Vehicle data processing method, device and system

Abstract

The invention discloses a vehicle data processing method, device and system, and belongs to the technical field of vehicle design. The vehicle data processing method comprises the steps that driving data of a vehicle and illumination data of the vehicle are obtained, the illumination data are obtained based on data collected by an optical sensor installed on the vehicle, and the driving data and the illumination data both correspond to timestamps; aggregating timestamps by using the driving data and the illumination data; and counting the duration of the vehicle in the sunlight by using the aggregated result so as to design a vehicle structure related to the sunlight based on the counted result. According to the method, data with reference value can be provided for the design of the vehicle structure related to sunlight, so that the designed vehicle structure related to sunlight can better meet the use requirements of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle design, and in particular to a method, device and system for processing vehicle data. Background Art

[0002] Designing the vehicle structure (such as the design of on-board solar cells and sunshade structures) based on the user's use of the vehicle can greatly improve the user's car experience. However, the data currently used in designing vehicle structures either comes from the designer's experience or from vehicle testing, neither of which can truly reflect the user's actual car usage. Therefore, there is an urgent need for a technology that can provide reference data for vehicle structure design. Summary of the invention

[0003] In view of this, the present invention provides a vehicle data processing method, device and system, which can provide data with reference value for the design of vehicle structures related to sunlight, so that the designed vehicle structures related to sunlight can better meet the vehicle use requirements.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for processing vehicle data, comprising:

[0006] Acquire driving data of the vehicle and illumination data of the vehicle, wherein the illumination data is obtained based on data collected by an optical sensor installed on the vehicle, wherein both the driving data and the illumination data correspond to a timestamp;

[0007] aggregating the timestamp using the driving data and the illumination data;

[0008] The aggregated results are used to count the duration of the vehicle under sunlight, so as to design a vehicle structure related to sunlight based on the statistical results.

[0009] Optionally, the aggregating the timestamp includes:

[0010] dividing the driving data and the illumination data into data periods;

[0011] In the same data period, the timestamps are aggregated according to the driving data and the illumination data.

[0012] Optionally, the illumination data includes characteristic information indicating whether the vehicle is in a sunlight mode or a non-sunlight mode;

[0013] The driving data includes the speed of the vehicle;

[0014] The aggregating the timestamp further comprises:

[0015] Dividing a timestamp corresponding to the characteristic information into a plurality of first time intervals, wherein one of the first time intervals corresponds to a sunshine mode or a non-sunlight mode;

[0016] According to whether the vehicle speed is greater than 0, dividing the timestamp corresponding to the vehicle speed into a plurality of second time intervals, wherein one of the second time intervals corresponds to a driving mode indicating that the vehicle speed is greater than 0 or a parking mode indicating that the vehicle speed is equal to 0;

[0017] A plurality of the first time intervals and a plurality of the second time intervals are aggregated.

[0018] Optionally, the aggregating the plurality of the first time intervals and the plurality of the second time intervals includes:

[0019] According to the characteristic information corresponding to the first time intervals and the driving modes or parking modes corresponding to the second time intervals, the first time intervals and the second time intervals are divided into target time intervals corresponding to any one or more combinations of the following modes;

[0020] A mode combination of sunlight mode and driving mode, a mode combination of sunlight mode and parking mode, a mode combination of non-sunlight mode and driving mode, and a mode combination of non-sunlight mode and parking mode.

[0021] Optionally, the vehicle data processing method further includes:

[0022] Determine a sunshine time period and a non-sunshine time period corresponding to the timestamp;

[0023] The aggregation result is corrected by using the sunshine time period and the non-sunshine time period.

[0024] Optionally, the result of the correction aggregation includes:

[0025] For the case where the aggregated result includes a target time interval corresponding to any one or more combinations of modes,

[0026] Screening out multiple target time intervals that are within a preset sampling period and correspond to the frame number of the same vehicle, sorting the screened out multiple target time intervals, and determining the sunshine time period and non-sunshine time period corresponding to the frame number of the vehicle;

[0027] For each of the selected target time intervals, perform the following operations:

[0028] Matching the timestamps at the ends of the target time interval and the corresponding mode combination with the sunshine time period and the non-sunshine time period corresponding to the vehicle frame number of the vehicle;

[0029] Correction is performed based on the matching results.

[0030] Optionally, the correction according to the matching result includes:

[0031] When the matching result indicates that the timestamp at the end of the target time interval belongs to a non-sunlight period, and the corresponding mode combination includes a sunshine mode, adjusting the sunshine mode included in the mode combination to a non-sunlight mode;

[0032] Circularly analyzing whether a mode corresponding to a previous timestamp of the current timestamp in the target time interval needs correction, until correction is no longer needed.

[0033] In a second aspect, an embodiment of the present invention provides a vehicle data processing device, including: an interaction module, an aggregation module and a statistical module, wherein:

[0034] The interaction module is used to obtain driving data of the vehicle and illumination data of the vehicle, wherein the illumination data is obtained based on data collected by an optical sensor installed on the vehicle, wherein the driving data and the illumination data both correspond to timestamps;

[0035] The aggregation module is used to aggregate the timestamp using the driving data and the illumination data;

[0036] The statistical module is used to use the aggregated results to count the duration of the vehicle being in sunlight, so as to design a vehicle structure related to sunlight based on the statistical results.

