CN112036695A - Weather information prediction method and device, readable storage medium and electronic equipment - Google Patents
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Abstract
The embodiment of the invention discloses a method and a device for predicting weather information, a readable storage medium and electronic equipment. The embodiment of the invention receives data from a plurality of target resource distribution terminals in any distribution area; analyzing the data to obtain the average real-time speed of the target distribution resources in a set time period; determining a difference value between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and determining weather information of any distribution area at the next moment of the set time period according to the difference value. By the method, the weather of the distribution area at the next moment can be automatically determined through the difference value between the average real-time speed and the historical average speed in the distribution area within the set time period, so that the waste of human resources is reduced, and the accuracy of the weather information of the distribution area is improved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a device for predicting weather information, a readable storage medium and electronic equipment.
Background
With the development of science and technology and the progress of society, industries such as express delivery and take-out bring more and more convenience to daily life of people, and in the distribution process, weather information of a distribution area can respond to distribution pressure, distribution difficulty, distribution time and the like of the distribution area.
In the prior art, in general, weather information is acquired through the following two conditions, namely, the weather information is acquired through a weather service platform in the first condition; carrying out manual real-time feedback through the station leader of the distribution area; specifically, when the weather information is acquired through the first condition, the weather information acquired by the weather service platform is the weather information of a large area, and the specific weather condition of each distribution area cannot be specifically acquired, so that the problem that the acquired weather information is inaccurate may exist; and a large amount of human resources are consumed when weather information is acquired through the second condition.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for predicting weather information, a readable storage medium, and an electronic device, which can improve accuracy of weather information in a distribution area and reduce waste of human resources.
In a first aspect, an embodiment of the present invention provides a method for predicting weather information, where the method includes: receiving data from a plurality of target distribution resource terminals in any distribution area; analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period; determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
With reference to the first aspect, in a first implementation manner of the first aspect, the analyzing the data by the at least one processor to obtain an average real-time speed of the target distribution resource within a set time period specifically includes: analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks; determining the average real-time speed from the plurality of real-time speeds.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the multiple real-time speeds are filtered through a setting method, where the setting method is kalman filtering.
With reference to the first aspect, in a third implementation manner of the first aspect, the determining, by the difference, weather information of any one of the distribution areas at a time next to the set time period specifically includes: determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade; and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
With reference to the first aspect, in a fourth implementation manner of the first aspect, the determining, by the difference, weather information of any one of the distribution areas at a next time of the set time period specifically includes: inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information; and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
With reference to the fourth implementation manner, in a fifth implementation manner of the first aspect, the classification model includes any one of a decision tree, an XGBoost, a support vector machine SVM, and a gradient lifting tree GBDT.
In a second aspect, an embodiment of the present invention provides a device for predicting weather information, where the device includes: a receiving unit, configured to receive data from a plurality of target distribution resource terminals in any distribution area; the acquisition unit is used for analyzing the data through at least one processor and acquiring the average real-time speed of the target distribution resources in a set time period; the processing unit is used for determining a difference value between the average real-time speed and a historical average speed through at least one processor, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and the determining unit is used for determining the weather information of any distribution area at the next moment of the set time period according to the difference value.
In a third aspect, the present invention provides a computer-readable storage medium on which computer program instructions are stored, the computer program instructions, when executed by a processor, implementing the method according to any one of the first aspect or any one of the first aspect implementations.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, where the one or more computer program instructions are executed by the processor to implement the following steps: receiving data from a plurality of target distribution resource terminals in any distribution area; analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period; determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
With reference to the fourth aspect, in a first implementation manner of the fourth aspect, in the embodiment of the present invention, the processor specifically executes the following steps: analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks; determining the average real-time speed from the plurality of real-time speeds.
With reference to the first implementation manner of the fourth aspect, in a second implementation manner of the fourth aspect, the processor further performs the following steps: and filtering the plurality of real-time speeds by a set method, wherein the set method is Kalman filtering.
