Network data processing method

Abstract

The embodiment of the invention provides a network data processing method. The network data processing method comprises the steps that first dimension relation information and at least one piece of second dimension relation information corresponding to a target network system are acquired; multi-dimensional relation information corresponding to the target network system is generated based on the first-dimensional relation information and the second-dimensional relation information, so that a target task result corresponding to a target task is obtained according to the multi-dimensional relation information, the target task is a task for the target network system, and the target task is a task for the target network system. The multi-dimensional relationship information is structured data fused with at least two dimension network association features. The multi-dimensional relation information is constructed through the first-dimensional relation information and the second-dimensional relation information, so that a data island is eliminated, the network object relevance and query efficiency are improved, the maintenance cost is reduced, and the observation and analysis capability can be enhanced in the execution process of a subsequent target task.

Description

Technical Field

[0001] The embodiments of the present specification relate to the field of computer technology, and in particular to a network data processing method. Background Art

[0002] With the continuous expansion of cloud network scale and the increasing complexity of task scenarios, the dimensions of cloud network data collection continue to extend, bringing comprehensive coverage of multi-dimensional data such as tenant routing, timing indicators, and status information.

[0003] At present, cloud network management realizes multi-source data storage through relational database, configuration management database and log service system, but the discreteness of data leads to the lack of association description between network objects, and there are significant obstacles to query efficiency and semantic unification. Therefore, a network data processing method is needed that can unify the definition of object information and integrate the association relationship of multi-dimensional data. Summary of the invention In view of this, the embodiments of this specification provide a network data processing method, a network data processing method applied to a cloud device. One or more embodiments of this specification also relate to a network data processing device, a computing device, a computer-readable storage medium, and a computer program product to solve the technical defects existing in the prior art.

[0004] According to a first aspect of an embodiment of this specification, a network data processing method is provided, including: Acquire first dimension relationship information and at least one second dimension relationship information corresponding to the target network system; Based on the first dimensional relationship information and each second dimensional relationship information, multidimensional relationship information corresponding to the target network system is generated to obtain a target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

[0005] According to a second aspect of an embodiment of this specification, a network data processing method is provided, which is applied to a cloud device, including: A network data processing request for a target network system sent by a receiving terminal side device, and obtaining first dimension relationship information and at least one second dimension relationship information corresponding to the target network system based on the network data processing request; Based on the first dimensional relationship information and each second dimensional relationship information, multidimensional relationship information corresponding to the target network system is generated, and the multidimensional relationship information is sent to the terminal side device, so that the terminal side device obtains the target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

[0006] According to a third aspect of an embodiment of this specification, a computing device is provided, including: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned network data processing method are implemented.

[0007] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned network data processing method are implemented.

[0008] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, including a computer program / instruction, which implements the steps of the above-mentioned network data processing method when executed by a processor.

[0009] By applying the solution of the embodiments of this specification, multi-dimensional relationship information is generated by integrating the first-dimensional relationship information and the second-dimensional relationship information of the target network system, and a multi-dimensional association description between network devices is established, thereby solving the semantic differences and lack of association of various data sources in cloud network management; and then the target task results are dynamically obtained based on the multi-dimensional relationship information, realizing the unified modeling of network object relationships and status data, improving the query efficiency and task execution accuracy across data sources, and providing global association support for network status diagnosis and policy optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a flow chart of a network data processing method provided by an embodiment of this specification; Figure 2 It is a schematic diagram of a network system analysis structure provided by an embodiment of this specification; Figure 3 is a flow chart of a network data processing method applied to a cloud device provided by an embodiment of this specification; Figure 4 is an architecture diagram of a network data processing system provided by an embodiment of this specification; Figure 5It is a processing flow chart of a network data observation and analysis method provided by an embodiment of this specification; Figure 6 It is a structural diagram of a network data processing device provided by an embodiment of this specification; Figure 7 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0011] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0012] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0013] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0014] In addition, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0015] First, the terms involved in one or more embodiments of this specification are explained.

[0016] Configuration Management Database (CMDB): As a core component of the information technology infrastructure library, it is used to systematically store and manage various configuration item data and their relationships. Through standardized modeling and dynamic update mechanisms, it supports scenarios such as event analysis and change impact assessment in IT service management. Integrating automated discovery tools and visual topology capabilities can effectively improve the accuracy and availability of configuration information. The implementation of this system requires the establishment of a standardized data maintenance process to ensure that information lifecycle management meets actual operation and maintenance needs.

[0017] Log Service (SLS): is a service for collecting, storing and analyzing log data. It can collect logs from multiple data sources, provide efficient storage methods, and have powerful query and analysis functions, which facilitates users to quickly locate problems and gain insight into task status, providing strong support for system operation and optimization.

[0018] Media Access Control Address Table (MAC Table): It is the core data structure used by network switching devices to record the mapping relationship between ports and physical addresses. This table dynamically maintains the connection information of physical layer devices through an automatic learning mechanism, and associates the MAC address of each access port with the corresponding interface.

[0019] In this specification, a network data processing method is provided, and a network data processing method applied to a cloud device is provided. One or more embodiments of this specification also involve a network data processing device, a computing device, a computer-readable storage medium, and a computer program product, which are described in detail one by one in the following embodiments.

[0020] In the current cloud network management and maintenance process, there are many challenges in terms of efficiency and accuracy. One of the main problems is the discreteness of cloud network data and the resulting low query efficiency. At present, data such as tenant routing, instance / device timing indicators, status information, configuration parameters, and resource relationships in cloud networks are scattered and stored in different data systems, such as relational databases, CMDB (Configuration Management Database), SLS (Log Service), etc. This data separation leads to a lack of correlation description between different data of the same cloud network object, and a lack of specific semantic correlation description between cloud network objects. Therefore, the task system faces the problem of high threshold and low efficiency when querying and utilizing this data.

[0021] In addition, there are also problems of confusion and duplication in the definition and maintenance of cloud network object information. The definition and description of the same cloud network object in different task systems are often different, and even the way to obtain the same type of data is quite different. This phenomenon directly leads to the difficulty in unifying the semantics of cloud network objects between task systems, and each system is forced to maintain its cloud network object description and construction logic, resulting in a waste of resources.

[0022] Moreover, each task system often faces the duplication of means when conducting observation and analysis of cloud network objects. Since observation and analysis of cloud network objects are required in different systems to support task operations, such as changes, monitoring, abnormal blocking, and fault self-healing, this leads to repeated investment and construction of many common observation and analysis strategies for the same type of objects. This situation not only increases the cost of development and maintenance, but also affects the data consistency across systems and the collaborative efficiency of observation strategies.

