CN116521374A - Data processing method, device, vehicle and computer program product - Google Patents
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Abstract
The present disclosure provides a data processing method, apparatus, vehicle and computer program product, the method is that, in the case of detecting a change in a vehicle behavior, a duration of the changed vehicle behavior is determined, and current calculation force load information of the vehicle is determined, wherein the current calculation force load information characterizes calculation force information required by the vehicle behavior; determining the priority execution rate of each subtask in the data mining task; and scheduling and executing each subtask according to the duration time, the current power load information and the priority execution rate of each subtask, so that the time planning of the data mining task is dynamically analyzed and the power resource utilization rate and the data mining efficiency are improved.
Description
Technical Field
The present disclosure relates to the field of vehicle technology, and in particular, to a data processing method, apparatus, vehicle, and computer program product.
Background
At present, the vehicle end data mining means that the data mining task can be executed at the vehicle end, and then the required data after the mining processing can be obtained, but the data mining task is executed at the vehicle end, so that certain calculation power resources, storage resources and the like are occupied, normal driving behavior of a vehicle can be possibly influenced, if the data mining task is executed again in a vehicle stopping state, the vehicle stopping state is random, and idle resources in the vehicle driving state are wasted, so that time planning on the data mining task is more reasonable is very necessary.
Disclosure of Invention
Embodiments of the present disclosure provide at least a data processing method, apparatus, vehicle, and computer program product.
In a first aspect, an embodiment of the present disclosure provides a data processing method, including:
under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, and determining the current calculation force load information of the vehicle, wherein the current calculation force load information represents calculation force information required by the vehicle behavior;
determining the priority execution rate of each subtask in the data mining task;
and scheduling and executing each subtask according to the duration time, the current power load information and the priority execution rate of each subtask.
In the embodiment of the disclosure, under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, determining the current calculation load information of the vehicle, and determining the priority execution rate of each subtask in the data mining task; and then, scheduling and executing each subtask according to the duration time, the current calculation load information and the priority execution rate of each subtask, so that the time plan of the data mining task is dynamically adjusted based on the current calculation load information and the duration time, the influence of the data mining task on the driving behavior of the vehicle can be reduced, the driving safety and reliability of the vehicle are ensured, the utilization rate of vehicle resources can be improved, and the execution time is allocated to the data mining task more reasonably and efficiently.
In an alternative embodiment, the determining the duration of the changed vehicle behavior in the case that the change of the vehicle behavior is detected includes:
determining the changed vehicle behavior under the condition that the change of the vehicle behavior is detected;
and acquiring the vehicle state information and the current road condition information of the vehicle, and determining the duration of the changed vehicle behavior according to the vehicle state information and the current road condition information.
In the embodiment of the disclosure, the vehicle behavior change can be detected in real time, the duration time is determined, and the calculation power use condition of the vehicle can be further predicted, so that more refined dynamic time planning is performed on the data mining task.
In an alternative embodiment, the method further comprises:
dividing the data mining task into sub-tasks according to data acquisition objects and/or data functions;
each of the subtasks is classified into a plurality of critical subtasks and a plurality of non-critical subtasks according to the criticality.
In the embodiment of the disclosure, the data mining tasks are classified in a finer manner, so that the precision of time planning can be improved, and the efficiency can be improved.
In an alternative embodiment, the determining the priority execution rate of each subtask in the data mining task includes:
Determining time demand information and calculation force demand information required by execution of each subtask according to the data scale and the complexity corresponding to each subtask in the data mining task;
determining the priority of each subtask according to the criticality;
and respectively determining the priority execution rate of each subtask according to the time demand information, the power demand information and the priority corresponding to each subtask.
In the embodiment of the disclosure, the priority, time requirement information and calculation force requirement information of each subtask are integrated, the priority execution rate of each subtask is determined, the execution sequence of each subtask can be evaluated, and the priority execution sequences of the critical subtask and the non-critical subtask can be adjusted.
In an optional implementation manner, the determining the priority execution rate of each subtask according to the time requirement information, the power requirement information and the priority corresponding to each subtask includes:
determining a first product of a first weight value and an exponential function for any one of the subtasks, wherein the exponent of the exponential function is the priority, the base number of the exponential function is a first difference value, and the first difference value is a difference value between the first ratio of the time demand information to the force demand information and the second weight value;
Determining a second product of the time demand information and the priority, then determining a second ratio between the time demand information and the force demand information, and determining a third product of the second ratio and a third weight value;
and determining the priority execution rate of the subtasks according to the sum between the first product and the third product.
