A method and system for grassland evaluation for a power plant area

Abstract

The present disclosure provides a method and system for grass and livestock evaluation of a power station area, relating to the technical field of environment. The method comprises: performing adaptive sampling processing on a target power station area based on each pasture resource data to obtain a pasture yield distribution corresponding to each land use type, determining a total carrying capacity distribution corresponding to the target power station area based on each pasture yield distribution and a preset grass and livestock evaluation parameter corresponding to each pasture yield distribution, determining an ecological gain parameter distribution corresponding to the target power station area under different scenario combinations based on an actual carrying capacity distribution and the total carrying capacity distribution, determining a confidence interval corresponding to the total carrying capacity distribution, performing ecological evaluation on the ecological gain parameter distribution, the total carrying capacity distribution and the confidence interval distribution under different scenario combinations, and generating a grass and livestock evaluation report corresponding to the target power station area. Through the above method, quantitative evaluation of pasture resources, carrying capacity and ecological gain under different ecological grazing scenarios is realized.

Description

Technical Field

[0001] This disclosure relates to the field of environmental technology, and more specifically, to a method and system for assessing grass and livestock in power plant areas. Background Technology

[0002] With the development of the new energy industry, new energy facilities such as photovoltaic power stations and solar thermal power stations are gradually being built in ecological areas such as grasslands and deserts. In composite application scenarios such as complementary grazing and photovoltaic, solar thermal and ecological animal husbandry, the areas where new energy facilities are built usually simultaneously undertake energy production and ecological utilization functions. It is necessary to reasonably assess the pasture resources and the number of livestock that can be supported in the areas where new energy facilities are built, so as to provide data basis for grazing management, grass-livestock balance maintenance and ecological benefit evaluation.

[0003] In related technologies, grassland resource assessment and carrying capacity assessment methods typically begin by obtaining quadrat survey data of the target area, then calculate a simple arithmetic mean of the grass yield per quadrat based on the quadrat survey data, and finally substitute this arithmetic mean into an empirical formula to estimate the carrying capacity. Since the external environment of the target area changes dynamically over time, related technologies usually use a fixed annual rate of variation to adjust the carrying capacity, thereby obtaining the carrying capacity of the target area.

[0004] However, due to the influence of photovoltaic panels, support structures, and different land use patterns, the construction area of ​​photovoltaic and solar thermal power plants has formed various regional types, including areas under the panels, between the panels, and natural open spaces. Significant microenvironmental differences exist between these areas, resulting in strong spatial heterogeneity in forage production capacity. Traditional calculation methods based on average yield per quadrat are insufficient to accurately reflect the actual changes in forage yield across different regions, leading to biases in the assessment of carrying capacity.

[0005] Meanwhile, existing methods for assessing carrying capacity typically calculate for specific years or only consider single utilization patterns such as harvesting, without taking into account the impact of different climate years and grassland utilization patterns on forage yield and available resources. They lack dynamic adaptability to different climate and production management conditions.

[0006] In addition, the relevant technologies mainly focus on the calculation of the overall pasture resources and carrying capacity in the region. However, photovoltaic and solar thermal facilities can change the local microenvironment by shading, reducing wind speed, and improving local water and heat conditions, so that the pasture production capacity of the facility coverage area is different from that of the natural grassland area. The above methods are difficult to distinguish the resource gains between the facility coverage area and the natural grassland.

[0007] Therefore, accurately assessing the pasture resources and carrying capacity of the target area has become a major problem to be solved.

[0008] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The purpose of this disclosure is to provide a method, system, electronic device, and computer-readable storage medium for assessing grass and livestock in power plant areas, enabling quantitative assessment of pasture resources, carrying capacity, and ecological gains under different ecological grazing scenarios.

[0010] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0011] According to a first aspect of the present disclosure, a method for assessing grass and livestock in a power plant area is provided, the method comprising: Obtain quadrat survey data corresponding to the target power plant area, wherein the quadrat survey data includes pasture resource data corresponding to different land use types; Based on the data of various pasture resources, adaptive sampling processing is performed on the target power plant area to obtain the pasture yield distribution corresponding to each land use type. Under different scenario combinations in the preset ecological grazing scenario, the total carrying capacity distribution corresponding to the target power station area is determined based on each forage yield distribution and the preset forage-livestock assessment parameters corresponding to each forage yield distribution; wherein, the preset ecological grazing scenario includes combinations of different environmental conditions and different grazing strategies. Determine the actual carrying capacity distribution of the control area of ​​the target power station area under different scenario combinations, and based on the actual carrying capacity distribution and the total carrying capacity distribution, determine the ecological gain parameter distribution of the target power station area under different scenario combinations; Determine the confidence interval corresponding to the total carrying capacity distribution, conduct ecological assessments on the distribution of the ecological gain parameters, the total carrying capacity distribution, and the confidence interval distribution under different scenario combinations, and generate a grass-livestock assessment report corresponding to the target power station area.

[0012] In some example embodiments of this disclosure, based on the foregoing scheme, obtaining the sample plot survey data corresponding to the target power plant area includes: Multiple quadrats and their corresponding location information are determined from the target power plant area; Determine the forage type determination result for each quadrat, and based on the forage type determination result, determine the forage yield information for each quadrat. The forage yield information corresponding to all quadrats in each land use type is statistically analyzed to obtain the forage resource data corresponding to each land use type. Based on the data of each pasture resource and the location information of each quadrat in each land use type, the quadrat survey data corresponding to the target power plant area is generated.

[0013] In some example embodiments of this disclosure, based on the foregoing scheme, determining the forage type determination result corresponding to each quadrat, and determining the forage yield information corresponding to each quadrat based on the forage type determination result, includes: If the quadrat contains plants of a specified forage type, then the plant weight and quadrat area corresponding to the quadrat are determined, and the forage yield information is generated based on the plant weight and the quadrat area. If the quadrat does not contain plants of the specified forage type, then the area of ​​the quadrat region corresponding to the quadrat is determined, and the weight of the plants corresponding to the quadrat is set to a preset value. Based on the preset value and the area of ​​the quadrat region, the forage yield information is generated.

[0014] In some example embodiments of this disclosure, based on the foregoing scheme, the adaptive sampling processing of the target power plant area based on various pasture resource data to obtain the pasture yield distribution corresponding to each land use type includes: Perform the following operations for each land use type: The number of quadrats corresponding to each quadrat type in each land use type and the forage yield information corresponding to each quadrat are counted. If the number of sample plots is not greater than the first sample plot number threshold, then the average forage yield is determined based on the forage yield information corresponding to each sample plot, and the sample plots are sampled multiple times based on a preset number of resampling times to obtain the forage yield distribution. If the number of quadrats is greater than the first quadrat number threshold and less than the second quadrat number threshold, then each quadrat is sampled without replacement based on the preset number of resampling times and the preset sampling ratio, and the forage yield distribution is determined based on the forage yield information of each sampled quadrat; wherein, the first quadrat number threshold is less than the second quadrat number threshold. If the number of quadrats is not less than the second quadrat number threshold, then each quadrat is sampled with replacement based on the preset number of resampling times, and the forage yield distribution is determined based on the forage yield information of each sampled quadrat; wherein, the proportion of quadrat corresponding to a quadrat that appears once in each sampling is not less than a preset proportion threshold.

[0015] In some example embodiments of this disclosure, based on the foregoing scheme, determining the total carrying capacity distribution corresponding to the target power plant area based on each forage yield distribution and the preset forage-livestock assessment parameters corresponding to each forage yield distribution includes: For each land use type in the target power plant area, the following steps are performed for each single sampling: The grassland forage regeneration rate, annual variation rate of grassland production, grassland utilization rate, standard hay conversion factor, daily feed intake per unit of livestock, and number of grazing days per year are extracted from the preset grassland and livestock assessment parameters. Based on the grassland forage regeneration rate, the annual variation rate of grassland forage production, and the forage yield, the annual available yield corresponding to each land use type in the target power plant area is determined. Based on the grassland utilization rate, the standard hay conversion factor, the daily feed intake per unit of livestock, the number of grazing days per year, and the annual available yield, the reasonable carrying capacity per unit area for each land use type in the target power station area is determined. Multiply the reasonable carrying capacity per unit area by the usable area corresponding to each land use type to obtain the total carrying capacity corresponding to each land use type in a single sampling. The total carrying capacity distribution is generated based on the total carrying capacity corresponding to each sampling for each land use type.

