Ground cover plant assembly type module generation method and device

Abstract

The invention relates to the technical field of carbon emission, in particular to a ground cover plant assembly type module generation method and device.The method comprises the steps that plant demand parameters, input by a user, of a target laying area are obtained, and the plant demand parameters at least comprise growth habits, environment demands, ecological functions, ornamental characteristics and economic cost; the growth habits are used for indicating morphological characteristic types of the plants, the environmental requirements are used for indicating environmental conditions required by plant growth and survival, the ecological functions are used for indicating roles and contributions of the plants in an ecological system, and the ornamental characteristics are used for indicating aesthetic values and ornamental potentials of the plants. The economic cost is used for indicating economic factors of plant cultivation, maintenance and use; matching a combination of various plant data from a database according to the plant demand parameters; and determining at least one of the combinations of the multiple plant data as a target plant combination, and generating a ground cover plant assembly type module according to the target plant combination. According to the scheme, the ground cover plant selection accuracy can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of carbon emission technology, and more particularly to a method and device for generating a ground cover plant assembly module. Background Art

[0002] In the process of rapid urbanization, urban space and population density have increased dramatically, the intensity of economic activities has significantly increased, and land use and land cover types have undergone tremendous changes. These changes are accompanied by an increase in energy consumption, especially the increase in anthropogenic greenhouse gas emissions, mainly carbon dioxide, which has further aggravated the problem of global warming. The planting of ground cover plants has become an effective strategy to cope with this challenge due to its significant effects in absorbing carbon dioxide, reducing surface temperature, and maintaining soil moisture. Therefore, how to achieve the scientific configuration and rapid batch planting of ground cover plants has become an urgent problem to be solved.

[0003] Currently, the selection of ground cover plants mainly relies on manual selection to determine the plant species in the target paving area.

[0004] However, traditional manual screening methods often fail to fully consider the diverse needs of the target area, resulting in a lack of precision and scientificity in the selection of ground cover plants. This limitation restricts the scientific nature and ecological benefits of the planting plan. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a method and device for generating a ground cover plant assembly module to at least partially solve the above-mentioned problems.

[0006] According to a first aspect of an embodiment of the present application, a method for generating a ground cover plant assembly module is provided, comprising: obtaining plant requirement parameters of a target paving area input by a user, wherein the plant requirement parameters include at least: growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs, the growth habits are used to indicate the morphological characteristic types of plants, the environmental requirements are used to indicate the environmental conditions required for plant growth and survival, the ecological functions are used to indicate the role and contribution of plants in an ecosystem, the ornamental characteristics are used to indicate the aesthetic value and ornamental potential of plants, and the economic costs are used to indicate the economic factors of plant planting, maintenance and use; according to the growth habits, the environmental requirements, the ecological functions, the ornamental characteristics and the economic costs, matching a combination of multiple plant data from a database; determining at least one from the combinations of the multiple plant data as a target plant combination, and generating a ground cover plant assembly module according to the target plant combination.

[0007] According to a second aspect of an embodiment of the present application, a device for generating a ground cover plant assembly module is provided, comprising: an acquisition module, used to acquire plant requirement parameters of a target paving area input by a user, wherein the plant requirement parameters include at least: growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs, the growth habits are used to indicate the morphological characteristic types of plants, the environmental requirements are used to indicate the environmental conditions required for plant growth and survival, the ecological functions are used to indicate the role and contribution of plants in an ecosystem, the ornamental characteristics are used to indicate the aesthetic value and ornamental potential of plants, and the economic costs are used to indicate the economic factors of plant planting, maintenance and use; a matching module, used to match a combination of multiple plant data from a database according to the growth habits, the environmental requirements, the ecological functions, the ornamental characteristics and the economic costs; a generation module, used to determine at least one as a target plant combination from the combination of the multiple plant data, and generate a ground cover plant assembly module according to the target plant combination.

[0008] The above-mentioned technical scheme selects the target plant combination by considering the growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs of the target paving area, and generates ground cover plant assembly modules, which can improve the accuracy and scientificity of ground cover plant selection, thereby improving the scientificity and ecological benefits of the planting plan. In addition, the ground cover plant assembly modules are easy to lay, which can improve the efficiency of vegetation planting in the target paving area. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 A flowchart of the steps of a method for generating a ground cover plant assembly module according to an embodiment of the present application; Figure 2 A flow chart of the steps of a plant combination matching model training method according to an embodiment of the present application; Figure 3 A flowchart of the steps of a method for generating a ground cover plant assembly module according to an embodiment of the present application; Figure 4 This is a structural block diagram of a ground cover plant assembly module generation device according to an embodiment of the present application; Figure 5 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0012] The specific implementation of the embodiment of the present application is further explained below in conjunction with the accompanying drawings of the embodiment of the present application.