[0023] To solve the above problems, we urgently need to develop an efficient and integrated system that can unify and coordinate the management of multi-dimensional data in cloud networks and provide consistent object information definitions and efficient observation and analysis tools for each task system. This will not only improve the efficiency of cloud network management, but also significantly reduce the complexity and cost of system maintenance.

[0024] See also Figure 1 , Figure 1 A flowchart of a network data processing method provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0025] Step 102: Acquire first dimension relationship information and at least one second dimension relationship information corresponding to the target network system.

[0026] In actual applications, the target network system is a dynamic network environment that includes network devices, instances, configurations, and operating data; the first-dimensional relationship information is a basic set of associated features for building a multi-dimensional fusion framework; the second-dimensional relationship information is an extended set of associated features that complements the basic dimension.

[0027] The target network system can be understood as a complex environment consisting of interconnected network devices, virtual instances and their real-time operating data. For example, in a cloud computing scenario, the system includes virtual switches, container clusters, security policy instances and their configuration parameters. These entities form interactive relationships through physical links, virtual tunnels or logical policies. The connection status, resource allocation and performance indicators of each object in the system need to be uniformly described through multi-dimensional data integration.

[0028] The first dimension relationship information can be understood as a structured association feature that serves as a reference framework in the multi-dimensional fusion process. For example, in data center network management, the physical connection topology between devices or the dependency between resource pools can be defined as the first dimension. In addition, the timing relationship of the network interface between devices can also be defined as the first dimension. This specification does not limit the specific content of the first dimension. The first dimension can be further understood as a basic interaction rule used to describe network objects. This type of information is usually generated by parsing device configuration tables, resource allocation strategies, or physical wiring logic to provide an anchor point for subsequent superposition of other dimensional relationships. The second dimension relationship information can be understood as a set of extensible association features that work in synergy with the basic dimension. For example, the mounting relationship between virtual machines and storage volumes, the call link dependency between service instances, or the timing correlation of network interface traffic indicators, etc. This specification also does not limit the specific content of the second dimension. This type of information constructs a non-structural association of network objects at the operating state and resource scheduling level by analyzing dynamically generated log events, performance monitoring data, or policy change records, supplementing the static description of the basic dimension.

[0029] It should be noted that obtaining the first dimension relationship information and at least one second dimension relationship information corresponding to the target network system can be understood as extracting different levels of correlation features through structured analysis of network operation data to support multi-dimensional fusion analysis. The specific method is to extract single-dimensional data such as routing relationships, resource relationships, and timing indicator relationships from network record information, and determine the first dimension and other second dimensions based on preset rules. For example: by parsing the routing table, a hop-by-hop reachable relationship between devices is constructed as the first dimension, and at the same time, resource binding relationships are extracted from the instance configuration log, and interface traffic timing indicators are collected from the monitoring system as the second dimension; device configuration parameters and monitoring data streams can also be collected in real time, and physical connection relationships between network objects are dynamically generated as the basic dimension, and extended dimensions can be generated based on policy dependencies or performance fluctuation characteristics; network log event streams can also be parsed to identify device interaction patterns to form basic dimensions, and auxiliary dimensions can be generated in combination with alarm association rules or resource scheduling records, etc. This manual does not impose any restrictions on this.

[0030] By determining a basic dimension and integrating other dimensions to construct multi-dimensional relationship information, a unified description framework covering the physical deployment, logical configuration and real-time status of network objects can be formed in the future, solving the problem of association breaks caused by inconsistent semantics of data across systems; based on dynamically updated multi-dimensional relationships, network topology and resource scheduling decision-making basis are generated, and when locating anomalies, physical connection anomalies of equipment and resource allocation conflicts are simultaneously analyzed, thereby improving the execution efficiency and accuracy of operation and maintenance tasks in complex network environments.

[0031] Further, obtaining first dimension relationship information and at least one second dimension relationship information corresponding to the target network system includes: Acquire at least one network record information corresponding to the target network system; Acquire at least two single-dimensional relationship information according to each network record information; The first dimension relationship information and at least one second dimension relationship information are determined in each single-dimensional relationship information.

[0032] In actual applications, network record information is a set of original data including network device configuration parameters, instance operation indicators and operation logs; single-dimensional relationship information is structured data of association relationships between network objects with independent semantics extracted from network records.

[0033] Network record information can be understood as a collection of raw data that reflects the operating characteristics of the network system, collected from data sources such as data management platforms and log storage systems. For example, routing table files record forwarding path rules between devices, configuration management databases store binding relationships between instances and resources, network interface throughput timing indicators collected by monitoring systems, and device operation events recorded by log systems. This type of information is extracted in real time or periodically from data sources such as CMDB and relational databases through interfaces to form raw records of network object configuration, status, and interaction behavior.

[0034] Single-dimensional relationship information can be understood as network object relationship structured data from a single perspective parsed from network records based on specific association rules. For example, the structured data of the route reachability relationship between devices generated by hop-by-hop calculation by analyzing the routing table file, or the structured data of the virtual machine and storage volume mounting relationship constructed by parsing the instance configuration log. The process of extracting this type of information includes: identifying network object identifiers from the original records, deriving the association relationship between objects according to preset semantic rules (such as routing forwarding logic, resource dependency strategy), and then forming structured data that can describe the interaction characteristics of a specific dimension.

[0035] It should be noted that obtaining at least two single-dimensional relationship information based on each network record information can be understood as extracting network object association relationships of different dimensions from multi-source network records according to specific semantic rules to support subsequent fusion analysis. The specific method is to parse the routing table file to calculate the routing reachability relationship between devices hop by hop as the first single-dimensional information, and at the same time extract the mounting relationship between virtual machines and storage volumes from the instance configuration log as the second single-dimensional information. For example: the routing reachability relationship is generated by analyzing the next-hop address and the target network segment rule in the routing table, and the resource dependency relationship is generated based on the instance resource binding information recorded in the configuration management database; the traffic timing data of the network interface can also be analyzed to generate the communication load fluctuation trend relationship information between devices, and the abnormal propagation relationship information is generated in combination with the alarm trigger record in the log event stream; the security policy configuration can also be parsed to generate the access control relationship, and the call link relationship between service instances can be constructed based on the container orchestration log, etc. This manual does not impose any restrictions on this.

[0036] By extracting independent dimension relationship information such as route reachability and resource dependency from multi-source network records, a basic feature set is provided for multi-dimensional data fusion. Basic dimensions and extended dimensions are dynamically screened based on preset rules to support flexible adaptation to different network analysis scenarios in the subsequent multi-dimensional relationship information construction process, thereby improving the efficiency of data integration and task execution in complex network environments.

[0037] Furthermore, at least two single-dimensional relationship information are obtained according to each network record information, including: The routing relationship information, resource relationship information and timing indicator relationship information are obtained according to the record information of each network.

