Distributed data sampling and synchronization method for multi-layer networks with high-dimensional node characteristics

Abstract

The present invention relates to a distributed data sampling and synchronization method for a multi-layer network with high-dimensional node characteristics, comprising the following steps: S1: establishing a multi-layer mathematical model with unconstrained inter-layer and intra-layer parameters; S2: based on the multi-layer mathematical model with unconstrained inter-layer and intra-layer parameters, treating a target state as a virtual node embedded in a network topology, and designing a data sampling controller containing virtual nodes; S3: defining a synchronization error, and establishing an output asymptotic synchronization problem for a multi-layer network with high-dimensional node characteristics; S4: based on the data sampling controller, solving a non-asymptotic output synchronization problem for a multi-layer network with high-dimensional node characteristics, establishing an output asymptotic synchronization criterion applicable to a multi-layer network with high-dimensional node characteristics, and realizing carrier synchronization in the multi-layer network, so that a modulated signal sent by any node can be synchronously demodulated by other nodes in the global network.

Description

Technical Field

[0001] The present invention relates to the technical field of production networks, and in particular to a distributed data sampling and synchronization method for a multi-layer network with high-dimensional node characteristics. Background Art

[0002] In industrial applications, manufacturing systems typically consist of geographically dispersed factories or workshops, forming a highly distributed architecture. These systems achieve efficient execution of multiple industrial manufacturing processes and product assembly tasks through autonomous coordination and collaboration mechanisms. To effectively characterize the structural characteristics and internal interactions of industrial manufacturing systems, the single-layer complex network model has been proposed as an idealized tool and widely used to describe the topological characteristics and dynamic behavior of industrial processes. However, industrial manufacturing systems typically consist of multiple layers and heterogeneous components, including production lines, equipment, warehouses, and supply chains. These components exhibit significant differences in behavior and functional characteristics, making them difficult to model through simple overlay. For example, when completing product manufacturing and assembly tasks, multiple subsystems, such as communication networks, control networks, and power grids, must closely collaborate to efficiently achieve production goals. These networks are deeply integrated, forming a complex multi-layer network system. Therefore, single-layer network models are unable to meet the modeling needs of real-world manufacturing systems, giving rise to the theory of multi-layer networks.

[0003] With the rapid development of intelligent manufacturing technologies, especially the gradual implementation of high-end intelligent manufacturing production processes, traditional network hierarchical approaches face increasingly severe challenges in addressing complex industrial scenarios. Existing hierarchical approaches typically assume a strict one-to-one correspondence between nodes in the physical and cyber domains, where each physical object (e.g., process, equipment, etc.) is mapped to a unique cyber node (e.g., sensor, data acquisition device, etc.). However, in real industrial environments, this strict correspondence is often difficult to maintain. Complex many-to-many or many-to-one mapping relationships between physical and cyber domain nodes are common. Furthermore, the number of nodes at different levels can vary significantly (e.g., the number of physical layer devices far outnumbers the number of cyber layer data acquisition points), further exacerbating modeling complexity. Therefore, developing a node mapping mechanism suitable for industrial manufacturing environments that supports non-one-to-one correspondences and adapts to the dynamic inconsistency of node numbers at each layer has become a key scientific issue that needs to be addressed. Summary of the Invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a multi-layer network distributed data sampling synchronization method with high-dimensional node characteristics, which effectively solves the synchronization control problem of multi-dimensional coupling of intelligent manufacturing systems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A distributed data sampling and synchronization method for a multi-layer network with high-dimensional node characteristics comprises the following steps:

[0007] S1: Establish a multi-layer mathematical model with no constraints on inter-layer and intra-layer parameters;

[0008] S2: Based on a multi-layer mathematical model with unconstrained inter-layer and intra-layer parameters, the target state is considered as a virtual node embedded in the network topology, and a data sampling controller with virtual nodes is designed;

[0009] S3: Define synchronization error and establish a multi-layer network output asymptotic synchronization problem with high-dimensional node characteristics;

[0010] S4: Based on a data sampling controller, the non-asymptotic output synchronization problem of multi-layer networks with high-dimensional node characteristics is solved, and an output asymptotic synchronization criterion applicable to multi-layer networks with high-dimensional node characteristics is established to achieve carrier synchronization in multi-layer networks, so that the modulated signal sent by any node can be synchronously demodulated by other nodes in the global network.

