Global common wild rice seed quality resource collecting and sampling method and system

Abstract

The invention discloses a global common wild rice germplasm resource collecting and sampling method and system, and the method comprises the steps: processing global common wild rice distribution and environmental factor data, including the longitude and latitude of a distribution point, the range selection of environmental factors in different periods, and format conversion; and verifying a simulation effect, comparing and analyzing contribution conditions of different environment variables in each period to common wild rice distribution, performing matching and grouping analysis on potential distribution in each period and key environment factor data of the potential distribution, performing sampling area division, and performing repeated operation on common wild rice sampling areas in different periods. According to the method, the sensitivity difference of the biological characteristics of the common wild rice to the environment is considered, so that the future prediction of the distribution of the common wild rice and the large-scale sampling are more reasonable and scientific.

Description

Technical Field

[0001] The invention relates to the technical field of global common wild rice germplasm resource collection and sampling, and in particular to a global common wild rice germplasm resource collection and sampling method and system. Background Art

[0002] Common wild rice exhibits extremely rich genetic diversity and is a chip for rice improvement. In recent years, with changes in climate, natural environment, planting structure, and land management methods, the habitat of common wild rice has been damaged, and it has been identified as a national second-level protected endangered species. Among the wild rice germplasm resources preserved in my country, more than 90% of the germplasm comes from domestic surveys and collections. my country urgently needs to investigate and collect foreign wild rice genetic resources to expand my country's germplasm resource pool. Therefore, it is particularly important to determine the areas where common wild rice germplasm resources are richly distributed, and to explore the possible evolution of its distribution pattern under different climate change scenarios.

[0003] Looking at the current survey and collection of common wild rice germplasm resources, it has gone through a process from small-scale surveys conducted by individuals in a sporadic and random manner to the current large-scale surveys based on literature, interviews and field investigations. At the same time, the Global Positioning System (GPS) is used to precisely locate the sample collection site, and images are used to record sample information to show the sample's habitat. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] The present invention proposes a method for collecting and sampling common wild rice germplasm resources worldwide. The present invention takes into account the differences in the sensitivity of the biological characteristics of common wild rice to the environment, making future predictions of the distribution of common wild rice and large-scale sampling more reasonable and scientific.

[0006] Another object of the present invention is to provide a global common wild rice germplasm resource collection and sampling system.

[0007] To achieve the above-mentioned purpose, the present invention provides a method for collecting and sampling common wild rice germplasm resources worldwide, comprising: The MaxEnt (Maximum Entropy) model is used to simulate the potential distribution probability of common wild rice in different periods based on the global common wild rice distribution data and environmental factor data after data preprocessing to obtain potential distribution data; According to the contribution rate, the key environmental factor data that affects the temporal and spatial distribution of common wild rice were obtained, and used to compare and analyze the contribution of different environmental variables to the distribution of common wild rice in different periods; Match the potential distribution data with the key environmental factor data that affect the temporal and spatial distribution of common wild rice to extract the key environmental factor data within the potential distribution area of ​​common wild rice; The key environmental factor data within the potential distribution area of ​​common wild rice were grouped and analyzed to spatially classify the potential distribution area, and the best grouping report results were used for secondary analysis to obtain the spatial classification results of the potential distribution area of ​​common wild rice.

[0008] The method for collecting and sampling common wild rice germplasm resources worldwide according to the embodiment of the present invention may also have the following additional technical features: In one embodiment of the present invention, data preprocessing includes: Obtain global common wild rice distribution data and environmental factor data; Use preset software to process all environmental factor data into layer data; Use the preset tool to remove highly correlated variable data to filter the layer data to remove highly correlated environmental factors.

[0009] In one embodiment of the present invention, after obtaining the potential distribution data, the method further includes: The potential distribution data were imported into ArcGIS 10.7 software for visualization to generate a geographic distribution map, and different colors were used to distinguish each category according to the different ranges of existence probability; The existence probability of common wild rice in the potential distribution data is defined as the suitability index, which is used to characterize the potential distribution of common wild rice. The simulated potential distribution data and the actual distribution data were superimposed and analyzed to verify the model simulation results, and the consistency between the simulation results and the actual distribution data was compared. The AUC value in the model simulation results was used as an indicator to measure the accuracy of the model.

