Regional carbon sink change prediction method, device, equipment, medium and product

Abstract

The invention relates to the technical field of electronics, and discloses a method, a device, equipment, a medium and a product for predicting the change of regional carbon sequestration, and the method comprises the steps: determining the growth time of vegetation in a corresponding unit according to the initial time of the change of land utilization information in each unit; calculating the vegetation carbon sink amount of the corresponding unit based on the vegetation type and vegetation growth time in each unit, and inputting the land utilization information corresponding to different historical periods in the target area and the land utilization information of the target period into a hydrological model, so that the model outputs underground runoff key parameters of each unit in the target period, the karst carbon sink amount of the corresponding unit in the target time period is calculated based on the underground runoff key parameter of each unit, the total carbon sink amount of the target area in the target time period is calculated based on the vegetation carbon sink amount and the karst carbon sink amount of each unit in the target time period, and the carbon sink amount of the target time period and the historical carbon sink amount are utilized to determine a regional carbon sink amount change prediction value. And the change of the determined regional carbon sink quantity is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of electronic technology, and particularly to a method, device, equipment, medium and product for predicting changes in regional carbon sink volume. Background Art

[0002] Carbon sequestration by terrestrial ecosystems is one of the key ways to achieve the "dual carbon" strategic goal. Accurately predicting changes in the carbon sink volume of terrestrial ecosystems is crucial for supporting regions in formulating carbon emission reduction strategies and guiding green and low-carbon development. Generally speaking, the carbon sink volume of terrestrial ecosystems includes two types: the carbon sink volume of vegetation ecosystems and the carbon sink volume of karst. In related technologies, the carbon sink volume under the land use (cover) scenario at a specific period is mostly predicted through field experiments or using remote sensing data, and then the change in the carbon sink volume relative to the historical period is determined. When predicting the carbon sink volume, it is generally based on the current vegetation distribution in the study area. However, when the vegetation growth type in the area has changed, it will have a certain impact on the subsequent carbon sink volume in this area, because the ability of vegetation to absorb carbon dioxide is different in different growth periods. If only the current vegetation type is used to calculate the basin carbon sink without considering the impact of land use transfer and transformation on the subsequent carbon sink volume, it will lead to incomplete and inaccurate accounting of the total basin carbon sink volume, and thus unable to accurately predict the change in the regional carbon sink volume. Summary of the Invention

[0003] In view of this, the present invention provides a method, device, equipment, medium and product for predicting changes in regional carbon sink volume, so as to solve the problem in related technologies that when predicting the carbon sink volume, the impact of land use change on the subsequent carbon sink volume is not considered, resulting in incomplete and inaccurate accounting of the total basin carbon sink volume, and thus unable to accurately predict the change in the regional carbon sink volume.

[0004] In a first aspect, the present invention provides a method for predicting changes in regional carbon sink amounts. The method includes: obtaining the land use information corresponding to different historical periods in a target area, the growth functions corresponding to different types of vegetation, the historical carbon sink amount of the target area, and the land use information of a target period, where the growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes multiple units; determining the vegetation type and vegetation growth time of each unit in the target period based on the land use information corresponding to different historical periods and the land use information of the target period, and the vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes; inputting the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period, and the target growth function of each unit is determined by the vegetation type of the corresponding unit and the growth functions corresponding to different types of vegetation; calculating the vegetation carbon sink amount of the corresponding unit in the target period based on the biomass characteristic parameters of each unit; inputting the land use information corresponding to different historical periods in the target area and the land use information of the target period into a pre-constructed hydrological model so that the model outputs the key parameters of groundwater runoff of each unit in the target period; calculating the karst carbon sink amount of the corresponding unit in the target period based on the key parameters of groundwater runoff of each unit; calculating the total carbon sink amount of the target area in the target period based on the vegetation carbon sink amount and karst carbon sink amount of each unit in the target period, and determining the predicted value of the carbon sink amount change information of the target area based on the carbon sink amount of the target area in the target period and the historical carbon sink amount.

[0005] The prediction method for the change in regional carbon sink provided by the present invention determines the vegetation type and vegetation growth time of each unit in the target period through the land use information corresponding to different historical periods of the target area and the land use information of the target period. The vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes. The vegetation growth time of each unit is input into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period; based on the biomass characteristic parameters of each unit, the vegetation carbon sink of the corresponding unit in the target period is calculated. By determining the starting time when the land use information in each unit changes, the growth time of the vegetation in the corresponding unit is accurately defined. Based on the type and growth time of the vegetation in each unit, the carbon sink of the corresponding unit is calculated, effectively considering the impact of the change in the land use information of each unit on the subsequent vegetation carbon sink, making the calculation result of the vegetation carbon sink more accurate. The land use information corresponding to different historical periods of the target area and the land use information of the target period are input into a pre-constructed hydrological model, so that the model outputs the key parameters of the groundwater runoff of each unit in the target period, considering the impact of the change in the land use information on the subsequent groundwater runoff parameters, making the obtained groundwater runoff parameters more accurate. Based on the key parameters of the groundwater runoff of each unit, the karst carbon sink of the corresponding unit in the target period is calculated. Based on the vegetation carbon sink and karst carbon sink of each unit in the target period, the total carbon sink of the target area in the target period is calculated. The predicted value of the carbon sink change information of the target area is determined by using the carbon sink of the target area in the target period and the historical carbon sink, more comprehensively and accurately predicting the total carbon sink of the basin, making the predicted value of the finally determined carbon sink change information more accurate.