[0011] Furthermore, a multi-layer mathematical model with no constraints on inter-layer and intra-layer parameters is established, as follows:

[0012] Consider an undirected weighted network consisting of layers, each layer has The same node system, Layer dynamics:

[0013] (1)

[0014] Where q represents the adjacent node, represents the intra-layer connection matrix, represents the inter-layer connection matrix, Indicates the layer, The definition is The first derivative of The definition is Layer The status of the node, The definition is Layer The status of the node, is the control gain, is the state matrix, is the input state matrix, represents the internal coupling matrix, represents the external coupling matrix, represents the output coefficient matrix, Indicates the Layer nodes The input vector, Indicates the Layer nodes The output vector of express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space;

[0015] Describes the state vector of the entire multi-layer network, T represents the transpose of the vector, yes The state vector of the layer, The definition is Layer The status of each node;

[0016] is a vector describing the output of the entire multi-layer network system, where yes Layer output variables, Indicates the Layer nodes The output vector of

[0017] is a vector describing the input of the entire multi-layer network system, where It is The input vector of the layer is the control input of the nodes, Indicates the Layer nodes The input vector of

[0018] The production goal is a target trajectory, the output of the following autonomous system:

[0019] (2)

[0020] in Represents the state matrix of the entire leader 0, represents the output matrix of the entire leader 0, and Represents the status and output of leader 0;

[0021] The target system (2) is regarded as a virtual vertex of the network (1), marked as nodes, let the intermediate variable , the augmented coupling graph of the multilayer network (2) is defined as , superscript Indicates the Layer, the vertex set is , the directed edge set is , Representation node To Node A directed edge of Rule No. Tier Node and Connections between neighboring nodes ,otherwise Obviously, , Indicates the Tier Node and Connections between neighbor nodes; represents the connection weight; and , the adjacency matrix for:

[0022] ;

[0023] in represents the 0 vector, Represents the original network connection matrix, vector , represents the original network Laplacian matrix, Represents an edge The connection weights of The Laplace matrix of Expressed as:

[0024] ,

[0025] in express The element in the p-th row and p-th column of the matrix, represents a diagonal matrix, for is a positive definite matrix.

[0026] Furthermore, the design of the data sampling controller is as follows:

[0027] Designed Layer Node The reference generator is

[0028] (3)

[0029] in Indicates the Layer Node The reference generator state of , and . Indicates the Layer Node The reference generator output of , and Indicates the Layer output, Indicates output. Indicates the Layer Node The distributed control law of the reference generator is Indicates the Layer control law, represents the control law of the entire network;

[0030] Due to the limitation of communication, the sampling data control method is adopted, and the zero-order holder maintains the value within the sampling interval. satisfy , the subscript s represents the sampling node time; the sampling interval is , design a distributed controller for It is non-periodic sampling, and the upper bound of sampling is ,Right now , represents the sampling interval, Data sampling distributed controller Designed to:

[0031] (4)

[0032] in represents the sampling interval, represents the control gain matrix, Indicates the number of nodes, Indicates the number of layers, express Tier The status of the node, express Tier The status of a node.

[0033] Based on the sampled data distributed control law in (4), the dynamics of the reference generator in (3) can be rewritten as:

[0034] (5).

