A vehicle driving intelligent control method based on vehicle networking

Abstract

The application provides a vehicle driving intelligent control method based on Internet of Vehicles, relates to the technical field of vehicle control methods based on Internet of Vehicles, and comprises the following steps: receiving trip information issued by a user, selecting a to-be-selected parking lot for the user's current trip according to the user's current positioning information, a parking end time and an opening time period of each to-be-processed parking lot. The to-be-processed parking lot is determined according to the user's trip destination; and then, the to-be-selected parking lot is recommended and displayed based on attribute values of the to-be-selected parking lot under multiple parking lot attributes. The parking lot attributes include part or all of parking fees, walking distances and driving durations. In the above process, the to-be-selected parking lot is recommended and displayed based on the parking lot attributes of the to-be-selected parking lot in multiple dimensions, so that the user can quickly find the parking lot used for the current trip, and thus the utilization rate of parking resources is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control method based on Internet of Vehicles, and particularly relates to a vehicle driving intelligent control method based on Internet of Vehicles. BACKGROUND

[0002] The current automobile ownership is growing, and it is impossible to build and expand parking lots without limit in urban construction. In recent years, the concept of shared parking has been proposed and applied to many cities, which has achieved remarkable results in alleviating parking difficulties.

[0003] Shared parking refers to the mode that the parking lot manager and the parking space holder publish the open period, charging, location and other related information of the idle parking space to the shared parking platform for time-sharing rental of the parking space. The car owner with parking demand can find and rent parking space through the platform application, thereby realizing parking space sharing.

[0004] When the shared parking platform displays the idle parking space, only the location of the parking lot where the idle parking space is located is marked. It is impossible to intelligently recommend the optimal parking lot suitable for travel and parking for the user in combination with the actual travel demand of the user, and there is a problem of insufficient utilization of parking resources. SUMMARY

[0005] The embodiment of the present application provides a vehicle driving intelligent control method based on Internet of Vehicles. The method is used for intelligently recommending a parking lot suitable for travel and parking for a user according to travel information of the user, thereby improving the utilization rate of parking resources.

[0006] To achieve the above purpose, the technical scheme of the embodiment of the present application is as follows:

[0007] The embodiment of the present application provides a vehicle driving intelligent control method based on Internet of Vehicles, and the method comprises the following steps:

[0008] In response to a parking instruction, receiving travel information of a user; wherein the travel information comprises a parking end time of the user and a travel destination;

[0009] According to the current positioning information of the user, the parking end time and the open period of each to-be-processed parking lot, determining a to-be-selected parking lot suitable for the user to park in this trip from the to-be-processed parking lots; wherein the to-be-processed parking lot is determined according to the travel destination;

[0010] Based on the attribute values of each to-be-selected parking lot under multiple parking lot attributes, recommending and displaying the to-be-selected parking lot in a parking recommendation page; wherein the multiple parking lot attributes include part or all of walking distance, driving time and parking fee; the walking distance is the walking distance from the parking lot to the travel destination; and the driving time is the time required for the user to drive to the parking lot.

[0011] In some possible embodiments, the determining, from the parking lots to be processed, a candidate parking lot applicable to the current trip of the user according to the current positioning information of the user, the parking end time and the opening time period of each of the parking lots to be processed comprises:

[0012] For any parking lot to be processed, determining a driving time of the user to drive to the parking lot to be processed according to the positioning information of the user and the parking lot to be processed in a road network map;

[0013] determining a parking time period corresponding to the parking lot to be processed based on the driving time and the parking end time;

[0014] taking, as the candidate parking lot, a parking lot corresponding to the parking time period located within the opening time period of the parking lot itself from among the parking lots to be processed.

[0015] In some possible embodiments, after the taking, as the candidate parking lot, a parking lot corresponding to the parking time period located within the opening time period of the parking lot itself from among the parking lots to be processed, the method further comprises:

[0016] determining whether the number of candidate parking lots is greater than a number threshold;

[0017] if the number of candidate parking lots is not greater than the number threshold, delaying a parking start time in the parking time period of any candidate parking lot by a preset time period to obtain a corrected parking time period corresponding to the candidate parking lot, wherein the candidate parking lot is a parking lot to be processed corresponding to a parking time period located outside the opening time period of the parking lot itself;

[0018] taking, as the candidate parking lot, a parking lot corresponding to the corrected parking time period located within the opening time period of the parking lot itself from among the candidate parking lots.