[0006] In an alternative embodiment, the step of determining the vegetation type and vegetation growth time of each unit in the target period based on the land use information corresponding to different historical periods and the land use information of the target period includes: based on the land use information corresponding to different historical periods and the land use information of the target period, determining the first type of units in which the land use information changes, the second type of units in which the land use information does not change, and the starting moment when the vegetation type of each first type of unit changes; based on the starting time of each first type of unit and the target time in the target period, determining the vegetation growth time of the corresponding first type of unit; based on the target time in the target period, determining the vegetation growth time of each second type of unit.

[0007] In an alternative embodiment, the land use information of the target period is determined through the following steps: obtaining the vegetation coverage information corresponding to different historical periods within the target area; inputting the land use information and vegetation coverage information corresponding to different historical periods within the target area into a pre-constructed prediction model, so that the prediction model outputs the land use information of the target area in the target period.

[0008] In an alternative embodiment, the steps of calculating the vegetation carbon sink amount of the corresponding unit in the target period based on the biomass characteristic parameters of each unit include: calculating the total primary productivity value of the vegetation ecosystem and the vegetation ecosystem respiration value of the corresponding unit based on the biomass characteristic parameters of each unit; calculating the vegetation carbon sink amount of the corresponding unit in the target period based on the total primary productivity value of the vegetation ecosystem and the vegetation ecosystem respiration value of each unit.

[0009] In an alternative embodiment, the key parameters of groundwater runoff include groundwater runoff modulus, the concentration of target ions in groundwater, and groundwater net flow. Calculating the karst carbon sink amount of the corresponding unit in the target period based on the key parameters of groundwater runoff of each unit includes: obtaining the area of each unit; calculating the karst carbon sink amount of the corresponding unit in the target period based on the area of each unit, groundwater runoff modulus, the concentration of target ions in groundwater, and groundwater net flow.

[0010] In an alternative embodiment, the growth functions corresponding to different types of vegetation are determined through the following steps: obtaining the biomass characteristic parameters of each type of vegetation in the target area at different historical times; constructing the growth function of the corresponding type of vegetation based on the biomass characteristic parameters of each type of vegetation at different historical times.

[0011] Second aspect, the present invention provides a prediction device for regional carbon sink quantity change, the device comprising: an acquisition module, configured to acquire the land use information corresponding to different historical periods of a target area, the growth functions corresponding to different types of vegetation, the historical carbon sink quantity of the target area, and the land use information of a target period, where the growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes a plurality of units; a first determination module, configured to determine the vegetation type and vegetation growth time of each unit in a target period based on the land use information corresponding to different historical periods and the land use information of the target period, and the vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes; a second determination module, configured to input the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period, and the target growth function of each unit is determined by the vegetation type of the corresponding unit and the growth functions corresponding to different types of vegetation; a first calculation module, configured to calculate the vegetation carbon sink quantity of the corresponding unit in the target period based on the biomass characteristic parameters of each unit; a third determination module, configured to input the land use information corresponding to different historical periods in the target area and the land use information of the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of groundwater runoff of each unit in the target period; a second calculation module, configured to calculate the karst carbon sink quantity of the corresponding unit in the target period based on the key parameters of groundwater runoff of each unit; a third calculation module, configured to calculate the total carbon sink quantity of the target area in the target period based on the vegetation carbon sink quantity and the karst carbon sink quantity of each unit in the target period, and determine the predicted value of the carbon sink quantity change information of the target area based on the carbon sink quantity of the target area in the target period and the historical carbon sink quantity.

[0012] Third aspect, the present invention provides a computer device, comprising: a memory and a processor, which are communicatively connected to each other, where the memory stores computer instructions, and the processor executes the computer instructions to execute the prediction method for regional carbon sink quantity change according to the first aspect or any corresponding embodiment thereof.

[0013] Fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the prediction method for regional carbon sink quantity change according to the first aspect or any corresponding embodiment thereof.

