A crop data management system based on big data

Abstract

The invention discloses a crop data management system based on big data, comprising: a data import unit, used for importing relevant data of each historical growth stage of crops in a big database into a crop management platform for pre-processing and screening to obtain spatial data of crops at different growth stages; the crop spatial data comprises: soil spatial data, climate spatial data, and planting density parameters; a data processing unit, used for extracting spatial information of crop spatial data at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages; a data analysis unit, used for performing spatial distribution dynamic analysis on crop layer vectors at different growth stages to obtain growth pattern coefficients of crops; and a data synchronization module, used for performing information synchronization and updating to the crop management platform according to the size of the growth pattern coefficient.

Description

Technical Field

[0001] The present invention relates to the technical field of crop data management, and in particular to a crop data management system based on big data. Background Art

[0002] In modern agriculture, big data technology plays a vital role, especially in crop data management systems. By utilizing big data technology, we can achieve fine management of agricultural production and optimal allocation of resources, thereby increasing crop yields, reducing production costs and reducing resource waste.

[0003] The smart agricultural management system utilizes the most advanced Internet of Things technology, relying on smart terminal devices and Internet platforms to accurately collect agricultural data, realize the dispersion and sharing of agricultural data, and form a scale management effect.

[0004] However, the current agriculture has the problem of untimely growth status management in the normalized and dynamic data distribution update and density management of crops. This design analyzes the growth data of crops in different cycles by building a GIS crop management platform for smart agriculture, forming an accurate data application process for the management and distribution of crops in agriculture. Summary of the invention

[0005] The purpose of the present invention is to provide a crop data management system based on big data to solve the following technical problems:

[0006] How to achieve dynamic data update and agricultural promotion and application of crop management platform through accurate digital analysis of the periodic growth distribution status of crops.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A crop data management system based on big data, comprising:

[0009] The data import unit is used to import the relevant data of each growth stage of crops in the big database into the crop management platform for pre-processing and screening to obtain the spatial data of crops at different growth stages;

[0010] Crop spatial data include: soil spatial data, climate spatial data, and planting density parameters;

[0011] A data processing unit is used to extract spatial information of crop spatial data at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages;

[0012] A data analysis unit, used to dynamically analyze the spatial distribution of crop layer vectors at different growth stages to obtain crop growth pattern coefficients;

[0013] The data synchronization module is used to synchronize and update information to the crop management platform according to the size of the growth pattern coefficient.

[0014] Preferably, the data import unit imports the data related to each growth stage of crops in the big database into the crop management platform for preprocessing and screening to obtain the spatial data of crops at different growth stages in the following method:

[0015] S1. Determine the target planting area of ​​crops, and establish a historical planting area characteristic network system based on the historical characteristic information of the target planting area; the historical planting area characteristic network system is a functional system equipped for the crop management platform;

[0016] S2, obtaining spatial distribution information of crops at different growth stages based on the historical planting area characteristic network system;

[0017] S3. Normalize the spatial distribution information of crops and transform it to obtain spatial data of crops.

[0018] Preferably, the method for constructing the historical planting area characteristic network system is:

[0019] S11, constructing a planting area information input module based on the historical data of each growth stage of crops;

[0020] S12. Divide crop area planting types according to the environmental characteristics of historical planting areas and construct a planting zoning planning module;

[0021] S13. Acquire a planting information storage module according to the data storage platform.

[0022] Preferably, the method in which the data processing unit extracts spatial information from spatial data of crops at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages is:

[0023] SS1. Obtain spatial distribution characteristic parameters of crop spatial data at different growth stages based on ArcGIS software positioning; spatial distribution characteristic parameters include soil spatial area, spatial temperature and humidity ratio, and planting space coverage rate;

[0024] SS2. Statistically input the spatial distribution characteristic parameter set of crops in different growth stages into the preset analysis model, estimate the target planting area of ​​the statistical crops through the spatial interpolation method, and confirm the spatial distribution characteristic coefficient within the target planting area;

[0025] SS3. Determine whether the spatial distribution characteristic coefficient meets the requirements:

[0026] If so, the distribution characteristics of the target planting areas of crops at different growth stages are identified and vectorized, and the crop layer vectors at different growth stages are output;

[0027] If not, crop spatial distribution adjustment information is generated and fed back to the crop management platform.

