Camping place recommendation method, electronic device and program product

Abstract

The invention provides a camping place recommendation method, electronic equipment and a program product, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring camping place feature description information, and acquiring a surrounding environment image of a destination; wherein the camping place feature description information is used for describing features of a camping place interested by a user; generating a camping place recommendation list based on the camping place feature description information and the destination surrounding environment image; wherein the camping place recommendation list comprises at least one alternative camping place and evaluation information corresponding to the alternative camping place. The method is used for improving the camping place recommendation efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent driving technology, and in particular to a campsite recommendation method, electronic equipment, and program product. Background Art

[0002] With the increasing popularity of automobiles, driving has become a common practice, and the demand for drive-in camping is growing rapidly. Currently, when driving near a destination, users often need to get out of their vehicles and slowly search for a suitable campsite, which is time-consuming and laborious, and in many cases, it is difficult to find a satisfactory campsite. Summary of the Invention

[0003] In view of this, embodiments of the present disclosure provide a campsite recommendation method, electronic device, and program product to improve the efficiency of campsite recommendation.

[0004] In a first aspect, the present disclosure provides a campsite recommendation method, comprising:

[0005] Acquiring campsite feature description information and obtaining an image of the surrounding environment of the destination; wherein the campsite feature description information is used to describe the characteristics of the campsite that the user is interested in;

[0006] A campsite recommendation list is generated based on the campsite feature description information and the destination surrounding environment image; wherein the campsite recommendation list includes at least one candidate campsite and evaluation information corresponding to the candidate campsite.

[0007] In a second aspect, the present disclosure provides an electronic device, including:

[0008] at least one processor; and

[0009] a memory communicatively connected to the at least one processor; wherein,

[0010] The memory stores at least one computer program executable by the at least one processor. The at least one computer program is executed by the at least one processor to enable the at least one processor to perform the campsite recommendation method according to the first aspect.

[0011] In a third aspect, the present disclosure provides a computer program product, comprising a computer program, which implements the campsite recommendation method according to the first aspect when executed in a processor.

[0012] In the embodiments provided by the present disclosure, a vehicle collects campsite feature description information and an image of the surrounding environment of a destination, determines each candidate campsite and its corresponding evaluation information based on the campsite feature description information and the image of the surrounding environment of the destination, and generates a campsite recommendation list, so that campsites in the campsite recommendation list can be recommended to users, thereby enabling automatic selection of campsites. The automatically selected campsites satisfy the collected campsite feature description information, that is, satisfy the user's demand for campsites, thereby improving the efficiency of campsite recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0014] Figure 1 Shown is a schematic diagram of the process of the campsite recommendation method in an embodiment of the present disclosure;

[0015] Figure 2 FIGURE 1 is a block diagram of a campsite recommendation device according to an embodiment of the present disclosure;

[0016] Figure 3 FIG. 1 is a schematic structural diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0018] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0019] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0020] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded. Similar words such as "connected" or "connected" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0021] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly defined as such herein.

[0022] Exemplary Methods

[0023] The campsite recommendation method provided by the embodiments of the present disclosure is applied to a vehicle, and specifically can be applied to a controller of the vehicle.

[0024] The campsite recommendation method for a vehicle provided in the embodiment of the present disclosure is as follows: Figure 1 As shown, it mainly includes the following steps 101-102:

[0025] Step 101 : Collect campsite feature description information and collect images of the surrounding environment of the destination; wherein the campsite feature description information is used to describe the features of the campsite that the user is interested in.

[0026] The destination is the general area where you will be camping, such as Park A, Village B, etc.

[0027] In some embodiments, a campsite selection interface is displayed on the vehicle's on-board central control screen, and the campsite selection interface includes options such as campsite type, environmental characteristics, infrastructure, etc. For example, the campsite selection interface includes options such as whether it is a tent campsite, whether it is close to a forest, whether there are charging piles, whether there are streams nearby, and the flatness of the site. Campsite feature description information is obtained based on the user's selection operation on the campsite selection interface.

[0028] For example, when collecting campsite feature description information, there are multiple ways to call up the campsite selection interface, including but not limited to: displaying the campsite selection interface on the vehicle's central control screen through a motor, or displaying the campsite selection interface on the vehicle's central control screen through voice control, or controlling the vehicle's central control screen to display the campsite selection interface through a user's mobile phone APP.

