TWI593942B - Methods for generating a map comprising weather forecasts or nowcasts and ground navigation devices and non-transitory computer readable medium thereof - Google Patents

Description

Method for generating a map including weather forecast or nowcasting and its ground navigation device and non-transitory computer readable medium [Related Application Cross Reference]

The present application claims priority to the following commonly owned and co-invented patent applications: U.S. Patent Application Serial No. 13/856,923, filed on Apr. 4, 2013; U.S. Patent Application Serial No. 13/947,331, filed on Jul. 22, 2013, and the U.S. Provisional Application No. 61/839,675, filed on Jun. 16, 2013, and U.S. Provisional Patent Application No. 61/835,626 And the application of the U.S. Provisional Application No. 61/836,713, filed on June 19, 2013, the entire contents of which are incorporated by reference.

The subject matter disclosed is generally directed to a system for determining weather forecasts.

The conventional weather forecasting system provides weather forecasts from the current time of 12 hours to several days. If a short-term forecast or a forecast with a fine time scale is required, the best information available is usually forecasted on an hourly basis.

The conventional weather forecasting system produces an average forecast of the areas in which it is equal. Therefore, a forecast for a precise location within this region may be inaccurate, and even the current weather displayed for a region may differ from the actual weather at one of the precise locations within the region.

In addition, conventional weather forecasts are shown on a time scale that is too coarse to allow the user to know when the exact location and time of the weather event occurred. Even for hourly habits Knowing the weather forecast, it is impossible for the user to know whether the predicted weather event lasts for one hour or one minute, and for the latter, it is impossible for the user to know the exact time when the predicted event occurred within the one hour.

There is a need in the market for a method that allows for the estimation of the weather path along the starting and destination locations so that the user can attempt to use alternative paths to avoid extreme weather conditions.

According to an embodiment, a computer implementation method for generating a map includes receiving a map request including a departure location and a destination location. In response to receiving the map request, the method includes obtaining map material that, when implemented on a display device, causes the display device to display a map comprising one of the paths drawn between the departure location and the destination location. The method also includes obtaining a one-way forecast along one of the one or more key points of the path; modifying the map data to include a visual addition to one of the ones of the neighboring forecasts representing the key points on the path drawn on the map And displaying the modified map data to an computing device for presentation to a user.

According to an embodiment, the method further comprises transmitting location information associated with each of the key points to a proximity predictor to obtain a nowcast.

According to an embodiment, the method further comprises: transmitting a time at which each of the key points requires the proximity prediction.

According to an embodiment, transmitting a time comprises: transmitting a current time.

According to an embodiment, transmitting the time comprises estimating an arrival time indicating a time when the user starting from a current location will arrive at a given key point.

According to an embodiment, estimating the time of arrival is based on at least one of: departure time, weather condition, speed limit, current speed, and distance between the current position and the key point.

According to an embodiment, the method further comprises: receiving updated location information indicating a current location of the computing device on which the map is displayed and using the current location as a new The departure location to update the proximity forecast on the map.

According to an embodiment, obtaining the map material comprises: generating the map data by using the information stored by the local end.

According to an embodiment, obtaining the map material comprises forwarding the map request to a remote server.

In another aspect, a method for providing a proximity prediction displayed on a map on a GPS navigation device (GPS) is provided, the method comprising: receiving from the GPS a location comprising a departure location and a destination location Location information and a predetermined path between the departure location and the destination location; identifying one or more key points along the path; associating a time with each point; obtaining a proximity prediction for each key point; and using The proximity prediction displayed on the path drawn between the departure location and the destination location is forwarded to one of the displays associated with the GPS for display to a user.

According to an embodiment, the method further comprises: transmitting one of the location information associated with each of the key points to a proximity predictor to obtain a proximity prediction.

According to an embodiment, the method further comprises: transmitting a time at which each of the key points requires the proximity prediction.

According to an embodiment, transmitting a time comprises: transmitting a current time.

According to another embodiment, transmitting a time comprises estimating an arrival time of a time when the user starting to arrive at a given key point at a current location is estimated.

According to an embodiment, estimating the time of arrival is based on at least one of: departure time, weather condition, speed limit, current speed, and distance between the current position and the key point.

According to an embodiment, the method further comprises: receiving updated location information indicating a current location of the GPS on which the map is displayed and using the current location as a new departure location to update the proximity prediction on the map.

definition

A near-term predictor system prepares a very short period of a very small area on the earth (5 meters, 10 meters, 50 meters, 100 meters, 500 meters, 1,000 meters, etc.) (for example, 1 minute, 5 minutes, 15 minutes, 30 minutes) Minutes, etc.) Forecast one of the weather forecasting devices.

