阅读说明：本技术 一种燃料电池车远程故障监测方法及系统 (Remote fault monitoring method and system for fuel cell vehicle ) 是由 全琎 胡雄晖 全睿 叶麦克 陈辞 于 2021-08-26 设计创作，主要内容包括：本发明涉及一种燃料电池车远程故障监测方法及系统,其方法包括：远程获取待测燃料电池车的动力系统的多个参数信息；将所述动力系统的多个参数信息输入到训练好的神经网络模型中,得到燃料电池发动机内的多个电堆电压的预测值；根据所述燃料电池发动机内的每个电堆电压的预测值判断待测燃料电池车是否出现故障。本发明利用深度学习的方法建立了燃料电池发动机的输入输出模型,在实际车载运行中通过各个电堆的电压残差识别燃料电池发动机的故障,并进行远程监控中心和车载监控单元的故障报警,具有实时、高效、可靠性强、数据吞吐量大、传输距离远和故障识别精度高等特点。(The invention relates to a remote fault monitoring method and a system for a fuel cell vehicle, wherein the method comprises the following steps: remotely acquiring a plurality of parameter information of a power system of a fuel cell vehicle to be tested; inputting a plurality of parameter information of the power system into a trained neural network model to obtain predicted values of a plurality of stack voltages in the fuel cell engine; and judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine. The invention establishes the input and output model of the fuel cell engine by using a deep learning method, identifies the fault of the fuel cell engine through the voltage residual error of each electric pile in the actual vehicle-mounted operation, carries out fault alarm of the remote monitoring center and the vehicle-mounted monitoring unit, and has the characteristics of real time, high efficiency, strong reliability, large data throughput, long transmission distance, high fault identification precision and the like.)

1. A fuel cell vehicle remote fault monitoring method is characterized by comprising the following steps:

remotely acquiring a plurality of parameter information of a power system of a fuel cell vehicle to be tested;

inputting a plurality of parameter information of the power system into a trained neural network model to obtain predicted values of a plurality of stack voltages in the fuel cell engine;

and judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine.

2. The fuel cell vehicle remote fault monitoring method according to claim 1, wherein the step of remotely acquiring information of a plurality of parameters of the power system of the fuel cell vehicle under test comprises the steps of:

collecting a plurality of parameter information of a power system of a fuel cell vehicle to be tested;

and remotely acquiring a plurality of parameter information of the power system of the fuel cell vehicle to be tested through the 5G communication module.

3. The fuel cell vehicle remote fault monitoring method of claim 1, wherein the trained neural network model is trained by:

acquiring a plurality of parameter information of a power system of the fuel cell vehicle in different running states and voltage values of corresponding galvanic piles, and respectively using the parameter information and the voltage values as samples and labels to construct a sample set;

extracting the characteristics of the parameter information, and mapping the parameter information to a multi-dimensional vector;

and training a neural network model by using the multi-dimensional vector and the sample set until the error of the neural network model tends to be stable and is lower than a threshold value, so as to obtain the trained neural network model.

4. The fuel cell vehicle remote failure monitoring method according to claim 3, wherein the plurality of parameter information includes respective parameter and status information of each of an air circuit, a hydrogen circuit, and a cooling circuit of the fuel cell engine.

5. The fuel cell vehicle remote fault monitoring method according to claim 1, wherein the determining whether the fuel cell vehicle under test is faulty according to the predicted value of each stack voltage in the fuel cell engine comprises:

comparing the predicted value of each galvanic pile voltage with the corresponding galvanic pile actual value to obtain a plurality of galvanic pile voltage deviations;

and if the maximum value of the voltage deviations of the plurality of the electric piles is larger than the threshold value, judging that the fuel cell vehicle to be tested has a fault.

6. The fuel cell vehicle remote fault monitoring method according to any one of claims 1 to 5, further comprising adjusting or alarming the working state of the fuel cell vehicle to be tested according to the fault state of the fuel cell vehicle.

7. A fuel cell vehicle remote failure monitoring system, comprising:

the acquisition module is used for remotely acquiring a plurality of parameter information of a power system of the fuel cell vehicle to be tested;

the prediction module is used for inputting a plurality of parameter information of the power system into the trained neural network model to obtain the predicted values of a plurality of stack voltages in the fuel cell engine;

and the judging module is used for judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine.

