CN115796384B - System for predicting wheat bread quality - Google Patents
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- CN115796384B CN115796384B CN202211656596.1A CN202211656596A CN115796384B CN 115796384 B CN115796384 B CN 115796384B CN 202211656596 A CN202211656596 A CN 202211656596A CN 115796384 B CN115796384 B CN 115796384B
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- 235000008429 bread Nutrition 0.000 title claims abstract description 69
- 241000209140 Triticum Species 0.000 title claims abstract description 49
- 235000021307 Triticum Nutrition 0.000 title claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 8
- 238000007637 random forest analysis Methods 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 6
- 238000001556 precipitation Methods 0.000 claims description 4
- 230000005855 radiation Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 abstract description 5
- 238000011160 research Methods 0.000 description 7
- 235000013305 food Nutrition 0.000 description 5
- 235000013312 flour Nutrition 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 102100031184 C-Maf-inducing protein Human genes 0.000 description 1
- 101000993081 Homo sapiens C-Maf-inducing protein Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000000855 fermentation Methods 0.000 description 1
- 230000004151 fermentation Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The application relates to a system for predicting the quality of wheat bread, which belongs to the technical field of agricultural planting planning, and comprises: the scene setting module is used for generating weather estimated data of a predicted scene according to future weather data in a future weather scene database based on a setting instruction input by a user; the prediction processing module is used for performing prediction processing by utilizing a pre-constructed bread quality prediction model according to the climate prediction data to obtain prediction result information of the wheat bread quality in the prediction scene; and the data output module is used for outputting the prediction result information outwards. According to the technical scheme, based on future climate data in the future climate situation database, the future change prediction of the wheat bread quality is realized through the pre-constructed bread quality prediction model, and relevant basic data support can be effectively provided for wheat planting planning.
Description
Technical Field
The application belongs to the technical field of agricultural planting planning, and particularly relates to a system for predicting wheat bread quality.
Background
Wheat is a staple food crop widely planted around the world, and wheat bread manufactured by grinding wheat into flour is a daily staple food for people in many areas. Because of regional climate factors, the yield quality of the same wheat variety in different wheat planting areas is different, and the variation of the difference of the wheat quality directly affects the quality of the wheat bread.
In the prior art, in agricultural planting planning, planning research is usually only carried out from the yield dimension of main grain crops, and based on factors such as climate change, the yield of which wheat planting areas can be increased and the yield of which wheat planting areas can be reduced are predicted and judged, so that relevant countermeasures are formulated. With the development of socioeconomic performance, the requirements of people on food are increasingly increasing, so that on the basis of the inherent correlation of wheat bread and wheat quality, it is obvious that research on planting planning from another dimension relative to the yield dimension is required, and for this reason, prediction on future changes in wheat bread quality needs to be realized, so as to provide basic data support for related planning research.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
To overcome the problems in the related art at least to some extent, the present application provides a system for wheat bread quality prediction to solve the problem of how to provide relevant basic data support for wheat planting planning.
In order to achieve the above purpose, the application adopts the following technical scheme:
the present application provides a system for wheat bread quality prediction, the system comprising:
the scene setting module is used for generating weather estimated data of a predicted scene according to future weather data in a future weather scene database based on a setting instruction input by a user;
the prediction processing module is used for performing prediction processing by utilizing a pre-constructed bread quality prediction model according to the climate prediction data to obtain prediction result information of the wheat bread quality in the prediction scene;
and the data output module is used for outputting the prediction result information outwards.
Optionally, the future climate data comprises a plurality of grid point data representing meteorological element index information;
the scene setting module is configured to perform grid precision adjustment processing on the grid point data based on a kriging interpolation method so as to obtain the climate estimated data meeting the model input requirement.
Optionally, the pre-construction process of the bread quality prediction model includes:
acquiring record data representing bread quality in a historical period of a plurality of stations throughout the country, and acquiring historical climate data of corresponding stations in the historical period;
based on the recorded data and the historical climate data, adopting a random forest method to analyze and determine regression relations between data items in the recorded data and meteorological element indexes in the climate data so as to construct and obtain the bread quality prediction model;
wherein the data items are specifically bread volume and bread score;
the weather element index is specifically an average air temperature index, a daily highest air temperature index, a daily lowest air temperature index, a daily precipitation index, a daily sunshine hours index, an effective radiation index, a humidity index and a daily highest temperature index of 30 ℃ or more of days in the growing period of wheat.