[0028] Preferably, the calculation method of the spatial distribution characteristic coefficient is:

[0029] By formula Calculate and obtain the real-time spatial distribution characteristic coefficient Spa(T);

[0030] Where n is the total number of blocks within the target planting area, and i∈[1,n]; T is the total number of time points in the current growth stage; ΔT is the preset growth stage time interval;

[0031] By formula Calculate the spatial distribution characteristic parameter Sd of the i-th block i ; Sd i (T) is the real-time value of the spatial distribution characteristic parameter recorded at each time point of the i-th block;

[0032] Among them, f is the preset function; γ 1 is the first preset weight coefficient, γ 2 is the second preset weight coefficient, γ 3 is the third preset weight coefficient, and γ 1 , γ 2 , γ 3 All greater than 0; S i is the soil space area of ​​the ith block, R i is the spatial temperature and humidity ratio of the i-th block, P i is the planting space coverage rate of the i-th block; S i0 is the standard soil space area of ​​the ith block; R i0 is the standard space temperature and humidity ratio of the i-th block; P i0 is the standard planting space coverage of the i-th block.

[0033] Preferably, the method further comprises comparing the real-time spatial distribution characteristic coefficient Spa(T) with a preset spatial distribution characteristic coefficient threshold interval [Spa A , Spa B ] for comparison:

[0034] If Spa(T)∈[Spa A , Spa B ], then it is judged that the current spatial distribution characteristic coefficient meets the requirements;

[0035] like It is judged that the current spatial distribution characteristic coefficient does not meet the requirements.

[0036] Preferably, the data analysis unit performs spatial distribution dynamic analysis on crop layer vectors at different growth stages to obtain the growth pattern coefficient of the crop in the following manner:

[0037] By formula Calculate the growth pattern coefficient Pat of the jth growth stage j ;

[0038] Among them, pd ij is the block distribution density of the i-th block in the j-th growth stage; g ij is the number of cells of similar adjacent block type in the jth growth stage of the i-th block, g ijmax Qh is the maximum number of cells of similar adjacent block type in the jth growth stage of the i-th block; j1 Qh is the first conversion function of the preset j-th growth stage; j2 is the second conversion function of the preset j-th growth stage; β 1 is the preset weight coefficient of the influence of block distribution density on growth pattern; β 2 is the preset weight coefficient for the effect of cell number on growth pattern.

[0039] Preferably, the method by which the data synchronization module performs information synchronization update to the crop management platform according to the size of the growth pattern coefficient is:

[0040] The growth pattern coefficient Pat of the jth growth stage j Compared with the preset growth pattern coefficient standard value Pat thr To compare:

[0041] If Pat j <Pat thr , then it is judged that the distribution of crop blocks is normal in the current growth stage;

[0042] If Pat j ≥Pat thr , it is judged that the distribution of crop blocks in the current growth stage is abnormal, and adjustment information is generated and synchronized to the crop management platform.

[0043] Beneficial effects of the present invention:

[0044] (1) The present invention performs data preprocessing and screening through a crop management platform to obtain spatial data of crops at different growth stages, further constructs spatial information of the crops based on the spatial data of crops at different growth stages, and implements the framework preparation process before constructing the crop spatial information by setting a data processing unit.

[0045] (2) The present invention constructs a spatial information framework for crop layer vectors at different growth stages by setting up a data analysis unit, and determines the relationship between the spatial layout and growth mode of crops by setting up a spatial distribution dynamic analysis method, thereby obtaining the growth pattern coefficient of crops.

[0046] (3) The present invention analyzes and judges the size of the growth pattern coefficient by setting up a data synchronization module, and synchronizes and updates the information to the crop management platform based on the analysis results of the growth pattern size; it realizes the judgment of the spatial distribution and growth pattern of crops in each growth stage and thus realizes the management of crops.

[0047] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 This is a module diagram of a crop data management system based on big data of the present invention;

[0050] Figure 2 This is a method diagram of the data import unit steps of the present invention;

[0051] Figure 3 A diagram of a method for constructing a historical planting area characteristic network system of the present invention;

[0052] Figure 4 This is a method diagram of the data processing unit steps of the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] See also Figure 1 As shown, the present invention is a crop data management system based on big data, comprising:

[0055] The data import unit is used to import the relevant data of each growth stage of crops in the big database into the crop management platform for pre-processing and screening to obtain the spatial data of crops at different growth stages;

[0056] Crop spatial data include: soil spatial data, climate spatial data, and planting density parameters;

[0057] A data processing unit is used to extract spatial information of crop spatial data at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages;

[0058] A data analysis unit, used to dynamically analyze the spatial distribution of crop layer vectors at different growth stages to obtain crop growth pattern coefficients;

[0059] The data synchronization module is used to synchronize and update information to the crop management platform according to the size of the growth pattern coefficient.

