Population dynamic prediction method for bluegrass in high-cold region and related device

Abstract

The invention discloses a population dynamic prediction method, a population dynamic prediction device and population dynamic prediction equipment for bluegrass in a high-cold region, and a computer readable storage medium. The method comprises the following steps: based on a temperature parameter, a precipitation parameter, a soil humidity parameter, an illumination parameter, a soil nutrition parameter, an interspecific competition parameter, a growth cycle parameter, a growth influence factor, a reproduction influence factor and a community structure influence factor, carrying out poa annua dynamic model construction processing to obtain an initial poa annua population dynamic model; performing parameterization processing on the initial bluegrass population dynamic model based on the obtained observation data and experimental data to obtain a bluegrass population dynamic model; dynamically adjusting the bluegrass population dynamic model based on different environmental factors to obtain a bluegrass population prediction model; performing real-time prediction based on the bluegrass population prediction model and the climate condition data to obtain a prediction result; and performing early warning processing based on the prediction result.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and more specifically, relates to a method for predicting the population dynamics of Poa alpina in alpine regions, a device for predicting population dynamics, a device for predicting population dynamics, and a computer-readable storage medium. Background Art

[0002] The alpine grassland ecosystem has characteristics such as harsh environmental conditions, drastic climate fluctuations, and limited plant growth cycles, and its ecological restoration work faces many challenges. At present, the research in the field of grassland ecological restoration mainly focuses on static intervention means such as improving soil physical and chemical properties, screening suitable plants, and fertilization techniques. However, the above methods generally lack a systematic consideration of the plant population dynamics process and its interaction with environmental factors, and it is difficult to achieve accurate prediction and dynamic regulation of the grassland community structure succession. Especially in the context of climate change, the spatio-temporal heterogeneity of environmental factors such as temperature, precipitation, and light has increased significantly. Traditional restoration techniques often lead to problems such as unstable restoration effects and insufficient sustainability due to ignoring the adaptive adjustment mechanism of plant population growth and reproduction strategies.

[0003] In the prior art, the ecological restoration research on dominant species such as Poa alpina in alpine regions mostly focuses on the static optimization of a single environmental factor (such as water or nutrients), and fails to integrate the long-term impact of the coupling effect of multiple factors on population dynamics. In addition, existing models often simplify key ecological processes such as plant asexual reproduction characteristics, interspecific competition relationships, and soil-plant feedback mechanisms, resulting in limited model prediction accuracy and difficulty in supporting scientific decision-making under complex environmental conditions. For example, in alpine regions where drought or extreme low-temperature events occur frequently, the lack of an adaptive management plan based on dynamic simulation is likely to lead to the risk of grassland degradation after restoration.

[0004] Therefore, there is an urgent need to develop a method that can integrate ecological principles and mathematical model technologies, systematically analyze the driving mechanism of environmental factors on the population dynamics of Poa alpina, and achieve precise intervention through dynamic simulation and optimization strategies. Summary of the Invention

[0005] The purpose of the present application is to provide a method for predicting the population dynamics of Poa alpina in alpine regions, a device for predicting population dynamics, a device for predicting population dynamics, and a computer-readable storage medium, so as to improve the accuracy and precision of predicting the population dynamics of Poa alpina in alpine regions.

[0006] In view of the above deficiencies or improvement requirements of the prior art, the present invention provides a method for predicting the population dynamics of Poa alpina in alpine regions, including:

[0007] Construct an initial Poa annua population dynamic model by processing the Poa annua dynamic model based on temperature parameters, precipitation parameters, soil moisture parameters, light parameters, soil nutrient parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors;

[0008] Parametrize the initial Poa annua population dynamic model based on the obtained observation data and experimental data to obtain the Poa annua population dynamic model;

[0009] Dynamically adjust the Poa annua population dynamic model based on different environmental factors to obtain the Poa annua population prediction model;

[0010] Perform real-time prediction based on the Poa annua population prediction model and climate condition data to obtain the prediction results; among them, the prediction results include: growth status data and community dynamic data;

[0011] Perform early warning processing based on the prediction results.