[0013] Figure 1 FIG. 1 is a flowchart of a method for generating a ground cover plant assembly module according to an embodiment of the present application, as shown in FIG. Figure 1 As shown, the method for generating the ground cover plant assembly module includes: Step 101: Obtain plant requirement parameters of a target paving area input by a user.

[0014] Step 102: Match a combination of multiple plant data from a database according to growth habits, environmental requirements, ecological functions, ornamental characteristics, and economic costs.

[0015] Step 103: determine at least one of the combinations of the plurality of plant data as a target plant combination, and generate a ground cover plant assembly module according to the target plant combination.

[0016] In order to guide the planting of the target paving area, the user can input the plant requirement parameters of the target paving area. The plant requirement parameters include at least: growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs. Growth habits are used to indicate the morphological characteristics of plants. Environmental requirements are used to indicate the environmental conditions required for plant growth and survival. Ecological functions are used to indicate the role and contribution of plants in the ecosystem. Ornamental characteristics are used to indicate the aesthetic value and ornamental potential of plants. Economic costs are used to indicate the economic factors of plant planting, maintenance and use. Specifically, growth habits may include types of grass ground cover, vine ground cover, shrub ground cover, etc. Environmental factors may include factors such as climate adaptability, light requirements and soil requirements. Ornamental characteristics may include growth characteristics, plant height, flowering and fruiting period, flower color and fruit color, etc. Ecological functions may include food source function, nectar source function, water saving ability and carbon sequestration ability, etc. Economic costs may include planting costs, plant specifications, maintenance costs and life cycle costs. After obtaining the plant requirement parameters, a combination of multiple plant data can be matched from the database according to growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs. For example, the plant requirement parameters can be: shrub ground cover, adapted to acidic clay, sun-loving, high water demand, strong water-saving ability, medium carbon fixation ability, one- or two-year-old ornamental flower ground cover, medium height (10-25cm), spring and summer flowers, elegant flower colors, medium unit price, small specifications. According to the plant requirement parameters, a combination of multiple plant data can be matched from the database, all of which can meet the above conditions. For example, the following are examples of combinations of plant data for target paving areas under different environmental scenarios: (1) Seed ratio for the northern drought-resistant nectar source scenario: henna 34.50%, thistle 31.95%, ryegrass 13.42%, sage 9.20%, agapanthus 5.75%, lotus 2.30%, sweet clover 1.92%, and rudbeckia 0.96%.

[0017] (2) Seed ratios for the northern moist nectar source scenario: 24.55% ryegrass, 10.67% iris, 10.67% aster, 10.67% oleander, 8.00% inula, 5.33% daisy, 5.33% patrinia, 5.33% alfalfa, 5.33% daylily, 5.33% thistle, 5.33% white clover, 2.13% schizonepeta, and 1.33% loosestrife.

[0018] (3) Seed ratio for shade-tolerant nectar source scenario in the north: 46.00% ryegrass, 14.00% large-leaf clematis, 8.00% dactylum, 8.00% february orchid, 8.00% echinacea, 6.00% dandelion, 6.00% creeping cinquefoil, and 4.00% salvia.

[0019] (4) Seed ratios for the shade-tolerant mammal food source scenario: 18.51% for Croton tiglium, 15.84% for Medicago truncatula, 15.84% for Oryza sativa, 14.50% for Viola yedoensis, 13.20% for Duchesnea sinensis, 12.22% for Lolium perenne, and 9.89% for Potentilla.

[0020] (5) Seed ratios in the food source scenario for sun-loving mammals: 48.90% ryegrass, 25.50% Viola yedoensis, 10.24% sweet daisy, 5.12% Potentilla fruticosa, 5.12% Duchesnea sinensis, 3.07% Duchesnea chinensis, and 2.05% Alfalfa.

[0021] (6) Seed ratio for northern drought-tolerant food source scenario: pea 75.68%, nightshade 7.57%, wild strawberry 6.86%, alfalfa 4.55%, Codonopsis pilosula 3.83%, snakeberry 1.01%, and Bletilla striata 0.50%.

[0022] (7) Seed ratios for sun-loving bird food sources: 23.00% ryegrass, 13.00% Aquilegia vulgaris, 13.00% Physalis alkekengi, 13.00% Setaria viridis, 13.00% alfalfa, 9.00% Solanum nigrum, 9.00% Duchesnea indica, 4.00% Wild pea, and 3.00% Bindweed.

[0023] (8) Seed ratio for shade-tolerant bird food source scenario: ryegrass 35.00%, snakeberry 24.00%, bluegrass 24.00%, and wild pea 17.00%.

[0024] (9) Seed ratios for butterfly-attracting chrysanthemums: Echinacea 23.00%, ryegrass 21.00%, 'Blue Sorrow' Nepeta 15.00%, Gaillardia 11.00%, Coreopsis 11.00%, Caryopteris 7.00%, Vanilla 4.00%, Rudbeckia grandiflora 3.00%, Cyperus rotundus 3.00%, Potentilla 2.00%.