[0035] Furthermore, we define the synchronization error and establish the problem of progressive synchronization of multi-layer network outputs with high-dimensional node characteristics, as follows:

[0036] Define the error vector :

[0037] (6)

[0038] in Indicates the number of nodes, Indicates the number of layers, represents the error vector, and express Layer error vector, Definition Tier node error vectors;

[0039] Calculation can be obtained:

[0040] ; (7)

[0041] in represents the state of leader 0, and

[0042] ;

[0043] in, Indicates the Interlayer Node and Interlayer Node connections, Indicates the Interlayer Node and Interlayer nodes connected; therefore,

[0044] (8)

[0045] in, represents the p-dimensional identity matrix, represents the direct sum of non-diagonal elements between layers, Represents the diagonal elements between layers, Indicates the number of layers, represents the circular multiplication,

[0046] Further, we calculate:

[0047] (9)

[0048] in express dimensional identity matrix, represents the intra-layer Laplacian matrix, represents the direct sum of matrices, represents the addition of two matrices, represents the circular multiplication, Indicates a A unit vector with one component being 1 and the rest being 0.

[0049] Furthermore, S4 is as follows:

[0050] Distributed fixed-time control of data sampling Parameterized as ,in is a solution to the algebraic Riccati inequality, choosing the scalar as a positive scalar; if there exists a positive definite matrix and So that:

[0051]

[0052] Established, h represents the upper bound of non-periodic sampling, represents the intermediate variable, then and The link is stable, Represents an intermediate variable.

[0053] Proof: Calculation

[0054] (10)

[0055] in represents the transfer function, represents the identity matrix;

[0056] Therefore, the closed-loop system consists of and The feedback interconnection is represented by represents an operator;

[0057] Defining the transfer function for:

[0058] (11)

[0059] Indicates input, Indicates output, represents the input signal of the system, and the time-delay integral operator for:

[0060] (12)

[0061] arrow Indicates from arrive The conversion or mapping of Indicates time Time Output Integrate because:

[0062]

[0063] I represents the identity matrix, Indicates taking the 2 norm and selecting the Lyapunov function for:

[0064] ,

[0065] therefore, The derivative of is:

[0066] (13)

[0067] in

[0068] , .

[0069] Therefore, we get:

[0070] (14)

[0071] in Represents intermediate variables:

[0072] ;

[0073] if , we can get ; By Schur's lemma, If and only if the following inequality holds:

[0074] ,

[0075] Multiply the above matrix by the diagonal block matrix get:

[0076] ,

[0077] make ,

[0078] .

[0079] Therefore, if there exists a positive definite matrix and Make ,but According to Lyapunov's stability theorem, the stability of the closed-loop system is guaranteed.

[0080] The present invention has the following beneficial effects:

[0081] 1. Based on the distributed data sampling control and progressive synchronization method of multi-layer network output, this paper establishes a new multi-layer network model with no constraints on inter-layer and intra-layer parameters, so that the nodes in all layers have inconsistent characteristics and structures, thus meeting the diverse modeling requirements of complex industrial applications.

[0082] 2. The target trajectory designed in the present invention is the output of an autonomous linear time-invariant system. It is used as a virtual node to enhance the original network, which can eliminate topological connectivity constraints, reduce the conservatism of synchronization conditions, and design a new multi-layer network (MCNS) sampling data control strategy for industrial applications. It overcomes the traditional assumption that physical domain nodes and information domain nodes must correspond one-to-one. For nodes with different time scales in MCNS, the controller performs distributed computing based on local information. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 This is a principle block diagram of the control method of the present invention;

[0084] Figure 2 A topological diagram of a network in one embodiment of the present invention;

[0085] Figure 3 A diagram of a synchronous demodulation architecture according to an embodiment of the present invention;

[0086] Figure 4 This is a signal modulation and demodulation framework diagram based on a carrier synchronization network in one embodiment of the present invention;

[0087] Figure 5 A state diagram of a leader and a follower in one embodiment of the present invention;

[0088] Figure 6 FIG. 4 is a signal modulation diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0090] refer to Figure 1 In this embodiment, a method for distributed data sampling and synchronization in a multi-layer network with high-dimensional node characteristics is provided, comprising the following steps:

[0091] S1: Establish a multi-layer mathematical model with no constraints on inter-layer and intra-layer parameters;

[0092] S2: Based on a multi-layer mathematical model with unconstrained inter-layer and intra-layer parameters, the target state is considered as a virtual node embedded in the network topology, and a data sampling controller with virtual nodes is designed;

[0093] S3: Define synchronization error and establish a multi-layer network output asymptotic synchronization problem with high-dimensional node characteristics;

[0094] S4: Based on a data sampling controller, the non-asymptotic output synchronization problem of multi-layer networks with high-dimensional node characteristics is solved, and an output asymptotic synchronization criterion applicable to multi-layer networks with high-dimensional node characteristics is established to achieve carrier synchronization in multi-layer networks, so that the modulated signal sent by any node can be synchronously demodulated by other nodes in the global network.

