Regional crop production potential assessment method and system based on planting mode

Abstract

The invention relates to the field of agricultural science and technology, and discloses a regional crop production potential assessment method and system based on a planting mode, and the method comprises the steps: collecting accumulated temperature data and rainfall data of a target assessment region, and calculating a theoretical multiple cropping index corresponding to the target assessment region; determining an ideal planting mode of the target evaluation area according to the theoretical multiple cropping index in combination with an existing planting mode; setting a target evaluation area for planting according to an ideal planting mode, and determining a theoretical sowing area of crops in the target evaluation area; setting a multi-scene mode, and selecting a theoretical planting mode; according to the theoretical sowing area, the actual sowing area and the actual crop yield per unit area, the crop production potential of the target evaluation area is evaluated, the method combines the theoretical multiple cropping index and the planting mode, is closer to production reality, sets a multi-contextual mode, flexibly adjusts the planting mode, and evaluates the crop production potential; and a scientific basis is provided for formulating a regional crop production policy.

Description

Technical Field

[0001] The present invention relates to the field of agricultural science and technology, and in particular to a method and system for evaluating regional crop production potential based on planting patterns. Background Art

[0002] With global climate change and population growth, food security issues are receiving increasing attention. In order to achieve the goal of increasing food production capacity, breakthroughs are needed in the efficient use of arable land resources, optimization of planting structure, natural disaster prevention and control, and reduction of food processing losses.

[0003] In existing technologies, improvements are usually made in the areas of crop pattern optimization, model application, and variety improvement. However, existing research mostly focuses on the interactions between crops, the mutual influence between crops and the environment, and the evaluation of economic and ecological benefits. There is a lack of systematic evaluation methods, and there is insufficient adaptability to climate change and limited multi-scenario analysis capabilities. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for evaluating regional crop production potential based on planting patterns to solve the problem in the prior art that crop production potential cannot be systematically evaluated.

[0005] In a first aspect, the present invention provides a method for evaluating regional crop production potential based on planting patterns, the method comprising:

[0006] Collect accumulated temperature data and precipitation data of the target assessment area, and calculate the theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and precipitation data;

[0007] Determine the ideal planting pattern for the target assessment area based on the theoretical multiple cropping index and combined with the existing planting pattern;

[0008] Set the target assessment area and plant crops according to the ideal planting pattern to determine the theoretical sowing area of crops in the target assessment area;

[0009] Set up multiple scenario modes and select theoretical planting modes;

[0010] Based on the theoretical sown area, actual sown area and actual crop yield per unit area, the crop production potential of the target assessment area is evaluated.

[0011] This invention proposes for the first time a method for evaluating regional crop production potential based on planting patterns, filling the gap in existing technology. It combines the theoretical multiple cropping index and planting patterns, is closer to actual production, sets multiple scenario models, flexibly adjusts planting patterns, and evaluates the crop production potential of the target assessment area, providing a scientific basis for the formulation of regional crop production policies and facilitating the formulation of targeted crop production policies.

[0012] In an optional embodiment, calculating the theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and the precipitation data includes:

[0013] Based on the accumulated temperature data, calculate the accumulated temperature multiple cropping index potential corresponding to the target assessment area;

[0014] Based on precipitation data, calculate the precipitation multiple cropping index potential corresponding to the target assessment area;

[0015] The minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential is determined as the theoretical multiple cropping index corresponding to the target assessment area.

[0016] The present invention takes into account the influence of accumulated temperature and precipitation, calculates the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential respectively, and takes the minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential as the theoretical multiple cropping index to reflect the regional multiple cropping potential and avoid the evaluation deviation caused by considering a single factor.

[0017] In an optional embodiment, the accumulated temperature multiple cropping index potential is calculated according to the following formula:

[0018]

[0019] Among them, M T is the accumulated temperature multiple cropping index potential, and T≥0℃ is the accumulated temperature.

[0020] The present invention determines the corresponding accumulated temperature multiple cropping index potential by segmenting different accumulated temperature ranges, reflecting the multiple cropping index potential corresponding to the regional accumulated temperature, so as to reasonably arrange the planting plan and select the appropriate crop types and planting times.

