Method, device and equipment for intelligent scheduling of upgrade time of OTA upgrade and storage medium

Abstract

The application discloses an OTA upgrading time intelligent scheduling method and device, equipment and a storage medium, and relates to the technical field of OTA upgrading. The application comprises the following steps: acquiring parking data and network condition data of a vehicle; analyzing the parking data to determine the parking probability of the vehicle in different time periods; determining a target upgrading time according to the parking data, the network condition data and the parking probability in the different time periods; and when a new upgrading task is detected, taking the target upgrading time as an upgrading start time and executing the new upgrading task. The application can efficiently and safely perform OTA upgrading of an intelligent vehicle, select the most suitable upgrading time period, use vehicle usage and network condition data to select the best opportunity for upgrading, improve the success rate and efficiency of upgrading, and reduce upgrading failures caused by network problems.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of OTA upgrade, in particular to an OTA upgrade time intelligent scheduling method and device, equipment and a storage medium. BACKGROUND

[0002] With the development of intelligent vehicle technology, software updates for vehicles are becoming more frequent. The traditional OTA (Over-The-Air) upgrade method has the problems of inflexible upgrade time selection, interrupted user experience during upgrade, and the like. Even if the user schedules a specific time for upgrade, it is difficult to avoid situations such as the vehicle being parked in a network environment in a parking garage. Currently, when an OTA upgrade is in progress, it is usually performed at night. When the vehicle is parked and connected to Wi-Fi, the update is automatically downloaded and installed. The user can also view the upgrade details on the touch screen of the vehicle and choose to upgrade immediately or schedule an upgrade time. However, this approach can affect the user's use of the vehicle when the upgrade time is long, and not everyone does not use the vehicle at night, which can also affect the user's use of the vehicle.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide an OTA upgrade time intelligent scheduling method, device, equipment and storage medium, which aims to solve the technical problem that the prior art cannot determine a suitable OTA upgrade time period according to the user's driving habits.

[0005] To achieve the above purpose, the present application provides an OTA upgrade time intelligent scheduling method, which comprises:

[0006] acquiring parking data and network status data of a vehicle;

[0007] analyzing the parking data to determine the parking probability of the vehicle at different time periods;

[0008] determining a target upgrade time according to the parking data, the network status data and the parking probability at the different time periods;

[0009] when a new upgrade task is detected, taking the target upgrade time as the upgrade start time to execute the new upgrade task.

[0010] In an embodiment, the step of acquiring parking data and network status data of a vehicle comprises:

[0011] acquire a power-off time of the vehicle machine and a next power-on time of the vehicle machine, and obtain a parking time span according to the power-off time of the vehicle machine and the next power-on time of the vehicle machine;

[0012] acquire positioning information of the vehicle machine at the power-off time, and determine a parking position of the vehicle;

[0013] obtain parking data according to the parking time span and the parking position;

[0014] acquire network signal strength of the vehicle machine within the parking time span;

[0015] obtain average network signal strength according to the network signal strength and the parking time span, and take the average network signal strength as network condition data.

[0016] In an embodiment, the step of analyzing the parking data and determining parking probability of the vehicle in different time periods comprises:

[0017] analyzing the parking data to obtain parking time span of the vehicle and the parking position of the vehicle;

[0018] when the parking position is a bound position parking position, comparing the parking time span with a valid parking time span, and determining parking behavior corresponding to the parking time span as valid parking when the parking time span is within the valid parking time span;

[0019] dividing time of a day into multiple equal time periods;

[0020] distributing the valid parking corresponding parking time to the corresponding time periods to obtain parking times of the vehicle in different time periods;

[0021] determining total parking times of the vehicle in a statistical period;

[0022] obtaining parking probability by respectively multiplying parking times in different time periods by the total parking times.

[0023] In an embodiment, the step of determining target upgrade time according to the parking data, the network condition data and the parking probability in different time periods comprises:

[0024] analyzing the parking data to obtain parking time span of the vehicle and the parking position of the vehicle;

[0025] determining time period score according to the parking time span, determining parking position score according to the parking position, determining network condition score according to the network condition data, and determining parking probability score according to the parking probability;

[0026] According to the time period score, the parking location score, the network condition score and the parking probability score, an upgrade score of the current time period is obtained;

[0027] According to the upgrade score, a target upgrade time is determined.