[0038] In practical applications, routing relationship information is the association information that describes the path reachability between network objects; resource relationship information is the structural data that characterizes the resource dependency or binding relationship between network objects; and timing indicator relationship information is the timing association feature that reflects the fluctuation trend of the operating status of network objects.

[0039] Routing relationship information can be understood as structured information that describes the reachability of communication paths between objects based on the routing configuration of network devices. For example, by parsing the routing table file of a virtual router or physical switch, the mapping relationship between the target network segment and the next hop address is calculated hop by hop, and the topology of the reachable path between devices is constructed. In the cloud network scenario, this information can represent the interconnection rules between subnets within a virtual private cloud, or the traffic forwarding logic of cross-regional gateways, providing basic dimensional data for subsequent path analysis.

[0040] Resource relationship information can be understood as structured data that describes resource dependencies between objects by analyzing network instance configurations. For example, the service binding relationship between a load balancer and a backend virtual machine, the mounting relationship between an elastic storage volume and a computing instance, or the association relationship between a security group policy and a network interface. This type of information is generated by parsing instance deployment records and resource allocation policy files in the configuration management database, extracting logical binding features between network objects, and forming association information in the resource topology dimension.

[0041] Time series indicator relationship information can be understood as the correlation information generated based on network monitoring data that reflects the time series fluctuation characteristics of the object's operating status. For example, the change trend of the incoming / outgoing traffic rate of the network interface, the correlation between the CPU utilization and memory usage of the virtual machine, or the linkage characteristics of the packet loss rate and delay indicators of the gateway device. By collecting the indicator data stream in the time series database and combining it with the abnormal trigger records in the log events, the time series dimension characteristics that describe the dynamic correlation of the network object's operating status are constructed.

[0042] By extracting path accessibility, resource dependency and status fluctuation characteristics from routing configuration, resource allocation records and time-series monitoring data, a multi-dimensional independent analysis perspective is formed; based on the superposition characteristics of single-dimensional relationship information, it supports dynamic fusion to generate a global network view, provides a cross-dimensional correlation analysis basis for tasks such as anomaly location and resource scheduling, and improves the efficiency of data modeling and decision support in complex network environments.

[0043] Considering that obtaining representative relationship information in the network can effectively improve the efficiency of data processing, the single-dimensional relationship information is any one of routing relationship information, resource relationship information or timing indicator relationship information; Further, determining the first dimension relationship information and at least one second dimension relationship information in each single-dimensional relationship information includes: Determining that the routing relationship information is first dimension relationship information; The resource relationship information is determined to be the second dimension relationship information and the timing indicator relationship information is determined to be the second dimension relationship information.

[0044] When constructing multi-dimensional network association information, routing relationship information is used as the first dimension relationship information, mainly based on its characteristics as the basic framework of network communication. Routing accessibility directly reflects the path rules of physical or logical connections between network objects. For example, by parsing the topological relationship generated by the routing table one by one, it can clearly describe the core communication logic such as inter-subnet interconnection and cross-regional forwarding, which provides a stable structural foundation for the subsequent superposition of extended dimensions such as resource dependence and performance fluctuation. Resource relationship information and timing indicator relationship information as the second dimension are used to supplement the dynamic operation characteristics of network objects: resource relationship reveals the dependency chain of network function implementation by parsing the binding rules in the instance configuration (such as the association between the load balancer and the backend service); the timing indicator relationship is based on monitoring data such as interface traffic and device load, reflecting the real-time change trend of the operation status.

[0045] By taking routing relationships as the basic dimension, it is possible to dynamically superimpose the impact range of resource scheduling strategies or the propagation path of performance anomalies while ensuring the core characteristics of network connectivity. This allows multi-dimensional correlation analysis to anchor the network infrastructure and flexibly respond to changes in resource configuration and fluctuations in operating status, thereby improving the accuracy of data correlation and the adaptability of policy generation in scenarios such as fault location and capacity planning.

[0046] Step 104: Generate multidimensional relationship information corresponding to the target network system based on the first dimensional relationship information and each second dimensional relationship information, so as to obtain a target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

[0047] In practical applications, multidimensional relationship information is structured data that integrates network correlation features at different levels; the target task is the analysis and operation requirements for network operation and maintenance or resource management; and the target task result is a state assessment or execution strategy that combines the output of multidimensional correlation features.

[0048] Multidimensional relationship information can be understood as a multi-scale data association framework formed by integrating basic dimensional relationships and extended dimensional relationships. For example, in a cloud network scenario, this information set includes both the basic relationship of physical connection or logical binding between devices and extended relationships such as resource allocation strategies and performance indicator trends, forming a panoramic view that can describe the interaction rules, resource dependencies, and operating status of network objects. This type of information continuously updates the multi-level association features between devices by dynamically collecting configuration changes, log events, and monitoring data.

[0049] The target task can be understood as an analysis or execution instruction designed for the operating status of the network system or resource scheduling requirements. For example, network resource query tasks, network emergency tasks, network change tasks, network scheduling tasks, network topology tasks, network diagnosis tasks or network detection tasks, etc. This type of task relies on the correlation features of different levels in the multi-dimensional relationship information to achieve a comprehensive analysis from basic connection status to extended operation indicators. The target task result can be understood as the result obtained for the target task based on the analysis of multi-dimensional relationship information. For example, output the associated resource topology map and repair priority list of abnormal devices, generate configuration adjustment suggestions for resource pool expansion, or provide path optimization solutions for traffic scheduling strategies.

[0050] By taking the basic dimensional relationship as the data fusion anchor point and combining it with the extended dimensional relationship to build a multi-scale association framework, we can achieve a globally consistent description of network object connection rules, resource allocation and operation indicators, and solve the limitations of association analysis caused by cross-system data discreteness. Based on dynamic association characteristics, we can quickly respond to task requirements such as network diagnosis and resource planning, and simultaneously output the scope of device interaction impact and the basis for policy adjustment, so as to improve the accuracy of task execution and decision-making efficiency in complex network environments.

[0051] Further, generating multi-dimensional relationship information corresponding to the target network system based on the first dimensional relationship information and each second dimensional relationship information includes: Determining a first dimensional relationship between network devices in the target network system according to the first dimensional relationship information; Determining the second dimensional relationship between each network device in the target network system based on each second dimensional relationship information; The first dimension relationship and the second dimension relationship between the network devices are integrated to generate multi-dimensional relationship information corresponding to the target network system.

[0052] In practical applications, the first dimensional relationship is the basic association information selected when multi-dimensional data is integrated; network equipment is the entity or virtualized component that carries network functions; and the second dimensional relationship is the extended dimensional association feature superimposed on the basic association information.

