Network relation analysis method and system based on intelligent equipment in intelligent factory manufacturing

Abstract

The invention relates to the technical field of Internet of Things, in particular to a network relation analysis method and system based on intelligent equipment in intelligent factory manufacturing, and the method comprises the steps: collecting the interaction, communication and cooperation data of the equipment, and carrying out the cleaning and standardization processing; abstracting equipment as network nodes, and constructing a network graph model; calculating centrality indexes of the nodes, judging key nodes, identifying node clusters through a spectral clustering algorithm, and constructing a group structure of the key nodes; and analyzing a connection relationship between the nodes, calculating an optimal connection path between the key node and other nodes by using a weighted shortest path algorithm, and adjusting node connection in the network according to a calculation result. According to the method, the network relationship of the equipment in the intelligent factory can be effectively analyzed, the connection between the equipment is optimized, and the production efficiency and the system robustness are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a network relationship analysis method and system based on intelligent devices in intelligent factory manufacturing. Background Art

[0002] Smart devices involve a large number of complex interactive behaviors and communication relationships in collaborative work, which makes the network relationship analysis between devices increasingly important. By analyzing the network relationship of smart devices, we can effectively explore the collaboration mode between devices, optimize the production process and improve the overall operation efficiency.

[0003] The existing methods for analyzing the network relationships of smart devices are usually relatively simple and lack comprehensiveness, making it difficult to fully reveal the complex interaction patterns between devices. Many methods are limited to analyzing a single data source (such as interaction logs or communication records), while ignoring the comprehensive effects of multiple data sources. In addition, existing methods also have certain limitations in key node identification, node cluster structure construction, and connection path optimization, and fail to fully consider the balance between the global and local characteristics of the network structure.

[0004] Regarding the network relationships of smart devices, it is particularly necessary to propose a comprehensive analysis method based on smart device interaction logs, communication records and collaborative data, so as to more accurately reveal the correlation between devices, identify key nodes and their cluster structures, and further improve the overall efficiency and stability of the smart factory manufacturing system by optimizing the connection relationship between nodes.

[0005] In view of this, the present invention proposes a network relationship analysis method and system based on intelligent devices in intelligent factory manufacturing. Summary of the invention

[0006] To achieve the above objectives, the present invention provides a network relationship analysis method and system based on intelligent devices in intelligent factory manufacturing. The specific technical solutions are as follows:

[0007] The network relationship analysis method based on intelligent devices in smart factory manufacturing includes:

[0008] Collect interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization;

[0009] Abstract smart devices as nodes in the network, use the interactions and communication behaviors between devices as edges, and build a network graph model that reflects the node relationships;

[0010] Calculate the degree centrality, betweenness centrality and closeness centrality indicators of the nodes, set thresholds, and identify the key nodes in the network based on the thresholds;

[0011] According to the identified key nodes, the spectral clustering algorithm is used to identify node clusters and construct the group structure of key nodes in the network;

[0012] Based on the identified key nodes and the group structure of key nodes, the connection relationship between nodes is analyzed, and the weighted shortest path algorithm is used to calculate the optimal connection path between key nodes and other nodes. According to the calculation results, the connection between key nodes and nodes inside and outside the group is adjusted in the network.

[0013] Preferably, the interaction logs, communication records and production collaboration data of the smart devices are collected, and data cleaning and standardization are performed, including: collecting the interaction logs of the smart devices and recording them as data sets ,in Indicates Interaction log data, is the total number of interaction logs;

[0014] Collect communication records between devices, recorded as data sets ,in Indicates Communication records, is the total number of communication records;

[0015] Collect the collaborative data of equipment in the production process, recorded as data set ,in Indicates 10 ... is the total number of collaborative data;

[0016] Preprocess the collected data, including deduplication, format unification, missing value filling, data transcoding, and numerical data normalization;

[0017] The preprocessed dataset , and Denoted as , and .

