Method and system for integrated monitoring of network equipment

Abstract

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

Description

Technical Field

[0001] The present invention belongs to the field of computer and information technology, and relates to a method and system for integrated monitoring of network devices. Background Art

[0002] At present, with the rapid development of network technology, the stable operation of network devices plays a crucial role in ensuring the normal development of various services. Therefore, efficient, real-time, intelligent and well-compatible monitoring of network devices has become a key requirement in the field of network management. However, there are still many problems to be solved in the current existing network device monitoring technologies.

[0003] In terms of the monitoring method, it currently shows a decentralized characteristic. Different types of network devices, such as switches, routers, servers, etc., often need to rely on independent tools or specific protocols for monitoring. For example, the commonly used SNMP protocol obtains device status information through OID (Object Identifier), and protocols such as Telnet are also often applied to device monitoring operations. At the same time, there are also independent tools such as Zabbix and Nagios, which usually can only perform polling monitoring on single types of devices. This makes various monitoring data isolated from each other. When a network failure occurs, managers need to correlate data and locate faults among multiple platforms, greatly reducing management efficiency and increasing the complexity and workload of operation and maintenance.

[0004] Real-time performance is another important indicator for network device monitoring, but the existing technologies perform poorly in this regard. Traditional monitoring technologies mainly rely on the polling mechanism, that is, sending query requests to devices at fixed intervals to obtain status information. However, in a network environment with frequent dynamic changes, this polling interval is often too long, resulting in failures not being detected in time. Key failures may occur and persist during the polling interval, but the monitoring system fails to detect them in time, thus delaying the fault handling time and possibly having a serious impact on the business.

[0005] The lack of intelligence is also a major shortcoming of the existing network device monitoring technologies. Currently, many monitoring systems mainly rely on manual analysis for alarm handling. The system can only trigger alarms according to simple threshold settings. When device status parameters exceed the preset thresholds, alarm information is generated and notified to managers. However, in the face of complex network fault scenarios, these simple alarm messages are often difficult to reveal the real root cause of the fault. Managers need to spend a lot of time and effort to comprehensively analyze various alarm data based on their own experience in an attempt to find the fault source, which is not only inefficient but also prone to misjudgment, affecting the speed and quality of fault resolution.

[0006] In addition, compatibility issues also pose great challenges to network device monitoring. In an actual network environment, devices often come from different manufacturers, and the devices of these manufacturers may adopt different monitoring protocols and interface standards. To achieve effective monitoring of cross-vendor devices, existing monitoring systems need to adapt to multiple protocols, which requires a large amount of customized development work. The differences between different protocols increase the difficulty and cost of system integration, and at the same time lead to poor compatibility of cross-vendor devices, restricting the generality and scalability of the monitoring system.

[0007] In summary, the existing network device monitoring technologies have obvious deficiencies in terms of monitoring efficiency, real-time performance, intelligence, and compatibility, and cannot meet the high requirements of modern network environments for device monitoring. Therefore, it is of great practical significance to develop a new network device monitoring technology that can solve the above problems. Summary of the Invention

[0008] The purpose of the present invention is to solve the problem that the existing network device monitoring technologies have obvious deficiencies in terms of monitoring efficiency, real-time performance, intelligence, and compatibility, and to provide a method and system for integrated monitoring of network devices.

[0009] To achieve the above object, the present invention adopts the following technical solutions: A method for integrated monitoring of network devices includes the following steps: Multi-source heterogeneous data collection: Deploy lightweight proxy programs to build a protocol compatibility layer, support multi-protocol adaptation, adopt an active push mechanism, and trigger real-time Trap alarms when the device is abnormal to shorten the response delay; at the same time, adopt a passive collection optimization strategy, and dynamically adjust the polling frequency for devices of different importance levels through a polling strategy. Data fusion and intelligent analysis: Dynamic topology construction: Parse the device configuration file to generate a network topology map. Root cause analysis model, time series prediction: Use an LSTM neural network to model device CPU, memory, and traffic metrics to predict abnormal trends; association rule mining: Analyze historical alarm data through the Apriori algorithm to extract fault association rules; knowledge graph assistance: Construct a device fault knowledge base to accelerate root cause location. Adaptive alarm threshold: Analyze the historical data distribution and dynamically adjust the threshold. Visualization decision-making and automated disposal: Provide a 3D topology map, support hierarchical display, and the fault nodes flash and mark the impact range; Fault classification and disposal: Comprehensively calculate the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity, map it to the fault level and work order type according to the impact score interval, and formulate dynamic work order generation rules.

[0010] Standardize the collected multi-source heterogeneous data, and the specific steps are as follows: Data collection and protocol parsing: Call the corresponding protocol parser according to the data source type; Extract key fields from the protocol message or interface response; Data cleaning and standardization: Map the fields of different data sources to a unified JSON structure; Perform data type conversion, including uniformly converting timestamps to ISO format and unifying numerical units; Fill default values or mark as null for missing fields; JSON structure generation: Design a basic JSON template, including common fields and extended fields of all data sources; According to the data source type, merge the parsed fields into metrics or source_info; Data verification and consistency guarantee: Check whether the JSON conforms to the predefined template; Verify the rationality of numerical values; For multiple reported data of the same device, merge them into one JSON record according to the time window; Output and extension: Push the final JSON to the data lake or storage system; When adding a new data source, quickly adapt by configuring field mapping rules without modifying the core logic.

[0011] For the dynamic topology construction, parse the device configuration file, and the specific process is as follows: Configuration file preprocessing: Convert the configuration files of different manufacturers into a unified format, extract key information through regular expressions or pre-trained models, and mark the field types; LLM semantic parsing: Based on the device type and configuration context, the LLM identifies the configuration intent, extracts ACL rules and VLAN configuration information, and performs conflict detection; Structured data output: Convert the parsing result into structured data for subsequent generation.

[0012] For the generation of the network topology diagram, the specific process is as follows: Node and link modeling: Create nodes according to the device information in the configuration file, and infer the direct connection relationship and logical connection of devices through the interface IP and Trunk port binding relationship; Policy association and layering: Map the ACL rules to filtering marks on the links, divide different colors or layers according to the VLAN ID, and display the broadcast domain range; Topology layout optimization: Use the force-directed algorithm or hierarchical layout to arrange the nodes, and adjust the node size / color according to the device importance or fault status; Visualization rendering: Input the structured data into the graphics engine to generate an interactive topology diagram, supporting zooming and clicking to view details.

[0013] The time series prediction specifically includes the following steps: Collect data, including the basic index data of the device CPU, memory, and traffic, as well as device metadata, environmental information, and external dependency context information. Collect all metrics at fixed time intervals and align the timestamps to form multi-dimensional time series data; Perform data preprocessing and analysis, including data cleaning, feature engineering, and anomaly pattern mining; Build an LSTM model, design the model structure, determine the training strategy, and generate labels; Perform anomaly trend prediction and root cause analysis. The model outputs the predicted values or anomaly probabilities of metrics for future time periods, judges the anomaly trend in combination with thresholds, and conducts root cause analysis through feature importance ranking, causal reasoning, and association rule mining; Perform deployment and monitoring, predict device metrics in real time and output the prediction results, establish a feedback mechanism, and regularly update the training data.

[0014] For the above-mentioned association rule mining, analyze historical alarm data through the Apriori algorithm to extract fault association rules. Specifically: Perform data preparation and preprocessing, cleaning, standardizing, and transactionalizing alarm data; Perform frequent item set mining, determine the minimum support, generate candidate sets, and find frequently co-occurring alarm combinations; Perform association rule generation and screening, calculate confidence, lift, and interest, and extract rules; Perform rule verification and optimization, verify through spatio-temporal correlation, merge redundant rules, and optimize rules with expert intervention; Output and application, establish a strong association rule library and apply it to the fault prediction model and root cause location assistance.

