Intelligent Juncao Transplanting Method and System Based on Internet of Things Technology

Abstract

The present invention discloses an intelligent Juncao transplanting method and system based on Internet of Things technology, which relates to the field of intelligent agricultural technology. The invention collects key environmental data such as soil microbial environment, wind speed and pressure, mechanical stress, soil moisture and light conditions in real time, and uses convolutional neural network technology to establish a transplanting environment prediction model. This can screen the Juncao transplanting adaptability of each sub-region, promote the improvement of transplanting success rate, and optimize resource allocation. During the growth process of Juncao transplanting, data on the compatibility of root exudates and pest attraction effects are continuously collected to establish a dynamic growth data set, and the growth risk coefficient is monitored in real time. This process can timely detect adverse trends during growth and ensure that countermeasures are taken when problems first appear. According to the real-time risk assessment results, the environmental optimization strategy is adjusted to ensure the healthy growth of Juncao throughout the growth cycle and reduce unnecessary resource waste.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agriculture, and specifically to an intelligent Juncao transplanting method and system based on Internet of Things technology. Background Art

[0002] Juncao transplanting technology is an agricultural technology that combines specific fungi and herbaceous plants through artificial intervention and enables them to quickly adapt and grow healthily in a new growth environment through appropriate transplanting methods. This technology is widely used in ecological restoration, soil remediation, and agricultural production. Especially in areas with poor or damaged soil, Juncao transplanting can effectively improve soil quality, increase land use efficiency, and promote the restoration of ecological balance. However, traditional Juncao transplanting technology often relies on manual experience, lacks comprehensive monitoring and real-time feedback of environmental data, resulting in unstable transplanting effects and certain risks.

[0003] The success of Juncao transplanting is affected by various environmental factors, such as the microbial environment of the soil, climatic conditions, moisture status, and light. Traditional Juncao transplanting methods usually have difficulty fully considering these complex environmental factors, and only select appropriate transplanting times and locations through manual judgment, lacking precise perception and real-time monitoring of environmental factors. This way that relies on manual experience makes the transplanting effect vulnerable to environmental changes and difficult to achieve refined management and control. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent Juncao transplanting method and system based on Internet of Things technology to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent Juncao transplanting method based on Internet of Things technology, including the following steps:

[0006] S1. Divide the transplanting area into several sub-areas, and use distributed Internet of Things sensing devices to collect environmental biological data in the sub-areas. The environmental biological data includes: soil microbial environment data, wind speed and wind pressure data, mechanical stress data of Juncao roots, soil moisture data, and spatial variation data of light conditions;

[0007] S2. Preprocess the collected environmental biological data. After removing outliers and filling in missing values, use dimensionless technology to standardize the environmental biological data and construct a transplanting environment database in a unified format;

[0008] S3. Use convolutional neural network technology to establish a transplanting environment prediction model. After training, extract features and perform calculations on the transplanting environment database to obtain the microbial environment influence coefficient of the i-th sub-area , Root mechanical stress coefficient , Soil moisture uniformity coefficient , Wind environment impact coefficient and light spatial variation coefficient ; And associated with obtaining the comprehensive environmental impact index of Juncao transplanting in the i-th sub-region ;

[0009] S4. Preset the first risk threshold Y 1 , Compare the comprehensive environmental impact index of Juncao transplanting in the i-th sub-region with the first risk threshold Y 1 for comparative evaluation to obtain the environmental adaptability level, and generate corresponding environmental optimization strategies according to the corresponding level;

[0010] S5. After implementing the corresponding environmental optimization strategy, perform Juncao transplanting operations, and continuously collect the compatibility data of Juncao root exudates and the pest attraction effect data after transplanting during the growth process of Juncao transplanting, establish a growth data set, and construct the growth risk coefficient of the i-th sub-region based on the growth data set , and preset the second risk threshold Y 2 , Compare the growth risk coefficient of the i-th sub-region with the second risk threshold Y 2 for comparison to obtain the growth risk assessment result.

[0011] Preferably, S1 includes:

[0012] S11. Use drone remote sensing mapping equipment to obtain high-definition images of the transplanting area and calculate the total area, mark low-lying areas, highlands, and shading areas, divide the transplanting area into several sub-regions, use the GIS geographic information system to draw a regional network, and synchronously number the several sub-regions in the regional network as , ,..., , where n is the number of sub-regions;

[0013] S12. Set a sampling point in each sub-region, arrange soil sensors, pH sensors, temperature sensors, and microbial sensors with microfluidic chips to collect soil microbial environment data in real time. The soil microbial environment data includes: microbial species, microbial density, soil pH value sjd, soil temperature wd, and CO concentration htcd of microbial metabolites 2 ;

[0014] S13. Set a sampling point in each sub-region, arrange an anemometer and a wind pressure sensor. The installation heights of the anemometer and the wind pressure sensor are set to 1 meter, 2 meters, and 3 meters respectively, and collect wind speed and wind pressure data in real time. The sampling interval is set to record data every 10 minutes. The wind speed and wind pressure data include: wind speed v and wind pressure p; and combine with the geographical coordinates of the sampling point to generate a spatio-temporal distribution map of wind speed and wind pressure in the sub-region.

[0015] Preferably, S1 further includes:

[0016] S14. Set sampling points in each sub-region. Pressure sensors are installed at depths of 5 cm, 10 cm, and 20 cm from the ground surface at the sampling points to collect mechanical stress data of the Juncao roots. The mechanical stress data of the Juncao roots includes mechanical stress ;

[0017] S15. Arrange a sampling point every 5 - 20 square meters in each sub-region. Install humidity sensors at a depth of 0 - 20 cm from the ground surface at each sampling point to collect soil moisture data. The soil moisture data includes soil moisture content M;

[0018] S16. Arrange a sampling point every 5 - 20 square meters in each sub-region. Install light intensity sensors at a height of one meter from the ground at each sampling point to collect spatial variation data of light conditions. The spatial variation data of light conditions includes light intensity L;

[0019] S17. Summarize the soil microbial environment data, wind speed and wind pressure data, mechanical stress data of Juncao roots, soil moisture data, and spatial variation data of light conditions collected in S12 - S16, and summarize and establish environmental biological data.

[0020] Preferably, S2 includes:

[0021] S21. One-to-one correspond the environmental biological data of several transplanted sub-regions collected in S1 with the number of each sub-region to form an original data set;

[0022] S22. Check the original data set to check whether there are missing values, check whether there are outliers, and check whether the timestamps and geographical coordinates of the original data table are complete; missing values include Null or NaN data;

[0023] S23. Set a threshold range to judge whether there are outliers, including:

[0024] The threshold range of the pH value sjd of the soil is 4.5 - 9;

[0025] The threshold range of the soil temperature wd is -10°C to 50°C;

[0026] Microbial metabolite CO2 The concentration htcd threshold range is 0 - 500 ppm;

[0027] The wind speed v threshold range is 0 - 20 m / s;

[0028] The wind pressure p threshold range is 0 - 1.5 kPa;

[0029] The soil moisture content M threshold range is 5% - 50%;

[0030] S24. Mark and remove the data outside the threshold range, and use the box plot analysis method to mark the original data set below 1.5 times the interquartile range (IQR) of the first quartile Q1 or above 1.5 times the interquartile range (IQR) of the third quartile Q3 as outliers; IQR represents the interquartile range, indicating the range of the middle 50% of the original data set distribution; Q1 is the first quartile of the original data set, which is the value corresponding to the 25% position after sorting the original data set; Q3 is the third quartile of the original data set, which is the value corresponding to the 25% position after sorting the original data set.

[0031] S25. After filling the missing values between adjacent time points using linear interpolation or spline interpolation, dimensionless the original data set and import it into the transplanting environment database in a unified format.

[0032] Preferably, S3 includes:

[0033] S31. Use a convolutional neural network to construct an initial convolutional neural network model, train and test the initial convolutional neural network model with the transplanting environment database, and use the trained initial convolutional neural network model as the transplanting environment prediction model. At the same time, use the intermediate layer output of the transplanting environment prediction model as a feature vector to identify feature information, and train and test the transplanting environment prediction model with the obtained feature information. Use the trained transplanting environment prediction model for data operation prediction;

[0034] S32. Use the transplanting environment prediction model to extract and calculate features from the transplanting environment database to obtain the microbial environment impact coefficient of the i-th sub-region, the root mechanical stress coefficient the soil moisture uniformity coefficient the wind environment impact coefficient and the light spatial variation coefficient.