[0019] In some possible embodiments, the determining, based on the driving time and the parking end time, a parking time period corresponding to the parking lot to be processed comprises:

[0020] detecting whether a trip time of the current trip of the user is reported;

[0021] if the trip time is received, determining the parking time period according to the trip time, the driving time and the parking end time;

[0022] if the trip time is not received, determining the parking time period according to a time of receiving the trip information, the driving time and the parking end time.

[0023] In some possible embodiments, the recommending and displaying, in the parking recommendation page, of each candidate parking lot based on the attribute values of each candidate parking lot in the plurality of parking lot attributes comprises:

[0024] obtaining a plurality of pieces of parking data generated in a historical period, wherein each piece of parking data is associated with a target parking lot and historical travel information reported by the user, and the target parking lot associated with any piece of parking data is a candidate parking lot obtained based on the historical travel information associated with the piece of parking data, and any piece of parking data comprises attribute values of the target parking lot associated with the piece of parking data in the plurality of parking lot attributes and a parking selection result;

[0025] determining an attribute weight of each parking lot attribute according to the attribute values of each target parking lot in the plurality of parking lot attributes and the parking selection result;

[0026] determining a ranking order of each candidate parking lot based on the attribute weight and the attribute values of each candidate parking lot in each parking lot attribute, and recommending and displaying each candidate parking lot in the parking recommendation page in the ranking order.

[0027] In some possible embodiments, the determining of the ranking order of each candidate parking lot based on the attribute weight and the attribute values of each candidate parking lot in each parking lot attribute comprises:

[0028] for each candidate parking lot, determining a recommendation value of the candidate parking lot according to the attribute values of the candidate parking lot in each parking lot attribute and the attribute weight;

[0029] ranking the recommendation values of the candidate parking lots from large to small as the ranking order of the candidate parking lots.

[0030] In some possible embodiments, the parking recommendation page further comprises a recommendation component for displaying parking lots with a parking space reservation function, and the method further comprises:

[0031] in response to a triggering operation of the recommendation component, selecting, from the candidate parking lots, reservation parking lots with the parking space reservation function, and obtaining attribute values of each reservation parking lot in each parking lot attribute;

[0032] displaying, in the parking recommendation page, each reservation parking lot in an order of attribute values of each reservation parking lot in each parking lot attribute from small to large.

[0033] In some possible embodiments, the method further comprises:

[0034] for any candidate parking lot, determining whether a difference between a parking end time of the candidate parking lot and an end time of an opening period of the candidate parking lot is less than a difference threshold value;

[0035] If less than the difference threshold, a prompt identifier representing the end time is added to the candidate parking lot when the candidate parking lot is displayed on the parking recommendation page.

[0036] In the embodiment of the present application, the travel information issued by the user is received, and the candidate parking lot for the user's current trip is selected from the user's current positioning information, the parking end time and the opening period of each to-be-processed parking lot. The above-mentioned each to-be-processed parking lot is determined according to the travel destination of the user; and then the candidate parking lot is recommended and displayed based on the attribute value of each candidate parking lot under multiple parking lot attributes. The parking lot attributes include part or all of the parking fee, the walking distance and the driving time. In the above-mentioned process, the candidate parking lot is recommended and displayed based on the parking lot attributes of each candidate parking lot in multiple dimensions, which facilitates the user to quickly find the parking lot used in the current trip, thereby improving the utilization rate of parking resources.

