Green plant selection method, system and equipment and storage medium

Abstract

The invention discloses a green plant selection method, system and device and a storage medium, and the technical scheme is characterized in that soil analysis data and plant physiological data corresponding to each candidate plant in a sub-region are acquired; constructing a structural equation model according to the congestion delay indexes, the soil analysis data and the plant physiological data of the sub-regions, and determining significant factors of each candidate plant influenced by traffic according to the structural equation model; calculating the first fitness of each candidate plant according to the congestion delay index of the sub-region and the significant factor of each candidate plant in the region; a plant selection model corresponding to each sub-region is constructed based on a genetic algorithm, the candidate plants are substituted into the plant selection model of each sub-region, target plants corresponding to each sub-region are obtained, and fitness functions of the plant selection models are constructed based on the first fitness, the congestion delay index and the significant factors. According to the method, the plants with high adaptability and high carbon sequestration capability can be selected based on the traffic jam condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological engineering, and in particular relates to a method, system, equipment and storage medium for selecting green plants. Background Art

[0002] As an important carbon sink, the carbon sequestration potential of urban green spaces is influenced by multiple factors, including plant species, environmental conditions, and management practices. Existing methods for assessing the carbon sequestration capacity of urban greening plants are mostly based on static environments (such as single light intensity, temperature, or humidity), lacking research on their response mechanisms to dynamic urban microenvironments such as traffic congestion.

[0003] Air pollutants generated by traffic congestion during peak hours significantly affect the photosynthetic efficiency of green plants. However, existing methods do not fully consider the impact of automobile exhaust emissions on plant photosynthesis during peak traffic hours. Although soil carbon pool components and extracellular enzyme activity detection have been widely used in ecology-related research, they have not yet been integrated into the dynamic management model of urban green space carbon sinks, nor have they revealed the dynamic coupling relationship between soil carbon pools and extracellular enzyme activity in the carbon cycle process. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system, device and storage medium for selecting green plants, which can select plants with high adaptability and strong carbon sequestration capacity based on traffic congestion conditions.

[0005] A first aspect of the present invention provides a method for selecting green plants, comprising:

[0006] Divide the target area into sub-areas with different congestion levels according to the congestion delay index;

[0007] Acquire soil analysis data and plant physiological data corresponding to each candidate plant in the sub-area;

[0008] constructing a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region, and determining significant factors affecting traffic flow for each candidate plant based on the structural equation model;

[0009] Calculating a first fitness of the candidate plants in the region according to the congestion delay index of the sub-region and the significance factor of each candidate plant in the region;

[0010] A plant selection model corresponding to each sub-area is constructed based on a genetic algorithm, and the candidate plants are substituted into the plant selection model of each sub-area to obtain the target plants corresponding to each sub-area, wherein the fitness function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significance factor.

[0011] In some embodiments, constructing a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region includes:

[0012] performing data processing on the soil analysis data to obtain soil processing data, and performing data processing on the plant physiological data to obtain plant processing data;

[0013] establishing an initial theoretical model based on the congestion delay index, soil treatment data, and plant treatment data;

[0014] Parameters of the initial theoretical model are estimated, and when evaluation indicators of the parameter estimation results meet preset indicators, a structural equation model is obtained.

[0015] In some embodiments, establishing an initial theoretical model based on the congestion delay index, soil treatment data, and plant physiological data includes:

[0016] Performing principal component analysis on the extracellular enzyme activities in the soil treatment data to obtain key extracellular enzyme activities;

[0017] Performing principal component analysis on the physical and chemical properties in the soil treatment data to obtain key physical and chemical properties;

[0018] Determine exogenous variables and endogenous variables, establish the first relationship between exogenous variables and endogenous variables, and establish the second relationship between endogenous variables;

[0019] Determine the endogenous latent variables and endogenous manifest variables of the endogenous variables, and establish the third relationship between the endogenous latent variables and the endogenous manifest variables;

[0020] An initial theoretical model is established according to the first relationship, the second relationship and the third relationship.