A meteorological observation can be an image, a video, a free-form text (tweet, message, email, etc.), a weather value of any category (such as temperature, pressure, visibility, type and intensity of rainfall, Ice accumulation, cloud volume, wind, etc.).

A weather related event is, for example, at least one of hail, a gust of wind, lightning, a temperature change, and the like.

A point of observation is one of the observations at a given time, as defined herein, at a particular location (sometimes referred to as a "location").

A specific location is the location on Earth that is being observed. One of the accuracy of 5 meters to 10 meters is suitable for the embodiments described herein, but the variation in position can be large, such as 25 meters, 50 meters, 100 meters, 1000 meters or more (ie, low precision). The means for obtaining a particular location includes any type of geographic location determining component or positioning system that is available upon application of the present patent application. The geographic location determination component or positioning system can be automated or non-automated. The automated geolocation decision component or positioning system includes global positioning system, RF positioning system, radio positioning technology, Internet Protocol (IP) addressing, MAC addressing, WiFi, radio frequency identification (RFID), and the like. The positioning system can also be manual, such as providing a street address, street corner, building or landmark, and the like.

A given time is defined as hours, minutes, and seconds at which point observations are made in a time zone corresponding to a particular location. Hours, minutes, and seconds for a given time may also be recorded according to World Standard Time (UTC) or Greenwich Mean Time (GMT) such that a given time is independent of a particular location. The accuracy of a given time can be greater or less than one second. For example, in some embodiments, one of 5 seconds, 10 seconds, 30 seconds, 60 seconds, or more may be sufficient for the embodiments described herein.

A user forwards a weather forecast to a person or a machine.

An observer provides an entity that is automated and/or human observed. An observer can be a person or an automated machine. An observer may also be a user as defined herein.

A grid image includes an image of one of latitude and longitude coordinates. Therefore, the grid image is a collection of two-dimensional geographic points/pixels.

Each pixel in a trellis image corresponds to a location and can represent a single weather value, a probability distribution of values, or a confidence level.

The features and advantages of the subject matter will be apparent from the following detailed description of the embodiments. As will be realized, the subject matter disclosed and claimed can be modified in various aspects, all of which are not in the scope of the claims. Accordingly, the drawings and description are to be regarded as illustrative in nature

200‧‧‧System/Nearby Forecaster

201‧‧‧Weather source

201-1‧‧‧Weather observation

201-2‧‧ ‧ observation

201-3‧‧‧Weather radar

201-4‧‧‧ Satellite

201-5‧‧‧ Numerical weather prediction model output

201-6‧‧‧Weather Forecasting and Consulting

202‧‧‧Rain type (PType) distribution forecaster

204‧‧‧Rain rate (PRate) distribution predictor

206‧‧‧ Forecast combiner

250‧‧‧Remote Server

252-1‧‧‧Customer Computer

252-2‧‧‧User computer

252-3‧‧‧User computer

254‧‧‧Telecom network

300‧‧‧ system

310‧‧‧Map generation module

312‧‧‧Smart Module

320‧‧‧Map source

330‧‧‧Global Positioning System (GPS) navigation device

332‧‧‧ satellite

720‧‧‧ computer

721‧‧‧ processor unit

722‧‧‧ system memory

723‧‧‧System Bus

724‧‧‧Reading Memory (ROM)

725‧‧‧ Random Access Memory (RAM)

726‧‧‧Basic Input/Output System (BIOS)

727‧‧‧ hard disk drive

728‧‧‧Disk machine

729‧‧‧Removable Disk

730‧‧‧CD player

731‧‧‧Removable CD

732‧‧‧hard drive interface

733‧‧‧Disk interface

734‧‧‧CD player interface

735‧‧‧ operating system

736‧‧‧Application

737‧‧‧Program Module

738‧‧‧Program data

740‧‧‧ keyboard

742‧‧‧ indicator device

746‧‧‧Serial interface

747‧‧‧Monitor

748‧‧‧Video Adapter

749‧‧‧ remote computer

750‧‧‧Memory storage device

751‧‧‧Local Network (LAN)

752‧‧‧ Wide Area Network (WAN)