[0028] In an embodiment, the steps of determining the time period score according to the parking time span, determining the parking location score according to the parking location, determining the network condition score according to the network condition data and determining the parking probability score according to the parking probability include:

[0029] Determining whether the parking time span contains a night time period, and if so, obtaining a time period score;

[0030] Determining a ratio of the number of parking times in the current time period to the total number of parking times in a statistical period to obtain a parking probability score;

[0031] Determining whether the parking location is a bound parking location, and if so, obtaining a parking location score according to a parking time proportion of an average parking duration of the parking location and a parking probability of the parking location;

[0032] Determining a ratio of a network speed of the bound parking location to a maximum network speed in all parking locations to obtain a network condition score;

[0033] Determining the time period score according to the parking time span, determining the parking location score according to the parking location, determining the network condition score according to the network condition data and determining the parking probability score according to the parking probability.

[0034] In an embodiment, the step of determining the target upgrade time according to the upgrade score includes:

[0035] Arranging the upgrade scores in descending order to obtain an arrangement result;

[0036] Traversing the arrangement result, and when there are continuous upgrade times of a preset time span corresponding to the upgrade scores in the arrangement result, taking the continuous upgrade times of the preset time span as the target upgrade time.

[0037] In an embodiment, the step of, when a new upgrade task is detected, taking the target upgrade time as an upgrade start time and executing the new upgrade task includes:

[0038] Obtaining a real-time position of the vehicle, and when the real-time position enters a preset area, automatically checking for an upgrade;

[0039] When a new upgrade task is detected, taking the target upgrade time as an upgrade start time and executing the new upgrade task.

[0040] In addition, to achieve the above object, the present application also provides an OTA upgrade time intelligent scheduling device, which comprises:

[0041] a data collection module, configured to acquire parking data and network condition data of the vehicle;

[0042] a behavior analysis module, configured to analyze the parking data and determine parking probabilities of the vehicle in different time periods;

[0043] an intelligent scheduling module, configured to determine a target upgrade time according to the parking data, the network condition data and the parking probabilities in the different time periods;

[0044] a decision output module, configured to, when a new upgrade task is detected, execute the new upgrade task with the target upgrade time as an upgrade start time.

[0045] In addition, to achieve the above object, the present application also provides an OTA upgrade time intelligent scheduling device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the OTA upgrade time intelligent scheduling method.

[0046] In addition, to achieve the above object, the present application also provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executable on a processor to implement the steps of the OTA upgrade time intelligent scheduling method.

[0047] In addition, to achieve the above object, the present application also provides a computer program product, which comprises a computer program, and the computer program is executable on a processor to implement the steps of the OTA upgrade time intelligent scheduling method.

[0048] The one or more technical solutions provided by the present application have at least the following technical effects: acquiring parking data and network condition data of the vehicle; analyzing the parking data and determining parking probabilities of the vehicle in different time periods; determining a target upgrade time according to the parking data, the network condition data and the parking probabilities in the different time periods; when a new upgrade task is detected, executing the new upgrade task with the target upgrade time as an upgrade start time, which can efficiently and safely perform OTA upgrade of the intelligent vehicle, select the most suitable upgrade time period, use vehicle use and network condition data to select the best opportunity for upgrade, improve the success rate and efficiency of upgrade, and reduce upgrade failure caused by network problems. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate preferred embodiments of the present application and, together with the description, serve to explain the principles of the application.

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings required by the embodiments or the prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0051] Figure 1 The flowchart provided for the first embodiment of the upgrade time intelligent scheduling method of the OTA upgrade of the present application;

[0052] Figure 2 The parking frequency data schematic diagram provided for the first embodiment of the upgrade time intelligent scheduling method of the OTA upgrade of the present application;

[0053] Figure 3 The time period and location binding schematic diagram provided for the first embodiment of the upgrade time intelligent scheduling method of the OTA upgrade of the present application;

[0054] Figure 4 The module structure schematic diagram of the upgrade time intelligent scheduling device of the OTA upgrade of the embodiment of the present application;

[0055] Figure 5 The device structure schematic diagram of the hardware running environment involved in the upgrade time intelligent scheduling method of the OTA upgrade in the embodiment of the present application.