[0035] Preferably, the said S32 includes:

[0036] S321. The microbial environment impact coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0037] Among them, is the density of the j-th type of microorganism in the soil of the i-th sub-region, is the adaptability score of the j-th type of microorganism to the forage grass in the soil of the i-th sub-region, obtained from experimental data, represents the number of microorganism species in the soil of the i-th sub-region, represents the pH value of the soil in the i-th sub-region, represents the soil temperature in the i-th sub-region, represents the concentration of the microbial metabolite CO 2 in the i-th sub-region; , , and represent the weight values;

[0038] S322, the root mechanical stress coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0039] Among them, and are respectively the maximum test volume boundary and the minimum test volume boundary within the i-th sub-region, represents the soil mechanical stress within the i-th sub-region, represents the test volume of the i-th sub-region, dV represents the infinitesimal volume element, representing the volume part accumulated during the integration process;

[0040] S323, the soil moisture uniformity coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0041] Among them, represents the average soil moisture content of the i-th sub-region, represents the number of sampling points in the i-th sub-region, represents the soil moisture content at the k-th sampling point in the i-th sub-region;

[0042] S324, the wind environment influence coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0043] Among them, represents the wind speed at the k-th sampling point in the i-th sub-region at height , the wind pressure at the k-th sampling point in the i-th sub-region at height , and Represents the weight value; Represents the number of sampling points in the i-th sub-region;

[0044] S325. The light spatial variability coefficient of the i-th sub-region Is obtained by calculation through the following formula:

[0045] Among them, Represents the light intensity of the k-th sampling point in the i-th sub-region; Represents the average light intensity of the i-th sub-region, Represents the number of sampling points in the i-th sub-region;

[0046] S326. After dimensionless processing of the microbial environment impact coefficient , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment impact coefficient and light spatial variability coefficient , the comprehensive environmental impact index of Juncao transplanting in the i-th sub-region is obtained through the following associated formula ;

[0047] Among them, , , , and respectively represent the microbial environment impact coefficient , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment impact coefficient and light spatial variability coefficient of the weight value; Represents the first correction constant.

[0048] Preferably, S4 includes:

[0049] S41. Preset the first risk threshold Y 1 , and compare and evaluate the comprehensive environmental impact index of Juncao transplanting in the i-th sub-region with the first risk threshold Y1 to obtain the environmental adaptability level, including:

[0050] If the comprehensive environmental impact index of Juncao transplanting in the i-th sub-region 1 > the first risk threshold Y × 150%, it means that the adaptability of the current sub-region is unqualified and it is not suitable for Juncao transplanting. Directly screen out this sub-region and mark it;

[0051] If the first risk threshold Y 1 ×120% ≤ the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region ≤ the first risk threshold Y 1 ×150%, it means that the adaptability of the current sub-region is unqualified and the first grade is obtained;

[0052] If the first risk threshold Y 1 ≤ the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region <the first risk threshold Y 1 ×120%, it means that the adaptability of the current sub-region is unqualified and the second grade is obtained;

[0053] If the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region <the first risk threshold Y 1 , it means that the adaptability of the current sub-region is qualified and the third grade is obtained;

[0054] S42. After obtaining the environmental adaptability level, generate corresponding environmental optimization strategies, including:

[0055] Generate the first environmental optimization strategy according to the first grade, including: the current sub-region needs to be comprehensively optimized, add 5 grams of nitrogen-fixing bacteria, phosphorus-solubilizing bacteria species and microbial agents per square meter, and repeat the application 2 times at intervals of one week; carry out tillage operations on the sub-region, with a tillage depth of 11 - 20 cm, and apply 200 grams of organic fertilizer per square meter; loosen the soil evenly in each sub-region and add 1 kg of wood chips per square meter; increase the sunshade net coverage rate by 30% - 50%, install sunshade nets in each sub-region, and reduce the light intensity to the target value L = 200 lumens; arrange windbreak walls around the sub-region or plant a 2-meter-high windbreak forest; after the implementation of the first environmental optimization strategy, carry out Juncao transplanting, and set the first transplanting depth of this sub-region to 5 cm;

[0056] Generate the second environmental optimization strategy according to the second grade, including: the current sub-region needs partial optimization, add 3 grams of nitrogen-fixing bacteria, phosphorus-solubilizing bacteria species and microbial agents per square meter, and repeat the application 1 time at intervals of one week; carry out tillage operations on the sub-region, with a tillage depth of 5 - 10 cm, and apply 100 grams of organic fertilizer per square meter; loosen the soil evenly in each sub-region and add 0.5 kg of wood chips per square meter; increase the sunshade net coverage rate by 10% - 15%, install sunshade nets in each sub-region, and reduce the light intensity to the target value L = 300 lumens; arrange windbreak walls around the sub-region or plant a 1-meter-high windbreak forest; after the implementation of the second environmental optimization strategy, carry out Juncao transplanting, and set the second transplanting depth of this sub-region to 6 - 7 cm;

[0057] Generate the third environmental optimization strategy according to the third grade, including: directly carry out Juncao transplanting, and set the third transplanting depth of this sub-region to 8 - 9 cm.

[0058] Preferably, S5 includes:

[0059] S51. After the corresponding environmental optimization strategy is executed, perform the operation of transplanting Juncao, and continuously collect the compatibility data of Juncao root exudates, the pest attraction effect data after transplantation, and the soil density disturbance data during the growth process of Juncao transplantation, and establish a growth dataset; and construct the root exudate compatibility coefficient of the i-th sub-region based on the growth dataset , pest attraction coefficient and soil disturbance influence coefficient , and associate the root exudate compatibility coefficient of the i-th sub-region , pest attraction coefficient and soil disturbance influence coefficient to obtain the growth risk coefficient of the i-th sub-region ;

[0060] S511. Within the sampling point range of each sub-region, install a liquid collection device in the Juncao root distribution area, set the sampling depth to 10 - 20 cm, obtain the root exudate liquid sample, set the collection frequency to once every 2 days, continuously collect for 6 - 8 weeks, and conduct experimental measurements on the root exudate liquid sample to obtain the organic acid concentration nd1, enzyme concentration nd2, and the pH value of the exudate , and establish the compatibility data of Juncao root exudates;

[0061] S512. Arrange pest monitoring equipment at the sampling points of each sub-region. The pest monitoring equipment includes an insect situation forecasting lamp and a ground trap; the layout interval of the pest monitoring equipment is 10 - 20 square meters, and collect the density of flying pests and the density of underground pests , and establish the pest attraction effect data after transplantation;

[0062] S513. Install soil density sensors at 1 - 9 cm from the surface layer and 10 - 20 cm from the subsurface layer at the sampling points within each sub-region, and collect the soil density disturbance data. The soil density disturbance data includes the soil density before transplantation and the soil density after transplantation ;

[0063] S514. The root exudate compatibility coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0064] where, represents the organic acid concentration of the i-th sub-region, represents the enzyme concentration of the i-th sub-region, represents the pH value of the exudate of the i-th sub-region;

[0065] Pest attraction coefficient of the i-th sub-region It is calculated by the following formula:

[0066] Wherein, represents the density of flying pests in the i-th sub-region, represents the density of underground pests in the i-th sub-region; represents the area of the i-th sub-region;

[0067] Soil disturbance influence coefficient of the i-th sub-region It is calculated by the following formula:

[0068] Wherein, represents the soil density before transplanting in the i-th sub-region, represents the soil density after transplanting in the i-th sub-region;

[0069] S515. Make the root system secretion compatibility coefficient of the i-th sub-region , pest attraction coefficient and soil disturbance influence coefficient After dimensionless processing, the growth risk coefficient of the i-th sub-region is obtained through the following associated formula :

[0070] Wherein, , and respectively represent the root system secretion compatibility coefficient of the i-th sub-region , pest attraction coefficient and soil disturbance influence coefficient Weight values; represents the second correction constant.