[0021] In an optional embodiment, the precipitation multiple cropping index potential is calculated according to the following formula:

[0022]

[0023] Among them, M R is the precipitation multiple cropping index potential, and R is the multi-year average precipitation.

[0024] The present invention determines the corresponding precipitation multiple cropping index potential by segmenting different precipitation ranges, measures the impact of precipitation on multiple cropping potential, and provides a scientific basis for agricultural production planning.

[0025] In an optional embodiment, the crop production potential of the target assessment area is assessed based on the theoretical sown area, the actual sown area, and the actual crop yield per unit area, including:

[0026] Calculate the difference between theoretical sowing area and actual sowing area;

[0027] The crop production potential is calculated based on the difference between the theoretical sown area and the actual sown area, and the actual crop yield per unit area.

[0028] The present invention calculates the difference between the theoretical sown area and the actual sown area to assess the unplanted area in the region. Combined with the actual crop yield per unit area, the crop production potential is calculated to analyze the regional grain production potential and provide a data basis for formulating targeted grain production policies.

[0029] In an optional embodiment, the crop production potential is calculated according to the following formula:

[0030] Y SA =SA gap ×Y A

[0031] SA gap =SA1-SA2

[0032] Among them, Y SA is the crop production potential, SA1 is the theoretical sown area, SA2 is the actual sown area, SA gap is the difference between the theoretical sowing area and the actual sowing area, Y A It is the actual crop yield per unit area.

[0033] The present invention calculates the crop production potential by multiplying the difference between the theoretical sowing area and the actual sowing area by the actual crop yield per unit area, thereby providing a clear quantitative indicator basis for regional crop potential.

[0034] In a second aspect, the present invention provides a regional crop production potential assessment system based on planting patterns, the system comprising:

[0035] The acquisition module is used to collect the accumulated temperature data and precipitation data of the target assessment area, and calculate the theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and precipitation data;

[0036] The ideal planting pattern determination module is used to determine the ideal planting pattern of the target assessment area based on the theoretical multiple cropping index and the existing planting pattern;

[0037] Theoretical sowing area determination module is used to set the target assessment area for planting according to the ideal planting pattern and determine the theoretical sowing area of crops in the target assessment area;

[0038] Multi-scenario mode setting module, used to set multiple scenario modes and select theoretical planting modes;

[0039] The crop production potential assessment module is used to assess the crop production potential of the target assessment area based on the theoretical sowing area, actual sowing area, and actual crop yield per unit area.

[0040] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the method for assessing regional crop production potential based on planting patterns according to the first aspect or any corresponding embodiment thereof.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for assessing regional crop production potential based on planting patterns according to the first aspect or any corresponding embodiment thereof.

[0042] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for enabling a computer to execute the method for assessing regional crop production potential based on planting patterns according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 is a flow chart of a method for evaluating regional crop production potential based on planting patterns according to an embodiment of the present invention;

[0045] Figure 2 is a flow chart of calculating regional food production potential according to an embodiment of the present invention;

[0046] Figure 3 is a structural block diagram of a regional crop production potential assessment system based on planting patterns according to an embodiment of the present invention;

[0047] Figure 4 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0049] Existing technological progress mainly includes the following three aspects:

[0050] Optimization of cropping patterns: Optimization of cropping patterns is one of the important means to increase grain production capacity. At present, the multi-cropping pattern occupies an important position in grain production. 3 / 4 of grain production (9 / 10 of rice and wheat, 3 / 5 of corn), 9 / 10 of rapeseed, and 9 / 10 of vegetables are produced on arable land with multi-cropping.

[0051] Model Application: In recent years, the WOFOST (World Food Studies Model) has been widely used to assess the impact of climate change on food production. Based on crop growth mechanisms and combining historical climate data with future climate scenarios, the model can quantitatively assess the potential impacts of climate change on food production. Furthermore, the GAEZ (Global Agro-Ecological Zones Model) has been used to assess the potential yields and yield increases of major food crops.

[0052] Technological Integration and Innovation: Utilizing key technologies that "store grain in technology," we have selected and promoted a number of high-quality, high-yield varieties, significantly increasing grain yields per unit area. For example, the widespread adoption of rice varieties like "Zhongjia Zao 17," wheat "Zhongmai 895," corn varieties "Zhongdan 808 / 909," and soybean variety "Zhonghuang 13" has laid a solid foundation for increasing grain production capacity.