[0025] It should be noted that when matching a combination of multiple plant data from a database based on growth habits, environmental requirements, ecological functions, ornamental characteristics, and economic costs, a query formula can be constructed first, in which at least one of the growth habits, environmental requirements, ecological functions, ornamental characteristics, and economic costs has a valid value. For example, the valid value of environmental requirements is high water demand, and the other valid values ​​are null, then the constructed query formula is "high water demand". For another example, the valid value of environmental requirements is high water demand, the valid value of growth habits is shrub ground cover, and the other valid values ​​are null, then the constructed query formula is "high water demand AND shrub ground cover". The corresponding query formula can be constructed according to actual needs.

[0026] After obtaining a combination of multiple plant data, the combination of multiple plant data can be screened again to determine at least one as a target plant combination, and then a ground cover plant assembly module is generated according to the target plant combination. The target paving area can be guided for paving according to the ground cover plant assembly module.

[0027] It should be noted that in the process of establishing a database, a basic database can be established first, and indicator information including growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs can be set in the database. According to the indicator information, the corresponding data can be extracted from various plant data websites (such as Plant Database, Botanical Gardens), and the extracted data can be written into the basic database according to the indicator information, thereby obtaining a database that can be queried according to the plant requirement parameters. Specifically, in the data extraction process, the web page structure is first analyzed, and an accurate XPath selector is defined to accurately extract the specific data corresponding to each indicator information. After extraction, data cleaning is performed, duplicate data is removed and formatted to ensure the neatness and consistency of the data. Given that the acquired data are all structured text data, it is chosen to store the acquired data in real time in the relational database MySQL, and a reasonable data model is designed to support efficient query and management. The crawler program is run regularly to update the information in the database, and a monitoring mechanism is set up to ensure the timeliness and accuracy of the data.

[0028] In an embodiment of the present application, by considering the growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs of the target paving area to select a target plant combination and generate a ground cover plant assembly module, the accuracy and scientificity of the ground cover plant selection can be improved, thereby improving the scientificity and ecological benefits of the planting plan, and the ground cover plant assembly module is easy to lay, which can improve the efficiency of vegetation planting in the target paving area.

[0029] In one feasible embodiment, the process of determining at least one target plant combination from a combination of multiple plant data may also include: obtaining environmental information of the target paving area, and determining the elevation, slope, soil properties, annual precipitation and annual average temperature of the target paving area based on the environmental information; calculating environmental factors based on the elevation, slope, soil properties, annual precipitation and annual average temperature; and determining at least one target plant combination from a combination of multiple plant data based on the environmental factors.

[0030] In order to determine the target plant combination, the environmental information of the target paving area can be obtained first, and then the elevation, slope, soil characteristics, annual precipitation and annual average temperature of the target paving area can be directly determined. The environmental factors can be calculated by combining the above parameters, and then the combination of multiple plant data can be screened according to the environmental factors to determine at least one as the target plant combination. The specific method of screening can be to digitize the parameters in the environmental factors and plant data, and then compare the values, and select at least one combination of plant data that is closest to the environmental factors as the target plant combination.

[0031] In the embodiment of the present application, environmental factors are determined by comprehensively analyzing the environmental information of the target paving area, and combinations of multiple plant data are screened by the environmental factors, so as to improve the accuracy of matching the target plant combination with the plant requirement parameters.

[0032] In a feasible manner, the process of determining at least one target plant combination from a combination of multiple plant data according to environmental factors may also include: Acquire environmental information of the target paving area, and determine the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index of the target paving area based on the environmental information; calculate the biodiversity index based on the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index; and determine at least one target plant combination from a combination of multiple plant data based on the biodiversity index and environmental factors.

[0033] When screening a combination of multiple plant data, the biodiversity index can be further integrated for screening. First, the environmental information of the target paving area is obtained, and the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index of the target paving area are determined according to the environmental information of the target paving area. The habitat quality index is used to indicate the ecological suitability of the target paving area. The habitat quality index can be based on land cover change (LUCC, Land Use and Land Cover Change) data, and the classification system adopts the remote sensing classification system in the "China Resources and Environment Database" of the Chinese Academy of Sciences to divide land types into 6 categories: cultivated land, forest land, grassland, water area, construction land and unused land. Water yield is used to indicate the moisture status information of the target paving area, which can be used as a reference for judging the dry and wet properties of the soil. The enhanced vegetation index is used to indicate the growth status of vegetation in the target paving area, which can evaluate the vegetation coverage and health, distinguish vegetation types, and respond to environmental changes. The Shannon diversity index is used to indicate the heterogeneity of the landscape in the target paving area. The more diversified the land use and the higher the degree of fragmentation, the greater the uncertainty of its information, and the corresponding Shannon diversity index value will be higher. After obtaining the biodiversity index, at least one combination of multiple plant data can be determined as the target plant combination based on the biodiversity index and environmental factors.