[0018] Preferably, the smart devices are abstracted as nodes in the network, and the interaction and communication behaviors between the devices are used as edges, including: preprocessing the smart device interaction log , Communication records and production collaboration data Extract the smart device ID from the smart device ID to form a smart device ID set ,in Indicates Smart devices, is the total number of smart devices;

[0019] Collect by smart device ID As a set of nodes in a network diagram, smart devices Abstract into nodes , each node Represents a smart device;

[0020] According to the interaction log , extract the interaction relationship between smart devices and construct directed edge sets ; For each record in the interaction log ,in is the source smart device ID, is the target smart device ID, is the interaction time, For the interaction type, in the node and Directed edges between , the edge weight is ;

[0021] According to the communication records , extract the communication relationship between smart devices and construct directed edge sets ; For each record in the communication record ,in is the source smart device ID, is the target smart device ID, is the communication time, For the communication protocol, at the node and Directed edges between , the edge weight is ;

[0022] According to collaborative data , extract the collaborative relationship between smart devices and construct an undirected edge set ; For each record in the collaborative data ,in The ID of the smart device participating in the collaboration. For collaborative time, For collaborative tasks, here Undirected edges are established between smart device nodes to form undirected edges, and the edge weight is .

[0023] Preferably, constructing a network graph model reflecting node relationships includes: constructing a node network graph based on the interaction relationship between smart devices, the communication relationship between smart devices, and the collaborative relationship between smart devices. ,in is a node set, For edge sets, is the edge weight set;

[0024] For edge sets For each edge in Calculated by weighted summation.

[0025] Preferably, the degree centrality, betweenness centrality and closeness centrality indicators of the nodes are calculated, and thresholds are set to identify key nodes in the network according to the thresholds, including: Each node in , compute nodes The degree centrality of ;

[0026] The degree centrality includes the in-degree centrality and out-degree centrality ;

[0027] For network diagrams Each node in , compute nodes Betweenness Centrality ;

[0028] For network diagrams Each node in , compute nodes Closeness Centrality ;

[0029] Setting degree centrality threshold , betweenness centrality threshold and close to the centrality threshold ; If the degree centrality of a node , betweenness centrality and closeness centrality satisfy:

[0030]

[0031] Then the node Identify key nodes in the network and build a key node network subgraph based on all key nodes .

[0032] Preferably, according to the identified key nodes, a spectral clustering algorithm is used to identify node clusters and construct a group structure of key nodes in the network, including: for a key node network subgraph composed of key nodes , use spectral clustering algorithm to cluster key nodes and construct the group structure of key nodes in the network;

[0033] The spectral clustering algorithm clusters based on the eigenvalues ​​and eigenvectors of the graph's Laplacian matrix; constructs subgraphs The adjacency matrix of ;

[0034] Computational subgraph The degree matrix ;

[0035] Computational subgraph The Laplace matrix of ;

[0036] Laplacian Matrix Perform eigenvalue decomposition and obtain the eigenvalue and the corresponding eigenvector ;

[0037] Before selection The eigenvectors corresponding to the smallest non-zero eigenvalues ​​constitute the characteristic matrix ,in is the number of target categories for clustering, is a sparse matrix;

[0038] For the feature matrix Normalize each row of to get the normalized feature matrix ;

[0039] Normalize the feature matrix Each row is considered A point in space;

[0040] use -means algorithm clusters the points and obtains Clustering results .

[0041] Preferably, according to the identified key nodes, a spectral clustering algorithm is used to identify node clusters and construct a group structure of key nodes in the network, further comprising: for each cluster , analyze the attribute characteristics of internal nodes and calculate the edge density within the cluster , Indicates the degree of connection between nodes within the cluster;

[0042] Calculate edge density between clusters , Representation clustering and The tightness of the connection between them.

[0043] Preferably, based on the identified key nodes and the key node group structure, the connection relationship between nodes is analyzed, including: key node network subgraph Each key node in , analyze key nodes The connection relationship between nodes in the network;

[0044] Defining key nodes With Node The connection strength between , is the sum of the edge weights of all paths between nodes;

[0045] Calculate key nodes Its cluster The average connection strength between internal nodes ;

[0046] Calculate key nodes With clustering The average connection strength between internal nodes .

