A method for generating attribute mapping identification for a space-time multi-dimensional identification network

Abstract

The application belongs to the technical field of network identification generation, and discloses an attribute mapping identification generation method for a space-time multi-dimensional identification network, which performs unified space-time reference normalization and cross-layer demand decomposition on a service request, extracts a function point and calculates a demand intensity; according to a cross-layer mapping permission relationship between the function point and a candidate attribute key, a mapping correlation is calculated in combination with a historical association probability and a semantic association intensity to construct a demand attribute correlation matrix; a comprehensive weight of the candidate attribute key is calculated based on the matrix and the function point demand intensity, a target attribute key is screened, an attribute value is generated, and then an object dimension, a connection dimension and an application dimension target attribute set are formed, and object identification, connection identification and application identification are generated. The application realizes automatic conversion of a service request to a space-time multi-dimensional identification attribute combination, and improves adaptability and consistency of identification generation in a dynamic service environment.

Description

Technical Field

[0001] This invention belongs to the field of network identifier generation technology, specifically relating to an attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks. Background Technology

[0002] With the rapid development of new network forms such as mobile internet, industrial internet, and vehicle-to-everything (V2X), the objects carried by networks have expanded from traditional fixed hosts to various entities including terminal devices, mobile nodes, sensors, computing resources, business systems, content resources, and intelligent agents. Network communication requirements have also evolved from simple end-to-end connections to a comprehensive communication model oriented towards objects, services, content, and tasks. Different services have varying requirements for latency, bandwidth, reliability, security, mobility, spatial coverage, and resource capabilities. Networks need to uniformly express, rapidly parse, and collaboratively schedule communication objects, connection relationships, service requirements, and operational status in a dynamic environment.

[0003] As the types of network objects and services continue to diversify, existing technologies have proposed multi-dimensional identification systems. These systems use object attributes, connection attributes, application attributes, and other relevant attributes to represent network entities, network connection relationships, and application service requirements in multiple dimensions. Compared to methods that rely solely on a single address or identifier, multi-dimensional identification can associate multiple types of information, such as object identity, network connection, service capabilities, and business requirements, within a unified identification system. This provides a foundation for subsequent operations such as identifier resolution, object discovery, addressing, and forwarding.

[0004] However, existing multidimensional identification systems often involve spatiotemporal information such as object location, business scope, attribute validity period, task duration, and network status changes. To enable the association and processing of multidimensional attributes corresponding to different objects, connections, and applications under a unified reference, it is necessary to further introduce a unified spatiotemporal benchmark into the multidimensional identification system. This would allow for a unified expression of relevant time and space information, forming a spatiotemporal multidimensional identifier, and further establishing a unified time and space reference in the expression of multidimensional attributes of objects, connections, and applications.

[0005] However, the establishment of a spatiotemporal multidimensional identifier system cannot directly generate corresponding identifiers based on arbitrary business requests. Actual business requests are typically expressed in the form of business category, target object, spatial constraints, valid time interval, latency, bandwidth, reliability, security level, and terminal or network status. Spatiotemporal multidimensional identifiers, on the other hand, require the combination and encoding of defined object attributes, connection attributes, and application attributes to be generated. Therefore, a problem of converting business requirements into identifier attributes still exists between abstract business requests and specific spatiotemporal multidimensional identifiers. Existing methods relying on manual configuration, fixed templates, or static mapping rules struggle to automatically determine the importance of different candidate attributes based on the current business request when business requirements, network status, and spatiotemporal conditions dynamically change. Furthermore, it is difficult to establish relationships between attributes across network layers. Therefore, how to automatically complete cross-layer requirement parsing, candidate attribute mapping, target attribute selection, and the generation of object identifiers, connection identifiers, and application identifiers based on business requests within a spatiotemporal multidimensional identifier system is a pressing issue that needs to be addressed. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks. By performing cross-layer requirement parsing on business requests, a mapping relationship between functional points and candidate attribute keys is established, and the target attribute set is determined based on the current business request, thereby automatically generating corresponding object identifiers, connection identifiers, and application identifiers.

[0007] The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks described in this invention includes the following steps: S1. Obtain business requests and call preset attribute space: Receive business requests, normalize the original business requests to form a standardized request representation, and call candidate attribute keys from the preset attribute space. S2. Perform cross-layer requirement decomposition and function point extraction for business requests: Based on standardized business requests, decompose business requirements into application layer requirements, network layer requirements, link layer requirements, and physical layer requirements, and encode the features of each layer requirement; extract function points from each layer requirement, determine the network layer corresponding to each function point, and calculate the requirement intensity of each function point based on standardized business requests to form a function point requirement intensity vector with hierarchical information. S3. Establish a requirement attribute relevance matrix based on the mapping function: For each function point and candidate attribute key, determine the mapping permission relationship between the function point and the candidate attribute key according to the network level to which the function point belongs, the network level to which the candidate attribute key belongs, and the preset cross-layer mapping rule set; For function points and candidate attribute keys that satisfy the mapping permission relationship, calculate the mapping relevance based on the historical association probability and semantic association strength, and construct the requirement attribute relevance matrix based on the mapping relevance between each function point and each candidate attribute key. S4. Filter the target attribute set and generate attribute values ​​based on attribute weights: Calculate the comprehensive weight of candidate attribute keys for the current business request based on the requirement attribute correlation matrix and the functional point requirement intensity vector; sort the candidate attribute keys according to the comprehensive weights and select the target attribute key set; for the selected target attribute keys, generate corresponding attribute values ​​in combination with the business request to obtain the target attribute sets in the object dimension, connection dimension and application dimension. S5. Generate spatiotemporal multidimensional identifiers based on target attribute sets: Encode the target attribute sets of object dimension, connection dimension and application dimension according to the preset identifier encoding rules to generate object identifier, connection identifier and application identifier.

[0008] Furthermore, S1 specifically refers to: S11. Receive original business requests: Receive original business requests that include business type, request purpose, application requirements, service quality constraints, spatial scope, time scope and context state. S12. Normalize the original business request: Pre-configure a unified spatiotemporal reference, including a unified spatial reference and a unified time reference; perform a reference transformation on the spatial information in the original business request according to the unified spatial reference to obtain spatial constraints; perform a reference transformation on the time information in the original business request according to the unified time reference to obtain the effective time interval and form a standardized business request. S13. Call the preset attribute space: The preset attribute space includes the object attribute space, the connection attribute space and the application attribute space; S14. Perform hierarchical indexing of the preset attribute space: Assume that the network layer set includes the application layer, network layer, link layer and physical layer; for any identifier type, its corresponding candidate attribute set is divided into candidate attribute subsets of each layer according to the network layer; S15. Determine the candidate attribute keys of each layer and their possible value range: For the candidate attribute set of the identifier type under the network layer, determine the set of candidate attribute keys and the set of attribute values ​​allowed for each candidate attribute key.

[0009] Furthermore, S2 specifically refers to: Standardized business requests are decomposed across layers and mapped to the application layer, network layer, link layer, and physical layer respectively to obtain the requirement view of each layer; Feature encoding is performed on each layer of the requirement view to transform text descriptions, numerical constraints, status information and strategy requirements into computable requirement features; Extract a set of functional points based on the requirements of each layer, where each functional point records the corresponding source network layer; Various demand characteristics are normalized to form a demand characteristic vector, and the demand intensity of each functional point is calculated based on the contribution coefficient between each functional point and the demand characteristics to form a functional point demand intensity vector.