[0060] In the above technical scheme, the current agriculture has the problem of untimely growth status management in the normalized and dynamic data distribution update and density management of crops; this design analyzes the growth data of crops in different cycles by building a GIS crop management platform for smart agriculture, and designs a crop data management system based on big data to realize the accurate data application process of analyzing and forming crop management and resource allocation.

[0061] Specifically, first, by setting up a data import unit, the relevant data information of each historical growth stage of a certain type of crop in the big database is obtained, and the crop data type is screened through the crop management platform. The crop management platform here is built based on the information of the relevant departments of common remote sensing monitoring, and is used to screen the required crop data. This design uses the crop management platform to preprocess and screen the data to obtain the spatial data of crops at different growth stages, and further build the spatial information of the crop based on the spatial data of crops at different growth stages. Then, by setting up a data processing unit, the framework preparation process before building the crop spatial information is realized. Specifically, the spatial information is extracted from the crop spatial data in the crop management platform of ArcGIS software, and finally the crop layer vectors at different growth stages are obtained with different growth stages as dynamic intervals; the crop layer vector is a type of data structure in ArcGIS, which is used to store crop data in vector units.

[0062] Next, the spatial information framework of the crop layer vectors at different growth stages is constructed by setting up a data analysis unit. Specifically, the relationship between the spatial layout and growth mode of crops is determined by setting up a spatial distribution dynamic analysis method, and then the growth pattern coefficient of crops is obtained. Finally, a data synchronization module is set up to analyze and judge the size of the growth pattern coefficient, and the information is synchronized and updated to the crop management platform based on the analysis results of the growth pattern size; the spatial distribution and growth pattern of crops at each growth stage are judged, and then the management of crops is realized.

[0063] As an embodiment of the present invention, please refer to Figure 2 As shown, the data import unit imports the relevant data of each growth stage of crops in the big database into the crop management platform for preprocessing and screening to obtain the spatial data of crops at different growth stages. The method is:

[0064] S1. Determine the target planting area of ​​crops, and establish a historical planting area characteristic network system based on the historical characteristic information of the target planting area; the historical planting area characteristic network system is a functional system equipped for the crop management platform;

[0065] S2, obtaining spatial distribution information of crops at different growth stages based on the historical planting area characteristic network system;

[0066] S3. Normalize the spatial distribution information of crops and transform it to obtain spatial data of crops.

[0067] In the above technical scheme, the method of acquiring specific crop spatial data for the data import unit in this embodiment is realized through the above steps, by determining the target planting area range of a certain crop, the target planting area range is determined according to the planning range obtained by the Agricultural Bureau, and the historical characteristic information is acquired according to the target planting area range information, and the planting area characteristic network system is constructed based on the historical characteristic information, thereby realizing the acquisition of crop spatial distribution information corresponding to the data of different growth stages of crops and the data conversion generation process. The data conversion process here is realized after data normalization processing of the spatial distribution vector information of crop remote sensing monitoring.

[0068] As an embodiment of the present invention, please refer to Figure 3 As shown in the figure, the construction method of the historical planting area characteristic network system is as follows:

[0069] S11, constructing a planting area information input module based on the historical data of each growth stage of crops;

[0070] S12. Divide crop area planting types according to the environmental characteristics of historical planting areas and construct a planting zoning planning module;

[0071] S13. Acquire a planting information storage module according to the data storage platform.

[0072] In the above technical solution, the above steps are used in this embodiment to construct a historical planting area feature network system through multiple modules. Specifically, first, a planting area sample is constructed by a certain amount of crop historical growth stage data, and a planting area information input module is obtained. Then, the crop area planting type is divided according to the environmental characteristics of the historical planting area, and a planting zoning planning module is constructed to realize the pre-division of the planting area into blocks; finally, a planting information storage module is obtained through a data storage platform to store the acquired data information.

