CN113037534A

CN113037534A - Communication network optimization method and system based on block chain and edge calculation

Info

Publication number: CN113037534A
Application number: CN202110156538.1A
Authority: CN
Inventors: 石高峰
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-05-20
Filing date: 2020-05-20
Publication date: 2021-06-25
Also published as: CN111585823B; CN111585823A; CN113037535A

Abstract

The communication network optimization method and system based on the block chain and the edge calculation firstly determine a first information set and a second information set corresponding to each industrial production device, secondly, determining the mapping relation between the first information set and the second information set by combining the current resource allocation topology corresponding to the industrial production equipment, then determining a first channel migration matrix corresponding to the current production line state and a second channel migration matrix corresponding to the target production line state according to each mapping relation, the first channel migration matrix and the second channel migration matrix are input into a channel modeling server to obtain a channel migration model, and then, operating the channel migration model, reconstructing the first network structure topology under the current production line state to obtain a second network structure topology, and finally forming a target communication network corresponding to the target production line state according to the second network structure topology. In this way, the industrial communication network can be optimized to ensure reliability of the industrial communication network.

Description

Communication network optimization method and system based on block chain and edge calculation

Technical Field

The present application relates to the field of communication technologies based on block chain and edge computation, and in particular, to a communication network optimization method and system based on block chain and edge computation.

Background

With the development of science and technology, the diversity demand of various products is continuously increased, and the demand of personalized customized products is rapidly increased. However, the traditional mass production and rigid production mode is difficult to meet the requirement. Therefore, an adaptive reconfigurable internet of things intelligent production technology for personalized customized products is developed. The self-adaptive reconstruction production system technology decouples the mechanical structure of the production system into a plurality of mutually independent production units based on a block chain and an edge calculation technology, and then quickly reconstructs the required production units into a new production system according to product design and process flow, thereby quickly and efficiently realizing the production mode switching aiming at the personalized customized products. However, with the decoupling and reconfiguration of the production system, the reliability of the industrial communication network of the production system may be reduced, which may cause the reconfigured production system to fail to operate properly.

Disclosure of Invention

The application provides a communication network optimization method and system based on block chains and edge calculation, so as to solve the technical problem that the reliability of an industrial communication network is reduced in the prior art.

In a first aspect, a communication network optimization method based on blockchain and edge computation is provided, which is applied to an edge computation server communicatively connected to a channel modeling server and a plurality of industrial production devices, and the method at least includes:

extracting network resource information of each industrial production device, forming a first information set, determining current device state information and a state label of each industrial production device, and generating a second information set corresponding to each industrial production device based on the current device state information and the state label; the first information set and the second information set comprise information fields with different dynamic coefficients, and the dynamic coefficients are used for characterizing the channel mobility distortion rate of the information fields;

after determining a field coding value of an information field corresponding to one dynamic coefficient in a first information set corresponding to each industrial production device, determining an information field corresponding to the maximum dynamic coefficient in a second information set corresponding to the industrial production device as a reference information field;

mapping the field coding value in a field sequence formed by the reference information field based on the current resource allocation topology corresponding to the industrial production equipment to obtain a mapping coding value corresponding to the field coding value in the field sequence; determining a mapping relation between the first information set and the second information set according to a reference coding value and the mapping coding value;

determining a channel migration sequence of each mapping relation relative to the current production line state, and importing each channel migration sequence into a preset sequence list to form a first channel migration matrix corresponding to the current production line state; determining a second channel migration matrix corresponding to the state of the target production line according to the received service requirement; inputting the first channel migration matrix and the second channel migration matrix into the channel modeling server to obtain a channel migration model between the current production line state and the target production line state; the channel migration model is used for representing difference information of network resource allocation between the current production line state and the target production line state;

operating the channel migration model to obtain the network resource requirement of each industrial production device in the target production line state, and reconstructing the first network structure topology of the industrial production device in the current production line state according to the network resource requirement to obtain a second network structure topology;

and issuing the second network structure topology to each industrial production device so as to decouple each industrial production device based on the second network structure topology, and then forming a target communication network corresponding to the target production line state.