[0010] Furthermore, S3 specifically refers to: S31. Merge the global candidate attribute key set: Merge the candidate attribute keys in the object attribute space, the join attribute space, and the application attribute space into a global candidate attribute key set, and retain the identifier type and network layer to which each candidate attribute key belongs; S32. Configure a mapping function for the function point: For any function point and candidate attribute key, configure a mapping function; the mapping function takes the cross-layer mapping permission parameter, historical association probability and semantic association strength between the function point and the candidate attribute key as input, and outputs the mapping relevance between the function point and the candidate attribute key; S33. Determine cross-layer mapping permission relationship: The preset cross-layer mapping rule set consists of multiple cross-layer mapping rules. Each cross-layer mapping rule includes at least the source network layer, source function point label, target network layer, target attribute key, and mapping permission flag. The cross-layer mapping rule set is matched according to the source network layer and function point label of the function point, and the network layer and attribute key of the candidate attribute key. The cross-layer mapping permission parameters between the function point and the candidate attribute key are determined according to the mapping permission flag in the matching rule. S34. Construct a historical association probability matrix: Select completed historical business requests within the preset historical statistics window as historical samples; count the historical association probabilities when function points are activated and candidate attribute keys are used simultaneously, and apply smoothing; construct a historical association probability matrix based on each historical association probability. S35. Construct a semantic association matrix: Pre-establish a unified semantic tag library and convert different business expressions into unified semantic tags through a preset synonym mapping relationship; calculate the semantic association strength based on the ratio of the intersection size to the union size of the semantic tag sets corresponding to the function points and candidate attribute keys; construct a semantic association matrix based on each semantic association strength. S36. Generate a requirement attribute correlation matrix using a mapping function: determine the effective historical fusion weights based on the number of historical samples corresponding to each function point. The mapping relevance is expressed as: ,in, Function points With candidate attribute keys The mapping correlation between them Indicates cross-layer mapping permission parameters, For mapping functions, Function points Candidate attribute keys are used when activated. Historical correlation probability, Function points With candidate attribute keys The semantic association strength between them; construct the relevance matrix of the requirement attributes based on the total mapping relevance.

[0011] Furthermore, S4 specifically refers to: S41. Calculate the comprehensive weight of the candidate attribute keys and normalize the comprehensive weight; S42. Sort the candidate attribute keys from high to low according to the normalized comprehensive weight; S43. Determine the set of required attribute keys according to the business request. First, add the required attribute keys to the target attribute key set. Then, select the candidate attribute keys in order according to the sorting results until the cumulative normalized comprehensive weight of the selected attribute keys reaches the preset cumulative weight threshold. S44. For the target attribute key, generate the corresponding attribute value based on the spatial constraints, the effective time interval, the service target vector, the auxiliary context, and the functional point requirement intensity vector. S45. Classify the target attribute keys and their attribute values ​​hierarchically: Based on the identifier type and network layer to which the attribute keys belong, divide the target attribute keys and their attribute values ​​into the object dimension target attribute set, the connection dimension target attribute set, and the application dimension target attribute set. S46. Generate a complete set of target attributes for each identifier type: merge the target attribute sets of each layer to obtain a complete set of target attributes for the object dimension, a complete set of target attributes for the connection dimension, and a complete set of target attributes for the application dimension.

[0012] Furthermore, the generation process of object identifier, connection identifier, and application identifier in S5 includes: S51. Establish unified identification coding rules: adopt a unified attribute coding method for object identifiers, connection identifiers and application identifiers, and convert the target attribute set into a standardized coding sequence; S52. Generate object identifier: Generate object identifier according to the unified identifier encoding rule based on the complete target attribute set of the object dimension; S53. Generate a connection identifier: Generate a connection identifier according to the unified identifier encoding rule based on the complete target attribute set of the connection dimension. S54. Generate application identifier: Generate application identifier according to the unified identifier encoding rules based on the complete target attribute set of the application dimension.

[0013] Furthermore, S51 specifically refers to: S511. Assign a unique attribute key code to each target attribute key according to the preset attribute dictionary, and arrange the target attribute items corresponding to the same identifier type according to the preset order of the attribute key codes; S512. For the target attribute key and its corresponding attribute value, construct a target attribute encoding unit, wherein the target attribute encoding unit includes the attribute key code, the data length of the target attribute value after normalization encoding, and the byte sequence concatenation of the normalization encoding result; S513. Connect the identifier header with the target attribute encoding units arranged in a preset order to obtain a standardized encoding sequence. The identifier header includes at least identifier version information and identifier type information.

[0014] Furthermore, S5 also includes: From the target attribute set, determine the minimum set of target attributes that meets the target discrimination capability requirement and has the fewest attributes, and generate the minimum target attribute identifier; wherein, the target discrimination capability requirement means that the number of remaining target candidates after using attribute subsets for target discrimination is not greater than a preset target discrimination threshold; perform hash processing on object identifier, connection identifier and application identifier respectively to generate corresponding auxiliary hash identifiers.

[0015] Furthermore, generate the minimum target attribute identifier, including: The complete target attribute set of the object dimension, the complete target attribute set of the connection dimension, and the complete target attribute set of the application dimension are merged to obtain the total target attribute set; Based on the business request, determine the set of destination candidates from the identifier management database; define the destination discrimination index of the attribute subset as the number of remaining destination candidates after using the attribute subset for destination discrimination; The set of mandatory attributes that cannot be deleted is determined based on one or more of the following in the business request: the target object, the target network domain, the business type, and the mandatory security requirements. Under the conditions of satisfying the objective discrimination capability requirement and the mandatory attribute constraint, the attribute subset with the fewest attributes is selected from the total target attribute set as the objective minimum attribute set; wherein, during the search process, for the extended attribute subset formed by adding candidate attribute keys to the candidate attribute subset, if the objective discrimination index of the extended attribute subset is the same as the objective discrimination index of the candidate attribute subset, it is determined that the added candidate attribute key has not improved the objective discrimination capability, and the extended attribute subset is no longer used as the parent node for subsequent searches; The minimum set of target attributes is encoded according to the preset target attribute identifier encoding rules to generate the minimum target attribute identifier.

[0016] Further, an auxiliary hash identifier is generated, including: For any identifier type, hash the complete spatiotemporal multidimensional identifier to obtain the corresponding auxiliary hash identifier; Establish a hash index table, using auxiliary hash identifiers as index keys and the corresponding complete spatiotemporal multidimensional identifiers and their complete target attribute sets as index values; When the downstream module performs an identifier query based on the auxiliary hash identifier, it obtains the corresponding complete spatiotemporal multidimensional identifier according to the hash index table, and performs attribute parsing, destination discrimination, addressing or routing processing based on the complete spatiotemporal multidimensional identifier.

