An urban green space plant growth status monitoring and analysis system and method

Abstract

The present invention discloses a monitoring and analysis system and method for the growth status of urban green space plants, which relates to the technical field of big data analysis. Through meticulous regional division and coordinate marking, the system can accurately track the health status of plants in each sub-region, improving the scientificity and effectiveness of urban green space management. By using a plant growth simulation model and an ideal environment growth model constructed by neural networks, the system can dynamically predict the growth status of plants and conduct personalized management of the growth of plants in different regions. By integrating various acquisition means such as environmental factors and remote sensing data, the growth status of urban green space plants is comprehensively monitored, overcoming the limitations of traditional methods. By setting an abnormal status threshold and combining it with a growth deviation trend chart, the system can implement a real-time warning mechanism, quickly identify potential growth anomalies, ensure timely discovery and solution of problems, thereby reducing the error of remote sensing technology monitoring and improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and specifically to a monitoring and analysis system and method for the growth status of urban green space plants. Background Art

[0002] Urban green space plants are of great significance in improving air quality, reducing urban temperature, and improving soil and water conservation. They can effectively absorb carbon dioxide and release oxygen, improving the living environment of residents. In addition, green space plants provide habitats, support biodiversity, and enhance the stability of the ecosystem. At the same time, these plants can provide beautiful landscapes, improve the aesthetics of the city, and enhance the mental health and quality of life of residents. In addition, green spaces also provide places for urban residents to relax, exercise and socialize, enhance community cohesion, and promote the sustainable development of the city.

[0003] Urban green space plants are widely distributed in the city. However, existing remote sensing monitoring technologies usually rely on a single data collection method, resulting in the lack of diversity and comprehensiveness of monitoring results. Due to the inability to capture the growth characteristics of multiple plants and the changes in environmental factors simultaneously, single monitoring data often cannot comprehensively reflect the health status of plants. In this way, the limitations of a single data source make it difficult to accurately evaluate the growth status of urban green space plants, lacking personalized analysis and management plans for the health of plants in different regions. Summary of the Invention

[0004] The purpose of the present invention is to provide a monitoring and analysis system and method for the growth status of urban green space plants to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for monitoring and analyzing the growth status of urban green space plants, the growth status monitoring and analysis method includes the following steps:

[0006] Step S1: Divide the area according to the distribution characteristics of urban green space plants, and based on the divided area, establish corresponding coordinate markings.

[0007] Step S1-1: Divide the urban green space into multiple sub-areas according to the location distribution characteristics of urban green space plants, combined with plant density and environmental conditions.

[0008] Step S1-2: According to the divided multiple sub-areas, use the geographic coordinate system to set coordinate markings for each sub-area, set the main coordinate marking at the geometric center point of each sub-area, and set the secondary coordinate marking at the boundary corner points.

[0009] Dividing urban green spaces into multiple sub - regions and setting coordinate markers helps to accurately locate and manage the plant growth status of each region, improving the accuracy and efficiency of monitoring. By combining plant distribution characteristics, density, and environmental conditions, refining regional analysis facilitates dynamic tracking of the growth status in different regions, enabling targeted management and optimization, and promoting the overall health and ecological benefits of urban green spaces.

[0010] Step S2: According to the marked - divided regions, comprehensively collect the growth status data of urban green space plants in each region, and record the corresponding plant characteristics and health conditions of each region.

[0011] Comprehensively collect the growth status data of urban green space plants in each of the divided sub - regions. The comprehensively collected data includes growth characteristic data, environmental factor data, plant health data, and remote sensing data, and store and record the plant characteristic data and health condition data corresponding to each sub - region.

[0012] Step S3: Utilize the comprehensively collected growth status data of urban green space plants to construct a plant growth simulation model through a neural network. At the same time, construct an ideal environment growth model based on preset ideal environmental parameters. Use the plant growth simulation model to simulate the growth status of the data collected for each region, and regularly collect key environmental factor data.

[0013] Step S3 - 1: Input the growth characteristic data, environmental factor data, plant health data, and remote sensing data into the neural network structure for training to construct a plant growth simulation model and an ideal environment growth model.

[0014] Step S3 - 2: Set the key environmental factor data collection period Q. Obtain key environmental factor data by deploying sensors in each sub - region. The key environmental factor data includes light intensity, soil humidity, temperature, and atmospheric carbon dioxide concentration. Input the key environmental factor data obtained in each collection period into the plant growth simulation model to simulate growth.

[0015] Step S3 - 3: Replace the set key environmental factor data with the standard data under ideal environmental conditions and input it into the ideal environment growth model for simulated growth.

[0016] By inputting the growth characteristic data, environmental factor data, plant health data, and remote sensing data into a neural network to construct a plant growth simulation model, it is possible to accurately predict the growth status of plants in each region.

[0017] Step S4: Set the plant remote sensing acquisition period, construct a remote sensing monitoring set, a simulation mapping remote sensing set, and an ideal environment simulation mapping remote sensing set. According to the plant remote sensing acquisition period, use remote sensing technology to monitor each area marked by coordinates, and store the plant remote sensing data actually collected by the remote sensing technology into the remote sensing monitoring set. At the same time, store the remote sensing data generated by the synchronous real environment plant growth model into the simulation mapping remote sensing set, and store the remote sensing data generated by the ideal environment plant growth model into the ideal environment simulation mapping remote sensing set;

[0018] Step S4-1: Set the plant remote sensing acquisition period T, construct a remote sensing monitoring set, a simulation mapping remote sensing set, and an ideal environment simulation mapping remote sensing set. The remote sensing monitoring set is used to store the plant remote sensing data actually collected by the remote sensing technology. The simulation mapping remote sensing set is used to store the plant remote sensing data mapped by the plant growth simulation model. The ideal environment simulation mapping remote sensing set is used to store the plant remote sensing data mapped by the ideal environment growth model;

[0019] Step S4-2: According to the plant remote sensing acquisition period, use remote sensing technology to monitor each sub-area marked by coordinates;

[0020] Step S4-3: Store the plant remote sensing data of each sub-area collected by the actual remote sensing technology into the remote sensing monitoring set, store the plant remote sensing data generated in the corresponding area of the plant growth simulation model into the simulation mapping remote sensing set, and store the plant remote sensing data generated in the corresponding area of the ideal environment growth model into the ideal environment simulation mapping remote sensing set.

