Optimization Method and System for SDN Controller Deployment in Power Communication Networks

Abstract

This invention belongs to the field of power communication network technology, and relates to a method and system for optimizing the deployment of SDN controllers in power communication networks. The method includes: constructing a data communication network cloud platform, acquiring deployment topology data of the power communication network, forming a data communication network diagram model, monitoring and updating the connections of devices in the communication network in real time; accessing the SDN controllers in the power communication network, collecting their operational data, analyzing the communication performance of each device, and generating a communication network data matrix; analyzing the communication network data matrix to determine whether there are optimization needs for the deployment of each SDN controller; marking SDN controllers that meet the optimization need criteria to generate a deployment optimization list; analyzing the optimization needs of the SDN controllers in the deployment optimization list and providing feedback to relevant personnel. This facilitates the stable operation of the power system and avoids the complexity and inefficiency caused by decentralized management.

Description

Technical Field

[0001] This invention belongs to the field of power communication network technology, and specifically relates to an SDN controller deployment optimization method and system for power communication networks. Background Technology

[0002] With the development of networks, traditional network architectures can no longer meet current communication needs, making SDN (Software-Defined Networking) an inevitable trend. The SDN controller is the core of the SDN architecture, communicating with the data plane through a network programming interface to control network traffic.

[0003] Existing technologies typically deploy SDN controllers in clusters to improve processing power, with each node in the cluster undertaking a portion of the computing tasks and collectively handling the communication network.

[0004] As a crucial component of the power system, the power communication network undertakes important tasks such as power dispatching, automated control, and information exchange. However, due to the massive scale of the power system and the diverse business requirements, how to deploy SDN controllers in the power communication network, ensure its stable operation, and fully consider the controller's scalability while optimizing the clustered SDN controller deployment remains a challenging problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide an SDN controller deployment optimization system and method for power communication networks to solve the problems mentioned in the background art.

[0006] To achieve the objectives of this invention, the following technical solution will be adopted: An optimization method for SDN controller deployment in power communication networks, comprising the following steps:

[0007] Step S100: Construct a data communication network cloud platform to coordinate all devices in the communication network, acquire the deployment topology data of the power communication network, form a data communication network diagram model, monitor the connection of devices in the communication network, and update the data communication network diagram model in real time.

[0008] Step S200: Access the SDN controller in the power communication network, collect the SDN controller's operating data, collect the instructions received by each device through the data communication network cloud platform during each control execution, and generate a communication list based on the instructions; randomly select an SDN controller, analyze the communication performance of each device based on the communication list, and generate a communication network data matrix;

[0009] Step S300: Analyze the communication network data matrix based on cloud computing to determine whether there is an optimization need for the deployment of each SDN controller; mark the SDN controllers that meet the criteria for optimization needs and generate a deployment optimization list;

[0010] Step S400: Analyze the SDN controller optimization requirements in the deployment optimization list based on the data communication network diagram model, and provide feedback on the deployment optimization requirements in the power communication network to relevant personnel.

[0011] As a preferred embodiment of the present invention, the implementation process of step S100 includes the following steps:

[0012] Step S101: Construct a data communication network cloud platform to coordinate all devices in the communication network and assign coding prefixes according to device type, and uniformly encode each device; identify the connection methods between devices in the communication network and obtain the deployment topology data of the power communication network; when any SDN controller performs control communication with any device, collect the northbound and southbound interface protocols of the control communication, mark the connection lines with the same intermediate layer service processing request when the same SDN controller performs control communication with the device as the Qth type of control communication, and connect the i-th SDN controller with device A. j The connection line is referred to as SDN. i →Q→B; where SDN i Let B represent the encoding of the i-th SDN controller, Q represent the Q-th middleware service processing request, and B = {A}. j |j∈[1,m]},A j This represents the code of the j-th device, and m represents the total number of devices that process requests through the Q-th intermediate layer service when the i-th SDN controller performs control communication.

[0013] Step S102: Based on the control communication records, a data communication network diagram model is formed, which includes the connection lines between each SDN controller and the device; the connection of the device in the communication network is monitored; when a new device is added to the communication network, step S101 is repeated to update the connection lines of the SDN controller; when the new device does not belong to the existing connection lines, a new connection line is generated and the data communication network diagram model is expanded; when an existing device in the communication network changes its interface protocol or its encoding is deleted, the connection lines of the SDN controller and the data communication network diagram model are refreshed.

