Agricultural data visualization analysis system and method

Abstract

The invention discloses an agricultural data visualization analysis system and method. The system comprises a data storage and processing module used for storing and processing a large-scale data set; the algorithm analysis module is used for executing various data analysis tasks; the visual presentation module is used for visually presenting the analysis result to a user; the interaction module is used for inputting query data by a user; the space-time visualization module is used for displaying the distribution condition of agricultural data at different time and places; a data association analysis module; the data acquisition module is used for acquiring relevance among different agricultural data; and the real-time data stream processing module is used for receiving and processing agricultural data streams in real time and providing real-time analysis and visualization. The agricultural data management system has the advantages that efficient management and accurate analysis of large-scale agricultural data are realized by integrating data storage, analysis, visualization and real-time processing modules. The intelligent optimization and space-time visualization functions help users to visually understand data distribution, and the efficiency and precision of agricultural decision making are improved.

Description

Technical Field

[0001] The present invention relates to agricultural visualization analysis, and in particular to an agricultural data visualization analysis system and method. Background Art

[0002] A system and method for visualizing and analyzing agricultural data utilizes a data storage and processing module to process large-scale agricultural data, while an algorithm analysis module performs various data analysis tasks to reveal underlying trends and patterns. A visualization module displays analysis results in intuitive charts and maps, making them easy for users to understand and apply. The system also utilizes a spatiotemporal visualization module to display the distribution of agricultural data at different times and locations, supporting real-time data stream processing and providing immediate analytical feedback. A data association analysis module helps identify relationships between different data sets, comprehensively improving agricultural data management and decision support.

[0003] Current agricultural data visualization and analysis systems on the market primarily integrate advanced data analysis technologies and visualization tools to help agricultural producers, researchers, and government agencies monitor and analyze various data from agricultural production processes in real time. These systems typically cover multi-dimensional data such as meteorology, soil, crop growth, and pests and diseases, leveraging big data, cloud computing, and the Internet of Things (IoT) to accurately collect and process data. Many systems also feature data sharing and collaboration capabilities, facilitating information exchange and cooperation between different regions and departments, thereby improving the overall efficiency and sustainability of agricultural production. Summary of the Invention

[0004] To improve existing agricultural data visualization and analysis systems, this paper presents an agricultural data visualization and analysis system and method. This method integrates data storage, processing, analysis, and visualization to help agricultural practitioners efficiently manage and make decisions. Through real-time analysis, spatiotemporal visualization, and intelligent optimization, the system improves agricultural data utilization efficiency and decision-making support capabilities.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] An agricultural data visualization analysis system, characterized by comprising:

[0007] Data storage and processing module: The data storage and processing module is used to store and process large-scale data sets;

[0008] Algorithm analysis module: The algorithm analysis module is used to perform various data analysis tasks;

[0009] Visualization presentation module: The visualization presentation module is used to visually present the analysis results to the user;

[0010] Interaction module: The interaction module is used for users to input query data;

[0011] Spatiotemporal visualization module: The spatiotemporal visualization module is used to display the distribution of agricultural data at different times and locations based on spatiotemporal data;

[0012] Data association analysis module; the data association analysis module is used to obtain the association and correlation between different agricultural data;

[0013] Real-time data stream processing module: The real-time data stream processing module is used to receive and process the acquired agricultural data stream in real time, and provide real-time analysis and visualization functions.

[0014] Preferably, the spatiotemporal visualization module specifically includes:

[0015] A ground information processing module, which is used to obtain data from each agricultural area at each time period to form ground data;

[0016] Ground data mapping module, used to map ground data to geographic space and time axis;

[0017] A visualization interface module allows users to explore the distribution of data at different geographical locations and time points.

[0018] Preferably, the ground data mapping module is used to map the ground data onto the geographic space and time axis and specifically includes:

[0019] The ground data mapping method specifically uses multi-dimensional data fusion technology to associate and fuse spatiotemporal data, attribute data, and event data;

[0020] The ground data mapping method combines spatiotemporal data clustering and dimensionality reduction techniques to effectively process and simplify large-scale spatiotemporal data, highlight key information and patterns, and show the inherent structure and relationship of spatiotemporal data.

[0021] Preferably, the ground data mapping method combines spatiotemporal data clustering and dimensionality reduction technology and specifically includes:

[0022] Data preprocessing: cleaning, conversion and normalization of acquired agricultural spatiotemporal data;

[0023] Feature extraction: Extract relevant features from preprocessed spatiotemporal data. These features may include timestamps, geographic locations, and attribute information.

