Urban greening management system and method based on Internet of Things

Abstract

The invention discloses an urban landscaping management system and method based on the Internet of Things, relates to the technical field of information processing, and aims to evaluate the growth change of green plants in a unit supervision period, collect attribute tags and plant growth change evaluation values of a plurality of plants, and obtain the evaluation value of the plant growth change. According to the method, optimal plant attribute tags under different workloads of greening maintenance work are obtained, optimal selection strategies of greening plants under different workload conditions are established, the optimal attribute tags are obtained according to the expected workload of the greening maintenance work of a green belt area, and reasonable reference suggestions are provided. Plants with similar habits can be selected in the same green belt, and resource waste caused by extra maintenance work for special green plants is reduced. And for the to-be-planned greening area, the additional burden generated in the later-stage maintenance process is reduced by obtaining the plants in the greening maintenance work capability bearing range.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and in particular to an urban greening management system and method based on the Internet of Things. Background Art

[0002] Good urban greening helps improve the urban ecological environment, affecting the quality of life of citizens and the sustainable development of the city. In the urban environment, human activities have a direct impact on the growth of plants, such as watering, fertilizing, pruning, weeding, pest control and other maintenance work during the plant growth cycle.

[0003] In traditional greening design plans, the main consideration is the plant's own conditions, color matching, morphological pruning and other requirements, and insufficient consideration is given to the impact of human interference on the local environment of the growth environment. Under the same maintenance strategy, there is a significant difference in the growth of different plants in the same green belt, and some plant species are more prone to diseases, which can easily pose a safety hazard to surrounding plants and the living environment of citizens. Summary of the invention

[0004] The purpose of the present invention is to provide a method to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: an urban greening management method based on the Internet of Things: Step S100: Obtain the historical records of greening maintenance work in the green belt area and calculate the evaluation value of the workload in the unit green belt area; Step S200: collecting data on leaf density and leaf color uniformity of green plants in the green belt, and evaluating the growth status of the green plants; Step S300: Evaluate the growth changes of green plants during the unit supervision cycle, and establish an evaluation list of the workload of greening maintenance work and the growth changes of green plants; Step S400: labeling the plant attributes of various types of plants in the green belt area through attribute labels, collecting attribute labels and plant growth change evaluation values ​​of several plants, and obtaining optimal plant attribute labels under different workloads of greening maintenance work; Step S500: Obtain a green belt area to be planned, obtain preferred attribute tags according to the expected workload of greening maintenance work in the green belt area, and recommend plants including the attribute tags to relevant managers.

[0006] Furthermore, step S100 includes: Step S101: Set a certain greening maintenance area as a characteristic area, obtain the area range D of the characteristic area, set the construction records of all greening maintenance in the unit supervision cycle of time length T1, and the area range includes a plane range or a three-dimensional space range; Step S102: Obtain the workload W of greening maintenance within a certain unit supervision period from the construction record, and calculate the workload evaluation value η of the characteristic area, η=W / (D×T1).

[0007] Further, step S200 includes: Step S201: acquiring image information of a feature area to obtain a first feature image, wherein the image information includes a visual image and a spectral image, and extracting an image of a plant leaf from the first feature image to obtain a second feature image; Step S202: gridding the second feature image, dividing the second feature image into n unit images, separating the data values ​​corresponding to each color channel in all unit images, respectively obtaining the variance of the data values ​​corresponding to each color channel, and calculating the color evaluation value v of the second feature image. , where m represents the total number of color channels in the image information, and uc i represents the variance of the data value corresponding to the i-th color channel in the second feature image; The second feature image is divided into n unit images, the same color channel data in the n unit images are extracted, the discrete degree of the color channel in the n images is calculated, and the discrete degrees of the m color channels are accumulated to obtain a discrete degree description value of the second feature image; Step S203: Calculate the discrete degree evaluation value α through the discrete degree evaluation function F(v), α= F(v), where, , e is a natural constant, a and k are adjustment coefficients, satisfying the conditions a>0, k>0; The larger the variance, the greater the degree of dispersion of the data. In the scheme, it is necessary to use a decreasing function to optimize the situation with a smaller variance. That is, in the plant evaluation coefficient, the larger the variance, the smaller the dispersion evaluation value. The variance of the input function is always greater than or equal to 0, so α is always greater than or equal to 0; a controls the amplitude of the function change, k controls the initial value, and by controlling the size of a and k, the weight of α in the calculation process of plant evaluation coefficient is controlled; Step S204: obtaining the area pro1 of the first feature image and the area pro2 of the second feature image, and calculating the area evaluation value β of the first image, β= pro2 / pro1; Step S205: Calculate the plant evaluation coefficient PL in the first feature image, PL=α×β.

