A network resource modeling method and system

Abstract

Embodiments of the present application disclose a kind of algorithm network resource modeling method and system.The method comprises: collecting original resource data from algorithm network resource;Based on original resource data, the resource ontology definition of each resource object in algorithm network resource is carried out, and the uniform resource model template of algorithm network resource is obtained;Based on resource model template, the modeling calculation of algorithm network resource is carried out, and the resource semantic representation is obtained, to obtain the uniform algorithm network resource model of algorithm network resource based on resource semantic representation, wherein resource semantic representation includes at least one of resource state vector, resource relationship graph and resource portrait score.The technical scheme of the embodiment of the present application can carry out uniform modeling to algorithm network resource.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of electronic digital data processing technology, and in particular to a method and system for modeling computing network resources. Background Technology

[0002] In a distributed cloud scenario, a cloud platform typically consists of multiple resource pools and multi-level network domains, and displays resources in the form of "resource list + monitoring indicators" to complete resource management and status monitoring.

[0003] However, it is currently impossible to uniformly model computing network resources, which means that resources can only be viewed, but cannot be calculated, queried, matched, or scheduled, which urgently needs to be resolved. Summary of the Invention

[0004] This invention provides a method and system for modeling computing network resources, which can perform unified modeling of computing network resources.

[0005] According to one aspect of the present invention, a method for modeling network resources is provided, which may include:

[0006] Collect raw resource data from computing network resources;

[0007] Based on the original resource data, resource ontology definitions are performed on each resource object in the computing network resources to obtain a unified resource model template for computing network resources;

[0008] Based on the resource model template, the computing network resources are modeled and calculated to obtain resource semantic representations. A unified computing network resource model is obtained based on the resource semantic representations. The resource semantic representations include at least one of the following: resource state vector, resource relationship graph, and resource profile score.

[0009] According to another aspect of the present invention, a network resource modeling system is provided, which may include: an acquisition adaptation layer, a resource ontology model, and a modeling computing engine; wherein,

[0010] The data acquisition and adaptation layer is used to collect raw resource data from computing network resources.

[0011] The resource ontology model is used to define the resource ontology of each resource object in the computing network based on the original resource data, so as to obtain a unified resource model template for computing network resources.

[0012] The modeling and computing engine is used to model and compute computing network resources based on resource model templates to obtain resource semantic representations, and to obtain a unified computing network resource model based on the resource semantic representations. The resource semantic representations include at least one of resource state vectors, resource relationship graphs, and resource profile scores.

[0013] According to another aspect of the present invention, an electronic device is provided, which may include:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that when the at least one processor executes the program, it implements the network resource modeling method provided in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided having computer instructions stored thereon, which are used to cause a processor to execute and implement the network resource modeling method provided in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the network resource modeling method provided in any embodiment of the present invention.

[0019] The technical solution of this invention collects raw resource data from computing network resources and, based on this raw resource data, defines the resource ontology of each resource object in the computing network resources to obtain a unified resource model template for the computing network resources. Further, based on the resource model template, it performs modeling calculations on the computing network resources to obtain resource semantic representations (at least one of resource state vectors, resource relationship graphs, and resource profile scores), thereby obtaining a unified computing network resource model based on these semantic representations. This technical solution, through resource data collection, resource ontology definition, and modeling calculations, constructs a unified computing network resource model, transforming heterogeneous computing and network resources into a computable, queryable, matchable, and schedulable data structure. This significantly improves the automated orchestration and intelligent operation and maintenance capabilities of resources, reducing manual intervention.

[0020] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a network resource modeling method provided according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of another network resource modeling method provided according to an embodiment of the present invention;

[0024] Figure 3 This is an architecture diagram of a unified modeling system for computing network resources provided in an embodiment of the present invention;

[0025] Figure 4 This is a data processing flowchart of the unified modeling system for computing network resources provided in an embodiment of the present invention;

[0026] Figure 5 This is a structural block diagram of a computing network resource modeling system provided according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the network resource modeling method of this invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in the technical solution of this invention all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to maintain user personal information security and network security.

[0031] Figure 1 This is a flowchart of a computing network resource modeling method provided in an embodiment of the present invention. This embodiment is applicable to the case of modeling computing network resources. The method can be executed by the computing network resource modeling system provided in this embodiment of the present invention. The system can be implemented by software and / or hardware. The system can be integrated on an electronic device, which can be various user terminals or servers, especially resource modeling servers in cloud management platforms or independently deployed computing network resource modeling service clusters.

[0032] See Figure 1 The method of this invention specifically includes the following steps:

[0033] S110. Collect raw resource data from the computing network resources.

[0034] In this context, computing and network resources can be understood as a set of resources that includes at least computing resources and network resources, and more specifically, a set of resources that includes computing resources, storage resources, network resources, intelligent computing resources, and related security, location, and operational state constraints. Based on this, computing resources may include one or more of the following: virtual machines, bare metal, container nodes, and graphics processing units (GPUs) / neural network processing units (NPUs) inference nodes. Network resources may include one or more of the following: virtual private clouds (VPCs), subnets, routing, load balancing, network address translation (NAT) gateways, security groups, perimeter firewall policies, leased links, and bandwidth and latency metrics.