[0012] Optionally, parameterizing the initial Poa annua population dynamic model based on the obtained observation data and experimental data to obtain the Poa annua population dynamic model includes:

[0013] Parametrize the initial Poa annua population dynamic model based on the obtained observation data and experimental data to obtain a parameterized population dynamic model;

[0014] Correct the parameterized population dynamic model based on the obtained growth dynamic data to obtain the Poa annua population dynamic model.

[0015] Optionally, performing early warning processing based on the prediction results includes:

[0016] Determine the grassland restoration problem data based on the prediction results and perform early warning processing based on the grassland restoration problem data.

[0017] Optionally, it further includes:

[0018] Generate an optimization management method by processing the management plan based on the prediction results.

[0019] This application also provides a population dynamic prediction device for Poa annua in alpine regions, including:

[0020] A model construction module, configured to construct an initial Poa annua population dynamic model by processing the Poa annua dynamic model based on temperature parameters, precipitation parameters, soil moisture parameters, light parameters, soil nutrient parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors;

[0021] A model parameterization module, which is used to parameterize the initial Poa annua population dynamics model based on the acquired observation data and experimental data to obtain a Poa annua population dynamics model;

[0022] A model adjustment module, which is used to dynamically adjust the Poa annua population dynamics model based on different environmental factors to obtain a Poa annua population prediction model;

[0023] A dynamic prediction module, which is used to perform real-time prediction based on the Poa annua population prediction model and climate condition data to obtain a prediction result; wherein, the prediction result includes: growth state data and community dynamics data;

[0024] An early warning processing module, which is used to perform early warning processing based on the prediction result.

[0025] Optionally, the model parameterization module is specifically used to parameterize the initial Poa annua population dynamics model based on the acquired observation data and experimental data to obtain a parameterized population dynamics model; and to correct the parameterized population dynamics model based on the acquired growth dynamics data to obtain the Poa annua population dynamics model.

[0026] Optionally, the early warning processing module is specifically used to determine grassland restoration problem data based on the prediction result and perform early warning processing based on the grassland restoration problem data.

[0027] Optionally, it further includes: a solution generation module, which is used to perform management solution generation processing based on the prediction result to obtain an optimized management method.

[0028] This application also provides a population dynamics prediction device, including:

[0029] A memory, which is used to store a computer program;

[0030] A processor, which is used to implement the steps of the population dynamics prediction method as described above when executing the computer program.

[0031] This application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the population dynamics prediction method as described above are implemented.

[0032] A method for predicting the population dynamics of Poa crymophila provided by the present application includes: constructing a dynamic model of Poa crymophila based on temperature parameters, precipitation parameters, soil moisture parameters, light parameters, soil nutrient parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors to obtain an initial population dynamics model of Poa crymophila; parameterizing the initial population dynamics model of Poa crymophila based on the obtained observation data and experimental data to obtain a population dynamics model of Poa crymophila; dynamically adjusting the population dynamics model of Poa crymophila based on different environmental factors to obtain a population prediction model of Poa crymophila; performing real-time prediction based on the population prediction model of Poa crymophila and climate condition data to obtain a prediction result; wherein the prediction result includes: growth status data and community dynamics data; and performing early warning processing based on the prediction result.

[0033] It has the following beneficial effects:

[0034] By comprehensively integrating multiple environmental and biological factors to construct a model, it can more accurately reflect the population dynamics law of Poa crymophila than traditional single-factor research. Through parameterization processing of observation and experimental data, the accuracy of the model is greatly improved. It can also be dynamically adjusted with environmental changes to predict growth and community dynamics data in real time. Once the data is abnormal, it will trigger an early warning, which is convenient for timely intervention. Overall, it can help accurately grasp the population changes of Poa crymophila, respond to potential risks in advance, provide a scientific basis for grassland ecological restoration in alpine regions, and improve the restoration effect and sustainability. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0036] Figure 1 It is a flowchart of a method for predicting the population dynamics of Poa crymophila provided by an embodiment of the present application;

[0037] Figure 2 It is a schematic structural diagram of a device for predicting the population dynamics of Poa crymophila provided by an embodiment of the present application;

[0038] Figure 3 It is a schematic structural diagram of a device for predicting population dynamics provided by an embodiment of the present application. Detailed Embodiments

[0039] The purpose of this application is to provide a method, a device, a device, and a computer-readable storage medium for predicting the population dynamics of Poa crymophila, so as to improve the accuracy and precision of predicting the population dynamics of Poa crymophila in alpine regions.