[0071] Preferably, S5 further includes: S52. Preset the second risk threshold Y 2 , and compare the growth risk coefficient of the i-th sub-region with the second risk threshold Y 2 to obtain the growth risk assessment result, including:

[0072] If the growth risk coefficient of the i-th sub-region > the second risk threshold Y 2 , it indicates that there is a risk in the growth environment of this sub-region, and a fourth environmental optimization strategy is generated, including: adding 1-2 kg of sandy soil per square meter to improve ventilation; applying 2 g of microbial inoculant per square meter, repeating once every 1 week for 3 consecutive times; arranging 1-2 trap lights in each sub-region, spraying biological pesticides once a week for 2-3 consecutive weeks;

[0073] If the growth risk coefficient of the i-th sub-region ≤ the second risk threshold Y 2 , it indicates that there is no risk in the growth environment of this sub-region, and continuous monitoring is carried out.

[0074] The intelligent Juncao transplanting system based on the Internet of Things technology includes:

[0075] The segmentation unit is used to divide the transplanting area into several sub-regions, draw a regional network using the GIS geographic information system, and synchronously number several sub-regions in the regional network as , ,... , where n is the number of sub-regions;

[0076] The first Internet of Things acquisition unit is used to collect environmental biological data in the sub-region by using distributed Internet of Things sensing devices. The environmental biological data includes: soil microbial environment data, wind speed and wind pressure data, mechanical stress data of Juncao roots, soil moisture data, and spatial variation data of light conditions;

[0077] The model establishment unit is used to establish a transplanting environment prediction model by using convolutional neural network technology. After training, feature extraction and calculation are performed on the transplanting environment database to obtain the microbial environment influence coefficient of the i-th sub-region , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment influence coefficient and light spatial variation coefficient ; and the comprehensive influence index of the Juncao transplanting environment of the i-th sub-region is obtained by association; ;

[0078] The first evaluation unit is used to preset the first risk threshold Y 1 , and compare and evaluate the comprehensive influence index of the Juncao transplanting environment of the i-th sub-region with the first risk threshold Y 1 to obtain the environmental adaptability level, and generate corresponding environmental optimization strategies according to the corresponding level;

[0079] The second Internet of Things monitoring unit is used to perform Juncao transplanting operations after the corresponding environmental optimization strategies are executed, and continuously collect the compatibility data of Juncao root exudates and the pest attraction effect data after transplanting during the growth process of Juncao, and establish a growth data set;

[0080] The second evaluation unit is used to construct the growth risk coefficient of the i-th sub-region according to the growth data set , and preset the second risk threshold Y 2, compare the growth risk coefficient of the $i$-th sub-region with the second risk threshold $Y$ 2 to obtain the growth risk assessment result.

[0081] The present invention provides an intelligent Juncao transplanting method and system based on Internet of Things technology, with the following beneficial effects:

[0082] (1) Through the application of distributed Internet of Things sensing devices, the present invention can real-time monitor multiple environmental data such as soil microbial environment, wind speed, wind pressure, mechanical stress of Juncao roots, soil moisture, and light conditions. It solves the defect of insufficient monitoring of environmental parameters in traditional methods. Traditional Juncao transplanting relies on manual experience and cannot real-time monitor and handle the impacts brought by environmental changes. The present invention can calculate the microbial environment impact coefficient 、root mechanical stress coefficient 、soil moisture uniformity coefficient 、wind environment impact coefficient and light spatial variation coefficient of the $i$-th sub-region during the environmental adaptability assessment process; and correlate to obtain the comprehensive environmental impact index of Juncao transplanting in the $i$-th sub-region, and intelligently generate corresponding optimization strategies. In traditional methods, environmental judgment often relies on experience, while the present invention realizes intelligent optimization and dynamic adjustment of the environment under the influence of multiple environmental factors through a data-driven method, which helps to improve the adaptability and growth success rate of Juncao transplanting.

[0083] (2) During the growth process of Juncao transplanting, the present invention continuously collects the compatibility data of Juncao root exudates and pest attraction effect data, establishes a growth data set, and constructs a growth risk coefficient. This method can real-time monitor the growth status after transplanting and early warning potential growth risks, especially in aspects such as pest attraction and root growth, improving the overall growth effect of Juncao.

[0084] (3) At the sub-region sampling points, the present invention can accurately reflect the growth status of roots in the soil and the adaptability of roots to the external environment by setting different heights to obtain wind speed and wind pressure data and collecting mechanical stress data of Juncao roots at different depths. It avoids data deviation caused by ignoring or single sampling height in traditional methods. Brief Description of the Drawings

[0085] Figure 1 is a schematic diagram of the steps of the intelligent Juncao transplanting method based on Internet of Things technology of the present invention;

[0086] Figure 2 is a schematic diagram of the process of the intelligent Juncao transplanting system based on Internet of Things technology of the present invention. Detailed Embodiments

[0087] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. 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.

[0088] Embodiment 1

[0089] Please refer to Figure 1 , the present invention provides an intelligent Juncao transplanting method based on Internet of Things technology, including the following steps:

[0090] S1. Divide the transplanting area into several sub-areas, and use distributed Internet of Things sensing devices to collect environmental biological data in the sub-areas. The environmental biological data includes: soil microbial environment data, wind speed and wind pressure data, mechanical stress data of Juncao roots, soil moisture data, and spatial variation data of light conditions;

[0091] S2. Preprocess the collected environmental biological data. After removing outliers and filling in missing values, use dimensionless technology to standardize the environmental biological data and construct a transplanting environment database in a unified format;

[0092] S3. Use convolutional neural network technology to establish a transplanting environment prediction model. After training, perform feature extraction and calculation on the transplanting environment database to obtain the microbial environment influence coefficient of the i-th sub-area , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment influence coefficient and light spatial variation coefficient ; and associate to obtain the comprehensive influence index of the Juncao transplanting environment of the i-th sub-area ;

[0093] S4. Preset the first risk threshold Y 1 , compare and evaluate the comprehensive influence index of the Juncao transplanting environment of the i-th sub-area with the first risk threshold Y 1 to obtain the environmental adaptability level, and generate corresponding environmental optimization strategies according to the corresponding level;

[0094] S5. After implementing the corresponding environmental optimization strategy, perform the Juncao transplanting operation, and continuously collect the compatibility data of Juncao root exudates and the pest attraction effect data after transplanting during the growth process of Juncao, establish a growth data set, and construct the growth risk coefficient of the i-th sub-area according to the growth data set , and preset a second risk threshold Y2, and compare the growth risk coefficient of the i-th sub-region with the second risk threshold Y2 to obtain a growth risk assessment result.

[0095] In this embodiment, through the application of distributed Internet of Things sensing devices, the present invention can real-time monitor multiple environmental data such as soil microbial environment, wind speed, wind pressure, mechanical stress of the roots of Juncao, soil moisture, and light conditions. Such comprehensive data collection can provide a more accurate environmental basis for Juncao transplantation, and solve the defect of insufficient monitoring of environmental parameters in traditional methods. Traditional Juncao transplantation relies on manual experience and cannot monitor and process the impact of environmental changes in real time. The present invention effectively ensures the quality and consistency of data through data preprocessing, outlier removal, missing value filling, and standardization techniques, and establishes an environmental prediction model through convolutional neural network technology, realizing real-time evaluation and feedback of environmental conditions in different sub-regions. This process can effectively avoid errors in manual operations, and timely adjust the transplantation strategy to optimize environmental conditions. The present invention can calculate the microbial environment impact coefficient of the i-th sub-region , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment impact coefficient and light spatial variation coefficient ; and associate and obtain the comprehensive impact index of the Juncao transplantation environment in the i-th sub-region , and intelligently generate corresponding optimization strategies. In traditional methods, environmental judgment often relies on experience, while the present invention realizes intelligent optimization and dynamic adjustment of the environment through a data-driven method under the influence of multiple environmental factors, which helps to improve the adaptability and growth success rate of Juncao transplantation.

[0096] During the growth process of Juncao transplantation, the present invention continuously collects the compatibility data of Juncao root exudates and pest attraction effect data, establishes a growth data set, and constructs a growth risk coefficient. This method can monitor the growth status after transplantation in real time, and early warning of potential growth risks. Especially in terms of pest attraction and root growth, it can provide precise control for the growth of transplanted plants, significantly reduce the risk factors in traditional methods, and improve the overall growth effect. According to the environmental characteristics and growth conditions of different sub-regions, the present invention can customize transplantation strategies for each sub-region, which can better promote the growth of Juncao, enhance the ecological restoration function, and promote the restoration of soil quality, and solve the problems of poor restoration effect and long cycle in traditional technologies.