[0017] The beneficial effects of this invention are as follows: 1) This invention normalizes the spatial and temporal information in business requests by using a unified spatial reference and a unified temporal reference with a unified spatiotemporal benchmark. This enables business requests from different sources and with different forms of expression to be converted into a unified spatiotemporal constraint representation, thereby providing a consistent data foundation for subsequent function point extraction, attribute mapping, and attribute value generation, and reducing the differences in attribute expression caused by inconsistent spatiotemporal references. 2) This invention decomposes standardized service requests into the application layer, network layer, link layer, and physical layer, while retaining the source network layer corresponding to the function point. This allows application requirements, network transmission requirements, link status, and physical resource conditions to jointly participate in the determination of target attributes. Compared with the method of fixed mapping only at a single network layer, this invention can avoid the fragmentation of service requirements during cross-layer transmission, and make the generated target attribute set more completely reflect the cross-layer constraints of the current service. 3) This invention limits the range of candidate attributes that function points can be associated with through cross-layer mapping permission relationships, and calculates the mapping relevance by integrating historical association probability and semantic association strength between the function points that are allowed to be mapped and the candidate attribute keys. When there are sufficient historical samples, the actual common adoption relationship in historical business is used to reflect the existing mapping rules. When there are insufficient historical samples, the weight of historical information is reduced and semantic association information is used to participate in the mapping, thereby reducing the lack of adaptability caused by simply relying on fixed static rules or single historical statistics. 4) This invention utilizes the functional point requirement intensity and requirement attribute correlation matrix to calculate the comprehensive weight of candidate attribute keys for the current business request, and combines mandatory attribute constraints and cumulative weight thresholds for sorting and filtering, so that the candidate attribute selection is transformed from manual configuration or fixed templates into a computable selection process oriented towards the current business request; wherein, mandatory attributes are used to ensure that necessary constraints in the business are not missed due to weight sorting, and cumulative weight filtering is used to control the range of candidate attributes entering the target attribute set while meeting the main business requirements; 5) Based on the identifier type to which the target attribute key belongs, the present invention forms target attribute sets of object dimension, connection dimension and application dimension respectively according to the filtering results, and generates object identifier, connection identifier and application identifier according to unified encoding rules, so that a consistent processing link is formed from business request parsing, attribute selection to identifier generation, providing a structured identifier foundation for subsequent parsing, addressing and routing processing based on spatiotemporal multidimensional identifiers. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method described in this invention; Figure 2 This is a flowchart of the method described in this invention. Detailed Implementation

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0020] The spatiotemporal multidimensional identifier described in this invention refers to a multidimensional identifier formed by combining object identifiers, connection identifiers, application identifiers, and their necessary attributes, based on existing multidimensional identifiers and a unified spatiotemporal benchmark.

[0021] The spatiotemporal multidimensional identifier server is a functional component responsible for the generation, parsing, verification, maintenance and updating of spatiotemporal multidimensional identifiers. It can select the target attribute set that matches the business request from the attribute space according to the mapping function, and generate object identifiers, connection identifiers and application identifiers based on the target attribute set.

[0022] The attribute space described in this invention is a set of attributes pre-configured and stored in a spatiotemporal multidimensional identification server, including an object attribute space, a connection attribute space, and an application attribute space, and organized according to the application layer, network layer, link layer, and physical layer. Upon receiving a service request, the spatiotemporal multidimensional identification server executes an attribute mapping and identification generation process based on the preset attribute space.

[0023] like Figure 1 and Figure 2 As shown, the attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to the present invention includes the following steps:

[0024] S1. Obtaining business requests and calling preset attribute spaces: The spatiotemporal multidimensional identifier server receives business requests and calls preset attribute spaces from the local or distributed identifier management database; the business request includes one or more of the following: business category, target object, spatial constraints, service quality requirements, time validity interval, terminal status, session status, and security requirements; the spatiotemporal multidimensional identifier server normalizes the original business request to form a standardized request representation.

[0025] Furthermore, S1 includes the following steps: S11. Receive original service requests: The spatiotemporal multidimensional identification server receives original service requests from users, terminals, application systems, or network control modules. The original service requests include one or more of the following information: service type, request purpose, application requirements, service quality constraints, spatial range, time range, and context state.

[0026] S12. Normalize the original business requests: The spatiotemporal multidimensional identification server is pre-configured with a unified spatiotemporal reference. ,in, Represents a unified spatial reference. Indicates a unified time reference; the spatiotemporal multidimensional identifier server uses a unified spatial reference. The spatial information in the original business request is transformed to obtain spatial constraints. ; based on a unified time reference The time information in the original business request is transformed to obtain the valid time range. and form standardized business requests. Specifically, it includes: S121, Extracting Business Categories Determine the business category based on the business name, business type, application type, or service description in the business request. ; S122, Extracting Spatial Constraints Determine spatial constraints based on the geographical location, regional scope, network domain, logical domain, or node range in the business request. ; S123, Extract the service target vector Based on the requirements of latency, bandwidth, reliability, security level, priority, mobility, and resource capabilities in the business requests, construct a service target vector. ; S124, Extraction time valid interval Determine the valid time range based on the effective time, duration, expiration time, or task cycle in the business request. ; S125, Extract auxiliary context Construct auxiliary context based on information such as user status, terminal status, session status, network status, task status, and environment status. .

[0027] S13. Call the preset attribute space: Let the preset attribute space be: ,in, Indicates the preset attribute space; Represents the object attribute space, used to store candidate attributes such as object type, object identity, object capabilities, object location, and object state; This represents the connection attribute space, used to store candidate attributes such as network domain, access method, link type, link quality, routing capability, and transmission capability. This represents the application attribute space, used to store candidate attributes such as application type, service category, content type, business priority, and service quality requirements.

[0028] S14. Perform hierarchical indexing of the preset attribute space: Let the network hierarchy set be: ,in, Represents the application layer. Represents the network layer. Indicates the link layer. Represents the physical layer; for any identifier type ,satisfy , Indicates the object identifier type; Indicates the connection identifier type; Indicates the application identifier type; Identifier type The corresponding set of candidate attributes is represented as follows: ,in, Indicates the identifier type The corresponding set of candidate attributes; Indicates the identifier type At the network level The set of candidate attributes.

[0029] S15. Determine the candidate attribute keys for each layer and their possible value ranges: For identifier types At the network level The following is a set of candidate attributes: ,in, Indicates the identifier type At the network level The set of candidate attribute keys; Indicates the candidate attribute key; Indicates attribute key In the identifier type and network layers The set of allowed attribute values; This represents an attribute item consisting of an attribute key and its allowed value range.

[0030] S2. Perform cross-layer requirement decomposition and function point extraction for business requests: The spatiotemporal multidimensional identification server decomposes business requirements into application layer requirements, network layer requirements, link layer requirements, and physical layer requirements based on standardized business requests, and performs feature encoding on each layer requirement; extracts function points from each layer requirement, determines the network layer corresponding to each function point, and calculates the requirement intensity of each function point based on standardized business requests to form a function point requirement intensity vector with hierarchical information.

[0031] Furthermore, S2 includes the following steps: S21. Perform cross-layer requirement decomposition on standardized business requests: The spatiotemporal multidimensional identification server maps standardized business requests to the application layer, network layer, link layer, and physical layer respectively, obtaining the requirement view for each layer; the application layer requirements mainly describe the business type, application content, and service capability requirements; the network layer requirements mainly describe the routing range, network domain, transmission strategy, and service quality requirements; the link layer requirements mainly describe the access method, link quality, and link reliability requirements; and the physical layer requirements mainly describe the node location, physical resources, equipment capabilities, and environmental status requirements.