[0010] In one embodiment of the present invention, the existence probability of the common wild rice is obtained by simulating and verifying the potential distribution of environmental factors after the model combines all environmental variables and screens them according to the correlation between the variables.

[0011] In one embodiment of the present invention, the area under the receiver operating characteristic curve automatically generated during the model operation is used as an indicator to measure the accuracy of the model, including: When the model-simulated potential distribution data of common wild rice is completely inconsistent with the actual distribution data, the AUC value is 0; When the potential distribution data simulated by the model completely matches the actual distribution data, that is, in an ideal state, the AUC value is 1; The model accuracy is judged based on the AUC value automatically generated by the model.

[0012] In one embodiment of the present invention, the variables ranked top three in descending order of contribution rate of each environmental variable are selected as the current key environmental factor data.

[0013] In one embodiment of the present invention, the key environmental factor data are captured after raster transformation is performed on the areas other than the low-suitable areas.

[0014] In one embodiment of the present invention, during the group analysis, the best evaluation group report is obtained, and reclassification is performed based on the best group report result.

[0015] In one embodiment of the present invention, the pseudo F statistic is calculated by group analysis:

[0016] Where T represents the total sum of squares, P g represents the within-group sum of squares when divided into g groups, where g is the number of groups, n is the total number of samples.

[0017] To achieve the above-mentioned purpose, the present invention further provides a global common wild rice germplasm resource collection and sampling system, comprising: A potential distribution data acquisition module uses the global common wild rice distribution data and environmental factor data after data preprocessing to simulate the potential distribution probability of common wild rice in different periods to obtain potential distribution data; The key environmental factor acquisition module is used to obtain the key environmental factor data that affect the temporal and spatial distribution of common wild rice according to the contribution rate, and to compare and analyze the contribution of different environmental variables to the distribution of common wild rice in different periods; The key environmental factor matching module is used to match the potential distribution data with the key environmental factor data that affect the temporal and spatial distribution of common wild rice, so as to extract the key environmental factor data within the potential distribution area of ​​common wild rice; The grouping analysis spatial classification module is used to group and analyze the key environmental factor data within the potential distribution area of ​​common wild rice to spatially classify the potential distribution area, and to use the best grouping report results for secondary analysis to obtain the spatial classification results of the potential distribution area of ​​common wild rice.

[0018] The global common wild rice germplasm resource collection and sampling method and system of the embodiment of the present invention are intended to provide a scientific basis for the investigation, collection and protection of common wild rice germplasm resources.

[0019] The beneficial effects of the present invention are: (1) Most of the traditional research on the temporal and spatial distribution of common wild rice and its environmental relationship has focused on small-scale areas and mainly conducted manual surveys. The present invention fully considered the differences and contribution rates of various environmental variables to the distribution of common wild rice in different periods, explored the influence of different environmental factors on common wild rice, and screened out key environmental factors for sampling division.

[0020] (2) The present invention fully considers the biological characteristics of common wild rice and its differences in sensitivity to the environment, making the environmental variables selected for predicting its distribution in the future more reasonable and scientific, enhancing the reliability of the forecast model, and completing the sampling zoning of common wild rice in different periods.

[0021] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 is a flow chart of a method for collecting and sampling global common wild rice germplasm resources according to an embodiment of the present invention; Figure 2 is a wild rice distribution map simulated by a MaxEnt model according to an embodiment of the present invention; Figure 3 is a diagram for describing the simulation accuracy of the MaxEnt model in the current period according to an embodiment of the present invention; Figure 4 is a diagram for describing the simulation accuracy of the MaxEnt model during the LGM period according to an embodiment of the present invention; Figure 5 is a diagram for describing the simulation accuracy of the MaxEnt model during the LIG period according to an embodiment of the present invention; Figure 6 is a potential distribution map of common wild rice at the current stage according to an embodiment of the present invention; Figure 7 is a potential distribution map of common wild rice during the LGM period according to an embodiment of the present invention; Figure 8 is a potential distribution map of common wild rice during the LIG period according to an embodiment of the present invention; Fig. 9 is a sampling partition diagram of common wild rice at the current stage according to an embodiment of the present invention; Fig.10 is a sampling partition diagram of common wild rice during the LGM period according to an embodiment of the present invention; Fig.11is a sampling partition diagram of common wild rice during the LIG period according to an embodiment of the present invention; Fig.12 It is a structural diagram of a global common wild rice germplasm resource collection and sampling system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0025] The following describes the method and system for collecting and sampling common wild rice germplasm resources around the world according to an embodiment of the present invention with reference to the accompanying drawings.