[0008] Furthermore, step S300 includes: Step S301: acquiring a first first characteristic image at the beginning of a certain unit supervision cycle, acquiring a plant evaluation coefficient PL1 of the first first characteristic image, acquiring a second first characteristic image at the end of a certain unit supervision cycle, acquiring a plant evaluation coefficient PL2 of the second first characteristic image; Step S302: Calculate the plant growth evaluation value GR of a certain unit supervision cycle, GR=PL2-PL1; Step S303: Obtain the total number R of plant species in the area D, obtain the growth evaluation value of each plant, and obtain the maximum value GR among the growth evaluation values. max , GR max As a reference evaluation value of the workload evaluation value η, the plant species corresponding to the reference evaluation value is recorded as the reference plant species; Step S304: Set a plant evaluation benchmark value PG, where PG>0, merge all plant species in the area D except the reference plant species into the plant evaluation set, and record the evaluation index of the fth plant species in the plant evaluation set as bot f , , where GR f is the evaluation index of the fth plant species; Step S305: taking the plant evaluation benchmark value as the evaluation index of the reference plant species, collecting the evaluation indexes of all plant species, and obtaining an evaluation list of the workload evaluation value η.

[0009] Furthermore, step S400 includes: Step S401: Obtain attribute labels of all types of plants in area D, where the attribute label set pro corresponding to the bth type of plant b , pro b Included in att 1 ,att 2 ,att 3 , ... and att x , where att 1 ,att 2 ,att 3 , ... and att x Respectively represent pro b the 1st, 2nd, 3rd, ... and xth labels in ; Step S402: taking a certain attribute tag as the target tag, obtaining a set of all attribute tags including the target tag, further obtaining plant species corresponding to the attribute tag machines, calculating the average value of the evaluation index of the plant species, and obtaining the tag evaluation value of the target tag under the condition of the workload evaluation value η; Different types of plants may have the same plant attribute label. The evaluation values ​​of the same label in different types of plants are collected to obtain the evaluation value of the label. This evaluation value corresponds to the workload evaluation value. By comparing the evaluation values ​​of different labels with the same workload evaluation value, the label is optimized. Step S403: Collect the tag evaluation values ​​of the target tag under several different workload evaluation value conditions to obtain a tag evaluation list of the target tag.

[0010] Furthermore, step S500 includes: Step S501: a certain green belt planning area is set as a target area, the target area and the characteristic area belong to the same city, the area range D1 of the target area and the planned workload W1 of the greening maintenance of the target area within the unit supervision cycle time range are obtained, and the workload estimate ω of the target area is calculated, ω=W1 / (D1×T1); Step S502: obtaining candidate plants to be planted in the target area, obtaining label evaluation values ​​of all attribute labels of any candidate plant under the workload estimation value ω, and summing the label evaluation values ​​to obtain the plant evaluation value of the candidate plant; Step S503: Gather the corresponding plant evaluation values ​​of all candidate plants, arrange the candidate plants in descending order of the plant evaluation values, obtain a plant recommendation sequence, and recommend the plant recommendation sequence to the green belt management personnel.