[0073] As an embodiment of the present invention, please refer to Figure 4 As shown, the method for the data processing unit to extract spatial information of crop spatial data at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages is as follows:

[0074] SS1. Obtain spatial distribution characteristic parameters of crop spatial data at different growth stages based on ArcGIS software positioning; spatial distribution characteristic parameters include soil spatial area, spatial temperature and humidity ratio, and planting space coverage rate;

[0075] SS2. Statistically input the spatial distribution characteristic parameter set of crops in different growth stages into the preset analysis model, estimate the target planting area of ​​the statistical crops through the spatial interpolation method, and confirm the spatial distribution characteristic coefficient within the target planting area;

[0076] SS3. Determine whether the spatial distribution characteristic coefficient meets the requirements:

[0077] If so, the distribution characteristics of the target planting areas of crops at different growth stages are identified and vectorized, and the crop layer vectors at different growth stages are output;

[0078] If not, crop spatial distribution adjustment information is generated and fed back to the crop management platform.

[0079] In the above technical scheme, in this embodiment, the data processing unit obtains the crop layer vector and realizes the process of adjusting the spatial distribution of crops through the above steps, by generating crop spatial distribution adjustment information and updating the management information of the crop management platform.

[0080] The specific implementation process is as follows: first, the spatial distribution characteristic parameters are located through ArcGIS software to determine the soil spatial area, spatial temperature-humidity ratio and planting space coverage; then, the spatial distribution characteristic parameter set samples are trained through the preset analysis model to obtain the optimization parameters, and the spatial interpolation method is used to estimate the scope of the target planting area of ​​crops covering the spatial distribution characteristic parameter set, and the spatial distribution characteristic coefficient within the target planting area is confirmed. The spatial distribution characteristic coefficient is obtained by formula calculation, and the judgment is made based on the spatial distribution characteristic coefficient. The distribution characteristics of the target planting area of ​​crops that meet the requirements are vectorized, and the crop layer vectors at different growth stages are output. Otherwise, the spatial distribution of crops is adjusted, the spatial distribution adjustment information of crops is generated, and the crop management platform is optimized and updated.

[0081] As an implementation mode of the present invention, the calculation method of the spatial distribution characteristic coefficient is:

[0082] By formula Calculate and obtain the real-time spatial distribution characteristic coefficient Spa(T);

[0083] Where n is the total number of blocks within the target planting area, and i∈[1,n]; T is the total number of time points in the current growth stage; ΔT is the preset growth stage time interval;

[0084] By formula Calculate the spatial distribution characteristic parameter Sd of the i-th block i ; Sd i (T) is the real-time value of the spatial distribution characteristic parameter recorded at each time point of the i-th block;

[0085] Among them, f is the preset function; γ 1 is the first preset weight coefficient, γ 2 is the second preset weight coefficient, γ 3 is the third preset weight coefficient, and γ 1 , γ 2 , γ 3 All greater than 0; S i is the soil space area of ​​the ith block, R i is the spatial temperature and humidity ratio of the i-th block, P i is the planting space coverage rate of the i-th block; S i0 is the standard soil space area of ​​the ith block; R i0 is the standard space temperature and humidity ratio of the i-th block; P i0 is the standard planting space coverage of the i-th block.

[0086] In the above technical solution, the calculation formula for the real-time spatial distribution characteristic coefficient Spa(T) in this embodiment is: By calculating the real-time value Sd of the spatial distribution characteristic parameter in the corresponding time zone of the current growth stage i The spatial distribution characteristic coefficient is obtained by summing up the accumulated blocks of (T), and real-time analysis and generation of adjustment information are performed according to the size range of the real-time spatial distribution characteristic coefficient.

[0087] It should be explained that the preset function f is based on the spatial distribution characteristic parameter Sd i The size of the model is set as an optimization function to ensure that the numerical range of the sum of the soil space area, space temperature and humidity ratio and planting space coverage after the specific gravity is set is optimized; the weight coefficient γ is preset 1 , γ 2 , γ 3 It is set based on historical experience data and will not be described in detail here; S i0 , R i0 , P i0 They are all obtained through machine simulation based on the corresponding historical data and will not be elaborated here.

[0088] As an implementation mode of the present invention, the real-time spatial distribution characteristic coefficient Spa(T) is further included in the process of comparing the real-time spatial distribution characteristic coefficient Spa(T) with the preset spatial distribution characteristic coefficient threshold interval [Spa A , Spa B ] for comparison:

[0089] If Spa(T)∈[Spa A , Spa B ], then it is judged that the current spatial distribution characteristic coefficient meets the requirements;

[0090] like It is judged that the current spatial distribution characteristic coefficient does not meet the requirements.

