CN115409254A - Meteorological prediction method, device, medium and equipment - Google Patents
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Abstract
The application relates to a meteorological prediction method, a device, a medium and equipment, which comprise the following steps: acquiring temperature data information at a moment within a preset number of days; performing Fourier fitting according to the temperature data information at the moment within the preset days to obtain a Fourier fitting model; and predicting temperature data at a predicted moment of a future preset time based on the Fourier fitting model. According to the method and the device, the temperature prediction of the future preset time can be realized through the Fourier fitting model under the condition that a large amount of historical meteorological data are absent in a special geographic area, namely, the meteorological data are less, so that the influence of severe weather on plant growth is reduced.
Description
Technical Field
The present application relates to the field of weather prediction, and in particular, to a weather prediction method, apparatus, medium, and device.
Background
The weather forecast is to use modern scientific technology to forecast the state of the earth atmosphere at a certain place in the future. The meteorological department can comprehensively analyze the collected large amount of meteorological data to predict future weather phenomena. Weather prediction is an important means for weather work serving national economy and national defense construction, and especially plays an important role in predicting disastrous weather, protecting lives and properties of people, promoting economic development and the like.
In many places of China, soil is fertile, sunlight and rainwater are sufficient due to special geographical positions of the plants, and the plants are suitable for growth of some plants, but the special geographical positions are often accompanied by severe weather such as high temperature and frost, and adverse effects are generated on growth of seedling stages of the plants, periodic growth and development of the plants, yield and quality of the plants and the like. However, in the same special geographical positions, the influence degrees of the same region by the meteorological disasters are different due to different landforms, and the special geographical positions are remote, so that small meteorological stations are not popularized, historical meteorological data are difficult to acquire, and the prediction of the weather is difficult.
In view of the above technical shortcomings, weather prediction cannot be performed in a special geographical area without a large amount of historical weather data, wherein severe weather cannot be dealt with in time and is liable to have adverse effects on plant growth.
Disclosure of Invention
In order to predict weather according to less weather data and cope with severe weather in time, the application provides a weather prediction method.
In a first aspect of the present application, a weather prediction method is provided, which adopts the following technical scheme:
a weather prediction method, comprising:
acquiring temperature data information at a moment within a preset number of days;
performing Fourier fitting according to the temperature data information at the moment within the preset days to obtain a Fourier fitting model;
and predicting temperature data at a predicted moment of a future preset time based on the Fourier fitting model.
By adopting the technical scheme, the temperature prediction of the future preset time is realized through the Fourier fitting model in the special geographic area without a large amount of historical meteorological data, namely, under the condition of less meteorological data, and the adverse weather is responded in time so as to reduce the influence of the adverse weather on the plant growth.
Preferably, after predicting the temperature data at a predicted time of the preset time in the future, the method further includes the following steps: acquiring actual time temperature data information of a plurality of hours before a prediction day; and carrying out temperature correction on the temperature data at the first prediction moment according to the actual moment temperature data information of a plurality of hours before the prediction day and a preset correction formula to obtain temperature data at the second prediction moment.
By adopting the technical scheme, the actual temperature of the previous hours of the prediction day is obtained, the correction difference value between the model temperature and the actual temperature at a certain moment in the previous hours is calculated, the temperature in the prediction model is subjected to addition and subtraction correction by using the correction value, and the model fitting is more accurate through double prediction temperature correction.
Preferably, the correction formula is:
wherein
For the purposes of the dual prediction of time-of-day temperature data,
in order to predict the time-of-day temperature data,
the temperature is predicted for the corresponding model at the actual moment,
i is the corresponding time serial number for the actual time temperature data,
by adopting the technical scheme, the corrected double predicted temperatures can be obtained according to the correction formula, so that the predicted temperature result is more accurate.
Preferably, the fourier fitting formula is:
wherein a is 0 Aj, bj being Friedel-craftsThe expansion coefficient of the leaf series, n is the order number, x is the time sequence number, x =1,2,3 \8230and24.
