Park green land vegetation optimization system and method based on carbon sequestration capability improvement

Abstract

The invention provides a park green land vegetation optimization system based on carbon sink capability improvement and a method thereof, relates to the field of urban ecological environment, and aims to improve the carbon sink benefit of a green land through scientific optimization, the system comprises a data acquisition module, a carbon sink evaluation module, a vegetation optimization module and an intelligent monitoring module. The data acquisition module acquires data such as soil quality, climate conditions and vegetation types by using a multi-source sensor; the carbon sequestration evaluation module quantifies the carbon sequestration capacity of the green land through a carbon fixed quantity formula and a carbon storage quantity model; the vegetation optimization module adopts an intelligent optimization algorithm to generate a capability of giving consideration to carbon sink based on the optimization objective function; and the intelligent monitoring module evaluates a green land optimization effect through real-time monitoring data and dynamically adjusts vegetation configuration. The carbon sink optimization method provided by the invention comprises four steps of data acquisition, carbon sink evaluation, vegetation optimization design and dynamic monitoring, and efficient configuration is realized for different green land types by utilizing an accurate model and an optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration, and in particular to a park green space vegetation optimization system and method based on carbon sequestration capacity enhancement. Background Art

[0002] As the main place for human activities, cities are not only one of the main sources of greenhouse gas emissions, but also a key area for carbon reduction and carbon sink function enhancement. In the urban environment, ecological spaces such as parks and green spaces have important carbon sink functions. They absorb carbon dioxide through photosynthesis of vegetation and fix it in organisms and soil, which plays an irreplaceable role in mitigating the greenhouse effect and improving the urban ecological environment. However, the development of the carbon sink function of urban green spaces is facing multiple challenges at this stage, and a scientific and efficient optimization plan is urgently needed to give full play to the carbon sink potential of green spaces.

[0003] 1. Main problems in current green space design:

[0004] (1) Ignoring the carbon sink function:

[0005] At present, the design of most urban green spaces is mainly oriented towards landscape beautification and residents' leisure functions, ignoring the differences in the carbon sequestration capacity of vegetation. For example, trees have higher carbon storage capacity due to their large biomass and long life cycle, while lawns and some shrubs have lower carbon sequestration benefits. However, in actual design, lawns and low vegetation are often overused due to their easy maintenance and high ornamental value, resulting in the ecological benefits of green spaces not being maximized.

[0006] (2) The ecological planning is not scientific enough:

[0007] Traditional green space planning lacks scientific optimization strategies and usually does not fully consider the comprehensive matching of soil conditions, climate characteristics, vegetation types and regional environmental needs. Wrong vegetation selection or unreasonable planting density may lead to a decrease in carbon sequestration capacity. For example, planting plants with high water requirements in arid or semi-arid areas not only increases maintenance costs, but may also threaten the healthy growth of vegetation.

[0008] (3) Lack of dynamic monitoring and management:

[0009] The carbon sequestration capacity of urban green space is a dynamic process, which is affected by many factors such as vegetation growth stage, environmental conditions (such as light, temperature, humidity) and human interference. However, traditional green space management methods are often static and single, lacking real-time dynamic monitoring methods, and it is difficult to adjust and optimize strategies according to actual environmental changes, thus limiting the improvement of green space ecological functions.

[0010] 2. Limitations of carbon sink assessment technology: Accurate assessment of carbon sinks is the basis for improving the carbon sink capacity of green land. However, existing carbon sink assessment methods generally have the following problems:

[0011] Insufficient data sources: Many traditional assessments rely on manual measurements or statistical yearbook data, which have poor real-time and accuracy and are difficult to reflect the dynamic changes in vegetation carbon sequestration capacity.

[0012] Over-simplification of the model: Carbon sink calculation models usually assume that environmental conditions are stable and vegetation growth rates are uniform, which is inconsistent with the actual situation and leads to large errors in the assessment results.

[0013] Lack of system integration: Most existing assessment tools operate independently and cannot be integrated with optimization and monitoring systems, making it difficult to directly use carbon sink assessment results to guide green space optimization design.