[0053] The shortcomings of the existing technology are reflected in the following three aspects:

[0054] Lack of systematic evaluation methods: Existing studies mostly start from a micro perspective, focusing on the interactions and benefit evaluation within cropping patterns, and lack a systematic evaluation method for regional food production potential based on cropping patterns.

[0055] Insufficient adaptability to climate change: Although the WOFOST model has played an important role in assessing the impact of climate change on food production, its regional application still has problems such as complex parameter adjustment and insufficient applicability verification.

[0056] Limited multi-scenario analysis capabilities: Existing assessment methods lack flexible multi-scenario analysis capabilities when responding to different demands (such as high grain yields and economic benefits), and are unable to meet the actual needs of regional grain production policy formulation.

[0057] To address these issues, embodiments of the present invention provide a method for assessing regional crop production potential based on cropping patterns. This method, combined with the multiple cropping index and planting structure, better reflects actual production conditions and provides a scientific basis for regional grain production policy formulation. Multiple scenarios are set to flexibly adjust cropping patterns and analyze regional grain production potential. This method, combined with the latest cropping pattern research, climate models, and regional data, fills a gap in existing technology.

[0058] According to an embodiment of the present invention, an embodiment of a method for evaluating regional crop production potential based on planting patterns is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] In this embodiment, a method for evaluating regional crop production potential based on planting patterns is provided. Figure 1 FIG. 1 is a flow chart of a method for evaluating regional crop production potential based on planting patterns according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0060] Step S101 : collecting accumulated temperature data and precipitation data of a target assessment area, and calculating a theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and precipitation data.

[0061] In this embodiment of the present invention, basic data is collected for the target assessment area. This data includes meteorological data for the area, including precipitation data such as daily or monthly precipitation, as well as accumulated temperature data such as maximum, minimum, and average temperatures. Accumulated temperature data and precipitation data refer to the sum of daily average temperatures during the crop growth phase, reflecting the impact of temperature on crop growth. Precipitation refers to precipitation records, reflecting the impact of precipitation on crop growth. Based on this basic data, a theoretical multiple cropping index for the area is calculated using a multiple cropping index potential model.

[0062] Step S102: Determine the ideal planting pattern for the target assessment area based on the theoretical multiple cropping index and in combination with the existing planting pattern.

[0063] In this embodiment of the present invention, existing cropping patterns reported in journals, magazines, and other literature provide a reference for sorting and organizing cropping patterns. Discussions, field visits, on-site surveys, and expert consultations serve as important supplements for sorting out ideal cropping patterns. Agricultural cropping zoning and key regional cropping patterns are collected, and the target assessment area and local agricultural cropping zoning are fitted to obtain the region's primary cropping zoning. The primary crops grown in the region are analyzed based on the crop sown area, and the ideal cropping pattern is refined based on regional characteristics for further analysis.

[0064] Among them, the ideal planting model refers to a planting model that conforms to the agricultural farming zoning and local crop planting habits.

[0065] Step S103: setting the target assessment area to be planted according to the ideal planting pattern, and determining the theoretical planting area of the crops in the target assessment area.

[0066] In the embodiment of the present invention, according to the farmland conditions, it is assumed that the target assessment area is planted entirely according to the ideal planting pattern, and the theoretical sowing area of the crops in the area is calculated.

[0067] Step S104: setting multiple scenario modes and selecting a theoretical planting mode.

[0068] In the embodiments of the present invention, multiple scenario modes are set according to different needs. The advantages of the planting modes under each scenario mode are analyzed, and then a certain planting mode is selected as the theoretical planting mode for that scenario. The multi-scenario mode design can be adjusted according to different needs, with high flexibility and a wide range of applications.

[0069] Step S105 , evaluating the crop production potential of the target evaluation area based on the theoretical sowing area, the actual sowing area, and the actual crop yield per unit area.

[0070] In the embodiment of the present invention, crop production data are collected, including crop planting distribution data, sowing area, sowing date, growth period, harvest date, harvest index and unit area yield. Field survey statistical data is conducted to determine the actual sowing area, such as Figure 2 As shown in the figure, based on the calculated theoretical sowing area, combined with the actual sowing area and actual crop yield per unit area (i.e., yield), the production potential of grain crops and other crops in the target assessment area is evaluated, providing a reference for formulating agricultural development policies and production plans.

