Addressing matrix analysis method for multi-dimensional identification system

Abstract

The application provides a kind of addressing matrix analysis method for multi-dimensional identification system, by the instruction of terminal input is disassembled into different components, combined into addressing information;Structured data based on multi-dimensional identification attribute, namely addressing matrix, is constructed;Terminal sends addressing request message to access routing device;The addressing request message is forwarded to regional server;K-d tree index is constructed;Using the pre-constructed k-d tree index, the terminal node coordinate C closest to the query point P in Euclidean distance is found;Match appropriate terminal node coordinate;After locking the target terminal through coordinate mapping and similarity matching algorithm, extract the key identifier as the analysis result of query;The method can significantly improve the positioning accuracy and query efficiency of terminal node, can significantly reduce the delay of data transmission, and provide faster response time.

Description

Technical Field

[0001] This invention relates to an addressing matrix parsing method for multi-dimensional identification systems, belonging to the field of vehicle network communication technology. Background Technology

[0002] Current vehicular network addressing mechanisms mainly rely on fixed address allocation and basic routing algorithms, which have the following significant limitations: First, the addressing strategy is rigid, using preset static information for terminal positioning, which cannot adapt to highly dynamic environments such as vehicle speed and network topology; second, it lacks multi-dimensional attribute awareness, making it difficult to accurately match based on information such as geographical location, time, and service type, resulting in low addressing accuracy and poor transmission efficiency; third, it suffers from insufficient scalability and performance, as the number of terminals and network traffic surges, request processing speed slows down significantly, making it difficult to guarantee accurate positioning.

[0003] These defects lead to data transmission errors and high latency, severely restricting the application of vehicle-to-everything (V2X) in key scenarios such as real-time navigation and traffic management, and failing to meet the requirements for efficiency and reliability in complex environments. Summary of the Invention

[0004] The purpose of this invention is to provide an addressing matrix parsing method for multidimensional identification systems to solve the problems of low addressing accuracy and poor efficiency caused by the reliance on fixed addresses and inability to adapt to highly dynamic environments in the existing technology.

[0005] The technical solution of this invention is: A method for addressing matrix parsing in a multidimensional identifier system includes the following steps: S1. Decompose the terminal input command into different components, including Service and Content Identifier (SID), Device Identifier (EID), and Device Network Identifier (NID), and combine the components into addressing information; S2. By extracting and organizing identifiers from multiple dimensions from the addressing information, a structured data, namely the addressing matrix, based on multi-dimensional identifier attributes is constructed. S3. Fill the addressing matrix into the addressing request message, and the terminal sends the addressing request message to the access routing device; S4. After receiving the addressing request message from the terminal, the access routing device will forward the addressing request message to the area server deployed in the area where the access routing device is located, based on its own network configuration and the location requirements of the target device. S5. The regional server extracts the multidimensional identification attributes of the terminal nodes from the database in advance and generates an attribute view. The multidimensional identification attributes of the terminal nodes in the attribute view are converted into multidimensional coordinate components through a numerical mapping algorithm. The multidimensional coordinate components form a set of coordinate points, and a kd-tree index is built based on the set of coordinate points. S6. When the regional server receives the addressing request message, it starts the query parsing process. The query parsing process extracts the addressing attributes from the addressing request message and converts the addressing attributes into a query point P in the multi-dimensional coordinate space. Using the pre-built kd tree index, it executes the nearest neighbor search algorithm NSS combined with dynamic pruning to find the coordinates C of the terminal node that is closest to the query point P in Euclidean distance and determines it as a candidate terminal node. S7. The regional server uses the same mapping rules as in step S6 to perform matching calculations on the candidate terminal nodes obtained in step S6. Based on the matching degree calculation results, it filters out the coordinates of terminal nodes that meet the preset conditions and determines the terminal corresponding to the coordinates of the terminal node as the target terminal. S8. Extract the key identifiers of the target terminal determined in step S7. The key identifiers include the device identity identifier (EID) and the associated device network identifier (NID). These key identifiers, as the parsing results of the query, will be encapsulated in the addressing attribute field of the response message header. The response message will then be sent back to the terminal that made the corresponding request, thereby granting the target terminal network identity and location information.