[0097] Embodiment 2

[0098] This embodiment is an explanatory description based on Embodiment 1. Specifically, S1 includes:

[0099] S11. Use a drone remote sensing mapping device to obtain a high-definition image of the transplanting area, calculate the total area, mark low-lying areas, highlands, and shaded areas, divide the transplanting area into several sub-areas, use a GIS (Geographic Information System) to draw a regional network, and simultaneously number the several sub-areas in the regional network as A1, A2, ..., An, where n is the number of sub-areas; obtaining a high-definition image through drone remote sensing technology can efficiently and accurately divide the entire transplanting area and calculate the area. Compared with traditional manual measurement methods, drone remote sensing has higher work efficiency and accuracy, and can identify and mark different terrain features such as low-lying areas, highlands, and shaded areas, providing a scientific basis for subsequent transplanting scheme design. In addition, using a GIS to draw a regional network and assigning a unique number to each sub-area makes subsequent management and data collection more orderly, facilitating tracking and analysis.

[0100] S12. Set a sampling point in each sub-area, arrange soil sensors, pH sensors, temperature sensors, and microbial sensors with microfluidic chips to collect soil microbial environment data in real time. The soil microbial environment data includes: microbial species, microbial density, soil pH value sjd, soil temperature wd, and the concentration of microbial metabolite CO 2 concentration htcd; traditional methods often rely on manual sampling, which has problems such as poor sampling timeliness and insufficient data accuracy. This step ensures the timeliness and accuracy of the data by collecting data in real time, providing a scientific means for monitoring the microbial environment of Juncao transplanting.

[0101] S13. Set a sampling point in each sub-area, arrange an anemometer and a wind pressure sensor. The installation heights of the anemometer and the wind pressure sensor are set to 1 meter, 2 meters, and 3 meters respectively, and collect wind speed and wind pressure data in real time. The sampling interval is set to record data every 10 minutes. The wind speed and wind pressure data includes: wind speed v and wind pressure p; and combine with the geographical coordinates of the sampling point to generate a spatio-temporal distribution map of wind speed and wind pressure in the sub-area. This process helps to master the changes in wind speed and wind pressure, avoiding data deviation caused by neglect or single sampling height in traditional methods. In addition, the impact of wind speed and wind pressure on the growth of Juncao cannot be ignored, and real-time monitoring helps to provide a basis for transplanting strategies adapted to different environmental conditions.

[0102] S14. Set sampling points within each sub-area, install pressure sensors at depths of 5 cm, 10 cm, and 20 cm from the ground surface to collect mechanical stress data of Juncao roots. The mechanical stress data of Juncao roots includes mechanical stress ; By collecting the mechanical stress data of the Juncao roots through pressure sensors set at multiple depths, it can accurately reflect the growth state of the roots in the soil and the adaptability of the roots to the external environment. These data help to evaluate the growth of the roots and their contact with the soil, thus providing a scientific basis for the growth of Juncao during transplantation. Compared with the traditional manual observation method, this high-precision monitoring can timely detect problems in root growth, reduce root damage, and improve the success rate of Juncao growth.

[0103] S15. Arrange a sampling point every 5 - 20 square meters within each sub-region. Install a humidity sensor at a depth of 0 - 20 cm from the ground surface at each sampling point to collect soil moisture data. The soil moisture data includes the soil moisture content M; by arranging humidity sensors and collecting soil moisture data at different depths, real-time monitoring of the soil moisture content can be achieved. The moisture changes in different soil layers have an important impact on the transplantation and growth of Juncao, and this step can accurately reflect the distribution of soil moisture, facilitating timely adjustment of the moisture management strategy. Traditional methods often lack accurate moisture monitoring and usually rely on manual sampling at regular intervals, making it difficult to ensure the timeliness and integrity of the moisture data.

[0104] S16. Arrange a sampling point every 5 - 20 square meters within each sub-region. Install a light intensity sensor at a distance of one meter from the ground at each sampling point to collect the spatial variation data of the light conditions. The spatial variation data of the light conditions includes the light intensity L; by arranging light intensity sensors, the light changes at different spatial positions can be monitored. Light is an important environmental factor affecting the growth of Juncao. Timely understanding of the light condition changes can effectively guide the selection of the transplantation time and location. In addition, this step can obtain the spatial distribution data of the light conditions, helping to analyze the light differences at different positions and ensuring that Juncao grows under the best light conditions.

[0105] S17. Summarize the soil microbial environment data, wind speed and wind pressure data, mechanical stress data of Juncao roots, soil moisture data, and spatial variation data of light conditions collected in S12 - S16, and summarize and establish environmental biological data. By summarizing the data of multiple environmental factors (including soil microbial environment, wind speed and wind pressure, root mechanical stress, soil moisture, and light conditions), a comprehensive environmental biological data set can be established. This data set can comprehensively reflect the environmental characteristics of the transplantation area and provide reliable basic data for subsequent analysis, evaluation, and strategy formulation. Compared with the single data collection of traditional methods, this step provides a more refined analysis framework for the transplantation effect through the integration of multiple environmental data, thus optimizing the growth conditions and management measures of Juncao.

[0106] Example 3

[0107] This embodiment is an explanation of the embodiment 1. Specifically, S2 includes:

[0108] S21, matching the environmental biological data of several transplanting sub-areas collected in S1 with the numbers of each sub-area one by one to form an original data set;

[0109] S22. Check the original data set to see if there are any missing values, any abnormal values, and whether the timestamps and geographic coordinates of the original data table are complete; missing values ​​include Null or NaN data;

[0110] S23. Setting a threshold range to determine whether there are abnormal values, including:

[0111] The pH value sjd threshold range of soil is 4.5-9;

[0112] The soil temperature wd threshold range is -10℃ to 50℃;

[0113] Microbial metabolites CO 2 The concentration htcd threshold range is 0-500ppm;

[0114] The wind speed v threshold range is 0-20m / s;

[0115] The wind pressure p threshold range is 0-1.5 kPa;

[0116] The soil moisture content M threshold range is 5%-50%;

[0117] S24. Mark the data beyond the threshold range as outliers and remove them. Use the box plot analysis method to mark the original data set below the first quartile Q1×1.5 times IQR or above the third quartile Q3×1.5 times IQR as outliers. IQR means position, which means the middle 50% range of the original data set distribution. Q1 is the first quartile of the original data set, which is the value corresponding to the lower 25% position of the original data set after the original data set is centrally sorted. Q3 is the third quartile of the original data set, which is the value corresponding to the upper 25% position of the original data set after the original data set is centrally sorted.

[0118] S25. After filling the missing values ​​between adjacent moments using linear interpolation or spline interpolation, the original data set is dimensionless and then imported into a transplant environment database in a unified format.

[0119] In this embodiment, step S2 ensures the accuracy and integrity of the data through rigorous data preprocessing, including data verification, outlier removal, missing value filling and data standardization. This provides reliable data support for subsequent transplant environment analysis and decision-making, while improving the efficiency of data management and the operability of the data set.

[0120] Example 4

[0121] This example is an explanatory note based on Example 1. Specifically, S3 includes:

[0122] S31. Using a convolutional neural network, construct an initial convolutional neural network model, train and test the initial convolutional neural network model with the transplanting environment database, and use the trained initial convolutional neural network model as the transplanting environment prediction model. At the same time, use the intermediate layer output of the transplanting environment prediction model as a feature vector to identify feature information, and train and test the transplanting environment prediction model with the obtained feature information. Use the trained transplanting environment prediction model for data operation prediction;

[0123] By constructing a convolutional neural network and training the transplanting environment database, step S31 uses the neural network to learn the latent features in the data, thereby establishing a transplanting environment prediction model. This can not only improve the prediction accuracy but also capture the complex relationships in the environmental data, making the transplanting environment prediction more refined and targeted. In addition, using the intermediate layer output as a feature vector further optimizes the data extraction method, making the feature information more prominent, thus improving the training effect of the subsequent model.

