CN116012044A - Multi-scale grain safety supply and demand early warning method, device and storage medium - Google Patents
Multi-scale grain safety supply and demand early warning method, device and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device and a storage medium for multi-scale grain safety supply and demand early warning, wherein the method comprises the following steps: determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain index historical values according to the grain basic data; acquiring a grain supply and demand total quantity predicted value according to the grain index historical value, and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value; and carrying out supply and demand early warning according to the supply and demand balance prediction result. According to the invention, the supply-demand balance prediction is realized according to grain data in the grain area scale range, the development trend of the future grain market is judged according to the supply-demand balance prediction result, and supply-demand early warning is carried out, so that the prospective pre-judgment of the future grain market by related institutions is helped; the multi-scale supply and demand balance scheme can be provided according to the supply and demand early warning, so that the related institutions can carry out supply and demand balance adjustment in multiple scales such as areas, countries and the world.
Description
Technical Field
The invention relates to the technical field of grain supply and demand early warning, in particular to a method, a device and a storage medium for multi-scale grain safety supply and demand early warning.
Background
In recent years, the comprehensive production and the guarantee capability of grains in China are continuously improved, the grain supply capability is continuously improved, but the contradiction of unbalanced supply and demand of grains is still existed under the influence of various factors. Therefore, higher requirements are put on how the grain data should deal with the situation change of the supply and demand market, the uncertainty of the external environment and the like, global, predictive and prospective predictions of the grain data are needed, and supply and demand balance early warning is timely carried out so as to ensure the stable development of the grain market in China.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-scale grain safety supply and demand early warning method, a device and a storage medium.
In a first aspect, a method for early warning of multi-scale grain safety supply and demand includes:
determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain supply and demand historical values according to the grain basic data;
acquiring a grain supply and demand total quantity predicted value according to the grain supply and demand historical value, and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value;
and carrying out supply and demand early warning according to the supply and demand balance prediction result.
Further, determining the grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain index historical values according to the grain basic data, wherein the grain index historical values specifically comprise:
determining a grain area scale, and acquiring grain basic conditions in the grain area scale range, wherein the grain basic conditions comprise grain types, grain historical supply conditions and grain historical consumption conditions;
setting a per-capita ration intake standard according to the grain type;
acquiring a grain index historical value according to the average grain intake standard, the grain historical supply condition and the grain historical consumption condition;
the grain supply and demand history values comprise grain yield, grain net import and export amount, grain stock amount, ration consumption, industrial grain consumption, total feed grain and total seed grain in the past year.
Further, the method obtains a predicted value of the total supply and demand of the grain according to the historical value of the grain index, and performs balance prediction of supply and demand according to the predicted value of the total supply and demand of the grain, specifically comprises the following steps:
determining a time span, and acquiring a grain index historical value in the time span range;
inputting the grain index historical value into a prediction model to obtain a grain supply and demand index predicted value;
calculating a grain supply total quantity predicted value and a grain demand total quantity predicted value according to the grain supply and demand index predicted value;
and carrying out supply-demand balance prediction according to the grain supply total quantity predicted value and the grain demand total quantity predicted value, wherein the supply-demand balance predicted result comprises supply-demand unbalance and supply-demand balance.
Further, the pre-warning of supply and demand according to the supply and demand balance prediction result specifically includes:
if the supply-demand balance prediction result is supply-demand balance, trend early warning is carried out based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
if the supply-demand balance prediction result is that the supply-demand is unbalanced, carrying out supply-demand early warning based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
the supply and demand early warning comprises, but is not limited to, supply and demand total quantity early warning, supply and demand increment early warning and supply and demand acceleration early warning.
Further, the method further comprises the following steps:
generating a multi-scale warning scheme based on the supply and demand early warning, wherein the multi-scale warning scheme comprises an intra-zone balancing scheme and an extra-zone balancing scheme;
the in-zone balancing comprises in-zone supply and demand balance adjustment based on the grain zone scale, and the out-of-zone balancing comprises out-of-zone supply and demand balance adjustment based on the upper zone scale of the grain zone scale.