[0035] Raw resource data can be understood as data collected from network resources, specifically data collected from one or more of the following: network controllers, cloud platforms, configuration management databases (CMDB), IP address management systems (IPAM), monitoring systems, and service ticketing systems. In this embodiment of the invention, optionally, raw resource data can be collected using one or more methods such as timed polling, event subscription, message queue consumption, and log parsing. Further optionally, for resource creation, deletion, and modification events, incremental events are prioritized; for existing resources, periodic snapshot compensation is used. It should be noted that the above are merely examples and not specific limitations.

[0036] S120. Based on the original resource data, define the resource ontology of each resource object in the computing network resources to obtain a unified resource model template for the computing network resources.

[0037] In this context, a resource object can be understood as a single manageable entity in the computing network resources, such as a virtual machine, a subnet, or a routing policy. This is related to the actual situation and is not specifically limited here.

[0038] A resource ontology can be understood as a unified semantic model used to define one or more of the following: resource categories, attributes, relationships, and constraints. Furthermore, the resource ontology definition can be used to define a unified expression for computing network resources. Optionally, as shown in Table 1 below, each resource object may include one or more of the following: basic attributes, capability attributes, location attributes, network attributes, state attributes, constraint attributes, and relationship attributes.

[0039] Table 1 Attribute Information of Resource Objects

[0040]

[0041] A resource model template can be understood as a unified structure that defines one or more of the following: resource category, attribute set, relationship type, constraint dimension, and lifecycle. For example, based on resource type (such as computing resource, network resource, and location resource), the fields of resource objects are categorized into seven major categories: basic attributes, capability attributes, location attributes, network attributes, state attributes, constraint attributes, and relationship attributes, forming a complete resource model template. This is a definition based on attribute classification. As another example, when defining a resource object, its relationship type with external resources (such as one or more of the following: contains, connects_to, depends_on, serves, protected_by, and monitored_by) is also defined, incorporating these relationship types into the resource model template, so that the resource model template simultaneously includes node attributes and edge semantics. This is a definition based on relationship types.

[0042] In this step, heterogeneous computing and network resources are abstracted into a unified semantic model template through resource ontology definition. This enables resources from different sources and of different types to have a consistent expression, providing a structurally complete and semantically clear template foundation for subsequent modeling and computation.

[0043] S130. Model and calculate the computing network resources based on the resource model template to obtain the resource semantic representation, and obtain a unified computing network resource model based on the resource semantic representation. The resource semantic representation includes at least one of the resource state vector, resource relationship graph and resource profile score.

[0044] Among them, resource semantic representation can be understood as including at least one of the resource state vector, resource relationship graph and resource profile score of computing network resources, used for machine-processable data representation.

[0045] Based on this, a resource state vector can be understood as a structured vector that encodes one or more of the following for each resource object: static specifications, dynamic indicators, network quality, security constraints, and lifecycle status. In other words, the resource state vector R can include one or more of the following components: resource capability component C, network quality component N, availability component A, security compliance component G, and risk component Risk.

[0046] A resource relationship graph can be understood as a data structure that uses nodes and edges to express one or more of the following relationships: inclusion, connection, dependency, service, and protection between resources. In this embodiment of the invention, optionally, computable resources, network resources, and business objects can be represented using a directed graph. Nodes in the graph represent resources or business objects, and edges represent the technical relationships between resources. For example, a virtual machine node is associated with a subnet node via a "belong_to" edge, a subnet node is associated with a routing table node via a "route_to" edge, a routing table node is associated with a NAT gateway or firewall policy node via a "connects_to" edge, and a business application node is associated with a virtual machine, container, or bare metal node via a "deployed_on" edge.

[0047] Resource profiling and scoring can be understood as a comprehensive score calculated for each resource object based on the components and weight parameters in its resource state vector. This score is used to quantitatively assess the degree of matching between resources and business needs. For example, the resource profiling score S = w1 x C + w2 x N + w3 x A + w4 x G - w5 x Risk. Where C is the resource capability component (i.e., computing capability matching degree), N is the network quality component (i.e., network quality matching degree), A is the availability component (i.e., availability matching degree), G is the security and compliance component (i.e., security and compliance matching degree), Risk is the risk component (i.e., resource risk score), and w1 to w5 are weight parameters.

[0048] The computing network resource model can be understood as a unified model that is consistent, computable, queryable, matchable, and can be used for orchestration and scheduling.

[0049] Based on resource model templates, computational models are performed on network resources to obtain resource semantic representations. For example, a resource state vector containing components such as specifications, dynamic indicators, network quality, and constraints is generated for each resource object, and the inclusion, connection, and dependency relationships between resource objects are automatically derived to obtain a resource relationship graph. Further, when multi-source data is inconsistent, conflicts are resolved based on trusted source priority, timestamps, and event sequences to generate a new lifecycle version, and resource semantic representations are obtained based on this.

