1. A reliability assessment method of an artificial intelligence system, the method comprising:

collecting log files, sensor data and real-time operation data of an artificial intelligence system;

performing multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

processing the data of the multi-source heterogeneous fusion to obtain reliability data of an artificial intelligence system;

constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process according to the reliability data;

and calculating a fault mean function and a failure intensity function of the artificial intelligence system to obtain the reliability evaluation of the artificial intelligence system.

2. The method of claim 1, wherein the performing multi-source heterogeneous fusion comprises performing de-exception processing, loss-filling processing, and de-duplication processing on the log file, the sensor data, and the real-time operating data.

3. The method of claim 1, further comprising obtaining an average failure interval, an average time to repair a fault, and an average time to no fault of the artificial intelligence system according to the reliability data after obtaining the reliability data of the artificial intelligence system; respectively expressed as:

the mean failure interval MTBF is calculated as follows:

wherein, t₀<t₁<…<t_nThe time point of the artificial intelligence system fault is n, and the total number of faults observed in the artificial intelligence system is n;

the Mean Time To Repair (MTTR) of the fault is calculated as follows:

wherein, T_iThe time point of the fault corresponding to the artificial intelligent system is repaired;

the mean time to failure a is calculated as follows:

A＝MTBF/(MTBF+MTTR)。

4. the method of claim 1, wherein the constructing the accumulated fault model of the artificial intelligence system using the heterogeneous poisson process comprises obtaining accumulated failure counts of a software system and a hardware system in the artificial intelligence system, respectively, obtaining an accumulated fault mean function of the software system using a first model, and obtaining an accumulated fault mean function of the hardware system using a second model; and summing the accumulated fault mean function of the software system and the accumulated fault mean function of the hardware system to obtain the accumulated fault function of the artificial intelligence system.

5. A reliability assessment system for an artificial intelligence system, the system comprising:

the data acquisition module is used for acquiring log files, sensor data and real-time operation data of the artificial intelligence system;

the multi-source heterogeneous fusion module is used for carrying out multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

the reliability data extraction module is used for processing the multi-source heterogeneous fusion data to obtain the reliability data of the artificial intelligence system;

and the reliability real-time evaluation module is used for constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process, calculating a fault mean function and a failure intensity function of the artificial intelligence system and obtaining the reliability evaluation of the artificial intelligence system.

6. The reliability evaluation system of an artificial intelligence system according to claim 5, wherein the multi-source heterogeneous data fusion module comprises a de-exception processing unit, a loss-filling processing unit and a de-duplication processing unit, and the de-exception processing unit performs de-exception processing on the log file, the sensor data and the real-time operation data; the loss compensation processing unit is used for performing loss compensation processing on the log file, the sensor data and the real-time operation data; the deduplication unit deduplicates the log file, the sensor data, and the real-time operation data.

7. The system according to claim 5, further comprising an online failure monitoring module for monitoring the failure of the artificial intelligence system in real time and calling the reliability data to obtain the mean failure interval, mean time to repair the failure and mean time to no failure of the artificial intelligence system; respectively expressed as:

the mean failure interval MTBF is calculated as follows:

wherein, t₀<t₁<…<t_nThe time point of the artificial intelligence system fault is n, and the total number of faults observed in the artificial intelligence system is n;

the Mean Time To Repair (MTTR) of the fault is calculated as follows:

wherein, T_iThe time point of the fault corresponding to the artificial intelligent system is repaired;

the mean time to failure a is calculated as follows:

A＝MTBF/(MTBF+MTTR)。

8. the system of claim 5, wherein the real-time reliability evaluation module comprises a first model unit, a second model unit and a summation unit, the first model unit obtains an accumulated failure mean function of the software system, and the second model unit obtains an accumulated failure mean function of the hardware system; and the summation unit sums the accumulated fault mean function of the software system and the accumulated fault mean function of the hardware system to obtain the accumulated fault function of the artificial intelligence system.

9. A computer device, characterized in that the computer device comprises:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more programs or the one or more processors, cause the one or more processors to implement a method for reliability assessment of an artificial intelligence system as claimed in any one of claims 1 to 4.

Background

At present, the artificial intelligence technology is rapidly developed, the artificial intelligence relates to all industries, such as robots, unmanned driving, smart cities, smart homes and the like, and more scenes are developed in the future. The artificial intelligence system is a complex intelligent system combining software and hardware, and has very powerful and diversified functions.

