Meteorological data-based system and method for guiding compound planting of grain and rice

Abstract

The invention discloses a system and a method for guiding grain compound planting based on meteorological data, and relates to the technical field of agricultural planting. Comprising the steps that historical meteorological data and real-time meteorological data of a target area are collected, growth models of various kinds of grain crops are obtained, and future meteorological data are obtained according to fusion processing of the historical meteorological data and the real-time meteorological data; according to future meteorological data, matching growth models of various types of grain crops, simulating to obtain estimated growth conditions of various types of grain crops, and determining planting types and optimal planting periods of the grain crops according to the estimated growth conditions; through the growth model, the most suitable future meteorological data for guiding the fertilizer and water management and pest control of the grain crops is obtained, the yield and quality of the crops are improved, the selection of the grain crops is prevented from being subjectively limited, the estimated maturation time is obtained according to the estimated growth condition, and the future meteorological data of the estimated maturation time is obtained. Subsequent planted crops are selected in advance, the land vacancy time is shortened, and the land utilization rate is increased.

Description

Technical Field

[0001] The present application relates to the field of agricultural planting technology, and specifically to a system and method for guiding the integrated planting of grain and economic crops based on meteorological data. Background Art

[0002] As a new agricultural production model, grain-cash cropping is a method of combining different types of grain crops with cash crops. Through this model, "one field with multiple industries" and "one land with multiple uses" can be achieved, and the multiple cropping index can be improved. For example, there are various grain-cash cropping models such as rice-vegetable / berry rotation, fruit-grain, fruit-vegetable, fruit-flower, and fruit-drug rotation. After the early spring vegetables are harvested, a season of rice is planted, and vegetables or strawberries are planted after the rice is harvested. For example, grain and cash crops are interplanted between fruit trees, etc. Grain-cash cropping releases soil fertility while improving the soil, and improves land utilization and economic benefits through interdependence and mutual complementation.

[0003] At present, the grain-cash cropping patterns in various regions are relatively fixed, and there are relatively few grain and cash crop varieties participating in the rotation, which are usually familiar varieties passed down by word of mouth by local people. However, under the current climate conditions that change year by year, the climate in different years and different rotation times is different, and it is not completely suitable for the growth of familiar varieties. In addition, the fixed varieties of long-term and multiple rotations can easily lead to a gradually monotonous soil structure. Therefore, there is an urgent need for a system and method based on meteorological data to guide the grain-cash cropping system. According to historical meteorological data and current meteorological data, future meteorological data can be predicted, and the data required for the growth of multiple grain and cash crops can be matched according to future meteorological data. The currently suitable crops can be selected for planting according to meteorological data, and the types of grain and cash crops participating in the rotation are no longer limited due to the differences in the degree of understanding of different crops by the planting personnel. Summary of the invention

[0004] This application aims at the fact that the current grain and cash crop composite planting patterns in various regions are relatively fixed, and there are few grain and cash crop varieties participating in the rotation. However, the climates in different years and at different rotation times are different, and are not completely suitable for the growth of familiar varieties. In addition, the varieties are fixed after long-term and multiple rotations, which easily leads to the problem of gradually monotonous soil structure. A system and method for guiding grain and cash crop composite planting based on meteorological data are provided. By obtaining the growth models of various grain and cash crops in advance, as well as the future meteorological data, the grain and cash crops that are most suitable for their growth during the planting period are selected for planting, thereby increasing crop yields and avoiding subjective restrictions on the selection of grain and cash crops. The specific technical scheme is as follows: In a first aspect of the present application, a system for guiding the integrated cropping of crops and crops based on meteorological data is provided, comprising: A data collection module collects historical meteorological data and real-time meteorological data of the target area, and obtains growth models of various types of grain and cash crops, wherein the growth models include growth conditions of grain and cash crops at various stages under various environmental data, and the historical meteorological data and the real-time meteorological data include temperature, precipitation and light; A data processing module, which obtains future meteorological data by fusing the historical meteorological data with the real-time meteorological data; A planting selection module matches the growth models of various grain and economic crops according to the future meteorological data, simulates the estimated growth conditions of various grain and economic crops, and selects grain and economic crops for planting according to the estimated growth conditions; The planting planning module obtains the estimated maturity time according to the estimated growth conditions of the grain and cash crops, obtains the future meteorological data of the estimated maturity time, and plans and selects the grain and cash crops to be planted in the future through the planting selection module.

[0005] In one embodiment of the present application, it also includes an equipment control module, which identifies the growth stage of the grain and cash crops through an image acquisition device, obtains the data range that is most suitable for the growth of the grain and cash crops in each growth stage according to the growth model, and an external execution device adjusts the environmental data of the grain and cash crops according to the real-time meteorological data, so that each growth stage of the grain and cash crops is in the most suitable data range for growth, or closer to the most suitable data range for growth.

[0006] In one embodiment of the present application, the equipment control module also includes a control update submodule, which obtains the controllable range of the environmental data of the available external execution device under each of the real-time meteorological data; when the future meteorological data matches the growth model of various types of grain and cash crops, the future meteorological data is combined with the controllable range of the external execution device to obtain the controllable meteorological data, and the controllable meteorological data makes the future meteorological data closer to the data range that is most suitable for the growth of various types of grain and cash crops at various growth stages; before planting, the planting selection module selects the controllable meteorological data to match the growth model of various types of grain and cash crops, simulates the estimated growth conditions, and selects grain and cash crops for planting; the planting planning module selects the controllable meteorological data to obtain the estimated maturity time during the growth of the grain and cash crops.