In the embodiment of the disclosure, a specific calculation mode of the priority execution rate is provided, the importance degrees of the critical subtasks and the non-critical subtasks can be comprehensively adjusted, the importance degrees of the priority, the time demand information and the calculation force demand information can be adjusted, and the accuracy and the reliability of the priority execution rate are improved.
In an alternative embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the preferential execution rate of each subtask includes:
when the duration is not smaller than a first time length threshold value and the current computational power load information is smaller than a first computational power size, screening key subtasks with the computational power demand information smaller than a second computational power size and/or non-key subtasks with the computational power demand information larger than or equal to the second computational power size from the subtasks;
And determining a first target subtask from the screened critical subtasks and/or non-critical subtasks according to the corresponding priority execution rate, and scheduling and executing the first target subtask.
In the embodiment of the disclosure, when the duration is not less than the first time threshold and the current calculation load information is less than the first calculation load, the key subtask with small data size or the non-key subtask with large data size is executed, so that the method and the device more conform to the current calculation service condition scene, not only can ensure the safety of the driving behavior of the vehicle, but also can fully utilize the current residual available calculation resources.
In an alternative embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the preferential execution rate of each subtask includes:
and under the condition that the duration time is not smaller than a first time length threshold value and the current computing power load information is larger than or equal to a first computing power size, not scheduling to execute each subtask.
In the embodiment of the disclosure, when the duration is not less than the first duration threshold and the current calculation load information is greater than or equal to the first calculation load, the current long-time high calculation load condition is described, and the data mining task is not executed at this time, so that the influence on the driving behavior of the vehicle is avoided and the safety is improved.
In an alternative embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the preferential execution rate of each subtask includes:
screening key subtasks with the calculation force demand information being more than or equal to the second calculation force from all the subtasks when the changed vehicle behaviors are no-driving behaviors;
and determining a second target subtask from the screened critical subtasks according to the corresponding priority execution rate, and scheduling and executing the second target subtask.
In the embodiment of the disclosure, when no driving behavior exists at present, the key subtasks with large data volume can be executed, the computing power resource is fully utilized, and the execution effect of the key subtasks is ensured.
In an alternative embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the preferential execution rate of each subtask includes:
when the current calculation force load information is determined to be smaller than a first calculation force size and the duration is smaller than a first time length threshold value, or the current calculation force load information is larger than or equal to the first calculation force size and the duration is smaller than the first time length threshold value, non-critical subtasks, of which calculation force demand information is smaller than a second calculation force size, are screened out from the subtasks;
And determining a third target subtask from the screened non-critical subtasks according to the corresponding priority execution rate, and scheduling and executing the third target subtask.
In the embodiment of the disclosure, under the condition of high-computation-force load or low-computation-force load, but with relatively short duration, the high-computation-force load and the low-computation-force load are temporally alternated, and at the moment, non-critical subtasks with small data size can be executed, so that the utilization rate of computation-force resources is improved.
In a second aspect, embodiments of the present disclosure further provide a data processing apparatus, including: a processor, a memory and a computer program; wherein the computer program is stored on the memory, which when executed by the processor, causes the apparatus to:
under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, and determining the current calculation force load information of the vehicle, wherein the current calculation force load information represents calculation force information required by the vehicle behavior;
determining the priority execution rate of each subtask in the data mining task;
and scheduling and executing each subtask according to the duration time, the current power load information and the priority execution rate of each subtask.
In a third aspect, embodiments of the present disclosure further provide a vehicle including the data processing apparatus in the second aspect described above.
In a fourth aspect, an alternative implementation of the present disclosure also provides a computer program product having a computer program stored thereon, which when executed by a processor, implements the steps of the first aspect, or any of the possible implementation manners of the first aspect.
The description of the effects of the data processing apparatus, the vehicle, and the computer program product is referred to the description of the vehicle control method, and is not repeated here.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the aspects of the disclosure.
The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for the embodiments are briefly described below, which are incorporated in and constitute a part of the specification, these drawings showing embodiments consistent with the present disclosure and together with the description serve to illustrate the technical solutions of the present disclosure. It is to be understood that the following drawings illustrate only certain embodiments of the present disclosure and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may admit to other equally relevant drawings without inventive effort.
