Urban greening monitoring and management system based on Internet of Things

Abstract

The invention relates to the field of urban landscaping management, and discloses an urban landscaping monitoring and management system based on the Internet of Things, the monitoring and management system comprises a division module, a plurality of monitoring modules and an analysis module, and a landscaping area is divided into a plurality of target areas according to plant varieties through the division module; monitoring the corresponding target area through a monitoring module, and obtaining plant monitoring data and environment data of plants in the corresponding target area; analyzing the plant monitoring data through an analysis module to obtain the growth stage of the plant, performing analysis according to the growth stage of the plant, the plant monitoring data and the environment data, and judging whether the plant in the corresponding target area is abnormal or not according to analysis; the accuracy of plant health state evaluation is improved, so that supervisors can know the health condition of plants in each target area in the greening area more intuitively, and plant abnormity can be found in time for corresponding processing.

Description

Technical Field

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

[0002] With the rapid development of economy and the increasing demand of people for the quality of living environment, urban greening work has been carried out rapidly. At present, major cities have built a large number of leisure parks or urban forests to provide residents with leisure places and improve the ecological environment of the city.

[0003] Unlike wild forests, plants in urban forests and leisure parks are planned and planted according to people's needs. The same variety of plants are planted in each area. In order to ensure the health of plants in urban forests and leisure parks, managers are usually assigned to monitor and manage plants in each area.

[0004] However, this management method is highly dependent on the experience and skills of managers. Due to the large area of ​​urban forests and leisure parks, many plants are planted, and the plant species in each area are different, and the growth stages at the same time are also different, the management personnel’s experience and skills are insufficient, making it difficult to accurately judge the health status of plants, and unable to detect abnormal plant conditions in time for timely treatment. Summary of the invention

[0005] The purpose of the present invention is to provide an urban greening monitoring and management system based on the Internet of Things to solve the following technical problems:

[0006] How to assess the health status of different plant species in green areas.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An urban greening monitoring and management system based on the Internet of Things, the monitoring and management system comprising:

[0009] A division module is used to divide the greening area into several target areas according to plant species;

[0010] Multiple monitoring modules, corresponding one to one with monitoring areas, are used to monitor corresponding target areas and obtain plant monitoring data and environmental data of plants in the corresponding target areas;

[0011] The analysis module is used to analyze the plant monitoring data, obtain the growth stage of the plant, and analyze according to the growth stage of the plant, the plant monitoring data and the environmental data, and judge whether the plants in the corresponding target area are abnormal according to the analysis.

[0012] As a further solution of the present invention: the plant monitoring data includes plant image data and real-time photosynthesis rate in the corresponding target area;

[0013] The environmental data includes real-time light intensity, real-time ambient temperature and real-time carbon dioxide concentration.

[0014] As a further solution of the present invention: the analysis module includes an identification unit and an analysis unit;

[0015] The recognition unit is a trained neural network model, which is used to recognize the image data of the plant and obtain the growth stage of the plant in the corresponding target area.

[0016] As a further embodiment of the present invention: the growth stages include a growing period, a flowering period, a fruiting period and a dormant period.

[0017] As a further solution of the present invention: the analysis unit performs analysis based on the growth stage of the plants in the corresponding target area, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration and the real-time photosynthesis rate of the plants in the corresponding target area to obtain a health assessment index of the plants in the corresponding target area; and performs health rating based on the health assessment index; the staff performs corresponding processing based on the health rating of the plants in the corresponding target area.

[0018] As a further solution of the present invention: the working process of the analysis unit is:

[0019] S1: Randomly select N time periods in the preset time period, n∈N;

[0020] S2: The duration of the nth time period is recorded as Δt n ;

[0021] S3: Obtaining the growth stage of the plants in the target area corresponding to the preset time period through the recognition unit;

[0022] S4: obtaining, through a monitoring unit, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration, and the real-time photosynthesis rate of plants in the target area corresponding to the preset time period;

[0023] S5: Obtaining a health assessment index of plants in the corresponding target area by analyzing the growth stage of plants in the corresponding target area, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration and the real-time photosynthesis rate of plants in the corresponding target area;

[0024] S6: Obtain health ratings of plants in the corresponding target area by analyzing the health assessment index.

