CN111224825B - Edge autonomous model construction device - Google Patents
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Abstract
本发明公开了一种边缘自治模型构建装置，所述装置包括：信息获取模块，用于获取应用场景的数据信息；信息分类模块，用于根据预设服务类型对所述数据信息进行分类生成分类后的数据信息；信息关联匹配模块，用于将所述分类后的数据信息和预设计算模式进行匹配关联生成匹配关联后的数据信息；模型生成模块，用于将所述匹配关联后的数据信息进行建模生成边缘自治模型。因此，采用本申请实施例，可以提高任务执行效率。
The invention discloses a device for constructing an edge autonomous model. The device comprises: an information acquisition module for acquiring data information of an application scenario; an information classification module for classifying the data information according to a preset service type to generate a classification The information association matching module is used to match and associate the classified data information and the preset calculation mode to generate the matched and associated data information; the model generation module is used to associate the matched and associated data. Information is modeled to generate edge autonomy models. Therefore, by adopting the embodiments of the present application, the task execution efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of software, in particular to an edge autonomous model construction device.
Background
With the rapid development of the internet of things technology, the electronic equipment of intelligent transportation, the electronic equipment of unmanned aerial vehicle group monitoring and the electronic equipment of intelligent home are combined with the internet of things technology, so that the electronic equipment is intelligent, and more convenience is brought to the life of people.
At present, the smart device that has combined internet of things can produce certain data message and upload to the high in the clouds and handle in the course of the work, can bring huge pressure for the high in the clouds when the data accumulation of high in the clouds is too much, brings huge pressure for the high in the clouds in order to solve, adopts traditional cloud to calculate at present and carries out high in the clouds pressure and alleviate, and traditional cloud calculates because the time delay is great to the information interaction efficiency between the smart device has been reduced.
Disclosure of Invention
The embodiment of the application provides an edge autonomous model building device. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The embodiment of the application provides an edge autonomous model building device, which comprises:
the information acquisition module is used for acquiring data information of an application scene;
the information classification module is used for classifying the data information according to a preset service type to generate classified data information;
the information association matching module is used for carrying out matching association on the classified data information and a preset computing mode to generate matched and associated data information;
and the model generation module is used for modeling the matched and associated data information to generate an edge autonomous model.
Optionally, the apparatus further comprises:
and the information acquisition module is used for acquiring the equipment information and generating the data information of the application scene after the equipment information is stored.
Optionally, the apparatus further comprises:
the task acquisition module is used for acquiring a target task;
and the task completion module is used for completing the edge autonomy and the cooperative calculation of the target task based on the edge autonomy model.
Optionally, the task completion module includes:
a first protocol acquisition unit, configured to acquire a network protocol of a peer-to-peer network;
a second protocol acquisition unit configured to acquire a network protocol of the content distribution network;
a protocol generation unit, configured to input a network protocol of the content distribution network into a network protocol of the peer-to-peer network, and generate an input network protocol of the peer-to-peer network;
a communication implementation unit, configured to implement, based on the input network protocol of the peer-to-peer network, peer-to-peer communication between devices in a preset device set;
and the task completion unit is used for completing the edge autonomy and the cooperative calculation of the target task according to the edge autonomy model and the point-to-point communication.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, data information of an application scene is obtained firstly, then the data information is classified according to a preset service type to generate classified data information, then the classified data information is matched and associated with a preset calculation mode to generate matched and associated data information, and finally the matched and associated data information is modeled to generate an edge autonomous model. According to the scheme, information interaction between the devices is processed by constructing the heterogeneous edge device collaborative calculation and the edge autonomous model, so that not only can the single-point load of the system be effectively reduced, but also the task execution efficiency can be greatly improved, the information collaborative interaction between the devices can be improved, and the method and the device can be more conveniently applied to application scenes such as intelligent transportation, unmanned plane group monitoring and the like.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flowchart of a method for constructing an edge autonomic model according to an embodiment of the present application;
