A method for constructing an addressing matrix based on multidimensional identifiers

Abstract

This invention belongs to the field of multidimensional identifier communication network technology and discloses a method for constructing an addressing matrix based on multidimensional identifiers. The specific process is as follows: a commander enters the instruction to be executed into the client of the multidimensional identifier network; after receiving a complete instruction, the client extracts the addressing attribute information text from the instruction entered by the commander; then it determines whether the addressing is multi-attribute addressing or single-attribute addressing; based on the addressing attributes, a target addressing matrix is ​​constructed; and the constructed target addressing matrix is ​​sent to the multidimensional identifier network server for addressing. This application establishes relationships between attributes, matrixes the multidimensional identifier attributes of communication entities, constructs an addressing matrix supporting multi-attribute addressing, and realizes the process of using multiple attributes to search in the multidimensional identifier database and return the multidimensional identifier of the communication peer that meets the conditions.

Description

Technical Field

[0001] This invention belongs to the field of multidimensional identifier communication network technology, specifically relating to a method for constructing an addressing matrix based on multidimensional identifiers. Background Technology

[0002] In a multidimensional identifier communication network system, three dimensions of identifiers—object, connection, and application—are used to represent nodes, network status information, and service characteristics within the network. The Object Identifier (EID) is the identifier for the object dimension. The EID's role is to uniquely identify devices within the network; finding the EID allows location of a specific device. The Network Identifier (NID) is the identifier for the connection dimension, representing a region or subnet within the network. It is a network-level identifier responsible for determining which network region a data packet should enter, performing routing and addressing functions. The Service Identifier (SID) is the identifier for the application dimension. In practical application scenarios, finding the corresponding EID based on the SID is the process of multidimensional identifier addressing, including single-attribute addressing and multi-attribute addressing.

[0003] In communication networks, single-attribute addressing refers to the process of finding a network communication peer based on a single attribute. In IP networks, each device is assigned a unique IP address, and these addresses can be used to find the corresponding target node during addressing. In Ethernet, each network interface has a unique MAC address, and devices can be found using their MAC addresses within a local area network. In multidimensional identification networks, each device has different attributes, i.e., text data. When a service needs to find a communication peer, it searches for the target communication device by searching for relevant attribute characteristics and returns the device's EID.

[0004] Multidimensional attribute addressing is categorized into single-attribute addressing and multi-attribute addressing based on the number of addressing attributes. Single-attribute addressing uses a single identifier attribute as the sole filtering criterion to search for the communication peer. This unique identifier attribute search method is similar to IP addressing on the Internet, but the multiple choices of unique attributes enrich the addressing content. Commonly used identifier attributes for single-attribute addressing include geographic location, node alias, and SID. Multi-attribute addressing searches for multidimensional identifier attributes in a database by combining attribute combinations.

[0005] Existing addressing methods provide ways to locate target devices, but single-attribute addressing is not flexible enough. In complex business scenarios, it can only locate the target device from one dimension, and the results obtained using single-attribute addressing are not accurate enough to fully meet business requirements. Therefore, multi-attribute addressing is incorporated into multi-dimensional identification communication networks. Multi-dimensional identification networks define multiple attributes for each device, and by combining multiple attributes, the communication peer is located, resulting in more accurate addressing results that maximize the fulfillment of business needs. The search is more precise and scalable when facing complex networks and business scenarios. Multi-attribute addressing, through the combination of multiple attributes, can adapt to different network environments and application scenarios, while also increasing network security and improving resource utilization. However, multiple attributes mean that multiple searches are required, increasing search time. Existing multi-attribute addressing lacks a unified addressing method and a standardized process, failing to meet the goal of quickly and accurately locating the communication peer in business processes. Summary of the Invention

[0006] To achieve the above objectives, this application provides a method for constructing an addressing matrix based on multidimensional identifiers. This method establishes relationships between attributes, matrixizes the multidimensional identifier attributes of communication entities, constructs an addressing matrix that supports multi-attribute addressing, and realizes the process of using multiple attributes to search in a multidimensional identifier database and return the multidimensional identifier of the communication peer that meets the conditions.