[0006] Furthermore, in step S2, the addressing matrix includes an object description, which includes a Service and Content Identifier (SID), a Device Identifier (EID), and a Device Network Identifier (NID). The object description is used to define the specific entity to which the instruction is applied.

[0007] Furthermore, in step S2, the addressing matrix also includes spatiotemporal information and an action description, which clarifies the specific operation to be applied to the specific entity.

[0008] Furthermore, in step S3, the addressing request message is a query message or a reply message, and the access routing device is a vehicle-mounted or fixed edge node.

[0009] Further, in step S6, the query parsing process extracts the addressing attributes from the addressing request message and converts the addressing attributes into a query point P in a multi-dimensional coordinate space. Using a pre-built kd-tree index, the nearest neighbor search algorithm (NSS) combined with dynamic pruning is executed to find the coordinates C of the terminal node closest to the query point P in Euclidean distance, and this terminal node is determined as a candidate terminal node. The specific steps include: S61. The regional server extracts attribute information from the received addressing matrix to form a coordinate system. Assuming each terminal node's attributes contain n dimensions, with each dimension corresponding to one attribute, these attributes are mapped to the n-dimensional coordinate system. Each terminal node is assigned a terminal node coordinate C: C = (c1, c2, ..., c...). nIn this context, c1 represents geographic location information, used to define the spatial coordinates of the terminal; c2 represents time information, including timestamps or task activation times; c3 represents Service and Content Identifier (SID), serving as a unique identifier for a specific service or content; c4 represents object description, used to define the specific entity to which the instruction acts; and c5 represents action description, used to clarify the specific operation applied to the specific entity. n This indicates optional multidimensional identifier attributes set according to actual application requirements; S62. The addressing request is converted into a query point P in the coordinate system. The regional server determines the matching similarity by calculating the Euclidean distance between the query point P and the coordinates C of each terminal node. The smaller the Euclidean distance, the more the attributes of the terminal node match the description of the addressing request. S63. Starting from the root node of the kd-tree, recursively search downwards. At each node, compare the corresponding dimension coordinates of the query point P with the split dimension coordinates of the current node to determine whether to enter the left subtree or the right subtree, until a leaf node is reached. Then, determine the coordinates corresponding to the leaf node as the initial candidate nearest neighbor coordinates. S64. Starting from the leaf node, backtrack to the root node, and perform subtree search or dynamic pruning based on the hypersphere intersection situation until the coordinates of the terminal node C that is closest to the query point P in Euclidean distance are finally locked.

[0010] Further, in step S64, starting from the leaf node and backtracking to the root node, subtree search or dynamic pruning is performed based on the hypersphere intersection situation, specifically: Starting from the leaf node, backtrack to the root node. On the backtracking path, check whether the hypersphere centered at the query point P and with a radius equal to the Euclidean distance to the nearest terminal node C intersects with the other side of the split hyperplane of the current node. If they intersect, it means that there may be a closer node in the subtree on the other side, and enter that subtree for further search; if they do not intersect, the subtree on that side is completely pruned and no further search is needed.

[0011] The beneficial effects of this invention are: I. This addressing matrix parsing method for a multi-dimensional identification system maps the attribute information of terminal nodes to a multi-dimensional coordinate system, enabling terminal nodes with similar attributes to cluster in areas of similar coordinate values. Compared to traditional addressing methods based on IP addresses or static attributes, this attribute-matching-based addressing method can significantly improve the positioning accuracy and query efficiency of terminal nodes, and reduce invalid queries caused by attribute mismatches.

[0012] Second, this invention introduces a kd-tree as the core index structure to spatially partition and organize the multi-dimensional attribute data of massive terminals. During terminal matching queries, the NNS algorithm of the kd-tree is used to reduce the query complexity from linear O(N) to logarithmic O(logN). This allows the system to maintain extremely high query response speed even in scenarios with a huge number of terminals and a surge in network scale, demonstrating scalability and performance advantages far exceeding traditional static search or traversal matching methods.