[0095] In this embodiment, a multi-layer mathematical model without constraints on inter-layer and intra-layer parameters is established, specifically as follows:

[0096] Consider an undirected weighted network consisting of layers, each layer has The same node system, Layer dynamics:

[0097] (1)

[0098] Where q represents the adjacent node, represents the intra-layer connection matrix, represents the inter-layer connection matrix, Indicates the layer, The definition is The first derivative of The definition is Layer The status of the node, The definition is Layer The status of the node, is the control gain, is the state matrix, is the input state matrix, represents the internal coupling matrix, represents the external coupling matrix, represents the output coefficient matrix, Indicates the Layer nodes The input vector, Indicates the Layer nodes The output vector of express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space;

[0099] Describe the state vector of the entire multi-layer network (T represents the transpose of the vector), yes The state vector of the layer, The definition is Layer The status of each node;

[0100] is a vector describing the output of the entire multi-layer network system, where yes Layer output variables, Indicates the Layer nodes The output vector of

[0101] is a vector describing the input of the entire multi-layer network system, where It is The input vector of the layer is the control input of the nodes, Indicates the Layer nodes The input vector of

[0102] The production goal is a target trajectory, the output of the following autonomous system:

[0103] (2)

[0104] in Represents the state matrix of the entire leader 0, represents the output matrix of the entire leader 0, and Represents the status and output of leader 0;

[0105] The target system (2) is regarded as a virtual vertex of the network (1), marked as nodes, let the intermediate variable , the augmented coupling graph of the multilayer network (2) is defined as , superscript Indicates the Layer, the vertex set is , the directed edge set is , Representation node To Node A directed edge of Rule No. Tier Node and Connections between neighboring nodes ,otherwise Obviously, , Indicates the Tier Node and Connections between neighbor nodes; represents the connection weight; ( Indicates the Tier Node and Neighbor nodes are connected) and , the adjacency matrix for:

[0106] ;

[0107] in represents the 0 vector, Represents the original network connection matrix, vector , represents the original network Laplacian matrix, Represents an edge The connection weights of The Laplace matrix of Expressed as:

[0108] ,

[0109] in express The element in the p-th row and p-th column of the matrix, represents a diagonal matrix, for is a positive definite matrix.

[0110] In this embodiment, the design of the data sampling controller is as follows:

[0111] Designed Layer Node The reference generator is

[0112] (3)

[0113] in Indicates the Layer Node The reference generator state of , and . Indicates the Layer Node The reference generator output of , and Indicates the Layer output, Indicates output. Indicates the Layer Node The distributed control law of the reference generator is Indicates the Layer control law, represents the control law of the entire network;

[0114] Due to the limitation of communication, the sampling data control method is adopted, and the zero-order holder maintains the value within the sampling interval. satisfy , the subscript s represents the sampling node time; the sampling interval is , design a distributed controller for It is non-periodic sampling, and the upper bound of sampling is ,Right now , represents the sampling interval, Data sampling distributed controller Designed to:

[0115] (4)

[0116] in represents the sampling interval, represents the control gain matrix, Indicates the number of nodes, Indicates the number of layers, express Tier The status of the node, express Tier The status of a node.

[0117] Based on the sampled data distributed control law in (4), the dynamics of the reference generator in (3) can be rewritten as:

[0118] (5).