[0047] Preferably, a weighted shortest path algorithm is used to calculate the optimal connection path between the key node and other nodes, and according to the calculation result, the connection between the key node and the nodes inside and outside the group is adjusted in the network, including: for the key node With Node The connection between nodes is calculated using the weighted shortest path algorithm;

[0048] Defining key nodes arrive The optimal connection path The path with the smallest sum of edge weights is:

[0049] For key nodes , calculate the optimal connection paths between the internal nodes of the cluster and the internal nodes of the non-cluster, and obtain the optimal connection path set ;

[0050] According to the calculated connection strength and optimal connection path, the connection relationship between the key nodes and other nodes in the network is adjusted;

[0051] For key nodes , if the key node The average connection strength between the nodes and other nodes in the cluster Below the preset threshold , then add new edges within the cluster or adjust the weights of existing edges;

[0052] For key nodes , if the key node The average connection strength between nodes outside the cluster Below the preset threshold , then add new edges between clusters or adjust the weights of existing edges;

[0053] For the optimal connection path set Each path in , if there is an edge on the path whose weight is lower than the preset threshold , then the value below the preset threshold The weights of the edges are adjusted.

[0054] A network relationship analysis system based on intelligent devices in intelligent factory manufacturing, which is used to implement the network relationship analysis method based on intelligent devices in intelligent factory manufacturing, including: a data acquisition module, a graph model construction module, a key node identification module, a node group construction module and a node connection adjustment module;

[0055] The data collection module is used to collect interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization processing;

[0056] The graph model building module is used to abstract smart devices into nodes in the network, and use the interaction and communication behaviors between devices as edges to build a network graph model that reflects the node relationship;

[0057] The key node identification module is used to calculate the degree centrality, betweenness centrality and closeness centrality indicators of the nodes, and set thresholds to identify the key nodes in the network according to the thresholds;

[0058] The node group construction module identifies node clusters based on the identified key nodes through a spectral clustering algorithm to construct a group structure of key nodes in the network;

[0059] The node connection adjustment module analyzes the connection relationship between nodes based on the identified key nodes and the group structure of the key nodes, calculates the optimal connection path between the key nodes and other nodes using a weighted shortest path algorithm, and adjusts the connection between the key nodes and nodes inside and outside the group in the network based on the calculation results.

[0060] Beneficial effects of the present invention: The present invention systematically collects and cleans the interaction logs, communication records and production collaboration data of smart devices to ensure the accuracy and consistency of the data, provide a high-quality data basis for subsequent network analysis, and reduce errors and noise.

[0061] The present invention converts smart devices into network nodes and uses their interaction and communication behaviors as edges to build an intuitive network graph model, which helps visualize the relationships and dependencies between devices, facilitates the identification of key connection points and optimizes space.

[0062] The present invention calculates degree centrality, betweenness centrality and closeness centrality indicators and sets thresholds to accurately identify key nodes in the network, which helps to optimize resource allocation, enhance the reliability of important equipment and reduce the overall risk of the system.

[0063] The present invention utilizes a spectral clustering algorithm to divide node clusters according to key nodes, construct a group structure, identify groups of devices with similar functions or working in collaboration, optimize resource allocation and task coordination, and improve production efficiency and collaboration effects.

[0064] The present invention is based on key nodes and group structures, uses a weighted shortest path algorithm to calculate the optimal connection path, and adjusts the connection between key nodes and nodes inside and outside the group, thereby enhancing network connectivity and information flow efficiency, and improving system robustness and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a network relationship analysis method based on intelligent devices in intelligent factory manufacturing provided by the present invention;

[0066] Figure 2 This is a structural diagram of the network relationship analysis system based on intelligent devices in the intelligent factory manufacturing provided by the present invention. DETAILED DESCRIPTION

[0067] In order to better understand the present invention, a more detailed description will be made of various aspects of the present invention with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0068] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0069] It should also be understood that expressions such as "comprises", "including", "having", "includes" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention". And, the term "exemplary" is intended to refer to an example or illustration.

