A vehicle state control method

Abstract

This invention provides a vehicle status control method. By generating a daily dedicated restart time based on the vehicle's unique identifier, real-time date, GPS coordinates, and voltage fluctuation characteristics, the restart times of a large number of vehicles are randomly distributed within an optimized time window. Combined with a four-stage progressive condition check mechanism, this method achieves personalized allocation and intelligent scheduling of vehicle restart times, effectively avoiding server congestion caused by simultaneous restarts of a large number of vehicles. It significantly improves the vehicle initialization success rate and system reliability, ensuring a normal user experience.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle technology, and in particular to a vehicle state control method. Background Technology

[0002] With the increasing number of intelligent connected vehicles, most of them employ a scheduled restart strategy for their infotainment systems to avoid system lag and other issues caused by prolonged periods without power. This strategy typically involves a scheduled restart at night, which also restarts the 4G module inside the cabin, allowing it to connect to an internet server after restarting.

[0003] Therefore, when a large number of vehicle infotainment systems (e.g., more than 100,000) restart simultaneously, a large number of vehicles initiate access to the Internet server, resulting in instantaneous network congestion on the server. Consequently, some vehicle infotainment systems fail to initialize due to their inability to connect to the server, affecting normal user operation. Summary of the Invention

[0004] This invention provides a vehicle status control method, which aims to overcome the deficiencies in the prior art and realize personalized allocation and intelligent scheduling of vehicle restart time.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A vehicle state control method, comprising:

[0007] Based on preset restart start and restart end times, a restart time window is determined, and the restart time window is adjusted in conjunction with historical parking data;

[0008] Based on the vehicle's unique identifier, current date, GPS coordinates, and voltage fluctuation characteristics, a unique restart time is generated for each vehicle daily.

[0009] When the system time reaches the designated restart time, a progressive restart condition check is performed, which includes four sequentially executed judgment stages: vehicle basic status check, driver off-vehicle status check, driver usage intention check, and environmental condition check.

[0010] If all restart conditions are met, then control the vehicle to restart.

[0011] Specifically, the formula for calculating the total length of the calculation time window is: T window =(1440-T start )+T end ;

[0012] The formula for calculating the optimized time window is:

[0013]

[0014] Among them, T start To restart the start time, T end For the restart end time, μ S Indicates the historical average parking start time, μ E σ represents the historical average start time of vehicle usage. S and σ E These represent the standard deviations for the corresponding time periods.

[0015] Specifically, the generation of the dedicated restart time includes:

[0016] Calculate the voltage fluctuation characteristic ΔV;

[0017] Construct the input string S in ;

[0018] For the input string S in Perform a hash operation to obtain the seed value;

[0019] Calculate the random offset Offset based on the seed value;

[0020] The specific reboot time, RebootTime, is calculated based on the random offset Offset and the time window.

[0021] Specifically, the formula for calculating the voltage fluctuation characteristic ΔV is as follows:

[0022]

[0023] Where v1, v2, and v3 are continuous voltage sample values, and the ⌊⌋ symbol indicates rounding down;

[0024] The constructed input string S in This includes: constructing the input string S based on the vehicle's unique identifier, current date, GPS coordinates, and voltage fluctuation characteristics. in :

[0025]

[0026] Where VIN represents the vehicle's unique identifier, D represents the current date, Quant(GPS) represents the GPS coordinates, ΔV represents the voltage fluctuation characteristics, and ⊕ represents string concatenation;

[0027] The formula for calculating the random offset Offset is: Offset = Int(Seed hash ) mod T window ;

[0028] The formula for calculating the reboot time point RebootTime is:

[0029]

[0030] Specifically, the vehicle basic status check includes determining whether the following conditions are met simultaneously:

[0031] The engine ignition system is off;

[0032] The vehicle speed sensor reading is zero;

[0033] The gearbox is in park.

[0034] If any condition is not met, the restart time window will be redefined.

[0035] Specifically, the driver's off-vehicle status check includes:

[0036] If the driver has left the vehicle and the duration of all doors being closed has reached the first preset time, proceed to the next inspection stage; otherwise, delay for the second preset time and then re-detect the driver's departure status.

[0037] Specifically, the driver's intent check includes:

[0038] Determine whether at least two of the following conditions are met: charging status activated, vehicle located in a preset safe area, parking time exceeded, or the distance between the smart key and the vehicle is greater than a preset distance.

[0039] If fewer than two conditions are met, proceed to the next inspection stage;

[0040] If two or more conditions are met, the detection will be repeated after a third preset time delay, where the third preset time is determined according to the following formula:

[0041] T3=min(120,max(5,30×(3-N aux )))

[0042] Where T3 is the third preset duration, N aux This indicates the number of items that satisfy the above conditions.

