Garden engineering data acquisition management system and method

Abstract

The invention discloses a garden engineering data acquisition management system and method, and relates to the field of digital data processing, the garden engineering data acquisition management system comprises a data acquisition layer, an edge calculation layer and a cloud platform layer, the data acquisition layer comprises an unmanned aerial vehicle-mounted multispectral imaging module, a ground sensor array and a meteorological monitoring station; the edge calculation layer comprises a data cleaning unit, a feature extraction unit and a dynamic clustering unit, the dynamic clustering unit adopts a DBSCAN algorithm to realize data clustering, a core parameter epsilon of the dynamic clustering unit is dynamically calculated through a formula epsilon = 1.5 * Dmedian, and Dmedian is a median of distances among all objects in a current data set; the cloud platform layer comprises a multi-dimensional data warehouse, an intelligent analysis module and a visual terminal; according to the invention, three-dimensional data collection of air, sky and land is realized, garden engineering data can be collected and analyzed more comprehensively and accurately, plant growth abnormity and pest and disease damage conditions can be found in time, accurate decision support is provided for maintenance management, and the maintenance quality and efficiency of garden engineering are effectively improved.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of digital data processing, and in particular to a garden engineering data collection management system and method. Background Art

[0002] With the acceleration of urbanization, garden projects play an important role in improving the urban ecological environment and enhancing the quality of life of residents. Accurate soil parameter monitoring is a key link in garden project management. It can provide a scientific basis for plant growth and ensure the healthy growth of garden plants.

[0003] At present, in the existing garden monitoring system, there are problems such as single data type, difficulty in integrating heterogeneous data, and slow response to abnormal detection. Incomplete data collection may lead to misjudgment of plant growth conditions, delays in analysis and processing may delay the prevention and control of pests and diseases, and inaccurate decision support may lead to waste of resources. At the same time, traditional methods rely on fixed threshold judgments, which are difficult to adapt to complex and changing environments and cannot realize intelligent identification of plant growth status. Therefore, there is an urgent need for a garden engineering monitoring system that can integrate multi-source data, realize intelligent analysis and accurate decision-making. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a garden engineering data collection management system and method to solve the technical problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A garden engineering data collection and management system, comprising a data collection layer, an edge computing layer and a cloud platform layer, wherein the data collection layer comprises an unmanned aerial vehicle multispectral imaging module, a ground sensor array and a meteorological monitoring station, wherein the unmanned aerial vehicle multispectral imaging module is configured with an optical lens with a resolution of 0.5 cm and a five-band spectral sensor, and is used to obtain high-resolution multispectral image data of a garden area, which can reflect the growth status of plants and information on pests and diseases; The edge computing layer includes a data cleaning unit, a feature extraction unit and a dynamic clustering unit. The data cleaning unit cleans the data to remove noise and abnormal data. The feature extraction unit extracts 23 feature parameters from the cleaned data. The dynamic clustering unit uses the DBSCAN algorithm to implement data clustering. Its core parameter ε is dynamically calculated by the formula ε=1.5×D_median, where D_median is the median of the distances between all objects in the current data set. The cloud platform layer includes a multidimensional data warehouse, an intelligent analysis module and a visualization terminal, wherein the multidimensional data warehouse adopts a dual storage architecture of a time series database and a spatial database, and the data index accuracy reaches 0.1 meter level.

[0006] Specifically, the ground sensor array deployment density is 3-5 nodes per 50 square meters, and the LoRaWAN protocol is used to build an ad hoc network between each sensor node, with a communication distance of ≥500 meters and a data collection frequency of 5-30 minutes / time; The meteorological monitoring station collects meteorological data and uploads it to the cloud platform layer via a wireless network.

[0007] Specifically, the operations performed by the dynamic clustering unit include: a. For the eigenvector To standardize the process, to Contains 23 characteristic parameters including NDVI vegetation index, soil conductivity, and canopy temperature; b. Use Mahalanobis distance to calculate the similarity matrix between data objects to eliminate the influence of different dimensions; c. Through density accessibility analysis, the monitoring area is divided into three clusters: healthy growth area, maintenance area and disease warning area.

[0008] Specifically, the intelligent analysis module includes a growth trend prediction model and a pest and disease identification model. The growth trend prediction model uses an LSTM neural network to process time series data. The input layer has feature data of 12 time steps, and the hidden layer is set with 64 neurons. The pest and disease recognition model implements leaf image classification based on the ResNet-50 architecture, adopts a transfer learning method during training, and performs fine-tuning based on the ImageNet pre-training model.

