Vehicle navigation method and device, electronic equipment and medium

Abstract

The invention provides a vehicle navigation method and device, electronic equipment, a computer readable storage medium and a computer program product, and relates to the field of computers, in particular to the technical fields of automatic driving, map navigation and data processing. According to the implementation scheme, position information of a target vehicle is determined; gridding an area in a preset distance range taking the position information as a center to determine a plurality of grid areas; for each grid area, determining the probability that a corresponding miraculous view exists in the grid area, the corresponding miraculous view being a preset miraculous view; in response to the fact that the probability that the corresponding mimicry exists in the target grid area is larger than the corresponding threshold value, first prompt information is generated, the first prompt information is used for indicating that the corresponding mimicry is predicted to exist in the target grid area, and the target grid is a grid area; and in response to a received trigger instruction aiming at the first prompt information, generating navigation information taking the target grid area as a destination so as to guide the target vehicle to go to the target grid area.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, in particular to the fields of autonomous driving, map navigation, and data processing technology, and specifically to a vehicle navigation method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Map navigation is a comprehensive field that leverages multiple technologies, including satellite positioning, network communications, and geographic information systems, to provide users with services such as map display, location tracking, route planning, and navigation guidance. It is widely used in transportation, logistics, tourism, smart devices, and other areas, profoundly changing people's travel methods and lifestyles.

[0003] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides a vehicle navigation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0005] According to one aspect of the present disclosure, a vehicle navigation method is provided, comprising: determining location information of a target vehicle; gridding an area within a preset distance range centered on the location information to determine a plurality of grid areas; for each of the plurality of grid areas, determining a probability of a corresponding wonder existing in the grid area, wherein the corresponding wonder is a wonder among at least one preset wonder; in response to determining that the probability of a corresponding wonder existing in a target grid area is greater than a corresponding threshold, generating first prompt information, wherein the first prompt information is used to indicate that the corresponding wonder is predicted to exist in the target grid area, wherein the target grid is at least one grid area among the plurality of grid areas; and in response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as a destination to guide the target vehicle to the target grid area.

[0006] According to another aspect of the present disclosure, a vehicle navigation device is provided, comprising: a position information determination module configured to determine position information of a target vehicle; a grid area determination module configured to grid an area within a preset distance range centered on the position information to determine a plurality of grid areas; a probability determination module configured to determine, for each grid area of ​​the plurality of grid areas, a probability that a corresponding wonder exists in the grid area, wherein the corresponding wonder is a wonder among at least one preset wonder; a prompt information generation module configured to generate first prompt information in response to determining that the probability that a corresponding wonder exists in a target grid area is greater than a corresponding threshold, wherein the first prompt information is used to indicate that the corresponding wonder is predicted to exist in the target grid area, wherein the target grid is at least one grid area among the plurality of grid areas; and a navigation information generation module configured to generate navigation information with the target grid area as a destination in response to receiving a trigger instruction for the first prompt information, so as to guide the target vehicle to the target grid area.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method described in the present disclosure.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method described in the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements the method described in the present disclosure when executed by a processor.

[0010] According to one or more embodiments of the present disclosure, by dynamically predicting the probability of occurrence of natural wonders, dynamic path planning and personalized wonder recommendations are provided during the navigation process, thereby enhancing the emotional value and interactive experience of the navigation system.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0013] Figure 1 A schematic diagram illustrating an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure; Figure 2 A schematic flow chart of a vehicle navigation method according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of a driving scenario for natural wonder prediction according to an embodiment of the present disclosure is shown; Figure 4 A structural block diagram of a vehicle navigation device according to an embodiment of the present disclosure is shown; and Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0014] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0015] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0016] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0017] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0018] Figure 1FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0019] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the vehicle navigation method.

[0020] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0021] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0022] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to receive predicted wonder points, issue travel instructions, etc. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0023] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service kiosks, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as Google Chrome OS); or various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablets, personal digital assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices and internet-enabled gaming devices. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may utilize various communication protocols.

[0024] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0025] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0026] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0027] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0028] In some embodiments, server 120 may be a server in a distributed system or a server integrated with blockchain. Server 120 may also be a cloud server or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0029] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as navigation information, path planning, spectacle prediction algorithms, etc. The databases 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0030] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0031] Figure 1The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0032] Map navigation is a comprehensive field that leverages multiple technologies, including satellite positioning, network communications, and geographic information systems, to provide users with services such as map display, location tracking, route planning, and navigation guidance. It is widely used in transportation, logistics, tourism, smart devices, and other areas, profoundly changing people's travel methods and lifestyles.

[0033] Traditional maps are unable to predict and understand the needs of viewing natural wonders. Astronomy or weather apps aren't integrated with navigation systems, making them ineffective for driving. Users are unable to predict or determine the best viewing locations, times, viewing angles, and routes for natural wonders.

[0034] Therefore, according to an embodiment of the present disclosure, a vehicle navigation method is provided. Figure 2 A flowchart of a navigation method according to an embodiment of the present disclosure is shown. Figure 2 As shown, the navigation method 200 includes: determining the location information of the target vehicle (step 210); gridding an area within a preset distance range centered on the location information to determine a plurality of grid areas (step 220); for each grid area of ​​the plurality of grid areas, determining a probability that a corresponding wonder exists in the grid area, wherein the corresponding wonder is a wonder among at least one preset wonder (step 230); in response to determining that the probability that the corresponding wonder exists in the target grid area is greater than a corresponding threshold, generating first prompt information, wherein the first prompt information is used to indicate that the corresponding wonder is predicted to exist in the target grid area, wherein the target grid is at least one grid area among the plurality of grid areas (step 240); and in response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as the destination to guide the target vehicle to the target grid area (step 250).

[0035] According to an embodiment of the present disclosure, the target vehicle's location information can be obtained by, for example, obtaining real-time positioning data of the target vehicle via the vehicle's CAN bus. The preset distance range can, for example, be a range of 100 kilometers around the target vehicle, which can be a potential observation area that the target vehicle may reach during the existence of the spectacle.

[0036] In some examples, gridding an area within a preset distance from the location information may refer to dividing an area within a preset distance (e.g., 100 kilometers) from the location of the target vehicle into multiple grid areas of, for example, 1000 m x 1000 m. This division method may be combined with meteorological data (e.g., China Meteorological Administration FY-4A satellite cloud Figure 1 km×1km resolution), as well as the accuracy adaptation of geographic data, thereby improving the accuracy of spectacle prediction.