With reference to the fourth aspect, in a third implementation manner of the fourth aspect, in the embodiment of the present invention, the processor specifically executes the following steps: determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade; and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
With reference to the fourth aspect, in a fourth implementation manner of the fourth aspect, in the embodiment of the present invention, the processor specifically executes the following steps: inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information; and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
With reference to the fourth aspect, in a fifth implementation manner of the fourth aspect, the classification model includes any one of a decision tree, an XGBoost, a support vector machine SVM, and a gradient lifting tree GBDT.
The embodiment of the invention receives data from a plurality of target resource distribution terminals in any distribution area; analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period; determining a difference value between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and determining weather information of any distribution area at the next moment of the set time period according to the difference value. By the method, the weather of the distribution area at the next moment can be automatically determined through the difference value between the average real-time speed and the historical average speed in the distribution area within the set time period, so that the waste of human resources is reduced, and the accuracy of the weather information of the distribution area is improved.
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The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
fig. 1 is a flowchart of a method for predicting weather information according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method for predicting weather information according to a second embodiment of the present invention;
FIG. 3 is a diagram of an application scenario of the third embodiment of the present invention;
FIG. 4 is a diagram illustrating a weather information prediction apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic view of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
The present disclosure is described below based on examples, but the present disclosure is not limited to only these examples. In the following detailed description of the present disclosure, certain specific details are set forth. It will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present disclosure.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout this specification, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present disclosure, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified.
In the prior art, weather information is typically obtained by two situations, including:
acquiring weather information through a weather service platform under the first condition; specifically, when the weather information is acquired through the first condition, the weather information acquired by the weather service platform is the weather information of a large area, and the specific weather condition of each distribution area cannot be specifically acquired, so that the problem that the acquired weather information is inaccurate may exist; for example, the distribution area is a route-learning and clearing area of the hail lake area of beijing city, and only a first-level administrative scope of the area can be obtained through the weather service platform, for example, the weather information of the hail lake area of beijing city, but the specific weather condition of the route-learning and clearing cannot be obtained specifically, if the weather information of the hail lake area is small rain, however, the area of the hail lake area is large, the route-learning and clearing area may be clear, and there is no rain, and if the weather information of the hail lake area is set as the weather information of the route-learning and clearing, the obtained weather information is inaccurate.
Carrying out manual real-time feedback through the station leader of the distribution area; and a large amount of human resources are consumed when weather information is acquired through the second condition.
Fig. 1 is a flowchart of a method for predicting weather information according to a first embodiment of the present invention. As shown in fig. 1, the method specifically comprises the following steps:
step S100 is to receive data from a plurality of target distributed resource terminals in any distribution area.
In a possible implementation manner, any delivery area may be a region, a street, a business district, or a range covered by a delivery site, and the target resource delivery terminal may be a mobile phone of a rider or a terminal device of an automatic delivery terminal, which is not limited in the embodiment of the present invention.
In one possible implementation, the data is trajectory data of the target delivery resource.
And step S101, analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period.
Specifically, the at least one processor analyzes the trajectory of the plurality of target distribution resource terminals, and determines a plurality of real-time speeds of the plurality of target distribution resources within a set time period according to the trajectory; determining the average real-time speed from the plurality of real-time speeds.
For example, it is assumed that a distribution area includes 5 target distribution resources, which may be tens, hundreds, or thousands in practical applications, and this is only an example, where the 5 target distribution resources are respectively a target distribution resource 1, a target distribution resource 2, a target distribution resource 3, a target distribution resource 4, and a target distribution resource 5, the set time is from 9 o 'clock to 9 o' clock 5 minutes in 12/month and 4/month in 2019, where 12/month and 4/month in 2019 are working days, a plurality of real-time speeds of each target distribution resource are determined by a trajectory of a target distribution resource terminal, an average speed of each target distribution resource is then determined, and it is assumed that an average speed of the target distribution resource 1 is 15km/h, an average speed of the target distribution resource 2 is 25km/h, an average speed of the target distribution resource 3 is 20km/h, and an average speed of the target distribution resource 4 is 23km/h, The average speed of the target distribution resources 5 is 17km/h, and then the average real-time speed of the distribution area, namely (15km/h +25km/h +20km/h +23km/h +17km/h)/5 is 20km/h, is determined according to the average speed of each target distribution resource.