[0014] Fifth aspect, the present invention provides a computer program product, comprising computer instructions, and the computer instructions are used to cause a computer to execute the prediction method for regional carbon sink quantity change according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0016] Figure 1 is a schematic flowchart of a method for predicting regional carbon sink volume change according to an embodiment of the present invention; Figure 2 is a schematic diagram of the growth curve function of different types of vegetation in an embodiment of the present invention; Figure 3 is a schematic diagram for comparative analysis of the total carbon sink volume of the terrestrial ecosystem in different historical periods and the target period in an embodiment of the present invention; Figure 4 is a schematic flowchart of another method for predicting regional carbon sink volume change according to an embodiment of the present invention; Figure 5 is a schematic diagram of the change of vegetation biomass characteristic parameters in an embodiment of the present invention; Figure 6 is a schematic flowchart of yet another method for predicting regional carbon sink volume change according to an embodiment of the present invention; Figure 7 is a structural block diagram of a device for predicting regional carbon sink volume change according to an embodiment of the present invention; Figure 8 is a schematic diagram of the hardware structure of a computer device in an embodiment of the present invention. Specific Embodiments

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0018] In related technologies, the carbon sink amount under the land use (cover) scenario at a specific period is mostly predicted through field test monitoring or by using remote sensing data, and then the change of the carbon sink amount relative to the historical period is determined. When predicting the carbon sink amount, it is generally based on the current vegetation distribution in the research area. However, when the vegetation growth type in the area has changed, it will have a certain impact on the subsequent carbon sink amount in this area, because the ability of vegetation to absorb carbon dioxide is different in different growth periods. If only the current vegetation type is used to calculate the basin carbon sink without considering the impact of land use transfer and transformation on the subsequent carbon sink amount, it will lead to incomplete and inaccurate calculation of the total basin carbon sink amount, and thus unable to accurately predict the change of the carbon sink amount in the area.

[0019] In view of this, a prediction method for regional carbon sink amount change provided by an embodiment of the present application can be applied to a server to achieve the prediction of regional carbon sink amount change. The method provided by the present invention determines the growth time of vegetation in the corresponding unit by the starting time when the land use information in each unit changes, realizes the accurate definition of the vegetation growth time, calculates the carbon sink amount of the corresponding unit based on the type and growth time of vegetation in each unit, effectively considers the impact of the change of land use information in each unit on the subsequent vegetation carbon sink amount, and makes the calculation result of the vegetation carbon sink amount more accurate. Input the land use information corresponding to different historical periods in the target area and the land use information of the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of groundwater runoff in each unit at the target period, considers the impact of the change of land use information on the subsequent groundwater runoff parameters, makes the obtained groundwater runoff parameters more accurate, calculates the karst carbon sink amount of the corresponding unit at the target period based on the key parameters of groundwater runoff in each unit, calculates the total carbon sink amount of the target area at the target period based on the vegetation carbon sink amount and karst carbon sink amount of each unit at the target period, and determines the predicted value of the carbon sink amount change information of the target area by using the carbon sink amount and historical carbon sink amount of the target area at the target period, more comprehensively and accurately predicts the total basin carbon sink amount, and makes the finally determined predicted value of the carbon sink amount change information more accurate.

[0020] According to an embodiment of the present invention, an embodiment of a prediction method for regional carbon sink amount change is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0021] In this embodiment, a prediction method for regional carbon sink amount change is provided, which can be used for the above-mentioned server. Figure 1 It is a flowchart of the prediction method for regional carbon sink amount change according to an embodiment of the present invention, as Figure 1As shown in the figure, the process includes the following steps: Step S101: Obtain the land use information corresponding to the target area in different historical periods, the growth functions corresponding to different types of vegetation, the historical carbon sink amount of the target area, and the land use information of the target period. The growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes multiple units.

[0022] Exemplarily, the target area can be a research area where the change in regional carbon sink amount needs to be predicted. The soil use information is used to characterize the vegetation type information growing on the land, and can also be understood as vegetation cover information; the historical carbon sink amount can be the carbon sink amount in any historical period. The target period can include but is not limited to a certain future period.

[0023] In some alternative embodiments, the land use information of the target period is determined through the following steps: Step a1: Obtain the vegetation cover information corresponding to different historical periods within the target area. Exemplarily, in the embodiments of the present application, through multi-temporal remote sensing image data of the target area in different historical periods, supervised classification of land use is carried out using decision trees + manual visual interpretation, and the vegetation cover is calculated using characteristic bands, so as to obtain the vegetation cover in different historical periods.

[0024] Step a2: Input the land use information and vegetation cover information corresponding to different historical periods within the target area into a pre-constructed prediction model, so that the prediction model outputs the land use information of the target area in the target period.