[0037] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0038] one or more processors;

[0039] a storage device for storing one or more programs,

[0040] When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle data processing method provided in the above-mentioned first aspect embodiment.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having a computer program for implementing a vehicle data processing method stored thereon.

[0042] When the computer program is executed by the on-board processor, the vehicle data processing method provided in the first aspect embodiment described above is implemented.

[0043] The technical solution of the above invention has the following advantages or beneficial effects:

[0044] The technical solution provided by the embodiment of the present invention aggregates the timestamps corresponding to the driving data and the lighting data so that the timestamps with the same or similar driving conditions and lighting conditions are aggregated together. Since the aggregation process introduces the driving data of the vehicle, the length of time the vehicle is in daylight based on the aggregation result is related to the driving condition of the vehicle. The length of time the vehicle is in daylight based on the driving condition of the vehicle can more truly reflect the vehicle usage status, and can provide a more valuable reference for vehicle design, so that the designed vehicle structure related to sunlight can better meet the vehicle usage needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0046] Figure 2 is a schematic diagram of the main flow of a method for processing vehicle data provided in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the main process of aggregating timestamps according to an embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of a main process of correcting the result of aggregation according to an embodiment of the present invention;

[0049] Figure 5 is a schematic diagram of a main process implemented by a main architecture for processing vehicle data provided in another embodiment of the present invention;

[0050] Figure 6 is a schematic diagram of the structure of a vehicle data processing device provided according to an embodiment of the present invention;

[0051] Figure 7 It is a schematic diagram of the structure of a computer system suitable for implementing the processing of vehicle data in the embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0053] It should be pointed out that the embodiments of the present invention and the technical features therein may be combined with each other without conflict.

[0054] In addition, the terms “first” and “second” etc. involved in the embodiments of the present invention are used to distinguish features of different scopes or purposes, and are not intended to limit the number or order of features.

[0055] The vehicle involved in the embodiment of the present invention may be an internal combustion engine vehicle using an engine as a power source, a hybrid vehicle using an engine and an electric motor as a power source, an electric vehicle using an electric motor as a power source, or the like.

[0056] The following describes a technical scenario to which the technical solution provided by the embodiment of the present invention is applicable based on the system architecture on which the technical solution provided by the embodiment of the present invention relies.

[0057] Figure 1 An exemplary system architecture 100 is shown, to which a vehicle data processing method or a vehicle data processing device according to an embodiment of the present invention may be applied.

[0058] like Figure 1 As shown, the system architecture 100 may include a database 101, a server 102, terminal devices 103, 104, a vehicle production line 105, and communication modules 106, 108. The communication modules 106, 108 are used to provide a medium for communication between the database 101 and the server 102, between the server 102 and the terminal devices 103, 104, between the server 102 and the vehicle production line 105, and between the terminal devices 103, 104 and the vehicle production line 105. The communication modules 106, 108 may include networks of various connection types, such as wired and wireless communications such as Bluetooth, infrared communication, and data line communication, and may also include a communication base station.

[0059] In addition, the database 101 and the server 102 can also communicate with a large number of sold vehicles 107 through the communication module 108 respectively. The sold vehicles 107 can be a part of the system architecture 100 or exist independently of the system architecture 100.

[0060] Specifically, the system architecture 100 is mainly divided into two parts: a vehicle data processing part and a data usage part. Among them, the database 101, the server 102 and the sold vehicles 107 mainly involve the vehicle data processing part; the terminal devices 102, 104 and the vehicle production line 105 mainly involve the data usage part. It is worth noting that the data processing part and the data usage part are not completely isolated. In special cases, the terminal devices 102, 104 can also be used as devices of the data processing part, and the server 102 can also be used as a device of the data usage part.

[0061] For the data processing part, two data processing scenarios are mainly involved. The first scenario: a large number of sold vehicles 107 send their own characteristic data (such as frame number, vehicle code, location, etc.), driving data (such as speed) and sunshine data (such as light intensity, daylight mode, non-daylight mode, etc.) to the database 101; the database 101 stores a large number of received sold vehicles 107 with their own characteristic data, driving data and sunshine data; the server 102 obtains the sold vehicles 107 with their own characteristic data, driving data and sunshine data from the database, and processes the obtained data. The second scenario: a large number of sold vehicles 107 directly send their own characteristic data (such as frame number, vehicle code, location, etc.), driving data (such as speed) and sunshine data (such as light intensity, daylight mode, non-daylight mode, etc.) to the server 102; the server 102 directly processes the received sold vehicles 107 with their own characteristic data, driving data and sunshine data.