[0015] For the above-mentioned knowledge graph assistance, specifically: Collect the data types required for building the knowledge base, including device alarm data, device configuration and status data, external knowledge data, and environmental and context data; Establish a knowledge base according to the process of data collection and cleaning, knowledge structuring and association, knowledge representation and storage, model training and intelligent reasoning, verification and iteration.

[0016] For the above-mentioned adaptive alarm threshold, analyze the historical data distribution and dynamically adjust the threshold. Specifically: Perform data preparation and feature extraction, select the historical operation data of the device, and conduct feature engineering such as time window division, normalization processing, and context supplementation.

[0017] Perform the K-Means clustering process, determine the number of clusters K, initialize the cluster centers, and perform iterative assignment and update; Perform classification result and threshold mapping, analyze the clustering results, and generate dynamic thresholds; Establish a dynamic adjustment mechanism to achieve real-time feedback, incremental updates, and exception filtering.

[0018] A system for integrated monitoring of network devices, comprising: A multi-source heterogeneous data collection module that deploys a lightweight proxy program to build a protocol compatibility layer, supports multi-protocol adaptation, adopts an active push mechanism, and triggers real-time Trap alarms when the device is abnormal to shorten the response delay; at the same time, it adopts a passive collection optimization strategy to dynamically adjust the polling frequency of devices with different importance levels through a polling strategy; Data fusion and intelligent analysis module: Dynamic topology construction sub-module: Parse the device configuration file to generate a network topology map; Root cause analysis model sub-module: Time series prediction, using an LSTM neural network to model device CPU, memory, and traffic metrics to predict abnormal trends; Association rule mining, analyzing historical alarm data through the Apriori algorithm to extract fault association rules; Knowledge graph assistance, constructing a device fault knowledge base to accelerate root cause location; Adaptive alarm threshold sub-module: Analyze the historical data distribution and dynamically adjust the threshold; Visualization decision-making and automated handling module: Provide a 3D topology map, support hierarchical display, with fault nodes flashing and marking the impact range; Fault classification handling, comprehensively calculate the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity, map to the fault level and work order type according to the impact score interval, and formulate dynamic work order generation rules.

[0019] The visualization decision-making and automated handling module specifically includes: Panoramic monitoring view sub-module: Provide a 3D topology map, support hierarchical display, with fault nodes flashing on the topology map and marking the impact range; At the same time, integrate various visualization forms such as tables, line charts, and heat maps to display device health, performance TOP lists, and alarm trend information; Intelligent work order system sub-module: Fault classification handling unit: Comprehensively calculate the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity; Map to the fault level and work order type according to the impact score interval, and formulate dynamic work order generation rules, including basic rules and dynamic adjustment mechanisms; Linkage repair unit: Call the manufacturer's device SDK through the API to achieve configuration distribution.

[0020] Compared with the prior art, the present invention has the following beneficial effects: A method for integrated monitoring of network devices in the present invention constructs a protocol compatibility layer by deploying a lightweight proxy program, supports multi-protocol adaptation, and can comprehensively cover various network devices such as switches, routers, servers, firewalls, and load balancers. The comprehensive protocol adaptation ability avoids monitoring blind spots caused by device protocol differences and ensures effective monitoring of various devices in the network.

[0021] The active push mechanism triggers real-time Trap alarms when device anomalies occur, greatly shortening the response latency. Compared with the traditional polling method, the active push can detect device anomalies faster, enabling operation and maintenance personnel to take timely measures and reducing the impact time of faults on the network. At the same time, the passive collection optimization strategy dynamically adjusts the polling frequency for devices of different importance levels through the polling strategy, which not only ensures high-frequency monitoring of core devices but also avoids unnecessary resource waste for non-core devices, improving the monitoring efficiency.

[0022] The data fusion and intelligent analysis module integrates multi-source heterogeneous data, breaking down the barriers between different data sources. By parsing the device configuration files to generate a network topology diagram, operation and maintenance personnel can intuitively understand the overall structure of the network and the connection relationships between devices, facilitating quick location of the fault occurrence location and the affected scope. The comprehensive data analysis method enables monitoring to no longer be limited to a single device or a single indicator, but to monitor the network from a global perspective, improving the comprehensiveness and accuracy of monitoring.

[0023] Time series prediction uses an LSTM neural network to model key indicators such as device CPU, memory, and traffic, and can accurately predict abnormal trends. By learning historical data, the LSTM neural network can discover the potential relationships and change patterns between indicators, predict possible faults in advance, provide early warnings for operation and maintenance personnel, and enable them to have sufficient time for prevention and handling to avoid the occurrence or expansion of faults.

[0024] Association rule mining analyzes historical alarm data through the Apriori algorithm to extract fault association rules. It can help operation and maintenance personnel understand the internal connections between different alarms. When a new alarm appears, it can quickly judge the possible root cause of the fault and accelerate the root cause location process.

[0025] The knowledge graph assists in constructing a device fault knowledge base, integrating a large amount of device fault information, solutions, and the association relationships with historical alarm data. When a fault occurs, operation and maintenance personnel can quickly find relevant fault cases and solutions through the knowledge graph, greatly shortening the fault troubleshooting and repair time and improving the operation and maintenance efficiency.

[0026] Adaptive alarm threshold optimization, which analyzes the historical data distribution and dynamically adjusts the threshold, can set reasonable alarm thresholds for different devices according to the actual operating conditions of the devices and business requirements. This avoids false alarms or missed alarms that may be caused by fixed thresholds and improves the accuracy of alarms. Brief Description of the Drawings

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0028] Figure 1 It is the system hierarchical architecture diagram of the present invention; Figure 2 It is the core flowchart of the fault handling of the present invention; Figure 3 It is the dynamic threshold adjustment logic diagram of the present invention; Figure 4 It is the data fusion and intelligent analysis module of the present invention. Detailed Embodiments

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0031] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0032] A method for integrated monitoring of network devices in the present invention includes the following steps: Multi-source heterogeneous data collection, deploy lightweight proxy programs to build a protocol compatibility layer, support multi-protocol adaptation, adopt an active push mechanism, trigger real-time Trap alarms when the device is abnormal to shorten the response latency; at the same time, adopt a passive collection optimization strategy, and dynamically adjust the polling frequency for devices of different importance levels through the polling strategy. Standardize the collected multi-source heterogeneous data, and the specific steps are as follows: Data collection and protocol parsing: Call the corresponding protocol parser according to the data source type; extract key fields from the protocol message or interface response; Data cleaning and standardization: Map the fields of different data sources to a unified JSON structure; perform data type conversion, including uniformly converting timestamps to ISO format and unifying numerical units; fill default values or mark as null for missing fields; JSON structure generation: Design a basic JSON template, including common fields and extended fields of all data sources; according to the data source type, merge the parsed fields into metrics or source_info; Data verification and consistency guarantee: Check whether the JSON conforms to the predefined template; verify the rationality of numerical values; for multiple reported data of the same device, merge them into one JSON record according to the time window; Output and extension: Push the final JSON to the data lake or storage system; when adding a new data source, quickly adapt by configuring field mapping rules without modifying the core logic.

[0033] Data fusion and intelligent analysis: Dynamic topology construction, parse the device configuration file, and generate a network topology diagram.

[0034] The specific process is as follows: Configuration file preprocessing: Convert the configuration files of different manufacturers into a unified format, extract key information through regular expressions or pre-trained models, and mark the field types; LLM semantic parsing: Based on the device type and configuration context, the LLM identifies the configuration intent, extracts ACL rules and VLAN configuration information, and performs conflict detection; Structured data output: Convert the parsing result into structured data for subsequent generation.