[0071] The method for evaluating regional crop production potential based on planting patterns provided in this embodiment is the first to propose a method for evaluating regional crop production potential based on planting patterns, filling a gap in the existing technology. It combines the theoretical multiple cropping index and planting patterns, is closer to production practice, sets multiple scenario models, flexibly adjusts planting patterns, evaluates the crop production potential of the target assessment area, and provides a scientific basis for the formulation of regional crop production policies, facilitating the formulation of targeted crop production policies.

[0072] In this embodiment, a method for evaluating regional crop production potential based on planting patterns is provided. The process includes the following steps:

[0073] Step S201 : collecting accumulated temperature data and precipitation data of a target assessment area, and calculating a theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and precipitation data.

[0074] Specifically, the calculation of the theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and precipitation data in step S201 includes:

[0075] Step S2011: Calculate the accumulated temperature multiple cropping index potential corresponding to the target assessment area based on the accumulated temperature data.

[0076] Step S2012: Calculate the precipitation multiple cropping index potential corresponding to the target assessment area based on the precipitation data.

[0077] Step S2013: determining the minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential as the theoretical multiple cropping index corresponding to the target assessment area.

[0078] In an embodiment of the present invention, accumulated temperature data ≥ 0°C and multi-year average precipitation data of the target assessment area are collected.

[0079] The accumulated temperature multiple cropping index potential is determined by the accumulated temperature (T) ≥ 0°C. Based on the accumulated temperature data of the target assessment area, the accumulated temperature multiple cropping index potential is calculated according to the following formula:

[0080]

[0081] Among them, M T is the accumulated temperature multiple cropping index potential, and T≥0℃ is the accumulated temperature.

[0082] By determining the corresponding accumulated temperature multiple cropping index potential for different accumulated temperature ranges, the multiple cropping index potential corresponding to the regional accumulated temperature is reflected, so as to reasonably arrange the planting plan and select the appropriate crop types and planting times.

[0083] The precipitation multiple cropping index potential is determined by precipitation (R). Based on the precipitation data of the target assessment area, the precipitation multiple cropping index potential is calculated according to the following formula:

[0084]

[0085] Among them, M R is the precipitation multiple cropping index potential, and R is the multi-year average precipitation.

[0086] By determining the corresponding precipitation multiple cropping index potential for different precipitation ranges, the impact of precipitation on multiple cropping potential is measured, providing a scientific basis for agricultural production planning.

[0087] Theoretical multiplication index MCI=MIN(M T , M R ), that is, taking the minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential.

[0088] By taking into account the influence of accumulated temperature and precipitation, the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential are calculated respectively. The minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential is taken as the theoretical multiple cropping index to reflect the regional multiple cropping potential and avoid the evaluation bias caused by considering a single factor.

[0089] Step S202: Determine the ideal planting pattern for the target assessment area based on the theoretical multiple cropping index and in combination with the existing planting pattern.

[0090] For details, please see Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0091] Step S203: setting the target assessment area to be planted according to the ideal planting pattern, and determining the theoretical planting area of the crops in the target assessment area.

[0092] For details, please see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0093] Step S204: Set multiple scenario modes and select a theoretical planting mode.

[0094] For details, please see Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0095] Step S205 , evaluating the crop production potential of the target evaluation area based on the theoretical sowing area, the actual sowing area, and the actual crop yield per unit area.

[0096] Specifically, the above step S205 includes:

[0097] Step S2051, calculating the difference between the theoretical sowing area and the actual sowing area.

[0098] Step S2052: Calculate the crop production potential based on the difference between the theoretical sowing area and the actual sowing area and the actual crop yield per unit area.

[0099] In the embodiment of the present invention, the difference (SA1) between the theoretical sowing area (SA2) and the actual sowing area (SA2) is calculated. gap ), based on the difference between the theoretical sown area and the actual sown area, and the actual crop yield per unit area, the crop production potential is calculated according to the following formula:

[0100] Y SA =SA gap ×Y A

[0101] SA gap =SA1-SA2

[0102] Among them, Y SA is the crop production potential, SA1 is the theoretical sown area, SA2 is the actual sown area, SA gap is the difference between the theoretical sowing area and the actual sowing area, Y A It is the actual crop yield per unit area.