In a second aspect, a multi-scale grain safety supply and demand early warning device includes:
and a data acquisition module: the method comprises the steps of determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain supply and demand historical values according to grain basic data;
supply and demand prediction module: the method is used for obtaining a grain supply and demand total quantity predicted value according to the grain supply and demand historical value and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value;
supply and demand early warning module: and the system is used for carrying out supply and demand early warning according to the supply and demand balance prediction result.
Further, the supply and demand prediction module is specifically configured to:
determining a time span, and acquiring a grain index historical value in the time span range;
inputting the grain index historical value into a prediction model to obtain a grain supply and demand index predicted value;
calculating a grain supply total quantity predicted value and a grain demand total quantity predicted value according to the grain supply and demand index predicted value;
and carrying out supply-demand balance prediction according to the grain supply total quantity predicted value and the grain demand total quantity predicted value, wherein the supply-demand balance predicted result comprises supply-demand unbalance and supply-demand balance.
Further, the supply and demand early warning module is specifically configured to:
if the supply-demand balance prediction result is supply-demand balance, trend early warning is carried out based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
if the supply-demand balance prediction result is that the supply-demand is unbalanced, carrying out supply-demand early warning based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
the supply and demand early warning comprises, but is not limited to, supply and demand total quantity early warning, supply and demand increment early warning and supply and demand acceleration early warning.
In a third aspect, a multi-scale grain safety supply and demand early warning apparatus includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is configured to store a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions to perform the method according to the first aspect.
In a fourth aspect, a computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method according to the first aspect.
The beneficial effects of the invention are as follows: according to grain data in a grain area scale range, grain basic conditions are obtained, grain index historical values are calculated and obtained according to the grain basic conditions, data prediction and comprehensive calculation are carried out according to the grain index historical values by adopting a prediction model, a grain supply and demand total quantity predicted value is obtained, supply and demand balance prediction is realized based on the grain supply and demand total quantity predicted value, the development trend of a future grain market is judged according to a supply and demand balance predicted result, supply and demand early warning is carried out, and prospective pre-judgment of a future grain market by related institutions is helped; the multi-scale supply and demand balance scheme can be provided according to the supply and demand early warning, so that the related institutions can carry out supply and demand balance adjustment in multiple scales such as areas, countries and the world.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a multi-scale grain safety supply and demand early warning method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a multi-scale grain safety supply and demand early warning device according to an embodiment of the present invention;
fig. 3 is a block diagram of a multi-scale grain safety supply and demand early warning device according to a second embodiment of the present invention.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
As shown in fig. 1, a multi-scale grain safety supply and demand early warning method includes:
s1: determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain supply and demand historical values according to the grain basic data;
specifically, the grain region scale refers to a grain region space scale, including but not limited to a global region scale, a national region scale, and an administrative region scale, where the global region scale is an upper scale of the national region scale, and the national region scale is an upper scale of the administrative region scale. The area scale can be selected according to the actual required early warning area, and generally, supply and demand prediction is performed from the lowest-level area scale, so that layer-by-layer supply and demand adjustment measures are adopted at the upper-layer scale of the lowest-level area scale, and multi-scale grain safety supply and demand balance is realized. Preferably, the global area scale, the national area scale and the administrative area scale can be subdivided in practical application.
And acquiring grain basic conditions in the determined grain area scale range, wherein the grain basic conditions comprise grain types and grain historical supply and demand conditions. Wherein the grain types include, but are not limited to, grain crops such as corn, rice, soybean, or meats such as pork, beef, mutton, and the like; the historical supply and demand conditions of the grains include, but are not limited to, industrial product yield such as beer or white wine, grain yield, sowing area, livestock product yield, seed consumption per unit area and grain import and export quantity.
Preferably, the grain base condition can be obtained from the disclosed related grain data through various channels, and the data sources include, but are not limited to, chinese statistics annual survey, chinese food industry annual survey, chinese light industry annual survey, chinese rural statistics annual survey, national agricultural product cost benefit data assembly, customs statistics month report, customs data platform and the like.
In the embodiment, nutritional dietary requirements are introduced in the supply and demand balance prediction, and the average ration intake standard is set. Specifically, the heat and protein corresponding to each grain type are obtained, the conversion coefficient of the grains, the heat and the protein is determined, and the average grain consumption is obtained by applying a coefficient conversion method, wherein the average grain consumption is the average grain intake standard.
