Meteorological early warning method and system and related equipment

Abstract

The invention discloses a meteorological early warning method, a meteorological early warning system and related equipment. Comprising the following steps: acquiring crop information, acquiring growth data of crops according to the crop information, and substituting the growth data into a crop growth model to calculate a simulated growth model of the crops; obtaining future environment data of the crops based on the simulated growth model, wherein the future environment data comprises water demand, illumination intensity and photosynthetic radiation of the crops; acquiring meteorological data, judging whether the meteorological data accords with meteorological disaster data according to a meteorological disaster rule, and if yes, generating meteorological disaster early warning information; if not, establishing a meteorological influence prediction model according to the meteorological data and the simulated growth model, judging whether the meteorological data can influence the crops or not based on the meteorological influence prediction model, and if yes, generating meteorological disaster early warning information; and sending the meteorological disaster early warning information to a preset department according to a preset rule. According to the invention, the meteorological data and the real-time meteorological data of the crop area can be combined, and the accuracy and rapid early warning of meteorological early warning are realized.

Description

Technical Field

[0001] The present invention relates to the field of meteorological warning, and in particular to a meteorological warning method, system and related equipment. Background Art

[0002] Agricultural meteorological disasters refer to natural disasters caused by meteorological conditions that have an adverse impact on agricultural production. Such disasters can be divided into many categories according to their causes: those caused by abnormal temperature, such as heat damage, freezing damage, frost, cold damage to tropical crops, and low temperature damage; droughts, floods, snow damage and hail damage caused by water imbalance; wind damage caused by excessive wind; and dry hot winds, cold rains and freezing waterlogging caused by the interweaving of multiple meteorological factors. It is worth noting that the key to defining agricultural meteorological disasters lies in their direct impact on agricultural production, which is essentially different from simple meteorological phenomena or processes. For example, weather phenomena such as cold waves, late spring cold, cold dew winds and continuous rain may be just ordinary climate changes in meteorology, but when they pose a threat to key crops such as wheat and rice, they are transformed into destructive agricultural meteorological disasters.

[0003] Agricultural meteorological disasters refer to natural disasters caused by meteorological conditions that have an adverse impact on agricultural production. Such disasters can be divided into many categories according to their causes: those caused by abnormal temperature, such as heat damage, freezing damage, frost, cold damage to tropical crops, and low temperature damage; droughts, floods, snow damage and hail damage caused by water imbalance; wind damage caused by excessive wind; and dry hot winds, cold rains and freezing waterlogging caused by the interweaving of multiple meteorological factors. It is worth noting that the key to defining agricultural meteorological disasters lies in their direct impact on agricultural production, which is essentially different from simple meteorological phenomena or processes. For example, weather phenomena such as cold waves, late spring cold, cold dew winds and continuous rain may be just ordinary climate changes in meteorology, but when they pose a threat to key crops such as wheat and rice, they are transformed into destructive agricultural meteorological disasters.

[0004] At present, although the meteorological early warning system has been established, it generally faces two major challenges: first, the prediction accuracy is limited, and it is difficult to accurately capture the subtle changes in disastrous weather; second, the reaction speed is lagging, and it is unable to respond quickly to emergency needs in agricultural production. What is particularly critical is that most of these systems fail to deeply integrate the physiological parameters of crops with meteorological conditions, resulting in a lack of sufficient intelligence and pertinence when predicting the specific impacts on crops. Therefore, in order to more effectively prevent and mitigate agricultural meteorological disasters, we need to continue to explore and innovate, and develop an intelligent early warning system that can closely combine crop growth characteristics with real-time meteorological data to achieve accurate prediction and rapid response to agricultural meteorological disasters. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problems of low accuracy and slow response speed of the existing weather forecast system. The present invention provides a weather early warning method, system and related equipment, which can realize dynamic forecast and intelligent early warning by combining meteorological data and crop physiological parameters.