[0007] In one embodiment of the present application, a dynamic adjustment module is also included, which obtains the current growth stage of the grain and cash crops through the image acquisition device, obtains the difference with the estimated growth situation, and adjusts the estimated maturity time of the grain and cash crops in real time according to the difference; and generates a dynamic planning plan based on the future meteorological data adjusted in real time.

[0008] In one embodiment of the present application, the equipment control module also includes a pre-control submodule, which identifies the current growth stage of the grain and cash crops through the image acquisition device, obtains the data range that is most suitable for the growth of the grain and cash crops in the current growth stage, and when the external execution device adjusts the environmental data, obtains the time required for the future meteorological data to change to the environmental data range that is most suitable for the current growth stage of the grain and cash crops, as well as the consumption of various types of environmental data per unit time in the grain and cash crop area under different meteorological data, calculates and determines the control time and control amount of the external execution device, so that when the environmental data regulated by the external execution device is consumed to the edge value of the data range that is most suitable for the current growth stage of the grain and cash crops, the real-time meteorological data changes to the data range that is most suitable for the current growth stage of the grain and cash crops.

[0009] In one embodiment of the present application, the device control module further includes: The disaster warning submodule performs a warning operation when the future meteorological data may cause a disaster. By estimating the extent of the disaster, the food and cash crops and / or the external execution equipment are processed in advance. The disaster includes natural disasters and biological disasters. The processing includes rushing to harvest, transferring and adding external execution equipment. The meteorological combination submodule combines the future meteorological data to recommend the time for fertilizing and applying pesticides to the grain and cash crops.

[0010] In one embodiment of the present application, a market suggestion module is also included, and the market suggestion module includes: The first suggestion submodule obtains the market conditions of various grain and cash crops for many years before planting, predicts the market changes during this year's harvest period through a large model, and obtains the future meteorological data to obtain the time required for the maturity of various grain and cash crops. The time required for maturity is combined with the predicted market changes during this year's harvest period to provide the grain and cash crop selection suggestions and sowing time suggestions; The second suggestion submodule obtains the market situation of the grain and cash crops before maturity, predicts the market changes in the future through a large model, and simultaneously obtains the future meteorological data to obtain the time when the grain and cash crops can be harvested. The time when the crops can be harvested is combined with the predicted market changes in the future to provide suggestions for the harvesting time of the grain and cash crops.

[0011] In one embodiment of the present application, the market recommendation module also includes a profit budget sub-module, which obtains the planting costs of various types of grain and cash crops under the future meteorological data. The planting costs include seed costs, labor and land costs, fertilizer and pesticide costs, and the cost of using the external execution equipment. The planting costs are combined with the predicted market conditions during this year's harvest period to derive the profits of various types of grain and cash crops.

[0012] In one embodiment of the present application, the market recommendation module also includes a risk prediction sub-model, which derives the probabilities of bumper yields, normal yields, reduced yields and total yields of various grain and economic crops based on the future meteorological data, obtains the predicted market conditions of various grain and economic crops during the harvest period this year, and derives the profit and loss amounts and probabilities of various grain and economic crops.

[0013] In a second aspect of the present application, a method for guiding the integrated cropping of crops and crops based on meteorological data is provided, comprising: Collecting historical meteorological data and real-time meteorological data of the target area, and obtaining growth models of various types of grain and cash crops, wherein the growth models include growth conditions of grain and cash crops at various stages under various environmental data, and the historical meteorological data and the real-time meteorological data include temperature, precipitation and light; Obtain future meteorological data by fusing the historical meteorological data with the real-time meteorological data; Matching the growth models of various grain and economic crops according to the future meteorological data, simulating the estimated growth conditions of various grain and economic crops, and selecting grain and economic crops for planting according to the estimated growth conditions; According to the estimated growth conditions of the grain and cash crops, the estimated maturity time is obtained, the future meteorological data of the estimated maturity time is acquired, and the grain and cash crops to be planted in the future are planned and selected.

[0014] In one embodiment of the present application, the growth stage of the grain and cash crops is identified by an image acquisition device, and the data range most suitable for the growth of the grain and cash crops in each growth stage is obtained according to the growth model. An external execution device adjusts the environmental data of the grain and cash crops according to the real-time meteorological data, so that each growth stage of the grain and cash crops is in the most suitable data range for growth, or closer to the most suitable data range for growth.

[0015] In one embodiment of the present application, the adjustable range of the environmental data of the available external execution device under each of the real-time meteorological data is obtained; when the future meteorological data matches the growth model of various types of grain and cash crops, the future meteorological data is combined with the adjustable range of the external execution device to obtain the regulated meteorological data, and the regulated meteorological data relatively makes the future meteorological data closer to the data range that is most suitable for the growth of various types of grain and cash crops at various growth stages; before planting, the regulated meteorological data is selected to match the growth model of various types of grain and cash crops, the estimated growth conditions are simulated, and grain and cash crops are selected for planting; during the growth of the grain and cash crops, the regulated meteorological data is selected to obtain the estimated maturity time.

[0016] In one embodiment of the present application, the current growth stage of the grain and cash crops is acquired through the image acquisition device, and the difference with the estimated growth situation is obtained, and the estimated maturity time of the grain and cash crops is adjusted in real time according to the difference; and a dynamic planning plan is generated based on the future meteorological data adjusted in real time.

[0017] In one embodiment of the present application, the current growth stage of the grain and cash crop is identified by the image acquisition device, and the data range that is most suitable for the growth of the grain and cash crop in the current growth stage is obtained. When the external execution device adjusts the environmental data, the time required for the future meteorological data to change to the environmental data range that is most suitable for the current growth stage of the grain and cash crop, and the consumption of various types of environmental data per unit time in the grain and cash crop area under different meteorological data are obtained. The regulation time and regulation amount of the external execution device are calculated and determined, so that when the environmental data regulated by the external execution device is consumed to the edge value of the data range that is most suitable for the growth of the grain and cash crop in the current growth stage, the real-time meteorological data changes to the data range that is most suitable for the growth of the grain and cash crop in the current growth stage.