[0037] Figure 3 FIG. 1 shows a schematic diagram of a driving scenario for natural wonder prediction according to an embodiment of the present disclosure. Figure 3 As shown, when target vehicle 301 is traveling along road 302 or parked at a corresponding location on road 302, the area within a preset range surrounding the target vehicle can be divided into multiple grid areas based on the current target vehicle's location information. Step 230 can be performed for each grid area to determine the probability of the presence of at least one of the wonders in that area. After performing the above-described wonder probability determination operation for each grid area, a target grid area can be determined from the multiple divided grid areas to guide the target vehicle to the target grid area.

[0038] In some embodiments, the corresponding wonder may refer to at least one of the preset wonders, such as a rainbow, a meteor shower, a sea of ​​clouds, and rime. The probability of occurrence of different wonders may be inferred by using corresponding probability calculations.

[0039] In some embodiments, the corresponding thresholds can be preset standards corresponding to the probability of occurrence of each wonder. Setting the thresholds can filter out high-probability observation areas. The first prompt information can be presented in the form of a wonder icon on the in-vehicle map. For example, a rainbow icon can be set in the target grid area with the highest predicted probability of a rainbow, and a meteor shower icon can be set in the target grid area with the highest predicted probability of a meteor shower. The trigger instruction can, for example, be a user clicking a "Go Now" button associated with the first prompt information to generate navigation information with the target grid area as the destination.

[0040] Therefore, the spatial accuracy of spectacle probability calculation is improved through vehicle positioning and grid partitioning, and the navigation system's predictive ability in spectacle viewing scenes is enhanced.

[0041] According to an embodiment of the present disclosure, the at least one wonder includes a rainbow wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: obtaining the rainfall probability and the solar elevation angle within a first preset time period corresponding to the grid area; in response to determining that the rainfall probability is greater than a first preset threshold and the solar elevation angle is within a first preset range, performing an operation of determining the probability of the rainbow wonder existing in the grid area; and in response to determining that the rainfall probability is not greater than the first preset threshold or the solar elevation angle is not within the first preset range, not performing the operation of determining the probability of the rainbow wonder existing in the grid area.

[0042] In some embodiments, the first preset time period can be the current or future hour. This one-hour window can cover the potential cycle of a rainbow from formation to dissipation. In some examples, the probability of rainfall can be obtained from meteorological data obtained through, for example, the National Meteorological Administration's open interface (API) or regional automatic weather stations. For example, the probability of rainfall in the current or future time period can be obtained once every preset time period (e.g., for rainfall information prompts). In this way, the most recently obtained data can be directly accessed when making rainbow predictions.

[0043] In the present disclosure, the solar elevation angle is also called the solar altitude angle, which refers to the angle between the incident direction of sunlight and the ground plane. This angle can reflect the height of the sun in the sky. In some embodiments, it can be calculated based on the geographical location of the observation point in the grid area and the current time, relying on data from, for example, meteorological and astronomical observation centers. Since the difference in solar elevation angle within 100 kilometers around the vehicle is very small, the calculation can also be simplified. For each grid area, its observation point can be, for example, the center point of the grid, such as Figure 3 The center point of the grid area 303 shown in FIG.

[0044] In some examples, the first preset threshold may be 40%, for example. A rainfall probability below this threshold may not provide sufficient water vapor, and the basic conditions for rainbow formation may be considered to be unavailable. The first preset range may be, for example, 15°-50°, because below 15° or above 50°, the angle of sunlight refraction may not meet the requirements for a visible rainbow.

[0045] In some embodiments, after obtaining the aforementioned rainfall probability and sun elevation angle corresponding to a grid area, a determination can be made as to whether the rainfall probability is greater than a first preset threshold (e.g., >40%) and the sun elevation angle is within a first preset range (e.g., 15°-50°). If these conditions are met, the rainbow probability calculation operation can be performed. However, if the rainfall probability is lower than the first preset threshold or the sun elevation angle is outside the first preset range, a rainbow may not form, and in this case, the calculation operation can be skipped to reduce wasted computations.

[0046] Therefore, by clarifying the prerequisites for calculating the probability of rainbow spectacle, we can avoid redundant calculations in the vehicle-mounted or remote systems and ensure that subsequent calculations are only performed on grids that meet the basic conditions for rainbow formation. This significantly improves the accuracy of rainbow spectacle probability prediction and provides a basis for generating effective prompt information and navigation information.

[0047] When the at least one wonder includes a rainbow wonder, according to an embodiment of the present disclosure, determining the probability of the corresponding wonder existing in the grid area may include: determining the probability of the rainbow wonder existing in the grid area by the following operations: obtaining the rainfall probability corresponding to the grid area within a second preset time period; obtaining the low-altitude cloud cover percentage data and the sun elevation angle data corresponding to the grid area; obtaining the first scoring data corresponding to the grid area, wherein the first scoring data is used to identify the obstruction of the horizon of the grid area; and determining the probability of the rainbow wonder existing in the grid area based on the rainfall probability, the low-altitude cloud cover percentage data, the sun elevation angle data and the first scoring data.

[0048] In some embodiments, the second preset time period can be, for example, a 5-minute time interval 2.5 minutes before and after the current time, for example, to calculate the probability of rainfall at 10:00 When , the average rainfall probability from 9:57:30 to 10:02:30 can be taken. The rainfall probability can be either a probability value or a value standardized according to a preset rule, for example, the rainfall probability When the probability value is 0, the rainfall probability can be standardized according to the preset rules. It can be recorded as 0.5, the probability of rainfall It can be recorded as 0.8, It can be recorded as 1.0, etc.

[0049] In some embodiments, low-altitude cloud cover percentage data and solar elevation angle data It can be data within the current or next hour, or data at the current moment, or within a preset time window before and after the current moment (such as the second preset time period). It can refer to the proportion of low-altitude clouds with a cloud height of less than 2000m. This value can be obtained based on the acquired meteorological data. After mapping the longitude and latitude to a 1000m×1000m grid, the proportion of low-altitude clouds corresponding to the grid area is determined as the low-altitude cloud proportion data.

[0050] In some embodiments, when determining the proportion of low-altitude cloud cover corresponding to each grid, it can be first determined whether the grid is a grid in the sun-backlit area. For example, the sun-backlit area can be a fan-shaped area with the sun-backlit direction as the axis of symmetry, for example, a 60° fan-shaped area formed by 30° to the left and right of the sun-backlit direction. For example, if any area of ​​a grid is in the sun-backlit area or a preset proportion of areas are in the sun-backlit area, the grid can be regarded as being in the sun-backlit area. When it is determined that the grid is in the sun-backlit area, the proportion of low-altitude cloud cover corresponding to the grid area can be determined based on the meteorological data as described above, such as 60%, 30%, etc.; and when it is determined that the grid is not in the sun-backlit area, it can be directly set to a maximum value, such as 1 (that is, the proportion of low-altitude cloud cover is a value between 0 and 1).