[0103] The method for evaluating regional crop production potential based on planting patterns provided in this embodiment calculates the difference between the theoretical sown area and the actual sown area to evaluate the unplanted area in the region. Combined with the actual crop yield per unit area, the crop production potential is calculated to analyze the regional grain production potential, provide a clear quantitative indicator basis for regional crop potential, and provide a data basis for formulating targeted grain production policies.

[0104] As a specific application example of the embodiment of the present invention, the 40-year average accumulated temperature ≥0°C T in City A from 1981 to 2020 is 8261.7°C, and the 40-year average precipitation R is 1926.6 mm. The calculated M T =300,M R =300, so the multiple cropping index potential MCI = 300, which means that the city can grow three crops a year.

[0105] According to existing research, City A belongs to the South China Low Plains Agriculture, Forestry, and Fishery Area within the South China Hilly Plains Paddy Field and Dryland Three-Crop Area, with a rich variety of crops such as rice, sugarcane, rubber, and fruits and vegetables. In 2020, City A's primary grain crops were rice and potatoes, accounting for 48.6% and 5.1% of the city's sown area, respectively. Vegetables and edible fungi accounted for 36.6%, and peanuts, among cash crops, accounted for 1.3%.

[0106] Based on the existing research results and by sorting out the existing planting patterns in City A, it is concluded that the ideal planting pattern for City A is a three-cropping pattern of double-season rice + potatoes, double-season rice + vegetables, and double-season rice + winter corn.

[0107] Scenario 1 is set as high grain yield: considering that potatoes are grain crops with high yield, it is assumed that all paddy fields and irrigated land are planted with double-season rice + potato planting pattern, and all dry land is planted with potatoes.

[0108] Scenario 2 is set to take into account both food and economic benefits: double-season rice + vegetable planting model.

[0109] Scenario 3 is set as actual production: During the actual planting process, farmers will comprehensively consider various factors such as labor, market sales, economic benefits, and planting techniques, and make various choices, which may be a single-crop or double-crop model, or they may only grow vegetables or only grow grain crops. Therefore, this scenario represents the current actual grain output.

[0110] According to the main data bulletin of the third national land survey in City A, there are 75,956.48 hectares (1.1393 million mu) of cultivated land. Of this, 60,593.24 hectares (908,900 mu) of paddy fields account for 79.77%; 4,321.24 hectares (64,800 mu) of irrigated land account for 5.69%; and 11,042.00 hectares (165,600 mu) of dry land account for 14.54%.

[0111] The grain yields under the three scenarios are shown in Table 1.

[0112] Table 1

[0113]

[0114]

[0115] Compared with Scenario 1 and Scenario 3, the food production potential Y SA is 1,798,187.9 tons; compared with scenario 3, the food production potential Y SA It is 150,612.6 tons.

[0116] In this embodiment, a regional crop production potential assessment system based on a planting pattern is also provided. This system is used to implement the above-mentioned embodiments and preferred embodiments, and the details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0117] This embodiment provides a regional crop production potential assessment system based on planting patterns, such as Figure 3 Shown, including:

[0118] The collection module 301 is used to collect the accumulated temperature data and precipitation data of the target assessment area, and calculate the theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and precipitation data.

[0119] The ideal planting pattern determination module 302 is used to determine the ideal planting pattern of the target assessment area based on the theoretical multiple cropping index and the existing planting pattern.

[0120] The theoretical sowing area determination module 303 is used to set the target assessment area to be planted according to the ideal planting pattern and determine the theoretical sowing area of the crops in the target assessment area.

[0121] The multi-scenario mode setting module 304 is used to set the multi-scenario mode and select the theoretical planting mode.

[0122] The crop production potential assessment module 305 is used to assess the crop production potential of the target assessment area based on the theoretical sowing area, the actual sowing area, and the actual crop yield per unit area.

[0123] In some optional implementations, the acquisition module 301 includes:

[0124] The first calculation unit is used to calculate the accumulated temperature multiple cropping index potential corresponding to the target assessment area based on the accumulated temperature data.