[0124] S32. Use the transplanting environment prediction model to extract and calculate features from the transplanting environment database to obtain the microbial environment impact coefficient of the i-th sub-region , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment impact coefficient and light spatial variation coefficient . The quantitative calculation of each factor helps to understand the specific impact of different environmental factors on the transplanting effect, and thus provides a basis for formulating optimization plans.

[0125] The said S32 includes:

[0126] S321. The microbial environment impact coefficient of the i-th sub-region is obtained by calculating through the following formula:

[0127] Wherein, is the density of the j-th type of microorganism in the soil of the i-th sub-region, is the adaptability score of the j-th type of microorganism in the soil of the i-th sub-region to the Juncao, obtained through experimental data, represents the number of microorganism species in the soil of the i-th sub-region, represents the pH value of the soil in the i-th sub-region, represents the soil temperature of the i-th sub-region, represents the concentration of microbial metabolites CO in the i-th sub-region 2 ; , , and represent the weight value;

[0128] S322, the root mechanical stress coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0129] where and are respectively the maximum test volume boundary and the minimum test volume boundary within the i-th sub-region, represents the soil mechanical stress within the i-th sub-region, represents the test volume of the i-th sub-region, dV represents the infinitesimal volume element, representing the volume part accumulated during the integration process;

[0130] S323, the soil moisture uniformity coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0131] where represents the average soil moisture content of the i-th sub-region, represents the number of sampling points in the i-th sub-region, represents the soil moisture content at the k-th sampling point in the i-th sub-region;

[0132] S324, the wind environment impact coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0133] where represents the wind speed at the k-th sampling point in the i-th sub-region at height , the wind pressure at the k-th sampling point in the i-th sub-region at height , and represent the weight value; represents the number of sampling points in the i-th sub-region;

[0134] S325, the light spatial variation coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0135] where represents the light intensity at the k-th sampling point in the i-th sub-region; represents the average light intensity of the i-th sub-region, represents the number of sampling points in the i-th sub-region;

[0136] S326. After dimensionless processing of the microbial environment impact coefficient , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment impact coefficient and light spatial variability coefficient , the comprehensive environmental impact index for the transplanting of Juncao in the i-th sub-region is obtained through the following associated formula ;

[0137] wherein, , , , and respectively represent the weight values of the microbial environment impact coefficient , root mechanical stress coefficient , soil moisture uniformity coefficient , wind environment impact coefficient and light spatial variability coefficient in the i-th sub-region; and the sum of the weight values is 1, represents the first correction constant.

[0138] In this embodiment, in step S326, through the dimensionless processing of different impact factors, the environmental impact factors of each sub-region are made comparable. This process ensures that parameters with different units or dimensions can compete fairly in the same evaluation system, avoiding biases caused by unit differences. Finally, the comprehensive environmental impact index for the transplanting of Juncao calculated through the associated formula provides a comprehensive and integrated score for the transplanting environment of different sub-regions, facilitating comparison and selection of the optimal scheme.

[0139] By comprehensively considering multiple environmental factors (microorganisms, soil, moisture, wind, light, etc.) and their variations in different sub-regions, step S3 can achieve a comprehensive evaluation of the transplanting environment. The comprehensive impact index provides a quantitative comparison standard for the transplanting effects under different environmental conditions, thereby helping to formulate scientific and reasonable transplanting strategies, optimizing the transplanting effects and improving the success rate. S3 not only improves the prediction accuracy but also provides a strong decision-making basis for optimizing the transplanting environment, promoting scientific and precise agricultural or environmental management.

[0140] Embodiment 5

[0141] This embodiment is an explanatory description based on Embodiment 1. Specifically, S4 includes:

[0142] S41. Preset the first risk threshold Y 1 , compare and evaluate the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region with the first risk threshold Y1 to obtain the environmental adaptability level, including:

[0143] If the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region > the first risk threshold Y 1 × 150%, it means that the adaptability of the current sub-region is unqualified and it is not suitable for Juncao transplanting. Directly screen out this sub-region and mark it;

[0144] If the first risk threshold Y 1 × 120% ≤ the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region ≤ the first risk threshold Y 1 × 150%, it means that the adaptability of the current sub-region is unqualified and the first level is obtained;

[0145] If the first risk threshold Y 1 ≤ the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region < the first risk threshold Y 1 × 120%, it means that the adaptability of the current sub-region is unqualified and the second level is obtained;

[0146] If the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region < the first risk threshold Y 1 , it means that the adaptability of the current sub-region is qualified and the third level is obtained;

[0147] S42. After obtaining the environmental adaptability level, generate corresponding environmental optimization strategies, including:

[0148] Generate the first environmental optimization strategy according to the first level, including: the current sub-region needs to be comprehensively optimized. Add 5 grams of nitrogen-fixing bacteria, phosphorus-solubilizing bacteria species and microbial agents per square meter, and repeat the application 2 times at intervals of one week; conduct tillage operations on the sub-region, with a tillage depth of 11 - 20 cm, and apply 200 grams of organic fertilizer per square meter; loosen the soil evenly in each sub-region and add 1 kg of wood chips per square meter; increase the sunshade net coverage rate by 30% - 50%, install sunshade nets in each sub-region, and reduce the light intensity to the target value L = 200 lux; arrange windbreak walls or plant windbreak forests 2 meters high around the sub-region; after implementing the first environmental optimization strategy, conduct Juncao transplanting and set the first transplanting depth of this sub-region to 5 cm;

[0149] Generate the second environmental optimization strategy according to the second level, including: partial optimization is required for the current sub-region, add 3 grams of nitrogen-fixing bacteria, phosphorus-solubilizing bacteria species and microbial agents per square meter, and repeat the application once every week; conduct plowing operations on the sub-region, with a plowing depth of 5-10 cm, and apply 100 grams of organic fertilizer per square meter; loosen the soil evenly in each sub-region, and add 0.5 kg of wood chips per square meter; increase the sunshade net coverage rate by 10%-15%, install sunshade nets in each sub-region, and reduce the light intensity to the target value L = 300 lumens; arrange windbreak walls around the sub-region or plant a 1-meter-high windbreak forest; after the implementation of the second environmental optimization strategy, carry out the transplanting of Juncao, and set the second transplanting depth of this sub-region to 6-7 cm;

[0150] Generate the third environmental optimization strategy according to the third level, including: directly transplant Juncao, and set the third transplanting depth of this sub-region to 8-9 cm.

[0151] In this embodiment, S41 conducts a hierarchical evaluation by comparing the comprehensive influence index of the Juncao transplanting environment in the i-th sub-region with the preset risk threshold (Y 1 ). This method can scientifically and objectively judge the environmental adaptability of each sub-region, and classify the sub-regions into different levels according to the size of the index, ensuring that Juncao transplantation is carried out only in suitable regions. This grading method can avoid waste of resources by screening out unsuitable transplanting regions. According to the sub-regions of different levels, S42 formulates specific environmental optimization strategies for each level. The strategies for each level include targeted adjustment measures to ensure that the environmental conditions can be optimized in a timely manner to meet the growth requirements of Juncao. Implementing different intensities of optimization measures for the sub-regions of each level avoids a one-size-fits-all approach. The needs of each sub-region are accurately responded to, improving the management efficiency. By applying different types of fertilizers, microbial agents, wood chips, sunshade nets and other substances, and conducting plowing operations, the growth conditions required for Juncao are provided, and the soil quality, light and moisture conditions are optimized. By increasing wind protection measures, adjusting light intensity and other environmental adjustments, Juncao is helped to adapt to the changes in the external environment, thereby improving its survival rate and growth rate.