[0013] Third, this addressing matrix parsing method for a multi-dimensional identification system maps addressing information and the target terminal's EID and NID to the packet header, enabling the source terminal to quickly obtain the target terminal's identification information and achieve rapid data transmission. Compared with traditional multiple-lookup methods, this method can significantly reduce data transmission latency and provide a faster response time. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the addressing matrix parsing method for a multidimensional identification system according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the query parsing process in the embodiment; Figure 3 This is a schematic diagram illustrating the addressing process in the embodiment; Figure 4 This is an illustration of an addressing request message in the embodiment, wherein (a) is an illustration of a query message and (b) is an illustration of a reply message; Figure 5 This is a schematic diagram comparing the response time of the addressing matrix parsing method for a multidimensional identification system with that of traditional methods in this embodiment; Figure 6 This is a schematic diagram illustrating the address matching degree of the addressing matrix parsing method for a multi-dimensional identification system under different addressing scenarios. Detailed Implementation

[0015] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0016] The embodiment provides an addressing matrix parsing method for a multi-dimensional identifier system, such as... Figure 1 and Figure 3 This includes the following steps: S1. Decompose the terminal input instructions into different components, including Service and Content Identifier (SID), Device Identifier (EID), and Device Network Identifier (NID).

[0017] In step S1, the service or session commands originating from the terminal device are systematically parsed. At this stage, the original commands are deconstructed into a series of standardized basic components, such as core commands, parameters, and options. Within the framework of the multi-dimensional identifier network system, these parameters are further categorized into three types of key identifiers. The first type is the Service Identifier (SID), which serves as a unique identifier for a specific service or content that the network can provide, constituting the addressing identifier for service requirements. The second type is the Entity Identifier (EID), which assigns a globally unique identifier to each physical device in the network. This identifier can be designed as a hash value of the device's public key, thereby providing a verifiable trust anchor for the source of the service or content, facilitating verification of the source of content / service generation or deployment. The third type is the Network Identifier (NID), used to identify network interfaces or hosts. These components carry the specific operations and details that the user wishes to perform, further integrating the various components from the command parsing stage to construct structured "addressing information." The core function of this addressing information is to act as a logical indicator, precisely pointing to the corresponding resource, service, or functional module within the system architecture. In this way, the target location and execution mode of the operation are clearly defined at the logical level, thus providing fundamental input data for all subsequent addressing and positioning protocols.

[0018] S2. By extracting and organizing identifiers from addressing information in multiple dimensions, a structured data, namely the addressing matrix, is constructed based on multi-dimensional identifier attributes.

[0019] In step S2, the addressing matrix includes an object description, which includes a Service and Content Identifier (SID), a Device Identifier (EID), and a Device Network Identifier (NID). The object description is used to define the specific entity to which the instruction is applied.

[0020] In step S2, the addressing matrix also includes spatiotemporal information and action description, which is used to clarify the specific operation to be applied to the specific entity.

[0021] In step S2, when searching for the required identification attributes of the communication peer, an appropriate identification attribute is usually selected based on the complexity of the actual application. The identification attribute can be a single attribute or a more complex addressing matrix, depending on the system requirements and the accuracy of the communication. For single-attribute identification, a unique identifier such as device ID, user ID, service ID, or static attributes is typically used to locate the communication peer. These single identifiers are usually relatively simple and suitable for scenarios with clear and simple communication goals. However, in complex communication environments, a single identifier may not be sufficient to describe all the information of the communication peer, especially when the system needs to consider multiple dimensions (such as spatiotemporal, behavioral, etc.), and the identification attributes of the communication peer become complex. Addressing matrices provide a multi-dimensional structure to accurately describe the target. Addressing matrices can combine single attributes and cover multiple attribute dimensions. The construction of an addressing matrix includes Service and Content Identifier (SID), Device Identity Identifier (EID), and Device Network Identifier (NID). Furthermore, depending on the accuracy requirements of the actual application scenario, the terminal input instructions also include spatiotemporal information and action descriptions. The addressing matrix can optionally be extended to include spatiotemporal information and action descriptions, thereby achieving more complex peer location and resolution. A specific example of an addressing matrix is ​​shown in Table 1 below: Table 1 Addressing Matrix