FIG. 2 is a process diagram of an edge autonomic model building process provided in an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating a firmware security detection before an edge autonomic model is built according to an embodiment of the present application;
FIG. 4 is a schematic diagram of system security detection before an edge autonomic model is constructed according to an embodiment of the present application;
fig. 5 is a schematic diagram of service security detection before an edge autonomic model is constructed according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an edge autonomic model building apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all 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.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Up to now, the smart machine that has combined internet of things can produce certain data message and upload to the high in the clouds and handle in the course of the work, can bring huge pressure for the high in the clouds when the data accumulation of high in the clouds is too much, brings huge pressure for the high in the clouds in order to solve, adopts traditional cloud to calculate at present and carries out high in the clouds pressure and alleviate, traditional cloud calculates because the time delay is great to the information interaction efficiency between the smart machine has been reduced. Therefore, the present application provides a method, an apparatus, a storage medium, and a terminal for constructing an edge autonomic model, so as to solve the problems in the related art. In the technical scheme provided by the application, since the information interaction between the devices is processed by constructing the heterogeneous edge device cooperative computing and the edge autonomous model, the single-point load of the system can be effectively reduced, the task execution efficiency can be greatly improved, the information cooperative interaction between the devices can be improved, and the method and the device can be more conveniently applied to application scenes such as intelligent transportation, unmanned cluster monitoring and the like.
The edge autonomic model construction method provided by the embodiment of the present application will be described in detail below with reference to fig. 1 to 3. The method may be implemented in dependence on a computer program, operable on a von neumann architecture-based edge autonomous model building apparatus. The computer program may be integrated into the application or may run as a separate tool-like application. The edge autonomic model building device in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Referring to fig. 1, a schematic flow chart of a method for constructing an edge autonomic model according to an embodiment of the present application is provided. As shown in fig. 1, the method of the embodiment of the present application may include the steps of:
s101, acquiring data information of an application scene;
the data information of the application scene is data information generated by intelligent electronic equipment deployed in the application scene, for example, data information generated by electronic equipment corresponding to municipal service management, data information generated by electronic equipment corresponding to urban traffic coordination, information generated by electronic equipment corresponding to public screen analysis, data information generated by electronic equipment corresponding to equipment implementation monitoring, and data information generated in electronic equipment corresponding to emergency handling.
In the embodiment of the application, when the edge autonomic model is constructed, data information generated by intelligent electronic equipment deployed in an application scene needs to be collected and stored, and the data information of the application scene is generated after the data information is successfully stored.
S102, classifying the data information according to a preset service type to generate classified data information;
the preset service type is a service type classified according to an application scenario of the smart device, such as municipal service, urban traffic service, public video service, and the like.
In the embodiment of the application, the service classification is carried out on the application scene by taking the human demand as the center, the corresponding calculation mode is matched according to the classified service type, and meanwhile, the virtualization modeling is carried out on various related calculation modes. Further, an edge calculation model is constructed for the performance and behavior characteristics of the entity in the edge calculation environment, edge calculation modeling is performed through an edge calculation unit, an edge calculation execution body and an edge calculation combination body, an edge calculation system structure is constructed, and sharing and collaborative optimization of resources according to scene needs are achieved. Therefore, a novel calculation model for edge-end equipment cooperative calculation is constructed by combining user requirements and engineering requirements.
S103, performing matching association on the classified data information and a preset calculation mode to generate matched associated data information;
and S104, modeling the matched and associated data information to generate an edge autonomous model.
In the embodiment of the application, from the design angle of a software structure, a novel calculation model for edge-end equipment cooperative calculation is constructed by combining user requirements and engineering requirements. For example, as shown in fig. 3, fig. 3 is an edge autonomic topology diagram, where the edge autonomic topology diagram includes heterogeneous edge device cooperative computing and edge autonomy.