[0016] In some exemplary embodiments of this disclosure, based on the foregoing scheme, determining the distribution of ecological gain parameters corresponding to the target power station area under different scenario combinations based on the actual carrying capacity distribution and the total carrying capacity distribution includes: Each time the target power station area is sampled, the total carrying capacity corresponding to the target power station area is obtained, and the actual carrying capacity corresponding to the control area is obtained. Based on the difference between the total carrying capacity and the actual carrying capacity corresponding to each sampling, the gain carrying capacity distribution is determined; Determine the current actual carrying capacity corresponding to the target power station area, and determine the incremental carrying capacity distribution based on the difference between each total carrying capacity in the total carrying capacity distribution and the current actual carrying capacity; The ecological gain parameter distribution is generated based on the gain carrying capacity distribution and the incremental carrying capacity distribution.

[0017] In some example embodiments of this disclosure, based on the foregoing scheme, determining the confidence interval corresponding to the total carrying capacity distribution includes: The total carrying capacity is sorted according to a preset order for each total carrying capacity in the total carrying capacity distribution to obtain a total carrying capacity sequence; Based on the pre-stored first quantile and second quantile, determine the first livestock load corresponding to the first quantile and the second livestock load corresponding to the second quantile from the total livestock load sequence; Based on the first carrying capacity and the second carrying capacity, the confidence interval corresponding to the total carrying capacity distribution is generated.

[0018] In some exemplary embodiments of this disclosure, based on the foregoing scheme, the ecological assessment of the distribution of the ecological gain parameters, the distribution of the total carrying capacity, and the confidence interval under different scenario combinations, and the generation of a grass-livestock assessment report corresponding to the target power station area, includes: Determine the gain confidence interval corresponding to the gain carrying capacity distribution in the ecological gain parameter distribution, and the incremental confidence interval corresponding to the incremental carrying capacity distribution; Based on the gain carrying capacity distribution, the gain confidence interval, the incremental carrying capacity distribution, the incremental confidence interval, the total carrying capacity distribution, and the confidence interval, an ecological assessment of the target power station area under different scenario combinations is conducted to obtain the grass-livestock assessment report.

[0019] In some example embodiments of this disclosure, based on the foregoing scheme, determining the gain confidence interval corresponding to the gain carrying capacity distribution and the incremental confidence interval corresponding to the incremental carrying capacity distribution in the ecological gain parameter distribution includes: The gain carrying capacity is sorted in a preset order for each gain carrying capacity in the gain carrying capacity distribution to obtain a gain carrying capacity sequence, and the incremental carrying capacity is sorted in the preset order for each incremental carrying capacity in the incremental carrying capacity distribution to obtain an incremental carrying capacity sequence. Based on the pre-stored first quantile and second quantile, parameters are extracted from the gain carrying capacity sequence and the incremental carrying capacity sequence, respectively, to obtain the gain confidence interval corresponding to the gain carrying capacity distribution and the incremental confidence interval corresponding to the incremental carrying capacity distribution.

[0020] According to a second aspect of the present disclosure, a grass-livestock assessment system for a power plant area is provided, the system comprising: The data acquisition module is used to acquire sample plot survey data corresponding to the target power station area. The sample plot survey data includes pasture resource data corresponding to different land use types. The quadrat sampling module is used to perform adaptive sampling processing on the target power plant area based on various pasture resource data to obtain the pasture yield distribution corresponding to each land use type. The total carrying capacity module is used to determine the total carrying capacity distribution corresponding to the target power station area under different scenario combinations in a preset ecological grazing scenario, based on each pasture yield distribution and the preset grass-livestock evaluation parameters corresponding to each pasture yield distribution; wherein, the preset ecological grazing scenario includes combinations of different environmental conditions and different grazing strategies. The gain parameter module is used to determine the actual carrying capacity distribution of the control area of ​​the target power station area under different scenario combinations, and based on the actual carrying capacity distribution and the total carrying capacity distribution, to determine the ecological gain parameter distribution of the target power station area under different scenario combinations. The grass-livestock assessment module is used to determine the confidence interval corresponding to the total carrying capacity distribution, to conduct ecological assessments on the distribution of the ecological gain parameters, the total carrying capacity distribution, and the confidence interval distribution under different scenario combinations, and to generate a grass-livestock assessment report corresponding to the target power station area.

[0021] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions that, when executed by the processor, implement the grass-livestock assessment method for a power plant area as described in the first aspect.

[0022] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the grass and livestock assessment method for a power plant area as described in the first aspect.

[0023] The technical solutions provided in this disclosure may have the following beneficial effects: The grassland-livestock assessment method for power plant areas in this embodiment adaptively switches sampling methods based on the number of sample plots for each land use type. This ensures that land use types with small, medium, and large sample plots can output pasture yield distributions with reasonable dispersion. This overcomes the instability of assessment results under small samples and the excessive duplication of standard replacement resampling samples under large samples. Therefore, it can still obtain robust yield distributions even in scenarios with large differences in sample plot numbers, avoiding bias in carrying capacity assessment caused by spatial heterogeneity. Furthermore, it cross-combines climate year and grassland use patterns to form a scenario matrix, outputting the total carrying capacity distribution and its confidence interval in batches under each scenario combination. This reflects the combined impact of climate fluctuations and management methods on pasture resources and carrying capacity, overcoming the problem that traditional static calculations for specific years and single use patterns cannot adapt to different climate and production management conditions. This provides a quantitative basis for flexibly adjusting grazing schemes under different year conditions.

[0024] Furthermore, ecological assessments are conducted on the distribution of ecological gain parameters, total carrying capacity, and confidence intervals under different scenario combinations to obtain a grass-livestock assessment report for the target power station area. This improves the adaptability and completeness of grass-livestock resource assessment in the target power station area and enables quantitative assessment of pasture resources, carrying capacity, and ecological gains under different ecological grazing scenarios.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0027] Figure 1 The schematic diagram illustrates a flow chart of a grass-livestock assessment method for a power plant area according to an embodiment of the present disclosure.

[0028] Figure 2 The diagram illustrates a hierarchical discrimination logic for adaptive sampling processing according to an embodiment of the present disclosure.

[0029] Figure 3 The diagram schematically illustrates a framework diagram of a grass and livestock assessment system for a power plant area according to an embodiment of the present disclosure.

[0030] Figure 4 A schematic diagram of the structure of a computer system of an electronic device according to an embodiment of the present disclosure is shown.

[0031] Figure 5 A schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure is shown.

[0032] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0034] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0035] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0036] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0037] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0038] Furthermore, the accompanying drawings are for illustrative purposes only and are not necessarily drawn to scale. The block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0039] In previous techniques, grassland resource assessment and carrying capacity assessment methods typically began by obtaining quadrat survey data for the target area. Then, a simple arithmetic mean of the forage yield per quadrat was calculated based on this data. This arithmetic mean was then substituted into an empirical formula to estimate the carrying capacity, and a fixed annual rate of variation was used to adjust for this carrying capacity, thus obtaining the carrying capacity of the target area. However, significant microenvironmental differences exist between different areas within the construction area of ​​photovoltaic and solar thermal power plants. Traditional calculation methods based on average quadrat yields cannot accurately reflect the actual changes in forage yield in different areas, leading to biases in the carrying capacity assessment results. Furthermore, the impact of different climatic years and grassland use patterns on forage yield and available resources is not considered, lacking dynamic adaptability to different climatic and production management conditions.

[0040] Furthermore, existing technologies primarily focus on calculating the overall pasture resources and carrying capacity of a region. However, photovoltaic and solar thermal facilities alter the local microenvironment through shading, reducing wind speed, and improving local hydrothermal conditions. This results in differences in pasture production capacity between the facility-covered areas and natural grassland areas within the target region. Therefore, the aforementioned methods struggle to differentiate the resource gains between the facility-covered areas and natural grasslands. Consequently, accurately assessing the pasture resources and carrying capacity of a target region remains a key challenge.