[0021] In some embodiments, the exogenous variable is a congestion delay index, and the endogenous variables include: a first endogenous variable, a second endogenous variable, a third endogenous variable, a fourth endogenous variable and a fifth endogenous variable; wherein, the first endogenous variable is the plant carbon sequestration amount in the plant physiological data, the second endogenous variable is the key photosynthesis enzyme activity in the plant physiological data, the third endogenous variable is the easily oxidizable organic carbon content in the soil treatment data, the fourth endogenous variable is the key extracellular enzyme activity, and the fifth endogenous variable is the key physical and chemical properties.

[0022] In some embodiments, calculating the first fitness of the candidate plants in the region based on the congestion delay index of the sub-region and the significance factor of each candidate plant in the region includes:

[0023] The traffic delay index and significant factors are used as original variables to perform principal component analysis to obtain the first principal component and the second principal component;

[0024] Calculate the traffic delay index and the first weight coefficient of the significance factor according to the load vector of the first principal component and the load vector of the second principal component;

[0025] A weighted calculation is performed according to the traffic delay index and its first weight coefficient, the significance factor and its first weight coefficient to obtain the first fitness of the candidate plants in the area.

[0026] In some embodiments, constructing a plant selection model corresponding to each sub-region based on a genetic algorithm includes:

[0027] establishing a population set based on the sub-regions and the training plants;

[0028] Constructing a fitness function of a plant selection model according to the congestion delay index, the significant factor, and the first fitness, setting a constraint condition on the significant factor, and calculating a second fitness corresponding to the training plants in the population set according to the fitness function;

[0029] Selection, crossover and mutation operations are iteratively performed on the training plants according to the second fitness. During the iteration process, the second weight coefficient of the fitness function is determined by sensitivity analysis until the iteration termination condition is reached, thereby obtaining a plant selection model.

[0030] In some embodiments, calculating the second fitness corresponding to the training plants in the population set according to the fitness function includes:

[0031] Calculating a congestion suppression factor based on the traffic delay index and a preset maximum traffic delay index;

[0032] Calculating the actual carbon sink based on the congestion suppression factor and the plant carbon sequestration amount in the significant factors of the trained plants;

[0033] Calculating the weighted sum of the factors other than the plant carbon fixation amount among the significant factors of the training plant to obtain the regulation intensity;

[0034] The sum of the actual carbon sink, the regulation intensity and the first fitness is calculated to obtain the second fitness of the trained plant.

[0035] A second aspect of the present invention provides a green plant selection system, comprising:

[0036] A division module is used to divide the target area into sub-areas with different congestion levels according to the congestion delay index;

[0037] An acquisition module, configured to acquire soil analysis data and plant physiological data corresponding to each candidate plant in the sub-area;

[0038] A construction module is used to construct a structural equation model based on the congestion delay index, soil analysis data and plant physiological data of the sub-region, and determine the significant factors of traffic impact on each candidate plant according to the structural equation model;

[0039] a calculation module, configured to calculate a first fitness of the candidate plants in the region based on the congestion delay index of the subregion and a significance factor of each candidate plant in the region;

[0040] A determination module is used to construct a plant selection model corresponding to each sub-area based on a genetic algorithm, substitute the candidate plants into the plant selection model of each sub-area, and obtain the target plants corresponding to each sub-area, wherein the fitness function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significance factor.

[0041] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0042] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0043] The technical solution provided by the present invention has the following advantages and effects: significant factors significantly affected by traffic are screened out through the structural equation model, and the first fitness of the candidate plants is calculated based on this, which is used as the comprehensive fitness of the candidate plants. Then, a plant selection model is constructed based on the selected significant factors and the genetic algorithm. The significant factors determined by the structural equation model play a guarantee role in the plant selection model constructed based on the genetic algorithm, and can achieve pre-verification and hard constraint embedding, and can select plants with high adaptability and strong carbon sequestration capacity. The structural equation model also enhances the interpretability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic flow chart of the greening plant selection method provided by the present invention;

[0045] Figure 2 1. It is a graph showing the measurement data of PPO, CBH, LOC and MOC of Pittosporum tobira and Ligustrum lucidum provided by the present invention;

[0046] Figure 3 The GADPH, PGK, Rubisco and A of Pittosporum tobira and Ligustrum lucidum provided by the present invention max Measured data diagram;

[0047] Figure 4 This is a structural block diagram of the greening plant selection system provided by the present invention;

[0048] Figure 5 It is a diagram of the internal structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To facilitate understanding of the present invention, specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0050] Unless otherwise specified or defined, the "first, second..." used in this article is only used to distinguish names and does not represent a specific quantity or order.