[0035] Generate a network topology diagram, and the specific process is as follows: Node and link modeling: Create nodes according to the device information in the configuration file, and infer the direct connection relationship and logical connection of the devices through the interface IP and Trunk port binding relationship; Policy association and layering: Map the ACL rules to filtering marks on the link, divide different colors or levels according to the VLAN ID, and display the broadcast domain range; Topological layout optimization: Use force-directed algorithms or hierarchical layouts to arrange nodes, and adjust the size / color of nodes according to device importance or fault status; Visual rendering: Input structured data into a graphics engine to generate an interactive topology diagram, supporting zooming and clicking to view details.

[0036] Root cause analysis model, time series prediction. Use LSTM neural networks to model device CPU, memory, and traffic metrics to predict abnormal trends; Association rule mining. Analyze historical alarm data through the Apriori algorithm to extract fault association rules; Knowledge graph assistance. Construct a device fault knowledge base to accelerate root cause location.

[0037] Time series prediction specifically includes the following steps: Collect data. Collect basic metrics data of device CPU, memory, and traffic, as well as device metadata, environmental information, and external dependency context information. Collect all metrics at fixed time intervals and align timestamps to form multi-dimensional time series data; Perform data preprocessing and analysis, including data cleaning, feature engineering, and abnormal pattern mining; Build an LSTM model, design the model structure, determine the training strategy, and generate labels; Perform abnormal trend prediction and root cause analysis. The model outputs predicted values or abnormal probabilities of metrics for future time periods, combines with thresholds to judge abnormal trends, and conducts root cause analysis through feature importance ranking, causal reasoning, and association rule mining; Perform deployment and monitoring. Real-time predict device metrics and output prediction results, establish a feedback mechanism, and regularly update training data.

[0038] Association rule mining. Analyze historical alarm data through the Apriori algorithm to extract fault association rules, specifically: Perform data preparation and preprocessing, clean, standardize, and transactionalize alarm data; Perform frequent itemset mining, determine the minimum support, generate candidate sets, and find frequently co-occurring alarm combinations; Perform association rule generation and screening, calculate confidence, lift, and interest, and extract rules; Perform rule verification and optimization, verify through spatio-temporal correlation, merge redundant rules, and optimize rules with expert intervention; Output and application. Establish a strong association rule library and apply it to fault prediction models and root cause location assistance.

[0039] Knowledge graph assistance, specifically: Collect data types required for building a knowledge base, including device alarm data, device configuration and status data, external knowledge data, and environmental and context data; Build a knowledge base according to the process of data collection and cleaning, knowledge structuring and association, knowledge representation and storage, model training and intelligent reasoning, verification and iteration.

[0040] Adaptive alarm threshold, analyze the historical data distribution, and dynamically adjust the threshold. Specifically: Perform data preparation and feature extraction, select the historical operation data of the device, and perform feature engineering such as time window division, normalization processing, and context supplementation.

[0041] Perform the K-Means clustering process, determine the number of clusters K, initialize the cluster centers, and perform iterative assignment and update; Perform classification result and threshold mapping, analyze the clustering results, and generate dynamic thresholds; Establish a dynamic adjustment mechanism to achieve real-time feedback, incremental update, and anomaly filtering.

[0042] Visualized decision-making and automated disposal, provide a 3D topological map, support hierarchical display, the fault nodes flash and mark the influence range; perform fault classification and disposal, comprehensively calculate the influence score of the fault through dimensions such as business impact degree, urgency, fault range, and historical similarity, map to the fault level and work order type according to the influence score interval, and formulate dynamic work order generation rules. Embodiment

[0043] The present invention proposes a network device integrated monitoring system and method, and realizes full-link intelligent monitoring through the following technical modules: 1. Multi-source heterogeneous data collection module (1) Protocol compatibility layer: Deploy a lightweight proxy program (Agent), support multi-protocol adaptation such as SNMP (v1 / v2 / v3), NetFlow, Syslog, RESTful API, etc., and cover devices such as switches, routers, servers, firewalls, and load balancers; · Active push mechanism: Trigger real-time Trap alarms when device anomalies occur, shortening the response latency; · Passive collection optimization: Dynamically adjust through polling strategies (such as 5 seconds / time for core devices and 30 seconds / time for non-core devices).

[0044] (2) Data standardization processing: Uniformly convert multi-source data into JSON format, and mark metadata such as time stamps, device IDs, and protocol types for subsequent correlation analysis.

[0045] The specific conversion process is as follows: 1. Data collection and protocol parsing Multi-protocol adaptation: Call the corresponding protocol parser according to the data source type (such as SNMP, NetFlow, Syslog, RESTful API, etc.).

[0046] SNMP: Obtain device status values through OID (Object Identifier) (for example, 1.3.6.1.2.1.1.5 corresponds to the device temperature).

[0047] NetFlow: Parse traffic records in UDP packets (such as source IP, destination IP, traffic volume).

[0048] Syslog: Parse text logs (such as <pri>Oct 10 12:00:00 host Process restarted).

[0049] API / XML / CSV: Call the standard library or a custom parser (e.g., use requests to get REST API data and xml.etree to parse XML).

[0050] Raw data extraction: Extract key fields from protocol messages or interface responses, e.g.: SNMP: OID = 1.3.6.1.2.1.1.5, Value = 45 → Extract as {"oid": "1.3.6.1.2.1.1.5", "value": 45}.

[0051] NetFlow: src_ip = 192.168.1.1, dst_ip = 10.0.0.1, bytes = 1024 → Extract as {"src_ip": "192.168.1.1", "dst_ip": "10.0.0.1", "bytes": 1024}.

[0052] 2. Data cleaning and standardization Field mapping and completion: Map fields from different data sources into a unified JSON structure, e.g.: Map the original fields to unified field names.

[0053] Example sysUpTime (SNMP) uptime12345 → {"uptime": 12345} prtName (device log) portGigabitEthernet0 / 1 → {"port": "GigabitEthernet0 / 1"} Data type conversion: Timestamp unification: Convert Oct 10 12:00:00 (Syslog) to ISO format 2023-10-10T12:00:00Z.

[0054] Numeric unit unification: Convert kb / s to bps (e.g., 100 kb / s → 800 bps).

[0055] Missing value handling: Fill default values (e.g., "status": "unknown") or mark as null for missing fields.

[0056] 3. JSON structure generation General Template Definition: Design a basic JSON template that includes common fields (such as device ID, timestamp, data type) for all data sources and extended fields (such as protocol-specific information).

[0057] { "device_id": "router-01", / / Unique device identifier "timestamp": "2023-10-10T12:00:00Z", / / Unified timestamp "data_type": "performance", / / Data category (such as performance, log, configuration) "metrics": { / / Specific metrics (dynamically extended according to the data source) "cpu_usage": 75, / / From SNMP "flow_bytes": 1024, / / From NetFlow "log_message": "Process restarted" / / From Syslog }, "source_info": { / / Data source meta information "protocol": "SNMP", / / Or NetFlow / Syslog / API "original_data": "<raw_data>" / / Optional: Retain the original data fragment } } Dynamic Field Merging: According to the data source type, merge the parsed fields into metrics or source_info. For example: SNMP data: Supplement metrics.cpu_usage, metrics.memory_free.

[0058] NetFlow data: Supplement metrics.flow_bytes, metrics.src_ip.

[0059] 4. Data Verification and Consistency Assurance Format Verification: Check if the JSON conforms to the predefined template (such as whether the required fields device_id and timestamp exist).

[0060] Range Verification: Verify the rationality of numerical values (such as cpu_usage should be between 0 - 100).

[0061] Deduplication and Merging: For multiple reports from the same device, merge them into a single JSON record according to a time window (e.g., 1 minute).

[0062] 5. Output and Expansion Standardized JSON Output: Push the final JSON to a data lake (such as Kafka, Elasticsearch) or a storage system.

[0063] Dynamic Expansion Support: When adding a new data source, quickly adapt by configuring field mapping rules (such as YAML / JSON files) without modifying the core logic.