[0091] In the above technical scheme, this embodiment presets a standard threshold interval of the spatial distribution characteristic coefficient according to the actual situation of historical experience through comparative analysis, and then compares the sizes. When it belongs to the set standard threshold interval, it is judged that the size of the current real-time spatial distribution characteristic coefficient meets the requirements, and it is also judged that the real-time spatial distribution change under the block is normal. If it exceeds or is less than the set standard threshold interval, it is judged that the real-time spatial distribution change is abnormal, and by accumulating the sizes of all real-time spatial distribution characteristic coefficients of the set time zone, it is applied to the subsequent spatial pattern adjustment of crops, thereby further refining the accurate state of the spatial distribution pattern.

[0092] As an implementation mode of the present invention, the data analysis unit performs spatial distribution dynamic analysis on crop layer vectors at different growth stages to obtain the growth pattern coefficient of the crop in the following manner:

[0093] By formula Calculate the growth pattern coefficient Pat of the jth growth stage j ;

[0094] Among them, pd ij is the block distribution density of the i-th block in the j-th growth stage; g ij is the number of cells of similar adjacent block type in the jth growth stage of the ith block, g ijmax Qh is the maximum number of cells of similar adjacent block type in the jth growth stage of the i-th block; j1 Qh is the first conversion function of the preset j-th growth stage; j2 is the second conversion function of the preset j-th growth stage; β 1 is the preset weight coefficient of the influence of block distribution density on growth pattern; β 2 is the preset weight coefficient for the effect of cell number on growth pattern.

[0095] In the above technical solution, in this embodiment, the formula Calculate the growth pattern coefficient Pat of the jth growth stage j ; According to the block distribution density of different blocks and different growth stages and the changes in the number of cells of similar adjacent block types, the growth pattern of all blocks in the target area is analyzed to achieve the spatial management process of adjusting crops; because the block (patch) division similar to landscape greening in the distribution process of crops, the planting space is optimized according to the zoning of different planting blocks, the land use efficiency is optimized, and the purpose of sustainable development is also achieved. Therefore, the block distribution density and the number of cells of similar adjacent block types are used as the basis for the division of crop growth patterns in this design.

[0096] Generally speaking, it is best if the density of block distribution is appropriate and the number of cells of similar adjacent block types does not exceed the maximum value; the results of the growth pattern coefficient are further analyzed to achieve adjustments to the crop management platform and information updates.

[0097] As an embodiment of the present invention, the method in which the data synchronization module performs information synchronization update to the crop management platform according to the size of the growth pattern coefficient is:

[0098] The growth pattern coefficient Pat of the jth growth stage j Compared with the preset growth pattern coefficient standard value Pat thr To compare:

[0099] If Pat j <Pat thr , then it is judged that the distribution of crop blocks is normal in the current growth stage;

[0100] If Pat j ≥Pat thr , it is judged that the distribution of crop blocks in the current growth stage is abnormal, and adjustment information is generated and synchronized to the crop management platform.

[0101] In the above technical solution, the size of the growth pattern coefficient of the current growth stage is determined by comparison to perform a comparison analysis process of the standard value. If it is less than the current standard value Pat thr The distribution of crop blocks in the current growth stage is judged to be normal; otherwise, it is judged to be abnormal and adjusted, and the adjustment information is fed back to realize the synchronization process of the crop management platform.

[0102] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0103] The above specific embodiments of this specification are described. Other embodiments are within the scope of the attached documents. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. A crop data management system based on big data, characterized in that: include: The data import unit is used to import the relevant data of each growth stage of crops in the big database into the crop management platform for pre-processing and screening to obtain the spatial data of crops at different growth stages; The crop spatial data includes: soil spatial data, climate spatial data, and planting density parameters; A data processing unit is used to extract spatial information of crop spatial data at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages; A data analysis unit, used to dynamically analyze the spatial distribution of crop layer vectors at different growth stages to obtain crop growth pattern coefficients; The data synchronization module is used to synchronize and update information to the crop management platform according to the size of the growth pattern coefficient.

2. A crop data management system based on big data according to claim 1, characterized in that: The method in which the data import unit imports the data related to each growth stage of crops in the big database into the crop management platform for preprocessing and screening to obtain spatial data of crops at different growth stages is as follows: S1. Determine the target planting area of ​​crops, and establish a historical planting area feature network system based on the historical feature information of the target planting area; the historical planting area feature network system is a functional system equipped by the crop management platform; S2, spatial distribution information of crops obtained from the network system of historical planting area characteristics at different growth stages; S3. Normalize the spatial distribution information of crops and transform it to obtain spatial data of crops.