[0050] In this step, resources are upgraded from "viewable" to "computable, matchable, and schedulable," enabling upper-layer applications to make automated decisions directly based on a unified computing network resource model.

[0051] Optionally, the computing network resource model exposes model service interfaces, allowing for at least one of the following: computation, querying, matching, and scheduling of computing network resources. These model service interfaces can be understood as exposed Application Programming Interfaces (APIs), employing one or more methods such as Representational State Transfer (REST), Graph Query Language (GraphQL), Remote Procedure Call (RPC), message subscription, and Model Context Protocol (MCP) tool interfaces. They can provide input for one or more of the following: resource querying, capacity assessment, service location selection, automatic orchestration, and intelligent operation and maintenance. This unified model is exposed in a standardized interface format, enabling scenarios such as resource querying, capacity assessment, service location selection, automatic orchestration, and intelligent operation and maintenance to directly obtain machine-processable data, reducing manual querying and judgment.

[0052] The aforementioned computing network resource model can be applied in various scenarios. Here are four examples. First, cloud site selection for business deployment. Specifically, after the business system submits its computing power specifications, network connectivity, security partitioning, and latency requirements, the model service interface filters resource pools that meet the constraints based on the unified resource model and returns candidate deployment locations based on resource scores. Second, elastic scaling. Specifically, the scaling service needs to determine whether the target resource pool has sufficient computing power, whether it has the target subnet address, whether the route is reachable, and whether the load balancer can be mounted. In this case, the unified model can return a feasibility and risk description for scaling in one go. Third, fault impact analysis. Specifically, when a link, route, gateway, or resource pool experiences an anomaly, the affected virtual machines, containers, business applications, and external access paths are located along the resource relationship graph, and the scope of impact is output. Fourth, cloud-edge collaborative scheduling. Specifically, based on the edge node's computing power, latency to the business data source, edge egress bandwidth, and security policies, it is determined whether the model inference task should be deployed in the central cloud, edge cloud, or local node. And so on, without specific limitations.

[0053] The technical solution of this invention collects raw resource data from computing network resources and, based on this raw resource data, defines the resource ontology of each resource object in the computing network resources to obtain a unified resource model template for the computing network resources. Further, based on the resource model template, it performs modeling calculations on the computing network resources to obtain resource semantic representations (at least one of resource state vectors, resource relationship graphs, and resource profile scores), thereby obtaining a unified computing network resource model based on these semantic representations. This technical solution, through resource data collection, resource ontology definition, and modeling calculations, constructs a unified computing network resource model, transforming heterogeneous computing and network resources into a computable, queryable, matchable, and schedulable data structure. This significantly improves the automated orchestration and intelligent operation and maintenance capabilities of resources, reducing manual intervention.

[0054] An optional technical solution, based on the original resource data, defines the resource ontology of each resource object in the computing network resources, which may include:

[0055] For the raw resource data collected from different data sources of computing network resources, the raw resource data is standardized to obtain standard resource data;

[0056] Based on standard resource data, resource ontology definitions are performed for each resource object in the computing network resources.

[0057] The different data sources can be two or more of the cloud platform, network controller, CMDB, IPAM, monitoring system and business ticket system mentioned above.

[0058] Standard resource data can be understood as data with unified fields, consistent units, and standardized status obtained after standardization processing. The standardization processing here can be one or more of the following: field mapping, unit conversion, status code conversion, resource type mapping, and null value padding, without specific limitations.

[0059] The original resource data is standardized to obtain standardized resource data. For example, fields and units are unified; for instance, a mapping table is looked up based on the data source type to map "flavor_vcpus", "cpu_num", and "core_count" to "cpu_cores", and Gbps is uniformly converted to Mbps. Another example is status standardization; for instance, different status codes are uniformly converted to creating, running, stopped, error, and deleted statuses, thus enabling unified calculation and sorting of resources.

[0060] Based on standard resource data, resource ontology definitions are performed for each resource object in the computing network resources. For example, standard resource data is mapped to predefined resource ontology templates according to resource type. Further, when a new resource type is encountered, the ontology definition is dynamically expanded based on its attributes, and the template library is updated.

[0061] The above technical solution, through a two-stage process of standardization and resource ontology definition, completely solves the problems of inconsistent formats and semantics of multi-source heterogeneous data, thus laying a data foundation for unified modeling and cross-system integration of computing network resources.

[0062] Figure 2 This is a flowchart of another computing network resource modeling method provided by an embodiment of the present invention. This embodiment is based on and optimized from the above-described technical solutions. In this embodiment, optionally, modeling and calculating computing network resources based on a resource model template to obtain resource semantic representations may include: generating a global resource identifier for each resource object; determining the object semantic representation of the resource object represented by each global resource identifier based on the resource model template; and obtaining the resource semantic representation of the computing network resource based on the object semantic representations. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0063] See Figure 2 The method in this embodiment may specifically include the following steps:

[0064] S210. Collect raw resource data from the computing network resources.

[0065] S220. Based on the original resource data, define the resource ontology of each resource object in the computing network resources to obtain a unified resource model template for the computing network resources.

[0066] S230. Generate a global resource identifier for each resource object.