[0006] In order to solve the above technical problems, the embodiment of the present invention discloses a weather warning method, comprising:

[0007] Acquire crop information, obtain crop growth data based on the crop information, and bring the growth data into the crop growth model to calculate and obtain a simulated growth model of the crop;

[0008] Based on the simulated growth model, the future environmental data of crops are obtained, including the water demand, light intensity and photosynthetic radiation of crops;

[0009] Obtain meteorological data, and determine whether the meteorological data meets the meteorological disaster data according to the meteorological disaster rules. If so, generate meteorological disaster warning information;

[0010] If not, a meteorological impact prediction model is established based on meteorological data and simulated growth models. Based on the 4 meteorological impact prediction models, it is determined whether the meteorological data will affect crops. If so, meteorological disaster warning information is generated;

[0011] When meteorological disaster warning information is generated, it will be sent to the preset department according to the preset rules.

[0012] By adopting the above technical solution, it is possible to combine meteorological data and crop physiological parameters to achieve dynamic forecasting and intelligent early warning.

[0013] According to another specific embodiment of the present invention, the embodiment of the present invention discloses obtaining crop growth data according to crop information, and bringing the growth data into a crop growth model to calculate a simulated growth model of the crop, including:

[0014] Get crop information, identify crop names and images,

[0015] Acquire crop growth data based on crop images, including crop spacing, current growth period, and crop height;

[0016] Obtain historical crop growth data and establish a crop growth model based on the historical growth data. The historical growth data includes crop field management data, historical meteorological data, historical growth period and yield trait observation data;

[0017] The growth data is brought into the crop growth model to calculate the simulated growth model of the crop.

[0018] According to another specific embodiment of the present invention, the embodiment of the present invention discloses that after the growth data is brought into the crop growth model to calculate the simulated growth model of the crop, the method further includes:

[0019] The growth data is obtained again after a preset time interval, and the simulated growth model is updated based on the growth data obtained again.

[0020] According to another specific embodiment of the present invention, the embodiment of the present invention discloses obtaining real-time meteorological data of all meteorological stations within a regional range and real-time meteorological monitoring data of crop areas based on the national comprehensive meteorological information sharing platform interface to obtain meteorological data.

[0021] According to another specific embodiment of the present invention, the embodiment of the present invention discloses establishing a meteorological impact prediction model based on meteorological data and a simulated growth model, including:

[0022] Obtain meteorological data, bring the meteorological data into the simulation growth model, and obtain the meteorological impact prediction model under the influence of meteorological data.

[0023] According to another specific embodiment of the present invention, the embodiment of the present invention discloses judging whether meteorological data will affect crops based on a meteorological impact prediction model, and if so, generating meteorological disaster warning information, including:

[0024] According to the current growth stage of the crop, the optimal meteorological data of the crop in the current growth stage is obtained, and the preset yield of the crop is obtained based on the prediction of the optimal meteorological data;

[0025] Based on the meteorological impact model, the predicted crop yield is predicted to determine whether the predicted yield is within the preset yield threshold range. If not, meteorological disaster warning information is generated according to the disaster type.

[0026] According to another specific embodiment of the present invention, the embodiment of the present invention discloses updating meteorological data at preset time intervals, and synchronously updating the meteorological impact prediction model.

[0027] The embodiment of the present invention further discloses a weather warning system, comprising:

[0028] The crop identification module obtains crop information, obtains crop growth data based on the crop information, and brings the growth data into the crop growth model to calculate and obtain a simulated growth model of the crop;

[0029] Growth model building module, which obtains the future environmental data of crops based on the simulated growth model. The future environmental data includes the water requirement, light intensity and photosynthetic radiation of crops;

[0030] The meteorological warning module obtains meteorological data and determines whether the meteorological data meets the meteorological disaster data according to the meteorological disaster rules. If so, it generates meteorological disaster warning information;

[0031] If not, a meteorological impact prediction model is established based on meteorological data and a simulated growth model. Based on the meteorological impact model, the predicted yield of the crop is predicted to determine whether the predicted yield is within the threshold range of the preset yield. If not, meteorological disaster warning information is generated according to the disaster type;

[0032] The information sending module, when generating meteorological disaster warning information, sends the meteorological disaster warning information to the preset department according to the preset rules.