[0043] Specifically, the environmental condition check includes:

[0044] First, determine if it is an underground parking garage environment. If so, ignore the rainfall status check and proceed directly to the restart operation.

[0045] If not, determine if it is in a rainy state. If it is not in a rainy state, proceed with the restart operation. If it is in a rainy state, re-detect the environmental conditions after a fourth preset time.

[0046] Specifically, determining whether it is an underground parking garage environment includes:

[0047] Obtaining GPS feature vectors , where N sat S represents the number of visible satellites. dBm The average signal strength is given by α, and the GPS signal attenuation rate is given by α.

[0048] Obtaining optical feature vectors Where L is the light intensity, δ L γ represents the light variability, and γ represents the confidence level of the artificial light source.

[0049] Obtaining cellular feature vectors , where β is the RSRP attenuation ratio, η is the network fallback rate, and τ is the latency jitter;

[0050] The corresponding feature scoring function is determined based on the GPS feature vector, optical feature vector, and cellular feature vector;

[0051] The underground probability is calculated based on the feature scoring function.

[0052] Based on the aforementioned underground probability, determine whether it is an underground parking garage environment.

[0053] Specifically, the feature score is calculated as follows:

[0054] GPS Feature Score Function S g Optical feature scoring function S o Cellular Feature Scoring Function S c for:

[0055]

[0056] The formula for calculating the underground probability is:

[0057] P ug =0.45S g +0.35S o +0.20S c

[0058] Where H(x) is the step function:

[0059]

[0060] Φ(x;a,b) is an interval indicator function:

[0061]

[0062] The beneficial effects of this invention are as follows: This invention generates a daily exclusive restart time based on the vehicle's unique identifier, real-time date, GPS coordinates, and voltage fluctuation characteristics. It randomly distributes the restart times of a large number of vehicles within an optimized time window. Combined with a four-stage progressive condition check mechanism, it realizes personalized allocation and intelligent scheduling of vehicle restart times, effectively avoiding server congestion caused by the simultaneous restart of a large number of vehicles, significantly improving the vehicle initialization success rate and system reliability, and ensuring the normal user experience. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the vehicle state control method of the present invention. Detailed Implementation

[0064] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The drawings are for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention.

[0065] In the process described in the specification, claims, or drawings of this invention, each step is numbered (e.g., step 10, 20, etc.). These numbers are used only to distinguish the steps and do not represent any execution order. It should be noted that the terms "first," "second," etc., used herein are only for distinguishing the objects being described and do not represent a chronological order, nor do they indicate that "first," "second," etc., are different types.

[0066] like Figure 1 As shown, the present invention provides a vehicle state control method, comprising:

[0067] Step 1: Determine the restart time window.

[0068] In this embodiment, step 1 includes:

[0069] First, obtain the preset restart start time T. start (For example, 22:00, 1320 minutes), restart end time T end (For example, 06:00, 360 minutes);

[0070] Then, according to the T start T end Calculate the total length of the time window (in minutes) using the following formula:

[0071] T window =(1440-T start )+T end

[0072] For example, T window =(1440-1320)+360=480 minutes.

[0073] In practice, the restart start time and restart end time can be dynamically determined based on historical parking data to optimize the restart time window.

[0074]

[0075] Among them, T' start Indicates the optimized restart start time, T end Indicates the optimized restart end time, μ S Indicates the historical average parking start time, μ E σ represents the historical average start time of vehicle usage. S and σ E These represent the standard deviations for the corresponding time periods.

[0076] Step 2: Generate a unique restart time for each vehicle daily.

[0077] In this embodiment, step 2 includes:

[0078] Step 201: Obtain the following parameters: Vehicle Unique Identifier (VIN) (string), Current Date (D) (format: YYYYMMDD, e.g., "20250804"), Vehicle GPS Coordinates (Quant) (GPS), Continuous Voltage Sampling: V b =[v1,v2,v3], preset time window parameter T start ,T end ,T window .

[0079] Step 202: Calculate voltage fluctuation characteristics:

[0080]

[0081] The ⌊⌋ symbol indicates rounding down.

[0082] Step 203: Construct the input string S in :

[0083]

[0084] Here, ⊕ represents string concatenation.