[0067] The global resource identifier can be understood as a unified identifier (global_id) that uniquely identifies the same resource object across multiple data sources. In this embodiment of the invention, it can optionally be obtained based on one or more of the following: resource type, tenant, region, availability zone, resource source identifier, resource semantic identifier, and key attribute fingerprint. This can be set according to actual needs and is not specifically limited here.

[0068] A global resource identifier is generated for each resource object. For example, it can be directly calculated using a hash of the factors mentioned above. As another example, when the resource source identifier is missing or inconsistent, the resource semantic identifier is compared first. If they are the same, the fingerprints of key attributes such as the Central Processing Unit (CPU), memory, and Media Access Control (MAC) of the network interface card are further compared. If the similarity exceeds a threshold, they are merged into the same identifier.

[0069] This step addresses the issue of the inability to automatically associate resources (i.e., resource objects) due to different identifiers in different data sources, enabling accurate identification and merging of resources across heterogeneous data sources, thus providing a reliable identification foundation for unified modeling.

[0070] S240. For each resource object represented by a global resource identifier, determine the object semantic representation of the resource object based on the resource model template.

[0071] The semantic representation of an object can be understood as a computable data representation describing a single resource object. In the implementation of this invention, it may include object state vectors and object matching scores, etc., without specific limitations.

[0072] Based on the resource model template, a corresponding semantic representation of the resource object is determined. For example, one or more components such as resource capability, network, availability, security compliance, and risk are extracted from the resource model template to form a structured object state vector. Further, based on the object state vector, an object matching score is calculated by combining each component and its weight parameters to obtain the object semantic representation.

[0073] In this step, the static specifications, dynamic indicators, and constraints of a single resource object are transformed into machine-processable structured data, making the resource objects comparable and sortable, and providing a quantitative basis for resource matching and scheduling.

[0074] S250. Based on the semantic representation of each object, obtain the resource semantic representation of the computing network resources, and obtain a unified computing network resource model based on the resource semantic representation. The resource semantic representation includes at least one of the resource state vector, resource relationship graph and resource profile score.

[0075] Specifically, resource semantic representations are obtained based on the semantic representations of each object. For example, the object state vectors of all resource objects are aggregated to form a global resource state vector. Further, based on the dependencies, inclusions, and connections between resource objects (e.g., contains, connects_to, and depends_on), a resource relationship graph is constructed with resources as nodes and relationships as edges.

[0076] In this step, the focus expands from individual resources to the overall resource space, forming a globally computable resource semantic representation, which provides a unified data foundation for fault impact analysis, business link tracing, and capacity assessment.

[0077] The technical solution of this invention, through the layer-by-layer construction of global resource identification, object semantic representation and then overall resource semantic representation, realizes a complete computable expression from a single resource to the overall resource, solves the problem that heterogeneous data sources cannot be automatically associated and cannot be uniformly represented, and can provide a structured semantic foundation for upper-layer scheduling and operation and maintenance.

[0078] An optional technical solution involves generating a global resource identifier for each resource object, which may include:

[0079] For each resource object, the resource information of the resource object is obtained, including the resource source identifier, the resource semantic identifier, and the key attribute fingerprint;

[0080] In response to determining that there are duplicate objects among the resource objects based on the resource information of each resource object, the duplicate objects are merged and the resource objects are updated.

[0081] For each resource object, a global resource identifier is generated based on the resource information of the resource object.

[0082] Resource information can be understood as including resource source identifier (source_id), resource semantic identifier (such as one or more of IP address, instance name, VPC name and device serial number) and key attribute fingerprint (such as one or more of CPU, memory, network card MAC, subnet and routing domain).

[0083] Duplicate objects can be understood as the same physical or logical resource appearing in multiple systems, identified as different representations of the same resource through resource information matching. Based on this, merging can be understood as combining the same resource object from multiple sources into a single unified resource object, retaining information from each source and updating relevant fields.

[0084] For example, when resource source identifiers are inconsistent but resource semantic identifiers are the same, they are merged by semantic key. As another example, a matching threshold is set, and resources are merged when the similarity of key attribute fingerprints exceeds the threshold; otherwise, a new resource object is created and marked as a suspected duplicate. This eliminates resource duplication and mismatch in multi-source data sources, improves the accuracy and consistency of the computing network resource model, and avoids scheduling errors and statistical biases caused by duplication.

[0085] Furthermore, a unique and stable global resource identifier is generated for each resource object across data sources, enabling precise location and traceability of resources both within the model and in external services, supporting subsequent version control and conflict resolution. For example, to address the issue of inconsistent identifiers for the same resource across different data sources, this example uses a combination of "resource source identifier + resource semantic identifier + key attribute fingerprint" to generate the global resource identifier. For instance, the global resource identifier `global_id = Hash(resource_type, tenant_id, region, az, source_id, semantic_key, fingerprint)`. Here, `resource_type` represents the resource type, `tenant_id` represents the tenant or project identifier, `region` represents the region, `az` represents the availability zone, `source_id` represents the original resource identifier in the data source (i.e., the resource source identifier), `semantic_key` represents a semantic key that can be understood by the business (i.e., the resource semantic identifier), such as an Internet Protocol (IP) address, instance name, VPC name, or device serial number, and `fingerprint` represents a fingerprint obtained from one or more key attributes such as CPU, memory, network interface card (NIC) MAC address, subnet, and routing domain.