According to the average grain consumption and the historical grain supply and demand conditions, grain index historical values are obtained, the grain index historical values comprise grain supply data and grain demand data of the past year, the grain supply data can be directly obtained through the historical grain supply and demand conditions, the grain supply data comprise grain yield, grain net import and export quantity and grain stock quantity of the past year, and the grain demand data can be obtained through calculation according to the historical grain supply and demand conditions, and comprise ration consumption, industrial grain consumption, total grain for feed and total grain for seeds of the past year.
Wherein, the ration consumption calculation formula is:
R ij ＝(RPC ijr ×P jr )/(1-r jr )+(RPC igu ×P ju )/(1-r ju ) (1)
wherein R is ij Is the ration consumption (10 4 t)，RPC ijr ,RPC ijr Average grain consumption (kg/per) r of rural and urban areas respectively jr ,r ju The proportion of dining in the outside of rural and urban population, P jr ,P ju Respectively rural and urban population.
The calculation formula of the consumption amount of the industrial grain is as follows:
wherein P is ij Is the industrial grain consumption (10 4 t)，P bj ,P wj ,P aj ,P mj The yields of beer, white spirit, alcohol and monosodium glutamate respectively (10) 4 t)；ρ b ,ρ w ,ρ a ,ρ m The grain consumption coefficients (0.17,2.33,3,5) and r of beer, white spirit, alcohol and monosodium glutamate respectively i The proportion of the i-th grain in the total industrial grain is calculated.
The calculation formula of the total amount of the feed grain is as follows:
wherein F is ij The total grain amount (10) 4 t)；P kj For j region, production of class k livestock products (10 4 t)；δ k Feed to meat ratio for the k-th stock; k is the kind of animal products, including 7 kinds of pork, beef, mutton, fowl, milk, fowl eggs, aquatic products, etc., and the feed conversion ratio is 2.01,0.93,0.81,0.35,1.72,1.62,1.20, r respectively ik The proportion of the type i grain required for the production of the type k livestock products.
The calculation formula of the total grain amount for seeds is as follows:
S ij ＝s ij ×A ij (4)
wherein S is ij Is the total grain quantity (10 4 t)；s ij The seed quantity (t/hm 2) is used for the unit area of the i-th grain in the j region; a is that ij For the sowing area (10) of the i-th grain in the j region 4 hm 2 ) The seed amount of tuber crop was calculated from 10% of the yield.
S2: acquiring a grain supply and demand total quantity predicted value according to the grain index historical value, and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value;
specifically, after obtaining the grain index historical values, determining a time span, obtaining the grain index historical values within the time span range, and respectively inputting the grain index historical values into a prediction model to predict each grain index so as to obtain future grain supply and demand index predicted values.
Preferably, the prediction model may be selected according to practical situations, including but not limited to a gray model, which is not limited herein. Taking a GM (1, 1) gray model as an example, if the supply and demand balance of the next several years of 2022 is to be predicted, acquiring the grain basic condition before 2022 from multiple channels, acquiring the grain yield, the net import and export quantity of the grain and the grain stock quantity of the past year according to the grain basic condition, calculating according to formulas (1) to (4) according to the grain basic condition to obtain the ration consumption, the industrial grain consumption, the total feed grain quantity and the total seed grain quantity of the past year, and taking the grain index history value acquired according to the grain basic condition as a plurality of data sequences to be respectively input into the GM (1, 1) gray model. And programming and predicting the GM (1, 1) gray model by utilizing software, so as to determine a prediction equation corresponding to each grain index, and substituting each grain index into the prediction equation to perform prediction calculation, thereby obtaining the grain supply and demand index predicted values, namely the predicted values of grain yield, grain net import and export quantity, grain stock quantity, ration consumption, industrial grain consumption, total feed grain and total seed grain.
Calculating a grain supply total quantity predicted value and a grain demand total quantity predicted value according to the grain supply and demand index predicted value, wherein the calculation formula is as follows:
FS＝G+I-E+S (5)
FD＝R+F+S+P+W (6)
wherein FS represents a predicted value of a total amount of grain supplied, FD represents a predicted value of a total amount of grain required, G represents a yield of grain, I represents a net inlet amount of grain, E represents a net outlet amount of grain, S represents a stock amount of grain, R represents a consumption amount of ration, F represents a total amount of grain for feed, P represents a consumption amount of industrial grain, and W represents a loss of grain. Wherein, the grain loss W can be set as a fixed coefficient, and the range of the area value is 6% -9%.