[0025] Exemplarily, the prediction model can predict the land use information in the target period based on the land use information and vegetation coverage information in the historical period. In the embodiments of the present application, machine learning algorithms such as random forest and neural network are used to establish a non-linear mapping relationship between meteorological and socio-economic key factors and the change of vegetation coverage (Leaf Area Index, LAI); physical models such as the Patch-generating Land Use Simulation (PLUS) model are used to establish boundary constraints on the transfer and transformation of land use by meteorological and social key factors. The machine learning method, taking the neural network as an example, is a black box model, including an input layer, a hidden layer and an output layer. Its basic principle is to input feature items, and then through one round or multiple rounds of 'pseudo-feature' construction, and optimally solved by an algorithm (activation function) to obtain output items, so as to establish a non-linear relationship between input and output; the PLUS model includes two major modules: the Land Expansion Analysis Strategy (LEAS) and the Cellular Automaton (CA) model based on multi-type random patch seeds and the Cellular Automata Model with Multiple Random Patch Seeds (CARS) model, and embeds a Markov chain for land use quantity prediction. The LEAS module is used to extract the part of the expansion of various land uses between two periods of land use changes, and based on the Random Forest Classification (RFC) algorithm, mine the driving factors and their weights of the expansion of various land uses to establish boundary constraints, so as to generate the development probability of various land uses; the CARS module combines the development probability and the domain effect, uses the random seed generation and threshold decreasing mechanism to simulate the spatio-temporal dynamic changes of future land use, and finally uses the Markov chain for land use quantity prediction. Its basic principle is shown in the following formula:

[0026] Wherein, It is determined by the following formula:

[0027] In the formula, S t+1 and S t respectively represent the land use in the t+1 and t periods, P ij represents the transition probability matrix, which is obtained according to the boundary constraints on the transfer and transformation of land use by meteorological and social key factors;n is the land use type.

[0028] On this basis, the multi-scenario future climate change data and socioeconomic change data are brought into the mapping relationship between the meteorological and socioeconomic key factors and the vegetation cover change established by the above machine learning algorithm and physical model to predict the future land use change in the area to be studied.

[0029] In some alternative embodiments, the growth functions corresponding to different types of vegetation are determined through the following steps: Step b1, obtain the biomass characteristic parameters of each type of vegetation in the target area at different historical times.

[0030] Exemplarily, in the embodiments of the present application, multi-period remote sensing image data of the area to be studied in historical times are collected, supervised classification of land use is carried out using decision trees + manual visual interpretation, and vegetation coverage is calculated using characteristic bands; Secondly, nested processing of land use and vegetation coverage at different times is carried out, and units with constant vegetation types and fluctuating vegetation coverage (leaf area index) are selected as potential survey sample points; Finally, sample point surveys are carried out to collect the biomass characteristic parameter indicators of each vegetation type at different growth stages, and the biomass characteristic parameters of different types of vegetation at different historical times are obtained.

[0031] Step b2, construct the growth function of the corresponding type of vegetation based on the biomass characteristic parameters of each type of vegetation at different historical times.

[0032] Exemplarily, support the establishment of growth curve functions for different types of vegetation. As Figure 2 shown, including the change functions of vegetation DBH, height, coverage, etc. over time:

[0033] Among them, D i , H i , LAI i respectively represent the DBH, height, and coverage biomass characteristic indicators of the i vegetation type; f 1i (t), f 2i (t), f 3i (t) respectively represent the change functions of each characteristic parameter over time.

[0034] Step S102: Based on the land use information corresponding to different historical periods and the land use information of the target period, determine the vegetation type and vegetation growth time of each unit in the target period. The vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes.

[0035] Exemplarily, in the embodiments of the present application, the transfer and transformation analysis of the land use information of multiple historical periods and the target period can be carried out through the ARCGIS tool of the geographic information system software, so as to determine the time for which the vegetation of the growth type of each unit in the target period continues to grow.

[0036] Step S103: Input the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period. The target growth function of each unit is determined by the vegetation type of the corresponding unit and the growth functions corresponding to different types of vegetation.

[0037] Exemplarily, based on the plant types of each unit in the target period, the determined target growth functions corresponding to different types of plants. Input the vegetation growth time of each unit into the corresponding target growth function for calculation to obtain the biomass characteristic parameters of the corresponding unit in the target period.

[0038] Step S104: Calculate the vegetation carbon sink amount of the corresponding unit in the target period based on the biomass characteristic parameters of each unit.

[0039] Exemplarily, in the embodiments of the present application, the biomass characteristic parameters may include but are not limited to the breast diameter, height, and coverage of the vegetation. Calculate the vegetation carbon sink amount of the corresponding unit based on the biomass characteristic parameters of each unit. The embodiments of the present application do not limit the specific calculation method, as long as the calculation of the vegetation carbon sink amount can be achieved.

[0040] Step S105: Input the land use information corresponding to different historical periods and the land use information of the target period in the target area into a pre-constructed hydrological model, so that the model outputs the key parameters of the subsurface runoff of each unit in the target period.

[0041] Exemplarily, the hydrological model may include but is not limited to a distributed hydrological model. In the embodiments of the present application, a distributed hydrological model is constructed based on the topography and geomorphology of the target area, historical land use, meteorological and hydrological data, etc., such as the Soil and Water Assessment Tool (SWAT) model. Input the land use information corresponding to different historical periods and the land use information of the target period into the pre-constructed hydrological model to simulate the spatio-temporal changes of the key parameters of surface runoff and subsurface runoff in the target area.

[0042] Step S106: Calculate the karst carbon sink amount of the corresponding unit in the target period based on the key parameters of the underground runoff of each unit.

