Intelligent decision support system for landscaping based on big data analysis

Abstract

The invention relates to the field of data analysis and support technology, and discloses an intelligent decision support system for gardening based on big data analysis, which specifically comprises: a greening data acquisition module for collecting growth status data and soil status data of green plants in a transition area in a garden, a morphological evolution evaluation module for analyzing and evaluating changes in the morphology of green plants in the transition area in the garden, a resource balance evaluation module for analyzing and evaluating resource occupation between green plants in the transition area in the garden, a decision support model construction module for building a decision support model, and a decision suggestion output module for converting decision support coefficients generated by the decision support model into maintenance suggestions for gardening. The invention solves the problem in the prior art that when plant varieties need to be replaced in gardening, the growth status and soil conditions of green plants in the transition area of ​​the garden cannot be monitored in real time and comprehensively.

Description

Technical Field

[0001] The present invention relates to the field of data analysis and support technology, and in particular to a landscaping intelligent decision support system based on big data analysis. Background Art

[0002] The status of gardening and greening in urban planning and construction is becoming increasingly important. In gardening and greening, the growth status of plants and soil conditions are key factors that determine the adaptability and growth health of plants. Existing gardening and greening management mainly relies on manual observation and empirical judgment, which requires a lot of manpower and material resources, especially in the planting transition area. When the plant varieties need to be replaced in gardening and greening, it is crucial to ensure the growth adaptability of plants and the stability of the garden ecosystem. How to provide digital decision support for gardening and greening and realize comprehensive monitoring and analysis of the growth status and soil conditions of green plants in the garden transition area is a huge challenge.

[0003] Among the existing disclosed invention technologies, the patent application with publication number CN117436656A discloses an urban landscaping maintenance decision support method and system based on artificial intelligence. It adopts machine learning and deep learning methods to construct a vegetation growth and pest and disease monitoring model, analyzes the vegetation growth in the landscaping maintenance area, generates decision support reports and suggestions, and automatically generates personnel-related maintenance tasks and maintenance plans through a natural language processing model, records them, and iteratively optimizes the maintenance plan.

[0004] The above patent cannot conduct targeted monitoring of the growth adaptability of newly planted plant varieties, and has the problems described in the background technology. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, when plant varieties need to be replaced in gardening, the growth status and soil conditions of green plants in the transition area of ​​the garden cannot be monitored in real time and comprehensively. An intelligent decision support system for gardening based on big data analysis is proposed.

[0006] In order to achieve the above objectives, the landscaping intelligent decision support system based on big data analysis of the present invention includes the following modules:

[0007] Greening data collection module, morphological evolution assessment module, resource balance assessment module, decision support model building module and decision suggestion output module;

[0008] The greening data collection module is used to collect the growth status data and soil status data of greening plants in the transition area of ​​the garden;

[0009] The morphological evolution evaluation module is used to analyze and evaluate the changes in the morphology of green plants in the transition area of ​​the garden;

[0010] The resource balance evaluation module is used to analyze and evaluate the resource occupancy between green plants in the transition area of ​​the garden;

[0011] The decision support model construction module is used to construct a decision support model and optimize the decision support model based on the evaluation results of the morphological evolution evaluation module and the resource balance evaluation module;

[0012] The decision suggestion output module is used to convert the decision support coefficients generated by the decision support model into landscaping maintenance suggestions for output.

[0013] Specifically, the greening data acquisition module includes: a growth status data acquisition unit and a soil status data acquisition unit;

[0014] The growth status data collection unit is used to collect: the total number of newly planted plants in the transition zone, the total number of leaves of each newly planted plant, the length of the crown of each newly planted plant, and the bending angle between each leaf and the rhizome of the newly planted plant; the growth status data collection unit is also used to collect: the total number of original plants in the transition zone, the total number of leaves of each original plant, the length of the crown of each original plant, and the bending angle between each leaf and the rhizome of the original plant;

[0015] The soil state data acquisition unit is used to acquire: soil moisture data, soil nutrient element types and the content of each nutrient element in the soil.