[0034] Specifically, the habitat quality index is determined according to the following formula:

[0035] in, Used to characterize target paving areas Middle Habitat quality of habitat types, To characterize the Habitat suitability of habitat types, Used to characterize target paving areas Middle The level of pressure on each habitat type, Used to characterize half-saturation constants, target laying areas The habitat types include: cultivated land, forest land, grassland, water area, construction land and unused land.

[0036] according to Habitat quality of each habitat type Habitat Quality Index (Habitat QualityIndex).

[0037] The water production is determined according to the following formula:

[0038] in, Used to characterize target paving areas Land use type Water output (mm), Used to characterize target paving areas Evapotranspiration of land use type l in the middle (mm); Used to characterize target paving areas Land use type Precipitation (mm).

[0039] according to Water yield per land use type Determine water production (Water Yield).

[0040] The enhanced vegetation index is determined according to the following formula:

[0041] in, (Enhanced Vegetation Index) is used to characterize the enhanced vegetation index. Used to characterize near-infrared surface reflectance that has been at least partially corrected for the atmosphere, is used to characterize the red surface reflectance, at least partially corrected for the atmosphere, is used to characterize the blue light surface reflectance, at least partially corrected for the atmosphere, 、 、 is a constant.

[0042] The Shannon diversity index is determined according to the following formula:

[0043] in, (Shannon's Diversity Index) is used to characterize the Shannon diversity index. Used to characterize the number of landscape types, To characterize landscape types The proportion of area occupied.

[0044] Specifically, the biodiversity index is determined according to the following formula:

[0045] in, (Biodiversity Index) is used to characterize the biodiversity index. , , and In order to calculate the weights corresponding to the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index when calculating environmental factors, by assigning different weights to the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index, more paving areas can be adapted. At the same time, the weights of the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index can be determined according to the environmental information of the target paving area.

[0046] In an embodiment of the present application, by screening the combination of multiple plant data, the biodiversity index determined by the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index is screened, which can further improve the matching degree between the target plant combination and the plant requirement parameters.

[0047] In one feasible embodiment, the process of determining at least one target plant combination from a combination of multiple plant data based on a biodiversity index and environmental factors may also include: determining at least one target plant combination from a combination of multiple plant data based on a biodiversity index, environmental factors, and weights of the biodiversity index and environmental factors.

[0048] In the process of screening the combination of various plant data according to the biodiversity index and environmental factors at the same time, the weights of the biodiversity index and environmental factors can be introduced, and the weights of the biodiversity index and environmental factors can be set accordingly according to the environmental information of the target paving area.

[0049] In the embodiment of the present application, by setting weights for the biodiversity index and environmental factors, the screening results can be made more accurate.

[0050] In one feasible embodiment, the process of matching multiple plant combinations from a database based on plant requirement parameters may also include: inputting the growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs included in the plant requirement parameters into a plant combination matching model, so that the plant combination matching model matches plants from the database and obtains multiple plant combinations output by the plant combination matching model.

[0051] Specifically, the growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs included in the plant demand parameters are input into the plant combination matching model by inputting the growth habits (grass ground cover, vine ground cover, etc.), environmental requirements (soil texture, light requirements, etc.), ecological functions (water-saving ability, carbon sequestration ability, etc.), ornamental characteristics (flowering period, plant height, etc.), and economic costs (planting costs, plant specifications, etc.) as feature matrix X into the plant combination matching model, and then the plant combination matching model generates a target variable Y, which is the plant planting function requirements included in the plant demand parameters, such as specific growth habits, ornamental characteristics, etc., and then the plants are matched in the database through the target variable Y to obtain multiple plant combinations output by the plant combination matching model.

[0052] In the embodiment of the present application, multiple plant combinations are outputted by the plant combination matching model, so that the accuracy of matching plant combinations from the database can be improved.

[0053] In a feasible manner, the target laying area can also be simulated to obtain a regional simulation map, and then the regional simulation map is segmented according to the size of the ground cover plant assembly module to obtain a segmented map, so as to guide the installation of the ground cover plant assembly module according to the segmented map.

[0054] After obtaining the ground cover plant assembly module, in order to facilitate laying, the target laying area can be simulated to obtain a regional simulation map. Then, the regional simulation map is segmented according to the size of the ground cover plant assembly module to obtain a segmented map. Since the segmented map is divided into multiple sub-maps of the size of the ground cover plant assembly module, it is convenient for guiding laying.

[0055] In the embodiment of the present application, by simulating the target laying area and dividing it according to the size of the ground cover plant assembly module, the difficulty of laying the ground cover plant assembly module can be reduced.