[0011] In order to better implement the above method, an urban greening management system based on the Internet of Things is also proposed, and the system includes: Workload management module, plant evaluation module, growth relationship evaluation module, plant attribute optimization module and plant recommendation module, wherein the workload management module is used to record the history of greening maintenance work in the green belt area and calculate the evaluation value of the workload in the unit green belt area, the plant evaluation module is used to evaluate the growth characteristics of plants in the greening area, the growth relationship evaluation module is used to establish an evaluation list of the workload of greening maintenance work and the growth changes of greening plants, the plant attribute optimization module is used to manage the preferred plant attribute labels under different workloads of greening maintenance work, and the plant recommendation module is used to obtain the preferred attribute labels and recommend the plants including the attribute labels to relevant managers; Further, the workload management module includes: a historical data management unit and a workload evaluation unit, wherein the historical data management unit is used to obtain the construction records and unit supervision cycle of greening maintenance in the greening maintenance area, and the workload evaluation unit is used to calculate the workload evaluation value in the greening area; Further, the plant evaluation module includes: a feature image acquisition unit, a color evaluation value calculation unit, an evaluation value calculation unit and an evaluation coefficient calculation unit, wherein the feature image acquisition unit is used to obtain a first feature image and a second feature image of the greening area, the color evaluation value calculation unit is used to obtain data values ​​of each color channel in the image, and calculate the color evaluation value of the second feature image, the evaluation value calculation unit is used to calculate a discrete degree evaluation value and an area evaluation value, and the evaluation coefficient calculation unit is used to calculate a plant evaluation coefficient in the first feature image; Further, the growth relationship evaluation module includes: a growth evaluation value management unit, an evaluation index calculation unit and an evaluation list management unit, the growth evaluation value management unit is used to obtain the plant evaluation coefficient to calculate the plant growth evaluation value, the evaluation index calculation unit is used to calculate the evaluation index of each plant species, and the evaluation list management unit is used to manage the evaluation list corresponding to the workload; Further, the plant attribute optimization module includes: a label management unit, a label evaluation value management unit and a label evaluation list management unit, wherein the label management unit is used to manage the attribute labels of the plants, the label evaluation value management unit is used to calculate the label evaluation value of the attribute label under a certain workload evaluation value condition, and the label evaluation list management unit is used to manage the label evaluation list of the labels; Furthermore, the plant recommendation module includes: a workload estimation unit, a plant evaluation unit and a recommendation management unit, wherein the workload estimation unit is used to obtain a workload estimation value, the plant evaluation unit is used to calculate the plant evaluation value of the candidate plants, and the recommendation management unit is used to obtain a plant recommendation sequence and recommend the plant recommendation sequence to the green belt management personnel.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: by analyzing the impact of greening maintenance work on plant growth, a mathematical model of plant growth under different greening maintenance workloads is established. The correlation between plant attributes and greening maintenance workload is further analyzed, and a preferred strategy for greening plants under different workload conditions is established to provide reasonable reference suggestions. It is beneficial to select plants with similar habits in the same green belt, reducing the waste of resources caused by additional maintenance work for special greening plants. And for the greening area to be planned, by obtaining plants within the capacity of greening maintenance work, the additional burden generated in the later maintenance process can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a schematic diagram of the structure of an urban greening management system based on the Internet of Things of the present invention; Figure 2 The present invention is a flowchart of an urban greening management method based on the Internet of Things. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Example: Figure 1 and Figure 2 As shown, the present invention provides a technical solution, an urban greening management system and method based on the Internet of Things, the method comprising: Step S100: Obtain the historical records of greening maintenance work in the green belt area and calculate the evaluation value of the workload in the unit green belt area; Wherein, step S100 includes: Step S101: Set a certain greening maintenance area as a characteristic area, obtain the area range D of the characteristic area, set the construction records of all greening maintenance in the unit supervision cycle of time length T1, and the area range includes a plane range or a three-dimensional space range; Step S102: Obtain the workload W of greening maintenance within a certain unit supervision period from the construction record, and calculate the workload evaluation value η of the characteristic area, η=W / (D×T1); In the embodiment, the workload of greening maintenance work is collected and uploaded through the Internet of Things coefficient, and the workload of greening maintenance work is collected and managed by establishing an Internet of Things information management platform.

