Vehicle data acquisition method and electronic equipment

Abstract

The invention discloses a vehicle data acquisition method and electronic equipment, and belongs to the technical field of vehicles, and the method comprises the steps: determining the current operation state of a vehicle according to first state data; determining an M-dimensional parameter value according to the second state data and the current operation state; determining a dynamic importance score according to the M-dimensional parameter value, wherein the dynamic importance score represents the influence degree of the sampling interval of the collected vehicle data on the health condition of the vehicle in the current running state; determining a current reference level according to the state level of the current operation state and the dynamic importance score; and adjusting a sampling interval for collecting the vehicle data according to the current reference level, and collecting the vehicle data according to the adjusted target sampling interval. According to the invention, the technical problem that the related technology for collecting vehicle data is difficult to meet the requirement for monitoring the health condition of the vehicle is solved.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle technology, and in particular relates to a vehicle data collection method and electronic equipment. Background Art

[0002] As vehicles become increasingly intelligent, various sensors and monitoring systems are equipped with real-time data such as the vehicle's operating status and surrounding environment. The sampling interval for vehicle data is typically set to a fixed value. This fixed-sampling interval data collection method generates a large amount of redundant information in practical applications, not only occupying a large amount of data storage space but also consuming excessive network bandwidth resources during data transmission. During vehicle operation, parameters such as acceleration, engine load, and brake pressure can change dramatically under conditions such as sudden acceleration, sudden braking, and high-speed driving. Low-frequency acquisition cannot capture this transient data, thus compromising the ability to monitor the vehicle's real-time health status, preventing the timely detection of potential fault hazards, and reducing the safety and reliability of vehicle operation.

[0003] It can be seen that with the rapid development of Internet of Vehicles technology and the increasing demand for refined analysis of vehicle operation data, the technology of collecting vehicle data with a fixed sampling frequency can no longer meet the needs of monitoring the health status of vehicles. Summary of the Invention

[0004] The embodiments of the present invention provide a vehicle data collection method and electronic device to solve the technical problem that related technologies for collecting vehicle data are difficult to meet the demand for monitoring the health status of the vehicle.

[0005] According to a first aspect of the present invention, a vehicle data collection method is provided, comprising:

[0006] Acquire first state data and second state data of a vehicle, wherein the first state data is vehicle data related to a current operating state of the vehicle, and the second state data is vehicle data of the vehicle collected during a historical period;

[0007] determining a current operating state of the vehicle according to the first state data;

[0008] Determine an M-dimensional parameter value according to the second state data and the current operating state, wherein the M-dimensional parameter value is data that affects the health status of the vehicle in the current operating state, and M is a positive integer;

[0009] Determining a dynamic importance score based on the M-dimensional parameter value, the dynamic importance score representing the degree of influence of a sampling interval for collecting vehicle data on the health status of the vehicle in a current operating state;

[0010] Determining a current reference level according to the status level of the current operating state and the dynamic importance score;

[0011] The sampling interval for collecting vehicle data is adjusted according to the current reference level, and the vehicle data of the vehicle is collected according to the adjusted target sampling interval.

[0012] In conjunction with the first aspect, in some embodiments, the first state data includes one or more of the most recently sampled vehicle speed parameters, battery operating parameters, engine operating parameters, battery temperature, and vehicle fault information; the vehicle operating state is pre-divided into a stationary state, an emergency state, and N vehicle speed states corresponding to N vehicle speed intervals, where N is a positive integer;

[0013] Determining the current operating state of the vehicle according to the first state data includes:

[0014] determining, based on the vehicle speed parameter, whether the vehicle is in one of the N vehicle speed states;

[0015] determining whether the vehicle is in the stationary state based on at least one of the vehicle speed parameter, the battery operating parameter, and the engine operating parameter; and

[0016] It is determined whether the vehicle is in the emergency state according to the battery temperature and the vehicle fault information.

[0017] In combination with the first aspect, in some embodiments, determining the M-dimensional parameter value according to the second state data and the current operating state includes:

[0018] determining, based on the second state data, a data change rate of at least one dimension of a parameter affecting a health condition of the vehicle in a current operating state;

[0019] determining an ambient temperature at a location of the vehicle based on the second state data;

[0020] The number of abnormal sampling times in the historical period is determined according to the second status data.

[0021] In conjunction with the first aspect, in some embodiments, scoring rules are pre-configured for different data dimensions, and determining the dynamic importance score according to the M-dimensional parameter value includes:

[0022] For each dimension parameter value in the M-dimensional parameter values, score the dimension parameter value according to the scoring rule configured for the data dimension of the dimension parameter value to obtain a target score value corresponding to the dimension parameter value;

[0023] The dynamic importance score is determined according to the M target rating values corresponding to the M-dimensional parameter values.