[0119] In this embodiment, the synchronization error is defined and the problem of progressive synchronization of multi-layer network outputs with high-dimensional node characteristics is established, as follows:

[0120] Define the error vector :

[0121] (6)

[0122] in Indicates the number of nodes, Indicates the number of layers, represents the error vector, and express Layer error vector, Definition Tier node error vectors;

[0123] Calculation can be obtained:

[0124] ; (7)

[0125] in represents the state of leader 0, and

[0126] ;

[0127] in, Indicates the Interlayer Node and Interlayer Node connections, Indicates the Interlayer Node and Interlayer nodes connected. Therefore,

[0128] (8)

[0129] in, represents the p-dimensional identity matrix, represents the direct sum of non-diagonal elements between layers, Represents the diagonal elements between layers, Indicates the number of layers, represents the circular multiplication,

[0130] Further, we calculate:

[0131] (9)

[0132] in express dimensional identity matrix, represents the intra-layer Laplacian matrix, represents the direct sum of matrices, represents the addition of two matrices, represents the circular multiplication, Indicates a A unit vector with one component being 1 and the rest being 0.

[0133] In this embodiment, S4 is as follows:

[0134] Distributed fixed-time control of data sampling Parameterized as ,in is a solution to the algebraic Riccati inequality, choosing the scalar as a positive scalar; if there exists a positive definite matrix and So that:

[0135]

[0136] Established, h represents the upper bound of non-periodic sampling, represents the intermediate variable, then and The link is stable, Represents an intermediate variable.

[0137] Proof: Calculation

[0138] (10)

[0139] in represents the transfer function, represents the identity matrix;

[0140] Therefore, the closed-loop system consists of and The feedback interconnection is represented by Represents an operator; defines a transfer function for:

[0141] (11)

[0142] Indicates input, Indicates output, represents the input signal of the system, and the time-delay integral operator for:

[0143] (12)

[0144] arrow Indicates from arrive The conversion or mapping of Indicates time Time Output Integrate. Because:

[0145]

[0146] I represents the identity matrix, Indicates taking the 2 norm and selecting the Lyapunov function for:

[0147] ,

[0148] therefore, The derivative of is:

[0149] (13)

[0150] in

[0151] , .

[0152] Therefore, we get:

[0153] (14)

[0154] in Represents an intermediate variable

[0155] ;

[0156] if , we can get ; By Schur's lemma, If and only if the following inequality holds:

[0157] ,

[0158] Multiply the above matrix by the diagonal block matrix get:

[0159] ,

[0160] make ,

[0161] .

[0162] Therefore, if there exists a positive definite matrix and Make ,but According to Lyapunov's stability theorem, the stability of the closed-loop system is guaranteed. Example

[0163] In order to introduce the present invention in detail, a specific example is given below to reflect the proposed multi-layer network synchronization control method with high-dimensional node characteristics.

[0164] In the intelligent production process, the detection and transmission of mechanical equipment status is an indispensable part of the production monitoring system. Since analog signal transmission usually encounters noise interference and causes signal distortion, in order to ensure reliable transmission of physical layer signals, the analog signal sender usually needs to modulate the signal before transmission, and the receiver needs to demodulate it.

[0165] In the demodulation process, three technologies are generally used: synchronous demodulation, envelope detection, and phase-sensitive detection. Because synchronous demodulation has a higher signal-to-noise ratio (SNR), noise immunity, and better spectral efficiency, it can more accurately reproduce the original signal and is more adaptable to signal transmission in the presence of spectrum holes. This makes synchronous demodulation widely used in signal transmission in smart industrial sites and 5G communications.

[0166] The sensor network, field debugging network and central control network in the manufacturing network are established as a multi-layer network mathematical model. In order to solve the data (signal) transmission problem and the carrier synchronization difficulties in the modulation and demodulation process, the proposed carrier synchronization scheme can quickly build a globally synchronized carrier signal, provide a high-precision data transmission method for the manufacturing process, and thus improve the multi-level equipment collaboration capability in the intelligent manufacturing system. Signal conditioning and conversion are indispensable links in the manufacturing process, and signal modulation and demodulation are important processes in signal transmission. In the modulation and demodulation process, synchronous demodulation can better retain the amplitude and phase information, but due to the characteristics of the synchronization signal being difficult to generate and easy to offset, it is difficult to be widely used in the industrial production process. To solve this problem, the present invention aims to design a synchronous carrier generation device suitable for any signal receiver (provided that the receiver is stable). The device consists of three parts, including a carrier generation node (synchronous state), a carrier sampler (RG) and a carrier receiver (RE). The carrier generation node is usually physically implemented as a signal transposition. The reference design scheme is as follows. Figure 2 shown.