[0016] Step S200: collecting data on leaf density and leaf color uniformity of green plants in the green belt, and evaluating the growth status of the green plants; Wherein, step S200 includes: Step S201: acquiring image information of a feature area to obtain a first feature image, wherein the image information includes a visual image and a spectral image, and extracting an image of a plant leaf from the first feature image to obtain a second feature image; Step S202: gridding the second feature image, dividing the second feature image into n unit images, separating the data values ​​corresponding to each color channel in all unit images, respectively obtaining the variance of the data values ​​corresponding to each color channel, and calculating the color evaluation value v of the second feature image. , where m represents the total number of color channels in the image information, and uc i represents the variance of the data value corresponding to the i-th color channel in the second feature image; In the embodiment, the RGB channel, HSV channel of the visual image or the multi-spectral channel of the multi-spectral image may be used. When the RGB channel or HSV channel is used, m is 3. When the multi-spectral image is used, the value is taken according to the actual number of spectral channels, usually a value in the range of 3 to 10. Step S203: Calculate the discrete degree evaluation value α through the discrete degree evaluation function F(v), α= F(v), where, , e is a natural constant, a and k are adjustment coefficients, satisfying the conditions a>0, k>0; Step S204: obtaining the area pro1 of the first feature image and the area pro2 of the second feature image, and calculating the area evaluation value β of the first image, β= pro2 / pro1; Step S205: Calculate the plant evaluation coefficient PL in the first feature image, PL=α×β.

[0017] Step S300: Evaluate the growth changes of green plants during the unit supervision cycle, and establish an evaluation list of the workload of greening maintenance work and the growth changes of green plants; Wherein, step S300 includes: Step S301: acquiring a first first characteristic image at the beginning of a certain unit supervision cycle, acquiring a plant evaluation coefficient PL1 of the first first characteristic image, acquiring a second first characteristic image at the end of a certain unit supervision cycle, acquiring a plant evaluation coefficient PL2 of the second first characteristic image; Step S302: Calculate the plant growth evaluation value GR of a certain unit supervision cycle, GR=PL2-PL1; Step S303: Obtain the total number R of plant species in the area D, obtain the growth evaluation value of each plant, and obtain the maximum value GR among the growth evaluation values. max , GR max As a reference evaluation value of the workload evaluation value η, the plant species corresponding to the reference evaluation value is recorded as the reference plant species; Step S304: Set a plant evaluation benchmark value PG, where PG>0, merge all plant species in the area D except the reference plant species into the plant evaluation set, and record the evaluation index of the fth plant species in the plant evaluation set as bot f , , where GR f is the evaluation index of the fth plant species; Step S305: taking the plant evaluation benchmark value as the evaluation index of the reference plant species, collecting the evaluation indexes of all plant species, and obtaining an evaluation list of the workload evaluation value η.

[0018] Step S400: labeling the plant attributes of various types of plants in the green belt area through attribute labels, collecting attribute labels and plant growth change evaluation values ​​of several plants, and obtaining optimal plant attribute labels under different workloads of greening maintenance work; Wherein, step S400 includes: Step S401: Obtain attribute labels of all types of plants in area D, where the attribute label set pro corresponding to the bth type of plant b , pro b Included in att 1 ,att 2 ,att 3 , ... and att x , where att 1 ,att 2 ,att 3 , ... and att x Respectively represent pro b the 1st, 2nd, 3rd, ... and xth labels in ; Step S402: taking a certain attribute tag as a target tag, obtaining a set of all attribute tags including the target tag, further obtaining plant species corresponding to the attribute tag machines, calculating the average value of the evaluation index of the plant species, and obtaining the tag evaluation value of the target tag under the condition of the workload evaluation value η; Step S403: Collecting the label evaluation values ​​of the target label under several different workload evaluation value conditions to obtain a label evaluation list of the target label; In the embodiment, there are 4 types of plants, plant1, plant2, plant3 and plant4, the evaluation indexes are respectively: dex1, dex2, dex3 and dex4, and the attribute label sets are respectively: pro 1 (att2, att3, att4), pro 2 (att1, att3, att5), pro 3 (att1, att3, att5) and pro 4 (att1, att3, att5); The label evaluation values ​​are: label1=(dex2+dex3) / 2, label2=dex1, label3=(dex1+dex2+dex3+dex4) / 4,label4=dex1, label4=(dex2+dex3+dex4) / 3.