[0024] In conjunction with the first aspect, in some embodiments, the scoring rule for each data dimension includes a correspondence between multiple preset parameter intervals and multiple scoring values, and for each dimension parameter value in the M-dimensional parameter values, scoring the dimension parameter value according to the scoring rule configured for the data dimension of the dimension parameter value to obtain a target scoring value corresponding to the dimension parameter value includes:

[0025] For each dimension parameter value in the M-dimensional parameter values, determine the target parameter interval in which the dimension parameter value is located from each of the preset parameter intervals in the scoring rule configured for the data dimension of the dimension parameter value, and determine the target scoring value corresponding to the dimension parameter value based on the target parameter interval.

[0026] In conjunction with the first aspect, in some embodiments, determining the current reference level according to the status level of the current operating state and the dynamic importance score includes:

[0027] determining a status level of the current operating state according to status levels preset for various operating states of the vehicle;

[0028] Determining a first weighted value of a state level of the current operating state and a first weight, and determining a second weighted value of the dynamic importance score and a second weight;

[0029] The sum of the first weighted value, the second weighted value, and a target adjustment factor is determined as the current reference level.

[0030] In combination with the first aspect, in some embodiments, before determining the sum of the first weighted value, the second weighted value, and the target adjustment factor as the reference level, the method further includes:

[0031] Obtaining weather data for the location of the vehicle;

[0032] determining a vehicle usage scenario based on the current operating state of the vehicle and the weather data;

[0033] The target adjustment factor is determined according to the vehicle usage scenario.

[0034] In conjunction with the first aspect, in some embodiments, adjusting the sampling interval for collecting vehicle data according to the current reference level includes:

[0035] Determining a target level interval where the current reference level is located from a plurality of preset level intervals;

[0036] Determining a target adjustment strategy configured for the target level interval from a plurality of preset adjustment strategies;

[0037] The reference sampling interval is adjusted according to the target adjustment strategy to obtain the target sampling interval.

[0038] In combination with the first aspect, in some embodiments, the plurality of preset level intervals include a first level interval, a second level interval, and a third level interval, the levels of which increase in sequence;

[0039] The adjusting the reference sampling interval according to the target adjustment strategy to obtain the target sampling interval includes:

[0040] If the target level interval is the first level interval, adjusting the sampling interval for collecting vehicle data from the vehicle to P times the reference sampling interval, where P is a positive number greater than 1;

[0041] If the target level interval is the second level interval, linearly adjust the reference sampling interval according to the current reference level to obtain the target sampling interval;

[0042] If the target level interval is the third level interval, the benchmark sampling interval is exponentially adjusted according to the current reference level to obtain the target sampling interval.

[0043] According to a second aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the memory is coupled to the processor and stores instructions, and when the instructions are executed by the processor, the vehicle data collection method described in any embodiment of the first aspect is implemented.

[0044] The one or more technical solutions provided by the embodiments of the present invention achieve at least the following technical effects or advantages:

[0045] The technical solution provided by the embodiment of the present invention determines the current operating state of the vehicle based on first state data; determines an M-dimensional parameter value that affects the health status of the vehicle in the current operating state based on the second state data of the historical period and the current operating state; then determines a dynamic importance score that characterizes the degree of influence of the sampling interval on the health status of the vehicle in the current operating state based on the M-dimensional parameter value; then determines a current reference level based on the state level of the current operating state and the dynamic importance score, and adjusts the sampling interval for collecting vehicle data based on the current reference level. Thus, the sampling interval for collecting vehicle data is dynamically adjusted by comprehensively considering the operating state of the vehicle and the dynamic importance of the data in that operating state. This enables the sampling interval for collecting vehicle data to meet the data monitoring requirements of the vehicle in different operating states in real time, reducing redundant data and thus data storage and transmission costs, and increasing the collection frequency when high-frequency vehicle data collection is required, ensuring effective monitoring of the vehicle's health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0047] Figure 1 A flow chart showing a vehicle data collection method in some embodiments of the present invention is shown;

[0048] Figure 2 A schematic structural diagram of a vehicle data acquisition device in some embodiments of the present invention is shown;

[0049] Figure 3 A schematic structural diagram of an electronic device in some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] Figure 1 FIG. 1 shows a flow chart of a vehicle data collection method in some embodiments of the present invention. Figure 1 As shown, an embodiment of the present invention provides a vehicle data collection method, which includes the following steps S101 to S106.

[0052] In step S101 : first state data and second state data of a vehicle are acquired, where the first state data is vehicle data related to a current operating state of the vehicle, and the second state data is vehicle data of the vehicle collected during a historical period.

[0053] It is understood that the vehicle data collected at a sampling interval includes various parameters. When sampling vehicle data at a sampling interval, the first state data is the value of at least one parameter dimension obtained from the most recent sampling, used to determine the current operating state of the vehicle. The second state data is the parameter values obtained from multiple samplings of multi-dimensional parameters affecting the vehicle's health status over a historical period.