[0021] By setting the plant remote sensing acquisition period and constructing the remote sensing monitoring set and the simulation mapping remote sensing set, the actually collected remote sensing data and the data generated by the model can be systematically collected and stored.

[0022] Step S5: Set the environmental monitoring threshold, obtain the deviation value between the actual plant remote sensing data of each area and the remote sensing data generated by the plant growth simulation model, which is recorded as the simulation growth deviation value; at the same time, obtain the deviation value between the remote sensing monitoring set and the ideal environment simulation mapping remote sensing set, which is recorded as the ideal environment deviation value. Compare the difference in remote sensing data between adjacent actual sub-areas and the difference in remote sensing data in the synchronous corresponding areas to form a regional growth gradient value. Summarize the simulation growth deviation value and the regional growth gradient value, construct a growth deviation trend chart and a gradient error trend chart, quantify the impact of environmental factors on plant growth according to the ideal environment deviation value, select the maximum impact factor, and perform data warning on the growth deviation trend chart in combination with the environmental monitoring threshold;

[0023] Step S5-1: Based on the remote sensing monitoring set, the simulated mapping remote sensing set, and the ideal environment simulated mapping remote sensing set, obtain the deviation value between the actual plant remote sensing data of each sub-region and the plant remote sensing data generated by the plant growth simulation model, denoted as the simulated growth deviation value M. Obtain the deviation value between the actual plant remote sensing data of each sub-region and the plant remote sensing data generated by the ideal environment growth model, denoted as the ideal environment deviation value;

[0024] Step S5-2: Aggregate the simulated growth deviation values M of each sub-region within the plant remote sensing acquisition period T to construct a growth deviation trend graph;

[0025] Step S5-3: Based on the remote sensing monitoring set and the simulated mapping remote sensing set, obtain the difference in plant remote sensing data between each actual adjacent sub-region and the difference in remote sensing data of the synchronous corresponding region in the plant growth simulation model, and compare these two differences to form the regional growth gradient value G error , and the formula for calculating the difference in plant remote sensing data between actual adjacent sub-regions is as follows:

[0026] G actual =R actual，i -R actual，o ;

[0027] In the formula, G actual represents the difference in plant remote sensing data between actual adjacent sub-regions; R actual，i represents the actual plant remote sensing data value of sub-region i; R actual，o represents the actual plant remote sensing data value of sub-region o;

[0028] The formula for calculating the difference in plant remote sensing data of the corresponding adjacent sub-regions in the plant growth simulation model is as follows:

[0029] G model =R model，i -R model，o ;

[0030] In the formula, G model represents the difference in plant remote sensing data of the corresponding adjacent sub-regions in the plant growth simulation model; R model，i represents the plant remote sensing data value of sub-region i in the plant growth simulation model; R model，o represents the plant remote sensing data value of sub-region o in the plant growth simulation model;

[0031] The formula for calculating the regional growth gradient value G error is as follows:

[0032] G error =|G actual -G model |;

[0033] wherein, G error represents the difference in plant remote sensing data between actual adjacent sub-regions and the difference in remote sensing data in the corresponding synchronous regions in the plant growth simulation model;

[0034] Step S5-4: Aggregate the regional growth gradient values J obtained within the plant remote sensing acquisition period T to construct a gradient error trend chart.

[0035] By analyzing the deviation values between the remote sensing monitoring set and the simulated mapping remote sensing set, the growth status and health level of urban green space plants can be effectively evaluated;

[0036] Step S5-5: Quantify the specific impact of actual environmental factors on plant growth according to the ideal environmental deviation values of each sub-region within the plant remote sensing acquisition period T. The actual environmental factors include light intensity, soil humidity, temperature, and atmospheric carbon dioxide concentration. By comparing the growth performance under actual environmental conditions with that under ideal environmental conditions, select the environmental factor with the largest impact value as the key environmental monitoring object;

[0037] Step S5-6: Compare the actually obtained value of the key environmental monitoring object with the environmental monitoring threshold:

[0038] When the actually obtained value of the key environmental monitoring object is less than the environmental monitoring threshold, it is judged that the data fluctuation of the growth deviation trend chart is in a normal state;

[0039] When the actually obtained value of the key environmental monitoring object is greater than or equal to the environmental monitoring threshold, it is judged that the data fluctuation of the growth deviation trend chart is in an abnormal state, and an abnormal signal of the key environmental monitoring object is sent.

[0040] Step S6: Set a status abnormal threshold, combine the growth deviation trend chart to give an early warning of the growth status of the monitored urban green space plants, extract the maximum absolute deviation value in the gradient error trend chart, determine the corresponding region according to the maximum absolute deviation value, and conduct secondary monitoring, and judge the growth status of the urban green space plants in this region according to the data of the secondary monitoring.