[0064] Example: SNMP Data Conversion Process Original Data: SNMP GetResponse: OID=1.3.6.1.2.1.1.5 (sysUpTime), Value=12345 Parsing and Mapping: { "device_id": "router-01", "timestamp": "2023-10-10T12:00:00Z", "data_type": "performance", "metrics": { "uptime": 12345 / / Mapped from sysUpTime }, "source_info": { "protocol": "SNMP", "original_data": "OID=1.3.6.1.2.1.1.5, Value=12345" } } Final JSON: { "device_id": "router-01", "timestamp": "2023-10-10T12:00:00Z", "data_type": "performance", "metrics": { "uptime": 12345, "cpu_usage": 75, / / Fields supplemented by other data sources "flow_bytes": 1024 }, "source_info": { "protocol": "SNMP", "original_data": "OID=1.3.6.1.2.1.1.5, Value=12345" } } Key technical points Protocol parsing library: Use tools such as pysnmp (SNMP), flowtools (NetFlow), grok (Syslog) to parse raw data.

[0065] Field mapping configuration: Define field mapping rules for different data sources through configuration files to avoid hard coding.

[0066] Time synchronization: Timestamps of all data sources need to be unified to UTC or a specified time zone for easy correlation analysis.

[0067] Error tolerance: Log data records that fail to parse and mark them as "parse_error": true to avoid interrupting the overall process.

[0068] Through the above steps, multi-source heterogeneous data can be efficiently converted into structured JSON, providing a consistent data basis for subsequent storage, analysis, and visualization.

[0069] 2. Data Fusion and Intelligent Analysis Engine (1)Dynamic topology construction: Based on the LLM (Large Language Model), parse device configuration files (such as ACL rules, VLAN partitioning), automatically generate a network topology diagram, and update the link status in real time; The following is the detailed parsing process: I. Parsing process of device configuration files 1. Preprocessing of configuration files Format standardization: Convert configuration files from different vendors (such as Cisco, Juniper, Huawei) to a unified format (such as JSON or XML) to eliminate syntax differences.

[0070] Word segmentation and tagging: Extract key information (such as IP addresses, VLAN IDs, ACL rules) through regular expressions or pre-trained models, and mark field types (such as action=permit, protocol=TCP).

[0071] 2. LLM Semantic Analysis Context Understanding: The LLM identifies the configuration intent based on the device type (such as routers, switches) and configuration context (such as interface, vlan paragraphs).

[0072] Rule Extraction: ACL Rules: Parse the source / destination IP, port, protocol, action (allow / deny), and associate them with the corresponding interface or VLAN.

[0073] VLAN Configuration: Extract the VLAN ID, name, bound interfaces (such as switchport access vlan 10), and the VLAN transparent transmission relationship across devices (such as trunkport).

[0074] Conflict Detection: Identify conflicting rules (such as the same port being configured with different VLANs multiple times), and repair or mark anomalies through LLM reasoning.

[0075] 3. Structured Data Output Convert the parsed results into structured data (such as device list, interface table, VLAN topology relationship, ACL policy table) for subsequent topology generation.

[0076] II. Network Topology Generation Process 1. Node and Link Modeling Device Nodes: Create nodes based on the device IP, model, and role (such as core switch) in the configuration file.

[0077] Link Relationships: Physical Links: Infer the direct connection relationship between devices through interface IP and Trunk port binding relationships (such as GigabitEthernet0 / 1 connecting to SwitchB).

[0078] Logical Links: Supplement logical connections based on VLAN transparent transmission rules (such as untagged VLAN 10) and routing protocols (such as OSPF neighbor relationships).

[0079] 2. Policy Association and Layering ACL Visualization: Map ACL rules to filtering marks on the link (such as red / green arrows indicating allow / deny), and associate them with the affected interfaces or VLANs.

[0080] VLAN Layering: Divide different colors or levels according to the VLAN ID to show the broadcast domain scope.

[0081] 3. Topology Layout Optimization Automatic layout: Use force-directed algorithm or hierarchical layout to arrange nodes, ensuring that key devices (such as core routers) are centered.

[0082] Dynamic adjustment: Adjust the node size / color according to device importance (such as traffic load) or fault status.

[0083] 4. Visual rendering Input structured data into a graph engine (such as D3.js, Graphviz) to generate an interactive topology diagram, supporting zooming and clicking to view details (such as interface configuration, ACL rules).

[0084] III. Key technologies and challenges 1. Limitations and solutions of LLM Problem: LLM may misunderstand non-standard configurations or vendor-specific syntax (such as Cisco's ip access-list vs. Juniper's firewall filter).

[0085] Solution: Combine with a domain knowledge base (such as a device instruction set dictionary) to assist in parsing.

[0086] Fine-tune the LLM by injecting a large number of device configuration samples (such as Cisco IOS, Juniper CLI).

[0087] 2. Dynamic configuration adaptation Real-time update: Dynamically update the topology diagram by continuously listening for device configuration changes (such as NetConf / YANG push).

[0088] Policy conflict warning: Use the inference ability of LLM to detect potential conflicts (such as ACL rule overriding VLAN pass-through requirements).

[0089] 3. Multi-source data fusion Combine traffic data (such as NetFlow) to verify the actual communication status of topology links and supplement connection relationships not clearly defined in the configuration file.

[0090] IV. Example process Input: A fragment of a switch configuration file interface GigabitEthernet0 / 1 description Link to RouterA switchport mode trunk switchport trunk allowed vlan 10,20 vlan 10 name Sales_Network LLM parsing output: { "device": "SwitchX", "interfaces": { "name": "GigabitEthernet0 / 1", "type": "trunk", "allowed_vlans": [10, 20], "connected_to": "RouterA" } , "vlans": { "vlan_id": 10, "name": "Sales_Network", "interfaces": ["GigabitEthernet0 / 1"] } } Topology generation: Nodes: SwitchX (switch) and RouterA (router).

[0091] Links: Trunk connection between GigabitEthernet0 / 1 and RouterA, labeled with allowed VLAN 10 / 20.

[0092] VLAN layering: VLAN 10 is identified in blue and associated with network devices.

[0093] (2) Root cause analysis model: Time series prediction: Use LSTM neural network to model device CPU, memory, traffic and other metrics to predict abnormal trends; Specific construction process I. Data collection 1. Basic metric data CPU: Utilization rate (percentage), core load, process occupancy rate, temperature, etc.

[0094] ​Memory: Usage rate (percentage), Swap usage rate, memory leak rate, etc.

[0095] Traffic: Network in / out bandwidth, proportion of TCP / UDP traffic, number of connections, retransmission rate, etc.

[0096] Storage: Disk I / O read / write rate, usage rate, latency, etc.

[0097] Others: Device logs (error codes, alerts), process status, number of threads, response time, etc.

[0098] 2. Context information Device metadata: Device model, operating system version, hardware configuration, network topology location, etc.

[0099] Environmental information: Temperature and humidity in the computer room, power supply status, load cycle (such as business peak period).

[0100] External dependencies: Status of upstream and downstream devices, success rate of API calls, availability of third-party services, etc.

[0101] 3. Time series alignment All metrics need to be collected at fixed time intervals (such as seconds / minutes level) and aligned with timestamps to form multi-dimensional time series data.

[0102] II. Data preprocessing and analysis 1. Data cleaning Missing value handling: Filling (interpolation, mean / median filling) or deleting incomplete time periods.

[0103] Outlier detection: Identifying and handling noisy data through statistical methods (such as 3σ principle) or isolation forest algorithm.

[0104] Normalization: Standardizing metrics with different dimensions (such as Min-Max, Z-Score) to avoid numerical overflow.

[0105] 2. Feature engineering Time window slicing: Dividing the continuous time series into fixed windows (such as 5-minute sliding window) and extracting statistical features (mean, variance, maximum value, trend slope, etc.) within the window.

[0106] Frequency domain features: Performing Fourier transform on periodic metrics (such as CPU load) and extracting the main frequency component.