And carrying out supply-demand balance prediction according to the calculated grain supply total quantity predicted value FS and the grain demand total quantity predicted value FD, if the grain supply total quantity predicted value FS is equal to the grain demand total quantity predicted value FD, balancing the supply and the demand, otherwise unbalanced the supply and the demand.
S3: performing supply and demand early warning according to the supply and demand balance prediction result;
specifically, if the supply-demand balance prediction result is supply-demand balance, it is indicated that future grain supply and grain demand can reach a basic balance state without taking intervention measures, and then grain supply-demand trend analysis is performed based on the grain supply total quantity predicted value FS and the grain demand total quantity predicted value FD, and trend early warning is performed according to the trend analysis result, so as to intuitively provide grain supply-demand development trend within the regional scale range for several years in the future for users; and if the supply-demand balance prediction result is unbalanced supply-demand, carrying out supply-demand early warning based on the grain supply total quantity prediction value FS and the grain demand total quantity prediction value FD, wherein the supply-demand early warning comprises but is not limited to supply-demand total quantity early warning, supply-demand increment early warning and supply-demand acceleration early warning.
Further, the early warning of supply and demand based on the predicted total grain supply amount FS and the predicted total grain demand amount FD includes:
if the FS-FD is less than 0, sending out a supply and demand total quantity early warning;
if meeting the requirement of delta FS-delta FD is less than or equal to beta, sending out supply and demand increment early warning, wherein delta FS is the variation of the total supply quantity of the future grains, delta FD is the variation of the total demand quantity of the future grains, and beta is an increment unbalance early warning value;
if it meets
Or->
Sending out early warning of supply and demand acceleration, wherein DeltaFS ↑ Indicating the rate of increase in total grain supply in the future, ΔFS ↓ Indicating the rate of decrease in total grain supply in the future Δfd ↑ Indicating the rate of increase in total grain demand, Δfd ↓ Indicating the rate at which the total amount of food demand increases in the future.
Further, the method also comprises the step of generating a multi-scale warning scheme based on the supply and demand warning so as to help a related mechanism to adjust the supply and demand balance state from multiple scales. The multi-scale warning scheme comprises an intra-area balance scheme and an extra-area balance scheme, wherein the intra-area balance is used for carrying out intra-area supply and demand balance adjustment based on the grain area scale, and the extra-area balance is used for carrying out extra-area supply and demand balance adjustment based on the upper layer area scale of the grain area scale. For example, in order to perform grain safety supply and demand early warning on x county, determining the space scale of the x county as the grain area scale, obtaining grain basic condition of the x county for years, predicting grain supply and demand total quantity predicted values of several years in the future according to the grain basic condition, performing supply and demand balance prediction based on the grain supply and demand total quantity predicted values, if the predicted result is unbalanced supply and demand, generating an intra-area balance scheme, and performing intra-area supply and demand balance adjustment by a related mechanism according to the intra-area balance scheme in the range of the x county area scale, if the problem of unbalanced supply and demand cannot be solved by the intra-area balance scheme, adopting supply and demand balance adjustment measures at the upper-layer scale of the x county according to the extra-area balance scheme, namely adopting supply and demand balance adjustment at the level of the Y city area scale of the x county, and if the supply and demand balance cannot be realized, further adopting supply and demand balance adjustment at the level of the national scale to realize the extra-area supply and demand balance adjustment.
Based on the same inventive concept, the embodiment of the invention provides a multi-scale grain safety supply and demand early warning device, as shown in fig. 2, comprising:
and a data acquisition module: the method comprises the steps of determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain supply and demand historical values according to grain basic data;
supply and demand prediction module: the method is used for obtaining a grain supply and demand total quantity predicted value according to the grain supply and demand historical value and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value;
supply and demand early warning module: and the system is used for carrying out supply and demand early warning according to the supply and demand balance prediction result.