[0152] Example 6

[0153] This embodiment is an explanatory description based on Embodiment 1. Specifically, S5 includes:

[0154] S51. After the implementation of the corresponding environmental optimization strategy, perform the Juncao transplanting operation, and continuously collect the compatibility data of Juncao root exudates, the pest attraction effect data after transplantation, and the soil density disturbance data during the growth of Juncao transplantation to establish a growth data set; and construct the root secretion compatibility coefficient of the i-th sub-region based on the growth data set 、the pest attraction coefficient and the soil disturbance influence coefficient and associate with the root system secretion compatibility coefficient of the i-th sub-region , pest attraction coefficient and soil disturbance influence coefficient , and obtain the growth risk coefficient of the i-th sub-region ;

[0155] S511. Install a liquid collection device in the root system distribution area of the Juncao within the sampling point range of each sub-region, set the sampling depth to 10 - 20 cm, obtain the root exudate liquid sample, set the collection frequency to once every 2 days, continuously collect for 6 - 8 weeks, and conduct experimental measurements on the root exudate liquid sample to obtain the organic acid concentration nd1, enzyme concentration nd2, and the pH value of the exudate , and establish the compatibility data of the Juncao root exudates;

[0156] S512. Arrange pest monitoring equipment at the sampling points of each sub-region. The pest monitoring equipment includes a pest forecasting lamp and a ground trap; the layout interval of the pest monitoring equipment is 10 - 20 square meters, and collect the density of flying pests and the density of underground pests , and establish the pest attraction effect data after transplantation;

[0157] S513. Install soil density sensors at 1 - 9 cm from the surface layer and 10 - 20 cm from the subsurface layer at the sampling points within each sub-region, and collect soil density disturbance data. The soil density disturbance data includes the soil density before transplantation and the soil density after transplantation ;

[0158] S514. The root system secretion compatibility coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0159] where, represents the organic acid concentration of the i-th sub-region, represents the enzyme concentration of the i-th sub-region, represents the pH value of the exudate of the i-th sub-region;

[0160] The pest attraction coefficient of the i-th sub-region is calculated and obtained through the following formula:

[0161] where, represents the density of flying pests in the i-th sub-region, represents the density of underground pests in the i-th sub-region; represents the area of the i-th sub-region;

[0162] Soil disturbance influence coefficient of the $i$-th sub-region It is calculated by the following formula:

[0163] Wherein, represents the soil density before transplanting in the $i$-th sub-region, represents the soil density after transplanting in the $i$-th sub-region;

[0164] S515. After dimensionless processing of the root exudate compatibility coefficient , pest attraction coefficient and soil disturbance influence coefficient of the $i$-th sub-region, the growth risk coefficient of the $i$-th sub-region is obtained through the following associated formula :

[0165] Wherein, , and respectively represent the weight values of the root exudate compatibility coefficient , pest attraction coefficient and soil disturbance influence coefficient of the $i$-th sub-region; and the sum of the weight values is 1, represents the second correction constant.

[0166] In this embodiment, the present invention can achieve precise monitoring and optimization of the growth process of Juncao transplantation by collecting and analyzing relevant data on root exudates, pest attraction effects, and soil disturbance during Juncao transplantation. By monitoring the compatibility data of root exudates and the impact of soil disturbance, it can provide a quantitative basis for improving soil quality, provide guidance for the implementation of subsequent soil improvement measures, and thus promote soil health and ecological restoration. Combining the analysis results of the pest attraction coefficient can provide accurate data support for formulating pest control strategies, reduce the threat of pests to transplanted Juncao, thereby reducing management costs and improving crop growth effects. Through the comprehensive evaluation of environmental factors in each sub-region, the present invention can provide personalized optimization solutions for transplantation operations, effectively improving the survival rate and growth rate of Juncao.

[0167] Embodiment 7

[0168] This embodiment is an explanatory description carried out in Embodiment 6. Specifically, S5 further includes: S52. Preset a second risk threshold Y 2 , and compare the growth risk coefficient of the $i$-th sub-region with the second risk threshold Y 2 to obtain a growth risk assessment result, including:

[0169] If the growth risk coefficient of the $i$-th sub-region > The second risk threshold Y 2 , indicating that there is a risk in the growth environment of this sub-region, generating a fourth environmental optimization strategy, including: adding 1-2 kg of sandy soil per square meter to improve ventilation; applying 2 g of microbial inoculant per square meter, repeating once every 1 week for 3 consecutive times; arranging 1-2 trapping lights in each sub-region, spraying biological pesticides once a week for 2-3 consecutive weeks;

[0170] If the growth risk coefficient of the i-th sub-region ≤ The second risk threshold Y 2 , indicating that there is no risk in the growth environment of this sub-region, and continuous monitoring is carried out.

[0171] In this embodiment, when the growth risk coefficient exceeds the second risk threshold Y 2 , by adding sandy soil to improve soil ventilation, the water permeability of the soil and the root growth environment are improved, which helps to enhance the development of the roots of the Juncao and improve its growth potential. Applying microbial inoculants can accelerate the reproduction of beneficial microorganisms in the soil, improve the biological characteristics of the soil, promote the decomposition of organic matter and the circulation of nutrients, and effectively enhance the health and sustainability of the soil. By setting up trapping lights and spraying biological pesticides, the threat of pests to Juncao can be effectively reduced, the negative impact of pesticides on the environment can be avoided, and the sustainable development of the agricultural ecosystem can be guaranteed. When the growth risk coefficient is lower than the second risk threshold Y 2 , the present invention can continuously monitor and evaluate environmental changes, timely adjust management strategies, ensure that the ecological environment of the sub-region is always maintained in a state suitable for the growth of Juncao, and improve the accuracy and operability of management. By comprehensively evaluating environmental factors, flexibly adjusting strategies, effectively combining agricultural production and environmental protection, and promoting ecological restoration in the agricultural production process, the present invention meets the requirements of sustainable agricultural development.

[0172] Embodiment 8

[0173] Please refer to Figure 2 , an intelligent Juncao transplanting system based on Internet of Things technology, including:

[0174] A segmentation unit, used to divide the transplanting area into several sub-regions, draw a regional network using the GIS geographic information system, and synchronously number several sub-regions in the regional network as A 1 、A 2 、...、A n , where n is the number of sub-regions;

[0175] A first Internet of Things acquisition unit, used to collect environmental biological data in the sub-region by using distributed Internet of Things sensing devices, and the environmental biological data includes: soil microbial environment data, wind speed and wind pressure data, mechanical stress data of Juncao roots, soil moisture data, and spatial change data of light conditions;

[0176] A model unit is established to establish a transplanting environment prediction model using convolutional neural network technology. After training, it extracts features and performs calculations on the transplanting environment database to obtain the microbial environment impact coefficient of the i-th sub-region. , the root mechanical stress coefficient , the soil moisture uniformity coefficient , the wind environment impact coefficient and the light spatial variation coefficient ; and associatively obtain the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region ;

[0177] The first evaluation unit is used to preset the first risk threshold Y 1 , compare and evaluate the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region with the first risk threshold Y 1 to obtain the environmental adaptability level, and generate corresponding environmental optimization strategies according to the corresponding level;

[0178] The second Internet of Things monitoring unit is used to perform Juncao transplanting operations after the corresponding environmental optimization strategies are executed, and continuously collect the compatibility data of Juncao root exudates and the pest attraction effect data after transplanting during the growth process of Juncao to establish a growth data set.

[0179] The second evaluation unit is used to construct the growth risk coefficient of the i-th sub-region based on the growth data set , and preset the second risk threshold Y 2 , compare the growth risk coefficient of the i-th sub-region with the second risk threshold Y 2 to obtain the growth risk assessment result.

[0180] In this embodiment, through the first Internet of Things acquisition unit, key environmental data such as soil microbial environment, wind speed and wind pressure, mechanical stress, soil moisture, and light conditions are collected in real time, and a transplanting environment prediction model is established using convolutional neural network technology. This can accurately evaluate the environmental impact of each sub-region and timely discover potential risk factors. The system compares the comprehensive environmental impact index with the preset risk threshold Y 1Compare to develop personalized environmental optimization strategies for each sub-region to ensure that Juncao can grow under the most suitable environmental conditions. This can not only improve the transplant success rate but also optimize resource allocation. During the transplanting and growth process of Juncao, the second Internet of Things monitoring unit continuously collects data on the compatibility of root exudates and the pest attraction effect, establishes a dynamic growth dataset, and real-time monitors the growth risk coefficient. This process can promptly detect adverse trends during growth and ensure that countermeasures are taken as soon as problems arise. Through the second evaluation unit, compare the growth risk coefficient with the second risk threshold Y 2 Compare, and the system can adjust the environmental optimization strategy according to the real-time risk assessment results to ensure the healthy growth of Juncao throughout the growth cycle and reduce unnecessary resource waste.