[0056] The purpose implementation, functional features and advantages of the present application will be further explained with reference to the accompanying drawings in combination with the embodiments. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0058] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings in the specification and specific embodiments.

[0059] The main solution of the embodiment of the present application is: obtaining parking data and network condition data of a vehicle; analyzing the parking data to determine parking probabilities of the vehicle in different time periods; determining a target upgrade time according to the parking data, the network condition data and the parking probabilities in the different time periods; when a new upgrade task is detected, taking the target upgrade time as an upgrade start time to execute the new upgrade task.

[0060] In this embodiment, for ease of description, the following describes an upgrade time intelligent scheduling device for identifying OTA upgrade as the execution subject.

[0061] Since the prior art usually upgrades at night, when the vehicle is parked and connected to Wi-Fi, the update is automatically downloaded and installed. The user can also view the upgrade details on the touch screen of the vehicle and choose to upgrade immediately or schedule the upgrade time, but this way will affect the user's use of the vehicle when the upgrade time is long, and not everyone does not use the vehicle at night, which will also affect the user's use of the vehicle.

[0062] The present application provides a solution to reduce the impact on the user's daily use by intelligently selecting the upgrade time, improve the overall satisfaction of the user's use of the vehicle, select the best opportunity for upgrade by using vehicle usage and network condition data, improve the success rate and efficiency of the upgrade, reduce the upgrade failure caused by network problems, upgrade when the network condition is good, reduce the security risk in the data transmission process, protect the safety of the vehicle and user data, avoid upgrading during the network peak period through intelligent scheduling, reduce the network burden, and optimize the use of network resources.

[0063] From the above embodiment, the present application can obtain the parking data and network condition data of the vehicle; analyze the parking data to determine the parking probability of the vehicle in different time periods; determine the target upgrade time according to the parking data, the network condition data and the parking probability in different time periods; and when a new upgrade task is detected, the target upgrade time is used as the upgrade start time, and the new upgrade task is executed, which can efficiently and safely perform OTA upgrade of the intelligent vehicle, select the most suitable upgrade time period, select the best opportunity for upgrade by using vehicle usage and network condition data, and improve the success rate and efficiency of the upgrade to reduce the upgrade failure caused by network problems.

[0064] It should be noted that the execution subject of the present embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an OTA upgrade upgrade time intelligent scheduling device, etc. The following describes the present embodiment and each of the following embodiments by taking the OTA upgrade upgrade time intelligent scheduling device as an example.

[0065] Based on this, the present application embodiment provides an OTA upgrade upgrade time intelligent scheduling method, which is described in detail with reference to Figure 1 , Figure 1 The flowchart of the first embodiment of the OTA upgrade upgrade time intelligent scheduling method of the present application is shown in the figure.

[0066] In this embodiment, the OTA upgrade upgrade time intelligent scheduling method comprises steps S10-S40:

[0067] Step S10, obtaining parking data and network condition data of the vehicle.

[0068] It should be noted that the parking data includes the position information and the parking time information of the vehicle, and the network condition data describes and records the network quality of the vehicle at any time.

[0069] In a specific implementation, after the vehicle machine is activated, the position information of the vehicle in the driving process or the vehicle stop state, the parking time and other parking data, and the real-time network signal quality network condition data and other data can be obtained through the GPS positioning signal inside the vehicle, the timer, the network diagnosis tool, etc., and the obtained parking data and network condition data can be stored in the vehicle machine for localized storage. In order to avoid continuous accumulation of data, the stored data can be automatically cleaned, such as cleaning the data 30 days ago, and only keeping the parking data and network condition data within 30 days.

[0070] In a feasible implementation, the step of obtaining the parking data and the network condition data of the vehicle comprises:

[0071] Obtaining the power-off time of the vehicle machine and the next power-on time of the vehicle machine, and obtaining the parking time span according to the power-off time of the vehicle machine and the next power-on time of the vehicle machine;

[0072] Obtaining the positioning information of the vehicle machine at the power-off time, and determining the parking position of the vehicle;

[0073] Obtaining the parking data according to the parking time span and the parking position;

[0074] Obtaining the network signal strength of the vehicle machine within the parking time span;

[0075] Obtaining the average network signal strength according to the network signal strength and the parking time span, and taking the average network signal strength as the network condition data.