[0026] Figure 1 FIG. 1 is a flow chart of a method for collecting and sampling common wild rice germplasm resources worldwide according to an embodiment of the present invention. Figure 1 As shown, the method includes: S1, based on the global common wild rice distribution data and environmental factor data after data preprocessing, simulate the potential distribution probability of common wild rice in different periods to obtain potential distribution data; S2, based on the contribution rate, obtain the key environmental factor data that affect the temporal and spatial distribution of common wild rice, and use it to compare and analyze the contribution of different environmental variables to the distribution of common wild rice in different periods; S3, matching the potential distribution data with the key environmental factor data that affect the temporal and spatial distribution of common wild rice to extract the key environmental factor data within the potential distribution area of ​​common wild rice; S4, by grouping and analyzing the key environmental factor data within the potential distribution area of ​​common wild rice to spatially classify the potential distribution area, and using the best grouping report results for secondary analysis to obtain the spatial classification results of the potential distribution area of ​​common wild rice.

[0027] Specifically, the method for collecting and sampling global common wild rice germplasm resources of the present invention may include the following specific steps: First, the global common wild rice distribution and environmental factor data were processed. The data included the longitude and latitude of the distribution points, the range of environmental factors in different periods, and format conversion. All environmental data were processed into layer data using ArcGIS 10.7 software. The environmental factors were screened using the SDM Tool to remove highly correlated variable data and remove highly correlated environmental factors to prevent autocorrelation. The MaxEnt model was used to combine the processed common wild rice data and environmental data to simulate the potential distribution of common wild rice in different periods. Secondly, ArcGIS10.7 software was used to visualize and classify the probability distribution results of common wild rice in each period, and different colors were used to distinguish the categories. The probability of common wild rice existence was defined as the suitability index, which was used to characterize the potential distribution of common wild rice and superimposed with the actual distribution data to verify the simulation effect. The AUC (Area Under Curve) value in the model simulation results was used as an indicator to measure the accuracy of the model.

[0028] Then, we compared and analyzed the contribution of different environmental variables to the distribution of common wild rice in different periods, and selected the top three variables according to their contribution rates as the key environmental factors affecting the temporal and spatial distribution of common wild rice. Then, we compared and analyzed the contribution of different environmental variables to the distribution of common wild rice in different periods, and selected the top three variables according to their contribution rates as the key environmental factors affecting the temporal and spatial distribution of common wild rice. Then, the potential distribution areas were spatially classified through grouping analysis, and the best grouping report results were used for secondary analysis to obtain the final results; Finally, environmental data from different stages were selected to re-establish the MaxEnt model to simulate the potential distribution of common wild rice, and the above steps were repeated to carry out zoning.

[0029] In step S2, the area value AUC under the receiver operating characteristic curve (ROC) automatically generated during the model operation is used as an indicator to measure the accuracy of the model, specifically: when the potential distribution of common wild rice simulated by the model is completely inconsistent with its actual distribution, the AUC value is 0; when the potential distribution simulated by the model is completely consistent with the actual distribution, that is, in an ideal state, the AUC value is 1; the model accuracy is judged according to the AUC value automatically generated by the model.

[0030] In one embodiment of the present invention, the number of environmental variables is not limited. Based on the contribution of each environmental variable to species distribution in different time periods, the top three variables with the highest contribution rate are selected as the key environmental factors for that month. Unlike previous studies, the present invention fully considers the influence of each environmental variable on the temporal and spatial distribution of common wild rice at different times, making the research results more scientific.