[0029] For example, when collecting campsite feature description information, there are multiple option operation methods in the retrieved campsite selection interface, including: based on the campsite selection interface displayed on the vehicle's central control screen, obtaining the user's target operation selection for each option in the interface or filling in the characteristics of the campsite of interest. The target operation includes checking, sliding, text input, etc. For example, check the "RV campsite" option, set the expected level of "site flatness" through the slider, and enter "I hope there are streams nearby" through the text box.

[0030] Exemplarily, for the directly entered data corresponding to each campsite option obtained, natural language processing (NLP) technology is used to perform semantic analysis, and the directly entered data of each option is converted into structured data as campsite feature description information. For example, the structured data includes "environmental characteristics-stream: high demand", and the structured campsite feature description information is stored in the user preference database for direct use during subsequent camping.

[0031] In some embodiments, collecting the image of the surrounding environment of the destination includes: obtaining the image of the surrounding environment of the destination by shooting with a camera of the vehicle when the vehicle enters a preset range of the destination.

[0032] Exemplarily, when the vehicle enters the preset range of the destination, the adaptive adjustment mechanism of the vehicle camera is triggered, and the camera shooting frequency is dynamically adjusted through the adaptive adjustment mechanism; when the vehicle speed is higher than a first threshold, the shooting frequency is increased to ensure the integrity of image acquisition in fast-moving scenes; when the vehicle speed is lower than a second threshold, the shooting frequency is reduced to save storage resources.

[0033] For example, while the vehicle camera continuously captures images of the destination's surrounding environment, the vehicle's positioning module (such as GPS positioning), inertial measurement unit and other sensors synchronously obtain the vehicle's position, driving direction, driving speed and other driving data, and timestamp the destination's surrounding environment images and driving data for subsequent data analysis.

[0034] For example, the collected images of the destination's surrounding environment are pre-processed, including but not limited to noise reduction and compression, to reduce the data volume, and uploaded to a cloud server via the network in real time for storage and subsequent analysis. The vehicle can transmit the collected images of the destination's surrounding environment to an onboard edge computing device for pre-processing.

[0035] Step 102 : Generate a campsite recommendation list based on the campsite feature description information and the destination surrounding environment image; wherein the campsite recommendation list includes at least one candidate campsite and evaluation information corresponding to the candidate campsite.

[0036] The evaluation information corresponding to the alternative campsites includes evaluation information in multiple dimensions, including but not limited to: hazardous factors, environmental risks, weather disasters, etc.

[0037] In some embodiments, generating a recommended campsite list based on the campsite feature description information and the destination surrounding environment image includes: determining at least one candidate campsite that meets the campsite feature description information based on the campsite feature description information and the surrounding environment image; obtaining a target environment image corresponding to the candidate campsite from the destination surrounding environment image, and determining evaluation information corresponding to the candidate campsite based on the target environment image; and generating the recommended campsite list based on the at least one candidate campsite and the evaluation information corresponding to the candidate campsite. If the selected candidate campsites meet the campsite feature description information, i.e., meet the user's camping needs, the candidate campsites are further evaluated in multiple dimensions to obtain evaluation information to provide a basis for further screening.

[0038] In some embodiments, the assessment information of the alternative campsite includes potential risk factor assessment information and environmental risk assessment information; wherein the potential risk factor assessment information is used to assess risk factors existing on the surface; and the environmental risk assessment information is used to assess geographical risks.

[0039] In some embodiments, determining corresponding potential risk factor assessment information based on the target environment image includes: inputting the target environment image into a pre-trained deep learning model to obtain potential risk factor assessment information output by the deep learning model; the potential risk factor assessment information includes the type, degree of risk and location information of the potential risk factors.

[0040] There is no limitation on the specific deep learning model used here. For example, the YOLO model is used to analyze the target environment image.

[0041] Exemplarily, the target environment images are input into the pre-trained deep learning model in order of acquisition time to obtain potential risk factor assessment information.

[0042] Exemplarily, potential risk factors include but are not limited to the following types: signs of landslides (such as identification of exposed rocks, cracks, etc.), dangerous boulders, fire hazards (such as identification of smoke, flames, etc.), wild animals, abandoned sharp objects, etc.

