CN108768857B - Virtual route forwarding method, device and system - Google Patents
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Abstract
The invention belongs to the field of data processing, and discloses a virtual route forwarding method, a device and a system, wherein the method is used for processing an industrial edge cloud virtual route forwarding request of third-party industrial application and comprises the following steps: obtaining a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, wherein each industrial edge cloud virtual route forwarding request comprises an evaluation index; performing deep analysis and deep analysis evaluation on evaluation indexes in the industrial edge cloud virtual route forwarding requests to generate forwarding analysis results of the industrial edge cloud virtual route forwarding requests; and sending the forwarding analysis result to a local industrial edge cloud, so that the local industrial edge cloud executes virtual routing forwarding according to the forwarding analysis result and feeds back the execution result to a third-party industrial application. The method and the device can reduce the forwarding time delay, the flow cost and the routing cost of the industrial edge cloud virtual routing forwarding of the third-party industrial application.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a virtual router forwarding method, apparatus, and system.
Background
With the rapid development of the internet of things, the number of edge terminal devices has rapidly increased, and the amount of data generated by the edge terminal devices has reached the level of Zeyte (ZB). The centralized data processing cannot effectively process the massive data generated by the edge terminal device, and the edge cloud has been generally recognized as one of the main trends of the next generation digital transformation in the industry. Mobile Edge Computing (MEC) is to migrate part of the Computing tasks of a traditional cloud Computing platform to an access domain, and deeply merge traditional services with internet services, so as to reduce end-to-end time delay of traditional service delivery, bring a brand new mode to the operation of an operator, and establish a brand new industrial chain and an ecosphere. Under the circumstance, in the face of increasingly urgent development requirements of edge clouds and virtual routes, rapid and continuous development of a virtual route forwarding mechanism based on the industrial edge cloud is of great significance.
The problems of high time delay, high traffic cost, high routing cost and the like are not fully considered in the conventional cloud computing system, and the problems of high time delay, high traffic cost, high routing cost and the like are increasingly highlighted along with the rapid growth of edge cloud and virtual routing services.
It should be noted that the above background description is only for the sake of clarity and complete description of the technical solutions of the present invention and for the understanding of those skilled in the art. Such solutions are not considered to be known to the person skilled in the art merely because they have been set forth in the background section of the invention.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art, and provides a virtual route forwarding method, a virtual route forwarding device and a virtual route forwarding system, which can reduce the forwarding time delay, the flow cost and the route cost of the industrial edge cloud virtual route forwarding applied to the third-party industry.
In order to achieve the above object, the present invention provides a virtual route forwarding method for processing an industrial edge cloud virtual route forwarding request of a third party industrial application, including:
obtaining a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, wherein each industrial edge cloud virtual route forwarding request comprises an evaluation index;
performing deep analysis and deep analysis evaluation on evaluation indexes in the industrial edge cloud virtual route forwarding requests to generate forwarding analysis results of the industrial edge cloud virtual route forwarding requests;
and sending the forwarding analysis result to a local industrial edge cloud for the local industrial edge cloud, executing virtual routing forwarding according to the forwarding analysis result, and feeding back the execution result to a third-party industrial application.
Optionally, the obtaining of the multiple industrial edge cloud virtual route forwarding requests of the third-party industrial application specifically includes:
acquiring the industrial edge cloud virtual route forwarding request through a regularly queried mechanism; and/or acquiring the industrial edge cloud virtual route forwarding request actively reported every preset time.
Optionally, the evaluation index in the industrial edge cloud virtual route forwarding request includes: forwarding delay, traffic cost, and routing cost.