[0019] Step S500: obtaining a green belt area to be planned, obtaining preferred attribute tags according to the expected workload of greening maintenance work in the green belt area, and recommending plants including the attribute tags to relevant managers; Wherein, step S500 includes: Step S501: a certain green belt planning area is set as a target area, the target area and the characteristic area belong to the same city, the area range D1 of the target area and the planned workload W1 of the greening maintenance of the target area within the unit supervision cycle time range are obtained, and the workload estimate ω of the target area is calculated, ω=W1 / (D1×T1); Step S502: obtaining candidate plants to be planted in the target area, obtaining label evaluation values ​​of all attribute labels of any candidate plant under the workload estimation value ω, and summing the label evaluation values ​​to obtain the plant evaluation value of the candidate plant; Step S503: Gather the corresponding plant evaluation values ​​of all candidate plants, arrange the candidate plants in descending order of the plant evaluation values, obtain a plant recommendation sequence, and recommend the plant recommendation sequence to the green belt management personnel.

[0020] The system includes: a workload management module, a plant evaluation module, a growth relationship evaluation module, a plant attribute optimization module and a plant recommendation module; The workload management module is used for the historical records of greening maintenance work in the green belt area and for calculating the evaluation value of the workload in the unit green belt area. The workload management module includes: a historical data management unit and a workload evaluation unit. The historical data management unit is used to obtain the construction records and unit supervision cycle of greening maintenance in the greening maintenance area, and the workload evaluation unit is used to calculate the workload evaluation value in the greening area. The plant evaluation module is used to evaluate the plant growth characteristics in the greening area, wherein the plant evaluation module includes: a feature image acquisition unit, a color evaluation value calculation unit, an evaluation value calculation unit and an evaluation coefficient calculation unit, wherein the feature image acquisition unit is used to obtain a first feature image and a second feature image of the greening area, the color evaluation value calculation unit is used to obtain data values ​​of each color channel in the image, calculate the color evaluation value of the second feature image, the evaluation value calculation unit is used to calculate a discrete degree evaluation value and an area evaluation value, and the evaluation coefficient calculation unit is used to calculate a plant evaluation coefficient in the first feature image; The growth relationship evaluation module is used to establish an evaluation list of the workload of greening maintenance work and the growth changes of greening plants. The growth relationship evaluation module includes: a growth evaluation value management unit, an evaluation index calculation unit and an evaluation list management unit. The growth evaluation value management unit is used to obtain a plant evaluation coefficient to calculate a plant growth evaluation value. The evaluation index calculation unit is used to calculate the evaluation index of each plant species. The evaluation list management unit is used to manage the evaluation list corresponding to the workload. The plant attribute optimization module is used to manage the preferred plant attribute labels under different workloads of greening maintenance work, wherein the plant attribute optimization module includes: a label management unit, a label evaluation value management unit and a label evaluation list management unit, wherein the label management unit is used to manage the attribute labels of plants, the label evaluation value management unit is used to calculate the label evaluation value of the attribute label under a certain workload evaluation value condition, and the label evaluation list management unit is used to manage the label evaluation list of the label; Among them, the plant recommendation module is used to obtain preferred attribute labels and recommend plants including attribute labels to relevant managers. Among them, the plant recommendation module includes: a workload estimation unit, a plant evaluation unit and a recommendation management unit. Among them, the workload estimation unit is used to obtain the workload estimation value, the plant evaluation unit is used to calculate the plant evaluation value of the alternative plants, and the recommendation management unit is used to obtain the plant recommendation sequence and recommend the plant recommendation sequence to the green belt managers.