[0033] An embodiment of the present invention further discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the meteorological warning method described in the above specification is implemented.

[0034] The embodiments of the present invention further disclose a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the meteorological warning method described in the above specification is implemented.

[0035] The beneficial effect of this application is: combining meteorological data with real-time meteorological data of crop areas to achieve accurate and rapid meteorological warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart showing a meteorological warning method according to an embodiment of the present invention;

[0037] Figure 2 A schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may extend based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0039] It should be noted that in this specification, similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0041] Reference Figure 1 , the present application provides a weather warning method, comprising:

[0042] Acquire crop information, obtain crop growth data based on the crop information, and bring the growth data into the crop growth model to calculate and obtain a simulated growth model of the crop;

[0043] In this embodiment, taking the crop meteorological warning in a unit area as an example, crop information is obtained. The crop information is obtained by video surveillance, personnel reporting, and drone video acquisition, etc. The crop information in the unit area is obtained, and the current growth data of each crop is obtained based on the latest data of each crop. The growth data is brought into the crop growth model of each crop to generate a simulated growth model of each crop. The simulated growth model is used to obtain the optimal growth curve of the crop based on the current growth data of the crop and the historical growth data.

[0044] Based on the simulated growth model, the optimal growth curve of the crop is obtained, and the future environmental data required by the crop under the optimal growth curve is obtained. The future environmental data includes the water requirement, light intensity and photosynthetic radiation of the crop;

[0045] Obtain meteorological data, and determine whether the meteorological data meets the meteorological disaster data according to the meteorological disaster rules. If so, generate meteorological disaster warning information;

[0046] The meteorological disaster rules are based on national standards, industry standards, and literature on agricultural meteorological disaster classification and disaster severity evaluation, combined with field data from local agricultural science and technology departments, to classify and summarize crop meteorological disasters to form a series of meteorological disaster rules for various crops and growth and development stages.

[0047] By comparing meteorological data with meteorological disaster rules, if the meteorological data conforms to meteorological disaster data, it is directly judged as meteorological disaster data, and meteorological disaster warning information is generated. The meteorological disaster warning information is sent to local departments or farmers to remind them to take emergency measures in time.

[0048] If not, a meteorological impact prediction model is established based on meteorological data and simulated growth models, and whether the meteorological data will affect crops is determined based on the meteorological impact prediction model. If so, meteorological disaster warning information is generated;

[0049] If the meteorological data does not match the meteorological disaster data, the meteorological data will be combined with the simulated growth model to establish a meteorological impact prediction model. Based on the meteorological data and the simulated impact data of crops, it is determined whether the meteorological data will affect crop growth. If so, it is determined to be meteorological disaster data, and meteorological disaster warning information is generated. The meteorological disaster warning information is sent to local departments or farmers to remind them to take emergency measures in time.

[0050] According to the current growth stage of the crop, for example, taking corn as an example, the growth period of corn is divided into: sowing period, emergence period, jointing period, tasseling and silking period and filling and maturity period. If corn is in the growth stage of tasseling and silking period, the number of corn ears, the number of rows of grains in the ears, and the number of grains in the ears are all formed at this time. The growth environment required for corn tasseling and silking period is sunny, and the suitable temperature is 24-26℃. If it is cloudy and rainy at this time, it will have a great impact on the heading, flowering and filling of corn. Therefore, if the meteorological data received during the tasseling and silking period of corn is cloudy and rainy, the meteorological data at this time will affect the growth of crops for corn, and it needs to be determined as meteorological disaster data. Meteorological data needs to be sent to local departments or farmers to remind them to take emergency measures in time.

[0051] In this embodiment, the above-mentioned meteorological warning method can also provide warnings for multiple crops at the same time. According to the different growth periods of different crops, the meteorological data suitable for the growth of each crop in each growth stage is obtained, and respective simulation growth models are established respectively. Then, combined with the meteorological impact prediction model, different meteorological assessment and warning are carried out for each crop respectively, so as to realize warnings in each critical growth period of each crop. Real-time warnings can be realized in critical periods of crops, so that staff can receive key information in time and take timely measures.

