Intelligent vehicle searching method, device, system and product

Abstract

The invention discloses an intelligent vehicle searching method, device, system and product. The method comprises the steps of obtaining a judgment result of whether a current vehicle position is a resident place or not; obtaining the type of a parking lot where the current vehicle is located, mode parameters of a physical environment and whether to start a mark; determining the vehicle searching difficulty; according to the judgment result, the vehicle searching difficulty and whether a mark is started or not, obtaining the degree grade of the vehicle searching intention of the user; judging whether the handheld terminal and the vehicle are within an effective range or not; when it is determined that the vehicle control content is within the effective range, corresponding vehicle control content is determined according to the degree grade and the mode parameter; and executing the vehicle control content until a signal of opening the vehicle door is obtained. According to the invention, different vehicle control contents are set by acquiring the judgment result of whether the vehicle is a resident place, the type of the parking lot, the mode parameter of the physical environment and whether the mark is started or not as conditions. Through the vehicle active mode, the user can accurately find the vehicle, and the vehicle finding efficiency is improved. Mainly used in the technical field of vehicles.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, specifically to a smart vehicle-finding method, device, system, and product. Background Technology

[0002] In relevant technical solutions, vehicle location typically employs technologies such as vehicle positioning or image recognition to locate vehicles in unfamiliar, large, and complex parking environments. One approach involves acquiring real-time location information from mobile devices and using this information to determine navigation routes on a map of the current indoor environment, thus enabling vehicle location within the indoor space. Another approach is to locate vehicles based on driving routes and environmental photographs. However, all these solutions rely on navigation signals, and when these signals are inaccurate, the problem of finding the vehicle persists. Therefore, addressing the issues inherent in these technologies is a pressing research topic within the industry. Summary of the Invention

[0003] This invention provides a smart vehicle-finding method, device, system, and product to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0004] This invention provides a smart vehicle-finding method, including: obtaining a judgment result on whether the current vehicle location is a permanent residence; obtaining the parking lot category where the vehicle is currently located; obtaining the pattern parameters of the physical environment of the current vehicle location; and obtaining the user's flag for whether the vehicle-finding application is enabled by accessing a handheld terminal. The difficulty of finding your car in the current parking lot is determined based on the parking lot category. The degree of the user's intent to find their car is determined based on the judgment result, the difficulty of finding the car, and whether a marker is enabled. Determine whether the handheld terminal and the vehicle are within a valid range; when it is determined that the handheld terminal and the vehicle are within a valid range, determine the corresponding vehicle control content based on the aforementioned severity level and physical environment mode parameters; control the vehicle to execute the vehicle control content until a signal to open the door is obtained.

[0005] Furthermore, determining whether the current vehicle location is a permanent location specifically includes: obtaining the latitude and longitude of the current vehicle location; clustering the latitude and longitude of the vehicle location using the DBscan algorithm; obtaining the center points of the clusters; calculating the proportion distribution of each center point; determining whether the proportion distribution of the center points is greater than or equal to a first threshold; if the proportion distribution of the center points is greater than or equal to the first threshold, determining that the center point is a permanent location, and determining that the current vehicle location is a permanent location; if the proportion distribution of the center points is less than a second threshold, then calculating the improvement degree of the parking weekday attribute and time period of the vehicle location; if the improvement degree of the center point is greater than or equal to the second threshold, and the number of times the center point appears is greater than or equal to a third threshold, then determining that the center point is a permanent location, and determining that the current vehicle location is a permanent location; when it is impossible to determine that the center point is a permanent location, then determining that the center point is a non-permanent location, and determining that the current vehicle location is a non-permanent location.

[0006] Furthermore, the intelligent car-finding method also includes: before obtaining the degree level of the user's car-finding intention through the judgment result, parking lot list, car-finding difficulty, and whether the marker is enabled, by waiting for a set buffer time to filter out invalid scenarios of quick temporary parking and departure.

[0007] Furthermore, obtaining the mode parameters of the physical environment of the vehicle's current location specifically includes: taking photos of the environment around the vehicle using an onboard camera, analyzing the environmental photos to determine the physical environment, and determining the corresponding mode parameters based on the physical environment. The physical environment includes: weather information, time period information, and indoor / outdoor information.

[0008] Furthermore, the intelligent vehicle-finding method also includes: displaying the real-time location of the current user and vehicle through the human-computer interaction interface of the vehicle-finding application.

[0009] Furthermore, the intelligent vehicle-finding method also includes: displaying environmental photos taken by the vehicle's camera through the human-computer interaction interface of the vehicle-finding application.

[0010] Furthermore, the intelligent vehicle-finding method also includes: displaying navigation information between the user and the vehicle's location through the human-computer interaction interface of the vehicle-finding application.

[0011] On the other hand, a smart car-finding device is provided, comprising: a processor and a memory, wherein the memory is used to store a computer-readable program; when the computer-readable program is executed by the processor, the processor enables the processor to implement the smart car-finding method as described in any of the above technical solutions.