[0009] The technical solution is specific, the ground sensor array is composed of a circular mounting plate, an internal lifting plate, a soil temperature and humidity sensor, a PH value sensor and an EC value sensor installed on the lifting plate, and a cleaning mechanism for cleaning and drying the sensor detection end, the soil temperature and humidity sensor, the PH value sensor and the EC value sensor are arranged in a triangle and the detection ends all pass through the lifting plate, the upper surface of the circular mounting plate is installed with a protective shell by screws, the lower surface of the circular mounting plate is installed with a protective shell by screws, a sliding L-shaped closing plate is symmetrically provided at the movable through groove at the bottom end of the protective shell, an electric push rod is installed on the inner top wall of the protective shell by screws, and the telescopic end of the electric push rod is fixed with screws on the upper surface of the lifting plate; The cleaning mechanism includes two linkage components, a cleaning component and a mounting shell arranged on the end face of the protective shell. The linkage components are respectively connected to the lifting plate and the L-shaped closing plate. The linkage components are used to control the L-shaped closing plate to move following the lifting and lowering of the lifting plate. The cleaning component is used to flush the residual soil at the sensor detection end. An electric heating tube is installed in the interior of the mounting shell that is connected to the protective shell, and a fan is symmetrically embedded in the end face of the mounting shell.

[0010] Specifically, each linkage assembly includes an L-shaped bracket fixed to one side of the upper surface of the lifting plate, a horizontal guide rod is installed at the inner bottom of the protective shell, the bottom of the L end of the L-shaped bracket is connected to the L end of the L-shaped closing plate through a pull rope, the pull rope contacts the outer wall of the guide rod through the guide rod, and the pull rope passes through the side wall of the protective shell; L-shaped protective plates are fixed to both side walls of the circular mounting plate, and horizontal abutment plates are fixed to the L ends of the L-shaped protective plates. The lower surface of the L-shaped closing plate is in contact with the upper surface of the abutment plate and is slidably connected. Sliding rods are symmetrically fixed between the L-shaped protective plate and the protective shell, and the L ends of the L-shaped closing plates are sleeved on the sliding rods and slidably connected thereto, and each of the sliding rods is sleeved with a return spring.

[0011] Specifically, the inner top of the protective shell is located outside the electric push rod and a water tank is fixed thereon, and the lower surface of the lifting plate is located at the sensor detection end and a storage groove is opened, and the three storage grooves are connected by a strip groove; The cleaning assembly includes an annular plate located in the storage groove, the three annular plates are connected by connecting plates, each of the connecting plates is arranged in the strip groove, a number of nozzles are evenly installed on the inner ring walls of the three annular plates, three water pumps are installed inside the water tank, the output port of each water pump is connected to a water supply pipe, and two telescopic tubes are connected below each water supply pipe, the two telescopic tubes pass through the water tank and the lifting plate and are connected to the inner cavity of the annular plate, three electric telescopic cylinders are installed on the upper surface of the lifting plate and are arranged in a triangle, and the telescopic ends of the three electric telescopic cylinders are fixed to the connecting plate screws.

[0012] According to the above technical scheme, a method for garden engineering data collection and management system is also provided, comprising the following steps: Step 1: Space grid division: divide the monitoring area into 50m×50m grids, and deploy 3-5 ground sensor arrays in each grid; Step 2: Multimodal data collection: The multispectral imaging module on the drone performs aerial photography once a week, with an adjustable flight altitude of 30-100 meters. The ground sensor array collects environmental data every 15 minutes, and the meteorological monitoring station uploads real-time data every minute. Step 3: Dynamic clustering analysis: After normalizing the collected data, the density peak clustering algorithm is used to generate the initial cluster center, and the final clustering result is determined through iterative optimization, and the clustering validity index DBI < 0.6 is set as the qualified threshold; Step 4: Decision output: Generate a three-dimensional heat map based on the clustering results, automatically mark the coordinates of the areas that require manual intervention, and send control instructions to the maintenance equipment through the MQTT protocol.

[0013] Specifically, the specific process of the dynamic clustering analysis in step 3 of this technical solution includes: a. Construct feature vector space: , where T is temperature, H is humidity, NDVI is normalized difference vegetation index, and EC is soil electrical conductivity; b. Calculate local density , where d_c is the cutoff distance, taking the 15% quantile of all pairwise distances in the data set; c. Determine the cluster center: select the point with local density ρ_i>ρ_mean and δ_i>δ_mean as the initial center, where δ_i represents the minimum distance from the point to a higher density point; d. Assign the remaining points: assign the non-central points to the cluster with higher density and closest distance to the central point; e. Anomaly detection: The points with density values ​​2 standard deviations lower than the overall mean are marked as abnormal data.