[0014] 3. Deficiencies in optimization design and management methods:

[0015] Green space optimization design is a key link in improving carbon sequestration function, but the current optimization design and management have the following shortcomings:

[0016] Mainly based on manual experience: The current green space configuration mostly relies on the designer's experience and judgment, lacks data support and intelligent optimization methods, and cannot achieve refined configuration in complex environments.

[0017] Insufficient multi-objective trade-offs: Green space design needs to take into account carbon sequestration benefits, landscape aesthetics and functional use, but existing solutions often excel in one aspect while ignoring other objectives, making it difficult to achieve an overall optimal solution.

[0018] Lack of dynamic adjustment: The optimized green space configuration is usually fixed and cannot be adjusted in real time according to environmental conditions or vegetation growth status, resulting in a gradual decline in ecological benefits over time.

[0019] 4. Introduction and potential of new technologies:

[0020] In recent years, the development of artificial intelligence, big data and remote sensing technology has brought new opportunities for green space optimization:

[0021] Multi-source data collection: UAV remote sensing, soil sensors and meteorological monitoring equipment can collect soil, vegetation and climate data of green spaces in real time, providing high-precision dynamic data support.

[0022] Accurate carbon sink model: The carbon sink calculation model that combines plant physiological characteristics, climate conditions and dynamic monitoring data can significantly improve the assessment accuracy of carbon fixation and carbon storage.

[0023] Intelligent optimization algorithm: Based on intelligent optimization technologies such as genetic algorithm and particle swarm algorithm, efficient multi-objective optimization can be achieved, thereby designing a vegetation configuration plan that takes into account carbon sequestration benefits and other functions.

[0024] Dynamic monitoring and real-time control: Combining sensor networks and artificial intelligence analysis, green space management can achieve a transition from static to dynamic, adjust vegetation optimization plans based on real-time data, and continuously improve carbon sequestration capacity.

[0025] Therefore, we urgently need to design a park green space vegetation optimization system and its method based on improving carbon sequestration capacity to solve the above problems. Summary of the invention

[0026] The purpose of the present invention is to address the deficiencies of the prior art and provide a park green space vegetation optimization system and method based on carbon sequestration capacity improvement to solve the problems raised in the background technology.

[0027] To achieve the above object, the present invention provides the following technical solutions:

[0028] A park green space vegetation optimization system based on carbon sequestration capacity improvement includes a data acquisition module, a carbon sequestration assessment module, a vegetation optimization module and an intelligent monitoring module, wherein:

[0029] The data collection module is used to collect multi-dimensional data on soil quality, climate conditions, vegetation types, and growth status of park green spaces;

[0030] The carbon sink assessment module quantitatively assesses the carbon sink capacity of park green space through the carbon fixation calculation formula and carbon storage model; the carbon sink assessment module adopts the following carbon fixation calculation formula:

[0031] F c =P×LAI×PAR×η

[0032] Among them, F c It represents the carbon fixation per unit time (unit: gC / mz / day), P is the photosynthetic efficiency, LAI is the leaf area index, PAR is the effective photosynthetic radiation (unit: MJ / mz / day), and η is the radiation utilization rate;

[0033] The vegetation optimization module optimizes the vegetation configuration plan based on the evaluation results, combined with the soil conditions and climate characteristics of the green space;

[0034] The intelligent monitoring module monitors the carbon sequestration capacity and ecological status of vegetation in real time based on multi-source sensors, and dynamically adjusts the optimization plan.

[0035] As a preferred technical solution of the present invention, the carbon sink assessment module further assesses the carbon storage capacity through a carbon storage calculation model, and the formula is:

[0036] S c =B×R c +T s

[0037] Among them, S c represents the carbon storage per unit area (unit: gC / ㎡), B is the biomass, R c is the carbon storage coefficient of organisms, T s is the soil carbon storage.

[0038] As a preferred technical solution of the present invention, the vegetation optimization module is optimized according to the comprehensive evaluation results of carbon fixation and carbon storage, using the optimization objective function:

[0039]

[0040] Among them, α and β are weight coefficients, and x is the vegetation type and community structure configuration parameter.

[0041] As a preferred technical solution of the present invention, the vegetation optimization module adopts genetic algorithm or particle swarm algorithm to solve the objective function optimization problem, and generates the optimal configuration scheme through population initialization, fitness calculation, selection, crossover and mutation operations.