[0018] In one embodiment of the present application, when the future meteorological data may cause a disaster, an early warning operation is performed, and the grain and cash crops and / or the external execution equipment are processed in advance by estimating the degree of the disaster. The disaster includes natural disasters and biological disasters, and the processing includes rushing to harvest, transferring and adding external execution equipment; combined with the future meteorological data, the time for fertilizing and applying medicines to the grain and cash crops is recommended.

[0019] In one embodiment of the present application, the market conditions of various types of grain and cash crops for many years are obtained before planting, and the market changes in this year's harvest period are predicted through a large model. At the same time, the future meteorological data is obtained to obtain the time required for the maturity of various types of grain and cash crops. The time required for maturity is combined with the predicted market changes in this year's harvest period to provide the grain and cash crop selection suggestions and sowing time suggestions; The market situation of the grain and cash crops is obtained before maturity, and the market changes in the future are predicted through a large model. At the same time, the future meteorological data is obtained to obtain the time when the grain and cash crops can be harvested. The time when the crops can be harvested is combined with the predicted market changes in the future to provide harvesting time recommendations for the grain and cash crops.

[0020] In one embodiment of the present application, the planting costs of various types of grain and cash crops under the future meteorological data are obtained, and the planting costs include seed costs, labor and land costs, fertilizer and pesticide costs, and the cost of using the external execution equipment. The planting costs are combined with the predicted market conditions during this year's harvest period to derive the profits of various types of grain and cash crops.

[0021] In one embodiment of the present application, the probabilities of bumper, normal, reduced and zero yields of various types of grain and cash crops are obtained based on the future meteorological data, the forecast market conditions of various types of grain and cash crops during the harvest period this year are obtained, and the profit and loss amounts and probabilities of various types of grain and cash crops are obtained.

[0022] This application has the following beneficial effects: 1. Pre-acquire growth models of various types of grain and economic crops, and the future meteorological data. Before planting, obtain the future meteorological data through the data processing module. The future meteorological data changes with time, and the suitable environmental data of various types of grain and economic crops also changes with different growth stages. Input the future meteorological data into the growth models of various types of grain and economic crops to obtain the estimated growth conditions of various types of grain and economic crops under the future meteorological data. The more the future meteorological data of grain and economic crops at each stage conform to their own suitable growth environment, the better the growth of the grain and economic crops, and the more suitable the future time is for planting the grain and economic crops; output various types of grain and economic crops through the interface The estimated growth conditions of crops are used for planting selection. The most suitable grain and cash crops to grow under the future meteorological data are obtained through the growth model, thereby increasing crop yields and avoiding subjective restrictions on the selection of grain and cash crops. Before harvesting grain and cash crops, the estimated maturity time can be obtained based on the estimated growth conditions. The estimated maturity time is a time period. Combined with the future meteorological data within the time period, a specific harvest time suitable for harvesting is recommended. At the same time, various types of grain and cash crops are matched again according to the future meteorological data after the harvest. After selecting the crops to be planted in advance, preparatory work before planting is carried out to reduce land vacancy time and improve land utilization rate.

[0023] 2. The environmental data of the area where the grain and cash crops grow is adjusted by the external device. When the real-time meteorological data makes the environmental data not within the suitable range for the grain and cash crops, the external execution device is controlled to make the grain and cash crops fit more closely to the suitable environment during the whole growth process, thereby effectively increasing the yield of the grain and cash crops. Furthermore, since the environmental data of the area where the grain and cash crops grow can be adjusted by the external execution device, the grain and cash crops grow more appropriately under the adjustment of the environmental data by the external execution device. Therefore, the estimated growth of the grain and cash crops without considering the external execution device may be lower than the actual situation, and the expected harvest time may be later than the actual date. Therefore, the results obtained by the planting selection module in seed selection and the planting planning module in estimating the maturity time are more correct when combined with the external execution device. At the same time, the adjustment of the environmental data by the external execution device may result in grain and cash crops that are closer to the optimal growth model, thereby further increasing the land yield.

[0024] 3. Considering that different external execution devices will generate different costs when used, such as power consumption, water consumption and equipment loss, and after the external execution device is adjusted, the environmental data will gradually change according to the real-time meteorological data and the consumption of grain and cash crops, and the change of meteorological data is continuous. Therefore, during the growth of grain and cash crops, while the external execution device adjusts the environmental data, the change of the real-time meteorological data is obtained to obtain the first time when the real-time meteorological data returns to the suitable growth range of grain and cash crops, and at the same time, the second time when the environmental data after adjustment by the external execution device is consumed to the edge of the suitable growth range of grain and cash crops is calculated. When the first time is equal to the second time, the function of adjusting the environmental data of the external execution device is turned off. When the adjusted environmental data is consumed to the edge of the suitable growth range of grain and cash crops, the real-time meteorological data can just make the grain and cash crops in the suitable growth area, avoiding waste of resources and saving costs. Applicable scenarios include the irrigation device stopping water supply before raining and the temperature control device stopping heating before the temperature rises at noon.