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] The following describes a method for predicting the population dynamics of Poa crymophila provided by this application through an embodiment.

[0042] Please refer to Figure 1 , Figure 1 , which is a flowchart of a method for predicting the population dynamics of Poa crymophila provided by an embodiment of this application.

[0043] In this embodiment, the method may include:

[0044] S101, perform processing on the Poa crymophila dynamic model construction based on temperature parameters, precipitation parameters, soil moisture parameters, light parameters, soil nutrient parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors to obtain an initial Poa crymophila population dynamic model;

[0045] In this step, the growth, reproduction, and population dynamics of Poa crymophila are comprehensively affected by various environmental factors and internal biological factors. These parameters cover physical, chemical, and biological factors in the environment. By integrating these factors to build a model, the dynamic change law of the Poa crymophila population in the natural environment can be reflected from multiple dimensions.

[0046] First, it is necessary to determine the value range and quantization method of each parameter. For example, the temperature parameter can obtain historical temperature data from the local meteorological station or use sensors to monitor in real time in the target area; the soil moisture parameter can be measured by a soil moisture sensor. Then, using mathematical modeling methods such as differential equations, difference equations, or artificial intelligence-based algorithms (such as neural networks), these parameters are incorporated into the model construction as variables to establish a preliminary mathematical relationship describing the population dynamics of Poa crymophila, thereby obtaining an initial Poa crymophila population dynamic model.

[0047] Comprehensively considering the influence of multiple factors makes the model more suitable for the actual ecosystem. Compared with models that only consider single or a few factors, it can more accurately reflect the real dynamic changes of the Poa crymophila population, laying a solid foundation for subsequent accurate prediction and analysis.

[0048] S102. Parametrize the initial Poa annua population dynamics model based on the obtained observational data and experimental data to obtain the Poa annua population dynamics model;

[0049] On the basis of S101, the initial model is only constructed based on theory and general assumptions, and there are many uncertainties and complexities in the actual ecosystem. Through observational data (such as the growth status of Poa annua and the change of population quantity observed on the spot) and experimental data (such as the experimental results of studying the influence of various factors on Poa annua under controlled conditions), the parameters in the model can be calibrated and optimized to make the model more in line with the actual situation.

[0050] Sort out and analyze the collected observational data and experimental data, and extract information related to the model parameters. Use parameter estimation methods, such as maximum likelihood estimation, least squares method, etc., to adjust the parameters in the initial model. For example, if it is observed that the growth rate of Poa annua under a certain soil moisture condition does not match the prediction of the initial model, the parameters related to soil moisture can be adjusted according to the data. After multiple iterations of optimization, a more accurate Poa annua population dynamics model reflecting the actual situation can be obtained.

[0051] Improve the accuracy and reliability of the model, enhance the simulation ability of the model for the actual ecosystem, enable it to more accurately predict the Poa annua population dynamics, and provide a more reliable basis for ecological restoration decision-making.

[0052] S103. Dynamically adjust the Poa annua population dynamics model based on different environmental factors to obtain the Poa annua population prediction model;

[0053] On the basis of S102, environmental factors are in dynamic change. For example, climate change will cause fluctuations in factors such as temperature and precipitation. In order to make the model adapt to this change and continuously and accurately predict, it is necessary to make corresponding adjustments to the existing Poa annua population dynamics model according to real-time or predicted environmental factor changes.

