Intelligent agriculture big data cloud service supervision platform fusing aerial remote sensing and spatial distance measurement information

Abstract

The invention relates to the technical field of space ranging, in particular to a smart agriculture big data cloud service supervision platform fusing aerial remote sensing and space ranging information. The method comprises the steps that a data acquisition unit acquires agricultural data based on a multi-source acquisition module; the data processing unit performs cleaning, correction and matching processing based on the agricultural data acquired by the data acquisition unit; the data analysis unit performs multi-aspect evaluation analysis through a multi-party analysis technology based on the agricultural data processed by the data processing unit; and the storage management unit manages and protects agricultural data through a cloud computing distributed storage technology based on an analysis result of the data analysis unit. According to the design of the invention, multi-dimensional evaluation and prediction are carried out on agricultural data by adopting a multi-party analysis technology, and a scientific basis and intelligent decision support are provided for agricultural production; through a cloud computing distributed storage technology, rapid access, backup and sharing of large-scale agricultural data are realized, and meanwhile, the security and privacy of the data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of space distance measurement technology, and in particular to a smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space distance measurement information. Background Art

[0002] A smart agricultural big data cloud service supervision platform that integrates aerial remote sensing and spatial ranging information is a comprehensive agricultural management solution built on the basis of new generation information technology. The platform integrates aerial remote sensing technology (such as drones or multispectral satellite images) and spatial ranging technology (such as GNSS global navigation satellite system, LiDAR laser radar, etc.) to achieve all-round and high-precision monitoring of farmland environment, crop growth status, soil characteristics and meteorological conditions. At the same time, relying on cloud computing, big data analysis and artificial intelligence algorithms, the platform can integrate multi-source data, conduct in-depth mining and intelligent analysis, and provide accurate decision-making support for agricultural production. Specifically, it can not only monitor crop growth, pest and disease occurrence and soil moisture and nutrient status in real time, but also predict yield trends, optimize irrigation and fertilization plans, and support agricultural product quality traceability and market supply and demand analysis. In addition, with the help of geographic information system (GIS) and visualization technology, the platform can generate intuitive three-dimensional digital twin models to help managers fully grasp the distribution and dynamic changes of agricultural resources.

[0003] In the prior art, the smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information has problems such as insufficient data acquisition accuracy, low processing efficiency, limited analysis ability and weak data security. To this end, the present invention proposes an improved solution. Summary of the invention

[0004] The purpose of the present invention is to provide a smart agricultural big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, so as to solve the problems of insufficient data acquisition accuracy, low processing efficiency, limited analysis capability and weak data security in the existing smart agricultural big data cloud service supervision platform that integrates aerial remote sensing and space ranging information proposed in the above background technology.

[0005] To achieve the above-mentioned object, the present invention aims to provide a smart agricultural big data cloud service supervision platform integrating aerial remote sensing and space ranging information, including a data acquisition unit, which acquires agricultural data based on a multi-source acquisition module; A data processing unit, wherein the data processing unit performs cleaning, correction and matching processing based on the agricultural data collected by the data collection unit; A data analysis unit, which performs multi-faceted evaluation and analysis based on the agricultural data processed by the data processing unit through multi-party analysis technology; A storage management unit manages and protects agricultural data through cloud computing distributed storage technology based on the analysis results of the data analysis unit.

[0006] As a further improvement of the technical solution, the multi-source acquisition module includes an aerial remote sensing module, a space ranging module and a ground sensor module; Among them, agricultural data include farmland area, soil moisture, temperature and nutrient content; The aerial remote sensing module is used to collect high-resolution multispectral, thermal infrared and visible light images; The spatial distance measurement module is used to obtain farmland area information; The ground sensor module is used to monitor soil moisture, temperature, nutrient content and meteorological conditions.

[0007] As a further improvement of the technical solution, the data processing unit includes a data cleaning module, a data correction module and a data matching module; Wherein, the data cleaning module is used to remove noise, outliers and redundant data; The specific process of removing noise, outliers and redundant data is as follows: Use the mean filter algorithm to remove noise from remote sensing images: ; In the formula, Represents the pixel value after denoising; Indicates that the original image Centered window The pixel value within Indicates The neighborhood window centered at ; Display Window The total number of pixels in the Detect outliers using the standard deviation method: ; In the formula, Indicates the value of the current data point; Represents the standardized value; represents the mean of the data; Indicates the standard deviation of the data; Use correlation analysis to remove redundant features: ; In the formula, Representation characteristics and Features The correlation coefficient of Representation characteristics and Features The covariance of Representation characteristics The standard deviation of Representation characteristics The standard deviation of Represents the feature index variable; represents another feature index variable; The data correction module is used to perform radiation correction, geometric correction and atmospheric correction on the image of the aerial remote sensing module; The data matching module is used to match agricultural data in time and space dimensions.