8. The fuel cell vehicle remote failure monitoring system of claim 7, wherein the acquisition module comprises an acquisition module and a communication module,

the acquisition module is used for acquiring a plurality of parameter information of a power system of the fuel cell vehicle to be detected;

the communication module remotely acquires a plurality of parameter information of the power system of the fuel cell vehicle to be tested through the 5G communication module.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the fuel cell vehicle remote failure monitoring method of any one of claims 1 to 6.

10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the fuel cell vehicle remote failure monitoring method according to any one of claims 1 to 6.

Technical Field

The invention belongs to the technical field of fuel cell vehicle fault monitoring, and particularly relates to a fuel cell vehicle remote fault monitoring method and system.

Background

Fuel cells are widely used in vehicles and traffic due to a series of advantages such as cleanliness, high efficiency, no pollution, no noise, etc. In recent years, with the demonstration application and the commercial popularization of fuel cell vehicles, the number of vehicles is increasing, the demonstration scale is increasing, and in order to improve the maintainability and the safety and reliability of demonstration operation vehicles, it is necessary to perform remote state monitoring and offline/online fault diagnosis on the vehicles, perform early warning on faults which may occur in the operation of the vehicles, or give a diagnosis result in time when the faults occur so that a control system takes action or after-sales personnel perform field maintenance. At present, the remote monitoring of the fuel cell vehicle is mainly performed based on a GPRS or 4G platform, and the communication speed, the communication distance and the communication stability of the remote monitoring of the fuel cell vehicle increasingly do not meet the data requirements of the remote monitoring of the large-scale fuel cell vehicle. In recent years, 5G communication technology has matured and formally commercialized, and because it has a series of advantages of high transmission speed, high data throughput, high interference resistance, etc., it is necessary to apply it to remote monitoring and fault monitoring of fuel cell vehicles in large-scale demonstration operation.

Disclosure of Invention

In order to realize remote monitoring and early warning of the fault of the fuel cell vehicle, the invention provides a remote fault monitoring method of the fuel cell vehicle in a first aspect, which comprises the following steps: remotely acquiring a plurality of parameter information of a power system of a fuel cell vehicle to be tested; inputting a plurality of parameter information of the power system into a trained neural network model to obtain predicted values of a plurality of stack voltages in the fuel cell engine; and judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine.

In some embodiments of the present invention, the remotely acquiring information of a plurality of parameters of a power system of a fuel cell vehicle under test includes the following steps: collecting a plurality of parameter information of a power system of a fuel cell vehicle to be tested; and remotely acquiring a plurality of parameter information of the power system of the fuel cell vehicle to be tested through the 5G communication module.

In some embodiments of the present invention, the trained neural network model is trained by: acquiring a plurality of parameter information of a power system of the fuel cell vehicle in different running states and voltage values of corresponding galvanic piles, and respectively using the parameter information and the voltage values as samples and labels to construct a sample set;

extracting the characteristics of the parameter information, and mapping the parameter information to a multi-dimensional vector;

and training a neural network model by using the multi-dimensional vector and the sample set until the error of the neural network model tends to be stable and is lower than a threshold value, so as to obtain the trained neural network model.

Preferably, the plurality of parameter information includes respective parameter and state information of each of the air circuit, the hydrogen circuit, and the cooling circuit of the fuel cell engine.

In some embodiments of the present invention, the determining whether the fuel cell vehicle under test has a fault according to the predicted value of each stack voltage in the fuel cell engine includes:

comparing the predicted value of each galvanic pile voltage with the corresponding galvanic pile actual value to obtain a plurality of galvanic pile voltage deviations; and if the maximum value of the voltage deviations of the plurality of the electric piles is larger than the threshold value, judging that the fuel cell vehicle to be tested has a fault.

In the above embodiment, the method further includes adjusting or giving an alarm to the operating state of the fuel cell vehicle to be tested according to the fault state of the fuel cell vehicle.

In a second aspect of the present invention, there is provided a fuel cell vehicle remote failure monitoring system, comprising:

the acquisition module is used for remotely acquiring a plurality of parameter information of a power system of the fuel cell vehicle to be tested;

the prediction module is used for inputting a plurality of parameter information of the power system into the trained neural network model to obtain the predicted values of a plurality of stack voltages in the fuel cell engine;

and the judging module is used for judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine.

In some embodiments of the present invention, the acquiring module includes an acquiring module and a communication module, the acquiring module is configured to acquire a plurality of parameter information of a power system of the fuel cell vehicle to be tested; the communication module remotely acquires a plurality of parameter information of the power system of the fuel cell vehicle to be tested through the 5G communication module.