[0042] The present invention also provides a method for optimizing park green space vegetation based on improving carbon sequestration capacity, comprising the following steps:

[0043] Data collection: Obtain environmental parameters such as soil, climate, and vegetation through the data collection module;

[0044] Carbon sink assessment: Use the carbon fixation formula and carbon storage formula to assess the carbon sink capacity of green land;

[0045] Vegetation optimization design: Based on the above objective function, select high carbon sink plant species and optimize the plant community structure;

[0046] Intelligent monitoring and dynamic adjustment: Real-time monitoring of the optimized vegetation status and dynamic adjustment of the configuration through artificial intelligence algorithms.

[0047] As a preferred technical solution of the present invention, in the carbon sink assessment step, the calculation formula of the leaf area index LAI is:

[0048]

[0049] Among them, A i is the leaf area of ​​the i-th vegetation (unit: m2), T is the total green area (unit: m2)

[0050] As a preferred technical solution of the present invention, in the vegetation optimization design step, the calculation formula of biomass B is:

[0051]

[0052] Among them, W i is the unit plant weight of the i-th vegetation (unit: kg), N i is the population density of vegetation (unit: plants / ㎡).

[0053] As a preferred technical solution of the present invention, the intelligent monitoring and dynamic adjustment step adopts the following dynamic adjustment function:

[0054]

[0055] Among them, x t is the vegetation configuration parameter at the tth moment, and γ is the learning rate.

[0056] As a preferred technical solution of the present invention, the system and method are applicable to different types of green spaces (such as urban parks and wetland reserves), and maximize the carbon sequestration capacity by optimizing the objective function, while meeting the requirements of ecological aesthetics and green space use functions.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. This invention introduces an accurate carbon fixation and carbon storage assessment model, combines it with an intelligent optimization algorithm, selects plant species with high carbon sink capacity, and designs a reasonable vegetation community structure, which significantly improves the carbon absorption and storage capacity of park green spaces, providing important support for cities to achieve carbon neutrality goals. At the same time, the dynamic monitoring module can adjust the optimization plan in real time to ensure the continuous improvement of the carbon sink capacity of green spaces.

[0059] 2. This invention uses multi-source sensors, artificial intelligence algorithms and dynamic monitoring technology to break through the limitation of traditional green space management relying on manual experience, and realizes the scientific and intelligent whole process from data collection to optimization design. The optimization scheme takes into account the soil conditions, climate characteristics and landscape function requirements of the region, ensures the balance between carbon sink benefits and ecological beauty, and lays the foundation for low-carbon city construction.

[0060] 3. The present invention is applicable to various scenarios such as urban parks, wetland protection areas and ecological restoration projects, and can generate personalized optimization plans according to the characteristics of different regions and green space types. Through dynamic monitoring and adjustment functions, it can cope with environmental fluctuations such as climate change and vegetation growth status, and the optimization effect is more efficient and stable, with good promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of the park green space vegetation optimization system and method based on carbon sequestration capacity improvement proposed by the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] The following is combined with Figure 1 With reference to the accompanying drawings and a number of embodiments, specific embodiments of the present invention are described in detail.

[0064] 1. Implementation method of system composition: The park green space vegetation optimization system based on carbon sequestration capacity improvement includes data acquisition module, carbon sequestration assessment module, vegetation optimization module and intelligent monitoring module:

[0065] 1. Data acquisition module: The data acquisition module uses multi-source sensors (such as drone remote sensing, soil sensors, and weather stations) to collect the following data:

[0066] Soil parameters: soil moisture, soil organic matter content;

[0067] Climate conditions: temperature, light intensity, rainfall;

[0068] Vegetation characteristics: leaf area index (LAI), plant growth rate, biomass, etc.