[0037] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present disclosure. The objects and other advantages of the present application can be achieved and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A traditional shared parking platform schematic diagram provided for the embodiment of the present application;

[0039] Figure 2 The overall flowchart of the vehicle driving intelligent control method based on the Internet of Vehicles provided for the embodiment of the present application;

[0040] Figure 3 The to-be-processed parking lot selection schematic diagram provided for the embodiment of the present application;

[0041] Figure 4 The flowchart of obtaining the candidate parking lot provided for the embodiment of the present application;

[0042] Figure 5 The search bar schematic diagram provided for the embodiment of the present application;

[0043] Figure 6 The schematic diagram of how to obtain the parking period provided for the embodiment of the present application;

[0044] Figure 7 The schematic diagram of how to obtain the corrected parking period provided for the embodiment of the present application;

[0045] Figure 8 The parking data schematic diagram provided for the embodiment of the present application;

[0046] Figure 9 Attribute value schematic diagram in multiple pieces of parking data provided for an embodiment of the present application;

[0047] Figure 10 Standardization processing result schematic diagram of attribute values provided for an embodiment of the present application;

[0048] Figure 11 Schematic diagram of recommending display of a reserved parking lot provided for an embodiment of the present application;

[0049] Figure 12 Another schematic diagram of recommending display of a reserved parking lot provided for an embodiment of the present application; DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. The embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Moreover, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0051] The terms “first” and “second” in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term “comprises” and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units that are not listed, or can optionally include other steps or units inherent to the process, method, product, or device. “Multiple” in the present application can mean at least two, for example, can be two, three, or more, and the embodiments of the present application are not limited.

[0052] Before introducing the vehicle driving intelligent control method based on vehicle networking provided by the embodiments of the present application, in order to facilitate understanding, the technical background of the embodiments of the present application is introduced in detail first.

[0053] As described above, shared parking refers to that the responsible person of a parking lot and the holder of a parking space publish the information such as the opening period, charge, and location of an idle parking space to a shared parking platform for time-sharing rental of the parking space. A vehicle owner with a parking demand can find a parking space through the platform application and rent it, thereby realizing sharing of the parking space.

[0054] However, when displaying available parking spaces, shared parking platforms only mark the location of each available space within the parking lot. Figure 1 An example of the application interface of a shared parking platform is shown. Users can enter their location in the search bar (such as "Shuanglinqiao Metro Station" in the illustration). Currently, shared bicycle platforms only mark parking lots near the user's location on the road network map that have posted parking space rental information on the platform.

[0055] That is, Figure 1 The image shows parking space rental information for parking lots near Shuanglinqiao Metro Station, including "Tiansheng Parking Lot," "Jinghu Parking Lot," and "Shuanglin Residential Area Underground Parking Lot." This demonstrates that current shared parking platforms cannot intelligently recommend suitable parking options based on users' actual travel needs, resulting in insufficient utilization of parking resources.

[0056] To address the aforementioned issues, the inventive concept of this application is as follows: Receiving trip information from a user, and selecting a potential parking lot for the user's trip based on the user's current location, parking end time, and the opening hours of each pending parking lot. These pending parking lots are determined based on the user's trip destination. Then, based on the attribute values ​​of each potential parking lot under multiple parking lot attributes, the inventive concept recommends and displays the parking lots accordingly. These parking lot attributes include some or all of parking fees, walking distance, and driving time. By recommending and displaying potential parking lots based on their multi-dimensional parking lot attributes, the user can quickly find the parking lot for their trip, thereby improving the utilization rate of parking resources.

[0057] Next, as follows Figure 2 As shown, Figure 2 The present application illustrates the overall flow of a vehicle driving intelligent control method based on the Internet of Vehicles, including the following steps:

[0058] Step 201: In response to a parking instruction, receive the user's trip information; wherein the trip information includes the user's parking end time and trip destination;

[0059] In this embodiment, after a user logs in, the shared parking platform displays a parking recommendation page. This page includes a search bar for users to enter their travel information. Users can enter their travel information in this search bar, allowing the shared parking platform to determine suitable parking lots for the user's current trip based on the uploaded information, through step 202 described below.

[0060] Step 202: determining, from the parking lots to be processed, a candidate parking lot suitable for the user's current trip according to the current location information of the user, the parking end time, and the opening time period of each parking lot to be processed; wherein the parking lot to be processed is determined according to the trip destination;

[0061] Specifically, as shown in Figure 3 , the user can input the trip destination and the estimated parking end time of the current trip into the shared parking platform application. The platform application takes the location coordinates of the trip destination in the road network map as the center, and takes the parking lots within the preset distance (the preset distance in the embodiment of the present application is set to two kilometers) as the parking lots to be processed, thereby obtaining the parking lots to be processed 1-3 shown in Figure 3 .