753‧‧‧ Adapter

754‧‧‧Data machine

800‧‧‧Computer implementation method

810‧‧‧Steps

820‧‧‧Steps

830‧‧ steps

840‧‧‧Steps

850 ‧ ‧ steps

860‧‧‧Steps

Further features and advantages of the present invention will become apparent from the following detailed description, in which: FIG. 1 is a block diagram of a system for displaying a collateral prediction along a path on a map; FIG. 2 is used to implement the embodiments. A block diagram of one of the suitable proximity predictors; FIG. 3a to FIG. 3c illustrate an example of a map and an indication returned by a map generation module; FIG. 4 illustrates an example of a GPS navigation device; FIG. 5a and FIG. 5b illustrates an example of a map of a plurality of near-forecasts showing a path and a plurality of key points along the path; FIG. 6 is an example of a network environment in which one of the embodiments may be practiced; FIG. 7 is a practical embodiment thereof. An example of another network environment; FIG. 8 is a flow chart of a computer implementation method for generating a map according to an embodiment; FIG. 9 illustrates a suitable computing operating environment in which one embodiment of the present invention may be practiced. An illustrative diagram.

It should be noted that similar features are identified by like reference numerals throughout the drawings.

The present invention will now be described more fully hereinafter with reference to the accompanying drawings. The embodiments are also described so that the disclosure conveys the scope of the invention to those skilled in the art. However, the embodiments may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein.

This embodiment can be embodied as a method or apparatus, among other things. Thus, embodiments may take the form of an entirely hardware embodiment, a full software embodiment, a combination of software and a hardware embodiment (and the like). Moreover, although the embodiments have been described with reference to a portable or handheld device, they can be implemented in a desktop computer, laptop computer, tablet device, or any computing operation with sufficient computing resources in the embodiments. On the device.

Briefly, this embodiment describes a system and method for displaying a proximity prediction along a path on a map. The system receives a map request including a departure location and a destination location from a user, and obtains map material including a path between the destination location and the departure location. A proximity predictor is used to output a neighboring forecast for a plurality of key points along the path. The system modifies the map material to include a visual indicator of the proximity prediction associated with each keypoint such that when the modified map material is displayed for a display, the proximity forecast is displayed along the path between the destination location and the departure location.

1 is a block diagram of a system 300 for displaying a forward forecast along a path on a map. As shown in FIG. 1, the system 300 includes a proximity predictor 200 (also referred to as a system for generating a near-forecast), a map generation module 310, and a smart module 312. The smart module 312 is used for map generation. Group 310 communicates with the proximity predictor 200 to output a map showing one of the proximity forecasts along a plurality of key points on the path.

Proximity forecaster

FIG. 2 is a block diagram of one of the suitable proximity predictors 200, such as described in U.S. Patent Application Serial No. 13/856, 923, filed on Apr. 4, 2013.

As shown in FIG. 2, the proximity predictor 200 receives meteorological observations from different sources 201, such as meteorological observation sources, including, but not limited to, point observations 201-2 (eg, provided by users and automated stations) Feedback, weather radar 201-3, satellite 201-4 and other types of meteorological observations 201-1 and weather forecast sources, such as numerical weather prediction (NWP) model output 201-5 and weather forecast and consultation 201-6.

In an embodiment, the proximity predictor 200 includes a rainfall type (PType) distribution predictor 202 and a rain rate (PRate) distribution predictor 204. The PType distribution predictor 202 receives meteorological observations from different sources 201 and outputs a probability distribution of a given latitude and longitude (and/or position) of the type of rainfall over a time interval. In a non-limiting example, the probability distribution of the rain type can be:

1) Snow: 10%

2) Rain: 30%

3) Freezing rain: 60%

4) Hail: 0%

5) Ice beads: 0%

One of ordinary skill in the art will appreciate that various other types and numbers of categories exist in addition to the examples provided above.

Similarly, the PRate distribution predictor 204 receives meteorological observations of a given latitude and longitude from different sources 201 and indicates one of the probability distributions of the output one rainfall rate (PRate) as one of the expression uncertainties. For example, one of the probability distribution predictions of the PRate can be output as a probability distribution of a given latitude and longitude for a rain rate or a range of rates over a time interval. In a non-limiting example, the probability distribution of the rain rate can be:

1) No rainfall: 30%

2) Light rain: 40%

3) Moderate rain: 20%

4) Heavy rain: 10%

One of ordinary skill in the art will appreciate that various other types and numbers of categories exist in addition to the examples provided above.