[0052] At the same time, the above-mentioned meteorological warning method can also act on fruit trees at the same time. Its implementation method is the same as the above-mentioned crop warning and will not be described in detail.

[0053] In an implementable embodiment, key meteorological disaster indicators associated with each crop that are likely to cause crop damage during the growth period can also be collected and organized, such as the influence of air temperature, the influence of soil temperature, the influence of soil volume moisture content, the influence of late spring cold, the influence of cold dew wind, and the influence of air temperature. Then, upper and lower limit warning values ​​for each crop's susceptibility to various disasters during the growth period can be constructed. In this embodiment, for example, by setting meteorological and soil sensors in the farmland, real-time temperature and humidity data of the farmland, as well as data such as the temperature of the farmland soil and the volume moisture content of the soil can be obtained in a timely manner. Combined with local historical meteorological data, such as whether there are climate influences such as late spring cold and cold dew wind, more accurate predictions of future weather forecasts can be obtained.

[0054] As shown in Table 1, the upper and lower warning values ​​of each crop's susceptibility to various disasters during its growth period. There may be differences in thresholds between different crops. The staff can adjust the thresholds according to the different crops. Table 1 takes corn and rice as examples.

[0055] Table 1

[0056]

[0057] It can also be divided into different disaster levels according to the degree of disaster. When the value of the sensor or agricultural weather forecast reaches the set threshold condition, the corresponding disaster category and disaster degree will be output, and different levels of automatic early warning prompts will be issued.

[0058] In an practicable embodiment, the growth data of the crop is obtained according to the crop information, and the growth data is brought into the crop growth model to calculate and obtain a simulated growth model of the crop, including:

[0059] Get crop information, identify crop names and images,

[0060] Acquire crop growth data based on crop images, including crop spacing, current growth period, and crop height;

[0061] Obtain historical crop growth data and establish a crop growth model based on the historical growth data. The historical growth data includes crop field management data, historical meteorological data, historical growth period and yield trait observation data;

[0062] The growth data is brought into the crop growth model to calculate the simulated growth model of the crop.

[0063] In this embodiment, the crop growth model is used to obtain a predicted growth model of the crop based on historical growth data according to the growth data of the crop. First, the crop information to be predicted is obtained, and the image information or crop name of the crop is obtained based on video monitoring, personnel reporting and drone video acquisition. According to the provided crop image, the crop spacing, current growth stage, crop height and other data can be identified and obtained, and these data are input into the system. Then, according to the historical growth data of the crop, the historical growth data includes crop field management data, historical meteorological data, crop growth and yield and other observation data, which are brought into the crop growth model to calculate and obtain a simulated growth model of the crop.

[0064] In an practicable embodiment, the embodiment of the present invention discloses that after the growth data is brought into the crop growth model to calculate the simulated growth model of the crop, the method further includes:

[0065] The growth data is obtained again after a preset time interval, and the simulated growth model is updated based on the growth data obtained again.

[0066] In this embodiment, in order to make the simulated growth model closer to the actual crops, the crop growth data needs to be obtained again after a preset time interval to update the simulated growth model, which can make the simulated growth model closer to the actual crops and facilitate the use of the meteorological impact prediction model to determine whether the meteorological data will affect crop growth.

[0067] In an implementable embodiment, the implementation of the present invention discloses obtaining real-time meteorological data of all meteorological stations within a region and real-time meteorological monitoring data of crop areas based on a national comprehensive meteorological information sharing platform interface to obtain meteorological data.

[0068] In this embodiment, in order to obtain meteorological data of the crop area more accurately, while obtaining real-time meteorological data of all meteorological stations within the region based on the national comprehensive meteorological information sharing platform interface, real-time meteorological monitoring data of the crop area is also required. Because the current meteorological early warning system usually faces low accuracy, the main reason is that the current meteorological data source is for large areas and spatial ranges, high altitude, and macroclimate data. The transmission process from meteorological data collection, processing to information release is long. For crop areas, meteorological data usually have low prediction accuracy and slow warning response speed. By obtaining real-time meteorological monitoring data in the crop area and obtaining near-ground data of the ground microclimate in the crop area, the ground and underground data can be obtained in real time at the same time, and directly transmitted to the system through the network and cloud server. It has the characteristics of fast response and accurate acquisition of ground and underground data. It can also better verify and correct the shortcomings of current meteorological early warnings, which is a better optimization and mutual complement of current meteorological early warnings.