Optionally, the data output module is configured to: and outputting the prediction result information in a time evolution characteristic form and/or a space evolution characteristic form.
Optionally, during the model construction process, the acquired record data and the historical climate data are stored in a base database;
the data output module is further configured to respond to a data query instruction input by a user and correspondingly output data in the basic database and/or the future climate situation database.
Optionally, the setting instruction includes a climate mode selection configuration of the predicted scene and a climate scenario selection configuration;
types of the climate modes include: FGOALS-g3 mode, MRI-ESM2-0 mode, canESM5 mode, and IPSL-CM6A-LR mode;
the types of climate scenarios include: SSP1-1.9 scenes, SSP1-2.6 scenes, SSP2-4.5 scenes, SSP5-8.5 scenes.
The application adopts the technical proposal and has at least the following beneficial effects:
in the present application, a system for predicting the quality of wheat bread comprises: the scene setting module is used for generating weather estimated data of a predicted scene according to future weather data in a future weather scene database based on a setting instruction input by a user; the prediction processing module is used for performing prediction processing by utilizing a pre-constructed bread quality prediction model according to the climate prediction data to obtain prediction result information of the wheat bread quality in a prediction scene; and the data output module is used for outputting the prediction result information outwards. The system provided by the technical scheme of the application realizes the prediction of the future change of the wheat bread quality through the pre-constructed bread quality prediction model based on the future climate data in the future climate scene database based on the specific system configuration, and can effectively provide relevant basic data support for the wheat planting planning.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application.
Drawings
The accompanying drawings are included to provide a further understanding of the technical aspects or prior art of the present application, and are incorporated in and constitute a part of this specification. The drawings, which are used to illustrate the technical scheme of the present application, are not limited to the technical scheme of the present application.
Fig. 1 is a schematic structural diagram of a system for predicting quality of wheat bread according to an embodiment of the present application;
FIG. 2 is a schematic illustration of a process for constructing and applying bread quality prediction models in accordance with the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, based on the examples herein, which are within the scope of the application as defined by the claims, will be within the scope of the application as defined by the claims.
As described in the background art, in the related art, in the agricultural planting planning, planning research is generally performed only from the yield dimension of the staple food crops, and based on factors such as climate change, it is predicted which wheat planting regions will increase in yield and which wheat planting regions will decrease in yield, so that relevant countermeasures are formulated. With the development of socioeconomic performance, the requirements of people on food are increasingly increasing, so that on the basis of the inherent correlation of wheat bread and wheat quality, it is obvious that research on planting planning from another dimension relative to the yield dimension is required, and for this reason, prediction on future changes in wheat bread quality needs to be realized, so as to provide basic data support for related planning research.
In view of this, the application provides a system for wheat bread quality prediction to solve the problem of how to provide relevant basic data support for wheat planting planning.
In one embodiment, as shown in fig. 1, the system for predicting the quality of wheat bread according to the present application comprises:
the scenario setting module 100 is configured to generate, based on a setting instruction input by a user, climate estimated data of a predicted scenario according to future climate data in a future climate scenario database;
it should be noted that the future climate situation database is a deduction database of future climate situations, specifically, the database adopts an international coupling mode comparison program sixth edition (CMIP 6) data set (obtained from https:// kgf-node:. Llnl. Gov/search/CMIP6 /);
in this embodiment, the future climate data obtained from the database includes a plurality of grid data representing the index information of the meteorological element, for example, the meteorological element index is specifically 8 indexes of daily average air temperature index, daily maximum air temperature index, daily minimum air temperature index, daily precipitation index, daily sunshine hours index, effective radiation index, humidity index and daily maximum temperature index of 30 ℃ or more during the growth period of wheat;
since the subsequent prediction process has a resolution precision requirement on the grid point data, in this embodiment, the scene setting module 100 is configured to perform grid precision adjustment processing on the grid point data based on the kriging interpolation method, so as to obtain climate estimated data that meets the model input requirement.