[0167] RG stands for carrier sampler, which is physically implemented as a signal sample-and-hold circuit and a signal generator of the same specifications. Because signal transmission in industrial settings is susceptible to various interferences, the robust stability of the signal reception method designed using the data sampling method described above has been demonstrated in the theoretical proof (small gain theorem). This carrier sampling method can better handle carrier reception under various interference conditions in industrial settings.

[0168] RE stands for carrier reception transposition, which is physically implemented as a signal conditioning circuit arranged in front of the signal receiver of each node. It is used to further read the carrier signal from the carrier sampler RG and input it into the signal receiver of the local segment. Such reception allows the addition of anti-interference devices and abnormality monitoring devices in this process to ensure the correct construction of the synchronous carrier network.

[0169] The signal receiver in this structure can be of any model, as long as it remains stable. This means that using this patent to generate a global carrier does not require replacing the original signal receiver. Furthermore, the network topology of the RG layer is the same as that of the original node network, meaning that the solution of this invention does not require the deployment of new communication links and can be completed through the existing communication links.

[0170] Through the carrier synchronization network designed in this structure, global signal modulation and synchronous demodulation can be achieved. That is, the signal can be sent from any node in the network and modulated using the carrier signal of the node, and then synchronously demodulated through the signal of any other node, so that the original signal with no amplitude or phase error can be received at any node.

[0171] Carrier synchronization in a multi-layer network is achieved through a global leader, so that the modulated signal sent by any node can be synchronously demodulated by other nodes in the global network, such as Figure 3-4 shown.

[0172] Following by the leader

[0173] (15)

[0174] As a carrier generator, the carrier signal expression is The carrier replicator is a multi-layer network node in this example. To ensure that the Shannon sampling theorem is satisfied, the sampling frequency is set to h = 1000 Hz. The controller proposed in this paper can achieve global carrier synchronization, which can be verified in the numerical simulation example. In this example, the message signal is ,in and represents the message signal frequency and . Add noise signal , Represents the original message signal, which is then used as the modulated signal. Randomly select nodes The carrier signal is modulated and then demodulated by the carrier signal of each node globally. The specific simulation is as follows Figure 5-6 shown.