[0012] On the other hand, a smart car-finding system is provided, including: an acquisition module, a determination module, an acquisition module, a judgment module, and a control module; The acquisition module is used to: obtain the judgment result of whether the current vehicle location is a permanent residence; obtain the parking lot category where the current vehicle is located; obtain the mode parameters of the physical environment of the current vehicle location; and obtain the user's flag for whether the car-finding application is enabled by accessing the handheld terminal. The determining module is used to: determine the difficulty of finding the car in the current parking lot based on the parking lot category; The obtaining module is used to: determine the degree level of the user's car-finding intention based on the judgment result, the difficulty of finding the car, and whether the marker is enabled; The judgment module is used to: determine whether the handheld terminal and the vehicle are within an effective range; The control module is used to: determine the corresponding vehicle control content based on the severity level and physical environment mode parameters when it is determined that the handheld terminal and the vehicle are within an effective range; and control the vehicle to execute the vehicle control content until a signal to open the door is obtained.

[0013] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the intelligent vehicle-finding method described in any of the above technical solutions.

[0014] This invention has at least the following beneficial effects: The method of this invention uses the judgment result of whether it is a permanent residence, parking lot category, physical environment mode parameters, and whether a marker is enabled as conditions to set different vehicle control content. By controlling the vehicle to execute the corresponding vehicle control content, the vehicle generates a signal to guide the user to find the vehicle. Through the vehicle's active approach, the user can accurately find the vehicle, improving the efficiency of vehicle finding. It solves the technical problem of inaccurate vehicle finding in the prior art. It avoids the inefficiency of existing solutions that rely on people to find vehicles in navigation due to inaccurate navigation. This invention improves the efficiency of vehicle finding for users by using vehicle control content to achieve vehicle-to-person matching. Simultaneously, this invention also provides corresponding devices, systems, and products. The beneficial effects of these devices, systems, and products are similar to those of the method and will not be repeated here. This invention is mainly used in the field of vehicle technology. Attached Figure Description

[0015] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0016] Figure 1 This is a flowchart of the steps involved in the intelligent car-finding method; Figure 2 This is a structural diagram of the intelligent vehicle-finding device; Figure 3 This is the hardware structure of a smart car-finding device according to another embodiment; Figure 4 This is a schematic diagram of the intelligent vehicle-finding system. Figure 5 This is a flowchart illustrating the steps involved in determining whether the current vehicle location is a permanent residence. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0020] A handheld terminal refers to a portable smart device that integrates data acquisition and processing functions.

[0021] DBscan, short for Density-Based Spatial Clustering of Applications with Noise, is a classic and practical unsupervised clustering algorithm.

[0022] In vehicle-related technical solutions, inaccurate navigation signals often make it difficult for users to quickly locate their vehicles. Therefore, how to enable users to quickly find their vehicles is a technical issue that urgently needs to be researched in the industry.

[0023] refer to Figure 1 , Figure 1 This is a flowchart of the steps involved in the intelligent vehicle search method.

[0024] To address the technical problems existing in the current related technologies, this application discloses a smart vehicle-finding method, which can be run by a software program. The steps implemented during software program execution include: Step 1: Obtain the result of determining whether the current vehicle location is a permanent residence; obtain the parking lot category where the vehicle is currently located; obtain the physical environment mode parameters of the vehicle's current location; obtain the user's flag for whether the car-finding application is enabled by accessing the handheld terminal.

[0025] In step 1, the software program primarily acquires various information. Specifically, it can establish a connection with a cloud platform to obtain a determination of whether the current vehicle location is a permanent residence. Of course, the vehicle is equipped with positioning sensors to obtain its current location and transmits this location to the cloud platform via the vehicle-to-everything (V2X) network. The cloud platform then uses historical data to determine whether the vehicle location is a permanent residence. In some further embodiments, the vehicle itself can also determine its permanent residence location based solely on its current location.

[0026] For the method of determining whether the current vehicle location is a permanent residence, please refer to... Figure 5 , Figure 5 This is a flowchart illustrating the steps of a method for determining whether the current vehicle location is a permanent residence. In some further specific embodiments, determining whether the current vehicle location is a permanent residence specifically includes: Step 11, obtaining the latitude and longitude of the current vehicle location; Step 12: Use the DBscan algorithm to cluster the latitude and longitude of the vehicle locations; Step 13: Obtain the center points of the clusters; Step 14: Calculate the percentage distribution of each center point; Step 15: Determine whether the proportion of the center points is greater than or equal to the first threshold. Step 16: When the proportion of the center point is greater than or equal to the first threshold, determine that the center point is a permanent location and determine that the current vehicle location is a permanent location; Step 17: If the proportion of the center point is less than the second threshold, then calculate the improvement of the parking weekday attribute and time period of the vehicle location. Step 18: If the elevation of the center point is greater than or equal to the second threshold, and the number of times the center point appears is greater than or equal to the third threshold, then the center point is determined to be a permanent location, and the current vehicle location is determined to be a permanent location. Step 19: If it cannot be determined that the center point is a permanent stationary point, then the center point is determined to be a non-permanent stationary point, and the current vehicle position is determined to be a non-permanent stationary location.