[0024] Clustering algorithm: Use clustering algorithms such as K-means, DBSCAN, and hierarchical clustering to perform cluster analysis on the extracted features. Through clustering, similar spatiotemporal data points are grouped together to form different clusters.

[0025] Application of dimensionality reduction technology: perform dimensionality reduction processing on clustered data to reduce the dimension and complexity of the data;

[0026] Visualization: Visualize the data after dimensionality reduction by using one or more of scatter plots, heat maps, and trajectory maps to obtain the distribution, relationship, and evolution between different clusters or clusters through visualization.

[0027] Preferably, the multi-dimensional data fusion specifically includes:

[0028] Data collection and integration: Collect relevant datasets from different sources and dimensions, such as spatiotemporal data, sensor data, and text data, integrate these datasets, and ensure that they are aligned in the same coordinate system or reference frame;

[0029] Feature extraction and transformation: For each dimension of data, relevant features are extracted and feature transformation and mapping are performed, including the extraction of geographic location and timestamp information of spatiotemporal data;

[0030] Data standardization and fusion: The extracted features are standardized to eliminate the dimension and scale differences between data of different dimensions. Then, fusion algorithms or techniques are used to fuse the features of different dimensions to form a unified multi-dimensional data representation.

[0031] Association analysis and mining: Apply association rule mining, cluster analysis, or other data mining algorithms to perform association analysis and pattern mining on the fused multi-dimensional data to help discover the associations, dependencies, and hidden patterns between different dimensions;

[0032] Result visualization: The multi-dimensional association patterns and results obtained by mining are visualized, and the relationships and trends between different dimensions are displayed in the form of charts and heat maps. At the same time, explanatory tools and methods are provided.

[0033] Furthermore, a method for visualizing and analyzing agricultural data is characterized by comprising:

[0034] Collect and acquire data sets from different sources and dimensions, including spatiotemporal data, sensor data, and text data. These data sets come from different databases, API interfaces, and third-party data sources. During the collection process, ensure the accuracy and completeness of the data, and perform necessary data cleaning and preprocessing.

[0035] Store the collected data sets in appropriate data storage systems, such as distributed file systems and cloud storage platforms. Select appropriate data storage formats and compression algorithms based on the scale and characteristics of the data to improve the efficiency of data storage and access.

[0036] Data preprocessing and feature extraction: This includes cleaning, converting, and normalizing the raw data to eliminate noise, outliers, and missing values. At the same time, relevant features are extracted. These features can be based on statistical, time-domain, and frequency-domain characteristics. The goal of feature extraction is to convert the raw data into a feature representation that is more suitable for analysis and visualization.

[0037] Based on feature extraction and conversion technology, data of different dimensions are associated and integrated. This includes extracting geographic location and timestamp information from spatiotemporal data, extracting keywords and semantic features from text data, and integrating features of different dimensions through fusion algorithms or technologies to form a unified multi-dimensional data representation.

[0038] Based on association rule algorithms and clustering algorithms, association analysis and pattern mining are performed on the fused multi-dimensional data. Through association rule mining, the correlation and dependency between different variables and events in the data can be discovered. Through cluster analysis, similar data points can be grouped together to form different clusters.

[0039] Based on the multi-dimensional association patterns and results obtained from mining, visual presentation is performed. According to the type and characteristics of the data, appropriate visualization methods and techniques are selected, such as scatter plots, heat maps and trajectory maps. Through visual presentation, users can intuitively observe and analyze the distribution, relationship and evolution between different clusters or clusters. At the same time, explanatory tools and methods are provided.

[0040] Compared with the prior art, the advantages of the present invention are:

[0041] The system of the present invention has efficient data storage, processing and analysis capabilities through the integration of multiple modules, and can achieve accurate decision support in the agricultural field. The data storage and processing module ensures the efficient management of large-scale agricultural data, and the algorithm analysis module provides a variety of data analysis methods. The visualization presentation module displays the analysis results in an intuitive manner. The spatiotemporal visualization module can display the distribution of agricultural data at different times and places, providing strong support for regional agricultural development and trend analysis. The data association analysis module can reveal the intrinsic connection between different agricultural data. The real-time data stream processing module realizes rapid response and analysis of real-time agricultural data, improving the timeliness of the system. These modules work together to give the system the advantages of accuracy, real-time and efficiency in agricultural data analysis, decision-making and management, which can greatly improve the level of intelligence and precision in agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the agricultural data visualization analysis method proposed in the present invention;