[0062] In the process of processing the characteristic data, driving data and sunshine data of the sold vehicle 107 by the server 102, the data to be processed can be selected according to the demand. For example, in the case of a customized vehicle, the server 102 only processes the driving data and sunshine data of the vehicle already owned by the individual involved in the customized vehicle; for example, in the case of vehicle production demand in a specific area, the server 102 only processes the driving data and sunshine data of the sold vehicle 107 involved in the specific area, etc.

[0063] Regardless of whether it is for the case of customized vehicles or for the vehicle production needs of a specific area, the server 102 implements the following by executing a data processing program: utilizing the driving data and lighting data to aggregate the timestamps included in the driving data and lighting data; utilizing the aggregation results to count the length of time the vehicle is in the sunlight, so that the vehicle production line can design a vehicle structure related to sunlight based on the statistical results.

[0064] Alternatively, the data processing program executed by the server 102 may also be completed in the terminal devices 103 and 104 .

[0065] For the data usage part, the server 102 can send the statistical results to the terminal devices 103 and 104, and the terminal devices 103 and 104 display the received statistical results to the designers. The designers can directly send the statistical results to the vehicle production line 105, or adjust the statistical results and send the modified results to the vehicle production line 105, so that the vehicle production line 105 can produce or assemble the vehicle 109 with solar cells according to the results. In addition, the server 102 can also directly send the statistical results to the vehicle production line 105.

[0066] It should be noted that the vehicle data processing method provided in the embodiment of the present invention can be completed by the server 102 or the terminal devices 103 and 104. Accordingly, each module of the vehicle data processing device can be arranged in the server 102 or the terminal devices 103 and 104.

[0067] It should be understood that Figure 1 The number of databases, servers, terminal devices, vehicle production lines, and vehicles sold in the embodiment is only illustrative. According to the implementation requirements, there can be any number of databases, servers, terminal devices, vehicle production lines, and vehicles sold.

[0068] Figure 2 FIG. 1 is a schematic diagram of the main steps of a method for processing vehicle data according to an embodiment of the present invention. Specifically, Figure 2 As shown, the vehicle data processing method mainly includes the following steps:

[0069] Step S201: Acquire driving data and illumination data of the vehicle, wherein the illumination data is obtained based on data collected by an optical sensor installed on the vehicle, wherein both the driving data and the illumination data correspond to a timestamp;

[0070] The driving data and lighting data acquired in this step may belong to one or a large number of vehicles. The specific number of vehicles can be determined according to the vehicle design requirements. For example, if the vehicle design requirement is to customize a vehicle for a private person, this step only acquires the driving data and lighting data of other vehicles of the person who customized the vehicle; if the vehicle design requirement is to design a special vehicle for a certain area, this step needs to acquire a large amount of driving data and lighting data of vehicles that have been sold in the area.

[0071] Among them, the vehicle's driving data and vehicle lighting data can be directly obtained from the database, and can also be directly received from the transmission of the sold vehicle.

[0072] The driving data of the vehicle generally includes the vehicle speed and a timestamp corresponding to the vehicle speed. The illumination data of the vehicle includes characteristic information indicating that the vehicle is in a sunlight mode or a non-sunlight mode and a timestamp corresponding to the characteristic information; wherein, the characteristic information is generally obtained from data collected by a light sensor installed on the vehicle. For example, if the light intensity in the data collected by the light sensor is lower than an intensity threshold, it is determined to be a non-sunlight mode; if the light intensity in the data collected by the light sensor is higher than or equal to the intensity threshold, it is determined to be a sunlight mode. The characteristic information indicating the sunlight mode or the non-sunlight mode can be directly determined by the vehicle's on-board system or light sensor, or it can be determined by a database or server based on the light intensity in the data collected by the light sensor. How to determine the characteristic information indicating the sunlight mode or the non-sunlight mode is not limited here.

[0073] It is worth noting that the above-mentioned vehicle driving data and vehicle illumination data do not involve user privacy, so the process of obtaining data in step S201 can ensure the privacy security of the vehicle user. In addition, step S201 can also only obtain the driving data and illumination data of the vehicle authorized by the vehicle user.

[0074] Step S202: using the driving data and the illumination data to aggregate the timestamp;

[0075] Specifically, the timestamp includes the year, month, day and specific time. By dividing the data period for the driving data and the illumination data, the timestamps are aggregated according to the driving data and the illumination data in the same data period. This can make the subsequent results aggregated in the same data period more targeted. Among them, the data period can be pre-set or determined according to the region where the vehicle is located. For example, the data period is divided into January to March, April to June, July to September and October to December according to the quarter; the data period is only set for April to July; each month is a data period; each week is a data period, etc.

[0076] In addition, in this step, the aggregation timestamp mainly merges adjacent timestamps of the same type of driving data and the same type of illumination data, where what kind of driving data belongs to the same type and what kind of illumination data belongs to the same type can be set according to needs. For example, driving data with a speed greater than 0 belongs to the same type; characteristic information indicating a daylight mode belongs to the same type of illumination data, etc.

[0077] Step S203: using the aggregated results, counting the duration of the vehicle being in sunlight, so as to design a vehicle structure related to sunlight based on the statistical results.

