Marketing operation and maintenance monitoring visualization system based on distributed architecture

Abstract

The invention discloses a marketing operation and maintenance monitoring visualization system based on a distributed architecture. The marketing operation and maintenance monitoring visualization system comprises a data acquisition module, a business mapping management module, a deployment management module, a load monitoring module and a business monitoring module, the data acquisition module is used for acquiring distributed architecture information of the marketing management system; the service mapping management module is used for carrying out association binding with a preset service process according to the distributed architecture information to obtain mapping relation information; the deployment management module is used for deploying a corresponding load monitoring program according to the distributed architecture information; the load monitoring module is used for acquiring load state data under current deployment through a monitoring program; the service monitoring module is used for predicting a service state according to the load state data and the mapping relation information; according to the invention, load and service state monitoring and prediction can be realized, abnormity can be sensed in advance, service fault reporting is avoided, and system stability is improved; and meanwhile, the architecture and the business process can be accurately associated, the system problem can be quickly positioned, the troubleshooting period is shortened, and the business continuity is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance monitoring technology, and more specifically to a marketing operation and maintenance monitoring visualization system based on a distributed architecture. Background Technology

[0002] To accelerate digital transformation and the construction of new power systems, the centralized construction of multiple basic platforms has been completed, and information systems are gradually being deployed centrally to advanced power grid nodes. Against this backdrop, the system architecture has become more complex, data links have been lengthened, business continuity and stability requirements are higher, and operational complexity has increased significantly. Furthermore, with business growth, greater risks to safe operation are emerging.

[0003] One type of critical information system currently supports core electricity billing operations, ensuring meter reading and billing for a large number of users every month, resulting in a heavy workload and significant support pressure. It is deployed across three levels of nodes, with long call chains and complex call relationships between modules.

[0004] Currently, there is a lack of effective operation and maintenance tools to support operations and maintenance personnel in carrying out system maintenance work. When system functions malfunction, the complexity of the architecture and the lack of effective monitoring tools lead to long troubleshooting cycles, making it difficult to ensure efficient and stable system operation, which may in turn affect business continuity. Furthermore, current monitoring systems primarily monitor the status of hardware and software components such as databases and middleware, but lack monitoring of core business call chains, critical business interfaces, and other scenarios deeply intertwined with business logic. This often results in situations where system operating indicators appear normal, but business operations report failures.

[0005] To improve operational continuity, it is necessary to enhance the system's anomaly detection capabilities, identifying and addressing issues before they become apparent to the business, thus preventing disruptions to business continuity. Furthermore, the existing system lacks sufficient security protection and early warning mechanisms, making it difficult to meet increasingly complex information security needs, network security requirements, and annual network protection requirements.

[0006] Therefore, improving the system's ability to notify of anomalies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a marketing operation and maintenance monitoring visualization system based on a distributed architecture, which can realize load and business status monitoring and prediction, detect anomalies in advance, avoid business failures, and improve system stability; at the same time, it can accurately associate architecture and business processes, quickly locate system problems, shorten the troubleshooting cycle, and ensure business continuity.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A marketing operations and maintenance monitoring visualization system based on a distributed architecture includes a data acquisition module, a business mapping management module, a deployment management module, a load monitoring module, and a business monitoring module.

[0010] The data acquisition module is used to acquire the distributed architecture information of the marketing management system; the business mapping management module is used to associate and bind the distributed architecture information with preset business processes to obtain mapping relationship information; the deployment management module is used to deploy corresponding load monitoring programs according to the distributed architecture information; the load monitoring module is used to acquire load status data under the current deployment through the monitoring program; and the business monitoring module is used to predict business status based on the load status data and the mapping relationship information.

[0011] Preferably, the data acquisition module includes a network topology acquisition submodule, a metadata parsing submodule, and a first transmission submodule.

[0012] The network topology submodule is used to continuously send probe data packets to each server node in the marketing management system for network probing and extract the corresponding node information to form a network topology structure. The metadata parsing submodule is used to obtain the configuration management data of the marketing management system and extract application module information through a data parsing algorithm. The first transmission submodule is used to retrieve the network topology structure and the application module information and send them to the business mapping management module and the deployment management module.

