Upgrading time intelligent scheduling method and device for OTA upgrading, equipment and storage medium

Abstract

The invention discloses an intelligent scheduling method, device and equipment for upgrading time of OTA upgrading and a storage medium, and relates to the technical field of OTA upgrading, and the method comprises the steps: obtaining parking data and network condition data of a vehicle; analyzing the parking data, and determining parking probabilities of the vehicle in different time periods; determining target upgrading time according to the parking data, the network condition data and the parking probabilities of the different time periods; and when a new upgrading task is detected, executing the new upgrading task by taking the target upgrading time as upgrading starting time, so that OTA upgrading of the intelligent vehicle can be efficiently and safely carried out, the most suitable upgrading time period is selected, the vehicle use and network condition data are utilized, and the optimal opportunity is selected for upgrading, and the upgrading success rate and efficiency are improved. And upgrading failures caused by network problems are reduced.

Description

Technical Field

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

[0002] With the development of smart vehicle technology, vehicle software updates are becoming increasingly frequent. The traditional OTA (Over-The-Air) upgrade method has problems such as inflexible upgrade time selection and upgrade interruption of user experience. Even if the user makes an appointment for the upgrade at a specified time, it is difficult to avoid the network environment where the vehicle is parked in an underground parking lot after the designated time. At present, OTA upgrades are usually performed at night. When the vehicle is stopped and connected to Wi-Fi, the update is automatically downloaded and installed. Users can also view the upgrade details on the vehicle's touch screen and choose to upgrade immediately or schedule an upgrade time. However, this method will affect the user's use of the vehicle when the upgrade time is long. At the same time, not everyone does not use the vehicle at night, which will also affect the user's use of the vehicle.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide an intelligent scheduling method, device, equipment and storage medium for OTA upgrade time, aiming to solve the technical problem that the prior art cannot determine the appropriate OTA upgrade time period according to the user's car usage habits.

[0005] To achieve the above purpose, the present application proposes an upgrade time intelligent scheduling method for OTA upgrade, and the upgrade time intelligent scheduling method for OTA upgrade includes:

[0006] Obtain parking data and network status data of the vehicle;

[0007] Analyzing the parking data to determine the parking probability of the vehicle in different time periods;

[0008] Determine a target upgrade time according to the parking data, the network status data and the parking probabilities in different time periods;

[0009] When a new upgrade task is detected, the new upgrade task is executed with the target upgrade time as the upgrade start time.

[0010] In one embodiment, the step of obtaining the parking data and network status data of the vehicle includes:

[0011] Obtaining the power-off time of the vehicle computer and the next power-on time of the vehicle computer, and obtaining the parking time span according to the power-off time of the vehicle computer and the next power-on time of the vehicle computer;

[0012] Acquire the positioning information of the vehicle computer when it is powered off to determine the parking position of the vehicle;

[0013] Obtain parking data according to the parking time span and the parking location;

[0014] Obtaining the network signal strength of the vehicle computer during the parking time span;

[0015] An 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 status data.

[0016] In one embodiment, the step of analyzing the parking data to determine the parking probability of the vehicle in different time periods includes:

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

[0018] When the parking position is a bound parking position, the parking time span is compared with the effective parking time span, and when the parking time span is within the effective parking time span, the parking behavior corresponding to the parking time span is determined as effective parking;

[0019] Divide the day into multiple equal-length time periods;

[0020] The parking time corresponding to the effective parking is apportioned to the corresponding time period to obtain the number of parking times of the vehicle in different time periods;

[0021] Determine the total number of stops of the vehicle during the statistical period;

[0022] The parking number in different time periods and the total parking number are respectively added to obtain the parking probability.

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

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

[0025] Determine a time period score according to the parking time span, determine a parking location score according to the parking location, determine a network status score according to the network status data, and determine a parking probability score according to the parking probability;

[0026] Obtaining an upgrade score for the current time period according to the time period score, the parking location score, the network status score and the parking probability score;

[0027] A target upgrade time is determined according to the upgrade score.