[0078] The duration of the vehicle in daylight generally refers to the average duration of a large number of vehicles in the same region (such as North China, South China, Northwest China, etc.) in daylight within one day (0:00-24:00) calculated within the above data period. In addition, for private customized vehicles, the duration of the vehicle in daylight may also refer to the duration of a vehicle corresponding to a specific user in daylight within one day (0:00-24:00) or the average duration of multiple vehicles in daylight within one day (0:00-24:00) calculated within the above data period.

[0079] It is worth noting that, according to different statistical results, it is up to the designer to decide which vehicle structure to design in relation to sunlight. It is understandable that the technical solution provided by the embodiment of the present invention is mainly to provide relatively real and reliable reference data for vehicle production.

[0080] The above-mentioned vehicle structures related to sunlight can be vehicle-mounted solar cells, sunshade structures, etc.

[0081] By aggregating the timestamps corresponding to the driving data and lighting data, the timestamps with the same or similar driving conditions and lighting conditions are aggregated together. Since the aggregation process introduces the vehicle's driving data, the length of time the vehicle is in daylight based on the aggregation result is related to the vehicle's driving condition. The length of time the vehicle is in daylight based on the driving condition can more truly reflect the vehicle's usage status, provide a more valuable reference for vehicle design, and enable the designed vehicle structure related to sunlight to better meet the vehicle's usage needs.

[0082] The specific implementation method of aggregating timestamps in the same data cycle is as follows: Figure 3 As shown, the following steps may be included:

[0083] Step S301: dividing the illumination data into a plurality of first time intervals, specifically, dividing the timestamps corresponding to the characteristic information into a plurality of first time intervals, wherein one first time interval corresponds to a sunshine mode or a non-sunlight mode;

[0084] This step mainly merges adjacent timestamps corresponding to the same feature information.

[0085] For example, 2024-7-10, 1:10, non-sunlight mode; 2024-7-10, 1:20, non-sunlight mode; 2024-7-10, 1:50, non-sunlight mode; ...; 2024-7-10, 5:30, non-sunlight mode; 2024-7-10, 5:40, sunshine mode; 2024-7-10, 6:00, sunshine mode; ...; 2024-7-10, 12:00, non-sunlight mode; 2024-7-10, 13:00, non-sunlight mode; 2 024-7-10, 14:00, sunshine mode; 2024-7-10, 15:00, sunshine mode; ...; 2024-7-10, 19:00, sunshine mode, etc. After processing through this step, multiple first time intervals are obtained, respectively, [2024-7-10-1:10, 2024-7-10-5:30]-non-sunshine mode; (2024-7-10-5:30, 2024-7-10-6:00]-sunshine mode; ...; [2024-7-10-12 :00, 2024-7-10-13:00] - non-sunshine mode; [2024-7-10-14:00, 2024-7-10-19:00] - sunshine mode, etc. It is worth noting that the above is only an example and does not represent the specific timestamp or sunshine data collection time interval. Generally speaking, for two first time intervals adjacent in time, the end time of the first time interval in time is the same as or close to the start time of the first time interval in time. For example, [2024 -7-10-1:10, 2024-7-10-5:30]-non-sunlight mode and (2024-7-10-5:30, 2024-7-10-6:00]-sunlight mode, the end time of the first time interval in the former time is 2024-7-10-5:30, and the start time of the first time interval in the latter time is also determined to be 2024-7-10-5:30, to ensure that all time or most time in a day can be counted, to ensure the accuracy and reliability of subsequent statistical results.

[0086] Step S302: dividing the driving data into a plurality of second time intervals, specifically, dividing the timestamp corresponding to the vehicle speed into a plurality of second time intervals according to whether the vehicle speed is greater than 0, wherein one second time interval corresponds to a driving mode indicating that the vehicle speed is greater than 0 or a parking mode indicating that the vehicle speed is equal to 0;

[0087] For example, 2024-7-10, 1:10, the vehicle speed is 0; 2024-7-10, 3:20, the vehicle speed is 0; ...; 2024-7-10, 5:30, the vehicle speed is 0; ...; 2024-7-10, 6:40, the vehicle speed is greater than 0; ...; 2024-7-10, 8:40, the vehicle speed is greater than 0; 2024-7-10, 8:50, the vehicle speed is 0; ...; 2024-7-10, 12:00, the vehicle speed is 0; 2024-7-10, 13:00, the vehicle speed is 0; 2024-7-10, 14:00, the vehicle speed is 0; 2024-7-10, 15:00, the vehicle speed is 0; ...; 2024-7-10, 18:00, the vehicle speed is greater than 0 At 0; 2024-7-10, 19:00, the vehicle speed is equal to 0, etc. After processing through this step, multiple second time intervals are obtained, namely, [2024-7-10-1:10, 2024-7-10-6:40)-parking mode; [2024-7-10-6:40, 2024-7-10-8:40]-driving mode; ...; [2024-7-10-8:50, 2024-7-10-18:00)-parking mode; [2024-7-10-18:00, 2024-7-10-19:00]-driving mode, etc. It is worth noting that the above is only an example, which does not represent a specific timestamp or a collection time interval of driving data. Generally speaking, for two second time intervals adjacent in time, the end time of the first second time interval is the same as or close to the start time of the second time interval. For example, [2024-7-10-1:10, 2024-7-10-6:40] - non-sunlight mode and (2024-7-10-6:40, 2024-7-10-8:40] - sunshine mode, the end time of the first time interval in the former is 2024-7-10-6:40, and the start time of the first time interval in the latter is also determined to be 2024-7-10-6:40, to ensure that all time or most time in a day can be counted, thereby ensuring the accuracy and reliability of subsequent statistical results.