[0051] Unless stated otherwise or defined otherwise, the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0052] It should be noted that, in this document, “fixed to” or “connected to” may mean directly fixing or connecting to an element, or indirectly fixing or connecting to an element.

[0053] like Figure 1 As shown, this embodiment provides a method for selecting green plants, including the following steps S1 to S5:

[0054] Step S1: Divide the target area into sub-areas with different congestion levels according to the congestion delay index.

[0055] In actual applications, the congestion delay index can be obtained in real time by accessing the AutoNavi Map API. The target area can be a section of highway and its surrounding areas. According to the congestion delay index, the target area is divided into sub-areas with different congestion levels, such as lightly congested sub-areas, moderately congested sub-areas, and heavily congested sub-areas.

[0056] Step S2: Acquire soil analysis data and plant physiological data corresponding to each candidate plant in the sub-area.

[0057] In practical applications, sample plots are set up in lightly congested sub-areas, moderately congested sub-areas, and heavily congested sub-areas respectively. In sub-areas with different congestion levels, healthy plants of candidate plants are selected for plant physiological data collection. In sub-areas with different congestion levels, the 0-20 cm surface soil where healthy plants of candidate plants grow is selected for soil analysis data collection.

[0058] Specifically, the collected plant physiological data include: maximum net photosynthetic rate and key photosynthetic enzyme activities, etc. The plant carbon fixation amount is calculated based on the maximum net photosynthetic rate. The key photosynthetic enzyme activities include: 3-phosphoglyceraldehyde dehydrogenase (Glyceraldehyde-3-phosphate dehydrogenase, GAPDH) activity, 3-phosphoglycerate kinase (3-Phosphoglycerate kinase, PGK) activity, and ribulose diphosphate carboxylase / oxygenase (Rubisco) activity. The collected soil analysis data include: soil carbon pool components, extracellular enzyme activities and physical and chemical properties. Soil carbon pool components include: inorganic carbon (IC) content, dissolved organic carbon (DOC) content, labile organic carbon (LOC) content, readily oxidizable organic carbon (ROC) content, mineral-associated organic carbon (MOC) content, etc.; extracellular enzyme activities include: solid-β-glutamylase (β-GC) activity, cellobiohydrolase (CBH) activity, polyphenol oxidase (PPO) activity, etc.

[0059] Step S3: constructing a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region, and determining significant factors of traffic impact on each candidate plant based on the structural equation model.

[0060] Specifically, the constructing of a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region includes:

[0061] performing data processing on the soil analysis data to obtain soil processing data, and performing data processing on the plant physiological data to obtain plant processing data;

[0062] establishing an initial theoretical model based on the congestion delay index, soil treatment data, and plant treatment data;

[0063] Parameters of the initial theoretical model are estimated, and when evaluation indicators of the parameter estimation results meet preset indicators, a structural equation model is obtained.

[0064] In practical applications, soil analysis data and plant physiological data are processed through data cleaning, normality verification, and dimensionality standardization to obtain soil-processed data corresponding to the soil analysis data and plant-processed data corresponding to the plant physiological data. Specifically, data that falls outside the reasonable range or exhibits logical contradictions in the soil analysis and plant physiological data are removed to ensure data accuracy. Outliers can be removed by drawing boxplots. If missing values ​​exist, multiple imputation is preferred, predicting missing values ​​based on intervariate correlations to maintain data integrity. The Shapiro-Wilk test is then used to verify data normality, and logarithmic transformation is performed if the data are severely skewed. Data that meet normality in the soil analysis and plant physiological data are then subjected to Z-score standardization to unify dimensions for ease of subsequent calculations.