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

[0029] Determine whether the parking time span includes a night time period, and if so, obtain a time period score;

[0030] Determine the parking probability score by determining the ratio of the number of parking times in the current time period to the total number of parking times in the statistical period;

[0031] Determine whether the parking location is a bound parking location, and if so, obtain a parking location score according to the parking time ratio of the average parking time of the parking location and the parking probability of the parking location;

[0032] Determine the ratio of the network speed of the bound parking location to the maximum network speed of all parking locations to obtain a network status score;

[0033] A time period score is determined according to the parking time span, a parking location score is determined according to the parking location, a network status score is determined according to the network status data, and a parking probability score is determined according to the parking probability.

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

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

[0036] The arrangement results are traversed, and when there are continuous upgrade times with a preset time span formed by the upgrade time corresponding to the upgrade score in the arrangement results, the continuous upgrade time with the preset time span is used as the target upgrade time.

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

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

[0039] When a new upgrade task is detected, the new upgrade task is executed with the target upgrade time as the upgrade start time.

[0040] In addition, to achieve the above purpose, the present application also proposes an upgrade time intelligent scheduling device for OTA upgrade, and the upgrade time intelligent scheduling device for OTA upgrade includes:

[0041] A data collection module, used to obtain parking data and network status data of vehicles;

[0042] A behavior analysis module, used to analyze the parking data and determine the parking probability of the vehicle in different time periods;

[0043] An intelligent scheduling module, used to determine a target upgrade time according to the parking data, the network status data and the parking probability in different time periods;

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

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes an upgrade time intelligent scheduling device for OTA upgrade, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the upgrade time intelligent scheduling method for OTA upgrade as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the upgrade time intelligent scheduling method of OTA upgrade as described above are implemented.

[0047] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the upgrade time intelligent scheduling method for OTA upgrade as described above.

[0048] One or more technical solutions proposed in the present application have at least the following technical effects: obtaining the parking data and network status 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 status data and the parking probability in the different time periods; when a new upgrade task is detected, using the target upgrade time as the upgrade start time, and executing the new upgrade task, so as to efficiently and safely perform OTA upgrades of smart vehicles, select the most appropriate upgrade time period, and use the vehicle usage and network status data to select the best time for the upgrade, thereby improving the success rate and efficiency of the upgrade and reducing upgrade failures caused by network problems. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

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

[0052] Figure 2 A schematic diagram of parking frequency data provided for an embodiment of an intelligent scheduling method for upgrading time of OTA upgrading of this application;

[0053] Figure 3 A schematic diagram of binding time periods and locations provided in an embodiment of an intelligent scheduling method for upgrading OTA upgrades of this application;

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

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

[0056] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. 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 solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of the embodiment of the present application is: obtaining the parking data and network status 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 status data and the parking probability in the different time periods; when a new upgrade task is detected, taking the target upgrade time as the upgrade start time, and executing the new upgrade task.

[0060] In this embodiment, for ease of description, the upgrade time intelligent scheduling device for identifying OTA upgrade is used as the execution subject for explanation below.

[0061] As existing technologies usually perform upgrades at night, when the vehicle is parked and connected to Wi-Fi, the update is automatically downloaded and installed. Users can also view the upgrade details on the vehicle's touch screen and choose to upgrade immediately or schedule an upgrade, but this method will affect the user's use of the vehicle if the upgrade takes a long time. At the same time, 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 that reduces the impact on users' daily use and improves users' overall satisfaction with vehicle use by intelligently selecting the upgrade time. It uses vehicle usage and network status data to select the best time for upgrading, improves the success rate and efficiency of upgrading, reduces upgrade failures caused by network problems, upgrades when the network conditions are good, reduces security risks during data transmission, protects the security of vehicle and user data, avoids upgrading during network peak hours through intelligent scheduling, reduces network burden, and optimizes the use of network resources.

