A field plant data visualization management system based on grid storage architecture

Abstract

The invention discloses a field plant data visualization management system based on a grid storage architecture, relates to the technical field of data visualization, and improves the efficiency of field plant inspection. A plant data relationship tree is established according to a plurality of pre-stored plant multi-source information data sets, a regional visualization grid map is established according to various real-time data, a target index condition is traversed in the plant data relationship tree according to basic index information, a plurality of initial detection grids are randomly set in the regional visualization grid map, the target index condition is matched with each initial detection grid, each initial detection grid is recorded as a target regional grid according to the matching result, the basic index information is matched with the adjacent regional grids of each target regional grid, and then the shortest exploration driving route is divided in the regional visualization grid map according to the matching result.

Description

Technical Field

[0001] The invention relates to the technical field of data visualization, and in particular to a field plant data visualization management system based on a grid storage architecture. Background Art

[0002] In the current ecological environment monitoring and protection work, accurate identification and management of wild plant resources is an important task. Traditional plant census methods usually rely on manual field surveys, which is not only time-consuming and labor-intensive, but also subject to human and resource limitations, making it difficult to achieve large-scale and efficient monitoring.

[0003] Existing technologies still have some defects in exploring path planning. Traditional path planning methods are often based on preset routes or simple algorithms, lacking pertinence and flexibility. When faced with complex field environments and diverse plant distributions, these methods may not be able to effectively plan the optimal inspection path, resulting in a waste of computing resources and low efficiency. To this end, a field plant data visualization management system based on a grid storage architecture is provided. Summary of the invention

[0004] In order to solve the above technical problems, the object of the present invention is to provide a field plant data visualization management system based on a grid storage architecture.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A field plant data visualization management system based on a grid storage architecture includes a cloud control terminal, wherein the cloud control terminal is communicatively connected to a plant feature analysis module, a field environment visualization module, and a patrol path planning module;

[0007] The plant feature analysis module is used to establish a plant data relationship tree based on a plurality of pre-stored plant multi-source information data sets;

[0008] The field environment visualization module is communicatively connected to a plurality of unmanned aerial vehicles and a ground data collection unit, collects a plurality of real-time data of a target field target area through the unmanned aerial vehicles and the ground data collection unit, and establishes a regional visualization grid map according to the various real-time data, wherein the regional visualization grid map is composed of a plurality of regional grids;

[0009] The inspection path planning module is used to obtain basic index information, and then traverse the target index conditions in the plant data relationship tree according to the basic index information, randomly set a number of initial detection grids in the regional visualization grid map, match the target index conditions with each initial detection grid, and record each initial detection grid as a target regional grid according to the matching result. Then, with each target regional grid as the starting position, the basic index information is matched with the adjacent regional grids of each target regional grid until all regional grids are matched with the basic index information, and then the shortest exploration driving route is divided in the regional visualization grid map according to the matching result.

[0010] Furthermore, the process of establishing the plant data relationship tree includes:

[0011] Generate a number of plant data nodes according to the number of multi-source information data sets, and set three slave data nodes for each plant data node, and then input the name of each plant into the plant data node, and input the characteristic image area of ​​each growth period, suitable soil properties and suitable environment data into each slave data node, and then the slave data node generates a number of child data nodes according to the types of data included;

[0012] The same types of different plant data nodes are matched with the sub-data nodes in the data nodes. If the data contained in the sub-data nodes are the same or have repeated parts, an associated data line is set between the corresponding two sub-data nodes to obtain a plant data relationship tree.

[0013] Furthermore, the drone is equipped with a camera, the ground data acquisition unit is equipped with a ring camera, a soil detection device, a temperature sensor and a humidity sensor, and the ground data acquisition unit is evenly installed at various locations of the target field target area.

[0014] Furthermore, the process of establishing the regional visualization grid map includes:

[0015] A drone flight route is set for each drone, and each drone collects real-time regional image data of a target field target area along the drone flight route;

[0016] At the same time, the ground data acquisition unit collects real-time regional image data, real-time range temperature data, real-time range humidity data and real-time soil property data within its data acquisition range;

[0017] The field environment visualization module matches, stitches and fuses the real-time image data collected by each drone and the ground data acquisition unit in sequence to obtain the real-time regional total image data, and then establishes a regional visualization grid map based on the real-time regional total image data.