[0032] S22. Feature encoding of each layer of requirement view: The spatiotemporal multidimensional identification server performs structured processing on each layer of requirement view, transforming text descriptions, numerical constraints, state information and policy requirements into computable requirement features.

[0033] S23. Generate a global requirement representation: The spatiotemporal multidimensional identification server integrates the requirement features of the application layer, network layer, link layer, and physical layer to obtain a global requirement representation for function point extraction and attribute mapping.

[0034] S24. Determine the set of function points: Assume the business request The corresponding set of functional points is: ,in, Indicates from business request The set of functional points extracted from it; Indicates the first One functional point; This indicates the total number of functionalities corresponding to the current business request; For each function point Record its source network hierarchy ,in: .

[0035] S25. Generate a functional point requirement intensity vector: The spatiotemporal multidimensional identifier server normalizes the various requirement features obtained in step S22 to form a requirement feature vector: ,in, Indicates the first A normalized demand characteristic, This indicates the number of demand features.

[0036] For function points Pre-configure the contribution coefficient between this function point and each requirement feature. And satisfy: Then the business request In functional points The intensity of demand is expressed as: .

[0037] The function point requirement intensity vector is composed of the requirement intensity of each function point: For numerical requirements such as latency, bandwidth, and reliability, normalization is performed based on the preset service parameter range; for discrete requirements such as service category, security level, and terminal status, they are converted into requirement feature values ​​within the range of [0,1] based on the preset feature mapping table.

[0038] S3. Establish a requirement attribute relevance matrix based on the mapping function: For each function point and candidate attribute key, determine the mapping permission relationship between the function point and the candidate attribute key according to the network layer to which the function point belongs, the network layer to which the candidate attribute key belongs, and the preset cross-layer mapping rule set; for function points and candidate attribute keys that satisfy the mapping permission relationship, calculate the mapping relevance based on the historical association probability and semantic association strength, and construct the requirement attribute relevance matrix based on the mapping relevance between each function point and each candidate attribute key; the relevance is jointly determined by the historical statistical probability and the semantic association strength, and finally forms the requirement attribute relevance matrix.

[0039] Furthermore, S3 includes the following steps: S31. Merging the Global Candidate Attribute Key Set: The spatiotemporal multidimensional identifier server merges the candidate attribute keys in the object attribute space, join attribute space, and application attribute space into a global candidate attribute key set. ,in, Represents the global set of candidate attribute keys; Indicates the first One candidate attribute key; Indicates the total number of candidate attribute keys; For each candidate attribute key Preserve its identifier type and the network layer to which it belongs ,in: The merging of the global candidate attribute key set is only used to uniformly perform subsequent correlation calculations, and does not change the original identifier type and network layer of the candidate attribute keys.

[0040] S32. Configure mapping functions for function points: For any function point and candidate attribute keys Configure mapping functions The mapping function takes as input the cross-layer mapping permission parameter between function points and candidate attribute keys, the historical association probability, and the semantic association strength, and outputs the function points. With candidate attribute keys Mapping correlation between .

[0041] S33. Determine cross-layer mapping permission relationship: The spatiotemporal multidimensional identification server pre-configures a cross-layer mapping rule set, which consists of multiple cross-layer mapping rules. Each rule includes at least a source network layer, a source function point label, a target network layer, a target attribute key, and a mapping permission flag. Among them, the source network layer is used to indicate the network layer to which the function point belongs, the source function point label is used to indicate the functional requirements to which the rule applies, the target network layer is used to indicate the network layer to which the candidate attribute key belongs, the target attribute key is used to indicate the candidate attribute that is allowed to establish a mapping relationship, and the mapping permission flag is used to indicate whether the corresponding function point and the candidate attribute key are allowed to establish a mapping relationship.

[0042] For any function point and candidate attribute keys The spatiotemporal multidimensional identification server is based on functional points Source network hierarchy and functional point tags, as well as candidate attribute keys The network layer and attribute key are used to match a preset set of cross-layer mapping rules; when a matching cross-layer mapping rule exists and its mapping permission flag is enabled, then... Otherwise, .

[0043] For example, for a function point originating from the application layer and labeled "real-time", the allowed mapping rules between it and the network layer "low-latency routing" attribute and the link layer "link quality" attribute can be pre-configured; for candidate attribute keys without configured allowed mapping rules, they will not be included in the subsequent fusion calculation of the function point's historical association probability and semantic association strength.

[0044] S34. Constructing a historical correlation probability matrix: The spatiotemporal multidimensional identification server selects completed historical business requests within a preset historical statistics window as historical samples; when functional points are in the same historical business request... Activated, and candidate attribute key When a function point is selected into the target attribute set corresponding to a historical business request, the function point is determined. With candidate attribute keys A joint adoption occurred.

[0045] Set up function points within the historical statistics window The number of activated historical service requests is Candidate attribute keys are used simultaneously. The number of historical business requests is The historical association probability after smoothing is expressed as: ;in, Represents the probability smoothing parameter; Function points Candidate attribute keys are used when activated. Historical correlation probability.

[0046] Construct a historical correlation probability matrix based on the historical correlation probabilities: The preset historical statistics window can be determined based on the time length or the number of historical business requests; for historical samples with a sample size lower than the preset minimum sample size... The function is to reduce or cancel the weight of historical association probability in subsequent mapping relevance calculations, so as to handle the situation of insufficient historical samples through semantic association information.

[0047] S35. Construct a semantic association matrix: The spatiotemporal multidimensional identifier server pre-establishes a unified semantic tag library. The semantic tags include at least one or more of the following: real-time performance, low latency, bandwidth, reliability, security, mobility, location, network domain, access method, and resource capabilities. Different business expressions are converted into unified semantic tags through a preset synonym mapping relationship.

[0048] Set function points The corresponding semantic tag set is Candidate attribute keys The corresponding semantic tag set is Then function points With candidate attribute keys The semantic association strength between them is represented as: ;in, ; The larger the value, the higher the function point. With candidate attribute keys The higher the degree of semantic association, the stronger the semantic association; construct a semantic association matrix based on the strength of each semantic association: .

[0049] S36. Generate a requirement attribute correlation matrix using a mapping function: based on functional points. The effective historical fusion weight is determined by the corresponding number of historical samples. : ,in, This indicates the preset historical fusion weights; when the number of historical samples is insufficient, let This allows the mapping relevance to be determined by the semantic association strength; For function points With candidate attribute keys The mapping function is expressed as: ,in, Function points With candidate attribute keys The mapping correlation between them; Construct a requirement attribute relevance matrix based on all mapping relevances: .

[0050] S4. Filter the target attribute set and generate attribute values ​​based on attribute weights: The spatiotemporal multidimensional identification server calculates the comprehensive weight of candidate attribute keys for the current business request based on the requirement attribute correlation matrix and the functional point requirement intensity vector; sorts the candidate attribute keys according to the comprehensive weights and selects the target attribute key set; for the selected target attribute keys, the spatiotemporal multidimensional identification server generates corresponding attribute values ​​in combination with the business request, and obtains the target attribute set in the object dimension, connection dimension and application dimension.