[0014] In summary, the present invention has the following beneficial effects: the present invention realizes three-dimensional data collection from the air, sky and ground, can collect and analyze garden engineering data more comprehensively and accurately, timely discover plant growth abnormalities and pests and diseases, provide accurate decision support for maintenance management, and effectively improve the maintenance quality and efficiency of garden engineering; In order to prevent the detection ends of the soil temperature and humidity sensors, pH sensors and EC value sensors of the ground sensor array from being inserted into the soil for a long time, which would lead to a decrease in measurement accuracy, regular cleaning is required. During cleaning, the lifting plate is first controlled to move up by the electric push rod to separate the detection end of the sensor from the soil. At the same time, driven by the pull rope, the L-shaped closing plate moves along with the upward movement of the lifting plate. After the lifting plate is moved up and reset, the two L-shaped closing plates are in close contact to seal the protective shell. Then the electric telescopic cylinder drives the annular plate downward through the connecting plate, and the water pump is started. The nozzle flushes the outer wall of the sensor detection end to flush down the attached soil and impurities. The water and impurities fall down after the subsequent L-shaped closing plate is opened, and then the fan and electric heating tube are started. The introduced air is heated and blown to the sensor detection end for drying, thereby effectively removing the soil residue on the sensor surface, reducing the wear and corrosion on the sensor surface, and extending the service life of the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a system framework diagram of the present invention; Figure 2 It is a schematic diagram of the structure of the ground sensor array of the present invention; Figure 3 For the present invention Figure 2 Schematic diagram of the axonometric section; Figure 4 For the present invention Figure 3 Oblique axonometric diagram; Figure 5 It is a schematic diagram of the isometric structure of the cleaning mechanism of the present invention; Figure 6 For the present invention Figure 5 Main view structure diagram; Figure 7 It is a schematic diagram of the lifting plate structure when viewed from above of the present invention; Figure 8 It is a diagram of the method steps of the present invention.

[0016] Description of the drawings: 1. Data acquisition layer; 101. UAV-mounted multispectral imaging module; 102. Ground sensor array; 103. Meteorological monitoring station; 2. Edge computing layer; 201. Data cleaning unit; 202. Feature extraction unit; 203. Dynamic clustering unit; 3. Cloud platform layer; 301. Multidimensional data warehouse; 302. Intelligent analysis module; 3021. Growth trend prediction model; 3022. Pest and disease identification model; 303. Visual terminal; 4. Retractable mounting plate; 401. Protective shell; 402. Protective shell; 4021. L-shaped closing plate; 403. Water tank; 404. Electric push rod; 405. Support leg; 5. Lifting plate; 501. Soil temperature and humidity sensor; 502. PH value sensor; 503. EC value sensor; 504. Storage slot; 5041. Strip slot; 6. Cleaning mechanism; 7. Linkage assembly; 701. L-shaped bracket; 702. Pull rope; 703. Guide rod; 704. L-shaped protection plate; 7041. Slide rod; 7042. Return spring; 705. Abutment plate; 8. Cleaning assembly; 801. Water pump; 802. Water supply pipe; 803. Telescopic pipe; 804. Ring plate; 8041. Nozzle; 8042. Connecting plate; 805. Electric telescopic cylinder; 9. Mounting shell; 901. Electric heating tube; 902. Fan. DETAILED DESCRIPTION

[0017] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] The following describes an embodiment of the present invention based on its overall structure.