[0051] It's generally understood that rainbows form in the direction of the sun's backlight, but low-altitude cloud cover can obscure them. Therefore, this data can be used to quantify the impact of cloud cover on rainbow observations. Therefore, if precipitation conditions permit but low-altitude cloud cover is too high in the sun-backlit area, rainbows may be difficult to observe. In this case, the low-altitude cloud cover data corresponding to all grid cells outside the sun-backlit area is a single value, with a maximum value of 1.

[0052] In some embodiments, the sun elevation angle data It can be obtained based on, for example, meteorological or astronomical data, the geographical location of the grid center point and the current time. Specifically, for example, it can be characterized using a piecewise function after being standardized according to a preset rule. For example, when the solar elevation angle θ<15° or θ>50°, =0; when 15°≤θ<22°, =(θ-15°) / (22°-15°); when 22°≤θ≤42°, =1; when 42°<θ≤50°, =(50°-θ) / (50°-42°).

[0053] In some embodiments, the first scoring data The obstruction of the horizon in the grid area can be identified. For example, this can be obtained by taking the altitude of the highest point within 1 km of the observation point (e.g., the grid center) in the direction of the sun's backlight, subtracting it from the altitude of the observation point in the grid area, and then performing a normalization process. The normalization process can be, for example, 1-(the above difference / preset altitude value). For example, if the altitude of the highest point within 1 km is 200 meters higher than the altitude of the observation point, the score is 1-200 / 1000 = 0.8, which is used to quantify the impact of terrain obstruction on rainbow observation.

[0054] According to an embodiment of the present disclosure, the probability of the rainbow wonder existing in the grid area is determined based on the following formula: : in, Indicates the preset correction coefficient, represents the rainfall probability, represents the proportion of low-altitude cloud cover, represents the solar elevation angle data, Indicates the first scoring data.

[0055] In some embodiments, when determining the probability of a rainbow spectacle, the normalized rainfall probability can be used as , low-altitude cloud cover data , solar elevation angle data , first rating data , combined with the empirical correction coefficient α (for example, the base value is 0.8, 0.85 in summer and 0.75 in winter), the probability value of the rainbow spectacle is obtained.

[0056] In some embodiments, the rainfall probability obtained or calculated as described above , low-altitude cloud cover percentage data , solar elevation angle data , first rating data , and the empirical correction coefficient α, the probability of the rainbow spectacle can be obtained according to the above formula When a grid , the grid can be marked as a “high probability observation area”.

[0057] Therefore, this scheme quantifies the impact of each parameter on rainbow observation by obtaining and standardizing the key parameters of rainbow formation, significantly improving the accuracy of the probability prediction of rainbow spectacle, so as to quickly lock the rainbow observation area during navigation.

[0058] It is understandable that the description of determining the probability of occurrence of the word rainbow based on corresponding meteorological data in the above embodiment is merely exemplary and is not intended to be limiting herein.

[0059] According to an embodiment of the present disclosure, the at least one spectacle includes a meteor shower spectacle, wherein determining the probability of the corresponding spectacle existing in the grid area includes: obtaining the zenith hourly occurrence rate of the grid area within a third preset time in the future; upon determining that the grid area has a zenith hourly occurrence rate greater than a preset value within the third preset time period in the future, performing an operation of determining the probability of the existence of a meteor shower spectacle in the grid area; and upon determining that the grid area does not have a zenith hourly occurrence rate greater than a preset value within the third preset time period in the future, not performing the operation of determining the probability of the existence of a meteor shower spectacle in the grid area.

[0060] In some embodiments, the third preset time period in the future can be the next 24 hours, and the 24-hour time window can completely cover the effective observation period of most meteor showers; the zenith hourly occurrence rate can be the ZHR in astronomy, that is, the zenith hourly flow rate, which usually refers to the number of meteors that can be observed per hour under ideal observation conditions (the observer is at the zenith of the meteor shower radiant point, there are no clouds or light pollution, the view is unobstructed, and the weather is clear). For example, its data can be obtained by connecting to the International Astronomical Union (IAU) database.

[0061] When the ZHR is below 20, the number of meteors observed per hour is extremely small (usually less than 20), resulting in a poor user observation experience and lack of practical observation value. Therefore, the default value can be set to 20 based on astronomical observation experience. After obtaining the ZHR data for the grid area for the next 24 hours, you can first determine whether the ZHR is greater than 20. If it is greater than 20, it indicates that the meteor shower activity intensity in the grid area will meet the standard for the next 24 hours, and the meteor shower spectacle probability determination operation can be performed. If it is not greater than 20, it indicates that the meteor shower activity is weak, and the meteor shower spectacle probability determination operation can be performed in this case.

[0062] Therefore, by clarifying the main parameter conditions before calculating the probability of meteor shower spectacle, redundant calculations of vehicle-mounted or remote systems are avoided.

[0063] When the at least one spectacle includes a meteor shower spectacle, according to an embodiment of the present disclosure, determining the probability of the corresponding spectacle existing in the grid area may include: determining the probability of the meteor shower spectacle existing in the grid area by the following operations: obtaining the zenith hourly appearance rate and high-altitude cloud cover percentage data corresponding to the grid area; obtaining the light pollution level data and the altitude correction coefficient corresponding to the grid area, wherein the altitude correction coefficient is used to represent the data after the altitude of the grid area is standardized; and determining the probability of the meteor shower spectacle existing in the grid area based on the zenith hourly appearance rate, high-altitude cloud cover percentage data, the light pollution level data and the altitude correction coefficient.

[0064] In some embodiments, the zenith hourly appearance rate corresponding to each grid area can be either the current moment or within a preset time window before and after the current moment, and is not limited here. Furthermore, the zenith hourly appearance rate corresponding to the grid area obtained at this time can be a standardized zenith hourly appearance rate. For example, numerical standardization can be performed based on the ratio of the current ZHR to the historical maximum ZHR of the meteor shower. For example, if the historical maximum ZHR of the Perseid meteor shower is 120 and the current ZHR is 100, the standardized value can be 100 / 120, thereby eliminating the impact of the ZHR baseline differences of different meteor showers.

[0065] In some embodiments, the high-altitude cloud cover percentage data This value can refer to the percentage of cloud cover above 6000m. This value can be directly obtained from meteorological data. Similarly, this value can be normalized to a range of 0 to 1. It is worth noting that when the cloud cover percentage is ≥ 60%, since high-altitude clouds may completely block meteors, the high-altitude cloud cover data can be forced to a value of 1.