However, while providing services, the artificial intelligence system faces various threats and attacks, and may bring about various faults to the system, which may cause problems of privacy disclosure, functional failure, and the like; therefore, the defense capability of the artificial intelligence system is improved, and the reliability of the artificial intelligence system can also be improved by monitoring and evaluating the fault condition of the artificial intelligence system in time.

Most of fault monitoring methods and reliability evaluation methods in the prior art split a software system and a hardware system in an artificial intelligence system, and evaluate the software system and the hardware system respectively, while the artificial intelligence system is a complex system combining software and hardware, and evaluation of the artificial intelligence system by adopting a traditional method will result in inaccurate evaluation results and fail to reflect the true situation of the artificial intelligence system.

Disclosure of Invention

In order to solve the technical problem of reliability evaluation of the artificial intelligence system, the invention provides a reliability evaluation method and device of the artificial intelligence system and computer equipment for evaluating the reliability of the artificial intelligence system and obtaining the fault monitoring condition of the artificial intelligence system in real time.

In a first aspect of the present invention, the present invention provides a reliability evaluation method for an artificial intelligence system, the method comprising:

collecting log files, sensor data and real-time operation data of an artificial intelligence system;

performing multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

processing the data of the multi-source heterogeneous fusion to obtain reliability data of an artificial intelligence system;

constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process according to the reliability data;

and calculating a fault mean function and a failure intensity function of the artificial intelligence system to obtain the reliability evaluation of the artificial intelligence system.

Further, the multi-source heterogeneous fusion includes performing exception removal processing, deletion supplement processing and repetition removal processing on the log file, the sensor data and the real-time operation data.

Further, after obtaining the reliability data of the artificial intelligence system, obtaining the average failure interval, the average fault repair time and the average non-fault time of the artificial intelligence system according to the reliability data; respectively expressed as:

the mean failure interval MTBF is calculated as follows:

wherein, t₀<t₁<…<t_nThe time point of the artificial intelligence system fault is n, and the total number of faults observed in the artificial intelligence system is n;

the Mean Time To Repair (MTTR) of the fault is calculated as follows:

wherein, T_iThe time point of the fault corresponding to the artificial intelligent system is repaired;

the mean time to failure a is calculated as follows:

A＝MTBF/(MTBF+MTTR)。

further, the method for constructing the accumulated fault model of the artificial intelligence system by adopting the heterogeneous poisson process comprises the steps of respectively obtaining accumulated failure numbers of a software system and a hardware system in the artificial intelligence system, obtaining an accumulated fault mean function of the software system by adopting a first model, and obtaining an accumulated fault mean function of the hardware system by adopting a second model; and summing the accumulated fault mean function of the software system and the accumulated fault mean function of the hardware system to obtain the accumulated fault function of the artificial intelligence system.

In a second aspect of the present invention, the present invention is a reliability evaluation system of an artificial intelligence system, the system comprising:

the data acquisition module is used for acquiring log files, sensor data and real-time operation data of the artificial intelligence system;

the multi-source heterogeneous fusion module is used for carrying out multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

the reliability data extraction module is used for processing the multi-source heterogeneous fusion data to obtain the reliability data of the artificial intelligence system;

and the reliability real-time evaluation module is used for constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process, calculating a fault mean function and a failure intensity function of the artificial intelligence system and obtaining the reliability evaluation of the artificial intelligence system.

Furthermore, the multi-source heterogeneous data fusion module comprises an exception removing processing unit, a loss compensating processing unit and a repetition removing processing unit, wherein the exception removing processing unit is used for removing exceptions of the log file, the sensor data and the real-time running data; the loss compensation processing unit is used for performing loss compensation processing on the log file, the sensor data and the real-time operation data; the deduplication unit deduplicates the log file, the sensor data, and the real-time operation data.

Furthermore, the reliability evaluation system also comprises an online fault monitoring module which is used for monitoring the fault of the artificial intelligence system in real time and calling the reliability data to acquire the average failure interval, the average fault repairing time and the average non-fault time of the artificial intelligence system; respectively expressed as:

the mean failure interval MTBF is calculated as follows:

wherein, t₀<t₁<…<t_nThe time point of the artificial intelligence system fault is n, and the total number of faults observed in the artificial intelligence system is n;

the Mean Time To Repair (MTTR) of the fault is calculated as follows:

wherein, T_iThe time point of the fault corresponding to the artificial intelligent system is repaired;

the mean time to failure a is calculated as follows:

A＝MTBF/(MTBF+MTTR)。

further, the reliability real-time evaluation module comprises a first model unit, a second model unit and a summation unit, wherein the first model unit acquires an accumulated fault mean function of the software system, and the second model unit acquires an accumulated fault mean function of the hardware system; and the summation unit sums the accumulated fault mean function of the software system and the accumulated fault mean function of the hardware system to obtain the accumulated fault function of the artificial intelligence system.