[0007] To achieve the above objectives, this application employs the following technical solution:

[0008] This application discloses a method for constructing an addressing matrix based on multidimensional identifiers. This method, designed for multidimensional identifier networks, represents attribute combinations using an addressing matrix. The specific steps of the addressing matrix construction method are as follows:

[0009] Step 1: The commander enters the desired command, i.e., text data, into the client of the multidimensional identification network;

[0010] Step 2: After receiving a complete instruction, the client extracts the addressing attribute information text from the instruction entered by the commander.

[0011] Step 3: Based on the addressing attribute information text extracted in Step 2, determine whether the addressing is multi-attribute addressing or single-attribute addressing;

[0012] Step 4: Based on the addressing attributes determined in Step 3, construct the target addressing matrix;

[0013] Step 5: Send the target addressing matrix constructed in Step 4 to the multidimensional identification network server for addressing.

[0014] A further improvement of this application is that: in step 2, the commander issues an instruction to describe the object and behavior dimensions and extract the addressing attribute information text, the content of which includes spatiotemporal information and equipment information.

[0015] A further improvement of this application is that, in step 3, if the attribute information text content of the address contains only one attribute, it is called single-attribute addressing; if the attribute information text content of the address contains two or more attributes, it is called multi-attribute addressing.

[0016] A further improvement of this application is that, in step 4, if it is single-attribute addressing, construct... The target addressing matrix, if multi-attribute addressing, is constructed. The target addressing matrix.

[0017] A further improvement of this application lies in: constructing The target addressing matrix specifically includes the following steps:

[0018] Step 4.1: Determine all attribute information texts of the addressed object and assign an identifier to each attribute information text. Suppose an addressed object has... Attribute information text

[0019] Step 4.2: Assuming relevance is used to measure the relationship between attribute information texts, for each pair of attribute information texts, Word2Vec is used to capture the semantic and syntactic relationships of the texts, converting the attribute information texts into attribute information text vectors, denoted as follows: Calculate the first Text vector of attribute information and the Text vector of attribute information Correlation coefficient between :

[0020]

[0021] in, It is the first Text vector of attribute information and the Text vector of attribute information covariance, for standard deviation for Standard deviation;

[0022] Step 4.3: Assuming we use similarity to measure the relationship between attribute information texts, similarity is calculated using the following formula:

[0023]

[0024] in, and They are and The value of , and These are the maximum and minimum values ​​of all attribute information text.

[0025] Step 4.4: Based on the business type, construct the row and column relationships of the target addressing matrix so that the attributes between rows and columns are related;

[0026] Step 4.5: Assign weights to each attribute information text according to actual needs. Weights can be calculated using methods such as the Analytic Hierarchy Process (AHP). The weight matrix is ​​represented as follows:

[0027]

[0028] in, Indicates the first The weight of each attribute information text.

[0029] A further improvement of this application is that: in step 4.4, the attribute association between rows and columns is a vertical association, which means that each column of the addressing matrix processes and summarizes the attribute information text into the same category from the perspective of semantic analysis and syntactic parsing, and addresses from different categories according to the vertical association.

[0030] A further improvement in this application is that step 5 specifically includes the following steps:

[0031] Step 5.1: In two-dimensional addressing, the attribute information text of the addressing matrix is ​​stored in the two-dimensional memory structure of the multidimensional identifier network. For a given... The target addressing matrix, and the two-dimensional address of the attribute information text of the target addressing matrix, are calculated using the following formula:

[0032]

[0033] in, It is a row index. It is a column index;

[0034] Step 5.2: In block addressing, the addressing matrix is ​​divided into multiple blocks, each block being a smaller submatrix. The target addressing matrix, and the block address of the attribute information text of the target addressing matrix are calculated using the following formula:

[0035]

[0036] in, It is the size of the block. Indicates rounding down;

[0037] Step 5.3: The database of the multidimensional identification network stores all attribute information text of all communication peers. Each row represents a different communication peer, and each column is a different matrix attribute information text. Based on the attribute information text in the addressing matrix, the corresponding attribute information text in the constructed target addressing matrix is ​​extracted from the database and formed into several small matrices with the same structure as the target addressing matrix.

[0038] Step 5.4: Match the small matrix generated by the database in Step 5.3 with the target addressing matrix.