[0008] As a further improvement of the technical solution, the specific process of performing radiation correction, geometric correction and atmospheric correction is as follows: Radiometric correction: ; In the formula, Indicates the light radiation brightness value; represents the sensor-specific scaling factor; Represents a digital value; Indicates the sensor-specific offset; Geometric correction: ; In the formula, represents the corrected coordinate matrix; Represents the original coordinate matrix; represents the affine transformation matrix; represents the translation matrix; Atmospheric correction: ; In the formula, Represents the surface reflectivity; Indicates the atmospheric path radiation value; represents the atmospheric transmittance.

[0009] As a further improvement of this technical solution, the specific process of matching agricultural data in the time and space dimensions is as follows: Use spatial interpolation to align time series data: ; In the formula, Indicates the target time point The characteristic value of Indicates time point The characteristic value of farmland area; Indicates time point The characteristic value of soil moisture; Indicates time point The temperature characteristic value of Indicates time point Characteristic value of nutrient content; Indicates time point The weight of Indicates time point The weight of Indicates time point The weight of Indicates time point The weight of Represents a time-indexed variable.

[0010] As a further improvement of the technical solution, the specific steps of performing multi-faceted evaluation and analysis by multi-party analysis technology are as follows: Among them, multi-analysis techniques include crop growth dynamic models, remote sensing image machine learning algorithms, and soil quality assessment methods; S6.1. Combine agricultural data and use soil quality assessment methods to assess soil quality; S6.2. Identify the occurrence area and severity of pests and diseases through remote sensing image machine learning algorithms; S6.3. Use historical agricultural data and real-time agricultural data to build a dynamic crop growth model to predict crop growth and yield.

[0011] As a further improvement of the technical solution, in S6.1, the specific process of evaluating and analyzing the soil quality by the soil quality evaluation method is as follows: Calculate the soil nutrient comprehensive index: ; In the formula, It represents the comprehensive index of soil nutrients; Indicates the nitrogen content; Indicates the phosphorus content; Indicates the potassium content; represents the weight of nitrogen; represents the weight of phosphorus; represents the weight of potassium; Estimation of organic matter content using remote sensing inversion models: ; In the formula, Indicates organic content; It indicates the influence of normalized difference vegetation index on organic matter content; Indicated in The basic value of soil organic matter content at zero; represents vegetation index; After standardizing each indicator, a weighted sum is performed to obtain a comprehensive soil quality score: ; In the formula, It represents the comprehensive score of soil quality; Indicates Standardized values ​​of the indicators; Indicates The weight of each indicator; Indicates the total number of indicators; Represents an indicator index variable.

[0012] As a further improvement of the technical solution, in S6.2, the specific process of identifying the occurrence area and severity of pests and diseases through the remote sensing image machine learning algorithm is as follows: Extract spectral features from remote sensing images and normalize vegetation index : ; In the formula, Represents the reflectivity in the near-infrared band; Use support vector machines to classify pest and disease areas: ; In the formula, represents the classification function; represents the input feature vector; represents the number of input feature vectors; A variable that represents the number of input feature vectors; represents the Lagrange multiplier; represents the kernel function; represents the bias term; Represents the training sample label; represents the input feature vector label; Among them, the input feature vector for: ; Determine whether the pixel belongs to the pest area based on the classification result: ; Calculate the proportion of area covered by pests and diseases to the total planted area: ; In the formula, It indicates the proportion of the area covered by pests and diseases to the total planted area; Indicates resolution; Considering that the pesticide coverage area has an impact on the occurrence area and severity of pests and diseases, the pesticide coverage area variable is introduced to optimize the remote sensing image machine learning algorithm: ; In the formula, represents the classification function of the introduced pesticide coverage area variable; represents the variable of pesticide coverage area; After introducing the pesticide coverage area variable, we can determine whether the pixel belongs to the pest and disease area based on the classification results: ; Calculate the proportion of area covered by pests and diseases to the total planted area: ; In the formula, It represents the proportion of pest and disease covered area to the total planted area after the introduction of the pesticide coverage area variable; Represents the pesticide effectiveness coefficient.

[0013] As a further improvement of the technical solution, in S6.3, the specific process of constructing the crop growth dynamic model is: Use crop growth dynamics models to predict final yields: ; In the formula, represents the predicted output; represents the proportionality coefficient; Indicates the offset; Indicates the crop at time The amount of growth; in, Specifically: ; In the formula, Indicates the maximum growth value; represents the growth rate; Indicates the time to reach half-saturation value; Considering that soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture have an impact on crop growth, soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture variables are introduced to optimize the crop growth dynamic model: ; In the formula, Indicates the effect of soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture on crops over time. The amount of growth; represents the proportionality coefficient affected by soil oxygen concentration, temperature and soil nutrients; Indicates the offset affected by soil nutrients and soil acidity and alkalinity; in, for: ; In the formula, It indicates the maximum growth value affected by soil oxygen concentration, light radiation brightness, soil nutrients and soil moisture; Indicates growth rate affected by soil oxygen concentration, temperature, and soil acidity; Indicates soil oxygen concentration; Indicates temperature; Indicates soil acidity and alkalinity; Indicates soil moisture; Considering that the coverage area of ​​pests and diseases has an inhibitory effect on the maximum growth value and growth rate of crops, the dynamic model of crop growth optimization based on the coverage area of ​​pests and diseases is introduced: ; In the formula, represents the final predicted output; It indicates the inhibition coefficient of pests and diseases on yield; It indicates the amount of growth that is optimized again after the introduction of pest and disease coverage area; in, for: ; In the formula, It indicates the inhibition coefficient of pests and diseases on the maximum growth value; It indicates the inhibition coefficient of pests and diseases on growth rate; Considering that the yield is affected by the multi-stage cumulative effect, the multi-stage cumulative effect is introduced to optimize the crop growth dynamic model: ; In the formula, It indicates the predicted yield of introducing soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and alkalinity, soil moisture and multi-stage cumulative effects; Indicates the growth amount during the tillering stage; Indicates the amount of growth during the flowering period; It indicates the amount of growth during the filling period; Use the root mean square error to evaluate the model prediction accuracy: ; In the formula, represents the root mean square error; represents the actual observed value; represents the model prediction value; represents the number of samples; Represents the sample index variable.