[0022] The addressing matrix is ​​a structured data used to parse and locate the communication peer, constructed by combining multi-dimensional identifier attributes extracted from command information. The addressing matrix can perform in-depth analysis of peer information involved in the command according to different dimensions, thereby achieving precise target location and resource scheduling. In the vertical dimension of the addressing matrix, information is deconstructed into two core components: object description and action description. The object description aims to define the specific entity or target object on which the command acts and to clearly define its related attributes. Its scope covers not only physical entities such as servers and sensors, but also extends to abstract concepts such as virtual services and software modules. Meanwhile, the action description clarifies the specific operation or expected behavior applied to the aforementioned object. This category encompasses a series of standardized operation commands such as query, update, delete, and start. The action description is highly coupled with the functional requirements of the object; its core function is to enable the system to accurately parse the operational intent and final goal for that object.

[0023] S3. Fill the addressing matrix into the addressing request message, and the terminal sends the addressing request packet to the access routing device.

[0024] In step S3, the addressing request message can be either a query message or a reply message. The headers of query messages and reply messages differ, such as... Figure 4 . Figure 4In (a) and (b), the Header: defines the message type (query message / response message) and the source / destination identifiers. The Question field: a specific field used to store addressing criteria (such as an addressing matrix or single attribute). The Additional field: a field used to extend functionality or store auxiliary network parameters. A query message is initiated by the requester and is primarily used to request specific information or data from a target system or service. The header carries the device identifier (EID) of the source terminal. A response message is a reply returned by the server or receiver after processing the query message. The response message typically contains the processing result or response content of the query, indicating whether the request was successful or providing the requested information. The header carries the EID of the requester's own address, along with the target EID and NID obtained through subsequent queries. The addressing matrix constructs a multi-dimensional structure by organizing Service and Content Identifiers (SIDs), Device Identifiers (EIDs), Device Network Identifiers (NIDs), and optional spatiotemporal, object, and action descriptions. This provides structured parsing support for addressing in highly dynamic and complex environments, facilitating subsequent logarithmic-level fast retrieval based on kd-trees. After standardized encapsulation, the terminal delivers the addressing request message via its network interface to its directly connected access routing device. The access routing device can be a vehicle-mounted or fixed edge node. This operation constitutes the first hop transmission of the addressing request in the network, marking the formal entry of the request from the terminal's internal processing stage into the network forwarding stage.

[0025] S4. After receiving the addressing request message from the terminal, the access routing device will forward the addressing request message to the area server deployed in the area where the access routing device is located, based on its own network configuration and the target device's location requirements.

[0026] In step S4, after receiving the addressing request message from the terminal, the access routing device undertakes the next-hop forwarding decision. This decision is based on its local network configuration policy and the parsing results of the addressing parameters contained in the request message. Accordingly, the addressing request message will be forwarded to the area server deployed within the corresponding geographical or logical area. This forwarding operation is a critical routing link connecting the network edge and core processing facilities. Its function is to guide the addressing request message from the access layer to a designated computing node with centralized processing and parsing capabilities, i.e., the area server.

[0027] S5. The regional server extracts the multidimensional identification attributes of the terminal nodes from the database in advance and generates an attribute view. The multidimensional identification attributes of the terminal nodes in the attribute view are converted into multidimensional coordinate components through a numerical mapping algorithm. The multidimensional coordinate components form a set of coordinate points, and a kd-tree index is built based on the set of coordinate points.