[0054] The data obtained by sampling the multi-dimensional parameters affecting the vehicle health status multiple times during a historical period may also include the value of at least one dimension of the parameter obtained in the most recent sampling. The first state parameter may include one or more of the vehicle speed parameter, battery operating parameter, engine operating parameter, battery temperature, and vehicle fault information obtained in the most recent sampling. The vehicle speed parameter includes at least one of the vehicle speed signal, acceleration value, brake signal, steering angle, etc. obtained in the most recent sampling. The battery operating parameter includes at least one of the battery load, charge and discharge current, and charge and discharge voltage obtained in the most recent sampling. The engine operating parameter includes at least one of the engine speed, torque, and power obtained in the most recent sampling.

[0055] In step S102: the current operating state of the vehicle is determined according to the first state data.

[0056] In some embodiments, the operating state of the vehicle is divided into the following types: a stationary state, an emergency state, and N types of speed states corresponding to N speed intervals. The current operating state of the vehicle is determined from multiple operating states based on the first state data. It can be understood that the stationary state is a state in which the vehicle is connected to a charging pile, the engine is turned off and parked, etc., the emergency state is a state in which the vehicle issues a collision warning, the vehicle control system fails, etc., and the N types of speed states may include the following three types: a low-speed driving state, a medium-speed driving state, and a high-speed driving state. For example, the low-speed driving state is a vehicle speed of 1-30km / h, the medium-speed driving state is a vehicle speed of 30-60km / h (excluding 30km / h), and the high-speed driving state is a vehicle speed of 60-120km / h (excluding 60km / h).

[0057] When the vehicle's operating state is divided into a stationary state, an emergency state, and N speed states corresponding to N speed ranges, determining the vehicle's current operating state based on the first state data may include: determining whether the vehicle is in one of the N speed states based on a speed parameter; determining whether the vehicle is in a stationary state based on at least one of a speed parameter, a battery operating parameter, and an engine operating parameter; and determining whether the vehicle is in an emergency state based on the battery temperature and vehicle fault information.

[0058] In some embodiments, it is possible to determine whether the vehicle is in a stationary state based on at least one of the vehicle speed parameters, battery operating parameters, and engine operating parameters. In some embodiments, it is determined whether the vehicle speed signal is zero or lower than the stationary threshold. If so, it is determined that the vehicle is in a stationary state. Since the vehicle shakes slightly when idling, the speed of a fuel vehicle is considered to be stationary when it is ≤0.5km / h, and the speed of an electric vehicle is considered to be stationary when it is ≤0.2km / h. In other embodiments, if the battery operating parameter indicates that the battery is charging, or indicates that the vehicle is connected to the charging pile, or indicates that the discharge current of the battery is lower than a preset current threshold, it is determined that the vehicle is in a charging state in a stationary state. In some other embodiments, if the engine speed is in the idle range (for example: the idle range of a fuel vehicle is about 600-1000RPM, and the idle range of an electric vehicle is about 0RPM), it is determined that the vehicle is in a stationary state. In some further embodiments, the vehicle can be determined to be in a stationary state when the vehicle speed is ≤5km / h and the acceleration ≈0 (acceleration ≈0 can be the absolute value of the acceleration ≤0.2m / s 2 ), when the throttle opening is ≤5% and the brake signal Brake=0 (non-braking state), it is determined that the vehicle is in a stationary state.

[0059] In some embodiments, a vehicle speed signal is obtained through a controller area network (CAN) bus, a threshold detector is set, and the speed range in which the vehicle speed signal is located is determined by the threshold detector. Thus, it is determined which of the N speed states the vehicle is in according to the speed range in which the speed signal is located.

[0060] In some embodiments, the time to collision is calculated by combining millimeter-wave radar and / or camera data. If the calculated time to collision is less than a preset time threshold, a collision warning is issued. A diagnostic trouble code (DTC) is obtained through the second-generation on-board diagnostics system (OBD-II), such as the battery overtemperature code P0A80. If a collision warning or trouble code is obtained, the vehicle is determined to be in an emergency state.

[0061] State levels are preset for various operating states of the vehicle. The state levels and typical scenarios preset for different operating states are different, as shown in Table 1 below.

[0062] Table 1. Correspondence between status levels, operating status, and typical scenarios

[0063]

[0064] In step S103: an M-dimensional parameter value is determined according to the second state data and the current operating state of the vehicle. The M-dimensional parameter value is data that affects the health of the vehicle in the current operating state, and M is a positive integer.

[0065] M-dimensional parameter values include, but are not limited to, the following three-dimensional parameter values: the data change rate of at least one dimension, the ambient temperature at the vehicle's location, and the number of abnormal sampling times within a historical period. It should be understood that the number of abnormal sampling times within a historical period refers to the number of times the vehicle data sampled during that period was abnormal. The historical period can be a week, a day, or other length of time. The data change rate of each dimension parameter can be the per-minute change in that dimension parameter.