Heterogeneous edge device collaborative computing has strong regulation and optimization capacity on resource allocation, edge computing oriented to the smart city influences energy consumption of each part of the smart city to a great extent, collaborative operation among different home terminal devices is achieved, power load data of different devices are computed at the edge of the devices, results are uploaded to a cloud after local processing, the cloud controls switching of electrical appliances, and collaborative operation and management are achieved.
The edge autonomy can reduce the data transmission bandwidth, can better protect private data, and reduces the scale that the risk of privacy leakage of terminal sensitive data is flexibly expanded from smart homes to communities or even cities.
For example, in a smart home, by providing a home gateway with computing capability, even in a disconnected state, a biometric lock, a robot, etc. can operate normally. However, if cloud computing is added, based on cloud big data analysis and judgment, on the premise of linkage, the intelligent device in the whole family scene becomes more personalized and intelligent, for example, when a door is closed, the sweeping robot starts to operate.
In the embodiment of the application, data information of an application scene is obtained firstly, then the data information is classified according to a preset service type to generate classified data information, then the classified data information is matched and associated with a preset calculation mode to generate matched and associated data information, and finally the matched and associated data information is modeled to generate an edge autonomous model. According to the scheme, information interaction between the devices is processed by constructing the heterogeneous edge device collaborative calculation and the edge autonomous model, so that not only can the single-point load of the system be effectively reduced, but also the task execution efficiency can be greatly improved, the information collaborative interaction between the devices can be improved, and the method and the device can be more conveniently applied to application scenes such as intelligent transportation, unmanned plane group monitoring and the like.
Referring to fig. 2, a schematic flow chart of another edge autonomic model construction method is provided in the embodiment of the present application. As shown in fig. 2, the method of the embodiment of the present application may include the steps of:
s201, acquiring equipment information, storing the equipment information, and generating data information of an application scene;
s202, acquiring data information of an application scene;
s203, classifying the data information according to a preset service type to generate classified data information;
s204, performing matching association on the classified data information and a preset calculation mode to generate matched associated data information;
s205, modeling the matched and associated data information to generate an edge autonomous model;
s206, acquiring a target task;
s207, acquiring a network protocol of the peer-to-peer network;
the peer-to-peer network (P2P) is an internet system that does not have a central server and relies on user groups (peers) to exchange information, and it is used to reduce nodes in the past network transmission to reduce the risk of data loss. Different from a central network system with a central server, each user side of the peer-to-peer network is a node and has the function of the server, and any node cannot directly find other nodes and must rely on the user group to exchange information.
S208, acquiring a network protocol of the content distribution network;
the Content Delivery Network (CDN) is an intelligent virtual Network built on the basis of the existing Network, and enables users to obtain required Content nearby by means of functional modules of load balancing, Content Delivery, scheduling and the like of a central platform by means of edge servers deployed in various places, so that Network congestion is reduced, and the access response speed and hit rate of the users are improved.
S209, inputting the network protocol of the content distribution network into the network protocol of the peer-to-peer network, and generating the input network protocol of the peer-to-peer network;
in the embodiment of the application, the management mechanism and the service capability of the CDN are introduced into the P2P network, so as to form a structure with the CDN as a reliable content core and the P2P as a service edge. The storage devices of the CDN are organized in a P2P manner, and content exchange between the CDN storage devices is realized by using the directory service and the multipoint transmission capability of P2P, so that the content delivery capability of the CDN is improved. The P2P edge collaboration technology mainly includes CDN data and computational model distribution, P2P data interaction, and P2P task collaboration. The CDN data and calculation model distribution technology mainly researches how edge side nodes are based on a P2P network to realize mutual CDN cache nodes among the nodes, so that a dynamic and intelligent CDN network is established; the P2P data exchange technology studies the point-to-point high-speed data exchange mechanism of the edge side device. The P2P task cooperation realizes the task distribution, task cooperation synchronization and data communication of how the multi-edge side nodes utilize the P2P communication mechanism.