[0181] The size of the threshold is set for easy comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0182] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formulas are set by those skilled in the art according to the actual situation. As described above, this is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent Juncao transplanting method based on Internet of Things technology, characterized in that: The following steps are involved: S1. Divide the transplanting area into several sub-areas, and use distributed Internet of Things sensing devices to collect environmental biological data in the sub-areas, wherein the environmental biological data include: soil microbial environment data, wind speed and wind pressure data, mechanical stress data of the roots of the grass, soil moisture data, and spatial variation data of light conditions; S2. Preprocess the collected environmental biological data, remove outliers and fill in missing values; standardize the environmental biological data using dimensionless technology to build a transplant environment database in a unified format; S3. Use convolutional neural network technology to establish a transplanting environment prediction model. After training, extract and calculate the features of the transplanting environment database to obtain the microbial environmental impact coefficient of the i-th sub-area. , root mechanical stress coefficient , soil moisture uniformity coefficient , Wind environmental impact coefficient and the spatial coefficient of variation of illumination ; and associate to obtain the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region ; Microbial environmental impact coefficient of the i-th sub-region Calculated by the following formula: in, is the density of the jth type of microorganism in the soil of the ith sub-region, is the adaptability score of the j-th microorganism in the soil of the ith sub-region to Juncao, obtained through experimental data. represents the number of microbial species in the soil of the ith sub-region, represents the pH value of the soil in the ith sub-region, represents the soil temperature of the ith sub-region, represents the CO2 concentration of microbial metabolites in the ith sub-region; , , and Represents the weight value; Root mechanical stress coefficient of the i-th sub-region Calculated by the following formula: in, and are the maximum test volume boundary and the minimum test volume boundary in the i-th sub-region, respectively. represents the soil mechanical stress in the ith sub-region, represents the test volume of the ith sub-region, It represents a small volume unit, representing the volume part accumulated during the integration process; Soil moisture uniformity coefficient of the i-th sub-region Calculated by the following formula: in, represents the average soil moisture content in the ith sub-region, represents the number of sampling points in the i-th sub-region, represents the soil moisture content at the kth sampling point in the ith sub-area; Wind environment impact coefficient of the i-th sub-area Calculated by the following formula: in, Indicates that the kth sampling point in the i-th sub-region is at height The wind speed, The kth sampling point in the i-th sub-region is at height The wind pressure, and Represents the weight value; represents the number of sampling points in the i-th sub-region; The spatial variation coefficient of illumination in the i-th sub-area Calculated by the following formula: in, Represents the light intensity of the kth sampling point in the i-th sub-area; represents the average light intensity of the ith sub-region, represents the number of sampling points in the i-th sub-area; The microbial environmental impact coefficient of the i-th sub-region , root mechanical stress coefficient , soil moisture uniformity coefficient , Wind environmental impact coefficient and the spatial coefficient of variation of illumination After dimensionless processing, the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region is obtained by the following correlation formula: ; in, , , , and Represent the microbial environmental impact coefficient of the i-th sub-region , root mechanical stress coefficient , soil moisture uniformity coefficient , Wind environmental impact coefficient and the spatial coefficient of variation of illumination The weight value of represents the first correction constant; S4, preset the first risk threshold Y1, and set the comprehensive impact index of the Juncao transplanting environment in the i-th sub-area Compare and evaluate with the first risk threshold Y1 to obtain the environmental adaptability level, and generate the corresponding environmental optimization strategy according to the corresponding level; S5. After the corresponding environmental optimization strategy is executed, the Juncao transplanting operation is performed, and the compatibility data of the Juncao root secretions and the pest attraction effect data after transplanting are continuously collected during the growth of the Juncao transplanting, and a growth data set is established. Based on the growth data set, the growth risk coefficient of the i-th sub-area is constructed. , and preset the second risk threshold Y2, and set the growth risk coefficient of the i-th sub-region Compare with the second risk threshold Y2 to obtain a growth risk assessment result; S5 includes: S51. After the corresponding environmental optimization strategy is executed, the Juncao transplanting operation is performed, and during the growth of Juncao transplanting, the compatibility data of Juncao root secretions, the pest attraction effect data after transplanting and the soil density disturbance data are continuously collected to establish a growth data set; and the root secretion compatibility coefficient of the i-th sub-area is constructed based on the growth data set. , Pest attraction coefficient and soil disturbance influence coefficient , and associated with the root secretion compatibility coefficient of the i-th sub-region , Pest attraction coefficient and soil disturbance influence coefficient , get the growth risk coefficient of the i-th sub-region ; S511. Within the sampling point range of each sub-area, install a liquid collection device in the root distribution area of ​​the Juncao, set the sampling depth to 10-20 cm, obtain root secretion liquid samples, set the collection frequency to once every 2 days, and keep collecting for 6-8 weeks. Conduct experimental measurements on the root secretion liquid samples to obtain the organic acid concentration nd1, enzyme concentration nd2 and secretion pH value. , establish compatibility data of Juncao root exudates; S512. Pest monitoring equipment is arranged at sampling points in each sub-area. The pest monitoring equipment includes insect monitoring lights and ground traps. The pest monitoring equipment is arranged at intervals of 10-20 square meters to collect flying pest density. and underground pest density , establish pest attraction effect data after transplanting; S513. Install soil density sensors at sampling points in each sub-area at a distance of 1-9 cm from the surface layer and 10-20 cm from the sub-surface layer to collect soil density disturbance data. The soil density disturbance data includes soil density before transplanting. and soil density after transplanting ; S514, root secretion compatibility coefficient of the i-th sub-region Calculated by the following formula: in, represents the organic acid concentration in the ith sub-region, represents the enzyme concentration in the ith sub-region, represents the pH value of secretion in the i-th sub-region; Pest attraction coefficient of the i-th sub-region Calculated by the following formula: in, represents the density of flying pests in the ith sub-area, represents the underground pest density in the ith sub-region, represents the area of ​​the i-th sub-region; Soil disturbance influence coefficient of the i-th sub-area Calculated by the following formula: in, represents the soil density before transplanting in the ith sub-region, represents the soil density after transplanting in the ith sub-area; S515, the root secretion compatibility coefficient of the i-th sub-region , Pest attraction coefficient and soil disturbance influence coefficient After dimensionless processing, the growth risk coefficient of the ith sub-region is obtained through the following correlation formula: : in, , and are the root secretion compatibility coefficients of the i-th sub-region , Pest attraction coefficient and soil disturbance influence coefficient The weight value of Represents the second correction constant.

2. The intelligent Juncao transplanting method based on Internet of Things technology according to claim 1 is characterized in that: S1 includes: S11. Use UAV remote sensing mapping equipment to obtain high-definition images of the transplanting area and calculate the total area, mark low-lying areas, highlands, and shaded areas, divide the transplanting area into several sub-areas, use GIS geographic information system to draw a regional network, and simultaneously number several sub-areas in the regional network. , ,..., , n is the number of sub-regions; S12. Set a sampling point in each sub-area, arrange soil sensors, pH sensors, temperature sensors and microbial sensors with microfluidic chips, and collect soil microbial environmental data in real time. The soil microbial environmental data include: microbial species, microbial density, soil pH value sjd, soil temperature wd and microbial metabolite CO2 concentration htcd; S13. Set a sampling point in each sub-area, arrange anemometers and wind pressure sensors, and set the arrangement heights of anemometers and wind pressure sensors to 1 meter, 2 meters and 3 meters respectively. Collect wind speed and wind pressure data in real time. The sampling interval is set to record data every 10 minutes. The wind speed and wind pressure data include: wind speed v and wind pressure p. Combined with the geographic coordinates of the sampling points, generate a spatiotemporal distribution map of wind speed and pressure in the sub-area.

3. The intelligent Juncao transplanting method based on Internet of Things technology according to claim 2 is characterized in that: S1 also includes: S14. Set sampling points in each sub-area, install pressure sensors at depths of 5 cm, 10 cm and 20 cm from the ground surface, and collect mechanical stress data of the roots of the Juncao. The mechanical stress data of the roots of the Juncao include mechanical stress ; S15, a sampling point is arranged at an interval of 5-20 square meters in each sub-area, and a humidity sensor is installed at a depth of 0-20 cm from the ground surface at each sampling point to collect soil moisture data, the soil moisture data including soil moisture content M; S16. Arrange a sampling point at an interval of 5-20 square meters in each sub-area, and arrange a light intensity sensor at a distance of one meter from the ground at each sampling point to collect spatial variation data of light conditions, where the spatial variation data of light conditions includes light intensity L; S17. Summarize the soil microbial environment data, wind speed and pressure data, mechanical stress data of the grass root system, soil moisture data and spatial variation data of light conditions collected in S12-S16, and summarize and establish environmental biological data.