[0041] In this embodiment of the application, a method for assessing grass and livestock resources in a power plant area is provided to accurately calculate the pasture resources and carrying capacity of the power plant area. Figure 1 This illustration schematically shows a flowchart of a grass-livestock assessment method for a power plant area according to an embodiment of this application. The executing entity can be a server or a system. (Reference) Figure 1 As shown, the method for assessing grass and livestock in power plant areas may include the following steps: Step S101: Obtain the sample survey data corresponding to the target power plant area.

[0042] The sample plot survey data includes pasture resource data corresponding to different land use types. The pasture resource data is used to characterize the pasture production capacity under the corresponding land use type. The target power station area is an area that undertakes the dual functions of energy production and ecological utilization. Due to the influence of photovoltaic panels, support structures, and different surface use methods, various land use types have been formed within the target power station area.

[0043] This embodiment of the disclosure requires determining multiple quadrats and the location information of each quadrat within the target power plant area. A quadrat is a sampling unit selected within the target power plant area, and each quadrat corresponds to a quadrat region containing at least one plant type. The quadrat location information is used to characterize the location of the quadrat within the target power plant area.

[0044] In some embodiments, the quadrat location information is a location description text of the quadrat, which may be under the fixed axis plate, between the fixed axis plates of the empirical base, etc.; the quadrat location information may also be the geographical coordinates of the quadrat, such as latitude and longitude coordinates or plane rectangular coordinates.

[0045] Next, the system determines the forage type classification result for each quadrat, and based on the forage type classification result, determines the forage yield information for each quadrat. The forage type classification result is used to indicate whether plants of the specified forage type exist within the corresponding quadrat; the forage yield information is used to indicate the forage yield per unit area of ​​the corresponding quadrat. The specific process is as follows: If the quadrat contains plants of a specified forage type, the plant weight and quadrat area are determined, and forage yield information is generated based on the plant weight and quadrat area.

[0046] In some embodiments, the plant weight is the sum of the aboveground dry weights of all plants belonging to the specified forage type within the quadrat, i.e., the total edible hay of the quadrat, in grams. The system divides the total edible hay of the quadrat by the area of ​​the quadrat region to obtain the basic forage yield per unit area of ​​the quadrat, and then uses the basic forage yield per unit area as forage yield information.

[0047] For example, when the area of ​​a sample plot is 1 square meter, the total edible hay in the sample plot is divided by 1 square meter to obtain the basic yield of forage per unit area in grams per square meter.

[0048] If the quadrat does not contain plants of the specified forage type, then determine the area of ​​the quadrat region corresponding to the quadrat, set the plant weight corresponding to the quadrat to a preset value, and generate forage yield information based on the preset value and the area of ​​the quadrat region.

[0049] Specifically, if the quadrat does not contain plants of the specified forage type, the system determines the area of ​​the quadrat region corresponding to that quadrat and sets the plant weight corresponding to that quadrat to a preset value of 0. Based on the preset value and the area of ​​the quadrat region, forage yield information is generated.

[0050] In some embodiments, the basic yield of forage per unit area of ​​the sample plot is recorded as 0, thereby ensuring that forage yield information is recorded for all sample plots and that no item is missing in the statistics due to individual sample plots having no edible forage.

[0051] In some embodiments, the above-mentioned preset value can also be set to a small value greater than 0, such as a lower limit value that characterizes extremely low yield based on experience in the art. This application does not limit the specific value of the preset value, as long as it can enable quadrats with no edible forage to participate in subsequent statistics.

[0052] The forage type set above is a pre-defined forage type included in the carrying capacity calculation. The forage type can be edible forage, inedible weeds, poisonous weeds, or special items. In this embodiment of the disclosure, the forage type is set to edible forage, that is, the subsequent carrying capacity calculation only considers edible forage.

[0053] For example, edible forage grasses include needlegrass, lysimachia, sand-fixing grass, and wheatgrass; inedible weeds include dog's-ear flower and dandelion; poisonous grasses include wolfsbane and drowsy grass; and special items include dead branches and fallen leaves. The system compares each species appearing in the quadrat with each forage type to classify the species; for species not appearing in any forage type, they are marked as other types.

[0054] Based on the above method, sample plots containing and not containing a specified forage type are processed separately. This ensures that forage yield information is contributed solely by edible forage, while retaining sample plots without edible forage in the statistical sample by assigning preset values. Compared to directly removing sample plots without edible forage, this embodiment retains the contribution of sample plots with low yield values ​​to the distribution, avoiding overestimation of the forage production capacity of the corresponding land use type due to the removal of low-yield sample plots. This makes the forage yield distribution obtained by subsequent adaptive sampling closer to the actual fluctuation.

[0055] The forage yield information corresponding to all quadrats in each land use type is statistically analyzed to obtain forage resource data corresponding to each land use type. Then, based on each forage resource data and the quadrat location information corresponding to each quadrat in each land use type, quadrat survey data corresponding to the target power plant area is generated.

[0056] Specifically, this embodiment of the disclosure pre-stores the correspondence between location description text and land use types, and the association between sample plots and land use types can be realized through keyword matching. For example, the area under a fixed axis plate is mapped to the area under a fixed plate, and the area between fixed axis plates in the empirical base is mapped to the area between fixed plates.

[0057] In some embodiments, the quadrat location information can be the geographic coordinates of the quadrat's geometric center. Based on the quadrat's geographic coordinates and the regional boundaries of each land use type, the quadrat can be mapped to the corresponding land use type through spatial placement. In this embodiment, the forage yield information of each quadrat mapped to the same land use type is summarized to obtain forage resource data corresponding to that land use type. The forage resource data can include forage yield information of each quadrat under that land use type and the number of quadrats.

[0058] Before generating forage resource data, the retrieved forage yield information is preprocessed. Preprocessing can include column name standardization and missing value handling. For example, column names from different sources and with different names are standardized, and fields such as coverage and dry weight are converted from text to numeric types. For missing numeric fields, missing values ​​can be filled or marked according to preset missing value handling rules. Through preprocessing, the system unifies raw data from different batches and formats into a standardized data structure, thereby improving statistical efficiency.

[0059] Based on the above method, after mapping the quadrat location information to land use type, the forage yield information is summarized according to land use type. This allows subsequent adaptive sampling processing and carrying capacity calculation to be carried out on a land use type basis, thereby enabling the forage production capacity of different microhabitats such as under the boards, between the boards, and natural open spaces to be characterized separately. This provides a data basis for distinguishing the resource differences between facility-covered areas and natural grasslands.

[0060] It should be noted that when the target power station is a photovoltaic power station, the land use type includes at least two of the following: under fixed panels, between fixed panels, under flat single-axis panels, between flat single-axis panels, under inclined single-axis panels, between inclined single-axis panels, natural open space, and fenced area; when the target power station is a solar thermal power station, the land use type can also be replaced with under mirror field panels, between mirror field panels, and tower base area, etc.

[0061] Among the aforementioned land use types, areas covered by facilities such as those under or between slabs benefit from improved microclimates through shading, humidification, and wind reduction. The pasture production capacity of these facility-covered areas differs from that of unaffected natural open spaces. Additionally, some land use types can be marked as not included in pastoral calculations.

[0062] For example, if the fenced area is not currently used for grazing, it can be marked as not participating in grazing calculations during grazing resource assessment and retained only as reference data. In this embodiment of the disclosure, whether or not it participates in grazing calculations is a configurable attribute, and there is no limitation on the specific land use type marked as not participating in grazing calculations.

[0063] The aforementioned natural open space is used as a control area for evaluating the ecological gains of the power station. That is, the control area is an area within the target power station area that has not been equipped with new energy facilities and is not affected by the microenvironment of the facilities. This control area can also be a natural grassland area selected from the vicinity of the target power station area. The specific source of the control area is not limited here. The control area is used to characterize the forage production capacity of natural grassland under the same natural conditions.

[0064] The above quadrat survey data comes from on-site sampling in the target power plant area.

[0065] For example, using 1m×1m quadrats, random sampling is conducted within the target power station area during the peak vegetation growth period, with one or more quadrats set up for each land use type. Each land use type can have anywhere from 2 to 21 quadrats, for a total of 32 quadrats. The observation records for each quadrat may include at least one of the following: plant species, number of plant clusters, average plant height, average crown width, coverage, aboveground fresh weight, and aboveground dry weight.

[0066] The quadrat size, sampling period, and number of quadrats for each type are all configurable items and are not limited here.