[0043] Figure 2This is a schematic diagram of the agricultural data visualization analysis system proposed by the present invention;

[0044] Figure 3 This is a schematic diagram of the spatiotemporal visualization module of the agricultural data visualization analysis system proposed in the present invention;

[0045] Figure 4 This is a diagram of the architecture of the electronic equipment in this solution;

[0046] Figure 5 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0047] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0048] An agricultural data visualization analysis system, comprising:

[0049] Data storage and processing module: The data storage and processing module is used to store and process large-scale data sets;

[0050] Algorithm analysis module: The algorithm analysis module is used to perform various data analysis tasks;

[0051] Visualization presentation module: The visualization presentation module is used to visually present the analysis results to the user;

[0052] Interaction module: The interaction module is used for users to input query data;

[0053] Spatiotemporal visualization module: The spatiotemporal visualization module is used to display the distribution of agricultural data at different times and locations based on spatiotemporal data;

[0054] Data association analysis module; the data association analysis module is used to obtain the association and correlation between different agricultural data;

[0055] Real-time data stream processing module: The real-time data stream processing module is used to receive and process the acquired agricultural data stream in real time, and provide real-time analysis and visualization functions.

[0056] See Figure 1 As shown, a method for visualizing and analyzing agricultural data includes:

[0057] Step 1: Collect and obtain data sets from different sources and dimensions, including spatiotemporal data, sensor data, and text data. These data sets come from different databases, API interfaces, and third-party data sources. During the collection process, ensure the accuracy and completeness of the data, and perform necessary data cleaning and preprocessing.

[0058] Step 2: Store the collected data sets in appropriate data storage systems, such as distributed file systems and cloud storage platforms. Based on the scale and characteristics of the data, select appropriate data storage formats and compression algorithms to improve the efficiency of data storage and access.

[0059] Step 3: Data preprocessing and feature extraction: This includes cleaning, transforming, and normalizing the raw data to eliminate noise, outliers, and missing values. At the same time, relevant features are extracted. These features can be based on statistical, time-domain, or frequency-domain characteristics. The goal of feature extraction is to convert the raw data into a feature representation that is more suitable for analysis and visualization.

[0060] Step 4: Based on feature extraction and conversion technology, data of different dimensions are associated and integrated. This includes extracting geographic location and timestamp information from spatiotemporal data, extracting keywords and semantic features from text data, and integrating features of different dimensions through fusion algorithms or technologies to form a unified multi-dimensional data representation.

[0061] Step 5: Based on association rule algorithms and clustering algorithms, association analysis and pattern mining are performed on the fused multi-dimensional data. Association rule mining can discover the correlation and dependency between different variables and events in the data. Cluster analysis can group similar data points together to form different clusters.

[0062] Step 6: Visualize the multi-dimensional association patterns and results obtained by mining. Select appropriate visualization methods and techniques based on the type and characteristics of the data, such as scatter plots, heat maps, and trajectory maps. Through visualization, users can intuitively observe and analyze the distribution, relationships, and evolution between different clusters or clusters. At the same time, explanatory tools and methods are provided.

[0063] See Figure 2 、 3 ,An agricultural data visualization and analysis system, the spatiotemporal visualization module further includes a geographic information module, a spatiotemporal data mapping module, and an interactive visualization interface module;

[0064] A ground information processing module, which is used to obtain data from each agricultural area at each time period to form ground data;

[0065] Ground data mapping module, used to map ground data to geographic space and time axis;

[0066] A visualization interface module allows users to explore the distribution of data at different geographical locations and time points.

[0067] Understandably, mapping ground data onto geographic space and time can lead to inaccurate spatial and temporal alignments. Data from different regions may have inconsistent timestamps or inaccurate geographic coordinates, leading to inaccurate mapping results. A unified time synchronization protocol ensures the accuracy and consistency of timestamps across data sources. Using GPS time or a unified time zone standard can mitigate time zone differences. When processing data from large agricultural regions, spatial interpolation algorithms (such as kriging) can be used to accurately map data from different locations onto geographic space.