[0053] The first dimension relationship can be understood as the structured correlation information that serves as the benchmark framework in the process of multi-dimensional relationship fusion. For example, in the network security analysis scenario, the security policy dependency relationship between devices can be used as the basic dimension to generate the access control topology by parsing the firewall rules; in the protocol interaction analysis scenario, the communication protocol handshake relationship between devices can be used as the basic dimension to generate the protocol interaction link diagram based on the session log. The selection of this dimension needs to adapt to the specific analysis objectives to ensure that the extended dimension can be effectively superimposed based on its structure.

[0054] Network devices can be understood as hardware or virtualized units with independent network identification and communication functions. For example, physical switches implement Layer 2 forwarding based on MAC Table (Media Access Control Address Table), virtual router instances in cloud environments manage inter-subnet traffic through routing policies, and service mesh components in container networks are responsible for communication management and control between microservices. The configuration change records, interface status indicators, session logs and other data generated by such devices during operation constitute the data source for multi-dimensional correlation analysis.

[0055] The second dimension relationship can be understood as the supplementary correlation features superimposed on the basic dimension correlation information. For example, in the security policy basic dimension, the correlation between the device vulnerability scan results and the number of policy hits is superimposed; in the protocol interaction basic dimension, the timing correlation features of the communication delay index and the protocol retransmission rate are superimposed. This type of relationship is generated by associating resource allocation records, performance monitoring data, or log event streams, and is used to describe the multi-angle characteristics of network behavior.

[0056] It should be noted that fusing the first dimension relationship and the second dimension relationship between each network device to generate the multi-dimensional relationship information corresponding to the target network system can be understood as constructing composite network information by hierarchically fusing the association characteristics between devices of different dimensions to support multi-scenario analysis. The specific method is to construct the initial topology graph structure with the first dimension relationship, and then inject the second dimension relationship as an additional attribute into the connection relationship between nodes. For example: after generating the basic connection graph between devices based on routing reachability, embed resource binding relationships (such as load balancer and virtual machine group service dependencies) and timing performance associations (such as interface traffic and target device load fluctuation synchronization) in the edge links to form composite edge attributes; it is also possible to convert different dimensional relationships into association matrices, and generate the fused multi-dimensional relationship information through matrix weighted superposition; it is also possible to establish a hierarchical skeleton based on the first dimensional relationship, dynamically select the second dimensional relationship according to the task scenario to generate a hierarchical subgraph for joint analysis, etc. This manual does not impose any restrictions on this.

[0057] By dynamically selecting the basic dimensional relationships that adapt to the analysis needs, the flexibility of the multi-dimensional fusion framework is guaranteed; based on the superposition of network equipment operation data, the dimensional relationships are extended to form composite information that takes into account both static architecture and dynamic behavior. The fusion information generated in this way can support multi-scenario analysis needs such as network configuration optimization and abnormal behavior detection, and enhance the integrity of network status representation and the pertinence of decision support.

[0058] Further, obtaining a target task result corresponding to the target task according to the multidimensional relationship information includes: Generate network status information corresponding to the target network system and multi-dimensional relationships between network devices in the target network system through the multi-dimensional relationship information; According to the network status information and / or the multi-dimensional relationship between each network device, a target task result corresponding to the target task is obtained.

[0059] In practical applications, network status information is a data set that reflects the real-time operating characteristics of the target network system; multi-dimensional relationships are structured information that describes various correlation characteristics between network devices.

[0060] Network status information can be understood as a dynamic data set that reflects the system operation characteristics generated by integrated network observation and analysis tools. For example, the interface bandwidth utilization collected based on traffic probes, abnormal session patterns identified by intelligent algorithms (such as sudden high-latency traffic), and device health status scores (such as CPU overload alarms) output by integrated network diagnostic tools. This type of information continuously collects configuration change records, interface performance indicators, log event streams and other data to form a real-time snapshot of the network operation status, providing data support for abnormal location and capacity assessment.

[0061] Multi-dimensional relationships can be understood as composite association information between network devices built based on different data layers. For example, the routing paths between devices (such as the neighbor relationship of cross-regional gateways), resource binding relationships (such as the mounting dependency of virtual machine groups and storage volumes), and performance indicator associations (such as the time synchronization of firewall packet loss rate and backend service response delay). This type of relationship forms an association graph that supports cross-dimensional queries by integrating multi-source information such as routing topology, resource configuration strategies, and time-series monitoring data, such as querying the backend instances associated with a specified load balancer and their historical performance fluctuation trends.

[0062] refer to Figure 2 , Figure 2A schematic diagram of a network system analysis structure provided for an embodiment of the present specification, wherein the application layer includes network resources, network changes, network scheduling, network topology, network diagnosis and network detection, which are used to initiate operations according to different task requirements and execute processes such as observation scenario management and detection task coverage; the network data processing layer obtains network status information and multi-dimensional relationships between network devices through multi-dimensional relationship information, so that the network data processing layer obtains task interfaces such as network resources, network operation and maintenance, network planning, network indicators, network evaluation, network water level and network usage analysis, so that subsequent application layers can interact with the support of the above task interfaces; in the single-dimensional information layer, routing relationship information, resource relationship information and timing indicator relationship information jointly construct the associated information of the network structure, providing support for the system to build and update network information; the data layer covers network devices or clusters, network instances or configurations, network indicators and task logs, obtains and stores monitoring data through steps such as associated resource query and link query and detection task issuance, and provides basic support for subsequent resource feature and configuration query, network information update, diagnosis object analysis, diagnosis result query, analysis algorithm startup and analysis result query.

[0063] The target task corresponding to the target task is obtained according to the network status information and / or the multi-dimensional relationship between each network device. Multiple task interfaces can be encapsulated by utilizing the network status information and / or the multi-dimensional relationship between each network device, such as the network topology interface: query and dynamically build the network topology according to the resource and routing relationship in the multi-dimensional relationship between each network device, and provide a panoramic network topology view; network indicator interface: quickly extract the associated indicator items of the target network object through the multi-dimensional relationship between each network device, and provide basic support for further network object analysis or indicator observation; network evaluation interface: first build the topology of the network object through the resource and routing relationship, and pull the network indicators of its related objects, based on the topology and Indicators, combined with network detection, network diagnosis and network object anomaly detection algorithm analysis and other means combined with a global assessment of network status information, give the health status of network objects; Network water level interface: for the input target object, query and analyze the water level indicators (combined with historical water level indicators, current water level indicators and future water level forecasts), and output the expansion and contraction suggestions for the target object; Network analysis interface: query and build a panoramic network topology of the target object, analyze the user networking characteristics based on the topology, identify the risks and non-standard architectures in the user networking, output the user's networking optimization items, etc. This manual does not impose any restrictions on this, and then determine the interface to be called according to the requirements of the target task to improve the operation and maintenance efficiency of the task.