[0014] Based on the above methods, the connection lines in the power communication network are divided according to the communication protocol and the content processed by the intermediate layer service. The deployment characteristics of the SDN controller are reflected in the vertical dimension of the network architecture, providing a data source for subsequent data analysis.

[0015] As a preferred embodiment of the present invention, the implementation process of step S200 includes the following steps:

[0016] Step S201: Access the SDN controller in the power communication network based on the communication cycle; within the Tth communication cycle, collect the operating data of the SDN controller; during each control execution, collect the instructions received by each device through the data communication network cloud platform; wherein, the instructions sent by the same SDN controller to the device corresponding to the same connection line are integrated into one instruction cluster; the data communication network cloud platform coordinates all collected instruction clusters to generate a communication list for the kth communication cycle; the communication list includes the connection line label of each instruction cluster, and any instruction cluster is denoted as ZC(iQB), where ZC(iQB) = {[L z (A j ), L t (A j )]|j∈[1,m]},L z (A j ) indicates device A j The corresponding latest instruction content, L t (A j ) indicates device A j The corresponding latest instruction reception time is used to denot the communication list of the k-th communication cycle as LZC. k And sort the instruction clusters in the communication list according to the order of the latest instruction received by the device;

[0017] Step S202: In the power communication network, multiple SDN controllers work collaboratively, with each controller managing a region or subnet. An SDN controller is randomly selected, and its communication list is obtained. Based on the communication list corresponding to each communication cycle, the communication performance of each device is analyzed. The communication performance score between the selected SDN controller and the devices in the power communication network is calculated using the following formula:

[0018]

[0019] Among them, X ij Let F(LZC) represent the communication performance score of the j-th device in the communication list of the i-th SDN controller, and W represent the total number of communication cycles recorded for the i-th SDN controller. k+1 )∩F(LZC k )] represents the discriminant function, F(LZC k+1 F(LZC) represents the instruction set containing instructions sent to the j-th device in the communication list of the (k+1)-th communication cycle; k ) represents the instruction set containing instructions sent to the j-th device in the communication list of the k-th communication cycle; when (F(LZCk+1 )∩F(LZC k If ) is empty, if[(F(LZC k+1 )∩F(LZC k )]=0; when (F(LZC k+1 )∩F(LZC k If [(F(LZC)] is not empty, then [(F(LZC)] k+1 )∩F(LZC k )] = 1;

[0020] Step S203: Generate a communication network data matrix based on the communication performance scores of each SDN controller and the devices connected to the SDN controller in the power communication network. In this matrix, rows represent different SDN controllers, columns represent different devices, and the value in the i-th row and j-th column is X. ij .

[0021] Based on the above method, the command reception of devices in the power communication network is monitored, and the communication performance of each SDN controller and the devices connected to the SDN controller in the power communication network is mapped into a matrix. Based on the network architecture, the area managed by each SDN controller is analyzed, which can reflect the communication frequency and traffic of each area, so as to facilitate further analysis of the capabilities of each SDN controller.

[0022] As a preferred embodiment of the present invention, the implementation process of step S300 includes the following steps:

[0023] Step S301: Analyze the communication network data matrix based on cloud computing, define the network status of each SDN controller according to the communication network data matrix, check whether the values ​​in the matrix are evenly distributed, and calculate the optimization index of the SDN controller corresponding to the i-th row of the matrix according to the following formula:

[0024]

[0025] Where N represents the total number of SDN controllers, M represents the total number of devices connected to the SDN controllers in the power communication network, and μ i Let μ represent the average of all elements in the i-th row of the matrix, and let μ represent the average of all elements in the matrix.

[0026] Step S302: When the optimization index of the SDN controller corresponding to the i-th row of the matrix exceeds the preset threshold, it is considered that the deployment of the i-th SDN controller in the power communication network has optimization needs; mark the SDN controllers with optimization needs and generate a deployment optimization list.