[0088] Step S303: Aggregate the multiple first time intervals and the multiple second time intervals.

[0089] The aggregation process in this step mainly involves the aggregation between the first time interval and the second time interval.

[0090] Specifically, according to the characteristic information corresponding to the plurality of first time intervals and the driving modes or parking modes corresponding to the plurality of second time intervals, the plurality of first time intervals and the plurality of second time intervals are split into target time intervals corresponding to any one or more combinations of the following modes;

[0091] A mode combination of sunlight mode and driving mode, a mode combination of sunlight mode and parking mode, a mode combination of non-sunlight mode and driving mode, and a mode combination of non-sunlight mode and parking mode.

[0092] This step is mainly to obtain the intersection of the first time interval and the second time interval corresponding to the pattern combination.

[0093] For example, the above-mentioned multiple first time intervals are respectively, [2024-7-10-1:10, 2024-7-10-5:30]-non-sunlight mode; (2024-7-10-5:30, 2024-7-10-6:00]-sunlight mode; ...; [2024-7-10-12:00, 2024-7-10-13:00]-non-sunlight mode; [2024-7-10-14:00, 2024 -7-10-19:00]-sunlight mode; the multiple second time intervals are respectively, [2024-7-10-1:10, 2024-7-10-6:40)-parking mode; [2024-7-10-6:40, 2024-7-10-8:40]-driving mode; ...; [2024-7-10-8:50, 2024-7-10-18:00)-parking mode; [2024-7-10-1 8:00, 2024-7-10-19:00]-driving mode. For this example, the aggregation results obtained through this step are: [2024-7-10-1:10, 2024-7-10-5:30]-non-sunlight mode + parking mode; (2024-7-10-5:30, 2024-7-10-6:00]-sunlight mode + parking mode; [2024-7-10-6:40, 2024-7- 10-8:40]-sunlight mode + driving mode;...; [2024-7-10-12:00, 2024-7-10-13:00]-non-sunlight mode + parking mode; [2024-7-10-14:00, 2024-7-10-18:00)-sunlight mode + parking mode; [2024-7-10-18:00, 2024-7-10-19:00]-sunlight mode + driving mode, etc.

[0094] Through the above process, the data is divided into intervals according to driving mode or non-driving mode and sunlight mode or non-sunlight mode, so as to better find the time period corresponding to the sunlight mode, as well as the vehicle driving time and vehicle parking time in the time period corresponding to the sunlight mode.

[0095] Furthermore, in order to ensure data accuracy and avoid misjudgment due to the vehicle being parked in an underground garage or due to the influence of underground garage lighting, an embodiment of the present invention also includes: determining the sunshine time period and non-sunlight time period corresponding to the timestamp; using the sunshine time period and non-sunlight time period to correct the aggregation result. Among them, the sunshine time period and non-sunlight time period are related to the region and the month of the timestamp. For example, in North China from July to September, the sunshine time period is 5:30 to 19:00 on average, and the non-sunlight time period is 00:00 to 5:29 and 19:01 to 24:00. The sunshine time period and non-sunlight time period are generally pre-set. This part of the correction process is mainly for timestamps belonging to non-sunlight time periods that are misjudged as sunshine mode. It is worth noting that for the target time interval indicated by the aggregation result as a non-sunlight mode, if it is in the sunshine period, this may be caused by the vehicle being in the garage. In this case, the target time interval is still maintained as a non-sunlight mode, so that the situation where the vehicle is located in the garage and cannot be exposed to sunlight will be treated as non-sunlight, so that the statistical results are consistent with the driving and parking habits of the vehicle, further improving the accuracy of the statistical results.

[0096] Specifically, the aggregation result includes the target time interval corresponding to any one or more mode combinations, such as Figure 4 As shown, the specific implementation of the correction aggregation result may include the following steps:

[0097] Step S401: screening out multiple target time intervals that are within a preset sampling period and correspond to the vehicle frame number of the same vehicle, and sorting the screened out multiple target time intervals;

[0098] The sorting is to arrange the multiple target time intervals in chronological order.

[0099] Step S402: Determine the sunshine time period and non-sunshine time period corresponding to the vehicle frame number of the vehicle;

[0100] There is no strict execution order between the above step S401 and step S402.

[0101] For each selected target time interval, perform the following operations:

[0102] Step S403: matching the timestamp at the end of the target time interval and the corresponding mode combination with the sunshine time period and the non-sunshine time period corresponding to the vehicle frame number;

[0103] Step S404: perform correction according to the matching result.