[0069] In an practicable embodiment, the embodiment of the present invention discloses establishing a meteorological impact prediction model based on meteorological data and a simulated growth model, including:

[0070] Obtain meteorological data, bring the meteorological data into the simulation growth model, and obtain the meteorological impact prediction model under the influence of meteorological data.

[0071] In this embodiment, the air temperature and humidity, rainfall and soil temperature and humidity obtained by the meteorological sensor and the soil sensor are used, and the real-time meteorological data of all meteorological stations in the region are obtained based on the national comprehensive meteorological information sharing platform interface, and are brought into the simulation growth model to generate a meteorological impact prediction model under the influence of meteorological data. The disaster meteorological elements that affect crops include:

[0072] High temperature disasters: including mild heat damage, moderate heat damage and severe heat damage in various growth stages of crops, as well as the setting of early warning values ​​for various types of heat damage;

[0073] Low temperature and chilling damage: including the warning value settings for each growth period of crops (mild chilling damage, moderate chilling damage, and severe chilling damage) and various types of heat damage;

[0074] Waterlogging: severe waterlogging, including daily rainfall, soil volume moisture content, and warning value settings under this type;

[0075] Frost: refers to spring frost, which is divided into light frost, moderate frost, heavy frost, and warning value settings for each type of frost.

[0076] Among them, meteorological sensors mainly monitor air temperature, humidity and rainfall; soil sensors mainly monitor soil temperature and humidity.

[0077] As shown in Table 2, the meteorological data and soil data monitored by the meteorological sensor.

[0078] Table 2

[0079]

[0080] In an implementable embodiment, whether the meteorological data will affect crops is determined based on the meteorological impact prediction model, and if so, meteorological disaster warning information is generated, including:

[0081] According to the current growth stage of the crop, the optimal meteorological data of the crop in the current growth stage is obtained, and the preset yield of the crop is obtained based on the prediction of the optimal meteorological data;

[0082] Based on the meteorological impact model, the predicted crop yield is predicted to determine whether the predicted yield is within the preset yield threshold range. If not, meteorological disaster warning information is generated according to the disaster type.

[0083] The meteorological data is updated at preset intervals, and the meteorological impact prediction model is updated synchronously.

[0084] In this embodiment, the specific way in which the meteorological impact prediction model determines whether meteorological data will affect crops is that, first, based on the current growth stage of the crops, the optimal meteorological data of the crops in the current growth stage is obtained through a predictive growth model. The optimal meteorological data includes the numerical range of the optimal air temperature and humidity and the numerical range of the optimal soil moisture content in the crop area based on historical data in the current growth stage. The estimated value of the optimal preset yield that the crops can achieve is predicted based on the optimal meteorological data.

[0085] Then, based on the meteorological impact prediction model, the actual meteorological data of the crop area is obtained. Under the influence of the meteorological impact prediction model, the estimated value of the crop's future predicted yield is calculated. The predicted yield is compared with the estimated yield to determine whether the predicted yield is within the threshold range of the estimated yield. The threshold range is usually 80%-100%. If the predicted yield is not within the threshold range, a corresponding disaster warning is issued based on the detected disaster type.

[0086] In an implementable embodiment, the meteorological impact prediction model can also directly determine whether the acquired meteorological data is disaster data according to national standards, industry standards and agricultural meteorological disaster classification. If so, a disaster warning is directly issued.

