Variety promotion region analysis method based on variety trials and environmental similarity

Abstract

The present invention proposes a method for analyzing the region of variety promotion based on variety trials and environmental similarity, including: collecting meteorological data and multi-year multi-test point variety trial data of crop varieties; pre-processing the data, integrating and processing the data files into data files of preset specifications; using the big data platform to deduce the algorithm of the law of development of the growth period based on the collected multi-year multi-point growth period data and the meteorological data of previous years; calculating the national growth period time range; finding out the high-yield regions and years with excellent performance; querying the meteorological data of the growth period of previous years of the high-yield test points for statistical analysis; deducing the regional algorithm suitable for variety promotion; analyzing and obtaining the similarity of the suitable promotion regions across the country; statistically analyzing the similarity data of the suitable promotion regions across the country, and finding the regions where the similarity of the suitable promotion regions across the country reaches a preset ratio or more. The present invention effectively points out the regions where the varieties are suitable for promotion, and ensures the accuracy and timeliness of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural data analysis, and in particular to a variety promotion area analysis method based on variety trials and environmental similarity. Background Art

[0002] Agriculture is the foundation of the national economy. In my country, planting is the main body of agriculture. Planting is greatly affected by natural conditions. There must be enough light, heat and water for the growth of crops. my country's planting areas are also mainly distributed in the plains, basins and hilly areas of the humid and semi-humid eastern monsoon zone. Affected by climatic conditions, there are obvious differences in farming systems and crop types between southern and northern my country. Moreover, we mainly grow more crops, and the promotion of suitable areas for modern crop varieties mostly relies on the previous experience of breeders and seed companies to make artificial judgments. The result is that the planting risk is high and the promotion of new crop varieties is slow. Summary of the invention

[0003] The object of the present invention is to solve at least one of the technical drawbacks.

[0004] To this end, the purpose of this invention is to propose a variety promotion area analysis method based on variety trials and environmental similarity, effectively point out the area where the variety is suitable for promotion, and ensure the accuracy and timeliness of the data.

[0005] In order to achieve the above object, an embodiment of the present invention provides a variety promotion area analysis method based on variety trials and environmental similarity, comprising the following steps:

[0006] Step S1, collecting meteorological data and crop variety test data of multiple years and multiple test sites, and storing them in the form of files on a server;

[0007] Step S2, pre-processing the data, integrating the data files into data files of preset specifications, and the big data platform periodically obtains the above data files and stores them in the database;

[0008] Step S3, the big data platform calculates an algorithm for the development law of the growth period based on the multi-year multi-point growth period data collected and combined with the meteorological data of previous years; and calculates the national growth period time range based on the algorithm for the development law of the growth period;

[0009] Step S4, screening based on multi-year multi-point test data to find high-yield areas and years with excellent performance;

[0010] Step S5, querying the meteorological data of the high-yield test points during the growth period of previous years through the high-yield areas and years for statistical analysis;

[0011] Step S6, repeatedly verifying the meteorological data of the growing seasons in multiple high-yield areas over the years, obtaining the climate condition index and weight ratio suitable for the variety; and calculating the regional algorithm suitable for variety promotion through the index and weight ratio;

[0012] Step S7, calculating and analyzing the similarity of suitable promotion regions across the country based on the regional algorithm for suitable variety promotion, national real-time growth period data, and real-time forecast meteorological data;

[0013] Step S8, statistically analyzing the similarity data of the suitable promotion regions across the country, and finding regions across the country whose similarity of the suitable promotion regions reaches a preset ratio or above.

[0014] Furthermore, in the step S1, the meteorological data includes: historical meteorological data, site meteorological data, grid hourly meteorological data and grid daily meteorological data.

[0015] Furthermore, in step S1, the multi-year and multi-test point variety test data includes: growth period, pests and diseases, meteorological disasters, crop growth, and yield.

[0016] Furthermore, in step S2, the data file integration process includes the following steps:

[0017] Grid the historical site meteorological data and real-time grid meteorological data;

[0018] Grid the test point by multi-year and multi-point variety information data;

[0019] The gridded data of the above-mentioned sites and the gridded data of the test points are preprocessed to generate data files, which are then stored in the database by the big data platform to complete data sorting and unified management.

[0020] Furthermore, in step S3, based on the growth period data of multi-year multi-point experiments, combined with historical meteorological data, the effective accumulated temperature rules in the same growth period in different years are statistically analyzed to infer the conditions for the growth period.

[0021] Furthermore, in the step S8, the preset ratio is 70% to 90%.