[0056] Figure 2 is a flow chart of the steps of a plant combination matching model training method according to an embodiment of the present application, such as Figure 2 As shown, the plant combination matching model training method includes the following steps: Step 201: Acquire multiple plant data and multiple plant requirements from a database.

[0057] Step 202: extract features from multiple plant data according to multiple plant requirements to obtain multiple plant feature data.

[0058] Step 203: construct multiple training data sets based on multiple plant feature data.

[0059] Step 204: construct multiple decision trees according to the multiple data sets to be trained, and construct a plant combination matching model according to the multiple decision trees and the plant requirements corresponding to the decision trees.

[0060] In order to train the plant combination matching model, we must first build a data set. Specifically, we can obtain multiple plant data from the database and obtain multiple plant requirements as input. Then, we can extract features from the multiple plant data according to the multiple plant requirements to obtain multiple plant feature data. In the process of feature extraction, we can numerically process the multiple plant data and multiple plant requirements, assign corresponding numerical values ​​to them, and perform standardization or normalization to ensure that different features are on the same scale. The normalization method can be Min-Max normalization. When generating multiple plant feature data, we can perform weighted calculations based on adaptability, water-saving ability, etc. while performing numerical processing to obtain multiple plant feature data. In the feature extraction and data preparation stage, input the feature matrix X mentioned above.

[0061] Then, multiple training data sets are constructed based on multiple plant feature data. Specifically, samples can be randomly selected. For each decision tree , from the original dataset: Randomly extract samples to construct multiple training sets, where: It is The training data set of trees is Sampling method Sampling is obtained, It is the specific sample drawn. At the same time, sampling with replacement can enhance the diversity of data, and the plant feature data in multiple training data sets are not exactly the same.

[0062] Then, multiple decision trees are constructed based on multiple training data sets. The CART (Classification and Regression Trees) algorithm can be used to construct each tree. When constructing each decision tree, a feature subset can be randomly selected. To split: ,in: For all feature sets, For A randomly selected subset of features from Representing feature subsets The number of features, is less than the total number of features This choice reduces the correlation between trees, making the model more stable when processing high-dimensional features and enhancing the generalization ability of the model.

[0063] Then, multiple decision trees are constructed by recursively splitting the nodes. The specific steps of constructing the decision trees are as follows: (1) At the current node, from the feature subset Select the best split feature and its threshold , so that the child nodes after splitting are optimal in the objective function. For regression problems, the goal is to minimize the mean square error (MSE): ,in: is the number of samples of the current node, is the true value, is the predicted value.

[0064] (2) Repeat the node splitting process and recursively build a decision tree until the stopping condition is met. For input samples Make predictions: .

[0065] Then, the prediction results of all decision trees are integrated to get the final model prediction result. For regression problems, the average of all decision tree prediction values ​​is taken: ,in: is the total number of decision trees, is the final prediction result.

[0066] Through the above method, multiple decision trees are constructed. Each tree will learn different planting scheme patterns, and then multiple training data sets are input into each tree to obtain prediction results, and scores are given according to the corresponding multiple plant requirements. Then, the random forest model is trained to optimize its prediction accuracy when generating planting schemes.

[0067] Specifically, the fitness of the trained model can also be evaluated: ,in: is the fitness score of the plant combination, Indicates the ornamental score of the plant combination, represents the ecological benefit score of the plant combination, and is a weight parameter, reflecting the importance of ornamental value and ecological benefit, and Specifically, the fitness of plant combinations can be evaluated based on plant needs and ecological benefits, and weights can be assigned to plant needs and ecological benefits respectively to represent the importance of ornamental and ecological benefits.

[0068] In addition, the plant combination matching model can also be optimized when the model is used. Specifically, the plant requirements generated by the model are compared with the plant requirements input by the user, a loss function is constructed, and an optimization algorithm such as the gradient boosting method is used to minimize the loss: ,in: is the number of samples of the current node, is the true value, The random forest model is regularly updated and retrained based on user feedback and new data to improve prediction accuracy.

[0069] In the embodiment of the present application, the accuracy of matching plants from the database can be improved by training the plant combination matching model by using multiple training data sets and constructing a decision tree.

[0070] Figure 3 is a flowchart of the steps of a method for generating a ground cover plant assembly module according to an embodiment of the present application, such as Figure 3 As shown, the method for generating a ground cover plant assembly module includes the following steps: Step 301: Mix the original soil and peat soil to form multiple planting media.

[0071] Step 302: Place the non-woven fabric on the planting tray.

[0072] Step 303: Spread a water-retaining agent of a first thickness on the non-woven fabric, and spread a water-retaining agent of a second thickness on the edge of the planting tray.

[0073] Step 304: Place one of the plurality of planting substrates on a planting tray to obtain a first substrate.