From the perspective of a fusion mode, the CDN and the P2P are fused into two forms, one is to organize Cache devices of the CDN in a P2P manner, and implement content exchange between the CDNCache devices by using directory service and multipoint transmission capability of P2P, so as to improve content distribution capability of the CDN; in addition, a management mechanism and a service capability of the CDN are introduced into the P2P network to form an architecture with the CDN as a reliable content core and the P2P as a service edge, so that the CDN service capability can be effectively improved without increasing CDN cost through the architecture, and many disadvantages of P2P application are more effectively avoided, under which a user needs to obtain services through a P2P client.
After the CDN and the P2P are fused, the load balance is greatly improved, and the hardware investment and the operation cost can be greatly reduced.
S210, realizing point-to-point communication among all devices in a preset device set based on the input network protocol of the peer-to-peer network;
and S220, finishing the edge autonomy and the cooperative calculation of the target task according to the edge autonomy model and the point-to-point communication.
In the embodiment of the application, in a heterogeneous environment, the computing capacities of different computing nodes in a cluster are largely different, so that according to an original task average allocation mode, part of nodes with higher performance are in a starvation state, and nodes with lower performance are always in a saturation state. Only when the last task completes the whole task, the poor performance nodes assigned the same multiple tasks will severely slow down the execution time, thereby affecting the overall performance.
The heterogeneous edge side task dynamic partitioning technology mainly controls a task partitioning algorithm of data level parallelism and control flow decomposition, and is used for supporting intelligent partitioning of tasks under specified resource constraint, including automatic parallelization of high data volume tasks and automatic collaboration of resource complex tasks, so that single-point load of a system is effectively reduced, and task execution efficiency is greatly improved.
Task partitioning, evaluation, distribution, and migration techniques may accelerate task allocation and completion of subtasks. When the target task is allocated, firstly, the most suitable source task is found by calculating the similarity between the current task and the historical task, then the allocation mode of the source task is migrated to the target task, and migration learning is used in the process of completing the subtasks.
In the embodiment of the application, data information of an application scene is obtained firstly, then the data information is classified according to a preset service type to generate classified data information, then the classified data information is matched and associated with a preset calculation mode to generate matched and associated data information, and finally the matched and associated data information is modeled to generate an edge autonomous model. According to the scheme, information interaction between the devices is processed by constructing the heterogeneous edge device collaborative calculation and the edge autonomous model, so that not only can the single-point load of the system be effectively reduced, but also the task execution efficiency can be greatly improved, the information collaborative interaction between the devices can be improved, and the method and the device can be more conveniently applied to application scenes such as intelligent transportation, unmanned plane group monitoring and the like.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 4, a schematic structural diagram of an edge autonomic model building apparatus according to an exemplary embodiment of the present invention is shown. The edge autonomic model building means may be implemented as all or part of the terminal in software, hardware or a combination of both. The device 1 comprises an information acquisition module 10, an information classification module 20, an information association matching module 30 and a model generation module 40.
The information acquisition module 10 is used for acquiring data information of an application scene;
the information classification module 20 is configured to classify the data information according to a preset service type to generate classified data information;
an information association matching module 30, configured to perform matching association on the classified data information and a preset calculation mode to generate matched and associated data information;
and the model generation module 40 is used for modeling the matched and associated data information to generate an edge autonomous model.
Optionally, as shown in fig. 5, the apparatus 1 further includes:
the information acquisition module 50 is used for acquiring the equipment information, storing the equipment information and generating data information of an application scene;
a task obtaining module 60, configured to obtain a target task;
and a task completion module 70, configured to complete edge autonomous and collaborative computation of the target task based on the edge autonomous model.