[0043] For example, the danger level can be a danger level or score, etc., determined based on preset rules to quantify the danger factor. For example, the score is 1-10, and a score of 8 indicates a high danger level for a large, sharp boulder located within the camping area. The preset rules can be pre-configured or self-learned by a learning model.

[0044] For example, the various identified potential risk factors, their risk levels, and locations are integrated to generate potential risk factor assessment information. This potential risk factor assessment information can be a visual report that displays the information in the form of a heat map and annotates detailed information and corresponding suggestions. The suggestions can be either not recommending a selection or recommending a selection.

[0045] In some embodiments, determining the corresponding environmental risk assessment information based on the target environment image includes: obtaining geographic information of the location corresponding to the target environment image from a geographic information system, and constructing a terrain model of the alternative campsite based on the geographic information and the target environment image; performing geological analysis and risk simulation based on the terrain model, and determining the environmental risk assessment information of the alternative campsite based on the geological analysis results and the risk simulation results.

[0046] For example, geographic information corresponding to the target environment image is obtained from a cloud-based geographic information system. The target environment image location can be estimated based on the vehicle's positioning position and the camera's shooting angle and range. The geographic information is combined with the target environment image to construct a terrain model of the candidate campsite using 3D reconstruction technology.

[0047] For example, a geological structure analysis algorithm is used to perform a geological analysis of candidate campsites based on a terrain model, evaluating soil type, bearing capacity, groundwater level, and other indicators to determine whether the site is suitable for camping activities such as setting up tents and parking vehicles. The geological structure analysis algorithm used is not limited herein.

[0048] For example, the specific process of risk simulation includes: simulating the engineering process for possible construction activities (such as site leveling, road paving, etc.) at alternative campsites, analyzing the possible ecological damage (such as vegetation destruction, soil erosion, etc.) and geological disasters (such as landslides, etc.), and estimating the probability of occurrence.

[0049] For example, environmental risk assessment information may include the environmental risk type, as well as the probability of occurrence and avoidance measures. Environmental risk types may include vegetation damage, soil erosion, landslides, etc. Avoidance measures may be determined based on a pre-configured correspondence between environmental risk types and avoidance measures. For example, environmental risk assessment information may include "The soil in this area is soft and has a low bearing capacity, making it unsuitable for heavy vehicles to park."

[0050] In some embodiments, the assessment information of the candidate campsites further includes weather hazard assessment information;

[0051] The method also includes: obtaining real-time meteorological data and historical meteorological data of the location corresponding to the target environment image; inputting the historical meteorological data and the real-time meteorological data into a weather disaster prediction model to obtain disaster meteorological prediction information output by the weather disaster prediction model; predicting disaster meteorological impact information based on the disaster meteorological prediction information and the terrain model; and generating the weather disaster assessment information based on the disaster meteorological prediction information and the disaster meteorological impact information.

[0052] For example, by accessing multiple data sources such as meteorological satellites, radar monitoring, and ground meteorological stations, real-time meteorological data of the location corresponding to the target environment image is obtained, and historical meteorological data of the location is obtained from the cloud. The historical meteorological data may include meteorological data in recent months or longer.

[0053] Here, there is no restriction on the specific learning model used in the weather disaster prediction model. For example, the LSTM model is used to build the weather disaster prediction model.

[0054] Using weather disaster prediction models, we analyze real-time meteorological data (such as humidity, temperature, wind speed, air pressure, etc.) and historical meteorological data, explore meteorological change patterns and trends, and obtain disaster deadline forecast information based on meteorological change patterns and trends. For example, we can predict the possibility of disasters such as heavy rain, mountain torrents, lightning, and strong winds within the next 72 hours. Then, we combine the terrain model to simulate the disaster propagation path and impact range to obtain disaster meteorological impact information.

[0055] For example, weather disaster assessment information may include the disaster type, expected time of occurrence, affected area, and protective recommendations. Protective recommendations may be determined based on pre-configured rules and the predicted disaster type, expected time of occurrence, and affected area. These rules may include protective recommendations corresponding to the disaster type, expected event, and affected area. For example, weather disaster assessment information may include "There is a 70% probability of thunderstorms in this area within the next 12 hours. It is recommended to avoid camping under large trees."