[0019] For examples, see Figure 1 As shown, a garden engineering data collection and management system includes a data collection layer 1, an edge computing layer 2 and a cloud platform layer 3. The data collection layer 1 includes an unmanned aerial vehicle multispectral imaging module 101, a ground sensor array 102 and a meteorological monitoring station 103. The unmanned aerial vehicle multispectral imaging module 101 is configured with an optical lens with a resolution of 0.5 cm and a five-band spectral sensor, which is used to obtain high-resolution multispectral image data of the garden area, which can reflect the growth status of plants and information on pests and diseases. The ground sensor array 102 has a deployment density of 3-5 nodes per 50 square meters. The LoRaWAN protocol is used to build an ad hoc network between each sensor node, and the communication distance is ≥500 meters. The data collection frequency is configured to be 5-30 minutes / time. The meteorological monitoring station 103 collects meteorological data and uploads it to the cloud platform layer 3 through a wireless network; The drone-mounted multispectral imaging module 101 uses a DJI Matrice 300 RTK equipped with a RedEdge-MX multispectral camera, which can obtain 0.5cm / pixel resolution images at a flight altitude of 80 meters, automatically perform grid flight according to the set time, and obtain multispectral image data of the garden area; The ground sensor array 102 deploys 4 nodes at the center point of each 50m×50m grid. The UWB positioning technology is used between the nodes. The deployment spacing is ≤200 meters, and the positioning error is <0.3 meters. The ground sensor array 102 is inserted into the soil through the supporting legs, and the detection ends of the soil temperature and humidity sensor 501, the pH value sensor 502 and the EC value sensor 503 are controlled to be inserted into the soil. The self-organizing network is constructed through the LoRaWAN protocol, and the collected soil temperature and humidity, pH value, EC value and other data are transmitted to the edge computing layer 2; The sampling frequency of meteorological monitoring station 103 is once per minute, and the data is uploaded to the cloud platform layer 3 in real time via 4G DTU; The edge computing layer 2 includes a data cleaning unit 201, a feature extraction unit 202, and a dynamic clustering unit 203. The data cleaning unit 201 cleans the data to remove noise and abnormal data. The feature extraction unit 202 extracts 23 feature parameters from the cleaned data. The dynamic clustering unit 203 uses the DBSCAN algorithm to implement data clustering. Its core parameter ε is dynamically calculated by the formula ε=1.5×D_median. D_median is the median of the distance between all objects in the current data set. The operations performed by the dynamic clustering unit 203 include: a. For the eigenvector To standardize the process, to Contains 23 characteristic parameters including NDVI vegetation index, soil conductivity, and canopy temperature; b. Use Mahalanobis distance to calculate the similarity matrix between data objects to eliminate the influence of different dimensions; c. Through density accessibility analysis, the monitoring area is divided into three clusters: healthy growth area, maintenance area and disease warning area; After the edge computing layer 2 receives the data, the data cleaning unit 201 first cleans the data using the sliding window method (window size = 10 sampling points). When three consecutive data points deviate from the moving average by ±2σ, anomaly removal is triggered. Then, the feature extraction unit 202 extracts 23 feature parameters from the cleaned data, such as NDVI vegetation index, soil conductivity, canopy temperature, etc. Then, the dynamic clustering unit 203 uses the DBSCAN algorithm to perform cluster analysis on the feature data and divides the monitoring area into healthy growth areas, maintenance areas, and disease warning areas. The adaptive ε value calculation: D_median is recalculated every 24 hours, and recalculation is automatically triggered when the environment changes suddenly (such as heavy rain); The cloud platform layer 3 includes a multidimensional data warehouse 301, an intelligent analysis module 302 and a visualization terminal 303. The multidimensional data warehouse 301 adopts a dual storage architecture of a time series database and a spatial database, and the data index accuracy reaches 0.1 meters. The intelligent analysis module 302 includes a growth trend prediction model 3021 and a pest and disease identification model 3022. The growth trend prediction model 3021 uses LSTM neural network to process time series data. The input layer has 12 time-step feature data and the hidden layer has 64 neurons. The pest and disease recognition model 3022 implements leaf image classification based on the ResNet-50 architecture. The transfer learning method is used during training and fine-tuned based on the ImageNet pre-training model. The cloud platform layer 3 receives the data and clustering results transmitted by the edge computing layer 2, and stores them in the multidimensional data warehouse 301, where the data storage includes: the InfluxDB time series database stores sensor data, PostgreSQL+PostGIS stores spatial data, and establishes a spatiotemporal joint index; The intelligent analysis module 302 further analyzes the data. The growth trend prediction model 3021 uses the LSTM neural network to predict the growth trend of the plant. The pest and disease identification model 3022 identifies the type of pest and disease based on the ResNet-50 architecture. The visualization terminal 303 displays the analysis results to the user in the form of intuitive charts, maps, etc., which facilitates the user to make decisions and management; Based on the clustering results, prediction and recognition results, the system generates a three-dimensional heat map and automatically marks the coordinates of the areas that require manual intervention. Maintenance personnel can carry out targeted maintenance and pest control in the areas that require maintenance and disease warning areas according to the system prompts, thereby improving the maintenance efficiency and effectiveness of garden projects. This enables three-dimensional data collection from the air, sky and ground, and enables more comprehensive and accurate collection and analysis of garden project data, timely detection of abnormal plant growth and pests and diseases, providing precise decision-making support for maintenance management, and effectively improving the maintenance quality and efficiency of garden projects.

[0020] See also Figure 2-4 As shown, the ground sensor array 102 consists of a circular mounting plate 4, an internal lifting plate 5, a soil temperature and humidity sensor 501, a pH value sensor 502 and an EC value sensor 503 installed on the lifting plate 5, and a cleaning mechanism 6 for cleaning and drying the sensor detection end. The four corners of the lower surface of the circular mounting plate 4 are fixed with support legs. The soil temperature and humidity sensor 501, the pH value sensor 502 and the EC value sensor 503 are arranged in a triangle and the detection ends all pass through the lifting plate 5. A protective shell 401 is installed on the upper surface of the circular mounting plate 4 by screws, and a protective shell 402 is installed on the lower surface of the circular mounting plate 4 by screws. A sliding L-shaped closing plate 4021 is symmetrically provided at the movable through groove at the bottom end of the protective shell 402. An electric push rod 404 is installed on the inner top wall of the protective shell 401 by screws, and the telescopic end of the electric push rod 404 is fixed with screws on the upper surface of the lifting plate 5. The cleaning mechanism 6 includes two linkage components 7, a cleaning component 8, and a mounting shell 9 arranged on the end face of the protective shell 402. The linkage component 7 is respectively connected to the lifting plate 5 and the L-shaped closing plate 4021. The linkage component 7 is used to control the L-shaped closing plate 4021 to move following the lifting of the lifting plate 5. The cleaning component 8 is used to flush the residual soil at the sensor detection end. The interior of the mounting shell 9 that is connected to the protective shell 402 is installed with an electric heating tube 901. The end face of the mounting shell 9 is symmetrically embedded with a fan 902. The outer wall of the protective shell 402 is screwed with a dust filter at the two fans 902. It should be noted that a solar panel and a controller are provided at the top of the protective shell 401. The controller is electrically connected to all electrical components in the device and is connected to the system via a wireless network.