[0051] Furthermore, S4 includes the following steps: S41. Calculate the comprehensive weight vector of candidate attribute keys. ,in Where n is the number of weights, the overall weights are normalized: and satisfy ;when At that time, comprehensive weight normalization and cumulative weight filtering are not performed, and the target attribute key set is determined based on the set of required attribute keys and the preset basic attributes; S42. According to the normalized comprehensive weight Sort the candidate attribute keys from highest to lowest; S43. Determine the set of required attribute keys based on the business request. First, add the required attribute keys to the target attribute key set, then select the candidate attribute keys in order of sorting until... ;in, This represents the final set of target attribute keys; This indicates a preset cumulative weight threshold, and , ; S44. For any target attribute key Based on spatial constraints obtained from a unified spatiotemporal reference Valid time interval and service target vector Auxiliary context Function point requirement intensity vector Generate corresponding attribute values ,in, Indicates attribute key The corresponding attribute value generation rules; S45. Classify the target attribute keys and their attribute values ​​hierarchically; the spatiotemporal multidimensional identification server divides the target attribute keys and their attribute values ​​into object-dimensional target attribute sets, connection-dimensional target attribute sets, and application-dimensional target attribute sets according to the identification type and network layer to which the attribute keys belong; S46. Generate a complete set of target attributes for each identifier type: The spatiotemporal multidimensional identifier server merges the target attribute sets of each layer to obtain a complete set of target attributes for the object dimension. Complete set of target attributes for connection dimension Complete set of target attributes for application dimensions ;in, This represents the set of target attributes corresponding to the object identifier; This represents the set of target attributes corresponding to the join identifier; This represents the set of target attributes corresponding to the application identifier.

[0052] S5. Generate spatiotemporal multidimensional identifiers based on target attribute sets: Encode the target attribute sets of object dimension, connection dimension and application dimension according to the preset identifier encoding rules to generate object identifier, connection identifier and application identifier.

[0053] Furthermore, S5 includes the following steps: S51. Establish unified identifier encoding rules: The spatiotemporal multidimensional identifier server adopts a unified attribute encoding method for object identifiers, connection identifiers, and application identifiers, converting the target attribute set into a standardized encoding sequence; specifically including: S511, the spatiotemporal multidimensional identification server assigns unique attribute keys to each target attribute key according to a preset attribute dictionary, and arranges the target attribute items corresponding to the same identification type according to the preset order of the attribute keys; For any identifier type ,satisfy: For identifier types The corresponding complete set of target attributes The target attribute items are obtained according to the preset order of the attribute keys. ;in, Indicates the identifier type The corresponding number of target attribute items; S512, For any target attribute key Construct the target attribute encoding unit based on the target attribute and its corresponding attribute values: ;in, Indicates the target attribute key Corresponding attribute key codes; Indicates the length of the data after normalization encoding of the target attribute value; This represents the normalized encoding result corresponding to the target attribute value; This indicates a byte sequence concatenation operation; For enumerated attribute values, they are converted to corresponding encoded values ​​according to a preset enumeration code table; for numeric attribute values, they are binary encoded according to a preset data length and byte order; for character attribute values, they are converted to byte sequences according to a preset unified character encoding method. For target attribute values ​​that contain spatial or temporal attributes, normalized spatial or temporal attribute values ​​obtained based on a unified spatiotemporal benchmark are used for encoding.

[0054] S513, the spatiotemporal multidimensional identifier server connects the identifier header with the target attribute encoding units arranged in a preset order to obtain the identifier type. The corresponding normalized coding sequence ;in, Indicates the identifier type The corresponding identifier header includes at least identifier version information and identifier type information; Therefore, identifier type The corresponding identifier encoding process is represented as follows: ;in, This indicates that the identifier type is encoded according to the encoding rules described in steps S511-S513. The corresponding complete set of target attributes is converted into a normalized encoded sequence using an identifier encoding function.

[0055] S52. Generate object identifiers: The spatiotemporal multidimensional identifier server generates a complete set of target attributes based on the object dimension. Generate object identifiers according to the unified identifier encoding rules: ,in, Indicates a business request The corresponding complete set of target attributes for the object dimension; Represents the object identifier.

[0056] S53. Generate Connection Identifier: The spatiotemporal multidimensional identification server generates a complete set of target attributes based on the connection dimension. Generate a connection identifier according to the unified identifier encoding rules. ;in, Indicates a business request The corresponding complete set of target attributes for the connection dimension; Indicates a connection identifier.

[0057] S54. Generate Application Identifier: The spatiotemporal multidimensional identifier server generates a complete set of target attributes based on the application dimension. The application identifier is generated according to the unified identifier encoding rules: ;in, Indicates a business request The corresponding application dimension complete target attribute set; Indicates the application identifier.

[0058] Furthermore, in an alternative implementation, a minimum target attribute identifier and an auxiliary hash identifier may also be generated.

[0059] S55. Generate a minimum target attribute identifier: The spatiotemporal multidimensional identifier server determines a minimum target attribute set that meets the target discrimination capability requirements and has the fewest attributes based on the complete target attribute set of the object dimension, the complete target attribute set of the connection dimension, and the complete target attribute set of the application dimension, and generates a minimum target attribute identifier based on the minimum target attribute set; including: S551, Complete target attribute set for object dimension Complete set of target attributes for connection dimension Complete set of target attributes for application dimensions By merging, we obtain the total target attribute set: ;in, Indicates a business request The corresponding set of overall target attributes.

[0060] S552, the spatiotemporal multidimensional identification server, based on business requests Determine the target candidate set from the identifier management database. ; For any subset of attributes In the attribute subset The target candidates that can be matched under all attribute constraints constitute the matching candidate set: ;in, Indicates the purpose of the candidate Satisfying attribute subset All attribute constraints in the; Define attribute subsets The objective discrimination index is: ,in, Indicates the use of attribute subsets The number of remaining target candidates after target discrimination; let the target discrimination threshold be... ,when At that time, determine the attribute subset Meets the requirement for destination identification capability. When it is necessary to uniquely identify a destination object, destination network domain, or destination service instance, set the following: .

[0061] S553, the spatiotemporal multidimensional identifier server determines the set of mandatory attributes that cannot be deleted based on one or more of the following in the business request: the target object, the target network domain, the business type, and the mandatory security requirements. ; Under the conditions of satisfying the requirements for objective discrimination and the constraints of mandatory attributes, from the total set of objective attributes... Select the subset of attributes with the fewest attributes as the target minimum attribute set. : And satisfy: .

[0062] S554, Spatiotemporal Multidimensional Identification Server from Required Attribute Set Initially, the subsets of candidate attributes are sorted by the number of attributes they contain, from fewest to most, to select the subsets containing the required attributes. A layer-by-layer search is performed on the subset of candidate attributes; For each candidate attribute subset Query the identifier management database and determine the corresponding matching candidate set. When the first occurrence satisfies When a subset of candidate attributes is found, the search stops at search levels with a larger number of attributes, and the subset of candidate attributes that meets the above conditions is determined as the target minimum attribute set. ; When there are multiple attribute subsets that meet the requirements of the objective discrimination capability for the same number of attributes, the attribute subset with the largest sum of normalized comprehensive weights can be selected as the minimum set of attributes for the objective based on the sum of the normalized comprehensive weights of each attribute.