FIG. 1 illustrates a flow chart of a data processing method provided by an embodiment of the present disclosure;
FIG. 2 shows a schematic diagram of a data processing apparatus provided by an embodiment of the present disclosure;
fig. 3 shows a schematic view of a vehicle provided by an embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments. The components of the disclosed embodiments generally described and illustrated herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure is not intended to limit the scope of the disclosure, as claimed, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be made by those skilled in the art based on the embodiments of this disclosure without making any inventive effort, are intended to be within the scope of this disclosure.
For the convenience of understanding the technical solutions of the present disclosure, technical terms in the embodiments of the present disclosure will be described first:
And (3) vehicle-end data mining: the data mining algorithm model represents a data mining algorithm model which can be specifically operated on the hardware of the vehicle end when the data mining task is executed on the vehicle end.
And (3) data mining: indicating which data is not well processed by the current algorithm model, such as by data mining, to obtain the required or useful data, affects the vehicle's automatic decision-making and driving experience.
Time planning: in the embodiment of the disclosure, the time planning is performed on the data mining task to plan the reasonable operation time of the data mining task.
According to research, it is found that the vehicle-end data mining needs to occupy a certain amount of calculation resources, storage resources and the like, and the normal driving behavior of the vehicle can be influenced, and if the data mining task is executed again in the vehicle stopping state, the vehicle stopping state is random, the complete calculation time cannot be ensured, and the idle resources in the vehicle driving state are wasted, so that it is very necessary to schedule the data mining task more reasonably.
Based on the above study, the disclosure provides a data processing method, under the condition that the change of the vehicle behavior is detected, the duration of the changed vehicle behavior is determined, the current calculation load information of the vehicle is determined, the priority execution rate of each subtask in the data mining task is determined, and then the scheduling execution is carried out on each subtask according to the duration and the current calculation load information and the priority execution rate of each subtask, so that the time plan of the data mining task is dynamically adjusted based on the current calculation load information, the influence of the execution of the data mining task on the driving behavior of the vehicle is reduced, the driving safety and the driving reliability of the vehicle are ensured, the utilization rate of the vehicle resources is also improved, and the time plan of the data mining task is more reasonable and efficient.
The present invention is directed to a method for manufacturing a semiconductor device, and a semiconductor device manufactured by the method.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
For ease of understanding, the data processing method in the embodiments of the present disclosure will be described first.
Referring to fig. 1, a flowchart of a data processing method according to an embodiment of the disclosure is shown, where the method includes:
s101: and under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, and determining the current calculation force load information of the vehicle, wherein the current calculation force load information represents calculation force information required by the vehicle behavior.
In the embodiment of the disclosure, the data mining is mainly aimed at the vehicle end, and the vehicle is not limited, for example, the vehicle can be an electric vehicle, a fuel oil vehicle and the like, and the computing power resource of the vehicle can be fully utilized to execute the data mining task in the driving process or the idle state of the vehicle.
Wherein the vehicle behavior includes, but is not limited to, any of the following: ordinary straight driving behavior, critical driving behavior, idle behavior (or no driving behavior), specifically, critical driving behavior is, for example, lane change from side to side, acceleration behavior, deceleration behavior, turning behavior, etc., and idle behavior is, for example, charging behavior, maintenance state, neutral state, parked stationary state, etc.
The current calculation force load information characterizes calculation force information required by the vehicle behavior, for example, the changed vehicle behavior is detected to be a turning behavior, and the duration of the turning behavior can be predicted when the vehicle is required to continuously turn on a curve, and calculation force information consumed by the turning behavior can be determined.
In particular performing this step S101, the present disclosure provides possible embodiments:
1) In the event that a change in vehicle behavior is detected, determining a duration of the changed vehicle behavior includes: under the condition that the change of the vehicle behavior is detected, determining the changed vehicle behavior; and acquiring vehicle state information and current road condition information of the vehicle, and determining the duration of the changed vehicle behavior according to the vehicle state information and the current road condition information.
The vehicle state information includes, for example, speed, acceleration or deceleration, position, navigation information, and the like, and the current road condition information includes, for example, environmental information, road information, a vehicle congestion condition, and the like, which are not limited in the embodiment of the present disclosure.
2) Current power load information of the vehicle is determined. For example, one possible embodiment is that after the behavior of the vehicle changes, the usage of the processor of the vehicle, the usage of the resources of the graphics processor (Graphics Processing Unit, GPU) and the like can be obtained, i.e. the current calculation load information is obtained.