[0027] Based on the above method, the control status of SDN controllers over their managed areas can be reflected. The higher the optimization index of an SDN controller, the more control operations are performed within its managed area. In order to avoid excessive load on the controller and reduced network stability, and to prevent the controller configuration from becoming more complicated, SDN controllers whose optimization index exceeds a preset threshold are marked as SDN controllers with optimization needs.

[0028] As a preferred embodiment of the present invention, the implementation process of step S400 includes the following steps:

[0029] Step S401: The deployment optimization list includes several SDN controller codes with optimization needs; the optimization needs of the SDN controllers in the deployment optimization list are analyzed based on the data communication network diagram model, and the anomaly degree of the device represented by the p-th row of the matrix corresponding to any SDN controller in the deployment optimization list is calculated according to the following formula:

[0030]

[0031] Among them, X gp μ represents the value in the g-th row and p-th column of the matrix. p X represents the average value of the p-th column of the matrix. gj μ represents the value in the g-th row and j-th column of the matrix. j This represents the average of all elements in the j-th column of the matrix;

[0032] Step S402: When the anomaly degree of the device represented by the p-th row of the matrix connected to any SDN controller in the deployment optimization list exceeds the preset threshold, it is considered that the device has an impact on the currently connected SDN controller; based on the optimization requirement analysis results of each SDN controller in the deployment optimization list, the deployment optimization requirements in the power communication network are fed back to relevant personnel, and the relevant personnel manage the devices that affect the SDN controller.

[0033] Based on the above methods, the anomalies of devices connected to SDN controllers with optimization needs are analyzed to help relevant personnel locate the devices affecting the capabilities of SDN controllers, so that relevant personnel can re-manage the deployment and management areas of SDN controllers for specific devices.

[0034] An SDN controller deployment optimization system for power communication networks includes: a communication data acquisition module, a controller performance analysis module, an optimization requirement identification module, and a deployment feedback module;

[0035] The communication data acquisition module is used to acquire the deployment topology data of the power communication network, form a data communication network diagram model, monitor the connection of devices in the communication network, and update the data communication network diagram model in real time.

[0036] The controller performance analysis module is used to access the SDN controller in the power communication network, collect the operating data of the SDN controller, and analyze the communication performance of each device.

[0037] The optimization requirement discrimination module is used to analyze the communication network data matrix to determine whether there is an optimization requirement for the deployment of each SDN controller; it marks the SDN controllers that meet the optimization requirement discrimination conditions and generates a deployment optimization list;

[0038] The deployment feedback module is used to analyze the SDN controller optimization requirements in the deployment optimization list and provide feedback on the deployment optimization requirements in the power communication network to relevant personnel.

[0039] Furthermore, the controller performance analysis module includes an instruction acquisition unit, a list generation unit, and a matrix generation unit;

[0040] The instruction acquisition unit accesses the SDN controller in the power communication network based on the communication cycle; within the Tth communication cycle, it acquires the operating data of the SDN controller, and during each control execution, it acquires the instructions received by each device through the data communication network cloud platform; wherein, the instructions sent by the same SDN controller to the device corresponding to the same connection line are integrated into an instruction cluster.

[0041] The list generation unit coordinates all collected instruction clusters through the data communication network cloud platform to generate a communication list for the k-th communication cycle. The communication list includes connection line tags for each instruction cluster. Any instruction cluster is denoted as ZC(iQB), where ZC(iQB) = {[L...} z (A j ), L t (A j )]|j∈[1,m]},L z (A j ) indicates device A j The corresponding latest instruction content, L t (A j ) indicates device A j The corresponding latest instruction reception time is used to denot the communication list of the k-th communication cycle as LZC. k ZC(iQB)∈LZC k And sort the instruction clusters in the communication list according to the order in which the latest instruction was received by the device;

[0042] The matrix generation unit analyzes the communication performance of each device based on the communication list corresponding to each communication cycle. It generates a communication network data matrix based on the communication performance scores of each SDN controller and the devices connected to the SDN controller in the power communication network. In this matrix, rows represent different SDN controllers, columns represent different devices, and the value in the i-th row and j-th column is X. ij The X ij Calculate using the following formula:

[0043]