[0125] The second calculation unit is used to calculate the precipitation multiple cropping index potential corresponding to the target assessment area based on the precipitation data.

[0126] The determination unit is used to determine the minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential as the theoretical multiple cropping index corresponding to the target assessment area.

[0127] In some optional embodiments, the crop production potential assessment module 305 includes:

[0128] The third calculation unit is used to calculate the difference between the theoretical sowing area and the actual sowing area.

[0129] The fourth calculation unit is used to calculate the crop production potential according to the difference between the theoretical sowing area and the actual sowing area and the actual crop yield per unit area.

[0130] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0131] The regional crop production potential assessment system based on planting patterns in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0132] The embodiment of the present invention also provides a computer device having the above Figure 3 The regional crop production potential assessment system based on planting patterns is shown.

[0133] See also Figure 4 , Figure 4 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0134] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0135] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0136] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0137] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0138] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0139] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, etc. The output device 40 can include a display device, etc.

[0140] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0141] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0142] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are intended to fall within the scope of this application.

Claims

1. A method for evaluating regional crop production potential based on planting patterns, characterized in that: The method comprises: Collecting accumulated temperature data and precipitation data of the target assessment area, and calculating a theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and the precipitation data; Determine the ideal planting pattern for the target assessment area based on the theoretical multiple cropping index and in combination with the existing planting pattern; Setting the target assessment area for planting according to the ideal planting pattern to determine the theoretical sowing area of crops in the target assessment area; Set up multiple scenario modes and select theoretical planting modes; Based on the theoretical sown area, actual sown area and actual crop yield per unit area, the crop production potential of the target assessment area is evaluated.

2. The method according to claim 1, characterized in that Calculating the theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and the precipitation data includes: Calculate the accumulated temperature multiple cropping index potential corresponding to the target assessment area based on the accumulated temperature data; Calculate the precipitation multiple cropping index potential corresponding to the target assessment area based on the precipitation data; The minimum value of the accumulated temperature multiple cropping index potential and the precipitation multiple cropping index potential is determined as the theoretical multiple cropping index corresponding to the target assessment area.

3. The method according to claim 2, characterized in that The accumulated temperature multiple cropping index potential is calculated according to the following formula: Among them, M T is the accumulated temperature multiple cropping index potential, and T≥0℃ is the accumulated temperature.

4. The method according to claim 2, characterized in that The precipitation multiple cropping index potential is calculated according to the following formula: Among them, M R is the precipitation multiple cropping index potential, and R is the multi-year average precipitation.

5. The method according to claim 1, wherein The aforementioned assessment of the crop production potential of the target assessment area based on the theoretical sown area, actual sown area, and actual crop yield per unit area includes: Calculate the difference between theoretical sowing area and actual sowing area; The crop production potential is calculated based on the difference between the theoretical sowing area and the actual sowing area and the actual crop yield per unit area.

6. The method according to claim 5, characterized in that The crop production potential is calculated according to the following formula: Y SA =IN gap ×Y A SO gap =SA1-SA2 Among them, Y SA is the crop production potential, SA1 is the theoretical sown area, SA2 is the actual sown area, SA gap is the difference between the theoretical sowing area and the actual sowing area, Y A It is the actual crop yield per unit area.

7. A regional crop production potential assessment system based on planting patterns, characterized in that: The system comprises: An acquisition module is used to collect accumulated temperature data and precipitation data of the target assessment area, and calculate a theoretical multiple cropping index corresponding to the target assessment area based on the accumulated temperature data and the precipitation data; An ideal planting pattern determination module is used to determine the ideal planting pattern of the target assessment area based on the theoretical multiple cropping index and the existing planting pattern; Theoretical sowing area determination module, used to set the target assessment area for planting according to the ideal planting pattern, and determine the theoretical sowing area of crops in the target assessment area; Multi-scenario mode setting module, used to set multiple scenario modes and select theoretical planting modes; The crop production potential assessment module is used to assess the crop production potential of the target assessment area based on the theoretical sowing area, actual sowing area, and actual crop yield per unit area.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the regional crop production potential assessment method based on planting patterns as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the regional crop production potential assessment method based on planting patterns according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for evaluating regional crop production potential based on planting patterns according to any one of claims 1 to 6.

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