[0025] As a further solution of the present invention: the health assessment index is obtained by the formula:

[0026]

[0027] Calculate the health assessment index Q;

[0028] Where f(X) is the judgment function. When X>0, f(X)=X; when X≤0, f(X)=0; v t (t) is the real-time photosynthesis rate curve of plants in the corresponding target area over time; δ n is the photosynthesis environmental impact index of plants in the corresponding target area in the nth random time period; v i is the preset photosynthesis rate of the plant species in the target area in the current growth stage and in the most suitable environmental standard; W0 is the preset error; θ i It is the environmental impact adjustment coefficient corresponding to the current growth stage of plants in the target area.

[0029] As a further solution of the present invention: the process of obtaining the photosynthesis environmental impact index of the plants in the target area in the nth random time period is through the formula:

[0030]

[0031] Calculation of photosynthesis environmental impact index δ n ;

[0032] Among them, Q L (t) is the light intensity change curve of the corresponding target area over time; Q i is the most suitable light intensity for plants in the target area at the current growth stage; T e (t) is the temperature variation curve of the real-time ambient temperature of the corresponding target area over time; T i is the most suitable ambient temperature for plants in the target area at the current growth stage; C t (t) is the concentration variation curve of real-time carbon dioxide concentration in the corresponding target area over time; C i is the most suitable carbon dioxide concentration for plants in the target area at the current growth stage; γ1 is the first weight coefficient; γ2 is the second weight coefficient; γ3 is the third weight coefficient; ε is the unit-removing coefficient; C1 is the first preset constant; C2 is the second preset constant; and C3 is the third preset constant.

[0033] Beneficial effects of the present invention:

[0034] (1) The present invention divides the greening area into several target areas according to plant species through a division module; monitors the corresponding target area through a monitoring module, and obtains plant monitoring data and environmental data of the plants in the corresponding target area; then analyzes the plant monitoring data through an analysis module to obtain the growth stage of the plant, and analyzes the plant growth stage, plant monitoring data and environmental data, and determines whether the plants in the corresponding target area are abnormal based on the analysis; improves the accuracy of plant health status assessment, so that supervisors can more intuitively understand the health status of plants in each target area in the greening area, and promptly discover plant abnormalities and take corresponding measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 A system module framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] See also Figure 1 As shown, in one embodiment, an urban greening monitoring and management system based on the Internet of Things is provided, including:

[0039] A division module is used to divide the greening area into several target areas according to plant species;

[0040] Multiple monitoring modules, corresponding one to one with monitoring areas, are used to monitor corresponding target areas and obtain plant monitoring data and environmental data of plants in the corresponding target areas;

[0041] The analysis module is used to analyze the plant monitoring data, obtain the growth stage of the plant, and analyze the growth stage of the plant, the plant monitoring data and the environmental data, and determine whether the plants in the corresponding target area are abnormal based on the analysis;

[0042] Through the above technical scheme, this embodiment divides the greening area into several target areas according to plant varieties through the division module; monitors the corresponding target area through the monitoring module, and obtains the plant monitoring data and environmental data of the plants in the corresponding target area; then analyzes the plant monitoring data through the analysis module to obtain the growth stage of the plant, and analyzes it according to the growth stage of the plant, the plant monitoring data and the environmental data, and judges whether the plants in the corresponding target area are abnormal based on the analysis; improves the accuracy of the assessment of the health status of the plants, so that the supervisors can more intuitively understand the health status of the plants in each target area in the greening area, and promptly discover plant abnormalities and take corresponding measures.

[0043] As an embodiment of the present invention, the plant monitoring data includes plant image data and real-time photosynthesis rate in the corresponding target area;

[0044] The environmental data includes real-time light intensity, real-time ambient temperature and real-time carbon dioxide concentration;

[0045] Through the above technical scheme, this embodiment can obtain plant image data, real-time photosynthesis rate, real-time light intensity, real-time ambient temperature and real-time carbon dioxide concentration through cameras, photosynthesis detectors, light intensity detectors, temperature sensors and gas detectors set in the corresponding target areas.

[0046] As an embodiment of the present invention, the analysis module includes an identification unit and an analysis unit;

[0047] The recognition unit is a trained neural network model, which is used to recognize the image data of the plant and obtain the growth stage of the plant in the corresponding target area;

[0048] The growth stages include growth period, flowering period, fruiting period and dormancy period;

[0049] The analysis unit performs analysis based on the growth stage of the plants in the corresponding target area, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration and the real-time photosynthesis rate of the plants in the corresponding target area to obtain a health assessment index of the plants in the corresponding target area; and performs health rating based on the health assessment index; the staff performs corresponding processing based on the health rating of the plants in the corresponding target area;

[0050] It should be noted that the specific training process of the neural network model used by the recognition unit is a prior art and will not be described in detail here.