[0039] The beneficial effects of this application are:

[0040] This application addresses resources by combining various attributes. Unlike single-attribute addressing, multi-attribute combination enhances the accuracy of resource identification and increases the flexibility of the addressing process. It also allows for better adaptation of services to network resources, effectively solving the problem of flexible scheduling of network resources. Furthermore, under multi-attribute combination, different weights can be set for each attribute according to service preferences, and addressing can be performed based on the priority of the attributes. For scenarios requiring complex data analysis and decision support, multi-dimensional attribute combination provides strong support.

[0041] This application combines attributes into a matrix, which can construct relationships between various attributes. The relationships between the rows and columns of the matrix reflect information from different dimensions of the business. The dimensions of the matrix can be expanded as needed to adapt to businesses of varying complexity. This flexibility makes matrix addressing applicable to businesses of all sizes. When the server addresses the addressing matrix, it can first analyze the row and column relationships and continue searching from top to bottom or bottom to top based on these relationships, using the longest prefix matching method. Because there are certain connections between the rows and columns of the constructed addressing matrix, the search range can be quickly narrowed. This is more systematic and faster than searching one attribute at a time without using a matrix. For example, if the first row of the addressing matrix describes spatiotemporal information, i.e., the behavioral dimension, such as time information and geographical location information, and the second row describes the object dimension, such as node alias, node affiliation, and node type, the server can first initially locate the target based on the spatiotemporal information, return the identifiers that meet the conditions, and then search again among the initially found objects based on the object dimension information in the next row. This process gradually narrows the search range until the finally found identifier is returned. Furthermore, matrix addressing allows for optimization of the attribute matrix, such as using hash tables or inverted indexes to accelerate the query process. This enables quick returns of results when querying for a device with a specific attribute.

[0042] This application constructs an addressing matrix. Computationally, the matrix structure supports parallel computing, and the matrix form naturally divides the search task into multiple smaller subtasks. In a distributed environment, these subtasks can be distributed across different computing nodes for parallel execution. For example, for a large addressing matrix, different rows or columns can be assigned to different nodes for searching, significantly accelerating data processing. Different rows or columns in the matrix can be searched on servers at different levels. The search results are compared, and the objects that meet all search criteria are returned as the final desired result. This speeds up addressing, and the addressing task is distributed across each server, dynamically adapting to each server's processing capacity and allowing for adjustments at any time. Attached Figure Description

[0043] Figure 1 This is the overall flowchart of this application.

[0044] Figure 2 This application Methods for constructing addressing matrices.

[0045] Figure 3 These are the matching rules between the addressing matrix of this application and the database.

[0046] Figure 4 This is a schematic diagram of the addressing matrix constructed when using single-attribute addressing in this application.

[0047] Figure 5 This is a schematic diagram of the addressing matrix constructed during multi-attribute addressing in this application.

[0048] Figure 6 This is a general schematic diagram of the addressing matrix of this application. Detailed Implementation

[0049] The embodiments of the present invention will be disclosed below with reference to the drawings. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the present invention. That is, in some embodiments of the present invention, these practical details are not essential. In addition, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0050] like Figure 1 As shown, this application discloses a method for constructing an addressing matrix based on multidimensional identifiers. This method is designed for multidimensional identifier networks and represents attribute combinations in the form of an addressing matrix. The addressing matrix construction method specifically includes the following steps:

[0051] Step 1: The commander enters the desired command, i.e., text data, into the client of the multidimensional identification network.

[0052] Step 2: After receiving a complete instruction, the client extracts the addressing attribute information text from the instruction entered by the commander. The commander issues instructions from two dimensions: object dimension and behavior dimension, extracting the addressing attribute information text. The content of the addressing attribute information text includes spatiotemporal information and device information. The spatiotemporal information includes time and geographical location, and the device information includes battery level, whether it is idle, and a description of the device itself, such as the device alias and the organization to which it belongs.

[0053] Step 3: Based on the addressing attribute information text extracted in Step 2, determine whether the addressing is multi-attribute addressing or single-attribute addressing. If the addressing attribute information text contains only one attribute, it is single-attribute addressing; if the addressing attribute information text contains two or more attributes, it is multi-attribute addressing.