[0070] Unless otherwise defined, all terms (including engineering terms and scientific and technological terms) used in this document have the same meaning as commonly understood by ordinary technicians in the field to which the present invention belongs. It should also be understood that unless otherwise clearly stated in the present invention, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0071] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0072] Example 1

[0073] Reference Figure 1 , which is the first embodiment of the present invention, provides a network relationship analysis method based on smart devices in smart factory manufacturing.

[0074] Step 1: Collect the interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization. Specifically include:

[0075] Collect the interaction logs of smart devices, including device ID, interaction time, interaction type (such as data transmission, control instructions, etc.) and interaction object information, and record them as data sets ,in Indicates Interaction log data, is the total number of interaction logs.

[0076] Collect communication records between devices, including source device ID, target device ID, communication time, communication protocol and communication content. The total number of communication records.

[0077] Collect collaborative data of equipment in the production process, including task allocation, goods flow, and product processing collaborative events, recorded as data sets ,in Indicates 10 ... is the total number of collaborative data.

[0078] Preprocess the collected data, including deduplication, format unification, missing value filling, data transcoding, and normalization of numerical data.

[0079] For example, the numerical field (such as communication duration) is normalized and mapped to The specific formula is:

[0080]

[0081] in, is the original value, and are the minimum and maximum values ​​of the field, respectively. is the normalized value.

[0082] Exemplarily, the categorized fields (such as interaction type, communication protocol, etc.) are encoded and converted into numerical variables; for example, one-hot encoding is used to map each category into a binary vector, where the position corresponding to the category in the vector is 1 and the remaining positions are 0; after the above data preprocessing steps, standardized smart device interaction logs, communication records, and production collaboration data are obtained, laying a data foundation for subsequent network relationship analysis.

[0083] The preprocessed dataset , and Denoted as , and .

[0084] Step 2: Abstract smart devices as nodes in the network, use the interactions and communication behaviors between devices as edges, and build a network graph model that reflects the node relationships. Specifically, it includes:

[0085] The preprocessed smart device interaction log , Communication records and production collaboration data Extract the smart device ID from the smart device ID to form a smart device ID set ,in Indicates Smart devices, is the total number of smart devices.

[0086] Collect by smart device ID As a set of nodes in a network diagram, smart devices Abstract into nodes , each node Represents a smart device.

[0087] According to the interaction log , extract the interaction relationship between smart devices and construct directed edge sets ; For each record in the interaction log ,in is the source smart device ID, is the target smart device ID, is the interaction time, For the interaction type, in the node and Directed edges between , the edge weight is ; Edge weight Calculated based on indicators such as interaction frequency or interaction duration.

[0088] According to the communication records , extract the communication relationship between smart devices and construct directed edge sets ; For each record in the communication record ,in is the source smart device ID, is the target smart device ID, is the communication time, For the communication protocol, at the node and Directed edges between , the edge weight is ; Edge weight Calculated based on indicators such as communication frequency or communication data volume.

[0089] According to collaborative data , extract the collaborative relationship between smart devices and construct an undirected edge set ; For each record in the collaborative data ,in The ID of the smart device participating in the collaboration. For collaborative time, For collaborative tasks, here Undirected edges are established between each pair of smart device nodes to form undirected edges, and the edge weight is ; Edge weight Calculated based on indicators such as collaboration frequency or collaboration duration.

[0090] Construct a node network diagram based on the interaction relationship, communication relationship and collaborative relationship between smart devices ,in is a node set, For edge sets, is the edge weight set.

[0091] For edge sets For each edge in Calculated by weighted summation, the formula is: ;in, , , is the weight factor.