[0065] Specifically, establishing an initial theoretical model based on the congestion delay index, soil treatment data, and plant physiological data includes:

[0066] Performing principal component analysis on the extracellular enzyme activities in the soil treatment data to obtain key extracellular enzyme activities;

[0067] Performing principal component analysis on the carbon pool components in the soil treatment data to obtain key carbon pool components;

[0068] Performing principal component analysis on the physical and chemical properties in the soil treatment data to obtain key physical and chemical properties;

[0069] Determine exogenous variables and endogenous variables, establish the first relationship between exogenous variables and endogenous variables, and establish the second relationship between endogenous variables;

[0070] Determine the endogenous latent variables and endogenous manifest variables of the endogenous variables, and establish the third relationship between the endogenous latent variables and the endogenous manifest variables;

[0071] An initial theoretical model is established according to the first relationship, the second relationship and the third relationship.

[0072] In practical applications, principal component analysis was performed on extracellular enzyme activity, carbon pool components, and physical and chemical properties to reduce variable dimensions, achieve data dimensionality reduction, and address collinearity issues. The exogenous variable determined was the congestion delay index, and the endogenous variables determined included: a first endogenous variable, a second endogenous variable, a third endogenous variable, a fourth endogenous variable, and a fifth endogenous variable; wherein, the first endogenous variable was the plant carbon sequestration amount in the plant physiological data, the second endogenous variable was the key photosynthetic enzyme activity in the plant physiological data, the third endogenous variable was the key carbon pool component in the soil treatment data, the fourth endogenous variable was the key extracellular enzyme activity, and the fifth endogenous variable was the key physical and chemical property.

[0073] Specifically, in this application, the activities of key enzymes of photosynthesis, key carbon pool components, key extracellular enzyme activities and key physicochemical properties are used as endogenous latent variables, GADPH activity, PGK activity and Rubisco activity are used as endogenous manifest variables of key enzyme activities of photosynthesis, PPO activity and CBH activity are used as endogenous manifest variables of key extracellular enzyme activities, ROC, LOC and MOC are used as endogenous manifest variables of key carbon pool components, and organic matter content is used as endogenous manifest variable of key physicochemical properties.

[0074] Specifically, an initial theoretical model was established based on the first, second, and third relationships. The robust maximum likelihood method was then used to estimate the parameters of the initial theoretical model. The root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR) constituted a model fit index evaluation system. The preset indices were: RMSEA ≤ 0.08, CFI ≥ 0.95, and SRMR ≤ 0.08. If the parameter estimation results met the preset indices, the resulting equation model was obtained. Variables significantly affected by traffic were identified based on the path coefficients of the structural equation and were considered significant factors.

[0075] Step S4: Calculate the first fitness of the candidate plants in the area according to the congestion delay index of the sub-area and the significance factor of each candidate plant in the area.

[0076] Specifically, the calculating of the first fitness of the candidate plants in the region according to the congestion delay index of the sub-region and the significance factor of each candidate plant in the region includes:

[0077] The traffic delay index and significant factors are used as original variables to perform principal component analysis to obtain the first principal component and the second principal component;

[0078] Calculate the traffic delay index and the first weight coefficient of the significance factor according to the load vector of the first principal component and the load vector of the second principal component;

[0079] A weighted calculation is performed according to the traffic delay index and its first weight coefficient, the significance factor and its first weight coefficient to obtain the first fitness of the candidate plants in the area.

[0080] In practical applications, after performing principal component analysis on the original variables, the comprehensive coefficients of the original variables are calculated. The calculation formula of the comprehensive coefficients is:

[0081] γ=(Z1·VPC1 +Z2·V PC2 ) / V

[0082] Among them, Z1 represents the load vector of the original variable in the first principal component, V PC1 represents the variance contribution rate of the first principal component, Z2 represents the load vector of the original variable in the second principal component, V PC2 represents the variance contribution rate of the second principal component, V represents the cumulative variance contribution rate of the first and second principal components, and after calculating the comprehensive coefficient of the traffic delay index and the significant factor according to the comprehensive coefficient calculation formula, the comprehensive coefficient is normalized to obtain the first weight coefficient of the traffic delay index and the significant factor. Assuming that the cumulative variance contribution rate of the first and second principal components is 90%, the load vector of the first principal component is X = -0.3, Y1 = 0.85, Y2 = 0.82, and the load vector of the second principal component is Y3 = 0.78, where X represents the traffic delay index and Y1-Y3 represent the significant factors. The comprehensive coefficient of the load vector of the first principal component and the load vector of the second principal component is calculated, and the comprehensive coefficient of X is (-0.3×6 0%+0×30%) / 90%=-0.2, the comprehensive coefficient of Y1 is (0.85×60%+0×30%) / 90%=0.57, the comprehensive coefficient of Y2 is (0.82×60%+0×30%) / 90%=0.55, and the comprehensive coefficient of Y3 is (0×60%+0.78×30%) / 90%=0.26. The comprehensive coefficients of the significant factors of the congestion delay index are normalized to obtain the corresponding first weight coefficient.