[0027] The first, second, and third thresholds are obtained through experimentation and preset. In this specific embodiment, the first threshold is set to 15%, the second threshold to 2%, and the third threshold to 3. The first threshold primarily reflects the frequency threshold for a specific location. This frequency can be calculated by dividing the number of times a user (vehicle) has visited a particular location within a certain period by the total number of times the user has visited all locations.

[0028] The principle for determining whether a location is a permanent station is that if it cannot be determined to be a permanent station, it can be considered a non-permanent station.

[0029] In addition to determining whether the vehicle is currently in a permanent location, the software program also obtains the parking lot category where the vehicle is located. When a vehicle enters a parking lot, its location is used to determine the current parking lot category. The classification criteria for parking lot categories are pre-set, and in some embodiments, the parking lot categories and classification criteria are shown in Table 1 below.

[0030]

[0031] Table 1.

[0032] Table 1 shows that a parking lot is classified as follows: Class I if it has more than 300 parking spaces or a total building area of ​​more than 10,000 square meters. Class II if it has more than 150 parking spaces but less than or equal to 300, or a total building area of ​​more than 5,000 square meters but less than or equal to 10,000 square meters. Class III if it has more than 50 parking spaces but less than or equal to 150, or a total building area of ​​more than 2,000 square meters but less than or equal to 5,000 square meters. Class IV if it has 50 parking spaces or less and a total building area of ​​less than or equal to 2,000 square meters. Parking lots that are not classified as Class I, II, III, or IV are classified as unclassified.

[0033] In addition to obtaining the parking lot type, the software program also acquires the pattern parameters of the physical environment at the vehicle's current location. After confirming that it has parked in a parking space, the vehicle uses an environmental detection and sensing device to perceive the physical environment of its location. This perception is typically performed when the vehicle is powered off and reports its location. The perception process generates pattern parameters of the physical environment. In some specific embodiments, these pattern parameters include: daytime mode, evening mode, and nighttime mode. The environmental detection and sensing device can be an onboard camera or a light sensor.

[0034] Specifically, obtaining the mode parameters of the physical environment of the vehicle's current location includes: taking photos of the surrounding environment using an onboard camera, analyzing the environmental photos to determine the physical environment, and determining the corresponding mode parameters based on the physical environment. The physical environment includes: weather information, time period information, and indoor / outdoor information.

[0035] The correspondence between model parameters and the physical environment is determined by a pre-set correspondence table. Table 2 shows the correspondence between model parameters and the physical environment in some embodiments.

[0036]

[0037] Table 2.

[0038] Table 2 shows that when the indoor / outdoor information is determined to be "not outdoor," regardless of the time period or weather conditions, the mode parameter is always the evening mode. "Not outdoor" refers to any situation where the indoor / outdoor information cannot be definitively identified as "outdoor." When the indoor / outdoor information is determined to be "outdoor," the time period is determined to be "sunrise," and the weather conditions are "sunny" or "partly cloudy," the mode parameter is the daytime mode. When the indoor / outdoor information is determined to be "outdoor," the time period is determined to be "sunrise," and the weather conditions are "overcast," "rainy," or "snowy," the mode parameter is the evening mode. When the indoor / outdoor information is determined to be "outdoor," the time period is determined to be "sunset," and regardless of the weather conditions, the mode parameter is the nighttime mode.

[0039] In addition to obtaining the parking lot category, the software program also obtains a flag indicating whether the user's car-finding application is enabled. This flag reflects whether the user has activated the car-finding application. When the user has activated the application, it indicates a strong intention to use it to find their car. Conversely, when the application is not activated, it suggests a less strong intention to use it. The car-finding application is installed on the handheld device.

[0040] Step 2: Determine the difficulty of finding your car in the current parking lot based on the parking lot category.

[0041] After obtaining the parking lot category, the software program can determine the difficulty of finding the car in that parking lot based on the category. In some specific embodiments, the software program can use Table 3 to determine the difficulty of finding the car corresponding to each parking lot category. Table 3 shows the relationship between the difficulty of finding the car and the parking lot category.

[0042]

[0043] Table 3.

[0044] Table 3 shows that when the parking lot is classified as Class I or II, the difficulty of finding the car is Level 3. In some practical applications, Class I or II parking lots are commonly found in shopping malls, transportation hubs, or scenic spots. When the parking lot is classified as Class III, the difficulty of finding the car is Level 2. In some practical applications, Class III parking lots are commonly found in commercial office buildings, public utilities, or residential areas. When the parking lot is classified as Class IV or has no category, the difficulty of finding the car is Level 1.