[0051] As an implementation mode of the present invention, the working process of the analysis unit is as follows:

[0052] S1: Randomly select N time periods in the preset time period, n∈N;

[0053] S2: The duration of the nth time period is recorded as Δt n ;

[0054] S3: Obtaining the growth stage of the plants in the target area corresponding to the preset time period through the recognition unit;

[0055] S4: obtaining, through a monitoring unit, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration, and the real-time photosynthesis rate of plants in the target area corresponding to the preset time period;

[0056] S5: Obtaining a health assessment index of plants in the corresponding target area by analyzing the growth stage of plants in the corresponding target area, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration and the real-time photosynthesis rate of plants in the corresponding target area;

[0057] S6: Obtain the health rating of plants in the corresponding target area by analyzing the health assessment index;

[0058] Through the above technical solution, the analysis unit of this embodiment first randomly selects N time periods in the preset time period, and records the length of the nth time period as Δt n ; Then, the growth stage of the plants in the target area corresponding to the preset time period is obtained through the identification unit; then, the real-time light intensity, real-time ambient temperature, real-time carbon dioxide concentration and real-time photosynthesis rate of the plants in the target area corresponding to the preset time period are obtained through the monitoring unit; then, the growth stage of the plants in the corresponding target area, the real-time light intensity, real-time ambient temperature, real-time carbon dioxide concentration and real-time photosynthesis rate of the plants in the corresponding target area are analyzed to obtain the health assessment index of the plants in the corresponding target area; finally, the health rating of the plants in the corresponding target area is obtained by analyzing the health assessment index; the accuracy of the assessment of the health status of the plants is improved, so that the supervisors can more intuitively understand the health status of the plants in each target area within the greening area, and promptly discover plant abnormalities and take corresponding measures.

[0059] As an embodiment of the present invention, in step S5, the health assessment index is obtained by the formula:

[0060]

[0061] Calculate the health assessment index Q;

[0062] Where f(X) is the judgment function. When X>0, f(X)=X; when X≤0, f(X)=0; v t(t) is the real-time photosynthesis rate curve of plants in the corresponding target area over time; δ n is the photosynthesis environmental impact index of plants in the corresponding target area in the nth random time period; v i is the preset photosynthesis rate of the plant species in the target area in the current growth stage and in the most suitable environmental standard; W0 is the preset error; θ i is the environmental impact adjustment coefficient corresponding to the current growth stage of plants in the target area;

[0063] Through the above technical solution, this embodiment is the actual photosynthesis accumulation rate value of plants in the target area in the nth random time period; is the actual average photosynthesis rate of plants in the target area in the nth random time period; δ n *v i *θ i The preset photosynthesis rate of the plant species in the corresponding target area in the nth random time period in the current growth stage and under the influence of the actual environment; is the actual average photosynthesis rate of plants in the target area in the nth random time period The preset photosynthesis rate δ of the plant species in the corresponding target area in the nth random time period in the current growth stage and under the influence of the actual environment n *v i The difference between The average difference between the actual average photosynthesis rate in N time periods and the preset photosynthesis rate in the current growth stage and actual environment; is the difference between the average difference between the actual photosynthesis rate in N time periods and the preset photosynthesis rate in the current growth stage and actual environment and the preset error; When , it means that the average difference between the actual photosynthesis rate in N time periods and the preset photosynthesis rate in the current growth stage and actual environment is within the preset error; therefore, the corresponding target area plant status is healthy; when When , it means that the average difference between the actual average photosynthesis rate in N time periods and the preset photosynthesis rate in the current growth stage and actual environment exceeds the preset error; therefore, the plant state in the corresponding target area is not good; The larger it is, the more serious the plant health problem is in the corresponding target area;

[0064] It should be noted that the preset photosynthesis rate v corresponding to the target area plant species is in a healthy state at the current growth stage and under the most suitable environmental standards iThe optimal light intensity of the target area plants in the current growth stage, the optimal ambient temperature of the target area plants in the current growth stage, the photosynthesis rate of the target area plants in the current growth stage under the optimal carbon dioxide concentration environment, and the environmental impact adjustment coefficient θ of the target area plants in the current growth stage i Determined according to the plant species and current growth stage in the corresponding target area, the preset photosynthesis rate v of the plant species in the corresponding target area is in a healthy state at the current growth stage and under the most suitable environmental standards i , corresponding to the environmental impact adjustment coefficient θ of the current growth stage of plants in the target area i And the preset error W0 are preset values, which are obtained based on experience and will not be described in detail here.