[0022] According to the method for analyzing the variety promotion area based on variety trials and environmental similarity of the embodiment of the present invention, by collecting multi-year multi-point variety trials and national historical real-time grid meteorological data, and collating and storing the data in a server, a large amount of data is used to repeatedly verify the correlation and weight ratio between the performance of the variety at multiple points over the years and the meteorological data indicators. A method for finding suitable variety promotion areas across the country in the form of environmental similarity using the relationship between meteorological indicators and the weight ratio. The present invention integrates and analyzes the experience of cultivating crop varieties based on many years of crop variety test points distributed in many places across the country and many years of national grid-format meteorological data, and can efficiently and accurately locate areas where varieties are suitable for promotion, and can quickly develop new crop varieties and promote suitable areas across the country at high speed. The present invention can effectively point out areas where varieties are suitable for promotion, and through manual multi-year multi-point variety data collection and real-time collating and calculation of meteorological data, the correctness and timeliness of the data are guaranteed.

[0023] 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

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0025] Figure 1 A flowchart of a variety promotion region analysis method based on variety trials and environmental similarity according to an embodiment of the present invention;

[0026] Figure 2 A flow chart of the collection and integration management of multi-year multi-point test data and meteorological data according to an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of growth period prediction according to an embodiment of the present invention;

[0028] Figure 4 Schematic diagram of a national variety promotion region analysis method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0030] The present invention proposes a variety promotion area analysis method based on variety trials and environmental similarity, which involves the collection of multi-year multi-point trial variety data and the collation of multi-year national meteorological data.

[0031] like Figure 1 As shown, the variety promotion area analysis method based on variety trials and environmental similarity in an embodiment of the present invention includes the following steps:

[0032] Step S1, collecting meteorological data and crop variety test data of multiple years and multiple test sites, and storing them in the server in the form of files.

[0033] Among them, meteorological data include: historical meteorological data, site meteorological data, grid hourly meteorological data and grid daily meteorological data. Multi-year and multi-test point variety test data include: growth period, pests and diseases, meteorological disasters, crop growth and yield.

[0034] Step S2, pre-process the data, integrate the data files into data files of preset specifications, and the big data platform regularly obtains the above data files and stores them in the database. In step S2, customize the data file rules and generate TXT format files.

[0035] Specifically, Figure 2 As shown, the data files are integrated and processed, including the following steps:

[0036] Step S21, gridding the historical site meteorological data and the real-time grid meteorological data;

[0037] Step S22, gridding the multi-year multi-point variety information data;

[0038] Step S23, preprocessing the above-mentioned station gridded data and test point gridded data to generate a data file;

[0039] Step S24, the big data platform stores the data in the database to complete the data sorting and manage it in a unified manner.

[0040] Step S3, the big data platform calculates the growth period development law algorithm based on the multi-year multi-point growth period data collected and combined with the meteorological data of previous years, and calculates the national growth period time range based on the growth period development law algorithm.

[0041] like Figure 3 As shown, based on the data of the growth period of many years and multiple points tests, combined with historical meteorological data, the meteorological indicators and weight ratios required for the development of the growth period are found (step S31), and the effective accumulated temperature rules in the same growth period in different years are counted to infer the conditions for the growth period. The growth period occurrence algorithm is defined according to the growth period occurrence conditions (step S32). Real-time forecast meteorological data is obtained (step S33), and according to the defined growth period algorithm, the recent national growth period forecast is carried out (step S34).

[0042] Step S4, screening based on multi-year multi-point test data to find high-yield areas and years with excellent performance.

[0043] Step S5, query the meteorological data of the years showing high yield through the high yield areas and years for statistical analysis.

[0044] Step S6, through repeated verification of meteorological data during the growth period of multiple high-yield areas over the years, obtain the climate condition indicators suitable for the varieties and the weight ratios; and deduce the regional algorithm suitable for variety promotion through the indicators and weight ratios.

[0045] Step S7, based on the algorithm of suitable promotion areas, national real-time growth period data, and real-time forecast meteorological data, the similarity of suitable promotion areas in the national grid is calculated and analyzed.

[0046] Step S8, statistically analyzing the national grid climate similarity data, and finding the regions in the country where the similarity of suitable promotion regions reaches a preset ratio or above; in one embodiment of the present invention, the preset ratio is 70% to 90%. Preferably, the preset ratio is 80%.

[0047] refer to Figure 4 , according to the high-yield areas and years combined with the statistical integration of historical meteorological data, the climate characteristics of the area suitable for the promotion of the variety, as well as the relevant climate indicators and weight ratios are found (step S41); according to the obtained climate data indicators and weight ratios of the suitable promotion area, an algorithm is calculated (step S42), combined with the national real-time growth period data and real-time forecast meteorological data, the national variety suitable promotion area index is calculated (step S43), and the area with a similarity of more than 80% with the suitable promotion area across the country is found (step S44), and the promotion area suitable for the variety in the national region is found through the method of environmental similarity.

[0048] The present invention proposes a variety promotion region analysis method based on crop variety experiments and environmental similarity, which has the following functions:

[0049] (1) Define data specifications and generate regular data files to uniformly manage multi-year and multi-point crop variety data and meteorological data.