[0066] It should be noted that no matter which current operating state is, the two-dimensional parameter values of the ambient temperature at the vehicle's location and the number of abnormal sampling times in the historical period are required. However, the data type of the data change rate will vary depending on the current operating state. In some embodiments, in a stationary state, the data change rate of one or more parameters including battery temperature, battery charging current, and battery SOC is included. In a low-speed driving state, the data change rate of one or more parameters including motor torque, brake pressure, and battery output current is included. In a medium-speed driving state, the data change rate of one or more parameters including vehicle speed, tire pressure, and coolant temperature is included. In a high-speed driving state, the data change rate of one or more parameters including engine speed, transmission oil temperature, and air flow sensor data is included. In an emergency state, one or more parameters including collision sensor data, battery voltage difference, and brake system pressure are included.

[0067] In some embodiments, based on the second state data, the data change rate of at least one dimensional parameter that affects the health status of the vehicle in the current operating state is determined; the ambient temperature of the vehicle's location is determined based on the second state data; and the number of abnormal sampling times within a historical period is determined based on the second state data.

[0068] In step S104 : a dynamic importance score is determined according to the M-dimensional parameter value, where the dynamic importance score represents the degree of influence of the sampling interval for collecting vehicle data on the health status of the vehicle in the current operating state.

[0069] In some embodiments, scoring rules are pre-configured for different data dimensions. For each dimension parameter value in the M-dimensional parameter values, the dimension parameter value is scored according to the scoring rules configured for the data dimension of the dimension parameter value to obtain the target scoring value corresponding to the dimension parameter value; the dynamic importance score is determined based on the M target scoring values corresponding to the M-dimensional parameter value.

[0070] In some embodiments, scoring rules are defined for each of the M data dimensions. The scoring rules for each data dimension include a correspondence between multiple preset parameter intervals and multiple scoring values. For example, the scoring rules for different data dimensions are shown in Table 2 below:

[0071] Table 2. Scoring rules for different data dimensions (total score range: 0-10 points)

[0072]

[0073] It should be noted that the specific values in Table 2 above are only examples and can be adjusted according to actual needs during the specific implementation process.

[0074] It should be noted that the scoring rules configured for data change rates of different data types have different preset parameter ranges and scoring values. Below, we will use the data change rate of battery temperature as an example to explain the scoring rules for data change rate: the battery temperature change per minute is divided into multiple change ranges, and each change range corresponds to a different scoring value: a temperature change per minute of 0-x1°C / min corresponds to a score of 1, and so on. A temperature change per minute of x1-x2 corresponds to a score of 2, a temperature change per minute of x2-x3 corresponds to a score of 3, a temperature change per minute of x3-x4 corresponds to a score of 4, and a temperature change per minute of greater than x4 or less than x1 corresponds to a score of 5. Scoring rules for data change rates of other data types can be configured based on the same or similar principles. For the sake of brevity, they are not further described here.

[0075] In some embodiments, for each dimensional parameter value, determining a target score corresponding to the dimensional parameter value according to a scoring rule configured for the data dimension of the dimensional parameter value may include: for each dimensional parameter value, determining a target parameter interval within which the dimensional parameter value lies from each preset parameter interval in the scoring rule configured for the data dimension of the dimensional parameter value, and determining the target score corresponding to the dimensional parameter value based on the target parameter interval. In other words, the score corresponding to the target parameter interval is used as the target score for the dimensional parameter value.

[0076] The dynamic importance score is determined based on the M target rating values corresponding to the M-dimensional parameter values. The dynamic importance score may be the sum of the M target rating values or the weighted sum of the M target rating values.

[0077] Taking the vehicle's current operating state as an example, which is stationary and charging, if the battery temperature change rate is 0.5°C / min, the ambient temperature is normal, and the number of abnormal sampling times is 0, then the dynamic importance score = battery temperature change 0.5°C / min (1 point) + normal temperature (1 point) + no abnormality (0 point) = 2 points.

[0078] Taking the vehicle's current operating state as a low-speed driving state as an example, if the current change rate is 2A / min, the ambient temperature is high (greater than 35°C), and the number of abnormal sampling is 1, then the dynamic importance score = current fluctuation 2A / min (2 points) + high temperature (3 points) + 1 abnormality (1 point) = 6 points.

[0079] Taking the vehicle's current operating state of medium speed as an example, if the tire pressure drops by 0.1 Bar / min, the ambient temperature is normal temperature [0℃-35℃], and the number of abnormal sampling is 0, then the dynamic importance score = tire pressure drop of 0.1 Bar / min (1 point) + normal weather (1 point) + no abnormality (0 point) = 2 points.

[0080] Taking the vehicle's current high-speed driving state as an example, if the transmission oil temperature rise rate is 2°C / min, the ambient temperature is low (less than 0°C), and the number of abnormal sampling is 2, then the dynamic importance score = transmission oil temperature rise 2°C / min (2 points) + low temperature (3 points) + 2 abnormalities (2 points) = 7 points.

[0081] Taking the vehicle's current operating state as an emergency state as an example, if the battery temperature change rate is 5°C / min, the ambient temperature is high, and the number of abnormal sampling times is 5 times, then the dynamic importance score = battery temperature change rate 5°C / min (5 points) + high temperature (3 points) + multiple abnormalities (2 points) = 10 points.