Optionally, the performing deep analysis and deep analysis evaluation on the evaluation indexes in the multiple industrial edge cloud virtual route forwarding requests, and generating the forwarding analysis result of the industrial edge cloud virtual route forwarding request specifically includes:
step S1, setting iteration initial parameters and maximum iteration times;
step S2, analyzing evaluation indexes in the industrial edge cloud virtual routing forwarding requests by a multidimensional space undirected sparse graph chaotic shortest path learning strategy and generating an initial result;
step S3, determining whether the initial result generated in step S2 satisfies the deep analysis evaluation condition, if yes, executing step S5, and if not, executing step S4;
step S4, adding 1 to the iteration number, and judging whether the current iteration number exceeds the maximum iteration number, if not, executing step S3, and if so, executing step S5;
and step S5, outputting a forwarding analysis result of the industrial edge cloud virtual route forwarding request.
Optionally, the depth analysis evaluation condition in step S3 includes a joint evaluation function, which is specifically as follows:
the multi-dimensional space undirected sparse graph chaotic shortest path learning strategy in the step S2 includes a chaotic shortest path optimization function, which is specifically as follows:
wherein M in the formula (1-4)ijt kIncluded
In the information vector of the three aspects, k in the expressions (1-1) to (1-5) represents the k-th iteration, d represents the maximum number of iterations, and k is 1,2, … …, d,
indicating the current time delay of the k-th time,
representing the current k-th time traffic cost,
indicating the current cost of the k-th route,
represents the (k + 1) th information vector,
which represents the information vector of the k-th time,
represents the (k + 1) th optimal utilization adjustment factor,
represents the k +1 th learning factor of optimum utilization, LminKDenotes the kth minimum delay, CminKDenotes the kth minimum flow cost, WminKDenotes the kth minimum routing cost, LminGIndicating historical minimum delay, CminGRepresents historical minimum traffic cost, WminGRepresenting historical minimum routing costs.
In order to achieve the above object, the present invention further provides a virtual route forwarding apparatus for processing an industrial edge cloud virtual route forwarding request of a third party industrial application, including:
the request acquisition module is used for acquiring a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, wherein each industrial edge cloud virtual route forwarding request comprises an evaluation index;
the analysis generation module is used for carrying out deep analysis and deep analysis evaluation on evaluation indexes in the industrial edge cloud virtual route forwarding requests to generate forwarding analysis results of the industrial edge cloud virtual route forwarding requests;
and the sending module is used for sending the forwarding analysis result to a local industrial edge cloud so that the local industrial edge cloud can execute virtual routing forwarding according to the forwarding analysis result and feed back the execution result to a third-party industrial application.
Optionally, the request obtaining module is specifically configured to obtain the industrial edge cloud virtual route forwarding request through a regularly queried mechanism; and/or acquiring the industrial edge cloud virtual route forwarding request actively reported every preset time.
Optionally, the evaluation index in the industrial edge cloud virtual route forwarding request includes: forwarding delay, traffic cost, and routing cost.
Optionally, the analysis generating module specifically includes:
the parameter setting submodule is used for setting an iteration initial parameter and the maximum iteration times;
the analysis generation submodule is used for analyzing evaluation indexes in the industrial edge cloud virtual routing forwarding requests by a multidimensional space undirected sparse graph chaotic shortest path learning strategy and generating an initial result;
the judgment submodule is used for judging whether the initial result meets the deep analysis evaluation condition;
and the output submodule is used for outputting a forwarding analysis result of the industrial edge cloud virtual route forwarding request.
In order to achieve the above object, the present invention further provides a virtual route forwarding system, configured to process an industrial edge cloud virtual route forwarding request for third-party industrial application, where the system includes:
the third-party application user equipment end is used for sending an industrial edge cloud virtual route forwarding request to the third-party industrial application;
the third-party industrial application terminal is used for sending an industrial edge cloud virtual route forwarding request to the central cloud analysis layer;
a central cloud analysis layer comprising an industrial database and a plurality of industrial analysis processors, the central cloud analysis layer for processing an industrial edge cloud virtual route forwarding request from a third party industrial application;
the regional edge cloud layer comprises a plurality of regional edge clouds, and is used for accessing the regional edge clouds and transmitting the industrial edge cloud virtual routing forwarding request;
the industrial edge cloud virtual route access layer comprises a plurality of industrial edge cloud virtual routes and is used for forwarding the industrial edge cloud virtual routes;
the provider base station edge network transmission layer comprises a plurality of provider base stations and is used for accessing the provider base stations and transmitting industrial data;
a local industrial edge cloud layer comprising a plurality of local industrial edge clouds, the local industrial edge cloud layer configured to analyze and process data of each local industrial edge cloud;
wherein the industrial analysis processor comprises a virtual route forwarding device as described above.