[0041] Step S6-1: Set a status abnormal threshold Z max , obtain the data point values in the growth deviation trend chart, denoted as the deviation value X, and combine the status abnormal threshold Z max to judge and give an early warning of the growth status of the monitored urban green space plants;

[0042] When X < Z max it is judged that the growth status of the monitored urban green space plants meets the growth expectation in the plant growth simulation model, and the growth status of the urban green space plants is normal;

[0043] When X ≥ Z maxWhen it is determined that the growth status of the urban green space plants under monitoring does not meet the growth expectation in the plant growth simulation model, and there is an abnormality in the growth status of the urban green space plants, a warning of abnormal growth status is issued;

[0044] Step S6-2: For the gradient error trend chart, extract the maximum absolute deviation G max , based on G max Determine its corresponding regional location, and conduct secondary remote sensing monitoring on this area. Compare the data collected in the secondary monitoring with the data from the previous monitoring to determine whether there is an abnormality or continuous deviation in the growth status of the plants. The calculation formula for the average value of the numerical values of each data point in the gradient error trend chart is as follows:

[0045]

[0046] In the formula, G avg represents the average value of the numerical values of each data point in the gradient error trend chart; N represents the number of differences between the remote sensing data of plants in each actual adjacent sub-region and the remote sensing data of the corresponding region in the plant growth simulation model;

[0047] The calculation formula for the maximum absolute deviation G max is as follows:

[0048] G max = max(|G error，io - G avg |);

[0049] In the formula, G max represents the maximum absolute deviation in the gradient error trend chart; G error，io represents the numerical values of the corresponding data points in sub-region i and sub-region o in the gradient error trend chart; max represents taking the maximum value from a set of data;

[0050] Extract the data of sub-region i and sub-region o corresponding to G max , and conduct secondary monitoring on sub-region i and sub-region o. Take the minimum value of the two monitoring results, and judge the growth status of the urban green space plants in this area according to the minimum value combined with the NDVI judgment standard.

[0051] NDVI < 0, water body, soil or vegetation-free area: Negative values usually indicate water bodies or bare soil;

[0052] 0 ≤ NDVI < 0.2, sparse vegetation: The NDVI value within this range usually indicates a low vegetation coverage rate, which may be sparse vegetation such as grassland or shrubs;

[0053] 0.2 ≤ NDVI < 0.5, medium vegetation coverage: This range represents medium-density vegetation, such as farmland or grassland;

[0054] 0.5 ≤ NDVI < 0.8, High vegetation cover: NDVI values in this range usually indicate high-density vegetation, such as forests or thick shrubs;

[0055] 0.8 ≤ NDVI ≤ 1, Very healthy and dense vegetation: Extremely high NDVI values indicate a very healthy and lush vegetation state, usually mature forests or other dense vegetation.

[0056] By setting the status anomaly threshold and combining it with the growth deviation trend chart for early warning, it is possible to achieve timely monitoring and anomaly identification of the growth status of urban green space plants.

[0057] An urban green space plant growth status monitoring and analysis system, the growth status monitoring and analysis system includes a data acquisition module, a regional management module, a growth simulation module, a set management module, a trend chart management module, and a status evaluation and early warning module;

[0058] The data acquisition module is used for data information acquisition; the regional management module is used to divide according to the location distribution characteristics of urban green space plants and construct coordinates for marking; the growth simulation module is used to train according to the collected data input into the neural network structure and simulate the growth of urban green space plants; the set management module is used to manage the addition and deletion of data in each set; the trend chart management module is used to construct a corresponding trend chart according to the set; the status evaluation and early warning module is used to judge the growth status according to the trend chart and perform early warning processing on the areas that meet the early warning conditions;

[0059] The output end of the data acquisition module is electrically connected to the input end of the regional management module; the output end of the regional management module is electrically connected to the input end of the growth simulation module; the output end of the growth simulation module is electrically connected to the input end of the set management module; the output end of the set management module is electrically connected to the input end of the trend chart management module; the output end of the trend chart management module is electrically connected to the input end of the status evaluation and early warning module.

[0060] The data acquisition module includes an environmental factor acquisition unit and a remote sensing acquisition unit; the environmental factor acquisition unit is used to acquire light intensity, soil humidity, temperature, and atmospheric carbon dioxide concentration; the remote sensing acquisition unit is used to obtain plant remote sensing data of each sub-region by using remote sensing technology;

[0061] The regional management module includes a regional division unit and a coordinate marking unit; the regional division unit is used to divide regions according to the location distribution of urban green space plants; the coordinate marking unit is used to mark the location of the regions;

[0062] The growth simulation module includes a model training unit and a simulation growth unit; the model training unit is used to input data into a neural network structure for training to construct a plant growth simulation model; the simulation growth unit is used to simulate growth according to the input parameters.

[0063] The set management module includes an actual set unit and a simulation set unit; the actual set unit is used to store the data actually collected and obtained; the simulation set unit is used to store the data generated by the simulated growth.

[0064] The trend chart management module includes a direct trend unit and an adjacent difference trend chart unit; the direct trend unit is used to construct a gap trend chart of the actual and simulated growth; the adjacent difference trend chart unit is used to construct a trend chart according to the difference between the adjacent two sub-regions in the actual collection and the simulated mapping.

[0065] The status evaluation and early warning module includes a status evaluation unit and an early warning unit; the status evaluation unit is used to evaluate the growth status according to the collected data; the early warning unit is used to perform early warning processing on the growth status of urban green space plants in the areas that meet the early warning conditions.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0067] 1. Through a variety of data collection means, including environmental factors and remote sensing data, the present invention can comprehensively monitor the growth status of urban green space plants. This comprehensive data collection method overcomes the limitations of traditional methods, making the monitoring results more accurate and comprehensive. Through detailed regional division and coordinate marking, the system can accurately track the health status and growth characteristics of plants in each sub-region, realizing scientific evaluation of the plant health level and improving the scientificity and effectiveness of urban green space management.