Optionally, mapping the field code value in a field sequence formed by the reference information field based on the current resource allocation topology corresponding to the industrial production device to obtain a mapping code value corresponding to the field code value in the field sequence, where the mapping code value includes:

determining the node priority corresponding to the resource node of the current resource allocation topology and the node label of the resource node; wherein the node label represents a node class of a resource node of the current resource allocation topology, the node label comprising at least: a first node class and a second node class of resource nodes representing the current resource allocation topology;

acquiring a priority ordering sequence corresponding to the node priority; the priority ordering sequence comprises a pre-configured sequence number, and the sequence number represents a mapping sequence of resource nodes which are positioned in the node priority in the priority ordering sequence and correspond to the node priority;

searching a target sequence number matched with each coded character in the field coded value in the priority sequencing sequence according to the node priority and the node label, and determining the node priority corresponding to the target sequence number;

and determining the sequence position of each code character in the field code value in the field sequence according to the node priority corresponding to the target sequence number, mapping each code character to the corresponding sequence position to obtain the corresponding mapping character, and combining the mapping characters according to the corresponding sequence positions to obtain the mapping code value.

Optionally, determining a mapping relationship between the first information set and the second information set according to a reference coding value and the mapping coding value includes:

determining a distance value between each code character in the reference code value and a mapping character corresponding to the code character in the mapping code value; wherein the distance value is used to characterize a similarity value between the encoded character and the mapped character;

splitting the reference coding value according to the sequence of the distance values from large to small to obtain a plurality of coding fields;

filling each coding field into a preset blank list, and filling a list unit corresponding to the pointing position of each coding field in the blank list according to a mapping character corresponding to each coding character in each coding field to obtain a mapping relation between the first information set and the second information set.

Optionally, determining a channel migration sequence of each mapping relationship with respect to the current production line state includes:

acquiring list structure description information corresponding to each mapping relation;

determining a plurality of list structure labels corresponding to the current production line state from the list structure description information; the list structure label is used for representing the information type of the list structure description information;

and calculating the migration weight of each list structure label corresponding to each list structure description information according to the character concentration degree represented by each list structure description information, and sequencing the list structure labels corresponding to each list structure description information according to the descending order of the migration weight to obtain the channel migration sequence.

Optionally, determining, according to the received service requirement, a second channel migration matrix corresponding to the target production line state includes:

analyzing the service requirement to obtain production line logic information corresponding to the service requirement; the production line logic information is used for representing the connection relation between the industrial production equipment in the target production line state;

integrating the network resource change records of each industrial production device according to the production line logic information to obtain the second channel migration matrix corresponding to the target production line state; and the network resource change record comprises the resource demand interval of the network resource of each industrial production device.

Optionally, the operating the channel migration model to obtain a network resource requirement of each industrial production device in the target production line state, and reconstructing a first network structure topology of the industrial production device in the current production line state according to the network resource requirement to obtain a second network structure topology includes:

acquiring a target script file for extracting model parameters of the channel migration model from the channel modeling server; judging whether the current data format of the target script file is consistent with a preset data format; if the channel migration model is consistent with the target script file, operating the target script file to obtain a model parameter set corresponding to the channel migration model; if not, format conversion is carried out on the target script file according to the preset data format, and the target script file with the converted format is operated to obtain a model parameter set corresponding to the channel migration model;

determining target model parameters of a second target identifier for characterizing network resource information from the model parameter set; for each target model parameter, determining a second target identifier matched with the first target identifier corresponding to the target model parameter; wherein the second target identifier is an equipment identifier of the industrial production equipment;

determining a parameter section corresponding to the latest generation moment in the target model parameters as the network resource requirement of the industrial production equipment corresponding to the target model parameters;

setting a time length threshold value for a corresponding network node of the corresponding industrial production equipment in the first network structure topology under the current production line state according to the requirement of each network resource; the time length threshold value is smaller than a preset effective reconstruction time length of the industrial production equipment corresponding to the network node in the first network structure topology;

calculating a time length difference value between each time length threshold value and a preset reconstruction effective time length corresponding to the time length threshold value, and sequentially reconstructing the network nodes according to the sequence of the time length difference values from small to large to obtain a second network structure topology; and reconstructing the network node comprises network resource adjustment and directed connection reconstruction of the network node.