[0063] S555, the spatiotemporal multidimensional identification server assigns the minimum set of destination attributes according to a preset destination attribute identification encoding rule. Encode the target minimal attribute identifier: ;in, Represents the minimum attribute identifier of the purpose; This represents the encoding function for the minimum attribute identifier of the target;

[0064] The target attribute identifier encoding rule adopts the same attribute key sorting method and attribute encoding unit format as step S51, so that each attribute item in the target minimum attribute identifier can be parsed according to the corresponding attribute key.

[0065] S56. Generate auxiliary hash identifiers and establish hash indexes: The spatiotemporal multidimensional identifier server performs hash processing on the normalized encoding results of object identifiers, join identifiers, and application identifiers respectively, and establishes an index relationship between auxiliary hash identifiers and complete spatiotemporal multidimensional identifiers in the identifier management database; specifically including: S561, For any identifier type For complete spatiotemporal multidimensional identification Perform hash processing to obtain the corresponding auxiliary hash identifier: Wherein, Hash represents a preset hash function, which can be a hash function such as SHA-256 or SHA-3 that can map an input of arbitrary length to a hash value of fixed length; in this embodiment, the SHA-256 hash function is used to hash the normalized encoded byte sequences corresponding to the object identifier, the link identifier, and the application identifier to obtain a 256-bit auxiliary hash identifier. Represents a complete spatiotemporal multidimensional identifier The corresponding auxiliary hash identifier; respectively obtain the object auxiliary hash identifier. Link auxiliary hash identifier and application auxiliary hash identifier ; S562. The spatiotemporal multidimensional identifier server establishes a hash index table in the identifier management database. To assist hash identifiers As an index key, with the corresponding complete spatiotemporal multidimensional identifier and its complete set of target attributes As an index value; S563. When the downstream module performs an identifier query based on the auxiliary hash identifier, the spatiotemporal multidimensional identifier server uses the hash index table. Obtain the corresponding complete spatiotemporal multidimensional identifier, and perform attribute parsing, destination determination, addressing or routing processing based on the complete spatiotemporal multidimensional identifier; When at least two complete spatiotemporal multidimensional identifiers generate the same auxiliary hash identifier, the complete spatiotemporal multidimensional identifiers with the same auxiliary hash identifier are associated with the same index item, and the actual matching result is determined by comparing the complete spatiotemporal multidimensional identifiers; the auxiliary hash identifier is only used to establish the index association with the complete spatiotemporal multidimensional identifier, and does not replace the object identifier, link identifier and application identifier for multidimensional attribute expression and parsing.

[0066] S57. In an optional implementation of generating the minimum destination attribute identifier and auxiliary hash identifier, a spatiotemporal multidimensional identifier generation result is formed; the spatiotemporal multidimensional identifier server associates the generated object identifier, connection identifier, application identifier, minimum destination attribute identifier, and the three types of auxiliary hash identifiers to form a business request. The corresponding spatiotemporal multidimensional identifier generation results are as follows: ;in, Indicates a business request The corresponding spatiotemporal multidimensional identifier generation results, Represents the object identifier. Indicates the connection identifier. Indicates the application identifier. Indicates the minimum attribute identifier of the purpose. Represents the auxiliary hash identifier of the object. Indicates the join auxiliary hash identifier, This indicates the application of an auxiliary hash identifier.

[0067] To make the present invention easier to understand, the following explanation uses the identifier generation process of the real-time video transmission and control coordination service of mobile inspection equipment as an example.

[0068] In this embodiment, the relevant attributes of the mobile inspection equipment, edge control center, regional wireless link, edge network domain, and video control service have been registered to the spatiotemporal multidimensional identification server through an identification agent. The spatiotemporal multidimensional identification server has already formed a preset attribute space, including an object attribute space, a connection attribute space, and an application attribute space. The object attribute space includes attributes such as object type, object number, object location, object capabilities, and object status; the connection attribute space includes attributes such as network domain, access method, link quality, available bandwidth, and routing capabilities; and the application attribute space includes attributes such as service type, service priority, latency requirements, bandwidth requirements, reliability requirements, and security level.

[0069] When a mobile inspection device initiates a real-time video transmission and control coordination service, the spatiotemporal multidimensional identification server receives the original service request. In this request, the service type is real-time video transmission and control coordination, the destination is the edge control center, and the destination object number is [missing information]. The current inspection area is The target network domain is The expected end-to-end latency is no more than 30ms, and the expected available bandwidth is no less than [amount missing]. Expected reliability is no less than The security level is The mission is valid from 10:00 to 12:00 on June 29, 2026. Meanwhile, the mobile inspection equipment is online with 78% battery remaining, the current wireless link quality score is 0.86, and the edge control center is in a serviceable state.

[0070] The spatiotemporal multidimensional identification server normalizes the original business requests to obtain standardized business requests; among which, business categories Values This indicates video and control collaborative services; spatial constraints. Values and This indicates the service location and target network domain; the service target vector... The values ​​are: latency not exceeding 30ms, bandwidth not less than 20Mbps, reliability not less than 0.999, and security level of [value missing]. Valid time interval The value is 2026. From 10:00 to 12:00 on the 29th of the month; auxiliary context The values ​​are: terminal online, battery level 78%, link quality 0.86, and edge control center available for service.

[0071] Subsequently, the spatiotemporal multidimensional identification server invokes a preset attribute space and extracts candidate attribute keys from the object attribute space, connection attribute space, and application attribute space. For ease of explanation, the candidate attribute keys in this embodiment include attributes such as object type, object number, target network domain, access method, link quality, available bandwidth, service type, latency requirements, security level, reliability requirements, and service priority.

[0072] The spatiotemporal multidimensional identification server performs cross-layer requirement decomposition on standardized business requests. Application layer requirements include real-time video backhaul, control command interaction, and task status synchronization; network layer requirements include low-latency forwarding, target network domain reachability, and stable path selection; link layer requirements include wireless access, link quality maintenance, and bandwidth adequacy; and physical layer requirements include the current location of mobile inspection equipment, equipment online status, and edge node serviceability status. Based on these requirements, the spatiotemporal multidimensional identification server extracts five functional points: real-time performance, bandwidth requirements, reliability, security, and mobility, and sets corresponding requirement intensities. Specifically, the real-time performance requirement intensity is 0.95, the bandwidth requirement intensity is 0.90, the reliability requirement intensity is 0.85, the security requirement intensity is 0.70, and the mobility requirement intensity is 0.65.

[0073] The spatiotemporal multidimensional identifier server calls the mapping function to map the above functional points to candidate attribute keys. For real-time functional points, latency requirements, link quality, and routing capabilities are prioritized; for bandwidth requirement functional points, available bandwidth, link quality, and service type are prioritized; for reliability functional points, link quality, target network domain, and reliability requirements are prioritized; for security functional points, security level and service priority are prioritized; and for mobility functional points, access method, device location, and link quality are prioritized.