The invention has the following beneficial effects:
the virtual route forwarding method provided by the invention comprises the steps of obtaining a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, carrying out deep analysis and deep analysis evaluation on evaluation indexes in the plurality of industrial edge cloud virtual route forwarding requests, generating a forwarding analysis result of the industrial edge cloud virtual route forwarding requests, and sending the forwarding analysis result to a local industrial edge cloud so that the local industrial edge cloud can execute virtual route forwarding according to the forwarding analysis result and feed back the execution result to the third-party industrial application. The method and the device can reduce the forwarding time delay, the flow cost and the routing cost of the industrial edge cloud virtual routing forwarding of the third-party industrial application.
Specific embodiments of the present invention are disclosed in detail with reference to the following description and drawings, indicating the manner in which the principles of the invention may be employed. It should be understood that the embodiments of the invention are not so limited in scope. The embodiments of the invention include many variations, modifications and equivalents within the spirit and scope of the appended claims.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments, in combination with or instead of the features of the other embodiments.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps or components.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a scene diagram of a virtual routing forwarding system according to an embodiment of the present invention;
fig. 2 is a flowchart of a virtual route forwarding method implemented by an industrial analysis processor according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for depth analysis and depth analysis evaluation provided by an embodiment of the present invention;
fig. 4 is a diagram of an analysis logic structure for virtual routing forwarding according to an embodiment of the present invention;
FIGS. 5 a-5 c are schematic diagrams of depth analysis provided by embodiments of the present invention;
FIG. 6 is a schematic diagram of a storage model for providing deep analysis according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a virtual router forwarding apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an analysis generation module according to an embodiment of the present invention;
fig. 9 is a server device according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following clear and complete description of the technical solution of the present invention is made with reference to the accompanying drawings, and it is obvious that the described embodiments are a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a scene diagram of a virtual routing forwarding system according to an embodiment of the present invention, and as shown in fig. 1, the scene of the virtual routing forwarding system mainly includes five layers: 1) each local industrial edge cloud comprising: the industrial edge clouds are used for realizing data analysis and processing of each local industrial edge cloud; 2) the provider base station edge network transport layer comprises: the operator base station realizes the access of the operator base station and industrial data transmission; 3) the industrial edge cloud virtual route access layer is composed of a plurality of industrial edge cloud virtual routes, and high-performance forwarding of the industrial edge cloud virtual routes is achieved; 4) the regional edge cloud layer is composed of a plurality of regional edge clouds, and is used for realizing the access of the regional edge clouds and transmitting the virtual routing forwarding request of the industrial edge cloud; 5) and the central cloud analysis layer consists of a plurality of industrial analysis processors and an industrial database and is used for processing the industrial edge cloud virtual route forwarding request from the third-party industry.
Further, the scenario of the virtual route forwarding system further includes: and the third-party application user is used for sending the industrial edge cloud virtual route forwarding request to the third-party industrial application. And the third-party industrial application is used for sending the industrial edge cloud virtual route forwarding request to the central cloud analysis layer. The third-party application user can be a third-party application user equipment terminal, and the third-party industrial application can be a third-party industrial application terminal.
In the scenario shown in fig. 1, the processing flow of the virtual routing forwarding method is specifically as follows, wherein reference numerals (I), (II), (III), (IV), (III), (IV), and (c) respectively represent the processing steps.