By adopting the technical scheme, the amount of historical meteorological data is small, the Fourier fitting model is suitable for the condition of small data order, the accuracy rate of the fitting model is high, and the calculation is simple.
Preferably, the highest temperature data in the double prediction time temperature data is obtained according to the double prediction time temperature data; judging whether the highest temperature data in the double prediction moment temperature data is higher than preset highest temperature data or not; and if the temperature is higher than the preset highest temperature data, sending a preset high-temperature early warning information prompt.
By adopting the technical scheme, after double temperature prediction is carried out, if the highest temperature data in the temperature data at the double prediction moments is higher than the preset highest temperature data, high-temperature early warning prompt can be carried out, so that personnel can know the high-temperature weather in advance to make cooling preparation measures so as to reduce the influence of the high-temperature weather on the growth of plants.
Preferably, after obtaining the double predicted time temperature data, the method further comprises the following steps: calculating average temperature data of the temperature data at the double prediction moments according to the temperature data at the double prediction moments; and matching the plants suitable for the temperature according to the average temperature data of the temperature data at the double prediction moments and a preset plant growth suitable temperature table.
By adopting the technical scheme, the temperature requirement of the growth of some plants in the seedling stage is strict, whether the temperature in the current period is the most suitable temperature for planting some plants can be judged through the average temperature data of the temperature data at the double prediction moments, and the adaptive planting of the plants is carried out according to the predicted temperature values, so that the growth of the plants is guaranteed.
Preferably, after obtaining the double predicted time temperature data, the method further comprises the following steps: acquiring temperature data information at an actual moment; storing and sending the temperature data information at the actual moment to a database to generate a historical meteorological database; calling actual time temperature data information in the historical meteorological database and taking the actual time temperature data information as a sample data set, and training a preset neural network model according to the sample data set to obtain a temperature prediction model; predicting the time temperature data of preset days according to the temperature prediction model; and if the lowest temperature in the temperature data of the predicted preset days is lower than the preset lowest temperature, sending a preset low-temperature early warning information prompt.
By adopting the technical scheme, after long-time historical temperature data are accumulated, the preset neural network model can be used for temperature prediction, the prediction result is more accurate, meanwhile, the conditions of sudden change of weather, such as cold tide and low temperature, can be more accurately predicted, if the lowest temperature in the predicted temperature data information is lower than the preset lowest temperature, the preset low-temperature early warning information prompt is sent out, so that personnel can be prompted to make low-temperature prevention preparation measures in advance to reduce the influence of low-temperature weather on the growth of plants.
In a second aspect of the present application, a system for weather forecasting is provided.
A system for weather forecasting, comprising:
the acquisition module is used for acquiring temperature data information at the moment within preset days;
the fitting module is used for performing Fourier fitting according to the temperature data information at the moment within the preset days to obtain a Fourier fitting model;
and the temperature prediction module is used for predicting temperature data at a predicted moment of a future preset time based on the Fourier fitting model.
In a third aspect of the present application, a computer storage medium is provided, which adopts the following technical solutions:
a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to carry out the above-mentioned method steps.
In a fourth aspect of the present application, an electronic device is provided, which adopts the following technical solutions:
an electronic device includes: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
In summary, the present application includes at least one of the following beneficial technical effects:
1. according to the method, the temperature prediction of the future preset time is realized through the Fourier fitting model in a special geographic area without a large amount of historical meteorological data, namely under the condition that the meteorological data are less, so that the influence of severe weather on the growth of plants is reduced;
2. this application is after carrying out the temperature prediction, if there is high temperature weather in predicting a plurality of hours in the future, then can carry out the high temperature early warning suggestion, lets personnel learn high temperature weather in advance and make preparation measures in order to reduce the influence of high temperature weather to vegetation.