[0044] Among them, X ij Let F(LZC) represent the communication performance score of the j-th device in the communication list of the i-th SDN controller, and W represent the total number of communication cycles recorded for the i-th SDN controller. k+1 )∩F(LZC k )] represents the discriminant function, F(LZC k+1 F(LZC) represents the instruction set containing instructions sent to the j-th device in the communication list of the (k+1)-th communication cycle; k ) represents the instruction set containing instructions sent to the j-th device in the communication list of the k-th communication cycle; when (F(LZC k+1 )∩F(LZC k If ) is empty, if[(F(LZC k+1 )∩F(LZC k )]=0; when (F(LZC k+1 )∩F(LZC k If [(F(LZC)] is not empty, then [(F(LZC)] k+1 )∩F(LZC k )]=1

[0045] Furthermore, the optimization requirement discrimination module includes a matrix analysis unit and a requirement analysis unit;

[0046] The matrix analysis unit analyzes the communication network data matrix based on cloud computing, defines the network status of each SDN controller according to the communication network data matrix, checks whether the values ​​in the matrix are evenly distributed, and calculates the optimization index of the SDN controller corresponding to the i-th row of the matrix according to the following formula:

[0047]

[0048] Where N represents the total number of SDN controllers, M represents the total number of devices connected to the SDN controllers in the power communication network, and μ i Let μ represent the average of all elements in the i-th row of the matrix, and let μ represent the average of all elements in the matrix.

[0049] The demand analysis unit is used to determine whether there is an optimization requirement for the deployment of the SDN controller based on the optimization index of the SDN controller, and to mark the SDN controllers with optimization requirements and generate a deployment optimization list.

[0050] Beneficial effects:

[0051] (1) This invention manages all devices in a unified manner through the data communication network cloud platform, realizes centralized management and monitoring of the network, pays attention to network topology changes, can cope with the uncontrollable disaster recovery problem caused by the expansion of business demand, and provides an abnormal autonomous perception method for SDN controller, which can analyze the operating status of the communication network in real time, avoiding the complexity and inefficiency caused by decentralized management.

[0052] (2) The present invention adopts a distributed controller architecture and combines the concept of centralized control to ensure the reliability and stability of each subnet; at the same time, it can dynamically adjust and optimize the discrimination strategy according to the actual needs of the network, effectively avoiding the limitations brought about by static configuration. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the SDN controller deployment optimization method for power communication networks according to the present invention. Detailed Implementation

[0054] The technical solutions of the present invention will now be clearly and completely described with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] As an embodiment of the present invention, such as Figure 1 As shown, this invention provides an optimized deployment method for SDN controllers in power communication networks, comprising the following steps:

[0056] Step S100: Construct a data communication network cloud platform to coordinate all devices in the communication network, acquire the deployment topology data of the power communication network, form a data communication network diagram model, monitor the connection of devices in the communication network, and update the data communication network diagram model in real time.

[0057] Step S101: Construct a data communication network cloud platform to coordinate all devices in the communication network and assign coding prefixes according to device type, uniformly coding each device; obtain interface information and connection status from network devices through the SNMP protocol; automatically discover the connection relationship between devices through the LLDP protocol; obtain device status and connection information through the API interface; identify the connection method between each device in the communication network and obtain the deployment topology data of the power communication network; when any SDN controller performs control communication with any device, collect the northbound and southbound interface protocols of the control communication, mark the connection lines with the same intermediate layer service processing request when the same SDN controller performs control communication with the device as the Qth type of control communication, and connect the i-th SDN controller with device A. j The connection line is referred to as SDN. i →Q→B; where SDN i Let B represent the encoding of the i-th SDN controller, Q represent the Q-th middleware service processing request, and B = {A}. j |j∈[1,m]},A j This represents the code of the j-th device, and m represents the total number of devices that process requests through the Q-th intermediate layer service when the i-th SDN controller performs control communication.

[0058] Step S102: Based on the control communication records, a data communication network diagram model is formed, which includes the connection lines between each SDN controller and the device; the connection of the device in the communication network is monitored; when a new device is added to the communication network, step S101 is repeated to update the connection lines of the SDN controller; when the new device does not belong to the existing connection lines, a new connection line is generated and the data communication network diagram model is expanded; when an existing device in the communication network changes its interface protocol or its encoding is deleted, the connection lines of the SDN controller and the data communication network diagram model are refreshed.