[0045] Step 3: Based on the judgment result, the difficulty of finding the car, and whether the marker is enabled, obtain the degree level of the user's car-finding intention.

[0046] After determining the judgment result, the difficulty of finding the car, and whether the marker is enabled, the software program can determine the level of the user's car-finding intention. The level of intention reflects the urgency of the user's need to find the car. In some specific embodiments, the software program can determine the level of the user's car-finding intention using Table 4. Table 4 is a comparison table of the level of user's car-finding intention.

[0047]

[0048] Table 4.

[0049] As shown in Table 4, when the judgment result of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: Level 1, and the activation mark is: Yes, then the degree of the user's car-finding intention is: strong.

[0050] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: level 2, and the flag is enabled: yes; then the user's level of intent to find the car is: strong.

[0051] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: level three, and the flag is enabled: yes; then the user's level of intent to find the car is: strong.

[0052] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: Level 1, and the flag is: no; then the user's level of intent to find the car is: strong.

[0053] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: level 2, and the flag is: no; then the user's level of car-finding intent is: medium.

[0054] If the determination of whether the location is a permanent residence is: permanent residence, then regardless of the difficulty level of finding the car or whether the marking is enabled, the user's level of car-finding intent is: none.

[0055] Step 4: Determine whether the handheld terminal and the vehicle are within a valid range.

[0056] After the software program determines the level of the user's intent to locate the vehicle, it can determine whether to enable vehicle control features based on the effective range of the link signal between the handheld terminal and the vehicle. In some further specific embodiments, the handheld terminal determines the effective range based on the strength of the Bluetooth link signal between itself and the vehicle.

[0057] Step 5: When it is determined that the handheld terminal and the vehicle are within an effective range, the corresponding vehicle control content is determined according to the severity level and the mode parameters of the physical environment; the vehicle is controlled to execute the vehicle control content until a signal to open the door is obtained.

[0058] Once the software program determines that the handheld terminal and the vehicle are within a valid range, it can then determine the corresponding vehicle control content based on the severity level and physical environment mode parameters. Specifically, the software program can determine the corresponding vehicle control content using Table 5. Table 5 shows the relationship between the user's vehicle search intent severity level, physical environment mode parameters, and vehicle control content.

[0059]

[0060] Table 5.

[0061] As shown in Table 5, when the user's intention to find the car is strong and the physical environment mode parameter is daytime mode, the vehicle control content is: a single horn blast and the playback of a car-finding prompt.

[0062] When the user's intent to find the car is strong and the physical environment mode parameter is night mode, the vehicle control content is: turn on the headlights and play a car-finding prompt sound.

[0063] When the user's intent to find the car is strong and the physical environment mode parameter is evening mode, the vehicle control content is: a single horn blast, a car-finding prompt sound, and hazard lights.

[0064] When the user's intent to find the car is at the medium level and the physical environment mode parameter is daytime mode, the vehicle control content is: play a car-finding prompt sound, which will automatically stop after 10 seconds.

[0065] When the user's level of intent to find the car is "medium" and the physical environment mode parameter is "night mode", the vehicle control content is: play the car-finding prompt sound and the parking lights, which will automatically stop after 10 seconds.

[0066] When the user's intent to find the car is at the medium level and the physical environment mode parameter is at the evening mode, the vehicle control content is: play the car-finding prompt sound and hazard lights, which will automatically stop after 10 seconds.

[0067] When the user's intention to find the car is weak, the vehicle control content will be: no operation, regardless of the physical environment mode parameters.

[0068] When the user's intent to find a car is at the level of "none", then regardless of the mode parameters of the physical environment, the vehicle control content will be "no operation".

[0069] Once the software program finds the corresponding vehicle control content, it will control the vehicle to execute the corresponding vehicle control content until it receives a signal to open the door.

[0070] This invention uses the determination of whether a location is a permanent residence, parking lot type, physical environment mode parameters, and whether a marker is enabled as conditions to set different vehicle control content. By controlling the vehicle to execute the corresponding vehicle control content, the vehicle generates signals to guide the user in finding the vehicle. Through the vehicle's proactive approach, users can accurately locate their vehicles, improving vehicle-finding efficiency. This solves the technical problem of inaccurate vehicle location in existing technologies. It avoids the inefficiency of existing solutions that rely on navigation for finding vehicles due to navigation inaccuracies. This invention improves the efficiency of vehicle-finding by enabling vehicles to locate people through vehicle control content.

[0071] The vehicle control system includes sound signals and / or light signals. The vehicle emits sound signals and / or light signals, which propagate in the parking lot space to form a guide, allowing the user to locate the vehicle.

[0072] When the sound and / or light signals emitted by the vehicle provide guidance, they do not require electronic carriers such as screens. Instead, they directly utilize human physiological senses, improving the accuracy of converting guidance into actual direction and location, and enhancing the efficiency of vehicle positioning and locating.