[0107] Lag features: Introducing the metric values at historical moments (such as CPU utilization in the previous 1 / 5 / 10 minutes) as inputs.

[0108] Context encoding: Converting device metadata (such as device type) into One-Hot encoding or embedding vectors.

[0109] 3. Anomaly Pattern Mining Correlation Analysis: Calculate the correlation coefficients between metrics (such as the correlation between CPU and memory utilization) to identify strongly correlated features.

[0110] Clustering Analysis: Discover the distribution differences between normal and abnormal patterns through K-Means or DBSCAN clustering.

[0111] Seasonal Decomposition: Separate the trend, periodic, and residual terms to identify abnormal deviations (such as sudden traffic peaks).

[0112] III. LSTM Model Construction 1. Model Structure Design Input Layer: Multidimensional time series (such as the sliding window data of metrics like CPU, memory, traffic, etc.).

[0113] LSTM Layer: 1 - 2 layers of LSTM cells (adjusted according to data complexity), and the number of cells in each layer can be set to 64 - 128.

[0114] Fully Connected Layer: Output the predicted values for future time periods (regression problem) or the anomaly probability (classification problem).

[0115] Output Layer: Regression Task: Directly predict the metric value (such as CPU utilization); Classification Task: Output the anomaly probability (mark as abnormal if exceeding the threshold).

[0116] 2. Training Strategy Loss Function: Regression Task: Mean Squared Error (MSE) or Mean Absolute Error (MAE); Classification Task: Binary Cross - Entropy.

[0117] Optimizer: Adam or RMSProp (adaptive learning rate).

[0118] Overfitting Handling: Add Dropout layer, L2 regularization, or Early Stopping.

[0119] 3. Label Generation Anomaly Definition: Mark the abnormal time periods based on business experience or statistical methods (such as exceeding the historical mean + 3σ).

[0120] Time Alignment: Ensure that the labels are aligned with the input window (such as predicting whether it is abnormal in the next 5 minutes).

[0121] IV. Anomaly Trend Prediction and Root Cause Analysis 1. Predicted Output The model outputs the predicted values of indicators or the probability of anomalies for a future time period, and combines the threshold to judge the anomaly trend.

[0122] For example: If the predicted value of CPU utilization continues to exceed 90% in the next 5 minutes, an anomaly warning will be triggered.

[0123] 2. Root Cause Analysis Association Feature Importance Ranking: Identify key impact indicators through SHAP values or attention mechanisms (such as Transformer).

[0124] Causal Reasoning: Combine device logs and alarm information to locate the root cause of anomalies (e.g., high CPU is caused by a specific process).

[0125] Association Rule Mining: Analyze the co-occurrence patterns of multiple indicators during anomaly periods (e.g., high traffic + high memory leak → cyber attack).

[0126] V. Deployment and Monitoring 1. Real-time Prediction: Deploy the model as an online service to receive device indicators in real time and output prediction results.

[0127] 2. Feedback Mechanism: Manually label false alarm / missed alarm cases and retrain the model to improve accuracy.

[0128] 3. Dynamic Update: Regularly update the training data (e.g., daily incremental training) to adapt to changes in device performance or business growth.

[0129] Association Rule Mining: Analyze historical alarm data through the Apriori algorithm to extract fault association rules (such as "sudden increase in traffic + packet loss rate > 30% → link congestion"); Specific Analysis Process of the Algorithm 1. Data Preparation and Preprocessing Analysis Object: Basic Alarm Information: Alarm ID, occurrence time, duration, device ID, device type, alarm type (code), severity level, processing result, etc.

[0130] Context Information: Device operating status, network topology relationship, historical fault records, maintenance operation logs, etc.

[0131] Data Processing Methods: Cleaning: Remove duplicate, incomplete or noisy data (such as false alarms, irrelevant alarms).

[0132] Standardization: Unify the time format (such as converting to timestamp), encode categorical fields (such as converting device type to numerical values).

[0133] Transactionization: Divide the alarm data into transactions according to time windows (such as 5 minutes or 1 hour), and each transaction contains all alarm combinations within that time period.

[0134] Example: If device A continuously generates alarms X and Y within a time window, the transaction is represented as {X, Y}.

[0135] 2. Frequent Itemset Mining Analysis objective: Find alarm combinations that frequently co-occur (such as {Alarm 1, Alarm 2}), and the support needs to exceed a preset threshold.

[0136] Key parameters: Minimum support: The lowest frequency threshold for the occurrence of an itemset (such as 10%), which is used to filter out low-frequency and meaningless combinations.

[0137] Candidate set generation: Through the layer-by-layer search strategy of Apriori, expand from single alarms to multi-alarm combinations.

[0138] Analysis example: If the support of {Alarm A, Alarm B} is 15%, it is considered that the two frequently co-occur and may imply an association relationship.

[0139] 3. Association Rule Generation and Screening Analysis objective: Extract rules in the form of X → Y from the frequent itemsets, indicating that Y is likely to follow when X occurs.

[0140] Key metrics: Confidence: The reliability of the rule X→Y, that is, P(Y|X), which needs to exceed a threshold (such as 60%).

[0141] Lift: Measures the correlation between X and Y. Lift>1 indicates a positive correlation, and the larger the value, the stronger the association.

[0142] Interest: Exclude obvious rules (such as Y→Y) and retain rules with practical significance.

[0143] Analysis example: Rule Alarm A → Alarm B: Support 12%, Confidence 70%, Lift 1.5, indicating that Alarm B is likely to follow after Alarm A occurs.

[0144] 4. Rule Verification and Optimization Analysis method: Temporal and spatial correlation verification: Check whether the rule is reasonable in terms of time sequence (such as A before B) or device topology (such as upstream and downstream devices).

[0145] Redundant rule merging: Merge similar rules (e.g., {A, B} → C and {A, C} → B).

[0146] Expert intervention: Combine domain knowledge to eliminate false rules (e.g., associated alarms caused by the same root cause).

[0147] Optimization directions: Adjust the support / confidence thresholds to balance the quantity and quality of rules.

[0148] Introduce weighted support and assign higher weights to alarms with high severity levels.

[0149] 5. Output and application Analysis results: Strong association rule library: e.g., {CPU overload, memory leak} → service outage (support 15%, confidence 80%).

[0150] Fault prediction model: Based on rules, implement real-time alarm correlation analysis to predict potential faults in advance.

[0151] Root cause location assistance: Quickly narrow down the scope of fault troubleshooting through association rules.

[0152] Summary: Core analysis logic 1. Data transactionalization: Group alarms by time window to form a transaction set.

[0153] 2. Frequent co-occurrence analysis: Mine high-frequency alarm combinations through Apriori.

[0154] 3. Rule refinement: Select rules with high confidence and high lift, and exclude redundancy.

[0155] 4. Business mapping: Combine device topology and fault cases to verify the practicality of rules.

[0156] Knowledge graph assistance: Construct a device fault knowledge base (such as a common alarm code library for Cisco devices) to accelerate root cause location.

[0157] The process of establishing the knowledge base I. Data types required for knowledge base construction 1. Device alarm data Original alarm logs: Include alarm codes (such as Cisco's ACL-DENY), timestamps, device IDs, interface information, severity levels (Critical / Major / Minor), etc.

[0158] Vendor-defined alarm manuals: Such as Cisco's MIB library and fault code tables, which contain alarm meanings, possible causes, and recommended operations.

[0159] Historical Alarm Association Cases: Record the correspondence between alarms and actual faults (e.g., BGP Session Down → Physical link interruption).

[0160] 2. Device Configuration and Status Data Configuration files (such as Cisco's running-config): Include ACL rules, VLAN partitioning, routing protocol parameters, etc.

[0161] Performance data (such as CPU utilization, memory usage, interface traffic, packet loss rate).

[0162] Device logs (such as syslog): Record device running status, process exceptions, restart events, etc.