[0018] Furthermore, k regional grids of the same size are set in the regional visualization grid map, where k is a natural number greater than 100;

[0019] According to the spatial position of the field target area corresponding to each regional grid, the corresponding real-time range temperature data, real-time range humidity data and real-time soil property data are stored in the regional grid, and several plant image areas are segmented from the regional visualization grid map.

[0020] Further, according to the spatial position of the field target area corresponding to the regional grid in the regional visualization grid map, the ground data collection unit whose data range covers the spatial position of the corresponding field target area is associated with the corresponding regional grid;

[0021] At the same time, according to the spatial position of the field target area passed by each drone during its flight, each drone is dynamically associated with each corresponding regional grid;

[0022] According to the association status between the ground data collection unit and the UAV and the regional grid, and according to the dynamic data of various data collected by the ground data collection unit and the UAV, the data contained in each regional grid is updated in real time.

[0023] Furthermore, the process of generating the target index condition includes:

[0024] The basic index information includes user starting point location information and index plant information, wherein the index plant information includes index plant name, characteristic image and suitable growth factor;

[0025] Input the basic index information into the plant data relationship tree. If the basic index information includes the index plant name, directly match the corresponding plant data node from the plant data relationship tree according to the index plant name, and then generate the target index condition according to the data stored in the child data node associated with the plant data node;

[0026] If the basic index information does not include the indexed plant name, then firstly, multiple child data nodes are matched from the plant data relationship tree according to the characteristic image and the suitable growth factor, and recorded as conditional child data nodes;

[0027] Then, according to the conditional sub-data nodes, traverse the plant data relationship tree and associate the sub-data nodes of two or more conditional sub-data nodes at the same time;

[0028] Then, based on the plant data nodes associated with each slave data node, the number of conditional sub-data nodes associated with each plant data node is counted, and then the data stored in the sub-data nodes associated with the plant data node with the largest number of associated conditional sub-data nodes are selected to generate the target index condition. It should be noted that if there are multiple plant data nodes with the largest number of associated conditional sub-data nodes, the target index condition will be generated together.

[0029] Furthermore, the process of dividing the shortest exploration driving route includes:

[0030] Randomly set and select i regional grids in the regional visualization grid map as initial detection grids, match the data in the target index condition with the data stored in the initial detection grid, i is a natural number greater than 20 and less than k;

[0031] If the data stored in the initial detection grid matches more than half of the data in the target index condition, the corresponding initial detection grid is retained and marked as the target regional grid. Otherwise, the initial detection grid mark of the corresponding regional grid is cancelled, and a regional grid is randomly selected and recorded as the initial detection grid.

[0032] Match the stored data of the adjacent regional grids of the initial detection grid with the target index condition. If the stored data of the adjacent regional grids matches more than half of the data in the target index condition, the adjacent regional grid is recorded as the target regional grid, and the marked regional grid adjacent to the target regional grid is matched with the target index condition again;

[0033] Splice adjacent target area grids in the regional visualization grid map, and then set up several target exploration areas in the regional visualization grid map;

[0034] According to the user's starting point location information in the basic index information and the distribution of the target exploration area on the regional visualization grid map, a shortest exploration driving route is traversed on the regional visualization grid map and sent to the user, so that all target exploration areas are passed in the shortest journey.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention constructs a plant data relationship tree, collects several real-time data of the target field target area through drones and ground data acquisition units, and establishes a regional visual grid map based on the real-time data. It can quickly and accurately identify and analyze plant species and their characteristics, and provide a data basis for subsequent exploration path planning.

[0037] 2. By matching the target index condition with each initial detection grid, each initial detection grid is recorded as a target area grid according to the matching result, and then each target area grid is used as the starting position, the basic index information is matched with the adjacent area grid of each target area grid until all area grids are matched with the basic index information. Then, according to the matching results, the shortest exploration route is divided on the regional visualization grid map, thereby improving the efficiency of field plant inspections while effectively avoiding duplicate paths and invalid collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0040] like Figure 1 As shown, a field plant data visualization management system based on a grid storage architecture includes a cloud control terminal, wherein the cloud control terminal is communicatively connected to a plant feature analysis module, a field environment visualization module, and a patrol path planning module;