[0066] In some embodiments, light pollution level data This can refer to an indicator that measures the interference of artificial light sources in a region (e.g., quantified by a value between 1 and 9), such as can be obtained from a global light pollution map. Furthermore, it can also be normalized according to level. For example, when the obtained light pollution level is 1, it can be normalized to 0; when the obtained light pollution level is 2, it can be normalized to 0.125; when the obtained light pollution level is 3, it can be normalized to 0.25... When the obtained light pollution level is 8, it can be normalized to 0.875; when the obtained light pollution level is 9, it can be normalized to 1.0. Higher levels indicate more severe light pollution and greater difficulty in meteor observation.

[0067] In some embodiments, the altitude correction factor This can be data normalized based on the altitude of the corresponding grid area to reflect the advantages of thin air and weak atmospheric extinction at high altitudes. Specifically, when the altitude is greater than 1000m, the coefficient can be set to 1.2, when the altitude is 500m≤altitude≤1000m, the coefficient can be set to 1.0, and when the altitude is less than 500m, the coefficient can be set to 0.8, thereby correcting the impact of altitude on the observation effect.

[0068] Through the above embodiment, when determining the probability of meteor shower spectacle, the standardized zenith hourly occurrence rate ZHR and high-altitude cloud cover percentage data can be used to determine the probability of meteor shower spectacle. , light pollution level data , altitude correction factor Combined together to calculate the probability of meteor shower spectacle.

[0069] According to an embodiment of the present disclosure, the probability of the presence of the meteor shower spectacle in the grid area is determined based on the following formula: :

[0070] Wherein, ZHR represents the hourly appearance rate of the zenith, Indicates the percentage of high-altitude cloud cover, Indicates the light pollution level data, represents the altitude correction factor, 、 、 and is the preset weight value.

[0071] In some embodiments, according to the zenith hourly occurrence rate ZHR and high-altitude cloud cover percentage data obtained or calculated as described above, , light pollution level data , altitude correction factor Taking weighted average, we can get the probability of a meteor shower spectacle. For example, when a grid , you can mark the grid as a "recommended observation area".

[0072] Therefore, by quantifying the influencing parameters of meteor shower observations and integrating the impact of astronomical, meteorological and geographical factors on observations, the accuracy of the probability prediction of meteor shower spectacle has been significantly improved.

[0073] According to an embodiment of the present disclosure, the at least one wonder includes a sea of ​​clouds wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: when it is determined that the terrain corresponding to the grid area is mountainous or hilly, and there is a continuous increase in relative humidity and an inversion layer at high altitude within a fourth preset time period in the future, performing an operation of determining the probability of the sea of ​​clouds wonder existing in the grid area; and when it is determined that the terrain corresponding to the grid area is not mountainous or hilly, or there is no continuous increase in relative humidity or no inversion layer at high altitude within the fourth preset time period in the future, not performing the operation of determining the probability of the sea of ​​clouds wonder existing in the grid area.

[0074] In some embodiments, the fourth preset time period can be, for example, the current or next six hours. Furthermore, relative humidity can refer to the percentage of water vapor pressure in the air to the saturated water vapor pressure at the same temperature, or the ratio of the absolute humidity of moist air to the maximum absolute humidity that can be achieved at the same temperature. It can also be expressed as the ratio of the water vapor partial pressure in moist air to the saturated pressure of water at the same temperature.

[0075] In some examples, relative humidity can be obtained through open meteorological data platforms such as the National Meteorological Administration and regional automatic weather stations. Because sufficient and continuously rising humidity is the water vapor foundation for the formation of sea of ​​clouds, the criterion for determining a continuously rising relative humidity can be, for example, an increasing trend in relative humidity obtained at multiple consecutive data collection points (e.g., every 10 minutes or every 5 minutes), to avoid misjudgments caused by short-term fluctuations.

[0076] Generally speaking, in the lower atmosphere, the temperature decreases with increasing altitude. However, at certain levels, the opposite phenomenon may occur, with the temperature increasing with increasing altitude. This phenomenon is called a temperature inversion. The atmospheric layer where a temperature inversion occurs is called an inversion layer. Without an inversion layer, water vapor easily diffuses, making it difficult to form a sea of ​​clouds. In some embodiments, the presence of an inversion layer at high altitude can be determined by vertical atmospheric temperature distribution information in meteorological data, such as temperature profiles in Micaps format data.

[0077] In some embodiments, after obtaining the terrain type, relative humidity trend for the current or next six hours, and the presence of an inversion layer for the corresponding grid area, the probability of a sea of ​​clouds being observed is determined only when the terrain is mountainous or hilly, relative humidity is continuously rising, and an inversion layer exists at high altitude. Therefore, by establishing a terrain and meteorological condition screening mechanism for calculating the probability of a sea of ​​clouds, redundant calculations in the vehicle-mounted or remote system are avoided, ensuring the accurate identification of areas with a high probability of observing a sea of ​​clouds.

[0078] When the at least one wonder includes the sea of ​​clouds wonder, according to an embodiment of the present disclosure, determining the probability of the corresponding wonder existing in the grid area may include: determining the probability of the sea of ​​clouds wonder existing in the grid area by the following operations: obtaining the relative humidity data corresponding to the grid area; ; Get the wind speed corresponding to the grid area to determine the wind speed influence coefficient , wherein the wind speed influence coefficient is used to represent the normalized wind speed data of the grid area; obtain the inversion layer intensity data corresponding to the grid area The inversion layer strength data is used to represent the normalized temperature difference of the inversion layer when there is an inversion layer at high altitude in the grid area; obtain the temperature lapse rate correction data corresponding to the grid area , wherein the temperature lapse rate correction data is used to represent the standardized data of the temperature lapse rate within the preset high altitude range of the grid area; based on the relative humidity data, the wind speed influence coefficient, the inversion layer intensity data and the temperature lapse rate correction data, the probability of the existence of the sea of ​​clouds in the grid area is determined.

[0079] In some embodiments, the wind speed can be obtained synchronously from the above-mentioned meteorological data, and the wind speed influence coefficient The wind speed can be standardized data. For example, when the wind speed is ≥10m / s, the wind speed influence coefficient can be 0.5 (strong wind significantly disturbs the sea of ​​clouds), when the wind speed is <5m / s, the wind speed influence coefficient can be 1.0 (light breeze does not affect the stability of the sea of ​​clouds), and between 5m / s and 10m / s, a linear transition can be used. For example, when the wind speed is 7m / s, the coefficient can be 1.0-(7-5) / (10-5)×(1.0-0.5)=0.8.

[0080] In some embodiments, the inversion layer strength data It can be obtained by normalizing the upper-altitude inversion layer temperature difference (the temperature difference between the upper and lower layers within the inversion layer) based on the atmospheric vertical temperature profile in meteorological data (such as data in Micaps format). For example, when the temperature difference is ≥3°C, the inversion layer strength data can be 1.0 (indicating a strong inversion layer with extremely stable sea of ​​clouds). When the temperature difference is 1°C or less and is less than 3°C, it can be linearly reduced to 0.5. When the temperature difference is less than 1°C, it can be 0 (indicating a weak inversion layer with no stable sea of ​​clouds). For example, when the temperature difference is 2°C, the strength data can be 0.75.