[0062] The opening time period of any parking lot to be processed represents the time period in which the parking lot to be processed supports the user to select a parking space for lease, and needs to be reported when the platform publishes the parking space lease information of the parking lot, that is, equivalent to the leasable time period shown in the foregoing Figure 1 . Therefore, the opening time period of each parking lot to be processed can be directly called from the platform after the parking lot to be processed is determined.

[0063] Specifically, the determination of the candidate parking lot suitable for the user's current trip from the parking lots to be processed according to the current location information of the user, the parking end time, and the opening time period of each parking lot to be processed can be as shown in Figure 4 , including the following steps:

[0064] Step 401: for any parking lot to be processed, determining the driving time of the user to arrive at the parking lot to be processed according to the location information of the user and the parking lot to be processed in the road network map.

[0065] Specifically, the vehicle driving speed can be set in advance, for example, the current average speed of driving in the city is 30-60 km / h, and the vehicle driving speed can be set to 50 km / h. Then, the optimal driving path of the user to arrive at the parking lot to be processed is determined according to the current location information of the user and the location information of the parking lot to be processed in the road network map. Finally, the driving time of the user to arrive at the parking lot to be processed is determined according to the ratio of the path distance of the optimal driving path to the vehicle driving speed.

[0066] In addition, the application interface of a third-party application (for example, the commonly seen xx map application) with map navigation function can be accessed. The current location information of the user and the location information of the parking lot to be processed are informed to the third-party application through the application interface, so as to obtain the estimated driving time of the user to arrive at the parking lot to be processed from the third-party application.

[0067] Step 402: determining the parking time period corresponding to the to-be-processed parking lot based on the driving time and the parking end time;

[0068] The technical solution of the present application supports the user to set the trip departure place and the trip time by himself / herself. Specifically, as shown in Figure 5 the foregoing search bar provided for the user, the parking end time and the trip destination are set as mandatory items, and the trip time and the trip departure place are set as optional items;

[0069] If the user inputs the trip time, the parking time period is determined according to the trip time, the driving time and the parking end time when the foregoing step 403 is performed. Correspondingly, if the user does not input the trip time, the parking time period is determined according to the time of receiving the trip information, the driving time and the parking end time. Specifically, as shown in Figure 6 assuming that the driving time of the user to the to-be-processed parking lot A is 30 minutes, the trip time input by the user this time is 13:00, and the parking end time is 15:00, the parking time of the user at the to-be-processed parking lot A is 1.5 hours, and the corresponding parking time period is [13:30, 15:00].

[0070] If the user inputs the trip departure place, the driving time from the trip departure place of the user to the to-be-processed parking lot is determined according to the trip departure place of the user and the positioning information of the to-be-processed parking lot in the process of determining the driving time in the foregoing step 401. Correspondingly, if the user does not input the trip departure place, the current positioning information of the user is used as the trip departure place of the user by default in the foregoing step 401.

[0071] Step 403: taking the parking lot corresponding to the parking time period located in the opening time period of the parking lot itself as a candidate parking lot.

[0072] If the parking time period of the to-be-processed parking lot is located in the opening time period of the parking lot itself, it means that the to-be-processed parking lot can meet the parking demand of the user this time, and the to-be-processed parking lot can be taken as a candidate parking lot.

[0073] Step 404: determining whether the number of the currently selected candidate parking lots is greater than a number threshold value;

[0074] Step 405: if the number of the currently selected candidate parking lots is greater than the number threshold value, ending the selection process of the candidate parking lot;

[0075] Step 406: if the number of the currently selected candidate parking lots is not greater than the number threshold value, modifying the parking time period of each candidate parking lot to obtain a modified parking time period corresponding to the candidate parking lot;

[0076] Step 407: taking the parking lot corresponding to the modified parking time period within the self-outside opening time period as a candidate parking lot; and jumping to step 405.