[0021] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An urban greening management method based on the Internet of Things, characterized in that: The method comprises the steps of: Step S100: Obtain the historical records of greening maintenance work in the green belt area and calculate the evaluation value of the workload in the unit green belt area; Step S200: collecting data on leaf density and leaf color uniformity of green plants in the green belt, and evaluating the growth status of the green plants; Step S300: Evaluate the growth changes of green plants during the unit supervision cycle, and establish an evaluation list of the workload of greening maintenance work and the growth changes of green plants; Step S400: labeling the plant attributes of various types of plants in the green belt area through attribute labels, collecting attribute labels and plant growth change evaluation values ​​of several plants, and obtaining optimal plant attribute labels under different workloads of greening maintenance work; Step S500: Obtain a green belt area to be planned, obtain preferred attribute tags according to the expected workload of greening maintenance work in the green belt area, and recommend plants including the attribute tags to relevant managers.

2. The urban greening management method based on the Internet of Things according to claim 1 is characterized in that: Step S100 includes: Step S101: setting a certain greening maintenance area as a characteristic area, obtaining the area range D of the characteristic area, and setting the construction records of all greening maintenance in a unit supervision cycle of time length T1, wherein the area range includes a plane range or a three-dimensional space range; Step S102: Obtain the workload W of greening maintenance within a certain unit supervision period from the construction record, and calculate the workload evaluation value η of the characteristic area, η=W / (D×T1).

3. The urban greening management method based on the Internet of Things according to claim 2 is characterized in that: Step S200 includes: Step S201: acquiring image information of a feature area to obtain a first feature image, wherein the image information includes a visual image and a spectral image, and extracting an image of a plant leaf from the first feature image to obtain a second feature image; Step S202: gridding the second feature image, dividing the second feature image into n unit images, separating the data values ​​corresponding to each color channel in all unit images, respectively obtaining the variance of the data values ​​corresponding to each color channel, and calculating the color evaluation value v of the second feature image. , where m represents the total number of color channels in the image information, uc i represents the variance of the data value corresponding to the i-th color channel in the second feature image; Step S203: Calculate the discrete degree evaluation value α by using the discrete degree evaluation function F(v), α=F(v), where , e is a natural constant, a and k are adjustment coefficients, satisfying the conditions a>0, k>0; Step S204: obtaining the area pro1 of the first feature image and the area pro2 of the second feature image, and calculating the area evaluation value β of the first image, β= pro2 / pro1; Step S205: Calculate the plant evaluation coefficient PL in the first feature image, PL=α×β.

4. The urban greening management method based on the Internet of Things according to claim 3 is characterized by: Step S300 includes: Step S301: acquiring a first first characteristic image at the beginning of a certain unit supervision cycle, acquiring a plant evaluation coefficient PL1 of the first first characteristic image, acquiring a second first characteristic image at the end of the certain unit supervision cycle, acquiring a plant evaluation coefficient PL2 of the second first characteristic image; Step S302: Calculate the plant growth evaluation value GR of the unit supervision cycle, GR=PL2-PL1; Step S303: Obtain the total number R of plant species in the area D, obtain the growth evaluation value of each plant, and obtain the maximum value GR among the growth evaluation values. max , GR max As a reference evaluation value of the workload evaluation value η, the plant species corresponding to the reference evaluation value is recorded as the reference plant species; Step S304: Set a plant evaluation benchmark value PG, where PG>0, merge all plant species in the area D except the reference plant species into the plant evaluation set, and record the evaluation index of the fth plant species in the plant evaluation set as bot f , , where GR f is the evaluation index of the f-th plant species; Step S305: taking the plant evaluation benchmark value as the evaluation index of the reference plant species, collecting the evaluation indexes of all plant species, and obtaining an evaluation list of the workload evaluation value η.

5. The urban greening management method based on the Internet of Things according to claim 4 is characterized in that: Step S400 includes: Step S401: Obtain attribute labels of all types of plants in area D, where the attribute label set pro corresponding to the bth type of plant b , pro b Including att1, att2, att3, ... and att x , where att1, att2, att3, ... and att x Respectively represent pro b the 1st, 2nd, 3rd, ... and xth labels in ; Step S402: taking a certain attribute tag as a target tag, obtaining a set of all attribute tags including the target tag, further obtaining plant species corresponding to the attribute tag machines, calculating the average value of the evaluation index of the plant species, and obtaining the tag evaluation value of the target tag under the condition of the workload evaluation value η; Step S403: Collect the tag evaluation values ​​of the target tag under several different workload evaluation value conditions to obtain a tag evaluation list of the target tag.