[0083] In practical applications, principal component analysis of significant factors can extract the main effect pattern, eliminate secondary interference, objectively quantify the true co-occurrence of congestion delay index and significant factors, avoid subjective bias, and accurately capture the synergistic effect between variables.

[0084] Step S5: construct a plant selection model corresponding to each sub-area based on a genetic algorithm, substitute the candidate plants into the plant selection model of each sub-area, and obtain the target plants corresponding to each sub-area, wherein the fitness function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significance factor.

[0085] Specifically, the genetic algorithm-based construction of the plant selection model corresponding to each sub-region includes:

[0086] establishing a population set based on the sub-regions and the training plants;

[0087] Constructing a fitness function of a plant selection model according to the congestion delay index, the significant factor, and the first fitness, setting a constraint condition on the significant factor, and calculating a second fitness corresponding to the training plants in the population set according to the fitness function;

[0088] Selection, crossover and mutation operations are iteratively performed on the training plants according to the second fitness. During the iteration process, the second weight coefficient of the fitness function is determined by sensitivity analysis until the iteration termination condition is reached, thereby obtaining a plant selection model.

[0089] In practical applications, when designing the code, each chromosome represents a training plant. The number of training plants can be set to M, and i is set to 1. The training plants are marked with i as the number. The gene sequence contains the significant factors corresponding to the training species, reducing the redundant genes that may be contained in the random code and improving the convergence efficiency of the algorithm. The number of significant factors can be set to N, and j is set to 1. The significant factors are marked with j as the number. The significant factors of the corresponding training plants are obtained in the sub-areas of each traffic congestion level, and the first fitness of each training plant in the sub-area is calculated to obtain the population set of the sub-area.

[0090] Specifically, calculating the second fitness corresponding to the training plants in the population set according to the fitness function includes:

[0091] Calculating a congestion suppression factor based on the traffic delay index and a preset maximum traffic delay index;

[0092] Calculating the actual carbon sink based on the congestion suppression factor and the plant carbon sequestration amount in the significant factors of the trained plants;

[0093] Calculating the weighted sum of the factors other than the plant carbon fixation amount among the significant factors of the training plant to obtain the regulation intensity;

[0094] The sum of the actual carbon sink, the regulation intensity and the first fitness is calculated to obtain the second fitness of the trained plant.

[0095] In practical applications, the calculation formula of the fitness function is:

[0096]

[0097] Wherein, Y1 represents the amount of carbon sequestered by plants, X represents the congestion delay index, and X max Indicates the maximum congestion delay index, w j represents the second weight coefficient of the jth significant factor, Y j Indicates the jth significant factor. The congestion suppression factor (1-X / X max) is suppressed, and by quantifying the physiological limitations of environmental stress on plant carbon sequestration, the fitness function is made more relevant to real-world urban scenarios. Constraints are set, such as hard screening conditions such as a threshold for key photosynthetic enzyme activity and soil pH [5.5, 7.5]. F1 represents the first fitness, and F2 represents the second fitness. In this fitness function, the core objective is to maximize plant carbon sequestration while mitigating the negative impact of traffic congestion. The fitness function calculates the second fitness of each training plant in the population. During the iteration process, a hybrid strategy of elite retention and roulette wheel selection is employed, retaining the top 10% of training plants in terms of second fitness to advance to the next generation. The remaining plants are selected through probabilistic selection, followed by a single-point crossover (with an 80% probability). For example, a significant factor, such as photosynthetic enzyme activity or soil index, is swapped between two training plants to simulate genetic recombination. Mutation is then performed: a significant factor is randomly perturbed (with a probability of 1%-5%), such as by adjusting the ROC content and the values ​​of key physical and chemical properties to introduce diversity. The initial weight of the second weight coefficient can refer to the path coefficient of the structural equation model. The second weight coefficient is determined through sensitivity analysis. For the iteration termination condition, a maximum number of iterations (e.g., 200) or a number of consecutive generations (e.g., 10 generations) in which the second fitness changes less than a threshold value can be set. This threshold can be set to 1%.