A third-party application user puts forward an industrial edge cloud virtual route forwarding request to third-party industrial application through an Internet; step two, the third-party industrial application transmits the industrial edge cloud virtual route forwarding request to an industrial analysis processor of a central operation analysis layer; analyzing the industrial edge cloud virtual route forwarding request by the industrial analysis processor in combination with the industrial database, and transmitting an analysis result of the industrial edge cloud virtual route forwarding request to the regional edge cloud layer; sending an analysis result of the forwarding request of the industrial edge cloud virtual router to the industrial edge cloud virtual router by the regional edge cloud layer; fifthly, the industrial edge cloud virtual router sends the analysis result forwarded by the industrial edge cloud virtual router forwarding request to each local industrial edge cloud through the operator base station; step seventhly, executing analysis results forwarded by the industrial edge cloud virtual routing forwarding request by each local industrial edge cloud, and feeding back the execution results to third-party industrial application; and step eight, the third-party industrial application transmits the feedback result to a third-party application user. And step I, step II, step III and step IV, each local industrial edge cloud layer transmits the execution result of the industrial edge cloud virtual route forwarding request to an industrial analysis processor of the central cloud analysis layer through the operator base station, the industrial edge cloud virtual route and the regional edge cloud.
In the application scenario, the analysis of the forwarding request of the industrial edge cloud virtual route is realized through the industrial analysis processor, the optimal forwarding delay, flow cost and route cost are obtained by processing evaluation indexes such as the forwarding delay, the flow cost and the route cost in the forwarding request of the industrial edge cloud virtual route, a forwarding analysis result of the forwarding request of the industrial edge cloud virtual route is generated, and the industrial edge cloud virtual route is forwarded according to the forwarding analysis result, so that the functional effects of low delay, low flow cost and low route cost are realized.
It is worth mentioning that the industrial analysis processor is configured to analyze and process the virtual route forwarding request, generate a forwarding analysis result, and forward the forwarding analysis result to the local edge cloud that executes the corresponding virtual route forwarding request. The industrial analysis processor can simultaneously process a plurality of industrial edge cloud virtual route forwarding requests and generate corresponding forwarding analysis results, and the industrial edge cloud virtual route forwarding requests are independent and do not interfere with each other.
The following describes the virtual route forwarding function implemented by the industrial analysis processor according to the embodiment of the present invention.
Fig. 2 is a flowchart of a virtual route forwarding method implemented by an industrial analysis processor according to an embodiment of the present invention, and as shown in fig. 2, the method includes:
Specifically, a real-time active and passive mode can be adopted to collect the industrial edge cloud virtual route forwarding request and analyze the request in real time.
And 102, carrying out deep analysis and deep analysis evaluation on evaluation indexes in the plurality of industrial edge cloud virtual route forwarding requests to generate a forwarding analysis result of the industrial edge cloud virtual route forwarding requests.
And 103, sending the forwarding analysis result to a local industrial edge cloud, so that the local industrial edge cloud executes virtual routing forwarding according to the forwarding analysis result and feeds back the execution result to a third-party industrial application.
Fig. 3 is a flowchart of a method for deep analysis and deep analysis evaluation according to an embodiment of the present invention, and the deep analysis and deep analysis evaluation idea of the present invention is to determine and analyze an industrial edge cloud virtual route forwarding request, so that each industrial edge cloud virtual route forwarding request has a different priority level. The deep analysis of the embodiment combines with the multidimensional space undirected sparse graph chaotic shortest path learning strategy method to realize the advantages of low forwarding delay, low traffic cost and low routing cost.
As shown in fig. 3, the method of depth analysis and depth analysis evaluation includes the steps of:
and step S1, setting iteration initial parameters and maximum iteration times.
Such as: the iteration initial parameter is set to 0, and the maximum iteration number is set to 50. Wherein, the maximum iteration number is set according to requirements and generally does not exceed 100. If the value of the maximum number of iterations is set too small, it will be inaccurate, and if it is set too large, it is computationally expensive.
And step S2, analyzing evaluation indexes in the industrial edge cloud virtual routing forwarding requests by a multidimensional space undirected sparse graph chaotic shortest path learning strategy and generating an initial result.
In step S3, it is determined whether the initial result generated in step S2 satisfies the depth analysis evaluation condition, and if so, step S5 is performed, and if not, step S4 is performed.
Step S4, add 1 to the iteration count, and determine whether the current iteration count exceeds the maximum iteration count, if not, execute step S3, and if so, execute step S5.
And performing one iteration on the initial result every time 1 is added to the iteration number.
And step S5, outputting a forwarding analysis result of the industrial edge cloud virtual route forwarding request.
Fig. 4 is a logical structure diagram of analysis for virtual routing forwarding according to an embodiment of the present invention, and as shown in fig. 4, the logical structure includes three parts: receiving an initial result of the forwarding request of the industrial edge cloud virtual route, analyzing the forwarding request of the industrial edge cloud virtual route by a multidimensional space undirected sparse graph chaotic shortest path learning strategy, and outputting an analysis result. The information of each industrial edge cloud virtual route forwarding request mainly comprises the following information: the forwarding delay L, the flow cost C and the routing cost W of each industrial edge cloud virtual routing forwarding request are analyzed through analyzing the industrial edge cloud virtual routing forwarding requests, analysis is achieved through a multidimensional space undirected sparse graph chaotic shortest path learning strategy, analysis results are given, and finally generated optimal analysis results (namely analysis results meeting deep analysis evaluation conditions) are forwarding analysis results. In this embodiment, the deep analysis may be a multidimensional space undirected sparse graph chaotic shortest path learning strategy.
Fig. 5a to 5C are depth analysis schematic diagrams provided by an embodiment of the present invention, and as shown in fig. 5a, the learning and analysis idea of the multiway space undirected sparse graph shortest path learning strategy in each iteration is that in a multidimensional space of 1,2, … h, a plurality of depth analysis schemes (in this embodiment, the forwarding delay L, the traffic cost C, and the routing cost W) of 1,2, … W are migrated to a direction determined by an optimal optimization scheme (in this embodiment, the analysis result meeting the depth analysis and evaluation condition is also used) according to the multiway space undirected sparse graph shortest path learning strategy, that is, the position of a sphere in fig. 5 a. Fig. 5b illustrates a principle of a learning strategy of the shortest chaotic path, in which a virtual routing forwarding request is analyzed by an adjustment factor, a strategy of the shortest chaotic path, and a learning factor after being input, and then a corresponding forwarding analysis result is output. FIG. 5c is a schematic diagram of the ordered multi-weighted sparse graph storage matrix chain: the method comprises the steps of automatically sending interval survival signals of local industrial edge clouds (receiving the interval survival signals of the local industrial edge clouds and estimating initial distance and flow cost weight values by the regional edge clouds), bringing the local industrial edge clouds into an industrial edge cloud ordered multi-weight sparse graph one by one, wherein each tertiary node in the sparse graph represents one local industrial edge cloud. Furthermore, by combining the learning and analyzing idea of the chaos shortest path learning strategy of the multidirectional space undirected sparse graph, a forwarding analysis result is obtained based on deep analysis of theoretical advantages of the multidimensional space undirected sparse graph, the chaos theory, the shortest path, the probability theory, biology, operation and research, intelligent optimization, machine learning and the like.
In this embodiment, after a plurality of industrial edge cloud virtual route forwarding requests arrive, each virtual route forwarding request is analyzed into a corresponding deep analysis result. If the incoming industrial edge cloud virtual route forwarding request is delayed, it is given a current higher analysis scheduling priority.