[0074] Step 305: Mix the seeds of the target plant combination with one of the multiple planting substrates to obtain a second substrate.

[0075] Step 306: Lay the second substrate on the first substrate to obtain a ground cover plant assembly module.

[0076] In order to prepare the ground cover plant assembly module, firstly, the original soil and peat soil are mixed, specifically, the mixing ratio of the original soil and peat soil is 7:3, the thickness of the planting matrix can be 5cm, then the non-woven fabric is placed on the planting tray, and the first thickness of the water retaining agent is spread on the non-woven fabric, and the second thickness of the water retaining agent is spread on the edge of the planting tray, the first thickness is less than the second thickness, specifically, the first thickness can be 1mm, and the second thickness can be 2mm, one of the multiple planting matrices is placed on the planting tray to obtain the first matrix, and the seeds of the target plant combination are mixed with one of the multiple planting matrices to obtain the second matrix. Specifically, the seeds of the target plant combination can be 25-30g. Then the second matrix is ​​laid on the first matrix to obtain the ground cover plant assembly module.

[0077] In the embodiment of the present application, by laying a thicker water-retaining agent on the edge of the planting tray and laying a thinner water-retaining agent at the non-woven fabric position, the use of the water-retaining agent can be reduced while ensuring the water retention effect.

[0078] In one feasible embodiment, the process of generating the ground cover plant prefabricated module may also include: watering the ground cover plant prefabricated module at a first time interval in a first cycle, and watering the ground cover plant prefabricated module at a second time interval in a second cycle; applying amino acid water-soluble fertilizer, chlormequat chloride and fungicide to the ground cover plant prefabricated module in the first cycle and the second cycle.

[0079] After the ground cover plant prefabricated modules are generated, they need to be maintained. The ground cover plant prefabricated modules can be watered at a first time interval in a first cycle, and watered at a second time interval in a second cycle. The first time interval is smaller than the second time interval. For example, the first cycle is 14 days, the first time interval is watering once every two days, and the second time interval is watering once every three days.

[0080] In the embodiment of the present application, nitrogen fertilizer and fungicide work together to maintain the biodiversity of the community. The configuration of nitrogen fertilizer and fungicide can significantly improve the biodiversity and stability of the ground cover plant assembly module. The use of chlormequat can promote the sturdiness of plant stems, enhance the soil gripping ability and lodging resistance of plant roots, so that the plant roots are closely combined with the matrix soil, making the ground cover plant assembly module easy to transport and lay.

[0081] Figure 4 This is a structural block diagram of a ground cover plant assembly module generation device according to an embodiment of the present application, such as Figure 4 As shown, the ground cover plant assembly module generating device 400 may include: an acquisition module 401 , a matching module 402 and a generating module 403 .

[0082] Acquisition module 401 is used to obtain plant requirement parameters of the target paving area input by the user, wherein the plant requirement parameters include at least: growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs, growth habits are used to indicate the morphological characteristics of plants, environmental requirements are used to indicate the environmental conditions required for plant growth and survival, ecological functions are used to indicate the role and contribution of plants in the ecosystem, ornamental characteristics are used to indicate the aesthetic value and ornamental potential of plants, and economic costs are used to indicate the economic factors of plant planting, maintenance and use.

[0083] The matching module 402 is used to match a combination of multiple plant data from the database according to growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs.

[0084] The generation module 403 is used to determine at least one of the combinations of multiple plant data as a target plant combination, and generate a ground cover plant assembly module according to the target plant combination.

[0085] In order to guide the planting of the target paving area, the user can input the plant requirement parameters of the target paving area. The plant requirement parameters include at least: growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs. Growth habits are used to indicate the morphological characteristics of plants. Environmental requirements are used to indicate the environmental conditions required for plant growth and survival. Ecological functions are used to indicate the role and contribution of plants in the ecosystem. Ornamental characteristics are used to indicate the aesthetic value and ornamental potential of plants. Economic costs are used to indicate the economic factors of plant planting, maintenance and use. Specifically, growth habits may include types of grass ground cover, vine ground cover, shrub ground cover, etc. Environmental factors may include factors such as climate adaptability, light requirements and soil requirements. Ornamental characteristics may include growth characteristics, plant height, flowering and fruiting period, flower color and fruit color, etc. Ecological functions may include food source function, nectar source function, water saving ability and carbon sequestration ability, etc. Economic costs may include planting costs, plant specifications, maintenance costs and life cycle costs. After the acquisition module 401 obtains the plant demand parameters, the matching module 402 can match a combination of multiple plant data from the database according to growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs. For example, the plant demand parameters can be: shrub ground cover, adapted to acidic clay, sun-loving, high water demand, strong water-saving ability, medium carbon fixation ability, one- or two-year-old flower ground cover, medium height (10-25cm), spring and summer flowers, elegant flower colors, medium unit price, small specifications. According to the plant demand parameters, a combination of multiple plant data can be matched from the database, and all of them can meet the above conditions. After obtaining a combination of multiple plant data, the matching module 402 can screen the combination of multiple plant data again, and determine at least one as the target plant combination. Then the generation module 403 generates a ground cover plant assembly module according to the target plant combination, and the ground cover plant assembly module can be used to guide the laying of the target laying area.

