阅读说明：本技术 基于蚁群算法的应急物资运输路径规划方法和装置 (Ant colony algorithm-based emergency material transportation path planning method and device ) 是由 梁光华 关劲夫 王静舞 于淼淼 王宇 刘磊 柏跃领 吴津津 李振铎 于 2021-06-28 设计创作，主要内容包括：本申请提供一种基于蚁群算法的应急物资运输路径规划方法和装置,该方案包括：通过获得目标卫星图,对所述卫星图像进行处理,以识别道路被破坏情况,并输出可利用的道路图层；根据灾情对应急物资需求量进行预测,查询应急物资存储情况,确定抽调应急物资的地点、数量及待支援地点；根据所述步骤S20和所述步骤S30建立目标路径优化图；通过蚁群优化算法对所述目标路径优化图进行最优化路径规划。由此,上述方案基于蚁群优化算法,对于应急救援物资路径进行最优化规划,不仅能保证在灾难发生后以最快的速度到达灾区进行救援,最大限度的保障人民生命和财产安全,还极大地降低了灾难带来的巨大损失。(The application provides an ant colony algorithm-based emergency material transportation path planning method and device, and the scheme comprises the following steps: processing the satellite image by obtaining a target satellite image to identify the road damage condition and outputting a usable road layer; predicting the demand of emergency materials according to disaster conditions, inquiring the storage condition of the emergency materials, and determining the location and the quantity of emergency materials to be drawn and dispatched and the location to be supported; establishing a target path optimization graph according to the step S20 and the step S30; and carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm. Therefore, the scheme is based on the ant colony optimization algorithm, the emergency rescue goods and materials path is optimally planned, the situation that the emergency rescue goods and materials reach a disaster area for rescue at the highest speed after a disaster occurs can be guaranteed, the life and property safety of people is guaranteed to the maximum extent, and huge loss caused by the disaster is greatly reduced.)

1. An ant colony algorithm-based emergency material transportation path planning method is characterized by comprising the following steps:

step S10, obtaining a target satellite map;

step S20, processing the satellite image to identify the road damage condition and output the usable road map layer;

step S30, predicting the demand of emergency materials according to the disaster, inquiring the storage condition of the emergency materials, and determining the location, the quantity and the location to be supported of the emergency materials;

step S40, establishing a target path optimization graph according to the step S20 and the step S30;

and step S50, carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm.

2. The ant colony algorithm-based emergency material transportation path planning method according to claim 1, wherein the step S10 specifically includes the following steps:

step S11, when a disaster happens, a two-dimensional plane picture containing an emergency rescue point and a target point is shot through a satellite map;

and S12, transmitting the shot two-dimensional plane pictures of the emergency rescue points and the target points to a ground emergency rescue control center through a wireless satellite communication technology.

3. The ant colony algorithm-based emergency material transportation path planning method according to claim 2, wherein the step S20 specifically includes the following steps:

step S21, preprocessing the multispectral image and the panchromatic image in ENVI5.3 software, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction in sequence;

step S22, fusing the processed multispectral image and the panchromatic image to obtain a color image with higher resolution;

and step S23, processing the color image with higher resolution by using a remote sensing image pattern recognition technology to recognize the position, the boundary outline and the road section of the damaged road, and performing supervision and classification by using ENVIClassic software to output a road layer.

4. The ant colony algorithm-based emergency material transportation path planning method according to claim 2, wherein the step S40 specifically includes the following steps:

step S41, discretizing the output two-dimensional planar picture into a square grid of a × b;

step S42, converting the two-dimensional plane picture after the dispersion into a 0-1 matrix, wherein a square grid with a path is set as 0, and a square grid without the path is set as 1;

and step S43, marking the starting node and the ending node of the path in the road map according to the prediction condition of the step S30.

5. The utility model provides an emergent goods and materials transportation route planning device based on ant colony algorithm which characterized in that includes:

the acquisition module is used for acquiring a target satellite map;

the identification module is used for processing the satellite images so as to identify the damaged condition of the road and output an available road layer;

the determining module is used for predicting the emergency material demand according to disaster situations, inquiring the storage condition of the emergency materials, and determining the location and the number of emergency materials to be extracted and dispatched and the location to be supported;

a building module, configured to build a target path optimization graph according to the step S20 and the step S30;

and the optimization module is used for carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm.