In the embodiment of the invention, the deep analysis evaluation condition comprises a joint evaluation function:
FIG. 6 is a schematic diagram of a storage model for deep analysis according to an embodiment of the present invention
In fig. 6, m, n, and q represent three spatial dimensions in the depth analysis model corresponding to the depth analysis, respectively, and each parameter in fig. 6 can be calculated according to the following formula (1-3). In this embodiment, the depth analysis model may be a multidimensional space undirected sparse graph chaotic shortest path learning strategy, which includes a chaotic shortest path optimization function:
wherein M in the formula (1-4)ijt kIncluded
In the information vector of the three aspects, k in the expressions (1-1) to (1-5) represents the k-th iteration, d represents the maximum number of iterations, and k is 1,2, … …, d,
indicating the current time delay of the k-th time,
representing the current k-th time traffic cost,
indicating the current cost of the k-th route,
represents the (k + 1) th information vector,
which represents the information vector of the k-th time,
represents the (k + 1) th optimal utilization adjustment factor,
represents the k +1 th learning factor of optimum utilization, LminKDenotes the kth minimum delay, CminKDenotes the kth minimum flow cost, WminKDenotes the kth minimum routing cost, LminGIndicating historical minimum delay, CminGRepresents historical minimum traffic cost, WminGRepresenting historical minimum routing costs.
Based on the depth analysis model and the joint evaluation function, when the joint evaluation function is not satisfied (namely, the initial result does not satisfy the depth analysis evaluation condition), the virtual route forwarding algorithm based on the industrial edge cloud is triggered, and the multidimensional space undirected sparse graph chaotic shortest path strategy learning is used for performing depth analysis and optimization on the virtual route forwarding method, so that the low forwarding delay, the low flow cost and the low route cost of virtual route forwarding are realized.
It should be noted that while the operations of the method of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Based on the same inventive concept, after the method of the exemplary embodiment of the present invention is introduced, the virtual route forwarding apparatus of the exemplary embodiment of the present invention is next introduced with reference to fig. 7. The implementation of the device can be referred to the implementation of the method, and repeated details are not repeated. The terms "module" and "unit", as used below, may be software and/or hardware that implements a predetermined function. While the modules described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 7 is a schematic structural diagram of a virtual routing forwarding apparatus according to an embodiment of the present invention, and as shown in fig. 7, the apparatus includes: a request acquisition module 11, an analysis generation module 12 and a sending module 13.
The request obtaining module 11 is configured to obtain a plurality of industrial edge cloud virtual route forwarding requests of a third-party industrial application, where each of the industrial edge cloud virtual route forwarding requests includes an evaluation index. The analysis generating module 12 is configured to perform deep analysis and deep analysis evaluation on the evaluation indexes in the multiple industrial edge cloud virtual route forwarding requests, and generate a forwarding analysis result of the industrial edge cloud virtual route forwarding request. The sending module 13 is configured to send the forwarding analysis result to a local industrial edge cloud, so that the local industrial edge cloud forwards the forwarding analysis result to a user equipment end of a third-party industrial application.
Further, the request obtaining module 11 is specifically configured to obtain the industrial edge cloud virtual route forwarding request through a regularly queried mechanism; and/or acquiring the industrial edge cloud virtual route forwarding request actively reported every preset time. The evaluation index in the industrial edge cloud virtual route forwarding request comprises: forwarding delay, traffic cost, and routing cost.
Further, fig. 8 is a schematic structural diagram of an analysis generating module according to an embodiment of the present invention, and as shown in fig. 8, the analysis generating module includes: a parameter setting submodule 121, an analysis generation submodule 122, a judgment submodule 123 and an output submodule 124.
The parameter setting submodule 121 is configured to set an iteration initial parameter and a maximum iteration number. The analysis and generation submodule 122 is used for analyzing the evaluation indexes in the multiple industrial edge cloud virtual routing forwarding requests by a multidimensional space undirected sparse graph chaotic shortest path learning strategy and generating an initial result. The judgment submodule 123 is configured to judge whether the initial result satisfies a deep analysis evaluation condition. The output submodule 124 is configured to output a forwarding analysis result of the industrial edge cloud virtual route forwarding request.
In the embodiment of the present invention, the analysis and generation sub-module 122 stores a multidimensional space undirected sparse graph chaos shortest path learning strategy, and the determination sub-module 123 stores a deep analysis evaluation condition, where the deep analysis evaluation condition may be a joint evaluation function in specific implementation.