[0073] To further improve efficiency and conserve computing resources, in some specific embodiments, the intelligent car-finding method further includes: before determining the degree of the user's car-finding intention through the judgment result, parking lot list, car-finding difficulty, and whether a marker is enabled, a set buffer time is waited for to filter out invalid scenarios of quick parking and departure. In some specific embodiments, the buffer time is set to 10 minutes. Research has found that users sometimes park briefly after power failure. Moreover, calculated by walking distance, the user does not move far from the vehicle within 10 minutes. Therefore, scenarios within the buffer time after power failure are invalid. By filtering out these invalid scenarios, computing resources can be saved to the maximum extent and efficiency can be improved.

[0074] In some further specific embodiments, the intelligent vehicle-finding method also includes: displaying the real-time location of the user and the vehicle through the human-computer interaction interface of the vehicle-finding application. The vehicle-finding application is equipped with a human-computer interaction interface, through which the user can easily see their current location and the vehicle's location.

[0075] In some further specific embodiments, the smart car-finding method also includes displaying environmental photos taken by the vehicle's camera through the human-computer interaction interface of the car-finding application. Users can also view environmental photos taken by the vehicle's camera through the car-finding application, making it easier for them to understand the surroundings of their vehicle. The human-computer interaction interface of the car-finding application also displays information on determining the floor and parking space number based on photos taken from the dashcam.

[0076] In some further specific embodiments, the smart car-finding method also includes displaying navigation information between the user and vehicle locations through the human-computer interaction interface of the car-finding application. This navigation information includes: pedestrian navigation when the user's location is less than 2 kilometers from the vehicle's, and driving navigation when the user's location is greater than 2 kilometers from the vehicle's.

[0077] In some further specific embodiments, if the car-finding app learns that the vehicle's headlights were on 5 minutes before the engine was turned off, it will simultaneously capture footage from the camera before the headlights were turned off. The car-finding app triggers a notification sound when a handheld device approaches the vehicle. The car-finding app can remotely control the vehicle's horn and headlights. The car-finding app can calculate the vehicle's parking timing. The car-finding app can aggregate parking fee information to estimate parking costs and the optimal departure time. The car-finding app has a one-click sharing function, allowing users to share information including the vehicle's parking address and garage number. The car-finding app supports quick sharing of the vehicle's location to other applications such as WeChat, Moments, and DingTalk.

[0078] refer to Figure 2 , Figure 2 This is a schematic diagram of the intelligent vehicle-finding device.

[0079] On the other hand, a smart car-finding device is provided, comprising: a processor and a memory, wherein the memory stores a computer-readable program. When the computer-readable program is executed by the processor, the processor implements the smart car-finding method as described in any of the above specific embodiments.

[0080] Those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. As is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0081] Please see Figure 3 , Figure 3 This is another embodiment of the hardware structure of a smart car-finding device. The smart car-finding device includes: a processor 901, a memory 902, an input / output interface 903, a communication interface 904, and a bus 905.

[0082] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the intelligent vehicle finding method provided in the embodiments of this application.

[0083] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application.

[0084] The input / output interface 903 is used to implement information input and output.

[0085] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0086] Bus 905 transmits information between various components of the device, such as processor 901, memory 902, input / output interface 903, and communication interface 904.

[0087] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0088] refer to Figure 4 , Figure 4 This is a schematic diagram of the intelligent vehicle-finding system.

[0089] A smart vehicle-finding system is provided, comprising: an acquisition module, a determination module, an acquisition module, a judgment module, and a control module.

[0090] The acquisition module is used to: obtain the judgment result of whether the current vehicle location is a permanent residence; obtain the parking lot category where the current vehicle is located; obtain the mode parameters of the physical environment of the current vehicle location; and obtain the user's flag for whether the car-finding application is enabled by accessing the handheld terminal.

[0091] The acquisition module primarily acquires various information. Specifically, it can establish a connection with the cloud platform to obtain a determination of whether the current vehicle location is a permanent residence. Of course, the vehicle is equipped with positioning sensors to determine its current location and transmits this location to the cloud platform via the vehicle-to-everything (V2X) network. The cloud platform then uses historical data to determine whether the vehicle location is a permanent residence. In some further specific embodiments, the vehicle itself can also determine its permanent residence location based solely on its current location.

[0092] For the method of determining whether the current vehicle location is a permanent residence, please refer to... Figure 5 , Figure 5 This is a flowchart illustrating the steps of a method for determining whether the current vehicle location is a permanent residence. In some further specific embodiments, determining whether the current vehicle location is a permanent residence specifically includes: Step 11, obtaining the latitude and longitude of the current vehicle location; Step 12: Use the DBscan algorithm to cluster the latitude and longitude of the vehicle locations; Step 13: Obtain the center points of the clusters; Step 14: Calculate the percentage distribution of each center point; Step 15: Determine whether the proportion of the center points is greater than or equal to the first threshold. Step 16: When the proportion of the center point is greater than or equal to the first threshold, determine that the center point is a permanent location and determine that the current vehicle location is a permanent location; Step 17: If the proportion of the center point is less than the second threshold, then calculate the improvement of the parking weekday attribute and time period of the vehicle location. Step 18: If the elevation of the center point is greater than or equal to the second threshold, and the number of times the center point appears is greater than or equal to the third threshold, then the center point is determined to be a permanent location, and the current vehicle location is determined to be a permanent location. Step 19: If it cannot be determined that the center point is a permanent stationary point, then the center point is determined to be a non-permanent stationary point, and the current vehicle position is determined to be a non-permanent stationary location.