[0014] As a further improvement of this technical solution, the specific steps of managing and protecting agricultural data through cloud computing distributed storage technology are: S10.1. Use cloud computing distributed storage technology to efficiently store agricultural data; Among them, cloud computing distributed storage technologies include cloud object storage technology, relational databases, and distributed file systems; S10.2. Classify, manage and share agricultural data; S10.3. Use encryption technology to protect the security of data during transmission and storage.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In this smart agricultural big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, agricultural-related data is obtained from multiple channels through a multi-source acquisition module to provide the platform with a comprehensive, real-time, and high-precision agricultural data foundation, ensuring that subsequent analysis and decision-making have reliable data support. The original agricultural data obtained by the data acquisition unit is cleaned, corrected, and matched, and the messy original data is converted into a structured, high-quality data set.

[0016] 2. This smart agricultural big data cloud service supervision platform integrates aerial remote sensing and space ranging information. By adopting multi-party analysis technology, agricultural data is evaluated and predicted in multiple dimensions, providing scientific basis and intelligent decision-making support for agricultural production. Through cloud computing distributed storage technology, large-scale agricultural data can be quickly accessed, backed up and shared, while ensuring data security and privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Data acquisition unit; 11. Multi-source acquisition module; 111. Aerial remote sensing module; 112. Space ranging module; 113. Ground sensor module; 2. Data processing unit; 21. Data cleaning module; 22. Data correction module; 23. Data matching module; 3. Data analysis unit; 4. Storage management unit. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example See also Figure 1As shown, a smart agricultural big data cloud service supervision platform integrating aerial remote sensing and space ranging information is provided, including a data acquisition unit 1, which acquires agricultural data based on a multi-source acquisition module 11; In this example, the multi-source acquisition module 11 includes an aerial remote sensing module 111 , a space ranging module 112 , and a ground sensor module 113 ; Among them, agricultural data include farmland area, soil moisture, temperature and nutrient content; The aerial remote sensing module 111 is used to collect high-resolution multi-spectral, thermal infrared and visible light images; The spatial distance measurement module 112 is used to obtain farmland area information; The ground sensor module 113 is used to monitor soil moisture, temperature, nutrient content and meteorological conditions.

[0020] Specifically, in a smart agriculture big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, the data acquisition unit 1 achieves comprehensive acquisition of agricultural data through a multi-source acquisition module 11, where the aerial remote sensing module 111 is responsible for collecting high-resolution multispectral, thermal infrared and visible light images for monitoring crop growth status, health status and environmental changes; the space ranging module 112 provides farmland area and crop height information to assist in precise farmland planning and crop structure analysis; the ground sensor module 113 monitors soil moisture, temperature, nutrient content and meteorological conditions in real time, providing refined environmental data support for agricultural production. The three work together to build a "sky-ground integrated" data acquisition system, providing comprehensive and accurate basic data for subsequent data processing, analysis and decision-making, thereby improving the level of intelligence in agricultural production and resource utilization efficiency.

[0021] The data processing unit 2 performs cleaning, correction and matching processing based on the agricultural data collected by the data collection unit 1; In this example, the data processing unit 2 includes a data cleaning module 21, a data correction module 22 and a data matching module 23; Wherein, the data cleaning module 21 is used to remove noise, outliers and redundant data; The specific process of removing noise, outliers and redundant data is as follows: Use the mean filter algorithm to remove noise from remote sensing images: ; In the formula, Represents the pixel value after denoising; Indicates that the original image Centered window The pixel value within Indicates The neighborhood window centered at ; Display Window The total number of pixels in the Detect outliers using the standard deviation method: ; In the formula, Indicates the value of the current data point; Represents the standardized value; represents the mean of the data; Indicates the standard deviation of the data; Use correlation analysis to remove redundant features: ; In the formula, Representation characteristics and Features The correlation coefficient of Representation characteristics and Features The covariance of Representation characteristics The standard deviation of Representation characteristics The standard deviation of Represents the feature index variable; represents another feature index variable; The data correction module 22 is used to perform radiation correction, geometric correction and atmospheric correction on the image of the aerial remote sensing module 111; In this example, the specific process of radiation correction, geometric correction and atmospheric correction is as follows: Radiometric correction: ; In the formula, Indicates the light radiation brightness value; represents the sensor-specific scaling factor; Represents a digital value; Indicates the sensor-specific offset; Specifically, the light radiation brightness value is obtained through the aerial remote sensing module 111; Geometric correction: ; In the formula, represents the corrected coordinate matrix; Represents the original coordinate matrix; represents the affine transformation matrix; represents the translation matrix; Atmospheric correction: ; In the formula, Represents the surface reflectivity; Indicates the atmospheric path radiation value; represents the atmospheric transmittance.