[0104] Specifically, when the timestamp at the end of the target time interval indicated by the matching result belongs to the sunshine time period, the corresponding mode combination includes the non-sunlight mode, and the current mode combination is kept unchanged. For example, in the above example, [2024-7-10-12:00, 2024-7-10-13:00]-non-sunlight mode + parking mode, although 12:00-13:00 belongs to the sunshine time period, [2024-7-10-12:00, 2024-7-10-13:00]-non-sunlight mode + parking mode is still kept to consider the situation of parking in the garage as non-sunlight, thereby improving the accuracy of the statistical results. When the timestamp at the end of the target time interval indicated by the matching result belongs to the non-sunlight time period, the corresponding mode combination includes the sunshine mode, and the sunshine mode included in the mode combination is adjusted to the non-sunlight mode; through this process, the situation where the lights in the garage or the lights on the road are mistaken for sunshine can be eliminated, thereby further improving the accuracy of the statistical results. If the current timestamp is a timestamp that needs to be corrected, it is necessary to further analyze whether the mode corresponding to the previous timestamp of the current timestamp in the target time interval needs to be corrected, until correction is no longer needed. For example, [2024-7-10-18:00, 2024-7-10-21:00]-sunlight mode+parking mode. Exemplarily, there are timestamps corresponding to sunshine data in this interval: 2024-7-10-18:00, 2024-7-10-19:30, 2024-7-10-20:00, 2024-7-10-20:30, 2024-7-10-20:50, etc. Then, through the above process, the current timestamp 2024-7-10-21:00 is first matched with the non-sunlight time period and the sunshine time period, and it is concluded that 2024-7-10-21:00 is the non-sunlight time period, then it is necessary to match 2024-7-10-20:50 (i.e., the previous timestamp of the current timestamp) with the non-sunlight time period and the sunshine time period, and so on. To ensure the accuracy of the pattern combination corresponding to each interval after correction.

[0105] Based on the above correction aggregation results, the specific implementation of the above counting the time the vehicle is in the daylight may include: based on the correction results, respectively calculating the time the vehicle is in the daylight and the driving time of the vehicle in the daylight.

[0106] The duration of the vehicle being in the sunlight includes the driving duration of the vehicle in the sunlight and the parking duration of the vehicle in the sunlight.

[0107] In the case of counting the time that a large amount of vehicles are in daylight, the proportion of vehicles corresponding to the time that each type of vehicle is in daylight is counted, and the proportion of vehicles is used as the weight of the corresponding time in daylight. The time that each type of vehicle is in daylight and its weight are used to calculate the average time that a large number of vehicles are in daylight. Correspondingly, the driving time of vehicles in daylight is calculated by counting the proportion of vehicles corresponding to the driving time of each type of vehicle in daylight, and the proportion of vehicles is used as the weight of the corresponding driving time in daylight. The driving time of each type of vehicle in daylight and its weight are used to calculate the average driving time of a large number of vehicles in daylight.

[0108] The specific implementation of counting the time that the vehicle is in the sunlight may include: counting the time that the vehicle is in the sunlight and the driving time of the vehicle in the sunlight according to the vehicle type and / or the region where the vehicle is located. That is, the vehicles are classified according to the vehicle type and / or the region where the vehicle is located, so as to ensure that the statistical results meet the requirements of different types of vehicles or different regions on vehicle structures.

[0109] The following is an example in which a database provides a server with a large amount of vehicle driving data and sunshine data, the server processes the data, obtains statistical results, and sends the statistical results to a vehicle production line, and the vehicle production line deploys solar cells for the required production vehicles based on the statistical results. Specifically, the vehicle data processing method provided by the embodiment of the present invention is described in detail. Figure 5 As shown, the vehicle data processing method may include the following steps:

[0110] Step S501: Acquire vehicle data;

[0111] The server obtains a large amount of vehicle driving data and sunshine data for a specific region (such as North China) from the database. The large amount of vehicle driving data and sunshine data are provided to the database by vehicles sold in North China, and the database is obtained after being sorted.

[0112] Step S502: Divide the data cycle;

[0113] The data period is divided into quarters, months, etc.

[0114] Step S503: Aggregate vehicle data within the same data period;

[0115] This step is to divide the timestamps into: a target time interval corresponding to a mode combination of sunshine mode and driving mode, a target time interval corresponding to a mode combination of sunshine mode and parking mode, a target time interval corresponding to a mode combination of non-sunlight mode and driving mode, and a target time interval corresponding to a mode combination of non-sunlight mode and parking mode according to the time sequence of the timestamps of the driving data and the timestamps of the sunshine data.

[0116] Step S504: Correcting the aggregated result;

[0117] This step uses the preset sunshine time period and non-sunlight time period to correct the various target time intervals aggregated in step S504. It is worth noting that for the target time interval corresponding to the non-sunlight mode, but the timestamp in the target time interval belongs to the sunshine time period, the target time interval is still kept corresponding to the non-sunlight mode. For the target time interval corresponding to the sunshine mode, but the timestamp in the target time interval belongs to the non-sunlight time period, it is necessary to correct the mode of some or all timestamps in the target time interval.