Drawings
FIG. 1 is a schematic flow chart of a weather forecasting method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a three-day hourly temperature 24-hour time-averaged value modeling for an example of the present application;
FIG. 3 is a schematic diagram of a temperature curve model at a predicted time based on a Fourier fitting model according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a modified dual predicted time temperature profile model according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a system for weather forecasting according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of the reference numerals: 1. an acquisition module; 2. a fitting module; 3. a temperature prediction module; 1000. an electronic device; 1001. a processor; 1002. a communication bus; 1003. a user interface; 1004. a network interface; 1005. a memory.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.
In the description of the embodiments of the present application, the words "exemplary," "for example," or "for instance" are used to indicate instances, or illustrations. Any embodiment or design described herein as "exemplary," "for example," or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "illustrative," "such as," or "for example" are intended to present relevant concepts in a concrete fashion.
In the description of the embodiments of the present application, the term "and/or" is only one kind of association relationship describing an associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, B exists alone, and A and B exist at the same time. In addition, the term "plurality" means two or more unless otherwise specified. For example, the plurality of systems refers to two or more systems, and the plurality of screen terminals refers to two or more screen terminals. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the indicated technical feature. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The embodiment of the application discloses a meteorological prediction method. Referring to fig. 1, a weather prediction method includes:
s1: acquiring temperature data information at a moment within preset days;
specifically, the mobile intelligent internet of things equipment acquires time temperature data information within a preset number of days, wherein a temperature sensor is installed in the mobile intelligent internet of things equipment, the temperature sensor can detect the time temperature data information within a certain range, the time temperature data information within the preset number of days is sent to an intelligent terminal in a Lora communication mode to be stored and displayed, a time temperature average value of the same time temperature data within the preset number of days is calculated, a time point is used as an abscissa, a time temperature average value corresponding to each time point is used as an ordinate, 24 time-time temperature average value coordinate points are acquired, and a time-time temperature average value curve is generated according to the 24 time-time temperature average value coordinate points.
Such as: referring to fig. 2, the preset number of days in this embodiment is 3 days, in other embodiments, other days may also be used, and the time temperature data information of three days before the predicted day, that is, the time temperature data of each day in the three days before the predicted day, for example, the temperature data of 12 points on the first day is 28 ℃, the temperature data of 12 points on the second day is 30 ℃, the temperature data of 12 points on the third day is 29 ℃, then the average value of the time temperatures of 12 points is 29 ℃, then this coordinate point is (12, 29), and so on, 24 coordinate points are obtained, and modeling is performed according to the 24 coordinate points. The specific three-day hourly temperature 24-hour mean value was modeled as shown in figure 2.
S2: performing Fourier fitting according to the temperature data information at the moment within the preset number of days to obtain a Fourier fitting model; specifically, after 24 coordinate points of the time-time temperature average value are obtained, a 24-hour time temperature average value change curve is established, and the time temperature average value change curve is fitted according to a fourier fitting formula, wherein the fourier fitting formula is as follows:
wherein, a 0 ，a j ,b j The Fourier series expansion coefficient is obtained, n is the order, x is the time sequence number, and x =1,2,3 \823024.
S3: predicting temperature data at a first prediction moment of future preset time based on a Fourier fitting model;
specifically, referring to fig. 3, the preset time in the future of the present embodiment is 24 hours in the future, and based on the fourier fitting model, temperature data at a predicted time of 24 hours in the future is predicted. And modeling is carried out according to the temperature data of one predicted time of 24 hours in the future to obtain a temperature curve model of the one predicted time. A specific fourier fitting model based one-time prediction temperature curve model is shown in fig. 3.