In the embodiment of the disclosure, the current calculation load information and the duration can be detected in real time, so that the calculation load condition of the vehicle end in the next time period, the time information of the next time period and the like can be predicted, and the time period can be divided dynamically in real time.
S102: and determining the priority execution rate of each subtask in the data mining task.
In the embodiment of the disclosure, in order to further improve the computing power utilization rate, subtasks of the data mining task may be further divided, which specifically provides a possible implementation manner, and the data mining task is divided into subtasks according to the data acquisition object and/or the data function; each subtask is classified into a plurality of critical subtasks and a plurality of non-critical subtasks according to the criticality.
For example, subtasks related to the vehicle state, such as power consumption, remaining mileage, tire pressure monitoring, etc., are generally relatively simple and have small data volume, so that subtasks such as camera image processing, radar information processing, etc., which are relatively complex and have large data volume, are executed, so that the calculation force demand information is large, and further, by combining with the criticality, each subtask can be classified more finely, and each subtask is determined to be a critical task or a non-critical task with large calculation force demand information, and a critical task or a non-critical task with small calculation force demand information.
Therefore, the data mining tasks can be finely divided and classified according to the key degree, and then each subtask is scheduled, so that the efficiency and the data mining effect can be improved.
The present disclosure specifically provides possible embodiments when performing S102 described above:
1) And determining time demand information and calculation force demand information required by execution of each subtask according to the data scale and the complexity corresponding to each subtask in the data mining task.
In the embodiment of the present disclosure, the data size may represent the original data size for completing a certain data mining subtask, and it is known that the larger the data size is, the higher the complexity is, the higher the time requirement information and the power requirement information are required, and vice versa, the lower the time requirement information and the power requirement information are required.
2) And determining the priority of each subtask according to the criticality.
Specifically, the degree of criticality is positively correlated with the priority, the higher the degree of criticality, the higher the priority, the lower the degree of criticality, and the lower the priority, for example, in order to facilitate the calculation of the subsequent priority execution rate, the magnitude of the numerical value corresponding to different priorities may be defined, and the higher the priority, the larger the numerical value corresponding to the higher priority.
3) And respectively determining the priority execution rate of each subtask according to the time demand information, the calculation force demand information and the priority corresponding to each subtask.
In this way, in the embodiment of the disclosure, the power demand information, the time demand information and the priority of the subtasks are integrated, and the priority execution rate is determined, so that the execution sequence of each subtask can be determined, and more reasonable subtask scheduling can be performed.
S103: and scheduling and executing each subtask according to the duration time, the current calculation load information and the priority execution rate of each subtask.
According to the embodiment of the disclosure, according to the duration of the changed vehicle behavior and the current calculation load information, the calculation power use condition of the vehicle caused by the changed vehicle behavior can be predicted, for example, the conditions of long-time high calculation power load, long-time low calculation power load, frequent alternation of high calculation power load and low calculation power load, no driving behavior, namely, the current almost no calculation power load and the like are adopted, and then different types of subtasks are scheduled to be executed according to the predicted conditions.
The basic principle is that the current calculation load information comprises calculation information required by the driving behavior of the vehicle, the priority of tasks required to be executed by the driving behavior is higher than that of data mining tasks, wherein the priority of tasks of the artificial driving behavior is higher than that of automatic driving behavior, so in the embodiment of the disclosure, the sub-tasks of different data mining are scheduled to be executed under the condition of the current calculation load information, and the duration of the current calculation load information is considered, so that more reasonable execution time can be allocated to different sub-tasks, and the execution efficiency is improved.
For example, when each subtask for performing data mining is scheduled, a shadow pattern algorithm may be used to classify the collected data, determine which data to collect, and may not collect data unrelated to the subtask, avoid a large amount of invalid information, and further collect key data or related data, so as to perform data mining calculation.
In addition, it can be understood that, in the embodiment of the present disclosure, data mining is mainly aimed at a vehicle end, but based on consideration of hardware of the vehicle end, a vehicle and a cloud server may be adopted to jointly execute a data mining task, for example, the vehicle end executes data collection in the data mining task, and simple data mining calculation, for example, analysis and screening are performed on data feature similarity, and then the data is uploaded to the cloud server for deep mining calculation of data.