[0076] It should be noted that the power-off of the vehicle machine can be used as a signal that the current vehicle is completely stopped, and the next power-on of the vehicle machine can be used as a signal that the vehicle is converted from the stopped state to the driving state or the standby state. The time span between the power-off time of the vehicle machine and the next power-on time of the vehicle machine is the vehicle stop time, i.e. the vehicle parking time. During the vehicle stop process, the position of the vehicle does not change, so the vehicle positioning signal at this time is the parking position of the vehicle. The network condition data is used to describe the network quality of the vehicle in the parking state.

[0077] In a specific implementation, the power-off time and power-on time of the car machine can be obtained, the data information can be stored in the driving log of the vehicle, and the data records are arranged according to time, so that the power-off time and the next power-on time of the car machine can be obtained from the driving log, and the parking time span is obtained according to the power-off time and the next power-on time of the car machine, for example, the power-off time of the car machine is 08:00:00, and the next power-on time of the car machine is 09:00:00. Then it can be determined that the parking time span is 1 hour, and the current parking time is 1 hour. When the car machine is powered off, it is determined that the vehicle is in a stopped state, so the current GPS position of the vehicle can be determined as the parking position of the vehicle, and the parking data can be generated according to the parking time span and the parking position of the vehicle to describe the current parking behavior in a combined manner. At the same time, the network signal strength of the car machine in the current parking time span can be determined, and the average network signal strength is obtained according to the network signal strength and the parking time span, and the average network signal strength is used as network condition data.

[0078] In step S20, the parking data is analyzed to determine the parking probability of the vehicle in different time periods.

[0079] It should be noted that the parking probability refers to the probability that the vehicle may be parked in a certain time period, which is usually the ratio of the total number of parking times in a certain time period to the total number of parking times in the total statistical time.

[0080] In a specific implementation, the number of parking times in the parking data can be counted, and the number of parking times can be counted according to different time periods when counting, for example, the total number of parking times in the last 30 days can be compared with the total number of parking times in each hour or each time period to obtain the parking probability of each time period or each hour. It should be noted that if a parking span across multiple time periods, then the current parking times are counted in these time periods. For example, if the parking time span of a parking data is 08:00:00-10:00:00, and each hour is used as a time period, then it can be known that the current parking is recorded in the time periods of 08:00:00-9:00:00 and 09:00:00-10:00:00, that is, the vehicle is parked in the time periods of 08:00:00-9:00:00 and 09:00:00-10:00:00.

[0081] In a feasible implementation, the step of analyzing the parking data to determine the parking probability of the vehicle in different time periods comprises:

[0082] The parking data is analyzed to obtain the parking time span of the vehicle and the parking position of the vehicle.

[0083] When the parking position is a bound position parking position, the parking time span is compared with a valid parking time span, and when the parking time span is within the valid parking time span, a parking behavior corresponding to the parking time span is determined as valid parking.

[0084] The time of a day is divided into multiple equal time periods;

[0085] The parking time corresponding to the valid parking is allocated to the corresponding time period to obtain the number of times of parking of the vehicle in different time periods;

[0086] The total number of times of parking of the vehicle in a statistical period is determined;

[0087] The number of times of parking in different time periods is compared with the total number of times of parking to obtain a parking probability.

[0088] It should be noted that the valid parking refers to a parking behavior with a parking time exceeding a certain time, which is used to distinguish the interference of parking behaviors such as vehicle engine off or temporary parking.

[0089] In a specific implementation, the time of 10 minutes is taken as a period, and the time of a day is divided into 24*6=144 periods, that is, the time of a day is divided into 144 parking time periods, and the parking time is matched with each parking time period to obtain a parking number data diagram as shown in FIG. 6. Figure 2 The time length of the vehicle machine power-off time to the next vehicle machine power-on time obtained by the car service of the vehicle machine is greater than 10 minutes, and the time is less than 1 day, which can be considered as the valid number of times of parking, because the parking with a time greater than 1 day belongs to long-term parking and does not have habits, which is not conducive to the analysis of the driving habit, and the total number of times of parking in the past 30 days can be collected by the program, and the time span of parking is allocated to each time period according to the power-on time and the power-off time, so that the number of times of parking in each time period can be obtained. Further, the total number of times of parking in the statistical period and the number of times of parking in each time period can be obtained according to the parking data in the statistical period, and the number of times of parking in different time periods is compared with the total number of times of parking to obtain a parking probability.