[0067] Step S102: Based on the data of each pasture resource, perform adaptive sampling processing on the target power station area to obtain the pasture yield distribution corresponding to each land use type.

[0068] In this embodiment of the disclosure, the same operation is performed for each land use type. Therefore, the following operation is performed on a land use type: The number of quadrats corresponding to each quadrat in each land use type is counted. The number of quadrats is the total number of quadrats mapped to that land use type. The forage yield information corresponding to each quadrat is also determined.

[0069] If the number of quadrats is not greater than the threshold for the number of first quadrats, the average forage yield is determined based on the forage yield information corresponding to each quadrat, and multiple samplings are performed on each quadrat based on a preset number of resampling operations to obtain the forage yield distribution. The specific process is as follows: Calculate the arithmetic mean of the basic forage yield per unit area of ​​all sample plots under this land use type, and then use this average forage yield as the average forage yield. Instead of random sampling, repeat the average forage yield a preset number of times to obtain the forage yield distribution composed of the preset number of identical values.

[0070] For example, if the preset number of resampling times M is 100, then the forage yield distribution consists of 100 identical average forage yields.

[0071] Based on the above method, when the number of sample plots is too small, random resampling is insufficient to reliably estimate the fluctuation range of forage yield. Therefore, it is more robust to directly use the average forage yield to represent the yield. At the same time, the average forage yield is resampled a preset number of times to ensure that the distribution of forage yield for each land use type is consistent in data format.

[0072] In some embodiments, when the number of quadrats for a certain land use type is 0, the system marks that land use type as missing. For land use types marked as missing, the system may either backfill by using the average pasture yield of the nearest neighboring similar habitat type, or skip that land use type and explain it in the grass-livestock assessment report.

[0073] If the number of quadrats is greater than the first quadrat number threshold but less than the second quadrat number threshold, then each quadrat is sampled without replacement based on a preset number of resampling attempts and a preset sampling ratio. Based on the forage yield information of each sampled quadrat, the forage yield distribution is determined. The specific process is as follows: Each time, from all the quadrats of this land use type, a portion of the quadrats are randomly selected without replacement according to a preset sampling ratio. The average value of the basic forage yield per unit area of ​​the selected quadrats is calculated, and this average value is used as the average forage yield of this sampling. The above sampling and calculation process is repeated for a preset number of resampling times to obtain the forage yield distribution composed of the average forage yield of the preset number of resampling times.

[0074] For example, there are 6 quadrats for land use type A. The preset sampling ratio is 70%. Each time, 4 quadrats are randomly selected without replacement to calculate the average yield. This is repeated 100 times to obtain the forage yield distribution consisting of 100 average forage yields.

[0075] In some embodiments, the system also sets a minimum number of quadrats to be drawn each time. When the number of samples calculated according to the sampling ratio is less than the minimum number of quadrats, the minimum number of quadrats is used to draw samples, so as to ensure that the sample size for each sampling is not too small. The preset sampling ratio and the minimum number of quadrats are both configurable parameters, and there is no limitation on their specific values ​​here.

[0076] If the number of quadrats is not less than the second quadrat number threshold, then each quadrat is sampled with replacement based on a preset number of resampling attempts. Based on the forage yield information of each sampled quadrat, the forage yield distribution is determined. The proportion of quadrat that appears once in each sampling is not less than a preset proportion threshold. The specific process is as follows: Each time, from all quadrats of that land use type, a number of quadrats with replacement are randomly selected equal to the total number of quadrats. The average value of the basic forage yield per unit area of ​​the selected quadrats is calculated, and this average value is used as the average forage yield of this sampling. The above sampling and calculation process is repeated for a preset number of resampling times to obtain the forage yield distribution composed of the average forage yield of the preset number of resampling times.

[0077] For example, land use type B has 21 quadrats. Each time, 21 quadrats are sampled with replacement to calculate the average yield. This process is repeated 100 times to obtain a forage yield distribution consisting of 100 average forage yields.

[0078] Since the same sample may be selected multiple times during sampling with replacement, in order to avoid excessive repetition of sample plots in a single sampling and cause the average forage yield to deviate from the true distribution, the system constrains the proportion of the unique sample plot in each sampling. The unique sample plot appears only once, that is, the proportion of the sample plot corresponding to the unique sample plot is not lower than the preset proportion threshold.

[0079] In some embodiments, the preset proportion threshold is set to 60%. After each sampling with replacement, the proportion of the unique sample plot in the current sampling result is calculated. If the proportion of the sample plot is not lower than the preset proportion threshold, the sampling result is adopted; if the proportion of the sample plot is lower than the preset proportion threshold, sampling is repeated. The system can set a maximum number of attempts, which can be up to 20. If the constraint is still not met after reaching the maximum number of attempts, the last sampling result is adopted or processed according to preset rules. The preset proportion threshold and the maximum number of attempts are both configurable parameters and are not limited here.

[0080] When the number of quadrats is large, sampling with replacement can make full use of sample information and obtain a smooth distribution of forage yield. Applying a lower limit constraint on the proportion of the unique quadrat suppresses the defect of excessive quadrat duplication in conventional sampling with replacement, so that each sampling covers a sufficient number of different quadrats and avoids distortion of the average forage yield due to repeated sampling of a few quadrats.

[0081] In some embodiments, before performing the various random sampling operations described above, the system first sets a fixed random seed. This random seed serves as the initial input parameter for the random sampling algorithm, determining the sample selection sequence for random sampling to ensure that the results of adaptive sampling processing are reproducible under the same input. The random sampling algorithm can have a first branch, a second branch, and a third branch processing method.

[0082] In some embodiments, the preset number of resampling times is the same in various random samplings, and the preset number of resampling times is 100, so that each land use type adopts the corresponding sampling method according to the number of quadrats, and the distribution of pasture yield corresponding to each land use type is composed of the same number of values, which facilitates subsequent alignment and superposition.

[0083] refer to Figure 2The embodiments of the present disclosure provide a schematic diagram of hierarchical discrimination logic for adaptive sampling processing. First, the number of quadrats corresponding to a land use type is determined. The adaptive sampling processing is divided into three branches according to a first quadrat number threshold and a second quadrat number threshold. When the number of quadrats is less than or equal to the first quadrat number threshold, the adaptive sampling method of the first branch is adopted for processing; when the number of quadrats is greater than the first quadrat number threshold and less than the second quadrat number threshold, the adaptive sampling method of the second branch is adopted for processing; when the number of quadrats is greater than or equal to the second quadrat number threshold, the adaptive sampling method of the third branch is adopted for processing.

[0084] Further, the number of quadrats is n, the first quadrat number threshold is N1, and the second quadrat number threshold is N2. When n≤N1, the land use type is processed by the first branch, that is: the average value of the forage yield of the n quadrats is directly calculated, and then multiple samplings are performed on each quadrat of the land use type based on the preset resampling times to obtain the forage yield distribution corresponding to the land use type; when N1<n<N2, the land use type is processed by the second branch, that is: the non-replacement Monte Carlo resampling is adopted to obtain the forage yield distribution corresponding to the land use type; when n≥N2, the land use type is processed by the third branch, that is: the Bootstrap resampling is adopted to obtain the forage yield distribution corresponding to the land use type.

[0085] It should be noted that the second branch processing can perform non-replacement sampling on each quadrat based on the preset resampling times and the preset sampling ratio, and the third branch processing can perform replacement sampling on each quadrat based on the preset resampling times; the above-mentioned first branch processing, the above-mentioned second branch processing and the above-mentioned third branch processing are only examples, and no excessive description is made here.

[0086] Exemplarily, in the target power station area, 3 quadrats are set up in the natural open space, the number of quadrats is not greater than the first quadrat number threshold of 5, the first branch processing is adopted for the natural open space, and the arithmetic average value of the basic yield of the 3 quadrats is directly taken and repeated 100 times to obtain the forage yield distribution; 6 quadrats are set up under the fixed plates, the number of quadrats is between the first quadrat number threshold of 5 and the second quadrat number threshold of 21, the second branch processing is adopted for the area under the fixed plates, 4 quadrats are drawn without replacement each time to calculate the average yield, and the process is repeated 100 times to obtain the forage yield distribution; 21 quadrats are set up between the fixed plates, the number of quadrats is not less than the second quadrat number threshold of 21, the third branch processing is adopted for the area between the fixed plates, 21 quadrats are drawn with replacement each time, and the unique quadrat proportion is constrained to be no less than 60%, and the process is repeated 100 times to obtain the forage yield distribution.