3. The crop data management system based on big data according to claim 2, characterized in that: The method for constructing the historical planting area characteristic network system is as follows: S11, constructing a planting area information input module based on the historical data of each growth stage of crops; S12. Divide crop area planting types according to the environmental characteristics of historical planting areas and construct a planting zoning planning module; S13. Acquire a planting information storage module according to the data storage platform.

4. The crop data management system based on big data according to claim 2, characterized in that: The method in which the data processing unit extracts spatial information from spatial data of crops at different growth stages through ArcGIS software to obtain crop layer vectors at different growth stages is as follows: SS1. Obtain spatial distribution characteristic parameters of crop spatial data at different growth stages based on ArcGIS software positioning; the spatial distribution characteristic parameters include soil spatial area, spatial temperature and humidity ratio, and planting space coverage; SS2. Statistically input the spatial distribution characteristic parameter set of crops in different growth stages into the preset analysis model, estimate the target planting area of ​​the statistical crops through spatial interpolation method, and confirm the spatial distribution characteristic coefficient within the target planting area; SS3. Determine whether the spatial distribution characteristic coefficient meets the requirements: If so, the distribution characteristics of the target planting areas of crops at different growth stages are identified and vectorized, and the crop layer vectors at different growth stages are output; If not, crop spatial distribution adjustment information is generated and fed back to the crop management platform.

5. The crop data management system based on big data according to claim 4, characterized in that: The calculation method of the spatial distribution characteristic coefficient is: By formula Calculate and obtain the real-time spatial distribution characteristic coefficient Spa(T); Where n is the total number of blocks within the target planting area, and i∈[1,n]; T is the total number of time points in the current growth stage; ΔT is the preset growth stage time interval; By formula Calculate the spatial distribution characteristic parameter Sd of the i-th block i ; Sd i (T) is the real-time value of the spatial distribution characteristic parameter recorded at each time point of the i-th block; Wherein, f is a preset function; γ1 is a first preset weight coefficient, γ2 is a second preset weight coefficient, γ3 is a third preset weight coefficient, and γ1, γ2, and γ3 are all greater than 0; S i is the soil space area of ​​the ith block, R i is the spatial temperature and humidity ratio of the i-th block, P i is the planting space coverage rate of the i-th block; S i0 is the standard soil space area of ​​the ith block; R i0 is the standard space temperature and humidity ratio of the i-th block; P i0 is the standard planting space coverage of the i-th block.

6. The crop data management system based on big data according to claim 5, characterized in that: The present invention also includes comparing the real-time spatial distribution characteristic coefficient Spa(T) with the preset spatial distribution characteristic coefficient threshold interval [Spa A , Spa B ] for comparison: If Spa(T)∈[Spa A , Spa B ], then it is judged that the current spatial distribution characteristic coefficient meets the requirements; like It is judged that the current spatial distribution characteristic coefficient does not meet the requirements.

7. The crop data management system based on big data according to claim 1, characterized in that: The data analysis unit performs spatial distribution dynamic analysis on crop layer vectors at different growth stages to obtain crop growth pattern coefficients in the following manner: By formula Calculate the growth pattern coefficient Pat of the jth growth stage j ; Among them, pd ij is the block distribution density of the i-th block in the j-th growth stage; g ij is the number of cells of similar adjacent block type in the jth growth stage of the i-th block, g ijmax Qh is the maximum number of cells of similar adjacent block type in the jth growth stage of the i-th block; j1 Qh is the first conversion function of the preset j-th growth stage; j2 is the second conversion function of the preset j-th growth stage; β1 is the preset weight coefficient of the influence of block distribution density on the growth pattern; β2 is the preset weight coefficient of the influence of cell number on the growth pattern.

8. The crop data management system based on big data according to claim 7, characterized in that: The method in which the data synchronization module synchronizes and updates information to the crop management platform according to the size of the growth pattern coefficient is: The growth pattern coefficient Pat of the jth growth stage j Compared with the preset growth pattern coefficient standard value Pat thr To compare: If Pat j <Pat thr , then it is judged that the distribution of crop blocks is normal in the current growth stage; If Pat j ≥Pat thr , it is judged that the distribution of crop blocks in the current growth stage is abnormal, and adjustment information is generated and synchronized to the crop management platform.

Images