[0041] The plant feature analysis module is used to establish a plant data relationship tree based on a plurality of pre-stored plant multi-source information data sets;

[0042] The field environment visualization module is communicatively connected to a plurality of unmanned aerial vehicles and a ground data collection unit, collects a plurality of real-time data of a target field target area through the unmanned aerial vehicles and the ground data collection unit, and establishes a regional visualization grid map according to the various real-time data, wherein the regional visualization grid map is composed of a plurality of regional grids;

[0043] The inspection path planning module is used to obtain basic index information, and then traverse the target index conditions in the plant data relationship tree according to the basic index information, randomly set a number of initial detection grids in the regional visualization grid map, match the target index conditions with each initial detection grid, and record each initial detection grid as a target regional grid according to the matching result. Then, with each target regional grid as the starting position, the basic index information is matched with the adjacent regional grids of each target regional grid until all regional grids are matched with the basic index information, and then the shortest exploration driving route is divided in the regional visualization grid map according to the matching result.

[0044] Further, the working principle of the present invention is described below by way of examples:

[0045] The plant multi-source information data set includes plant names, suitable soil properties for growth, plant image data at various growth stages, and suitable environment data;

[0046] The plant feature analysis module obtains the pixel value of each pixel in the plant image data of various plants at various growth stages, performs a pixel value averaging operation on the pixels of each plant image data, and divides a number of feature image areas in the plant image data according to the pixel value averaging operation result;

[0047] Generate a number of plant data nodes according to the number of multi-source information data sets, and set three slave data nodes for each plant data node, and then input the name of each plant into the plant data node, and input the characteristic image area of ​​each growth period, suitable soil properties and suitable environment data into each slave data node, and then the slave data node generates a number of child data nodes according to the types of data included;

[0048] The data types of soil properties suitable for growth include pH value, soil temperature, soil particles (sand, silt, clay), etc.;

[0049] The data types of the suitable environmental data include suitable air humidity, suitable air temperature, etc.;

[0050] Match the sub-data nodes of the same type of different plant data nodes. If the data contained in the sub-data nodes are the same or have repeated parts, set a correlation data line between the corresponding two sub-data nodes.

[0051] Repeat the above operation of setting associated data lines between each sub-data node to obtain a plant data relationship tree.

[0052] Further, the plant feature analysis module sends the plant data relationship tree to the inspection path planning module;

[0053] The field environment visualization module is communicatively connected to m drones and n ground data acquisition units, where m and n are natural numbers greater than 0;

[0054] The drone is equipped with a camera, the ground data acquisition unit is equipped with a ring camera, a soil detection device, a temperature sensor and a humidity sensor, and the ground data acquisition units are evenly installed at various positions of the target field target area. It should be noted that the spatial distances of the ground data acquisition units on the horizontal plane are equal, and the edge positions of the data acquisition ranges of the ground data acquisition units at adjacent spatial positions partially overlap;

[0055] The field environment visualization module sets a UAV flight route for each UAV, and then each UAV collects real-time regional image data of the target field target area along the UAV flight route;

[0056] At the same time, the ground data acquisition unit collects real-time regional image data, real-time range temperature data, real-time range humidity data and real-time soil property data within its data acquisition range;

[0057] The field environment visualization module matches and splices the real-time image data collected by each drone and the ground data acquisition unit in sequence to obtain the real-time regional total image data, and then establishes a regional visualization grid map based on the real-time regional total image data;

[0058] The regional visualization grid map is provided with k regional grids of the same size, and each regional grid is provided with a number a1, a2, a3, ..., a k , k is a natural number greater than 100;

[0059] According to the spatial position of the field target area corresponding to each regional grid, the corresponding real-time range temperature data, real-time range humidity data and real-time soil property data are stored in the regional grid. At the same time, the characteristic image areas of various types of plants are collected and obtained, and several plant image areas are segmented from the regional visualization grid map. It should be noted that each plant image area corresponds to a plant.

[0060] It should be noted that, according to the spatial position of the field target area corresponding to the regional grid in the regional visualization grid map, the ground data acquisition unit whose data range covers the spatial position of the corresponding field target area is associated with the corresponding regional grid;

[0061] At the same time, according to the spatial position of the field target area passed by each drone during its flight, each drone is dynamically associated with each corresponding regional grid;

[0062] According to the association status between the ground data collection unit and the UAV and the regional grid, and according to the dynamic data of various data collected by the ground data collection unit and the UAV, the data contained in each regional grid is updated in real time.