[0082] In some embodiments, an over-limit threshold can be set for each dimension or some dimensions. If the score value calculated for the dimension is greater than the over-limit threshold, the over-limit threshold is used as the target score value for the dimension, which can improve the accuracy of the dynamic importance score.

[0083] In step S105 : the current reference level is determined according to the status level and the dynamic importance score of the current operating state.

[0084] In some embodiments, the sum of the state level and the dynamic importance score of the current operating state can be used as the current reference level. In other embodiments, the current reference level can be obtained by taking the weighted sum of the state level and the dynamic importance score of the current operating state. In other embodiments, the weighted sum of the state level and the dynamic importance score of the current operating state can be adjusted based on the target adjustment factor, or the sum of the state level and the dynamic importance score of the current operating state can be adjusted based on the target adjustment factor to obtain the current reference level, so as to improve the accuracy of the current reference level.

[0085] In some embodiments, based on different status levels preset for various operating states of the vehicle, a status level of the current operating state is determined; a first weighted value is determined for the status level of the current operating state and a first weight, and a second weighted value is determined for the dynamic importance score and a second weight; and the sum of the first weighted value, the second weighted value, and the target adjustment factor is determined as the current reference level. In a specific implementation, the current reference level can be determined by referring to the following level adjustment function:

[0086] f(S,I)=(α·S)+(β·I)+C

[0087] Among them, α and β are the first weight and the second weight respectively, S is the status level of the current operating state, I is the dynamic importance score, and C is the target adjustment factor.

[0088] It can be understood that the first weight and the second weight are fixed values determined in advance through experiments.

[0089] In some embodiments, in order to improve the accuracy of the target adjustment factor, weather data of the vehicle's location is obtained; the vehicle usage scenario is determined based on the vehicle's current operating status and weather data; and the target adjustment factor is determined based on the vehicle usage scenario.

[0090] Vehicle usage scenarios can be categorized as charging, regular driving, extreme weather, and emergency. Regular driving refers to non-extreme weather conditions where the vehicle is in motion. Extreme weather includes fog and haze, rain and snow, high temperatures (>35°C) or low temperatures (<0°C), and sudden drops or rises in temperature. Emergency scenarios are when the vehicle is in an emergency.

[0091] An adjustment factor is configured for each vehicle usage scenario. Based on the vehicle's current operating status and weather data, the vehicle's current vehicle usage scenario is matched from multiple vehicle usage scenarios.

[0092] The more dynamic the demand for collected data, the larger the configured adjustment factor. The adjustment factors configured for charging scenarios, regular driving scenarios, extreme weather scenarios, and emergency scenarios increase in order. For example, the adjustment factor of 0.8 for charging scenarios can be used to increase the sampling interval, thereby reducing the sampling frequency. The adjustment factor of 1.0 for regular driving scenarios is the default adjustment factor. The adjustment factor for extreme weather scenarios is selected within the range of 1.2-1.5. The sampling interval is shortened according to different extreme weather conditions to increase the sampling frequency and respond to environmental risks. The adjustment factor of 2.0 for emergency scenarios can be used to shorten the sampling interval to a greater extent, maximizing the sampling frequency to ensure real-time monitoring.

[0093] In some embodiments, it is possible to detect whether the vehicle usage scenario has switched by the following implementation: obtaining the vehicle speed signal in real time through the CAN bus, determining whether the vehicle speed signal is greater than the stationary threshold, and if so, and the weather data indicates that it is in normal weather, determining that the vehicle has entered a regular driving scenario. Monitor the voltage signal of the charging interface, and when the voltage jumps from 0V to the charging voltage (such as 400V), determine that the vehicle has entered the charging scenario, otherwise exit the charging scenario. Detect the ambient temperature of the vehicle's location through temperature and humidity sensors. If the ambient temperature rises / falls sharply by more than a preset change amount (such as 10°C) within a preset time period (such as 5 seconds), determine that the vehicle has entered an extreme weather scenario. Obtain the fault code in real time through the OBD-II diagnostic interface. If a fault code or collision warning is obtained, enter the emergency scenario.

[0094] In step S106 : the sampling interval for collecting vehicle data is adjusted according to the current reference level, and the vehicle data of the vehicle is collected according to the adjusted target sampling interval.

[0095] It can be understood that the larger the current reference level is, the smaller the adjusted target sampling interval is, so that the sampling frequency is higher.

[0096] In some embodiments, adjusting the sampling interval for collecting vehicle data according to the current reference level may include: determining a target sampling interval applicable to the current reference level based on a one-to-one correspondence between the reference level and the sampling interval, wherein the higher the reference level, the smaller the configured sampling interval.

[0097] In other embodiments, adjusting the sampling interval for collecting vehicle data based on the current reference level may include: determining a target level interval for the current reference level from a plurality of preset level intervals; and determining a target sampling interval applicable to the current reference level based on a one-to-one correspondence between level intervals and sampling intervals. The sampling interval is configured to be smaller for preset level intervals with higher reference levels.