The PRate value and the PType value output by the PRate distribution predictor 204 and the PType distribution predictor 202 are sent to a forecast combiner 206 to combine the equal values into a single value PTypeRate representing a rain result. For example, if the value of PType is "snow" and the value of PRate is large, the combined value of PTypeRate can be "grand snow".

For a given latitude and longitude, the system outputs a predicted PTypeRate distribution for a predefined time interval (fixed (eg, 1 minute) or variable (eg, 1 minute, then 5 minutes, then 10 minutes, etc.)). The system can pre-calculate and store the predicted PTypeRate distribution or calculate the PTypeRate distribution in real time over a sequence of time intervals. For each time interval, a PTypeRate distribution indicates that a certainty or uncertainty of a PTypeRate will occur.

Referring to FIG. 2, the forecast combiner 206 receives the final PRate distribution from the PType distribution predictor 202 and receives the final PRate distribution from the PRate distribution predictor 204 to combine them into one of the PTypeRate distribution values, each PTypeRate distribution value indicating reception. The probability of a certain type of rainfall at a certain rate. An example is provided below.

Assume that the PType distribution is as follows: snow: 50%, rain: 0%, freezing rain: 30%, hail: 0%, ice beads: 20%, and PRate distribution is as follows: none: 0%, small: 10%, medium: 20% , large: 30%, great: 40%, PTypeRate distribution can be as follows:

Thus, the forecast combiner 206 multiplies the probability of each type of rainfall by the probability of rainfall at each rate to obtain a probability of receiving a certain type of rainfall at a certain rate (eg, 20% likelihood of heavy snow, or The 12% probability is extremely freezing rain). In one embodiment, the probability range can be associated with textual information to display textual information to the user rather than digitally displaying the probability. For example, a chance between 5% and 15% can be associated with the word "small likelihood", and a chance between 40% and 70% can be related to the word "probability" or "very likely" Union and so on, it can be displayed: "There is a great possibility of heavy snow" instead of the display: 60% of the possibility is heavy snow.

In another embodiment, two or more different PTypeRates may be combined along one or more dimensions (the dimensions include: rate, type, or probability). For example, the result of this combination may include: it may be as small as moderate rain, it may be as small as moderate rain or heavy snow; it may rain or snow; it may rain or snow; it may be small to moderate rain or heavy snow or small Hail; may be rainy, snowy, or hail; it may rain, snow, or hail.

Thus, the proximity predictor 200 receives the location requiring the nearcast and the time and/or time interval required for the nearcast, and outputs the PtypeRate distribution for the given location and time.

There may be another embodiment of the proximity predictor 200. In this embodiment, the proximity predictor includes a PType selector/receiver and a PRate distribution predictor. Similar to Figure 2 In the illustrated embodiment, the PRate distribution predictor receives meteorological observations of a given latitude and longitude from different sources, and indicates one of the probability distributions of the output one rainfall rate (PRate) with one of the expression uncertainties. For example, one of the probability distribution predictions of the PRate can be output as a probability distribution of a given latitude and longitude for a rain rate or a range of rates over a time interval. In a non-limiting example, the probability distribution of the rain rate can be:

1) No rainfall: 30%

2) Light rain: 40%

3) Moderate rain: 20%

4) Heavy rain: 10%

One of ordinary skill in the art will appreciate that various other types and numbers of categories exist in addition to the examples provided above.

However, the PType selector/receiver does not output a probability distribution associated with different types of rainfall. Instead, the PType selector/receiver receives meteorological observations of a given latitude and longitude from different sources to select a type of rainfall from a different list of rainfall types. For example, based on inputs received from the sources, the PType selector/receiver selects one of the most likely occurrences of a given latitude and longitude (and/or location) from the following list of rainfall types:

1) Snow

2) Rain

3) Freezing rain

4) Hail

5) Ice beads

6) Mixing (eg 1 and 3, 1 and 4, 2 and 3, 1 and 5, 3 and 5, 4 and 5, etc.)

From the list of rainfall types (such as one of the above), only one type of rainfall is selected for a given location. For example, one of the most likely types of rainfall at a given time can be selected by mixing one of the snow and the freezing rain. The type of rainfall is not associated with a probability value. In fact, because only one type of rainfall is selected for any given location and the time corresponding to that location, The selected rainfall type will have a 100% effective probability value.

The list of rainfall types that can be used to select a type can include one of a mixture type that represents one of two different rainfall types (eg, snow and freezing rain, hail, ice, etc.). A hybrid type is considered to be useful for selecting one of the distinct rainfall types, and as shown in type (6) of the listing above, there may be many different hybrid types representing different combinations of various rainfall types.