[0021] When deploying the ground sensor array 102, after placing the device at the designated point, insert it into the ground through the four supporting legs to fix the position, and then start the electric push rod 404 through the controller, the telescopic end of the electric push rod 404 pushes the lifting plate 5 to move downward, and the lifting plate 5 drives the soil temperature and humidity sensor 501, the pH value sensor 502 and the EC value sensor 503 to move downward, and the two L-shaped closing plates 4021 are controlled to move to both sides through the linkage component 7, so that the moving through groove of the protective shell 402 is exposed to facilitate the removal of the lifting plate 5, until the detection ends of the three sensors are inserted into the soil and stop at 15 cm, and the deployment is completed; In order to prevent the detection ends of the soil temperature and humidity sensor 501, the pH value sensor 502 and the EC value sensor 503 from being inserted into the soil for a long time, resulting in a decrease in measurement accuracy, regular cleaning is required. During cleaning, the lifting plate 5 is first controlled to move up by the electric push rod 404, so that the detection ends of the three sensors are separated from the soil. At this time, under the action of the linkage component 7, the two L-shaped closing plates 4021 will gradually close until the lifting plate 5 moves up and resets. At this time, the ends of the two L-shaped closing plates 4021 are in close contact to block the moving groove of the protective shell 402, and then the cleaning component 8 works to clean the actuator. Move downward to flush the outer walls of the detection ends of the three sensors to flush away the attached soil and impurities. The flushing duration is 30-60 seconds, wherein the water and impurities fall down after the subsequent L-shaped closing plate 4021 is opened. After the flushing is completed, the fan 902 and the electric heating tube 901 work. The fan 902 introduces external air and blows it to the detection ends of the three sensors for drying after being heated by the electric heating tube 901. The drying time is 3 minutes, and the temperature of the electric heating tube 901 is 50°C, which can effectively remove the soil residue on the sensor surface, reduce the wear and corrosion on the sensor surface, and extend the service life of the sensor.

[0022] See also Figure 4-6 As shown, each linkage assembly 7 includes an L-shaped bracket 701 fixed to one side of the upper surface of the lifting plate 5, a horizontal guide rod 703 is installed at the inner bottom of the protective shell 402, and the bottom of the L end of the L-shaped bracket 701 is connected to the L end of the L-shaped closing plate 4021 through a pull rope 702, and the pull rope 702 contacts the outer wall of the guide rod 703, and the pull rope 702 passes through the side wall of the protective shell 402; L-shaped protective plates 704 are fixed to both side walls of the circular mounting plate 4, and horizontal abutment plates 705 are fixed to the L ends of the L-shaped protective plates 704. The lower surface of the L-shaped closing plate 4021 is in contact with the upper surface of the abutment plate 705 and is slidably connected. Slide rods 7041 are symmetrically fixed between the L-shaped protective plate 704 and the protective shell 402. The L end of the L-shaped closing plate 4021 is sleeved on the slide rod 7041 and is slidably connected to it, and each slide rod 7041 is sleeved with a return spring 7042.

[0023] When the lifting plate 5 moves downward, the L-shaped bracket 701 will be driven to move, and the downward moving L-shaped bracket 701 will relax the pull rope 702. Under the elasticity of the reset spring 7042, the reset spring 7042 drives the L-shaped closing plate 4021 to move along the sliding rod 7041 to the side away from the protective shell 402, and the L-shaped closing plate 4021 moves along the sliding rod 7041 and the abutment plate 705, and at the same time tightens the loose pull rope 702, thereby ensuring that when the lifting plate 5 moves downward, the two L-shaped closing plates 4021 are unfolded to both sides.