In a second aspect, an apparatus for optimizing a communication network based on blockchain and edge computation is provided, which is applied to an edge computation server communicatively connected to a channel modeling server and a plurality of industrial production devices, and includes at least:

the information determining module is used for extracting the network resource information of each industrial production device, forming a first information set, determining the current device state information and the state label of each industrial production device, and generating a second information set corresponding to each industrial production device based on the current device state information and the state label; the first information set and the second information set comprise information fields with different dynamic coefficients, and the dynamic coefficients are used for characterizing the channel mobility distortion rate of the information fields;

the field determining module is used for determining an information field corresponding to the maximum dynamic coefficient in a second information set corresponding to each industrial production device as a reference information field after determining a field encoding value of the information field corresponding to one dynamic coefficient in the first information set corresponding to each industrial production device;

the mapping determining module is used for mapping the field coding value in a field sequence formed by the reference information field based on the current resource allocation topology corresponding to the industrial production equipment to obtain a corresponding mapping coding value of the field coding value in the field sequence; determining a mapping relation between the first information set and the second information set according to a reference coding value and the mapping coding value;

the model obtaining module is used for determining a channel migration sequence of each mapping relation relative to the current production line state and importing each channel migration sequence into a preset sequence list to form a first channel migration matrix corresponding to the current production line state; determining a second channel migration matrix corresponding to the state of the target production line according to the received service requirement; inputting the first channel migration matrix and the second channel migration matrix into the channel modeling server to obtain a channel migration model between the current production line state and the target production line state; the channel migration model is used for representing difference information of network resource allocation between the current production line state and the target production line state;

the topology reconstruction module is used for operating the channel migration model to obtain the network resource requirement of each industrial production device in the target production line state, and reconstructing the first network structure topology of the industrial production device in the current production line state according to the network resource requirement to obtain a second network structure topology;

and the communication optimization module is used for issuing the second network structure topology to each industrial production device so as to decouple each industrial production device based on the second network structure topology, and then forming a target communication network corresponding to the target production line state.

Optionally, the mapping determining module is specifically configured to:

determining the sequence position of each code character in the field code value in the field sequence according to the node priority corresponding to the target sequence number, mapping each code character to the corresponding sequence position to obtain the corresponding mapping character, and combining the mapping characters according to the corresponding sequence positions to obtain the mapping code value;

Optionally, the model obtaining module is specifically configured to:

Optionally, the topology reconfiguration module is specifically configured to:

When the communication network optimization method and system based on block chain and edge calculation in the embodiment of the application are applied, a first information set and a second information set corresponding to each industrial production device are firstly determined, then the mapping relation between the first information set and the second information set is determined by combining the current resource allocation topology corresponding to the industrial production device, then a first channel migration matrix corresponding to the current production line state is determined according to each mapping relation, a second channel migration matrix corresponding to the target production line state is determined according to the received service requirement, the first channel migration matrix and the second channel migration matrix are input into a channel modeling server to obtain a channel migration model between the current production line state and the target production line state, then the channel migration model is operated, and the first network structure topology under the current production line state is reconstructed to obtain the second network structure topology, and finally, forming a target communication network corresponding to the target production line state according to the second network structure topology. In this way, the industrial communication network can be optimized to ensure network reliability.

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 application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a communication architecture diagram of a communication network optimization system according to an exemplary embodiment of the present application.

Fig. 2 is a flow chart illustrating a method for communication network optimization according to an exemplary embodiment of the present application.