S4: temperature correction is carried out on the temperature data at the first prediction moment to obtain temperature data at the second prediction moment;
specifically, the method comprises the steps of obtaining actual time temperature data information of a plurality of hours before a predicted day, sending the obtained actual time temperature data information of the plurality of hours before the predicted day to an intelligent terminal through a Lora communication mode for storage and display, carrying out temperature correction on a piece of predicted data according to the actual time temperature data of the plurality of hours before the predicted day, a piece of predicted time temperature data and a preset correction formula to obtain double predicted time temperature data, and finally carrying out double predicted temperature modeling according to the double predicted time temperature data, wherein the specific correction formula is that
Wherein
For the purposes of the dual prediction of time-of-day temperature data,
in order to predict the time-of-day temperature data,
is the temperature data at the actual moment of time,
i is the corresponding time sequence number,
in other embodiments of the present invention, the substrate may be,
is a plurality of smallThe average value of the actual time-of-day temperature,
the average of the corresponding predicted temperatures for the actual time of the first several hours.
Such as: referring to fig. 4, the present embodiment acquires actual time temperature data of the predicted six hours before the day, for example, actual time temperature data of three points in the previous six hours, that is
24 deg.C, and at three points a single predicted temperature data is
At 23 deg.C, which corresponds to the predicted temperature at 3 o' clock
At 23 deg.C, i.e. according to the correction formula
Double predicted time of day temperature data
23+ (24-23) =24 ℃. Namely, the difference value between the actual time temperature data of a certain hour in the previous six hours and the corresponding predicted temperature of the actual time is a corrected value, and the temperature at one predicted time is subjected to addition, subtraction and correction according to the corrected value to obtain the temperature data at the two predicted times. A specific corrected two-fold predicted time temperature profile model is shown in fig. 4.
S5: and if the highest temperature in the temperature data at the double prediction moments is higher than the preset highest temperature, sending a high-temperature early warning information prompt.
Specifically, after the double prediction moment temperature data are obtained, the highest temperature data in the double prediction moment temperature data are obtained according to the double prediction moment temperature data, whether the highest temperature data in the double prediction moment temperature data are higher than preset highest temperature data or not is judged, if the highest temperature data are higher than the preset highest temperature data, the intelligent terminal sends out preset high-temperature early warning prompt information and displays the preset high-temperature early warning prompt information on a display screen of the intelligent terminal, wherein the preset high-temperature early warning prompt information is displayed on the display screen in a text mode, and meanwhile, a voice alarm is sent out. The preset maximum temperature can be adjusted according to the corresponding proper temperature range of different growth periods of plants, for example, the proper temperature for apple fruit to expand in summer is 22-28 ℃, and if the temperature is higher than 35 ℃, the photosynthesis can be inhibited in high-temperature weather, and the conditions of fruit shrinkage, fruit drop, fruit surface burn and the like are caused. After receiving the high-temperature early warning information, preventive measures such as water spraying to the tree body, orchard watering, fruit bag ventilation and the like can be taken in advance to improve the microclimate of the soil and the orchard, adjust the space-time distribution of light and heat and humidity in the orchard, and reduce the threat of high-temperature heat damage.
After the double prediction time temperature data are obtained, the average temperature data of the double prediction time temperature data are calculated, and the plants suitable for the temperature are matched according to the average temperature data of the double prediction time temperature data and a preset plant growth suitable temperature table, wherein the plant growth suitable temperature table comprises plant name information and temperature data information suitable for growth of the corresponding plants in each stage, and because the temperature of some plants in a growth environment is strict, if the plants are sown at the suitable temperature, the survival rate of the plants is higher.