6. The urban greening management method based on the Internet of Things according to claim 5 is characterized by: Step S500 includes: Step S501: a certain green belt planning area is set as a target area, the target area and the characteristic area belong to the same city, the area range D1 of the target area and the planned workload W1 of the greening maintenance of the target area within the unit supervision cycle time range are obtained, and the workload estimate ω of the target area is calculated, ω=W1 / (D1×T1); Step S502: obtaining candidate plants to be planted in the target area, obtaining label evaluation values ​​of all attribute labels of any candidate plant under the workload estimation value ω, and summing the label evaluation values ​​to obtain the plant evaluation value of the candidate plant; Step S503: Gather the corresponding plant evaluation values ​​of all candidate plants, arrange the candidate plants in descending order of the plant evaluation values, obtain a plant recommendation sequence, and recommend the plant recommendation sequence to the green belt management personnel.

7. An urban greening management system based on the Internet of Things, used to implement an urban greening management method based on the Internet of Things according to claims 1-6, characterized in that: Workload management module, plant evaluation module, growth relationship evaluation module, plant attribute optimization module and plant recommendation module, wherein the workload management module is used to record the history of greening maintenance work in the green belt area and calculate the evaluation value of the workload in the unit green belt area; the plant evaluation module is used to evaluate the growth characteristics of plants in the greening area; the growth relationship evaluation module is used to establish an evaluation list of the workload of greening maintenance work and the growth changes of greening plants; the plant attribute optimization module is used to manage the preferred plant attribute labels under different workloads of greening maintenance work; the plant recommendation module is used to obtain preferred attribute labels and recommend plants including the attribute labels to relevant managers.

8. The urban greening management system based on the Internet of Things according to claim 7 is characterized by: The workload management module includes: a historical data management unit and a workload evaluation unit, wherein the historical data management unit is used to obtain the construction records and unit supervision cycle of greening maintenance in the greening maintenance area, and the workload evaluation unit is used to calculate the workload evaluation value in the greening area; The plant evaluation module includes: a feature image acquisition unit, a color evaluation value calculation unit, an evaluation value calculation unit and an evaluation coefficient calculation unit, wherein the feature image acquisition unit is used to obtain a first feature image and a second feature image of a green area, the color evaluation value calculation unit is used to obtain data values ​​of each color channel in the image, and calculate the color evaluation value of the second feature image, the evaluation value calculation unit is used to calculate a discrete degree evaluation value and an area evaluation value, and the evaluation coefficient calculation unit is used to calculate a plant evaluation coefficient in the first feature image.

9. The urban greening management system based on the Internet of Things according to claim 7 is characterized by: The growth relationship evaluation module includes: a growth evaluation value management unit, an evaluation index calculation unit and an evaluation list management unit. The growth evaluation value management unit is used to obtain the plant evaluation coefficient to calculate the plant growth evaluation value. The evaluation index calculation unit is used to calculate the evaluation index of each plant species. The evaluation list management unit is used to manage the evaluation list corresponding to the workload. The plant attribute optimization module includes: a label management unit, a label evaluation value management unit and a label evaluation list management unit, wherein the label management unit is used to manage the attribute labels of the plants, the label evaluation value management unit is used to calculate the label evaluation value of the attribute label under a certain workload evaluation value condition, and the label evaluation list management unit is used to manage the label evaluation list of the label.

10. The urban greening management system based on the Internet of Things according to claim 7 is characterized by: The plant recommendation module includes: a workload estimation unit, a plant evaluation unit and a recommendation management unit, wherein the workload estimation unit is used to obtain the workload estimation value, the plant evaluation unit is used to calculate the plant evaluation value of the candidate plants, and the recommendation management unit is used to obtain the plant recommendation sequence and recommend the plant recommendation sequence to the green belt managers.

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