[0013] Preferably, the business mapping module includes a business process parsing submodule, a semantic matching submodule, and a second transmission submodule.

[0014] The business process parsing submodule is used to obtain business process configuration information; and to decompose the business process configuration information into multiple sub-task units for each business process; the semantic matching submodule is used to calculate the text similarity between the functional description of the application module and the sub-task unit, and to filter out combinations that meet the similarity threshold for matching; the second transmission submodule is used to send the matching result to the business monitoring module and / or the business deployment module.

[0015] Preferably, the business mapping module further includes a mapping verification submodule, which is used to obtain historical load indicators and predict business indicators based on the current mapping relationship, and calculate the accuracy rate based on the predicted business indicators and the actual historical business indicators.

[0016] Preferably, the business mapping module further includes a manual visualization calibration submodule, which is used to display a candidate set of mapping relationships through a visualization interface, allowing operation and maintenance personnel to manually adjust the mapping relationships or add additional mapping rules.

[0017] Preferably, the deployment management module includes a resource assessment submodule, a strategy formulation submodule, a deployment execution submodule, and a deployment information management submodule.

[0018] The resource assessment submodule is used to assess the resources of each server node based on the distributed architecture information, and predict the resource carrying capacity of each node under different business loads based on resource usage. The strategy formulation submodule is used to determine the target application module that needs to deploy the load monitoring program based on the mapping relationship information, and select server nodes for the target application module based on the resource assessment results. The deployment execution submodule is used for the task distribution mechanism, sending the deployment task list to the node management program. The node management program completes the deployment of the load monitoring program according to the list. The deployment information management submodule is used to record the current deployment information.

[0019] Preferably, the load monitoring module includes a data acquisition submodule, a data preprocessing submodule, and a data analysis submodule.

[0020] The data acquisition submodule is used to obtain corresponding load operation indicators based on the operation monitoring program running on each server node; it is used to obtain module performance indicators through a predefined health check interface; the data analysis submodule is used to use a normal load behavior model built with machine learning algorithms to judge the real-time data by calculating the deviation between the real-time data and the historical normal data distribution.

[0021] Preferably, the load monitoring module further includes a first alarm submodule, which is used to trigger an alarm mechanism, generate alarm information, and transmit the information through a specific channel when it is determined that the deviation of real-time data exceeds a set alarm threshold.

[0022] Preferably, the business monitoring module includes a data management submodule and a business status assessment submodule; the data association submodule is used to establish a relationship mapping table based on the load status data and the mapping relationship, and to map each load status data to the corresponding business indicator type according to the relationship mapping table to generate structured data; the business status assessment submodule predicts and judges the corresponding business status based on the structured data.

[0023] Preferably, the business monitoring module further includes a second alarm submodule; the second alarm submodule is used to highlight the abnormal node on the business process diagram through the data visualization engine when the abnormal business status is determined, and to display detailed abnormal information and impact analysis report.

[0024] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a marketing operation and maintenance monitoring visualization system based on a distributed architecture, which can realize load and business status monitoring and prediction, perceive anomalies in advance, avoid business failures, and improve system stability; at the same time, it can accurately associate the architecture with the business process, quickly locate system problems, shorten the troubleshooting period, and ensure business continuity.

[0025] The present invention integrates architecture collection, load analysis, and business mapping to achieve intelligent prediction of anomalies in the technical layer and business layer, trigger alarms before users report problems, upgrade the fault response from passive handling to active prevention, and when abnormal trends appear in load data or business metrics, trigger alarms in a timely manner, changing the operation and maintenance mode from "post-fault handling of business failures" to "intervention before anomalies occur", effectively reducing the impact of system failures on business continuity.

[0026] Using the association relationship between the architecture and the business process established by the business mapping management module of the present invention, combined with the visualization interface display, after the system detects an anomaly, it can quickly lock the corresponding business link and technical module. Bid farewell to the inefficient method of manual hierarchical troubleshooting in traditional distributed systems, greatly shorten the fault troubleshooting period, and improve the operation and maintenance response efficiency.