In a third aspect of the present invention, there is provided an electronic device comprising: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the fuel cell vehicle remote failure monitoring method provided by the first aspect of the present invention.

In a fourth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the fuel cell vehicle remote fault monitoring method provided in the first aspect of the present invention.

The invention has the beneficial effects that:

1. the invention fully utilizes the remote transmission of each parameter information of the whole vehicle power system, simultaneously combines with massive normal data during the demonstration operation of the fuel cell vehicle, establishes the input and output model of the fuel cell engine by utilizing a deep learning method, and identifies the fault of the fuel cell engine by the voltage residual of each electric pile during the actual vehicle-mounted operation;

2. the invention can realize remote fault monitoring or alarming, even state adjustment or maintenance, of the fuel cell vehicle through fault alarming of the remote monitoring center and the vehicle-mounted monitoring unit;

3. the system provided by the invention utilizes the 5G communication function, so that the communication distance is long, the real-time performance is strong, and the data throughput is large; the prediction model integrates the information of each parameter of the whole vehicle power system, simplifies the prediction process, has high intelligent degree and is suitable for the requirements of remote state monitoring and fault diagnosis of large-scale fuel cell vehicles.

Drawings

FIG. 1 is a schematic basic flow diagram of a fuel cell vehicle remote fault monitoring method in some embodiments of the invention;

FIG. 2 is a schematic flow chart of a method for remote fault monitoring of a fuel cell vehicle according to some embodiments of the present invention;

FIG. 3 is a schematic structural view of a fuel cell vehicle according to some embodiments of the present invention;

FIG. 4 is a schematic diagram of the operation of a neural network in some embodiments of the present invention;

FIG. 5 is a schematic diagram of the basic structure of a fuel cell vehicle remote fault monitoring system in some embodiments of the invention;

fig. 6 is a schematic structural diagram of a fuel cell vehicle remote failure monitoring system according to some embodiments of the present invention;

fig. 7 is a schematic structural diagram of an electronic device in some embodiments of the invention.

Detailed Description

The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.

Referring to fig. 1 and 2, in a first aspect of the present invention, there is provided a fuel cell vehicle remote fault monitoring method, including: s100, remotely acquiring a plurality of parameter information of a power system of a fuel cell vehicle to be tested; s200, inputting a plurality of parameter information of the power system into a trained neural network model to obtain predicted values of a plurality of electric pile voltages in the fuel cell engine; s300, judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine.

In step S100 of some embodiments of the present invention, the remotely acquiring information of a plurality of parameters of a power system of a fuel cell vehicle under test includes: s101, collecting a plurality of parameter information of a power system of a fuel cell vehicle to be tested; s102, remotely acquiring a plurality of parameter information of the power system of the fuel cell vehicle to be tested through the 5G communication module. Optionally, the 5G communication module may also be replaced by a sixth generation or more WiFi module or other high-reliability high-capacity communication modules, so as to implement remote information interaction of the fuel cell vehicle.

Referring to fig. 4, in S200 of some embodiments of the present invention, the trained neural network model is trained by: s201, acquiring multiple parameter information of a power system of the fuel cell vehicle in different running states and voltage values of corresponding galvanic piles, and respectively using the parameter information and the voltage values as samples and labels to construct a sample set; optionally, in step S201, in order to improve training efficiency of the neural network and reduce redundant information, data in the sample set is cleaned and normalized, and then is clustered and reduced in dimension, where the dimension reduction method at least includes one of PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), LLE (local Linear embedding) or laplacian eigenmaps.

S202, extracting the characteristics of the parameter information, and mapping the parameter information to a multi-dimensional vector;

and S203, training a neural network model by using the multi-dimensional vector and the sample set until the error of the neural network model tends to be stable and is lower than a threshold value, and obtaining the trained neural network model.

Referring to fig. 3, the plurality of parameter information includes respective parameter and state information of each of the air circuit, the hydrogen circuit, and the cooling circuit of the fuel cell engine. The method specifically comprises the following steps: high pressure of hydrogen P_HHydrogen pressure P of reactor inlet_H＿inPressure P of hydrogen discharged from the reactor_H＿outTemperature T of air entering pile_Air＿inStack inlet air pressure P_Air＿inAnd the flow rate of the air entering the reactor F_Air＿inTemperature T of reactor inlet circulating water_Coolant＿inTemperature T of discharged circulating water_{CoolantL＿out}Pressure P of discharged circulating water_{Coolant＿out}Tail gas valve on-off state S_purgeProportional control valve opening degree O_WalveAnd the rotating speed V of the air compressor_airRotating speed V of hydrogen circulating pump_H＿pumpEtc.; it is understood that the above parameter information is only taken as an example of a power system, and is taken as an example to improve the accuracy of prediction, and other models of the fuel cell vehicle can be includedA block or a parameter or information in the system.