[0064] When building a network evaluation system, network status information provides basic data support for real-time operation characteristics. For example, by collecting dynamic indicators such as device interface packet loss rate and session connection peak, it is possible to identify abnormal fluctuations in the current network load; and by combining historical water level data (such as the trend of changes in storage resource occupancy), a capacity prediction model can be established. This cross-validation of real-time and historical data allows health status assessment to reflect instantaneous anomalies and identify potential resource bottlenecks, providing a quantitative basis for expansion and contraction strategies.

[0065] The structured information of multi-dimensional relationships provides a framework support for correlation analysis. When a processor overload alarm is detected in a virtual machine instance, the associated load balancer and backend service group can be quickly located based on the resource topology relationship, and the traffic source subnet can be traced through the routing relationship. This cross-dimensional correlation query capability enables abnormal cause analysis to penetrate multiple levels such as resource configuration and traffic path, avoiding misjudgment or omissions caused by traditional single-dimensional troubleshooting.

[0066] By integrating real-time status monitoring and multi-dimensional correlation analysis, operation and maintenance decisions can respond to both immediate anomalies and system bottlenecks. For example, in a traffic burst scenario, an alarm can be triggered by a sudden increase in interface bandwidth utilization, and based on historical water level data, it can determine whether it is a periodic task peak, thereby selecting elastic expansion or traffic scheduling strategies. This multi-dimensional data-driven decision-making mechanism significantly improves the accuracy of fault handling and the economy of resource scheduling.

[0067] Considering that network analysis tasks require a relatively large amount of data and require relatively complex judgments, the target tasks include network analysis tasks; According to the network status information and the multi-dimensional relationship between each network device, a target task result corresponding to the target task is obtained, including: Determine a current state of the network based on the network state information; In the case where the current state of the network is an abnormal state, determining at least one initial network device according to the network state information, and determining at least one reference network device corresponding to each initial network device according to the multi-dimensional relationship between the network devices; generating a network analysis result based on each initial network device and each reference network device; When the current state of the network is normal, a network analysis result is generated based on the network state information.

[0068] In practical applications, network analysis tasks are maintenance operations that require processing multi-source data and performing complex logical judgments; the current state of the network is the state classification of a data set that reflects the real-time operating characteristics of the system; the current state of the network is an abnormal state when an operating indicator or correlation relationship deviates from a preset threshold; the current state of the network is a normal state when all indicators and correlation relationships are within the expected range; the initial network device is the suspicious device node located in the first round during the anomaly detection process; the reference network device is an extended device set that has multi-dimensional associations with the initial device; the network analysis result is a disposal suggestion or optimization conclusion generated by combining the abnormal characteristics and associated paths.

[0069] Network analysis tasks can be understood as a systematic operational process for root cause location or performance evaluation based on multi-dimensional network data. For example, when a sudden increase in network latency is detected in a certain area, the system needs to simultaneously analyze the routing topology status (such as cross-domain gateway congestion), resource load distribution (such as virtual machine instance overload ratio), and traffic timing characteristics (such as specific application burst traffic patterns), and generate anomaly attribution reports by cross-verifying the correlation of different data layers. Such tasks usually involve complex operations such as routing reachability verification, resource dependency chain tracing, and performance indicator correlation analysis.

[0070] The current state of the network can be understood as the system operation status judgment formed by real-time collection of device performance indicators and topological relationships. For example, the comprehensive health score is calculated based on indicators such as interface bandwidth utilization, network request retransmission rate, and routing table oscillation frequency. When the score is lower than the threshold, it is judged as an abnormal state, otherwise it is a normal state. In the process of state judgment, it is necessary to integrate instantaneous indicator fluctuations (such as the utilization rate of a switch processor exceeding 90%) and historical baseline data (such as the average load of the device in the same period of the past week) to avoid misjudgment caused by occasional fluctuations.

[0071] The current state of the network is abnormal, which means that the system has detected a continuous deviation in key operating parameters or associations. For example, if the number of session connections of a load balancer exceeds the capacity limit for 5 consecutive minutes, and the response delay of its associated backend instance increases synchronously, it is considered abnormal. The determination of abnormal status may trigger analysis of associated links (such as checking whether the security group policy bound to the load balancer incorrectly intercepts traffic) or resource topology tracing (such as confirming whether the backend instance has service degradation due to a storage volume mounting failure).

[0072] The current state of the network is normal, which means that the system operation indicators and the relationship between devices are within the acceptable range. For example, the traffic distribution of each port of the core switch conforms to the preset load balancing strategy, and the session establishment rate of the firewall directly connected to it is within the historical fluctuation range. The network analysis results generated at this time may include preventive suggestions (such as predicting the risk of exhaustion of a subnet address pool in the next 3 days), or output performance optimization solutions (such as adjusting routing weights to improve cross-region transmission efficiency).

[0073] The initial network device can be understood as the problem triggering node that is first identified during the anomaly detection process. For example, when the network detection task detects that a virtual router has continuous packet loss, the router is marked as the initial device. The determination of the initial device may be based on threshold alarms (such as a sudden increase in interface error counts), topology weights (such as core layer device failures have a larger impact range), or task priorities (such as devices that carry critical tasks are checked first).

[0074] Reference network devices can be understood as a collection of extended devices that have routing, resource, or performance associations with the initial device. For example, for the initial load balancer processor overload exception, the reference device may include its bound backend virtual machine instance (resource binding relationship), the backup load balancer in the same availability zone (routing redundancy relationship), and the associated database connection pool (performance association relationship). The selection of reference devices needs to be combined with multi-dimensional relationship information, such as tracing the dependency chain through resource topology, or analyzing the traffic source device based on the routing path.

[0075] The results of network analysis can be understood as actionable conclusions that integrate abnormal features, associated paths, and disposal strategies. For example, when a network isolation failure occurs in a container cluster, the analysis results may include the root cause of the abnormality (such as a network plug-in configuration error on a node), the scope of impact (such as 10 microservices that depend on the node), repair suggestions (such as rolling back recent configuration changes and restarting the plug-in service), and preventive measures (such as adding a cross-node heartbeat detection mechanism). For normal state scenarios, the analysis results may output capacity planning suggestions (such as the bandwidth usage rate in a certain area is close to the threshold, and it is recommended to pre-expand) or architecture optimization solutions (such as adjusting subnet division to reduce the risk of broadcast storms).

[0076] By combining real-time network status determination with multi-dimensional correlation analysis, it is possible to quickly locate the source of the problem and the set of devices it affects in an abnormal state (such as tracing the abnormal propagation path based on routing relationships), and to generate preventive optimization strategies in a normal state (such as adjusting the load distribution in advance through resource water level prediction). This state-based processing mechanism improves analysis efficiency, avoids redundant calculations in a normal state, and ensures that the abnormal handling solution covers the complete chain of associated impacts.