[0081] In some embodiments, the preset altitude range may refer to the altitude from near the ground to 1000m, so the temperature lapse rate may refer to the temperature change rate within this range. The temperature lapse rate correction data The data may be normalized for the lapse rate. Because a lapse rate between -0.6°C / 100m and -0.8°C / 100m is most conducive to water vapor condensation and the maintenance of sea of ​​clouds, in some examples, the normalization rule may be: within this range, the correction data is set to 1.0, and deviations from this range decay linearly to 0. For example, for a lapse rate of -0.5°C / 100m, the correction data may be 0.5.

[0082] According to an embodiment of the present disclosure, the probability of the presence of the sea of ​​clouds in the grid area is determined based on the following formula: :

[0083] in, Indicates the preset correction coefficient, Represents the relative humidity data, Indicates the wind speed influence coefficient, Indicates the inversion layer strength data, Indicates the temperature lapse rate correction data.

[0084] In some embodiments, determining the probability of a sea of ​​clouds spectacle The above standardized relative humidity data can be , wind speed influence coefficient , inversion layer strength data , Temperature lapse rate correction data , multiplied by the empirical correction coefficient γ1 (base value 0.7, plus 0.1 for mountainous terrain and minus 0.1 for hilly terrain), the final probability of the sea of ​​clouds is obtained. For example, if the standardized humidity data of a mountain grid is 1.0, the wind speed influence coefficient is 0.8, the inversion layer strength data is 0.9, the temperature lapse rate correction data is 1.0, and γ1=0.8, then the probability is 0.8×1.0×0.8×0.9×1.0=0.576. In addition, for example, the probability of the sea of ​​clouds can be The area is marked as "high probability sea of ​​cloud area".

[0085] Therefore, by collecting the meteorological parameters of sea of ​​clouds and performing probability calculations, the impact of water vapor, wind speed, inversion layer, and temperature environment on the sea of ​​clouds was effectively quantified, significantly improving the accuracy of the probability prediction of the sea of ​​clouds spectacle.

[0086] According to an embodiment of the present disclosure, the at least one wonder includes the hoarfrost wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: when it is determined that the temperature in the fifth future time period corresponding to the grid area is lower than a preset threshold and the air humidity is greater than a preset threshold, performing an operation of determining the probability of the hoarfrost wonder existing in the grid area; and when it is determined that the temperature in the fifth future time period corresponding to the grid area is not lower than the preset threshold or the air humidity is not greater than the preset threshold, not performing the operation of determining the probability of the hoarfrost wonder existing in the grid area.

[0087] In some embodiments, the fifth future time period can be set to the next 12 hours. This 12-hour window can fully cover the key process from water vapor condensation to ice crystal accumulation, avoiding missing the opportunity for rime formation due to a too short observation period. Temperature data can be obtained through open meteorological data platforms such as the National Meteorological Administration and regional automatic weather stations. Because the condensation process of rime must occur below freezing, water vapor cannot directly condense into ice crystals when the temperature is ≥0°C, and the temperature basis for rime formation is not met. Therefore, in some examples, the preset threshold can be 0°C.

[0088] Similarly, air humidity can be obtained synchronously from the aforementioned meteorological data. A preset threshold of 80% is possible, for example. Below this threshold, the air's water vapor content is insufficient, making it difficult to reach supersaturation and meet the water vapor conditions required for rime condensation. Therefore, after obtaining the temperature trend and humidity data for the next 12 hours corresponding to the corresponding grid area, the probability of rime condensation can only be determined if both the temperature is below 0°C and the humidity is greater than 80%.

[0089] Therefore, by screening the temperature and humidity conditions, grid areas with no potential for rime formation are effectively excluded, the redundant computing load of the on-board system is reduced, and the accuracy of rime observation and guidance information is improved.

[0090] When the at least one wonder includes the hoarfrost wonder, according to an embodiment of the present disclosure, determining the probability of the existence of the corresponding wonder in the grid area includes: determining the probability of the existence of the hoarfrost wonder in the grid area by the following operations: obtaining the average temperature data of the sixth time period in the future corresponding to the grid area; obtaining the relative humidity coefficient corresponding to the grid area, wherein the relative humidity coefficient is used to represent the data after the relative humidity of the grid area is standardized; obtaining the wind speed corresponding to the grid area to determine the wind speed influence coefficient, wherein the wind speed influence coefficient is used to represent the data after the wind speed of the grid area is standardized; obtaining the vegetation coverage data corresponding to the grid area; and determining the probability of the existence of the hoarfrost wonder in the grid area based on the average temperature data, the relative humidity coefficient, the wind speed influence coefficient and the vegetation coverage data.

[0091] In some embodiments, the sixth time period in the future can be set to the next 6 hours. This data can be obtained through open meteorological data platforms such as the National Meteorological Administration and regional automatic weather stations. Furthermore, in some examples, when the average temperature is greater than -3°C, it can be normalized to 0; when -8°C ≤ the average temperature ≤ -3°C, a linear calculation can be performed using the linear formula (-3°C - temperature) / (-3°C - (-8°C)); when the temperature is less than -8°C, it can be normalized to 1.

[0092] In some embodiments, the relative humidity coefficient It can be the result of normalizing the relative humidity of the grid area. For example, when the relative humidity is ≥90%, it can be 1.0, when it is 80%≤ and the humidity is <90%, it decreases linearly to 0.5, and when the humidity is <80%, it can be 0. For example, when the humidity is 85%, the coefficient can be 0.75. Wind speed can also be obtained from meteorological data. Wind speed influence coefficient It can also be a standardized coefficient. For example, when the wind speed is ≥5m / s, it is 0.5; when it is <2m / s, it is 1.0; and there is a linear transition between 2m / s and 5m / s. For example, when the wind speed is 3m / s, the coefficient can be 1.0-(3-2) / (5-2)×(1.0-0.5)=0.83, to quantify the interference of wind speed on the stable formation of rime.

[0093] In some embodiments, vegetation coverage data can be obtained from geographic data. Similarly, when the vegetation coverage is greater than 60%, the vegetation coverage data can be standardized to 0.8, and when it is less than 30%, it can be standardized to 0.3. For example, when the vegetation coverage of a grid is 70%, the data can be 0.8.

[0094] According to an embodiment of the present disclosure, the probability of the existence of the rime wonder in the grid area is determined based on the following formula: :

[0095] in, Indicates the preset correction coefficient, Indicates the average temperature data, Represents the relative humidity coefficient, Indicates the wind speed influence coefficient, Indicates the vegetation coverage data.

[0096] In some embodiments, when determining the probability of rime spectacle, the above-mentioned standardized average temperature data can be used. , relative humidity coefficient , wind speed influence coefficient , vegetation coverage data , multiplied by the empirical correction coefficient γ2 (base value 0.6, minus 0.1 in the dry north, plus 0.1 in the humid south). For example, if the standardized temperature data of a grid in the south is 0.5, the relative humidity coefficient is 0.9, the wind speed influence coefficient is 0.9, and the vegetation coverage data is 0.8, γ2=0.7, then the probability can be 0.7×0.5×0.9×0.9×0.8=0.2268. For example, the area can be marked as a "suitable area for rime formation".

[0097] Therefore, by collecting the temperature, humidity, wind speed and surface parameters of rime formation and standardizing them, the influence of each parameter on rime formation was effectively quantified, significantly improving the accuracy of the probability prediction of rime spectacle.

[0098] According to an embodiment of the present disclosure, generating a first prompt message in response to determining that the probability of the existence of a corresponding wonder in a target grid area is greater than a corresponding threshold includes: among the multiple grid areas, determining a preset number of grid areas whose probability of the existence of the corresponding wonder is greater than a corresponding preset threshold and which are closest to the target vehicle; and based on the preset number of grid areas, generating first prompt messages corresponding to the preset number of grid areas respectively.

[0099] In some embodiments, the corresponding preset threshold value may refer to the judgment criteria for the observation value of each wonder, such as the rainbow probability ≥ 0.6, meteor shower ≥ 0.75, sea of ​​clouds ≥ 0.6, rime ≥ 0.7, etc. mentioned above. The threshold setting can be determined based on the formation conditions of each wonder and the observation experience to reduce the error push to the user. For example, among all the grid areas formed, when it is determined that there is a grid area where the preset probability of the corresponding wonder is greater than the corresponding threshold, then the grid area can be determined as the target grid area. When there is a lot of data in the grid area, the preset number of grid areas closest to the target vehicle can be used as the target grid area. Furthermore, corresponding prompt information can be generated for each target grid area. For example, the first prompt information can be a dedicated wonder icon on the in-vehicle map, such as the icons corresponding to meteor showers, sea of ​​clouds, and rime.

[0100] Therefore, through the above embodiments, the purpose of effectively helping users to quickly lock the observation target closest to the target vehicle in a driving scenario can be achieved.

[0101] According to an embodiment of the present disclosure, generating a first prompt message in response to determining that the probability of the existence of a corresponding wonder in a target grid area is greater than a corresponding threshold includes: determining, among the multiple grid areas, a preset number of grid areas whose corresponding probabilities of the existence of the corresponding wonder are the highest; and generating, based on the preset number of grid areas, first prompt messages corresponding to the preset number of grid areas respectively.

[0102] In some embodiments, the predetermined number of grid areas with the highest probability can be obtained by sorting all grid areas with probabilities exceeding a threshold from high to low by probability value, and then determining the predetermined number of grid areas. If there are grid areas with the same probability, such as two grid areas with a meteor shower probability of 0.8, the grid area closer to the target vehicle can be further prioritized to ensure that the screening results have both high probability and accessibility.

[0103] Therefore, by superimposing the areas with the highest probability and the shortest distance, it can effectively help users quickly identify the optimal observation target area while driving.

[0104] According to an embodiment of the present disclosure, in response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as the destination includes: in response to receiving the trigger instruction for the first prompt information, determining the time required for the target vehicle to reach the target grid area; determining the duration of the corresponding wonder predicted to exist in the target grid area; and in response to determining that the required time is less than the duration, generating navigation information with the target grid area as the destination.

[0105] In some embodiments, the time required for the target vehicle to reach the target grid area can be achieved through the path planning algorithm of the on-board map. The duration of the corresponding wonder can refer to the effective observation window of each wonder, which can be set based on the formation characteristics and dissipation rules of the wonder itself. For example, a rainbow can last for 5 minutes due to the limitations of sunlight angle and rainfall duration; a meteor shower can last for 60 minutes due to its relative stability during periods of high flow; a sea of ​​clouds can last for 120 minutes due to its strong ability to maintain an inversion layer; and rime can last for 240 minutes due to the difficulty of ice crystals melting in low temperature environments. The above durations can be the estimated critical time for effective observation of the wonder.

[0106] Therefore, after obtaining the required time and the duration of the spectacle, a time comparison can be performed. Only when the required time is less than the duration of the spectacle will navigation information with the target grid area as the destination be generated. Furthermore, this process can also include route planning and adjustment during driving. That is, if the real-time estimated arrival time at the spectacle prediction point differs significantly from the originally planned time, and the calculated arrival time based on current road conditions indicates that the arrival time has exceeded the observation window, the user can be immediately reminded to cancel the trip. The generated navigation information can include specific driving routes, turn-by-turn directions, real-time traffic alerts, and other content, directly guiding the target vehicle to the target grid area.

[0107] According to an embodiment of the present disclosure, in response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as the destination includes: in response to determining that the required time is not less than the duration, generating second prompt information, wherein the second prompt information is used to indicate that the wonder is predicted to disappear when arriving at the target grid area; in response to receiving a trigger instruction for the second prompt information, generating navigation information with the target grid area as the destination.

[0108] In some embodiments, if the required time is not less than the duration, this indicates that the wonder will have exceeded the effective observation window and will likely disappear by the time the user reaches the target grid area based on current road conditions. In this case, a second prompt may be generated. This second prompt can be presented, for example, as a pop-up window on the in-vehicle map, clearly informing the user that the wonder may disappear upon arrival. The triggering instruction for this second prompt can be an action where the user chooses to continue traveling after viewing the pop-up window (e.g., clicking the "Continue" button in the pop-up window). In this case, navigation information with the target grid area as the destination can still be generated based on this instruction.

[0109] Therefore, by introducing a comparison and judgment mechanism between the required time and the duration, the generated navigation information is ensured to have actual observation value, avoiding the situation where users miss the wonders when they arrive due to long navigation time, thus ensuring the practicality of navigation guidance.

[0110] According to an embodiment of the present disclosure, during the process of the target vehicle heading towards the target grid area based on the navigation information, the corresponding wonders predicted to exist in the target grid area are continuously observed; and in response to observing that the preset indicator corresponding to the corresponding wonder changes by exceeding a preset amplitude value, a third prompt information is generated, wherein the third prompt information is used to indicate the state of the wonder when the target grid area is predicted to be reached.