[0063] Further, the user uploads basic index information to the patrol route planning module, wherein the basic index information includes the user's starting point location information and index plant information, wherein the index plant information includes the index plant name, characteristic image, and suitable growth factor;

[0064] Input the basic index information into the plant data relationship tree. If the basic index information includes the index plant name, directly match the corresponding plant data node from the plant data relationship tree according to the index plant name, and then generate the target index condition according to the data stored in the child data node associated with the plant data node;

[0065] If the basic index information does not include the indexed plant name, then firstly, multiple child data nodes are matched from the plant data relationship tree according to the characteristic image and the suitable growth factor, and recorded as conditional child data nodes;

[0066] Then, according to the conditional sub-data nodes, traverse the plant data relationship tree and associate the sub-data nodes of two or more conditional sub-data nodes at the same time;

[0067] Then, based on the plant data nodes associated with each slave data node, the number of conditional sub-data nodes associated with each plant data node is counted, and then the data stored in the sub-data nodes associated with the plant data node with the largest number of associated conditional sub-data nodes are selected to generate the target index condition. It should be noted that if there are multiple plant data nodes with the largest number of associated conditional sub-data nodes, the target index condition will be generated together.

[0068] Furthermore, i regional grids are randomly selected in the regional visualization grid map as initial detection grids, and each data in the target index condition is matched with the data stored in the initial detection grid, where i is a natural number greater than 20 and less than k;

[0069] If the data stored in the initial detection grid matches more than half of the data in the target index condition, the corresponding initial detection grid is retained and marked as the target regional grid. Otherwise, the initial detection grid mark of the corresponding regional grid is cancelled, and a regional grid is randomly selected and recorded as the initial detection grid.

[0070] Match the stored data of the adjacent regional grids of the initial detection grid with the target index condition. If the stored data of the adjacent regional grids matches more than half of the data in the target index condition, the adjacent regional grid is recorded as the target regional grid, and the marked regional grid adjacent to the target regional grid is matched with the target index condition again;

[0071] Repeat the above process of matching and marking each regional grid until all regional grids are matched, splice adjacent target regional grids in the regional visualization grid map, and then set up several target exploration areas in the regional visualization grid map;

[0072] According to the user's starting point location information in the basic index information and the distribution of the target exploration area on the regional visualization grid map, a shortest exploration driving route is traversed on the regional visualization grid map and sent to the user, so that all target exploration areas are passed in the shortest journey.

[0073] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A field plant data visualization management system based on a grid storage architecture, including a cloud control terminal, characterized in that: The cloud control terminal is communicatively connected to a plant feature analysis module, a field environment visualization module, and a patrol path planning module; The plant feature analysis module is used to establish a plant data relationship tree based on a plurality of pre-stored plant multi-source information data sets; The field environment visualization module is communicatively connected to a plurality of unmanned aerial vehicles and a ground data collection unit, collects a plurality of real-time data of a target field target area through the unmanned aerial vehicles and the ground data collection unit, and establishes a regional visualization grid map according to the various real-time data, wherein the regional visualization grid map is composed of a plurality of regional grids; The inspection path planning module is used to obtain basic index information, and then traverse the target index conditions in the plant data relationship tree according to the basic index information, randomly set a number of initial detection grids in the regional visualization grid map, match the target index conditions with each initial detection grid, and record each initial detection grid as a target regional grid according to the matching result. Then, with each target regional grid as the starting position, the basic index information is matched with the adjacent regional grids of each target regional grid, and the shortest exploration driving route is divided in the regional visualization grid map according to the matching result.

2. According to claim 1, a field plant data visualization management system based on a grid storage architecture is characterized in that: The process of establishing the plant data relationship tree includes: Generate a number of plant data nodes according to the number of multi-source information data sets, and set three slave data nodes for each plant data node, and then input the name of each plant into the plant data node, and input the characteristic image area of ​​each growth period, suitable soil properties and suitable environment data into each slave data node, and then the slave data node generates a number of child data nodes according to the types of data included; The same types of different plant data nodes are matched with the sub-data nodes in the data nodes. If the data contained in the sub-data nodes are the same or have repeated parts, an associated data line is set between the corresponding two sub-data nodes to obtain a plant data relationship tree.

