阅读说明：本技术 实车服役工况的碳钢动态大气腐蚀影响因素挖掘及腐蚀速率预测方法、电子设备及存储介质 (Carbon steel dynamic atmospheric corrosion influence factor mining and corrosion rate prediction method under actual vehicle service working condition, electronic equipment and storage medium ) 是由 宋肖肖 张超 汪科宇 陈亚军 王付胜 郝岸霆 于 2021-08-03 设计创作，主要内容包括：本申请公开了一种实车服役工况的碳钢动态大气腐蚀影响因素挖掘及腐蚀速率预测方法、电子设备及存储介质,属于腐蚀数据挖掘及腐蚀速率预测技术领域。解决了现有预测方法无法适用于交通工具实际服役状况的问题。本申请采集试验车辆行驶特征数据及静态大气环境因素；挖掘计算车辆行驶特征关键因素；利用MATLAB计算时间权值；确定关键参数；采用机器学习算法构建腐蚀速率预测模型；预测碳钢动态大气腐蚀速率。本申请建立金属材料动态大气腐蚀关键影响因素挖掘及腐蚀速率预测模型具有重要的意义与价值。(The application discloses a carbon steel dynamic atmospheric corrosion influence factor mining and corrosion rate prediction method under actual vehicle service working conditions, electronic equipment and a storage medium, and belongs to the technical field of corrosion data mining and corrosion rate prediction. The problem that the existing prediction method cannot be applied to the actual service condition of the vehicle is solved. The method comprises the steps of collecting driving characteristic data of a test vehicle and static atmospheric environment factors; excavating and calculating key factors of vehicle running characteristics; calculating a time weight by using MATLAB; determining key parameters; constructing a corrosion rate prediction model by adopting a machine learning algorithm; and predicting the dynamic atmospheric corrosion rate of the carbon steel. The method has important significance and value for establishing the mining of key influence factors of dynamic atmospheric corrosion of the metal material and the prediction model of the corrosion rate.)

1. The method for mining carbon steel dynamic atmospheric corrosion influence factors and predicting the corrosion rate under the service working condition of the real vehicle is characterized by comprising the following steps of:

step 1: collecting test vehicle running characteristic data, test city meteorological station information, test city meteorological data and test city pollutant data; the information of the test urban meteorological station, the test urban meteorological data and the test urban pollutant data form static atmospheric environment factors;

step 2: mining and calculating key factors of vehicle running characteristics based on test vehicle running characteristic data;

and step 3: 1, calculating the time weight of the information of the test urban weather station by using MATLAB according to the running characteristic data of the test vehicle collected in the step 1;

and 4, step 4: calculating time weighted values of the test urban meteorological data and the test urban pollutant data;

and 5: determining key parameters of vehicle running characteristics and key parameters of a static atmospheric environment;

step 6: constructing a corrosion rate prediction model by adopting a machine learning algorithm;

and 7: and predicting the dynamic atmospheric corrosion rate of the carbon steel.

2. The method for mining carbon steel dynamic atmospheric corrosion influencing factors and predicting the corrosion rate under the actual vehicle service condition according to claim 1, wherein dimension reduction processing is added between the step 2 and the step 3, and specifically, dimension reduction processing is performed on key factors of running characteristics of a test vehicle by using a dimension optimization algorithm.

3. The method for mining carbon steel dynamic atmospheric corrosion influencing factors and predicting the corrosion rate under the actual vehicle service condition according to claim 1, wherein in the step 1, the test vehicle running characteristic data at least comprises the following data: longitude and latitude information, running speed information, vehicle running state information, running mileage information and acquisition time information; the test city weather station information at least comprises address and coordinate information; the test urban meteorological data at least comprises temperature and relative humidity data; the test urban pollutant data comprises sulfur dioxide data or sulfur dioxide data and chloride ion deposition amount data.