Further, the data acquisition module is specifically configured to:
determining a grain area scale, and acquiring grain basic conditions in the grain area scale range, wherein the grain basic conditions comprise grain types and grain historical supply and demand conditions;
setting a per-capita ration intake standard according to the grain type;
acquiring a grain index historical value according to the average grain intake standard and the grain historical supply and demand conditions;
the grain supply and demand history values comprise grain yield, grain net import and export amount, grain stock amount, ration consumption, industrial grain consumption, total feed grain and total seed grain in the past year.
Further, the supply and demand prediction module is specifically configured to:
determining a time span, and acquiring a grain index historical value in the time span range;
inputting the grain index historical value into a prediction model to obtain a grain supply and demand index predicted value;
calculating a grain supply total quantity predicted value and a grain demand total quantity predicted value according to the grain supply and demand index predicted value;
and carrying out supply-demand balance prediction according to the grain supply total quantity predicted value and the grain demand total quantity predicted value, wherein the supply-demand balance predicted result comprises supply-demand unbalance and supply-demand balance.
Further, the supply and demand early warning module is specifically configured to:
if the supply-demand balance prediction result is supply-demand balance, trend early warning is carried out based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
if the supply-demand balance prediction result is that the supply-demand is unbalanced, carrying out supply-demand early warning based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
the supply and demand early warning comprises, but is not limited to, supply and demand total quantity early warning, supply and demand increment early warning and supply and demand acceleration early warning.
Further, the system also comprises a multi-scale alarm module, wherein the multi-scale alarm module is specifically used for:
generating a multi-scale warning scheme based on the supply and demand early warning, wherein the multi-scale warning scheme comprises an intra-zone balancing scheme and an extra-zone balancing scheme;
the in-zone balancing comprises in-zone supply and demand balance adjustment based on the grain zone scale, and the out-of-zone balancing comprises out-of-zone supply and demand balance adjustment based on the upper zone scale of the grain zone scale.
According to the method, grain basic conditions are acquired according to grain data in a grain area scale range, grain index historical values are calculated and acquired according to the grain basic conditions, a prediction model is adopted to conduct data prediction and comprehensive calculation according to the grain index historical values, a grain supply and demand total quantity predicted value is obtained, supply and demand balance prediction is achieved based on the grain supply and demand total quantity predicted value, a development trend of a future grain market is judged according to supply and demand balance predicted results, supply and demand early warning is conducted, and prospective pre-judgment of a related institution on the future grain market is helped; the multi-scale supply and demand balance scheme can be provided according to the supply and demand early warning, so that the related institutions can carry out supply and demand balance adjustment in multiple scales such as areas, countries and the world.
Optionally, in another preferred embodiment of the present invention, as shown in fig. 3, the multi-scale grain safety supply and demand early warning device may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and a memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected by a bus 105. The memory 104 is used for storing a computer program comprising program instructions, which the processor 101 is configured to invoke for performing the method of the above-described method embodiment part.
It should be appreciated that in embodiments of the present invention, the processor 101 may be a central processing unit (Central Processing Unit, CPU), a deep learning graphics card (e.g., NPU, injedag GPU, google TPU), other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 102 may include a keyboard or the like, and the output device 103 may include a display (LCD or the like), a speaker or the like.
The memory 104 may include read only memory and random access memory and provides instructions and data to the processor 101. A portion of the memory 104 may also include non-volatile random access memory. For example, the memory 104 may also store information of device type.
In a specific implementation, the processor 101, the input device 102, and the output device 103 described in the embodiments of the present invention may execute the implementation described in the embodiments of the multi-scale grain security supply and demand early warning method provided in the embodiments of the present invention, which is not described herein again.
It should be noted that, in the embodiment of the present invention, the workflow and related details of a multi-scale grain safety supply and demand early warning device are more specific, please refer to the foregoing method embodiment section, and are not repeated here.
Further, corresponding to the foregoing method and apparatus, the embodiment of the present invention further provides a readable storage medium storing a computer program, where the computer program includes program instructions, and the program instructions when executed by a processor implement: the multi-scale grain safety supply and demand early warning method.