In another embodiment, instead of selecting the type of rainfall by the PType selector/receiver, the rainfall type is received from one of the sources external to the neighboring predictor. In other words, the proximity predictor 200 can request a remote source (eg, a third party weather service) to identify the type of rainfall most likely to occur at a given location at a given time and receive one of the most likely types of rainfall from the source. Respond. In this case, the selection of the type of rainfall is not performed by the proximity predictor. The proximity predictor is only input to the selected type of rainfall and thereby saves the computing power of the proximity predictor required to perform the selection.

The selected rainfall type and PRate value output by the PType selector/receiver and the PRate distribution predictor are combined. For example, if the selected rainfall type is snow and the PRate value is as described above, the combined information will indicate:

1) No snow: 30%

2) Xiaoxue: 40%

3) Zhongxue: 20%

4) Heavy snow: 10%.

Since only one type of rainfall is concerned, performing a combination to output the final weather forecast data requires only a minimal amount of computing power. Since the PType selector/receiver will output a type of rainfall (1) of a given position and time, if the PRate distribution predictor outputs m probability distributions, the final weather forecast data will only include m(m*1) Distribution of weather forecasts.

Similar to the embodiment shown in FIG. 2, when the final weather forecast data is output, the probability range can be associated with the text information to display the text information to the user instead of displaying the data. Probability. For example, a chance between 5% and 15% can be associated with the word "small likelihood", and a chance between 40% and 70% can be related to the word "probability" or "very likely" Union and so on, it can be displayed: "There is a great possibility of heavy snow" instead of the display: "60% of the possibilities are heavy snow." One of ordinary skill in the art will appreciate that many other variations are possible in addition to the examples provided above.

Thus, the proximity predictor receives the location requiring the nearcast and the time and/or time interval required for the nearcast, and outputs the selected PType and PRate distribution for the given location and time.

In some situations where efficiency is desired, the proximity predictor according to this alternative embodiment may be advantageous over the embodiment shown in FIG. This other embodiment can be implemented using processing capabilities that are much smaller than the embodiment of FIG. However, the embodiment of Figure 2 may be more stable than the other embodiment described above in providing a more detailed and accurate snapshot of weather forecast data for any given location and time.

Map generation module

The map generation module 310 can be a web-based module and/or a GPS-based module for generating paths and indications.

These web modules based on one of the examples may include Mapquest ^TM, Yahoo ^TM Maps, Google ^TM maps. In this type of module, data relating to the generation of paths and/or maps is stored on a remote server 250 accessible via a telecommunications network 254, such as the Internet. Using such modules, the user may request to view a map of a location (eg, city, town, country, street, etc.) and/or may request an indication from a first location A to a second location B, The module can return a path from A to B as indicated in Figure 3a and/or one of the paths plotted on one of the maps as shown in Figure 3b. In one embodiment, map generation module 310 can provide one or more path selections for the user to select from, as exemplified in Figure 3c.

In another embodiment, the data can be downloaded and/or pushed from the server 250 to the above. The computing device of the embodiment is implemented whereby the path and/or map can be displayed without accessing the remote server 250.

In an embodiment, the map generation module 310 can also include a GPS navigation device 330 that is coupled to one of the satellites 332 to determine the current location of the user. GPS may be embedded in one unit, such as a portable IPhone ^TM like apparatus. In another example, GPS navigation device 330 may be embedded in one of a series of means, such as a Garmin ^TM manufactured Magellan ^TM or the like of the portable GPS navigation device. An example of a navigation device is shown in FIG.

Smart module

In one embodiment, the smart module 312 can be coupled to a user interface to receive user input such as identification of locations A and B, departure time, and user preferences regarding locations requiring proximity prediction.

The smart module 312 transmits the identification of the locations A and B to the map generation module 310, and in response, receives a map of the path between A and B, as illustrated in Figure 3b.

Based on user preferences, the smart module 312 can identify/receive key points along the path. For example, the user may choose to view the neighboring forecasts of the major cities along the path or incrementally view the neighboring forecasts along the path by, for example, 30 Km. The smart module 312 sends the location information associated with each keypoint to the proximity predictor 200 along with the same time or time interval. In response, smart module 312 receives the proximity prediction from proximity predictor 200 and adds some or all of these proximity predictions (depending on the resolution and size of the display and the zoom level) to the map received from map generation module 310. Above, as illustrated in Figure 5a.