Fig. 3 is a block diagram illustrating an embodiment of a communication network optimization device according to an exemplary embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. 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 application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

In order to solve the problems, the invention discloses a communication network optimization method and a communication network optimization system based on block chains and edge calculation, wherein network resources of a production system are abstracted, channel migration models among different production systems are established, and the channel migration models can be operated based on service requirements of the different production systems to realize reconstruction of an industrial communication network of the production system, so that the optimization of the industrial communication network can be realized, and the reliability of the industrial communication network is ensured.

To achieve the above object, the present invention first discloses a communication architecture diagram of a communication network optimization system 100 as shown in fig. 1, the communication network optimization system 100 may include an edge computing server 200, a channel modeling server 300 and a plurality of industrial production devices 400. As a hub of the entire communication network optimization system 100, the edge computing server 200 is communicatively connected to the channel modeling server 300 and each industrial production facility 400, and performs the communication network optimization method as shown in fig. 2 by communicatively interacting with the channel modeling server 300 and each industrial production facility 400. The method shown in fig. 2 includes the following steps.

Step S210, extracting network resource information of each industrial production device and forming a first information set, determining current device state information and a state label of each industrial production device and generating a second information set corresponding to each industrial production device based on the current device state information and the state label; the first information set and the second information set comprise information fields with different dynamic coefficients, and the dynamic coefficients are used for characterizing the channel mobility distortion rate of the information fields.

In this embodiment, the larger the dynamic coefficient, the smaller the channel mobility distortion ratio.

Step S220, after determining the field encoding value of the information field corresponding to one of the dynamic coefficients in the first information set corresponding to each industrial production device, determining the information field corresponding to the largest dynamic coefficient in the second information set corresponding to the industrial production device as the reference information field.

Step S230, mapping the field code value in a field sequence formed by the reference information field based on the current resource allocation topology corresponding to the industrial production equipment to obtain a mapping code value corresponding to the field code value in the field sequence; and determining the mapping relation between the first information set and the second information set according to the reference coding value and the mapping coding value.

Step S240, determining a channel migration sequence of each mapping relation relative to the current production line state and importing each channel migration sequence into a preset sequence list to form a first channel migration matrix corresponding to the current production line state; determining a second channel migration matrix corresponding to the state of the target production line according to the received service requirement; inputting the first channel migration matrix and the second channel migration matrix into the channel modeling server to obtain a channel migration model between the current production line state and the target production line state; the channel migration model is used for representing difference information of network resource allocation between the current production line state and the target production line state.

Step S250, operating the channel migration model to obtain a network resource requirement of each industrial production device in the target production line state, and reconstructing a first network structure topology of the industrial production device in the current production line state according to the network resource requirement to obtain a second network structure topology.

In this embodiment, the network structure topology includes a plurality of network nodes, and the network nodes are connected by a directed connection line.

Step S260, the second network structure topology is issued to each industrial production device so that each industrial production device performs decoupling based on the second network structure topology, and then a target communication network corresponding to the target production line state is formed.

In this embodiment, the target communication network is used to represent the network resource occupancy rate, the communication interface type, and the communication protocol pairing result of each industrial production device in the target production line state.

It can be understood that, through the above steps S210 to S260, the first information set and the second information set corresponding to each industrial production equipment are determined first, secondly, determining the mapping relation between the first information set and the second information set by combining the current resource allocation topology corresponding to the industrial production equipment, then determining a first channel migration matrix corresponding to the current production line state according to each mapping relation and determining a second channel migration matrix corresponding to the target production line state according to the received service requirement, the first channel migration matrix and the second channel migration matrix are input into a channel modeling server to obtain a channel migration model between the current production line state and the target production line state, and then, operating the channel migration model, reconstructing the first network structure topology under the current production line state to obtain a second network structure topology, and finally forming a target communication network corresponding to the target production line state according to the second network structure topology. In this way, the industrial communication network can be optimized to ensure network reliability.