[0085] Step 204: Process the input string S in Perform a hash operation to obtain the seed value:

[0086]

[0087] Step 205: Calculate the random offset Offset based on the seed value:

[0088] Offset=Int(Seedhash ) mod T window

[0089] Step 206: Calculate the reboot time point RebootTime:

[0090]

[0091] Finally, convert RebootTime to reboot time, as follows:

[0092] The hour portion of the reboot time: Hour = ⌊RebootTime / 60⌋;

[0093] Reboot time in minutes only: Minute = RebootTime mod 60.

[0094] For example: VIN="LSVNB49Y8Y1234567", D="20250804"; GPS coordinates: latitude: 31.2304N, longitude: 121.4737E; v1=12.345, v2=12.358, v3=12.352, then:

[0095]

[0096] So, S in =LSVNB49Y8Y1234567|20250804|31.2304N|121.4737E|013.

[0097] By calculating S in The SHA256 value is obtained, and then the random offset Offset is calculated.

[0098] For example, according to the above S in The calculated offset is 139.

[0099] Since 139 > 1440 - 1320 = 120, therefore RebootTime = 139 - (1440 - 1320) = 19.

[0100] Replacing RebootTime with hours and minutes gives: 00:19.

[0101] Step 3: Restart the condition check.

[0102] When the system time reaches RebootTime:

[0103] (1) First, determine whether all of the following basic conditions are met. If they are met, proceed to the next step. If they are not met, repeat the process of re-determining the time window:

[0104] Condition A1: The engine ignition system is off (ignition_status=OFF)

[0105] Condition A2: The vehicle speed sensor reading is zero (v=0km / h)

[0106] Condition A3: The transmission is in Park (G=P) gear.

[0107] (2) Next, determine whether the driver has left the vehicle. If the driver has left the vehicle and all doors are closed for a first preset time T1 (e.g., 10 minutes), proceed to the next step. If the driver has not left the vehicle, delay for a second preset time T2 (e.g., 60 minutes) and then check whether the driver has left the vehicle again.

[0108] (3) Next, determine whether the driver has the intention to use the device. If not, proceed to the next step. Otherwise, delay for a third preset time T3 and then check again whether the driver has the intention to use the device.

[0109] In this embodiment, determining that the driver intends to use the device requires at least two of the following conditions to be met:

[0110] Condition C1: Charging state activated (Sc=ACTIVE).

[0111] Condition C2: The vehicle is located in a preset safe area (GPS∈Asafe, such as a home / company parking space).

[0112] Condition C3: Parking timeout, i.e., T park >μ T -σ T T park Indicates the duration of vehicle parking, μ T σ represents the historical average parking time. T This represents the standard deviation of the historical average parking time.

[0113] Condition C4: The distance between the smart key and the vehicle is greater than the preset distance (e.g., 10 meters).

[0114] In this embodiment, the third preset duration T3 is determined according to the following formula:

[0115] T3=min(120,max(5,30×(3-N aux ))), where N aux This indicates the number of items that satisfy the above conditions.

[0116] (4) Finally, determine whether it is an underground parking garage environment. If it is, ignore the rain condition and determine that the restart condition is met. Otherwise, determine whether it is a rain condition. If it is, delay for the fourth preset time T4 (e.g., 30 minutes) and then check whether it is a rain condition again. Otherwise, determine that the restart condition is met.

[0117] In this embodiment, determining whether it is an underground parking garage environment includes:

[0118] Step a: Obtain the number of visible satellites Nsat and the average signal strength SdBm, calculate the GPS signal attenuation rate α, and output the GPS feature vector.

[0119]

[0120] Where α = ΔS dBm / Δt.

[0121] Step b: Collect the current light intensity L and calculate the volatility δ L Analyze spectral features, generate artificial light source confidence γ, and output optical feature vector:

[0122]

[0123] Where, δ L =[(L max -L min ) / L avg ]*100%

[0124] Step c: Acquire cellular network signals, calculate RSRP attenuation ratio β, calculate network fallback rate η and latency jitter τ, and output cellular feature vector:

[0125]

[0126] Where β = RSRP ground / RSRP current RSRP ground This represents the RSRP value on the ground. current This indicates the current RSRP value.

[0127] Step d: Determine the corresponding feature scoring function based on the feature vectors obtained in steps a, b, and c.

[0128]

[0129] Where H(x) is the step function:

[0130]

[0131] Φ(x;a,b) is an interval indicator function:

[0132]

[0133] Step e: Calculate the underground probability based on the feature scoring function:

[0134] P ug =0.45S g +0.35S o +0.20S c

[0135] If P ug If the value is greater than 0.75, it is determined to be an underground parking garage environment.

[0136] Step 4: When the restart conditions are met, control the vehicle to restart.