[0087] Through the aforementioned adaptive sampling process, the system automatically switches the sampling method based on the number of quadrats for each land use type, ensuring that land use types with few, medium, and many quadrats can all output forage yield distributions with reasonable dispersion. Compared to uniformly using quadrat averages or standard replacement resampling for all types, this embodiment avoids both the instability of evaluation results under small samples and the excessive duplication of standard replacement resampling samples under large samples, thereby ensuring accurate carrying capacity calculation under spatially heterogeneous conditions.

[0088] Step S103: Under different scenario combinations in the preset ecological grazing scenario, determine the total carrying capacity distribution corresponding to the target power station area based on each pasture yield distribution and the preset grass-livestock assessment parameters corresponding to each pasture yield distribution.

[0089] The pre-set ecological grazing scenarios include combinations of different environmental conditions and grazing strategies. The environmental conditions include three types of years: bumper harvest, average year, and lean year. The grazing strategies include winter harvest and natural grazing.

[0090] For each land use type in the target power plant area, the following steps are performed for a single sampling: A single sampling corresponds to a pasture yield average in the pasture yield distribution for that land use type; that is, the following steps are performed for each pasture yield average in the pasture yield distribution: The system extracts grassland forage regeneration rate, annual variation rate of grassland yield, grassland utilization rate, standard hay conversion factor, daily feed intake per unit of livestock, and number of grazing days per year from preset grassland-livestock assessment parameters. Among these, the annual variation rate of grassland yield and grassland utilization rate vary depending on the scenario combination.

[0091] In some embodiments, the annual variation rate of grassland production is set to 1.35, 1.00, and 0.55 for bumper, average, and lean years, respectively; the grassland utilization rate is set to 0.90 and 0.45 for winter harvesting and natural grazing, respectively. The grassland forage regeneration rate is determined based on the site of the target power station area; for example, it is set to 0 in Gonghe County, Qinghai Province, and 0.05 in Haidong County. The standard hay conversion factor is set to 0.95, the daily feed intake per sheep unit is set to 1.38 kg per sheep unit per day, and the number of grazing days per year is set to 365. All of the above preset grassland-livestock assessment parameters are configurable, and this application does not limit the specific values ​​of the preset grassland-livestock assessment parameters.

[0092] Next, based on the grassland forage regeneration rate, the annual variation rate of grassland forage production, and forage yield, the annual available yield corresponding to this land use type in the target power plant area is determined.

[0093] Specifically, the system first converts the basic yield of forage per unit area corresponding to a single sampling into standard hay yield. The formula for calculating standard hay yield is as follows: P×10× in, P represents the standard hay yield per unit area, in kilograms per hectare; P represents the basic forage yield per unit area corresponding to a single sampling, in grams per square meter; 10 is the unit conversion factor from grams per square meter to kilograms per hectare. The moisture content of standard hay. This is the conversion factor for converting absolute dry weight to standard hay with a moisture content of 14%.

[0094] The formula for calculating the annual available output for each land use type in the target power plant area is as follows: ×(1+ )× in, The annual yield of edible grass in grassland is expressed in kilograms per hectare. Standard hay yield per unit area; For grassland pasture regeneration rate; The annual variation rate of grassland forage production is used as the basis. By introducing the grassland forage regeneration rate and the annual variation rate of grassland forage production, the system enables the annual available yield to reflect the regeneration capacity of forage and the impact of different years on forage production.

[0095] Then, based on grassland utilization rate, standard hay conversion factor, daily feed intake per unit of livestock, number of grazing days per year, and annual available yield, the system determines the reasonable carrying capacity per unit area for this land use type in the target power plant area. The formula for calculating the reasonable carrying capacity per unit area is as follows: in, The number of sheep units that can be supported per hectare of year-round grazing grassland during the grazing season is expressed as sheep units per hectare per year. This represents the annual available output. For grassland utilization rate, take 0.90 for winter harvest and 0.45 for natural grazing; The standard hay conversion factor is 0.95. The daily feed intake per sheep unit is 1.38 kg per sheep unit per day. The number of grazing days per year is taken as 365 days. The daily feed intake per sheep unit can be calculated according to relevant regulations, for example: based on the assumption that one sheep unit can consume 4.00 kg of fresh grass per day, and the dry-to-fresh grass ratio is 1:2.9, which is equivalent to standard hay.

[0096] Subsequently, the system multiplies the reasonable carrying capacity per unit area by the usable area corresponding to that land use type to obtain the total carrying capacity for that land use type in a single sampling. The formula for calculating the total carrying capacity for a single sampling is as follows: × in, This represents the total number of sheep units that this land use type can support, with sheep units as the unit. The reasonable carrying capacity per unit area; This represents the usable area for this land use type, in hectares. The usable area is the area within this land use type that is included in livestock calculations. Land use types marked as not included in livestock calculations are excluded from the total carrying capacity calculation.

[0097] The system generates a total carrying capacity distribution based on the total carrying capacity corresponding to each sampling for each land use type.

[0098] In some embodiments, the system performs the above calculations for each land use type to obtain a set of total carrying capacity for that land use type under a preset number of resampling times, which constitutes the total carrying capacity distribution of that land use type; the system then aligns and superimposes the total carrying capacity distributions of each land use type participating in the livestock calculation according to the sampling order to obtain the total carrying capacity distribution of the entire region.

[0099] For example, each land use type undergoes 100 iterations. The system sums the total carrying capacity corresponding to the i-th sampling of each land use type to obtain the total carrying capacity corresponding to the i-th sampling of the target power station area, where i ranges from 1 to 100, thus obtaining a total carrying capacity distribution consisting of 100 total carrying capacities for the entire area. The above-mentioned sequential alignment and overlay is based on the premise that the sample plots of each land use type are independent and that each land use type uses the same preset number of resampling iterations.

[0100] The system performs the above calculations for each scenario combination to obtain the total livestock carrying capacity distribution corresponding to that scenario combination. The total livestock carrying capacity distributions corresponding to each scenario combination collectively constitute the total livestock carrying capacity distribution of the target power station area under different scenario combinations.

[0101] By employing the methods described above, the distribution of forage yield is further integrated with grassland forage regeneration rate, annual variation rate of grassland forage production, grassland utilization rate, standard hay conversion factor, daily feed intake per unit of livestock, and number of grazing days per year, among other forage-livestock assessment parameters. This allows for the sequential calculation of annual available yield, reasonable carrying capacity per unit area, and total carrying capacity for different land use types, with the total carrying capacity distribution formed based on each sampling result. This approach effectively incorporates the uncertainty of forage yield into the carrying capacity assessment process, avoiding the use of a single average yield to calculate a definitive carrying capacity result. The final carrying capacity reflects the potential range of carrying capacity variations under different forage yield conditions in a distributed manner. Furthermore, calculating carrying capacity separately for different land use types and then summarizing them preserves the yield differences between different land use types and their impact on the overall carrying capacity of the region, improving the precision of the overall carrying capacity assessment for the power station area.

[0102] Step S104: Determine the actual carrying capacity distribution of the control area of ​​the target power station area under different scenario combinations, and based on the actual carrying capacity distribution and the total carrying capacity distribution, determine the ecological gain parameter distribution of the target power station area under different scenario combinations.

[0103] The process of determining the actual carrying capacity distribution of the control area under different scenario combinations is the same as the process of determining the total carrying capacity distribution of the target power station area. Therefore, the process of determining the actual carrying capacity distribution of the control area under different scenario combinations is described above. This embodiment of the disclosure will not repeat the description of determining the actual carrying capacity distribution of the control area under different scenario combinations.

[0104] Each time the target power station area is sampled, the total carrying capacity of the target power station area and the actual carrying capacity of the control area are obtained.

[0105] The total carrying capacity corresponding to the target power station area mentioned above refers to the total carrying capacity of the facility coverage area to be evaluated within the target power station area under this sampling. The actual carrying capacity corresponding to the control area mentioned above refers to the carrying capacity of natural grassland of the same area as the facility coverage area under this sampling.