[0077] Considering that the data in the network system changes in real time, the method further includes: Real-time monitoring of data change events corresponding to the target network system; The multidimensional relationship information is updated based on the data change event.

[0078] In practical applications, data change events are configuration adjustments or performance indicator fluctuations that occur in real time in the network system; multidimensional relationship information is structured data information that reflects the dynamic association relationship between network devices.

[0079] Data change events can be understood as atomic-level change records such as configuration updates, resource status changes, or performance indicator fluctuations generated during the operation of the network system. For example, the addition of a preset policy to a router causes a change in the traffic path, address reallocation caused by virtual machine instance migration, and alarm events triggered by interface bandwidth utilization exceeding the preset threshold. Such events are captured in real time by underlying probes or log collection modules to form an event stream containing the change content, timestamp, and scope of impact, providing a data input source for information updates.

[0080] Multidimensional relationship information can be understood as a collection of associations between network devices that are dynamically maintained based on real-time events. When a change is detected in the backend server list of a load balancer, the system will parse the data change event and update the instance binding information in the resource relationship; if an adjustment of the routing table entry of a cross-regional gateway is detected, the reachable path topology in the routing relationship will be corrected synchronously. This dynamic maintenance mechanism ensures that the association information always reflects the true state of the current network architecture, such as real-time tracking of changes in the binding relationship between a virtual machine and a storage volume or security group in a hot migration scenario.

[0081] By capturing network configuration changes and operating indicator fluctuations in real time, and dynamically updating core information elements such as route reachability and resource dependencies, it can not only quickly respond to high-frequency network change operations in cloud environments (such as the increase or decrease of instances triggered by elastic scaling), but also ensure that the anomaly detection algorithm performs analysis based on recent data to avoid misjudgments due to information lags, thereby improving the timeliness and accuracy of network status assessment and fault location.

[0082] By applying the solution of the embodiments of this specification, unified information covering the multi-dimensional relationships between network devices is constructed through multi-source data extraction and integration of routing relationship information, resource relationship information and timing indicator relationship information, eliminating the problem of isolated storage of routing, resource and indicator data in cloud network management; combining network status information with real-time data change event update mechanism, multi-dimensional relationship description is dynamically generated, the accuracy of correlation analysis between initial network devices and reference network devices during network anomaly diagnosis is enhanced, redundant operations of cross-system queries are reduced, and consistent multi-dimensional semantic support is provided for resource scheduling and link optimization.

[0083] Corresponding to the above method embodiment, this specification also provides a network data processing method embodiment applied to a cloud device, see Figure 3 , Figure 3 A flowchart of a network data processing method applied to a cloud device provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0084] Step 302: A network data processing request for a target network system sent by a receiving end-side device, and based on the network data processing request, first dimension relationship information and at least one second dimension relationship information corresponding to the target network system are acquired.

[0085] Step 304: Generate multidimensional relationship information corresponding to the target network system based on the first dimensional relationship information and each second dimensional relationship information, and send the multidimensional relationship information to the terminal side device, so that the terminal side device obtains the target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

[0086] In actual applications, the end-side device is a localized computing node that performs network status monitoring or policy execution; the network data processing request is an operation instruction that triggers the cloud to perform network relationship modeling and data analysis.

[0087] The end-side device can be understood as a terminal or gateway device deployed at the edge of the network and equipped with data processing capabilities. For example, in a cloud network architecture, smart routers, container nodes, or operation and maintenance terminals can obtain network topology relationships or resource status information by submitting data processing requirements to the cloud. Such devices usually need to perform localized tasks such as link detection and traffic scheduling based on real-time network status, relying on multi-dimensional relationship data pre-processed in the cloud to reduce local computing load.

[0088] Network data processing requests can be understood as network relationship modeling or analysis task trigger instructions initiated by the end-side device to the cloud. For example, when the edge gateway detects a local network delay anomaly, it sends a request containing the target device identifier to the cloud, requesting to obtain the routing path, resource binding relationship, and historical performance indicators associated with the device. The request usually contains the scope of the network objects to be analyzed, the task type identifier, and the data processing timeliness requirements, triggering the cloud to execute the dynamic construction and distribution of multi-dimensional relationship information.

[0089] The above is a schematic scheme of a network data processing method applied to a cloud device in this embodiment. It should be noted that the technical scheme of the network data processing method applied to the cloud device and the technical scheme of the network data processing method described above belong to the same concept, and the details of the technical scheme of the network data processing method applied to the cloud device that are not described in detail can all be referred to the description of the technical scheme of the network data processing method described above.

[0090] By applying the solution of the embodiments of this specification, after receiving the end-side network data processing request through the cloud device, the routing, resource and timing indicator data are extracted to build unified information covering the multi-dimensional relationships between devices, and realize the consistent mapping of cross-level network object semantics; the multi-dimensional relationship information is dynamically distributed to the end-side device, so that the end side can directly perform abnormal diagnosis or policy optimization tasks based on the associated information pre-processed by the cloud, reduce the transmission delay of the original data interaction, and simultaneously ensure the real-time data of the cloud data update and the end-side task execution, so as to form the network status collaborative perception capability under the distributed processing architecture.

[0091] See also Figure 4 , Figure 4 An architecture diagram of a network data processing system provided by an embodiment of the present specification is shown, and the network data processing system may include a client 100 and a server 200; The client 100 is used to send a network data processing request for a target network system to the server 200; The server 200 is used for receiving a network data processing request for a target network system sent by a terminal device, and obtaining first dimension relationship information and at least one second dimension relationship information corresponding to the target network system based on the network data processing request; generating multidimensional relationship information corresponding to the target network system based on the first dimension relationship information and each second dimension relationship information; and sending the multidimensional relationship information to the client 100; The client 100 is also used to receive the multidimensional relationship information sent by the server 200, and obtain the target task result corresponding to the target task based on the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

[0092] The solution of the embodiments of this specification is applied, through the centralized processing of routing, resource and timing indicator data by the server, to build unified information containing multi-dimensional relationships between devices, so as to achieve consistent understanding of the semantics of network objects between the cloud and the terminal; the client directly executes abnormal diagnosis or policy optimization tasks based on the multi-dimensional relationship information sent by the cloud, reduces the frequency of original data requests through pre-built association relationships, and combines with the real-time synchronization update mechanism of the cloud to ensure dynamic matching of terminal-side task execution and network status changes, thus forming cross-level device collaborative analysis capabilities, and simultaneously supporting the precise implementation of network path planning and resource scheduling decisions.