In an embodiment, the smart module 312 can transmit the location information of each key point along with the current time, so that the user can see the current weather conditions in different locations along the path.

In another embodiment, the smart module 312 can estimate the arrival time of one of the key points, which represents the estimated time that the user wishes to reach a given key point. The estimate of the arrival time can depend on several factors, including: departure time (unless specified by the user, otherwise it is treated as if Current time), the distance between the starting point and the respective key points, the traffic information received from the map generation module 310 (or another source), the weather information, the current speed, and the path between the current position and the respective key points The speed limit associated with the segment.

As discussed above, the map generation module 310 can provide different path selections whereby the user can view weather conditions along different key points and select one of the paths for navigation.

In one embodiment, if the user views the map using a computing device having GPS and/or webpage functions, the proximity prediction can be updated on the map based on the user's advancement on the route and changes in weather conditions.

In an embodiment, as illustrated in Figure 5b, a proximity prediction can be provided on the map along with the time/time interval associated with each nowcast. In one embodiment, the time displayed on the map is estimated by the smart module 312 based on the current location, speed, and meteorological and traffic conditions.

Figure 6 is an example of one of the network environments in which embodiments may be practiced. The proximity predictor 200 can be implemented on a server 250 that can be accessed by a plurality of client computers 252 via a telecommunications network 254. Client computer 252 can include, but is not limited to, a laptop, a desktop computer, a portable computing device, a tablet, and the like. Using a client computer 252, each user can enter an indication between two locations and preferably enter a departure time (or use the current time instead of the departure time). This information is sent to the remote server 250 via a telecommunications network 254. The server 250 returns an indication list with the same map containing one of the paths from position A to B along with the proximity prediction at certain key points on the path. As discussed in connection with FIG. 2, the server accesses the weather source 201 via a telecommunications network 254. The server 250 stores map data thereon and also accesses a map source 320 provided by a third party.

Preferably, the client computer 252 is provided with a GPS function. In this case, as discussed above, the computing device can provide an update to the server 250 to update the proximity prediction along the path.

7 is an example of another network environment in which embodiments of one of the methods for providing proximity prediction can be practiced. In this embodiment, the user enters the destination on a GPS navigation device. And view the map. The GPS navigation device treats the starting position as the current position. The current location and final destination, along with a predetermined path selected by a satellite 332, may be transmitted to the server 250 via satellite 332. The smart module 312 implemented in the server 250 can transmit the proximity prediction along the key point of the path, and send one of the proximity prediction and the key point identification to the GPS navigation device 330 for addition to the GPS navigation device 330. A map on the display.

FIG. 8 illustrates a computer implementation method 800 for generating a map. The method 800 includes receiving a map request 810 including a departure location and a destination location. In response to receiving the map request 810, the computer-implemented method 800 includes obtaining map material 820 that, when implemented on a display device, causes the display device to display a map display including one of the paths drawn between the departure location and the destination location 830. The method 800 also performs a proximity prediction 840 that obtains one or more key points along the path, and modifies the map material 820 for the representation 850 of one of the proximity predictions for each of the key points plotted on the map (including the addition of one Visual indicator). The computer implementation method 800 ultimately outputs the modified map data to display 860 to a user.

Hardware and operating environment

9 is an illustrative diagram of one suitable operational operating environment in which one embodiment of the present invention may be practiced. The following description is associated with FIG. 9 and is intended to provide a brief general description of one suitable computer hardware and a suitable computing environment that can be combined to implement the embodiments. The implementation of the embodiments is not required to all of the components, and variations in the configuration and type of components may be made without departing from the spirit or scope of the embodiments.

Although not required, the embodiments are described in the general context of computer-executable instructions, such as a computer (such as a personal computer, a handheld or handheld computer, intelligent). Telephone) or an embedded system (such as a consumer device or a computer in a dedicated industrial controller) to perform. In general, program modules contain routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types.

Moreover, those skilled in the art will appreciate that the embodiments can be practiced using other computer system configurations including handheld devices, microprocessor systems, microprocessor-based or programmable consumer electronics, Internet PCS, small computers, large computers, cellular phones, smart phones, display pagers, radio frequency (RF) devices, infrared (IR) devices, personal digital assistants (PDAs), laptops, wearable computers, A tablet, an IPOD device or an IPAD device family manufactured by Apple Computer, an integrated device that combines one or more of the foregoing devices, or any other computing device capable of performing the methods and systems described herein. The embodiments may also be practiced in a decentralized computing environment in which tasks are performed by remote processing devices that are coupled through a communications network. In a distributed computing environment, the program modules can be located in the local and remote memory storage devices.