[0086] In an embodiment of the present application, by considering the growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs of the target paving area to select a target plant combination and generate a ground cover plant assembly module, the accuracy and scientificity of the ground cover plant selection can be improved, thereby improving the scientificity and ecological benefits of the planting plan, and the ground cover plant assembly module is easy to lay, which can improve the efficiency of vegetation planting in the target paving area.

[0087] It should be noted that, as the information interaction, execution process and other contents between the modules in the above-mentioned ground cover plant prefabricated module generating device 400 are based on the same concept as the above-mentioned ground cover plant prefabricated module generating method embodiment, the specific contents can be found in the description in the above-mentioned ground cover plant prefabricated module generating method embodiment, and will not be repeated here.

[0088] In this embodiment, an electronic device 500 is provided, such as Figure 5 As shown, the electronic device 500 may include: a processor 501, a communication interface 502, a memory 503, and a communication bus 504. Among them: The processor 501 , the communication interface 502 , and the memory 503 communicate with each other via the communication bus 504 .

[0089] The communication interface 502 is used to communicate with other electronic devices or servers.

[0090] The processor 501 is used to execute the program 505, and specifically can execute the relevant steps in the aforementioned embodiment of the method for generating the ground cover plant assembly module.

[0091] Specifically, the program 505 may include program codes, which include computer operation instructions.

[0092] The processor 501 may be a CPU, or an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0093] The memory 503 is used to store the program 505. The memory 503 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0094] The program 505 can be specifically used to enable the processor 501 to execute the method for generating the ground cover plant assembly module in the aforementioned embodiment.

[0095] The specific implementation of each step in program 505 can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned ground cover plant assembly module generation method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0096] The electronic device 500 of the embodiment of the present application selects a target plant combination by considering the growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs of the target paving area, and generates a ground cover plant assembly module, which can improve the accuracy and scientificity of ground cover plant selection, thereby improving the scientificity and ecological benefits of the planting plan, and the ground cover plant assembly module is easy to lay, which can improve the efficiency of vegetation planting in the target paving area.

[0097] In this embodiment, a computer-readable storage medium is provided, which stores instructions for causing a machine to execute the method for generating a ground cover plant assembly module as described herein. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code for implementing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device is enabled to read and execute the program code stored in the storage medium.

[0098] In this case, the program code read from the storage medium itself can implement the function of any one of the above embodiments, so the program code and the storage medium storing the program code constitute part of the present application.

[0099] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0100] In this embodiment, a computer program product is provided, including computer instructions, which instruct a computing device to perform any corresponding operation in the above-mentioned multiple method embodiments.

[0101] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0102] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0103] It should be noted that in the description of this application, the terms "first" and "second" are only used to facilitate the description of different components or names, and cannot be understood as indicating or implying a sequential relationship, relative importance, or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0104] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0105] It should be noted that although the specific embodiments of the present application are described in detail in conjunction with the accompanying drawings, this should not be construed as limiting the scope of protection of the present application. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still fall within the scope of protection of the present application.

[0106] The examples of the embodiments of the present application are intended to concisely illustrate the technical features of the embodiments of the present application so that those skilled in the art can intuitively understand the technical features of the embodiments of the present application, and are not intended to be improper limitations of the embodiments of the present application.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating a ground cover plant assembly module, characterized in that: include: Obtaining plant requirement parameters of the target paving area input by the user, wherein the plant requirement parameters at least include: growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs, wherein the growth habits are used to indicate the morphological characteristics of the plants, the environmental requirements are used to indicate the environmental conditions required for the growth and survival of the plants, the ecological functions are used to indicate the roles and contributions of the plants in the ecosystem, the ornamental characteristics are used to indicate the aesthetic value and ornamental potential of the plants, and the economic costs are used to indicate the economic factors of the planting, maintenance and use of the plants; matching a combination of multiple plant data from a database according to the growth habits, the environmental requirements, the ecological functions, the ornamental characteristics and the economic costs; At least one of the combinations of the plurality of plant data is determined as a target plant combination, and a ground cover plant assembly module is generated according to the target plant combination.

2. The method according to claim 1, characterized in that The step of determining at least one target plant combination from the combination of the plurality of plant data comprises: Acquire environmental information of the target paving area, and determine the elevation, slope, soil properties, annual precipitation and annual average temperature of the target paving area according to the environmental information; Calculating environmental factors based on the elevation, the slope, the soil properties, the annual precipitation and the annual average temperature; At least one target plant combination is determined from the combinations of the plurality of plant data according to the environmental factors.