[0106] In some embodiments, the facility coverage area to be evaluated is a photovoltaic (PV) zone, which includes at least one of the following: under a fixed panel, between fixed panels, under a horizontal single-axis panel, between horizontal single-axis panels, under an inclined single-axis panel, and between inclined single-axis panels. Alternatively, the facility coverage area to be evaluated is an under-panel area, which includes at least one of the following: under a fixed panel, under a horizontal single-axis panel, and under an inclined single-axis panel. The system can calculate the distribution of ecological gain parameters separately for different facility coverage areas such as PV zones and under-panel areas.

[0107] For concentrated solar power (CSP) plants, the facility coverage area can also be the area under the mirror panels, between the mirror panels, etc. The specific division of the facility coverage area to be evaluated is not limited here.

[0108] In some embodiments, the average carrying capacity per unit area of ​​the control area is used as the control benchmark. The usable area of ​​the facility-covered area is multiplied by the control benchmark to obtain the actual carrying capacity of the control area in this sampling, that is, the theoretical carrying capacity that the same area of ​​natural grassland can support. The control benchmark can be calculated based on the distribution of forage yield in the control area.

[0109] Next, based on the difference between the total carrying capacity and the actual carrying capacity corresponding to each sampling, the gain carrying capacity distribution is determined.

[0110] Specifically, the system subtracts the actual carrying capacity of an equivalent area of ​​natural grassland from the total carrying capacity of each sample in the facility-covered area to obtain the gain carrying capacity for that sample. The system then aggregates the gain carrying capacity for each sample in the sampling order to obtain the gain carrying capacity distribution. The gain carrying capacity characterizes the number of sheep units that the facility-covered area can support relative to an equivalent area of ​​natural grassland, and is used to quantify the ecological benefits of pasture growth brought about by the facility microenvironment.

[0111] In some embodiments, when the number of sample plots in the control area is insufficient, the system uses the control baseline as a point estimate, that is, the average carrying capacity per unit area of ​​the control area is used as a fixed value in the above difference calculation. In this case, the gain carrying capacity distribution only reflects the sampling uncertainty of the facility coverage area itself. When the number of sample plots in the control area is sufficient, the system simultaneously performs adaptive sampling processing on the control area to obtain the actual carrying capacity distribution of the control area, and calculates the difference between the actual carrying capacity corresponding to each sampling in the control area and the total carrying capacity corresponding to each sampling in the facility coverage area, thereby incorporating the uncertainty of the control baseline into the gain carrying capacity distribution. Whether the number of sample plots in the control area is sufficient can be determined according to the aforementioned first sample plot number threshold, etc., which will not be explained in detail here.

[0112] Then, determine the current actual carrying capacity corresponding to the target power station area, and determine the incremental carrying capacity distribution based on the difference between each total carrying capacity and the current actual carrying capacity in the total carrying capacity distribution.

[0113] The above-mentioned current actual livestock carrying capacity refers to the current actual livestock population in the target power station area.

[0114] In some embodiments, the system subtracts the current actual carrying capacity from each total carrying capacity distribution in the target power plant area to obtain the corresponding incremental carrying capacity. Then, the incremental carrying capacities are summed to obtain the incremental carrying capacity distribution. The incremental carrying capacity characterizes the grass-livestock balance state of the target power plant area relative to the current actual livestock population; that is, when the incremental carrying capacity is positive, it indicates potential for increased livestock production; when the incremental carrying capacity is negative, it indicates that the current actual carrying capacity is excessive and the livestock population needs to be reduced.

[0115] For example, if the current actual livestock population in the target power station area is 4,000 sheep units, the system will subtract 4,000 from each total livestock population in the total livestock population distribution of the entire area to obtain the incremental livestock population distribution.

[0116] Finally, both the above-mentioned gain carrying capacity distribution and the above-mentioned incremental carrying capacity distribution are used as ecological gain parameter distributions.

[0117] Using the above method, the increased carrying capacity of the facility-covered area relative to natural grassland is quantified based on the control area, and the incremental carrying capacity of the target power station area is quantified based on the current actual breeding volume. Thus, the total carrying capacity is decomposed into two diagnostic indicators: the ecological gain brought by the facility and the potential for increased breeding or overloading relative to the current breeding situation. This solves the problems of difficulty in distinguishing the differences between the facility-covered area and natural grassland resources, and difficulty in diagnosing the grass-livestock balance in combination with the current breeding status.

[0118] Step S105: Determine the confidence interval corresponding to the total carrying capacity distribution, conduct ecological assessments on the distribution of ecological gain parameters, total carrying capacity distribution, and confidence interval distribution under different scenario combinations, and generate a grass-livestock assessment report corresponding to the target power station area.

[0119] The system sorts each total carrying capacity in the total carrying capacity distribution according to a preset order, resulting in a total carrying capacity sequence. This preset order can be ascending order from smallest to largest. The system arranges each total carrying capacity in the total carrying capacity distribution in ascending order to obtain the total carrying capacity sequence.

[0120] Based on the pre-stored first quantile and second quantile, determine the first livestock load corresponding to the first quantile and the second livestock load corresponding to the second quantile from the total livestock load sequence.

[0121] In some embodiments, the first quantile is 2.5% and the second quantile is 97.5%. In the total carrying capacity sequence, the system uses the value at the 2.5% quantile as the first carrying capacity and the value at the 97.5% quantile as the second carrying capacity. The first and second quantiles are configurable parameters; the first quantile can also be 5% and the second quantile can also be 95%, and no limitation is made here.

[0122] Based on the first and second carrying capacities, a confidence interval corresponding to the total carrying cap distribution is generated.

[0123] The system uses the first carrying capacity as the lower limit and the second carrying capacity as the upper limit to form a confidence interval. When the first quantile and the second quantile are 2.5% and 97.5% respectively, the resulting confidence interval is a 95% confidence interval.

[0124] The system performs the sorting and quantile extraction described above for the total carrying capacity distribution corresponding to each scenario combination, obtaining the confidence interval for that scenario combination. The confidence intervals for each scenario combination constitute a confidence interval distribution.

[0125] Based on the above method, the confidence interval is determined by sorting the distribution of total carrying capacity and extracting quantiles. The range of values ​​for the confidence interval is obtained directly from the total carrying capacity distribution obtained by simulated production, without assuming a distribution pattern. This quantifies the uncertainty of carrying capacity estimation, making the assessment result no longer a single value, but an interval estimate with a fluctuation range.

[0126] The specific process for conducting ecological assessments of the distribution of ecological gain parameters, total carrying capacity distribution, and confidence intervals under different scenario combinations, and generating grassland-livestock assessment reports, is as follows: The specific determination process is as follows: The gain confidence interval corresponding to the gain carrying capacity distribution and the increment confidence interval corresponding to the increment carrying capacity distribution are determined within the ecological gain parameter distribution. The gain carrying capacity is sorted according to a preset order, which can be ascending order from smallest to largest, to obtain a gain carrying capacity sequence. Similarly, the incremental carrying capacity is sorted according to a preset order to obtain an incremental carrying capacity sequence. Then, based on the pre-stored first quantile and second quantile, parameters are extracted from the gain carrying capacity sequence and the incremental carrying capacity sequence to obtain the gain confidence interval corresponding to the gain carrying capacity distribution and the incremental confidence interval corresponding to the incremental carrying capacity distribution.

[0127] In some embodiments, the system uses the value at the first quantile position as the lower limit of the gain confidence interval and the value at the second quantile position as the upper limit of the gain confidence interval in the gain load sequence; and uses the value at the first quantile position as the lower limit of the incremental load sequence and the value at the second quantile position as the upper limit of the incremental confidence interval.

[0128] In some embodiments, the first and second quantiles used to extract the gain confidence interval and the incremental confidence interval are the same as the first and second quantiles used to extract the total carrying capacity distribution confidence interval, that is: the first quantile is 2.5% and the second quantile is 97.5%, so that the confidence intervals of the total carrying capacity, the gain carrying capacity, and the incremental carrying capacity are at the same confidence level.