[0163] 3. External Knowledge Data Vendor technical documents (such as Cisco's troubleshooting guides, TAC case libraries).

[0164] Industry experience libraries (such as alarm association rules in Splunk and ELK, or fault solutions in community forums).

[0165] Notes from operation and maintenance personnel: Processes, command sets, and root cause analysis results for manual fault handling.

[0166] 4. Environment and Context Data Device topology relationships (such as connection relationships and dependencies between devices in the network).

[0167] Business impact labels (such as whether an alarm affects core business, priority classification).

[0168] Change records (such as configuration modifications, firmware upgrades, hardware replacements).

[0169] II. Knowledge Base Establishment Process 1. Data Collection and Cleaning Automated collection: Real-time collection of device alarms, configurations, and performance data through protocols such as SNMP, Syslog, and NetConf.

[0170] Integrate vendor APIs (such as Cisco Prime and DNA Center) to obtain standardized alarm information.

[0171] Data cleaning: Remove duplicate, incomplete, or noisy data (such as false alarms).

[0172] Standardize fields (such as unifying time formats and device model naming rules).

[0173] Supplement the missing context (e.g., associate device relationships through a topology diagram).

[0174] 2. Knowledge Structuring and Association Alarm Coding and Classification: Classify alarm codes according to vendor standards (e.g., MAJOR and MINOR in Cisco), and supplement custom tags (e.g., fault domain: network layer, hardware layer, application layer).

[0175] Establish a mapping table between alarms and symptoms (e.g., Interface Down → Possible causes: fiber optic break, port closed, optical module failure).

[0176] Root Cause Association Rules: Mine strong association relationships between alarms through historical data (e.g., ACL - DENY + TCP Retransmission → Potential DDoS attack).

[0177] Define rules in combination with expert experience (e.g., Cisco's BSOD alarm may be triggered by memory leak or software Bug).

[0178] Solution Library: Bind solution steps to each alarm / fault mode (e.g., command - line operations, configuration modification suggestions, spare part replacement procedures).

[0179] Mark the success rate and applicable conditions of the solution (e.g., specific IOS version).

[0180] 3. Knowledge Representation and Storage Structured Storage: Use a graph database (such as Neo4j) to store the association relationship between alarms and root causes.

[0181] Or adopt knowledge graph technology to concatenate fault phenomena, alarms, devices, and solutions into triples (entity - relationship - entity).

[0182] Retrieval Optimization: Build an inverted index: Support rapid location of relevant knowledge through alarm codes and keywords (such as "packet loss").

[0183] Add business tags: such as "affecting voice service" or "urgent repair" priority.

[0184] 4. Model Training and Intelligent Reasoning (Optional) Machine Learning Assistance: Train a classification model (such as decision tree, XGBoost) based on historical alarm data to predict the probability of the root cause corresponding to the alarm.

[0185] Use NLP to analyze text logs (such as syslog) and extract fault features (such as "buffer overflow").

[0186] Dynamic knowledge update: Automatically optimize alarm correlation rules through reinforcement learning (RL), or correct the knowledge base through feedback from operation and maintenance personnel.

[0187] 5. Verification and iteration Simulation test: Inject simulated alarms to verify whether the knowledge base can accurately match the root cause and solution.

[0188] Manual review: Experts verify the correlation rules generated by the machine and supplement missing scenarios.

[0189] Continuous update: Regularly synchronize the latest fault libraries of manufacturers (such as Cisco's SIRT announcements), and integrate newly emerging fault cases.

[0190] III. Application examples of the knowledge base 1. Orange flashing alarm analysis: Input: Cisco device alarm code: ACL-DENY (msg=src-ip X.X.X.X).

[0191] Knowledge base output: Possible root causes: Incorrect ACL rule configuration, source IP attack behavior, NAT conversion exception.

[0192] Recommended actions: Check the ACL policy, analyze the five-tuple of traffic, and view the NAT mapping table.

[0193] Associated alarms: If TCP Retransmission exists at the same time, prompt potential DDoS risks.

[0194] 2. Accelerate root cause location: Input: "%SYS-5-RESTART: System restart due to software watchdog" in the device log.

[0195] Knowledge base output: Root cause: It may be CPU overload, memory leak or software Bug (such as a specific IOS version).

[0196] Solution steps: Check the CPU occupancy rate of the process, upgrade the firmware, and contact TAC support.

[0197] IV. Recommended technical tools Data collection: Prometheus (monitoring metrics), Elastic Stack (log analysis), SNMP Walker.

[0198] Knowledge base construction: Neo4j (graph database), Apache Jena (knowledge graph), Confluence (document management).

[0199] Intelligent analysis: Python (PyTorch / Scikit-learn), Splunk (alarm correlation analysis), Cisco ThousandEyes.

[0200] (3) Adaptive alarm threshold: Analyze the historical data distribution using the K-Means clustering algorithm and dynamically adjust the threshold (e.g., the traffic threshold automatically floats by 20% according to the business peak).

[0201] The process of using k-means analysis is as follows: 1. Data preparation and feature extraction Data source: Select the historical operation data of the device (such as CPU utilization, memory usage, network traffic, etc.) and align it according to the time series.

[0202] Feature engineering: Time window division: Divide the continuous data into fixed time windows (such as 5 minutes or 1 hour), and extract the statistical features within each window (such as mean, maximum value, standard deviation, trend slope, etc.).

[0203] Normalization processing: Standardize the features with different dimensions to avoid the influence of numerical differences on the clustering results.

[0204] Context supplementation: Add auxiliary features such as device type, business cycle (such as peak / low valley), and environmental parameters (such as temperature).

[0205] 2. K-Means clustering process Step 1: Determine the number of clusters K Determine the value of K through business experience or algorithms. For example: If it is necessary to distinguish between "normal" and "abnormal" states, K = 2 can be set; if it is necessary to subdivide different load patterns (such as low, medium, and high baselines), K = 3 or more can be set.

[0206] Step 2: Initialize the cluster centers, randomly select K historical data points as the initial cluster centers, or optimize the initial point distribution through density clustering (such as K-Means++).

[0207] Step 3: Iterative assignment and update Assignment stage: Assign the feature vectors of each time window to the nearest cluster center (based on Euclidean distance).

[0208] Update stage: Recalculate the center points (mean vectors) of each cluster.

[0209] Termination condition: Repeat the assignment and update until the cluster centers no longer change significantly or the maximum number of iterations is reached.

[0210] 3. Classification Results and Threshold Mapping Cluster result analysis: Each cluster represents a typical data distribution pattern. For example: Cluster 1: Low-load mode (e.g., CPU average 30% ± 5%), corresponding to the "normal" state; Cluster 2: High-load mode (e.g., CPU average 80% ± 10%), possibly corresponding to "abnormal" or business peak.

[0211] Determine candidate threshold values by statistically analyzing the boundaries (e.g., mean + standard deviation) or quantiles (e.g., 95th percentile) of each cluster.

[0212] Dynamic threshold generation: Single-threshold strategy: Select the boundary of the largest cluster (e.g., normal mode) as the base threshold, and the boundaries of other clusters as auxiliary references.

[0213] Multi-threshold strategy: Set different thresholds for different clusters (e.g., higher baselines correspond to higher alarm thresholds).

[0214] Adaptive adjustment: Periodically recluster based on new data and update the thresholds to match the latest data distribution.

[0215] 4. Dynamic Adjustment Mechanism Real-time feedback: When a new data point falls into an abnormal cluster, trigger an alarm and record the actual failure situation for subsequent model calibration.

[0216] Incremental update: Periodically (e.g., daily) re-run K-Means with the latest data to prevent the model from becoming invalid due to changes in data distribution (e.g., equipment aging, business growth).

[0217] Anomaly filtering: Exclude known anomaly points (e.g., historical failure data) before clustering to ensure that the clustering results are closer to the "normal" mode.