[0028] In step S5, to optimize system performance and improve database response speed, the regional server employs an enhancement mechanism based on data preprocessing and spatial indexing. The core of this mechanism is that the regional server extracts the complete set of terminal nodes from the database in offline or periodic mode, based on specific addressing attributes, thereby generating a static data view. The regional server constructs a kd-tree from all the terminal node data in this view. This kd-tree organizes the multi-dimensional attribute coordinates of all terminals as an efficient index structure for subsequent fast queries. The construction of the kd-tree is completed offline or periodically, ensuring extremely high performance during queries. This view generation process is based on the data stored in the database. When a user requests data, the regional server does not need to perform complex queries and calculations again, but directly returns the pre-generated view data, greatly reducing the real-time computational load and response latency of the database, ensuring high-performance retrieval capabilities in high-concurrency query scenarios.

[0029] S6. When the regional server receives the addressing request message, it starts the query parsing process. The query parsing process extracts the addressing attributes from the addressing request message and converts the addressing attributes into a query point P in a multi-dimensional coordinate space. Using the pre-built kd-tree index, it executes the nearest neighbor search algorithm (NSS) combined with dynamic pruning to find the coordinates C of the terminal node that is closest to the query point P in Euclidean distance and determines it as a candidate terminal node.

[0030] In step S6, the query parsing process extracts the addressing attributes from the addressing request message and converts them into a query point P in a multi-dimensional coordinate space. Using a pre-built kd-tree index, it executes the nearest neighbor search algorithm (NSS) combined with dynamic pruning to find the coordinates C of the terminal node closest to the query point P in Euclidean distance, and determines it as a candidate terminal node. Figure 2 The specific steps include: S61. The regional server extracts attribute information from the received addressing matrix to form a coordinate system. Assuming each terminal node's attributes contain n dimensions, with each dimension corresponding to one attribute, these attributes are mapped to the n-dimensional coordinate system. Each terminal node is assigned a terminal node coordinate C: C = (c1, c2, ..., c...). n In this context, c1 represents geographic location information, used to define the spatial coordinates of the terminal; c2 represents time information, including timestamps or task activation times; c3 represents Service and Content Identifier (SID), serving as a unique identifier for a specific service or content; c4 represents object description, used to define the specific entity to which the instruction acts; and c5 represents action description, used to clarify the specific operation applied to the specific entity. n This indicates optional multidimensional identifier attributes set according to actual application requirements; S62. The addressing request is converted into a query point P in the coordinate system. The regional server determines the matching similarity by calculating the Euclidean distance between the query point P and the coordinates C of each terminal node. The smaller the Euclidean distance, the more the attributes of the terminal node match the description of the addressing request. S63. Starting from the root node of the kd-tree, recursively search downwards. At each node, compare the corresponding dimension coordinates of the query point P with the split dimension coordinates of the current node to determine whether to enter the left subtree or the right subtree, until a leaf node is reached. Then, determine the coordinates corresponding to the leaf node as the initial candidate nearest neighbor coordinates. S64. Starting from the leaf node, backtrack to the root node, and perform subtree search or dynamic pruning based on the hypersphere intersection situation until the coordinates of the terminal node C that is closest to the query point P in Euclidean distance are finally locked.

[0031] In step S64, starting from the leaf node, backtrack to the root node, and perform subtree search or dynamic pruning based on the hypersphere intersection situation, specifically: Starting from the leaf node, backtrack to the root node. On the backtracking path, check whether the hypersphere centered at the query point P and with a radius equal to the Euclidean distance to the nearest terminal node C intersects with the other side of the split hyperplane of the current node. If they intersect, it means that there may be a closer node in the subtree on the other side, and enter that subtree for further search; if they do not intersect, the subtree on that side is completely pruned and no further search is needed.

[0032] In step S64, through the backtracking and pruning process described above, the algorithm can efficiently find the coordinates C of the terminal node closest to the query point P in terms of Euclidean distance, thus achieving an accurate match. For fuzzy queries, range search or k-nearest neighbor search (k-NN) can be performed on this basis to find all nodes within the tolerance range.

[0033] In step S6, after receiving the addressing request message, the regional server initiates the query parsing process. This process first parses the attribute information of the addressing matrix from the message and applies the same mapping function as in the preprocessing stage to convert it into a query point P in an n-dimensional vector space. Subsequently, the server uses a pre-built kd-tree index to execute the NNS algorithm. This algorithm first recursively descends from the root node of the kd-tree to a leaf node to locate an initial candidate point, and then enters the backtracking phase. During backtracking, the algorithm dynamically prunes unnecessary subtree branches by comparing the distances between the query point P and the dividing hyperplanes of each node on the path, thus significantly reducing the search space.