In a third aspect of the present invention, the present invention also provides a computer apparatus comprising:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more programs or the one or more processors, cause the one or more processors to implement the method for reliability assessment of an artificial intelligence system according to the first aspect of the invention.

The invention has the beneficial effects that:

the invention respectively models a hardware system and a software system in the artificial intelligence system, can excavate the characteristics of the respective systems, and respectively collects log files, sensor data and real-time operation data of the artificial intelligence system; the fusion of the multi-source heterogeneous data can ensure that the fused data has higher reliability, and the non-homogeneous poisson process can be utilized to obtain the reliability result and the real-time monitoring result of the artificial intelligent system based on the reliability data.

Drawings

FIG. 1 is a flow chart of a method for evaluating the reliability of an artificial intelligence system in an embodiment of the invention;

FIG. 2 is a flow chart of a method for reliability assessment of an artificial intelligence system in accordance with a preferred embodiment of the present invention;

FIG. 3 is a diagram of a reliability assessment system architecture for an artificial intelligence system in an embodiment of the present invention;

fig. 4 is a diagram showing a structure of a computer device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a flowchart of a reliability evaluation method of an artificial intelligence system in an embodiment of the present invention, as shown in fig. 1, the method includes:

101. collecting log files, sensor data and real-time operation data of an artificial intelligence system;

in this step, the collection mode of the log file of the artificial intelligence system includes calling a log file interface of the artificial intelligence system, and obtaining the log file of the artificial intelligence system from the log file interface.

In the step, various sensor data are included, and the sensors can be digital sensors and analog sensors; the sensors may be specifically a sound recorder, a camera, etc., and the sensors generate corresponding sensor data in real time and transmit the sensor data to the artificial intelligence system through a certain manner, which may include a bus manner, etc.

In this step, the real-time operation data may include CPU operation data, power data, interface data, and the like in the artificial intelligence system.

The multi-source heterogeneous data includes multiple types of structured data, semi-structured data, and unstructured data. In the embodiment of the invention, the log file belongs to semi-structured data, has a certain basic fixed structure mode, but is of a non-relational type; the sensor data belongs to unstructured data, does not have a fixed mode, and can be embodied in different forms of audio, pictures, videos and the like; the operation data is structured data and can be embodied in a relational table database and the like.

102. Performing multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

the multi-source heterogeneous fusion refers to that data sources have multiple sources and data types and forms have complexity, namely heterogeneity; the multi-source heterogeneous data come from a plurality of data sources, including log files stored in different database systems, data sets collected by different sensor devices in work and the like.

The traditional data fusion mode can comprise data fusion of redundant information, data fusion of complementary information, data fusion of complex information and the like, the data from different data needs to be integrated by adopting a multi-source heterogeneous fusion mode, the difference of types and structures of the data is shielded, and the problems of complex sources and heterogeneous structures of the multi-source heterogeneous data are solved, so that the unified storage, management and analysis of the data are realized, the undifferentiated access of users is realized, and the value of the data is fully exerted.

After receiving the log file, the sensor data, and the real-time operation data, the log file, the sensor data, and the real-time operation data may be preprocessed, to filter out non-compliant data therein, and to clean up meaningless data therein, for example, data cleaning refers to processing the log file according to a requirement, where the data cleaning includes deleting irrelevant data, merging some records, performing appropriate processing on a record where an error occurs when a user requests a page, and the like. After the data is cleaned, format conversion and normalization can be carried out on the log files, and the log files can be separated into files of different types.

After the data is cleaned, the dimension of the data can be reduced, and since the multi-source heterogeneous data has the characteristics of various types and complex structures, in order to extract more reliable and effective data information from original log files, sensor data and real-time operation data, irrelevant and redundant features need to be eliminated, new feature data is generated, and the dimension reduction of the high-dimensional data is realized. In the development of modern manufacturing technology, mass multi-source heterogeneous data in the production process of the manufacturing industry is high in dimensionality and high in correlation among a large amount of data, which brings high difficulty to data dimension reduction.

103. Processing the data of the multi-source heterogeneous fusion to obtain reliability data of an artificial intelligence system;

in general, data dimensionality reduction can be achieved by feature selection or feature extraction of the data. The feature selection method obtains a subset of the original feature set by selecting elements in the original feature set, thereby realizing dimension reduction; the feature extraction method obtains a new feature set by combining different features, thereby achieving the purpose of data dimension reduction.

In order to fuse data, firstly, dimension attribute weights of log files, sensor data and real-time operation data are sequentially constructed, and the dimension attributes can be expressed as:

wherein the content of the first and second substances,representing the ith dimension attribute weight of the ith data, wherein l refers to log files, sensor data and real-time operation data; h is_iA vector representation representing the ith dimension attribute; a represents the average vector representation of all dimensional attributes; k represents the total number of dimension attributes; the data for multi-source heterogeneous fusion is represented as:

the conv represents the reliability data of the multi-source heterogeneous fused data set artificial intelligence system, and the reliability data can fully reflect the multi-dimensional characteristics of the artificial intelligence system; m represents the number of data types, wherein the types can include but are not limited to three major types of log files, sensor data and real-time operation data, and a plurality of minor types are formed after each major type is subdivided.

The invention improves the traditional fusion mode, fuses different dimensional characteristics of all data in a dimensional attribute weight mode, can realize dimensional fusion, can increase multi-granularity information of the data and enhance the reliability of the information; and noise information caused by dimension drift can be reduced, so that favorable data information is enhanced, and useless noise information is weakened.

104. Constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process according to the reliability data;

the artificial intelligence system is composed of a hardware system and a software system, and failure mechanisms of the software system and the hardware system are completely different, so that failure of the system is divided into software system failure and hardware system failure. The accumulated failure number N (t) of the artificial intelligence system is a random process, the invention is described by a non-homogeneous Poisson process, and the calculation formula is as follows:

N(t)＝N₁(t)+N₂(t)

wherein N (t) is the accumulated failure number of the artificial intelligence system, N₁(t) is the cumulative number of failures of the hardware system, N₂(t) is the cumulative number of failures of the software system.

105. And calculating a fault mean function and a failure intensity function of the artificial intelligence system to obtain the reliability evaluation of the artificial intelligence system.

The calculation formula of the fault mean function m (t) and the failure intensity function lambda (t) of the artificial intelligence system is as follows:

m(t)＝m₁(t)+m₂(t)

λ(t)＝m’(t)＝λ₁(t)+λ₂(t)

wherein m (t) and lambda (t) are respectively a fault mean function and a failure intensity function of the artificial intelligent system, and m₁(t) and lambda₁(t) mean function and failure intensity function of the hardware system, m₂(t) and lambda₂And (t) respectively representing a fault mean function and a failure strength function of the software system.

The reliability of the artificial intelligence system reflects the probability that failure does not occur in the (t, t + delta t) time period, and the system reliability function calculation formula is as follows:

R(Δt|t)＝P{N(t+Δt)-N(t)＝0}

＝exp{-[m(t+Δt)-m(t)]}

on the basis of the embodiment, the invention uses the G-O model to model the software system and uses the power law model to model the reliability of the hardware system. The cumulative failure mean function of the G-O model is m (t) ═ a (1-exp (-bt)), and the cumulative failure mean function of the power law model is m (t) ═ at^b。

Calculating an accumulated fault function m (t) of the artificial intelligence system:

wherein a is_jAnd b_j(j ═ 1,2) is an unknown parameter.

Using maximum likelihood estimation, an estimate of the unknown parameters can be obtainedValue ofAndbased on the estimated value obtainedAndfurther, the reliability function of the artificial intelligence system can be obtained as follows:

R(Δt|t)＝exp{-[m(t+Δt)-m(t)]}

the reliability of the artificial intelligence system in different time periods can be obtained through the reliability function, and whether the artificial intelligence system needs to be adjusted or not can be determined according to the reliability.

Fig. 2 is a flowchart of a reliability evaluation method of an artificial intelligence system in a preferred embodiment of the present invention, as shown in fig. 2, the method includes:

201. collecting log files, sensor data and real-time operation data of an artificial intelligence system;

202. performing multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

203. processing the data of the multi-source heterogeneous fusion to obtain reliability data of an artificial intelligence system;

203A, calling reliability data of the artificial intelligence system in real time, and acquiring the average failure interval, the average fault repairing time and the average fault-free time of the artificial intelligence system according to the reliability data; respectively expressed as:

the mean failure interval MTBF is calculated as follows:

wherein, t₀<t₁<…<t_nThe time point of the artificial intelligence system fault is n, and the total number of faults observed in the artificial intelligence system is n;

the Mean Time To Repair (MTTR) of the fault is calculated as follows:

wherein, T_iThe time point of the fault corresponding to the artificial intelligent system is repaired;

the mean time to failure a is calculated as follows:

A＝MTBF/(MTBF+MTTR)。

data such as pictures, audios and texts are analyzed in a multi-source heterogeneous fusion mode, and failure data similar to that shown in table 1, namely reliability data required by the method can be obtained through analysis and calculation.

TABLE 1 reliability data

Wherein, t_iAnd T_iRespectively representing the time points of the fault occurrence and the repair of the artificial intelligence system, wherein TRUE and FALSE respectively represent the fault occurrence and the non-fault occurrence of the software/hardware system.

204. Constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process according to the reliability data;

205. and calculating a fault mean function and a failure intensity function of the artificial intelligence system to obtain the reliability evaluation of the artificial intelligence system.

Fig. 3 is a diagram of the architecture of a reliability evaluation system of an artificial intelligence system according to an embodiment of the present invention, as shown in fig. 3, the system includes:

301. the data acquisition module is used for acquiring log files, sensor data and real-time operation data of the artificial intelligence system;

302. the multi-source heterogeneous fusion module is used for carrying out multi-source heterogeneous fusion on the preprocessed log file, the preprocessed sensor data and the preprocessed real-time running data;

the multi-source heterogeneous data fusion module comprises an exception removing processing unit, a loss compensating processing unit and a repetition removing processing unit, wherein the exception removing processing unit is used for removing exceptions of the log file, the sensor data and the real-time running data; the loss compensation processing unit is used for performing loss compensation processing on the log file, the sensor data and the real-time operation data; the deduplication unit deduplicates the log file, the sensor data, and the real-time operation data.

303. The reliability data extraction module is used for processing the multi-source heterogeneous fusion data to obtain the reliability data of the artificial intelligence system;

the reliability evaluation system also comprises an online fault monitoring module which is used for monitoring the fault of the artificial intelligence system in real time and calling the reliability data to obtain the average failure interval, the average fault repairing time and the average non-fault time of the artificial intelligence system; respectively expressed as:

the mean failure interval MTBF is calculated as follows:

wherein, t₀<t₁<…<t_nThe time point of the artificial intelligence system fault is n, and the total number of faults observed in the artificial intelligence system is n;

the Mean Time To Repair (MTTR) of the fault is calculated as follows:

wherein, T_iThe time point of the fault corresponding to the artificial intelligent system is repaired;

the mean time to failure a is calculated as follows:

a is MTBF/(MTBF + MTTR). 304. And the reliability real-time evaluation module is used for constructing an accumulated fault number model of the artificial intelligence system by adopting an inhomogeneous poisson process, calculating a fault mean function and a failure intensity function of the artificial intelligence system and obtaining the reliability evaluation of the artificial intelligence system.

The reliability real-time evaluation module comprises a first model unit, a second model unit and a summation unit, wherein the first model unit acquires an accumulated fault mean function of a software system, and the second model unit acquires an accumulated fault mean function of a hardware system; and the summation unit sums the accumulated fault mean function of the software system and the accumulated fault mean function of the hardware system to obtain the accumulated fault function of the artificial intelligence system.

Fig. 4 is a structural diagram of a computer device in an embodiment of the present invention, and as shown in fig. 4, the computer device includes:

one or more processors 410;

a memory 430 for storing one or more programs;

when the one or more programs or the one or more processors 410 are executed, the one or more processors 410 implement the reliability assessment method of the artificial intelligence system according to the present invention, wherein the processor 410 and the memory 430 may be connected by a system bus 420.

In another possible design, when the computer device is a chip, the method includes: a processing unit, which may be for example a processor, and a communication unit, which may be for example an input/output interface, a pin or a circuit, etc. The processing unit may execute the computer-executable instructions stored in the storage unit, so as to enable the chip in the terminal to execute the reliability evaluation method of the artificial intelligence system according to any one of the first aspect. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, and the like, and the storage unit may also be a storage unit located outside the chip in the terminal, such as a read-only memory (ROM) or another type of static storage device that can store static information and instructions, a Random Access Memory (RAM), and the like.

The processor mentioned in any of the above may be a general purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the programs of the above methods.

It should be noted that the above-described embodiments of the apparatus are merely schematic, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiments of the apparatus provided in the present application, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be implemented as one or more communication buses or signal lines.

Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods described in the embodiments of the present application.

In the above embodiments, the implementation may be wholly or partially realized 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 instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.