The exemplary hardware and operating environment of FIG. 9 includes a general purpose computing device in the form of a computer 720. The computer 720 includes a processor unit 721, a system memory 722, and a system bus 723. The system bus 723 will include Various system components of system memory are operatively coupled to processing unit 721. There may be only one processing unit 721 or more than one processing unit 721 such that the processor of computer 720 includes a single central processing unit (CPU) or a plurality of processing units collectively referred to as a parallel processing environment. The computer 720 can be a conventional computer, a decentralized computer, or any other type of computer; these embodiments are not so limited.

System bus 723 can be any of several types of bus bars, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory may also be referred to simply as a memory, and includes a read only memory (ROM) 724 and a random access memory (RAM) 725. A basic input/output system (BIOS) 726, which contains one of the basic routines, such as facilitating the transfer of information between components within computer 720 during startup, is stored in ROM 724. In an embodiment of the invention, the computer 720 further includes a hard disk drive 727 for reading from or writing to a hard disk (not shown) for reading from a removable disk 729. Take or write to a disk drive 728 of a removable disk 729 and for use with a removable optical disk 731 (such as a CD ROM or other light) The learning medium is read or written to one of the disc players 730 of the removable disc 731. In an alternative embodiment of the invention, volatile or non-volatile RAM is used to simulate the functions provided by hard disk drive 727, magnetic disk 729, and optical disk drive 730 to save power and reduce the size of the system. In such alternative embodiments, the RAM can be fixed in a computer system, or it can be a removable RAM device such as a compact flash memory card.

In one embodiment of the present invention, the hard disk drive 727, the magnetic disk drive 728, and the optical disk drive 730 are respectively connected to the system bus 723 by a hard disk drive interface 732, a disk drive interface 733, and a disk drive interface 734. The disk drives and their associated computer readable media provide non-volatile storage of computer readable instructions, data structures, program modules and other data of the computer 720. Those skilled in the art will appreciate that any type of computer readable medium (such as magnetic cymbals, flash memory cards, digital video discs, Bernoulli cassettes, random access) that can store data that can be accessed by a computer. Memory (RAM), read only memory (ROM), etc. can be used in an exemplary operating environment.

A plurality of program modules can be stored on the hard disk, the magnetic disk 729, the optical disk 731, the ROM 724 or the RAM 725. The programming module includes an operating system 735, one or more application programs 736, other program modules 737, and program data 738. . A user can input commands and information into the personal computer 720 through, for example, a keyboard 740 and an indicator device 742. Other input devices (not shown) may include a microphone, joystick, game board, satellite dish, scanner, touch sensitive panel, and the like. These and other input devices are typically coupled to processing unit 721 via a serial port 746 coupled to the system bus, but may be connected by other interfaces such as a parallel port, game cartridge, or a universal serial bus (USB). . Additionally, the input of the system can be provided by a microphone to receive the audio input.

A monitor 747 or other type of display device is also coupled to system bus 723 via an interface, such as a video adapter 748. In one embodiment of the invention, the monitor includes a liquid crystal display (LCD). In addition to monitors, computers typically include other peripheral output devices (not shown), such as speakers and printers. The monitor can include a touch sensitive surface Allows the user to interface with the computer by pressing or touching the surface.

Computer 720 can operate in a network environment using logical connections to one or more remote computers, such as a remote computer 749. These logical connections are made up of a portion of a communication device or computer 720 coupled to one of the portions of computer 720; embodiments are not limited to a particular type of communication device. The remote computer 749 can be another computer, a server, a router, a network PC, a client, a peer device, or other common network node, and although typically includes many of the above described with respect to the computer 720 or All components, but only one memory storage device 750 is shown in FIG. The logical connection depicted in Figure 9 includes a local area network (LAN) 751 and a wide area network (WAN) 752. These network environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet.

When used in a LAN network environment, computer 720 is coupled to regional network 751 via a network interface or adapter 753, which is a type of communication device. When in a WAN network environment, computer 720 typically includes a data machine 754, a type of communication device, or any other type of communication device for establishing communications over a wide area network 752, such as the Internet. Data machine 754, internal or external, can be coupled to system bus 723 via serial port 746. In a networked environment, the program modules described with respect to the personal computer 720 or portions thereof can be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other components and communication means for establishing a communication link between computers can be used.