4. The method for mining carbon steel dynamic atmospheric corrosion influencing factors and predicting the corrosion rate under the actual vehicle service condition according to claim 1, wherein in the step 2, the key factors of the vehicle running characteristics at least comprise a dynamic-static ratio gamma; the average speed V of the vehicle is in m/s; test time t_tIn units of days; stationary time t of vehicle_sIn units of days; vehicle running time t_rIn units of days; the driving mileage L of the vehicle is m, and the dynamic-static ratio gamma is t_r/t_sThe average speed V of the vehicle is L/86400t_r。

5. The method for mining carbon steel dynamic atmospheric corrosion influencing factors and predicting corrosion rate under real vehicle service working conditions according to claim 2, wherein the dimension optimization algorithm comprises the following steps: dynamic fuzzy clustering algorithm, principal component analysis method and K-means dynamic clustering algorithm.

6. The method for mining carbon steel dynamic atmospheric corrosion influence factors and predicting corrosion rate under real vehicle service conditions according to any one of claims 1 to 5, wherein in the step 3, the time weight for calculating the test urban weather station information is specifically as follows:

step 31: calculating the space Euclidean distance D between longitude and latitude data acquired during the running process of the crown block vehicle at the test time q and each meteorological station_ijWherein i is 1,2,3,4The index number, j ═ 1,2,3,4.. m, represents the weather station serial number;

step 32: determiningmm_iRepresenting the shortest distance between the collected vehicle driving longitude and latitude data and the distance of each meteorological station, and counting mm_iFrequency f of occurrence of corresponding weather station_j＝num(j)；

Step 33: calculating the time weight of each weather station

7. The method for mining carbon steel dynamic atmospheric corrosion influencing factors and predicting corrosion rate under real vehicle service conditions as claimed in claim 6, wherein in step 4, time weighted values of test urban meteorological data and test urban pollutant data are calculated according to a formulaCalculating a time-weighted value C of each static atmospheric factor; c is the value of the weighted influence factor in a certain test section, C_jIs the average of the static atmospheric influences of the weather station j over the test period.

8. The method for mining carbon steel dynamic atmospheric corrosion influencing factors and predicting the corrosion rate under the actual vehicle service condition according to claim 6, wherein in the step 5, key parameters of vehicle running characteristics are as follows: average speed, dynamic-static ratio; the static atmospheric environment key parameters include: weighted average temperature, weighted average humidity, and weighted sulfur dioxide deposition rate and chloride ion deposition rate; in step 7, the machine learning algorithm includes: a genetic algorithm model, a neural network model, a random forest algorithm model method and a support vector regression algorithm model.

9. An electronic device, characterized in that: comprising a processor and a memory for storing a computer program capable of running on the processor,

wherein the processor is adapted to perform the steps of the method of any one of claims 1 to 8 when running the computer program.

10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 8.

Technical Field

The application relates to the technical field of corrosion data mining and corrosion rate prediction, in particular to a method for mining key influence factors of dynamic atmospheric corrosion of carbon steel and predicting corrosion rate based on service working conditions of a real vehicle, electronic equipment and a storage medium.

Background

Corrosion has spread throughout the industry, and until today, the wealth of human painstaking creations has been engulfed by dramatic values. However, the research aiming at the corrosion of metal materials in atmospheric environment is mainly based on static exposure test, although the method can truly reflect the actual service conditions of stationary metal facilities and buildings, for vehicles, airplanes, ships and other moving vehicles, the actual service conditions are the combination of dynamic driving and static parking. When the vehicle is stationary, the corrosion of the metallic material is substantially the same as the static atmospheric corrosion; when the vehicle runs, not only the service environment of the metal material changes along with the motion of the vehicle, but also the running state and the running road condition of the vehicle influence the corrosion of the metal material. Therefore, corrosion of metallic materials used in vehicles is not only related to natural environmental factors but also closely related to the driving route conditions and the driving characteristics of the vehicle itself. The corrosion rule under the real service working condition is far away from the static atmosphere exposure test, so that the research on the dynamic atmospheric corrosion mechanism of the metal material based on the real service working condition of the vehicle is developed, and the important significance and value are realized by establishing the mining method for the key influence factors of the dynamic atmospheric corrosion of the metal material and the corrosion rate prediction model.

Disclosure of Invention

In view of the above, the application provides a carbon steel dynamic atmospheric corrosion influence factor mining and corrosion rate prediction method under an actual vehicle service condition, an electronic device and a storage medium, so as to solve the problem that the existing prediction method cannot be applied to the actual service condition of a vehicle.