Furthermore, although several modules of the virtual route forwarding apparatus are mentioned in the above detailed description, such division is not mandatory only. Indeed, the features and functions of two or more of the units described above may be embodied in one unit, according to embodiments of the invention. Also, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Fig. 9 is a server device according to an embodiment of the present invention, and as shown in fig. 9, the server device includes: a memory a and a processor b, wherein the memory a stores a computer program, and the computer program realizes the following functions when being executed by the processor b:
obtaining a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, wherein each industrial edge cloud virtual route forwarding request comprises an evaluation index;
performing deep analysis and deep analysis evaluation on evaluation indexes in the industrial edge cloud virtual route forwarding requests to generate forwarding analysis results of the industrial edge cloud virtual route forwarding requests;
and sending the forwarding analysis result to a local industrial edge cloud, so that the local industrial edge cloud executes virtual routing forwarding according to the forwarding analysis result and feeds back the execution result to a third-party industrial application.
The virtual route forwarding method, the device and the system disclosed by the embodiment of the invention are combined with the learning idea of the chaos shortest path strategy of the undirected sparse graph in the multidimensional space, and based on the theoretical advantages of the multidimensional space, the undirected sparse graph, the chaos theory, the shortest path, the probability theory, the biology, the operation research, the intelligent optimization, the machine learning and the like, and are combined with the special virtual route forwarding system scene provided by the embodiment of the invention, the dynamic deep analysis is carried out on the virtual route forwarding request of the industrial edge cloud in real time by the virtual route forwarding algorithm based on the industrial edge cloud, so that the low forwarding delay, the low flow cost and the low route cost of the virtual route forwarding are realized.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and the content of the present specification should not be construed as a limitation to the present invention. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (8)
1. A virtual route forwarding method is used for processing an industrial edge cloud virtual route forwarding request of a third-party industrial application, and comprises the following steps:
obtaining a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, wherein each industrial edge cloud virtual route forwarding request comprises an evaluation index;
performing deep analysis and deep analysis evaluation on evaluation indexes in the industrial edge cloud virtual route forwarding requests to generate forwarding analysis results of the industrial edge cloud virtual route forwarding requests;
sending the forwarding analysis result to a local industrial edge cloud, so that the local industrial edge cloud executes virtual routing forwarding according to the forwarding analysis result and feeds back the execution result to a third-party industrial application;
performing deep analysis and deep analysis evaluation on evaluation indexes in the plurality of industrial edge cloud virtual route forwarding requests, and generating a forwarding analysis result of the industrial edge cloud virtual route forwarding request specifically includes:
step S1, setting iteration initial parameters and maximum iteration times;
step S2, analyzing evaluation indexes in the industrial edge cloud virtual routing forwarding requests by a multidimensional space undirected sparse graph chaotic shortest path learning strategy and generating an initial result;
step S3, determining whether the initial result generated in step S2 satisfies the deep analysis evaluation condition, if yes, executing step S5, and if not, executing step S4;
step S4, adding 1 to the iteration number, and judging whether the current iteration number exceeds the maximum iteration number, if not, executing step S3, and if so, executing step S5;
and step S5, outputting a forwarding analysis result of the industrial edge cloud virtual route forwarding request.
2. The virtual route forwarding method according to claim 1, wherein the obtaining of the plurality of industrial edge cloud virtual route forwarding requests of the third party industrial application specifically comprises:
acquiring the industrial edge cloud virtual route forwarding request through a regularly queried mechanism; and/or acquiring the industrial edge cloud virtual route forwarding request actively reported every preset time.
3. The virtual route forwarding method according to claim 1, wherein the evaluation index in the industrial edge cloud virtual route forwarding request includes: forwarding delay, traffic cost, and routing cost.