In a specific example, the inventors have found that, when mapping a field code value, there is usually a case where the field code value does not correspond to a position of a field sequence, which may cause the length of the determined mapping code value to be inconsistent with the length of the field code value, and thus it is difficult to accurately perform the subsequent steps. In order to improve such a problem and ensure smooth optimization of the communication network, in step S230, the field code value is mapped in the field sequence formed by the reference information field based on the current resource allocation topology corresponding to the industrial production device, so as to obtain a mapping code value corresponding to the field code value in the field sequence, which may specifically include the contents described in the following steps S2311 to S2314.

Step S2311, determining a node priority corresponding to a resource node of the current resource allocation topology and a node label of the resource node; wherein the node label represents a node class of a resource node of the current resource allocation topology, the node label comprising at least: a first node class and a second node class of resource nodes representing the current resource allocation topology.

Step S2312, acquiring a priority ranking sequence corresponding to the node priority; the priority ordering sequence comprises a pre-configured sequence number, and the sequence number represents a mapping sequence of resource nodes which are located in the node priority in the priority ordering sequence and correspond to the node priority.

Step S2313, according to the node priorities and the node labels, searching a target sequence number matched with each code character in the field code values in the priority ranking sequence, and determining the node priorities corresponding to the target sequence numbers.

Step S2314, determining a sequence position of each code character in the field code value in the field sequence according to the node priority corresponding to the target sequence number, mapping each code character to a corresponding sequence position to obtain a corresponding mapping character, and combining the mapping characters according to the corresponding sequence positions to obtain the mapping code value.

It can be understood that, through the steps S2311 to S2312, the resource nodes of the current resource allocation topology can be analyzed, so as to determine the priority ordering sequence for positioning the encoded characters in the field encoded values, thereby ensuring the uniqueness of the positions of the encoded characters in the field encoded values in the field sequence, avoiding the omission or repetition of the positions of the encoded characters in the field encoded values in the field sequence, ensuring the one-to-one correspondence between the field encoded values and the positions of the field sequence, further ensuring the consistency between the lengths of the field encoded values and the mapping encoded values, and ensuring the subsequent smooth optimization of the communication network.

In particular implementation, in order to ensure the accurate establishment of the channel migration model subsequently to achieve accurate and reliable optimization of the communication network, in step S230, the mapping relationship between the first information set and the second information set is determined according to the reference coding value and the mapping coding value, and further may include the following steps S2321-S2323.

Step S2321, determining a distance value between each code character in the reference code value and a mapping character corresponding to the code character in the mapping code value; wherein the distance value is used to characterize a similarity value between the code character and the mapping character.

Step S2322, splitting the reference encoded value according to the order of the distance values from large to small, so as to obtain a plurality of encoded fields.

Step S2323, filling each code field into a preset blank list, and filling a list unit corresponding to a pointing position of each code field in the blank list according to a mapping character corresponding to each code character in each code field, so as to obtain a mapping relationship between the first information set and the second information set.

When the contents described in steps S2321 to S2323 are executed, the mapping relationship between the first information set and the second information set can be accurately determined, so as to ensure that the channel migration model is accurately established subsequently to implement accurate and reliable optimization of the communication network.

In one possible embodiment, in order to accurately determine the channel migration sequence and thus ensure the integrity of the first channel migration matrix, in step S240, the channel migration sequence of each mapping relationship with respect to the current production line state is determined, which may specifically include the contents described in the following steps S2411 to S2413.

Step S2411, obtaining list structure description information corresponding to each mapping relationship.

Step S2412, determining a plurality of list structure labels corresponding to the current production line state from the list structure description information; wherein the list structure label is used for representing the information type of the list structure description information.

Step S2413, calculating a migration weight of each list structure label corresponding to each list structure description information according to the character concentration ratio represented by each list structure description information, and sorting the list structure labels corresponding to each list structure description information according to the descending order of the migration weights to obtain the channel migration sequence.