[0077] The candidate parking lot is a to-be-processed parking lot corresponding to a parking time period outside the self-outside opening time period. That is, the candidate parking lot is the remaining to-be-processed parking lot that is not selected as a candidate parking lot in step 403.

[0078] The number threshold of the embodiment is 3. The purpose of the threshold comparison is to preferentially recommend the parking lot with no conflict between the opening time period and the parking time period to the user as a candidate parking lot. However, if the number of such parking lots is too small, in order to provide more selection space for the user, the parking time period of each candidate parking lot is modified to obtain the modified parking time period corresponding to the candidate parking lot.

[0079] In implementation, for any candidate parking lot, the starting time of the parking time period of the candidate parking lot is delayed by a preset time length to obtain the modified parking time period corresponding to the candidate parking lot.

[0080] The preset time length of the embodiment is 15 minutes. As shown in the foregoing Figure 6 The to-be-processed parking lot A is taken as a candidate parking lot for illustration, and details are shown in FIG. 4. Figure 7 It is known that the parking time period of the parking lot A is [13:30, 15:00]. At this time, the starting time (13:30) of the parking time period is delayed by 15 minutes to obtain the corresponding modified parking time period [13:45, 15:00].

[0081] The purpose of modifying the parking time period is that if the number of candidate parking lots with no conflict between the opening time period and the parking time period is too small, the parking lot that can be parked after the user slightly waits for a certain time after arriving at the parking lot is also taken as a candidate parking lot. The preset time length, that is, the default waiting time for parking accepted by the user, is 15 minutes.

[0082] Step 203: recommending and displaying each candidate parking lot in a parking recommendation page based on attribute values of the candidate parking lot in multiple parking lot attributes; wherein the multiple parking lot attributes include part or all of walking distance, driving time, and parking fee; the walking distance is the walking distance from the parking lot to the travel destination; and the driving time is the time required for the user to drive to the parking lot.

[0083] It has been mentioned that the purpose of the embodiment is to recommend and display the candidate parking lot based on the parking lot attributes of the candidate parking lot in multiple dimensions, so as to facilitate the user to quickly find the parking lot used for this trip, thereby improving the utilization rate of parking resources.

[0084] In the embodiments of the present application, three parking lot attributes are preset, specifically including walking distance, driving time and parking fee. The driving time is the time for the user to drive to the parking lot, the parking fee is the hourly parking fee of the parking lot, and the walking distance is the walking distance from the parking lot to the destination.

[0085] In the implementation, a plurality of parking data generated in a historical period is acquired in advance; each piece of parking data is associated with a target parking lot and a piece of historical travel information reported by the user; the target parking lot associated with any piece of parking data is a candidate parking lot obtained based on the historical travel information associated with the parking data; and any piece of parking data includes attribute values of the associated target parking lot under a plurality of parking lot attributes and a parking selection result.

[0086] In some possible embodiments, a plurality of pieces of parking data generated by the user in the last three months on the platform can be acquired. Each piece of parking data is associated with a target parking lot and a piece of historical travel information reported by the user. The historical travel information is a piece of travel information reported by the user in the last three months. The target parking lot is one of a plurality of candidate parking lots selected according to the historical travel information.

[0087] Specifically, as shown in Figure 8 , it is assumed that the user uploads a piece of travel information to the platform on a certain day in the last three months, and the platform can obtain N candidate parking lots corresponding to the travel information through the foregoing steps 201-202 at that time. For the present, the travel information is a piece of historical travel information 1, and each candidate parking lot in the N candidate parking lots is a target parking lot associated with the historical travel information 1.

[0088] That is, the user's behavior of uploading the travel information 1 in the historical period can generate N pieces of parking data, the N pieces of parking data are all associated with the travel information 1, and the target parking lot associated with the Nth piece of parking data is the Nth candidate parking lot corresponding to the historical travel information 1.

[0089] Next, the specific content contained in each piece of parking data is explained. Any piece of parking data includes attribute values of the target parking lot associated therewith under various parking lot attributes and a parking selection result. The parking lot attributes have been introduced in the foregoing content, and will not be described again here. It should be noted that the attribute values of any target parking lot under various parking lot attributes are acquired by the platform when the target parking lot is selected as a candidate parking lot according to the travel information uploaded by the user.