[0086] When the source_id is missing or inconsistent across different data sources, the semantic_key can be compared first. If the semantic_key is the same but there is a risk of mismatch, then the fingerprint can be compared further. A matching threshold can be set; when the fingerprint similarity exceeds the threshold, they are merged into the same global_id. Otherwise, a new resource object is created and marked as a suspected duplicate object for subsequent manual or rule-based confirmation.

[0087] The above technical solution systematically solves the problems of inconsistent resource identifiers and resource duplication and mismatch in multi-source heterogeneous data sources through three stages of processing: resource information extraction, duplicate object merging, and global resource identifier generation. It provides a reliable identifier foundation for unified resource modeling and correlation analysis.

[0088] Another optional technical solution, based on a resource model template, determines the object semantic representation of the resource object, which may include:

[0089] Based on the resource model template, determine the object state vector of the resource object;

[0090] Based on each component in the object state vector and the corresponding weight parameter, the object matching score of the resource object is obtained;

[0091] Based on the object state vector and the object matching score, the semantic representation of the object is obtained.

[0092] The main difference between the meaning of the object state vector and the resource state vector is that the former refers to a single resource object, while the latter refers to computing network resources. This will not be elaborated further here.

[0093] Based on the resource model template, the object state vector is determined. For example, capability attributes such as `cpu_cores`, `memory_gb`, and `storage_iops` are extracted from the resource model template to form capability components, and the object state vector is obtained based on these components. Further, the object state vector is updated by combining dynamic data such as utilization, latency, packet loss rate, and health status obtained from the monitoring system. This process compresses the multi-dimensional attributes of resources into a computable and comparable structured vector, providing a unified input format for subsequent resource matching and scheduling.

[0094] The components can be understood as components in the object's state vector. Each component can be, for example, two or more of the following: capability component C, network quality component N, availability component A, security compliance component G, and risk component Risk. This can be set according to actual needs and is not specifically limited here. In this technical solution, optionally, C can be calculated based on whether the capabilities of CPU, memory, storage, GPU / NPU, etc., meet business requirements; N can be calculated based on indicators such as end-to-end latency, bandwidth, packet loss rate, routing hop count, and whether it passes through a specified security boundary; A can be calculated based on resource health status, remaining capacity, historical failure frequency, and alarm level; G can be calculated based on security partitions, compliance labels, access policies, and data domain requirements; and Risk can be calculated based on factors such as resource overload, single point of failure risk, link congestion, and version inconsistency.

[0095] The weight parameter can be understood as a numerical value used to adjust the degree of influence of each component in the final object matching score. In this technical solution, optionally, the weight parameter can be configured by expert rules, or it can be obtained by training through historical scheduling effects, resource usage effects, and fault data; no specific limitation is made here.

[0096] Object matching score can be understood as a comprehensive quantitative value calculated based on each component of the resource state vector and its corresponding weight parameters. It can be used to measure the degree of matching between resources and business needs. Here, resource matching capability is quantified into a single score, making resource quality ranking and comparison possible, and providing a clear decision-making basis for automated scheduling and business location selection.

[0097] Furthermore, based on the object state vector and object matching score, an object semantic representation is obtained. For example, the resource state vector and object matching score are packaged into a unified resource object semantic structure, i.e., represented jointly by the two. As another example, in scheduling scenarios, the object matching score can be prioritized as the primary object semantic representation, with the object state vector serving as auxiliary information.

[0098] The above technical solution, through object state vector construction, object matching score calculation, and object semantic representation generation, transforms the static and dynamic attributes of resources into comparable and sortable quantitative expressions, solves the problem of the inability to uniformly quantify and match resource capabilities, and provides a direct quantitative decision basis for automated scheduling.

[0099] Another optional technical solution, based on a resource model template, determines the object semantic representation of resource objects, which may include:

[0100] In response to the resource model template, it is determined that there are conflicts in the returned data of different data sources for resource objects in the computing network resources. According to the trust source priority of each data source, the conflict is resolved for each returned data to obtain conflict-resolved data.

[0101] Based on conflict resolution data, an object semantic representation of the resource object is obtained.

[0102] The priority of trusted sources can be understood as the order of trustworthiness configured for different data sources. For example, resource specifications are ranked with the cloud platform API as the highest trusted source, network addresses are ranked with IPAM or cloud platform network interfaces as a relatively trusted source, operating status is judged by cloud platform events and monitoring systems, and business attribution is ranked with the ticket system and CMDB as a relatively trusted source, etc. No specific restrictions are made here.

[0103] Returned data can be understood as data returned by the corresponding data source for the same field of the same resource object, while a conflict can be understood as different data sources returning inconsistent data for the same field of the same resource object. For example, the cloud platform API returns that a virtual machine has 4 CPU cores, while the CMDB records the same virtual machine as having 2 CPU cores; this is a data conflict.