The technical scheme of the application is realized as follows:

the first scheme is as follows: the method for mining carbon steel dynamic atmospheric corrosion influence factors and predicting corrosion rate under the service working condition of a real vehicle comprises the following steps:

1. the method for mining carbon steel dynamic atmospheric corrosion influence factors and predicting the corrosion rate under the service working condition of the real vehicle is characterized by comprising the following steps of:

step 1: collecting test vehicle running characteristic data, test city meteorological station information, test city meteorological data and test city pollutant data; the information of the test urban meteorological station, the test urban meteorological data and the test urban pollutant data form static atmospheric environment factors;

step 2: mining and calculating key factors of vehicle running characteristics based on test vehicle running characteristic data;

and step 3: 1, calculating the time weight of the information of the test urban weather station by using MATLAB according to the running characteristic data of the test vehicle collected in the step 1;

and 4, step 4: calculating time weighted values of the test urban meteorological data and the test urban pollutant data;

and 5: determining key parameters of vehicle running characteristics and key parameters of a static atmospheric environment;

step 6: constructing a corrosion rate prediction model by adopting a machine learning algorithm;

and 7: and predicting the dynamic atmospheric corrosion rate of the carbon steel.

Further, dimension reduction processing is added between the step 2 and the step 3, specifically, dimension reduction processing is carried out on key factors of the running characteristics of the test vehicle by using a dimension optimization algorithm.

Further, in step 1, the test vehicle driving characteristic data at least includes: longitude and latitude information, running speed information, vehicle running state information, running mileage information and acquisition time information; the test city weather station information at least comprises address and coordinate information; the test urban meteorological data at least comprises temperature and relative humidity data; the test urban pollutant data comprises sulfur dioxide data or sulfur dioxide data and chloride ion deposition amount data.

Further, in step 2, the key factors of the vehicle running characteristics at least comprise dynamic-static ratioGamma; the average speed V of the vehicle is in m/s; test time t_tIn units of days; stationary time t of vehicle_sIn units of days; vehicle running time t_rIn units of days; the driving mileage L of the vehicle is m, and the dynamic-static ratio gamma is t_r/t_sThe average speed V of the vehicle is L/86400t_r。

Further, the dimension optimization algorithm includes: dynamic fuzzy clustering algorithm, principal component analysis method and K-means dynamic clustering algorithm.

Further, in step 3, calculating the time weight of the test city weather station information specifically comprises:

step 31: calculating the space Euclidean distance D between longitude and latitude data acquired during the running process of the crown block vehicle at the test time q and each meteorological station_ijWherein, i is 1,2,3,4.. n represents the number of longitude and latitude coordinates collected in the test section, and j is 1,2,3,4.. m represents the serial number of the meteorological station;

step 32: determiningmm_iRepresenting the shortest distance between the collected vehicle driving longitude and latitude data and the distance of each meteorological station, and counting mm_iFrequency f of occurrence of corresponding weather station_j＝num(j)；

Step 33: calculating the time weight of each weather station

Further, in step 4, calculating time weighted value of the test city meteorological data and the test city pollutant data according to formulaCalculating a time-weighted value C of each static atmospheric factor; c is the value of the weighted influence factor in a certain test section, C_jIs the average of the static atmospheric influences of the weather station j over the test period.

Further, in step 5, the key parameters of the driving characteristics of the vehicle are as follows: average speed, dynamic-static ratio; the static atmospheric environment key parameters include: weighted average temperature, weighted average humidity, and weighted sulfur dioxide deposition rate and chloride ion deposition rate; in step 7, the machine learning algorithm includes: a genetic algorithm model, a neural network model, a random forest algorithm model method and a support vector regression algorithm model.

Scheme II: an electronic device comprising a processor and a memory for storing a computer program capable of running on the processor,

wherein the processor is configured to perform the steps of the method of aspect one when running the computer program. .

The third scheme is as follows: a storage medium having stored thereon a computer program for execution by a processor to perform the steps of implementing a method as described herein.