[0063] It can be seen from the above embodiments that the present application obtains the parking data and network status data of the vehicle; analyzes the parking data to determine the parking probability of the vehicle in different time periods; determines the target upgrade time according to the parking data, the network status data and the parking probability in the different time periods; when a new upgrade task is detected, uses the target upgrade time as the upgrade start time and executes the new upgrade task, so as to efficiently and safely perform OTA upgrades of smart vehicles, select the most appropriate upgrade time period, and use the vehicle usage and network status data to select the best time for the upgrade, thereby improving the success rate and efficiency of the upgrade and reducing upgrade failures caused by network problems.

[0064] It should be noted that the execution subject of this 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 upgrade time intelligent scheduling device for OTA upgrade, etc. The following takes the upgrade time intelligent scheduling device for OTA upgrade as an example to illustrate this embodiment and the following embodiments.

[0065] Based on this, the embodiment of the present application provides an intelligent scheduling method for upgrading the OTA upgrade time, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the upgrade time intelligent scheduling method for OTA upgrade of the present application.

[0066] In this embodiment, the upgrade time intelligent scheduling method of the OTA upgrade includes steps S10 to S40:

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

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

[0069] In a specific implementation, after the vehicle's computer is activated, it can obtain the vehicle's location information during driving or when the vehicle is stopped, parking data such as parking time, and real-time network signal quality network status data through the vehicle's internal GPS positioning signal, timer, network diagnostic tools, etc., and can store the acquired parking data and network status data inside the vehicle for local storage. In order to avoid continuous accumulation of data, the stored data can be automatically cleaned up, such as cleaning up data before 30 days and retaining only parking data and network status data within 30 days.

[0070] In a feasible implementation manner, the step of obtaining the parking data and network status data of the vehicle includes:

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

[0072] Acquire the positioning information of the vehicle computer when it is powered off to determine the parking position of the vehicle;

[0073] Obtain parking data according to the parking time span and the parking location;

[0074] Obtaining the network signal strength of the vehicle computer during the parking time span;

[0075] An 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 status data.

[0076] It should be noted that the power-off of the vehicle computer can be used as a signal that the current vehicle has completely stopped, and the next power-on of the vehicle computer can be used as a signal that the vehicle has switched from a stopped state to a driving state or a standby state. The time span between the time the vehicle computer is powered off and the time the vehicle computer is powered on next time is the time the vehicle is stopped, that is, the vehicle parking time. During the stopping process, the position of the vehicle will not change. Therefore, the vehicle positioning signal at this time is the parking position of the vehicle. The network status data is used to describe the network quality when the vehicle is in a parked state.

[0077] In a specific implementation, the power-off time and power-on time of the vehicle computer can be obtained, and the data information can be stored in the vehicle's driving log, and the data records are arranged according to time. Therefore, the power-off time of the vehicle computer and the next power-on time of the vehicle computer can be obtained from the driving log, and the parking time span can be obtained according to the power-off time of the vehicle computer and the next power-on time of the vehicle computer. For example, the power-off time of the vehicle computer is 08:00:00, and the next power-on time of the vehicle computer is 09:00:00. Then it can be determined that the parking time span is 1 hour, and the corresponding parking time is 1 hour. When the vehicle computer 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 then the parking data can be generated according to the parking time span and parking position of the vehicle, and the current parking behavior can be described in a combined manner. And, at the same time, the network signal strength of the vehicle computer in this 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 the network status data.

[0078] Step S20: analyzing the parking data 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 a vehicle may stop within a certain time period, which is usually the ratio of the total number of stops in a certain time period to the total number of stops 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 also be counted according to different time periods when the statistics are performed. For example, the total number of parking times in the past 30 days can be compared with the total number of parking times in each hour or each time period to obtain the parking probability in each time period or each hour. It should be noted that if a parking spans multiple time periods, it is considered that these time periods all count the current number of parking times. For example, if the parking time span of a parking data is 08:00:00-10:00:00, if each hour is used as a time period, then it can be known that the parking is recorded in the time periods of 08:00:00-9:00:00 and 09:00:00-10:00:00, that is, the parking is done at 08:00:00-9:00:00 and 09:00:00-10:00:00, that is, the parking is done at 08:00:00-9:00:00 and 09:00:00-10:00:00.