[0090] In step S30, the target upgrade time is determined according to the parking data, the network condition data, and the parking probability in different time periods.

[0091] It should be noted that the target upgrade time refers to a time interval for performing an OTA upgrade behavior, and the target upgrade time is the time when the vehicle is in a parking state.

[0092] In a specific implementation, when the target upgrade time is determined according to the parking data, the network condition data and the parking probability of different time periods, the parking data, the network condition data and the parking probability of different time periods can be comprehensively analyzed, which can be considered as scoring each time period to measure the degree of suitability of the current time period for OTA upgrade in the form of score. The several time periods with the highest degree of suitability are determined as the target upgrade time.

[0093] In a feasible implementation, the step of determining the target upgrade time according to the parking data, the network condition data and the parking probability of different time periods comprises:

[0094] The parking data is analyzed to obtain the parking time span of the vehicle and the parking location of the vehicle;

[0095] The time period score is determined according to the parking time span, the parking location score is determined according to the parking location, the network condition score is determined according to the network condition data, and the parking probability score is determined according to the parking probability;

[0096] The upgrade score of the current time period is obtained according to the time period score, the parking location score, the network condition score and the parking probability score;

[0097] The target upgrade time is determined according to the upgrade score.

[0098] It should be noted that the time period score, the parking location score, the network condition score and the parking probability score are respectively data-based descriptions of the adaptation degree of the corresponding time period, parking location, network condition and parking probability to OTA upgrade. The higher the score, the more suitable for OTA upgrade.

[0099] In a specific implementation, the parking data is analyzed to obtain the parking time span of the vehicle and the parking location of the vehicle, and the time period and location binding diagram as shown in Figure 3 Based on the commonly used parking locations and the average start and end time of parking, we can divide the 144 time periods into 12 groups according to Figure 3The data statistics are performed in the illustrated manner, and each time period is counted where the time period is generally parked, and how many times of parking. Then, the place with the most times of parking is selected as the binding place of the time period, that is, the user is most likely to park at this position in the future. If there is no parking in the time period or the times of parking is less than 2 (compared to the statistics of nearly 30 days, 2 times do not have regularity), it is ignored, and the parking place of the time period is set to empty. Then, the 144 time periods can be scored. When scoring, it can be determined whether the parking time span contains a night time period, if yes, a time period score is obtained; a parking probability score is determined by the ratio of the times of parking in the current time period to the total times of parking in the statistics period; it is determined whether the parking position is a bound parking position, if yes, a parking position score is obtained according to the parking time proportion of the average parking time of the parking position and the parking probability of the parking position; a network condition score is obtained by determining the ratio of the network speed of the bound parking position to the maximum network speed in all parking places; the time period score is determined according to the parking time span, the parking position score is determined according to the parking position, the network condition score is determined according to the network condition data, and the parking probability score is determined according to the parking probability. Specifically, according to the total times of parking in nearly 30 days, the times of parking in the statistics of the time period are divided by the total times of parking, and the score is calculated according to the following formula:

[0100]

[0101] Each time period is bound to a parking place, and the score of the time period of the unbound place is 0, and the score is calculated according to the following formula:

[0102]

[0103] According to the network speed kb / s of the bound place of each time period, the score is calculated according to the following formula, the score of the time period of the unbound place is 0, and the score of the place with a network speed of 0 is 0.

[0104]

[0105] If the time period has a bound parking place and the time is between 0 and 6, the score is added by 5.

[0106] Finally, the obtained time period score, parking position score, network condition score and parking probability score can obtain the upgrade score of the current time period, the upgrade scores are sorted in size, and the corresponding parking time period corresponding to the larger upgrade score is selected as the target upgrade time.

[0107] In a possible implementation, the step of determining the target upgrade time according to the upgrade score comprises:

[0108] The upgrade scores are arranged in descending order to obtain an arrangement result.

[0109] The arrangement result is traversed, and when there are continuous upgrade times of a preset time span in the arrangement result corresponding to the upgrade scores, the continuous upgrade times of the preset time span are taken as target upgrade times.