[0022] The atmospheric path radiation value and the atmospheric transmittance are obtained through the aerial remote sensing module 111; Specifically, in a smart agriculture big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, the specific processes of radiation correction, geometric correction and atmospheric correction ensure the quality and reliability of remote sensing image data. Radiation correction uses formulas to convert digital values ​​into light radiation brightness values ​​to eliminate the impact of sensor characteristic differences on the data; geometric correction uses affine transformation matrices and translation matrices to convert original coordinates into corrected coordinates to correct the geometric distortion of the image caused by sensors or terrain factors; atmospheric correction uses formulas to remove the impact of atmospheric scattering and absorption on surface reflectivity and improve the authenticity of the image. These three correction processes work together to significantly improve the accuracy and availability of remote sensing images, provide high-quality basic data for subsequent data analysis, crop monitoring and decision support, and are an important technical guarantee for the realization of precision management of smart agriculture.

[0023] The data matching module 23 is used to match agricultural data in time and space dimensions.

[0024] In this example, the specific process of matching agricultural data in time and space dimensions is as follows: Use spatial interpolation to align time series data: ; In the formula, Indicates the target time point The characteristic value of Indicates time point The characteristic value of farmland area; Indicates time point The characteristic value of soil moisture; Indicates time point The temperature characteristic value of Indicates time point Characteristic value of nutrient content; Indicates time point The weight of Indicates time point The weight of Indicates time point The weight of Indicates time point The weight of Represents a time-indexed variable.

[0025] Specifically, in a smart agriculture big data cloud service supervision platform that integrates aerial remote sensing and spatial ranging information, through time series data registration, feature extraction and matching, matching is achieved in the spatiotemporal dimension of agricultural data. Specifically, the spatial interpolation method is used to register data at different time points, and continuous data at the target time point is generated by weighted summation to make up for the data loss caused by the time sampling interval; it provides high-quality and high-precision comprehensive data support for crop monitoring, environmental analysis and precision agricultural decision-making, and is the core technical link for realizing intelligent management of smart agriculture.

[0026] Furthermore, in a smart agriculture big data cloud service supervision platform that integrates aerial remote sensing and spatial ranging information, the data cleaning module uses the mean filtering algorithm to remove remote sensing image noise, the standard deviation method to detect outliers, and the correlation analysis to eliminate redundant features to ensure the accuracy and validity of the data; the data correction module performs radiation correction, geometric correction, and atmospheric correction on aerial remote sensing images to improve image quality and eliminate external environmental interference; the data matching module matches agricultural data from different sources and time and space dimensions to achieve seamless connection and unified expression of data. This series of processing procedures provides a high-quality and highly reliable data foundation for subsequent data analysis and decision support, and significantly improves the data processing capability and intelligence level of the smart agriculture big data cloud service supervision platform.

[0027] Data analysis unit 3, based on the agricultural data processed by data processing unit 2, data analysis unit 3 performs multi-faceted evaluation and analysis through multi-party analysis technology; In this example, the specific steps of conducting multi-faceted evaluation and analysis through multi-party analysis technology are as follows: Among them, multi-analysis techniques include crop growth dynamic models, remote sensing image machine learning algorithms, and soil quality assessment methods; S6.1. Combine agricultural data and use soil quality assessment methods to assess soil quality; In this example, the specific process of evaluating and analyzing soil quality through the soil quality assessment method is as follows: Calculate the soil nutrient comprehensive index: ; In the formula, It represents the comprehensive index of soil nutrients; Indicates the nitrogen content; Indicates the phosphorus content; Indicates the potassium content; represents the weight of nitrogen; represents the weight of phosphorus; represents the weight of potassium; Determine soil acidity and alkalinity: Acidic: ; neutral: ; Alkaline: ; Estimation of organic matter content using remote sensing inversion models: ; In the formula, Indicates organic content; It indicates the influence of normalized difference vegetation index on organic matter content; Indicated in The basic value of soil organic matter content at zero; represents vegetation index; After standardizing each indicator, a weighted sum is performed to obtain a comprehensive soil quality score: ; In the formula, It represents the comprehensive score of soil quality; Indicates Standardized values ​​of the indicators; Indicates The weight of each indicator; Indicates the total number of indicators; Represents an indicator index variable.