[0027] The deployment management module of the present invention dynamically adjusts the deployment strategy of the load monitoring program according to the architecture information and resource evaluation results to ensure that the monitoring does not affect system performance. At the same time, combined with the load status data and business status prediction, operation and maintenance personnel can perform resource scheduling in advance, such as capacity expansion and node switching, to ensure the stability of the core electricity business under large-scale and high-frequency operations.

[0028] The load monitoring module of the present invention monitors the security status of technical components in real time, and the business monitoring module deeply monitors operations such as key business interface calls to build a two-way security protection system for technology and business. The complete call chain audit log and real-time alarm mechanism help enterprises meet the network security supervision requirements of the power industry and prevent security risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0030] Figure 1 The attached drawing is a schematic structural diagram of a marketing operation and maintenance monitoring visualization system based on a distributed architecture provided by the present invention;

[0031] Figure 2This is a schematic diagram of the data acquisition module in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the business mapping management module in an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram of the deployment management module in an embodiment of the present invention;

[0034] Figure 5 This is a schematic diagram of the structure and monitoring logic of the business monitoring module in an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] like Figure 1 This invention discloses a marketing operations and maintenance monitoring visualization system based on a distributed architecture, including a data acquisition module, a business mapping management module, a deployment management module, a load monitoring module, and a business monitoring module.

[0037] The data acquisition module is used to acquire the distributed architecture information of the marketing management system; the business mapping management module is used to associate and bind the distributed architecture information with the preset business processes to obtain mapping relationship information; the deployment management module is used to deploy the corresponding load monitoring program according to the distributed architecture information; the load monitoring module is used to acquire the load status data under the current deployment through the monitoring program; and the business monitoring module is used to predict the business status based on the load status data and mapping relationship information.

[0038] like Figure 2 To further implement the above technical solutions, the data acquisition module extracts multi-dimensional information from the distributed architecture through active detection, data parsing, and structured transmission, providing basic data support for subsequent business mapping, load monitoring, and status prediction. This module includes a network topology acquisition submodule, a metadata parsing submodule, and a first transmission submodule.

[0039] The network topology submodule is used to continuously send probe data packets to each server node in the marketing management system to conduct network probes and extract the corresponding node information to form a network topology structure.

[0040] The metadata parsing submodule is used to obtain configuration management data from the marketing management system and extract application module information through data parsing algorithms. The first transmission submodule is used to retrieve the network topology and the application module information and send them to the business mapping management module and the deployment management module.

[0041] The network topology submodule continuously sends probe packets (such as ICMP Ping, TCP SYN, etc.) to each server node in the distributed architecture. Based on the response results (such as latency, reachability, port open status), it extracts information such as the node's IP address, port, service type, and connection relationship, and finally combines them into a dynamic network topology structure that reflects the physical / logical connection relationship of system components.

[0042] The metadata parsing submodule retrieves configuration files (such as XML, YAML, JSON), code metadata (such as class names, function names, interface definitions), and deployment description files (such as Kubernetes Manifest) from the Marketing Management System's Configuration Management Database (CMDB) or version control system (such as Git). It then extracts application module information, such as application name, version, dependencies (such as module A calling module B's API), service interface parameters, and data flow (such as the order module transmitting order numbers to the payment module).

[0043] The first transmission submodule transmits the topology data (such as node relationship diagram) generated by the network topology submodule and the application module information (such as module dependency list) output by the metadata parsing submodule to the business mapping management module and the deployment management module through a unified data interface (such as RESTAPI, message queue Kafka) to ensure the real-time performance and consistency of the data.

[0044] like Figure 3 To further implement the above technical solution, the business mapping module includes a business process parsing submodule, a semantic matching submodule, and a second transmission submodule.

[0045] The business process parsing submodule is used to obtain business process configuration information; it is used to break down the business process configuration information into multiple sub-task units for each business process; the semantic matching submodule is used to calculate the text similarity between the functional description of the application module and the sub-task unit, and to filter out the combinations that meet the similarity threshold for matching; the second transmission submodule is used to send the matching results to the business monitoring module and / or the business deployment module.