The computer readable storage medium may be an internal storage unit of the system according to any of the foregoing embodiments, for example, a hard disk or a memory of the system. The computer readable storage medium may also be an external storage device of the system, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the system. Further, the computer readable storage medium may also include both internal storage units and external storage devices of the system. The computer readable storage medium is used to store the computer program and other programs and data required by the system. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The multi-scale grain safety supply and demand early warning method is characterized by comprising the following steps of:
determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain index historical values according to the grain basic data;
acquiring a grain supply and demand total quantity predicted value according to the grain index historical value, and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value;
and carrying out supply and demand early warning according to the supply and demand balance prediction result.
2. The method for early warning of multi-scale grain safety supply and demand according to claim 1, wherein the determining the grain area scale, obtaining grain basic conditions within the grain area scale range, and obtaining grain index historical values according to the grain basic data, specifically comprises:
determining a grain area scale, and acquiring grain basic conditions in the grain area scale range, wherein the grain basic conditions comprise grain types and grain historical supply and demand conditions;
setting a per-capita ration intake standard according to the grain type;
acquiring a grain index historical value according to the average grain intake standard and the grain historical supply and demand conditions;
the grain index historical values comprise grain yield, grain net import and export amount, grain stock amount, ration consumption, industrial grain consumption, total feed grain amount and total seed grain amount in the past year.
3. The method for early warning of multi-scale grain safety supply and demand according to claim 2, wherein the method for acquiring the predicted value of the total supply and demand of grain according to the historical value of the grain index and predicting the balance of supply and demand according to the predicted value of the total supply and demand of grain comprises the following specific steps:
determining a time span, and acquiring a grain index historical value in the time span range;
inputting the grain index historical value into a prediction model to obtain a grain supply and demand index predicted value;
calculating a grain supply total quantity predicted value and a grain demand total quantity predicted value according to the grain supply and demand index predicted value;
and carrying out supply-demand balance prediction according to the grain supply total quantity predicted value and the grain demand total quantity predicted value, wherein the supply-demand balance predicted result comprises supply-demand unbalance and supply-demand balance.
4. The method for providing and warning for multi-scale grain safety according to claim 3, wherein the providing and warning for demand is performed according to the result of predicting the balance of demand, specifically:
if the supply-demand balance prediction result is supply-demand balance, trend early warning is carried out based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
if the supply-demand balance prediction result is that the supply-demand is unbalanced, carrying out supply-demand early warning based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
the supply and demand early warning comprises, but is not limited to, supply and demand total quantity early warning, supply and demand increment early warning and supply and demand acceleration early warning.
5. The multi-scale grain safety supply and demand early warning method according to claim 4, further comprising:
generating a multi-scale warning scheme based on the supply and demand early warning, wherein the multi-scale warning scheme comprises an intra-zone balancing scheme and an extra-zone balancing scheme;
the in-zone balancing comprises in-zone supply and demand balance adjustment based on the grain zone scale, and the out-of-zone balancing comprises out-of-zone supply and demand balance adjustment based on the upper zone scale of the grain zone scale.
6. A multi-scale grain safety supply and demand early warning device is characterized by comprising:
and a data acquisition module: the method comprises the steps of determining a grain area scale, acquiring grain basic conditions in the grain area scale range, and acquiring grain supply and demand historical values according to grain basic data;
supply and demand prediction module: the method is used for obtaining a grain supply and demand total quantity predicted value according to the grain supply and demand historical value and carrying out supply and demand balance prediction according to the grain supply and demand total quantity predicted value;
supply and demand early warning module: and the system is used for carrying out supply and demand early warning according to the supply and demand balance prediction result.
7. The multi-scale grain safety supply and demand early warning device according to claim 6, wherein the supply and demand prediction module is specifically configured to:
determining a time span, and acquiring a grain index historical value in the time span range;
inputting the grain index historical value into a prediction model to obtain a grain supply and demand index predicted value;
calculating a grain supply total quantity predicted value and a grain demand total quantity predicted value according to the grain supply and demand index predicted value;
and carrying out supply-demand balance prediction according to the grain supply total quantity predicted value and the grain demand total quantity predicted value, wherein the supply-demand balance predicted result comprises supply-demand unbalance and supply-demand balance.
8. The multi-scale grain safety supply and demand early warning device according to claim 7, wherein the supply and demand early warning module is specifically configured to:
if the supply-demand balance prediction result is supply-demand balance, trend early warning is carried out based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
if the supply-demand balance prediction result is that the supply-demand is unbalanced, carrying out supply-demand early warning based on the grain supply total quantity prediction value and the grain demand total quantity prediction value;
the supply and demand early warning comprises, but is not limited to, supply and demand total quantity early warning, supply and demand increment early warning and supply and demand acceleration early warning.