[0118] Step S505: using the calibrated result, counting the time the vehicle is in sunlight;

[0119] The proportion of vehicles corresponding to the time spent in daylight by each type of vehicle is counted, and the proportion of vehicles is used as the weight of the corresponding time spent in daylight. The time spent in daylight by each type of vehicle and its weight are used to calculate the average time spent in daylight by a large number of vehicles. The proportion of vehicles corresponding to the driving time spent in daylight by each type of vehicle is counted, and the proportion of vehicles is used as the weight of the corresponding driving time spent in daylight. The driving time spent in daylight by each type of vehicle and its weight are used to calculate the average driving time spent in daylight by a large number of vehicles.

[0120] Step S506: Provide the duration of the vehicle being in sunlight to the vehicle production line.

[0121] Further, Figure 6 FIG. 2 is a schematic diagram showing a part of the structure of a device for implementing the above-mentioned vehicle data processing method provided by an embodiment of the present invention. Figure 6 As shown, the vehicle data processing device may include: an interaction module 601, an aggregation module 602 and a statistics module 603, wherein:

[0122] Interaction module 601, used to obtain driving data and illumination data of the vehicle, wherein the illumination data is obtained based on data collected by an optical sensor installed on the vehicle, wherein both the driving data and the illumination data have corresponding timestamps;

[0123] Aggregation module 601, for aggregating timestamps using driving data and illumination data;

[0124] The statistical module 603 is used to use the aggregated results to count the duration of the vehicle in sunlight, so as to design the vehicle structure related to sunlight based on the statistical results.

[0125] Furthermore, the aggregation module 601 is further used to divide the driving data and the illumination data into data periods; and within the same data period, aggregate the timestamps according to the driving data and the illumination data.

[0126] In an embodiment of the present invention, the lighting data includes characteristic information indicating that the vehicle is in a sunlight mode or a non-sunlight mode; the driving data includes the vehicle speed; the above-mentioned aggregation module 601 is further used to divide the timestamp corresponding to the characteristic information into multiple first time intervals, wherein a first time interval corresponds to the sunlight mode or the non-sunlight mode; according to whether the vehicle speed is greater than 0, the timestamp corresponding to the speed is divided into multiple second time intervals, wherein a second time interval corresponds to a driving mode indicating that the speed is greater than 0 or a parking mode indicating that the speed is equal to 0; and multiple first time intervals and multiple second time intervals are aggregated.

[0127] In an embodiment of the present invention, the above-mentioned aggregation module 601 is further used to split the multiple first time intervals and the multiple second time intervals into target time intervals corresponding to any one or more of the following mode combinations according to the characteristic information corresponding to the multiple first time intervals and the driving modes or parking modes corresponding to the multiple second time intervals; the mode combination of sunlight mode and driving mode, the mode combination of sunlight mode and parking mode, the mode combination of non-sunlight mode and driving mode, and the mode combination of non-sunlight mode and parking mode.

[0128] In the embodiment of the present invention, the aggregation module 601 is further used to determine the sunshine time period and the non-sunshine time period corresponding to the timestamp; and correct the aggregation result by using the sunshine time period and the non-sunshine time period.

[0129] In an embodiment of the present invention, the above-mentioned aggregation module 601 is further used to, for example, select multiple target time intervals that are within a preset sampling period and correspond to the frame number of the same vehicle when the aggregation result includes a target time interval corresponding to any one or more pattern combinations, sort the selected multiple target time intervals, and determine the sunshine time period and non-sunshine time period corresponding to the frame number of the vehicle; for each selected target time interval, perform the following operations: match the timestamp at the end of the target time interval and the corresponding pattern combination with the sunshine time period and non-sunshine time period corresponding to the frame number of the vehicle; and perform correction according to the matching result.

[0130] In an embodiment of the present invention, the above-mentioned aggregation module 601 is further used to adjust the sunshine mode included in the pattern combination to a non-sunlight mode when the timestamp at the end of the target time interval indicated by the matching result belongs to a non-sunlight time period and the corresponding pattern combination includes a sunshine mode; and cyclically analyze whether the pattern corresponding to the previous timestamp of the current timestamp in the target time interval needs to be corrected until correction is no longer required.

[0131] Reference below Figure 7, which shows a schematic diagram of the structure of a computer system 700 suitable for implementing the vehicle data processing method according to an embodiment of the present invention. Figure 7 The computer system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0132] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0133] The following components are connected to the I / O interface 705: an input section 706 including an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0134] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present invention are executed.

[0135] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0136] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be provided in a processor. For example, they may be described as follows: a processor includes the above-mentioned interaction module, aggregation module and statistical module. The names of these modules or units do not, in some cases, constitute limitations on the modules or units themselves. For example, the interaction module may also be described as a "module or unit for obtaining vehicle driving data and vehicle lighting data".