6. The ant colony algorithm-based emergency material transportation path planning device according to claim 5, wherein the obtaining module specifically includes:

the shooting unit is used for shooting a two-dimensional plane picture containing an emergency rescue point and a target point through a satellite picture when a disaster happens;

and the transmission unit is used for transmitting the shot two-dimensional plane pictures of the emergency rescue points and the shot two-dimensional plane pictures of the target points to a ground emergency rescue control center through a wireless satellite communication technology.

7. The ant colony algorithm-based emergency material transportation path planning device according to claim 6, wherein the identification module specifically comprises:

the processing unit is used for preprocessing the multispectral image and the panchromatic image in ENVI5.3 software, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction in sequence;

the fusion unit is used for fusing the processed multispectral image and the full-color image to obtain a color image with higher resolution;

and the classification unit is used for processing the color image with higher resolution by using a remote sensing image pattern recognition technology so as to recognize the position, the boundary outline and the road section of the damaged road, and performing supervision and classification by using ENVIClassic software to output a road layer.

8. The ant colony algorithm-based emergency material transportation path planning device according to claim 6, wherein the establishing module specifically includes:

a discretization unit, configured to discretize the output two-dimensional planar picture into a × b squares;

the conversion unit is used for converting the two-dimensional plane picture after the dispersion into a 0-1 matrix, wherein the square grids with paths are set as 0, and the square grids without paths are set as 1;

and a marking unit for marking the starting node and the ending node of the path in the road map according to the prediction condition of the step S30.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1-4 when executing the computer program.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1-4.

Technical Field

The application relates to the technical field of emergency material transportation, in particular to an ant colony algorithm-based emergency material transportation path planning method and device.

Background

In recent years, natural disasters occur frequently in countries around the world, and cause great loss to the human society. In particular, earthquake disasters have great self destructive power and are difficult to observe, so that the damage caused by the earthquake disasters is countless. How to timely and efficiently schedule emergency resources after a natural disaster occurs is one of the keys for ensuring the working effect of emergency rescue. Therefore, the research on the transportation path of the emergency rescue materials becomes a key point in the field of emergency management.

In the face of uncertain conditions possibly occurring in emergencies, damage to traffic network structures and road blocking conditions, a decision maker needs to decide to rapidly and effectively send emergency rescue materials to a destination. Therefore, real-time planning of transportation paths for transporting goods and materials is very important for emergency rescue.

However, in the prior art, the planning of the emergency rescue goods and materials path is far away from the practical feasible path, so that the emergency rescue goods and materials cannot reach the disaster area for rescue at the highest speed after a disaster occurs, and the safety of people's life and property is guaranteed to the maximum extent.

Disclosure of Invention

The present application is directed to solving, at least to some extent, one of the technical problems in the related art.

Therefore, a first objective of the present application is to provide an ant colony algorithm-based emergency material transportation path planning method, so as to implement real-time optimized planning on an emergency rescue material transportation path.

The second purpose of this application lies in providing an emergent goods and materials transportation route planning device based on ant colony algorithm.

A third object of the present application is to propose a computer device.

A fourth object of the present application is to propose a non-transitory computer-readable storage medium.

In order to achieve the above object, an embodiment of the first aspect of the present application provides an emergency material transportation path planning method based on an ant colony algorithm, including the following steps:

step S10, obtaining a target satellite map;

step S20, processing the satellite image to identify the road damage condition and output the usable road map layer;

step S30, predicting the demand of emergency materials according to the disaster, inquiring the storage condition of the emergency materials, and determining the location, the quantity and the location to be supported of the emergency materials;

step S40, establishing a target path optimization graph according to the step S20 and the step S30;

and step S50, carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm.

Optionally, in this embodiment of the application, the step S10 specifically includes the following steps:

step S11, when a disaster happens, a two-dimensional plane picture containing an emergency rescue point and a target point is shot through a satellite map;

and S12, transmitting the shot two-dimensional plane pictures of the emergency rescue points and the target points to a ground emergency rescue control center through a wireless satellite communication technology.