[0034] S7. The regional server uses the same mapping rules as in step S6 to perform matching calculations on the candidate terminal nodes obtained in step S6. Based on the matching degree calculation results, it filters out the coordinates of terminal nodes that meet the preset conditions and determines the terminal corresponding to the coordinates of the terminal node as the target terminal. For fuzzy attribute matching, the region server uses the cosine similarity algorithm (CS algorithm) to evaluate the matching degree between different terminal nodes. If fuzzy attribute matching exists (e.g., region query), the server can map the attribute value to a region instead of a single coordinate point. In this case, all points within the region will be considered to have similar attribute values. A step-size search algorithm is used to search for nodes within the region. In scenarios requiring comprehensive evaluation of multiple candidate terminals, the system supports the application of a weighted average model, calculating the comprehensive matching score of each node according to preset weights to achieve optimal target ranking and selection. If it is necessary to evaluate the matching degree of multiple terminal nodes, a weighted average formula can be used to calculate the matching degree between the query coordinates C and the coordinates of multiple terminal nodes; the smaller the distance, the higher the matching degree.

[0035] In step S7, to adapt to fuzzy and imprecise query scenarios, multiple extended matching strategies are integrated. For queries with uncertain attributes, the system can use the cosine similarity algorithm (CS) to quantify the relevance by calculating the cosine of the angle between the query vector and the attribute vectors of each terminal node. For range queries, the query attribute can be mapped to a bounded n-dimensional spatial region instead of a single coordinate point, and a tolerance threshold δ can be set to retrieve all nodes contained within this region. Furthermore, in scenarios requiring comprehensive evaluation of multiple candidate terminals, the system supports the application of a weighted average model to calculate the comprehensive matching score of each node according to preset weights, thereby achieving the ranking and selection of the optimal target.

[0036] S8. Extract the key identifiers of the target terminal determined in step S7. The key identifiers include the device identity identifier (EID) and the associated device network identifier (NID). These key identifiers, as the parsing results of the query, will be encapsulated in the addressing attribute field of the response message header. The response message will then be sent back to the terminal that made the corresponding request, thereby granting the target terminal network identity and location information.

[0037] Step S8 marks the completion of the entire address resolution process, marking the end of the entire process from abstract query to specific network entity location.

[0038] This addressing matrix parsing method for multidimensional identification systems maps the attribute information of terminal nodes to a multidimensional coordinate system, enabling terminal nodes with similar attributes to cluster in areas of similar coordinate values. Compared to traditional addressing methods based on IP addresses or static attributes, this attribute-matching-based addressing method can significantly improve the positioning accuracy and query efficiency of terminal nodes, and reduce invalid queries caused by attribute mismatches.

[0039] This invention introduces a kd-tree as the core index structure to spatially partition and organize the multi-dimensional attribute data of massive terminals. During terminal matching queries, the NNS algorithm of the kd-tree is used to reduce the query complexity from linear O(N) to logarithmic O(logN). This allows the system to maintain extremely high query response speed even in scenarios with a huge number of terminals and a rapidly increasing network scale, demonstrating scalability and performance advantages far exceeding traditional static search or traversal matching methods.

[0040] This addressing matrix parsing method for multi-dimensional identification systems maps addressing information and the target terminal's EID and NID to the packet header, enabling the source terminal to quickly obtain the target terminal's identification information and achieve rapid data transmission. Compared to traditional multiple-lookup methods, this method significantly reduces data transmission latency and provides a faster response time.