The application has beneficial effects that:

the application fully considers the vehicle, and the real service working condition of the vehicle is the combination of dynamic driving and static parking. When the vehicle is stationary, the corrosion of the metallic material is substantially the same as the static atmospheric corrosion; when the vehicle runs, the service environment of the metal material changes along with the motion of the vehicle, and the running state and the running road condition of the vehicle also influence the corrosion of the metal material. The corrosion of metallic materials used on vehicles by the present application combines natural environmental factors with vehicle travel characteristics. The method is a metal material dynamic atmospheric corrosion mechanism research based on the real service working condition of the vehicle, and has important significance and value in establishing a metal material dynamic atmospheric corrosion key influence factor mining and corrosion rate prediction model.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a flow chart of a carbon steel dynamic atmospheric corrosion influence factor mining and corrosion rate prediction method under a real vehicle service condition according to the application;

FIG. 2 is a dynamic fuzzy clustering hierarchy map of the present application;

FIG. 3 is a logic diagram of a genetic algorithm coupled neural network of the present application;

FIG. 4 is a graph illustrating the results of GA-BP training data in accordance with the present application;

fig. 5 is a schematic diagram of an electronic device according to the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant application and are not limiting of the application. It should be noted that, for the convenience of description, only the portions relevant to the application are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Example one

The embodiment of the application provides a carbon steel dynamic atmospheric corrosion influence factor mining and corrosion rate predicting method under the service working condition of a real vehicle (see figure 1), which comprises the following steps:

step 1: collecting test vehicle running characteristic data, test city meteorological station information, test city meteorological data and test city pollutant data; the information of the test urban meteorological station, the test urban meteorological data and the test urban pollutant data form static atmospheric environment factors;

in step 1, the test vehicle driving characteristic data at least comprises: longitude and latitude information, running speed information, vehicle running state information, running mileage information and acquisition time information; the test city weather station information at least comprises address and coordinate information; the test urban meteorological data at least comprises temperature and relative humidity data; the data of the urban pollutant test at least comprises data of the deposition amount of sulfur dioxide and chloride ions.

Step 2: mining and calculating key factors of vehicle running characteristics based on test vehicle running characteristic data;

in step 2The key factors of the vehicle running characteristics at least comprise a dynamic-static ratio gamma; the average speed V of the vehicle is in m/s; test time t_tIn units of days; stationary time t of vehicle_sIn units of days; vehicle running time t_rIn units of days; the vehicle driving mileage L is m; the dynamic-static ratio gamma is t_r/t_sThe average speed V of the vehicle is L/86400t_r。

And step 3: performing dimension reduction processing on key factors of the running characteristics of the test vehicle by using a dimension optimization algorithm;

the dimension optimization algorithm includes, but is not limited to: dynamic fuzzy clustering algorithm, principal component analysis method and K-means dynamic clustering algorithm.

And 4, step 4: 1, calculating the time weight of the information of the test urban weather station in a certain test section by using MATLAB (matrix laboratory) according to the running characteristic data of the test vehicle collected in the step 1;

in step 4, the time weight for calculating the information of the test urban weather station specifically comprises the following steps:

step 41: calculating the space Euclidean distance D between longitude and latitude data acquired during the running process of the crown block vehicle at the test time q and each meteorological station_ijWherein, i is 1,2,3,4.. n represents the number of longitude and latitude coordinates collected in the test section, and j is 1,2,3,4.. m represents the serial number of the meteorological station;

step 42: determiningmm_iRepresenting the shortest distance between the collected vehicle driving longitude and latitude data and the distance of each meteorological station, and counting mm_iFrequency f of occurrence of corresponding weather station_j＝num(j)；

Step 43: calculating the time weight of each weather station

And 5: calculating time weighted values of the test urban meteorological data and the test urban pollutant data;

in step 5, the experimental city meteorological data is calculated andtesting the time weighted value of the urban pollutant data according to a formulaCalculating a time-weighted value C of each static atmospheric factor; c is the value of the weighted influence factor in a certain test section, C_jIs the average of the static atmospheric influences of the weather station j over the test period.

Step 6: determining key parameters of vehicle running characteristics and key parameters of a static atmospheric environment;

in step 6, the key parameters of the vehicle running characteristics are as follows: average speed, dynamic-static ratio; the static atmospheric environment key parameters include: weighted average temperature, weighted average humidity, and weighted sulfur dioxide deposition rate and chloride ion deposition rate.