[0031] In one embodiment of the present invention, the key environmental factors for each month are selected by selecting only the top three variables in descending order of contribution rate of each environmental variable in each month as the key environmental factors for that month, and the key environmental factors for each period are different.

[0032] In one embodiment of the present invention, the probability of common wild rice being suitable for growth is 0-0.4, which is a low suitable growth zone; 0.4-0.6, which is a medium suitable growth zone; and 0.6-1, which is a high suitable growth zone.

[0033] In one embodiment of the present invention, it is necessary to perform grid transformation on the areas other than the low-suitable areas and then spatially capture the key environmental factor data.

[0034] In one embodiment of the present invention, the above steps can be repeated when plotting the existence probability of common wild rice at different periods and dividing the regions.

[0035] Furthermore, if Figure 2-11 As shown, the potential partition prediction and sampling division processing process of global wild rice is selected by the present invention.

[0036] 1. Model construction.

[0037] The maximum entropy model MaxEnt in the present invention is a classic theory in the field of statistics and machine learning. It is a criterion for selecting the statistical characteristics of random variables that best meet the objective situation. Based on this theory, according to the species existence data and the environmental data of the entire study area, the distribution with the largest species existence probability, i.e., the largest entropy, is selected as the optimal distribution of its potential habitat in accordance with the constraints.

[0038] The latest MaxEnt software is used for model operation. The species presence data of the sample input layer (Samples) is divided into two parts, domestic distribution data and foreign distribution data. Domestic data mainly rely on the detailed surveys of common wild rice in China conducted by the research team over the years. These surveys cover the natural distribution range, habitat, ecological characteristics, etc. of wild rice, providing rich basic data for in-depth research on the protection and utilization of common wild rice. Through field surveys and sample collection, the research team systematically recorded the habitat of common wild rice, covering China's southern rice-growing areas and some wetland ecosystems. Such surveys have laid a solid foundation for the conservation of common wild rice germplasm resources in China and future breeding research. On the other hand, foreign distribution data mainly come from international gene banks, such as the Genesys gene bank (https: / / www.genesys-pgr.org). This database brings together plant genetic resource information worldwide, including the distribution of common wild rice in many countries. The global spatial distribution map of common wild rice is shown in the figure below. Figure 2 As shown in Figure 1 . The 20 key climate factors that affect species distribution are used as simulation variables, including annual mean temperature (BIO1), monthly mean day-night temperature difference (BIO2), annual temperature range (BIO7), annual mean precipitation (BIO12), wettest monthly precipitation (BIO13), driest monthly precipitation (BIO14), etc. The data are from the World Climate website (https: / / worldclim.org) with a resolution of 2.5 minutes. For details, see Table 1 . Before simulation, the environmental variables need to be correlated and converted into ASCII format by ArcGIS 10.7 software for storage. Before running the MaxEnt model, 75% of the species distribution data are used as training data, and the remaining 25% are used as test data. In order to eliminate randomness and repeatability, the number of model repetitions is set to 10. Specifically, the sample data will be randomly divided into training set and test set in each operation, and the data will be run in a cross-validation manner in each operation. The regularization multiplier and the number of iterations are set by the automatic optimal setting of the software. The final result is the average of the results of these 10 runs and is output in Logistic form.

[0039] Table 1

[0040] 2. Result verification: The present invention uses the receiver operating characteristic curve (ROC) automatically generated by the MaxEnt model to evaluate the experimental performance of the model. The ROC curve is drawn with the false positive rate as the horizontal axis and the true positive rate as the vertical axis. The size of the curve area value (AUC) enclosed by the horizontal and vertical axes is used as a measure of model accuracy, and the value range is [0, 1]. That is, when the potential distribution of species simulated by the model is completely inconsistent with the actual distribution, the AUC value is 0; when the two are completely consistent, the AUC value is 1. It is defined as model prediction failure, poor, average, good and excellent. The simulation accuracy of the model in each period is as follows. Figure 3 , Figure 4 and Figure 5 They are the present period, the last glacial maximum (LGM), and the last interglacial period (LIG). The simulation accuracy of each period is greater than 0.9, indicating that the simulation results are excellent.