[0028] Specifically, in a smart agriculture big data cloud service supervision platform that integrates aerial remote sensing and spatial ranging information, the soil quality assessment method provides a scientific basis for precision agriculture through quantitative analysis of key indicators such as soil nutrient comprehensive index (NPI), acidity (pH value), and organic matter content (OM). These indicators are standardized and weighted to generate a comprehensive soil quality score (SQI), which can fully reflect the health of the soil and help the platform to achieve dynamic monitoring, accurate diagnosis and optimized management of farmland soil status. Combining the spatial coverage capability of aerial remote sensing data and the high-precision positioning function of spatial ranging information, this method can support regional soil quality assessment, guide fertilization, improvement measures and crop planting planning, thereby improving agricultural production efficiency and sustainable development.

[0029] S6.2. Identify the occurrence area and severity of pests and diseases through remote sensing image machine learning algorithms; In this example, the specific process of identifying the occurrence area and severity of pests and diseases through remote sensing image machine learning algorithms is as follows: Extract spectral features from remote sensing images and normalize vegetation index : ; In the formula, Represents the reflectivity in the near-infrared band; Use support vector machines to classify pest and disease areas: ; In the formula, represents the classification function; represents the input feature vector; represents the number of input feature vectors; A variable that represents the number of input feature vectors; represents the Lagrange multiplier; represents the kernel function; represents the bias term; Represents the training sample label; represents the input feature vector label; Among them, the input feature vector for: ; Determine whether the pixel belongs to the pest area based on the classification result: ; Calculate the proportion of area covered by pests and diseases to the total planted area: ; In the formula, It indicates the proportion of the area covered by pests and diseases to the total planted area; Indicates resolution; Considering that the pesticide coverage area has an impact on the occurrence area and severity of pests and diseases, the pesticide coverage area variable is introduced to optimize the remote sensing image machine learning algorithm: ; In the formula, represents the classification function of the introduced pesticide coverage area variable; represents the variable of pesticide coverage area; After introducing the pesticide coverage area variable, we can determine whether the pixel belongs to the pest and disease area based on the classification results: ; Calculate the proportion of area covered by pests and diseases to the total planted area: ; In the formula, It represents the proportion of pest and disease covered area to the total planted area after the introduction of the pesticide coverage area variable; Represents the pesticide effectiveness coefficient.

[0030] Specifically, in this embodiment, the specific process of identifying the occurrence area and severity of pests and diseases through remote sensing image machine learning algorithms can achieve accurate monitoring and evaluation of the health status of farmland. First, the normalized difference vegetation index (NDVI) is used to extract the key features of the crop growth status, and the pest and disease areas are classified in combination with the support vector machine model to generate a high-precision pest and disease distribution map. A pesticide coverage area variable optimization algorithm is further introduced to consider the impact of pesticide use on pests and diseases, so as to more accurately calculate the proportion of pest and disease coverage area. This method combines the large-scale data collection capabilities of aerial remote sensing and the high-precision positioning function of spatial ranging information to provide the platform with real-time and dynamic pest and disease monitoring data, help formulate scientific prevention and control strategies, reduce pesticide abuse, improve agricultural production efficiency and sustainability, and provide important support for smart agricultural decision-making. Among them, P The physical meaning of is the area actually covered by pesticide spraying in the farmland area obtained by remote sensing technology (unit: hectare or square meter), and its value range is P ≥0 (non-negative value). or The physical meaning of is the actual effectiveness ratio of pesticides in the target area (actual utilization rate after considering environmental loss), and its value range should be 0≤ or ≤1, where or =1 means fully effective (no loss), or =0 means completely invalid.

[0031] S6.3. Use historical agricultural data and real-time agricultural data to build a dynamic crop growth model to predict crop growth and yield.