[0016] Specifically, the morphological evolution evaluation module includes: a root and stem posture evolution evaluation unit and a crown width spacing evolution evaluation unit;

[0017] The root and stem posture evolution evaluation unit is used to run the following specific steps:

[0018] S21: Importing the growth status data of the green plants in the transition area into the calculation strategy of the first root-stem posture factor, and calculating and obtaining the first root-stem posture factor , the calculation strategy of the first rhizome posture factor is as follows:

[0019] ;

[0020] Among them, t and t-1 are superscripts, indicating the time point of data collection;

[0021] n represents the nth newly planted plant in the transition area, and N is the total number of newly planted plants in the transition area;

[0022] Indicates the number of newly planted plants on the nth leaves, is the total number of leaves on the nth newly planted plant;

[0023] It means that at the data collection time point t, the number of The bending angle between the leaf blade and the rhizome;

[0024] It means that at the data collection time point t-1, the number of The bending angle between the leaves and the rhizome;

[0025] S22: At the same time, the growth status data of the green plants in the transition area is imported into the calculation strategy of the second root-stem posture factor, and the second root-stem posture factor is calculated and obtained. , the calculation strategy of the second root stem posture factor is as follows:

[0026] ;

[0027] m represents the mth original plant in the transition area, and M is the total number of original plants in the transition area;

[0028] Indicates the number of leaves, is the total number of leaves on the mth original plant;

[0029] It means that at the data collection time point t, the The bending angle between the leaf blade and the rhizome;

[0030] It means that at the data collection time point t-1, the The bending angle between the leaves and the rhizome;

[0031] S23: Calculate the root and stem posture evolution coefficient based on S21-S22 , the calculation strategy of the root-stem posture evolution coefficient is: .

[0032] Specifically, the crown width spacing evolution evaluation unit is used to run the following specific steps:

[0033] S24: Importing the growth status data of green plants in the transition area into the calculation strategy of the first crown width spacing factor, and calculating and obtaining the first crown width spacing factor , the calculation strategy of the first crown width spacing factor is as follows:

[0034] ;

[0035] in, It represents the overlapping length of the crown of the nth newly planted plant and the mth original plant at the data collection time point t;

[0036] It indicates the overlapping length of the crown width between the nth newly planted plant and the mth original plant at the data collection time point t-1;

[0037] is the crown length of the nth newly planted plant;

[0038] S25: Import the growth status data of green plants in the transition area into the calculation strategy of the second crown width spacing factor, and calculate and obtain the second crown width spacing factor , the calculation strategy of the second crown width spacing factor is as follows:

[0039] ;

[0040] in, is the crown length of the mth original plant;

[0041] S26: Calculate the crown width spacing evolution coefficient based on S24-S25 , the calculation strategy of the crown width spacing evolution coefficient is: .

[0042] Specifically, the resource balance evaluation module includes: a soil transpiration rate evaluation unit and a soil element ratio evaluation unit;

[0043] The soil transpiration rate evaluation unit is used to run the following specific steps:

[0044] S31: Taking each newly planted plant in the transition area as the evaluation center, dividing the evaluation sub-areas in the direction of the original plants and the direction of the newly planted plants in the transition area, wherein the original plant direction is divided outward, and the newly planted plant direction is divided inward; the evaluation sub-areas include: outer evaluation sub-areas 30 cm and 60 cm away from the evaluation center; the evaluation sub-areas also include: inner evaluation sub-areas 30 cm and 60 cm away from the evaluation center;

[0045] S32: Importing the soil state data of green plants in the transition area into the calculation strategy of the soil transpiration rate coefficient, and calculating and obtaining the soil transpiration rate coefficient , the calculation strategy of the soil transpiration rate coefficient is as follows:

[0046] ;

[0047] in, , Time and When represents the outer assessment sub-area 30cm and 60cm away from the assessment center respectively; and They represent the inner assessment sub-areas 30 cm and 60 cm away from the assessment center, respectively;

[0048] Assess soil moisture in the center where the new plants are planted;

[0049] Respectively represent the labels and The soil moisture of the corresponding assessment sub-area.

[0050] Specifically, the soil element ratio evaluation unit is used to run the following specific steps:

[0051] S33: Importing the soil state data of the green plants in the transition area into the calculation strategy of the soil element proportional coefficient, and calculating and obtaining the soil element proportional coefficient , the calculation strategy of the soil element proportional coefficient is as follows:

[0052] ;

[0053] Among them, k is a subscript, indicating the kth nutrient element in the soil;

[0054] is the content of the kth nutrient element in the soil of the evaluation center where the newly planted plants are located;

[0055] Respectively represent the labels and The content of the kth nutrient element in the soil of the corresponding assessment sub-area.