[0059] Based on the above methods, the connection lines in the power communication network are divided according to the communication protocol and the content processed by the intermediate layer service. The deployment characteristics of the SDN controller are reflected in the vertical dimension of the network architecture, providing a data source for subsequent data analysis.

[0060] Step S200: Access the SDN controller in the power communication network, collect the SDN controller's operating data, collect the instructions received by each device through the data communication network cloud platform during each control execution, and generate a communication list based on the instructions; randomly select an SDN controller, analyze the communication performance of each device based on the communication list, and generate a communication network data matrix;

[0061] Step S201: Access the SDN controller in the power communication network based on the communication cycle; within the Tth communication cycle, collect the operating data of the SDN controller; during each control execution, collect the instructions received by each device through the data communication network cloud platform; wherein, the instructions sent by the same SDN controller to the device corresponding to the same connection line are integrated into one instruction cluster; the data communication network cloud platform coordinates all collected instruction clusters to generate a communication list for the kth communication cycle; the communication list includes the connection line label of each instruction cluster, and any instruction cluster is denoted as ZC(iQB), where ZC(iQB) = {[L z (A j ), L t (A j )]|j∈[1,m]},L z (A j ) indicates device A j The corresponding latest instruction content, L t (A j ) indicates device A j The corresponding latest instruction reception time is used to denot the communication list of the k-th communication cycle as LZC. k ZC(iQB)∈LZC k And sort the instruction clusters in the communication list according to the order in which the latest instruction was received by the device;

[0062] Step S202: In the power communication network, multiple SDN controllers work collaboratively, with each controller managing a region or subnet. An SDN controller is randomly selected, and its communication list is obtained. Based on the communication list corresponding to each communication cycle, the communication performance of each device is analyzed. The communication performance score between the selected SDN controller and the devices in the power communication network is calculated using the following formula:

[0063]

[0064] Among them, X ij Let F(LZC) represent the communication performance score of the j-th device in the communication list of the i-th SDN controller, and W represent the total number of communication cycles recorded for the i-th SDN controller. k+1 )∩F(LZC k )] represents the discriminant function, F(LZC k+1 F(LZC) represents the instruction set containing instructions sent to the j-th device in the communication list of the (k+1)-th communication cycle; k ) represents the instruction set containing instructions sent to the j-th device in the communication list of the k-th communication cycle; when (F(LZC k+1 )∩F(LZC kIf ) is empty, if[(F(LZC k+1 )∩F(LZC k )]=0; when (F(LZC k+1 )∩F(LZC k If [(F(LZC)] is not empty, then [(F(LZC)] k+1 )∩F(LZC k )] = 1;

[0065] Step S203: Generate a communication network data matrix based on the communication performance scores of each SDN controller and the devices connected to the SDN controller in the power communication network. In this matrix, rows represent different SDN controllers, columns represent different devices, and the value in the i-th row and j-th column is X. ij .

[0066] Based on the above method, the command reception of devices in the power communication network is monitored, and the communication performance of each SDN controller and the devices connected to the SDN controller in the power communication network is mapped into a matrix. Based on the network architecture, the area managed by each SDN controller is analyzed, which can reflect the communication frequency and traffic of each area, so as to facilitate further analysis of the capabilities of each SDN controller.

[0067] Step S300: Analyze the communication network data matrix based on cloud computing to determine whether there is an optimization need for the deployment of each SDN controller; mark the SDN controllers that meet the criteria for optimization needs and generate a deployment optimization list;

[0068] Step S301: Analyze the communication network data matrix based on cloud computing, define the network status of each SDN controller according to the communication network data matrix, check whether the values ​​in the matrix are evenly distributed, and calculate the optimization index of the SDN controller corresponding to the i-th row of the matrix according to the following formula:

[0069]

[0070] Where N represents the total number of SDN controllers, M represents the total number of devices connected to the SDN controllers in the power communication network, and μ i Let μ represent the average of all elements in the i-th row of the matrix, and let μ represent the average of all elements in the matrix.

[0071] Step S302: When the optimization index of the SDN controller corresponding to the i-th row of the matrix exceeds the preset threshold, it is considered that the deployment of the i-th SDN controller in the power communication network has optimization needs; mark the SDN controllers with optimization needs and generate a deployment optimization list.