[0024] See also Figure 3 , Figure 4 and Figure 7 As shown, a water tank 403 is fixed to the inner top of the protective shell 401 outside the electric push rod 404, and a storage groove 504 is opened on the lower surface of the lifting plate 5 at the sensor detection end, and the three storage grooves 504 are connected by a strip groove 5041; The cleaning assembly 8 includes an annular plate 804 located in the storage groove 504, and the three annular plates 804 are connected by a connecting plate 8042, and each connecting plate 8042 is arranged in the strip groove 5041. The inner wall of the three annular plates 804 is evenly installed with a plurality of nozzles 8041. Three water pumps 801 are installed inside the water tank 403, and the output port of each water pump 801 is connected to a water supply pipe 802. Two telescopic tubes 803 are connected below each water supply pipe 802. The two telescopic tubes 803 pass through the water tank 403 and the lifting plate 5 and are connected to the inner cavity of the annular plate 804. Three electric telescopic cylinders 805 are installed on the upper surface of the lifting plate 5 and are arranged in a triangle. The telescopic ends of the three electric telescopic cylinders 805 are fixed with screws to the connecting plate 8042.

[0025] When cleaning the three sensor detection ends that have moved up and reset, the three electric telescopic cylinders 805 and the water pump 801 work. The water pump 801 sends the water in the water tank 403 into the inner cavity of the annular plate 804 through the water supply pipe 802 and the telescopic pipe 803, and sprays it out from the nozzle 8041. At this time, the telescopic end of the electric telescopic cylinder 805 pushes the connecting plate 8042 to move down out of the strip groove 5041, and the connecting plate 8042 will drive the storage groove 504 at the lower position of the annular plate 804, and then the three annular plates 804 move along the detection ends of the soil temperature and humidity sensor 501, the pH value sensor 502 and the EC value sensor 503 respectively, and the water sprayed from the nozzle 8041 flushes the outer wall of the sensor detection end that passes by, and washes away the soil and impurities attached to the outer wall.

[0026] See also Figure 8 As shown, according to the above embodiment, a method for garden engineering data collection and management system is also provided, comprising the following steps: Step 1: spatial grid division: the monitoring area is divided into 50m×50m grids, and 3-5 ground sensor arrays 102 are deployed in each grid; Step 2: Multimodal data collection: The multispectral imaging module 101 carried by the drone performs aerial photography once a week, and the flight altitude is adjustable from 30 to 100 meters. The ground sensor array 102 collects environmental data every 15 minutes, and the meteorological monitoring station 103 uploads real-time data every minute. Step 3: Dynamic clustering analysis: After normalizing the collected data, the density peak clustering algorithm is used to generate the initial cluster center, and the final clustering result is determined through iterative optimization, and the clustering validity index DBI < 0.6 is set as the qualified threshold; Step 4: Decision output: Generate a three-dimensional heat map based on the clustering results, automatically mark the coordinates of the area that requires manual intervention, and send control instructions to the maintenance equipment through the MQTT protocol; The specific process of dynamic cluster analysis described in step 3 includes: a. Construct feature vector space: , where T is temperature, H is humidity, NDVI is normalized difference vegetation index, and EC is soil electrical conductivity; b. Calculate local density , where d_c is the cutoff distance, taking the 15% quantile of all pairwise distances in the data set; c. Determine the cluster center: select the point with local density ρ_i>ρ_mean and δ_i>δ_mean as the initial center, where δ_i represents the minimum distance from the point to a higher density point; d. Assign the remaining points: assign the non-central points to the cluster with higher density and closest distance to the central point; e. Anomaly detection: The points with density values ​​2 standard deviations lower than the overall mean are marked as abnormal data.

[0027] The working principle of the present invention is: When carrying out garden management, a 50m×50m grid is used, and 4 nodes are deployed at the center point of each grid. After the circular mounting plate 4 is placed at the designated point, it is inserted into the ground through four supporting legs to fix the position, and then the electric push rod 404 is started by the controller. The telescopic end of the electric push rod 404 pushes the lifting plate 5 downward, and the lifting plate 5 drives the soil temperature and humidity sensor 501, the pH value sensor 502 and the EC value sensor 503 to move downward, and the two L-shaped closing plates 4021 are controlled to move to both sides through the linkage component 7, so that the moving through groove of the protective shell 402 is exposed to facilitate the removal of the lifting plate 5, until the detection ends of the three sensors are inserted into the soil 15cm and stop, the deployment is completed, and the collected soil temperature and humidity, pH value, EC value and other data are transmitted to the edge computing layer 2; The multispectral imaging module 101 carried by the drone performs grid flight to obtain multispectral image data of the garden area, and the data sampled by the meteorological monitoring station 103 is uploaded to the cloud platform layer 3; After the edge computing layer 2 receives the data, the data cleaning unit 201 first cleans the data. When three consecutive data points deviate from the moving average by ±2σ, anomaly removal is triggered. Then, the feature extraction unit 202 extracts 23 feature parameters from the cleaned data, such as NDVI vegetation index, soil conductivity, canopy temperature, etc. Then, the dynamic clustering unit 203 uses the DBSCAN algorithm to perform cluster analysis on the feature data and divide the monitoring area into healthy growth area, maintenance area and disease warning area; The cloud platform layer 3 receives the data and clustering results transmitted by the edge computing layer 2, and stores them in the multidimensional data warehouse 301, where the data storage includes: the InfluxDB time series database stores sensor data, PostgreSQL+PostGIS stores spatial data, and establishes a spatiotemporal joint index; The intelligent analysis module 302 further analyzes the data. The growth trend prediction model 3021 uses the LSTM neural network to predict the growth trend of the plant. The pest and disease identification model 3022 identifies the type of pest and disease based on the ResNet-50 architecture. The visualization terminal 303 displays the analysis results to the user in the form of intuitive charts, maps, etc., which facilitates the user to make decisions and management; Based on the clustering results, prediction and identification results, the system generates a three-dimensional heat map and automatically marks the coordinates of areas requiring manual intervention. Maintenance personnel carry out targeted maintenance and pest control in areas requiring maintenance and disease warning areas based on system prompts.