When the contents described in the above steps S2411 to S2413 are executed, migration weight calculation can be performed on the list structure tag in the list structure description information corresponding to each mapping relationship, so as to determine the channel migration sequence corresponding to the mapping relationship according to the calculated migration weight, thus conversion between the list structure tag and the migration weight can be realized, and thus the channel migration sequence can be accurately determined, thereby ensuring the integrity of the first channel migration matrix.

In an example, the step of determining the second channel migration matrix corresponding to the target production line state according to the received service requirement, which is described in step S240, may specifically include the following steps S2421 to S2422.

Step S2421, analyzing the service requirement to obtain production line logic information corresponding to the service requirement; and the production line logic information is used for representing the connection relation between the devices of each industrial production device in the target production line state.

Step S2422, integrating the network resource change records of each industrial production device according to the production line logic information to obtain the second channel migration matrix corresponding to the target production line state; and the network resource change record comprises the resource demand interval of the network resource of each industrial production device.

Based on the above step S2421 and step S2422, the service requirement can be analyzed to obtain the production line logic information for representing the inter-device connection relationship of the industrial production device, so that the network resource change records can be integrated based on the production line logic information to quickly obtain the second channel migration matrix, and the real-time property of determining the second channel migration matrix is improved.

In the specific implementation process, the inventor finds that, because the network nodes have the adaptive adjustment mechanism, when the network structure topology is reconstructed, the reconstruction effective duration between each network node needs to be considered, so that the effectiveness and the reliability of the second network structure topology are ensured. To achieve the above object, in step S250, the channel migration model is operated to obtain a network resource requirement of each industrial production device in the target production line state, and the first network structure topology of the industrial production device in the current production line state is reconstructed according to the network resource requirement to obtain a second network structure topology, which may specifically include the contents described in the following steps S251 to S255.

Step S251, obtaining a target script file for extracting model parameters of the channel migration model from the channel modeling server; judging whether the current data format of the target script file is consistent with a preset data format; if the channel migration model is consistent with the target script file, operating the target script file to obtain a model parameter set corresponding to the channel migration model; and if not, performing format conversion on the target script file according to the preset data format, and operating the target script file after the format conversion to obtain the model parameter set corresponding to the channel migration model.

Step S252, determining target model parameters of a second target identifier for representing network resource information from the model parameter set; for each target model parameter, determining a second target identifier matched with the first target identifier corresponding to the target model parameter; wherein the second target identifier is an equipment identifier of the industrial production equipment.

Step S253, determining a parameter segment corresponding to the latest generation time in the target model parameters as the network resource requirement of the industrial production equipment corresponding to the target model parameters.

Step S254, a duration threshold is set for a network node corresponding to the industrial production device in the first network structure topology in the current production line state according to each network resource requirement; and the time length threshold value is smaller than the preset effective reconstruction time length of the industrial production equipment corresponding to the network node in the first network structure topology.

Step S255, calculating a time length difference value between each time length threshold value and the corresponding preset reconstruction effective time length, and sequentially reconstructing the network nodes according to the sequence of the time length difference values from small to large to obtain a second network structure topology; and reconstructing the network node comprises network resource adjustment and directed connection reconstruction of the network node.

It can be understood that through the above steps S251 to S255, when reconstructing the network structure topology, the effective reconstruction duration between each network node can be determined by considering the adaptive adjustment mechanism existing in the network node. In this way, the network nodes can be sequentially reconstructed based on the time length difference between the time length threshold and the reconstruction effective time length, so that the validity and the reliability of the second network structure topology are ensured.

In an alternative embodiment, in order to improve the accuracy of issuing the second network structure topology and the efficiency of optimizing the communication network, in step S260, the second network structure topology is issued to each industrial production device so that each industrial production device performs decoupling based on the second network structure topology and forms a target communication network corresponding to the target production line state, which may specifically include the contents described in steps S261 to S263 below.