[0137] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A vehicle state control method, characterized in that, include: Based on preset restart start and restart end times, a restart time window is determined, and the restart time window is adjusted in conjunction with historical parking data; Based on the vehicle's unique identifier, current date, GPS coordinates, and voltage fluctuation characteristics, a unique restart time is generated for each vehicle daily. When the system time reaches the designated restart time, a progressive restart condition check is performed, which includes four sequentially executed judgment stages: vehicle basic status check, driver off-vehicle status check, driver usage intention check, and environmental condition check. If all restart conditions are met, then control the vehicle to restart; The dedicated restart time for the generated vehicle includes: Calculate the voltage fluctuation characteristic ΔV; Constructing input string S in ; For the input string S in Perform a hash operation to obtain the seed value; Calculate the random offset Offset based on the seed value; Based on the random offset Offset, the specific reboot time RebootTime is calculated using the time window. The formula for calculating the voltage fluctuation characteristic ΔV is: Where v1, v2, and v3 are continuous voltage sample values, The symbol indicates rounding down; The constructed input string S in This includes: constructing the input string S based on the vehicle's unique identifier, current date, GPS coordinates, and voltage fluctuation characteristics. in : Where VIN represents the vehicle's unique identifier, D represents the current date, Quant(GPS) represents the GPS coordinates, ΔV represents the voltage fluctuation characteristics, and ⊕ represents string concatenation; The formula for calculating the random offset Offset is: Offset = Int(Seed hash ) mod T window ; The formula for calculating the reboot time point RebootTime is: Among them, T start This is the restart start time.

2. The vehicle state control method according to claim 1, characterized in that, The restart time window is determined according to the following formula: T window =(1440 T start )+T end ; The adjustment of the restart time window based on historical parking data is determined according to the following calculation formula: Where T end T' is the restart end time. start Indicates the optimized restart start time, T' end Indicates the optimized restart end time, μ S Indicates the historical average parking start time, μ E σ represents the historical average start time of vehicle usage. S and σ E These represent the standard deviations for the corresponding time periods.

3. The vehicle state control method according to claim 1, characterized in that, The vehicle basic condition check includes determining whether the following conditions are met simultaneously: The engine ignition system is off; The vehicle speed sensor reading is zero; The gearbox is in park. If any condition is not met, the restart time window will be redefined.

4. The vehicle state control method according to claim 1, characterized in that, The driver's off-vehicle status check includes: If the driver has left the vehicle and the duration of all doors being closed has reached the first preset time, proceed to the next inspection stage; otherwise, delay for the second preset time and then re-detect the driver's departure status.

5. The vehicle state control method according to claim 1, characterized in that, The driver's intent check includes: Determine whether at least two of the following conditions are met: charging status activated, vehicle located in a preset safe area, parking time exceeded, or the distance between the smart key and the vehicle is greater than a preset distance. If fewer than two conditions are met, proceed to the next inspection stage; If two or more conditions are met, the detection will be repeated after a third preset time delay, where the third preset time is determined according to the following formula: T3=min(120,max(5,30×(3 N aux ))) Where T3 is the third preset duration, N aux This indicates the number of items that satisfy the above conditions.

6. The vehicle state control method according to claim 1, characterized in that, The environmental condition check includes: First, determine if it is an underground parking garage environment. If so, ignore the rainfall status check and proceed directly to the restart operation. If not, determine if it is in a rainy state. If it is not in a rainy state, proceed with the restart operation. If it is in a rainy state, re-detect the environmental conditions after a fourth preset time.

7. The vehicle state control method according to claim 5, characterized in that, The determination of whether it is an underground parking garage environment includes: Obtaining GPS feature vectors , where N sat S represents the number of visible satellites. dBm The average signal strength is given by α, and the GPS signal attenuation rate is given by α. Obtaining optical feature vectors Where L is the light intensity, δ L γ represents the light variability, and γ represents the confidence level of the artificial light source. Obtaining cellular feature vectors , where β is the RSRP attenuation ratio, η is the network fallback rate, and τ is the latency jitter; The corresponding feature scoring function is determined based on the GPS feature vector, optical feature vector, and cellular feature vector; The underground probability is calculated based on the feature scoring function. Based on the aforementioned underground probability, determine whether it is an underground parking garage environment.

8. The vehicle state control method according to claim 7, characterized in that, The feature score is calculated as follows: GPS Feature Score Function S g Optical feature scoring function S o Cellular Feature Scoring Function S c for: The formula for calculating the underground probability is: P ug =0.45S g +0.35S o +0.20S c Where H(x) is the step function: Φ(x;a,b) is an interval indicator function: 。

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