4. The intelligent Juncao transplanting method based on Internet of Things technology according to claim 1 is characterized in that S2 include: S21, matching the environmental biological data of several transplanting sub-areas collected in S1 with the numbers of each sub-area one by one to form an original data set; S22. Check the original data set to see if there are any missing values, check if there are any abnormal values, and check if the timestamps and geographic coordinates of the original data table are complete; S23. Setting a threshold range to determine whether there are abnormal values, including: The pH value sjd threshold range of soil is 4.5-9; The soil temperature wd threshold range is -10℃ to 50℃; The htcd threshold range of CO2 concentration of microbial metabolites is 0-500ppm; The wind speed v threshold range is 0-20m / s; The wind pressure p threshold range is 0-1.5 kPa; The soil moisture content M threshold range is 5%-50%; S24. Mark the data beyond the threshold range as outliers and remove them. Use the box plot analysis method to mark the original data set below the first quartile Q1×1.5 times IQR or above the third quartile Q3×1.5 times IQR as outliers. IQR means position, which means the middle 50% range of the original data set distribution. Q1 is the first quartile of the original data set, which is the value corresponding to the lower 25% position of the original data set after the original data set is centrally sorted. Q3 is the third quartile of the original data set, which is the value corresponding to the upper 25% position of the original data set after the original data set is centrally sorted. S25. After filling the missing values ​​between adjacent moments using linear interpolation or spline interpolation, the original data set is dimensionless and then imported into a transplant environment database in a unified format.

5. The intelligent Juncao transplanting method based on Internet of Things technology according to claim 1 is characterized in that S3 include: S31, using a convolutional neural network to construct an initial model of the convolutional neural network, and using the transplanting environment database to train and test the initial model of the convolutional neural network, and using the trained initial model of the convolutional neural network as a transplanting environment prediction model, and using the intermediate layer output of the transplanting environment prediction model as a feature vector to identify feature information, and using the acquired feature information to train and test the transplanting environment prediction model, and using the trained transplanting environment prediction model as data to run prediction; S32. Use the transplanting environment prediction model to extract and calculate the characteristics of the transplanting environment database to obtain the microbial environmental impact coefficient of the i-th sub-area , root mechanical stress coefficient , soil moisture uniformity coefficient , Wind environmental impact coefficient and the spatial coefficient of variation of illumination .

6. The intelligent Juncao transplanting method based on Internet of Things technology according to claim 1 is characterized in that S4 include: S41, preset the first risk threshold Y1, and set the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region Compare and evaluate with the first risk threshold Y1 to obtain the environmental adaptability level, including: If the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region > The first risk threshold Y1×150% indicates that the adaptability of the current sub-area is unqualified and unsuitable for Juncao transplanting. The sub-area is directly screened out and marked; If the first risk threshold Y1×120%≤the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region ≤ the first risk threshold Y1×150%, indicating that the adaptability of the current sub-region is unqualified and obtains the first level; If the first risk threshold Y1≤the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region < the first risk threshold Y1×120%, indicating that the adaptability of the current sub-region is unqualified and obtains the second level; If the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region < the first risk threshold Y1, indicating that the current sub-region is suitable for adaptation and obtains the third level; S42. After obtaining the environmental adaptability level, a corresponding environmental optimization strategy is generated, including: The first environmental optimization strategy is generated according to the first level, including: the current sub-area needs to be fully optimized, 5 grams of nitrogen-fixing bacteria, phosphate-dissolving bacteria and microbial preparations are added per square meter, and the application is repeated twice a week; the sub-area is plowed to a depth of 11-20 cm, and 200 grams of organic fertilizer is applied per square meter; the soil is loosened evenly in each sub-area, and 1 kg of sawdust / square meter is added; the coverage rate of the sunshade net is increased by 30%-50%, and a sunshade net is installed in each sub-area to reduce the light intensity to the target value L=200 lumens; a windbreak wall is arranged around the sub-area or a 2-meter-high windbreak forest is planted; after the first environmental optimization strategy is executed, the grass is transplanted, and the first transplanting depth of the sub-area is set to 5 cm; The second environmental optimization strategy is generated according to the second level, including: the current sub-area needs to be partially optimized, 3 grams of nitrogen-fixing bacteria, phosphate-dissolving bacteria and microbial preparations are added per square meter, and the application is repeated once a week; the sub-area is plowed to a depth of 5-10 cm, and 100 grams of organic fertilizer is applied per square meter; the soil is loosened evenly in each sub-area, and 0.5 kg of sawdust / square meter is added; the coverage rate of the sunshade net is increased by 10%-15%, and a sunshade net is installed in each sub-area to reduce the light intensity to the target value L=300 lumens; a windbreak wall is arranged around the sub-area or a 1-meter-high windbreak forest is planted; after the second environmental optimization strategy is executed, the grass is transplanted, and the second transplanting depth of the sub-area is set to 6-7 cm; The third environmental optimization strategy is generated according to the third level, including: direct transplanting of Juncao, and setting the third transplanting depth of the sub-area to 8-9 cm.

7. The intelligent Juncao transplanting method based on Internet of Things technology according to claim 1 is characterized in that: S5 also includes: S52, presetting a second risk threshold Y2, and setting the growth risk coefficient of the i-th sub-region Compare with the second risk threshold Y2 to obtain the growth risk assessment results, including: If the growth risk coefficient of the i-th sub-region > The second risk threshold Y2 indicates that the growth environment of the sub-area is at risk, and the fourth environmental optimization strategy is generated, including: adding 1-2 kg of sandy soil per square meter to improve aeration; applying 2 grams of microbial agents per square meter, repeating once a week, and applying three times in a row; arranging 1-2 trapping lights in each sub-area, spraying biological pesticides once a week for 2-3 consecutive weeks; If the growth risk coefficient of the i-th sub-region ≤ the second risk threshold Y2, indicating that there is no risk in the growth environment of the sub-region and continuous monitoring is required.

8. An intelligent Juncao transplanting system based on Internet of Things technology is applied to the intelligent Juncao transplanting method based on Internet of Things technology described in any one of claims 1 to 7, characterized in that: include: The segmentation unit is used to divide the transplanting area into several sub-areas, draw a regional network using the GIS geographic information system, and simultaneously number several sub-areas in the regional network. , ,..., , n is the number of sub-regions; The first IoT collection unit is used to collect environmental biological data in the sub-area using distributed IoT sensing devices, wherein the environmental biological data includes soil microbial environment data, wind speed and wind pressure data, mechanical stress data of the grass root system, soil moisture data, and spatial variation data of light conditions; Establish a model unit to establish a transplanting environment prediction model using convolutional neural network technology. After training, extract and calculate the features of the transplanting environment database to obtain the microbial environmental impact coefficient of the i-th sub-area. , root mechanical stress coefficient , soil moisture uniformity coefficient , Wind environmental impact coefficient and the spatial coefficient of variation of illumination ; and associate to obtain the comprehensive impact index of the Juncao transplanting environment in the i-th sub-region ; The first evaluation unit is used to preset the first risk threshold Y1 and calculate the comprehensive impact index of the Juncao transplanting environment in the i-th sub-area. Compare and evaluate with the first risk threshold Y1 to obtain the environmental adaptability level, and generate the corresponding environmental optimization strategy according to the corresponding level; The second IoT monitoring unit is used to perform the Juncao transplanting operation after the corresponding environmental optimization strategy is executed, and continuously collect the compatibility data of Juncao root secretions and the pest attraction effect data after transplanting during the growth of Juncao transplanting, and establish a growth data set; The second evaluation unit is used to construct the growth risk coefficient of the i-th sub-region based on the growth data set. , and preset the second risk threshold Y2, and set the growth risk coefficient of the i-th sub-region The value is compared with the second risk threshold Y2 to obtain a growth risk assessment result.

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