[0218] Example scenario Suppose the historical CPU utilization data of a certain server is clustered into two categories by K-Means: Cluster A: Mean 30%, standard deviation 5% (normal mode); Cluster B: Mean 75%, standard deviation 15% (high-load mode).

[0219] Threshold setting: Base alarm threshold = Mean of Cluster A + 3 × Standard deviation = 30% + 15% = 45%; If the data point exceeds 45% and falls into Cluster B, the "High Load" alarm is triggered.

[0220] Key advantages Data-driven: Automatically identify patterns based on historical distributions, reducing the subjectivity of manually setting thresholds.

[0221] Dynamic adaptability: Adapt to changes in device performance or business fluctuations through regular clustering updates.

[0222] Multi-mode differentiation: Support complex scenarios (such as multiple normal modes, gradual anomalies), improving alarm accuracy.

[0223] 3. Visual decision-making and automated handling (1) Panoramic monitoring view: 3D topology map: Supports hierarchical display (such as data center → rack → device), with faulty nodes flashing and marking the affected area; Multi-dimensional data dashboard: Integrates tables, line charts, and heat maps to display device health, performance top list, and alarm trends.

[0224] (2) Intelligent work order system: Fault classification handling: Automatically generate work orders according to the impact level (such as core link failure → high priority, automatically trigger device restart); The process is as follows: 1. Impact assessment dimensions The system comprehensively calculates the impact score of the fault (usually a standardized value from 0 to 100) through the following dimensions: 2. Impact quantification and classification rules The system generates an impact score through weighted calculation and maps it to the fault level and work order type according to the score range: 3. Dynamic work order generation rules (1) Basic rules Threshold trigger: When the real-time impact of the fault exceeds the preset threshold (such as service interruption duration > 5 minutes), the system automatically generates a work order of the corresponding level.

[0225] Multi-dimensional linkage: For example: Business impact (such as payment system failure) + Urgency (sharp increase in user complaints) → Directly determined as a first-level work order.

[0226] Fault scope (affecting more than 10 nodes) + historical similarity (similar faults have caused major accidents) → escalate to a second-level work order.

[0227] (2) Dynamic adjustment mechanism Real-time data update: If the impact deteriorates during the fault handling process (such as the fault scope expands), the system automatically upgrades the work order level (such as from level three to level two).

[0228] Manual intervention and correction: The operation and maintenance personnel can manually adjust the impact parameters (such as discovering hidden risks), and the system recalculates and updates the work order level.

[0229] 4. Work order flow and closed-loop Work order splitting: High-level work orders may be split into multiple sub-work orders (such as "fault location", "emergency recovery", "root cause analysis"), and are respectively assigned to different teams.

[0230] AI-assisted decision-making: The system recommends processing steps based on historical work order data (such as "execute command X", "replace module Y"), shortening the mean time to repair (MTTR).

[0231] Closed-loop feedback: After the work order is completed, the system records the actual processing results and feeds them back to the model to optimize the accuracy of future impact assessment.

[0232] 5. Example: Fault grading and work order generation Scenario: An alarm is triggered for high CPU load on a certain cloud server.

[0233] Impact assessment: Business impact: Affects non-core business (weight 30%), but user complaints are starting to rise (weight 10%); Urgency: Service response is delayed (weight 20%); Fault scope: Single server (weight 15%); Historical similarity: The average processing time for similar problems is 2 hours (weight 15%).

[0234] Total score: 30%×0.4 + 20%×0.3 + 15%×0.2 + 15%×0.1 = 64 points → second-level work order.

[0235] Work order generation: Sent to the operation and maintenance team, requiring a response within 30 minutes; Associated resources: Automatically retrieve the configuration document and performance log of this server; AI suggestion: Check for abnormal processes or DDoS attacks.

[0236] Linked repair: By calling the manufacturer's device SDK through the API (such as Huawei eSight, Cisco DNA), configuration distribution is achieved (such as adjusting the QoS policy).

[0237] A system for integrated monitoring of network devices in the present invention includes: A multi-source heterogeneous data collection module deploys a lightweight proxy program to build a protocol compatibility layer, supports multi-protocol adaptation, adopts an active push mechanism, and triggers real-time Trap alarms when the device is abnormal to shorten the response delay; at the same time, a passive collection optimization strategy is adopted, and the polling frequency is dynamically adjusted for devices of different importance levels through a polling strategy; Data fusion and intelligent analysis module: Dynamic topology construction sub-module: Parses the device configuration file to generate a network topology map; Root cause analysis model sub-module: Time series prediction, uses an LSTM neural network to model device CPU, memory, and traffic metrics to predict abnormal trends; Association rule mining, analyzes historical alarm data through the Apriori algorithm to extract fault association rules; Knowledge graph assistance, constructs a device fault knowledge base to accelerate root cause location; Adaptive alarm threshold sub-module: Analyzes the historical data distribution and dynamically adjusts the threshold; Visualization decision-making and automated handling module: Provides a 3D topology map, supports hierarchical display, the fault node flashes and marks the impact range; Fault classification handling, comprehensively calculates the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity, maps to the fault level and work order type according to the impact score interval, and formulates dynamic work order generation rules.

[0238] The visualization decision-making and automated handling module specifically includes: Panoramic monitoring view sub-module: Provides a 3D topology map, supports hierarchical display, the fault node flashes on the topology map and marks the impact range; At the same time, it integrates various visualization forms such as tables, line charts, and heat maps to display device health, performance TOP lists, and alarm trend information; Intelligent work order system sub-module: Fault classification handling unit: Comprehensively calculates the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity; Maps to the fault level and work order type according to the impact score interval, and formulates dynamic work order generation rules, including basic rules and dynamic adjustment mechanisms; Linked repair unit: Calls the manufacturer's device SDK through the API to achieve configuration distribution.

[0239] Intuitive visual decision support provides a 3D topological map, supports hierarchical display, blinks fault nodes and marks the affected scope, enabling operation and maintenance personnel to intuitively understand the operating status and fault conditions of devices in the network. Through this visual method, operation and maintenance personnel can quickly locate fault nodes, understand the impact scope of faults on the network, and thus make more accurate decisions. For example, on the 3D topological map, operation and maintenance personnel can clearly see the location of the faulty device and other devices connected to it, and judge whether it will affect critical services.

[0240] The multi-dimensional data dashboard integrates various visual forms such as tables, line charts, and heat maps to display information such as device health, performance TOP list, and alarm trends. Operation and maintenance personnel can comprehensively understand the operating conditions of network devices through these visual charts and discover potential problems in a timely manner. For example, through the line chart, the change trend of the device CPU utilization rate can be intuitively seen to judge whether there is a performance bottleneck; through the heat map, areas with frequent alarms in the network can be quickly discovered for key monitoring and handling.

[0241] Intelligent fault classification and automated handling. Fault classification and handling comprehensively calculate the impact score of faults through multiple dimensions such as business impact, urgency, fault scope, and historical similarity, and map it to the fault level and work order type according to the impact score interval, and formulate dynamic work order generation rules. This intelligent classification and handling method can reasonably allocate operation and maintenance resources according to the severity of faults, ensure that critical faults are given priority, and improve operation and maintenance efficiency. For example, for faults that affect core business and have a high degree of urgency, high-priority work orders will be automatically generated and corresponding emergency handling processes will be triggered.

[0242] Linkage repair calls the manufacturer's device SDK through the API to implement automated operations such as configuration issuance. When a fault occurs, the system can automatically execute corresponding repair operations according to preset rules, such as adjusting the QoS policy, restarting the device, etc., reducing the time and error rate of manual intervention, and improving the timeliness and accuracy of fault handling. At the same time, the automated handling process can be recorded and audited for subsequent fault analysis and summary.