[0098] In yet other embodiments, adjusting the sampling interval for collecting vehicle data based on the current reference level may include: determining a target level interval within which the current reference level falls from a plurality of preset level intervals; determining a target adjustment strategy configured for the target level interval from a plurality of preset adjustment strategies; and adjusting the baseline sampling interval based on the target adjustment strategy to obtain a target sampling interval. The baseline sampling interval is a pre-set fixed value, thereby enabling more refined adjustment of the sampling interval for collecting vehicle data.

[0099] In some embodiments, the above-mentioned multiple preset level intervals may include a first level interval, a second level interval, and a third level interval with increasing levels. Adjusting the reference sampling interval according to the target adjustment strategy to obtain the target sampling interval includes: if the target level interval is the first level interval, adjusting the sampling interval for the vehicle to collect vehicle data to P times the reference sampling interval, where P is a positive number greater than 1; if the target level interval is the second level interval, linearly adjusting the reference sampling interval according to the current reference level to obtain the target sampling interval; if the target level interval is the third level interval, exponentially adjusting the reference sampling interval according to the current reference level to obtain the target sampling interval.

[0100] In some embodiments, the first level interval is f(S, I) ≤ 1, and the adjustment strategy corresponding to the first level interval is to adjust the sampling interval for collecting vehicle data to 2 times the reference sampling interval to obtain the target sampling interval, realizing a conservative extension of the sampling interval:

[0101] T = T base ×2;

[0102] where T is the target sampling interval, and T base is the reference sampling interval.

[0103] The second level interval is 1 < f(S, I) ≤ 3, and the adjustment strategy corresponding to the second level interval is: taking the ratio value of the reference sampling interval to the current reference level as the target sampling interval, realizing a linear adjustment of the sampling interval within the second level interval, and the greater the current reference level, the smaller the target sampling interval, realizing a follow-up increase in the reference level and a linear shortening of the sampling interval:

[0104]

[0105] where T is the target sampling interval, and T base is the reference sampling interval, and f(S, I) is the current reference level;

[0106] The third level interval is f(S, I) ≥ 3, and the adjustment strategy corresponding to the third level interval is:

[0107]

[0108] where T is the target sampling interval, and T base is the reference sampling interval, f(S, I) is the current reference level, and k is a preset coefficient taking values in the range of 0 - 1, realizing an exponential adjustment of the sampling interval within the third level interval, and the greater the current reference level, the smaller the target sampling interval, realizing a follow-up increase in the reference level and a radical shortening of the sampling interval. Based on hardware limitations, the adjusted target sampling interval needs to satisfy being greater than or equal to 1 second.

[0109] In some embodiments, when the current reference level is in the third level interval (sampling interval ≤ 5 seconds), the collected vehicle data is analyzed in the vehicle-mounted electronic control unit (ECU) to extract abnormal data fragments, and the abnormal data fragments are uploaded to the cloud for the cloud to judge the health status of the vehicle. In the case of high-frequency collection of vehicle data, the transmission cost is reduced, and non-abnormal data is compressed, archived or cleared after being retained in the local storage of the vehicle for a preset period of time (for example: 30mi). Among them, when the data is detected to exceed the preset threshold, it is marked as an abnormal data fragment. When the current reference level is in the first level interval and when the current reference level is in the second level interval, since the frequency of collecting vehicle data is low, all collected vehicle data can be uploaded to the cloud for the cloud to judge the health status of the vehicle.

[0110] Based on the same inventive concept, an embodiment of the present invention provides a vehicle data acquisition device. Figure 2 FIG. 1 shows a schematic diagram of the structure of a vehicle data acquisition device in some embodiments of the present invention. Figure 2 As shown, the vehicle data collection device includes: a data acquisition unit 201, used to acquire first state data and second state data of the vehicle, the first state data is vehicle data related to the current operating state of the vehicle, and the second state data is vehicle data of the vehicle collected during a historical period; a state determination unit 202, used to determine the current operating state of the vehicle based on the first state data; a parameter determination unit 203, used to determine an M-dimensional parameter value based on the second state data and the current operating state, the M-dimensional parameter value being data affecting the health status of the vehicle in the current operating state, where M is a positive integer; a score determination unit 204, used to determine a dynamic importance score based on the M-dimensional parameter value, the dynamic importance score representing the degree of influence of the sampling interval for collecting vehicle data on the health status of the vehicle in the current operating state; a level determination unit 205, used to determine a current reference level based on the state level of the current operating state and the dynamic importance score; and an interval adjustment unit 206, used to adjust the sampling interval for collecting vehicle data based on the current reference level, and collect vehicle data of the vehicle based on the adjusted target sampling interval.