[0090] The parking selection result represents whether the user selects the target parking lot for parking from the candidate parking lots obtained based on the historical travel information associated with the piece of parking data. Still referring to the Figure 8For example, it is assumed that the user finally selects the candidate parking lot 3 from the candidate parking lots 1 to N for parking, and the value of the parking selection result in the parking data of the candidate parking lot 3 is set to 1. That is, the user finally selects the candidate parking lot 3 from the candidate parking lots determined by the travel information 1. The values of the parking selection results of the remaining candidate parking lots are set to 0.

[0091] Next, according to the attribute values of each target parking lot under various parking lot attributes and the parking selection result, the attribute weight of each parking lot attribute is determined.

[0092] In implementation, the selection emphasis of the user for each parking lot attribute can be obtained from the above parking data based on a linear regression algorithm. Specifically, a logistic regression algorithm can be used to establish a logistic function as shown in the following formula (1):

[0093] (1);

[0094] wherein β0 is the intercept value in the logistic regression; β1-β n represent the weights of the plurality of preset dimensions, C1-C n represent the numerical results under the plurality of preset dimensions; since three parking lot attributes are involved in the present application, β1-β n include β1, β2 and β3, which respectively correspond to the attribute weights of the three parking lot attributes; C1-C n include C1, C2 and C3, which respectively correspond to the three parking lot attributes; m represents the total number of parking data; x i represents the i-th parking data; y i represents the parking selection result (1 or 0) of the i-th parking data; represents the probability of the user parking at the target parking lot associated with the parking data X.

[0095] The β1-β in the above formula (1) are derived according to The specific derivation process is as follows:

[0096] Since represents the probability of the user parking at the target parking lot associated with the parking data X, therefore; represents the probability of the user parking at the target parking lot associated with the i-th parking data; 1- that is, represents the probability of the user not selecting the target parking lot associated with the i-th parking data for parking, and the loss function expression for calculating the i-th parking data can be obtained by simultaneously solving the equations by the maximum likelihood estimation method: .

[0097] Furthermore, in the process of solving the above formula (1), the logarithm is used beforehand to transform the logical function shown in formula (1) from a product form to an additive form as shown in the following formula (2):

[0098] (2);

[0099] By performing gradient descent to solve for the partial derivatives of the above formula (2), the convergence values ​​of β1, β2, and β3 can be directly obtained. The convergence values ​​of β1, β2, and β3 are the attribute weights corresponding to each of the vehicle yard attributes C1, C2, and C3. The above gradient descent solution for partial derivatives is a conventional solution method in linear algebra, and this application will not elaborate on it.

[0100] It should be noted that since the attributes of each parking lot belong to different dimensions, the attribute values ​​of each parking lot need to be standardized in advance before substituting them into formula (2) for calculation.

[0101] The foregoing Figure 8 As previously explained, the target parking lots associated with each parking data point related to the same historical trip information are all candidate parking lots selected by the platform based on that historical trip information. Therefore, for any parking data point, the following formula (3) can be used to standardize the attribute values ​​in that parking data point:

[0102] (3);

[0103] in, The standardized processing result of the k-th parking lot attribute in the j-th parking data; Let be the attribute value of the k-th parking lot attribute in the j-th parking data; Let be the maximum attribute value of the k-th parking lot attribute in j parking data points; Let $k$ be the maximum attribute value of the $k$-th parking lot attribute among $j$ parking data entries; the historical travel information associated with $j$ parking data entries is the same.

[0104] To facilitate understanding of the above standardized process, the specific details are as follows: Figure 9 As shown, Figure 9 The image shows the target parking lot associated with each of the three parking data j that are associated with the same historical travel information, as well as the attribute values ​​of each parking data j under each parking lot attribute k.

[0105] by Figure 9 Taking the target parking lot 1 as an example, the attribute value (500) under the parking lot attribute walking distance is the attribute value of the first type of parking lot attribute in the first parking data of the three parking data, that is... Among these three parking data points, the maximum attribute value for walking distance is 500, while the minimum attribute value is 200. , The standardized processing result of the attribute value of the walking distance in the parking data j1 is obtained by substituting the parameters into the above formula (3). Wherein, Figure 9 The standardized processing result of each attribute value in the parking data j1~3 is specifically as shown in the following table. Figure 10

[0106] After the attribute values in each parking data are standardized processed by the above process, the attribute weight is calculated by substituting the above formula (2).