[0046] The business process parsing submodule obtains business process configuration information (such as XML and BPMN format files) from the power system's business management platform and breaks down the complete business process into sub-task units using a process parsing algorithm. For example, the "electricity bill settlement and notification" process is broken down into independent steps such as "electricity consumption calculation," "electricity bill calculation," "bill generation," and "user notification," clearly defining the input, output, and execution logic of each sub-task.

[0047] The semantic matching submodule calculates text similarity between the application module function descriptions (such as code comments and interface documentation) provided by the data acquisition module and the subtask units. It uses Natural Language Processing (NLP) techniques (such as TF-IDF and BERT models) to extract keywords and semantic features, compares the semantic relevance of the two, and selects combinations with similarity higher than a threshold (such as 80%) for matching.

[0048] The second transmission submodule sends the business-module mapping relationship (e.g., "Subtask 2 → Electricity Billing Engine") generated by the semantic matching submodule to the business monitoring module and the business deployment module via a unified data interface (e.g., RESTful API) or message queue (e.g., Kafka). The business monitoring module tracks the business execution status based on this relationship, while the business deployment module optimizes resource allocation based on the matching results.

[0049] For example, in a smart grid marketing system, the "tiered electricity pricing settlement process" configuration file is obtained, and the following sub-task units are parsed out: Sub-task 1: Read the user's monthly electricity consumption data; Sub-task 2: Calculate the electricity fee according to the tiered electricity pricing standard; Sub-task 3: Generate and store the electronic bill; Sub-task 4: Push the bill notification via SMS / APP. During the semantic matching process,

[0050] The application module "Electricity Bill Calculation Engine" is described as "Calculating user electricity consumption data in real time based on tiered electricity pricing rules," which has a high semantic similarity to sub-task 2 "Calculating electricity bills according to tiered electricity pricing standards," thus meeting the matching criteria. The application module "Message Push Service" is described as "Sending system notifications via SMS and APP interface," which matches sub-task 4 "Pushing bill notifications via SMS / APP" to the preset threshold, thus successfully being associated.

[0051] Furthermore, the business mapping module also includes a mapping verification submodule and a manual visualization calibration submodule.

[0052] The mapping verification submodule is used to obtain historical load metrics and predict business metrics based on the current mapping relationship, and calculate the accuracy based on the predicted business metrics and actual historical business metrics. The manual visualization calibration submodule is used to display the candidate set of mapping relationships through a visual interface, allowing operations and maintenance personnel to manually adjust the mapping relationships or add additional mapping rules.

[0053] like Figure 4 To further implement the above technical solutions, the deployment management module includes a resource assessment submodule, a strategy formulation submodule, a deployment execution submodule, and a deployment information management submodule.

[0054] The resource assessment submodule is used to assess the resources of each server node based on the distributed architecture information, and predict the resource carrying capacity of each node under different business loads based on resource usage. The strategy formulation submodule is used to determine the target application module that needs to deploy the load monitoring program based on the mapping relationship information, and select server nodes for the target application module based on the resource assessment results. The deployment execution submodule is used for the task distribution mechanism, sending the deployment task list to the node management program. The node management program completes the deployment of the load monitoring program according to the list. The deployment information management submodule is used to record the current deployment information.

[0055] In the power system marketing, operation, and maintenance monitoring visualization system, the deployment management module leverages the characteristics of a distributed architecture to achieve efficient deployment and resource optimization of load monitoring programs through dynamic resource assessment, intelligent strategy planning, collaborative deployment execution, and end-to-end information management. Considering the high concurrency and multi-node collaboration characteristics of power business, each submodule is deeply integrated into the distributed architecture design.