[0068] 2. The present invention uses a neural network to construct a plant growth simulation model. By analyzing the plant growth characteristics and real-time environmental data, it can dynamically predict the growth status of plants. This intelligent growth simulation method enables the system to make timely responses to the plant growth conditions in different regions and provide personalized management solutions.

[0069] 3. By setting a status anomaly threshold and combining it with a growth deviation trend chart, the present invention realizes real-time early warning of the plant growth status. This early warning mechanism can quickly identify potential growth anomalies by analyzing the deviation between the actual monitoring data and the simulated data, and then take targeted measures. The secondary monitoring function allows the system to conduct in-depth analysis of the abnormal areas to ensure timely discovery and solution of problems, and can also reduce the errors generated by remote sensing technology in different regions. Description of the Drawings

[0070] Figure 1It is a schematic flow chart of a method for monitoring and analyzing the growth status of urban green space plants according to the present invention;

[0071] Figure 2 It is a schematic structural diagram of a system for monitoring and analyzing the growth status of urban green space plants according to the present invention. Specific embodiments

[0072] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0073] Embodiment 1: As Figure 1 shown, the present invention provides a technical solution, a method for monitoring and analyzing the growth status of urban green space plants. The growth status monitoring and analysis method includes the following steps:

[0074] Step S1: Divide the area according to the distribution characteristics of urban green space plants, and based on the divided area, establish corresponding coordinate markings;

[0075] Step S1-1: Divide the urban green space into multiple sub-areas according to the location distribution characteristics of urban green space plants, combined with plant density and environmental conditions;

[0076] Step S1-2: According to the divided multiple sub-areas, use the geographic coordinate system to set coordinate markings for each sub-area, set the main coordinate markings at the geometric center points of each sub-area, and set the secondary coordinate markings at the boundary corner points.

[0077] Step S2: According to the marked area, comprehensively collect the growth status data of urban green space plants in each area, and record the corresponding plant characteristics and health conditions of each area;

[0078] Comprehensively collect the growth status data of urban green space plants in each of the divided sub-areas. The comprehensively collected data includes growth characteristic data, environmental factor data, plant health data, and remote sensing data, and store and record the corresponding plant characteristic data and health condition data of each sub-area.

[0079] Step S3: Use the comprehensively collected growth status data of urban green space plants to construct a plant growth simulation model through a neural network. At the same time, construct an ideal environment growth model based on preset ideal environmental parameters. Use the plant growth simulation model to simulate the growth status of the data collected in each area, and regularly collect key environmental factor data;

[0080] Step S3-1: Input the growth characteristic data, environmental factor data, plant health data, and remote sensing data into the neural network structure for training to construct a plant growth simulation model and an ideal environment growth model;

[0081] Step S3-2: Set the acquisition period Q of the key environmental factor data. Obtain the key environmental factor data by deploying sensors in each sub-region. The key environmental factor data includes light intensity, soil humidity, temperature, and atmospheric carbon dioxide concentration. Input the key environmental factor data obtained in each acquisition period into the plant growth simulation model to simulate growth;

[0082] Step S3-3: Replace the set key environmental factor data with the standard data under ideal environmental conditions and input it into the ideal environment growth model for simulated growth.

[0083] Step S4: Set the plant remote sensing acquisition period, construct a remote sensing monitoring set, a simulated mapping remote sensing set, and an ideal environment simulated mapping remote sensing set. Monitor each region marked by coordinates using remote sensing technology according to the plant remote sensing acquisition period, and store the plant remote sensing data actually collected by the remote sensing technology in the remote sensing monitoring set. At the same time, store the remote sensing data generated by the synchronous real environment plant growth model in the simulated mapping remote sensing set, and store the remote sensing data generated by the ideal environment plant growth model in the ideal environment simulated mapping remote sensing set;

[0084] Step S4-1: Set the plant remote sensing acquisition period T, construct a remote sensing monitoring set, a simulated mapping remote sensing set, and an ideal environment simulated mapping remote sensing set. The remote sensing monitoring set is used to store the plant remote sensing data actually collected by the remote sensing technology. The simulated mapping remote sensing set is used to store the plant remote sensing data mapped by the plant growth simulation model. The ideal environment simulated mapping remote sensing set is used to store the plant remote sensing data mapped by the ideal environment growth model;

[0085] Step S4-2: Monitor each sub-region marked by coordinates using remote sensing technology according to the plant remote sensing acquisition period;

[0086] Step S4-3: Store the plant remote sensing data of each sub-region actually collected by the remote sensing technology in the remote sensing monitoring set, store the plant remote sensing data generated in the corresponding region of the plant growth simulation model in the simulated mapping remote sensing set, and store the plant remote sensing data generated in the corresponding region of the ideal environment growth model in the ideal environment simulated mapping remote sensing set.

[0087] Step S5: Set the environmental monitoring threshold, obtain the deviation value between the actual plant remote sensing data of each region and the remote sensing data generated by the plant growth simulation model, denoted as the simulated growth deviation value; at the same time, obtain the deviation value between the remote sensing monitoring set and the ideal environmental simulation mapping remote sensing set, denoted as the ideal environmental deviation value. Compare the difference in remote sensing data between adjacent actual sub-regions with the difference in remote sensing data in the synchronized corresponding regions to form a regional growth gradient value. Aggregate the simulated growth deviation value and the regional growth gradient value, construct a growth deviation trend chart and a gradient error trend chart, quantify the impact of environmental factors on plant growth according to the ideal environmental deviation value, select the maximum influencing factor, and combine the environmental monitoring threshold to perform data warning on the growth deviation trend chart;