[0043] Exemplarily, in the embodiments of the present application, the karst carbon sink amount of the corresponding unit is calculated through the key parameters of the underground runoff of each unit. The calculation process of the karst carbon sink amount in the embodiments of the present application is not limited as long as it is reasonable.

[0044] Step S107: Calculate the total carbon sink amount of the target area in the target period based on the vegetation carbon sink amount and the karst carbon sink amount of each unit in the target period, and determine the predicted value of the carbon sink amount change information of the target area based on the carbon sink amount and the historical carbon sink amount of the target area in the target period.

[0045] Exemplarily, in the embodiments of the present application, the vegetation carbon sink amount and the karst carbon sink amount of each unit in the target period are summed to obtain the total carbon sink amount of the corresponding target area in the target period. The difference between the carbon sink amount of the target area in the target period and the historical carbon sink amount is obtained to obtain the predicted value of the carbon sink amount change information of the target area. The carbon sink amount of the target area in the target period and the historical carbon sink amount can be calculated by the following formula:

[0046] Wherein, EC 未i and EC 历史 respectively represent the carbon sink amounts of the watershed terrestrial ecosystem in the i-scenario of the target period and the historical period; α represents the unit conversion coefficient.

[0047] Conduct a comparative analysis of the total carbon sink amounts of the terrestrial ecosystem in the historical period and the target period. For specific reference, see Figure 3 to obtain the predicted value of the carbon sink amount change information of the target area:

[0048] Wherein, Δ EC i represents the predicted value of the carbon sink amount change information of the target area.

[0049] The prediction method for regional carbon sink volume change provided in this embodiment determines the growth time of vegetation in corresponding units by the starting time when land use information changes in each unit, achieving accurate definition of the vegetation growth time. Based on the type of vegetation and the vegetation growth time in each unit, the carbon sink volume of the corresponding unit is calculated, effectively considering the impact of changes in land use information in each unit on the subsequent vegetation carbon sink volume, making the calculation result of the vegetation carbon sink volume more accurate. Input the land use information corresponding to different historical periods within the target area and the land use information of the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of groundwater runoff in each unit during the target period, considering the impact of changes in land use information on subsequent groundwater runoff parameters, making the obtained groundwater runoff parameters more accurate. Based on the key parameters of groundwater runoff in each unit, calculate the karst carbon sink volume of the corresponding unit during the target period. Based on the vegetation carbon sink volume and karst carbon sink volume of each unit during the target period, calculate the total carbon sink volume of the target area during the target period. Based on the carbon sink volume and historical carbon sink volume of the target area during the target period, determine the predicted value of the carbon sink volume change information of the target area, predicting the total carbon sink volume of the basin more comprehensively and accurately, making the predicted value of the finally determined carbon sink volume change information more accurate.

[0050] In this embodiment, a prediction method for regional carbon sink volume change is provided, which can be used in the above-mentioned server. Figure 4 It is a flowchart of the prediction method for regional carbon sink volume change according to an embodiment of the present invention, as Figure 4 shown, and this process includes the following steps: Step S401, obtain the land use information corresponding to different historical periods in the target area, the growth functions corresponding to different types of vegetation, the historical carbon sink volume of the target area, and the land use information of the target period. The growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes multiple units. For details, please refer to Figure 1 Step S101 of the embodiment shown, which will not be elaborated here.

[0051] Step S402, based on the land use information corresponding to different historical periods and the land use information of the target period, determine the vegetation type and vegetation growth time of each unit in the target period.

[0052] Specifically, the above step S402 includes: Step S4021, based on the land use information corresponding to different historical periods and the land use information of the target period, determine the first type of units where land use information changes, the second type of units where land use information does not change, and the starting moment when the vegetation type changes in each first type of unit.

[0053] Exemplarily, in the embodiments of the present application, the ARCGIS tool is used to perform the transfer and transformation analysis of historical and future multi-period land use, determine whether the land use of each unit in the area to be studied has changed, the time when the change occurred, and the vegetation type it has been transformed into, etc. According to the time when the land use changes and the transformation type on the unit, the vegetation growth curve function of the unit and the starting value of its independent variable (time) are determined, fully considering the growth process of the vegetation on different units, and calculating the change of the vegetation biomass characteristic parameters. Specifically, as Figure 5 shown, the biomass characteristic parameters include diameter at breast height, height, coverage, etc.

[0054] Step S4022: Based on the starting time of each first-type unit and the target time in the target period, determine the vegetation growth time corresponding to the first-type unit.

[0055] Exemplarily, the target time can be any moment in the target period. The embodiments of the present application do not limit the specific content of the target time, and those skilled in the art can determine it according to needs. For the unit where the land use information has changed, its vegetation growth time is t - t 0, t where represents the target time in the target period, t 0 represents the starting time when the land use information changes. For the unit where the land use has undergone transfer and transformation, the calculation of the vegetation biomass characteristic parameters is shown as the following formula:

[0056] where → represents that the land use changes from vegetation type i to vegetation type j.