[0056] The resource assessment submodule leverages the decentralized nature of a distributed architecture, employing a distributed data acquisition + global aggregation analysis model. Lightweight probes are deployed on each server node (e.g., metering server clusters, electricity consumption information collection node groups) to collect real-time metrics such as CPU core utilization, memory sharding usage, distributed disk storage I / O, and cross-node network bandwidth. Distributed computing frameworks (e.g., Spark, Flink) are used to perform parallel analysis of massive amounts of node data. Combined with the distributed processing logic of power business (e.g., cross-regional electricity consumption data synchronization), a resource carrying capacity model for each node in a distributed task chain is constructed to predict the resource coordination capabilities between nodes under different business loads.

[0057] Specifically, Prometheus Exporter is deployed on each server node, pushing resource data to a distributed message queue (such as Apache Pulsar) via the gRPC protocol to achieve distributed data collection with low latency. Combined with the network topology provided by the data acquisition module, graph computing algorithms (such as GraphX) are used to analyze resource dependencies between nodes. For example, when metering nodes in a certain area have insufficient resources, the impact on the load propagation of this to adjacent data forwarding nodes is predicted, achieving topology-aware analysis. Based on the Kubernetes HPA (Horizontal Autoscaling) principle, an elastic mapping relationship between resource requirements and business traffic is established, and evaluation thresholds are dynamically adjusted to achieve elastic model evaluation.

[0058] The strategy formulation submodule addresses business scenarios involving multi-node collaboration within a distributed architecture, focusing on the principles of global load balancing and data locality. Based on business mapping relationships, it breaks down the deployment requirements of the target application module's load monitoring program into distributed subtasks (e.g., the electricity billing engine monitoring program needs to be deployed across multiple billing nodes). Combining resource assessment results, and using distributed optimization algorithms (such as consistent hashing and Dijkstra's shortest path), it allocates the optimal deployment node to each subtask, considering inter-node network latency and data transmission costs, thus avoiding single-point overload and redundant cross-regional data transmission.

[0059] Task Segmentation Planning: For the deployment of the monitoring program for the electricity data processing module, the task is divided into multiple sub-tasks based on geographical regions, with priority given to deploying them on nodes near the data generation source. The deployment of the monitoring program for the electricity data processing module is also divided according to specific power grid regions. For example, the first power grid is divided into Region A, Region B, Region C, and Region D, involving four sub-tasks. The deployment strategy for each sub-task clearly defines the data collection scope. For instance, the Region D sub-task is responsible for collecting electricity data from the corresponding city, and the monitoring program is prioritized for deployment on the five edge nodes closest to the data center to reduce cross-regional data transmission latency.

[0060] Cross-node collaborative decision-making: A distributed consensus algorithm (such as Raft) is employed to synchronize resource status and deployment strategies across multiple management nodes, ensuring network-wide policy consistency. For example, using the Raft consensus algorithm, resource status and deployment strategies are synchronized among three management nodes (IPs: 10.0.0.10, 10.0.0.11, and 10.0.0.12). When node 10.0.0.10 receives a new deployment request, it synchronizes the request log to other nodes via the Raft protocol. After obtaining confirmation from at least two nodes, the deployment strategy is calculated. During the calculation process, a consistent hashing algorithm is used to evenly distribute the electricity billing engine monitoring program across different settlement nodes, ensuring load balancing.

[0061] Dynamic policy updates: When a node's resource utilization exceeds a threshold (e.g., 70%), a policy recalculation is automatically triggered, migrating some monitoring tasks to nodes with lower loads. For example, when a metering node in a Central China region is found to have resource utilization exceeding 70%, the system automatically triggers a policy recalculation. First, using Dijkstra's shortest path algorithm, the network latency and bandwidth between this node and other nodes are analyzed, selecting three backup nodes with loads below 40% and network latency within 5ms. Then, some monitoring tasks are migrated to the backup nodes, employing a rolling update strategy during the migration process, with each migration not exceeding 20% ​​of the total tasks to avoid impacting business operations.

[0062] The deployment and execution submodule is based on the concept of decentralized control. It achieves efficient deployment through a P2P task distribution network and containerized deployment technology. After each node completes deployment, it reports its status, forming a distributed consensus.

[0063] The deployment information management submodule constructs a distributed ledger structure to record deployment information, uses a Merkle tree to ensure that the information is immutable, and combines it with a service discovery mechanism to form a visual panoramic view of the deployment.