[0092] In step 2, the smart devices are abstracted as nodes in the network graph, and the interactions, communications, and collaborative relationships between devices are abstracted as edges. A network graph model that reflects the device relationships is constructed, providing a basis for subsequent network relationship analysis.

[0093] Step 3: Calculate the degree centrality, betweenness centrality and closeness centrality indicators of the nodes, set thresholds, and identify key nodes in the network based on the thresholds. Specifically include:

[0094] For network diagrams Each node in , compute nodes The degree centrality of .

[0095] The degree centrality includes the in-degree centrality and out-degree centrality , respectively reflecting the node's receiving and sending connection capabilities; in-degree centrality The calculation formula is: ;in is the adjacency matrix The elements in represent the slave nodes arrive The weight of the edge; out-degree centrality The calculation formula is: ;in, is the adjacency matrix The elements in represent the slave nodes arrive The weight of the edge; degree centrality The calculation formula is: .

[0096] For network diagrams Each node in , compute nodes Betweenness Centrality , betweenness centrality indicates the degree to which a node plays an "intermediary" role in the network, that is, the frequency of being on the shortest path between other nodes; betweenness centrality The calculation formula is: ;in, Representation Node and The number of shortest paths between Representation Node and Passing through nodes The number of shortest paths; nodes with higher betweenness centrality play important information transmission and control functions in the network.

[0097] For network diagrams Each node in , compute nodes Closeness Centrality ,Closeness centrality indicates the average distance between a node and other nodes in the network, reflecting the ,accessibility of a node in the network.

[0098] Closeness Centrality The calculation formula is ;in Representation Node and The shortest path length between nodes; nodes with higher proximity centrality have better accessibility in the network and can quickly interact and communicate with other nodes.

[0099] Setting degree centrality threshold , betweenness centrality threshold and close to the centrality threshold ; If the degree centrality of a node , betweenness centrality and closeness centrality satisfy:

[0100]

[0101] Then the node Identify key nodes in the network and build a key node network subgraph based on all key nodes .

[0102] Step 3 comprehensively considers degree centrality, betweenness centrality and closeness centrality, sorts the importance of nodes in the network, and identifies the most influential and key smart devices in the network; key devices need to be monitored and managed to ensure their stable operation and optimize their connection with other devices to improve the overall efficiency and reliability of the network; through the above steps, using indicators such as degree centrality, betweenness centrality and closeness centrality, key nodes and important devices in the smart device network are identified to provide decision support for subsequent network optimization and management.

[0103] Step 4: Based on the identified key nodes, the spectral clustering algorithm is used to identify node clusters and construct the group structure of key nodes in the network.

[0104] For the key node network subgraph composed of key nodes ,The spectral clustering algorithm is used to cluster the key nodes and construct the group structure of the key nodes in the network.

[0105] The spectral clustering algorithm performs clustering based on the eigenvalues ​​and eigenvectors of the Laplacian matrix of the graph. The specific steps are as follows:

[0106] Constructing a subgraph The adjacency matrix of , ;in Indicates key nodes and The weight of the edge between them.

[0107] Computational subgraph The degree matrix , ;in , Indicates key nodes The degree, is the number of key nodes, represents a diagonal matrix;

[0108] Computational subgraph The Laplace matrix of ,in ;

[0109] Laplacian Matrix Perform eigenvalue decomposition and obtain the eigenvalue and the corresponding eigenvector ;

[0110] Before selection The eigenvectors corresponding to the smallest non-zero eigenvalues ​​constitute the characteristic matrix ,in is the number of target categories for clustering, is a sparse matrix;

[0111] For the feature matrix Normalize each row of to get the normalized feature matrix ;

[0112] Normalize the feature matrix Each row is considered A point in space;

[0113] use -means algorithm clusters the points and obtains Clustering results .

[0114] For each cluster , analyze the attribute characteristics of internal nodes, including node type, function, subsystem, etc., identify the common characteristics of clusters; calculate the edge density within the cluster , which indicates the degree of connection between nodes within the cluster: ;in, Representation clustering The number of internal nodes, Representation clustering The number of edges between internal nodes.