[0041] The existence probability distribution result format output by the model is in ASCII format, which needs to be imported into ArcGIS10.7 software for visualization analysis. First, convert the ASCII format data into raster format, load the world map shp file to obtain the potential distribution map of common wild rice; define the existence probability as the habitat suitability index (HSI), and "reclassify" according to (low suitability area), (medium suitability area), (high suitability area), and assign different colors to each category, such as Figure 6 , Figure 7 and Figure 8 Shown are the current stage, LGM, and LIG, respectively.

[0042] 3. Selection of key environmental factors As shown in Table 2. According to the contribution rate of each environmental variable in each period, the first three variables with higher contribution rates were selected in descending order as the key environmental factors of that period.

[0043] Table 2

[0044] 4. Sampling area division The medium-suitable growth area and high-suitable growth area of ​​each period were selected to capture the key environmental factor data of each period. The key environmental factors in the suitable growth area were extracted using multi-value extraction points. The sampling areas of each period were divided through group analysis, mainly using key environmental factors as classification factors. The optimal number of groups needed to be evaluated during the analysis. According to the results of the optimal number of groups, the potential distribution area of ​​common wild rice in each period was divided twice to obtain the final sampling area. The results are as follows: Fig. 9 , Fig.10 and Fig.11As shown, they are the current stage, LGM, and LIG. Pseudo F statistics are calculated by group analysis:

[0045] Where T represents the total sum of squares, P g represents the within-group sum of squares when divided into g groups, where g is the number of groups, n is the total number of samples.

[0046] Among them, the pseudo F statistic evaluates the effect of dividing into g classes. If the division into g classes is reasonable, the sum of squares of deviations within the class (denominator) should be small, and the sum of squares between classes (numerator) should be relatively large, so the clustering level with a larger pseudo F statistic and a smaller number of classes should be selected. Through the pseudo F statistic, the optimal number of groups is obtained and regrouped.

[0047] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

[0048] According to the global common wild rice germplasm resource collection and sampling method of the embodiment of the present invention, based on the existing global-scale common wild rice distribution records and 20 climate factors, GIS spatial analysis technology and MaxEnt model are used to simulate the potential suitable areas of common wild rice LIG, LGM and the current period, and analyze the impact of climate change on its distribution, predict the optimal number of sampling groups, and obtain the sampling strategy for dividing different ecological zones, aiming to provide a scientific basis for the investigation, collection and protection of common wild rice germplasm resources.

[0049] In order to implement the above embodiment, Fig.12 As shown, this embodiment also provides a global common wild rice germplasm resource collection and sampling system 10, including: Potential distribution data acquisition module 100, using the global common wild rice distribution data and environmental factor data after data preprocessing to simulate the potential distribution probability of common wild rice in different periods to obtain potential distribution data; The key environmental factor acquisition module 200 is used to obtain the key environmental factor data that affects the temporal and spatial distribution of common wild rice according to the contribution rate, and to compare and analyze the contribution of different environmental variables to the distribution of common wild rice in different periods; The key environmental factor matching module 300 is used to match the potential distribution data with the key environmental factor data that affect the temporal and spatial distribution of common wild rice, so as to extract the key environmental factor data in the potential distribution area of ​​common wild rice; The group analysis spatial classification module 400 is used for group analysis of key environmental factor data within the potential distribution area of ​​common wild rice to spatially classify the potential distribution area, and to perform secondary analysis using the best group report results to obtain the spatial classification results of the potential distribution area of ​​common wild rice.

[0050] According to the global common wild rice germplasm resource collection and sampling system of the embodiment of the present invention, based on the existing global-scale common wild rice distribution records and 20 climate factors, GIS spatial analysis technology and MaxEnt model are used to simulate the potential suitable areas of common wild rice in the last interglacial period, the last glacial maximum and the current period, and analyze the impact of climate change on its distribution, predict the optimal number of sampling groups, and obtain the sampling strategy for dividing different ecological zones, aiming to provide a scientific basis for the investigation, collection and protection of common wild rice germplasm resources.