[0107] After the attribute weight β1 of the walking distance, the attribute weight β2 of the driving time and the attribute weight β3 of the parking cost are obtained by the above formula (2), the sorting order of each candidate parking lot in the parking recommendation page is determined according to the attribute value of each candidate parking lot under each parking attribute.

[0108] In implementation, for each candidate parking lot, the recommendation value of the candidate parking lot is determined according to the attribute value and the attribute weight (β1, β2, β3) of the candidate parking lot under each parking attribute.

[0109] Specifically, since the attribute value of each candidate parking lot under each parking attribute obtained in the above step 202 is known, after the above formula (3) is used to standardize process each attribute value, for each candidate parking lot, the sum of the product of the standardized processing result of each parking attribute in the candidate parking lot and the corresponding attribute weight is taken as the recommendation value of the candidate parking lot.

[0110] The specific process of obtaining the recommendation value can be represented by the following formula (4):

[0111] (4);

[0112] Wherein, is the wth candidate parking lot, is the standardized processing result of the kth parking attribute of the wth candidate parking lot, is the attribute weight of the kth parking attribute of the wth candidate parking lot; s is the number of types of parking attributes (s=3 in the present application).

[0113] Since the attribute weight of each parking attribute is determined according to the parking data generated by the user in the historical period, the attribute weight of each parking attribute can reflect the influence degree of each parking attribute on the user's selection of parking.

[0114] ​Therefore, the higher the recommendation value of each candidate parking lot obtained in the above formula (4), the more the parking lot property of the candidate parking lot meets the user's parking selection standard. Therefore, after obtaining the recommendation values of each candidate parking lot through the above process, the order of the recommendation values of each candidate parking lot from large to small can be used as the recommended order of each candidate parking lot displayed in the parking recommendation page.

[0115] In addition, considering the specific reservation function of some parking lots in actual application, a recommendation component representing parking lots with the specific reservation function can be set in the aforementioned parking recommendation page. The user clicks on the recommendation component to inform the platform that the user prefers to select a parking lot with a reservation function.

[0116] In this way, when performing the above step 202, the reservation parking lots with the reservation function can be selected from the candidate parking lots, and then the attribute values of each reservation parking lot under each parking lot attribute can be obtained. Then, according to the order of the attribute values of each reservation parking lot under each parking lot attribute from small to large, each reservation parking lot is recommended and displayed in the parking recommendation page.

[0117] As shown in Figure 11 , assuming that the candidate parking lots 1-3 among the candidate parking lots 1-N selected in step 202 are reservation parking lots with the reservation function, after the user clicks on the "priority reservation" button shown in Figure 11 , each reservation parking lot 1-3 can be sorted according to the walking time from small to large, the driving time from small to large, and the parking fee from small to large, and displayed to the user in the parking recommendation page, so that the user can quickly find a parking lot that meets his parking needs.

[0118] In addition, as shown in Figure 12 , a display component for recommending and displaying each reservation parking lot in the order of each parking lot attribute from small to large can be set in the parking recommendation page in advance. When displaying each candidate parking lot in the parking recommendation page, each reservation parking lot can be recommended and displayed in the order of a certain parking lot attribute (for example, walking time) from small to large by default. The user can click on the display component corresponding to the remaining parking lot attributes (driving time and parking fee) to recommend and display each reservation parking lot in the parking recommendation page in the order of the parking lot attribute corresponding to the display component clicked by the user from small to large.

[0119] In this way, the problem of too long display content that may exist when using the aforementioned Figure 11 display method can be avoided.

[0120] In addition, for each candidate parking lot, if a difference between the parking end time of the candidate parking lot and an end time of the opening time period of the candidate parking lot is less than a difference threshold, a prompt mark representing a near end time is added to the candidate parking lot in the parking recommendation page, so as to prompt the user not to be late for picking up the vehicle.