[0056] In some embodiments, a final campsite is selected based on the campsite recommendation list. Specifically, the method further includes: inputting the campsite recommendation list into a weighted scoring model, using the weighted scoring model to weight the evaluation indicators in the evaluation information of the candidate campsites to obtain evaluation scores for the candidate campsites; sorting the candidate campsites according to the evaluation scores, and selecting at least one target campsite with an evaluation score above a threshold based on the sorting results to generate a target campsite list.

[0057] The weighted scoring model includes weights for each evaluation indicator. The weights for each evaluation indicator, along with the evaluation indicators in the scoring information, are used to perform a weighted process to generate the evaluation score. For example, a weight of 40% is set for safety-related evaluation indicators, 30% for comfort-related evaluation indicators, 20% for convenience-related evaluation indicators, and 10% for user preference-related evaluation indicators. This is for illustrative purposes only and does not limit the specific weighting configuration.

[0058] For example, the target campsite list includes at least one target campsite and evaluation information corresponding to the target campsite. The evaluation information corresponding to the target campsite includes basic information about the target campsite, an evaluation score, risk warnings, suitable camping types, and activity recommendations. The basic information about the target campsite includes, for example, its location and area.

[0059] In some embodiments, a final campsite is selected based on the target campsite list. Specifically, the method further includes: marking the target campsite on a map based on the target campsite list and displaying the evaluation score and evaluation information corresponding to the target campsite, and displaying the map; obtaining user selection information for the target campsite based on the displayed map; and determining the final campsite based on the selection information.

[0060] For example, a map is displayed on the vehicle's central control screen or a mobile phone APP, and the target campsite is displayed on the map in the form of a picture and text card. The location and evaluation score of each target campsite are marked on the map with different colors and / or icons. Click the icon to pop up the picture and text card to display the evaluation information of the target campsite in the picture and text card, which makes it convenient for users to drive the vehicle to the target campsite.

[0061] For example, multiple target campsites selected by the user are obtained and compared and analyzed to determine a final target campsite. The results of the comparative analysis are graphically displayed to show the differences between the target campsites in terms of safety, comfort, and other dimensions. Furthermore, a risk simulation can be performed on these target campsites to help users intuitively understand the risk status of different campsites under different weather and environmental conditions.

[0062] In some embodiments, the method further includes: obtaining user feedback information on the final campsite usage, such as reasons for dissatisfaction, etc., so as to optimize the algorithm or model used in the campsite recommendation method by collecting feedback information, thereby improving the accuracy of campsite recommendations and user satisfaction.

[0063] In the embodiments provided by the present disclosure, a vehicle collects campsite feature description information and an image of the surrounding environment of a destination, determines each candidate campsite and its corresponding evaluation information based on the campsite feature description information and the image of the surrounding environment of the destination, generates a campsite recommendation list, and can recommend campsites in the campsite recommendation list, thereby enabling automatic selection of campsites. The automatically selected campsites satisfy the collected campsite feature description information, that is, satisfy the user's demand for campsites, thereby improving the efficiency of campsite recommendations.

[0064] It is understood that the above-mentioned various method embodiments mentioned in this disclosure can be combined with each other to form combined embodiments without violating the principle logic. Due to space limitations, this disclosure will not go into details. It is understood by those skilled in the art that in the above-mentioned methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic, and the execution order between steps is not limited to being implemented according to the step number.

[0065] Exemplary devices

[0066] The embodiment of the present disclosure also provides a campsite recommendation device. The corresponding technical solutions and descriptions are described in the method section and will not be repeated here. Figure 2 This is a block diagram of a campsite recommendation device provided in an embodiment of the present disclosure. The campsite recommendation device mainly includes:

[0067] Acquisition module 201 is used to acquire campsite feature description information and obtain an image of the surrounding environment of the destination; wherein the campsite feature description information is used to describe the characteristics of the campsite that the user is interested in;

[0068] The generating module 202 is configured to generate a campsite recommendation list based on the campsite feature description information and the destination surrounding environment image; wherein the campsite recommendation list includes at least one candidate campsite and evaluation information corresponding to the candidate campsite.

[0069] Figure 3 A block diagram of an electronic device provided in an embodiment of the present disclosure.