3. The method according to claim 2, characterized in that The step of determining at least one target plant combination from the combination of the plurality of plant data according to the environmental factors further comprises: Acquire environmental information of the target paving area, and determine the habitat quality index, water yield, enhanced vegetation index and Shannon diversity index of the target paving area according to the environmental information, wherein the habitat quality index is used to indicate the ecological suitability of the target paving area, the water yield is used to indicate the moisture status information of the target paving area, the enhanced vegetation index is used to indicate the growth status of vegetation in the target paving area, and the Shannon diversity index is used to indicate the heterogeneity of the landscape in the target paving area; calculating a biodiversity index based on the habitat quality index, the water yield, the enhanced vegetation index and the Shannon diversity index; At least one target plant combination is determined from the combinations of the plurality of plant data according to the biodiversity index and the environmental factors.

4. The method according to claim 3, characterized in that The step of determining at least one target plant combination from the combination of the plurality of plant data according to the biodiversity index and the environmental factor comprises: At least one target plant combination is determined from the combinations of the plurality of plant data according to the biodiversity index, the environmental factors, and the weights of the biodiversity index and the environmental factors.

5. The method according to claim 1, characterized in that: The matching of multiple plant combinations from a database according to the plant requirement parameters includes: The growth habits, environmental requirements, ecological functions, ornamental characteristics and economic costs included in the plant requirement parameters are input into a plant combination matching model so that the plant combination matching model matches plants from a database to obtain multiple plant combinations output by the plant combination matching model.

6. The method according to claim 5, characterized in that The plant combination matching model is trained by the following method: Acquire a plurality of plant data from the database, and acquire a plurality of plant requirements; Extracting features from the plurality of plant data according to the plurality of plant requirements to obtain a plurality of plant feature data; Constructing a plurality of to-be-trained data sets according to the plurality of plant feature data, wherein the plant feature data in the plurality of to-be-trained data sets are not completely the same; A plurality of decision trees are constructed according to the plurality of data sets to be trained, and the plant combination matching model is constructed according to the plurality of decision trees and plant requirements corresponding to the decision trees.

7. The method according to claim 3, characterized in that The habitat quality index is determined according to the following formula: in, Used to characterize the target paving area Middle Habitat quality of habitat types, To characterize the Habitat suitability of habitat types, Used to characterize the target paving area Middle The level of pressure on each habitat type, For characterizing the half-saturation constant, the target paving area The habitat types in the include: cultivated land, forest land, grassland, water area, built-up land and unused land; according to Habitat quality of each habitat type Habitat Quality Index ; The water production is determined according to the following formula: in, Used to characterize the target paving area Land use type The water production, Used to characterize the target paving area Land use type evapotranspiration; Used to characterize the target paving area Land use type of precipitation; according to Water yield per land use type Determine the enhanced vegetation index ; The enhanced vegetation index is determined according to the following formula: in, For characterizing the enhanced vegetation index, Used to characterize near-infrared surface reflectance that has been at least partially corrected for the atmosphere, is used to characterize the red surface reflectance, at least partially corrected for the atmosphere, is used to characterize the blue light surface reflectance, at least partially corrected for the atmosphere, , , is a constant; The Shannon diversity index is determined according to the following formula: in, Used to characterize the Shannon diversity index, Used to characterize the number of landscape types, To characterize landscape types The proportion of area occupied.

8. The method according to claim 1, characterized in that The method of generating a ground cover plant assembly module according to the target plant combination includes: Mix the original soil and peat soil to form multiple planting substrates; Place the nonwoven fabric on the planting tray; Laying a water-retaining agent of a first thickness on the non-woven fabric, and laying a water-retaining agent of a second thickness on the edge of the planting tray, wherein the first thickness is less than the second thickness; placing one of the plurality of planting substrates on the planting tray to obtain a first substrate; mixing seeds of the target plant combination with one of the plurality of planting substrates to obtain a second substrate; The second matrix is ​​laid on the first matrix to obtain the ground cover plant assembly module.

9. The method according to claim 8, characterized in that The method further comprises: The ground cover plant assembly module is watered at a first time interval in a first cycle, and the ground cover plant assembly module is watered at a second time interval in a second cycle, wherein the first time interval is smaller than the second time interval; In the first cycle and the second cycle, amino acid water-soluble fertilizer, chlormequat chloride and fungicide are applied to the ground cover plant assembly module.

10. The method according to claim 1, characterized in that The method further comprises: Simulating the target paving area to obtain a regional simulation map; The regional simulation map is segmented according to the size of the ground cover plant assembly module to obtain a segmented map, so as to guide the installation of the ground cover plant assembly module according to the segmented map.

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