value, such as a plurality of fuzzy sets of positive large, positive small, zero, negative small, negative large, and the like, which are respectively represented by PB, PS, ZE, NM, and NB, where each fuzzy set corresponds to a corresponding fuzzy interval. The domains are divided into PB, PS, ZE, NM and NB according to the characteristics of the temperature change of the grassland soil, which respectively represent 5 states of positive big, positive small, zero, negative small and negative big of the temperature, and the corresponding change ranges of the set temperature are (-20, 0), (-2.5, 7.5), (0, 15), (7.5, 22.5) and (17.5, 30). Dividing the grassland soil humidity into 5 grades, selecting a Gaussian membership function as a fuzzy membership function of a fuzzy inference input variable, dividing domains into PB, PS, ZE, NM and NB according to the characteristics of the pasture soil humidity change, and respectively setting the change ranges of the soil humidity as (0, 20), (10, 30), (30,50), (45, 70), (60, 80). That is to say, in the embodiment of the present disclosure, the corresponding fuzzy interval may be determined according to the fuzzy value of the grassland parameter obtained by the membership function, that is, the domain PB, PS, ZE, NM, or NB corresponding to the fuzzy interval may be determined as the corresponding first fuzzy set. The fuzzy sets in the disclosed embodiments may correspond to the universe of discourse corresponding to each interval.

In addition, the embodiment of the disclosure can also obtain a second fuzzy set corresponding to the carbon dioxide flux information. In some possible embodiments, if the obtained carbon dioxide throughput information is the carbon dioxide flux determined by the regression analysis model, the carbon dioxide flux in the carbon dioxide flux information may be processed by using a membership function to obtain a corresponding carbon dioxide flux fuzzy value, and the second fuzzy set is determined by using a range interval corresponding to the carbon dioxide fuzzy value.

Likewise, the membership function may be a gaussian membership function, which is not specifically limited by the present disclosure. And inputting the carbon dioxide flux into the membership function to obtain a corresponding carbon dioxide flux fuzzy value, and correspondingly determining a second fuzzy set according to an interval corresponding to the fuzzy value. The grassland carbon dioxide flux reflects the respiratory condition of pasture, the carbon dioxide flux is divided into 5 grades according to the actual measurement condition and the expert opinion, a Gaussian membership function is selected as an input membership function of fuzzy reasoning, domains of discourse are divided into PB, PS, ZE, NM and NB, the positive state, the negative state and the negative state of the carbon dioxide flux are represented respectively, and the carbon dioxide flux ranges are (1, 2.5), (1.5, 3.5), (2.5, 4.5), (3.5, 5.5) and (4.5, 7).

For each input parameter domain summarized above, different membership function eigenvalues are set as shown in table 3.

TABLE 3 characteristic value table of function with different membership degrees

Alternatively, in other embodimentsIf the obtained carbon dioxide flux information is carbon dioxide grade information, the second fuzzy set can be determined based on a range interval corresponding to the carbon dioxide flux grade in the carbon dioxide flux information. That is to say, in the embodiment of the present disclosure, there is a mapping relationship between the carbon dioxide flux level output by the neural network model and the domain of carbon dioxide flux, and each level corresponds to a unique domain of discourse, so that the obtained carbon dioxide flux level determines the second fuzzy set (domain) corresponding to carbon dioxide flux. The number of domains and the carbon dioxide flux level can correspond to one or more domains, for example, the embodiment of the disclosure can be y₁And y₂Corresponding to negative universe of discourse, y₆And y₇Corresponding to a positive large discourse field, the remaining levels y correspond to negative small, zero and positive small, respectively. The present disclosure does not specifically limit this.

Through the configuration, the fuzzification processing of the grassland biological parameters and the carbon dioxide flux information can be realized, the first fuzzy set of each parameter in the grassland biological parameters and the second fuzzy set corresponding to the carbon dioxide flux information are obtained, and then the grassland biomass grade can be determined by utilizing the domains corresponding to the determined first fuzzy set and the second fuzzy set and setting rules.

The rules of the present disclosure may be configured such that for each combination of a first fuzzy set of the parameter information and a second fuzzy set corresponding to the carbon dioxide flux information, there may be a grass biomass rating corresponding to the combination. For example, the setting rule may be constituted by a conditional function if. For a fuzzy rule "if x is a, y is B", the rule reflects the implication relationship between a and B, and R is a → B, and R is a fuzzy relationship matrix (set rule).

Specifically, in the embodiment of the present disclosure, the partial fuzzy rule is shown in table 4.

TABLE 4 characteristic value table of function with different membership degrees

Wherein input1 represents a first fuzzy set of soil temperature, input2 represents a first fuzzy set of soil humidity, input3 represents a second fuzzy set, output1 represents grassland biomass grade, and mf1, mf2, mf3 and mf4 represent four output state grades of grassland biomass from small to large.