[0054] When the instruction is issued to a specific object or a specific action is queried, single-attribute addressing is used, and attribute searches are performed using node aliases, node types, or SIDs. In this case, the construction... The addressing matrix. For example:

[0055] Scenario Design: In construction projects, multidimensional identification networks can be used for command and control. In one project, the commander issues the following order: First Engineering Team, also known as Taishan, all members shall commence full operations at the construction site located at 20 degrees north latitude and 135 degrees east longitude at 4:00 AM tomorrow.

[0056] This command was issued by the command center and uses the alias attribute for addressing. The alias attribute is "Taishan". Other information about the command is written into the data segment. The addressing matrix for this scenario is as follows: Figure 4 As shown.

[0057] If the command needs to search for multiple attributes, or the target is not just one type of communication peer, use multi-attribute addressing to construct vertical or horizontal relationships based on the associations between attributes. The addressing matrix. For example... Figure 2 As shown, construct The target addressing matrix specifically includes the following steps:

[0058] Step 4.1: Determine all attribute information texts of the addressed object and assign an identifier to each attribute information text. Suppose an addressed object has... Attribute information text

[0059] Step 4.2: Assuming relevance is used to measure the relationship between attribute information texts, for each pair of attribute information texts, Word2Vec is used to capture the semantic and syntactic relationships of the texts, converting the attribute information texts into attribute information text vectors, denoted as follows: , No. Text vector of attribute information and the Text vector of attribute information Correlation coefficient between :

[0060]

[0061] in, It is the first Text vector of attribute information and the Text vector of attribute information covariance, for standard deviation for Standard deviation;

[0062] Step 4.3: Assuming we use similarity to measure the relationship between attribute information texts, similarity is calculated using the following formula:

[0063]

[0064] in, and They are and The value of , and These are the maximum and minimum values ​​of all attribute information text.

[0065] Step 4.4: Based on the business type, construct the row and column relationships of the target addressing matrix so that the attributes between rows and columns are related.

[0066] The attribute associations between rows and columns are vertical associations. These vertical associations refer to the fact that each column of the addressing matrix processes and categorizes attribute information text into the same category from a semantic analysis and syntactic perspective. Addressing is then performed from different categories based on these vertical associations. Categories include: subject description and business description. This allows for addressing from different angles based on these associations. When issuing instructions, the commander needs to break down the instruction into the category of vertical association based on the target and fill in the corresponding attribute in the corresponding module of the multidimensional identifier client. The multidimensional identifier client is a visual interface divided into modules according to attribute categories. The commander only needs to fill in the specific name to be invoked for this instruction in the corresponding attribute module. For example, to instruct Team 2's excavator to operate, the excavator needs to be entered in the node alias, and Team 2 in the organization field.

[0067] Step 4.5: Assign weights to each attribute information text according to actual needs. Weights can be calculated using methods such as the Analytic Hierarchy Process (AHP). The weight matrix is ​​represented as follows:

[0068]

[0069] in, Indicates the first The weight of each attribute information text.

[0070] The specific scenario is as follows:

[0071] Scenario Design: In an engineering project, workers carry out operations according to the orders issued by the commander. The commander issues the following instructions: The engineering team located at 20° North latitude and 135° East longitude, also known as Songshan, must begin excavation at 05:00 tomorrow morning.

[0072] This scenario involves a command issued from the command center to members of the engineering team. The communication endpoint for this command is the first team member capable of completing it. In this command, the time information is 05:00 AM tomorrow, the geographical location information is 20°N 135°E, the node alias is Songshan, and the business category is mining, reflected in SID1. Therefore, addressing is performed using four identifier attributes: time, geographical location, node type, and business type. The addressing matrix for this scenario is as follows: Figure 5 As shown.

[0073] The first row, linking node aliases to geographic locations, describes the target area for the first team's operations. Node aliases represent specific units, while geographic locations indicate the exact location of their actions. Combining these two elements clearly defines the team's deployment location and operational direction. The second row, linking business type to time, determines the start time and task type of the action. The time element clarifies the specific moment of the action, while the business type indicates the nature of the action: mining. Together, they clarify when and what kind of action is being carried out. The first column, linking node aliases to business types, reflects the task nature of the target node. Analyzing the node type clarifies the team's identity, while the business type specifies the concrete task it performs—mining. Together, they help accurately define the team's function and mission. The second column, linking time to geographic location, clarifies the time and location of the action; this relationship ensures precise positioning of the action's spatiotemporal reference.