[0129] By sorting the gain carrying capacity distribution and the incremental carrying capacity distribution separately, and extracting the corresponding parameters using the same first and second quantiles, the gain confidence interval and the incremental confidence interval are obtained respectively. This allows for the quantification of the uncertainty of the power station's ecological gain and the uncertainty of the current grassland-livestock balance, ensuring that the ecological gain, shearing capacity, and overgrazing capacity under different scenarios not only have corresponding assessment values ​​but also corresponding ranges of variation. This improves the comparability and decision-making reference value of different diagnostic indicators in the grassland-livestock assessment report.

[0130] Ecological assessments of the target power plant area under different scenario combinations were conducted based on gain carrying capacity distribution, gain confidence interval, incremental carrying capacity distribution, incremental confidence interval, total carrying capacity distribution, and confidence interval, resulting in a grass-livestock assessment report.

[0131] Specifically, for each scenario combination, the mean of the total carrying capacity distribution, the mean of the gain carrying capacity distribution, and the mean of the incremental carrying capacity distribution are calculated, and combined with their respective confidence intervals, as the ecological assessment result for that scenario combination. The system summarizes the ecological assessment results for each scenario combination to obtain a grass-livestock assessment report.

[0132] In some embodiments, the system also assigns a management recommendation level to each scenario combination based on preset rules.

[0133] For example, the system assigns the scenario combination with the highest carrying capacity and greatest potential for benefit as the optimal level, such as the combination of a bumper harvest and winter harvest; assigns the scenario combination with the lowest carrying capacity and the highest risk of overgrazing as the cautious level, such as the combination of a poor year and natural grazing; and assigns the remaining scenario combinations as the intermediate level. The above-mentioned management recommendation level classification rules are configurable, and this embodiment of the disclosure does not limit the specific classification rules.

[0134] In some embodiments, the grass-livestock assessment report includes at least one of tables and statistical charts.

[0135] For example, the grass-livestock assessment report includes at least one of the following: a multi-scenario summary table, an annual change comparison table, a utilization pattern comparison table, and a management strategy recommendation table; the statistical charts include a bar chart comparing carrying capacity, with error bars indicating confidence intervals. The system can output the above content as a spreadsheet file.

[0136] Using the methods described above, the corresponding gain confidence intervals and increment confidence intervals are determined based on the gain carrying capacity distribution and incremental carrying capacity distribution in the ecological gain parameter distribution. These gain confidence intervals, increment confidence intervals, and carrying capacity distribution are then used together for ecological assessment. This allows both ecological gain analysis and grassland-livestock balance analysis to have uncertainty characterization capabilities, avoiding judgments based solely on a single value of gain carrying capacity or incremental carrying capacity. This improves the reliability of ecological gain and grassland-livestock balance assessment results under different scenarios. Furthermore, by uniformly incorporating total carrying capacity, gain carrying capacity, and incremental carrying capacity into the ecological assessment, a comprehensive assessment result can be formed, encompassing resource carrying capacity, power plant ecological gain, and the actual grassland-livestock balance state.

[0137] In some embodiments, the target power plant area is a mixed photovoltaic and solar thermal arrangement area, and the land use type includes both the under-panel and inter-panel types corresponding to photovoltaic supports and the under-panel and inter-panel types corresponding to mirror fields. All of the above-mentioned different types of target power plant areas can be evaluated using the same adaptive sampling, multi-scenario calculation, and gain diagnosis process; the only difference lies in the classification of land use types and the available area configuration for each type of target power plant area.

[0138] In some embodiments, the forage yield information pertains to sheep, and the carrying capacity is measured in sheep units. The livestock can also be cattle, horses, or other herbivorous animals. In this case, parameters such as daily feed intake per livestock unit can be adjusted according to the corresponding livestock unit conversion relationship, and the carrying capacity can be measured in the corresponding livestock units. The specific type of livestock is not limited here.

[0139] In some embodiments, environmental conditions may include at least one of precipitation conditions and soil conditions in addition to annual climate; grazing strategies may include at least one of rotational grazing and seasonal rest grazing in addition to winter harvesting and natural grazing. The system can combine the above environmental conditions and grazing strategies to form more scenario combinations, and each scenario combination is processed using the same total carrying capacity distribution calculation process.

[0140] By adaptively switching sampling methods based on the number of quadrats for each land use type, the method can output forage yield distributions with reasonable dispersion for land use types with small, medium, and large quadrats. This overcomes the shortcomings of unstable assessment results under small samples and excessive resampling under large samples. Therefore, it can still obtain robust yield distributions even in scenarios with large differences in quadrat numbers, avoiding bias in carrying capacity assessment caused by spatial heterogeneity. Furthermore, by cross-combining climate year and grassland use patterns to form a scenario matrix, the method outputs the total carrying capacity distribution and its confidence interval in batches under each scenario combination. This reflects the combined impact of climate fluctuations and management methods on forage resources and carrying capacity, overcoming the problem that traditional static calculations for specific years and single use patterns cannot adapt to different climate and production management conditions. This provides a quantitative basis for flexibly adjusting grazing schemes under different year conditions.

[0141] Furthermore, ecological assessments are conducted on the distribution of ecological gain parameters, total carrying capacity, and confidence intervals under different scenario combinations to obtain a grass-livestock assessment report for the target power station area. This improves the adaptability and completeness of grass-livestock resource assessment in the target power station area and enables quantitative assessment of pasture resources, carrying capacity, and ecological gains under different ecological grazing scenarios.

[0142] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0143] Furthermore, in this example embodiment, a grass-livestock assessment system for power plant areas is also provided. (Refer to...) Figure 3 As shown, the grass-livestock assessment system 300 for power plant areas may include: a data acquisition module 310, a quadrat sampling module 320, a total carrying capacity module 330, a gain parameter module 340, and a grass-livestock assessment module 350. Wherein: The data acquisition module 310 can be used to acquire sample plot survey data corresponding to the target power station area, wherein the sample plot survey data includes pasture resource data corresponding to different land use types. The quadrat sampling module 320 can be used to perform adaptive sampling processing on the target power plant area based on various pasture resource data to obtain the pasture yield distribution corresponding to each land use type; The total carrying capacity module 330 can be used to determine the total carrying capacity distribution corresponding to the target power station area under different scenario combinations in a preset ecological grazing scenario, based on each pasture yield distribution and the preset grass-livestock evaluation parameters corresponding to each pasture yield distribution; wherein, the preset ecological grazing scenario includes combinations of different environmental conditions and different grazing strategies. The gain parameter module 340 can be used to determine the actual carrying capacity distribution of the control area of ​​the target power station area under different scenario combinations, and based on the actual carrying capacity distribution and the total carrying capacity distribution, determine the ecological gain parameter distribution of the target power station area under different scenario combinations. The grass-livestock assessment module 350 can be used to determine the confidence interval corresponding to the total carrying capacity distribution, to conduct ecological assessments on the distribution of the ecological gain parameters, the total carrying capacity distribution, and the confidence interval distribution under different scenario combinations, and to generate a grass-livestock assessment report corresponding to the target power station area.

[0144] The specific details of each module of the grass-livestock assessment system for power plant areas have been described in detail in the corresponding grass-livestock assessment methods for power plant areas, so they will not be repeated here.

[0145] It should be noted that although several modules or units for the grass-livestock assessment system for power plant areas have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0146] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method for assessing grass and livestock in a power plant area is also provided.

[0147] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be embodied in the following forms: a completely hardware embodiment, a completely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0148] The following reference Figure 4 The storage unit stores program code that can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 421 and / or a cache 422, and may further include a read-only memory (ROM) 423.

[0149] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0150] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0151] Electronic device 400 can also communicate with one or more external devices 470 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0152] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware.