[0025] 4. Considering that unsuitable weather may bring disasters to grain and cash crops, and even lead to crop reduction or crop failure, including heavy rain causing floods to wash away or drown crops, and high temperature and drought causing crop withering, therefore, when it is identified that the future weather data will cause crop disasters, early warning operations are performed, and corresponding measures are taken according to the current state of the current crops and the degree of disaster, including early harvesting, transfer of seedlings, and even transfer of the external execution equipment when the grain and cash crops cannot be transferred due to severe disasters, so as to minimize economic losses; at the same time, new information on the external execution equipment that can respond to the disaster is provided according to the degree of the disaster, and new external execution equipment is purchased to respond to the disaster. For example, if the future weather data shows that there will be continuous rainfall in the next week, the water in the field cannot be discharged, and the roots of grain and cash crops cannot breathe and die, the excess water in the field can be discharged by adding drainage pipes and other devices to protect the grain and cash crops from the disaster.

[0026] 5. The first suggestion submodule predicts the market conditions before planting grain and cash crops, and provides suggestions on the type selection and sowing time of grain and cash crops based on the predicted market conditions at the maturity time of grain and cash crops, so as to achieve efficiency improvement when the market conditions are good during the crop maturity period; at the same time, the second suggestion submodule predicts the market conditions of the current crops at a relatively close time before the maturity of grain and cash crops, and corresponds to the time when grain and cash crops can be harvested in the future meteorological data. For example, it is not recommended to harvest rice on rainy days, and it is chosen to harvest and sell it at the recommended harvesting time and the time when the market conditions are predicted to be good; further, combined with the dynamic adjustment module, ripening operations or delayed maturation operations are performed according to the current growth stage and growth conditions of grain and cash crops and the predicted market conditions, so as to further control the maturity time of the grain and cash crops.

[0027] 6. Taking into account the different planting costs of different grain and cash crops, as well as different forecast market conditions, combined with the calculation of the profits obtained from planting different crops, select grain and cash crops with high profits per unit time to obtain higher benefits; further, obtain historical meteorological data and historical market conditions in the grain and cash crop planting areas, obtain meteorological stability and market stability. If the historical meteorological data is unstable, it is more likely to reduce or even completely stop production. The probability of high yield, normal yield, reduced yield, and complete failure of grain and cash crops is predicted through a neural network model. At the same time, considering the market stability, the predicted profit and loss amount and probability of various grain and cash crops are obtained, and grain and cash crops with different risks are selected for reference based on their own risk resistance capabilities; further, it is also possible to consider predicting the increase in the yield of the grain and cash crops after installing the external execution equipment, and comprehensively calculate whether to purchase the external execution equipment based on the forecast market conditions and the purchase cost and use cost of the external execution equipment, as well as the scope of use and expected service life of the external execution equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0029] Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in an embodiment of the present application.

[0030] Figure 2 It is a schematic diagram of the functional modules of the system for guiding the integrated crop-crop and cashew planting based on meteorological data provided in an embodiment of the present application.

[0031] Figure 3 This is a module construction diagram of a system for guiding the integrated crop and cash crop planting based on meteorological data provided in an embodiment of the present application.

[0032] Figure 4 It is a flowchart of the steps of the method for guiding the integrated crop planting based on meteorological data provided in an embodiment of the present application.

[0033] Symbols in the figure: 1001 - processor, 1002 - communication bus, 1003 - user interface, 1004 - network interface, 1005 - memory. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0035] The solution of the present application is further described below in conjunction with the accompanying drawings.

[0036] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0037] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0038] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module and a data storage module.

[0039] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the system for guiding the composite planting of grain and economic crops based on meteorological data stored in the data storage module in the memory 1005 through the processor 1001, and executes the method for guiding the composite planting of grain and economic crops based on meteorological data provided in the embodiment of the present application.

[0040] Based on the aforementioned hardware operating environment and system architecture, in the first aspect of the present application, refer to Figure 2 and Figure 3 As shown in the figure, a system for guiding the integrated cropping of crops and cash crops based on meteorological data is provided, including: A data collection module collects historical meteorological data and real-time meteorological data of the target area, and obtains growth models of various types of grain and cash crops, wherein the growth models include growth conditions of grain and cash crops at various stages under various environmental data, and the historical meteorological data and the real-time meteorological data include temperature, precipitation and light; It should be noted that the food crops and cash crops include grain crops and cash crops. The growth needs and preferences of various food crops are collected through the Internet and agricultural books, and a growth model is further derived. The growth model is the growth conditions of the food crops in different growth stages under different environmental data, including rapid growth, normal growth, poor growth and death; the real-time meteorological data is collected in real time through satellite remote sensing, ground meteorological stations and drones, and a data fusion algorithm is used to generate a more accurate meteorological model; the historical meteorological data is obtained through local meteorological stations and Internet databases; It should be noted that the environmental data refers to the area where grain and cash crops grow, and the data that affects the growth of grain and cash crops, including temperature, humidity and light, while the real-time meteorological data has a relatively large range of influence; the environmental data corresponds to the real-time meteorological data, and the real-time meteorological data can greatly affect the environmental data, but the environmental data can be affected by human intervention, such as using agricultural equipment to water crops, resulting in a difference between the environmental data and the real-time meteorological data; A data processing module, which obtains future meteorological data by fusing the historical meteorological data with the real-time meteorological data; It should be noted that the weather forecast model is constructed through machine learning, including linear regression and random forest, and the historical meteorological data and the real-time meteorological data are analyzed and integrated to predict the future meteorological data; it also includes predicting future weather changes through atmospheric physical processes based on prior knowledge of atmospheric physics; A planting selection module matches the growth models of various grain and economic crops according to the future meteorological data, simulates the estimated growth conditions of various grain and economic crops, and selects grain and economic crops for planting according to the estimated growth conditions; The planting planning module obtains the estimated maturity time according to the estimated growth conditions of the grain and cash crops, obtains the future meteorological data of the estimated maturity time, and plans and selects the grain and cash crops to be planted in the future through the planting selection module.