Optionally, as shown in fig. 6, the task completing module 70 includes:
a first protocol obtaining unit 710, configured to obtain a network protocol of a peer-to-peer network;
a second protocol acquisition unit 720, configured to acquire a network protocol of the content distribution network;
a protocol generating unit 730, configured to input the network protocol of the content distribution network into the network protocol of the peer-to-peer network, and generate an input network protocol of the peer-to-peer network;
a communication implementation unit 740, configured to implement, based on the input network protocol of the peer-to-peer network, peer-to-peer communication between devices in a preset device set;
and a task completing unit 750, configured to complete edge autonomous and collaborative computation of the target task according to the edge autonomous model and the peer-to-peer communication.
It should be noted that, when the edge autonomic model building apparatus provided in the foregoing embodiment executes the edge autonomic model building method, only the division of the functional modules is taken as an example, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules, so as to complete all or part of the functions described above. In addition, the edge autonomic model construction apparatus and the edge autonomic model construction method provided by the above embodiments belong to the same concept, and details of implementation processes are found in the method embodiments, which are not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, data information of an application scene is obtained firstly, then the data information is classified according to a preset service type to generate classified data information, then the classified data information is matched and associated with a preset calculation mode to generate matched and associated data information, and finally the matched and associated data information is modeled to generate an edge autonomous model. According to the scheme, information interaction between the devices is processed by constructing the heterogeneous edge device collaborative calculation and the edge autonomous model, so that not only can the single-point load of the system be effectively reduced, but also the task execution efficiency can be greatly improved, the information collaborative interaction between the devices can be improved, and the method and the device can be more conveniently applied to application scenes such as intelligent transportation, unmanned plane group monitoring and the like.
The present invention also provides a computer readable medium, on which program instructions are stored, and when the program instructions are executed by a processor, the method for constructing the edge autonomic model provided by the above method embodiments is implemented.
The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the edge autonomic model construction method described in the above method embodiments.
Please refer to fig. 7, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 7, the terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 7, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an edge autonomic model building application program.
In the terminal 1000 shown in fig. 7, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; the processor 1001 may be configured to call the edge autonomic model building application stored in the memory 1005, and specifically perform the following operations:
acquiring data information of an application scene;
classifying the data information according to a preset service type to generate classified data information;
performing matching association on the classified data information and a preset computing mode to generate matched associated data information;
and modeling the matched and associated data information to generate an edge autonomous model.
In one embodiment, the processor 1001, when executing the acquiring of the data information of the application scenario, further performs the following operations:
and acquiring the equipment information, storing the equipment information and generating data information of the application scene.
In one embodiment, after performing the modeling of the associated data information to generate an edge autonomic model, the processor 1001 further performs the following operations:
acquiring a target task;
and finishing the edge autonomy and the cooperative calculation of the target task based on the edge autonomy model.
In an embodiment, when the processor 1001 performs the edge autonomic and collaborative computation based on the edge autonomic model to complete the target task, the following operations are specifically performed:
acquiring a network protocol of a peer-to-peer network;
acquiring a network protocol of a content distribution network;
inputting the network protocol of the content distribution network into the network protocol of the peer-to-peer network, and generating the input network protocol of the peer-to-peer network;
realizing point-to-point communication among all devices in a preset device set based on the input network protocol of the peer-to-peer network;
and finishing the edge autonomy and the cooperative calculation of the target task according to the edge autonomy model and the point-to-point communication.
In the embodiment of the application, data information of an application scene is obtained firstly, then the data information is classified according to a preset service type to generate classified data information, then the classified data information is matched and associated with a preset calculation mode to generate matched and associated data information, and finally the matched and associated data information is modeled to generate an edge autonomous model. According to the scheme, information interaction between the devices is processed by constructing the heterogeneous edge device collaborative calculation and the edge autonomous model, so that not only can the single-point load of the system be effectively reduced, but also the task execution efficiency can be greatly improved, the information collaborative interaction between the devices can be improved, and the method and the device can be more conveniently applied to application scenes such as intelligent transportation, unmanned plane group monitoring and the like.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, it should be understood that the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
It should be understood that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. The present invention is not limited to the procedures and structures that have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
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