Disclosure of Invention
The application provides a method, a device, a vehicle and a storage medium for reminding of the power deficiency of a storage battery, so as to solve the problems of high energy consumption, low accuracy and the like of a storage battery power deficiency analysis method in the related art.
An embodiment of a first aspect of the present application provides a method for reminding a battery of power loss, including the steps of:
acquiring real-time vehicle data of a current vehicle;
inputting the real-time vehicle data into a pre-trained power deficiency reason identification model to obtain a power deficiency prediction result of the current vehicle, wherein the pre-trained power deficiency reason identification model is obtained by training a target neural network through vehicle data to be trained; and
if the power deficiency prediction result is the power deficiency of the storage battery, acquiring a power deficiency reason from the power deficiency prediction result, generating reminding information based on the power deficiency reason, and sending the reminding information to a preset mobile terminal.
According to one embodiment of the application, the power loss cause includes at least one of a controller exception hold, a controller exception wake-up, and a user bad use.
According to an embodiment of the application, the generating the reminding information based on the power deficiency causes and sending the reminding information to a preset mobile terminal includes:
if the power deficiency factor is that the controller is abnormally maintained or the controller is abnormally awakened, the reminding information is controller optimization reminding information;
and sending the controller optimization reminding information to the after-sale center of the current vehicle.
According to an embodiment of the application, the generating the reminding information based on the power deficiency causes and sending the reminding information to a preset mobile terminal includes:
if the power deficiency factor is bad use of the user, the reminding information is to start an engine for reminding;
and sending the engine starting prompt to a vehicle-to-vehicle system of the current vehicle so as to display the engine starting prompt through the vehicle-to-vehicle system.
According to one embodiment of the application, before inputting the real-time vehicle data into the pre-trained power deficiency cause identification model, further comprising:
acquiring offline vehicle data of a plurality of power-deficient vehicles in a first preset time before power deficiency;
screening out the vehicle data to be trained meeting preset training conditions based on the off-line vehicle data;
respectively training a plurality of target neural networks by utilizing the vehicle data to be trained to obtain a controller abnormal maintenance/awakening model, a user bad use model, a power loss and health degree association model and a frequent starting behavior and health degree association model;
and when the model identification results of the abnormal controller maintaining/waking model, the poor user use model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model are all larger than the preset result, obtaining the pre-trained power deficiency reason identification model according to the abnormal controller maintaining/waking model, the poor user use model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model.
According to one embodiment of the application, the real-time vehicle data includes at least one of vehicle status data, battery status data, power usage component status, and current ambient temperature.
According to the battery power shortage reminding method provided by the embodiment of the application, real-time vehicle data are input into a pre-trained power shortage reason identification model to obtain a power shortage prediction result of a current vehicle, if the power shortage prediction result is the battery power shortage, the power shortage reason is obtained, reminding information is generated, and the reminding information is sent to a preset mobile terminal. Therefore, the problems that the storage battery power shortage analysis method in the related technology consumes much energy and is low in accuracy and the like are solved, the power shortage identification is carried out through the Internet of vehicles big data, the flow and timeliness of power shortage treatment are optimized, and therefore the experience of a user in use is improved.
In a second aspect, embodiments of the present application provide a battery power shortage reminding device, including:
the acquisition module is used for acquiring real-time vehicle data of the current vehicle;
the prediction module is used for inputting the real-time vehicle data into a pre-trained power deficiency reason recognition model to obtain a power deficiency prediction result of the current vehicle, wherein the pre-trained power deficiency reason recognition model is obtained by training a target neural network through vehicle data to be trained; and
and the reminding module is used for acquiring the power deficiency reason from the power deficiency prediction result if the power deficiency prediction result is the power deficiency of the storage battery, generating reminding information based on the power deficiency reason, and sending the reminding information to a preset mobile terminal.
According to one embodiment of the application, the power loss cause includes at least one of a controller exception hold, a controller exception wake-up, and a user bad use.
According to one embodiment of the application, the reminding module is configured to:
if the power deficiency factor is that the controller is abnormally maintained or the controller is abnormally awakened, the reminding information is controller optimization reminding information;
and sending the controller optimization reminding information to the after-sale center of the current vehicle.
According to one embodiment of the application, the reminding module is configured to:
if the power deficiency factor is bad use of the user, the reminding information is to start an engine for reminding;
and sending the engine starting prompt to a vehicle-to-vehicle system of the current vehicle so as to display the engine starting prompt through the vehicle-to-vehicle system.
According to one embodiment of the application, the prediction module is further configured to, prior to inputting the real-time vehicle data into the pre-trained power deficiency cause identification model:
acquiring offline vehicle data of a plurality of power-deficient vehicles in a first preset time before power deficiency;
screening out the vehicle data to be trained meeting preset training conditions based on the off-line vehicle data;
respectively training a plurality of target neural networks by utilizing the vehicle data to be trained to obtain a controller abnormal maintenance/awakening model, a user bad use model, a power loss and health degree association model and a frequent starting behavior and health degree association model;
and when the model identification results of the abnormal controller maintaining/waking model, the poor user use model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model are all larger than the preset result, obtaining the pre-trained power deficiency reason identification model according to the abnormal controller maintaining/waking model, the poor user use model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model.
According to one embodiment of the application, the real-time vehicle data includes at least one of vehicle status data, battery status data, power usage component status, and current ambient temperature.
According to the storage battery power shortage reminding device provided by the embodiment of the application, real-time vehicle data are input into the pre-trained power shortage reason identification model to obtain the power shortage prediction result of the current vehicle, if the power shortage prediction result is the storage battery power shortage, the power shortage reason is obtained, reminding information is generated, and the reminding information is sent to the preset mobile terminal. Therefore, the problems that the storage battery power shortage analysis method in the related technology consumes much energy and is low in accuracy and the like are solved, the power shortage identification is carried out through the Internet of vehicles big data, the flow and timeliness of power shortage treatment are optimized, and therefore the experience of a user in use is improved.
An embodiment of a third aspect of the present application provides a vehicle, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the method for reminding the battery of the deficiency of electricity according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the battery power shortage reminding method according to the above embodiment.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The battery power shortage reminding method, device, vehicle and storage medium of the embodiments of the present application are described below with reference to the drawings. Aiming at the problems that the storage battery power shortage analysis method in the background technology is large in energy consumption and low in accuracy, the application provides a storage battery power shortage reminding method, in the method, real-time vehicle data are input into a pre-trained power shortage reason identification model to obtain a power shortage prediction result of a current vehicle, if the power shortage prediction result is the storage battery power shortage, the power shortage reason is acquired, reminding information is generated, and the reminding information is sent to a preset mobile terminal. Therefore, the problems that the storage battery power shortage analysis method in the related technology consumes much energy and has lower accuracy and the like are solved.
Specifically, fig. 1 is a schematic flow chart of a battery power shortage reminding method according to an embodiment of the present application.
As shown in fig. 1, the battery power shortage reminding method comprises the following steps:
in step S101, real-time vehicle data of a current vehicle is acquired.
Wherein in some embodiments, the real-time vehicle data includes at least one of vehicle state data, battery state data, power usage component state, and current ambient temperature.
Specifically, the embodiment of the application can acquire real-time vehicle data of the current vehicle through the internet of vehicles technology. It should be noted that, the above-mentioned acquisition of the real-time vehicle data of the current vehicle through the internet of vehicles technology is only exemplary and not limiting to the application, and those skilled in the art may adopt other ways to acquire the real-time vehicle data of the current vehicle according to actual situations, so that redundancy is avoided, and detailed descriptions thereof are omitted.
It should be noted that, the real vehicle data accumulation should be longer than a certain time, so as to ensure that the recognition output of the subsequent modeling can embody the actual situation of the vehicle.
In step S102, real-time vehicle data is input to a pre-trained power deficiency cause identification model, to obtain a power deficiency prediction result of the current vehicle, where the pre-trained power deficiency cause identification model is obtained by training a target neural network with vehicle data to be trained.
Wherein, in some embodiments, the power loss cause includes at least one of controller exception hold, controller exception wake-up, and user poor use.
Specifically, the vehicle may have a situation in which the controller is abnormally maintained or the controller is abnormally awakened due to overvoltage, overcurrent or excessive temperature, and the user may use the high-power electric components such as an air conditioner, audio-visual and the like for a long time in an ignition off state of the vehicle due to bad use.
Therefore, by using a pre-trained model, real-time vehicle data can be rapidly analyzed and identified, an accurate electricity deficiency prediction result is obtained, and a user or an after-sale center can take measures in time to avoid or solve the electricity deficiency problem. Because the neural network learns a large number of data modes and characteristics in the training process, the neural network has higher reliability and reliability, and can provide a high-efficiency and accurate prediction result. Meanwhile, the power deficiency prediction result can be connected with a downstream system, so that the problem of power deficiency of the storage battery can be timely processed, and the subsequent influence caused by the power deficiency can be reduced.
Further, in some embodiments, before inputting the real-time vehicle data into the pre-trained power deficiency cause identification model, further comprising: acquiring offline vehicle data of a plurality of power-deficient vehicles in a first preset time before power deficiency; screening out vehicle data to be trained meeting preset training conditions based on the offline vehicle data; respectively training a plurality of target neural networks by utilizing vehicle data to be trained to obtain a controller abnormal maintenance/awakening model, a user bad use model, a power loss and health degree association model and a frequent starting behavior and health degree association model; when the model identification results of the abnormal controller maintaining/waking model, the poor user model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model are all larger than the preset result, a pre-trained power deficiency cause identification model is obtained according to the abnormal controller maintaining/waking model, the poor user model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model.
The first preset time may be a time preset by a person skilled in the art, may be a time obtained through limited experiments, or may be a time obtained through limited computer simulation, and is not specifically limited herein. The offline vehicle data may include battery voltage, temperature, charge, health, power loss frequency, ignition key status, generator status, engine status, drive motor status (new energy vehicle), smart power up status (new energy vehicle), air conditioning status, vehicle application status, controller network status, etc.
Preferably, the preset result may be F1 score=70%, which is not specifically limited herein.
Optionally, in the embodiment of the application, vehicle record data can be respectively taken according to three sections of low ambient temperature (< 5 ℃), medium ambient temperature ([ 5 ℃,35 ℃) and high ambient temperature (> 35 ℃), according to the model of the storage battery and the model of the vehicle.
Specifically, offline vehicle data are obtained, vehicle data to be trained are screened out, a plurality of target neural networks are respectively trained by utilizing the vehicle data to be trained, so that the vehicle data meet fitting standards, and the vehicle data are migrated to a big data real-time application cluster for real-time power loss identification and scene identification.
Further, as shown in fig. 2, the controller abnormality maintenance/wake-up model of the embodiment of the present application may use a conventional logic decision model to identify the case of "ignition key OFF" within 24 hours before the electric power shortage of the electric power shortage vehicle, but the controller signal abnormality maintenance/wake-up lasts for 30 minutes ". The controllers are arranged from high to low according to the duration and the frequency, and a professional team of the cooperative battery checks whether the controller keeps/wakes up logic reasonably or not, and an optimization requirement is unreasonably put forward. When the controller is identified not to sleep, whether the controller is abnormally kept or abnormally waken up is judged, and meanwhile, the main controller is judged according to the conditions, so that the controller is conveniently set and optimized by research and development.
Further, the bad use model of the user in the embodiment of the application can adopt a conventional logic decision model to identify the condition that the engine is turned off within 72 hours before the electric power of the electric power-deficient vehicle, but the air conditioner/vehicle is continuously operated for 30 minutes. The electric equipment is arranged from high to low according to the duration and the frequency, and an application program or a car machine prompt is added to equipment frequently causing power shortage to remind a user to start an engine or close the electric equipment. When the bad use of the user is identified, the main power utilization direction of the user is judged and is used for setting the use label of the storage battery, so that the user can conveniently manage the thousands of people and thousands of faces of labels.
Further, when the abnormal maintenance/awakening model of the controller, the bad use model of the user, the power deficiency and health degree association model and the model identification results of the frequent starting behavior and health degree association model are all larger than the preset results, the identification results are summarized comprehensively and used as the input of the power deficiency cause multi-classification identification model, the relation between fitting data and each power deficiency cause is trained and built, and the power deficiency cause identification model is trained and built, so that the optimization of a storage battery technology system is supported
It should be noted that, the model only can embody the vehicle characteristics of the used data, when the value of KS (Kolmogorov-Smirnov, a model evaluation index) is less than 20%, the model can not stably identify the corresponding scene, and the model parameters no longer have the capability of representing the scene logic, so that the model needs to be iterated continuously, especially for the battery scene with continuously changing seasons, temperatures and usage habits, the model can be detected every 3 months, at this time, the data of the last 3 months need to be used for retraining, the training parameters are optimized, the data accumulation accords with the conditions, and the training iteration can be performed according to seasons and geographical areas, so that the quality and stability of the model are ensured.
In step S103, if the power shortage prediction result is the power shortage of the storage battery, the power shortage reason is obtained from the power shortage prediction result, the reminding information is generated based on the power shortage reason, and the reminding information is sent to the preset mobile terminal.
The preset mobile terminal includes a mobile phone with a radio short wave communication function or other handheld communication devices, which are not limited herein.
Specifically, the power deficiency reasons are obtained from the power deficiency prediction result, manual intervention and analysis are not needed, time and labor cost are saved, and the reminding information generated based on the power deficiency reasons can remind each storage battery differently according to the specific situation of each storage battery, so that the reminding accuracy and practicability are improved. Through timely sending reminding information to a preset mobile terminal and giving reasonable advice, a user can know the state of a battery and possible problems at any time and any place, so that the use of equipment is better managed, the vehicle habit of the user is optimized, and the user experience is improved.
Further, in some embodiments, generating the alert information based on the power loss cause and transmitting the alert information to the preset mobile terminal includes: if the power deficiency reason is that the controller is abnormally maintained or the controller is abnormally awakened, the reminding information is the controller optimization reminding information; and sending the optimized reminding information of the controller to an after-sales center of the current vehicle.
Specifically, as shown in fig. 3, if the power loss cause is that the controller is abnormally maintained or the controller is abnormally awakened, the vehicle body/vehicle machine/gateway/hybrid/battery management system controller uploads signals to the vehicle-mounted communication, the vehicle-mounted communication is summarized and reported to the cloud, and the cloud sends the controller optimization reminding information to the after-sales system.
Therefore, the controller optimization reminding information is sent to the after-sales center of the current vehicle, the after-sales center can know the running state of the vehicle and the abnormal condition of the controller in time, corresponding measures are taken for maintenance and optimization, and timeliness and effect of after-sales service are improved.
Further, in some embodiments, generating the alert information based on the power loss cause and transmitting the alert information to the preset mobile terminal includes: if the reason of the power deficiency is bad use of the user, the reminding information is to start the engine for reminding; and sending an engine starting prompt to a vehicle-mounted system of the current vehicle so as to display the engine starting prompt through the vehicle-mounted system.
For example, if the power loss is bad for the user, the after-sales team is docked, and the user is prompted by the vehicle system or the application program to "use the air conditioner or the vehicle for a long time after turning off the engine will cause the battery to be power loss, please turn on the engine or charge the battery in time for the your vehicle experience, thank for-! "
Therefore, the ignition state is kept when a client uses high-power components such as an air conditioner and audio-visual equipment by sending an engine starting prompt and guiding the client to use the high-power components through the display of a vehicle system or the display of an application program or a customer service call, the user can be timely reminded of the operation of starting the engine, the user is helped to avoid the condition of power shortage caused by bad use, the alertness of the user to the use of a vehicle battery is improved, the vehicle habit of the user is cultivated and optimized, and the vehicle experience of the user is improved.
Further, the embodiment of the application can also prompt a user that the power shortage will lead to the decrease of the health degree of the vehicle battery through a vehicle machine system or an application program for the after-sale team when the power shortage condition of the storage battery occurs, please charge in time and thank the user, meanwhile, the storage battery team is docked, a power shortage cause analysis report is generated, and the subsequent storage battery calibration is optimized.
In order to facilitate a clearer and more visual understanding of the battery power shortage reminding method according to the embodiment of the present application, the following detailed description is given with reference to fig. 4.
Specifically, fig. 4 is a block schematic diagram of a battery power shortage reminding system related to a battery power shortage reminding method according to an embodiment of the present application, where the system may include a big data background decision platform 1, a car networking background 2, a car communication module 3, a CAN (Controller Area Network ) bus 4, a car body controller 5, a car machine system 6, a central gateway 7, a hybrid control unit 8, and a battery management system controller 9.
The big data background decision platform 1 is responsible for collecting, analyzing and processing various vehicle data based on big data technology, judging whether the electric quantity of the storage battery is too low, and making corresponding decisions, such as sending a prompt to a driver or triggering other operations. The internet of vehicles background 2 receives data from the vehicle-mounted communication module 3 and transmits the data to the big data background decision platform 1 for further analysis and processing. The vehicle-mounted communication module 3 transmits the data of the vehicle to the internet of vehicles background 2 through a wireless network or other communication modes. The CAN bus 4 connects a plurality of devices such as the vehicle-mounted communication module 3, the vehicle body controller 5, the vehicle-mounted system 6, the hybrid power control unit 8 and the like, and data transmission and communication CAN be performed between the devices through the CAN bus 4. The body controller 5 controls the operation of various electronic devices of the vehicle. The vehicle system 6 integrates various functions of the vehicle-mounted electronic system such as navigation, entertainment, communication, and the like. The central gateway 7 is used for connecting the vehicle body controller 5, the vehicle machine system 6, the storage battery management system controller 9 and the like, and realizing data transmission and communication. The hybrid control unit 8 may communicate with a battery management system controller to obtain battery charge information. The battery management system controller 9 may provide the electric power information of the secondary battery to other devices and communicate with the vehicle body controller 5, the hybrid control unit 8, and the like.
Therefore, through the storage battery power shortage reminding system related to the storage battery power shortage reminding method according to the embodiment of the application, real-time monitoring of the storage battery of the vehicle is realized, the storage battery power shortage of a user is timely reminded, the vehicle habit of the user is cultivated and optimized, and the service life of the storage battery is prolonged.
According to the battery power shortage reminding method, real-time vehicle data are input into a pre-trained power shortage reason identification model to obtain a power shortage prediction result of a current vehicle, if the power shortage prediction result is the battery power shortage, the power shortage reason is obtained, reminding information is generated, and the reminding information is sent to a preset mobile terminal. . Therefore, the problems that the storage battery power shortage analysis method in the related technology consumes much energy and is low in accuracy and the like are solved, the power shortage identification is carried out through the Internet of vehicles big data, the flow and timeliness of power shortage treatment are optimized, and therefore the experience of a user in use is improved.
Next, a battery power shortage warning device according to an embodiment of the present application will be described with reference to the drawings.
Fig. 4 is a block schematic diagram of a battery power shortage warning device 10 according to an embodiment of the present application.
As shown in fig. 4, the battery power shortage warning device 10 includes: the system comprises an acquisition module 100, a prediction module 200 and a reminding module 300.
The acquiring module 100 is configured to acquire real-time vehicle data of a current vehicle; the prediction module 200 inputs real-time vehicle data into a pre-trained power deficiency reason recognition model to obtain a power deficiency prediction result of the current vehicle, wherein the pre-trained power deficiency reason recognition model is obtained by training a target neural network by the vehicle data to be trained; the reminding module 300 is configured to obtain a power loss reason from the power loss prediction result if the power loss prediction result is the power loss of the storage battery, generate reminding information based on the power loss reason, and send the reminding information to the preset mobile terminal.
Further, in some embodiments, the power loss cause includes at least one of controller exception hold, controller exception wake-up, and user poor use.
Further, in some embodiments, the reminder module 300 is configured to: if the power deficiency reason is that the controller is abnormally maintained or the controller is abnormally awakened, the reminding information is the controller optimization reminding information; and sending the optimized reminding information of the controller to an after-sales center of the current vehicle.
Further, in some embodiments, the reminder module 300 is configured to: if the reason of the power deficiency is bad use of the user, the reminding information is to start the engine for reminding; and sending an engine starting prompt to a vehicle-mounted system of the current vehicle so as to display the engine starting prompt through the vehicle-mounted system.
Further, in some embodiments, the prediction module 200 is further configured to, prior to inputting the real-time vehicle data into the pre-trained power deficiency cause identification model: acquiring offline vehicle data of a plurality of power-deficient vehicles in a first preset time before power deficiency; screening out vehicle data to be trained meeting preset training conditions based on the offline vehicle data; respectively training a plurality of target neural networks by utilizing vehicle data to be trained to obtain a controller abnormal maintenance/awakening model, a user bad use model, a power loss and health degree association model and a frequent starting behavior and health degree association model; when the model identification results of the abnormal controller maintaining/waking model, the poor user model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model are all larger than the preset result, a pre-trained power deficiency cause identification model is obtained according to the abnormal controller maintaining/waking model, the poor user model, the power deficiency and health degree association model and the frequent starting behavior and health degree association model.
Further, in some embodiments, the real-time vehicle data includes at least one of vehicle status data, battery status data, power usage component status, and current ambient temperature.
It should be noted that the foregoing explanation of the embodiment of the battery power shortage reminding method is also applicable to the battery power shortage reminding device of this embodiment, and will not be repeated here.
According to the storage battery power shortage reminding device provided by the embodiment of the application, real-time vehicle data are input into a pre-trained power shortage reason identification model to obtain a power shortage prediction result of a current vehicle, if the power shortage prediction result is the storage battery power shortage, the power shortage reason is obtained, reminding information is generated, and the reminding information is sent to a preset mobile terminal. Therefore, the problems that the storage battery power shortage analysis method in the related technology consumes much energy and is low in accuracy and the like are solved, the power shortage identification is carried out through the Internet of vehicles big data, the flow and timeliness of power shortage treatment are optimized, and therefore the experience of a user in use is improved.
Fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.
The processor 602 implements the battery power shortage warning method provided in the above embodiment when executing the program.
Further, the vehicle further includes:
a communication interface 603 for communication between the memory 601 and the processor 602.
A memory 601 for storing a computer program executable on the processor 602.
The memory 601 may comprise a high-speed RAM memory or may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may perform communication with each other through internal interfaces.
The processor 602 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the battery power shortage warning method as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.