[0069] 2. Carbon sink assessment module:

[0070] The carbon sink assessment module assesses the carbon sink capacity of vegetation through the following two main formulas:

[0071] Carbon fixation formula:

[0072] F c =P×LAI×PAR×η

[0073] F c : Carbon fixation per unit time (unit: gC / m 2 / day), which means the carbon absorbed by plants through photosynthesis per unit area;

[0074] P: photosynthesis efficiency, which depends on the plant species and environmental conditions, and usually ranges from 0.05-0.15;

[0075] LAI: Leaf Area Index, which indicates the total leaf area per unit land area and reflects the coverage of vegetation; PAR: Photosynthetically Active Radiation (unit: MJ / m 2 day), provided by the acquisition module; η: radiation utilization rate, which indicates the efficiency of plants in utilizing light energy, with a value range of 0.5-0.8.

[0076] Carbon storage formula:

[0077] S c =B×R c +T s

[0078] S c :Carbon storage per unit area (unit: gC / m 2 ), represents the total amount of carbon stored in plants and soil; B: plant biomass (unit: g / m 2 ), determined by the population density and individual plant weight of vegetation; R c : Biomass carbon storage coefficient, which indicates the proportion of carbon in biomass, usually 0.4-0.6; T s :Soil carbon storage (unit: g / m 2 ), calculated from the soil organic matter content and soil thickness.

[0079] 3. Vegetation Optimization Module

[0080]

[0081] The vegetation optimization module determines the optimal vegetation configuration scheme through the following objective function:

[0082] x: vegetation configuration parameters, including plant species, planting density and community structure;

[0083] α: weight coefficient of carbon fixation, reflecting its importance in the optimization objective;

[0084] β: weight coefficient of carbon storage, reflecting its importance in the optimization objective;

[0085] F c : Calculated by the carbon fixation formula;

[0086] S c : Calculated by the carbon storage formula.

[0087] Optimization algorithm: Genetic algorithm (GA) is used to solve the above objective function.

[0088] The specific process includes:

[0089] Population initialization: randomly generate a set of initial vegetation configurations;

[0090] Fitness function calculation: Use the objective function value as the fitness to evaluate each configuration scheme;

[0091] Selection: select individuals with higher fitness to enter the next generation;

[0092] Crossover and mutation: Generate new configurations through crossover operations and increase diversity through mutation;

[0093] Iteration termination: When the fitness no longer improves significantly or reaches the number of iterations, the optimal solution is output.

[0094] 4. Intelligent monitoring module

[0095] The intelligent monitoring module uses real-time data to monitor the growth status and carbon sequestration capacity of optimized vegetation, and optimizes the vegetation configuration through the following dynamic adjustment formula:

[0096]

[0097] x t : vegetation configuration parameters at the tth moment; γ: learning rate, used to control the adjustment amplitude; The gradient of the objective function with respect to the configuration parameters indicates the direction of adjustment. 2. Specific embodiments

[0099] Example 1: Urban park vegetation optimization:

[0100] Data collection A city park covers an area of ​​10 hectares, with a temperate monsoon climate. The collected data are: soil moisture: 25%; soil organic matter content: 4%; temperature: 20℃, photosynthetically active radiation (PAR): 10MJ / mz / day; leaf area index (LAI): 2.5. Carbon sink assessment is calculated according to the formula:

[0101] Carbon fixation:

[0102] F c =0.1×2.5×10×0.6=1.5gC / m 2 / day

[0103] Carbon storage:

[0104] S c =800×0.5+100=500gC / m 2

[0105] Optimization design: Through genetic algorithm, the optimized configuration is:

[0106] Increase the number of deep-rooted trees (such as ginkgo trees) and high-light-efficiency shrubs (such as azaleas);

[0107] Adjust the ratio of trees, shrubs and herbaceous plants to 5:3:2; increase the planting density by 20%.

[0108] Optimization effect: Carbon fixation increased to 2.0gC / m 2 / day;

[0109] Carbon storage capacity increased to 600gC / m 2 .

[0110] Example 2: Wetland Park Vegetation Optimization:

[0111] Data collection: A wetland park covers an area of ​​5 hectares and has a tropical monsoon climate. The following data were collected:

[0112] Soil moisture: 60%; soil organic matter content: 8%; air temperature: 30℃, photosynthetically active radiation (PAR): 12MJ / mz / day; leaf area index (LAI) of existing vegetation: 3.5.