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

[0082] Analyzing the parking data to obtain the parking time span of the vehicle and the parking position of the vehicle;

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

[0084] Divide the day into multiple equal-length time periods;

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

[0086] Determine the total number of stops of the vehicle during the statistical period;

[0087] The parking number in different time periods and the total parking number are respectively added to obtain the parking probability.

[0088] It should be noted that effective parking refers to parking behavior that exceeds a certain period of time, which is used to distinguish interference with parking behavior such as vehicle shutdown or temporary parking.

[0089] In the specific implementation, the time is divided into 10 minutes for a period, and a day is divided into 24*6=144 periods, that is, a day is divided into 144 parking time periods, and the parking time is matched with each parking time period to obtain the following: Figure 2 The schematic diagram of parking times data is shown. The car service of the car IVI obtains the time from the car power-off time to the next car power-on time. If the length of time is greater than 10 minutes and less than 1 day, it can be considered as the effective parking times. Because parking for more than 1 day is a long-term stay, it is not habitual and is not conducive to the analysis of car usage habits. The total number of parking times in the past 30 days can be collected by the program. At the same time, according to the power-on time and power-off time, the time span of parking is amortized to each time period, and the number of parking times in each time period can be obtained. Then, according to the parking data in the statistical period, the total number of parking times in the statistical period and the number of parking times in each time period can be obtained, and the number of parking times in different time periods and the total number of parking times can be used to obtain the parking probability.

[0090] Step S30, determining a target upgrade time according to the parking data, the network status data and the parking probabilities in different time periods.

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

[0092] In a specific implementation, when determining the target upgrade time based on parking data, network status data, and parking probabilities in different time periods, a comprehensive analysis can be performed on the parking data, network status data, and parking probabilities in different time periods, which can be considered as scoring each time period to measure the suitability of the current time period for OTA upgrades. The time periods with the highest suitability are determined as the target upgrade time.

[0093] In a feasible implementation manner, the step of determining the target upgrade time according to the parking data, the network status data and the parking probabilities in different time periods includes:

[0094] Analyzing the parking data to obtain the parking time span of the vehicle and the parking position of the vehicle;

[0095] Determine a time period score according to the parking time span, determine a parking location score according to the parking location, determine a network status score according to the network status data, and determine a parking probability score according to the parking probability;

[0096] Obtaining an upgrade score for the current time period according to the time period score, the parking location score, the network status score and the parking probability score;

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

[0098] It should be noted that the time period score, parking location score, network status score, and parking probability score are respectively used to describe the degree of adaptability of the corresponding time period, parking location, network status, and parking probability to OTA upgrades. The larger the score, the more suitable it is for OTA upgrades.

[0099] In a specific implementation, the parking data is analyzed to obtain the parking time span of the vehicle and the parking position of the vehicle, and the following is obtained based on the parking time span and the parking position of the vehicle: Figure 3 Based on the common parking locations and the average start and end times of parking, we can classify the 144 time periods into Figure 3The data is counted in the manner shown, and each time period is counted where the car is usually parked and how many times it is parked. Then, the place with the most parking times is selected as the binding place for the time period, that is, the user is very likely to park at this location in the future. If there is no parking in the time period, or the number of parking times is less than 2 times (compared with the statistics of the past 30 days, 2 times is not regular), it is ignored and the parking place for the time period is set to empty. Then, these 144 time periods can be scored. When scoring, it can be determined whether the parking time span includes the night time period. If so, the time period score can be obtained; the parking probability score can be determined by determining the ratio of the number of parking times in the current time period to the total number of parking times in the statistical period; it can be determined whether the parking location is a bound parking location. If so, the parking location score can be obtained based on the parking time ratio of the average parking time of the parking location and the parking probability of the parking location; the network status score can be obtained by determining the ratio of the network speed of the bound parking location to the maximum network speed of all parking locations; the time period score can be determined based on the parking time span, the parking location score can be determined based on the parking location, the network status score can be determined based on the network status data, and the parking probability score can be determined based on the parking probability. Specifically: based on the total number of parking times counted in the past 30 days, the number of parking times counted in this time period is divided by the total number of parking times, and the score is calculated according to the following formula:

[0100]

[0101] Each time period is bound to a parking location. The score for a time period that is not bound to a location is 0. The score is calculated according to the following formula:

[0102]

[0103] The score is calculated according to the kb / s of the network speed of the location bound to each time period using the following formula. The score for the time period without a bound location is 0, and the score for a location with a network speed of 0 is 0:

[0104]

[0105] If the time period is bound to a parking location and the time is between 0:00 and 6:00, 5 points will be added.

[0106] Finally, the obtained time period score, parking location score, network status score and parking probability score can be used to obtain the upgrade score of the current time period, and the upgrade scores are sorted by size, and the corresponding parking period with a larger upgrade score is selected as the target upgrade time.

[0107] In a feasible implementation manner, the step of determining the target upgrade time according to the upgrade score includes:

[0108] Arrange the upgrade scores in descending order to obtain an arrangement result;

[0109] The arrangement results are traversed, and when there are continuous upgrade times with a preset time span formed by the upgrade time corresponding to the upgrade score in the arrangement results, the continuous upgrade time with the preset time span is used as the target upgrade time.

[0110] In the specific implementation, when the platform is checked for new upgrade tasks, the vehicle OTA program calculates the scores of 144 time segments according to the above logic based on the data of nearly 30 days, sorts the time periods by scores, takes the first 30 time segments, and calculates by the program whether they can form 18 consecutive time segments, that is, 3 hours. The selection basis is the longest upgrade time required for a general OTA upgrade. If not, take the first 40 time segments and continue to find whether there are 18 consecutive time segments. If not, continue to expand the scope until 18 consecutive time segments can be found. Then the OTA program schedules the upgrade according to the start time of the time period.

[0111] Step S40: When a new upgrade task is detected, the new upgrade task is executed with the target upgrade time as the upgrade start time.

[0112] It should be noted that when the vehicle meets the set conditions, it can initiate OTA upgrade detection. When the OTA upgrade task is detected, the OTA upgrade behavior can be executed 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 for OTA upgrade, OTA upgrade can be performed immediately. Otherwise, OTA upgrade will be performed only when the target upgrade time is reached.

[0113] In the specific implementation, the real-time location of the vehicle can be obtained, and when the real-time location enters the preset area, the upgrade is automatically checked; when a new upgrade task is detected, the target upgrade time is used as the upgrade start time to execute the new upgrade task. Specifically, geo-fencing technology can be used, and users can set several specific areas that can be used for OTA upgrades, such as residences or office spaces. Considering that the upgrade will only be performed at the parking location during OTA upgrades, it can be agreed that when the vehicle speed is 0, the location where the stay time exceeds 1 hour is the effective parking location. The vehicle speed can be collected through the car service of the vehicle IVI, and the positioning can be collected through the vehicle positioning module. Multiple positionings with a speed of 0 and a distance of less than 500m can be aggregated into 1 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 of less than 500m, the newly generated position is aggregated with the existing position. After the vehicle arrives at the location, it can actively perform upgrade detection. When a new upgrade task is detected, the target upgrade time is used as the upgrade start time to execute the new upgrade task.

[0114] The present embodiment provides an intelligent scheduling method for upgrade time of OTA upgrade, by acquiring parking data and network status 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 status data and the parking probability in the different time periods; 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. This method can efficiently and safely perform OTA upgrades of smart vehicles, select the most appropriate upgrade time period, use vehicle usage and network status data to select the best time for upgrading, improve the success rate and efficiency of the upgrade, and reduce upgrade failures caused by network problems.