[0111] In some embodiments, the first state data includes one or more of the vehicle speed parameters, battery operating parameters, engine operating parameters, battery temperature and vehicle fault information of the vehicle sampled most recently; the vehicle's operating state is pre-divided into a stationary state, an emergency state, and N speed states corresponding to N speed intervals, where N is a positive integer; the state determination unit 202 is used to: determine whether the vehicle is in one of the N speed states based on the speed parameters; determine whether the vehicle is in the stationary state based on at least one of the speed parameters, the battery operating parameters and the engine operating parameters; and determine whether the vehicle is in the emergency state based on the battery temperature and the vehicle fault information.

[0112] In some embodiments, the parameter determination unit 203 is used to: determine the data change rate of at least one-dimensional parameter that affects the health status of the vehicle in the current operating state based on the second state data; determine the ambient temperature of the location of the vehicle based on the second state data; and determine the number of abnormal sampling times within the historical period based on the second state data.

[0113] In some embodiments, the score determination unit 204 includes: a scoring sub-unit, used to score each dimension parameter value in the M-dimensional parameter values according to the scoring rules configured for the data dimension of the dimension parameter value, and obtain a target score value corresponding to the dimension parameter value; a score addition sub-unit, used to determine the dynamic importance score based on the M target score values corresponding to the M-dimensional parameter value.

[0114] In some embodiments, the scoring rules for each data dimension include the correspondence between multiple preset parameter intervals and multiple scoring values. The scoring sub-unit is used to: for each dimension parameter value in the M-dimensional parameter values, determine the target parameter interval in which the dimension parameter value is located from each of the preset parameter intervals in the scoring rules configured for the data dimension of the dimension parameter value, and determine the target scoring value corresponding to the dimension parameter value based on the target parameter interval.

[0115] In some embodiments, the level determination unit 205 includes: a level subunit, used to determine the status level of the current operating state based on the status levels preset for various operating states of the vehicle; a weight subunit, used to determine the first weighted value of the status level of the current operating state and the first weight, and to determine the second weighted value of the dynamic importance score and the second weight; and a summation subunit, used to determine the sum of the first weighted value, the second weighted value and the target adjustment factor as the current reference level.

[0116] In some embodiments, a factor determination unit is further included, which is used to: obtain weather data at the location of the vehicle; determine a vehicle usage scenario based on the current operating status of the vehicle and the weather data; and determine the target adjustment factor based on the vehicle usage scenario.

[0117] In some embodiments, the interval adjustment unit 206 includes: an interval subunit, used to determine the target level interval in which the current reference level is located from multiple preset level intervals; a strategy subunit, used to determine the target adjustment strategy configured for the target level interval from multiple preset adjustment strategies; and an adjustment subunit, used to adjust the baseline sampling interval according to the target adjustment strategy to obtain the target sampling interval.

[0118] In some embodiments, the multiple preset level intervals include a first level interval, a second level interval, and a third level interval with increasing levels in sequence; an adjustment subunit is used to: if the target level interval is the first level interval, adjust the sampling interval for collecting vehicle data from the vehicle to P times the baseline sampling interval, where P is a positive number greater than 1; if the target level interval is the second level interval, linearly adjust the baseline sampling interval according to the current reference level to obtain the target sampling interval; if the target level interval is the third level interval, exponentially adjust the baseline sampling interval according to the current reference level to obtain the target sampling interval.

[0119] Based on the same inventive concept, an embodiment of the present invention provides an electronic device. Figure 3 Schematic diagram of the structure of electronic equipment in some embodiments of the present invention is shown. Figure 3 As shown, the electronic device provided by the embodiment of the present invention includes: a processor 302 and a memory 304, the memory 304 is coupled to the processor 302, and a computer program stored in the memory 304 and executable on the processor 302. When the processor 302 executes the program, the vehicle data collection method described in the above embodiment is implemented. For the sake of brevity of the specification, it will not be repeated here.

[0120] Among them, Figure 3In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown. Bus 300 may include any number of interconnected buses and bridges, and bus 300 links together various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same component, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0121] Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned vehicle data collection method when executed by a processor.

[0122] According to one or more embodiments of the present invention, the sampling interval for collecting vehicle data is dynamically adjusted according to the current operating state and dynamic importance of the vehicle, so that the collection frequency is reduced when the vehicle is in a stationary state (such as in a charging scenario and a parking scenario), avoiding the generation of a large amount of redundant data, thereby reducing data storage and transmission costs; and when the vehicle is in a driving state and an emergency state, the collection frequency is increased to ensure effective monitoring of the vehicle's health status. At the same time, since the sampling interval for collecting vehicle data is dynamically adjusted according to the dynamic importance score, it is possible to reduce the collection frequency for data with lower importance to save storage resources; for data with higher importance, the collection frequency is increased to meet monitoring requirements, ensuring effective monitoring of the vehicle's health status and saving storage resources. Moreover, the sampling interval for collecting vehicle data is determined based on a comprehensive consideration of the vehicle's operating state and the importance of the data, which is scientific and flexible.