And 7: constructing a corrosion rate prediction model by adopting a machine learning algorithm;

in step 7, the machine learning algorithm includes but is not limited to: a genetic algorithm model, a neural network model, a random forest algorithm model method and a support vector regression algorithm model.

And 8: and predicting the dynamic atmospheric corrosion rate of the carbon steel.

In the embodiment of the present application, the metal material atmospheric corrosion test based on the working conditions of the real vehicle, which has been developed in Tianjin, is taken as an example, and the logic flow steps and the specific embodiment shown in fig. 1 are combined to further describe the present application.

The specific test time is 29 days 8 and 29 months in 2018 to 29 days 8 and 29 months in 2020, and the total time is 2 years. The sampling interval for the first year was 3 months and the sampling interval for the second year was 6 months, and a total of 31 sets of effective corrosion rate (μm/year) data were obtained for the 2 year test. The data are as follows: corrosion rate ═ 24.8; 19.79; 18.94; 18.85; 18.36; 18.04; 16.83; 16.4; 16.39; 15.28; 15.16; 15.02; 14.29; 14.12; 13.57; 13.54; 13.23; 13.18; 12.11; 11.89; 11.78; 11.62; 10.76; 9.93; 9.85; 9.85; 9.35; 9.33; 7.11; 5.7; 5.5)

The vehicle characteristic data that traveles is through installing on-vehicle positioning data collection transmission system collection on every special test vehicle, sets for the characteristic data that traveles that once gathered test vehicle every 5 second time interval, includes: latitude and longitude information, running speed information, vehicle state information, running mileage information, acquisition time information and the like.

Based on the collected vehicle running characteristic data, corresponding to each effective corrosion rate data test time, respectively counting: dynamic-static ratio gamma, vehicle average speed V (m/s) and test time t_t(day), vehicle stationary time t_s(days), vehicle running time t_r(day) and vehicle driving mileage L (m), wherein the dynamic-static ratio gamma is t ═ t_r/t_sThe average speed V of the vehicle is L/86400t_r. The data are shown in table 1.

TABLE 1 vehicle Driving characteristics Key data

And clustering analysis is carried out on key factors of the vehicle running characteristics by using a dynamic fuzzy clustering algorithm, so that the optimization of multivariate data dimensionality is realized, and the data dimensionality is reduced. The specific calculation is carried out according to the following steps:

step one, establishing a data matrix. Let the domain A ═ x₁,x₂,···,x_kThe objects to be classified are represented by r indexes, that is, a characteristic index matrix a ═ x_uv)_k×r. Where k is 6, each represents a dynamic-static ratio γ, a vehicle average speed V (m/s), and a test time t_t(day), vehicle stationary time t_s(days), vehicle running time t_r(days), vehicle mileage L (m). r corresponds to 31 effective corrosion rate test data.

And step two, carrying out data dimensionless. Different sequences have different properties and differ by a large number of orders of magnitude, so that data generally needs to be subjected to non-dimensionalization processing. This example uses the range positiveThe specification is subjected to dimensionless method, namely:wherein the content of the first and second substances,

step three, the present embodiment uses the angle cosine method to construct the fuzzy similar matrix H,

then H ═ H_uv)_k×r；

Step four, synthesizing the transmission closure matrix G (H) ═ H by using a flat method^lIn this embodiment, l is 16.

And fifthly, constructing a dynamic clustering hierarchy map.

The constructed clustering hierarchy map according to the dynamic fuzzy clustering algorithm is shown in fig. 2.

And (3) displaying a clustering result: the relevance of the rest time, the running time, the driving mileage, the test time and the average speed of the vehicle is more than 0.87, and the average speed of the vehicle is selected as a key factor of the running characteristic of the vehicle by comprehensively considering the non-accumulative amount of the average speed of the vehicle, which is the same as the corrosion rate. Finally, through clustering analysis, the average speed and the dynamic-static ratio of the vehicle are optimized to be used as key index factors of the vehicle running characteristics for final modeling.

For the collection of atmospheric data related to static atmospheric factors, the addresses and specific coordinates of all weather stations in Tianjin are determined first. The total number of the plants is 23, and the plants relate to each urban area of Tianjin city. As shown in table 2.