[0115] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the intelligent scheduling method for OTA upgrade time of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0116] This application also provides an OTA upgrade time intelligent scheduling device, please refer to Figure 4 The upgrade time intelligent scheduling device of the OTA upgrade includes:

[0117] The data collection module 10 is used to obtain the parking data and network status data of the vehicle;

[0118] A behavior analysis module 20, for analyzing the parking data to determine the parking probability of the vehicle in different time periods;

[0119] An intelligent scheduling module 30, configured to determine a target upgrade time according to the parking data, the network status data and the parking probability in different time periods;

[0120] The decision output module 40 is used to execute the new upgrade task with the target upgrade time as the upgrade start time when a new upgrade task is detected.

[0121] The intelligent scheduling device for upgrading time of OTA upgrade provided by the present application adopts the intelligent scheduling method for upgrading time of OTA upgrade in the above-mentioned embodiment, which can solve the technical problem that the prior art cannot determine the appropriate OTA upgrade time period according to the user's car usage habits. Compared with the prior art, the beneficial effects of the intelligent scheduling device for upgrading time of OTA upgrade provided by the present application are the same as the beneficial effects of the intelligent scheduling method for upgrading time of OTA upgrade provided by the above-mentioned embodiment, and the other technical features in the intelligent scheduling device for upgrading time of OTA upgrade are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0122] The present application provides an upgrade time intelligent scheduling device for OTA upgrade, and the upgrade time intelligent scheduling device for OTA upgrade includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the upgrade time intelligent scheduling method for OTA upgrade in the above-mentioned embodiment one.

[0123] Reference below Figure 5 , which shows a schematic diagram of the structure of an upgrade time intelligent scheduling device suitable for implementing OTA upgrades in the embodiment of the present application. The upgrade time intelligent scheduling device for OTA upgrades in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The upgrade time intelligent scheduling device for OTA upgrade shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] like Figure 5As shown, the upgrade time intelligent scheduling device for OTA upgrade may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the upgrade time intelligent scheduling device for OTA upgrade are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, 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.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the upgrade time intelligent scheduling device for OTA upgrades to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an upgrade time intelligent scheduling device for OTA upgrades with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0125] In particular, according to the embodiments disclosed in the present application, 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 application 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 a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

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

[0127] It should be understood that the various parts disclosed in this application can be implemented 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 only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0129] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the upgrade time intelligent scheduling method for OTA upgrade in the above-mentioned embodiment.

[0130] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, 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 this embodiment, the 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, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0131] The above-mentioned computer-readable storage medium may be included in the upgrade time intelligent scheduling device for OTA upgrade; or it may exist independently without being assembled into the upgrade time intelligent scheduling device for OTA upgrade.

[0132] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the upgrade time intelligent scheduling device for OTA upgrade, the upgrade time intelligent scheduling device for OTA upgrade:

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

[0134] Analyzing 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 status data and the parking probabilities in different time periods;

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

[0137] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0138] 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 application. 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 module, the program segment or a part of the 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 sequence different from that 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 and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[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, which can solve the technical problem that the prior art cannot determine the appropriate OTA upgrade time period according to the user's car usage habits. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the OTA upgrade time intelligent scheduling method provided by the above-mentioned embodiment, and will not be repeated here.

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

[0142] The computer program product provided by this application can solve the technical problem that the prior art cannot determine the appropriate OTA upgrade time period according to the user's car usage habits. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the upgrade time intelligent scheduling method for OTA upgrade provided by the above embodiment, which will not be repeated here.

[0143] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An intelligent scheduling method for OTA upgrade time, characterized in that: The upgrade time intelligent scheduling method of the OTA upgrade includes: Obtain parking data and network status data of the vehicle; Analyzing the parking data to determine the parking probability of the vehicle in different time periods; Determine a target upgrade time according to the parking data, the network status data and the parking probabilities in different time periods; When a new upgrade task is detected, the new upgrade task is executed with the target upgrade time as the upgrade start time.