[0088] Step S5-1: According to the remote sensing monitoring set, the simulated mapping remote sensing set, and the ideal environmental simulation mapping remote sensing set, obtain the deviation value between the actual plant remote sensing data of each sub-region and the plant remote sensing data generated by the plant growth simulation model, denoted as the simulated growth deviation value M. Obtain the deviation value between the actual plant remote sensing data of each sub-region and the plant remote sensing data generated by the ideal environmental growth model, denoted as the ideal environmental deviation value;

[0089] Step S5-2: Aggregate the simulated growth deviation value M of each sub-region within the plant remote sensing acquisition period T to construct a growth deviation trend chart;

[0090] Step S5-3: According to the remote sensing monitoring set and the simulated mapping remote sensing set, obtain the difference in plant remote sensing data between adjacent actual sub-regions and the difference in remote sensing data in the synchronized corresponding regions in the plant growth simulation model, and compare these two differences to form a regional growth gradient value G error , and the calculation formula for the difference in plant remote sensing data in adjacent actual sub-regions is as follows:

[0091] G actual =R actual，i -R actual，o ;

[0092] In the formula, G actual represents the difference in plant remote sensing data in adjacent actual sub-regions; R actual，i represents the actual plant remote sensing data value of sub-region i; R actual，o represents the actual plant remote sensing data value of sub-region o;

[0093] The calculation formula for the difference in plant remote sensing data in the corresponding adjacent sub-regions in the plant growth simulation model is as follows:

[0094] G model =R model，i -R model，o ;

[0095] In the formula, G modelDenoted as the difference in plant remote sensing data between adjacent sub-regions in the plant growth simulation model; R model，i Denoted as the plant remote sensing data value of sub-region i in the plant growth simulation model; R model，o Denoted as the plant remote sensing data value of sub-region o in the plant growth simulation model;

[0096] Regional growth gradient value G error The calculation formula is as follows:

[0097] G error =|G actual -G model |;

[0098] In the formula, G error Denoted as the difference in plant remote sensing data between actual adjacent sub-regions and the difference in remote sensing data of the synchronous corresponding regions in the plant growth simulation model;

[0099] Step S5-4: Aggregate the regional growth gradient values J obtained within the plant remote sensing acquisition period T to construct a gradient error trend graph;

[0100] Step S5-5: Quantify the specific impact of actual environmental factors on plant growth based on the ideal environmental deviation values of each sub-region within the plant remote sensing acquisition period T. The actual environmental factors include light intensity, soil humidity, temperature, and atmospheric carbon dioxide concentration. By comparing the growth performance under actual environmental conditions with that under ideal environmental conditions, select the environmental factor with the largest impact value as the key environmental monitoring object;

[0101] Step S5-6: Compare the actually obtained value of the key environmental monitoring object with the environmental monitoring threshold:

[0102] When the actually obtained value of the key environmental monitoring object is less than the environmental monitoring threshold, it is judged that the data fluctuation of the growth deviation trend graph is in a normal state;

[0103] When the actually obtained value of the key environmental monitoring object is greater than or equal to the environmental monitoring threshold, it is judged that the data fluctuation of the growth deviation trend graph is in an abnormal state, and an abnormal signal of the key environmental monitoring object is issued.

[0104] Step S6: Set a status abnormal threshold, combine the growth deviation trend graph to give an early warning of the growth status of the monitored urban green space plants, extract the maximum absolute deviation value in the gradient error trend graph, determine the corresponding region according to the maximum absolute deviation value, and conduct secondary monitoring, and judge the growth status of the urban green space plants in this region based on the data of the secondary monitoring.

[0105] Step S6-1: Set a status abnormal threshold Z max, obtain the data point values in the growth deviation trend graph, denoted as the deviation value X, and combine it with the status anomaly threshold Z max Judge and give early warnings for the growth status of the urban green space plants under monitoring;

[0106] When X < Z max it is judged that the growth status of the urban green space plants under monitoring conforms to the growth expectation in the plant growth simulation model, and the growth status of the urban green space plants is normal;

[0107] When X ≥ Z max it is judged that the growth status of the urban green space plants under monitoring does not conform to the growth expectation in the plant growth simulation model, the growth status of the urban green space plants is abnormal, and an early warning of abnormal growth status is issued;

[0108] Step S6-2, gradient error trend graph, extract the maximum absolute deviation G max , according to G max determine its corresponding regional location, and conduct secondary remote sensing monitoring on this area. Compare the data collected by the secondary monitoring with the previous monitoring data to judge whether there are abnormalities or continuous deviations in the growth status of the plants. The average value calculation formula of the data point values in the gradient error trend graph is as follows:

[0109]

[0110] In the formula, G avg represents the average value of the data point values in the gradient error trend graph; N represents the number of differences between the plant remote sensing data of each adjacent sub-region in reality and the remote sensing data of the synchronous corresponding region in the plant growth simulation model;

[0111] The calculation formula of the maximum absolute deviation G max is as follows:

[0112] G max = max(|G error，io - G avg |);

[0113] In the formula, G max represents the maximum absolute deviation in the gradient error trend graph; G error，io represents the data point values corresponding to sub-region i and sub-region o in the gradient error trend graph; max represents taking the maximum value from a set of data;

[0114] Extract the data of sub-region i and sub-region o corresponding to G max , and conduct secondary monitoring on sub-region i and sub-region o. Take the minimum value of the two monitoring results, and judge the growth status of the urban green space plants in this area according to the minimum value combined with the NDVI judgment standard.

[0115] For example, Region A, Region B, and Region C; Region A and B are adjacent regions, and Region B and C are also adjacent regions;

[0116] The plant remote sensing data obtained by actual remote sensing technology is:

[0117] A: 0.8;

[0118] B: 0.6;

[0119] C: 0.5;

[0120] The plant remote sensing data obtained by the remote sensing technology corresponding to the simulated mapping is:

[0121] A: 0.7;

[0122] B: 0.65;

[0123] C: 0.55;

[0124] Calculate the difference in plant remote sensing data between actual adjacent regions:

[0125] Region A - B: 0.8 - 0.6 = 0.2;

[0126] Region B - C: 0.6 - 0.5 = 0.1;

[0127] Calculate the difference in plant remote sensing data between simulated adjacent regions:

[0128] Region A - B: 0.7 - 0.65 = 0.05;

[0129] Region B - C: 0.65 - 0.55 = 0.1;

[0130] Calculation of regional growth gradient value:

[0131] G error (A,B) = 0.2 - 0.05 = 0.015;

[0132] G error (B,C) = 0.1 - 0.1 = 0;

[0133] Set the status exception threshold Z max = 0.1;

[0134] Then for Region A: 0.8 - 0.7 = 0.1 = Z max ;

[0135] X = Z max , it is determined that the growth status of urban green space plants in Region A does not meet the growth expectation in the plant growth simulation model, the growth status of urban green space plants in Region A is abnormal, and a growth status abnormal warning is issued;

[0136] Extract the maximum absolute deviation G max ;

[0137] The corresponding ones that meet the conditions are areas A - B. Conduct secondary monitoring on areas A and B, and the secondary monitoring data are respectively:

[0138] A: 0.75;

[0139] B: 0.6;

[0140] According to the NDVI judgment standard, it is judged that both areas A and B have high vegetation coverage: The NDVI value in this range usually indicates high - density vegetation, such as forests or thick bushes.

[0141] Embodiment 2, as Figure 2 shown, the present invention provides an urban green space plant growth status monitoring and analysis system. The growth status monitoring and analysis system includes a data acquisition module, a region management module, a growth simulation module, a set management module, a trend chart management module, and a status evaluation and early warning module;

[0142] The data acquisition module is used for data information acquisition; the region management module is used for dividing according to the location distribution characteristics of urban green space plants and constructing coordinates for marking; the growth simulation module is used for training by inputting the collected data into a neural network structure to simulate the growth of urban green space plants; the set management module is used for managing the addition and deletion of data in each set; the trend chart management module is used for constructing a corresponding trend chart according to the set; the status evaluation and early warning module is used for judging the growth status according to the trend chart and performing early warning processing on the areas that meet the early warning conditions;

[0143] The output end of the data acquisition module is electrically connected to the input end of the region management module; the output end of the region management module is electrically connected to the input end of the growth simulation module; the output end of the growth simulation module is electrically connected to the input end of the set management module; the output end of the set management module is electrically connected to the input end of the trend chart management module; the output end of the trend chart management module is electrically connected to the input end of the status evaluation and early warning module.

[0144] The data acquisition module includes an environmental factor acquisition unit and a remote sensing acquisition unit; the environmental factor acquisition unit is used for acquiring light intensity, soil humidity, temperature, and atmospheric carbon dioxide concentration; the remote sensing acquisition unit is used for obtaining plant remote sensing data of each sub - region by using remote sensing technology;

[0145] The region management module includes a region division unit and a coordinate marking unit; the region division unit is used for dividing regions according to the location distribution of urban green space plants; the coordinate marking unit is used for marking the location of the region;

[0146] The growth simulation module includes a model training unit and a simulated growth unit; the model training unit is used to input data into a neural network structure for training to construct a plant growth simulation model; the simulated growth unit is used to simulate growth according to the input parameters.

[0147] The set management module includes an actual set unit and a simulated set unit; the actual set unit is used to store the actually collected data; the simulated set unit is used to store the data generated by simulated growth;

[0148] The trend chart management module includes a direct trend unit and an adjacent difference trend chart unit; the direct trend unit is used to construct a trend chart of the gap between actual and simulated growth; the adjacent difference trend chart unit is used to construct a trend chart according to the difference between two adjacent sub-regions in the difference between actual collection and simulated mapping;

[0149] The status evaluation and early warning module includes a status evaluation unit and an early warning unit; the status evaluation unit is used to evaluate the growth status according to the collected data; the early warning unit is used to give an early warning of the growth status of urban green space plants in the areas that meet the early warning conditions.

[0150] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed claims.

Claims

1. A method for monitoring and analyzing the growth status of urban green space plants, characterized in that: The growth status monitoring and analysis method comprises the following steps: Step S1, dividing the area according to the distribution characteristics of urban green space plants, and establishing corresponding coordinate marks based on the divided areas; Step S2: comprehensively collect the growth status data of urban green space plants in each area according to the divided and marked areas, and record the plant characteristics and health status corresponding to each area; Step S3: Using the fully collected urban green space plant growth status data, a plant growth simulation model is constructed through a neural network, and an ideal environment growth model is constructed based on preset ideal environmental parameters. The plant growth simulation model is used to simulate the growth status of the corresponding collected data in each area, and key environmental factor data are collected regularly; Step S4, setting a plant remote sensing acquisition cycle, constructing a remote sensing monitoring set, a simulated mapping remote sensing set and an ideal environment simulated mapping remote sensing set, using remote sensing technology to monitor each area marked with coordinates according to the plant remote sensing acquisition cycle, and storing the plant remote sensing data actually collected by remote sensing technology in the remote sensing monitoring set, while storing the remote sensing data generated by the synchronized real environment plant growth model in the simulated mapping remote sensing set, and storing the remote sensing data generated by the ideal environment plant growth model in the ideal environment simulated mapping remote sensing set; Step S5, setting the environmental monitoring threshold, obtaining the deviation value between the actual plant remote sensing data of each region and the remote sensing data generated by the plant growth simulation model, and recording it as the simulated growth deviation value; at the same time, obtaining the deviation value between the remote sensing monitoring set and the ideal environment simulation mapping remote sensing set, and recording it as the ideal environment deviation value, comparing the remote sensing data difference of the actual adjacent sub-regions with the remote sensing data difference of the synchronous corresponding region, forming a regional growth gradient value, summarizing the simulated growth deviation value and the regional growth gradient value, constructing a growth deviation trend graph and a gradient error trend graph, quantifying the influence of environmental factors on plant growth according to the ideal environment deviation value, selecting the maximum influencing factor, and performing data warning on the growth deviation trend graph in combination with the environmental monitoring threshold; Step S6, set the abnormal status threshold, combine the growth deviation trend chart to warn the growth status of the monitored urban green plants, extract the maximum deviation absolute value in the gradient error trend chart, determine the corresponding area according to the maximum deviation absolute value, and conduct secondary monitoring, and judge the growth status of the urban green plants in the area according to the data of the secondary monitoring.