[0243] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.< / pri>

Claims

1. A method for integrated monitoring of network devices, characterized in that, It includes the following steps: Multi-source heterogeneous data collection: Deploy a lightweight proxy program to build a protocol compatibility layer, support multi-protocol adaptation, adopt an active push mechanism, and trigger real-time Trap alarms in case of device anomalies to shorten the response latency; at the same time, adopt a passive collection optimization strategy, and dynamically adjust the polling frequency for devices with different importance levels through the polling strategy; Data fusion and intelligent analysis: Dynamic topology construction: Parse the device configuration file and generate a network topology map; Root cause analysis model, time series prediction: Use an LSTM neural network to model device CPU, memory, and traffic metrics to predict abnormal trends; Association rule mining: Analyze historical alarm data through the Apriori algorithm to extract fault association rules; Assist with a knowledge graph, construct a device fault knowledge base, and accelerate root cause location; Adaptive alarm threshold: Analyze the historical data distribution and dynamically adjust the threshold; Visualized decision-making and automated handling: Provide a 3D topology map, support hierarchical display, and the fault nodes flash and mark the affected range; Fault classification handling: Comprehensively calculate the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity, map it to the fault level and work order type according to the impact score interval, and formulate dynamic work order generation rules.

2. The method for integrated monitoring of a network device according to claim 1, characterized in that, Perform standardized processing on the collected multi-source heterogeneous data, and the specific steps are as follows: Data collection and protocol parsing: Call the corresponding protocol parser according to the data source type; Extract key fields from the protocol message or interface response; Data cleaning and standardization: Map the fields of different data sources to a unified JSON structure; Perform data type conversion, including uniformly converting the timestamp to the ISO format and unifying the numerical units; Fill default values or mark as null for missing fields; JSON structure generation: Design a basic JSON template, including common fields and extended fields of all data sources; According to the data source type, merge the parsed fields into metrics or source_info; Data verification and consistency guarantee: Check whether the JSON conforms to the predefined template; Verify the rationality of the numerical values; For multiple reported data of the same device, merge them into one JSON record according to the time window; Output and extension: Push the final JSON to the data lake or storage system; When adding a new data source, quickly adapt by configuring the field mapping rule without modifying the core logic.

3. A method for integrated monitoring of network devices according to claim 1, characterized in that, For the above-mentioned dynamic topology construction, the process of parsing the device configuration file is as follows: Configuration file preprocessing: Convert the configuration files of different manufacturers into a unified format, extract key information through regular expressions or pre-trained models, and mark the field types; LLM semantic parsing: Based on the device type and configuration context, the LLM identifies the configuration intent, extracts ACL rules and VLAN configuration information, and performs conflict detection; Structured data output: Convert the parsing result into structured data for subsequent generation.

4. The method for integrated monitoring of a network device according to claim 1, wherein, For the above-mentioned generation of the network topology map, the specific process is as follows: Node and Link Modeling: Create nodes based on device information in the configuration file, and infer the direct connection relationship and logical connection of devices through the interface IP and Trunk port binding relationship; Policy Association and Stratification: Map ACL rules to filtering tags on the link, divide different colors or levels according to the VLAN ID, and display the broadcast domain range; Topology Layout Optimization: Arrange nodes using the force-directed algorithm or hierarchical layout, and adjust the node size / color according to the device importance or fault status; Visualization Rendering: Input structured data into the graphics engine to generate an interactive topology diagram, supporting zooming and clicking to view details.

5. The method for integrated monitoring of a network device according to claim 1, wherein The timing prediction specifically includes the following steps: Perform data collection, collect basic metric data of device CPU, memory, and traffic, as well as device metadata, environmental information, and external dependency context information, and collect all metrics at fixed time intervals and align the timestamps to form multi-dimensional time series data; Perform data preprocessing and analysis, including data cleaning, feature engineering, and abnormal pattern mining; Build an LSTM model, design the model structure, determine the training strategy, and generate labels; Perform abnormal trend prediction and root cause analysis. The model outputs the predicted values of metrics or abnormal probabilities for future time periods, judges the abnormal trend in combination with the threshold, and performs root cause analysis through feature importance ranking, causal reasoning, and association rule mining; Perform deployment and monitoring, predict device metrics in real time and output the prediction results, establish a feedback mechanism, and regularly update the training data.

6. The method for integrated monitoring of a network device according to claim 1, characterized in that, The association rule mining specifically extracts fault association rules by analyzing historical alarm data through the Apriori algorithm, specifically: Perform data preparation and preprocessing, clean, standardize, and transactionalize alarm data; Perform frequent item set mining, determine the minimum support, generate candidate sets, and find alarm combinations that frequently appear together; Perform association rule generation and screening, calculate confidence, lift, and interest, and extract rules; Perform rule verification and optimization, verify through spatio-temporal correlation, merge redundant rules, and optimize rules with expert intervention; Output and application, establish a strong association rule library, and apply it to the fault prediction model and root cause location assistance.

7. The method for integrated monitoring of a network device according to claim 1, characterized in that, The knowledge graph assistance specifically is: Collect data types required for building a knowledge base, including device alarm data, device configuration and status data, external knowledge data, and environmental and context data; Establish a knowledge base according to the process of data collection and cleaning, knowledge structuring and association, knowledge representation and storage, model training and intelligent reasoning, verification and iteration.

8. A method for integrated monitoring of a network device as claimed in claim 1, wherein, The adaptive alarm threshold analyzes the historical data distribution and dynamically adjusts the threshold; specifically: Perform data preparation and feature extraction, select the historical operation data of the device, and perform feature engineering such as time window division, normalization processing, and context supplementation; Perform the K-Means clustering process, determine the number of clusters K, initialize the cluster centers, and perform iterative assignment and update; Perform classification result and threshold mapping, analyze the clustering results, and generate dynamic thresholds; Establish a dynamic adjustment mechanism to achieve real-time feedback, incremental update, and abnormal filtering.

9. A system for integrated monitoring of network devices, characterized in that, Including: Multi-source heterogeneous data acquisition module, which deploys a lightweight proxy program to build a protocol compatibility layer, supports multi-protocol adaptation, adopts an active push mechanism, and triggers real-time Trap alarms when the device is abnormal to shorten the response latency; at the same time, it adopts a passive acquisition optimization strategy and dynamically adjusts the polling frequency of devices with different importance levels through a polling strategy. Data fusion and intelligent analysis module: Dynamic topology construction sub-module: Parses the device configuration file and generates a network topology map. Root cause analysis model sub-module: Time series prediction, uses an LSTM neural network to model device CPU, memory, and traffic metrics to predict abnormal trends; Association rule mining, analyzes historical alarm data through the Apriori algorithm to extract fault association rules; Knowledge graph assistance, constructs a device fault knowledge base to accelerate root cause location. Adaptive alarm threshold sub-module: Analyzes the historical data distribution and dynamically adjusts the threshold. Visualization decision-making and automated handling module: Provides a 3D topology map, supports hierarchical display, and the fault nodes flash and mark the impact range. Fault classification and handling, comprehensively calculates the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity, maps to the fault level and work order type according to the impact score interval, and formulates dynamic work order generation rules.

10. A system for integrated monitoring of network devices according to claim 9, characterized in that, The visualization decision-making and automated handling module specifically includes: Panoramic monitoring view sub-module: Provides a 3D topology map, supports hierarchical display, and the fault nodes flash and mark the impact range on the topology map; at the same time, it integrates various visualization forms such as tables, line charts, and heat maps to display device health, performance TOP list, and alarm trend information. Intelligent work order system sub-module: Fault classification and handling unit: Comprehensively calculates the impact score of the fault through dimensions such as business impact, urgency, fault range, and historical similarity; Maps to the fault level and work order type according to the impact score interval, and formulates dynamic work order generation rules, including basic rules and dynamic adjustment mechanisms. Linkage repair unit: Calls the manufacturer's device SDK through the API to implement configuration distribution.

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