Step S102, determining a difference value between the average real-time speed and a historical average speed through at least one processor, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information.
In the embodiment of the invention, the weather information is naturally and regularly related to the speed of the target distribution resources, for example, when the standard weather information is sunny, the distribution speed of the target distribution resources is not affected by weather, and the distribution speed is high; when the weather is worse, the speed of the target for delivering the resource is affected, and the worse the weather, the slower the speed of the target for delivering the resource is, for example, the slower the speed of the target for delivering the resource is in a storm than in a sunny day.
In this embodiment of the present invention, the historical average speed may be determined according to an average speed of the plurality of target resource distribution terminals in any distribution area under standard weather information, may also be determined according to a median of the plurality of target resource distribution terminals in any distribution area, and may also be determined according to a 6-place value of the plurality of target resource distribution terminals in any distribution area.
For example, it is assumed that the historical average data is 25km/h, and the historical average speed is the average speed of target distribution resources in the same region and in the same type of days (such as working days and holidays) and in the same time period in sunny days.
And step S103, determining weather information of any distribution area at the next moment of the set time period according to the difference.
Specifically, the weather information at the next time can be determined in the following two ways, including:
determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade; and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
For example, the weather levels are preset, and each weather level corresponds to a set difference range, for example, as shown in table 1:
TABLE 1
| Weather grade | Weather information | Range of difference |
| 1 | Light rain | 1-5 |
| 2 | Medium rain | 6-10 |
| 3 | Storm rain | 10-15 |
| 4 | Storm rain | 16-20 |
If the historical average data corresponding to the set time period in any distribution area is 25km/h, the average real-time speed of the set time period is 20km/h, and the difference between the historical average data and the average real-time speed is 5, the table 1 is inquired, the weather of the distribution area in the set time period is light rain, and the light rain is determined as the weather information of the distribution area.
Inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information; and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
For example, first, position information, time information, third-party weather information, a difference value obtained according to historical data, and a weather information training classification model of historical data calibrated manually are obtained, where the time information specifically includes working days, holidays, time slices, and the like, the set area may be a political area, a business district, or a site, and longitude and latitude of all merchants responsible for the site corresponding to the business district are aggregated into a central point, and a range covering 3km with the central point may be referred to as a business district.
In a possible implementation manner, when weather information of a set area needs to be determined, position information, time information, third-party weather information of a target distribution resource in a set time period and a difference value obtained according to historical data need to be obtained, the data are sent to a classification model trained in advance, and weather of the set area is determined through the classification model.
In a possible implementation manner, the classification model includes any one of a decision tree, an XGBoost, a support vector machine SVM, and a gradient lifting tree GBDT, which is not limited in the embodiment of the present invention.
In the embodiment of the present invention, after determining the weather information according to step S103, the weather information is allocated to a task allocation platform or a target resource distribution terminal, where the task allocation platform may also be referred to as a control terminal, and after receiving the weather information, the control terminal may schedule a task according to the weather information; after receiving the weather information, the target distribution resource terminal can judge the distribution condition of the target distribution resource terminal according to the weather information, judge the number of the tasks carried by the target distribution resource terminal, and the like, and specifically determine the number according to the actual condition.
Fig. 2 is a flowchart of another weather information prediction method according to a second embodiment of the present invention. After step S100, the method further comprises the steps of:
and S104, filtering the real-time speeds by a setting method, wherein the setting method is Kalman filtering.
For example, data with a speed less than or greater than a set value may be filtered, e.g., less than 1km/h, or greater than 60km/h, and may be filtered for deviation data.
Fig. 3 is an application scenario diagram of a third embodiment of the present invention, including a server and a target resource distribution terminal, where the server may also be referred to as a platform, a system, and the like, the target resource distribution terminal may be a mobile phone, a tablet, and the like, and may be a device capable of positioning a position of a target resource distribution, the number of the target resource distribution terminals is multiple, and the server receives data from the target resource distribution terminal; analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period; determining a difference value between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and determining weather information of any distribution area at the next moment of the set time period according to the difference value. By the method, the weather of the distribution area at the next moment can be automatically determined through the difference value between the average real-time speed and the historical average speed in the distribution area within the set time period, so that the waste of human resources is reduced, and the accuracy of the weather information of the distribution area is improved.