[0087] The embodiment of the present invention further discloses a weather warning system, comprising:

[0088] The crop identification module obtains crop information, obtains crop growth data based on the crop information, and brings the growth data into the crop growth model to calculate and obtain a simulated growth model of the crop;

[0089] Growth model building module, which obtains the future environmental data of crops based on the simulated growth model. The future environmental data includes the water requirement, light intensity and photosynthetic radiation of crops;

[0090] The meteorological warning module obtains meteorological data and determines whether the meteorological data meets the meteorological disaster data according to the meteorological disaster rules. If so, it generates meteorological disaster warning information;

[0091] If not, a meteorological impact prediction model is established based on meteorological data and simulated growth models, and whether the meteorological data will affect crops is determined based on the meteorological impact prediction model. If so, meteorological disaster warning information is generated;

[0092] The information sending module, when generating meteorological disaster warning information, sends the meteorological disaster warning information to the preset department according to the preset rules.

[0093] In this embodiment, taking the crop meteorological warning in a unit area as an example, the crop identification module obtains crop information, obtains the crop information in the unit area, and obtains the current growth data of each crop based on the latest data of each crop. The crop identification module brings the growth data into the growth model establishment module, and the growth model establishment module establishes a crop growth model for each crop and generates a simulated growth model for each crop. The simulated growth model is used to obtain the optimal growth curve of the crop based on the current growth data of the crop and the historical growth data.

[0094] The meteorological disaster rules are based on national standards, industry standards, and literature on agricultural meteorological disaster classification and disaster severity evaluation, combined with field data from local agricultural science and technology departments, to classify and summarize crop meteorological disasters to form a series of meteorological disaster rules for various crops and growth and development stages.

[0095] The meteorological warning module compares meteorological data with meteorological disaster rules. If the meteorological data meets the meteorological disaster data, it will be directly judged as meteorological disaster data and generate meteorological disaster warning information. The information sending module will send the meteorological disaster warning information to local departments or farmers to promptly remind them to take emergency measures in response.

[0096] If not, the meteorological warning module establishes a meteorological impact prediction model based on the meteorological data and the simulated growth model, and determines whether the meteorological data will affect the crops based on the meteorological impact prediction model. If so, meteorological disaster warning information is generated;

[0097] If the meteorological data does not match the meteorological disaster data, the meteorological warning module will compare the meteorological data with the simulated growth model to establish a meteorological impact prediction model. Based on the meteorological data and the simulated impact data of the crops, it will be determined whether the meteorological data will affect the growth of crops. If so, it will be determined as meteorological disaster data and generate meteorological disaster warning information. The information sending module will send the meteorological disaster warning information to local departments or farmers to remind them to take emergency measures in time.

[0098] An embodiment of the present invention further discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the weather warning method described in the above specification is implemented.

[0099] The embodiments of the present invention further disclose a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the meteorological warning method described in the above specification is implemented.

[0100] like Figure 2 As shown, in this embodiment, it shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, and the electronic device may be a terminal or a server, etc. Specifically:

[0101] The electronic device may include components such as a processor 101 with one or more processing cores, a memory 102 with one or more computer-readable storage media, a power supply 103, and an input unit 104. Those skilled in the art will appreciate that Figure 2 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0102] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically in this embodiment, the processor 101 in the electronic device will load the executable file corresponding to the process of one or more applications into the memory 102 according to the following instructions, and the processor 101 will run the application stored in the memory 102, thereby realizing various functions as follows: the control system runs the test board at a first frequency, the test board interacts with the connector at a second frequency, and sends the operation data of the connector under the second frequency to the control system; wherein the first frequency is greater than the second frequency

[0103] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0104] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0105] To this end, an embodiment of the present application provides a computer-readable storage medium, which stores multiple instructions that can be loaded by a processor to execute the steps in any memory component testing device, method and related equipment provided in the embodiment of the present application.

[0106] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0107] Since the instructions stored in the computer-readable storage medium can execute the steps in any device management method and related equipment provided in the embodiments of the present application, the beneficial effects that can be achieved by any memory component testing device, method and related equipment provided in the embodiments of the present application can be achieved. Please see the previous embodiments for details and will not be repeated here.

[0108] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method provided in various optional implementations of the above-mentioned memory component-based test device.