[0098] After reaching the iteration termination condition, a plant selection model based on the genetic algorithm is obtained, which can select appropriate green plants according to the congestion level of the sub-area. For example, in the sub-areas with light, moderate and heavy congestion, Pittosporum tobira and Ligustrum lucidum are selected as candidate plants, and the significant factors corresponding to the candidate plants in the sub-areas with different congestion levels are obtained, such as the collection of significant factors such as net photosynthetic rate, GAPDH activity, PGK activity, Rubisco activity, PPO activity, CBH activity, LOC, MOC, etc. Figure 2 and Figure 3 As shown, the carbon sequestration capacity of the candidate plants is calculated based on the net photosynthetic rate. The first fitness of the candidate plants in each sub-region is then calculated based on the congestion delay index and the significant factor of the candidate plants. Each candidate plant is then substituted into the corresponding plant selection model to obtain the target plant for each sub-region. For example, in a heavily congested urban environment, Pittosporum tobira has a stronger adaptability. Pittosporum tobira has a higher photosynthetic rate and a higher content of stable components in its rhizosphere soil carbon pool, making it an excellent plant for improving urban carbon sequestration efficiency.

[0099] The greening plant selection method of the present invention screens out significant factors significantly affected by traffic through a structural equation model, and uses this to calculate the first fitness of candidate plants as the comprehensive fitness of the candidate plants. Then, a plant selection model is constructed based on the selected significant factors and a genetic algorithm. The significant factors determined by the structural equation model play a guarantee role in the plant selection model constructed based on the genetic algorithm, and can achieve pre-verification and hard constraint embedding. The structural equation model also enhances the interpretability of the results.

[0100] like Figure 4 As shown, an embodiment of the present invention further provides a green plant selection system, comprising:

[0101] A division module 10 is configured to divide the target area into sub-areas of different congestion levels according to the congestion delay index;

[0102] An acquisition module 20 is configured to acquire soil analysis data and plant physiological data corresponding to each candidate plant in the sub-region;

[0103] A construction module 30 is configured to construct a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region, and determine a significant factor of traffic impact on each candidate plant based on the structural equation model;

[0104] A calculation module 40 is configured to calculate a first fitness of the candidate plants in the region based on the congestion delay index of the sub-region and a significance factor of each candidate plant in the region;

[0105] Determination module 50 is used to construct a plant selection model corresponding to each sub-area based on a genetic algorithm, substitute the candidate plants into the plant selection model of each sub-area, and obtain the target plants corresponding to each sub-area, wherein the fitness function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significance factor.

[0106] Each module of the green plant selection system can be implemented in whole or in part through software, hardware, or a combination thereof. Each module and unit can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0107] like Figure 5 As shown, an embodiment of the present invention discloses a computer device, including a memory and a processor, wherein the memory stores a computer program;

[0108] The computer device may be a server, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the green plant selection method described in the above embodiments is implemented.

[0109] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0110] An embodiment of the present invention further discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the greening plant selection method described in the above embodiments.

[0111] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0112] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for selecting green plants, characterized in that: include: Divide the target area into sub-areas with different congestion levels according to the congestion delay index; Acquire soil analysis data and plant physiological data corresponding to each candidate plant in the sub-area; constructing a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region, and determining significant factors affecting traffic flow for each candidate plant based on the structural equation model; Calculating a first fitness of the candidate plants in the region according to the congestion delay index of the sub-region and the significance factor of each candidate plant in the region; A plant selection model corresponding to each sub-area is constructed based on a genetic algorithm, and the candidate plants are substituted into the plant selection model of each sub-area to obtain the target plants corresponding to each sub-area, wherein the fitness function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significance factor.

2. The greening plant selection method according to claim 1, characterized in that: The constructing of a structural equation model based on the congestion delay index, soil analysis data, and plant physiological data of the sub-region includes: performing data processing on the soil analysis data to obtain soil processing data, and performing data processing on the plant physiological data to obtain plant processing data; establishing an initial theoretical model based on the congestion delay index, soil treatment data, and plant treatment data; Parameters of the initial theoretical model are estimated, and when evaluation indicators of the parameter estimation results meet preset indicators, a structural equation model is obtained.