Fig. 4 is a schematic diagram of a weather information prediction apparatus according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus of the present embodiment includes a receiving unit 41, an acquiring unit 42, a processing unit 43, and a determining unit 44. The receiving unit 41 is configured to receive data from a plurality of target resource distribution terminals in any distribution area; an obtaining unit 42, configured to analyze the data through at least one processor, and obtain an average real-time speed of a target distribution resource within a set time period; a processing unit 43, configured to determine, by at least one processor, a difference between the average real-time speed and a historical average speed, where the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; a determining unit 44, configured to determine, according to the difference, weather information of the any distribution area at a next time of the set time period.
Further, the obtaining unit 42 is specifically configured to: analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks; determining the average real-time speed from the plurality of real-time speeds.
Further, the apparatus further comprises: and the filtering unit is used for filtering the plurality of real-time speeds by a setting method, wherein the setting method is Kalman filtering.
Further, the determining unit 44 is specifically configured to: determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade; and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
Further, the determining unit 44 is further specifically configured to: inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information; and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
Further, the classification model comprises any one of a decision tree, an XGboost, a Support Vector Machine (SVM) and a gradient lifting tree (GBDT).
Fig. 5 is a schematic view of an electronic device according to a fifth embodiment of the present invention. In this embodiment, the electronic device is a server. It should be understood that other electronic devices, such as raspberry pies, are also possible. As shown in fig. 5, the electronic device: at least one processor 501; and a memory 502 communicatively coupled to the at least one processor 501; and a communication component 503 in communicative connection with the scanning device, the communication component 503 receiving and transmitting data under the control of the processor 501; wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions being executable by the at least one processor 501 to implement: receiving data from a plurality of target distribution resource terminals in any distribution area; analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period; determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information; and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
Further, the processor specifically executes the following steps: analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks; determining the average real-time speed from the plurality of real-time speeds.
Further, the processor performs the steps of: and filtering the plurality of real-time speeds by a set method, wherein the set method is Kalman filtering.
Further, the processor specifically executes the following steps: determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade; and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
Further, the processor specifically executes the following steps: inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information; and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
Further, the classification model comprises any one of a decision tree, an XGboost, a Support Vector Machine (SVM) and a gradient lifting tree (GBDT).
Specifically, the electronic device includes: one or more processors 501 and a memory 502, with one processor 501 being an example in fig. 5. The processor 501 and the memory 502 may be connected by a bus or other means, and fig. 5 illustrates the connection by the bus as an example. Memory 502, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 501 executes various functional applications of the device and data processing, i.e., implements the above-described weather information prediction method, by executing nonvolatile software programs, instructions, and modules stored in the memory 502.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 502 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to an external device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 502 and, when executed by the one or more processors 501, perform the method of predicting weather information in any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.
A sixth embodiment of the invention is directed to a non-volatile storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific embodiments for practicing the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
The embodiment of the application discloses A1 and a method for predicting weather information, which comprises the following steps:
receiving data from a plurality of target distribution resource terminals in any distribution area;
analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period;
determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information;
and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
A2, the method as in a1, wherein the analyzing the data by at least one processor to obtain the average real-time speed of the target distribution resource within the set time period, specifically comprises:
analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks;
determining the average real-time speed from the plurality of real-time speeds.
A3, the method of a2, the method further comprising:
and filtering the plurality of real-time speeds by a set method, wherein the set method is Kalman filtering.
A4, the method as in a1, wherein the determining the weather information of any one of the distribution areas at the next moment of the set time period according to the difference specifically includes:
determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade;
and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
A5, the method as in a1, wherein the determining the weather information of any one of the distribution areas at the next moment of the set time period according to the difference specifically includes:
inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information;
and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
A6, the method of a5, the classification model comprising any one of decision trees, XGBoost, support vector machine, SVM, gradient boosting tree, GBDT.