3. A field plant data visualization management system based on a grid storage architecture according to claim 2, characterized in that: The drone is equipped with a camera, and the ground data acquisition unit is equipped with a ring camera, a soil detection device, a temperature sensor and a humidity sensor.

4. A field plant data visualization management system based on a grid storage architecture according to claim 3, characterized in that: The process of establishing the regional visualization grid map includes: A drone flight route is set for each drone, and each drone collects real-time regional image data of a target field target area along the drone flight route; At the same time, the ground data acquisition unit collects real-time regional image data, real-time range temperature data, real-time range humidity data and real-time soil property data within its data acquisition range; The real-time image data collected by each UAV and the ground data acquisition unit are matched, spliced ​​and fused in sequence to obtain the real-time regional total image data, and then a regional visualization grid map is established based on the real-time regional total image data.

5. A field plant data visualization management system based on a grid storage architecture according to claim 4, characterized in that: The regional visualization grid map is provided with k regional grids of the same size, where k is a natural number greater than 100; According to the spatial position of the field target area corresponding to each regional grid, the corresponding real-time range temperature data, real-time range humidity data and real-time soil property data are stored in the regional grid, and several plant image areas are segmented from the regional visualization grid map.

6. A field plant data visualization management system based on a grid storage architecture according to claim 5, characterized in that: According to the spatial position of the field target area corresponding to the regional grid in the regional visualization grid map, the ground data collection unit whose data range covers the spatial position of the corresponding field target area is associated with the corresponding regional grid; At the same time, according to the spatial position of the field target area passed by each drone during its flight, each drone is dynamically associated with each corresponding regional grid; According to the association status between the ground data collection unit and the UAV and the regional grid, and according to the dynamic data of various data collected by the ground data collection unit and the UAV, the data contained in each regional grid is updated in real time.

7. A field plant data visualization management system based on a grid storage architecture according to claim 6, characterized in that: The process of generating the target index condition includes: The basic index information includes user starting point location information and index plant information, wherein the index plant information includes index plant name, characteristic image and suitable growth factor; Input the basic index information into the plant data relationship tree. If the basic index information includes the index plant name, directly match the corresponding plant data node from the plant data relationship tree according to the index plant name, and then generate the target index condition according to the data stored in the child data node associated with the plant data node; If the basic index information does not include the indexed plant name, firstly, multiple child data nodes are matched from the plant data relationship tree according to the characteristic image and the suitable growth factor, recorded as conditional child data nodes, and then the conditional child data nodes are traversed in the plant data relationship tree, and the child data nodes of two or more conditional child data nodes are associated at the same time; According to the plant data nodes associated with each slave data node, the number of conditional sub-data nodes associated with each plant data node is counted, and then the data stored in the sub-data node associated with the plant data node with the largest number of associated conditional sub-data nodes is selected to generate the target index condition.

8. A field plant data visualization management system based on a grid storage architecture according to claim 7, characterized in that: The process of dividing the shortest exploration driving route includes: Randomly set and select i regional grids in the regional visualization grid map as initial detection grids, match the data in the target index condition with the data stored in the initial detection grid, i is a natural number greater than 20 and less than k; If the data stored in the initial detection grid matches more than half of the data in the target index condition, the corresponding initial detection grid is retained and marked as the target regional grid. Otherwise, the initial detection grid mark of the corresponding regional grid is cancelled, and a regional grid is randomly selected and recorded as the initial detection grid. Match the stored data of the adjacent regional grids of the initial detection grid with the target index condition. If the stored data of the adjacent regional grids matches more than half of the data in the target index condition, the adjacent regional grid is recorded as the target regional grid, and the marked regional grid adjacent to the target regional grid is matched with the target index condition again; Splice adjacent target area grids in the regional visualization grid map, and then set up several target exploration areas in the regional visualization grid map; According to the user's starting point location information in the basic index information and the distribution of the target exploration area on the regional visualization grid map, a shortest exploration driving route is traversed on the regional visualization grid map and sent to the user, so that all target exploration areas are passed in the shortest journey.

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