2. The method for monitoring and analyzing the growth status of urban green space plants according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1, dividing the urban green space into multiple sub-areas according to the location distribution characteristics of urban green space plants, combined with plant density and environmental conditions; Step S1-2: according to the divided multiple sub-regions, a coordinate mark is set for each sub-region using a geographic coordinate system, a primary coordinate mark is set at the geometric center point of each sub-region, and a secondary coordinate mark is set at the boundary corner point.

3. The method for monitoring and analyzing the growth status of urban green space plants according to claim 2, characterized in that: In step S2, the growth status data of urban green space plants are comprehensively collected for each divided sub-area. The comprehensively collected data include growth characteristic data, environmental factor data, plant health data and remote sensing data, and the plant characteristic data and health status data corresponding to each sub-area are stored and recorded.

4. The method for monitoring and analyzing the growth status of urban green space plants according to claim 3, characterized in that: The specific steps of step S3 are as follows: Step S3-1, inputting growth characteristic data, environmental factor data, plant health data and remote sensing data into a neural network structure for training, and constructing a plant growth simulation model and an ideal environment growth model; Step S3-2, setting a key environmental factor data collection period Q, acquiring key environmental factor data by deploying sensors in each sub-area, wherein the key environmental factor data includes light intensity, soil moisture, temperature and atmospheric carbon dioxide concentration, and inputting the key environmental factor data acquired in each collection period into a plant growth simulation model to simulate growth; Step S3-3: Replace the set key environmental factor data with standard data under ideal environmental conditions, and input the ideal environmental growth model to simulate growth.

5. The method for monitoring and analyzing the growth status of urban green space plants according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1, setting a plant remote sensing acquisition cycle T, constructing a remote sensing monitoring set, a simulated mapping remote sensing set and an ideal environment simulated mapping remote sensing set, wherein the remote sensing monitoring set is used to store plant remote sensing data actually collected by remote sensing technology, the simulated mapping remote sensing set is used to store plant remote sensing data mapped by a plant growth simulation model, and the ideal environment simulated mapping remote sensing set is used to store plant remote sensing data mapped by an ideal environment growth model; Step S4-2: monitor each sub-area marked with coordinates using remote sensing technology according to the plant remote sensing collection cycle; Step S4-3, store the plant remote sensing data of each sub-area collected by actual remote sensing technology into the remote sensing monitoring set, store the plant remote sensing data generated in the corresponding area in the plant growth simulation model into the simulation mapping remote sensing set, and store the plant remote sensing data generated in the corresponding area in the ideal environment growth model into the ideal environment simulation mapping remote sensing set.

6. The method for monitoring and analyzing the growth status of urban green space plants according to claim 5, characterized in that: The specific steps of step S5 are as follows: Step S5-1, according to the remote sensing monitoring set, the simulated mapping remote sensing set and the ideal environment simulated mapping remote sensing set, obtain the deviation value between the actual plant remote sensing data of each sub-region and the plant remote sensing data generated by the plant growth simulation model, recorded as the simulated growth deviation value M, obtain the deviation value between the actual plant remote sensing data of each sub-region and the plant remote sensing data generated by the ideal environment growth model, recorded as the ideal environment deviation value; Step S5-2, summarizing the simulated growth deviation values ​​M of each sub-region within the plant remote sensing acquisition period T to construct a growth deviation trend graph; Step S5-3: According to the remote sensing monitoring set and the simulated mapping remote sensing set, the plant remote sensing data difference between each adjacent sub-region and the remote sensing data difference of the synchronous corresponding region in the plant growth simulation model are obtained, and the two differences are compared to form the regional growth gradient value G error , the difference calculation formula of plant remote sensing data in actual adjacent sub-areas is as follows: G actual =R actual，i -R actual，o ; In the formula, G actual It is expressed as the difference of plant remote sensing data in actual adjacent sub-areas; R actual，i Represented as the actual plant remote sensing data value of sub-region i; R actual，o It is represented by the actual plant remote sensing data value of sub-area o; The difference calculation formula of plant remote sensing data in adjacent sub-areas in the plant growth simulation model is as follows: G model =R model，i -R model，o ; In the formula, G model It is expressed as the difference between the plant remote sensing data in the corresponding adjacent sub-areas in the plant growth simulation model; R model，i Represented as the plant remote sensing data value of sub-area i in the plant growth simulation model; R model，o It is represented by the plant remote sensing data value of sub-area o in the plant growth simulation model; Region growth gradient value G error The calculation formula is as follows: G error =|G actual -G model |; In the formula, G error It is expressed as the difference of plant remote sensing data between actual adjacent sub-regions and the difference of remote sensing data of synchronous corresponding regions in the plant growth simulation model; Step S5-4, summarizing the regional growth gradient values ​​J obtained within the plant remote sensing acquisition period T to construct a gradient error trend graph; Step S5-5, according to the ideal environmental deviation value of each sub-region within the plant remote sensing acquisition period T, quantify the specific impact of actual environmental factors on plant growth, the actual environmental factors include light intensity, soil moisture, temperature and atmospheric carbon dioxide concentration, and by comparing the growth performance under actual environmental conditions with that under ideal environmental conditions, select the environmental factor with the largest impact value as the key environmental monitoring object; Step S5-6: Compare the actual value of the key environmental monitoring object with the environmental monitoring threshold: When the value actually obtained for the key environmental monitoring object is less than the environmental monitoring threshold, it is judged that the data fluctuation of the growth deviation trend graph is in a normal state; When the value actually obtained by the key environmental monitoring object is greater than or equal to the environmental monitoring threshold, it is judged that the data fluctuation of the growth deviation trend chart is normal, and an abnormal signal of the key environmental monitoring object is issued.