[0093] The first, second, and third thresholds are obtained through experimentation and preset. In this specific embodiment, the first threshold is set to 15%, the second threshold to 2%, and the third threshold to 3. The first threshold primarily reflects the frequency threshold for a specific location. This frequency can be calculated by dividing the number of times a user (vehicle) has visited a particular location within a certain period by the total number of times the user has visited all locations.

[0094] The principle for determining whether a location is a permanent station is that if it cannot be determined to be a permanent station, it can be considered a non-permanent station.

[0095] In addition to determining whether the vehicle is currently in a permanent location, the acquisition module also obtains the parking lot category where the vehicle is located. When a vehicle enters a parking lot, its location is used to determine the current parking lot category. The classification criteria for parking lot categories are pre-set, and in some embodiments, the parking lot categories and classification criteria are shown in Table 1 below.

[0096]

[0097] Table 1.

[0098] Table 1 shows that a parking lot is classified as follows: Class I if it has more than 300 parking spaces or a total building area of ​​more than 10,000 square meters. Class II if it has more than 150 parking spaces but less than or equal to 300, or a total building area of ​​more than 5,000 square meters but less than or equal to 10,000 square meters. Class III if it has more than 50 parking spaces but less than or equal to 150, or a total building area of ​​more than 2,000 square meters but less than or equal to 5,000 square meters. Class IV if it has 50 parking spaces or less and a total building area of ​​less than or equal to 2,000 square meters. Parking lots that are not classified as Class I, II, III, or IV are classified as unclassified.

[0099] In addition to acquiring the parking lot type, the acquisition module also obtains the pattern parameters of the physical environment at the vehicle's current location. After confirming that it has parked in a parking space, the vehicle uses an environmental detection and sensing device to perceive the physical environment of its location. This perception is typically performed when the vehicle is powered off and reports its location. The perception process generates pattern parameters of the physical environment. In some specific embodiments, these pattern parameters include: daytime mode, evening mode, and nighttime mode. The environmental detection and sensing device can be an onboard camera or a light sensor.

[0100] Specifically, obtaining the mode parameters of the physical environment of the vehicle's current location includes: taking photos of the surrounding environment using an onboard camera, analyzing the environmental photos to determine the physical environment, and determining the corresponding mode parameters based on the physical environment. The physical environment includes: weather information, time period information, and indoor / outdoor information.

[0101] The correspondence between model parameters and the physical environment is determined by a pre-set correspondence table. Table 2 shows the correspondence between model parameters and the physical environment in some embodiments.

[0102]

[0103] Table 2.

[0104] Table 2 shows that when the indoor / outdoor information is determined to be "not outdoor," regardless of the time period or weather conditions, the mode parameter is always the evening mode. "Not outdoor" refers to any situation where the indoor / outdoor information cannot be definitively identified as "outdoor." When the indoor / outdoor information is determined to be "outdoor," the time period is determined to be "sunrise," and the weather conditions are "sunny" or "partly cloudy," the mode parameter is the daytime mode. When the indoor / outdoor information is determined to be "outdoor," the time period is determined to be "sunrise," and the weather conditions are "overcast," "rainy," or "snowy," the mode parameter is the evening mode. When the indoor / outdoor information is determined to be "outdoor," the time period is determined to be "sunset," and regardless of the weather conditions, the mode parameter is the nighttime mode.

[0105] In addition to obtaining the parking lot category, the acquisition module also obtains a flag indicating whether the user's car-finding application is enabled. This flag reflects whether the user has activated the car-finding application. If the user has activated the application, it indicates a strong intention to use it to find their car. If the user has not activated the application, it indicates a less strong intention to use it. The car-finding application is installed on the handheld terminal.

[0106] The determining module is used to: determine the difficulty of finding a car in the current parking lot based on the parking lot category.

[0107] After obtaining the parking lot category, the determination module can determine the difficulty of finding the car in that parking lot based on the category. In some specific embodiments, the determination module can use Table 3 to determine the difficulty of finding the car corresponding to each parking lot category. Table 3 is a table showing the relationship between the difficulty of finding the car and the parking lot category.

[0108]

[0109] Table 3.