3. The greening plant selection method according to claim 2, characterized in that: The initial theoretical model is established based on the congestion delay index, soil treatment data and plant physiological data, including: Performing principal component analysis on the extracellular enzyme activities in the soil treatment data to obtain key extracellular enzyme activities; Performing principal component analysis on the carbon pool components in the soil treatment data to obtain key carbon pool components; Performing principal component analysis on the physical and chemical properties in the soil treatment data to obtain key physical and chemical properties; Determine exogenous variables and endogenous variables, establish the first relationship between exogenous variables and endogenous variables, and establish the second relationship between endogenous variables; Determine the endogenous latent variables and endogenous manifest variables of the endogenous variables, and establish the third relationship between the endogenous latent variables and the endogenous manifest variables; An initial theoretical model is established according to the first relationship, the second relationship and the third relationship.

4. The greening plant selection method according to claim 3, characterized in that: The exogenous variable is the congestion delay index, and the endogenous variables include: a first endogenous variable, a second endogenous variable, a third endogenous variable, a fourth endogenous variable and a fifth endogenous variable; wherein, the first endogenous variable is the plant carbon sequestration amount in the plant physiological data, the second endogenous variable is the key photosynthesis enzyme activity in the plant physiological data, the third endogenous variable is the key carbon pool component in the soil treatment data, the fourth endogenous variable is the key extracellular enzyme activity, and the fifth endogenous variable is the key physical and chemical properties.

5. The greening plant selection method according to claim 1, wherein: The calculating of the first fitness of the candidate plants in the region according to the congestion delay index of the sub-region and the significance factor of each candidate plant in the region includes: The traffic delay index and significant factors are used as original variables to perform principal component analysis to obtain the first principal component and the second principal component; Calculate the traffic delay index and the first weight coefficient of the significance factor according to the load vector of the first principal component and the load vector of the second principal component; A weighted calculation is performed according to the traffic delay index and its first weight coefficient, the significance factor and its first weight coefficient to obtain the first fitness of the candidate plants in the area.

6. The greening plant selection method according to claim 1, characterized in that: The plant selection model corresponding to each sub-region is constructed based on the genetic algorithm, including: establishing a population set based on the sub-regions and the training plants; Constructing a fitness function of a plant selection model according to the congestion delay index, the significant factor, and the first fitness, setting a constraint condition on the significant factor, and calculating a second fitness corresponding to the training plants in the population set according to the fitness function; Selection, crossover and mutation operations are iteratively performed on the training plants according to the second fitness. During the iteration process, the second weight coefficient of the fitness function is determined by sensitivity analysis until the iteration termination condition is reached, thereby obtaining a plant selection model.

7. The greening plant selection method according to claim 1, characterized in that: The calculating, according to the fitness function, the second fitness corresponding to the training plants in the population set includes: Calculating a congestion suppression factor based on the traffic delay index and a preset maximum traffic delay index; Calculating the actual carbon sink based on the congestion suppression factor and the plant carbon sequestration amount in the significant factors of the trained plants; Calculating the weighted sum of the factors other than the plant carbon fixation amount among the significant factors of the training plant to obtain the regulation intensity; The sum of the actual carbon sink, the regulation intensity and the first fitness is calculated to obtain the second fitness of the trained plant.

8. The green plant selection system is characterized by: include: A division module is used to divide the target area into sub-areas with different congestion levels according to the congestion delay index; an acquisition module, configured to acquire soil analysis data of the sub-region and plant physiological data of candidate plants in the region; A construction module is used to construct a structural equation model based on the congestion delay index, soil analysis data and plant physiological data of the sub-region, and determine significant factors affected by traffic based on the structural equation model; a calculation module, configured to calculate a first fitness of the candidate plants in the region based on the congestion delay index of the subregion and a significance factor of each candidate plant in the region; A determination module is used to construct a plant selection model corresponding to each sub-area based on a genetic algorithm, substitute the candidate plants into the plant selection model of each sub-area, and obtain the target plants corresponding to each sub-area, wherein the fitness function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significance factor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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