[0065] As an implementation mode of the present invention, the process of obtaining the photosynthesis environmental impact index of the plants in the target area in the nth random time period is as follows:

[0066]

[0067] Calculation of photosynthesis environmental impact index δ n ;

[0068] Among them, Q L (t) is the light intensity change curve of the corresponding target area over time; Q i is the most suitable light intensity for plants in the target area at the current growth stage; T e (t) is the temperature variation curve of the real-time ambient temperature of the corresponding target area over time; T i is the most suitable ambient temperature for plants in the target area at the current growth stage; C t (t) is the concentration variation curve of real-time carbon dioxide concentration in the corresponding target area over time; C i is the most suitable carbon dioxide concentration for plants in the target area at the current growth stage; γ1 is the first weight coefficient; γ2 is the second weight coefficient; γ3 is the third weight coefficient; ε is the unit-removing coefficient; C1 is the first preset constant; C2 is the second preset constant; C3 is the third preset constant;

[0069] Through the above technical solution, this embodiment is the cumulative value of the real-time light intensity in the nth random time period of the corresponding target area; is the average real-time light intensity of the target area in the nth random time period; is the difference between the average real-time light intensity of the target area in the nth random time period and the optimal light intensity of the plants in the target area at the current growth stage; since the optimal light intensity of the plants in the target area at the current growth stage is Qi The photosynthesis speed is the fastest, so the difference between the average real-time light intensity of the target area in the nth random time period and the optimal light intensity of the plants in the target area at the current growth stage is The larger the value, the slower the photosynthesis rate in this state. Therefore, the photosynthesis environmental impact index δ n The smaller; is the accumulated value of the real-time ambient temperature in the nth random time period of the corresponding target area; is the average real-time ambient temperature of the target area in the nth random time period; is the difference between the average real-time ambient temperature of the target area in the nth random time period and the optimal ambient temperature of the plants in the target area at the current growth stage; since the optimal ambient temperature of the plants in the target area at the current growth stage is T i The photosynthesis speed is the fastest, so the difference between the average real-time ambient temperature of the target area in the nth random time period and the optimal ambient temperature of the plants in the target area at the current growth stage is The larger the value, the slower the photosynthesis rate in this state. Therefore, the photosynthesis environmental impact index δ n The smaller; is the real-time cumulative value of carbon dioxide concentration in the nth random time period of the corresponding target area; is the average real-time carbon dioxide concentration in the nth random time period of the corresponding target area; is the difference between the average real-time carbon dioxide concentration of the target area in the nth random time period and the optimal carbon dioxide concentration of the plants in the target area at the current growth stage; since the optimal carbon dioxide concentration of the plants in the target area at the current growth stage is C i As photosynthesis is the fastest, the difference between the average real-time carbon dioxide concentration in the nth random time period of the corresponding target area and the optimal carbon dioxide concentration of the plants in the corresponding target area at the current growth stage is The larger the value, the slower the photosynthesis rate in this state. Therefore, the photosynthesis environmental impact index δ n The smaller; when and and When the photosynthesis environmental impact index δ n =1; otherwise, photosynthesis environmental impact index δ n <1.

[0070] It should be noted that the optimal light intensity Q corresponding to the target area plants in the current growth stage is i , corresponds to the most suitable ambient temperature T for plants in the target area at the current growth stage i and the optimal carbon dioxide concentration C of the plants in the corresponding target area at the current growth stagei Determined according to the plant variety in the corresponding target area and the current growth stage, the most suitable light intensity Q of the plants in the corresponding target area at the current growth stage i and the most suitable environmental temperature T of the plants in the corresponding target area at the current growth stage i and the most suitable carbon dioxide concentration C of the plants in the corresponding target area at the current growth stage i The first weight coefficient γ1, the second weight coefficient γ2, the third weight coefficient γ3, the unit removal coefficient ε, the first preset constant C1, the second preset constant C2, and the third preset constant C3 are preset values, obtained based on experience and not elaborated here.

[0071] As a further solution of the present invention: In step S6, the rating method for the health rating of the plants in the corresponding target area is as follows:

[0072] Compare the health assessment index Q with the preset threshold [R1, R2]:

[0073] When Q = 0, the plants in the corresponding target area are rated as healthy;

[0074] When 0 < Q ≤ R1, the plants in the corresponding target area are rated as sub-healthy;

[0075] When R1 < Q ≤ R2, the plants in the corresponding target area are rated as slightly abnormal;

[0076] When R2 ≤ Q, the plants in the corresponding target area are rated as severely abnormal.