[0032] In this example, the specific process of constructing the crop growth dynamic model is as follows: Use crop growth dynamics models to predict final yields: ; In the formula, represents the predicted output; represents the proportionality coefficient; Indicates the offset; Indicates the crop at time The amount of growth; in, Specifically: ; In the formula, Indicates the maximum growth value; represents the growth rate; Indicates the time to reach half-saturation value; Considering that soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture have an impact on crop growth, soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture variables are introduced to optimize the crop growth dynamic model: ; In the formula, Indicates the effect of soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture on crops over time. The amount of growth; represents the proportionality coefficient affected by soil oxygen concentration, temperature and soil nutrients; Indicates the offset affected by soil nutrients and soil acidity and alkalinity; Specifically, The specific mathematical expression is: ; In the formula, represents the base scale factor; represents the temperature regulation coefficient; It represents the half-saturation constant of soil nutrients; It represents the oxygen enhancement factor; Indicates the optimum concentration of oxygen; Base temperature that indicates crop metabolic efficiency; in, The specific mathematical expression is: ; In the formula, Indicates the base offset; express Nutrient synergy coefficient; Neutral value; in, The specific mathematical expression is: ; In the formula, It indicates the maximum growth value affected by soil oxygen concentration, light radiation brightness, soil nutrients and soil moisture; Indicates growth rate affected by soil oxygen concentration, temperature, and soil acidity; Indicates soil oxygen concentration; Indicates temperature; Indicates soil acidity and alkalinity; Indicates soil moisture; Specifically, The specific mathematical expression is: ; In the formula, represents the theoretical maximum growth; The half-saturation constant representing the brightness of the illumination radiation; Indicates optimum soil moisture; represents the humidity deviation penalty coefficient; represents the half-saturation constant of oxygen; in, The specific mathematical expression is: ; In the formula, represents the theoretical maximum growth rate; represents the temperature sensitivity coefficient; represents the optimum temperature; Indicates the minimum value of soil acidity and alkalinity; Indicates the maximum value of soil acidity and alkalinity; represents the hypoxia stress coefficient; represents the oxygen response coefficient; Considering that the coverage area of ​​pests and diseases has an inhibitory effect on the maximum growth value and growth rate of crops, the dynamic model of crop growth optimization based on the coverage area of ​​pests and diseases is introduced: ; In the formula, represents the final predicted output; It indicates the inhibition coefficient of pests and diseases on yield; It indicates the amount of growth that is optimized again after the introduction of pest and disease coverage area; in, The specific mathematical expression is: ; In the formula, It indicates the inhibition coefficient of pests and diseases on the maximum growth value; It indicates the inhibition coefficient of pests and diseases on growth rate; Considering that the yield is affected by the multi-stage cumulative effect, the multi-stage cumulative effect is introduced to optimize the crop growth dynamic model: ; In the formula, It indicates the predicted yield of introducing soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and alkalinity, soil moisture and multi-stage cumulative effects; Indicates the growth amount during the tillering stage; Indicates the amount of growth during the flowering period; It indicates the amount of growth during the filling period; Specifically, , , They are: ; ; ; In the formula, Indicates the maximum growth value during the tillering stage; Indicates the maximum growth value during the flowering period; It indicates the maximum growth value during the filling period; Indicates the growth rate during the tillering stage; Indicates the growth rate during the flowering period; represents the growth rate during the grain filling period; Indicates the start time of tillering period; Indicates the start time of flowering period; Indicates the start time of the grouting period; Use the root mean square error to evaluate the model prediction accuracy: ; In the formula, represents the root mean square error; represents the actual observed value; represents the model prediction value; represents the number of samples; Represents the sample index variable.

[0033] Specifically, in a smart agricultural big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, the specific process of constructing a crop growth dynamic model can achieve accurate prediction of crop growth status and final yield. By introducing multiple factors such as soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and alkalinity, soil moisture, and pest and disease coverage area, the Logistic growth model is optimized in combination with the staged cumulative effect to make the model more in line with actual agricultural production conditions. The model uses high-resolution data provided by remote sensing images and the precise positioning capability of space ranging technology to monitor crop growth dynamics in real time, evaluate the impact of environmental factors on crops, and predict final yield. The results can provide scientific decision-making support for the platform, help with precise fertilization, irrigation management, and pest and disease control, thereby improving resource utilization efficiency, reducing agricultural input waste, and promoting the intelligent and sustainable development of agricultural production. At the same time, the root mean square error (RMSE) is used to evaluate the accuracy of the model to ensure the reliability of the prediction results and further improve the service quality of the platform.

[0034] Furthermore, in the smart agriculture big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, soil quality assessment methods are used to quantify key indicators such as soil nutrients, acidity, and organic matter content, providing a scientific basis for precision fertilization and soil improvement; remote sensing image machine learning algorithms are used to identify the occurrence areas and severity of pests and diseases, and to achieve early warning and precise prevention and control of pests and diseases; based on historical and real-time agricultural data, a crop growth dynamic model is constructed, and environmental factors (such as soil and meteorological conditions) and multi-stage cumulative effects are combined to predict crop growth and yield, helping agricultural production decision-making. The three work together to give full play to the spatial coverage capabilities of remote sensing data and the high-precision positioning advantages of ranging information, providing the platform with comprehensive and dynamic farmland monitoring and management support, and promoting the intelligent, precise and sustainable development of agricultural production.

[0035] The storage management unit 4 manages and protects the agricultural data through cloud computing distributed storage technology based on the analysis results of the data analysis unit 3.

[0036] In this example, the specific steps for managing and protecting agricultural data through cloud computing distributed storage technology are: S10.1. Use cloud computing distributed storage technology to efficiently store agricultural data; Among them, cloud computing distributed storage technologies include cloud object storage technology, relational databases, and distributed file systems; Specifically, using cloud computing distributed storage technology to efficiently store agricultural data is as follows: Store unstructured data such as remote sensing images and sensor data in cloud object storage (such as Alibaba Cloud OSS); Use a relational database (such as MySQL) to store structured data, such as crop growth model parameters and soil quality scores; Distributed storage of large-scale data is achieved with the help of distributed file systems (such as HDFS).

[0037] S10.2. Classify, manage and share agricultural data; Specifically, data are classified and managed by data type (remote sensing images, sensor data, etc.), time dimension (historical, real-time, predicted) and region (farmland plots).

[0038] Implement data lifecycle management strategies, regularly archive historical data to cold storage, and clean up useless data to optimize storage space.

[0039] Provides standardized API interfaces to support secure sharing of agricultural data and interaction with external systems.

[0040] S10.3. Use encryption technology to protect the security of data during transmission and storage.