[0074] Step 4: Based on the addressing attributes determined in Step 3, construct the target addressing matrix;

[0075] Step 5: Send the target addressing matrix constructed in Step 4 to the multidimensional identifier network server for addressing, such as... Figure 3 As shown, it includes the following steps:

[0076] Step 5.1: In two-dimensional addressing, the attribute information text of the addressing matrix is ​​stored in the two-dimensional memory structure of the multidimensional identifier network. For a given... The target addressing matrix, and the two-dimensional address of the attribute information text of the target addressing matrix, are calculated using the following formula:

[0077]

[0078] in, It is a row index. It is a column index;

[0079] Step 5.2: In block addressing, the addressing matrix is ​​divided into multiple blocks, each block being a smaller submatrix. The target addressing matrix, and the block address of the attribute information text of the target addressing matrix are calculated using the following formula:

[0080]

[0081] in, It is the size of the block. Indicates rounding down;

[0082] Step 5.3: The database of the multidimensional identification network stores all attribute information text of all communication peers. Each row represents a different communication peer, and each column is a different matrix attribute information text. Based on the attribute information text in the addressing matrix, the corresponding attribute information text in the constructed target addressing matrix is ​​extracted from the database and formed into several small matrices with the same structure as the target addressing matrix.

[0083] Step 5.4: Match the small matrix generated by the database in Step 5.3 with the target addressing matrix.

[0084] Various techniques are used to match the similarity between strings or encodings, some of the more common ones being:

[0085] (1) The Levenshtein distance (edit distance) algorithm measures the difference between two strings by calculating the minimum number of editing operations, such as insertion, deletion, and replacement, required to transform one string into another. Its mathematical definition is as follows:

[0086]

[0087] in, It is an indicator function, when = When the time condition is met, its value is 0; otherwise, it equals 1. represent The former byte to The former The distance is 1 byte.

[0088] The first formula in the operation represents from Delete characters to match The second formula represents inserting characters to match. The third indicates whether there is a match or not, depending on whether the symbols are the same.

[0089] Damerau-Levenshtein Distance: The Damerau–Levenshtein Distance is a string metric used to measure the edit distance between two character sequences. The Damerau–Levenshtein Distance between two words is the minimum number of operations required to transform one word into another. Unlike the Levenshtein Distance, it includes the transformation of two adjacent characters in addition to the insertion, deletion, and alteration of individual characters.

[0090] For two strings , ,function express The former characters and The former Edit distance per character:

[0091]

[0092] when In addition to calculating the operands for insert, delete, and modify operations as defined in Levenshtein Distance, it is also necessary to calculate the operands for adjacent character conversions, and then compare the four operands and take the minimum value.

[0093] Jaro-Winkler Distance is an algorithm for evaluating the similarity between two strings, particularly suitable for short strings such as names of people or places. This algorithm considers character matching, transposition, and prefix similarity.

[0094] For two strings and Their Jaro similarity Given by the following formula:

[0095]

[0096] in, and Representing strings respectively and Length, This indicates the number of matched characters between the two strings. This indicates that the transposition is half of the intended purpose.

[0097] Jaro-Winkler similarity, based on Jaro similarity, adds a weighted average to prefixes to emphasize the importance of prefix matching. The formula is as follows:

[0098]

[0099] in: It's Jaro similarity. This indicates the number of common prefix characters in two strings, with a maximum of 4. This is a scaling factor constant that describes the contribution of common prefixes to similarity. The value is usually between 0.1 and 0.25.

[0100] In summary, compared with traditional addressing methods, this application introduces a matrix structure to fill in multi-attribute combination addressing within the matrix. Combining the advantages of matrix and multi-attribute combination addressing, it makes addressing more accurate and applicable to more complex business scenarios. It fully demonstrates the advantages of multidimensional identification networks and avoids the shortcomings of traditional addressing, such as insufficient address space and difficulty in expansion. It is suitable for large-scale networks with a large number of communication devices and high requirements for real-time performance and accuracy, providing reliable technical support for addressing in multidimensional identification networks.