[0121] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), computer program products according to this application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor 202 of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor 202 of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowcharts and / or block diagrams.

[0123] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowcharts and / or block diagrams.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowcharts and / or block diagrams.

[0125] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A vehicle driving intelligent control method based on Internet of Vehicles, characterized in that, The method comprises: in response to the parking instruction, receiving trip information of the user; wherein the trip information comprises a parking end time of the user and a trip destination; determining, from each of the to-be-processed parking lots, a to-be-selected parking lot suitable for parking of the user in the current trip according to current positioning information of the user, the parking end time, and an opening time period of each of the to-be-processed parking lots; wherein the to-be-processed parking lot is determined according to the trip destination; based on attribute values of each of the to-be-selected parking lots in multiple parking lot attributes, recommending and displaying each of the to-be-selected parking lots in a parking recommendation page; wherein the multiple parking lot attributes comprise some or all of a walking distance, a driving time, and a parking fee; the walking distance is a walking distance from the parking lot to the trip destination; and the driving time is a time required for the user to drive to the parking lot; the determining, from each of the to-be-processed parking lots, a to-be-selected parking lot suitable for parking of the user in the current trip according to current positioning information of the user, the parking end time, and an opening time period of each of the to-be-processed parking lots comprises: for any to-be-processed parking lot, determining a driving time of the user to drive to the to-be-processed parking lot according to positioning information of the user and the to-be-processed parking lot in a road network map; determining a parking time period corresponding to the to-be-processed parking lot based on the driving time and the parking end time; taking, as the to-be-selected parking lot, a parking lot in each of the to-be-processed parking lots, for which the corresponding parking time period is within the opening time period of the parking lot itself; after the taking, as the to-be-selected parking lot, a parking lot in each of the to-be-processed parking lots, for which the corresponding parking time period is within the opening time period of the parking lot itself, the method further comprises: determining whether a number of the to-be-selected parking lots is greater than a number threshold; if the number of the to-be-selected parking lots is not greater than the number threshold, for any candidate parking lot, delaying a parking start time in the parking time period of the candidate parking lot by a preset time to obtain a corrected parking time period corresponding to the candidate parking lot; wherein any candidate parking lot is a to-be-processed parking lot for which the corresponding parking time period is outside the opening time period of the parking lot itself; taking, as the to-be-selected parking lot, a parking lot in each of the candidate parking lots, for which the corresponding corrected parking time period is within the opening time period of the parking lot itself; the determining a parking time period corresponding to the to-be-processed parking lot based on the driving time and the parking end time comprises: detecting whether a trip time of the current trip is reported by the user; if the trip time is received, determining the parking time period according to the trip time, the driving time, and the parking end time; if the trip time is not received, determining the parking time period according to a time when the trip information is received, the driving time, and the parking end time; the recommending and displaying each of the to-be-selected parking lots in a parking recommendation page based on attribute values of each of the to-be-selected parking lots in multiple parking lot attributes comprises: Obtain a plurality of pieces of parking data generated in a historical period; each piece of parking data is associated with a target parking lot and a piece of historical travel information reported by the user; any target parking lot associated with a piece of parking data is a candidate parking lot obtained based on the historical travel information associated with the piece of parking data; and any piece of parking data includes attribute values of the target parking lot associated with the piece of parking data under a plurality of parking lot attributes and a parking selection result; Determine attribute weights of each parking lot attribute based on the attribute values of each target parking lot under the plurality of parking lot attributes and the parking selection result; Determine a ranking order of each candidate parking lot based on the attribute weights and the attribute values of each candidate parking lot under each parking lot attribute; and recommend and display each candidate parking lot in a parking recommendation page in the ranking order.

2. The method of claim 1, wherein, The method further comprises: For any candidate parking lot, determine whether a difference between a parking end time of the candidate parking lot and an end time of an opening period of the candidate parking lot is less than a difference threshold value; If the difference is less than the difference threshold value, add a prompt identifier representing a time close to the end time to the candidate parking lot when the candidate parking lot is displayed in the parking recommendation page.

3. The method of claim 1, wherein, ​ ​ ​ 4. The method of claim 1, wherein, ​ ​ ​

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