[0041] In this embodiment, the growth model of each type of grain and economic crop and the future meteorological data are obtained in advance. Before planting, the future meteorological data is obtained by the data processing module. The future meteorological data changes with time, and the environmental data range suitable for the growth of each type of grain and economic crop also changes with the growth stage. The future meteorological data is input into the growth model of each type of grain and economic crop to obtain the estimated growth conditions of each type of grain and economic crop under the future meteorological data. The more the future meteorological data of each stage of the grain and economic crop conforms to its own suitable growth environment, the better the growth of the grain and economic crop, and the more suitable the future time is for planting the grain and economic crop; the input is input through the interface The estimated growth conditions of various grain and cash crops are obtained for planting selection, and the most suitable grain and cash crops to be grown under the future meteorological data are obtained through the growth model, so as to increase crop yield and avoid subjective restrictions on the selection of grain and cash crops. Before the grain and cash crops are harvested, the estimated maturity time can be obtained according to the estimated growth conditions. The estimated maturity time is a time period. Combined with the future meteorological data within the time period, a specific harvesting time suitable for harvesting is recommended. At the same time, various grain and cash crops are matched again according to the future meteorological data after the harvest. After the crops to be planted subsequently are selected in advance, preparatory work before planting is carried out to reduce the vacant time of land and improve the land utilization rate.

[0042] In one embodiment of the present application, it also includes an equipment control module, which identifies the growth stage of the grain and cash crops through an image acquisition device, obtains the data range that is most suitable for the growth of the grain and cash crops in each growth stage according to the growth model, and an external execution device adjusts the environmental data of the grain and cash crops according to the real-time meteorological data, so that each growth stage of the grain and cash crops is in the most suitable data range for growth, or closer to the most suitable data range for growth.

[0043] It should be noted that the image acquisition device is a device that can collect, analyze and identify images of grain and cash crops at their growth stages, including a monitoring camera; the external execution device is a device that adjusts environmental data in the growth area of ​​grain and cash crops, including an irrigation device, a spraying device and a heat preservation device; for example, the irrigation device can adjust the water content of the roots of grain and cash crops through piped water, the spraying device can adjust the humidity and temperature of the leaves by spraying water mist into the air, and the heat preservation device can adjust the temperature of grain and cash crops through heat preservation film and heating equipment in the greenhouse; In one embodiment of the present application, the equipment control module also includes a control update submodule, which obtains the controllable range of the environmental data of the available external execution device under each of the real-time meteorological data; when the future meteorological data matches the growth model of various types of grain and cash crops, the future meteorological data is combined with the controllable range of the external execution device to obtain the controllable meteorological data, and the controllable meteorological data makes the future meteorological data closer to the data range that is most suitable for the growth of various types of grain and cash crops at various growth stages; before planting, the planting selection module selects the controllable meteorological data to match the growth model of various types of grain and cash crops, simulates the estimated growth conditions, and selects grain and cash crops for planting; the planting planning module selects the controllable meteorological data to obtain the estimated maturity time during the growth of the grain and cash crops.

[0044] It should be noted that under different real-time meteorological data, the adjustable range of the external execution device is different. For example, when the real-time meteorological data is high temperature and drought, the irrigation device in the external execution device can adjust the soil moisture content in a larger range, but when the real-time meteorological data is high temperature and high humidity, the irrigation device in the external execution device can only adjust the soil moisture content in a relatively small range. Therefore, the range value of the environmental data of the grain and cash crop growing area that can be regulated by the external execution device under each real-time meteorological data is obtained; In one embodiment of the present application, a dynamic adjustment module is also included, which obtains the current growth stage of the grain and cash crops through the image acquisition device, obtains the difference with the estimated growth situation, and adjusts the estimated maturity time of the grain and cash crops in real time according to the difference; and generates a dynamic planning plan based on the future meteorological data adjusted in real time.

[0045] In this embodiment, the environmental data of the area where the grain and cash crops are grown is adjusted by the external device. When the real-time meteorological data makes the environmental data not within the data range that is most suitable for the growth of the grain and cash crops at each growth stage, the external execution device is controlled to make the grain and cash crops within the data range that is most suitable for their growth, or closer, throughout the growth process, thereby effectively increasing the yield of the grain and cash crops. Furthermore, since the environmental data of the area where the grain and cash crops are grown can be adjusted by the external execution device, the grain and cash crops are more suitable for growth under the adjustment of the environmental data by the external execution device. Therefore, the estimated growth of the grain and cash crops without considering the external execution device may be lower than the actual situation, and the expected harvest time may be later than the actual date. Therefore, the results obtained by the planting selection module in selecting seeds and the planting planning module in estimating the maturity time are more accurate when combined with the external execution device. At the same time, the adjustment of the environmental data by the external execution device may result in grain and cash crops that are closer to the optimal growth model, further increasing the land yield.

[0046] In one embodiment of the present application, the equipment control module also includes a pre-control submodule, which identifies the current growth stage of the grain and cash crops through the image acquisition device, obtains the data range that is most suitable for the growth of the grain and cash crops in the current growth stage, and when the external execution device adjusts the environmental data, obtains the time required for the future meteorological data to change to the environmental data range that is most suitable for the current growth stage of the grain and cash crops, as well as the consumption of various types of environmental data per unit time in the grain and cash crop area under different meteorological data, calculates and determines the control time and control amount of the external execution device, so that when the environmental data regulated by the external execution device is consumed to the edge value of the data range that is most suitable for the current growth stage of the grain and cash crops, the real-time meteorological data changes to the data range that is most suitable for the current growth stage of the grain and cash crops.