In the embodiment of the disclosure, under the condition that the change of the vehicle behavior is detected, the duration of the changed vehicle behavior is determined, the current calculation load information of the vehicle is determined, the priority execution rate of each subtask in the data mining task is determined, and then the scheduling execution is carried out on each subtask according to the duration and the current calculation load information and the priority execution rate of each subtask, so that the scheduling execution is carried out on each subtask by considering the current calculation load information and the duration, the influence of the data mining task on the vehicle behavior is avoided, the driving safety is ensured, the real-time dynamic analysis and planning are carried out, the calculation resource of the vehicle can be fully utilized, the efficiency is improved, the method is more flexible, the application scene is wider, the real-time scheduling execution can also reduce the intermediate data volume, the requirement of the data mining task on storage equipment is reduced, and the occupation of the storage cost and the read-write calculation on the calculation of the vehicle calculation force is reduced.
Based on the foregoing embodiments, in the embodiments of the present disclosure, the current calculation load information and the duration may be detected in real time, and then, in combination with the priority execution rate of each subtask in the data mining task, each subtask is dynamically scheduled to be executed, where the present disclosure provides a possible implementation manner of determining the priority execution rate, specifically, determining the priority execution rate of each subtask according to the time requirement information, the calculation requirement information and the priority corresponding to each subtask, respectively, including:
1) For any one of the subtasks, determining a first product of a first weight value and an exponential function, wherein the exponent of the exponential function is a priority, the base of the exponential function is a first difference value, and the first difference value is a difference value between the first difference value and a second weight value after a first ratio of time demand information to calculation force demand information.
For example, taking any sub-bin as an example, the first weight value is K K The second weight value is K 2 The priority is denoted as p, the time demand information is denoted as t, the power demand information is denoted as Af, and the first ratio of the time demand information and the power demand information may be denoted as \cfrac { t } { Af-wherein \cfrac is a function of the partial formula, the first difference is \cfrac { t } { Af } -K 2 The first product may be: k (K) 1 ·(\cfrac{t}{Af}-K 2 ) p 。
2) And determining a second product of the time demand information and the priority, calculating a second ratio between the time demand information and the force demand information, and determining a third product of the second ratio and a third weight value.
For example, the second product is tp, the second ratio is \cfrac { tp } { Af }, and the third weight value is K 3 The third product is: k (K) 3 ·\cfrac{tp}{Af}。
3) And determining the priority execution rate of the subtasks according to the sum between the first product and the third product.
For example, if the priority execution rate is res, the priority execution rate is: res=k 1 ·(\cfrac{t}{Af}-K 2 ) p +K 3 ·\cfrac{tp}{Af}。
Based on the calculation formula of the priority execution rate res, when the time demand information t and the calculation force demand information Af are fixed, the priority execution rate will decrease and then increase with the size of the priority p, and has a minimum value, but since the minimum value point is negative, the priority execution rate increases with the increase of the size of the priority p; when the priority p and the calculation force demand information Af are fixed, the priority execution rate will increase with the increase in the length of the time demand information t; when the priority p and the time demand information t are fixed, the priority execution rate will decrease as the size of the computing force demand information Af increases.
It should be noted that in the embodiment of the present disclosure, the first weight value, the second weight value, and the third weight value may be set according to practical experience and requirements, which is not limited, for example, in one possible embodiment, K is set 1 ＝5，K 2 ＝2，K 3 Preferably, the first weight value, the second weight value and the third weight value are positive values, the values of the first weight value and the third weight value are the same, and the second weight value can be taken arbitrarily, so that the critical degree of the subtask is ensured to be the same as the importance degree of three variables of the priority, the time requirement information and the calculation force requirement information required by calculation, and the final calculated priority is ensured The accuracy of the execution rate specifically describes the first weight value, the second weight value, and the third weight value:
1) The first weight value may be used to adjust how important critical subtasks and non-critical tasks are in the final scheduling decision.
2) The second weight value may be used to adjust criteria that determine whether a subtask is a critical subtask or a non-critical subtask.
For example, if \cfrac { t } { Af } -K 2 >1, the higher the exponential function rises, the greater the influence of the priority on the priority execution rate will be, so can be when \cfrac { t } { Af } is great, the higher the priority execution rate will be, namely can make the higher-critical subtasks can be executed preferentially.
Also for example, if 0<\cfrac{t}{Af}-K 2 <1, the exponential function decreases, the priority will have less impact on the priority execution rate, so that non-critical tasks can be executed when \cfrac { t } { Af } is smaller.
3) The third weight value may be used to adjust the importance of the three variables of priority, power demand information and time demand information in the final scheduling decision.
Further, in the embodiments of the present disclosure, for the step S103, several possible embodiments are further provided, including:
in one possible embodiment, scheduling each subtask according to the duration and the current computational load information and the priority execution rate of each subtask includes: when the duration time is not smaller than a first time threshold value and the current calculation force load information is smaller than the first calculation force, screening key sub-tasks with calculation force demand information smaller than the second calculation force and/or non-key sub-tasks with calculation force demand information larger than or equal to the second calculation force from all the sub-tasks; and determining a first target subtask from the screened critical subtasks and/or non-critical subtasks according to the corresponding priority execution rate, and scheduling and executing the first target subtask.
The first time threshold, the first calculation force magnitude and the second calculation force magnitude may be set according to actual experience and conditions, and are not limited in the embodiments of the present disclosure.
In the embodiment of the disclosure, when the duration is not less than the first time threshold and the current calculation load information is less than the first calculation load, the changed vehicle behavior can be considered to be changed into a long-time low calculation load condition, for example, a scene of constant-speed cruising at a high speed, at this time, the remaining available calculation force of the vehicle is still more, and in order to ensure the time integrity and reliability of the execution of the subtasks, the critical subtasks with small data volume and/or the non-critical subtasks with large data volume can be executed.
In order to ensure the data mining effect, a sub-task with the highest priority execution rate can be selected from the screened sub-tasks as a first target sub-task to further execute the first target sub-task, and if the vehicle is still under the condition of low calculation load for a long time after the first target sub-task is executed, the screened sub-tasks with the priority execution rate being inferior to that of the first target sub-task can be continuously executed.
In another possible embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the priority execution rate of each subtask includes: and under the condition that the duration time is not less than the first time length threshold value and the current calculation force load information is more than or equal to the first calculation force magnitude, not scheduling to execute each subtask.
In the embodiment of the disclosure, when the duration is not less than the first time threshold and the current calculation load information is greater than or equal to the first calculation load, the changed vehicle behavior can be considered to be changed into a long-time high calculation load condition, for example, a vehicle distance maintenance scene, a complex road condition driving scene, and the like, in order not to affect the vehicle driving behavior, the vehicle safety is ensured, and no data mining task is executed in the condition.
In another possible embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the priority execution rate of each subtask includes: screening key subtasks with calculation force demand information greater than or equal to the second calculation force from all the subtasks when the changed vehicle behaviors are no-driving behaviors; and determining a second target subtask from the screened critical subtasks according to the corresponding priority execution rate, and scheduling and executing the second target subtask.
The driving-free behavior such as a parking state, a charging state, a maintenance state and the like is that the current calculation load information is basically zero, so that a key subtask with larger data quantity can be executed, and the data mining effect of the key subtask is ensured.
In another possible embodiment, the scheduling execution of each subtask according to the duration and the current computing power load information and the priority execution rate of each subtask includes: when the current calculation force load information is determined to be smaller than the first calculation force size and the duration is smaller than a first time length threshold value, or the current calculation force load information is greater than or equal to the first calculation force size and the duration is smaller than the first time length threshold value, non-critical sub-tasks with calculation force demand information smaller than the second calculation force size are screened out of the sub-tasks; and determining a third target subtask from the screened non-critical subtasks according to the corresponding priority execution rate, and scheduling and executing the third target subtask.
In other words, in the embodiment of the disclosure, the conditions of high-calculation-force load or low-calculation-force load are determined, but the duration is relatively short, for example, the conditions of frequent and short-term alternate occurrence of the high-calculation-force load and the low-calculation-force load, for example, a vehicle parallel scene, a vehicle turning scene, a vehicle braking scene, a traffic jam scene and the like, and at the moment, some non-critical subtasks with smaller data volume can be executed, so that the calculation-force resources of the vehicle are fully utilized.
In this way, in the embodiment of the disclosure, according to the current calculation load information and the duration, different calculation force use conditions of the vehicle are further refined, and the data mining is also divided into different types of key subtasks and non-key subtasks, so that the current calculation force use conditions of the vehicle are dynamically analyzed, different subtasks are scheduled and executed, the utilization rate of calculation force resources can be improved on the premise that the driving behavior of the vehicle is not affected, and the data mining efficiency is improved.
It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.
Based on the same inventive concept, the embodiments of the present disclosure further provide a data processing device corresponding to the data processing method, and since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to that of the embodiments of the present disclosure, the specific implementation of the device may refer to the embodiments of the methods, and the repetition is omitted.
Referring to fig. 2, a schematic diagram of a data processing apparatus according to an embodiment of the disclosure is provided, where the data processing apparatus includes: a processor 21, a memory 22 and a computer program; wherein the computer program is stored on the memory 22, which, when executed by the processor 21, causes the device to:
Under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, and determining the current calculation force load information of the vehicle, wherein the current calculation force load information represents calculation force information required by the vehicle behavior;
determining the priority execution rate of each subtask in the data mining task;
and scheduling and executing each subtask according to the duration time, the current power load information and the priority execution rate of each subtask.
In a possible embodiment, the data processing device is configured to: determining the changed vehicle behavior under the condition that the change of the vehicle behavior is detected; and acquiring the vehicle state information and the current road condition information of the vehicle, and determining the duration of the changed vehicle behavior according to the vehicle state information and the current road condition information.
In a possible embodiment, the data processing device is configured to:
determining time demand information and calculation force demand information required by execution of each subtask according to the data scale and the complexity corresponding to each subtask in the data mining task;
determining the priority of each subtask according to the criticality;
And respectively determining the priority execution rate of each subtask according to the time demand information, the power demand information and the priority corresponding to each subtask.
The processor 21 may be a vehicle controller of a vehicle, and may acquire information such as vehicle behavior through a sensor of the vehicle, further analyze the information, and dynamically schedule each subtask of data mining according to the current power usage situation.
The memory 22 includes a memory 221 and an external memory 222; the memory 221 is also referred to as an internal memory, and is used for temporarily storing operation data in the processor 21 and data exchanged with the external memory 222 such as a hard disk, and the processor 21 exchanges data with the external memory 222 via the memory 221.
The specific implementation process of the computer program may refer to the steps of the data processing method described in the embodiments of the present disclosure, which is not described herein.
Referring to fig. 3, a schematic diagram of a vehicle according to an embodiment of the disclosure is provided, where the vehicle includes the data processing device in the foregoing embodiment.
In the embodiment of the disclosure, the vehicle may be, for example, a fuel-oil vehicle, a new energy electric vehicle, an electric fuel-mixing vehicle, etc., and the vehicle type may be, for example, a car, a truck, an automobile, etc., without limitation, where the data processing device is disposed in the vehicle, and the vehicle further includes various sensors, where the sensors may be in communication connection with the data processing device, for example, the sensors may obtain vehicle state information, vehicle behavior, etc., and may obtain data collected by the sensors when performing a data mining task, etc.
In the embodiment of the disclosure, the current calculation load information and the duration are detected in real time, the priority execution rate of each subtask in the data mining task is determined, and then each subtask is scheduled and executed according to the duration and the current calculation load information and the priority execution rate of each subtask, so that the execution time can be more reasonably allocated to each subtask, the time planning is performed based on load balancing, the driving behavior of a vehicle can not be influenced, the calculation resource utilization rate can be improved, and the efficiency of the data mining task is improved.
The data processing method in the embodiment of the disclosure can be applied to more scenes of vehicles, is more flexible, for example, is used for automatically driving a scene which is rapidly and continuously developed by a small-sized vehicle with light hardware at the vehicle end, is used for data mining of network vehicles, shared automobiles and the like which are frequently used and have complex and changeable scenes, and is used for continuously performing data mining tasks.
The embodiments of the present disclosure further provide a computer program product, on which a computer program is stored, where the computer program when executed by a processor implements the steps of the data processing method described in the foregoing method embodiments, and specifically reference may be made to the foregoing method embodiments, which are not described herein in detail.
The methods in this application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described herein are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a network device, a user equipment, a core network device, an operation administration maintenance (Operation Administration and Maintenance, OAM) or other programmable device.
The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; but also optical media such as digital video discs; but also semiconductor media such as solid state disks. The computer readable storage medium may be volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage medium.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present disclosure, and are not intended to limit the scope of the disclosure, but the present disclosure is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, it is not limited to the disclosure: any person skilled in the art, within the technical scope of the disclosure of the present disclosure, may modify or easily conceive changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features thereof; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (11)
1. A method of data processing, comprising:
under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, and determining the current calculation force load information of the vehicle, wherein the current calculation force load information represents calculation force information required by the vehicle behavior;
determining the priority execution rate of each subtask in the data mining task;
and scheduling and executing each subtask according to the duration time, the current power load information and the priority execution rate of each subtask.
2. The method of claim 1, wherein the determining the duration of the changed vehicle behavior in the event that a change in vehicle behavior is detected comprises:
determining the changed vehicle behavior under the condition that the change of the vehicle behavior is detected;
and acquiring the vehicle state information and the current road condition information of the vehicle, and determining the duration of the changed vehicle behavior according to the vehicle state information and the current road condition information.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
Dividing the data mining task into sub-tasks according to data acquisition objects and/or data functions;
each of the subtasks is classified into a plurality of critical subtasks and a plurality of non-critical subtasks according to the criticality.
4. A method according to claim 3, wherein determining the priority execution rate of each sub-task in the data mining task comprises:
determining time demand information and calculation force demand information required by execution of each subtask according to the data scale and the complexity corresponding to each subtask in the data mining task;
determining the priority of each subtask according to the criticality;
and respectively determining the priority execution rate of each subtask according to the time demand information, the power demand information and the priority corresponding to each subtask.
5. The method according to claim 4, wherein determining the priority execution rate of each subtask according to the time demand information, the power demand information, and the priority level corresponding to each subtask, respectively, comprises:
determining a first product of a first weight value and an exponential function for any one of the subtasks, wherein the exponent of the exponential function is the priority, the base number of the exponential function is a first difference value, and the first difference value is a difference value between the first ratio of the time demand information to the force demand information and the second weight value;
Determining a second product of the time demand information and the priority, then determining a second ratio between the time demand information and the force demand information, and determining a third product of the second ratio and a third weight value;
and determining the priority execution rate of the subtasks according to the sum between the first product and the third product.
6. The method according to claim 4 or 5, wherein said scheduling execution of each of said subtasks according to said duration and said current computational load information, and a priority execution rate of each of said subtasks, comprises:
when the duration is not smaller than a first time length threshold value and the current computational power load information is smaller than a first computational power size, screening key subtasks with the computational power demand information smaller than a second computational power size and/or non-key subtasks with the computational power demand information larger than or equal to the second computational power size from the subtasks;
and determining a first target subtask from the screened critical subtasks and/or non-critical subtasks according to the corresponding priority execution rate, and scheduling and executing the first target subtask.
7. The method according to claim 4 or 5, wherein said scheduling execution of each of said subtasks according to said duration and said current computational load information, and a priority execution rate of each of said subtasks, comprises:
And under the condition that the duration time is not smaller than a first time length threshold value and the current computing power load information is larger than or equal to a first computing power size, not scheduling to execute each subtask.
8. The method according to claim 4 or 5, wherein said scheduling execution of each of said subtasks according to said duration and said current computational load information, and a priority execution rate of each of said subtasks, comprises:
screening key subtasks with the calculation force demand information being more than or equal to the second calculation force from all the subtasks when the changed vehicle behaviors are no-driving behaviors;
and determining a second target subtask from the screened critical subtasks according to the corresponding priority execution rate, and scheduling and executing the second target subtask.
9. The method according to claim 4 or 5, wherein said scheduling execution of each of said subtasks according to said duration and said current computational load information, and a priority execution rate of each of said subtasks, comprises:
when the current calculation force load information is determined to be smaller than a first calculation force size and the duration is smaller than a first time length threshold value, or the current calculation force load information is larger than or equal to the first calculation force size and the duration is smaller than the first time length threshold value, non-critical subtasks, of which calculation force demand information is smaller than a second calculation force size, are screened out from the subtasks;
And determining a third target subtask from the screened non-critical subtasks according to the corresponding priority execution rate, and scheduling and executing the third target subtask.
10. A data processing apparatus, comprising: a processor, a memory and a computer program; wherein the computer program is stored on the memory, which when executed by the processor, causes the apparatus to:
under the condition that the change of the vehicle behavior is detected, determining the duration of the changed vehicle behavior, and determining the current calculation force load information of the vehicle, wherein the current calculation force load information represents calculation force information required by the vehicle behavior;
determining the priority execution rate of each subtask in the data mining task;
and scheduling and executing each subtask according to the duration time, the current power load information and the priority execution rate of each subtask.
11. A vehicle, characterized in that it comprises a data processing device according to claim 10.
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| CN117149860A (en) * | 2023-10-31 | 2023-12-01 | 安徽中科星驰自动驾驶技术有限公司 | Driving data mining method and system for automatic driving vehicle |
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