[0072] Based on the above methods, the control status of the SDN controller over its managed area can be reflected, supporting large-scale business elastic and agile control, providing a hierarchical and domain-based management mode, and facilitating system business operations. The higher the optimization index of the SDN controller, the more control operations are performed in its managed area. In order to avoid excessive load on the controller and reduced network stability, and to prevent the controller configuration complexity from becoming more complicated, SDN controllers whose optimization index exceeds the preset threshold are marked as SDN controllers with optimization needs.

[0073] Step S400: Analyze the SDN controller optimization requirements in the deployment optimization list based on the data communication network diagram model, and provide feedback on the deployment optimization requirements in the power communication network to relevant personnel;

[0074] Step S401: The deployment optimization list includes several SDN controller codes with optimization needs; the optimization needs of the SDN controllers in the deployment optimization list are analyzed based on the data communication network diagram model, and the anomaly degree of the device represented by the p-th row of the matrix corresponding to any SDN controller in the deployment optimization list is calculated according to the following formula:

[0075]

[0076] Among them, X gp μ represents the value in the g-th row and p-th column of the matrix. p X represents the average value of the p-th column of the matrix. gj μ represents the value in the g-th row and j-th column of the matrix. j This represents the average of all elements in the j-th column of the matrix;

[0077] Step S402: When the anomaly degree of the device represented by the p-th row of the matrix connected to any SDN controller in the deployment optimization list exceeds the preset threshold, it is considered that the device has an impact on the currently connected SDN controller; based on the optimization requirement analysis results of each SDN controller in the deployment optimization list, the deployment optimization requirements in the power communication network are fed back to relevant personnel, and the relevant personnel manage the devices that affect the SDN controller.

[0078] Based on the above methods, the anomalies of devices connected to SDN controllers with optimization needs are analyzed to help relevant personnel locate the devices affecting the capabilities of SDN controllers, so that relevant personnel can re-manage the deployment and management areas of SDN controllers for specific devices.

[0079] The technical solutions of the present invention have been described in detail above with reference to the embodiments / drawings. However, the present invention is not limited to the above technical solutions. For those skilled in the art, after learning the contents described in the present invention, several equivalent changes and substitutions can be made without departing from the principle of the present invention. These equivalent changes and substitutions should also be considered to fall within the protection scope of the present invention.

Claims

1. An optimization method for SDN controller deployment in power communication networks, characterized in that, Includes the following steps: Step S100: Construct a data communication network cloud platform to coordinate all devices in the communication network, acquire the deployment topology data of the power communication network, form a data communication network diagram model, monitor the connection of devices in the communication network, and update the data communication network diagram model in real time. Step S200: Access the SDN controller in the power communication network, collect the SDN controller's operating data, collect the instructions received by each device through the data communication network cloud platform during each control execution, and generate a communication list based on the instructions; randomly select an SDN controller, analyze the communication performance of each device based on the communication list, and generate a communication network data matrix; Step S300: Analyze the communication network data matrix based on cloud computing to determine whether there is an optimization need for the deployment of each SDN controller; Mark the SDN controllers that meet the criteria for optimization requirements and generate a deployment optimization list; Step S400: Analyze the SDN controller optimization requirements in the deployment optimization list based on the data communication network diagram model, and provide feedback on the deployment optimization requirements in the power communication network to relevant personnel; The implementation process of step S200 includes the following steps: Step S201: Access the SDN controller in the power communication network based on the communication cycle; within the Tth communication cycle, collect the operating data of the SDN controller; during each control execution, collect the instructions received by each device through the data communication network cloud platform; wherein, the instructions sent by the same SDN controller to the device corresponding to the same connection line are integrated into one instruction cluster; the data communication network cloud platform coordinates all collected instruction clusters to generate a communication list for the kth communication cycle; the communication list includes the connection line label of each instruction cluster, and any instruction cluster is denoted as ZC(iQB), where ZC(iQB) = {[L z (A j ), L t (A j )]|j∈[1,m]},L z (A j ) indicates device A j The corresponding latest instruction content, L t (A j ) indicates device A j The corresponding latest instruction reception time is used to denot the communication list of the k-th communication cycle as LZC. k And sort the instruction clusters in the communication list according to the order of the latest instruction received by the device; Step S202: Randomly select an SDN controller, obtain the communication list of the SDN controller, analyze the communication performance of each device based on the communication list corresponding to each communication cycle, and calculate the communication performance score between the selected SDN controller and the devices in the power communication network according to the following formula: Among them, X ij Let F(LZC) represent the communication performance score of the j-th device in the communication list of the i-th SDN controller, and W represent the total number of communication cycles recorded for the i-th SDN controller. k+1 )∩F(LZC k )] represents the discriminant function, F(LZC k+1 F(LZC) represents the instruction set containing instructions sent to the j-th device in the communication list of the (k+1)-th communication cycle; k ) represents the instruction set in the communication list of the k-th communication cycle that contains instructions sent to the j-th device; when F(LZC k+1 )∩F(LZC k If F(LZC) is empty, then... k+1 )∩F(LZC k )]=0; when F(LZC k+1 )∩F(LZC k If F(LZC) is not empty, then... k+1 )∩F(LZC k )] = 1; Step S203: Generate a communication network data matrix based on the communication performance scores of each SDN controller and the devices connected to the SDN controller in the power communication network. In this matrix, rows represent different SDN controllers, columns represent different devices, and the value in the i-th row and j-th column is X. ij .

2. The SDN controller deployment optimization method for power communication networks according to claim 1, characterized in that, The implementation process of step S100 includes the following steps: Step S101: Construct a data communication network cloud platform to coordinate all devices in the communication network, assign coding prefixes according to device type, and uniformly encode each device; identify the connection methods between devices in the communication network and obtain the deployment topology data of the power communication network; When any SDN controller communicates with any device, the connection line with the same intermediate layer service processing request when the same SDN controller communicates with the device is marked as the Q-th type of communication. The i-th SDN controller and device A are then connected... j The connection line is referred to as SDN. i →Q→B; where SDN i Let B represent the encoding of the i-th SDN controller, Q represent the Q-th middleware service processing request, and B = {A}. j |j∈[1,m]},A j This represents the code of the j-th device, and m represents the total number of devices that process requests through the Q-th intermediate layer service when the i-th SDN controller performs control communication. Step S102: Based on the control communication records, a data communication network diagram model is formed, which includes the connection lines between each SDN controller and the device; the connection of the device in the communication network is monitored; when a new device is added to the communication network, step S101 is repeated to update the connection lines of the SDN controller; when the new device does not belong to the existing connection lines, a new connection line is generated and the data communication network diagram model is expanded; when an existing device in the communication network changes its interface protocol or its encoding is deleted, the connection lines of the SDN controller and the data communication network diagram model are refreshed.

3. The SDN controller deployment optimization method for power communication networks according to claim 1, characterized in that, The implementation process of step S300 includes the following steps: Step S301: Analyze the communication network data matrix based on cloud computing, define the network status of each SDN controller according to the communication network data matrix, and calculate the optimization index of the SDN controller corresponding to the i-th row of the matrix according to the following formula: Where N represents the total number of SDN controllers, M represents the total number of devices connected to the SDN controllers in the power communication network, and μ i Let μ represent the average of all elements in the i-th row of the matrix, and let μ represent the average of all elements in the matrix. Step S302: When the optimization index of the SDN controller corresponding to the i-th row of the matrix exceeds the preset threshold, it is considered that the deployment of the i-th SDN controller in the power communication network has optimization needs; mark the SDN controllers with optimization needs and generate a deployment optimization list.

4. The SDN controller deployment optimization method for power communication networks according to claim 3, characterized in that, The implementation process of step S400 includes the following steps: Step S401: The deployment optimization list includes several SDN controller codes with optimization needs; the optimization needs of the SDN controllers in the deployment optimization list are analyzed based on the data communication network diagram model, and the anomaly degree of the device represented by the p-th row of the matrix corresponding to any SDN controller in the deployment optimization list is calculated according to the following formula: Among them, X gp μ represents the value in the g-th row and p-th column of the matrix. p X represents the average value of the p-th column of the matrix. gj μ represents the value in the g-th row and j-th column of the matrix. j This represents the average of all elements in the j-th column of the matrix; Step S402: When the anomaly degree of the device represented by the p-th row of the matrix connected to any SDN controller in the deployment optimization list exceeds the preset threshold, it is considered that the device has an impact on the currently connected SDN controller; based on the optimization requirement analysis results of each SDN controller in the deployment optimization list, the deployment optimization requirements in the power communication network are fed back to relevant personnel.

5. An SDN controller deployment optimization system for power communication networks, characterized in that, include: The module includes a communication data acquisition module, a controller performance analysis module, an optimization requirement identification module, and a deployment feedback module. The communication data acquisition module is used to acquire the deployment topology data of the power communication network, form a data communication network diagram model, monitor the connection of devices in the communication network, and update the data communication network diagram model in real time. The controller performance analysis module is used to access the SDN controller in the power communication network, collect the operating data of the SDN controller, analyze the communication performance of each device, and generate a communication network data matrix. The optimization requirement discrimination module is used to analyze the communication network data matrix and determine whether there is an optimization requirement for the deployment of each SDN controller; Mark the SDN controllers that meet the criteria for optimization requirements and generate a deployment optimization list; The deployment feedback module is used to analyze the SDN controller optimization requirements in the deployment optimization list and provide feedback on the deployment optimization requirements in the power communication network to relevant personnel. The controller performance analysis module includes an instruction acquisition unit, a list generation unit, and a matrix generation unit; The instruction acquisition unit accesses the SDN controller in the power communication network based on the communication cycle; within the Tth communication cycle, it acquires the operating data of the SDN controller, and during each control execution, it acquires the instructions received by each device through the data communication network cloud platform; wherein, the instructions sent by the same SDN controller to the device corresponding to the same connection line are integrated into an instruction cluster. The list generation unit coordinates all collected instruction clusters through the data communication network cloud platform to generate a communication list for the k-th communication cycle. The communication list includes connection line tags for each instruction cluster. Any instruction cluster is denoted as ZC(iQB), where ZC(iQB) = {[L...} z (A j ), L t (A j )]|j∈[1,m]},L z (A j ) indicates device A j The corresponding latest instruction content, L t (A j ) indicates device A j The corresponding latest instruction reception time is used to denot the communication list of the k-th communication cycle as LZC. k And sort the instruction clusters in the communication list according to the order of the latest instruction received by the device; The matrix generation unit analyzes the communication performance of each device based on the communication list corresponding to each communication cycle. It generates a communication network data matrix based on the communication performance scores of each SDN controller and the devices connected to the SDN controller in the power communication network. In this matrix, rows represent different SDN controllers, columns represent different devices, and the value in the i-th row and j-th column is X. ij The X ij Calculate using the following formula: Among them, X ij Let F(LZC) represent the communication performance score of the j-th device in the communication list of the i-th SDN controller, and W represent the total number of communication cycles recorded for the i-th SDN controller. k+1 )∩F(LZC k )] represents the discriminant function, F(LZC k+1 F(LZC) represents the instruction set containing instructions sent to the j-th device in the communication list of the (k+1)-th communication cycle; k ) represents the instruction set in the communication list of the k-th communication cycle that contains instructions sent to the j-th device; when F(LZC k+1 )∩F(LZC k If F(LZC) is empty, then... k+1 )∩F(LZC k )]=0; when F(LZC k+1 )∩F(LZC k If F(LZC) is not empty, then... k+1 )∩F(LZC k )]=1.

6. The SDN controller deployment optimization system for power communication networks according to claim 5, characterized in that: The optimization requirement discrimination module includes a matrix analysis unit and a requirement analysis unit; The matrix analysis unit analyzes the communication network data matrix based on cloud computing, defines the network status of each SDN controller according to the communication network data matrix, checks whether the values ​​in the matrix are evenly distributed, and calculates the optimization index of the SDN controller corresponding to the i-th row of the matrix according to the following formula: Where N represents the total number of SDN controllers, M represents the total number of devices connected to the SDN controllers in the power communication network, and μ i Let μ represent the average of all elements in the i-th row of the matrix, and let μ represent the average of all elements in the matrix. The demand analysis unit is used to determine whether there is an optimization requirement for the deployment of the SDN controller based on the optimization index of the SDN controller, and to mark the SDN controllers with optimization requirements and generate a deployment optimization list.

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