Table 2 information of each weather station in city Tianjin tested in this embodiment

In this embodiment, data of all weather stations in table 2 during 29.8.2018-29.2020-8.29 are collected, specifically including test urban weather data (temperature and relative humidity data) and test urban pollutant data (sulfur dioxide and chloride ion deposition amount data). Test time corresponding to each effective corrosion rate data according to formulaAnd calculating the average values of the temperature and humidity, the sulfur dioxide and the chloride ion deposition rate in each weather station. In this example, d is 1.

In the embodiment, the shortest test interval is 3 months, and the dimensionality of the acquired vehicle running characteristic data also has millions of data groups, so that the time weight r of each meteorological station in a certain test period is calculated by using MATLAB according to the following steps_j。

Step A: calculating the space Euclidean distance D between longitude and latitude data collected during the running process of the crown block vehicle at the test time q and each meteorological station_ijWherein, i is 1,2,3,4.. n represents the number of longitude and latitude coordinates collected in the test section, and j is 1,2,3,4.. m represents the serial number of the meteorological station.

And B: determiningStatistics mm_iFrequency f of occurrence of corresponding weather station_j＝num(j)。

And C: calculating the time weight in a certain test section of each meteorological station

Finally according to the formulaA time-weighted value C for each static atmospheric factor is calculated. C is the weighted shadow in a certain test sectionValue of noise factor, C_jIs the average value of a certain static influencing factor of the meteorological station j in the test section.

The weighted data of the key parameters of the static atmospheric environment calculated according to the effective corrosion rate data in the above steps are shown in table 3:

TABLE 3 weighted static atmospheric environmental key parameter data

Finally, the determined key parameters of the vehicle running characteristics in the embodiment are as follows: average speed, dynamic-static ratio; the static atmospheric environment key parameters include: weighted average temperature, weighted average humidity, and weighted sulfur dioxide deposition rate and chloride ion deposition rate.

In the embodiment, a genetic algorithm coupled neural network model (GA-BP) is used for constructing a corrosion rate prediction model, and the logic flow of the GA-BP is shown in FIG. 3. The operation parameters required to be determined for genetic algorithm initialization mainly comprise: population size N, number of inheritance G, crossover probability P_CAnd the mutation probability P_MAs shown in table 4.

TABLE 4 main operating parameters of the genetic Algorithm

And randomly selecting 5 groups of preprocessed test data as verification samples of the GA-BP model, and using the rest 26 groups of test data as training samples. With RMSE and R²As the evaluation index, see formula 1 and formula 2. The training data results are shown in fig. 4, where all data are located near y-x, indicating that the predicted data are close to the measured data.

The corrosion rate pairs predicted by the validation samples and the neural network model are shown in table 5. As can be seen from Table 5, the dynamic corrosion of the carbon steel in Tianjin is well predicted by a model established under the condition of comprehensively considering the key factors of the static atmospheric environment and the key parameters of the driving characteristics of the vehicle.

Table 5 test data test results

Example two

An electronic device is provided in the second embodiment of the present application, and referring to fig. 5, the electronic device is represented in the form of a general-purpose computing device. Components of the electronic device may include, but are not limited to: one or more processors or processing units, a memory for storing a computer program capable of running on the processor, a bus connecting the various system components (including the memory, the one or more processors or processing units).

Wherein the one or more processors or processing units are configured to execute the steps of the method according to one or both embodiments when the computer program is run. The type of processor used includes central processing units, general purpose processors, digital signal processors, application specific integrated circuits, field programmable gate arrays or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

Where a bus represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

EXAMPLE III

A third embodiment of the present application provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method of the first embodiment.

It should be noted that the storage media described herein can be computer readable signal media or storage media or any combination of the two. A 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 storage media 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 the present application, a 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 this application, however, the storage medium may comprise a propagated data signal with the 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 storage medium may also be any computer-readable medium 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: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The above-mentioned embodiments are provided to further explain the purpose, technical solutions and advantages of the present application in detail, and it should be understood that the above-mentioned embodiments are only examples of the present application and are not intended to limit the scope of the present application, and any modifications, equivalents, improvements and the like made on the basis of the technical solutions of the present application should be included in the scope of the present application.