[0153] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0154] refer to Figure 5 As shown, a program product 500 for implementing the above-described method for assessing grass and livestock in a power plant area, according to an embodiment of the present disclosure, is described. This product may be a portable compact disc read-only memory (CD-ROM) and includes program code, and may run on a terminal device, such as a personal computer. However, the program product of this disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0155] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0156] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0157] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0158] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for assessing grass and livestock in power plant areas, characterized in that, include: Obtain quadrat survey data corresponding to the target power plant area, wherein the quadrat survey data includes pasture resource data corresponding to different land use types; Based on the data of various pasture resources, adaptive sampling processing is performed on the target power plant area to obtain the pasture yield distribution corresponding to each land use type. Under different scenario combinations in the preset ecological grazing scenario, the total carrying capacity distribution corresponding to the target power station area is determined based on each forage yield distribution and the preset forage-livestock assessment parameters corresponding to each forage yield distribution; wherein, the preset ecological grazing scenario includes combinations of different environmental conditions and different grazing strategies. Determine the actual carrying capacity distribution of the control area of ​​the target power station area under different scenario combinations, and based on the actual carrying capacity distribution and the total carrying capacity distribution, determine the ecological gain parameter distribution of the target power station area under different scenario combinations; Determine the confidence interval corresponding to the total carrying capacity distribution, conduct ecological assessments on the distribution of the ecological gain parameters, the total carrying capacity distribution, and the confidence interval distribution under different scenario combinations, and generate a grass-livestock assessment report corresponding to the target power station area.

2. The method according to claim 1, characterized in that, The acquisition of sample plot survey data corresponding to the target power plant area includes: Multiple quadrats and their corresponding location information are determined from the target power plant area; Determine the forage type determination result for each quadrat, and based on the forage type determination result, determine the forage yield information for each quadrat. The forage yield information corresponding to all quadrats in each land use type is statistically analyzed to obtain the forage resource data corresponding to each land use type. Based on the data of each pasture resource and the location information of each quadrat in each land use type, the quadrat survey data corresponding to the target power plant area is generated.

3. The method according to claim 2, characterized in that, The step of determining the forage type classification result for each quadrat and, based on the forage type classification result, determining the forage yield information for each quadrat includes: If the quadrat contains plants of a specified forage type, then the plant weight and quadrat area corresponding to the quadrat are determined, and the forage yield information is generated based on the plant weight and the quadrat area. If the quadrat does not contain plants of the specified forage type, then the area of ​​the quadrat region corresponding to the quadrat is determined, and the weight of the plants corresponding to the quadrat is set to a preset value. Based on the preset value and the area of ​​the quadrat region, the forage yield information is generated.

4. The method according to claim 1, characterized in that, The adaptive sampling process based on pasture resource data for the target power plant area yields the pasture yield distribution for each land use type, including: Perform the following operations for each land use type: The number of quadrats corresponding to each quadrat type in each land use type and the forage yield information corresponding to each quadrat are counted. If the number of sample plots is not greater than the first sample plot number threshold, then the average forage yield is determined based on the forage yield information corresponding to each sample plot, and the sample plots are sampled multiple times based on a preset number of resampling times to obtain the forage yield distribution. If the number of quadrats is greater than the first quadrat number threshold and less than the second quadrat number threshold, then each quadrat is sampled without replacement based on the preset number of resampling times and the preset sampling ratio, and the forage yield distribution is determined based on the forage yield information of each sampled quadrat; wherein, the first quadrat number threshold is less than the second quadrat number threshold. If the number of quadrats is not less than the second quadrat number threshold, then each quadrat is sampled with replacement based on the preset number of resampling times, and the forage yield distribution is determined based on the forage yield information of each sampled quadrat; wherein, the proportion of quadrat corresponding to a quadrat that appears once in each sampling is not less than a preset proportion threshold.

5. The method according to claim 1, characterized in that, The determination of the total carrying capacity distribution corresponding to the target power plant area based on each forage yield distribution and the preset forage-livestock assessment parameters corresponding to each forage yield distribution includes: For each land use type in the target power plant area, the following steps are performed for each single sampling: The grassland forage regeneration rate, annual variation rate of grassland production, grassland utilization rate, standard hay conversion factor, daily feed intake per unit of livestock, and number of grazing days per year are extracted from the preset grassland and livestock assessment parameters. Based on the grassland forage regeneration rate, the annual variation rate of grassland forage production, and the forage yield, the annual available yield corresponding to each land use type in the target power plant area is determined. Based on the grassland utilization rate, the standard hay conversion factor, the daily feed intake per unit of livestock, the number of grazing days per year, and the annual available yield, the reasonable carrying capacity per unit area for each land use type in the target power station area is determined. Multiply the reasonable carrying capacity per unit area by the usable area corresponding to each land use type to obtain the total carrying capacity corresponding to each land use type in a single sampling. The total carrying capacity distribution is generated based on the total carrying capacity corresponding to each sampling for each land use type.

6. The method according to claim 1, characterized in that, The determination of the ecological gain parameter distribution of the target power station area under different scenario combinations based on the actual carrying capacity distribution and the total carrying capacity distribution includes: Each time the target power station area is sampled, the total carrying capacity corresponding to the target power station area is obtained, and the actual carrying capacity corresponding to the control area is obtained. Based on the difference between the total carrying capacity and the actual carrying capacity corresponding to each sampling, the gain carrying capacity distribution is determined; Determine the current actual carrying capacity corresponding to the target power station area, and determine the incremental carrying capacity distribution based on the difference between each total carrying capacity in the total carrying capacity distribution and the current actual carrying capacity; The ecological gain parameter distribution is generated based on the gain carrying capacity distribution and the incremental carrying capacity distribution.

7. The method according to claim 1, characterized in that, Determining the confidence interval corresponding to the total carrying capacity distribution includes: The total carrying capacity is sorted according to a preset order for each total carrying capacity in the total carrying capacity distribution to obtain a total carrying capacity sequence; Based on the pre-stored first quantile and second quantile, determine the first livestock load corresponding to the first quantile and the second livestock load corresponding to the second quantile from the total livestock load sequence; Based on the first carrying capacity and the second carrying capacity, the confidence interval corresponding to the total carrying capacity distribution is generated.

8. The method according to claim 1, characterized in that, The ecological assessment of the distribution of ecological gain parameters, the distribution of total carrying capacity, and the confidence interval under different scenario combinations, and the generation of a grass-livestock assessment report corresponding to the target power station area, includes: Determine the gain confidence interval corresponding to the gain carrying capacity distribution in the ecological gain parameter distribution, and the incremental confidence interval corresponding to the incremental carrying capacity distribution; Based on the gain carrying capacity distribution, the gain confidence interval, the incremental carrying capacity distribution, the incremental confidence interval, the total carrying capacity distribution, and the confidence interval, an ecological assessment of the target power station area under different scenario combinations is conducted to obtain the grass-livestock assessment report.

9. The method according to claim 8, characterized in that, Determining the gain confidence interval corresponding to the gain carrying capacity distribution in the ecological gain parameter distribution, and the incremental confidence interval corresponding to the incremental carrying capacity distribution, includes: The gain carrying capacity is sorted in a preset order for each gain carrying capacity in the gain carrying capacity distribution to obtain a gain carrying capacity sequence, and the incremental carrying capacity is sorted in the preset order for each incremental carrying capacity in the incremental carrying capacity distribution to obtain an incremental carrying capacity sequence. Based on the pre-stored first quantile and second quantile, parameters are extracted from the gain carrying capacity sequence and the incremental carrying capacity sequence, respectively, to obtain the gain confidence interval corresponding to the gain carrying capacity distribution and the incremental confidence interval corresponding to the incremental carrying capacity distribution.

10. A grass-livestock assessment system for power plant areas, characterized in that, include: The data acquisition module is used to acquire sample plot survey data corresponding to the target power station area. The sample plot survey data includes pasture resource data corresponding to different land use types. The quadrat sampling module is used to perform adaptive sampling processing on the target power plant area based on various pasture resource data to obtain the pasture yield distribution corresponding to each land use type. The total carrying capacity module is used to determine the total carrying capacity distribution corresponding to the target power station area under different scenario combinations in a preset ecological grazing scenario, based on each pasture yield distribution and the preset grass-livestock evaluation parameters corresponding to each pasture yield distribution; wherein, the preset ecological grazing scenario includes combinations of different environmental conditions and different grazing strategies. The gain parameter module is used to determine the actual carrying capacity distribution of the control area of ​​the target power station area under different scenario combinations, and based on the actual carrying capacity distribution and the total carrying capacity distribution, to determine the ecological gain parameter distribution of the target power station area under different scenario combinations. The grass-livestock assessment module is used to determine the confidence interval corresponding to the total carrying capacity distribution, to conduct ecological assessments on the distribution of the ecological gain parameters, the total carrying capacity distribution, and the confidence interval distribution under different scenario combinations, and to generate a grass-livestock assessment report corresponding to the target power station area.

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