[0104] In the event of conflicts, the returned data from each data source is resolved based on its trusted source priority. For example, when different data sources conflict on the same field, the data source is first selected based on its trusted source priority, and then the returned data from that data source is chosen. If the trusted source priorities are the same, the returned data with the updated timestamp and larger event sequence number is selected. If the conflict still cannot be resolved, multiple returned data sets are retained, the trust score of the resource is lowered, and a conflict flag is output. This systematically solves the multi-source data conflict problem through a combination of trusted source priority and timestamp + event sequence number resolution rules, outputting stable and consistent resource data and providing high-quality input for subsequent resource semantic representation.

[0105] The semantic representation of objects is obtained based on conflict resolution data, which ensures the consistent output of resolved fields. This enables upper-layer applications to perceive the credibility of resource data, avoid using low-credibility resource data in critical decision-making scenarios, and improve the security of automated decision-making.

[0106] The above technical solution solves the data conflict problem of multi-source heterogeneous data sources by configuring trusted source priorities, enabling resource semantic representation to output consistent results while transparently exposing uncertain information, thereby improving the consistency, reliability and auditability of multi-source fusion results.

[0107] Based on this, optionally, determining the object semantic representation of a resource object based on a resource model template may also include:

[0108] Update the lifecycle version of the resource object;

[0109] Based on conflict resolution data, the object semantic representation of the resource object is obtained, including:

[0110] Based on conflict resolution data and the updated lifecycle version, an object semantic representation of the resource object is obtained.

[0111] The lifecycle version can be understood as a new version number generated each time a key resource field changes, used to trace the evolution history of the resource's state. The lifecycle version is updated when a conflict is detected. Retaining the history of resource state changes makes the computing network resource model traceable, supporting issue backtracking, change auditing, and version rollback, thereby improving the model's reliability and operational capabilities.

[0112] Furthermore, based on conflict resolution data and updated lifecycle versions, object semantic representations are obtained. This ensures that resource semantic representations not only reflect the current consistent state but also relate to historical evolution trajectories, providing a stable and traceable data foundation for intelligent operation and maintenance and automated orchestration.

[0113] The above technical solution, by combining lifecycle version updates with conflict resolution data, can ensure the consistency of resource semantic representation while increasing the ability to trace lifecycle versions, thus solving the problems of lack of version control for resource status and inability to trace back to the past.

[0114] Based on this, in order to better understand the above technical solutions as a whole, we will use a unified modeling system for computing network resources oriented towards computing network convergence as an example to illustrate them.

[0115] For example, see Figure 3 , Figure 4 As shown in Table 2, the unified modeling system for computing network resources includes heterogeneous data sources, a data acquisition and adaptation layer, a standardized processing layer, a computing network resource model, a rule and constraint library, a modeling computing engine, a model service interface, and a model storage unit. Among these,

[0116] Heterogeneous data sources may include two or more of the following: cloud platform, network controller, CMDB, IPAM, monitoring system, and business ticket system.

[0117] Through a data acquisition adaptation layer, raw resource data is collected from heterogeneous data sources according to acquisition protocols, acquisition cycles, pagination rules, incremental cursors, and failure retry strategies. Specifically, incremental events are prioritized for resource creation, deletion, and modification events; periodic snapshot compensation is used for existing resources.

[0118] The standardization processing layer standardizes the raw resource data, such as performing one or more of the following: field mapping, unit unification, resource type mapping, status code conversion, and null value padding, to obtain standard resource data.

[0119] By using a resource ontology model, resource ontology is defined based on standard resource data. For example, one or more of the following can be defined: resource category, attribute set, relationship type, constraint dimension, and lifecycle, resulting in a unified resource model template.

[0120] Through the modeling and computing engine, based on standard resource data, resource model templates, and a rules and constraints library, global resource identifiers, resource state vectors, resource relationship graphs, and resource profile scores are generated and presented in a consistent format, resulting in a semantic representation of resources. Based on this, a unified computing network resource model is derived and stored in the model storage unit for subsequent use. Specifically,

[0121] A global resource identifier is generated based on one or more of the following: resource source, resource type, tenant, region, availability zone, resource source identifier, resource semantic identifier, and key attribute fingerprint. When the same physical or logical resource appears in multiple data sources, it is merged using identifier matching rules.

[0122] For each resource object, an object state vector is generated, which includes static specifications, dynamic metrics, network quality, security constraints, and lifecycle status, for subsequent resource matching, capacity assessment, and scheduling.

[0123] Based on data such as resource attributes, network addresses, routing tables, subnet affiliation, monitoring object relationships, load balancer listeners, and business work order relationships, the system automatically derives the inclusion, connection, dependency, and protection relationships between resources to obtain a resource relationship graph.

[0124] When different data sources return inconsistent data for the same resource object, conflict resolution is performed based on trusted source priority, collection timestamp, and change event sequence number, and a new lifecycle version is formed.

[0125] Through the model service interface, it provides one or more capabilities to upper-layer applications, such as querying, matching, topology generation, capacity assessment, and output of scheduling candidate sets.