The embodiment of the application discloses B1, a prediction device of weather information, the device includes:
a receiving unit, configured to receive data from a plurality of target distribution resource terminals in any distribution area;
the acquisition unit is used for analyzing the data through at least one processor and acquiring the average real-time speed of the target distribution resources in a set time period;
the processing unit is used for determining a difference value between the average real-time speed and a historical average speed through at least one processor, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information;
and the determining unit is used for determining the weather information of any distribution area at the next moment of the set time period according to the difference value.
The embodiment of the application discloses C1, a computer readable storage medium, on which computer program instructions are stored, which when executed by a processor implement the method according to any one of A1-A6.
The embodiment of the application discloses a D1 electronic device, comprising a memory and a processor, wherein the memory is used for storing one or more computer program instructions, and the one or more computer program instructions are executed by the processor to realize the following steps:
receiving data from a plurality of target distribution resource terminals in any distribution area;
analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period;
determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information;
and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
D2, the electronic device as recited in D1, the processor specifically performs the following steps:
analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks;
determining the average real-time speed from the plurality of real-time speeds.
D3, the electronic device as recited in D2, the processor further performing the steps of:
and filtering the plurality of real-time speeds by a set method, wherein the set method is Kalman filtering.
D4, the electronic device as recited in D1, the processor specifically performs the following steps:
determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade;
and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
D5, the electronic device as recited in D1, the processor specifically performs the following steps:
inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information;
and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
D6, the electronic device of D5, the classification model comprising any one of a decision tree, XGBoost, support vector machine, SVM, gradient boosting tree, GBDT.
Claims (10)
1. A method for predicting weather information, the method comprising:
receiving data from a plurality of target distribution resource terminals in any distribution area;
analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period;
determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information;
and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
2. The method of claim 1, wherein the analyzing the data by the at least one processor to obtain the average real-time speed of the target delivery resource over the set time period comprises:
analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks;
determining the average real-time speed from the plurality of real-time speeds.
3. The method of claim 2, further comprising:
and filtering the plurality of real-time speeds by a set method, wherein the set method is Kalman filtering.
4. The method according to claim 1, wherein the determining weather information of any one of the distribution areas at a next time of the set time period by the difference specifically includes:
determining a weather grade corresponding to the difference value, and determining corresponding weather information according to the weather grade;
and determining the weather information as the weather information of any distribution area at the next moment of the set time period.
5. The method according to claim 1, wherein the determining weather information of any one of the distribution areas at a next time of the set time period by the difference specifically includes:
inputting the difference and the acquired feature data into a pre-trained classification model, wherein the feature data comprises at least one of position information, time information and third-party weather information;
and outputting the weather information of any distribution area at the next moment of the set time period through the classification model.
6. The method of claim 5, wherein the classification model comprises any one of a decision tree, XGboost, Support Vector Machine (SVM), gradient boosting tree (GBDT).
7. An apparatus for predicting weather information, the apparatus comprising:
a receiving unit, configured to receive data from a plurality of target distribution resource terminals in any distribution area;
the acquisition unit is used for analyzing the data through at least one processor and acquiring the average real-time speed of the target distribution resources in a set time period;
the processing unit is used for determining a difference value between the average real-time speed and a historical average speed through at least one processor, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information;
and the determining unit is used for determining the weather information of any distribution area at the next moment of the set time period according to the difference value.
8. A computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-6.
9. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to perform the steps of:
receiving data from a plurality of target distribution resource terminals in any distribution area;
analyzing the data through at least one processor to obtain the average real-time speed of the target distribution resources in a set time period;
determining, by at least one processor, a difference between the average real-time speed and a historical average speed, wherein the historical average speed is determined according to historical speeds of a plurality of target resource distribution terminals in any distribution area under standard weather information;
and determining weather information of any distribution area at the next moment of the set time period according to the difference value.
10. The electronic device of claim 9, wherein the processor is further configured to perform the steps of:
analyzing the tracks of the target distribution resource terminals through at least one processor, and determining a plurality of real-time speeds of the target distribution resources within a set time period according to the tracks;
determining the average real-time speed from the plurality of real-time speeds.
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