[0028] Although an embodiment of the present invention has been shown and described, this specific embodiment is merely an explanation of the present invention and is not a limitation of the invention. The specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions and variations to the embodiments without creative contributions as needed without departing from the principles and purpose of the present invention. However, as long as they are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A garden engineering data collection and management system, comprising a data collection layer (1), an edge computing layer (2) and a cloud platform layer (3), characterized in that: The data collection layer (1) comprises an unmanned aerial vehicle-mounted multispectral imaging module (101), a ground sensor array (102) and a meteorological monitoring station (103); the unmanned aerial vehicle-mounted multispectral imaging module (101) is equipped with an optical lens with a resolution of 0.5 cm and a five-band spectral sensor, and is used to obtain high-resolution multispectral image data of a garden area, which can reflect the growth status of plants and information on pests and diseases; The edge computing layer (2) comprises a data cleaning unit (201), a feature extraction unit (202) and a dynamic clustering unit (203). The data cleaning unit (201) cleans the data to remove noise and abnormal data. The feature extraction unit (202) extracts 23 feature parameters from the cleaned data. The dynamic clustering unit (203) uses the DBSCAN algorithm to implement data clustering. Its core parameter ε is expressed by the formula Dynamic calculation, D_median is the median of the distances between all objects in the current data set; The cloud platform layer (3) comprises a multidimensional data warehouse (301), an intelligent analysis module (302) and a visualization terminal (303), wherein the multidimensional data warehouse (301) adopts a dual storage architecture of a time series database and a spatial database, and the data index accuracy reaches 0.1 meter level.

2. A garden engineering data collection and management system according to claim 1, characterized in that: The deployment density of the ground sensor array (102) is 3-5 nodes per 50 square meters, and the LoRaWAN protocol is used to build an ad hoc network between the sensor nodes, the communication distance is ≥500 meters, and the data collection frequency is configured to be 5-30 minutes / time; The meteorological monitoring station (103) collects meteorological data and uploads it to the cloud platform layer (3) via a wireless network.

3. A garden engineering data collection and management system according to claim 1, characterized in that: The operations performed by the dynamic clustering unit (203) include: a. For the eigenvector To standardize the process, to Contains 23 characteristic parameters including NDVI vegetation index, soil conductivity, and canopy temperature; b. Use Mahalanobis distance to calculate the similarity matrix between data objects to eliminate the influence of different dimensions; c. Through density accessibility analysis, the monitoring area is divided into three clusters: healthy growth area, maintenance area and disease warning area.

4. A garden engineering data collection and management system according to claim 1, characterized in that: The intelligent analysis module (302) comprises a growth trend prediction model (3021) and a pest and disease identification model (3022), wherein the growth trend prediction model (3021) uses an LSTM neural network to process time series data, the input layer has feature data of 12 time steps, and the hidden layer is provided with 64 neurons; The pest and disease identification model (3022) implements leaf image classification based on the ResNet-50 architecture, adopts a transfer learning method during training, and performs fine-tuning based on the ImageNet pre-training model.

5. The garden engineering data collection and management system according to claim 1 is characterized by: The ground sensor array (102) is composed of a circular mounting plate (4), an internal lifting plate (5), a soil temperature and humidity sensor (501), a pH value sensor (502) and an EC value sensor (503) mounted on the lifting plate (5), and a cleaning mechanism (6) for cleaning and drying the sensor detection ends; the soil temperature and humidity sensor (501), the pH value sensor (502) and the EC value sensor (503) are arranged in a triangular shape and the detection ends all pass through the lifting plate (5); a protective shell (401) is mounted on the upper surface of the circular mounting plate (4) by screws; a protective shell (402) is mounted on the lower surface of the circular mounting plate (4) by screws; a sliding L-shaped closing plate (4021) is symmetrically provided at the movable through groove at the bottom end of the protective shell (402); an electric push rod (404) is mounted on the inner top wall of the protective shell (401) by screws; and the telescopic end of the electric push rod (404) is fixed to the upper surface of the lifting plate (5) by screws; The cleaning mechanism (6) comprises two linkage components (7), a cleaning component (8) and a mounting shell (9) arranged on the end surface of the protective shell (402); the linkage component (7) is connected to the lifting plate (5) and the L-shaped closing plate (4021) respectively; the linkage component (7) is used to control the L-shaped closing plate (4021) to move following the lifting and lowering of the lifting plate (5); the cleaning component (8) is used to flush the residual soil at the sensor detection end; an electric heating tube (901) is installed in the interior of the mounting shell (9) that is in communication with the protective shell (402); and a fan (902) is symmetrically embedded in the end surface of the mounting shell (9).