[0109] Although the present invention has been illustrated and described with reference to certain preferred embodiments of the present invention, it should be understood by those skilled in the art that the above is a further detailed description of the present invention in conjunction with specific embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. Those skilled in the art may make various changes in form and details, including making several simple deductions or substitutions, without departing from the spirit and scope of the present invention.

Claims

1. A weather warning method, characterized in that: include: Acquire crop information, acquire crop growth data according to the crop information, and bring the growth data into a crop growth model to calculate and obtain a simulated growth model of the crop; Obtaining future environmental data of the crop based on the simulated growth model, wherein the future environmental data includes water requirement, light intensity and photosynthetic radiation of the crop; Acquire meteorological data, and determine whether the meteorological data meets the meteorological disaster data according to the meteorological disaster rules, and if so, generate meteorological disaster warning information; If not, a meteorological impact prediction model is established according to the meteorological data and the simulated growth model, and whether the meteorological data will affect the crop is determined based on the meteorological impact prediction model, and if so, meteorological disaster warning information is generated; After the meteorological disaster warning information is generated, the meteorological disaster warning information is sent to a preset department according to a preset rule.

2. The weather warning method according to claim 1, characterized in that: The step of acquiring the growth data of the crop according to the crop information and bringing the growth data into a crop growth model to calculate and obtain a simulated growth model of the crop includes: obtaining the crop information, identifying the name and image of the crop, Acquire the crop growth data according to the crop image, wherein the growth data includes crop plant-row spacing, current growth period, and crop height; Acquiring historical growth data of the crop, and establishing the crop growth model based on the historical growth data, wherein the historical growth data includes observation data such as crop field management data, historical meteorological data, historical growth period and yield traits; The growth data is brought into the crop growth model to calculate and obtain a simulated growth model of the crop.

3. The weather warning method according to claim 2, characterized in that: After the growth data is brought into the crop growth model to calculate and obtain the simulated growth model of the crop, the method further includes: The growth data is acquired again after a preset time interval, and the simulated growth model is updated based on the growth data acquired again.

4. The weather warning method according to claim 1, characterized in that: Based on the national comprehensive meteorological information sharing platform interface, real-time meteorological data of all meteorological stations within the region and real-time meteorological monitoring data of the crop area are obtained to obtain the meteorological data.

5. The weather warning method according to claim 1, characterized in that: The step of establishing a meteorological impact prediction model based on the meteorological data and the simulated growth model comprises: The meteorological data is obtained, the meteorological data is brought into the simulated growth model, and the meteorological impact prediction model under the influence of the meteorological data is obtained.

6. The weather warning method according to claim 1, characterized in that: The step of judging whether the meteorological data will affect the crops based on the meteorological impact prediction model, and if so, generating meteorological disaster warning information includes: According to the current growth stage of the crop, the optimal meteorological data of the crop in the current growth stage is obtained, and the preset yield of the crop is predicted based on the optimal meteorological data; Based on the meteorological impact model, the predicted yield of the crop is predicted, and it is determined whether the predicted yield is within the threshold range of the preset yield. If not, meteorological disaster warning information is generated according to the disaster type.

7. The weather warning method according to claim 1, characterized in that: The meteorological data is updated at preset time intervals, and the meteorological impact prediction model is updated synchronously.

8. A weather warning system, characterized in that: include: A crop identification module, which obtains crop information, obtains crop growth data according to the crop information, and brings the growth data into a crop growth model to calculate and obtain a simulated growth model of the crop; A growth model building module, which obtains future environmental data of the crop based on the simulated growth model, wherein the future environmental data includes water demand, light intensity and photosynthetic radiation of the crop; A meteorological warning module obtains meteorological data and determines whether the meteorological data meets the meteorological disaster data according to the meteorological disaster rules. If so, a meteorological disaster warning information is generated; If not, a meteorological impact prediction model is established according to the meteorological data and the simulated growth model, and whether the meteorological data will affect the crop is determined based on the meteorological impact prediction model, and if so, meteorological disaster warning information is generated; The information sending module, after generating the meteorological disaster warning information, sends the meteorological disaster warning information to the preset department according to the preset rules.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the weather warning method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the weather warning method according to any one of claims 1 to 7 is implemented.

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