[0101] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for constructing an addressing matrix based on multidimensional identifiers, the method being designed for multidimensional identifier networks, characterized in that: The addressing matrix construction method specifically includes the following steps: Step 1: The commander enters the instructions to be executed, i.e., text data, into the client of the multidimensional identification network; Step 2: After receiving a complete instruction, the client extracts the addressing attribute information text from the instruction entered by the commander. Step 3: Based on the addressing attribute information text extracted in Step 2, determine whether the addressing is multi-attribute addressing or single-attribute addressing; Step 4: Construct the target addressing matrix based on the addressing attributes determined in Step 3; Step 5: Send the target addressing matrix constructed in Step 4 to the multidimensional identification network server for addressing. This includes the following steps: Step 5.1: In two-dimensional addressing, the attribute information text of the addressing matrix is ​​stored in the two-dimensional memory structure of the multidimensional identifier network. For an m*n target addressing matrix, the two-dimensional address of the attribute information text of the target addressing matrix is ​​calculated using the following formula: ， in, It is a row index. It is a column index; Step 5.2: In block addressing, the addressing matrix is ​​divided into multiple blocks, each block being a sub-matrix. For an m*n target addressing matrix, the block address of the attribute information text of the target addressing matrix is ​​calculated using the following formula: ， in, It is the size of the block. Indicates rounding down; Step 5.3: The database of the multidimensional identification network stores all attribute information text of all communication peers. Each row represents a different communication peer, and each column is a different matrix attribute information text. Based on the attribute information text in the addressing matrix, the corresponding attribute information text in the constructed target addressing matrix is ​​extracted from the database and formed into several small matrices with the same structure as the target addressing matrix. Step 5.4: Match the small matrix generated from the database in step 5.3 with the target addressing matrix.

2. The addressing matrix construction method based on multidimensional identifiers according to claim 1, characterized in that: In step 2, the commander issues instructions to describe the target from two dimensions: object dimension and behavior dimension, and extracts the addressing attribute information text. The content of the addressing attribute information text includes spatiotemporal information and equipment information.

3. The addressing matrix construction method based on multidimensional identifiers according to claim 1, characterized in that: In step 3, if the attribute information text content of the address contains only one attribute, it is called single-attribute addressing; if the attribute information text content of the address contains two or more attributes, it is called multi-attribute addressing.

4. The addressing matrix construction method based on multidimensional identifiers according to claim 3, characterized in that: In step 4, if it is single-attribute addressing, construct a 1*1 target addressing matrix; if it is multi-attribute addressing, construct an m*n target addressing matrix.

5. The addressing matrix construction method based on multidimensional identifiers according to claim 4, characterized in that: Constructing an m*n target addressing matrix involves the following steps: Step 4.1: Determine all attribute information texts of the addressed object and assign an identifier to each attribute information text. Suppose an addressed object has... Attribute information text Step 4.2: Assuming relevance is used to measure the relationship between attribute information texts, for each pair of attribute information texts, Word2Vec is used to capture the semantic and syntactic relationships between the attribute information texts, converting the attribute information texts into attribute information text vectors, denoted as follows: Calculate the first Text vector of attribute information and the Text vector of attribute information Correlation coefficient between : ， in, It is the first Text vector of attribute information and the Text vector of attribute information covariance, for standard deviation for Standard deviation; Step 4.3: Assuming we use similarity to measure the relationship between attribute information texts, similarity is calculated using the following formula: ， in, and They are and The value of , and These are the maximum and minimum values ​​of all attribute information text. Step 4.4: Construct the row and column relationships of the target addressing matrix so that the attributes between rows and columns are related; Step 4.5: Assign weights to each attribute information text. The weight matrix is ​​represented as follows: ， in, Indicates the first The weight of each attribute information text.

6. The addressing matrix construction method based on multidimensional identifiers according to claim 5, characterized in that: In step 4.4, the attribute association between rows and columns is a vertical association. The vertical association means that each column of the addressing matrix processes and summarizes the attribute information text into the same category, and addresses from different categories according to the vertical association.

Images