As a specific embodiment, to improve the functionality of the system, the setting instructions herein include a climate mode selection configuration of the predicted scene and a climate scenario selection configuration;
for example, the types of climate patterns herein include: FGOALS-g3 mode of institute of atmospheric physics, national academy of sciences, MRI-ESM2-0 mode of hadey center, canadian climate simulation and analysis center, canESM5 mode, and IPSL-CM6A-LR mode of the praise institute of peaels Meng La, france;
for example, the types of climate scenarios herein include: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP 5-8.5;
in other words, in the setting of the prediction scenario, the climate estimated data in different climate scenarios in different climate modes of the corresponding prediction time periods (such as 2021-2041, 2041-2060, 2061-2080, 2081-2100) can be generated based on the future climate scenario database, so that the prediction processing and corresponding output can be performed based on the specific prediction scenario.
As shown in fig. 1, the system further includes a prediction processing module 200, which is configured to perform prediction processing according to the climate estimated data by using a pre-constructed bread quality prediction model, so as to obtain prediction result information of the wheat bread quality in the prediction scene.
It should be noted that, the bread quality prediction model is constructed based on analysis of the historical data of the wheat bread quality and the corresponding historical climate data, specifically, as shown in fig. 2, the pre-construction process of the bread quality prediction model in the present application includes:
acquiring record data representing bread quality in a historical period of a plurality of stations throughout the country, acquiring historical climate data of each station in the historical period, and storing the record data in a corresponding database (corresponding to the bread quality database and the weather index database in fig. 2);
for example, the data items in the record data of bread quality are specifically the bread volume (the fermentation volume of 100 g of flour is measured by national standard method) and the bread score (the indexes such as surface color, center color, surface texture, shape, smoothness, texture, elasticity and taste of bread are scored after flour is made into bread, and finally the comprehensive score is obtained), and the record data of bread quality are the bread volume and the bread score data of a plurality of stations 2006-2019 in China;
the historical climate data is weather element index data at points corresponding to recorded data of bread quality, and specifically, the weather element index data is data of 8 indexes such as daily average air temperature, daily maximum air temperature, daily minimum air temperature, daily precipitation, daily hours, effective radiation, humidity, and days when the daily maximum temperature reaches 30 ℃ or higher (or days when the daily maximum temperature is 30 ℃ or higher) in the growing period of wheat in 2006-2019.
Then, based on the recorded data and the historical climate data, adopting a random forest method to analyze and determine regression relations between data items in the recorded data and all meteorological element indexes in the climate data (the regression relations correspond to the bread quality and the meteorological factors in fig. 2) so as to construct and obtain a bread quality prediction model;
in the machine learning method, a random forest is a classifier containing a plurality of decision trees, and its output class is determined by the mode of the individual tree output class; according to the application, a random forest method is adopted to analyze the relation between bread quality and meteorological elements, bread quality data and meteorological element data of corresponding sites are respectively called from a database, the national wheat regions are subjected to random forest modeling by adopting R3.6.2 (R package randomForest), modeling results are evaluated by Root Mean Square Error (RMSE) and standard root mean square error (NRMSE), the two indexes represent the proximity degree of an analog value and an actual measurement value, and the related expression is as follows:
in expression (1): p is p i O as observed value i For the predicted value, the smaller the RMSE, the closer the representative analog value is to the actual value;
in expression (2):to measure the average value of the samples, the closer the NRMSE is to 0, the closer the representative analog value is to the actual value.
Specifically, in this embodiment, based on the above basic data, the NRMSE that is used to construct the bread volume prediction model is 0.0312, and the correlation coefficient is 0.9617; the NRMSE of the bread grading prediction model is 0.0407, the correlation coefficient is 0.9621, and the application requirements can be met.
And then as shown in fig. 2, the bread quality prediction model can be used to perform relevant prediction research on future evolution trend of bread quality in combination with climate situation data (such as prediction processing in the system of the application).
As shown in fig. 1, the system further includes a data output module 300, configured to output the prediction result information obtained by the prediction processing module.
Specifically, in this embodiment, the data output module 300 is configured to: outputting the prediction result information in a time evolution characteristic form and/or a space evolution characteristic form;
for example, in practice, based on a specific configuration, the ground system module of arcgis10.2 may be used to draw a spatial distribution diagram of a certain predicted time node of the quality of the wheat bread based on the predicted result information, and for example, the predicted result information may be displayed and output in the form of a line graph (time is a coordinate axis) by using a related drawing control.