[0175] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0177] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0179] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A distributed data sampling and synchronization method for a multi-layer network with high-dimensional node characteristics, characterized in that: The following steps are involved: S1: Establish a multi-layer mathematical model with no constraints on inter-layer and intra-layer parameters; S2: Based on a multi-layer mathematical model with unconstrained inter-layer and intra-layer parameters, the target state is considered as a virtual node embedded in the network topology, and a data sampling controller with virtual nodes is designed; S3: Define synchronization error and establish a multi-layer network output asymptotic synchronization problem with high-dimensional node characteristics; S4: Based on a data sampling controller, we solve the non-asymptotic output synchronization problem of multi-layer networks with high-dimensional node characteristics and establish an output asymptotic synchronization criterion applicable to multi-layer networks with high-dimensional node characteristics. This allows for carrier synchronization in multi-layer networks, so that the modulated signal sent by any node can be synchronously demodulated by other nodes globally. The multi-layer mathematical model with unconstrained inter-layer and intra-layer parameters is established as follows: Consider an undirected weighted network consisting of layers, each layer has The same node system, Layer dynamics: （1） Where q represents the adjacent node, represents the intra-layer connection matrix, represents the inter-layer connection matrix, Indicates the layer, The definition is The first derivative of The definition is Layer The status of the node, The definition is Layer The status of the node, is the control gain, is the state matrix, is the input state matrix, represents the internal coupling matrix, represents the external coupling matrix, represents the output coefficient matrix, Indicates the Layer nodes The input vector, Indicates the Layer nodes The output vector of express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space, express dimensional Euclidean space; Describes the state vector of the entire multi-layer network, T represents the transpose of the vector, yes The state vector of the layer, The definition is Layer The status of each node; is a vector describing the output of the entire multi-layer network system, where yes Layer output variables, Indicates the Layer nodes The output vector of is a vector describing the input of the entire multi-layer network system, where It is The input vector of the layer is the control input of the nodes, Indicates the Layer nodes The input vector of The production goal is a target trajectory, the output of the following autonomous system: （2） in Represents the state matrix of the entire leader 0, represents the output matrix of the entire leader 0, and Represents the status and output of leader 0; The target system is regarded as the virtual vertex of the network and marked as nodes, let the intermediate variable , the augmented coupling graph of the multi-layer network is defined as , superscript Indicates the Layer, the vertex set is , the directed edge set is , Representation node To Node A directed edge of Rule No. Tier Node and Connections between neighboring nodes ,otherwise ;in, , Indicates the Tier Node and Connections between neighbor nodes; represents the connection weight; and , the adjacency matrix for: ； in represents the 0 vector, Represents the original network connection matrix, vector , represents the original network Laplacian matrix, Represents an edge The connection weight of The Laplace matrix of Expressed as: ， in express The element in the p-th row and p-th column of the matrix, represents a diagonal matrix, for is a positive definite matrix.

2. The method for distributed data sampling and synchronization in a multi-layer network with high-dimensional node characteristics according to claim 1, characterized in that: The design of the data sampling controller is as follows: Designed Layer Node The reference generator is （3） in Indicates the Layer Node The reference generator state of , and ; Indicates the Layer Node The reference generator output of , and Indicates the Layer output, Indicates output; Indicates the Layer Node The distributed control law of the reference generator is Indicates the Layer control law, represents the control law of the entire network; Due to the limitation of communication, the sampling data control method is adopted. The zero-order holder maintains a fixed constant within the sampling interval. satisfy , the subscript s represents the sampling node time; the sampling interval is , design a distributed controller for It is non-periodic sampling, and the upper bound of sampling is ,Right now , represents the sampling interval, Represents identity; data sampling distributed controller Designed to: （4） in represents the sampling interval, represents the control gain matrix, Indicates the number of nodes, Indicates the number of layers, express Tier The status of the node, express Tier The status of each node; Based on the sampled data distributed control law in (4), the dynamics of the reference generator in (3) can be rewritten as: （5）。 3. The method for distributed data sampling and synchronization in a multi-layer network with high-dimensional node characteristics according to claim 2, characterized in that: The synchronization error is defined to establish a multi-layer network output asymptotic synchronization problem with high-dimensional node characteristics, as follows: Define the error vector : （6） in Indicates the number of nodes, Indicates the number of layers, represents the error vector, and express Layer error vector, Definition Tier node error vectors; Calculation can be obtained: ；（7） in represents the state of leader 0, and ； in, Indicates the Interlayer Node and Interlayer Node connections, Indicates the Interlayer Node and Interlayer nodes connected; therefore, （8） in, represents the p-dimensional identity matrix, represents the direct sum of non-diagonal elements between layers, Represents the diagonal elements between layers, Indicates the number of layers, represents the circular multiplication, And calculate: （9） in express dimensional identity matrix, represents the intra-layer Laplacian matrix, represents the direct sum of matrices, represents the addition of two matrices, represents the circular multiplication, Indicates a A unit vector with one component being 1 and the rest being 0.

4. The method for distributed data sampling and synchronization in a multi-layer network with high-dimensional node characteristics according to claim 1, characterized in that: The S4 is specifically as follows: Distributed fixed-time control of data sampling Parameterized as ,in is a solution to the algebraic Riccati inequality, choosing the scalar as a positive scalar; if there exists a positive definite matrix and So that: ； If established, and The link is stable, where h represents the upper bound of non-periodic sampling, represents the intermediate variable, Represents an intermediate variable.

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