[0054] Obtain environmental factor data in real time. For example, obtain temperature and precipitation change information for a future period through weather forecasts, and obtain dynamic data of soil moisture and nutrient content through soil monitoring equipment. According to these changes, use the model adjustment algorithm to adjust the relevant parameters or structures in the Poa annua population dynamics model. For example, if it is predicted that the precipitation will increase in a future period, the parameters related to precipitation in the model can be adjusted, and the model can be recalculated to obtain the Poa annua population prediction model adapted to the new environmental conditions.

[0055] Enable the model to have dynamic adaptability, be able to track environmental changes, provide more timely and accurate prediction results, which helps to formulate response measures in advance, such as adjusting the grassland irrigation and fertilization plans according to the prediction results, and better guiding the grassland ecological restoration work in alpine regions.

[0056] S104, based on the Poa annua population prediction model and climate condition data, conduct real-time prediction to obtain prediction results; among them, the prediction results include: growth status data and community dynamics data.

[0057] On the basis of S103, the Poa annua population prediction model has integrated the influence relationships of various factors. Combining real-time or predicted climate condition data (such as current and future temperature, light, etc.), through the calculation and simulation of the model, it can infer the growth status of Poa annua in the future period (such as plant height, biomass, etc.) and community dynamics (such as changes in population quantity, species richness, etc.).

[0058] Input the real-time or predicted climate condition data into the Poa annua population prediction model and perform operations according to the calculation logic set by the model. For example, if the influence formula of temperature on the growth rate of Poa annua is set in the model, calculate the growth rate according to the input temperature data, and then obtain the growth status data; by considering factors such as interspecific competition, calculate the changes in population quantity and community structure, and obtain the community dynamics data.

[0059] Provide intuitive and specific information for the ecological restoration work, help the staff understand the future development trend of the Poa annua population, and timely discover potential problems, such as poor growth of Poa annua or imbalance of community structure, etc., so as to take targeted measures for intervention.

[0060] S105, conduct early warning processing based on the prediction results.

[0061] On the basis of S104, through the analysis of the prediction results, judge whether the Poa annua population and the grassland ecosystem are in a normal state or there are potential risks. If the prediction results show that the growth status data or community dynamics data deviate from the normal range, an early warning signal will be issued to indicate the possible problems.

[0062] Set the early warning threshold. For example, when the predicted Poa annua population quantity is lower than a certain threshold, or the decline in community species richness exceeds a certain proportion, trigger the early warning mechanism. The early warning information can be sent to relevant personnel through text messages, emails, system pop-ups, etc., to inform the possible problems in grassland restoration.

[0063] It can discover abnormal situations in the grassland ecological restoration process in advance, gain time for the staff to handle problems, avoid the deterioration of problems, reduce economic losses and ecological damage, and ensure the smooth progress of the grassland ecological restoration work in alpine regions.

[0064] In summary, in this embodiment, a model is constructed by comprehensively integrating multiple environments and biological factors, which can more accurately reflect the population dynamics of Poa pratensis than traditional single-factor research. Through the parametric processing of observation and experimental data parameters, the accuracy of the model is greatly improved. It can also be dynamically adjusted according to environmental changes to predict the growth and community dynamics data in real time. Once the data is abnormal, an alarm will be triggered for timely intervention. Overall, it can help accurately grasp the changes in the Poa pratensis population, respond to potential risks in advance, provide a scientific basis for the ecological restoration of alpine grasslands, and improve the restoration effect and sustainability.

[0065] Optionally, parameterize the initial Poa pratensis population dynamics model based on the obtained observation data and experimental data to obtain the Poa pratensis population dynamics model, including:

[0066] Parameterize the initial Poa pratensis population dynamics model based on the obtained observation data and experimental data to obtain a parameterized population dynamics model;

[0067] Correct the parameterized population dynamics model based on the obtained growth dynamics data to obtain the Poa pratensis population dynamics model.

[0068] Optionally, perform warning processing based on the prediction result, including:

[0069] Determine the grassland restoration problem data based on the prediction result and perform warning processing based on the grassland restoration problem data.

[0070] Optionally, it further includes:

[0071] Generate a management plan based on the prediction result to obtain an optimized management method.

[0072] Next, a population dynamics prediction device for Poa pratensis in alpine regions provided by the embodiments of the present application will be introduced. The population dynamics prediction device for Poa pratensis in alpine regions described below can be mutually referred to with a population dynamics prediction method for Poa pratensis in alpine regions.

[0073] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a population dynamics prediction device for Poa pratensis in alpine regions provided by the embodiments of the present application.

[0074] In this embodiment, the device may include:

[0075] A model construction module 100, configured to perform Poa pratensis dynamic model construction processing based on temperature parameters, precipitation parameters, soil humidity parameters, light parameters, soil nutrient parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors to obtain an initial Poa pratensis population dynamics model;

[0076] The model parameterization module 200 is used to parameterize the initial Poa annua population dynamics model based on the acquired observation data and experimental data, and obtain the Poa annua population dynamics model;

[0077] The model adjustment module 300 is used to dynamically adjust the Poa annua population dynamics model based on different environmental factors, and obtain the Poa annua population prediction model;

[0078] The dynamic prediction module 400 is used to perform real-time prediction based on the Poa annua population prediction model and climate condition data, and obtain the prediction result; wherein, the prediction result includes: growth state data and community dynamics data;

[0079] The early warning processing module 500 is used to perform early warning processing based on the prediction result.

[0080] Optionally, the model parameterization module is specifically used to parameterize the initial Poa annua population dynamics model based on the acquired observation data and experimental data, and obtain the parameterized population dynamics model; and correct the parameterized population dynamics model based on the acquired growth dynamics data, and obtain the Poa annua population dynamics model.

[0081] Optionally, the early warning processing module is specifically used to determine the grassland restoration problem data based on the prediction result, and perform early warning processing based on the grassland restoration problem data.

[0082] Optionally, it further includes: a solution generation module, which is used to generate a management solution based on the prediction result, and obtain an optimized management method.

[0083] This application also provides a population dynamics prediction device, please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the population dynamics prediction device provided by the embodiment of this application. The population dynamics prediction device may include:

[0084] A memory, which is used to store computer programs;

[0085] A processor, which can implement the steps of any of the above-mentioned population dynamics prediction methods for Poa annua in alpine regions when executing the computer program.

[0086] As Figure 3 shown, it is a schematic composition structure diagram of the population dynamics prediction device. The population dynamics prediction device may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all complete communication with each other through the communication bus 13.

[0087] In the embodiments of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices, etc.

[0088] The processor 10 may call the programs stored in the memory 11. Specifically, the processor 10 may execute the operations in the embodiments of the exception IP recognition method.

[0089] The memory 11 is used to store one or more programs. The programs may include program codes, and the program codes include computer operation instructions. In the embodiments of the present application, the memory 11 stores at least programs for implementing the following functions:

[0090] Based on temperature parameters, precipitation parameters, soil humidity parameters, light parameters, soil nutrient parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors, perform processing for constructing a Poa annua dynamic model to obtain an initial Poa annua population dynamic model;

[0091] Based on the obtained observation data and experimental data, perform parameterization processing on the initial Poa annua population dynamic model to obtain a Poa annua population dynamic model;

[0092] Based on different environmental factors, perform dynamic adjustment on the Poa annua population dynamic model to obtain a Poa annua population prediction model;

[0093] Based on the Poa annua population prediction model and climate condition data, perform real-time prediction to obtain a prediction result; wherein, the prediction result includes: growth state data and community dynamic data;

[0094] Perform early warning processing based on the prediction result.

[0095] In a possible implementation manner, the memory 11 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system, and application programs required for at least one function, etc.; the data storage area may store the data created during use.

[0096] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage devices.

[0097] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0098] Of course, it should be noted that Figure 3The structure shown does not constitute a limitation on the population dynamics prediction device in the embodiments of the present application. In practical applications, the population dynamics prediction device may include more or fewer components than Figure 3 those shown, or combine certain components.

[0099] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above-mentioned population dynamics prediction methods for Poa alpina in alpine regions can be implemented.

[0100] The computer-readable storage medium may include: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0101] For the introduction of the computer-readable storage medium provided in the present application, please refer to the above method embodiments, and the present application will not elaborate here.

[0102] The various embodiments in the specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method part.

[0103] Those skilled in the art can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0104] The steps of the method or algorithm described in combination with the embodiments disclosed in this document can be directly implemented by hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0105] The above has introduced in detail a method for predicting the population dynamics of Poa alpina in alpine regions, a device for predicting population dynamics, a device for predicting population dynamics, and a computer-readable storage medium. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for predicting the population dynamics of bluegrass in alpine regions, characterized in that: include: Based on temperature parameters, precipitation parameters, soil moisture parameters, light parameters, soil nutrition parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors, a dynamic model of bluegrass was constructed to obtain an initial bluegrass population dynamic model; Parameterizing the initial bluegrass population dynamic model based on the acquired observation data and experimental data to obtain a bluegrass population dynamic model; Dynamically adjusting the bluegrass population dynamic model based on different environmental factors to obtain a bluegrass population prediction model; Based on the bluegrass population prediction model and climate condition data, real-time prediction is performed to obtain prediction results; wherein the prediction results include: growth status data and community dynamics data; Perform early warning processing based on the prediction results.

2. The population dynamics prediction method according to claim 1, characterized in that: The initial bluegrass population dynamic model is parameterized based on the acquired observation data and experimental data to obtain a bluegrass population dynamic model, including: Parameterizing the initial bluegrass population dynamics model based on the acquired observation data and experimental data to obtain a parameterized population dynamics model; The parameterized population dynamics model is corrected based on the acquired growth dynamics data to obtain the bluegrass population dynamics model.

3. The population dynamics prediction method according to claim 2, characterized in that: Performing early warning processing based on the prediction results includes: Grassland restoration problem data is determined based on the prediction results, and early warning processing is performed based on the grassland restoration problem data.

4. The population dynamics prediction method according to claim 3, characterized in that: Also includes: A management plan is generated based on the prediction results to obtain an optimized management method.

5. A population dynamics prediction device for bluegrass in alpine regions, characterized in that: include: A model building module is used to build a dynamic model of bluegrass based on temperature parameters, precipitation parameters, soil moisture parameters, light parameters, soil nutrition parameters, interspecific competition parameters, growth cycle parameters, growth influencing factors, reproduction influencing factors, and community structure influencing factors to obtain an initial bluegrass population dynamic model; A model parameterization module, used for parameterizing the initial bluegrass population dynamics model based on the acquired observation data and experimental data to obtain the bluegrass population dynamics model; A model adjustment module, used for dynamically adjusting the bluegrass population dynamic model based on different environmental factors to obtain a bluegrass population prediction model; A dynamic prediction module, used for performing real-time prediction based on the bluegrass population prediction model and climate condition data to obtain prediction results; wherein the prediction results include: growth status data and community dynamics data; The early warning processing module is used to perform early warning processing based on the prediction result.

6. The population dynamics prediction device according to claim 5, characterized in that: The model parameterization module is specifically used to parameterize the initial bluegrass population dynamics model based on the acquired observation data and experimental data to obtain a parameterized population dynamics model; and to correct the parameterized population dynamics model based on the acquired growth dynamics data to obtain the bluegrass population dynamics model.

7. The population dynamics prediction device according to claim 6, characterized in that: The early warning processing module is specifically used to determine grassland restoration problem data based on the prediction result, and perform early warning processing based on the grassland restoration problem data.

8. The population dynamics prediction device according to claim 7, characterized in that: Also includes: The solution generation module is used to generate a management solution based on the prediction result to obtain an optimized management method.

9. A population dynamics prediction device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the population dynamics prediction method as claimed in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the population dynamics prediction method according to any one of claims 1 to 4 are implemented.

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