[0110] Table 3 shows that when the parking lot is classified as Class I or II, the difficulty of finding the car is Level 3. In practical applications, Class I or II parking lots are commonly found in shopping malls, transportation hubs, or scenic spots. When the parking lot is classified as Class III, the difficulty of finding the car is Level 2. In practical applications, Class III parking lots are commonly found in commercial office buildings, public utilities, or residential areas. When the parking lot is classified as Class IV or has no category, the difficulty of finding the car is Level 1. The difficulty of finding the car at Level 3 is greater than that at Level 2, and the difficulty of finding the car at Level 2 is greater than that at Level 1.

[0111] The module is used to: determine the degree level of the user's intention to find the car based on the judgment result, the difficulty of finding the car, and whether the marker is enabled.

[0112] After determining the judgment result, the difficulty of finding the car, and whether the marker is enabled, the module can determine the level of the user's car-finding intention. The level of intention reflects the urgency of the user's need to find the car. In some specific embodiments, the module can determine the level of the user's car-finding intention using Table 4. Table 4 is a comparison table of the level of user's car-finding intention.

[0113]

[0114] Table 4.

[0115] As shown in Table 4, when the judgment result of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: Level 1, and the activation mark is: Yes, then the degree of the user's car-finding intention is: strong.

[0116] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: level 2, and the flag is enabled: yes; then the user's level of intent to find the car is: strong.

[0117] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: level three, and the flag is enabled: yes; then the user's level of intent to find the car is: strong.

[0118] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: Level 1, and the flag is: no; then the user's level of intent to find the car is: strong.

[0119] If the determination of whether it is a permanent residence is: non-permanent residence, the difficulty of finding the car is: level 2, and the flag is: no; then the user's level of car-finding intent is: medium.

[0120] If the determination of whether the location is a permanent residence is: permanent residence, then regardless of the difficulty level of finding the car or whether the marking is enabled, the user's level of car-finding intent is: none.

[0121] The judgment module is used to determine whether the handheld terminal and the vehicle are within a valid range.

[0122] After the judgment module determines the level of the user's intention to find the vehicle, it can determine whether to enable vehicle control content by measuring the effective range of the link signal between the handheld terminal and the vehicle. In some further specific embodiments, the handheld terminal determines the effective range by measuring the strength of the Bluetooth link signal between itself and the vehicle.

[0123] The control module is used to: determine the corresponding vehicle control content based on the severity level and physical environment mode parameters when it is determined that the handheld terminal and the vehicle are within an effective range; and control the vehicle to execute the vehicle control content until a signal to open the door is obtained.

[0124] Once the control module determines that the handheld terminal and the vehicle are within a valid range, it can then determine the corresponding vehicle control content based on the severity level and physical environment mode parameters. The software program can use Table 5 to determine the corresponding vehicle control content. Table 5 shows the relationship between the user's vehicle search intent severity level, physical environment mode parameters, and vehicle control content.

[0125]

[0126] Table 5.

[0127] As shown in Table 5, when the user's intention to find the car is strong and the physical environment mode parameter is daytime mode, the vehicle control content is: a single horn blast and the playback of a car-finding prompt.

[0128] When the user's intent to find the car is strong and the physical environment mode parameter is night mode, the vehicle control content is: turn on the headlights and play a car-finding prompt sound.

[0129] When the user's intent to find the car is strong and the physical environment mode parameter is evening mode, the vehicle control content is: a single horn blast, a car-finding prompt sound, and hazard lights.

[0130] When the user's intent to find the car is at the medium level and the physical environment mode parameter is daytime mode, the vehicle control content is: play a car-finding prompt sound, which will automatically stop after 10 seconds.

[0131] When the user's level of intent to find the car is "medium" and the physical environment mode parameter is "night mode", the vehicle control content is: play the car-finding prompt sound and the parking lights, which will automatically stop after 10 seconds.

[0132] When the user's intent to find the car is at the medium level and the physical environment mode parameter is at the evening mode, the vehicle control content is: play the car-finding prompt sound and hazard lights, which will automatically stop after 10 seconds.

[0133] When the user's intention to find the car is weak, the vehicle control content will be: no operation, regardless of the physical environment mode parameters.

[0134] When the user's intent to find a car is at the level of "none", then regardless of the mode parameters of the physical environment, the vehicle control content will be "no operation".

[0135] Once the control module finds the corresponding vehicle control content, it will control the vehicle to execute the corresponding vehicle control content until it receives a signal to open the door.

[0136] The vehicle control system includes sound signals and / or light signals. The vehicle emits sound signals and / or light signals, which propagate in the parking lot space to form a guide, allowing the user to locate the vehicle.

[0137] When the sound and / or light signals emitted by the vehicle provide guidance, they do not require electronic carriers such as screens. Instead, they directly utilize human physiological senses, improving the accuracy of converting guidance into actual direction and location, and enhancing the efficiency of vehicle positioning and locating.

[0138] This application also discloses a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. The processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the intelligent vehicle finding method as described in any of the preceding embodiments.