6. A garden engineering data collection and management system according to claim 5, characterized in that: Each linkage assembly (7) comprises an L-shaped bracket (701) fixed to one side of the upper surface of the lifting plate (5); a horizontal guide rod (703) is installed at the inner bottom of the protective shell (402); the bottom of the L end of the L-shaped bracket (701) is connected to the L end of the L-shaped closing plate (4021) via a pull rope (702); the pull rope (702) contacts the outer wall of the guide rod (703) through the guide rod (703), and the pull rope (702) passes through the side wall of the protective shell (402); L-shaped protective plates (704) are fixed to both side walls of the circular mounting plate (4), and horizontal abutment plates (705) are fixed to the L ends of the L-shaped protective plates (704). The lower surface of the L-shaped closing plate (4021) is in contact with the upper surface of the abutment plate (705) and is slidably connected. Sliding rods (7041) are symmetrically fixed between the L-shaped protective plate (704) and the protective shell (402). The L ends of the L-shaped closing plates (4021) are sleeved on the sliding rods (7041) and are slidably connected thereto, and each of the sliding rods (7041) is sleeved with a return spring (7042).

7. A garden engineering data collection and management system according to claim 5, characterized in that: A water tank (403) is fixed on the inner top of the protective shell (401) and located outside the electric push rod (404). The lower surface of the lifting plate (5) is provided with a storage groove (504) at the sensor detection end. The three storage grooves (504) are connected to each other through a strip groove (5041).

8. A garden engineering data collection and management system according to claim 7, characterized in that: The cleaning assembly (8) comprises an annular plate (804) located in the storage groove (504); the three annular plates (804) are connected to each other via a connecting plate (8042); each connecting plate (8042) is arranged in the strip groove (5041); a plurality of nozzles (8041) are evenly installed on the inner wall of the three annular plates (804); three water pumps (801) are installed inside the water tank (403); the output port of each water pump (801) is connected to a water supply pipe (802); two telescopic pipes (803) are connected below each water supply pipe (802); the two telescopic pipes (803) pass through the water tank (403) and the lifting plate (5) and are connected to the inner cavity of the annular plate (804); three electric telescopic cylinders (805) are installed on the upper surface of the lifting plate (5) and are arranged in a triangular shape; the telescopic ends of the three electric telescopic cylinders (805) are fixed to the connecting plate (8042) by screws.

9. A management method is applied to a garden engineering data collection and management system, characterized in that: The following steps are involved: Step 1: spatial grid division: divide the monitoring area into 50m×50m grids, and deploy 3-5 ground sensor arrays (102) in each grid; Step 2: multimodal data collection: the multispectral imaging module (101) carried by the drone performs aerial photography once a week, with an adjustable flight altitude of 30-100 meters; the ground sensor array (102) collects environmental data once every 15 minutes; and the meteorological monitoring station (103) uploads real-time data once a minute; Step 3: Dynamic clustering analysis: After normalizing the collected data, the density peak clustering algorithm is used to generate the initial cluster center, and the final clustering result is determined through iterative optimization, and the clustering validity index DBI < 0.6 is set as the qualified threshold; Step 4: Decision output: Generate a three-dimensional heat map based on the clustering results, automatically mark the coordinates of the areas that require manual intervention, and send control instructions to the maintenance equipment through the MQTT protocol.

10. The management method according to claim 9 is applied to a garden engineering data collection and management system, characterized in that: The specific process of dynamic cluster analysis described in step 3 includes: a. Construct feature vector space: , where T is temperature, H is humidity, NDVI is normalized difference vegetation index, and EC is soil electrical conductivity; b. Calculate local density , where d_c is the cutoff distance, taking the 15% quantile of all pairwise distances in the data set; c. Determine the cluster center: select the point with local density ρ_i>ρ_mean and δ_i>δ_mean as the initial center, where δ_i represents the minimum distance from the point to a higher density point; d. Assign the remaining points: assign the non-central points to the cluster with higher density and closest distance to the central point; e. Anomaly detection: The points with density values ​​2 standard deviations lower than the overall mean are marked as abnormal data.

Images