[0057] Step S4023: Based on the target time in the target period, determine the vegetation growth time of each second-type unit.

[0058] Exemplarily, for the second-type unit where the land use has not undergone transfer and transformation, its vegetation growth time is t , and the calculation of the vegetation biomass characteristic parameters is expressed as:

[0059] where the specific meanings of the parameters refer to the descriptions in the above steps and will not be elaborated here.

[0060] Step S403: Input the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period. For details, please refer to Figure 1 Step S103 of the embodiments shown, which will not be elaborated here.

[0061] Step S404: Calculate the vegetation carbon sink of the corresponding unit in the target period based on the biomass characteristic parameters of each unit. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0062] Step S405: Input the land use information corresponding to different historical periods in the target area and the land use information in the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of groundwater runoff of each unit in the target period. For details, please refer to Figure 1 Step S105 of the embodiment shown, which will not be elaborated here.

[0063] Step S406: Calculate the karst carbon sink of the corresponding unit in the target period based on the key parameters of groundwater runoff of each unit. For details, please refer to Figure 1 Step S106 of the embodiment shown, which will not be elaborated here.

[0064] Step S407: Calculate the total carbon sink of the target area in the target period based on the vegetation carbon sink and karst carbon sink of each unit in the target period, and determine the predicted value of the carbon sink change information of the target area based on the carbon sink in the target period and the historical carbon sink of the target area. For details, please refer to Figure 1 Step S107 of the embodiment shown, which will not be elaborated here.

[0065] In this embodiment, a prediction method for regional carbon sink change is provided, which can be used for the above-mentioned server. Figure 6 It is a flowchart of the prediction method for regional carbon sink change according to the embodiment of the present invention. As Figure 6 shown, this process includes the following steps: Step S601: Obtain the land use information corresponding to different historical periods in the target area, the growth functions corresponding to different types of vegetation, the historical carbon sink of the target area, and the land use information in the target period. The growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time. The target area includes multiple units. For details, please refer to Figure 4 Step S401 of the embodiment shown, which will not be elaborated here.

[0066] Step S602: Determine the vegetation type and vegetation growth time of each unit in the target period based on the land use information corresponding to different historical periods and the land use information in the target period.

[0067] Step S603: Input the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period. For details, please refer to Figure 4 Step S403 of the embodiment shown, which will not be elaborated here.

[0068] Step S604: Calculate the vegetation carbon sink of the corresponding unit in the target period based on the biomass characteristic parameters of each unit.

[0069] Specifically, the above-mentioned step S604 includes: Step S6041: Calculate the total primary productivity value of the vegetation ecosystem and the vegetation ecosystem respiration value of the corresponding unit based on the biomass characteristic parameters of each unit.

[0070] Step S6042: Calculate the vegetation carbon sink of the corresponding unit in the target period based on the total primary productivity value of the vegetation ecosystem and the vegetation ecosystem respiration value of each unit.

[0071] Exemplarily, in the embodiments of the present application, the vegetation carbon sink of each unit in the target period can be calculated by the following formula:

[0072] In the formula, GPP represents the total primary productivity of the vegetation ecosystem; RE represents the vegetation ecosystem respiration.

[0073] Among them, GPP and RE are calculated by the following formula:

[0074] Among them, C 地上 、 C 地下 respectively represent the aboveground and underground carbon storages of the vegetation; k 1, k 2 are empirical parameters derived from the data of the flux station, LAI represents the leaf area index; D 、 H respectively represent the breast diameter and plant height of the vegetation; CF represents the carbon content coefficient of a certain vegetation type; a 、 b are empirical fitting parameters; r represents the root-shoot ratio of the vegetation; k is a correction coefficient.

[0075] Step S605: Input the land use information corresponding to different historical periods in the target area and the land use information in the target period into the pre-constructed hydrological model, so that the model outputs the key parameters of the subsurface runoff of each unit in the target period.

[0076] Specifically, the above-mentioned step S605 includes: Step S6051: Obtain the area of each unit.

[0077] Exemplarily, in the embodiments of the present application, the area of each unit can be obtained through the map information data of the target area. The embodiments of the present application do not limit the method for obtaining the area of each unit, and those skilled in the art can determine it according to requirements.

[0078] Step S6052, calculate the karst carbon sink amount of the corresponding unit in the target period based on the area of each unit, the groundwater runoff modulus, the concentration of the target ion in the groundwater, and the net groundwater flow.

[0079] Exemplarily, in the embodiments of the present application, the karst carbon sink amount of each unit in the target period can be determined by the following formula:

[0080] Wherein, M i Determined by the following formula:

[0081] Wherein, CC represents the karst carbon sink amount; kk represents the unit conversion coefficient, generally taking 0.031536; M i represents the groundwater runoff modulus of unit i; [HCO3 - i represents the ion concentration in the groundwater of unit i, which can be obtained through on-site investigation and monitoring and model simulation; R di represents the groundwater runoff of unit i; Area i represents the area of unit i.