Further, as a specific implementation manner, in the model construction process, the acquired record data and the historical climate data are stored in the basic database;
the data output module 300 is further configured to correspondingly output data in the base database and/or the future climate scene database in response to a data query instruction input by a user;
for example, the data output module is configured to:
the system module of ArcGIS10.2 is adopted for drawing, and the Kriging interpolation is respectively and independently carried out on four climate modes (FGOALS-g 3, MRI-ESM2-0, canESM5 and IPSL-CM 6A-LR) and the climate variables of the emission scenes of SSP1-1.9, SSP1-2.6, SSP2-4.5 and SSP5-8.5 of a plurality of prediction time periods (such as 2021-2041, 2041-2060, 2061-2080 and 2081-2100) in each climate mode, and then the interpolation results in the four climate modes are subjected to superposition processing, so that the spatial distribution map of each meteorological element index is output.
The system provided by the technical scheme of the application realizes the prediction of the future change of the wheat bread quality through the pre-constructed bread quality prediction model based on the future climate data in the future climate scene database based on the specific system configuration, and can effectively provide relevant basic data support for the wheat planting planning.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (3)
1. A system for wheat bread quality prediction, comprising:
the scene setting module is used for generating weather estimated data of a predicted scene according to future weather data in a future weather scene database based on a setting instruction input by a user, wherein the setting instruction comprises: predicting climate mode selection configuration of a scene and climate scene selection configuration;
the prediction processing module is used for performing prediction processing by utilizing a pre-constructed bread quality prediction model according to the climate prediction data to obtain prediction result information of the wheat bread quality in the prediction scene;
the data output module is used for outputting the prediction result information outwards;
wherein the future climate data comprises a plurality of grid point data representing meteorological element index information;
the scene setting module is configured to perform grid precision adjustment processing on the grid point data based on a kriging interpolation method so as to obtain the climate estimated data meeting the model input requirement;
the pre-construction process of the bread quality prediction model comprises the following steps:
acquiring record data representing bread quality in a historical period of a plurality of stations throughout the country, and acquiring historical climate data of corresponding stations in the historical period;
based on the recorded data and the historical climate data, adopting a random forest method to analyze and determine regression relations between data items in the recorded data and meteorological element indexes in the climate data so as to construct and obtain the bread quality prediction model;
wherein the data items are specifically bread volume and bread score;
the weather element indexes comprise a daily average air temperature index, a daily highest air temperature index, a daily lowest air temperature index, a daily precipitation index, a daily sunshine hours index, an effective radiation index, a humidity index and a day index with the daily highest temperature being more than or equal to 30 ℃;
the data output module is configured to: and outputting the prediction result information in a time evolution characteristic form and/or a space evolution characteristic form.
2. The system for wheat bread quality prediction according to claim 1, wherein the mould
In the model construction process, the acquired record data and the historical climate data are stored in a basic database;
the data output module is further configured to respond to a data query instruction input by a user and correspondingly output data in the basic database and/or the future climate situation database.
3. A system for wheat bread quality prediction according to claim 1 or 2, characterized in that,
types of the climate modes include: FGOALS-g3 mode, MRI-ESM2-0 mode, canESM5 mode, and IPSL-CM6A-LR mode;
the types of climate scenarios include: SSP1-1.9 scenes, SSP1-2.6 scenes, SSP2-4.5 scenes, SSP5-8.5 scenes.
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| CN110443420A (en) * | 2019-08-05 | 2019-11-12 | 山东农业大学 | A kind of crop production forecast method based on machine learning |
| CN111639803A (en) * | 2020-05-29 | 2020-09-08 | 福州市规划设计研究院 | Prediction method applied to future vegetation index of area under climate change scene |
| CN112183575A (en) * | 2020-08-24 | 2021-01-05 | 盐城工学院 | Method for predicting potato yield based on regional meteorological parameters |
Non-Patent Citations (1)
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| "国外农产品气候品质评价研究进展";霍治国等;《中国农业资源与区划》;第43卷(第9期);第34-49页 * |
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