Optionally, in this embodiment of the application, the step S20 specifically includes the following steps:

step S21, preprocessing the multispectral image and the panchromatic image in ENVI5.3 software, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction in sequence;

step S22, fusing the processed multispectral image and the panchromatic image to obtain a color image with higher resolution;

and step S23, processing the color image with higher resolution by using a remote sensing image mode identification technology to identify the position, the boundary outline and the road section of the damaged road, performing supervision and classification by using ENVI Classic software, and outputting a road layer.

Optionally, in this embodiment of the application, the step S40 specifically includes the following steps:

step S41, discretizing the output two-dimensional planar picture into a square grid of a × b;

step S42, converting the two-dimensional plane picture after the dispersion into a 0-1 matrix, wherein a square grid with a path is set as 0, and a square grid without the path is set as 1;

and step S43, marking the starting node and the ending node of the path in the road map according to the prediction condition of the step S30.

In order to achieve the above object, an embodiment of a second aspect of the present application provides an emergency material transportation path planning device based on an ant colony algorithm, including:

the acquisition module is used for acquiring a target satellite map;

the identification module is used for processing the satellite images so as to identify the damaged condition of the road and output an available road layer;

the determining module is used for predicting the emergency material demand according to disaster situations, inquiring the storage condition of the emergency materials, and determining the location and the number of emergency materials to be extracted and dispatched and the location to be supported;

a building module, configured to build a target path optimization graph according to the step S20 and the step S30;

and the optimization module is used for carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm.

Optionally, in an embodiment of the present application, the obtaining module specifically includes:

the shooting unit is used for shooting a two-dimensional plane picture containing an emergency rescue point and a target point through a satellite picture when a disaster happens;

and the transmission unit is used for transmitting the shot two-dimensional plane pictures of the emergency rescue points and the shot two-dimensional plane pictures of the target points to a ground emergency rescue control center through a wireless satellite communication technology.

Optionally, in an embodiment of the present application, the identification module specifically includes:

the processing unit is used for preprocessing the multispectral image and the panchromatic image in ENVI5.3 software, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction in sequence;

the fusion unit is used for fusing the processed multispectral image and the full-color image to obtain a color image with higher resolution;

and the classification unit is used for processing the color image with higher resolution by using a remote sensing image mode identification technology so as to identify the position, the boundary outline and the road section of the damaged road, and performing supervision and classification by using ENVI Classic software to output a road layer.

Optionally, in an embodiment of the present application, the establishing module specifically includes:

a discretization unit, configured to discretize the output two-dimensional planar picture into a × b squares;

the conversion unit is used for converting the two-dimensional plane picture after the dispersion into a 0-1 matrix, wherein the square grids with paths are set as 0, and the square grids without paths are set as 1;

and a marking unit for marking the starting node and the ending node of the path in the road map according to the prediction condition of the step S30.

To achieve the above object, a third aspect of the present application provides a computer device, including: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to perform the method according to the embodiment of the first aspect of the present application.

In order to achieve the above object, a fourth aspect of the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program is configured to, when executed by a processor, implement the method according to the first aspect of the present application.

To sum up, according to the ant colony algorithm-based emergency material transportation path planning method, device, computer equipment and non-transitory computer-readable storage medium in the embodiment of the present application, the satellite image is processed by obtaining the target satellite image to identify the road damage condition, and output an available road layer; predicting the demand of emergency materials according to disaster conditions, inquiring the storage condition of the emergency materials, and determining the location and the quantity of emergency materials to be drawn and dispatched and the location to be supported; establishing a target path optimization graph according to the step S20 and the step S30; and carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm. Therefore, the scheme is based on the ant colony optimization algorithm, the emergency rescue goods and materials path is optimally planned, the situation that the emergency rescue goods and materials reach a disaster area for rescue at the highest speed after a disaster occurs can be guaranteed, the life and property safety of people is guaranteed to the maximum extent, and huge loss caused by the disaster is greatly reduced.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a flowchart of an emergency material transportation path planning method based on an ant colony algorithm according to an embodiment of the present application;

FIG. 2 is a simulation diagram of path planning in an embodiment of the present application;

FIG. 3 is a graph illustrating the trend of the convergence curve in the embodiment of the present application;

FIG. 4 is a flowchart of an ant colony algorithm in an embodiment of the present application; and

fig. 5 is a schematic structural diagram of an emergency material transportation path planning device based on an ant colony algorithm according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

The method and the device for classifying the fire of the building based on the K-means clustering in the embodiment of the application are described below with reference to the attached drawings.