[0047] In this embodiment, it is taken into account that different external execution devices will generate different costs when used, such as power consumption, water consumption and equipment loss, and after the external execution device is adjusted, the environmental data will gradually change according to the real-time meteorological data and the consumption of grain and cash crops, and the change of meteorological data is continuous. Therefore, during the growth of grain and cash crops, while the environmental data is adjusted by the external execution device, the change of the real-time meteorological data is obtained, and the first time when the real-time meteorological data returns to the suitable growth range of grain and cash crops is obtained. At the same time, the second time when the environmental data after adjustment by the external execution device is consumed to the edge of the suitable growth range of grain and cash crops is calculated. When the first time is equal to the second time, the function of adjusting the environmental data of the external execution device is turned off. When the adjusted environmental data is consumed to the edge of the suitable growth range of grain and cash crops, the real-time meteorological data can just make the grain and cash crops in a suitable growth area, avoiding waste of resources and saving costs. Applicable scenarios include the irrigation device stopping water supply before raining and the temperature control device stopping heating before the temperature rises at noon.

[0048] In one embodiment of the present application, the device control module further includes: The disaster warning submodule performs a warning operation when the future meteorological data may cause a disaster. By estimating the extent of the disaster, the food and cash crops and / or the external execution equipment are processed in advance. The disaster includes natural disasters and biological disasters. The processing includes rushing to harvest, transferring and adding external execution equipment. The meteorological combination submodule combines the future meteorological data to recommend the time for fertilizing and applying pesticides to the grain and cash crops.

[0049] In this embodiment, taking into account that unsuitable weather may bring disasters to grain and cash crops, and even lead to crop reduction or complete failure, including heavy rain causing floods to wash away or drown crops, and high temperature and drought causing crop withering, therefore, when it is identified that the future weather data will cause crop disasters, an early warning operation is performed, and corresponding measures are taken according to the current state of the current crops and the degree of disaster, including early harvesting, transfer of seedlings, and even when grain and cash crops cannot be transferred due to severe disasters, the external execution equipment is transferred to minimize economic losses; at the same time, new information on the external execution equipment that can respond to disasters is provided according to the degree of the disaster, and new external execution equipment is purchased to respond to the disaster. For example, if the future weather data shows that there will be continuous rainfall in the next week, water in the field cannot be drained, and the roots of grain and cash crops cannot breathe and die, the excess water in the field can be drained by adding drainage pipes and other devices to protect the grain and cash crops from the disaster.

[0050] In one embodiment of the present application, a market suggestion module is also included, and the market suggestion module includes: The first suggestion submodule obtains the market conditions of various grain and cash crops for many years before planting, predicts the market changes during this year's harvest period through a large model, and obtains the future meteorological data to obtain the time required for the maturity of various grain and cash crops. The time required for maturity is combined with the predicted market changes during this year's harvest period to provide the grain and cash crop selection suggestions and sowing time suggestions; The second suggestion submodule obtains the market situation of the grain and cash crops before maturity, predicts the market changes in the future through a large model, and simultaneously obtains the future meteorological data to obtain the time when the grain and cash crops can be harvested. The time when the crops can be harvested is combined with the predicted market changes in the future to provide suggestions for the harvesting time of the grain and cash crops.

[0051] It should be noted that the large model includes a price forecasting framework for grain and cash crops, which includes supply determination, demand forecasting, and crop price forecasting functional blocks, combining supply, demand, and year data to forecast prices; In one embodiment of the present application, the market recommendation module also includes a profit budget sub-module, which obtains the planting costs of various types of grain and cash crops under the future meteorological data. The planting costs include seed costs, labor and land costs, fertilizer and pesticide costs, and the cost of using the external execution equipment. The planting costs are combined with the predicted market conditions during this year's harvest period to derive the profits of various types of grain and cash crops.

[0052] It should be noted that the labor land cost includes labor cost and land cost. The labor cost is the labor required for the grain and cash crops. For example, different crops require different weeding frequencies, and the corresponding labor required is different. The land cost is the time it takes for the grain and cash crops to occupy the land from sowing to maturity, resulting in different returns from two harvests a year and one harvest a year, and different corresponding costs. The external execution equipment use cost is the cost incurred by using the equipment. For example, when using irrigation equipment to irrigate rice during drought, it is necessary to consider the loss cost of the irrigation equipment and the cost of using water sources. In one embodiment of the present application, the market recommendation module also includes a risk prediction sub-model, which derives the probabilities of bumper yields, normal yields, reduced yields and total yields of various grain and economic crops based on the future meteorological data, obtains the predicted market conditions of various grain and economic crops during the harvest period this year, and derives the profit and loss amounts and probabilities of various grain and economic crops.

[0053] In this embodiment, taking into account the different planting costs of different grain and cash crops and the different predicted market conditions, combined with the calculation of the profits obtained from planting different crops, grain and cash crops with high profits per unit time are selected to obtain higher benefits; further, historical meteorological data and historical market conditions of the grain and cash crop planting areas are obtained to obtain meteorological stability and market stability. If the historical meteorological data is unstable, it is more likely to reduce or even completely stop production. The probabilities of high yield, normal yield, reduced yield and completely stop production of grain and cash crops are predicted through a neural network model. At the same time, considering the market stability, the predicted profit and loss amounts and probabilities of various types of grain and cash crops are obtained, and grain and cash crops with different risks are selected for reference based on their own risk resistance capabilities; further, it is also possible to consider predicting the increase in the yield of the grain and cash crops after installing the external execution equipment, and comprehensively calculate whether to purchase the external execution equipment based on the predicted market conditions and the purchase cost and use cost of the external execution equipment, as well as the scope of use and expected service life of the external execution equipment.