[0068] See Figure 2 、 3 , an agricultural data visualization analysis system, a ground data mapping module, is used to map ground data to geographic space and time axis, specifically including:

[0069] The ground data mapping method specifically uses multi-dimensional data fusion technology to associate and fuse spatiotemporal data, attribute data, and event data;

[0070] The ground data mapping method combines spatiotemporal data clustering and dimensionality reduction techniques to effectively process and simplify large-scale spatiotemporal data, highlight key information and patterns, and show the inherent structure and relationship of spatiotemporal data.

[0071] See Figure 2 、 3 , an agricultural data visualization and analysis system, the ground data mapping method combines spatiotemporal data clustering and dimensionality reduction technology, specifically including:

[0072] Data preprocessing: cleaning, conversion and normalization of acquired agricultural spatiotemporal data;

[0073] Feature extraction: Extract relevant features from preprocessed spatiotemporal data. These features may include timestamps, geographic locations, and attribute information.

[0074] Clustering algorithm: Use clustering algorithms such as K-means, DBSCAN, and hierarchical clustering to perform cluster analysis on the extracted features. Through clustering, similar spatiotemporal data points are grouped together to form different clusters.

[0075] Application of dimensionality reduction technology: perform dimensionality reduction processing on clustered data to reduce the dimension and complexity of the data;

[0076] Visualization: Visualize the data after dimensionality reduction by using one or more of scatter plots, heat maps, and trajectory maps to obtain the distribution, relationship, and evolution between different clusters or clusters through visualization.

[0077] See Figure 2 、 3, an agricultural data visualization analysis system, multi-dimensional data fusion specifically includes:

[0078] Data collection and integration: Collect relevant datasets from different sources and dimensions, such as spatiotemporal data, sensor data, and text data, integrate these datasets, and ensure that they are aligned in the same coordinate system or reference frame;

[0079] Feature extraction and transformation: For each dimension of data, relevant features are extracted and feature transformation and mapping are performed, including the extraction of geographic location and timestamp information of spatiotemporal data;

[0080] Data standardization and fusion: The extracted features are standardized to eliminate the dimension and scale differences between data of different dimensions. Then, fusion algorithms or techniques are used to fuse the features of different dimensions to form a unified multi-dimensional data representation.

[0081] Association analysis and mining: Apply association rule mining, cluster analysis, or other data mining algorithms to perform association analysis and pattern mining on the fused multi-dimensional data to help discover the associations, dependencies, and hidden patterns between different dimensions;

[0082] Result visualization: The multi-dimensional association patterns and results obtained by mining are visualized, and the relationships and trends between different dimensions are displayed in the form of charts and heat maps. At the same time, explanatory tools and methods are provided.

[0083] Understandably, during association analysis and mining, data volumes can be excessive, making processing time-consuming and leading to performance bottlenecks. Mined patterns may also contain noise or overfitting. Associations between different dimensions can be complex, making it difficult to discover valid patterns. Cross-validation of mined patterns or validation with different datasets can help avoid overfitting. Statistical methods (such as confidence and support) can be used to evaluate the effectiveness of mining results.

[0084] Furthermore, the method according to the embodiment of the present application can also be used with the aid of Figure 4 The electronic device architecture shown in FIG. Figure 4 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store an agricultural data visualization analysis system and method provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 4The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 4 One or more components of an electronic device are shown.

[0085] Figure 5 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 5 1 shows a computer-readable storage medium 600 according to one embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by a processor, the agricultural data visualization analysis system and method according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0086] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An agricultural data visualization analysis system, characterized in that: include: Data storage and processing module: The data storage and processing module is used to store and process large-scale data sets; Algorithm analysis module: The algorithm analysis module is used to perform various data analysis tasks; Visualization presentation module: The visualization presentation module is used to visually present the analysis results to the user; Interaction module: The interaction module is used for users to input query data; Spatiotemporal visualization module: The spatiotemporal visualization module is used to display the distribution of agricultural data at different times and locations based on spatiotemporal data; Data association analysis module; the data association analysis module is used to obtain the association and correlation between different agricultural data; Real-time data stream processing module: The real-time data stream processing module is used to receive and process the acquired agricultural data stream in real time, and provide real-time analysis and visualization functions.

2. The agricultural data visualization analysis system according to claim 1, characterized in that: The spatiotemporal visualization module specifically includes: A ground information processing module, which is used to obtain data from each agricultural area at each time period to form ground data; Ground data mapping module, used to map ground data to geographic space and time axis; A visualization interface module allows users to explore the distribution of data at different geographical locations and time points.