[0041] This addressing matrix parsing method for multi-dimensional identification systems is a way for terminals to achieve addressing through in-vehicle routing devices and regional servers. It aims to improve the addressing accuracy, efficiency, and reliability of in-vehicle networks in highly dynamic and large-scale scenarios. By mapping the terminal's geographic, temporal, and service attributes to coordinate space, high-precision target positioning is achieved. Simultaneously, by introducing a k-dimensional tree index structure and the Nearest Neighbor Search (NNS) algorithm, the query complexity is reduced from linear to logarithmic levels, thereby significantly improving the parsing efficiency for massive numbers of terminals and ensuring that terminals can quickly and accurately find targets and establish communication connections.

[0042] Figure 5 This is a schematic diagram comparing the response time of the addressing matrix parsing method for a multi-dimensional identifier system with that of the traditional method. When encapsulating the addressing request message in step S3, the identifier attribute is configured as a single-attribute identifier or an addressing matrix as required. Taking a single-attribute identifier as a comparison: in a simple addressing scenario with a single attribute (Case 1), the response time of the comparison method is basically the same as that of the traditional method, at 344.97ms and 342.45ms respectively. This indicates that in low dimensions, the spatial indexing overhead of the kd-tree is comparable to that of the traditional basic index. However, due to... Figure 5It is clearly observable that as the addressing dimension and complexity increase (Case 2 to Case 3), the response time of the traditional method increases significantly, from 342.45ms to 388.85ms, exhibiting a linear growth trend. However, the implementation method, thanks to the multi-dimensional mapping of the addressing matrix and the pruning effect of the kd-tree, optimizes the response time from 344.97ms to 288.85ms. This strongly demonstrates that using a kd-tree can reduce query complexity from a linear level. Reduced to logarithmic level Furthermore, the higher the dimension, the more significant the advantages of the implementation method in excluding irrelevant nodes and shrinking the search space, and the stronger the real-time response and scalability advantages in large-scale, highly dynamic vehicle network environments.

[0043] Figure 6 This is a schematic diagram illustrating the addressing matching degree of the addressing matrix parsing method for a multi-dimensional identifier system under different addressing scenarios. Figure 6 As can be seen, in the process of evolving from single-attribute addressing (Case 1) to multi-attribute addressing (Case 2) and complex multi-attribute addressing (Case 3), the addressing matching accuracy of the method of this invention has remained stable at over 88%. Multi-attribute addressing involves a combination of SID, EID, and NID for localization; complex multi-attribute addressing further introduces extended dimensions such as spatiotemporal information and action descriptions on top of multi-attribute addressing, making it suitable for complex scenarios requiring refined multi-criteria matching. This demonstrates that the addressing matrix mapping algorithm has extremely high dimensional robustness, and the parsing accuracy does not decrease due to the increase in the identifier dimension, thus solving the problem of difficult multi-criteria matching in complex environments.

[0044] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for parsing addressing matrices for multidimensional identification systems, characterized in that: Includes the following steps, S1. Decompose the terminal input command into different components, including Service and Content Identifier (SID), Device Identifier (EID), and Device Network Identifier (NID), and combine the components into addressing information; S2. By extracting and organizing identifiers from multiple dimensions from the addressing information, a structured data, namely the addressing matrix, based on multi-dimensional identifier attributes is constructed. S3. Fill the addressing matrix into the addressing request message, and the terminal sends the addressing request message to the access routing device; S4. After receiving the addressing request message from the terminal, the access routing device will forward the addressing request message to the area server deployed in the area where the access routing device is located, based on its own network configuration and the location requirements of the target device. S5. The regional server extracts the multidimensional identification attributes of the terminal nodes from the database in advance and generates an attribute view. The multidimensional identification attributes of the terminal nodes in the attribute view are converted into multidimensional coordinate components through a numerical mapping algorithm. The multidimensional coordinate components form a set of coordinate points, and a kd-tree index is built based on the set of coordinate points. S6. When the regional server receives the addressing request message, it starts the query parsing process. The query parsing process extracts the addressing attributes in the addressing request message and converts the addressing attributes into a query point P in the multi-dimensional coordinate space. Using the pre-built kd-tree index, it executes the nearest neighbor search algorithm NSS combined with dynamic pruning to find the coordinates C of the terminal node that is closest to the query point P in Euclidean distance and determines it as a candidate terminal node. S7. The regional server uses the same mapping rules as in step S6 to perform matching calculations on the candidate terminal nodes obtained in step S6. Based on the matching degree calculation results, it filters out the coordinates of terminal nodes that meet the preset conditions and determines the terminal corresponding to the coordinates of the terminal node as the target terminal. S8. Extract the key identifiers of the target terminal determined in step S7. The key identifiers include the device identity identifier (EID) and the associated device network identifier (NID). These key identifiers, as the parsing results of the query, will be encapsulated in the addressing attribute field of the response message header. The response message will then be sent back to the terminal that made the corresponding request, thereby granting the target terminal network identity and location information.