[0113] Carbon sink assessment of carbon fixation:

[0114] F c =0.12×3.5×12×0.7=3.528gC / m 2 / day

[0115] Carbon storage:

[0116] S c =1000×0.5+200=700gC / m 2

[0117] Optimized design: Optimized solution: Add wetland plants with high carbon sink capacity (such as cattail and reed);

[0118] Adjust the planting density and increase it by 30%; design a multi-level vegetation structure to improve ecological stability.

[0119] Optimization effect: Carbon fixation increased to 4.2gC / m 2 / day; carbon storage increased to 850gC / m 2 .

[0120] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A park green space vegetation optimization system based on carbon sequestration capacity improvement, characterized by: The system includes data acquisition module, carbon sink assessment module, vegetation optimization module and intelligent monitoring module. The data collection module is used to collect multi-dimensional data on soil quality, climate conditions, vegetation types, and growth status of park green spaces; The carbon sink assessment module quantitatively assesses the carbon sink capacity of park green space through the carbon fixation calculation formula and carbon storage model; the carbon sink assessment module adopts the following carbon fixation calculation formula: F c =P×LAI×PAR×η Among them, F c It represents the amount of carbon fixed per unit time, P is the photosynthesis efficiency, LAI is the leaf area index, PAR is the effective photosynthetic radiation, and ηη is the radiation utilization rate; The vegetation optimization module optimizes the vegetation configuration plan based on the evaluation results, combined with the soil conditions and climate characteristics of the green space; The intelligent monitoring module monitors the carbon sequestration capacity and ecological status of vegetation in real time based on multi-source sensors, and dynamically adjusts the optimization plan.

2. A park green space vegetation optimization system based on carbon sequestration capacity improvement according to claim 1, characterized in that: The carbon sink assessment module further assesses the carbon storage capacity through a carbon storage calculation model, the formula of which is: S c =B×R c +T s Among them, S c represents the carbon storage per unit area, B is the biomass, R c is the carbon storage coefficient of organisms, T s is the soil carbon storage.

3. The park green space vegetation optimization system based on carbon sequestration capacity improvement according to claim 1 is characterized in that: The vegetation optimization module is optimized based on the comprehensive evaluation results of carbon fixation and carbon storage, using the optimization objective function: Among them, α and β are weight coefficients, and x is the vegetation type and community structure configuration parameter.

4. A park green space vegetation optimization system based on carbon sequestration capacity improvement according to claim 3, characterized in that: The vegetation optimization module adopts genetic algorithm or particle swarm algorithm to solve the objective function optimization problem, and generates the optimal configuration scheme through population initialization, fitness calculation, selection, crossover and mutation operations.

5. A method for optimizing park green space vegetation based on improving carbon sequestration capacity, characterized in that: The following steps are involved: Data collection: Obtain soil, climate, and vegetation environmental parameters through the data collection module; Carbon sink assessment: Use the carbon fixation formula and carbon storage formula to assess the carbon sink capacity of green land; Vegetation optimization design: Based on the above objective function, select high carbon sink plant species and optimize the plant community structure; Intelligent monitoring and dynamic adjustment: Real-time monitoring of the optimized vegetation status and dynamic adjustment of the configuration through artificial intelligence algorithms.

6. A method for optimizing park green space vegetation based on improving carbon sequestration capacity according to claim 5, characterized in that: In the carbon sink assessment step, the calculation formula of leaf area index LAI is: Among them, A i is the leaf area of ​​the ith vegetation, and T is the total green area.

7. The method for optimizing park green space vegetation based on improving carbon sequestration capacity according to claim 5, characterized in that: In the vegetation optimization design step, the calculation formula of biomass B is: Among them, W i is the unit plant weight of the i-th vegetation (unit: kg), N i is the population density of vegetation (unit: plants / ㎡).

8. The method for optimizing park green space vegetation based on improving carbon sequestration capacity according to claim 5, characterized in that: The intelligent monitoring and dynamic adjustment step uses the following dynamic adjustment function: Among them, x t is the vegetation configuration parameter at the tth moment, and γ is the learning rate.

9. The method for optimizing park green space vegetation based on improving carbon sequestration capacity according to claim 5, characterized in that: The system and method are applicable to different types of green spaces, and can maximize carbon sequestration capacity by optimizing the objective function, while meeting the requirements of ecological aesthetics and green space use functions.

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