[0139] On the other hand, a computer-readable storage medium is provided, wherein a processor-executable program is stored, which, when executed by a processor, is used to implement the intelligent vehicle-finding method as described in any of the above specific embodiments.

[0140] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0141] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0143] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0145] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0146] Although the description of this application has been quite detailed and particularly focused on several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment. Rather, it should be considered as effectively covering the intended scope of this application by referring to the appended claims and taking into account the prior art, which provides for a broad possible interpretation of these claims. Furthermore, the foregoing description of this application with respect to embodiments foreseeable by the inventors is intended to provide a useful description, and non-substantial modifications to this application that have not yet been foreseen may still represent equivalent modifications.

[0147] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

Claims

1. A smart vehicle-finding method, characterized in that, include: Obtain the result of determining whether the current vehicle location is a permanent residence; obtain the parking lot category where the current vehicle is located; Obtain the physical environment pattern parameters of the vehicle's current location; obtain the user's flag for whether the car-finding application is enabled by accessing the handheld terminal; The difficulty of finding your car in the current parking lot is determined based on the parking lot category. The degree of the user's intent to find their car is determined based on the judgment result, the difficulty of finding the car, and whether a marker is enabled. Determine whether the handheld terminal and the vehicle are within a valid range; Once it is determined that the handheld terminal and the vehicle are within an effective range, the corresponding vehicle control content is determined based on the severity level and the mode parameters of the physical environment; the vehicle is then controlled to execute the vehicle control content until a signal to open the door is received.

2. The intelligent vehicle finding method according to claim 1, characterized in that, Determining whether the current vehicle location is a permanent location specifically includes: obtaining the latitude and longitude of the current vehicle location; clustering the latitude and longitude of the vehicle location using the DBscan algorithm; obtaining the centroids of the clusters; calculating the percentage distribution of each centroid; determining whether the percentage distribution of the centroids is greater than or equal to a first threshold; if the percentage distribution of the centroids is greater than or equal to the first threshold, determining that the centroid is a permanent location, and the current vehicle location is a permanent location; if the percentage distribution of the centroids is less than a second threshold, calculating the improvement of the parking weekday attribute and time period of the vehicle location; if the improvement of the centroid is greater than or equal to the second threshold, and the number of times the centroid appears is greater than or equal to a third threshold, determining that the centroid is a permanent location, and the current vehicle location is a permanent location; if it cannot be determined that the centroid is a permanent location, determining that the centroid is a non-permanent location, and the current vehicle location is a non-permanent location.

3. The intelligent vehicle finding method according to claim 1, characterized in that, Also includes: Before determining the user's intent to find their car based on the judgment result, parking lot list, difficulty of finding the car, and whether a marker is enabled, an invalid scenario of quick parking and leaving is filtered out by waiting for a set buffer time.

4. The intelligent vehicle finding method according to claim 1, characterized in that, The specific steps for obtaining the mode parameters of the physical environment of the vehicle's current location include: taking photos of the environment around the vehicle using an onboard camera, analyzing the environmental photos to determine the physical environment, and determining the corresponding mode parameters based on the physical environment. The physical environment includes weather information, time period information, and indoor / outdoor information.

5. The intelligent vehicle finding method according to claim 1, characterized in that, Also includes: The real-time location of the user and vehicle is displayed through the human-computer interaction interface of the car-finding app.

6. The intelligent vehicle finding method according to claim 4, characterized in that, Also includes: The car-finding app uses a human-computer interaction interface to display environmental photos taken by the vehicle's camera.

7. The intelligent vehicle finding method according to claim 4, characterized in that, Also includes: The navigation information between the user and the vehicle's location is displayed through the human-computer interaction interface of the car-finding app.

8. A smart vehicle finding device, characterized in that, include: processor; Memory, used to store computer-readable programs; When the computer-readable program is executed by the processor, the processor implements the intelligent vehicle-finding method as described in any one of claims 1-7.

9. A smart vehicle-finding system, characterized in that, include: Acquisition module, determination module, obtaining module, judgment module, and control module; The acquisition module is used to: obtain the judgment result of whether the current vehicle location is a permanent residence; obtain the parking lot category where the current vehicle is located; obtain the mode parameters of the physical environment of the current vehicle location; and obtain the user's flag for whether the car-finding application is enabled by accessing the handheld terminal. The determining module is used to: determine the difficulty of finding the car in the current parking lot based on the parking lot category; The obtaining module is used to: determine the degree level of the user's car-finding intention based on the judgment result, the difficulty of finding the car, and whether the marker is enabled; The judgment module is used to: determine whether the handheld terminal and the vehicle are within an effective range; The control module is used to: determine the corresponding vehicle control content based on the severity level and physical environment mode parameters when it is determined that the handheld terminal and the vehicle are within an effective range; and control the vehicle to execute the vehicle control content until a signal to open the door is obtained.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent vehicle-finding method according to any one of claims 1 to 7.

Images