[0051] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0052] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A method for collecting and sampling common wild rice germplasm resources worldwide, characterized in that: include: Based on the pre-processed global common wild rice distribution data and environmental factor data, the potential distribution probability of common wild rice in different periods is simulated to obtain potential distribution data; According to the contribution rate, the key environmental factor data that affects the temporal and spatial distribution of common wild rice were obtained, and used to compare and analyze the contribution of different environmental variables to the distribution of common wild rice in different periods; Match the potential distribution data with the key environmental factor data that affect the temporal and spatial distribution of common wild rice to extract the key environmental factor data within the potential distribution area of ​​common wild rice; The key environmental factor data within the potential distribution area of ​​common wild rice were grouped and analyzed to spatially classify the potential distribution area, and the best grouping report results were used for secondary analysis to obtain the spatial classification results of the potential distribution area of ​​common wild rice.

2. The method according to claim 1, characterized in that Data preprocessing, including: Obtain global common wild rice distribution data and environmental factor data; Use preset software to process all environmental factor data into layer data; Use the preset tool to remove highly correlated variable data to filter the layer data to remove highly correlated environmental factors.

3. The method according to claim 1, characterized in that After obtaining the potential distribution data, the method further includes: The potential distribution data were imported into ArcGIS 10.7 software for visualization to generate a geographic distribution map, and different colors were used to distinguish each category according to the different ranges of existence probability; The existence probability of common wild rice in the potential distribution data is defined as the suitability index, which is used to characterize the potential distribution of common wild rice. The simulated potential distribution data and the actual distribution data were superimposed and analyzed to verify the model simulation results, and the consistency between the simulation results and the actual distribution data was compared. The AUC value in the model simulation results was used as an indicator to measure the accuracy of the model.

4. The method according to claim 3, characterized in that The existence probability of the common wild rice is obtained by combining all environmental variables with the model and performing potential distribution simulation and verification based on environmental factors screened according to the correlation between the variables.

5. The method according to claim 3, characterized in that: The area under the receiver operating characteristic curve automatically generated during the model operation is used as an indicator to measure the accuracy of the model, including: When the model-simulated potential distribution data of common wild rice is completely inconsistent with the actual distribution data, the AUC value is 0; When the potential distribution data simulated by the model completely matches the actual distribution data, that is, in an ideal state, the AUC value is 1; The model accuracy is judged based on the AUC value automatically generated by the model.

6. The method according to claim 3, characterized in that According to the contribution rate of each environmental variable from large to small, the variables ranked top three are selected in turn as the current key environmental factor data.

7. The method according to claim 6, characterized in that After raster conversion, the key environmental factor data are captured in areas other than the low-suitable areas.

8. The method according to claim 1, characterized in that During group analysis, obtain the best evaluation group report and reclassify based on the results of the best group report.

9. The method according to claim 8, characterized in that Calculate the pseudo F statistic through group analysis: Where T represents the total sum of squares, P g represents the within-group sum of squares when divided into g groups, where g is the number of groups, n is the total number of samples.

10. A global common wild rice germplasm resource collection and sampling system, characterized in that: include: A potential distribution data acquisition module uses the global common wild rice distribution data and environmental factor data after data preprocessing to simulate the potential distribution probability of common wild rice in different periods to obtain potential distribution data; The key environmental factor acquisition module is used to obtain the key environmental factor data that affect the temporal and spatial distribution of common wild rice according to the contribution rate, and to compare and analyze the contribution of different environmental variables to the distribution of common wild rice in different periods; The key environmental factor matching module is used to match the potential distribution data with the key environmental factor data that affect the temporal and spatial distribution of common wild rice, so as to extract the key environmental factor data within the potential distribution area of ​​common wild rice; The grouping analysis spatial classification module is used to group and analyze the key environmental factor data within the potential distribution area of ​​common wild rice to spatially classify the potential distribution area, and to use the best grouping report results for secondary analysis to obtain the spatial classification results of the potential distribution area of ​​common wild rice.

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