In step S300 of some embodiments of the present invention, the determining whether the fuel cell vehicle under test fails according to the predicted value of each stack voltage in the fuel cell engine includes:

s301, comparing the predicted value of each galvanic pile voltage with the corresponding galvanic pile actual value to obtain a plurality of galvanic pile voltage deviations; s302, if the maximum value of the voltage deviations of the plurality of the electric piles is larger than a threshold value, judging that the fuel cell vehicle to be tested breaks down.

In the above embodiment, the method further includes adjusting or giving an alarm to the operating state of the fuel cell vehicle to be tested according to the fault state of the fuel cell vehicle.

Specifically, referring to fig. 2 to 4, in one embodiment of the present invention, a fuel cell vehicle remote failure monitoring method includes:

the method comprises the following steps: the remote monitoring unit acquires the pressure P of the high-pressure hydrogen sent by the vehicle-mounted monitoring terminal unit through 5G communication_HHydrogen pressure P of reactor inlet_H＿inPressure P of hydrogen discharged from the reactor_H＿outTemperature T of air entering pile_Air＿inStack inlet air pressure P_Air＿inAnd the flow rate of the air entering the reactor F_Air＿inTemperature T of reactor inlet circulating water_Coolant＿inTemperature T of discharged circulating water_{CoolantL＿out}Pressure P of discharged circulating water_{Coolant＿out}Tail gas valve on-off state S_purgeProportional control valve opening degree O_WalveAnd the rotating speed V of the air compressor_airRotating speed V of hydrogen circulating pump_H＿pumpTotal output current I and N cell stack voltage values V₁～V_N；

Step two:

the pressure P of high-pressure hydrogen_HHydrogen pressure P of reactor inlet_H＿inPressure P of hydrogen discharged from the reactor_H＿outTemperature T of air entering pile_Air＿inStack inlet air pressure P_Air＿inAnd the flow rate of the air entering the reactor F_Air＿inTemperature T of reactor inlet circulating water_Coolant＿inTemperature T of discharged circulating water_{CoolantL＿out}Pressure P of discharged circulating water_{Coolant＿out}Tail gas valve switch shapeState S_purgeProportional control valve opening degree O_WalveAnd the rotating speed V of the air compressor_airRotating speed V of hydrogen circulating pump_H＿pumpInputting the total output current I into the stored neural network model to obtain the neural network model predicted voltage values V 'of the N electric piles'₁～V’_N。

Step three: as shown in FIG. 4, the voltage values V 'are predicted by using the neural network models of the N cell stacks'₁～V’_NSequentially comparing with N electric pile voltage actual values V₁～V_NComparing to obtain voltage deviation delta V of each electric pile₁～ΔV_NIf the maximum voltage deviation is greater than the set minimum threshold Vth (i.e., max [ Δ V ]₁，ΔV₂，…，ΔV_NAnd the rate is larger than or equal to Vth), judging that the fuel cell engine has a fault, simultaneously carrying out sound-light alarm prompt on the upper computer, sending the fault judgment result to the vehicle-mounted monitoring terminal unit through 5G, sending the fault judgment result to the vehicle controller and the fuel cell controller through the CAN bus, and adjusting the working state of the fuel cell engine through the vehicle-mounted monitoring terminal unit and the fuel cell controller.

Example 2

Referring to fig. 5, in a second aspect of the present invention, there is provided a fuel cell vehicle remote failure monitoring system 1, including: the acquisition module 11 is used for remotely acquiring a plurality of parameter information of a power system of the fuel cell vehicle to be tested; the prediction module 12 is used for inputting a plurality of parameter information of the power system into the trained neural network model to obtain the predicted values of a plurality of stack voltages in the fuel cell engine; and the judging module 13 is used for judging whether the fuel cell vehicle to be tested breaks down or not according to the predicted value of the voltage of each electric pile in the fuel cell engine.