[0138] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: obtaining the driving data of the vehicle and the illumination data of the vehicle, wherein the illumination data is obtained based on the data collected by the optical sensor installed on the vehicle, wherein the driving data and the illumination data both correspond to timestamps; using the driving data and the illumination data, aggregating the timestamps; using the aggregation results, counting the duration of the vehicle in the sunlight, so as to design the vehicle structure related to sunlight based on the statistical results.

[0139] According to the technical solution of the embodiment of the present invention, the timestamps corresponding to the driving data and the lighting data are aggregated so that the timestamps with the same or similar driving conditions and lighting conditions are aggregated together. Since the aggregation process introduces the driving data of the vehicle, the length of time the vehicle is in daylight based on the aggregation result is related to the driving condition of the vehicle. The length of time the vehicle is in daylight based on the driving condition of the vehicle can more truly reflect the vehicle usage status, and can provide a more valuable reference for vehicle design, so that the designed vehicle structure related to sunlight can better meet the vehicle usage needs.

[0140] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for processing vehicle data, characterized in that: include: Acquire driving data of the vehicle and illumination data of the vehicle, wherein the illumination data is obtained based on data collected by an optical sensor installed on the vehicle, wherein both the driving data and the illumination data correspond to a timestamp; aggregating the timestamp using the driving data and the illumination data; The aggregated results are used to count the duration of the vehicle under sunlight, so as to design a vehicle structure related to sunlight based on the statistical results.

2. The vehicle data processing method according to claim 1, characterized in that: The aggregating the timestamp includes: dividing the driving data and the illumination data into data periods; In the same data period, the timestamps are aggregated according to the driving data and the illumination data.

3. The vehicle data processing method according to claim 1 or 2, characterized in that: The illumination data includes characteristic information indicating whether the vehicle is in a sunlight mode or a non-sunlight mode; The driving data includes the speed of the vehicle; The aggregating the timestamp further comprises: Dividing a timestamp corresponding to the characteristic information into a plurality of first time intervals, wherein one of the first time intervals corresponds to a sunshine mode or a non-sunlight mode; According to whether the vehicle speed is greater than 0, dividing the timestamp corresponding to the vehicle speed into a plurality of second time intervals, wherein one of the second time intervals corresponds to a driving mode indicating that the vehicle speed is greater than 0 or a parking mode indicating that the vehicle speed is equal to 0; A plurality of the first time intervals and a plurality of the second time intervals are aggregated.

4. The vehicle data processing method according to claim 3, characterized in that: The aggregating the plurality of the first time intervals and the plurality of the second time intervals includes: According to the characteristic information corresponding to the first time intervals and the driving modes or parking modes corresponding to the second time intervals, the first time intervals and the second time intervals are divided into target time intervals corresponding to any one or more combinations of the following modes; A mode combination of sunlight mode and driving mode, a mode combination of sunlight mode and parking mode, a mode combination of non-sunlight mode and driving mode, and a mode combination of non-sunlight mode and parking mode.

5. The vehicle data processing method according to any one of claims 1 to 4, characterized in that: Also includes: Determine a sunshine time period and a non-sunshine time period corresponding to the timestamp; The aggregation result is corrected by using the sunshine time period and the non-sunshine time period.

6. The vehicle data processing method according to claim 5, characterized in that: The result of the correction aggregation includes: For the case where the aggregated result includes a target time interval corresponding to any one or more combinations of modes, Screening out multiple target time intervals that are within a preset sampling period and correspond to the frame number of the same vehicle, sorting the screened out multiple target time intervals, and determining the sunshine time period and non-sunshine time period corresponding to the frame number of the vehicle; For each of the selected target time intervals, perform the following operations: Matching the timestamps at the ends of the target time interval and the corresponding mode combination with the sunshine time period and the non-sunshine time period corresponding to the vehicle frame number of the vehicle; Correction is performed based on the matching results.

7. The vehicle data processing method according to claim 6, characterized in that: The correction according to the matching result includes: When the matching result indicates that the timestamp at the end of the target time interval belongs to a non-sunlight period, and the corresponding mode combination includes a sunshine mode, adjusting the sunshine mode included in the mode combination to a non-sunlight mode; Circularly analyzing whether a mode corresponding to a previous timestamp of the current timestamp in the target time interval needs correction, until correction is no longer needed.

8. A vehicle data processing device, characterized in that: include: Interaction module, aggregation module and statistics module, among which, The interaction module is used to obtain driving data of the vehicle and illumination data of the vehicle, wherein the illumination data is obtained based on data collected by an optical sensor installed on the vehicle, wherein the driving data and the illumination data both correspond to timestamps; The aggregation module is used to aggregate the timestamp using the driving data and the illumination data; The statistical module is used to use the aggregated results to count the duration of the vehicle being in sunlight, so as to design a vehicle structure related to sunlight based on the statistical results.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle data processing method as described in any one of claims 1 to 7 above.

10. A computer-readable medium, characterized in that A computer program for implementing a vehicle data processing method is stored thereon, When the computer program is executed by an on-board processor, the vehicle data processing method as described in any one of claims 1 to 7 is implemented.

Images