[0126] Table 2 Inputs, Processing Contents, and Outputs of Each Module

[0127]

[0128] The above example involves: acquiring raw resource data from multiple data sources through an adaptation layer; standardizing the raw resource data through a standardization processing layer; defining the resource ontology through a resource ontology model; generating resource semantic representations through a modeling computing engine; outputting a consistent computing network resource model through versioned storage and conflict resolution mechanisms; and finally outputting capabilities through a model service interface.

[0129] The above example has at least the following advantages:

[0130] 1. By using a global resource identifier and fingerprint matching mechanism, the problems of resource duplication, mismatch, and inability to automatically associate resources in heterogeneous data sources are solved.

[0131] 2. By unifying resource ontology and resource state vectors, information such as computing, network, security, location, and operational status is transformed into computable data structures, thereby improving the automation level of resource matching and scheduling.

[0132] 3. Resource dependencies are expressed through resource relationship graphs, providing a machine-processable data foundation for fault impact analysis, business link tracing, capacity assessment, and disaster recovery switching.

[0133] 4. Improve the consistency, traceability and stability of multi-source data fusion results through conflict resolution and version control mechanisms.

[0134] 5. Through the model service interface, a unified input can be provided for business cloud migration, resource expansion, computing network collaborative scheduling, automated orchestration and intelligent operation and maintenance, reducing manual queries and manual judgment.

[0135] Figure 5 This is a structural block diagram of a network resource modeling system provided in an embodiment of the present invention. This system is used to execute the network resource modeling method provided in any of the above embodiments. This system and the network resource modeling methods of the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the network resource modeling system can be found in the embodiments of the network resource modeling methods described above. See also... Figure 5 The system may specifically include: an acquisition adaptation layer 310, a resource ontology model 320, and a modeling calculation engine 330.

[0136] Among them, the acquisition adaptation layer 310 is used to collect raw resource data from computing network resources;

[0137] Resource ontology model 320 is used to define the resource ontology of each resource object in the computing network resource based on the original resource data, so as to obtain a unified resource model template for computing network resources.

[0138] The modeling and computing engine 330 is used to model and compute computing network resources based on resource model templates to obtain resource semantic representations, and to obtain a unified computing network resource model based on the resource semantic representations. The resource semantic representations include at least one of resource state vectors, resource relationship graphs, and resource profile scores.

[0139] Optionally, the modeling calculation engine 330 may include:

[0140] The global resource identifier generation module is used to generate a global resource identifier for each resource object.

[0141] The object semantic representation determination module is used to determine the object semantic representation of each resource object represented by each global resource identifier, based on the resource model template.

[0142] The resource semantic representation acquisition module is used to obtain the resource semantic representation of the computing network resources based on the semantic representation of each object.

[0143] Based on this, an optional global resource identifier generation module may include:

[0144] The resource information obtaining unit is used to obtain the resource information of each resource object, wherein the resource information includes resource source identifier, resource semantic identifier and key attribute fingerprint;

[0145] The merging processing unit is used to respond to determining, based on the resource information of each resource object, that there are duplicate objects among the resource objects, to merge the duplicate objects and update each resource object;

[0146] The global resource identifier generation unit can be used to generate a global resource identifier for each resource object based on the resource information of the resource object.

[0147] Another optional object semantic representation determination module may include:

[0148] The object state vector determination unit can be used to determine the object state vector of a resource object based on a resource model template.

[0149] The object matching score acquisition unit is used to obtain the object matching score of the resource object based on each component in the object state vector and the weight parameters corresponding to each component.

[0150] The first obtaining unit is used to obtain the semantic representation of the object based on the object state vector and the object matching score.

[0151] Alternatively, the object semantic representation determination module may include:

[0152] The conflict resolution unit is used to respond to the determination that there is a conflict in the returned data of different data sources for resource objects in the computing network resources based on the resource model template, and to resolve the conflict of each returned data according to the trust source priority of each data source to obtain conflict resolution data.

[0153] The second obtaining unit is used to obtain the object semantic representation of the resource object based on the conflict resolution data.

[0154] In addition, the optional object semantic representation determination module may further include:

[0155] Lifecycle version update unit, used to update the lifecycle version of resource objects;

[0156] The second receiving unit may include:

[0157] The object semantic representation yields sub-units, which are used to obtain the object semantic representation of the resource object based on conflict resolution data and the updated lifecycle version.

[0158] Optionally, the raw resource data is collected from different data sources of the computing network resources, and the system may further include: a standardization processing layer; wherein,

[0159] The standardization processing layer is used to standardize the raw resource data collected from different data sources to obtain standard resource data.

[0160] Resource ontology model 320 is specifically used to define the resource ontology of each resource object in the computing network based on standard resource data, so as to obtain a unified resource model template for computing network resources.

[0161] Optionally, based on any of the above systems, the computing network resource model exposes a model service interface to perform at least one of the following: calculation, query, matching, and scheduling of computing network resources.