Step S261, according to a preset dynamic random number corresponding to each industrial production device, verifies topology information corresponding to the industrial production device in the second network structure topology to obtain a first verification result, and sends the first verification result and the second network structure topology to the corresponding industrial production device.

Step S262, each industrial production device performs a calibration calculation based on the received topology information corresponding to the industrial production device in the second network structure topology and the agreed key pre-stored in the industrial production device, so as to obtain a second calibration result.

And step S263, when determining that the first check result and the second check result corresponding to each industrial production device are consistent, each industrial production device rolls back the communication protocol of the industrial production device based on the node list of the second network structure topology, and establishes the target communication network based on the protocol list of the second network structure topology to form the target communication network.

It can be understood that, through the steps S261 to S263, the accuracy of issuing the second network structure topology and the efficiency of optimizing the communication network can be improved.

Referring to fig. 3, a block diagram of functional modules of a communication network optimization apparatus 300 based on blockchain and edge calculation is also provided, the apparatus at least includes the following functional modules:

an information determining module 310, configured to extract network resource information of each industrial production device and form a first information set, determine current device state information and a state label of each industrial production device, and generate a second information set corresponding to each industrial production device based on the current device state information and the state label; the first information set and the second information set comprise information fields with different dynamic coefficients, and the dynamic coefficients are used for characterizing the channel mobility distortion rate of the information fields;

a field determining module 320, configured to determine, after determining a field encoding value of an information field corresponding to one of the dynamic coefficients in the corresponding first information set of each industrial production device, an information field corresponding to a maximum dynamic coefficient in a second information set corresponding to the industrial production device as a reference information field;

a mapping determining module 330, configured to map the field coding value in a field sequence formed by the reference information field based on a current resource allocation topology corresponding to the industrial production device, so as to obtain a mapping coding value corresponding to the field coding value in the field sequence; determining a mapping relation between the first information set and the second information set according to a reference coding value and the mapping coding value;

a model obtaining module 340, configured to determine a channel migration sequence of each mapping relationship relative to a current production line state and import each channel migration sequence into a preset sequence list to form a first channel migration matrix corresponding to the current production line state; determining a second channel migration matrix corresponding to the state of the target production line according to the received service requirement; inputting the first channel migration matrix and the second channel migration matrix into the channel modeling server to obtain a channel migration model between the current production line state and the target production line state; the channel migration model is used for representing difference information of network resource allocation between the current production line state and the target production line state;

a topology reconfiguration module 350, configured to run the channel migration model to obtain a network resource requirement of each industrial production device in the target production line state, and reconfigure a first network structure topology of the industrial production device in the current production line state according to the network resource requirement to obtain a second network structure topology;

the communication optimization module 360 is configured to issue the second network structure topology to each industrial production device so that each industrial production device performs decoupling based on the second network structure topology, and then forms a target communication network corresponding to the target production line state.

Optionally, the mapping determining module 330 is specifically configured to:

Optionally, the model obtaining module 340 is specifically configured to:

Optionally, the topology reconfiguration module 350 is specifically configured to:

For the description of the functional modules, reference is made to the description of the method, and no further description is made here.

On the basis, an edge computing server is further provided, and the specific description is as follows.

A1. An edge computing server communicatively coupled to a channel modeling server and a plurality of industrial production devices, the edge computing server configured to:

A2. The edge computing server according to a1, configured to:

verifying topology information corresponding to the industrial production equipment in the second network structure topology according to a preset dynamic random number corresponding to each industrial production equipment to obtain a first verification result, and sending the first verification result and the second network structure topology to the corresponding industrial production equipment;

each industrial production device carries out verification calculation based on received topology information corresponding to the industrial production device in the second network structure topology and an agreed key prestored in the industrial production device to obtain a second verification result;

and when each industrial production device determines that the first check result and the second check result corresponding to the industrial production device are consistent, rolling back the communication protocol of the industrial production device based on the node list of the second network structure topology, and constructing the target communication network based on the protocol list of the second network structure topology by each industrial production device to form the target communication network.