[0111] In some embodiments, the preset indicator can be a parameter that characterizes the state of the wonder. For example, for a rainbow, the preset indicator can be the predicted probability of a rainbow wonder within 10 minutes. For meteor showers, it can be high-altitude cloud cover ; For the sea of ​​clouds, it can be the inversion layer intensity data within 1 hour For rime, the average temperature and relative humidity for the next 6 hours can be used. The preset amplitude value can be the critical standard for judging whether the amplitude of the preset index change affects the observation of the wonders. For example, the preset amplitude value of the rainbow can be within 10 minutes. A decrease of ≥ 25%, meteor showers can be caused by high-altitude cloud cover, for example A sudden rise of more than 20% can cause the sea of ​​clouds to Decrease ≥ 40%. This amplitude is based on the dissipation patterns of each wonder. Only when the indicator changes beyond this amplitude will it mean that the wonder's status will deteriorate significantly.

[0112] Therefore, when it is observed that the preset indicator changes by more than the preset amplitude value, a third prompt message can be generated to immediately remind the user that the wonder may disappear and recommend canceling the trip, thereby ensuring that the user can intuitively know the predicted status of the wonder when arriving at the target grid area.

[0113] Therefore, through dynamic observation of wonders and judgment of the change range of preset indicators during navigation, real-time tracking of the state of wonders is achieved, which prevents users from unknowingly going to areas that are no longer worth observing and provides users with a basis for decision-making.

[0114] According to the embodiments of the present disclosure, Figure 4As shown, a vehicle navigation device 400 is also provided, including: a position information determination module 410, configured to determine the position information of a target vehicle; a grid area determination module 420, configured to grid an area within a preset distance range centered on the position information to determine a plurality of grid areas; a probability determination module 430, configured to determine, for each grid area of ​​the plurality of grid areas, a probability that a corresponding wonder exists in the grid area, wherein the corresponding wonder is a wonder among at least one preset wonder; a prompt information generation module 440, configured to generate first prompt information in response to determining that the probability that a corresponding wonder exists in a target grid area is greater than a corresponding threshold, wherein the first prompt information is used to indicate that the corresponding wonder is predicted to exist in the target grid area, wherein the target grid is at least one grid area among the plurality of grid areas; and a navigation information generation module 450, configured to generate navigation information with the target grid area as a destination in response to receiving a trigger instruction for the first prompt information, so as to guide the target vehicle to the target grid area.

[0115] Here, the operations of the above-mentioned units 410 to 450 of the vehicle navigation device 400 are similar to the operations of steps 210 to 250 described above, and are not repeated here.

[0116] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0117] According to an embodiment of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0118] refer to Figure 5 , a block diagram of an electronic device 500 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0119] like Figure 5As shown, electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of electronic device 500. Computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0120] Multiple components within electronic device 500 are connected to I / O interface 505, including an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. Input unit 506 can be any type of device capable of inputting information into electronic device 500. Input unit 506 can receive input numeric or character information and generate key signal input related to user settings and / or function control of the electronic device. It may include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 507 can be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 508 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 509 allows electronic device 500 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks. It may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0121] Computing unit 501 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 501 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of method 200 described above can be performed. Alternatively, in other embodiments, computing unit 501 can be configured to perform method 200 in any other suitable manner (e.g., via firmware).

[0122] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0127] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0128] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0129] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A vehicle navigation method, comprising: Determine the location information of the target vehicle; Gridding an area within a preset distance range centered on the location information to determine a plurality of grid areas; For each grid area among the plurality of grid areas, determining a probability that a corresponding wonder exists in the grid area, wherein the corresponding wonder is a wonder among at least one preset wonder; In response to determining that the probability of the corresponding wonder existing in the target grid area is greater than the corresponding threshold, generating first prompt information, wherein the first prompt information is used to indicate that the corresponding wonder is predicted to exist in the target grid area, wherein the target grid is at least one grid area among the plurality of grid areas; and In response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as a destination to guide the target vehicle to the target grid area.

2. The method according to claim 1, wherein The at least one wonder includes a rainbow wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: Obtaining the rainfall probability and the sun elevation angle within a first preset time period corresponding to the grid area; In response to determining that the rainfall probability is greater than a first preset threshold and the sun elevation angle is within a first preset range, performing an operation of determining a probability that the rainbow wonder exists in the grid area; and In response to determining that the rainfall probability is not greater than the first preset threshold, or the sun elevation angle is not within the first preset range, the operation of determining the probability of the rainbow spectacle existing in the grid area is not performed.

3. The method according to claim 1 or 2, wherein The at least one wonder includes a rainbow wonder, wherein determining the probability of the corresponding wonder existing in the grid area comprises: determining the probability of the rainbow wonder existing in the grid area by the following operations: Obtaining a rainfall probability corresponding to the grid area within a second preset time period; Obtain the low-altitude cloud cover percentage and solar elevation angle data corresponding to the grid area; Obtaining first scoring data corresponding to the grid area, wherein the first scoring data is used to identify an occlusion condition of a horizon of the grid area; and Based on the rainfall probability, the low-altitude cloud cover ratio, the solar elevation angle data, and the first scoring data, the probability of the rainbow spectacle existing in the grid area is determined.

4. The method according to claim 3, wherein: The probability of the rainbow spectacle existing in the grid area is determined based on the following formula: : in, Indicates the preset correction coefficient, represents the rainfall probability, represents the proportion of low-altitude cloud cover, represents the solar elevation angle data, Indicates the first scoring data.

5. The method according to claim 1, wherein The at least one spectacle includes a meteor shower spectacle, wherein determining the probability of the corresponding spectacle existing in the grid area comprises: Obtain the hourly appearance rate of the zenith in the grid area within the third preset time in the future; When it is determined that the zenith hourly occurrence rate in the grid area within the third preset time period in the future is greater than a preset value, performing an operation of determining the probability of a meteor shower spectacle existing in the grid area; and When it is determined that there is no zenith hourly appearance rate greater than the preset value in the grid area within the third preset time period in the future, the operation of determining the probability of the presence of a meteor shower spectacle in the grid area is not performed.

6. The method according to claim 1 or 5, wherein: The at least one spectacle includes a meteor shower spectacle, wherein determining the probability of the corresponding spectacle existing in the grid area comprises: determining the probability of the meteor shower spectacle existing in the grid area by the following operations: Get the hourly appearance rate of the zenith and the percentage of high-altitude cloud cover corresponding to the grid area; Obtaining light pollution level data and an altitude correction coefficient corresponding to the grid area, wherein the altitude correction coefficient is used to represent normalized data of the altitude of the grid area; and Based on the zenith hourly appearance rate, the proportion of high-altitude cloud cover, the light pollution level data and the altitude correction factor, the probability of the presence of a meteor shower spectacle in the grid area is determined.