[0054] In the second aspect of the present application, reference is made to Figure 4 As shown in the figure, a method for guiding the integrated cropping of grain and economic crops based on meteorological data is provided, including: Collecting historical meteorological data and real-time meteorological data of the target area, and obtaining growth models of various types of grain and cash crops, wherein the growth models include growth conditions of grain and cash crops at various stages under various environmental data, and the historical meteorological data and the real-time meteorological data include temperature, precipitation and light; Obtain future meteorological data by fusing the historical meteorological data with the real-time meteorological data; Matching the growth models of various grain and economic crops according to the future meteorological data, simulating the estimated growth conditions of various grain and economic crops, and selecting grain and economic crops for planting according to the estimated growth conditions; According to the estimated growth conditions of the grain and cash crops, the estimated maturity time is obtained, the future meteorological data of the estimated maturity time is acquired, and the grain and cash crops to be planted in the future are planned and selected.

[0055] In one embodiment of the present application, the growth stage of the grain and cash crops is identified by an image acquisition device, and the data range most suitable for the growth of the grain and cash crops in each growth stage is obtained according to the growth model. An external execution device adjusts the environmental data of the grain and cash crops according to the real-time meteorological data, so that each growth stage of the grain and cash crops is in the most suitable data range for growth, or closer to the most suitable data range for growth.

[0056] In one embodiment of the present application, the adjustable range of the environmental data of the available external execution device under each of the real-time meteorological data is obtained; when the future meteorological data matches the growth model of various types of grain and cash crops, the future meteorological data is combined with the adjustable range of the external execution device to obtain the regulated meteorological data, and the regulated meteorological data relatively makes the future meteorological data closer to the data range that is most suitable for the growth of various types of grain and cash crops at various growth stages; before planting, the regulated meteorological data is selected to match the growth model of various types of grain and cash crops, the estimated growth conditions are simulated, and grain and cash crops are selected for planting; during the growth of the grain and cash crops, the regulated meteorological data is selected to obtain the estimated maturity time.

[0057] In one embodiment of the present application, the current growth stage of the grain and cash crops is acquired through the image acquisition device, and the difference with the estimated growth situation is obtained, and the estimated maturity time of the grain and cash crops is adjusted in real time according to the difference; and a dynamic planning plan is generated based on the future meteorological data adjusted in real time.

[0058] In one embodiment of the present application, the current growth stage of the grain and cash crop is identified by the image acquisition device, and the data range that is most suitable for the growth of the grain and cash crop in the current growth stage is obtained. When the external execution device adjusts the environmental data, the time required for the future meteorological data to change to the environmental data range that is most suitable for the current growth stage of the grain and cash crop, and the consumption of various types of environmental data per unit time in the grain and cash crop area under different meteorological data are obtained. The regulation time and regulation amount of the external execution device are calculated and determined, so that when the environmental data regulated by the external execution device is consumed to the edge value of the data range that is most suitable for the growth of the grain and cash crop in the current growth stage, the real-time meteorological data changes to the data range that is most suitable for the growth of the grain and cash crop in the current growth stage.

[0059] In one embodiment of the present application, when the future meteorological data may cause a disaster, an early warning operation is performed, and the grain and cash crops and / or the external execution equipment are processed in advance by estimating the degree of the disaster. The disaster includes natural disasters and biological disasters, and the processing includes rushing to harvest, transferring and adding external execution equipment; combined with the future meteorological data, the time for fertilizing and applying medicines to the grain and cash crops is recommended.

[0060] In one embodiment of the present application, the market conditions of various types of grain and cash crops for many years are obtained before planting, and the market changes in this year's harvest period are predicted through a large model. At the same time, the future meteorological data is obtained to obtain the time required for the maturity of various types of grain and cash crops. The time required for maturity is combined with the predicted market changes in this year's harvest period to provide the grain and cash crop selection suggestions and sowing time suggestions; The market situation of the grain and cash crops is obtained before maturity, and the market changes in the future are predicted through a large model. At the same time, the future meteorological data is obtained to obtain the time when the grain and cash crops can be harvested. The time when the crops can be harvested is combined with the predicted market changes in the future to provide harvesting time recommendations for the grain and cash crops.

[0061] In one embodiment of the present application, the planting costs of various types of grain and cash crops under the future meteorological data are obtained, and the planting costs include seed costs, labor and land costs, fertilizer and pesticide costs, and the cost of using the external execution equipment. The planting costs are combined with the predicted market conditions during this year's harvest period to derive the profits of various types of grain and cash crops.

[0062] In one embodiment of the present application, the probabilities of bumper, normal, reduced and zero yields of various types of grain and cash crops are obtained based on the future meteorological data, the forecast market conditions of various types of grain and cash crops during the harvest period this year are obtained, and the profit and loss amounts and probabilities of various types of grain and cash crops are obtained.

[0063] It should be noted that the specific implementation method of the method for guiding the integrated planting of grain and economic crops based on meteorological data in the embodiment of the present application refers to the specific implementation method of the system for guiding the integrated planting of grain and economic crops based on meteorological data proposed in the first aspect of the aforementioned embodiment of the present application, and will not be repeated here.

[0064] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that an article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of more restrictions, the elements defined by the sentence "includes..." do not exclude the existence of other identical elements in the article or device including the elements.