7. The method for monitoring and analyzing the growth status of urban green space plants according to claim 6, characterized in that: The specific steps of step S6 are as follows: Step S6-1: Setting the abnormal state threshold Z max , obtain the data point value in the growth deviation trend chart, record it as the deviation value X, and combine it with the state abnormal threshold Z max To judge and warn the growth status of the monitored urban green space plants; When X<Z max When the growth state of the monitored urban green space plants is in line with the growth expectations in the plant growth simulation model, the growth state of the urban green space plants is normal; When X ≥ Z max When the monitored urban green space plant growth status does not meet the growth expectations in the plant growth simulation model, the urban green space plant growth status is abnormal, and an abnormal growth status warning is issued; Step S6-2: Gradient error trend graph, extract the maximum deviation absolute value G max , according to G max Determine the corresponding regional location and conduct secondary remote sensing monitoring of the area. Compare the data collected by the secondary monitoring with the previous monitoring data to determine whether there is an abnormality or continuous deviation in the growth status of the plant. The average value of each data point in the gradient error trend graph is calculated as follows: In the formula, G avg It is represented by the average value of each data point in the gradient error trend graph; N is represented by the difference in plant remote sensing data between each adjacent sub-region and the number of remote sensing data differences in the synchronous corresponding regions in the plant growth simulation model; Maximum absolute value of deviation G max The calculation formula is as follows: G max =max(|G error，io -G avg |); In the formula, G max It is expressed as the maximum absolute value of the deviation in the gradient error trend graph; G error，io It is represented by the corresponding data point values ​​of sub-area i and sub-area o in the gradient error trend graph; max represents taking the maximum value from a set of data; Extraction of G max The corresponding sub-area i and sub-area o data are collected, and sub-area i and sub-area o are monitored twice, the minimum value of the two monitorings is taken out, and the growth status of urban green plants in the area is judged based on the minimum value combined with the NDVI judgment standard.

8. A system for monitoring and analyzing the growth status of urban green space plants, which is applied to a method for monitoring and analyzing the growth status of urban green space plants as claimed in any one of claims 1 to 7, characterized in that: The growth status monitoring and analysis system includes a data acquisition module, a regional management module, a growth simulation module, a collection management module, a trend chart management module and a status assessment and early warning module; The data acquisition module is used for data information collection; the area management module is used for dividing urban green space plants according to their location distribution characteristics and constructing coordinates for marking; the growth simulation module is used for training according to the collected data input into the neural network structure to simulate the growth of urban green space plants; the collection management module is used to manage the addition and deletion of data in each collection; The trend graph management module is used to construct a trend graph corresponding to the set framework; the state assessment and early warning module is used to judge the growth state according to the trend graph and perform early warning processing on the areas that meet the early warning conditions; The output end of the data acquisition module is electrically connected to the input end of the area management module; the output end of the area management module is electrically connected to the input end of the growth simulation module; the output end of the growth simulation module is electrically connected to the input end of the collection management module; the output end of the collection management module is electrically connected to the input end of the trend chart management module; the output end of the trend chart management module is electrically connected to the input end of the status assessment and early warning module.

9. The urban green space plant growth status monitoring and analysis system according to claim 8, characterized in that: The data acquisition module includes an environmental factor acquisition unit and a remote sensing acquisition unit; the environmental factor acquisition unit is used to collect light intensity, soil moisture, temperature and atmospheric carbon dioxide concentration; the remote sensing acquisition unit is used to obtain plant remote sensing data of each sub-area using remote sensing technology; The area management module includes an area division unit and a coordinate marking unit; the area division unit is used to divide the area according to the location distribution of urban green space plants; the coordinate marking unit is used to mark the location of the area; The growth simulation module includes a model training unit and a simulated growth unit; the model training unit is used to input data into a neural network structure for training to construct a plant growth simulation model; the simulated growth unit is used to simulate growth according to the input parameters.

10. The urban green space plant growth status monitoring and analysis system according to claim 8, characterized in that: The collection management module includes an actual collection unit and a simulation collection unit; the actual collection unit is used to store the data actually collected and acquired; the simulation collection unit is used to store the data generated by the simulated growth; The trend graph management module includes a direct trend unit and an adjacent difference trend graph unit; the direct trend unit is used to construct a difference trend graph between actual and simulated growth; The adjacent difference trend graph unit is used to construct a trend graph according to the difference between the actual acquisition and the simulated mapping of the difference between two adjacent sub-regions; The state evaluation and early warning module includes a state evaluation unit and an early warning unit; the state evaluation unit is used to evaluate the growth state according to the collected data; the early warning unit is used to perform early warning processing on the growth state of urban green space plants in the area that meets the early warning conditions.

Images