The hardware and operating environment in which the embodiments of the present invention may be practiced are described. A computer that can be combined to practice embodiments of the present invention can be a conventional computer, a handheld or palmtop computer, a computer in an embedded system, a distributed computer, or any other type of computer; limit. Such a computer typically includes one or more processing units as its processor and a computer readable medium such as a memory. The computer may also include a communication device such as a network adapter or a data modem such that it can communicatively couple to other computers.

Although the preferred embodiment has been described above and illustrated in the drawings, it is familiar to the skilled art. The operator will understand that modifications may be made without departing from the disclosure. Such modifications are considered to include possible variations in the scope of the present disclosure.

200‧‧‧ Proximity forecaster

201-1‧‧‧Weather observation

201-2‧‧ ‧ observation

201-3‧‧‧Weather radar

201-4‧‧‧ Satellite

201-5‧‧‧ Numerical weather prediction model output

201-6‧‧‧Weather Forecasting and Consulting

202‧‧‧Rain type (PType) distribution forecaster

204‧‧‧Rain rate (PRate) distribution predictor

206‧‧‧ Forecast combiner

Claims (15)

A computer implemented method for generating a map for ground navigation and including a weather forecast, the method comprising: receiving a map request including a departure location and a destination location; obtaining a map in response to receiving the map request And causing the display to display at least a portion of a map when the map material is implemented on a display of a ground navigation device, the at least one portion of the map including the starting location, the destination location, and the departure location a path between a key point between the destination locations; obtaining an estimate of the arrival time of the ground navigation device reaching the key point; obtaining a weather forecast for the geographic location, the weather forecast of the geographic location including the key point And a time period including the estimated arrival time, wherein the weather forecast including the key point and the geographic location during the time period including the estimated arrival time is different from the departure location and the destination location Weather forecast; for the time when the key point is included and the estimated arrival time is included The period of location, modify the map data such that it represents one of the weather forecast includes a visual indicator; and an output of the modified data to map a computing device to present to a user. The method of claim 1, wherein obtaining the weather forecast comprises transmitting location information associated with the keypoint and the estimated time of arrival to a weather forecasting server, and receiving the weather forecast from the weather forecasting server. The method of claim 2, wherein the estimated arrival time is based on a current time set. The method of claim 2, wherein the departure location is a current location of one of the ground navigation devices. The method of claim 4, wherein the estimated arrival time is determined based on at least one of: a departure time, a weather condition, a speed limit, a current speed, and a distance between the current position and the key point. The method of claim 5, further comprising: receiving updated location information indicating the current location of the computing device; obtaining an update arrival time based on the updated location information to arrive at the keypoint; obtaining the key at the update arrival time One of the geographical locations of the points updates the weather forecast; and modifies the map data to include a visual indicator representative of the updated weather forecast. The method of any one of claims 1 to 6, wherein the map material is generated using information stored by the local end. The method of any one of claims 1 to 6, wherein the map request is forwarded to a remote server and the map material is obtained from the remote server. The method of any one of claims 1 to 6, wherein the key point is selected based on user preferences. The method of claim 9, wherein the key point is along a major city on the path. The method of claim 9, wherein the key point is a plurality of equidistant points along the path. The method of any one of claims 1 to 6, wherein the weather forecast includes a probability of a particular rainfall type of one of a particular rainfall rate. The method of claim 12, wherein the weather forecast is generated by combining the following probability distributions: a plurality of rainfall types and a probability of one probability associated with each rainfall type Cloth; and a probability distribution of a plurality of rainfall rates and one probability associated with each rainfall rate. A ground navigation device for generating a map including a weather forecast, the ground navigation device comprising: one or more processors, a display device, and a memory storing instructions of the one or more processors, And when the one or more processors execute the instructions stored in the memory, causing the apparatus to: receive a map request including one of a departure location and a destination location; and in response to receiving the map request, obtain Map material displayed on the display device to display at least a portion of the map material obtained by the path between the departure location, the destination location, and a key point between the departure location and the destination location Obtaining an estimated time of arrival at one of the key points; obtaining a weather forecast of the geographic location of the key point at the estimated time of arrival, including the key point and the geographic location of the time period including the estimated time of arrival The weather forecast is a plurality of weather forecasts different from the departure location and the destination location; for including the key point and including the estimate The location of the period of time for a time, such that it modifies the map data includes information indicating one of the visual indicator weather forecasting; and displaying the modified data in the map on the display device. A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the method of any one of claims 1 to 6.