A3. The edge computing server according to a1, configured to:

A4. The edge computing server according to a1, configured to:

A5. The edge computing server according to a4, configured to:

A6. The edge computing server according to a1, configured to:

A7. The edge computing server according to a1, configured to:

Further, an embodiment of the present invention further provides a communication network optimization system based on a block chain and edge calculation, and the detailed description about the system is as follows.

B1. A communication network optimization system based on block chains and edge computing comprises an edge computing server, a channel modeling server and a plurality of industrial production devices, wherein the channel modeling server is communicated with the edge computing server;

the edge computing server is configured to:

determining a channel migration sequence of each mapping relation relative to the current production line state, and importing each channel migration sequence into a preset sequence list to form a first channel migration matrix corresponding to the current production line state; determining a second channel migration matrix corresponding to the state of the target production line according to the received service requirement; inputting the first channel migration matrix and the second channel migration matrix into the channel modeling server;

the channel modeling server is configured to:

generating a channel migration model between the current production line state and the target production line state according to the first channel migration matrix and the second channel migration matrix, and transmitting the channel migration model back to the edge computing server; the channel migration model is used for representing difference information of network resource allocation between the current production line state and the target production line state;

the edge computing server is configured to:

issuing the second network structure topology to each industrial production device;

and the industrial production equipment is used for decoupling based on the second network structure topology and then forming a target communication network corresponding to the target production line state.

B2. The communication network optimization system of B1, wherein the edge computing server is specifically configured to:

B3. The communication network optimization system of B1, wherein the edge computing server is specifically configured to:

B4. The communication network optimization system of B3, wherein the edge computing server is specifically configured to:

B5. The communication network optimization system of B1, wherein the edge computing server is specifically configured to:

B6. The communication network optimization system of B1, wherein the edge computing server is specifically configured to:

On the basis, the edge computing server is further provided, and the edge computing server comprises: the system comprises a processor, a memory and a network interface, wherein the memory and the network interface are connected with the processor; the network interface is connected with a nonvolatile memory in the edge computing server; and the processor calls the computer program from the nonvolatile memory through the network interface when running, and runs the computer program through the memory to execute the method.

On the basis, a readable storage medium applied to a computer is further provided, and a computer program is burned on the readable storage medium and is used for realizing the method when the computer program runs in the memory of the edge computing server.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A communication network optimization method based on blockchain and edge computing, applied to an edge computing server communicatively connected to a channel modeling server and a plurality of industrial production devices, the method at least comprising:

issuing the second network structure topology to each industrial production device so that each industrial production device can decouple based on the second network structure topology, and then forming a target communication network corresponding to the target production line state;

wherein:

the target communication network is used for representing the network resource occupancy rate, the communication interface type and the communication protocol pairing result of each industrial production device in the target production line state.

2. The communication network optimization method of claim 1, wherein mapping the field code value in a field sequence formed by the reference information field based on a current resource allocation topology corresponding to the industrial production equipment to obtain a mapping code value corresponding to the field code value in the field sequence comprises:

3. The method of claim 1, wherein determining the mapping relationship between the first set of information and the second set of information based on a reference code value and the mapping code value comprises:

4. The method of claim 3, wherein determining a sequence of channel transitions for each mapping relationship relative to a current line state comprises:

5. The method of claim 1, wherein determining a second channel migration matrix corresponding to a target production line state according to the received service requirement comprises:

6. The method of claim 1, wherein the operating the channel migration model to obtain a network resource requirement of each industrial production device in the target production line state, and reconstructing a first network structure topology of the industrial production device in the current production line state according to the network resource requirement to obtain a second network structure topology comprises:

7. A communication network optimization system based on block chains and edge computing is characterized by comprising an edge computing server, a channel modeling server and a plurality of industrial production devices, wherein the channel modeling server is communicated with the edge computing server;

the edge computing server is configured to:

the channel modeling server is configured to:

the edge computing server is configured to: