Energy consumption optimization method for industrial system switching control based on multi-layer network

Abstract

The present invention relates to an industrial system switching control energy consumption optimization method based on a multi-layer network, comprising the following steps: S1: collecting data information of each level of the industrial system and constructing nodes to construct a multi-layer network structure of the industrial system; S2: constructing a controller based on the multi-layer network structure of the industrial system to synchronize communications between network layers; S3: calculating the energy consumed by the controller, determining the upper limit of energy consumption, and analyzing the multi-layer network synchronization energy consumption; S4: optimizing the energy cost within the multi-layer network according to the analysis results of the multi-layer network synchronization energy consumption. The present invention can effectively optimize the energy consumption management of the multi-layer network, improve the network operation efficiency and overall energy efficiency, and ensure network performance and service quality.

Description

Technical Field

[0001] The present invention relates to the field of industrial system optimization, and in particular to a method for optimizing energy consumption of industrial system switching control based on a multi-layer network. Background Art

[0002] A multi-layer networked industrial system is a multi-dimensional dynamic system used in industrial environments to interconnect various devices, sensors, controllers, and information systems to support data transmission, monitoring, control, and automated operations. In practical applications, control costs are very important and inevitable in industrial network system control. Control costs include the time cost required for the system to achieve the control target after applying the control input, as well as the energy cost consumed by the driving controller. The subsystems in the industrial network work together through multi-layer networks. With the improvement of intelligence and interconnection, energy consumption patterns have become more complex, and the amount of data has increased significantly, increasing the complexity and challenges of energy management.

[0003] In order to improve the operating efficiency and economy of industrial systems, periodic switching control and fixed or preset time control strategies in multi-layer networks have become research focuses. Periodic switching control optimizes energy efficiency by regularly adjusting the system operation mode, and is suitable for periodic scenarios such as industrial production line equipment scheduling and intelligent warehousing and logistics management. Preset time control achieves control goals at a specific time point, independent of the periodic changes of the system, and is suitable for planned operations such as regular maintenance of key equipment and process flow switching. Fixed time control is used in scenarios that require rapid response, such as emergency shutdown of equipment or fault recovery. All three methods can improve system performance, reduce energy consumption, and improve resource utilization.

[0004] Although the application of fixed-time and preset-time periodic switching control strategies can improve control efficiency in multi-layer networked industrial systems, there are still several unresolved issues. First, the analysis of the energy cost of the controller is not comprehensive enough. Second, there is a lack of clear understanding of the energy consumption pattern of the switching controllers within the multi-layer network infrastructure in the application scenario of fixed-time periodic control. In addition, the analysis of the energy cost of preset-time periodic switching control is also insufficient when it comes to multi-layer networks. Summary of the invention

[0005] In order to solve the above problems, the purpose of the present invention is to provide an industrial system switching control energy consumption optimization method based on a multi-layer network, which can effectively optimize the energy consumption management of the multi-layer network, improve the network operation efficiency and overall energy efficiency, and ensure network performance and service quality.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for optimizing energy consumption of industrial system switching control based on a multi-layer network comprises the following steps:

[0008] S1: Collect data information at all levels of the industrial system and build nodes to build a multi-layer network structure of the industrial system;

[0009] S2: Based on the multi-layer network structure of the industrial system, a controller is built to synchronize the communication between network layers;

[0010] S3: Calculate the energy consumed by the controller, determine the upper bound of energy consumption, and analyze the energy consumption of multi-layer network synchronization;

[0011] S4: Optimize the energy cost within the multi-layer network according to the results of the multi-layer network synchronization energy consumption analysis.

[0012] Further, S1 is as follows:

[0013] Collect data information from all levels of the industrial system, identify entities that can send, receive or transmit data in each layer as nodes, and define the dynamic behavior of nodes as:

[0014]

[0015] in, It's time. express Dimension the state of isolated nodes, It is dimensional state variables, yes About Time The first derivative of ;

[0016] For The characteristic attributes of each node, including state and performance parameters, are quantified into numerical vectors. express:

[0017]

[0018] in, express Dimensional vector The state variables;

[0019] The entire industrial system is considered as a weighted directed graph, where nodes represent devices or control units that can send, receive or transmit data, and edges represent the logical or physical connections between nodes. If there is a logical or physical connection, it is defined as 1, otherwise it is 0. Based on the relationship and communication path between nodes, the first Connection matrix of layer network ;

[0020] Build the network model:

[0021]

[0022] in, It is p Nodes at time t The state vector The first-order derivative with respect to time indicates the rate of change of the node state over time. It is A vector function of the inherent dynamic characteristics of each node, is the total number of layers in the network, is the number of nodes in the network, It is The intralayer coupling strength parameter of the layer, It is The first Nodes and The external coupling state matrix of each node is: It is The control input of each node is is the state vector of the jth node at time t, is the coupling matrix of the k-th layer network.

[0023] Furthermore, the industrial system includes but is not limited to the enterprise layer, supervisory layer, control layer, fieldbus layer and data information of each layer. At the enterprise layer, business and production scheduling data are collected; the supervisory layer obtains real-time monitoring and operator instruction information; in the control layer, the controller provides equipment operating status and control logic data; the fieldbus layer focuses on the real-time data of sensors and actuator terminal devices.

[0024] Furthermore, S2 is specifically:

[0025] Definition The synchronization error of nodes:

[0026]

[0027] in, Indicates at time At that time, The status of the node With reference node status The difference between

[0028] Establish a fixed-time controller that does not consider the impact of the number of layers on the industrial system:

[0029]

[0030] in, It is The control gain of each node is , , , is the adjustable parameter of the controller, is a positive integer, is the control period of the controller, is the sign function, the controller in one cycle Down, and is a parameter greater than zero, which will switch within the period interval;

[0031] Establish a fixed-time controller that considers the impact of the number of layers on the industrial system:

[0032]

[0033] in It is Layer The control gain of each node is is the number of nodes;

[0034] Establish a fixed time controller that does not consider the effect of the number of layers on energy consumption:

[0035]

[0036] in, is the time required to achieve fixed time synchronization, It is the time required for preset time synchronization;

[0037] The preset time controller considering the effect of the number of floors on energy consumption is designed as follows:

[0038]

[0039] in, is the time required to achieve fixed time synchronization, is the number of layers in the network.

[0040] Furthermore, S3 is specifically:

[0041] Analyze the energy of the controller, the energy consumption of the controller:

[0042]

[0043] in is the energy consumed by the controller, is the time required to achieve synchronization, and Equation (9) represents the energy consumed by the controller during the time period of completing synchronization;

[0044] Under the fixed time controller of formula (5), the energy consumption of the controller is estimated as:

[0045]

[0046] in, is the actual controller energy consumption, represents the minimum energy cost, is the optimized parameter, is the sum of the control gains;

[0047] Next, we will analyze the relationship between energy consumption and the number of layers in a multi-layer network;

[0048] At fixed time controller The energy consumption of the controller is estimated to be

[0049]

[0050] in is the actual controller energy consumption, Represents the minimum energy consumption of the controller, the optimized parameters ; is to compare specific quantities under all layers The maximum value among ;

[0051] Defining functions

[0052]

[0053] By optimizing the function to find the minimum value, the number of network layers can be obtained , number of layers It is the target value of network layer optimization;

[0054] The energy consumption of this controller is estimated to be:

[0055]

[0056] In the formula, is the actual controller energy consumption, is an intermediate variable, is the sum of the control gains, is an intermediate variable, is an intermediate variable, It is the preset time;

[0057] Energy consumption To optimize, Requirements:

[0058]

[0059] in, is the intermediate variable, at which the energy consumption reaches the minimum;

[0060] Analyze the relationship between the energy consumption of preset time control and the number of layers in a multi-layer network:

[0061] In preset time controller The energy consumption of the controller is estimated as:

[0062]

[0063] in is the time required to achieve fixed time synchronization, is the preset time and , is an intermediate variable, is an intermediate variable, is an intermediate variable.

[0064] Furthermore, the S4 is specifically as follows: through numerical simulation, it is obtained that the energy consumption of the controller shows a trend with the parameter , , , Positively correlated with parameter Negative correlation characteristics; that is, the energy consumption of the controller decreases with the parameter , , , As the parameter To optimize energy consumption, the optimization strategy shortens the preset time. On the other hand, reduce the parameters inside the controller , , , while adjusting the parameters , to reduce the overall energy consumption of the system.

[0065] The present invention has the following beneficial effects:

[0066] The present invention optimizes the energy consumption management of the multi-layer network, improves the network operation efficiency and overall energy efficiency, and ensures network performance and service quality by constructing a multi-layer network model, designing fixed-time and preset-time switching controllers, and calculating and analyzing the energy consumption of these controllers. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of the method of the present invention;

[0068] Figure 2 A schematic diagram of energy consumption of a switching controller according to an embodiment of the present invention;

[0069] Figure 3 It is a topological diagram of a network in one embodiment of the present invention. DETAILED DESCRIPTION

[0070] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0071] refer to Figure 1-3 In this embodiment, a method for optimizing energy consumption of industrial system switching control based on a multi-layer network is provided, comprising the following steps:

[0072] S1: Collect data information at all levels of the industrial system and build nodes to build a multi-layer network structure of the industrial system;

[0073] S2: Based on the multi-layer network structure of the industrial system, a controller is built to synchronize the communication between network layers;

[0074] S3: Calculate the energy consumed by the controller, determine the upper bound of energy consumption, and analyze the energy consumption of multi-layer network synchronization;

[0075] S4: Optimize the energy cost within the multi-layer network according to the results of the multi-layer network synchronization energy consumption analysis.

[0076] In this embodiment, S1 is specifically as follows:

[0077] Collect data information from all levels of the industrial system, identify devices or control units that can send, receive or transmit data in each layer as nodes, and define the dynamic behavior of the nodes as:

[0078]

[0079] in, It's time. express Dimension the state of isolated nodes, It is dimensional state variables, yes About Time The first-order derivative of The rate of change over time, is a vector function that defines the state the way it changes over time;

[0080] For The characteristic attributes of each node, such as state and performance parameters, are quantified into numerical vectors. express:

[0081]

[0082] in, express Dimensional vector The state variables;

[0083] The entire industrial system is considered as a weighted directed graph, where nodes represent devices or control units that can send, receive or transmit data, and edges represent the logical or physical connections between them. If there is a logical or physical connection, it is defined as 1, otherwise it is 0. Based on the relationship and communication path between nodes, the first Connection matrix of layer network ,This matrix describes which nodes can exchange data directly and the transmission delay or bandwidth limitations between them;

[0084] Build the network model:

[0085]

[0086] in, It is p Nodes at time t The state vector The first-order derivative with respect to time indicates the rate of change of the node state over time. It is A vector function of the inherent dynamic characteristics of each node, is the total number of layers in the network, is the number of nodes in the network, It is The intralayer coupling strength parameter of the layer, It is The first Nodes and The external coupling state matrix of each node is: It is The control input of each node is is the state vector of the jth node at time t, is the coupling matrix of the k-th layer network;

[0087] In this embodiment, the industrial system includes but is not limited to the enterprise layer, supervisory layer, control layer, field bus layer and data information of each layer. At the enterprise layer, business and production scheduling data are collected; the supervisory layer obtains real-time monitoring and operator instruction information; in the control layer, the controller provides equipment operating status and control logic data; the field bus layer focuses on the real-time data of sensors and actuator terminal devices.

[0088] In this embodiment, S2 is specifically:

[0089] Definition The synchronization error of nodes:

[0090]

[0091] in, Indicates at time At that time, The status of the node With reference node status The difference between

[0092] Establish a fixed-time controller that does not consider the impact of the number of layers on the industrial system:

[0093]

[0094] in, It is The control gain of each node is , , , is the adjustable parameter of the controller, is a positive integer, is the control period of the controller, is a symbolic function. The controller has a cycle Down, and is a parameter greater than zero, which will switch within the period interval;

[0095] Establish a fixed-time controller that considers the impact of the number of layers on the industrial system:

[0096]

[0097] in It is Layer The control gain of each node is is the number of nodes;

[0098] Establish a fixed time controller that does not consider the effect of the number of layers on energy consumption:

[0099]

[0100] in, is the time required to achieve fixed time synchronization, It is the time required for preset time synchronization;

[0101] The preset time controller considering the effect of the number of floors on energy consumption is designed as follows:

[0102]

[0103] in, is the time required to achieve fixed time synchronization, is the number of layers in the network.

[0104] In this embodiment, S3 is specifically:

[0105] Analyze the energy of the controller, the energy consumption of the controller:

[0106]

[0107] in is the energy consumed by the controller, is the time required to achieve synchronization. The above formula represents the energy consumed by the controller during the time period of completing synchronization;

[0108] Under the fixed time controller of formula (5), the energy consumption of the controller can be estimated as:

[0109]

[0110] in, is the actual controller energy consumption, represents the minimum energy cost, is the optimized parameter, is the sum of the control gains;

[0111] Next, we will analyze the relationship between energy consumption and the number of layers in a multi-layer network;

[0112] At fixed time controller The energy consumption of the controller can be estimated as:

[0113]

[0114] in is the actual controller energy consumption, Represents the minimum energy consumption of the controller, the optimized parameters , is to compare specific quantities under all layers The maximum value among ;

[0115] Define the function:

[0116]

[0117] By optimizing the function to find the minimum value, the number of network layers can be obtained , this number of layers is the target value of network layer optimization;

[0118] The energy consumption of this controller is estimated to be:

[0119]

[0120] In the formula, is the actual controller energy consumption, is an intermediate variable greater than zero, is an intermediate variable, is the sum of the control gains, is an intermediate variable, is an intermediate variable, It is the preset time;

[0121] Energy consumption To optimize, Requirements:

[0122]

[0123] in, is the intermediate variable, at which the energy consumption reaches the minimum;

[0124] Analyze the relationship between the energy consumption of preset time control and the number of layers in a multi-layer network:

[0125] Under the preset time controller, the energy consumption of the controller is estimated to be:

[0126]

[0127] in is the time required to achieve fixed time synchronization, is an intermediate variable greater than zero, is the preset time and , is an intermediate variable, is an intermediate variable, is an intermediate variable.

[0128] In this embodiment, S4 is specifically: through numerical simulation, it is obtained that the energy consumption of the controller shows a similarity with the parameter , , , Positively correlated with parameter Negative correlation characteristics; that is, the energy consumption of the controller decreases with the parameter , , , As the parameter To optimize energy consumption, the optimization strategy shortens the preset time. On the other hand, reduce the parameters inside the controller , , , while adjusting the parameters , to reduce the overall energy consumption of the system.

[0129] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing energy consumption of industrial system switching control based on a multi-layer network, characterized in that: The following steps are involved: S1: Collect data information at all levels of the industrial system and build nodes to build a multi-layer network structure of the industrial system; The industrial system includes, but is not limited to, the enterprise layer, supervisory layer, control layer, fieldbus layer, and data information of each layer. At the enterprise layer, business and production scheduling data are collected; The supervisory layer obtains real-time monitoring and operator instruction information; In the control layer, the collection controller provides equipment operating status and control logic data; the fieldbus layer focuses on the real-time data of sensors and actuators at the end devices; S2: Based on the multi-layer network structure of the industrial system, a controller is built to synchronize the communication between network layers; S3: Calculate the energy consumed by the controller, determine the upper bound of energy consumption, and analyze the energy consumption of multi-layer network synchronization; S4: Optimizing the energy cost within the multi-layer network according to the result of the multi-layer network synchronization energy consumption analysis; The S2 is specifically: Definition The synchronization error of nodes: ； in, Indicates at time At that time, The status of the node With reference node status The difference between Establish a fixed-time controller that does not consider the impact of the number of layers on the industrial system: ； in, It is The control gain of each node is is the adjustable parameter of the controller, is a positive integer, is the control period of the controller, is the sign function, the controller in one cycle Down, and is a parameter greater than zero, which will switch within the period interval; Establish a fixed-time controller that considers the impact of the number of layers on the industrial system: ； in It is Layer The control gain of each node is is the number of nodes; Establish a fixed time controller that does not consider the effect of the number of layers on energy consumption: ； in, is the time required to achieve fixed time synchronization, It is the time required for preset time synchronization; The preset time controller considering the effect of the number of floors on energy consumption is designed as follows: ； in, is the time required to achieve fixed time synchronization, is the number of layers in the network; S3 is specifically: Analyze the energy of the controller, the energy consumption of the controller: ； in is the energy consumed by the controller, is the time required to achieve synchronization, and Equation (9) represents the energy consumed by the controller during the time period of completing synchronization; Under the fixed time controller of formula (5), the energy consumption of the controller is estimated as: ； in, is the actual controller energy consumption, represents the minimum energy cost, is the optimized parameter, is the sum of control gains; Next, we will analyze the relationship between energy consumption and the number of layers in a multi-layer network; At fixed time controller The energy consumption of the controller is estimated as: ； in is the actual controller energy consumption, Represents the minimum energy consumption of the controller, the optimized parameters , is to compare specific quantities under all layers The maximum value among ; Define the function: ； By optimizing the function to find the minimum value, the number of network layers can be obtained , number of layers It is the target value of network layer optimization; is the number of nodes in the network; The energy consumption of this controller is estimated to be: In the formula, is the actual controller energy consumption, is an intermediate variable, is an intermediate variable greater than zero, is the sum of the control gains, is an intermediate variable, is an intermediate variable, It is the preset time; Energy consumption To optimize, Requirements: ； in, is the intermediate variable, at which the energy consumption reaches the minimum; Analyze the relationship between the energy consumption of preset time control and the number of layers in a multi-layer network: In preset time controller The energy consumption of the controller is estimated as: ； in is the time required to achieve fixed time synchronization, is a parameter greater than zero, is the preset time and , is an intermediate variable, is an intermediate variable, is an intermediate variable.

2. The method for optimizing energy consumption of industrial system switching control based on a multi-layer network according to claim 1, characterized in that: The S1 is specifically as follows: Collect data information from all levels of the industrial system, identify entities that can send, receive or transmit data in each layer as nodes, and define the dynamic behavior of nodes as: ； It's time. express Dimension the state of isolated nodes, It is dimensional state variables, yes About Time The first derivative of ; For The characteristic attributes of each node, including state and performance parameters, are quantified into numerical vectors. express: ； in, express Dimensional vector The state variables; The entire industrial system is considered as a weighted directed graph, where nodes represent devices or control units that can send, receive or transmit data, and edges represent the logical or physical connections between nodes. If there is a logical or physical connection, it is defined as 1, otherwise it is 0. Based on the relationship and communication path between nodes, the first Connection matrix of layer network ; Build the network model: ； in, It is p Nodes at time t The state vector The first-order derivative with respect to time indicates the rate of change of the node state over time. It is A vector function of the inherent dynamic characteristics of each node, is the total number of layers in the network, is the number of nodes in the network, It is The intralayer coupling strength parameter of the layer, It is The first Nodes and The external coupling state matrix of each node is: It is The control input of each node is is the state vector of the jth node at time t, is the coupling matrix of the k-th layer network.

3. The method for optimizing energy consumption of industrial system switching control based on a multi-layer network according to claim 1, characterized in that: The S4 is specifically: through numerical simulation, the energy consumption of the controller is obtained to be similar to the parameter Positively correlated with parameter Negative correlation characteristics; that is, the energy consumption of the controller decreases with the parameter As the parameter To optimize energy consumption, the optimization strategy shortens the preset time. On the other hand, reduce the parameters inside the controller , while adjusting the parameters , to reduce the overall energy consumption of the system.

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