Referring to fig. 6, in some embodiments of the present invention, the acquiring module 11 includes an acquiring module and a communication module, the acquiring module is used for acquiring information of a plurality of parameters of a power system of a fuel cell vehicle to be tested; the communication module remotely acquires a plurality of parameter information of the power system of the fuel cell vehicle to be tested through the 5G communication module.

Specifically, the vehicle-mounted monitoring terminal unit communicates with a whole vehicle power communication unit through a CAN bus to acquire message information of each unit of a whole vehicle power system of the fuel cell vehicle, and then performs information interaction with a remote monitoring unit through a 5G communication module; the remote monitoring unit firstly collects and stores various parameters and state information of the normal running of the whole vehicle power system sent by the vehicle-mounted monitoring terminal unit, takes the output voltage value of each electric pile of the fuel cell engine as output, takes the parameters and states of an air loop, a hydrogen loop and a cooling loop of the fuel cell engine as input, constructs an input and output model of the fuel cell engine through a neural network embedded in a training host computer, then receives the parameter information of the whole vehicle power system sent by the vehicle-mounted monitoring terminal unit in real time and inputs the parameter information into the trained neural network model to obtain the predicted value of each electric pile voltage of the fuel cell engine, compares the predicted value with the actual value of each electric pile voltage sent by the vehicle-mounted monitoring terminal unit, judges that the fuel cell engine runs and breaks down if the deviation is larger than a set threshold value, and then gives an alarm, and sending the fault information to a vehicle-mounted monitoring terminal unit for field real-time display.

The whole vehicle power communication unit connects and exchanges information with the fuel cell engine, the boosting DC/DC, the whole vehicle controller, the power battery pack, the motor and the hydrogen supply system through a whole vehicle power CAN network;

the vehicle-mounted monitoring terminal unit communicates with the whole vehicle power system through a CAN bus to acquire CAN message information of each component, and communicates with the remote monitoring unit through a 5G communication module:

if the remote monitoring unit obtains all normal information and states of the whole vehicle power system sent by the vehicle-mounted monitoring terminal unit, an upper computer is utilized to establish an input and output neural network model of the fuel cell engine, and meanwhile, the output voltages of a plurality of electric piles of the fuel cell engine are predicted according to the received parameters and state information of the fuel cell engine input to the established neural network model.

The vehicle-mounted monitoring terminal unit is connected with a fuel battery controller, a vehicle controller, a boosting DC/DC and a battery of a vehicle power communication unit through a CAN busThe management unit, the motor controller and the hydrogen management unit are connected for communication, and the remote monitoring unit obtains the high-pressure hydrogen pressure P when the fuel cell engine normally operates by carrying out 5G communication with the vehicle-mounted monitoring terminal unit_HHydrogen pressure P of reactor inlet_H＿inPressure P of hydrogen discharged from the reactor_H＿outTemperature T of air entering pile_Air＿inStack inlet air pressure P_Air＿inAnd the flow rate of the air entering the reactor F_Air＿inTemperature T of reactor inlet circulating water_Coolant＿inTemperature T of discharged circulating water_{CoolantL＿out}Pressure P of discharged circulating water_{Coolant＿out}Tail gas valve on-off state S_purgeProportional control valve opening degree O_WalveAnd the rotating speed V of the air compressor_airRotating speed V of hydrogen circulating pump_H＿pumpTotal output current I and N cell stack voltage values V₁～V_NAnd at the acquired high pressure hydrogen pressure P every one second_HHydrogen pressure P of reactor inlet_H＿inPressure P of hydrogen discharged from the reactor_H＿outTemperature T of air entering pile_Air＿inStack inlet air pressure P_Air＿inAnd the flow rate of the air entering the reactor F_Air＿inTemperature T of reactor inlet circulating water_Coolant＿inTemperature T of discharged circulating water_{CoolantL＿out}Pressure P of discharged circulating water_{Coolant＿out}Tail gas valve on-off state S_purgeProportional control valve opening degree O_WalveAnd the rotating speed V of the air compressor_airRotating speed V of hydrogen circulating pump_H＿pumpThe total output current I is input by the neural network and N electric pile voltage values V₁～V_NAnd for the output of the neural network, training the neural network by using an upper computer until the training error is lower than a set value, and then storing the trained neural network model.

Example 3

Referring to fig. 7, in a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the fuel cell vehicle remote failure monitoring method provided by the first aspect of the present invention.

The electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 7 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 7 may represent one device or may represent multiple devices as desired.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:

computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.