[0056] Specifically, the decision support model building module is used for the following specific steps:

[0057] S41: extracting the root and stem posture evolution coefficient, the crown width evolution coefficient, the soil transpiration rate coefficient and the soil element proportion coefficient;

[0058] S42: According to S41, a decision support model is established, and the output formula of the decision support model is: ;

[0059] in, is the decision support coefficient; All are multiple regression coefficients; is the regression error.

[0060] Specifically, the maintenance recommendations output in the decision recommendation output module include: a preset decision support baseline coefficient. When the decision support coefficient of the transition area is less than or equal to the decision support baseline coefficient, maintenance recommendations are output that the newly planted plants can adapt to and integrate into landscaping; when the decision support coefficient of the transition area is greater than the decision support baseline coefficient, maintenance recommendations are output that the newly planted plants are not suitable for landscaping.

[0061] A storage medium stores instructions, and when a computer reads the instructions, the computer runs the steps in the landscaping intelligent decision support system based on big data analysis.

[0062] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, the steps in the above-mentioned landscaping intelligent decision support system based on big data analysis are implemented.

[0063] Compared with the prior art, the technical effects of the present invention are as follows:

[0064] 1. The present invention uses a greening data acquisition module to comprehensively obtain the growth status data of newly planted plants and original plants in the transition area and the soil status data. The present invention can obtain more comprehensive and detailed data, providing a solid foundation for subsequent analysis and decision-making.

[0065] 2. The decision support model construction module of the present invention constructs and optimizes the decision support model based on the evaluation results of the morphological evolution evaluation module and the resource balance evaluation module, and generates a decision support coefficient. The model can comprehensively consider multiple factors, provide scientific and quantitative decision-making basis for garden greening management, and improve the accuracy and reliability of decision-making.

[0066] 3. The automated data collection and analysis function of the present invention improves the efficiency of landscaping management and reduces the time and cost of manual operations. Administrators can obtain real-time monitoring data on plant growth and soil status, make quick decisions, and optimize landscaping management processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0068] Figure 1It is a structural schematic diagram of the landscaping intelligent decision support system based on big data analysis of the present invention;

[0069] Figure 2 It is a schematic flow chart of the process of obtaining the root and stem posture evolution coefficient of the present invention;

[0070] Figure 3 It is a schematic flow chart of the process of obtaining the crown width spacing evolution coefficient of the present invention. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0073] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0074] Example 1

[0075] like Figure 1 As shown, the landscaping intelligent decision support system based on big data analysis according to the embodiment of the present invention includes the following modules:

[0076] Greening data collection module, morphological evolution assessment module, resource balance assessment module, decision support model building module and decision suggestion output module;

[0077] The greening data collection module is used to collect the growth status data and soil status data of greening plants in the transition area of ​​the garden;

[0078] The greening data acquisition module includes: a growth status data acquisition unit and a soil status data acquisition unit;

[0079] The growth status data collection unit is used to collect: the total number of newly planted plants in the transition zone, the total number of leaves of each newly planted plant, the length of the crown of each newly planted plant, and the bending angle between each leaf and the rhizome of the newly planted plant; the growth status data collection unit is also used to collect: the total number of original plants in the transition zone, the total number of leaves of each original plant, the length of the crown of each original plant, and the bending angle between each leaf and the rhizome of the original plant;

[0080] The soil state data acquisition unit is used to acquire: soil moisture data, soil nutrient element types and the content of each nutrient element in the soil.

[0081] The morphological evolution evaluation module is used to analyze and evaluate the changes in the morphology of green plants in the transition area of ​​the garden;

[0082] The morphological evolution evaluation module includes: a root and stem posture evolution evaluation unit and a crown width spacing evolution evaluation unit;

[0083] like Figure 2 As shown, the root and stem posture evolution evaluation unit is used to run the following specific steps:

[0084] S21: Importing the growth status data of the green plants in the transition area into the calculation strategy of the first root-stem posture factor, and calculating and obtaining the first root-stem posture factor , the calculation strategy of the first rhizome posture factor is as follows:

[0085] ;

[0086] Among them, t and t-1 are superscripts, indicating the time point of data collection;

[0087] n represents the nth newly planted plant in the transition area, and N is the total number of newly planted plants in the transition area;

[0088] Indicates the number of newly planted plants on the nth leaves, is the total number of leaves on the nth newly planted plant;

[0089] It means that at the data collection time point t, the number of The bending angle between the leaf blade and the rhizome;

[0090] It means that at the data collection time point t-1, the number of The bending angle between the leaf blade and the rhizome;