[0064] To further implement the above technical solution, the load monitoring module includes a data acquisition submodule, a data preprocessing submodule, and a data analysis submodule;

[0065] The data acquisition submodule is used to obtain relevant load operation indicators based on the running monitoring program on each server node; it is also used to obtain module performance indicators through a predefined health check interface.

[0066] The data analysis submodule is used to build a normal load behavior model using machine learning algorithms. It judges the real-time data by calculating the deviation between the real-time data and the historical normal data distribution.

[0067] Furthermore, the load monitoring module also includes a first alarm submodule, which is used to trigger an alarm mechanism, generate alarm information, and transmit the information through a specific channel when it is determined that the deviation of real-time data exceeds the set alarm threshold.

[0068] like Figure 5 To further implement the above technical solutions, the business monitoring module includes a data management submodule and a business status assessment submodule.

[0069] The data association submodule is used to establish a relationship mapping table based on the load status data and the mapping relationship, and to map each load status data to the corresponding business indicator type according to the relationship mapping table to generate structured data; the business status assessment submodule predicts and judges the corresponding business status based on the structured data.

[0070] The business monitoring module also includes a second alarm submodule; the second alarm submodule is used to highlight the abnormal node on the business process diagram through the data visualization engine when an abnormal business status is detected, and to display detailed abnormal information and impact analysis report.

[0071] For example, for monitoring monthly electricity bill settlement, the load monitoring module obtains real-time data from the electricity bill calculation server: CPU utilization 90%, memory usage 85%, and database query response time 500ms. The data association submodule performs vector transformation on these load status data and business indicator types: transforming indicators such as CPU utilization, memory usage, and response time into multi-dimensional vectors [0.9, 0.85, 0.5] (normalized, value range 0-1); pre-defining the "electricity bill calculation task processing efficiency" vector [0.7, 0.6, 0.2] (constructed based on normal business status thresholds) and the "bill generation data reading speed" vector [0.6, 0.5, 0.15]. By calculating the similarity between the load status vector and each vector in the business indicator vector library using cosine similarity, it was found that the similarity with the "electricity bill calculation task processing efficiency" vector reached 0.85, and the similarity with the "bill generation data reading speed" vector was 0.82. Based on this, a mapping relationship was established, generating a structured data vector group: {"business type":"electricity bill settlement","related vector group":[[0.9,0.85,0.5],[0.7,0.6,0.2]]}, which intuitively reflects the degree of correlation between load data and business indicators.

[0072] The business status assessment submodule compares the generated structured data vector set with the vector set from historical normal business status. By calculating the vector distance (such as Euclidean distance), it is found that the average distance between the current load status vector and the historical normal vector set exceeds the threshold of 30%. Combined with a time series vector prediction model (such as vector prediction based on Transformer), it is inferred that if no action is taken, the probability of electricity bill settlement delay reaches 80% after 3 hours, and the business status is determined to be abnormal.

[0073] After receiving the anomaly determination result, the second alarm submodule highlights the "Electricity Billing Engine" node in red in the electricity billing business process diagram. Simultaneously, it generates a visual analysis based on vector data: using a bar chart to compare the differences in various dimensions between the current load vector and the normal business vector, noting that CPU utilization exceeds normal levels by 28% and response time is extended by 150%, along with impact analysis and handling suggestions.

[0074] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0075] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A marketing operations and maintenance monitoring visualization system based on a distributed architecture, characterized in that, It includes a data acquisition module, a business mapping management module, a deployment management module, a load monitoring module, and a business monitoring module; the data acquisition module is used to acquire the distributed architecture information of the marketing management system. The business mapping management module is used to associate and bind the distributed architecture information with the preset business processes to obtain mapping relationship information. The deployment management module is used to deploy corresponding load monitoring programs based on the distributed architecture information; The load monitoring module is used to obtain load status data under the current deployment through the monitoring program; The business monitoring module is used to predict the business status based on the load status data and the mapping relationship information.