[0065] The system for guiding the integrated planting of grain and economic crops based on meteorological data is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the system for guiding the integrated planting of grain and economic crops based on meteorological data of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A system for guiding the integrated crop and cash cropping based on meteorological data, characterized in that: include: A data collection module collects historical meteorological data and real-time meteorological data of the target area, and obtains growth models of various types of grain and cash crops, wherein the growth models include growth conditions of grain and cash crops at various stages under various environmental data, and the historical meteorological data and the real-time meteorological data include temperature, precipitation and light; A data processing module, which obtains future meteorological data by fusing the historical meteorological data with the real-time meteorological data; A planting selection module matches the growth models of various grain and economic crops according to the future meteorological data, simulates the estimated growth conditions of various grain and economic crops, and selects grain and economic crops for planting according to the estimated growth conditions; The planting planning module obtains the estimated maturity time according to the estimated growth conditions of the grain and cash crops, obtains the future meteorological data of the estimated maturity time, and plans and selects the grain and cash crops to be planted in the future through the planting selection module.

2. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 1, characterized in that: It also includes an equipment control module, which identifies the growth stage of the grain and cash crops through an image acquisition device, obtains the most suitable data range for the growth of the grain and cash crops in each growth stage according to the growth model, and an external execution device adjusts the environmental data of the grain and cash crops according to the real-time meteorological data, so that each growth stage of the grain and cash crops is in the most suitable data range for growth, or closer to the most suitable data range for growth.

3. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 2, characterized in that: The equipment control module also includes a control update submodule, which obtains the controllable range of the environmental data of the available external execution device under each of the real-time meteorological data; when the future meteorological data matches the growth model of various types of grain and cash crops, the future meteorological data is combined with the controllable range of the external execution device to obtain the controllable meteorological data, and the controllable meteorological data makes the future meteorological data closer to the data range that is most suitable for the growth of various types of grain and cash crops at various growth stages; before planting, the planting selection module selects the controllable meteorological data to match the growth model of various types of grain and cash crops, simulates the estimated growth conditions, and selects grain and cash crops for planting; the planting planning module selects the controllable meteorological data to obtain the estimated maturity time during the growth of the grain and cash crops.

4. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 2, characterized in that: It also includes a dynamic adjustment module, which obtains the current growth stage of the grain and cash crops through the image acquisition device, obtains the difference with the estimated growth situation, and adjusts the estimated maturity time of the grain and cash crops in real time according to the difference; A dynamic planning scheme is generated according to the future meteorological data adjusted in real time.

5. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 2, characterized in that: The equipment control module also includes a pre-control submodule, which identifies the current growth stage of the grain and cash crops through the image acquisition device, obtains the data range that is most suitable for the growth of the grain and cash crops in the current growth stage, and when the external execution device adjusts the environmental data, obtains the time required for the future meteorological data to change to the environmental data range that is most suitable for the current growth stage of the grain and cash crops, as well as the consumption of various types of environmental data per unit time in the grain and cash crop area under different meteorological data, calculates and determines the control time and control amount of the external execution device, so that when the environmental data regulated by the external execution device is consumed to the edge value of the data range that is most suitable for the growth of the grain and cash crops in the current growth stage, the real-time meteorological data changes to the data range that is most suitable for the growth of the grain and cash crops in the current growth stage.

6. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 5, characterized in that: The device control module also includes: The disaster warning submodule performs a warning operation when the future meteorological data may cause a disaster. By estimating the extent of the disaster, the food and cash crops and / or the external execution equipment are processed in advance. The disaster includes natural disasters and biological disasters. The processing includes rushing to harvest, transferring and adding external execution equipment. The meteorological combination submodule combines the future meteorological data to recommend the time for fertilizing and applying pesticides to the grain and cash crops.

7. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to any one of claims 2 to 6, characterized in that: It also includes a market suggestion module, which includes: The first suggestion submodule obtains the market conditions of various grain and cash crops for many years before planting, predicts the market changes during this year's harvest period through a large model, and obtains the future meteorological data to obtain the time required for the maturity of various grain and cash crops. The time required for maturity is combined with the predicted market changes during this year's harvest period to provide the grain and cash crop selection suggestions and sowing time suggestions; The second suggestion submodule obtains the market situation of the grain and cash crops before maturity, predicts the market changes in the future through a large model, and simultaneously obtains the future meteorological data to obtain the time when the grain and cash crops can be harvested. The time when the crops can be harvested is combined with the predicted market changes in the future to provide suggestions for the harvesting time of the grain and cash crops.

8. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 7, characterized in that: The market recommendation module also includes a profit budget submodule, which obtains the planting costs of various types of grain and cash crops under the future meteorological data. The planting costs include seed costs, labor and land costs, fertilizer and pesticide costs, and the cost of using the external execution equipment. The planting costs are combined with the predicted market conditions during this year's harvest period to derive the profits of various types of grain and cash crops.

9. The system for guiding the integrated crop-crop and crop-economy planting based on meteorological data according to claim 8, characterized in that: The market recommendation module also includes a risk prediction sub-model, which obtains the probability of bumper, normal, reduced and zero yields of various grain and economic crops based on the future meteorological data, obtains the predicted market conditions of various grain and economic crops during the harvest period this year, and obtains the profit and loss amounts and probabilities of various grain and economic crops.

10. A method for guiding the integrated crop and cash cropping based on meteorological data, characterized in that: include: Collecting historical meteorological data and real-time meteorological data of the target area, and obtaining growth models of various types of grain and cash crops, wherein the growth models include growth conditions of grain and cash crops at various stages under various environmental data, and the historical meteorological data and the real-time meteorological data include temperature, precipitation and light; Obtaining future meteorological data by fusing the historical meteorological data with the real-time meteorological data; Matching the growth models of various grain and economic crops according to the future meteorological data, simulating the estimated growth conditions of various grain and economic crops, and selecting grain and economic crops for planting according to the estimated growth conditions; According to the estimated growth conditions of the grain and cash crops, the estimated maturity time is obtained, the future meteorological data of the estimated maturity time is acquired, and the grain and cash crops to be planted in the future are planned and selected.

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