[0074] In this embodiment, the historical statistics window is set to business requests completed within the last 30 days, with a preset minimum historical sample size. probability smoothing parameter Preset historical fusion weights For the number of historical samples that meets the following conditions... Functional points, making For functional points with insufficient historical sample size, let After mapping function calculation and comprehensive weight sorting, the spatiotemporal multidimensional identifier server obtains the following sorted results for candidate attribute keys, excluding the mandatory attribute key object type: link quality, available bandwidth, latency requirement, target network domain, object number, service type, security level, reliability requirement, service priority, and access method. In this embodiment, the object type is determined as a mandatory attribute key and is first added to the target attribute key set. The cumulative weight threshold in this embodiment... The weight is set to 0.85. The spatiotemporal multidimensional identification server first normalizes the comprehensive weight of each candidate attribute key according to step S41, then sorts them from high to low according to the normalized comprehensive weight, and selects attribute keys in turn until the cumulative normalized comprehensive weight of the selected attribute keys is not less than 0.85. The server selects attribute keys in order of weight, and finally determines the target attribute key set as follows: object type, object number, target network domain, link quality, available bandwidth, service type, latency requirement, security level, and reliability requirement.

[0075] For the aforementioned target attribute keys, the spatiotemporal multidimensional identifier server generates corresponding attribute values. The object number takes the value... The target network domain value is The link quality is set to 0.86, the available bandwidth is set to 25Mbps, and the service type is set to [value missing]. The latency requirement is set to 1. The security level is set to 1. The reliability requirement is set to 1. .

[0076] The spatiotemporal multidimensional identification server categorizes attributes based on their type, resulting in object-dimensional target attribute sets, connection-dimensional target attribute sets, and application-dimensional target attribute sets. The object-dimensional target attribute set includes object type. and object number The target attribute set for the connectivity dimension includes the target network domain. Link quality 0.86 and available bandwidth 25Mbps; the application dimension target attribute set includes service type. Latency requirements Security level and reliability requirements .

[0077] Subsequently, the spatiotemporal multidimensional identifier server generates three types of spatiotemporal multidimensional identifiers based on the aforementioned target attribute set. The object identifier generation result is as follows: Used to identify the number Edge control center; connection identifier generation result is It is used to identify the connection status, such as the target network domain, link quality, and available bandwidth; the application identifier generation result is... It is used to identify real-time video and control collaboration services and their quality of service requirements.

[0078] Furthermore, the spatiotemporal multidimensional identification server extracts the minimum target attribute identifier from the target attribute sets of the object dimension, connection dimension, and application dimension. In this embodiment, the target discrimination threshold is set to... The spatiotemporal multidimensional identification server searches for candidate attribute subsets in ascending order of the number of attributes, following steps S552-S554, and determines the number of target candidates corresponding to each attribute subset through the identification management database. After the search, the first subset that satisfies the required number of target candidates... The attribute subset includes object number, target network domain, service type, latency requirements and security level. Therefore, this attribute subset is determined as the minimum target attribute set and encoded according to step S555 to obtain the corresponding minimum target attribute identifier.

[0079] Finally, the spatiotemporal multidimensional identifier server performs hash processing on the three types of identifiers according to step S56. In this embodiment, the SHA-256 hash function is used. Taking the object identifier, link identifier, and application identifier as input according to the normalized encoded byte sequence obtained in step S51, the corresponding 256-bit object auxiliary hash identifier, link auxiliary hash identifier, and application auxiliary hash identifier are calculated. These auxiliary hash identifiers are then used as the index keys of the hash index table. The auxiliary hash identifiers are actually calculated from the corresponding normalized encoded byte sequences using the SHA-256 algorithm, without using pre-specified sample values.

[0080] Therefore, in this embodiment, the spatiotemporal multidimensional identification server ultimately outputs the object identifier. Connection identifier Application Identifier The invention includes a minimum attribute identifier and three types of hash identifiers. As can be seen from this embodiment, the invention can automatically complete request normalization, function point extraction, attribute mapping, attribute filtering, attribute value generation, and spatiotemporal multidimensional identifier generation based on specific business requests, after the attributes have been registered with the spatiotemporal multidimensional identifier server. This provides directly usable identifier results for subsequent parsing, addressing, routing, and forwarding.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to further limit the present invention. All equivalent changes made based on the description and drawings of the present invention are within the protection scope of the present invention.

Claims

1. A method for generating attribute mapping identifiers for spatiotemporal multidimensional identifier networks, characterized in that, Includes the following steps: S1. Obtain business requests and call preset attribute space: Receive business requests, normalize the original business requests to form a standardized request representation, and call candidate attribute keys from the preset attribute space. S2. Perform cross-layer requirement decomposition and function point extraction for business requests: Based on standardized business requests, decompose business requirements into application layer requirements, network layer requirements, link layer requirements, and physical layer requirements, and perform feature encoding on each layer requirement. Extract functional points from the requirements of each layer, determine the network layer corresponding to each functional point, and calculate the demand intensity of each functional point according to the standardized business requests to form a functional point demand intensity vector with hierarchical information. S3. Establish a requirement attribute relevance matrix based on the mapping function: For each function point and candidate attribute key, determine the mapping permission relationship between the function point and the candidate attribute key according to the network level to which the function point belongs, the network level to which the candidate attribute key belongs, and the preset cross-layer mapping rule set; For function points and candidate attribute keys that satisfy the mapping permission relationship, calculate the mapping relevance based on the historical association probability and semantic association strength, and construct the requirement attribute relevance matrix based on the mapping relevance between each function point and each candidate attribute key. S4. Filter the target attribute set and generate attribute values ​​based on attribute weights: Calculate the comprehensive weight of candidate attribute keys for the current business request based on the requirement attribute correlation matrix and the functional point requirement intensity vector. Candidate attribute keys are sorted according to comprehensive weights, and a set of target attribute keys is selected. For the selected target attribute keys, corresponding attribute values ​​are generated in conjunction with business requests to obtain the target attribute sets in the object dimension, connection dimension, and application dimension. S5. Generate spatiotemporal multidimensional identifiers based on target attribute sets: Encode the target attribute sets of object dimension, connection dimension and application dimension respectively according to the preset identifier encoding rules to generate object identifier, connection identifier and application identifier; Specifically, S4 is: S41. Calculate the comprehensive weight of the candidate attribute keys and normalize the comprehensive weight; S42. Sort the candidate attribute keys from high to low according to the normalized comprehensive weight; S43. Determine the set of required attribute keys according to the business request. First, add the required attribute keys to the target attribute key set. Then, select the candidate attribute keys in order according to the sorting results until the cumulative normalized comprehensive weight of the selected attribute keys reaches the preset cumulative weight threshold. S44. For the target attribute key, generate the corresponding attribute value based on the spatial constraints, the effective time interval, the service target vector, the auxiliary context, and the functional point requirement intensity vector. S45. Classify the target attribute keys and their attribute values ​​hierarchically: Based on the identifier type and network layer to which the attribute keys belong, divide the target attribute keys and their attribute values ​​into the object dimension target attribute set, the connection dimension target attribute set, and the application dimension target attribute set. S46. Generate a complete set of target attributes for each identifier type: merge the target attribute sets of each layer to obtain a complete set of target attributes for the object dimension, a complete set of target attributes for the connection dimension, and a complete set of target attributes for the application dimension.

2. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 1, characterized in that, S1 specifically refers to: S11. Receive original business requests: Receive original business requests that include business type, request purpose, application requirements, service quality constraints, spatial scope, time scope and context state. S12. Normalize the original business requests: Pre-configure a unified spatiotemporal reference, including a unified spatial reference and a unified time reference; The spatial information in the original business request is transformed according to a unified spatial reference to obtain spatial constraints; the time information in the original business request is transformed according to a unified time reference to obtain the effective time interval, and a standardized business request is formed. S13. Call the preset attribute space: The preset attribute space includes the object attribute space, the connection attribute space and the application attribute space; S14. Perform hierarchical indexing on the preset attribute space: Assume that the network layer set includes the application layer, network layer, link layer and physical layer; for any identifier type, its corresponding candidate attribute set is divided into candidate attribute subsets of each layer according to the network layer. S15. Determine the candidate attribute keys for each layer and their possible value ranges: For the candidate attribute set of the identifier type in the network layer, determine the set of candidate attribute keys and the set of allowed attribute values ​​for each candidate attribute key.

3. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 1, characterized in that, S2 specifically refers to: Standardized business requests are decomposed across layers and mapped to the application layer, network layer, link layer, and physical layer respectively to obtain the requirement view of each layer; Feature encoding is performed on each layer of the requirement view to transform text descriptions, numerical constraints, status information and strategy requirements into computable requirement features; Extract a set of functional points based on the requirements of each layer, where each functional point records the corresponding source network layer; Various demand characteristics are normalized to form a demand characteristic vector, and the demand intensity of each functional point is calculated based on the contribution coefficient between each functional point and the demand characteristics to form a functional point demand intensity vector.

4. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 1, characterized in that, S3 specifically refers to: S31. Merge the global candidate attribute key set: Merge the candidate attribute keys in the object attribute space, the join attribute space, and the application attribute space into a global candidate attribute key set, and retain the identifier type and network layer to which each candidate attribute key belongs; S32. Configure a mapping function for the function point: For any function point and candidate attribute key, configure a mapping function; the mapping function takes the cross-layer mapping permission parameter, historical association probability and semantic association strength between the function point and the candidate attribute key as input, and outputs the mapping relevance between the function point and the candidate attribute key; S33. Determine cross-layer mapping permission relationship: The preset cross-layer mapping rule set consists of multiple cross-layer mapping rules. Each cross-layer mapping rule includes at least the source network layer, source function point label, target network layer, target attribute key, and mapping permission flag. The cross-layer mapping rule set is matched according to the source network layer and function point label of the function point, and the network layer and attribute key of the candidate attribute key. The cross-layer mapping permission parameters between the function point and the candidate attribute key are determined according to the mapping permission flag in the matching rule. S34. Construct a historical correlation probability matrix: Select completed historical business requests within a preset historical statistics window as historical samples; The historical association probability of a function point being activated and a candidate attribute key being used simultaneously is calculated and smoothed out; a historical association probability matrix is ​​constructed based on each historical association probability. S35. Construct a semantic association matrix: Pre-establish a unified semantic tag library and convert different business expressions into unified semantic tags through preset synonym mapping relationships; The semantic association strength is calculated based on the ratio of the intersection size to the union size of the semantic tag sets corresponding to the function points and the candidate attribute keys. Construct a semantic association matrix based on the strength of each semantic association; S36. Generate a requirement attribute correlation matrix using a mapping function: determine the effective historical fusion weights based on the number of historical samples corresponding to each function point. The mapping relevance is expressed as: ,in, Function points With candidate attribute keys The mapping correlation between them Indicates cross-layer mapping permission parameters, For mapping functions, Function points Candidate attribute keys are used when activated. Historical correlation probability, Function points With candidate attribute keys The semantic association strength between them; construct the relevance matrix of the requirement attributes based on the total mapping relevance.

5. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 1, characterized in that, The generation process of object identifier, connection identifier, and application identifier in S5 includes: S51. Establish unified identification coding rules: adopt a unified attribute coding method for object identifiers, connection identifiers and application identifiers, and convert the target attribute set into a standardized coding sequence; S52. Generate object identifier: Generate object identifier according to the unified identifier encoding rule based on the complete target attribute set of the object dimension; S53. Generate a connection identifier: Generate a connection identifier according to the unified identifier encoding rule based on the complete target attribute set of the connection dimension. S54. Generate application identifier: Generate application identifier according to the unified identifier encoding rules based on the complete target attribute set of the application dimension.

6. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 5, characterized in that, S51 specifically refers to: S511. Assign a unique attribute key code to each target attribute key according to the preset attribute dictionary, and arrange the target attribute items corresponding to the same identifier type according to the preset order of the attribute key codes; S512. For the target attribute key and its corresponding attribute value, construct a target attribute encoding unit, wherein the target attribute encoding unit includes the attribute key code, the data length of the target attribute value after normalization encoding, and the byte sequence concatenation of the normalization encoding result; S513. Connect the identifier header with the target attribute encoding units arranged in a preset order to obtain a standardized encoding sequence. The identifier header includes at least identifier version information and identifier type information.

7. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 1, characterized in that, S5 also includes: From the target attribute set, determine the minimum set of target attributes that meets the target discrimination capability requirement and has the fewest attributes, and generate the minimum target attribute identifier; wherein, the target discrimination capability requirement means that the number of remaining target candidates after using attribute subsets for target discrimination is not greater than a preset target discrimination threshold; perform hash processing on object identifier, connection identifier and application identifier respectively to generate corresponding auxiliary hash identifiers.

8. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 7, characterized in that, Generate the minimum attribute identifier for the target purpose, including: The complete target attribute set of the object dimension, the complete target attribute set of the connection dimension, and the complete target attribute set of the application dimension are merged to obtain the total target attribute set; Based on the business request, determine the set of destination candidates from the identifier management database; define the destination discrimination index of the attribute subset as the number of remaining destination candidates after using the attribute subset for destination discrimination; The set of mandatory attributes that cannot be deleted is determined based on one or more of the following in the business request: the target object, the target network domain, the business type, and the mandatory security requirements. Under the conditions of satisfying the objective discrimination capability requirement and the mandatory attribute constraint, the attribute subset with the fewest attributes is selected from the total target attribute set as the objective minimum attribute set; wherein, during the search process, for the extended attribute subset formed by adding candidate attribute keys to the candidate attribute subset, if the objective discrimination index of the extended attribute subset is the same as the objective discrimination index of the candidate attribute subset, it is determined that the added candidate attribute key has not improved the objective discrimination capability, and the extended attribute subset is no longer used as the parent node for subsequent searches; The minimum set of target attributes is encoded according to the preset target attribute identifier encoding rules to generate the minimum target attribute identifier.

9. The attribute mapping identifier generation method for spatiotemporal multidimensional identifier networks according to claim 7, characterized in that, Generate auxiliary hash identifiers, including: For any identifier type, hash the complete spatiotemporal multidimensional identifier to obtain the corresponding auxiliary hash identifier; Establish a hash index table, using auxiliary hash identifiers as index keys and the corresponding complete spatiotemporal multidimensional identifiers and their complete target attribute sets as index values; When the downstream module performs an identifier query based on the auxiliary hash identifier, it obtains the corresponding complete spatiotemporal multidimensional identifier according to the hash index table, and performs attribute parsing, destination discrimination, addressing or routing processing based on the complete spatiotemporal multidimensional identifier.

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