[0082] Step S606, calculate the karst carbon sink amount of the corresponding unit in the target period based on the key parameters of the underground runoff of each unit. For details, please refer to Figure 4 Step S406 of the embodiment shown, which will not be elaborated here.

[0083] Step S607, calculate the total carbon sink amount of the target area in the target period based on the vegetation carbon sink amount and the karst carbon sink amount of each unit in the target period, and determine the predicted value of the carbon sink amount change information of the target area based on the carbon sink amount and the historical carbon sink amount of the target area in the target period. For details, please refer to Figure 4 Step S407 of the embodiment shown, which will not be elaborated here.

[0084] ​In this embodiment, a prediction device for regional carbon sink change is further provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0085] This embodiment provides a prediction device for regional carbon sink change, as Figure 7 shown, including: An acquisition module 701, configured to acquire the land use information corresponding to the target area in different historical periods, the growth functions corresponding to different types of vegetation, the historical carbon sink of the target area, and the land use information of the target period. The growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes multiple units; A first determination module 702, configured to determine the vegetation type and vegetation growth time of each unit in the target period based on the land use information corresponding to different historical periods and the land use information of the target period. The vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes; A second determination module 703, configured to input the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period; A first calculation module 704, configured to calculate the vegetation carbon sink of the corresponding unit in the target period based on the biomass characteristic parameters of each unit; A third determination module 705, configured to input the land use information corresponding to different historical periods in the target area and the land use information of the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of groundwater runoff of each unit in the target period; A second calculation module 706, configured to calculate the karst carbon sink of the corresponding unit in the target period based on the key parameters of groundwater runoff of each unit; A third calculation module 707, configured to calculate the total carbon sink of the target area in the target period based on the vegetation carbon sink and karst carbon sink of each unit in the target period, and determine the predicted value of the carbon sink change information of the target area based on the carbon sink of the target area in the target period and the historical carbon sink.

[0086] In some alternative implementation manners, the first determination module 702 includes: The first determination sub-module is configured to determine, based on the land use information corresponding to different historical periods and the land use information of the target period, the first type of units in which the land use information has changed, the second type of units in which the land use information has not changed, and the starting moment when the vegetation type corresponding to each first type of unit has changed; The second determination sub-module is configured to determine the vegetation growth time corresponding to each first type of unit based on the starting time of each first type of unit and the target time in the target period; The third determination sub-module is configured to determine the vegetation growth time of each second type of unit based on the target time in the target period.

[0087] In some alternative embodiments, the land use information of the target period is determined through the following steps: Obtain the vegetation cover information corresponding to different historical periods within the target area; Input the land use information and vegetation cover information corresponding to different historical periods within the target area into a pre-constructed prediction model, so that the prediction model outputs the land use information of the target area in the target period.

[0088] In some alternative embodiments, the first calculation module 704 includes: The first calculation sub-module is configured to calculate the total primary productivity value of the vegetation ecosystem and the vegetation ecosystem respiration value corresponding to each unit based on the biomass characteristic parameters of each unit; The second calculation sub-module is configured to calculate the vegetation carbon sink amount of each unit in the target period based on the total primary productivity value of the vegetation ecosystem and the vegetation ecosystem respiration value of each unit.

[0089] In some alternative embodiments, the key parameters of groundwater runoff include groundwater runoff modulus, the concentration of target ions in groundwater, and groundwater net flow. The second calculation module 706 includes: The acquisition sub-module is configured to acquire the area of each unit; The third calculation sub-module is configured to calculate the karst carbon sink amount of each unit in the target period based on the area of each unit, the groundwater runoff modulus, the concentration of target ions in groundwater, and the groundwater net flow.

[0090] In some alternative embodiments, the growth functions corresponding to different types of vegetation are determined through the following steps: Obtain the biomass characteristic parameters of each type of vegetation in the target area at different historical times; Construct the growth function of the corresponding type of vegetation based on the biomass characteristic parameters of each type of vegetation at different historical times.

[0091] The further function descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0092] The prediction device for regional carbon sink volume change in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0093] The embodiment of the present invention also provides a computer device having the above Figure 7 prediction device for regional carbon sink volume change as shown.