[0123] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0124] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A vehicle data collection method, characterized in that: include: Acquire first state data and second state data of a vehicle, wherein the first state data is vehicle data related to a current operating state of the vehicle, and the second state data is vehicle data of the vehicle collected during a historical period; determining a current operating state of the vehicle according to the first state data; Determine an M-dimensional parameter value according to the second state data and the current operating state, wherein the M-dimensional parameter value is data that affects the health status of the vehicle in the current operating state, and M is a positive integer; Determining a dynamic importance score based on the M-dimensional parameter value, the dynamic importance score representing the degree of influence of a sampling interval for collecting vehicle data on the health status of the vehicle in a current operating state; Determining a current reference level according to the status level of the current operating state and the dynamic importance score; The sampling interval for collecting vehicle data is adjusted according to the current reference level, and the vehicle data of the vehicle is collected according to the adjusted target sampling interval.

2. The vehicle data collection method according to claim 1, wherein: The first state data includes one or more of the most recently sampled vehicle speed parameters, battery operating parameters, engine operating parameters, battery temperature, and vehicle fault information; the vehicle operating state is pre-divided into a stationary state, an emergency state, and N vehicle speed states corresponding to N speed intervals, where N is a positive integer; Determining the current operating state of the vehicle according to the first state data includes: determining, based on the vehicle speed parameter, whether the vehicle is in one of the N vehicle speed states; determining whether the vehicle is in the stationary state based on at least one of the vehicle speed parameter, the battery operating parameter, and the engine operating parameter; and It is determined whether the vehicle is in the emergency state according to the battery temperature and the vehicle fault information.

3. The vehicle data collection method according to claim 2, wherein: The determining the M-dimensional parameter value according to the second state data and the current operating state includes: determining, based on the second state data, a data change rate of at least one dimension of a parameter affecting a health condition of the vehicle in a current operating state; determining an ambient temperature at a location of the vehicle based on the second state data; The number of abnormal sampling times in the historical period is determined according to the second status data.

4. The vehicle data collection method according to claim 3, wherein: Scoring rules are pre-configured for different data dimensions. The dynamic importance score is determined based on the M-dimensional parameter value, including: For each dimension parameter value in the M-dimensional parameter values, score the dimension parameter value according to the scoring rule configured for the data dimension of the dimension parameter value to obtain a target score value corresponding to the dimension parameter value; The dynamic importance score is determined according to the M target rating values corresponding to the M-dimensional parameter values.

5. The vehicle data collection method according to claim 4, characterized in that: The scoring rule for each data dimension includes a correspondence between multiple preset parameter intervals and multiple scoring values. For each dimension parameter value in the M-dimensional parameter values, the dimension parameter value is scored according to the scoring rule configured for the data dimension of the dimension parameter value to obtain the target scoring value corresponding to the dimension parameter value, including: For each dimension parameter value in the M-dimensional parameter values, determine the target parameter interval in which the dimension parameter value is located from each of the preset parameter intervals in the scoring rule configured for the data dimension of the dimension parameter value, and determine the target scoring value corresponding to the dimension parameter value based on the target parameter interval.

6. The vehicle data collection method according to any one of claims 1 to 5, characterized in that: The determining of the current reference level according to the status level of the current operating state and the dynamic importance score includes: determining a status level of the current operating state according to status levels preset for various operating states of the vehicle; Determining a first weighted value of a state level of the current operating state and a first weight, and determining a second weighted value of the dynamic importance score and a second weight; The sum of the first weighted value, the second weighted value, and a target adjustment factor is determined as the current reference level.

7. The vehicle data collection method according to claim 6, wherein: Before determining the sum of the first weighted value, the second weighted value, and the target adjustment factor as the reference level, the method further includes: Obtaining weather data for the location of the vehicle; determining a vehicle usage scenario based on the current operating state of the vehicle and the weather data; The target adjustment factor is determined according to the vehicle usage scenario.

8. The vehicle data collection method according to any one of claims 1 to 5, characterized in that: The adjusting the sampling interval for collecting vehicle data according to the current reference level includes: Determining a target level interval where the current reference level is located from a plurality of preset level intervals; Determining a target adjustment strategy configured for the target level interval from a plurality of preset adjustment strategies; The reference sampling interval is adjusted according to the target adjustment strategy to obtain the target sampling interval.

9. The vehicle data collection method according to claim 8, wherein: The plurality of preset level intervals include a first level interval, a second level interval, and a third level interval with increasing levels in sequence; The adjusting the reference sampling interval according to the target adjustment strategy to obtain the target sampling interval includes: If the target level interval is the first level interval, adjusting the sampling interval for collecting vehicle data from the vehicle to P times the reference sampling interval, where P is a positive number greater than 1; If the target level interval is the second level interval, linearly adjust the reference sampling interval according to the current reference level to obtain the target sampling interval; If the target level interval is the third level interval, the benchmark sampling interval is exponentially adjusted according to the current reference level to obtain the target sampling interval.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory is coupled to the processor and stores instructions. When the instructions are executed by the processor, the vehicle data collection method according to any one of claims 1 to 9 is implemented.

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