[0093] The network data processing system may include multiple clients 100 and a server 200, wherein the client 100 may be referred to as a terminal device and the server 200 may be referred to as a cloud device. Multiple clients 100 may establish a communication connection through the server 200. In the network data processing scenario, the server 200 is used to provide network data processing services between multiple clients 100. Multiple clients 100 may serve as a sender or a receiver, respectively, and realize communication through the server 200.

[0094] The user can interact with the server 200 through the client 100 to receive data sent by other clients 100, or send data to other clients 100, etc. In the network data processing scenario, the user can publish a data stream to the server 200 through the client 100, and the server 200 generates multi-dimensional relationship information according to the data stream, and pushes the multi-dimensional relationship information to other clients that establish communication.

[0095] The client 100 and the server 200 are connected via a network. The network provides a medium for a communication link between the client 100 and the server 200. The network may include various connection types, such as wired or wireless communication links or optical fiber cables, etc. The data transmitted by the client 100 may need to be encoded, transcoded, compressed, etc. before being released to the server 200.

[0096] The client 100 can be a browser, an APP (Application), or a web application such as an H5 (HyperText Markup Language 5, Hypertext Markup Language Version 5) application, or a light application (also known as a mini-program, a lightweight application) or a cloud application, etc. The client 100 can be based on the software development kit (SDK, Software Development Kit) of the corresponding service provided by the server 200, such as based on the real-time communication (RTC, Real Time Communication) SDK development and acquisition. The client 100 can be deployed in an electronic device and needs to rely on the device to run or some APPs in the device to run. For example, the electronic device can have a display screen and support information browsing, such as a personal mobile terminal such as a mobile phone, a tablet computer, a personal computer, etc. Various other types of applications can also be configured in the electronic device, such as human-computer dialogue applications, model training applications, text processing applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0097] The server 200 may include servers that provide various services, such as servers that provide communication services to multiple clients, servers for background training that support models used on clients, and servers that process data sent by clients. It should be noted that the server 200 can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. The server can also be a server of a distributed system, or a server combined with a blockchain. The server can also be a cloud server for basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology.

[0098] It is worth noting that the network data processing method provided in the embodiments of this specification is generally executed by the server, but in other embodiments of this specification, the client may also have similar functions to the server, thereby executing the network data processing method provided in the embodiments of this specification. In other embodiments, the network data processing method provided in the embodiments of this specification may also be jointly executed by the client and the server.

[0099] The following combination Figure 5 , taking the application of the network data processing method provided in this specification in the network data observation and analysis method as an example, the network data processing method is further explained. Figure 5 A processing flow chart of a network data observation and analysis method provided by an embodiment of the present specification is shown, which specifically includes the following steps.

[0100] Step 502: Obtain network record information corresponding to the target network system, including network timing indicators, CMDB, network alarm time, and network configuration table.

[0101] Step 504: Obtain routing relationship information, resource relationship information and timing indicator relationship information according to the above-mentioned network record information.

[0102] Step 506: Determine the routing relationship between each network device in the network system according to the routing relationship information.

[0103] Step 508: Determine the resource relationship and timing indicator relationship between each network device in the network system according to the resource relationship information and timing indicator relationship information.

[0104] Step 510: Based on the routing relationship between the various network devices, the resource relationship and timing indicator relationship between the various network devices are integrated into the routing relationship between the various network devices to obtain a multi-dimensional relationship between the various network devices.

[0105] Step 512: Generate multi-dimensional relationship information of the target network system according to the multi-dimensional relationships between the above-mentioned network devices.

[0106] Step 514: Identify the network devices that need to be observed according to the observation scenario selected by the user.

[0107] Step 516: Acquire resources and associated network devices associated with the network devices that need to be observed based on the multi-dimensional relationship information of the target network system, and store the resources and associated network devices associated with the network devices that need to be observed in an observation scenario library.

[0108] Step 518: Acquire resources associated with the network device to be observed and the associated network device in real time to detect the above anomaly and acquire the corresponding diagnosis result of the network device to be observed based on the data associated with the network device to be observed.

[0109] Step 520: When an abnormality occurs in the target network system, the resources associated with the above network devices and the associated network devices are analyzed to obtain the network problem analysis results.

[0110] Step 522: Performing a network analysis task with the initial network device and the reference network device to obtain a network analysis result.

[0111] Step 524: Acquire the real-time indicators and abnormal alarm events corresponding to the network devices that need to be observed, and store the real-time indicators and abnormal alarm events in the analysis result library.

[0112] Step 526: When the user needs network detection, the resources associated with the network device to be observed and the associated network device are obtained according to step 518.

[0113] Step 528: When the user needs network diagnosis, obtain the network problem analysis result according to step 520.

[0114] Step 530: When the user needs algorithm services, the resources associated with the above network devices and the associated network devices in the preset time period are obtained according to step 520, and the network problem analysis results are obtained according to the preset traffic balancing detection and / or general anomaly detection.

[0115] The solution of the embodiments of this specification is applied, through the centralized processing of routing, resource and timing indicator data by the server, to build unified information containing multi-dimensional relationships between devices, so as to achieve consistent understanding of the semantics of network objects between the cloud and the terminal; the client directly executes abnormal diagnosis or policy optimization tasks based on the multi-dimensional relationship information sent by the cloud, reduces the frequency of original data requests through pre-built association relationships, and combines with the real-time synchronization update mechanism of the cloud to ensure dynamic matching of terminal-side task execution and network status changes, thus forming cross-level device collaborative analysis capabilities, and simultaneously supporting the precise implementation of network path planning and resource scheduling decisions.

[0116] Corresponding to the above method embodiment, this specification also provides a network data processing device embodiment, Figure 6 FIG. 1 shows a schematic diagram of the structure of a network data processing device provided by an embodiment of the present specification. Figure 6 As shown, the device comprises: An acquisition module 602 is configured to acquire first dimension relationship information and at least one second dimension relationship information corresponding to a target network system; The generation module 604 is configured to generate multidimensional relationship information corresponding to the target network system based on the first dimensional relationship information and each second dimensional relationship information, so as to obtain a target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

[0117] Optionally, the acquisition module 602 is further configured to: Acquire at least one network record information corresponding to the target network system; Acquire at least two single-dimensional relationship information according to each network record information; The first dimension relationship information and at least one second dimension relationship information are determined in each single-dimensional relationship information.

[0118] Optionally, the acquisition module 602 is further configured to: The routing relationship information, resource relationship information and timing indicator relationship information are obtained according to the record information of each network.

[0119] Optionally, the single-dimensional relationship information is any one of routing relationship information, resource relationship information, or timing indicator relationship information; The acquisition module 602 is further configured to: Determining that the routing relationship information is first dimension relationship information; The resource relationship information is determined to be the second dimension relationship information and the timing indicator relationship information is determined to be the second dimension relationship information.