[0050] (2) A large amount of data is analyzed to analyze the correlation and weight ratio between variety information and meteorological data indicators in high-yield areas over many years, and an algorithm for defining areas where varieties are suitable for promotion is defined.

[0051] (3) Based on the variety promotion area algorithm combined with real-time national weather, the national variety promotion suitability index is calculated, and the areas with a promotion suitability index above 80% are sorted and selected for variety promotion.

[0052] The present invention can realize a method for dividing regions suitable for promotion of different crop varieties throughout the country. The method can be applied to the promotion of different crop varieties and the provision of suitable area data and meteorological conditions for different varieties.

[0053] According to the method for analyzing the variety promotion area based on variety trials and environmental similarity of the embodiment of the present invention, by collecting multi-year multi-point variety trials and national historical real-time grid meteorological data, and collating and storing the data in a server, a large amount of data is used to repeatedly verify the correlation and weight ratio between the performance of the variety at multiple points over the years and the meteorological data indicators. A method for finding suitable variety promotion areas across the country in the form of environmental similarity using the relationship between meteorological indicators and the weight ratio. The present invention integrates and analyzes the experience of cultivating crop varieties based on many years of crop variety test points distributed in many places across the country and many years of national grid-format meteorological data, and can efficiently and accurately locate areas where varieties are suitable for promotion, and can quickly develop new crop varieties and promote suitable areas across the country at high speed. The present invention can effectively point out areas where varieties are suitable for promotion, and through manual multi-year multi-point variety data collection and real-time collating and calculation of meteorological data, the correctness and timeliness of the data are guaranteed.

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" 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 representation of the above terms does 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.

[0055] Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. Those skilled in the art can change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing variety promotion regions based on variety trials and environmental similarity, characterized in that: The steps include: Step S1, collecting meteorological data and crop variety test data of multiple years and multiple test sites, and storing them in the form of files on a server; Step S2, pre-processing the data, integrating the data files into data files of preset specifications, and the big data platform periodically obtains the above data files and stores them in the database; Step S3, the big data platform calculates an algorithm for the development law of the growth period based on the multi-year multi-point growth period data collected and combined with the meteorological data of previous years; and calculates the national growth period time range based on the algorithm for the development law of the growth period; In step S3, the meteorological indicators and weight ratios required for the development of the growth period are found in combination with historical meteorological data, and the effective accumulated temperature rules in the same growth period in different years are counted to infer the conditions for the growth period to occur; Define the growth period occurrence algorithm through the growth period occurrence conditions; obtain real-time forecast meteorological data, and make a recent national growth period prediction based on the defined growth period algorithm; Step S4, screening based on multi-year multi-point test data to find high-yield areas and years with excellent performance; Step S5, querying the meteorological data of the high-yield test points during the growth period of previous years through the high-yield areas and years for statistical analysis; Step S6, repeatedly verifying the meteorological data of the growing seasons in multiple high-yield areas over the years, obtaining the climate condition index and weight ratio suitable for the variety; and calculating the regional algorithm suitable for variety promotion through the index and weight ratio; Step S7, calculating and analyzing the similarity of suitable promotion regions across the country based on the regional algorithm for suitable variety promotion, national real-time growth period data, and real-time forecast meteorological data; Based on the high-yield areas and years combined with the statistical integration of historical meteorological data, the climate characteristics of the area suitable for the promotion of the variety, as well as the relevant climate indicators and weight ratios are found; based on the obtained climate data indicators and weight ratios of the suitable promotion area, the algorithm is calculated, combined with the national real-time growth period data and real-time forecast meteorological data, the national variety suitable promotion area index is calculated, and the area with a similarity of more than a preset ratio with the suitable promotion area is found nationwide, and the promotion area suitable for the variety in the national region is found through environmental similarity; Step S8, statistically analyzing the similarity data of the suitable promotion regions across the country, and finding regions across the country whose similarity of the suitable promotion regions reaches a preset ratio or above.

2. The method for analyzing variety promotion regions based on variety trials and environmental similarity according to claim 1, characterized in that: In the step S1, the meteorological data includes: historical meteorological data, site meteorological data, grid hourly meteorological data and grid daily meteorological data.

3. The method for analyzing variety promotion regions based on variety trials and environmental similarity according to claim 1, characterized in that: In step S1, the multi-year and multi-test point variety test data includes: growth period, pests and diseases, meteorological disasters, crop growth, and yield.

4. The method for analyzing variety promotion regions based on variety trials and environmental similarity according to claim 1, characterized in that: In step S2, the data file integration process includes the following steps: Grid the historical site meteorological data and real-time grid meteorological data; Grid the test point by multi-year and multi-point variety information data; The gridded data of the above-mentioned sites and the gridded data of the test points are preprocessed to generate data files, which are then stored in the database by the big data platform to complete data sorting and unified management.

5. The method for analyzing variety promotion regions based on variety trials and environmental similarity according to claim 1, characterized in that: In the step S8, the preset ratio is 70% to 90%.

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