4. The virtual route forwarding method according to claim 1, wherein the deep analysis evaluation condition in step S3 includes a joint evaluation function, which is specifically as follows:
the multi-dimensional space undirected sparse graph chaotic shortest path learning strategy in the step S2 includes a chaotic shortest path optimization function, which is specifically as follows:
wherein M in the formula (1-4)ijt kIncluded
In the information vector of the three aspects, k in the expressions (1-1) to (1-5) represents the k-th iteration, d represents the maximum number of iterations, and k is 1,2, … …, d,
indicating the current time delay of the k-th time,
representing the current k-th time traffic cost,
indicating the current cost of the k-th route,
represents the (k + 1) th information vector,
which represents the information vector of the k-th time,
represents the (k + 1) th optimal utilization adjustment factor,
represents the k +1 th learning factor of optimum utilization, LminKDenotes the kth minimum delay, CminKDenotes the kth minimum flow cost, WminKDenotes the kth minimum routing cost, LminGIndicating historical minimum delay, CminGRepresents historical minimum traffic cost, WminGRepresenting historical minimum routing costs.
5. A virtual route forwarding device for processing an industrial edge cloud virtual route forwarding request of a third party industrial application, comprising:
the request acquisition module is used for acquiring a plurality of industrial edge cloud virtual route forwarding requests of third-party industrial application, wherein each industrial edge cloud virtual route forwarding request comprises an evaluation index;
the analysis generation module is used for carrying out deep analysis and deep analysis evaluation on evaluation indexes in the industrial edge cloud virtual route forwarding requests to generate forwarding analysis results of the industrial edge cloud virtual route forwarding requests;
the sending module is used for sending the forwarding analysis result to a local industrial edge cloud so that the local industrial edge cloud can execute virtual routing forwarding according to the forwarding analysis result and feed back the execution result to a third-party industrial application;
the analysis generation module specifically comprises:
the parameter setting submodule is used for setting an iteration initial parameter and the maximum iteration times;
the analysis generation submodule is used for analyzing evaluation indexes in the industrial edge cloud virtual routing forwarding requests by a multidimensional space undirected sparse graph chaotic shortest path learning strategy and generating an initial result;
the judgment submodule is used for judging whether the initial result meets the deep analysis evaluation condition;
and the output submodule is used for outputting a forwarding analysis result of the industrial edge cloud virtual route forwarding request.
6. The virtual route forwarding device according to claim 5, wherein the request obtaining module is specifically configured to obtain the industrial edge cloud virtual route forwarding request through a regularly queried mechanism; and/or acquiring the industrial edge cloud virtual route forwarding request actively reported every preset time.
7. The virtual route forwarding device according to claim 5, wherein the evaluation index in the industrial edge cloud virtual route forwarding request includes: forwarding delay, traffic cost, and routing cost.
8. A virtual route forwarding system for processing an industrial edge cloud virtual route forwarding request for a third party industrial application, the system comprising:
the third-party application user equipment end is used for sending an industrial edge cloud virtual route forwarding request to the third-party industrial application;
the third-party industrial application terminal is used for sending an industrial edge cloud virtual route forwarding request to the central cloud analysis layer;
a central cloud analysis layer comprising an industrial database and a plurality of industrial analysis processors, the central cloud analysis layer for processing an industrial edge cloud virtual route forwarding request from a third party industrial application;
the regional edge cloud layer comprises a plurality of regional edge clouds, and is used for accessing the regional edge clouds and transmitting the industrial edge cloud virtual routing forwarding request;
the industrial edge cloud virtual route access layer comprises a plurality of industrial edge cloud virtual routes and is used for forwarding the industrial edge cloud virtual routes;
the provider base station edge network transmission layer comprises a plurality of provider base stations and is used for accessing the provider base stations and transmitting industrial data;
a local industrial edge cloud layer comprising a plurality of local industrial edge clouds, the local industrial edge cloud layer configured to analyze and process data of each local industrial edge cloud;
wherein the industrial analysis processor comprises the virtual route forwarding device of any one of claims 5-7.
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