[0041] Specifically, encryption technology (such as SSL / TLS, AES) is used to protect the security of data during transmission and storage.

[0042] Implement role-based access control (RBAC) to limit unauthorized data access.

[0043] Configure an off-site disaster recovery mechanism, regularly back up important data and test the recovery process to ensure rapid business recovery in emergencies.

[0044] Furthermore, in a smart agricultural big data cloud service supervision platform that integrates aerial remote sensing and space ranging information, cloud computing distributed storage technology (including cloud object storage, relational database and distributed file system) is used to achieve efficient storage and management of massive agricultural data, ensuring data reliability and scalability. At the same time, agricultural data is classified, lifecycle managed and shared, optimizing data utilization efficiency and meeting the needs of different users. In addition, encryption technology is used to ensure the security of data during transmission and storage, prevent data leakage or tampering, and provide a safe and stable data support environment for agricultural production, decision analysis and information services. This series of functions ensures the intelligence, efficiency and security of the platform in data management, and promotes the sustainable development of smart agriculture.

[0045] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information, characterized in that: include A data collection unit (1), wherein the data collection unit (1) acquires agricultural data based on a multi-source collection module (11); A data processing unit (2), wherein the data processing unit (2) performs cleaning, correction and matching processing on the agricultural data collected by the data collection unit (1); A data analysis unit (3), wherein the data analysis unit (3) performs multi-faceted evaluation and analysis based on the agricultural data processed by the data processing unit (2) by using a multi-faceted analysis technique; A storage management unit (4), which manages and protects agricultural data based on the analysis results of the data analysis unit (3) through cloud computing distributed storage technology.

2. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 1 is characterized by: The multi-source acquisition module (11) comprises an aerial remote sensing module (111), a space ranging module (112) and a ground sensor module (113); Among them, agricultural data include farmland area, soil moisture, temperature and nutrient content; The aerial remote sensing module (111) is used to collect high-resolution multi-spectral, thermal infrared and visible light images; The spatial distance measurement module (112) is used to obtain farmland area information; The ground sensor module (113) is used to monitor soil moisture, temperature and nutrient content.

3. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 1 is characterized by: The data processing unit (2) comprises a data cleaning module (21), a data correction module (22) and a data matching module (23); Wherein, the data cleaning module (21) is used to remove noise, outliers and redundant data; The specific process of removing noise, outliers and redundant data is as follows: Use the mean filter algorithm to remove noise from remote sensing images: ； In the formula, Represents the pixel value after denoising; Indicates that the original image Centered window The pixel value within Indicates The neighborhood window centered at ; Display Window The total number of pixels in the Detect outliers using the standard deviation method: ； In the formula, Indicates the value of the current data point; Represents the standardized value; represents the mean of the data; Indicates the standard deviation of the data; Use correlation analysis to remove redundant features: ； In the formula, Representation characteristics and Features The correlation coefficient of Representation characteristics and Features The covariance of Representation characteristics The standard deviation of Representation characteristics The standard deviation of Represents the feature index variable; represents another feature index variable; The data correction module (22) is used to perform radiation correction, geometric correction and atmospheric correction on the image of the aerial remote sensing module (111); The data matching module (23) is used to match agricultural data in time and space dimensions.

4. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 3 is characterized in that: The specific process of the data correction module (22) performing radiation correction, geometric correction and atmospheric correction is as follows: Radiometric correction: ； In the formula, Indicates the light radiation brightness value; represents the sensor-specific scaling factor; Represents a digital value; Indicates the sensor-specific offset; Geometric correction: ； In the formula, represents the corrected coordinate matrix; Represents the original coordinate matrix; represents the affine transformation matrix; represents the translation matrix; Atmospheric correction: ； In the formula, Represents the surface reflectivity; Indicates the atmospheric path radiation value; represents the atmospheric transmittance.

5. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 4 is characterized in that: The specific process of the data matching module (23) matching agricultural data in the time and space dimensions is as follows: Use spatial interpolation to align time series data: ； In the formula, Indicates the target time point The characteristic value of Indicates time point The characteristic value of farmland area; Indicates time point The characteristic value of soil moisture; Indicates time point The temperature characteristic value of Indicates time point Characteristic value of nutrient content; Indicates time point The weight of Indicates time point The weight of Indicates time point The weight of Indicates time point The weight of Represents a time-indexed variable.

6. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 5 is characterized in that: The specific steps of the data analysis unit (3) performing multi-faceted evaluation and analysis using multi-faceted analysis technology are as follows: Among them, multi-analysis techniques include crop growth dynamic models, remote sensing image machine learning algorithms, and soil quality assessment methods; S6.

1. Combine agricultural data and use soil quality assessment methods to assess and analyze soil quality; S6.

2. Identify the occurrence area and severity of pests and diseases through remote sensing image machine learning algorithms; S6.

3. Use historical agricultural data and real-time agricultural data to build a dynamic crop growth model to predict crop growth and yield.

7. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 6 is characterized by: In S6.1, the specific process of evaluating and analyzing soil quality by the soil quality evaluation method is as follows: Calculate the soil nutrient comprehensive index: ； In the formula, It represents the comprehensive index of soil nutrients; Indicates the nitrogen content; Indicates the phosphorus content; Indicates the potassium content; represents the weight of nitrogen; represents the weight of phosphorus; represents the weight of potassium; Estimation of organic matter content using remote sensing inversion models: ； In the formula, Indicates organic content; It indicates the influence of normalized difference vegetation index on organic matter content; Indicated in The basic value of soil organic matter content at zero; represents vegetation index; After standardizing each indicator, a weighted sum is performed to obtain a comprehensive soil quality score: ； In the formula, It represents the comprehensive score of soil quality; Indicates Standardized values ​​of the indicators; Indicates The weight of each indicator; Indicates the total number of indicators; Represents an indicator index variable.

8. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 7 is characterized by: In S6.2, the specific process of identifying the occurrence area and severity of pests and diseases through remote sensing image machine learning algorithm is as follows: Extract spectral features from remote sensing images and normalize vegetation index : ； In the formula, Represents the reflectivity in the near-infrared band; Use support vector machines to classify pest and disease areas: ； In the formula, represents the classification function; represents the input feature vector; represents the number of input feature vectors; A variable that represents the number of input feature vectors; represents the Lagrange multiplier; represents the kernel function; represents the bias term; Represents the training sample label; represents the input feature vector label; Among them, the input feature vector for: ; Determine whether the pixel belongs to the pest area based on the classification result: ； Calculate the proportion of area covered by pests and diseases to the total planted area: ； In the formula, It indicates the proportion of the area covered by pests and diseases to the total planted area; Indicates resolution; Considering that the pesticide coverage area has an impact on the occurrence area and severity of pests and diseases, the pesticide coverage area variable is introduced to optimize the remote sensing image machine learning algorithm: ； In the formula, represents the classification function of the introduced pesticide coverage area variable; represents the variable of pesticide coverage area; After introducing the pesticide coverage area variable, we can determine whether the pixel belongs to the pest and disease area based on the classification results: ； Calculate the proportion of area covered by pests and diseases to the total planted area: ； In the formula, It represents the proportion of pest and disease covered area to the total planted area after the introduction of the pesticide coverage area variable; Represents the pesticide effectiveness coefficient.

9. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 8 is characterized by: In S6.3, the specific process of constructing the crop growth dynamic model is as follows: Use crop growth dynamics models to predict final yields: ； In the formula, represents the predicted output; represents the proportionality coefficient; Indicates the offset; Indicates the crop at time The amount of growth; in, Specifically: ； In the formula, Indicates the maximum growth value; represents the growth rate; Indicates the time to reach half-saturation value; Considering that soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture have an impact on crop growth, soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture variables are introduced to optimize the crop growth dynamic model: ； In the formula, represents the predicted yield after optimization, Indicates the effect of soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and soil moisture on crops over time. The amount of growth; represents the proportionality coefficient affected by soil oxygen concentration, temperature and soil nutrients; Indicates the offset affected by soil nutrients and soil acidity and alkalinity; in, for: ； In the formula, It indicates the maximum growth value affected by soil oxygen concentration, light radiation brightness, soil nutrients and soil moisture; Indicates growth rate affected by soil oxygen concentration, temperature, and soil acidity; Indicates soil oxygen concentration; Indicates temperature; Indicates soil acidity and alkalinity; Indicates soil moisture; Considering that the coverage area of ​​pests and diseases has an inhibitory effect on the maximum growth value and growth rate of crops, the dynamic model of crop growth optimization based on the coverage area of ​​pests and diseases is introduced: ； In the formula, represents the final predicted output; It indicates the inhibition coefficient of pests and diseases on yield; It indicates the amount of growth that is optimized again after the introduction of pest and disease coverage area; in, for: ； In the formula, It indicates the inhibition coefficient of pests and diseases on the maximum growth value; It indicates the inhibition coefficient of pests and diseases on growth rate; Considering that the yield is affected by the multi-stage cumulative effect, the multi-stage cumulative effect is introduced to optimize the crop growth dynamic model: ； In the formula, It indicates the predicted yield of introducing soil oxygen concentration, temperature, light radiation brightness, soil nutrients, soil acidity and alkalinity, soil moisture and multi-stage cumulative effects; Indicates the growth amount during the tillering stage; Indicates the amount of growth during the flowering period; It indicates the amount of growth during the filling period; Use the root mean square error to evaluate the model prediction accuracy: ； In the formula, represents the root mean square error; represents the actual observed value; represents the model prediction value; represents the number of samples; Represents the sample index variable.

10. The smart agriculture big data cloud service supervision platform integrating aerial remote sensing and space ranging information according to claim 1 is characterized in that: The specific steps of managing and protecting agricultural data through cloud computing distributed storage technology are: S10.

1. Use cloud computing distributed storage technology to efficiently store agricultural data; Among them, cloud computing distributed storage technologies include cloud object storage technology, relational databases, and distributed file systems; S10.

2. Classify, manage and share agricultural data; S10.

3. Use encryption technology to protect the security of data during transmission and storage.

Images