The embodiment can also perform weather prediction on extreme climates, specifically, the method includes acquiring daily actual time temperature data information in real time, storing the daily actual time temperature data information, sending the daily actual time temperature data information to a database to generate a historical weather database, calling the actual time temperature data information in the historical weather database and using the actual time temperature data information as a sample data set, performing model training on a preset neural network model according to the sample data set to obtain a temperature prediction model, wherein the preset neural network model can be a long-short term memory network (LSTM), predicting the preset time temperature data of the days according to the temperature prediction model, and the method is not described in the prior art. According to a historical meteorological database, model training is carried out by utilizing a neural network, predicted time temperature data are more accurate, sudden extreme weather such as frost, cold tide and other low-temperature weather can be effectively predicted, if the lowest temperature in temperature data information in preset days is predicted to be lower than the preset lowest temperature, a preset low-temperature early warning information prompt is sent and displayed on a display screen of an intelligent terminal, the preset low-temperature early warning prompt information is displayed on the display screen in a character mode, meanwhile, a voice alarm is sent to enable personnel to take preventive measures in advance, for example, an apple is in a flower bud device or a flowering season, if the apple is attacked by strong cold air and cold tide, flower buds or flower devices are frozen, the quality and the quality of the apple in the current year are seriously affected, if the temperature is lower than-3.0 ℃, the fatality rate of petals and a ovary is obviously increased along with the duration, particularly, late frost with different degrees often appears in spring, if low-temperature early warning is received, comprehensive measures such as 'avoidance, resistance, prevention' and the like can be taken, the flowering phase is delayed, the effects of regulating and controlling small climate of orchards fruit trees are actively prevented.
The implementation principle of the weather prediction method in the embodiment of the application is as follows: acquiring time temperature data information within preset days, and performing 24-hour modeling according to a time-time temperature average value; performing Fourier fitting according to the temperature data information at the moment within the preset days and a Fourier fitting formula to obtain a Fourier fitting model; predicting temperature data at a first prediction moment of future preset time based on a Fourier fitting model; acquiring actual time temperature data information of a plurality of hours before the day of the forecast day, and performing temperature correction on the temperature data of the first forecast time according to the actual time temperature data information of the plurality of hours before the day of the forecast day and a preset correction formula to obtain temperature data of the second forecast time; and if the highest temperature in the temperature data at the double prediction moments is higher than the preset highest temperature, sending a high-temperature early warning information prompt.
The embodiment of the application also discloses a system for meteorological prediction.
Referring to fig. 5, a system for meteorological prediction includes an acquisition module 1, a fitting module 2, and a temperature prediction module 3.
The system comprises an acquisition module 1, a storage module and a display module, wherein the acquisition module 1 is used for acquiring temperature data information at a moment within preset days;
the fitting module 2 is used for performing Fourier fitting according to the temperature data information at the moment in the preset days to obtain a Fourier fitting model;
and the temperature prediction module 3 is used for predicting temperature data at a predicted moment of a future preset time based on the Fourier fitting model.
It should be noted that: in the system provided in the above embodiment, when the functions of the system are implemented, only the division of the functional modules is illustrated, and in practical application, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to implement all or part of the functions described above. In addition, the system and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
The embodiment of the application further provides the electronic equipment.
Please refer to fig. 6, which provides a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 6, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
The communication bus 1002 is used to implement connection communication among these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), 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, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may alternatively be at least one memory device located remotely from the processor 1001. As shown in fig. 6, the memory 1005, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program of a weather prediction method therein.
It should be noted that: in the device provided in the foregoing embodiment, when the functions of the device are implemented, only the division of each functional module is illustrated, and in practical applications, the functions may be distributed by different functional modules as needed, that is, the internal structure of the device may be divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
In the electronic device 1000 shown in fig. 6, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be used to invoke an application program in the memory 1005 that stores a method for weather prediction, which when executed by the one or more processors, causes the electronic device to perform the method as described in one or more of the above embodiments.
An electronic device readable storage medium having instructions stored thereon. When executed by one or more processors, cause an electronic device to perform a method as described in one or more of the above embodiments.