[0070] Reference Figure 3An embodiment of the present disclosure provides an electronic device, comprising: at least one processor 301; at least one memory 302; and one or more I / O interfaces 303 connected between the processor 301 and the memory 302; wherein the memory 302 stores one or more computer programs executable by the at least one processor 301, and the one or more computer programs are executed by the at least one processor 301 to enable the at least one processor 301 to perform the above-mentioned campsite recommendation method.

[0071] Each module in the above-mentioned electronic device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0072] The embodiments of the present disclosure further provide a computer program product, including a computer program, which implements the campsite recommendation method when executed in a processor.

[0073] The computer program may be stored in a readable storage medium of a computer device or in the cloud; the processor of the computer device reads the computer program from the readable storage medium or the cloud.

[0074] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0075] It will be understood by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium).

[0076] As is well known to those skilled in the art, the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information (such as computer-readable program instructions, data structures, program modules or other data). Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically contains computer-readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0077] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0078] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0079] The computer program product described herein may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0080] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0081] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0082] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0083] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0084] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A campsite recommendation method, characterized in that: Applied to a vehicle, the method comprises: Collecting campsite feature description information and images of the surrounding environment of the destination; wherein the campsite feature description information is used to describe the characteristics of the campsite that the user is interested in; A campsite recommendation list is generated based on the campsite feature description information and the destination surrounding environment image; wherein the campsite recommendation list includes at least one candidate campsite and evaluation information corresponding to the candidate campsite.

2. The method according to claim 1, characterized in that The generating of a recommended campsite list based on the campsite feature description information and the destination surrounding environment image includes: determining, based on the campsite feature description information and the surrounding environment image, at least one candidate campsite that meets the campsite feature description information; acquiring a target environment image corresponding to the candidate campsite from the destination surrounding environment image, and determining evaluation information corresponding to the candidate campsite based on the target environment image; The camping site recommendation list is generated according to the at least one candidate camping site and the evaluation information corresponding to the candidate camping site.

3. The method according to claim 2, characterized in that The assessment information of the candidate campsites includes potential hazard factor assessment information and environmental risk assessment information; The potential risk factor assessment information is used to assess risk factors existing on the surface of the earth; and the environmental risk assessment information is used to assess geographical risks.

4. The method according to claim 3, characterized in that Determining corresponding potential risk factor assessment information based on the target environment image includes: The target environment image is input into a pre-trained deep learning model to obtain potential risk factor assessment information output by the deep learning model; the potential risk factor assessment information includes the type, risk level and location information of the potential risk factors.

5. The method according to claim 3, characterized in that Determining the corresponding environmental risk assessment information based on the target environment image includes: Acquire geographic information of a location corresponding to the target environment image from a geographic information system, and construct a terrain model of an alternative campsite based on the geographic information and the target environment image; A geological analysis and a risk simulation are performed based on the terrain model, and environmental risk assessment information of the candidate campsite is determined according to the geological analysis results and the risk simulation results.

6. The method according to claim 5, characterized in that The assessment information of the alternative campsites also includes weather hazard assessment information; The method further comprises: Acquiring real-time meteorological data and historical meteorological data for a location corresponding to the target environment image; Inputting the historical meteorological data and the real-time meteorological data into a weather disaster prediction model to obtain disaster meteorological prediction information output by the weather disaster prediction model; Predicting disaster meteorological impact information based on the disaster meteorological forecast information and the terrain model; The weather disaster assessment information is generated based on the disaster meteorological forecast information and the disaster meteorological impact information.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Inputting the recommended campsite list into a weighted scoring model, and using the weighted scoring model to weight the evaluation indicators in the evaluation information of the candidate campsites to obtain evaluation scores for the candidate campsites; The candidate campsites are sorted according to the evaluation scores, and at least one target campsite with an evaluation score higher than a threshold is screened according to the sorting result to generate a target campsite list.

8. The method according to claim 7, characterized in that The method further comprises: According to the target campsite list, marking the target campsite on a map and displaying the evaluation score and evaluation information corresponding to the target campsite, and displaying the map; obtaining user selection information of the target campsite based on the displayed map; A final campsite is determined based on the selection information.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores at least one computer program executable by the at least one processor. The at least one computer program is executed by the at least one processor to enable the at least one processor to perform the campsite recommendation method according to any one of claims 1 to 8.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed in a processor, the campsite recommendation method according to any one of claims 1 to 8 is implemented.

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