[0091] S22: At the same time, the growth status data of the green plants in the transition area is imported into the calculation strategy of the second root-stem posture factor, and the second root-stem posture factor is calculated and obtained. , the calculation strategy of the second root stem posture factor is as follows:

[0092] ;

[0093] m represents the mth original plant in the transition area, and M is the total number of original plants in the transition area;

[0094] Indicates the number of leaves, is the total number of leaves on the mth original plant;

[0095] It means that at the data collection time point t, the The bending angle between the leaf blade and the rhizome;

[0096] It means that at the data collection time point t-1, the The bending angle between the leaf blade and the rhizome;

[0097] S23: Calculate the root and stem posture evolution coefficient based on S21-S22 , the calculation strategy of the root-stem posture evolution coefficient is: .

[0098] like Figure 3 As shown, the crown width spacing evolution evaluation unit is used to run the following specific steps:

[0099] S24: Importing the growth status data of green plants in the transition area into the calculation strategy of the first crown width spacing factor, and calculating and obtaining the first crown width spacing factor , the calculation strategy of the first crown width spacing factor is as follows:

[0100] ;

[0101] in, It represents the overlapping length of the crown of the nth newly planted plant and the mth original plant at the data collection time point t;

[0102] It indicates the overlapping length of the crown width between the nth newly planted plant and the mth original plant at the data collection time point t-1;

[0103] is the crown length of the nth newly planted plant;

[0104] S25: Import the growth status data of green plants in the transition area into the calculation strategy of the second crown width spacing factor, and calculate and obtain the second crown width spacing factor , the calculation strategy of the second crown width spacing factor is as follows:

[0105] ;

[0106] in, is the crown length of the mth original plant;

[0107] S26: Calculate the crown width spacing evolution coefficient based on S24-S25 , the calculation strategy of the crown width spacing evolution coefficient is: .

[0108] The resource balance evaluation module is used to analyze and evaluate the resource occupancy between green plants in the transition area of ​​the garden;

[0109] The resource balance evaluation module includes: a soil transpiration rate evaluation unit and a soil element ratio evaluation unit;

[0110] The soil transpiration rate evaluation unit is used to run the following specific steps:

[0111] S31: Taking each newly planted plant in the transition area as the evaluation center, dividing the evaluation sub-areas in the direction of the original plants and the direction of the newly planted plants in the transition area, wherein the original plant direction is divided outward, and the newly planted plant direction is divided inward; the evaluation sub-areas include: outer evaluation sub-areas 30 cm and 60 cm away from the evaluation center; the evaluation sub-areas also include: inner evaluation sub-areas 30 cm and 60 cm away from the evaluation center;

[0112] S32: Importing the soil state data of green plants in the transition area into the calculation strategy of the soil transpiration rate coefficient, and calculating and obtaining the soil transpiration rate coefficient , the calculation strategy of the soil transpiration rate coefficient is as follows:

[0113] ;

[0114] in, , Time and When represents the outer assessment sub-area 30cm and 60cm away from the assessment center respectively; and They represent the inner assessment sub-areas 30 cm and 60 cm away from the assessment center, respectively;

[0115] Assess soil moisture in the center where the new plants are planted;

[0116] Respectively represent the labels and The soil moisture of the corresponding assessment sub-area.

[0117] The soil element ratio evaluation unit is used to run the following specific steps:

[0118] S33: Importing the soil state data of the green plants in the transition area into the calculation strategy of the soil element proportional coefficient, and calculating and obtaining the soil element proportional coefficient , the calculation strategy of the soil element proportional coefficient is as follows:

[0119] ;

[0120] Among them, k is a subscript, indicating the kth nutrient element in the soil;

[0121] is the content of the kth nutrient element in the soil of the evaluation center where the newly planted plants are located;

[0122] Respectively represent the labels and The content of the kth nutrient element in the soil of the corresponding assessment sub-area.

[0123] The decision support model construction module is used to construct a decision support model and optimize the decision support model based on the evaluation results of the morphological evolution evaluation module and the resource balance evaluation module;

[0124] The decision support model building module is used for the following specific steps:

[0125] S41: extracting the root and stem posture evolution coefficient, the crown width evolution coefficient, the soil transpiration rate coefficient and the soil element proportion coefficient;

[0126] S42: According to S41, a decision support model is established, and the output formula of the decision support model is: ;

[0127] in, is the decision support coefficient; All are multiple regression coefficients; is the regression error.

[0128] The decision suggestion output module is used to convert the decision support coefficients generated by the decision support model into landscaping maintenance suggestions for output.