Fig. 1 is a flowchart of an emergency material transportation path planning method based on an ant colony algorithm according to an embodiment of the present application.

As shown in fig. 1, an emergency material transportation path planning method based on an ant colony algorithm provided in an embodiment of the present application includes the following steps:

and step S10, obtaining a target satellite map.

In the embodiment of the application, the obtaining of the target satellite map specifically comprises the following steps:

step S11, when a disaster happens, a two-dimensional plane picture containing an emergency rescue point and a target point is shot through a satellite map;

and S12, transmitting the shot two-dimensional plane pictures of the emergency rescue points and the target points to a ground emergency rescue control center through a wireless satellite communication technology.

And step S20, processing the satellite images to identify the damaged road condition and outputting the usable road map layer.

In this embodiment of the present application, processing the satellite image to identify a road damage condition, and outputting an available road map layer specifically includes the following steps:

step S21, preprocessing the multispectral image and the panchromatic image in ENVI5.3 software, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction in sequence;

step S22, fusing the processed multispectral image and the panchromatic image to obtain a color image with higher resolution;

and step S23, processing the color image with higher resolution by using a remote sensing image mode identification technology to identify the position, the boundary outline and the road section of the damaged road, performing supervision and classification by using ENVI Classic software, and outputting a road layer.

And step S30, predicting the demand of the emergency materials according to the disaster, inquiring the storage condition of the emergency materials, and determining the location, the quantity and the location to be supported for sampling and adjusting the emergency materials.

Fig. 2 is a simulation diagram of path planning in the embodiment of the present application.

Step S40, a target path optimization graph is established according to step S20 and step S30, as shown in fig. 2.

In the application embodiment, the establishing of the target path optimization graph according to the steps S20 and S30 specifically includes the following steps:

step S41, discretizing the output two-dimensional planar picture into a square grid of a × b;

step S42, converting the two-dimensional plane picture after the dispersion into a 0-1 matrix, wherein a square grid with a path is set as 0, and a square grid without the path is set as 1;

and step S43, marking the starting node and the ending node of the path in the road map according to the prediction condition of the step S30.

And step S50, carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm.

FIG. 3 is a graph illustrating the trend of the convergence curve in the embodiment of the present application;

fig. 4 is a flowchart of an ant colony algorithm in the embodiment of the present application.

Further, as shown in fig. 3 and 4, the ant colony algorithm in the embodiment of the present application is expressed as follows:

at the initial moment of the algorithm, m ants are randomly placed on n nodes, and meanwhile, the first position of the tabu of each ant is set as the node where the ant is currently located. In this case, the amount of pheromone on each path is equal, let τ_ij(0) C (c is a small constant), and then each ant is unique according to the amount of pheromones remaining on the path and heuristic information (distance between two nodes)Immediately select the next node, at time t, the probability p that ant k transfers from node i to node j_ij ^k(t) is:

wherein, J_k(i)＝{1,2,…,n}—tabu_kRepresenting the set of nodes ant k is allowed to select next. In the formula eta_ijIs a heuristic factor that represents the expected degree of ant transfer from node i to node j. In the ant colony algorithm, eta_ijUsually the inverse of the distance between node i and node j is taken. Alpha and beta represent the relative importance of the pheromone heuristic and the desired heuristic, respectively.

When all ants complete one round trip, the pheromone on each path is updated according to the following formula:

τ_ij(t+n)＝(1-ρ)·τ_ij(t)+Δτ_ij (2)

wherein: ρ (0 < ρ < 1) represents the evaporation coefficient of pheromones on the path, 1- ρ represents the persistence coefficient of the pheromones; delta tau_ijRepresenting the increment of pheromone on the edge ij in the iteration; delta tau^k _ijRepresenting the amount of pheromone left on the edge ij by the kth ant in this iteration. If ant k does not pass edge ij, Δ τ^k _ijIs zero. Delta tau^k _ijExpressed as:

wherein Q is a normal number, L_kAnd the length of the path taken by the kth ant in the current round trip is shown.