[0094] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As shown in Figure 8 , the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as a server array, a set of blade servers, or a multi-processor system). Figure 8 In

[0095] Processor 10 can be a central processor, a network processor, or a combination thereof. Among them, processor 10 can further include a hardware chip. The above hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0096] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0097] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0098] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0099] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0100] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the methods described herein can be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0101] A part of the present invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can call or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include but are not limited to source files, executable files, installation package files, etc. Correspondingly, the ways for a computer to execute computer program instructions include but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0102] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting changes in regional carbon sinks, characterized in that: The method comprises: Obtaining land use information corresponding to different historical periods of the target area, growth functions corresponding to different types of vegetation, historical carbon sinks of the target area, and land use information of the target period, wherein the growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes multiple units; Based on the land use information corresponding to different historical periods and the land use information of the target period, the vegetation type and vegetation growth time of each unit in the target period are determined. The vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes; Inputting the vegetation growth time of each unit into the corresponding target growth function to obtain the biomass characteristic parameters of the corresponding unit in the target period, wherein the target growth function of each unit is determined by the vegetation type of the corresponding unit and the growth functions corresponding to different types of vegetation; Calculate the vegetation carbon sink of the corresponding unit in the target period based on the biomass characteristic parameters of each unit; Inputting the land use information corresponding to different historical periods in the target area and the land use information of the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of underground runoff of each unit in the target period; Based on the key parameters of underground runoff in each unit, the karst carbon sink of the corresponding unit in the target period is calculated; The total carbon sink of the target area during the target period is calculated based on the vegetation carbon sink and karst carbon sink of each unit during the target period. The predicted value of carbon sink change information of the target area is determined based on the carbon sink and historical carbon sink of the target area during the target period.

2. The method according to claim 1, characterized in that: The steps of determining the vegetation type and vegetation growth time of each unit in the target period based on the land use information corresponding to different historical periods and the land use information of the target period include: Based on the land use information corresponding to different historical periods and the land use information of the target period, determining the first type of units whose land use information has changed, the second type of units whose land use information has not changed, and the starting time when the vegetation type corresponding to each first type of unit has changed among the multiple units; Determining vegetation growth time corresponding to the first type unit based on the start time of each first type unit and the target time in the target period; The vegetation growth time of each second type unit is determined based on the target time in the target period.

3. The method according to claim 1, characterized in that The land use information of the target period is determined by the following steps: Obtain vegetation coverage information corresponding to different historical periods in the target area; The land use information and vegetation coverage information corresponding to different historical periods in the target area are respectively input into a pre-built prediction model, so that the prediction model outputs the land use information of the target area in the target period.

4. The method according to claim 1, characterized in that The step of calculating the vegetation carbon sink of the corresponding unit in the target period based on the biomass characteristic parameters of each unit includes: Calculating the total primary productivity value and the respiration value of the vegetation ecosystem of the corresponding unit based on the biomass characteristic parameters of each unit; The vegetation carbon sink of the corresponding unit in the target period is calculated based on the total primary productivity value of the vegetation ecosystem and the respiration value of the vegetation ecosystem of each unit.

5. The method according to claim 3, characterized in that: The key parameters of underground runoff include groundwater runoff modulus, target ion concentration in groundwater and net groundwater flow. The karst carbon sink of the corresponding unit in the target period is calculated based on the key parameters of underground runoff of each unit, including: Get the area of ​​each unit; The karst carbon sink of each unit in the target period is calculated based on the area of ​​each unit, groundwater runoff modulus, target ion concentration in groundwater and net groundwater flow.

6. The method according to claim 4, characterized in that The growth functions corresponding to different types of vegetation are determined by the following steps: Obtain biomass characteristic parameters of various types of vegetation in the target area in different historical periods; The growth function of the corresponding type of vegetation is constructed based on the biomass characteristic parameters of each type of vegetation in different historical periods.

7. A device for predicting changes in regional carbon sinks, characterized in that: The device comprises: An acquisition module is used to acquire land use information corresponding to different historical periods of the target area, growth functions corresponding to different types of vegetation, historical carbon sinks of the target area, and land use information of the target period, wherein the growth function is used to characterize the correlation between biomass characteristic parameters and vegetation growth time, and the target area includes multiple units; The first determination module is used to determine the vegetation type and vegetation growth time of each unit in the target period based on the land use information corresponding to different historical periods and the land use information of the target period. The vegetation growth time of each unit is determined according to the starting time when the land use information in the corresponding unit changes; A second determination module is used to input the vegetation growth time of each unit into a corresponding target growth function to obtain a biomass characteristic parameter of the corresponding unit in a target period, wherein the target growth function of each unit is determined by the vegetation type of the corresponding unit and the growth functions corresponding to different types of vegetation; The first calculation module is used to calculate the vegetation carbon sink of the corresponding unit in the target period based on the biomass characteristic parameters of each unit; The third determination module is used to input the land use information corresponding to different historical periods in the target area and the land use information of the target period into a pre-constructed hydrological model, so that the model outputs the key parameters of underground runoff of each unit in the target period; The second calculation module is used to calculate the karst carbon sink of the corresponding unit in the target period based on the key parameters of underground runoff of each unit; The third calculation module is used to calculate the total carbon sink of the target area in the target period based on the vegetation carbon sink and karst carbon sink of each unit in the target period, and determine the predicted value of the carbon sink change information of the target area based on the carbon sink of the target area in the target period and the historical carbon sink.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting changes in regional carbon sinks according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for predicting changes in regional carbon sinks according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for predicting changes in regional carbon sinks according to any one of claims 1 to 6.

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