9. A multi-scale grain safety supply and demand early warning device, comprising a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are connected with each other, and wherein the memory is configured to store a computer program comprising program instructions, and wherein the processor is configured to invoke the program instructions to perform the method of any of claims 1-5.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894588A (en) * | 2023-06-05 | 2023-10-17 | 中国科学院地理科学与资源研究所 | Big data-based intelligent grain supply and demand management method, system and medium |
| CN117078102A (en) * | 2023-08-28 | 2023-11-17 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Regional grain security guarantee capability quantitative evaluation method based on space matching degree |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101764758B1 (en) * | 2017-01-12 | 2017-08-03 | 경희대학교 산학협력단 | System and method of anaylzing water-energy-food nexus |
| CN108122137A (en) * | 2018-01-08 | 2018-06-05 | 中国农业科学院农业信息研究所 | A kind of method and system of intelligent equalization agricultural product supply and demand |
| CN108346069A (en) * | 2018-02-09 | 2018-07-31 | 中国农业科学院农业信息研究所 | A kind of agricultural product supply demand method for early warning and system |
| CN111539549A (en) * | 2020-03-11 | 2020-08-14 | 水利部交通运输部国家能源局南京水利科学研究院 | Water-energy-grain tie relationship prediction method based on resource life cycle process |
| CN112712206A (en) * | 2020-12-30 | 2021-04-27 | 航天信息股份有限公司 | Method and system for predicting food species supply and demand balance based on local equilibrium model |
| CN113222634A (en) * | 2020-12-30 | 2021-08-06 | 航天信息股份有限公司 | Method and system for predicting total corn demand based on local equilibrium model |
| CN113343553A (en) * | 2021-04-14 | 2021-09-03 | 中国农业大学 | Waterlogging water resource conservation allocation method with supply and demand bilateral prediction |
-
2023
- 2023-01-17 CN CN202310060489.0A patent/CN116012044A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101764758B1 (en) * | 2017-01-12 | 2017-08-03 | 경희대학교 산학협력단 | System and method of anaylzing water-energy-food nexus |
| CN108122137A (en) * | 2018-01-08 | 2018-06-05 | 中国农业科学院农业信息研究所 | A kind of method and system of intelligent equalization agricultural product supply and demand |
| CN108346069A (en) * | 2018-02-09 | 2018-07-31 | 中国农业科学院农业信息研究所 | A kind of agricultural product supply demand method for early warning and system |
| CN111539549A (en) * | 2020-03-11 | 2020-08-14 | 水利部交通运输部国家能源局南京水利科学研究院 | Water-energy-grain tie relationship prediction method based on resource life cycle process |
| CN112712206A (en) * | 2020-12-30 | 2021-04-27 | 航天信息股份有限公司 | Method and system for predicting food species supply and demand balance based on local equilibrium model |
| CN113222634A (en) * | 2020-12-30 | 2021-08-06 | 航天信息股份有限公司 | Method and system for predicting total corn demand based on local equilibrium model |
| CN113343553A (en) * | 2021-04-14 | 2021-09-03 | 中国农业大学 | Waterlogging water resource conservation allocation method with supply and demand bilateral prediction |
Non-Patent Citations (1)
| Title |
|---|
| 曾澜, 张勇, 张雪, 吴炳方: "基于遥感估产和农业统计的粮食供需平衡模型研究", 遥感学报, no. 06 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894588A (en) * | 2023-06-05 | 2023-10-17 | 中国科学院地理科学与资源研究所 | Big data-based intelligent grain supply and demand management method, system and medium |
| CN116894588B (en) * | 2023-06-05 | 2024-06-18 | 中国科学院地理科学与资源研究所 | Big data-based intelligent grain supply and demand management method, system and medium |
| CN117078102A (en) * | 2023-08-28 | 2023-11-17 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Regional grain security guarantee capability quantitative evaluation method based on space matching degree |
| CN117078102B (en) * | 2023-08-28 | 2024-05-17 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Regional grain security guarantee capability quantitative evaluation method based on space matching degree |
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