[0115] Calculate edge density between clusters , indicating clustering and The degree of connection between: ;in, Representation clustering and The number of edges between .

[0116] Step 4: Based on the clustering results, identify key equipment clusters with similar functions or working together to provide decision support for production scheduling, task allocation, etc.; for clusters with high edge density within the cluster, it means that the interactions between the devices within the cluster are frequent, and it is necessary to focus on optimizing their communication and coordination mechanisms to improve the working efficiency of the cluster; for cluster pairs with high edge density between clusters, it means that the coordination relationship between these clusters is close, and it is necessary to optimize the data exchange and task coordination process across clusters to improve the coordination efficiency between clusters; the clustering results are fed back to the production management system and equipment control system, and the coordination relationship and communication strategy between equipment are dynamically adjusted to realize intelligent production scheduling and optimization.

[0117] Step 5: Based on the identified key nodes and the group structure of key nodes, analyze the connection relationship between nodes, use the weighted shortest path algorithm to calculate the optimal connection path between key nodes and other nodes, and adjust the connection between key nodes and nodes inside and outside the group in the network based on the calculation results. Specifically include:

[0118] Key Node Network Subgraph Each key node in , analyze key nodes The connection relationship between nodes in the network.

[0119] Defining key nodes With Node The connection strength between , is the sum of the edge weights of all paths between nodes: ;in, Indicates key nodes arrive The set of all paths of Indicates the path, represents the edge on the path, Represents edge The weight of .

[0120] Calculate key nodes Its cluster The average connection strength between internal nodes : .

[0121] Calculate key nodes With clustering The average connection strength between internal nodes : .

[0122] For key nodes With Node The connection between nodes is calculated using the weighted shortest path algorithm.

[0123] Defining key nodes arrive The optimal connection path The path with the smallest sum of edge weights is:

[0124] For key nodes , calculate the optimal connection paths between the internal nodes of the cluster and the internal nodes of the non-cluster, and obtain the optimal connection path set .

[0125] According to the calculated connection strength and optimal connection path, the connection relationship between the key nodes and other nodes in the network is adjusted.

[0126] For key nodes , if the key node The average connection strength between the nodes and other nodes in the cluster Below the preset threshold , then new edges are added within the cluster or the weights of existing edges are adjusted.

[0127] For key nodes , if the key node The average connection strength between nodes outside the cluster Below the preset threshold , then new edges are added between clusters or the weights of existing edges are adjusted.

[0128] For the optimal connection path set Each path in , if there is an edge on the path whose weight is lower than the preset threshold , then the value below the preset threshold The weights of the edges are adjusted.

[0129] Step 5: Based on the identified key nodes and the group structure of key nodes, analyze the connection relationship between nodes, use the weighted shortest path algorithm to calculate the optimal connection path between the key nodes and other nodes, and adjust the connection between the key nodes and other nodes inside and outside the group in the network according to the calculation results, optimize the connection relationship of the network, and improve the efficiency and robustness of the network.

[0130] Example 2

[0131] Reference Figure 2 , which is the second embodiment of the present invention, provides a network relationship analysis system based on intelligent devices in intelligent factory manufacturing.

[0132] The system includes: a data acquisition module, a graph model construction module, a key node identification module, a node group construction module and a node connection adjustment module.

[0133] The data collection module is used to collect interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization processing.

[0134] The graph model building module is used to abstract smart devices into nodes in the network, use the interaction and communication behaviors between devices as edges, and build a network graph model that reflects the node relationship.

[0135] The key node identification module is used to calculate the degree centrality, betweenness centrality and closeness centrality indicators of the nodes, and set thresholds to identify the key nodes in the network according to the thresholds.

[0136] The node group construction module identifies node clusters based on the identified key nodes through a spectral clustering algorithm, and constructs a group structure of key nodes in the network.