3. The agricultural data visualization analysis system according to claim 2, characterized in that: The ground data mapping module is used to map ground data onto geographic space and time axis and specifically includes: The ground data mapping method specifically uses multi-dimensional data fusion technology to associate and fuse spatiotemporal data, attribute data, and event data; The ground data mapping method combines spatiotemporal data clustering and dimensionality reduction techniques to effectively process and simplify large-scale spatiotemporal data, highlight key information and patterns, and show the inherent structure and relationship of spatiotemporal data.

4. The agricultural data visualization analysis system according to claim 3, characterized in that: The ground data mapping method combines spatiotemporal data clustering and dimensionality reduction technology, specifically including: Data preprocessing: cleaning, conversion and normalization of acquired agricultural spatiotemporal data; Feature extraction: Extract relevant features from preprocessed spatiotemporal data. These features may include timestamps, geographic locations, and attribute information. Clustering algorithm: Use clustering algorithms such as K-means, DBSCAN, and hierarchical clustering to perform cluster analysis on the extracted features. Through clustering, similar spatiotemporal data points are grouped together to form different clusters. Application of dimensionality reduction technology: perform dimensionality reduction processing on clustered data to reduce the dimension and complexity of the data; Visualization: Visualize the data after dimensionality reduction by using one or more of scatter plots, heat maps, and trajectory maps to obtain the distribution, relationship, and evolution between different clusters or clusters through visualization.

5. The agricultural data visualization analysis system according to claim 2, characterized in that: The multi-dimensional data fusion specifically includes: Data collection and integration: Collect relevant datasets from different sources and dimensions, such as spatiotemporal data, sensor data, and text data, integrate these datasets, and ensure that they are aligned in the same coordinate system or reference frame; Feature extraction and transformation: For each dimension of data, relevant features are extracted and feature transformation and mapping are performed, including the extraction of geographic location and timestamp information of spatiotemporal data; Data standardization and fusion: The extracted features are standardized to eliminate the dimension and scale differences between data of different dimensions. Then, fusion algorithms or techniques are used to fuse the features of different dimensions to form a unified multi-dimensional data representation. Association analysis and mining: Apply association rule mining, cluster analysis, or other data mining algorithms to perform association analysis and pattern mining on the fused multi-dimensional data to help discover the associations, dependencies, and hidden patterns between different dimensions; Result visualization: The multi-dimensional association patterns and results obtained by mining are visualized, and the relationships and trends between different dimensions are displayed in the form of charts and heat maps. At the same time, explanatory tools and methods are provided.

6. A method for visualizing and analyzing agricultural data, applied to a big data visualizing and analyzing system as claimed in claims 1 to 5, characterized in that: Includes the following: Collect and acquire data sets from different sources and dimensions, including spatiotemporal data, sensor data, and text data. These data sets come from different databases, API interfaces, and third-party data sources. During the collection process, ensure the accuracy and completeness of the data, and perform necessary data cleaning and preprocessing. Store the collected data sets in appropriate data storage systems, such as distributed file systems and cloud storage platforms. Select appropriate data storage formats and compression algorithms based on the scale and characteristics of the data to improve the efficiency of data storage and access. Data preprocessing and feature extraction: This includes cleaning, converting, and normalizing the raw data to eliminate noise, outliers, and missing values. At the same time, relevant features are extracted. These features can be based on statistical, time-domain, and frequency-domain characteristics. The goal of feature extraction is to convert the raw data into a feature representation that is more suitable for analysis and visualization. Based on feature extraction and conversion technology, data of different dimensions are associated and integrated. This includes extracting geographic location and timestamp information from spatiotemporal data, extracting keywords and semantic features from text data, and integrating features of different dimensions through fusion algorithms or technologies to form a unified multi-dimensional data representation. Based on association rule algorithms and clustering algorithms, association analysis and pattern mining are performed on the fused multi-dimensional data. Through association rule mining, the correlation and dependency between different variables and events in the data can be discovered. Through cluster analysis, similar data points can be grouped together to form different clusters. Based on the multi-dimensional association patterns and results obtained from mining, visual presentation is performed. According to the type and characteristics of the data, appropriate visualization methods and techniques are selected, such as scatter plots, heat maps and trajectory maps. Through visual presentation, users can intuitively observe and analyze the distribution, relationship and evolution between different clusters or clusters. At the same time, explanatory tools and methods are provided.

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