2. The addressing matrix parsing method for a multidimensional identification system as described in claim 1, characterized in that: In step S2, the addressing matrix includes an object description, which includes a Service and Content Identifier (SID), a Device Identifier (EID), and a Device Network Identifier (NID). The object description is used to define the specific entity to which the instruction is applied.

3. The addressing matrix parsing method for a multidimensional identification system as described in claim 2, characterized in that: In step S2, the addressing matrix also includes spatiotemporal information and action description, which is used to clarify the specific operation to be applied to the specific entity.

4. The addressing matrix parsing method for a multidimensional identification system as described in any one of claims 1-3, characterized in that: In step S3, the addressing request message is either a query message or a reply message, and the access routing device is a vehicle-mounted or fixed edge node.

5. The addressing matrix parsing method for a multidimensional identification system as described in any one of claims 1-3, characterized in that: In step S6, the query parsing process extracts the addressing attributes from the addressing request message and converts the addressing attributes into a query point P in a multi-dimensional coordinate space. Using a pre-built kd-tree index, the nearest neighbor search algorithm (NSS) combined with dynamic pruning is executed to find the coordinates C of the terminal node closest to the query point P in Euclidean distance, and this terminal node is determined as a candidate terminal node. The specific steps include: S61. The regional server extracts attribute information from the received addressing matrix to form a coordinate system. Assuming each terminal node's attributes contain n dimensions, with each dimension corresponding to one attribute, these attributes are mapped to the n-dimensional coordinate system. Each terminal node is assigned a terminal node coordinate C: C = (c1, c2, ..., c...). n In this context, c1 represents geographic location information, used to define the spatial coordinates of the terminal; c2 represents time information, including timestamps or task activation times; c3 represents Service and Content Identifier (SID), serving as a unique identifier for a specific service or content; c4 represents object description, used to define the specific entity to which the instruction acts; and c5 represents action description, used to clarify the specific operation applied to the specific entity. n This indicates optional multidimensional identifier attributes set according to actual application requirements; S62. The addressing request is converted into a query point P in the coordinate system. The regional server determines the matching similarity by calculating the Euclidean distance between the query point P and the coordinates C of each terminal node. The smaller the Euclidean distance, the more the attributes of the terminal node match the description of the addressing request. S63. Starting from the root node of the kd-tree, recursively search downwards. At each node, compare the corresponding dimension coordinates of the query point P with the split dimension coordinates of the current node to determine whether to enter the left subtree or the right subtree, until a leaf node is reached. Then, determine the coordinates corresponding to the leaf node as the initial candidate nearest neighbor coordinates. S64. Starting from the leaf node, backtrack to the root node, and perform subtree search or dynamic pruning based on the hypersphere intersection situation until the coordinates of the terminal node C that is closest to the query point P in Euclidean distance are finally locked.

6. The addressing matrix parsing method for a multidimensional identification system as described in claim 5, characterized in that: In step S64, starting from the leaf node, backtrack to the root node, and perform subtree search or dynamic pruning based on the hypersphere intersection situation, specifically: Starting from the leaf node, backtrack to the root node. On the backtracking path, check whether the hypersphere centered at the query point P and with a radius equal to the Euclidean distance to the nearest terminal node C intersects with the other side of the split hyperplane of the current node. If they intersect, it means that there may be a closer node in the subtree on the other side, and enter that subtree for further search; if they do not intersect, the subtree on that side is completely pruned and no further search is needed.

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