[0110] In a specific implementation, when it is checked that there is a new upgrade task for the platform, the whole-vehicle OTA program calculates scores of 144 time segments according to the above logic based on nearly 30 days of data, sorts the time segments according to the scores, takes the first 30 time segments, and calculates by the program whether the 18 continuous time segments, that is, 3 hours, can be formed. The selection basis is the longest upgrade time generally required for one OTA upgrade. If not, the first 40 time segments are taken, and it is continued to be checked whether the 18 continuous time segments can be formed. If not, the range is continued to be expanded until the 18 continuous time segments can be found. Then, the OTA program makes a reservation for upgrade according to the start time of the time segment.

[0111] In step S40, when it is detected that there is a new upgrade task, the target upgrade time is taken as an upgrade start time, and the new upgrade task is executed.

[0112] It should be noted that when the vehicle reaches a set condition, the OTA upgrade can be initiated. When it is detected that there is an OTA upgrade task, the OTA upgrade behavior can be performed according to the determined OTA upgrade time. At this time, the current time can be detected. If the current time is the target upgrade time at which the OTA upgrade can be performed, the OTA upgrade can be performed immediately. Otherwise, the OTA upgrade is performed only when the target upgrade time is reached.

[0113] In a specific implementation, the real-time position of the vehicle can be obtained. When the real-time position enters a preset area, the upgrade is automatically checked. When it is detected that there is a new upgrade task, the target upgrade time is taken as an upgrade start time, and the new upgrade task is executed. Specifically, the geographic fence technology can be used. The user can set a plurality of specific areas that can be used for OTA upgrade, such as a residence or an office site. It is considered that the OTA upgrade is performed only at a parking position. It is agreed that the position at which the vehicle speed is 0 and the stay time is more than 1 hour is a valid parking position. The vehicle speed can be collected by the car service of the vehicle machine IVI. The positioning can be collected by the positioning module of the vehicle machine. A plurality of positions at which the vehicle speed is 0 and the distance between which is less than 500 m can be aggregated into one point. When a new valid parking position is generated, the distance to the existing position is calculated. If there is a valid parking position with a distance less than 500 m, the new position and the existing position are aggregated. After the vehicle reaches the position, the upgrade detection can be actively performed. When it is detected that there is a new upgrade task, the target upgrade time is taken as an upgrade start time, and the new upgrade task is executed.

[0114] The embodiment provides an OTA upgrade time intelligent scheduling method, which comprises the following steps: acquiring parking data and network condition data of a vehicle; analyzing the parking data to determine parking probabilities of the vehicle in different time periods; determining a target upgrade time according to the parking data, the network condition data and the parking probabilities in the different time periods; and when a new upgrade task is detected, taking the target upgrade time as an upgrade start time and executing the new upgrade task, so that OTA upgrade of the intelligent vehicle can be efficiently and safely performed, the most suitable upgrade time period is selected, the best opportunity for upgrade is selected by using the vehicle use and network condition data, the success rate and efficiency of upgrade are improved, and upgrade failure caused by network problems is reduced.

[0115] It should be noted that the above examples are only used for understanding the present application and do not constitute a limitation on the OTA upgrade time intelligent scheduling method of the present application, and more forms of simple transformation based on the technical concept are within the protection scope of the present application.

[0116] The present application also provides an OTA upgrade time intelligent scheduling device, please refer to Figure 4 The OTA upgrade time intelligent scheduling device comprises:

[0117] A data collection module 10 is configured to acquire parking data and network condition data of a vehicle.

[0118] A behavior analysis module 20 is configured to analyze the parking data to determine parking probabilities of the vehicle in different time periods.

[0119] An intelligent scheduling module 30 is configured to determine a target upgrade time according to the parking data, the network condition data and the parking probabilities in the different time periods.

[0120] A decision output module 40 is configured to, when a new upgrade task is detected, take the target upgrade time as an upgrade start time and execute the new upgrade task.

[0121] The OTA upgrade time intelligent scheduling device provided by the present application adopts the OTA upgrade time intelligent scheduling method in the above embodiment, and can solve the technical problem that the prior art cannot determine a suitable OTA upgrade time period according to the driving habits of a user. Compared with the prior art, the OTA upgrade time intelligent scheduling device provided by the present application has the same beneficial effects as the OTA upgrade time intelligent scheduling method provided by the above embodiment, and other technical features in the OTA upgrade time intelligent scheduling device are the same as the features disclosed in the above embodiment method, and thus will not be described here.

[0122] The application provides an OTA upgrade time intelligent scheduling device, which comprises at least one processor and a memory connected with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the OTA upgrade time intelligent scheduling method in the above embodiment one.

[0123] Reference will be made to the following description Figure 5 which shows a structural diagram of the OTA upgrade time intelligent scheduling device suitable for implementing the embodiments of the application. The OTA upgrade time intelligent scheduling device in the embodiments of the application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable MediaPlayer), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 5 The OTA upgrade time intelligent scheduling device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the application.

[0124] As Figure 5As shown, the OTA upgrade upgrade time intelligent scheduling device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1002 or loaded from a storage device 1003 into a random access memory (RAM) 1004. In the RAM 1004, various programs and data required for the OTA upgrade upgrade time intelligent scheduling device to operate are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the OTA upgrade upgrade time intelligent scheduling device to communicate with other devices wirelessly or by wire to exchange data. Although the OTA upgrade upgrade time intelligent scheduling device with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0125] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0126] The OTA upgrade time intelligent scheduling device provided by the application can solve the technical problem that the prior art cannot determine a suitable OTA upgrade time period according to the user's driving habits, by using the OTA upgrade time intelligent scheduling method in the above embodiment. Compared with the prior art, the OTA upgrade time intelligent scheduling device provided by the application has the same beneficial effects as the OTA upgrade time intelligent scheduling method provided by the above embodiment, and other technical features in the OTA upgrade time intelligent scheduling device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0127] It should be understood that various parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0129] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e. computer programs) for performing the OTA upgrade time intelligent scheduling method in the above embodiment.

[0130] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electrical connection with one or more conductive 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 embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electrical wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination of the above.

[0131] The computer readable storage medium described above can be included in the upgrade time intelligent scheduling device of OTA upgrade, or can exist separately without being assembled into the upgrade time intelligent scheduling device of OTA upgrade.

[0132] The computer readable storage medium described above carries one or more programs, which, when executed by the upgrade time intelligent scheduling device of OTA upgrade, cause the upgrade time intelligent scheduling device of OTA upgrade to:

[0133] Obtain parking data and network condition data of the vehicle;

[0134] Analyze the parking data to determine the parking probability of the vehicle in different time periods;

[0135] Determine a target upgrade time according to the parking data, the network condition data, and the parking probability in different time periods;

[0136] When a new upgrade task is detected, execute the new upgrade task with the target upgrade time as the upgrade start time.

[0137] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0138] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0139] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0140] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e., computer programs) for executing the above-mentioned OTA upgrade time intelligent scheduling method, and can solve the technical problem that the prior art cannot determine a suitable OTA upgrade time period according to the user's driving habits. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the OTA upgrade time intelligent scheduling method provided by the above-mentioned embodiments, and will not be described here.

[0141] The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the OTA upgrade time intelligent scheduling method as described above.

[0142] The computer program product provided by the application can solve the technical problem that the prior art cannot determine a suitable OTA upgrade time period according to the user's driving habits. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the OTA upgrade time intelligent scheduling method provided by the above-mentioned embodiments, and are not described here.

[0143] The above only describes some embodiments of the application, and does not limit the patent scope of the application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or direct / indirect application in other related technical fields within the technical concept of the application, including in the patent protection scope of the application.

Claims

1. An intelligent scheduling method for upgrade time of OTA upgrade, characterized in that, The OTA upgrade time intelligent scheduling method comprises: Obtaining parking data and network condition data of the vehicle; Analyzing the parking data to determine the parking probability of the vehicle in different time periods; Determining the target upgrade time according to the parking data, the network condition data and the parking probability in different time periods; When a new upgrade task is detected, taking the target upgrade time as the upgrade start time to execute the new upgrade task; The step of analyzing the parking data to determine the parking probability of the vehicle in different time periods comprises: Analyzing the parking data to obtain the parking time span of the vehicle and the parking position of the vehicle; When the parking position is a bound position parking position, comparing the parking time span with the effective parking time span, and determining the parking behavior corresponding to the parking time span as effective parking when the parking time span is within the effective parking time span, the bound position parking position being the place with the most parking times in a time period; Dividing the time of a day into multiple equal time periods; Distributing the parking time corresponding to the effective parking to the corresponding time periods to obtain the parking times of the vehicle in different time periods; Determining the total parking times of the vehicle in a statistical period; Obtaining the parking probability based on the parking times in different time periods and the total parking times.

2. The method of claim 1, wherein, The step of obtaining the parking data and the network condition data of the vehicle comprises: Obtaining the power-off time of the vehicle machine and the next power-on time of the vehicle machine to obtain the parking time span according to the power-off time of the vehicle machine and the next power-on time of the vehicle machine; Obtaining the positioning information of the vehicle machine when it is powered off to determine the parking position of the vehicle; Obtaining the parking data according to the parking time span and the parking position; Obtaining the network signal strength of the vehicle machine in the parking time span; Obtaining the average network signal strength according to the network signal strength and the parking time span, and taking the average network signal strength as the network condition data.

3. The method of claim 1, wherein, The step of determining the target upgrade time according to the parking data, the network condition data and the parking probability in different time periods comprises: Analyzing the parking data to obtain the parking time span of the vehicle and the parking position of the vehicle; Determining the time period score according to the parking time span, determining the parking position score according to the parking position, determining the network condition score according to the network condition data, and determining the parking probability score according to the parking probability; Obtaining the upgrade score of the current time period according to the time period score, the parking position score, the network condition score and the parking probability score; Determining the target upgrade time according to the upgrade score.

4. The method of claim 3, wherein, The step of determining the time period score according to the parking time span, determining the parking position score according to the parking position, determining the network condition score according to the network condition data, and determining the parking probability score according to the parking probability comprises: Determining whether the parking time span contains a night time period, and obtaining the time period score if yes; determining a parking probability score by determining a ratio of a number of times of parking in a current time period to a total number of times of parking in a statistical period; determining whether the parking location is a bound parking location, and if so, determining a parking location score according to a parking time proportion of an average parking duration of the parking location and a parking probability of the parking location; determining a network condition score according to a ratio of a network speed of the bound parking location to a maximum network speed in all parking locations; determining a time period score according to the parking time span, a parking location score according to the parking location, a network condition score according to the network condition data, and a parking probability score according to the parking probability.

5. The method of claim 3, wherein, The step of determining the target upgrade time according to the upgrade scores comprises: arranging the upgrade scores in descending order to obtain an arrangement result; when there are continuous upgrade times of a preset time span in the arrangement result, the continuous upgrade times of the preset time span are taken as the target upgrade time.

6. The method of any one of claims 1 to 5, wherein, The step of executing the new upgrade task when detecting the new upgrade task with the target upgrade time as the upgrade start time comprises: acquiring a real-time location of the vehicle, and automatically checking for upgrade when the real-time location enters a preset area; executing the new upgrade task when detecting the new upgrade task with the target upgrade time as the upgrade start time.

7. An apparatus for intelligent scheduling of upgrade time for OTA upgrade, characterized in that, The device comprises: a data collection module configured to acquire parking data and network condition data of the vehicle; a behavior analysis module configured to analyze the parking data to determine a parking probability of the vehicle in different time periods; an intelligent scheduling module configured to determine a target upgrade time according to the parking data, the network condition data, and the parking probability in the different time periods; a decision output module configured to execute a new upgrade task when detecting the new upgrade task with the target upgrade time as the upgrade start time; The step of analyzing the parking data to determine the parking probability of the vehicle in different time periods comprises: analyzing the parking data to obtain a parking time span of the vehicle and a parking location of the vehicle; when the parking location is a bound location parking location, comparing the parking time span with an effective parking time span, and determining a parking behavior corresponding to the parking time span as effective parking when the parking time span is within the effective parking time span, the bound location parking location being a location with the most times of parking in a time period; dividing a day into multiple time periods of equal length; allocating a parking time corresponding to the effective parking to a corresponding time period to obtain a number of times of parking of the vehicle in different time periods; determining a total number of times of parking of the vehicle in a statistical period; determining a parking probability based on the number of times of parking in different time periods and the total number of times of parking.

8. An apparatus for intelligent scheduling of upgrade time for OTA upgrade, characterized in that, The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the OTA upgrade intelligent scheduling method of upgrade time according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the OTA upgrade intelligent scheduling method of upgrade time according to any one of claims 1 to 6.

Images