2. The method according to claim 1, characterized in that The step of obtaining the parking data and network status data of the vehicle comprises: Obtaining the power-off time of the vehicle computer and the next power-on time of the vehicle computer, and obtaining the parking time span according to the power-off time of the vehicle computer and the next power-on time of the vehicle computer; Acquire the positioning information of the vehicle computer when it is powered off to determine the parking position of the vehicle; Obtain parking data according to the parking time span and the parking location; Obtaining the network signal strength of the vehicle computer during the parking time span; An 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 status data.

3. The method according to claim 1, characterized in that The step of analyzing the parking data to determine the parking probability of the vehicle in different time periods includes: 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 parking position, the parking time span is compared with the effective parking time span, and when the parking time span is within the effective parking time span, the parking behavior corresponding to the parking time span is determined as effective parking; Divide the day into multiple equal time periods; The parking time corresponding to the effective parking is apportioned to the corresponding time period to obtain the number of parking times of the vehicle in different time periods; Determine the total number of stops of the vehicle during the statistical period; The parking number in different time periods and the total parking number are respectively added to obtain the parking probability.

4. The method according to claim 1, characterized in that The step of determining the target upgrade time according to the parking data, the network status data and the parking probabilities 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; Determine a time period score according to the parking time span, determine a parking location score according to the parking location, determine a network status score according to the network status data, and determine a parking probability score according to the parking probability; Obtaining an upgrade score for the current time period according to the time period score, the parking location score, the network status score and the parking probability score; A target upgrade time is determined according to the upgrade score.

5. The method according to claim 4, characterized in that The steps of determining a time period score according to the parking time span, determining a parking location score according to the parking location, determining a network status score according to the network status data, and determining a parking probability score according to the parking probability include: Determine whether the parking time span includes a night time period, and if so, obtain a time period score; Determine the parking probability score by determining the ratio of the number of parking times in the current time period to the total number of parking times in the statistical period; Determine whether the parking location is a bound parking location, and if so, obtain a parking location score according to the parking time ratio of the average parking time of the parking location and the parking probability of the parking location; Determine the ratio of the network speed of the bound parking location to the maximum network speed of all parking locations to obtain a network status score; A time period score is determined according to the parking time span, a parking location score is determined according to the parking location, a network status score is determined according to the network status data, and a parking probability score is determined according to the parking probability.

6. The method according to claim 4, characterized in that The step of determining the target upgrade time according to the upgrade score comprises: Arrange the upgrade scores in descending order to obtain an arrangement result; The arrangement results are traversed, and when there are continuous upgrade times with a preset time span formed by the upgrade time corresponding to the upgrade score in the arrangement results, the continuous upgrade time with the preset time span is used as the target upgrade time.

7. The method according to any one of claims 1 to 6, characterized in that When a new upgrade task is detected, the step of taking the target upgrade time as the upgrade start time and executing the new upgrade task comprises: Obtaining the real-time location of the vehicle, and automatically checking for upgrades when the real-time location enters a preset area; When a new upgrade task is detected, the new upgrade task is executed with the target upgrade time as the upgrade start time.

8. An intelligent scheduling device for OTA upgrade time, characterized in that: The device comprises: A data collection module, used to obtain parking data and network status data of vehicles; A behavior analysis module, used to analyze the parking data and determine the parking probability of the vehicle in different time periods; An intelligent scheduling module, used to determine a target upgrade time according to the parking data, the network status data and the parking probability in different time periods; The decision output module is used to execute the new upgrade task with the target upgrade time as the upgrade start time when a new upgrade task is detected.

9. An upgrade time intelligent scheduling device for OTA upgrade, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the upgrade time intelligent scheduling method for OTA upgrade according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the upgrade time intelligent scheduling method for OTA upgrade according to any one of claims 1 to 7 are implemented.

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