[0129] The maintenance recommendations output in the decision recommendation output module include: a preset decision support baseline coefficient. When the decision support coefficient of the transition area is less than or equal to the decision support baseline coefficient, maintenance recommendations are output that the newly planted plants can adapt to and integrate into landscaping; when the decision support coefficient of the transition area is greater than the decision support baseline coefficient, maintenance recommendations are output that the newly planted plants are not suitable for landscaping.

[0130] Example 2

[0131] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0132] The processor runs the steps in the above-mentioned landscaping intelligent decision support system based on big data analysis by calling the computer program stored in the memory.

[0133] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and run by the processor to implement the steps in the landscaping intelligent decision support system based on big data analysis provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0134] Example 3

[0135] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0136] When the computer program runs on a computer device, the computer device executes the steps in the above-mentioned landscaping intelligent decision support system based on big data analysis.

[0137] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0138] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0139] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0140] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0141] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0146] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0147] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0148] 1. The present invention uses a greening data acquisition module to comprehensively obtain the growth status data of newly planted plants and original plants in the transition area and the soil status data. The present invention can obtain more comprehensive and detailed data, providing a solid foundation for subsequent analysis and decision-making.

[0149] 2. The decision support model construction module of the present invention constructs and optimizes the decision support model based on the evaluation results of the morphological evolution evaluation module and the resource balance evaluation module, and generates a decision support coefficient. The model can comprehensively consider multiple factors, provide a scientific and quantitative decision-making basis for garden greening management, and improve the accuracy and reliability of decision-making.

[0150] 3. The automated data collection and analysis function of the present invention improves the efficiency of landscaping management and reduces the time and cost of manual operations. Administrators can obtain real-time monitoring data on plant growth and soil status, make quick decisions, and optimize landscaping management processes.

[0151] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The intelligent decision support system for landscaping based on big data analysis is characterized by: The system includes the following modules: Greening data collection module, morphological evolution assessment module, resource balance assessment module, decision support model building module and decision suggestion output module; The greening data collection module is used to collect the growth status data and soil status data of greening plants in the transition area of ​​the garden; The morphological evolution evaluation module is used to analyze and evaluate the changes in the morphology of green plants in the transition area of ​​the garden; The resource balance evaluation module is used to analyze and evaluate the resource occupancy between green plants in the transition area of ​​the garden; The decision support model construction module is used to construct a decision support model and optimize the decision support model based on the evaluation results of the morphological evolution evaluation module and the resource balance evaluation module; The decision support model building module is used for the following specific steps: S41: Extract root and stem posture evolution coefficient, canopy spacing evolution coefficient, soil transpiration rate coefficient and soil element ratio coefficient; S42: According to S41, a decision support model is established, and the output formula of the decision support model is: ; in, is the decision support coefficient; All are multiple regression coefficients; is the root-stem posture evolution coefficient; is the crown width spacing evolution coefficient; is the soil transpiration rate coefficient; is the soil element proportion coefficient; is the regression error; The decision suggestion output module is used to convert the decision support coefficients generated by the decision support model into landscaping maintenance suggestions for output.

2. The landscaping intelligent decision support system based on big data analysis according to claim 1 is characterized in that: The greening data acquisition module includes: a growth status data acquisition unit and a soil status data acquisition unit; The growth status data acquisition unit is used to collect: the total number of newly planted plants, the total number of leaves of each newly planted plant, the length of the crown of each newly planted plant, and the bending angle between each leaf and the rhizome of the newly planted plant in the transition area; the growth status data acquisition unit is also used to collect: the total number of original plants, the total number of leaves of each original plant, the length of the crown of each original plant, and the bending angle between each leaf and the rhizome of the original plant in the transition area; The soil state data acquisition unit is used to acquire: soil moisture data, soil nutrient element types and the content of each nutrient element in the soil.