7. The method according to claim 6, wherein: The probability of the presence of the meteor shower spectacle in the grid area is determined based on the following formula: : in, Indicates the hourly appearance rate of the zenith, Indicates the percentage of high-altitude cloud cover, Indicates the light pollution level data, represents the altitude correction factor, 、 、 and is the preset weight value.

8. The method of claim 1, wherein The at least one wonder includes a sea of ​​clouds wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: When it is determined that the terrain corresponding to the grid area is mountainous or hilly, and that the relative humidity continues to increase and an inversion layer exists at high altitude within a fourth preset time period in the future, determining the probability of the sea of ​​clouds existing in the grid area; and When it is determined that the terrain corresponding to the grid area is not mountainous or hilly, or there is no continuous increase in relative humidity or no inversion layer in the high altitude within the fourth preset time period in the future, the operation of determining the probability of the presence of the sea of ​​clouds in the grid area is not performed.

9. The method according to claim 1 or 8, wherein The at least one wonder includes a sea of ​​clouds wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: determining the probability of the sea of ​​clouds wonder existing in the grid area by the following operations: Get the relative humidity data corresponding to the grid area; Obtaining the wind speed corresponding to the grid area to determine a wind speed influence coefficient, wherein the wind speed influence coefficient is used to represent normalized data of the wind speed in the grid area; Acquire the inversion layer strength data corresponding to the grid area, wherein the inversion layer strength data is used to represent the normalized temperature difference of the inversion layer when there is an inversion layer at high altitude in the grid area; Obtaining temperature lapse rate correction data corresponding to the grid area, wherein the temperature lapse rate correction data is used to represent standardized data of the temperature lapse rate within a preset high altitude range of the grid area; Based on the relative humidity data, the wind speed influence coefficient, the inversion layer strength data and the temperature lapse rate correction data, the probability of the sea of ​​clouds spectacle existing in the grid area is determined.

10. The method of claim 9, wherein: The probability of the existence of the sea of ​​clouds wonder in the grid area is determined based on the following formula: : in, Indicates the preset correction coefficient, Represents the relative humidity data, Indicates the wind speed influence coefficient, Indicates the inversion layer strength data, Indicates the temperature lapse rate correction data.

11. The method of claim 1, wherein The at least one wonder includes a rime wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: When it is determined that the temperature in the fifth future time period corresponding to the grid area is lower than a preset threshold and the air humidity is greater than a preset threshold, performing an operation of determining a probability of the presence of rime in the grid area; and When it is determined that the temperature in the fifth future time period corresponding to the grid area is not lower than the preset threshold or the air humidity is not greater than the preset threshold, the operation of determining the probability of the presence of the rime spectacle in the grid area is not performed.

12. The method according to claim 1 or 11, wherein The at least one wonder includes a rime wonder, wherein determining the probability of the corresponding wonder existing in the grid area includes: determining the probability of the rime wonder existing in the grid area by the following operations: Obtain the average temperature data for the sixth time period in the future corresponding to the grid area; Obtaining a relative humidity coefficient corresponding to the grid area, wherein the relative humidity coefficient is used to represent normalized data of the relative humidity in the grid area; Obtaining the wind speed corresponding to the grid area to determine a wind speed influence coefficient, wherein the wind speed influence coefficient is used to represent normalized data of the wind speed in the grid area; Obtaining vegetation coverage data corresponding to the grid area; and The probability of the existence of rime wonder in the grid area is determined based on the average temperature data, the relative humidity coefficient, the wind speed influence coefficient and the vegetation coverage data.

13. The method of claim 12, wherein: The probability of the existence of the rime wonder in the grid area is determined based on the following formula: : in, Indicates the preset correction coefficient, Indicates the average temperature data, Represents the relative humidity coefficient, Indicates the wind speed influence coefficient, Indicates the vegetation coverage data.

14. The method of claim 1, wherein: Generating first prompt information in response to determining that the probability of the corresponding wonder existing in the target grid area is greater than the corresponding threshold includes: Among the plurality of grid areas, determining a preset number of grid areas having a probability of the corresponding wonder being greater than a corresponding preset threshold and being closest to the target vehicle; Based on the preset number of grid areas, first prompt information corresponding to the preset number of grid areas is generated.

15. The method of claim 1, wherein Generating first prompt information in response to determining that the probability of the corresponding wonder existing in the target grid area is greater than the corresponding threshold includes: Determining, among the plurality of grid areas, a preset number of grid areas corresponding to the plurality of grid areas having the highest probability of the corresponding wonders existing therein; and Based on the preset number of grid areas, first prompt information corresponding to the preset number of grid areas is generated.

16. The method of claim 1 or 14 or 15, wherein: In response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as a destination includes: In response to receiving a trigger instruction for the first prompt information, determining a time required for the target vehicle to reach the target grid area; Determining the duration of the corresponding wonder predicted to exist in the target grid area; In response to determining that the required time is less than the duration, navigation information with the target grid area as a destination is generated.

17. The method of claim 16, wherein: In response to receiving a trigger instruction for the first prompt information, generating navigation information with the target grid area as a destination includes: In response to determining that the required time is not less than the duration, generating second prompt information, wherein the second prompt information is used to indicate that the wonder is predicted to disappear when reaching the target grid area; In response to receiving a trigger instruction for the second prompt information, generating navigation information with the target grid area as a destination.

18. The method of claim 16 or 17, further comprising: During the process of the target vehicle heading towards the target grid area based on the navigation information, continuously observing the corresponding wonders predicted to exist in the target grid area; as well as In response to observing that a change in a preset indicator corresponding to the corresponding wonder exceeds a preset amplitude value, a third prompt information is generated, wherein the third prompt information is used to indicate a predicted wonder state when the wonder reaches the target grid area.

19. A vehicle navigation device comprising: A location information determination module configured to determine the location information of the target vehicle; A grid area determination module configured to grid an area within a preset distance range centered on the location information to determine a plurality of grid areas; a probability determination module configured to determine, for each grid area among the plurality of grid areas, a probability that a corresponding wonder exists in the grid area, wherein the corresponding wonder is a wonder among at least one preset wonder; a prompt information generating module configured to generate first prompt information in response to determining that a probability of a corresponding wonder existing in a target grid area is greater than a corresponding threshold, wherein the first prompt information is used to indicate that the corresponding wonder is predicted to exist in the target grid area, wherein the target grid is at least one grid area among the plurality of grid areas; and The navigation information generating module is configured to generate navigation information with the target grid area as the destination in response to receiving a trigger instruction for the first prompt information, so as to guide the target vehicle to the target grid area.

20. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 18.

21. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-18.

22. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 18 is implemented.

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