Specifically, if the first fuzzy set of soil temperature and soil moisture is NB and the second fuzzy set is ZE, the grass biomass rating is mf1, which is the highest grass biomass rating.

If the first fuzzy set of soil temperature and soil moisture is NB and the second fuzzy set is PS, the grassy biomass rating is mf1, when the grassy biomass rating is highest.

If the first and second fuzzy sets of soil temperature are NM and the first fuzzy set of soil moisture is NB, the grass biomass rating is mf2, when the grass biomass rating is second.

If the first fuzzy set of soil temperatures is NM and the second fuzzy set is ZE, the first fuzzy set of soil moisture is NB, the grassland biomass rating is mf2, when the grassland biomass rating is second.

If the first and second fuzzy sets of soil temperature are ZE and the first fuzzy set of soil moisture is NB, the grass biomass rating is mf3, when the grass biomass rating is third.

If the first fuzzy set of soil temperatures is ZE and the second fuzzy set is NM, the first fuzzy set of soil moisture is NB, the grassland biomass rating is mf2, when the grassland biomass rating is third.

If the first fuzzy set of soil temperature and soil moisture is PS and the second fuzzy set is NB, the grassy biomass rating is mf1, and the grassy biomass rating is fourth.

If the first fuzzy set of soil temperature and soil moisture is PS and the second fuzzy set is NM, the grassy biomass rating is mf1, which is the fourth rating.

The above is merely an exemplary illustration of setting rules, and in other embodiments, other rules may be included, which are not specifically limited by the present disclosure.

In summary, the embodiment of the present disclosure can determine the carbon dioxide flux information by using the obtained grassland parameter information, and predict the grassland biomass according to the setting rule by using the carbon dioxide flux information as the main variation factor affecting the biomass, so as to realize the real-time monitoring and the grade evaluation of the grassland biomass. This is disclosed need not to carry out mathematical modeling to remote sensing data and meteorological observation data, and can reduce the human cost, can be real-timely predict the meadow biomass according to the information of gathering, can use locally simultaneously, can satisfy local regional nature and real-time nature business demand. Can make grazing and forage grass reaping scheme according to the aassessment result to do benefit to managers, rationally use meadow resource, enclose when the meadow biomass is little and forbid pasturing, the release grazing when the meadow biomass is many.

It will be understood by those skilled in the art that in the method of the present invention, the order of writing of each step in the method of the present invention does not imply a strict order of execution and should in any way limit the process of execution, and that the specific order of execution of each step should be determined by its function and possible inherent logic.

In addition, the present disclosure also provides a device, an electronic device, a computer-readable storage medium, and a program for predicting grassland biomass, which can be used to implement any method for predicting grassland biomass provided by the present disclosure, and further details are not repeated for the corresponding technical solutions and descriptions and corresponding descriptions in the methods section.

Fig. 8 is a block diagram illustrating a prediction apparatus of grass biomass according to an embodiment of the present disclosure, as shown in fig. 8, including:

the information acquisition module 10 is used for acquiring grassland parameter information, wherein the grassland parameter information comprises at least one of soil characteristics, meteorological characteristics, climate characteristics and additional characteristics of a grassland monitoring environment;

a determination module 20 for determining carbon dioxide flux information using the meadow parameter information;

and the prediction module 30 is used for obtaining the grassland biomass grade by using the grassland parameter information and the carbon dioxide flux information according to a set rule.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 9 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 9, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operating mode, such as a shooting mode or a video mode. Each of the front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in memory 804 or transmitted via communications component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing status assessments of various aspects to the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 10 shows a block diagram of another electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 10, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the method described above.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer readable storage medium having computer readable program instructions embodied therewith for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a variety of computing/processing devices, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting grassland biomass, comprising:

and obtaining the grassland biomass grade by using the grassland parameter information and the carbon dioxide flux information according to a set rule.

2. The method of claim 1, wherein said using said grass parameter information to determine carbon dioxide flux information comprises:

3. The method of claim 2, wherein determining the predictive model based on the obtained grass parameter information comprises:

4. The method of claim 2 or 3, wherein said deriving said carbon dioxide flux information from said predictive model comprises:

obtaining the carbon dioxide flux by using the regression analysis model;

5. The method of any one of claims 1-3, wherein obtaining the grass biomass rating using the grass parameter information and the carbon dioxide flux information according to a set rule comprises:

6. The method of claim 5, wherein the blurring the grass parameter information is performed, and the obtaining a first fuzzy set corresponding to the grass parameter information comprises:

7. The method of claim 5 or 6, wherein determining the second fuzzy set corresponding to the carbon dioxide flux information based on the carbon dioxide flux information comprises at least one of:

8. A prediction apparatus for grassland biomass, comprising:

9. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1-8.

10. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1-8.