[0162] The computing network resource modeling system provided in this invention collects raw resource data from computing network resources through the cooperation of an adaptation layer and a resource ontology model. Based on the raw resource data, it defines the resource ontology of each resource object in the computing network resources, obtaining a unified resource model template for the computing network resources. Then, through a modeling calculation engine, it performs modeling calculations on the computing network resources based on the resource model template to obtain resource semantic representations (at least one of resource state vectors, resource relationship graphs, and resource profile scores), thereby obtaining a unified computing network resource model based on the resource semantic representations. This system, through resource data collection, resource ontology definition, and modeling calculations, constructs a unified computing network resource model, transforming heterogeneous computing and network resources into a computable, queryable, matchable, and schedulable data structure. This significantly improves the automated orchestration and intelligent operation and maintenance capabilities of resources, reducing manual intervention.

[0163] The computing network resource modeling system provided in this embodiment of the invention can execute the computing network resource modeling method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0164] It is worth noting that in the above embodiments of the computing network resource modeling system, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0165] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0166] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0167] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0168] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as network resource modeling methods.

[0169] In some embodiments, the network resource modeling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the network resource modeling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the network resource modeling method by any other suitable means (e.g., by means of firmware).

[0170] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips or system-on-a-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0171] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0172] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0173] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0174] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0175] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0176] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0177] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0178] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for modeling network resources, characterized in that, include: Collect raw resource data from computing network resources; Based on the original resource data, resource ontology definitions are performed on each resource object in the computing network resources to obtain a unified resource model template for the computing network resources; The computing network resources are modeled and calculated based on the resource model template to obtain resource semantic representations. A unified computing network resource model is obtained based on the resource semantic representations. The resource semantic representations include at least one of resource state vectors, resource relationship graphs, and resource profile scores.

2. The method according to claim 1, characterized in that, The process of modeling and calculating the network resources based on the resource model template to obtain a resource semantic representation includes: Generate a global resource identifier for each of the resource objects; For each resource object represented by the global resource identifier, the object semantic representation of the resource object is determined based on the resource model template; Based on the semantic representation of each object, the resource semantic representation of the computing network resource is obtained.

3. The method according to claim 2, characterized in that, The step of generating a global resource identifier for each of the resource objects includes: For each resource object, resource information of the resource object is obtained, wherein the resource information includes resource source identifier, resource semantic identifier and key attribute fingerprint; In response to determining, based on the resource information of each resource object, that there are duplicate objects among the resource objects, the duplicate objects are merged and the resource objects are updated; For each resource object, a global resource identifier is generated based on the resource information of the resource object.

4. The method according to claim 2, characterized in that, The step of determining the object semantic representation of the resource object based on the resource model template includes: Based on the resource model template, determine the object state vector of the resource object; Based on each component in the object state vector and the weight parameter corresponding to each component, the object matching score of the resource object is obtained; Based on the object state vector and the object matching score, the semantic representation of the object is obtained.

5. The method according to claim 2, characterized in that, The step of determining the object semantic representation of the resource object based on the resource model template includes: In response to the determination that there is a conflict in the returned data of different data sources for the resource object in the computing network resource based on the resource model template, the conflict is resolved in each returned data according to the trusted source priority of each data source to obtain conflict-resolved data; Based on the conflict resolution data, the object semantic representation of the resource object is obtained.

6. The method according to claim 5, characterized in that, The step of determining the object semantic representation of the resource object based on the resource model template further includes: Update the lifecycle version of the resource object; The process of obtaining the object semantic representation of the resource object based on the conflict resolution data includes: Based on the conflict resolution data and the updated lifecycle version, the object semantic representation of the resource object is obtained.

7. The method according to claim 1, characterized in that, The step of defining the resource ontology for each resource object in the computing network resources based on the original resource data includes: For the raw resource data collected from different data sources of the computing network resources, the raw resource data is standardized to obtain standard resource data. Based on the standard resource data, resource ontology definitions are performed for each resource object in the computing network resources.

8. The method according to any one of claims 1-7, characterized in that, The computing network resource model exposes a model service interface, through which at least one of the following can be performed: calculation, query, matching, and scheduling of the computing network resources.

9. A network resource modeling system, characterized in that, include: The system includes an adaptation layer, a resource ontology model, and a modeling computation engine; among which, The acquisition adaptation layer is used to acquire raw resource data from computing network resources; The resource ontology model is used to define the resource ontology of each resource object in the computing network resource based on the original resource data, so as to obtain a unified resource model template for the computing network resource. The modeling and computing engine is used to perform modeling and computing on the computing network resources based on the resource model template to obtain resource semantic representations, and to obtain a unified computing network resource model based on the resource semantic representations. The resource semantic representations include at least one of resource state vectors, resource relationship graphs, and resource profile scores.

10. The system according to claim 9, characterized in that, The raw resource data is collected from different data sources of the computing network resources. The system also includes a standardization processing layer. The standardization processing layer is used to standardize the original resource data collected separately to obtain standard resource data. The resource ontology model is specifically used to define the resource ontology of each resource object in the computing network resource based on the standard resource data, so as to obtain a unified resource model template for the computing network resource.

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