It is clear to a person skilled in the art that the solution of the present application can be implemented by means of software and/or hardware. The term "unit" and "module" in this specification refers to software and/or hardware capable of performing a specific function independently or in cooperation with other components, wherein the hardware may be, for example, a Field-ProgrammaBLE Gate Array (FPGA), an Integrated Circuit (IC), or the like.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some service interfaces, indirect coupling or communication connection of devices or units, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application, in essence or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, and the memory may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. It is intended that all equivalent variations and modifications made in accordance with the teachings of the present disclosure be covered thereby. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A weather prediction method, the method comprising:
acquiring temperature data information at a moment within a preset number of days;
performing Fourier fitting according to the temperature data information at the moment within preset days to obtain a Fourier fitting model;
and predicting temperature data at a predicted moment of a future preset time based on the Fourier fitting model.
2. The weather forecasting method as claimed in claim 1, further comprising the following steps after forecasting temperature data at a forecast time of a future preset time:
acquiring actual time temperature data information of a plurality of hours before the day of the forecast day;
and carrying out temperature correction on the temperature data at the first prediction moment according to the actual moment temperature data information of a plurality of hours before the prediction day and a preset correction formula to obtain temperature data at the second prediction moment.
3. The weather prediction method according to claim 2, wherein the modification formula is:
wherein
For the purposes of the dual prediction of time-of-day temperature data,
in order to predict the time-of-day temperature data,
is the temperature data at the actual moment of time,
the predicted temperature for the model at the actual time, i is the corresponding time number,
4. the weather prediction method according to claim 1, wherein the fourier fitting equation is:
wherein a is 0 ，a j ，b j The expansion coefficient is Fourier series expansion coefficient, n is order number, x is time sequence number, and x =1,2,3 \823024.
5. The weather forecasting method according to claim 2, further comprising, after obtaining the double forecast time temperature data, the steps of:
obtaining the highest temperature data in the double prediction moment temperature data according to the double prediction moment temperature data;
judging whether the highest temperature data in the double prediction moment temperature data is higher than preset highest temperature data or not;
and if the temperature is higher than the preset highest temperature data, sending a preset high-temperature early warning information prompt.
6. The weather forecasting method according to claim 2, further comprising, after obtaining the double forecast time temperature data, the steps of:
calculating average temperature data of the temperature data at the double prediction moments according to the temperature data at the double prediction moments;
and matching the plants suitable for the temperature according to the average temperature data of the temperature data at the double prediction moments and a preset plant growth suitable temperature table.
7. The weather forecasting method according to claim 2, further comprising, after obtaining the double forecast time temperature data, the steps of:
acquiring temperature data information at an actual moment;
storing and sending the temperature data information at the actual moment to a database to generate a historical meteorological database;
calling actual time temperature data information in the historical meteorological database and taking the actual time temperature data information as a sample data set, and training a preset neural network model according to the sample data set to obtain a temperature prediction model;
predicting the time temperature data of preset days according to the temperature prediction model;
and if the lowest temperature in the temperature data of the predicted preset days is lower than the preset lowest temperature, sending a preset low-temperature early warning information prompt.
8. A system for weather forecasting according to any of claims 1 to 7, the system comprising:
the acquisition module (1) is used for acquiring temperature data information at a moment within preset days;
the fitting module (2) is used for carrying out Fourier fitting according to the temperature data information at the moment within the preset days to obtain a Fourier fitting model;
and the temperature prediction module (3) is used for predicting temperature data at a predicted moment of a future preset time based on the Fourier fitting model.
9. A computer-readable storage medium, characterized in that it stores instructions which, when executed, perform the method steps according to any one of claims 1 to 7.
10. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
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| CN117094452A (en) * | 2023-10-20 | 2023-11-21 | 浙江天演维真网络科技股份有限公司 | Drought state prediction method, and training method and device of drought state prediction model |
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| CN117094452A (en) * | 2023-10-20 | 2023-11-21 | 浙江天演维真网络科技股份有限公司 | Drought state prediction method, and training method and device of drought state prediction model |
| CN117094452B (en) * | 2023-10-20 | 2024-02-06 | 浙江天演维真网络科技股份有限公司 | Drought state prediction method, and training method and device of drought state prediction model |
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