Specifically, the ant colony algorithm in the embodiment of the present application is specifically implemented as follows:

(1) and initializing parameters. Let time t equal to 0 and cycle number N_cSetting the maximum circulation times G as 0, placing m ants on n nodes, and enabling the initialization information amount tau of each edge (i, j) on the directed graph_ij(t) ═ c, where c denotes a constant, and the initial time Δ τ_ij(0)＝0；

(2) Number of cycles N_c＝N_c+1；

(3) The taboo list index number k of the ant is 1;

(4) the ant number k is k + 1;

(5) the ant individual selects a node j according to the probability calculated by the state transition probability formula (1) and advances;

(6) modifying the taboo list pointer, namely moving the ants to a new node after selection, and moving the node to the taboo list of the ant individual;

(7) if the nodes in the set C are not traversed, namely k is less than m, jumping to the step 4; otherwise, executing the step 8;

(8) recording the current optimal route;

(9) updating the information amount on the daily path according to the formula (2) and the formula (3);

(10) if the end condition is satisfied. I.e. if the number of cycles N_cIf not, the circulation is ended and a program optimization result is output, otherwise, the taboo table is emptied and the step 2 is skipped.

In summary, according to the ant colony algorithm-based emergency material transportation path planning method in the embodiment of the application, the satellite image is processed by obtaining the target satellite image so as to identify the road damage condition, and the available road map layer is output; predicting the demand of emergency materials according to disaster conditions, inquiring the storage condition of the emergency materials, and determining the location and the quantity of emergency materials to be drawn and dispatched and the location to be supported; establishing a target path optimization graph according to the step S20 and the step S30; and carrying out optimization path planning on the target path optimization graph through an ant colony optimization algorithm. Therefore, the scheme is based on the ant colony optimization algorithm, the emergency rescue goods and materials path is optimally planned, the situation that the emergency rescue goods and materials reach a disaster area for rescue at the highest speed after a disaster occurs can be guaranteed, the life and property safety of people is guaranteed to the maximum extent, and huge loss caused by the disaster is greatly reduced.

Fig. 5 is a schematic structural diagram of an emergency material transportation path planning device based on an ant colony algorithm according to an embodiment of the present application.

As shown in fig. 5, an emergency material transportation path planning device based on an ant colony algorithm provided in an embodiment of the present application includes:

the acquisition module 10 is used for acquiring a target satellite map;

the identification module 20 is used for processing the satellite images to identify the damaged condition of the road and output an available road map layer;

the determining module 30 is used for predicting the emergency material demand according to disaster situations, inquiring the emergency material storage condition, and determining the location and the number of emergency material sampling and adjusting and the location to be supported;

a building module 40, configured to build a target path optimization graph according to the step S20 and the step S30;

and the optimization module 50 is configured to perform an optimized path planning on the target path optimization graph through an ant colony optimization algorithm.

Further, the obtaining module in the embodiment of the present application specifically includes:

the shooting unit is used for shooting a two-dimensional plane picture containing an emergency rescue point and a target point through a satellite picture when a disaster happens;

and the transmission unit is used for transmitting the shot two-dimensional plane pictures of the emergency rescue points and the shot two-dimensional plane pictures of the target points to a ground emergency rescue control center through a wireless satellite communication technology.

Further, the identification module in the embodiment of the present application specifically includes:

the processing unit is used for preprocessing the multispectral image and the panchromatic image in ENVI5.3 software, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction in sequence;

the fusion unit is used for fusing the processed multispectral image and the full-color image to obtain a color image with higher resolution;

and the classification unit is used for processing the color image with higher resolution by using a remote sensing image mode identification technology so as to identify the position, the boundary outline and the road section of the damaged road, and performing supervision and classification by using ENVI Classic software to output a road layer.

Further, the establishing module in the embodiment of the present application specifically includes:

a discretization unit, configured to discretize the output two-dimensional planar picture into a × b squares;

the conversion unit is used for converting the two-dimensional plane picture after the dispersion into a 0-1 matrix, wherein the square grids with paths are set as 0, and the square grids without paths are set as 1;

and a marking unit for marking the starting node and the ending node of the path in the road map according to the prediction condition of the step S30.

In order to implement the foregoing embodiment, the present application further provides a computer device, which is characterized by comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the method for planning an emergency material transportation path based on an ant colony algorithm according to the foregoing embodiment is implemented.

In order to implement the foregoing embodiments, the present application further proposes a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the ant colony algorithm-based emergency material transportation path planning method described in the foregoing embodiments.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.