[0137] The node connection adjustment module analyzes the connection relationship between nodes based on the identified key nodes and the group structure of the key nodes, calculates the optimal connection path between the key nodes and other nodes using a weighted shortest path algorithm, and adjusts the connection between the key nodes and nodes inside and outside the group in the network based on the calculation results.

[0138] The above sequence for the steps of the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless otherwise specifically stated.

[0139] In addition, in some embodiments, the present invention can also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Therefore, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0140] In addition, the parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0141] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A network relationship analysis method based on intelligent devices in intelligent factory manufacturing, characterized in that: include: Collect interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization; Abstract smart devices as nodes in the network, use the interactions and communication behaviors between devices as edges, and build a network graph model that reflects the node relationships; Calculate the degree centrality, betweenness centrality and closeness centrality indicators of the nodes, set thresholds, and identify the key nodes in the network based on the thresholds; According to the identified key nodes, the spectral clustering algorithm is used to identify node clusters and construct the group structure of key nodes in the network; Based on the identified key nodes and the group structure of key nodes, the connection relationship between nodes is analyzed, and the weighted shortest path algorithm is used to calculate the optimal connection path between key nodes and other nodes. According to the calculation results, the connection between key nodes and nodes inside and outside the group is adjusted in the network.

2. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 1 is characterized in that: Collect the interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization, including: Collect the interaction logs of smart devices and record them as data sets ,in Indicates Interaction log data, is the total number of interaction logs; Collect communication records between devices, recorded as data sets ,in Indicates Communication records, is the total number of communication records; Collect the collaborative data of equipment in the production process, recorded as data set ,in Indicates Collaborative data, is the total number of collaborative data; Preprocess the collected data, including deduplication, format unification, missing value filling, data transcoding, and numerical data normalization; The preprocessed dataset , and Denoted as , and .

3. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 2 is characterized in that: Abstract smart devices as nodes in the network, with the interaction and communication behaviors between devices as edges, including: pre-processing smart device interaction logs , Communication records and production collaboration data Extract the smart device ID from the smart device ID to form a smart device ID set ,in Indicates Smart devices, is the total number of smart devices; Collect by smart device ID As a set of nodes in a network diagram, smart devices Abstract into nodes , each node Represents a smart device; According to the interaction log , extract the interaction relationship between smart devices and construct directed edge sets ; For each record in the interaction log ,in is the source smart device ID, is the target smart device ID, is the interaction time, For the interaction type, in the node and Directed edges between , the edge weight is ; According to the communication records , extract the communication relationship between smart devices and construct directed edge sets ; For each record in the communication record ,in is the communication time, For the communication protocol, at the node and Directed edges between , the edge weight is ; According to collaborative data , extract the collaborative relationship between smart devices and construct an undirected edge set ; For each record in the collaborative data ,in The ID of the smart device participating in the collaboration. For collaborative time, For collaborative tasks, here Undirected edges are established between smart device nodes to form undirected edges, and the edge weight is .

4. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 3 is characterized in that: Construct a network graph model that reflects node relationships, including: constructing a node network graph based on the interaction relationship between smart devices, the communication relationship between smart devices, and the collaborative relationship between smart devices ,in is a node set, For edge sets, is the edge weight set; For edge sets For each edge in Calculated by weighted summation.

5. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 4 is characterized in that: Calculate the degree centrality, betweenness centrality and closeness centrality of nodes, set thresholds, and identify key nodes in the network based on the thresholds, including: Each node in , compute nodes The degree centrality of ; The degree centrality includes the in-degree centrality and out-degree centrality ; For network diagrams Each node in , compute nodes Betweenness Centrality ; For network diagrams Each node in , compute nodes Closeness Centrality ; Setting degree centrality threshold , betweenness centrality threshold and close to the centrality threshold ; If the degree centrality of a node , betweenness centrality and closeness centrality satisfy: ； Then the node Identify key nodes in the network and build a key node network subgraph based on all key nodes .

6. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 5 is characterized in that: According to the identified key nodes, the node clusters are identified through the spectral clustering algorithm, and the group structure of key nodes in the network is constructed, including: for the key node network subgraph composed of key nodes , use spectral clustering algorithm to cluster key nodes and construct the group structure of key nodes in the network; The spectral clustering algorithm clusters based on the eigenvalues ​​and eigenvectors of the graph's Laplacian matrix; constructs subgraphs The adjacency matrix of ; Computational subgraph The degree matrix ; Computational subgraph The Laplace matrix of ; Laplacian Matrix Perform eigenvalue decomposition and obtain the eigenvalue and the corresponding eigenvector ; Before selection The eigenvectors corresponding to the smallest non-zero eigenvalues ​​constitute the characteristic matrix ,in is the number of target categories for clustering, is a sparse matrix; For the feature matrix Normalize each row of to get the normalized feature matrix ; Normalize the feature matrix Each row is considered A point in space; use -means algorithm clusters the points and obtains Clustering results .

7. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 6 is characterized in that: According to the identified key nodes, the node clusters are identified through the spectral clustering algorithm to build the group structure of key nodes in the network, which also includes: for each cluster , analyze the attribute characteristics of internal nodes and calculate the edge density within the cluster , Indicates the degree of connection between nodes within the cluster; Calculate edge density between clusters , Representation clustering and The tightness of the connection between them.

8. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 7 is characterized in that: Based on the identified key nodes and key node group structure, analyze the connection relationship between nodes, including: key node network subgraph Each key node in , analyze key nodes The connection relationship between nodes in the network; Defining key nodes With Node The connection strength between , is the sum of the edge weights of all paths between nodes; Calculate key nodes Its cluster The average connection strength between internal nodes ; Calculate key nodes With clustering The average connection strength between internal nodes.

9. The network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to claim 8 is characterized in that: The weighted shortest path algorithm is used to calculate the optimal connection path between the key node and other nodes, and according to the calculation results, the connection between the key node and the nodes inside and outside the group is adjusted in the network, including: With Node The connection between nodes is calculated using the weighted shortest path algorithm; Defining key nodes arrive The optimal connection path The path with the smallest sum of edge weights is: For key nodes , calculate the optimal connection paths between the internal nodes of the cluster and the internal nodes of the non-cluster, and obtain the optimal connection path set ; According to the calculated connection strength and optimal connection path, the connection relationship between the key nodes and other nodes in the network is adjusted; For key nodes , if the key node The average connection strength between nodes in the cluster Below the preset threshold , then add new edges within the cluster or adjust the weights of existing edges; For key nodes , if the key node The average connection strength between nodes outside the cluster Below the preset threshold , then add new edges between clusters or adjust the weights of existing edges; For the optimal connection path set Each path in , if there is an edge on the path whose weight is lower than the preset threshold , then the value below the preset threshold The weights of the edges are adjusted.

10. A network relationship analysis system based on intelligent devices in intelligent factory manufacturing, which is used to implement the network relationship analysis method based on intelligent devices in intelligent factory manufacturing according to any one of claims 1 to 9, characterized in that: include: Data collection module, graph model building module, key node identification module, node group building module and node connection adjustment module; The data collection module is used to collect interaction logs, communication records and production collaboration data of smart devices, and perform data cleaning and standardization processing; The graph model building module is used to abstract smart devices into nodes in the network, and use the interaction and communication behaviors between devices as edges to build a network graph model that reflects the node relationship; The key node identification module is used to calculate the degree centrality, betweenness centrality and closeness centrality indicators of the nodes, and set thresholds to identify the key nodes in the network according to the thresholds; The node group construction module identifies node clusters based on the identified key nodes through a spectral clustering algorithm to construct a group structure of key nodes in the network; The node connection adjustment module analyzes the connection relationship between nodes based on the identified key nodes and the group structure of the key nodes, calculates the optimal connection path between the key nodes and other nodes using a weighted shortest path algorithm, and adjusts the connection between the key nodes and nodes inside and outside the group in the network based on the calculation results.

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