3. The landscaping intelligent decision support system based on big data analysis according to claim 2 is characterized in that: The morphological evolution evaluation module includes: a root and stem posture evolution evaluation unit and a crown width spacing evolution evaluation unit; The root and stem posture evolution evaluation unit is used to run the following specific steps: S21: Importing the growth status data of the green plants in the transition area into the calculation strategy of the first root-stem posture factor, and calculating and obtaining the first root-stem posture factor , the calculation strategy of the first rhizome posture factor is as follows: ； Among them, t and t-1 are superscripts, indicating the time point of data collection; n represents the nth newly planted plant in the transition area, and N is the total number of newly planted plants in the transition area; Indicates the number of newly planted plants on the nth leaves, is the total number of leaves on the nth newly planted plant; It means that at the data collection time point t, the number of The bending angle between the leaves and the rhizome; It means that at the data collection time point t-1, the number of The bending angle between the leaf blade and the rhizome; S22: At the same time, the growth status data of the green plants in the transition area is imported into the calculation strategy of the second root-stem posture factor, and the second root-stem posture factor is calculated and obtained. , the calculation strategy of the second root stem posture factor is as follows: ； m represents the mth original plant in the transition area, and M is the total number of original plants in the transition area; Indicates the number of leaves, is the total number of leaves on the mth original plant; It means that at the data collection time point t, the The bending angle between the leaves and the rhizome; It means that at the data collection time point t-1, the The bending angle between the leaves and the rhizome; S23: Calculate the root and stem posture evolution coefficient based on S21-S22 , the calculation strategy of the root-stem posture evolution coefficient is: .

4. The landscaping intelligent decision support system based on big data analysis according to claim 3 is characterized in that: The crown width spacing evolution evaluation unit is used to run the following specific steps: S24: Importing the growth status data of green plants in the transition area into the calculation strategy of the first crown width spacing factor, and calculating and obtaining the first crown width spacing factor , the calculation strategy of the first crown width spacing factor is as follows: ； in, It represents the overlapping length of the crown of the nth newly planted plant and the mth original plant at the data collection time point t; It indicates the overlapping length of the crown width between the nth newly planted plant and the mth original plant at the data collection time point t-1; is the crown length of the nth newly planted plant; S25: Import the growth status data of green plants in the transition area into the calculation strategy of the second crown width spacing factor, and calculate and obtain the second crown width spacing factor , the calculation strategy of the second crown width spacing factor is as follows: ； in, is the crown length of the mth original plant; S26: Calculate the crown width spacing evolution coefficient based on S24-S25 , the calculation strategy of the crown width spacing evolution coefficient is: .

5. The landscaping intelligent decision support system based on big data analysis according to claim 4 is characterized in that: The resource balance evaluation module includes: a soil transpiration rate evaluation unit and a soil element ratio evaluation unit; The soil transpiration rate evaluation unit is used to run the following specific steps: S31: Taking each newly planted plant in the transition area as the evaluation center, dividing the evaluation sub-areas in the direction of the original plants and the direction of the newly planted plants in the transition area, wherein the original plant direction is divided outward, and the newly planted plant direction is divided inward; the evaluation sub-areas include: outer evaluation sub-areas 30 cm and 60 cm away from the evaluation center; the evaluation sub-areas also include: inner evaluation sub-areas 30 cm and 60 cm away from the evaluation center; S32: Importing the soil state data of green plants in the transition area into the calculation strategy of the soil transpiration rate coefficient, and calculating and obtaining the soil transpiration rate coefficient , the calculation strategy of the soil transpiration rate coefficient is as follows: ； in, , Time and When represents the outer assessment sub-area 30cm and 60cm away from the assessment center respectively; and They represent the inner assessment sub-areas 30 cm and 60 cm away from the assessment center, respectively; Assess soil moisture in the center where the new plants are planted; Respectively represent the labels and The soil moisture of the corresponding assessment sub-area.

6. The landscaping intelligent decision support system based on big data analysis according to claim 5 is characterized in that: The soil element ratio evaluation unit is used to run the following specific steps: S33: Importing the soil state data of the green plants in the transition area into the calculation strategy of the soil element proportional coefficient, and calculating and obtaining the soil element proportional coefficient , the calculation strategy of the soil element proportional coefficient is as follows: ； Among them, k is a subscript, indicating the kth nutrient element in the soil; is the content of the kth nutrient element in the soil of the evaluation center where the newly planted plants are located; Respectively represent the labels and The content of the kth nutrient element in the soil of the corresponding assessment sub-area.

7. The landscaping intelligent decision support system based on big data analysis according to claim 6 is characterized in that: The maintenance recommendations output in the decision recommendation output module include: a preset decision support baseline coefficient. When the decision support coefficient of the transition area is less than or equal to the decision support baseline coefficient, maintenance recommendations are output that the newly planted plants can adapt to and integrate into landscaping; when the decision support coefficient of the transition area is greater than the decision support baseline coefficient, maintenance recommendations are output that the newly planted plants are not suitable for landscaping.

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