2. The marketing operation and maintenance monitoring visualization system based on a distributed architecture as described in claim 1, characterized in that, The data acquisition module includes a network topology acquisition submodule, a metadata parsing submodule, and a first transmission submodule; The network topology submodule is used to continuously send probe data packets to each server node in the marketing management system to conduct network probes and extract the corresponding node information to form a network topology structure. The metadata parsing submodule is used to obtain the configuration management data of the marketing management system and extract application module information through data parsing algorithms. The first transmission submodule is used to retrieve the network topology and application module information and send them to the service mapping management module and the deployment management module.

3. The marketing operation and maintenance monitoring visualization system based on a distributed architecture as described in claim 1, characterized in that, The business mapping module includes a business process parsing submodule, a semantic matching submodule, and a second transmission submodule; The business process parsing submodule is used to obtain business process configuration information; and to decompose each business process into multiple sub-task units based on the business process configuration information. The semantic matching submodule is used to calculate the text similarity between the functional description of the application module and the subtask unit, and to filter out the combinations that meet the similarity threshold for matching. The second transmission submodule is used to send the matching results to the service monitoring module and / or the service deployment module.

4. The marketing operation and maintenance monitoring visualization system based on a distributed architecture as described in claim 3, characterized in that, The business mapping module also includes a mapping verification submodule, which is used to obtain historical load indicators and predict business indicators based on the current mapping relationship, and calculate the accuracy rate based on the predicted business indicators and the actual historical business indicators.

5. A marketing operations and maintenance monitoring visualization system based on a distributed architecture as described in claim 3 or 4, characterized in that, The business mapping module also includes a manual visualization calibration submodule, which is used to display a set of mapping relationship candidates through a visualization interface, allowing operation and maintenance personnel to manually adjust the mapping relationship or add additional mapping rules.

6. The marketing operation and maintenance monitoring visualization system based on a distributed architecture as described in claim 1, characterized in that, The deployment management module includes a resource assessment submodule, a strategy formulation submodule, a deployment execution submodule, and a deployment information management submodule. The resource assessment submodule is used to assess the resources of each server node based on the distributed architecture information, and predict the resource carrying capacity of each node under different business loads in combination with the resource usage. The strategy formulation submodule is used to determine the target application module that needs to deploy the load monitoring program based on the mapping relationship information, and to select server nodes for the target application module based on the resource assessment results. The deployment execution submodule is used to send the deployment task list to the node management program according to the task distribution mechanism; The node management program completes the deployment of the load monitoring program according to the list; The deployment information management submodule is used to record the current deployment information.

7. A marketing operations and maintenance monitoring visualization system based on a distributed architecture as described in claim 1, characterized in that, The load monitoring module includes a data acquisition submodule, a data preprocessing submodule, and a data analysis submodule; The data acquisition submodule is used to obtain corresponding load operation indicators based on the operation monitoring program running on each server node; and to obtain module performance indicators through a predefined health check interface. The data analysis submodule is used to construct a normal load behavior model using machine learning algorithms. It judges the real-time data by calculating the deviation between the real-time data and the historical normal data distribution.

8. A marketing operation and maintenance monitoring visualization system based on a distributed architecture as described in claim 7, characterized in that, The load monitoring module also includes a first alarm submodule, which is used to trigger an alarm mechanism, generate alarm information and transmit the information through a specific channel when it is determined that the deviation of real-time data exceeds the set alarm threshold.

9. A marketing operations and maintenance monitoring visualization system based on a distributed architecture as described in claim 1, characterized in that, The business monitoring module includes a data management submodule and a business status assessment submodule; The data association submodule is used to establish a relationship mapping table based on the load status data and the mapping relationship, and to map each load status data to the corresponding business indicator type based on the relationship mapping table to generate structured data. The business status assessment submodule predicts and judges the corresponding business status based on the structured data.

10. A marketing operation and maintenance monitoring visualization system based on a distributed architecture as described in claim 9, characterized in that, The business monitoring module also includes a second alarm submodule; the second alarm submodule is used to highlight the abnormal node on the business process diagram through the data visualization engine when the abnormal business status is determined, and to display detailed abnormal information and impact analysis report.

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