[0077] The above has described a detailed description of an embodiment of the present invention, but the content described above is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. An urban greening monitoring and management system based on the Internet of Things, characterized in that: The monitoring and management system comprises: A division module is used to divide the greening area into several target areas according to plant species; Multiple monitoring modules, corresponding one to one with monitoring areas, are used to monitor corresponding target areas and obtain plant monitoring data and environmental data of plants in the corresponding target areas; The analysis module is used to analyze the plant monitoring data, obtain the growth stage of the plant, and analyze according to the growth stage of the plant, the plant monitoring data and the environmental data, and judge whether the plants in the corresponding target area are abnormal according to the analysis.

2. According to the Internet of Things-based urban greening monitoring and management system of claim 1, it is characterized in that: The plant monitoring data includes plant image data and real-time photosynthesis rate in the corresponding target area; The environmental data includes real-time light intensity, real-time ambient temperature and real-time carbon dioxide concentration.

3. The urban greening monitoring and management system based on the Internet of Things according to claim 2 is characterized in that: The analysis module includes an identification unit and an analysis unit; The recognition unit is a trained neural network model, which is used to recognize the image data of the plant and obtain the growth stage of the plant in the corresponding target area.

4. The urban greening monitoring and management system based on the Internet of Things according to claim 3 is characterized in that: The growth stages include the growing period, the flowering period, the fruiting period and the dormancy period.

5. The urban greening monitoring and management system based on the Internet of Things according to claim 4 is characterized in that: The analysis unit performs analysis based on the growth stage of the plants in the corresponding target area, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration and the real-time photosynthesis rate of the plants in the corresponding target area to obtain a health assessment index for the plants in the corresponding target area; and performs a health rating based on the health assessment index; the staff performs corresponding processing based on the health rating of the plants in the corresponding target area.

6. The urban greening monitoring and management system based on the Internet of Things according to claim 5 is characterized in that: The working process of the analysis unit is as follows: S1: Randomly select N time periods in the preset time period, n∈N; S2: The duration of the nth time period is recorded as Δt n ; S3: Obtaining the growth stage of the plants in the target area corresponding to the preset time period through the recognition unit; S4: obtaining, through a monitoring unit, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration, and the real-time photosynthesis rate of plants in the target area corresponding to the preset time period; S5: Obtaining a health assessment index of plants in the corresponding target area by analyzing the growth stage of plants in the corresponding target area, the real-time light intensity, the real-time ambient temperature, the real-time carbon dioxide concentration and the real-time photosynthesis rate of plants in the corresponding target area; S6: Obtain health ratings of plants in the corresponding target area by analyzing the health assessment index.

7. The urban greening monitoring and management system based on the Internet of Things according to claim 6 is characterized in that: The health assessment index is calculated by the formula: Calculate the health assessment index Q; Where f(X) is the judgment function. When X>0, f(X)=X; when X≤0, F(X)=0; v t (t) is the real-time photosynthesis rate curve of plants in the corresponding target area over time; δ n is the photosynthesis environmental impact index of plants in the corresponding target area in the nth random time period; v i is the preset photosynthesis rate of the plant species in the target area in the current growth stage and in the most suitable environmental standard; W0 is the preset error; θ i It is the environmental impact adjustment coefficient corresponding to the current growth stage of plants in the target area.

8. The urban greening monitoring and management system based on the Internet of Things according to claim 7 is characterized in that: The process of obtaining the photosynthesis environmental impact index of plants in the target area at the nth random time period is as follows: Calculation of photosynthesis environmental impact index δ n ; Among them, Q L (t) is the light intensity change curve of the corresponding target area over time; Q i is the most suitable light intensity for plants in the target area at the current growth stage; T e (t) is the temperature variation curve of the real-time ambient temperature of the corresponding target area over time; T i is the most suitable ambient temperature for plants in the target area at the current growth stage; C t (t) is the concentration variation curve of real-time carbon dioxide concentration in the corresponding target area over time; C i is the most suitable carbon dioxide concentration for plants in the target area at the current growth stage; γ1 is the first weight coefficient; γ2 is the second weight coefficient; γ3 is the third weight coefficient; ε is the unit-removing coefficient; C1 is the first preset constant; C2 is the second preset constant; and C3 is the third preset constant.

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