[0120] Optionally, the generating module 604 is further configured to: Determining a first dimensional relationship between network devices in the target network system according to the first dimensional relationship information; Determining the second dimensional relationship between each network device in the target network system based on each second dimensional relationship information; The first dimension relationship and the second dimension relationship between the network devices are integrated to generate multi-dimensional relationship information corresponding to the target network system.

[0121] Optionally, the network data processing device further includes a task processing module configured to: Generate network status information corresponding to the target network system and multi-dimensional relationships between network devices in the target network system through the multi-dimensional relationship information; According to the network status information and / or the multi-dimensional relationship between each network device, a target task result corresponding to the target task is obtained.

[0122] Optionally, the target task includes a network analysis task; The task processing module is further configured to: Determine a current state of the network based on the network state information; In the case where the current state of the network is an abnormal state, determining at least one initial network device according to the network state information, and determining at least one reference network device corresponding to each initial network device according to the multi-dimensional relationship between the network devices; generating a network analysis result based on each initial network device and each reference network device; When the current state of the network is normal, a network analysis result is generated based on the network state information.

[0123] Optionally, the network data processing device further includes a monitoring module configured to: Real-time monitoring of data change events corresponding to the target network system; The multidimensional relationship information is updated based on the data change event.

[0124] The above is a schematic scheme of a network data processing device of this embodiment. It should be noted that the technical scheme of the network data processing device and the technical scheme of the network data processing method described above are of the same concept, and the details not described in detail in the technical scheme of the network data processing device can be referred to the description of the technical scheme of the network data processing method described above.

[0125] The solution of the embodiment of this specification is applied to separate the network record information collection, single-dimensional relationship extraction and multi-dimensional relationship generation processes through a modular architecture, so as to realize the parallel processing and dynamic aggregation of routing, resource and indicator data; based on the real-time mapping mechanism of network status information and multi-dimensional relationships, the initial network device is quickly located in abnormal diagnosis and the topological dependency of the associated reference network device is reduced, thereby reducing the complexity of multi-source data retrieval; combined with the multi-dimensional relationship update triggered by data change events, it is ensured that the analysis results output by the task processing module are synchronized with the current network status, thereby improving the timeliness of resource scheduling and link optimization strategies, and reducing redundant storage of data across modules.

[0126] Figure 7 The block diagram of a computing device 700 according to an embodiment of the present specification is shown. The components of the computing device 700 include but are not limited to a memory 710 and a processor 720. The processor 720 is connected to the memory 710 via a bus 730, and the database 750 is used to store data.

[0127] The computing device 700 also includes an access device 740 that enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 740 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0128] In one embodiment of the present specification, the above components of the computing device 700 and Figure 7 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 7 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0129] The computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 700 may also be a mobile or stationary server.

[0130] The processor 720 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned network data processing method.

[0131] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above network data processing method belong to the same concept, and the details not described in detail in the technical scheme of the computing device can be referred to the description of the technical scheme of the above network data processing method.

[0132] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which can implement the steps of the above-mentioned network data processing method when executed by a processor.

[0133] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned network data processing method belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the above-mentioned network data processing method.

[0134] An embodiment of the present specification also provides a computer program product, including a computer program / instruction, which implements the steps of the above-mentioned network data processing method when executed by a processor.

[0135] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the above network data processing method belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be referred to the description of the technical scheme of the above network data processing method.

[0136] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0138] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0139] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A network data processing method, comprising: Acquire first dimension relationship information and at least one second dimension relationship information corresponding to the target network system; Based on the first dimensional relationship information and each second dimensional relationship information, multidimensional relationship information corresponding to the target network system is generated to obtain a target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

2. The method according to claim 1, obtaining first dimension relationship information and at least one second dimension relationship information corresponding to the target network system, comprising: Acquire at least one network record information corresponding to the target network system; Acquire at least two single-dimensional relationship information according to each network record information; The first dimension relationship information and at least one second dimension relationship information are determined in each single-dimensional relationship information.

3. The method according to claim 2, obtaining at least two single-dimensional relationship information according to each network record information, comprising: The routing relationship information, resource relationship information and timing indicator relationship information are obtained according to the record information of each network.

4. The method according to claim 2, wherein the single-dimensional relationship information is any one of routing relationship information, resource relationship information or timing indicator relationship information; Determining first dimension relationship information and at least one second dimension relationship information in each single dimension relationship information includes: Determining that the routing relationship information is first dimension relationship information; The resource relationship information is determined to be the second dimension relationship information and the timing indicator relationship information is determined to be the second dimension relationship information.

5. The method according to claim 1, generating multi-dimensional relationship information corresponding to the target network system based on the first dimensional relationship information and each second dimensional relationship information, comprising: Determining a first dimensional relationship between network devices in the target network system according to the first dimensional relationship information; Determining the second dimensional relationship between each network device in the target network system based on each second dimensional relationship information; The first dimension relationship and the second dimension relationship between the network devices are integrated to generate multi-dimensional relationship information corresponding to the target network system.

6. The method according to claim 1, obtaining a target task result corresponding to the target task according to the multidimensional relationship information, comprises: Generate network status information corresponding to the target network system and multi-dimensional relationships between network devices in the target network system through the multi-dimensional relationship information; According to the network status information and / or the multi-dimensional relationship between each network device, a target task result corresponding to the target task is obtained.

7. The method of claim 6, wherein the target task comprises a network analysis task; According to the network status information and the multi-dimensional relationship between each network device, a target task result corresponding to the target task is obtained, including: Determine a current state of the network based on the network state information; In the case where the current state of the network is an abnormal state, determining at least one initial network device according to the network state information, and determining at least one reference network device corresponding to each initial network device according to the multi-dimensional relationship between the network devices; generating a network analysis result based on each initial network device and each reference network device; When the current state of the network is normal, a network analysis result is generated based on the network state information.

8. The method of claim 1, further comprising: Real-time monitoring of data change events corresponding to the target network system; The multidimensional relationship information is updated based on the data change event.

9. A network data processing method, applied to a cloud device, comprising: A network data processing request for a target network system sent by a receiving terminal side device, and obtaining first dimension relationship information and at least one second dimension relationship information corresponding to the target network system based on the network data processing request; Based on the first dimensional relationship information and each second dimensional relationship information, multidimensional relationship information corresponding to the target network system is generated, and the multidimensional relationship information is sent to the terminal side device, so that the terminal side device obtains the target task result corresponding to the target task according to the multidimensional relationship information, wherein the target task is a task for the target network system, and the multidimensional relationship information is structured data that integrates at least two dimensional network association features.

10. A computing device comprising: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions. When the computer program / instructions are executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

11. A computer-readable storage medium storing a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.

12. A computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.

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