Intelligent IVR (Interactive Voice Response) multi-node monitoring system supporting high-availability service of power marketing

Abstract

The invention belongs to the technical field of power marketing and artificial intelligence, and particularly relates to an intelligent IVR (Interactive Voice Response) multi-node monitoring system supporting high-availability service of power marketing, which comprises a system initialization module, a data processing module, a data processing module and a data processing module, the database of the user portrait can be loaded; and the node monitoring data flow configuration module is used for regularly capturing data flow information of each node by using the node monitoring module and the real-time data flow monitoring logic, and dynamically generating a node running state report by means of a streaming analysis tool. The core of the invention lies in that the high availability and intelligent level of power marketing service are comprehensively improved by constructing an intelligent and modularized IVR multi-node monitoring system.

Description

Technical Field

[0001] The present invention relates to the fields of power marketing and artificial intelligence technology, and in particular to an intelligent IVR multi-node monitoring system that supports high-availability services for power marketing. Background Art

[0002] With the rapid development of the power industry, the intelligent and digital transformation of power marketing business has become an important direction for enterprises to improve service quality and operational efficiency. In the field of customer service, the interactive voice response system (IVR), as an important part of power customer service, undertakes many key businesses such as electricity bill payment, power outage inquiry, and fault repair. However, the existing IVR system has many problems in actual operation.

[0003] The current IVR system has deficiencies in node monitoring and exception handling. Due to the lack of real-time monitoring capabilities for key functional nodes, when abnormalities occur in nodes such as grayscale control and user type identification in the system, it is often difficult to locate and handle them in a timely manner, resulting in a high risk of service interruption. In addition, the existing system lacks efficient fault-tolerant mechanisms and backup process designs in the event of interface call failure or network fluctuations, which further aggravates service interruptions and a decline in customer experience. At the same time, due to the lag in the alarm mechanism, operation and maintenance personnel cannot quickly obtain relevant information after a system abnormality occurs, increasing the time cost of system repair and recovery. More importantly, the current system has a limited level of intelligence and cannot provide personalized services based on user portraits and behavior patterns, which is a big gap from the diversified and precise needs of modern customers for power services. At the same time, the operation and maintenance tools of the IVR system also lack intuitiveness and visualization, and the system operation status is difficult to dynamically control, further restricting the improvement of operation and maintenance efficiency and service quality. Therefore, an intelligent IVR multi-node monitoring system that supports high-availability services for power marketing is invented. Summary of the invention

[0004] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0005] An intelligent IVR multi-node monitoring system supporting high-availability services for power marketing, comprising:

[0006] The system initialization module is used to initialize the data of the process nodes and load the user portrait database while deploying each functional module;

[0007] The node monitoring data flow configuration module is used to regularly capture the data flow information of each node using the node monitoring module and real-time data flow monitoring logic, and then dynamically generate node operation status reports with the help of streaming analysis tools;

[0008] The exception processing data flow optimization module is used to automatically switch to the backup node or the default process through the fault tolerance mechanism when an exception occurs in the data flow of a certain node, and after the exception processing is completed, the processing record of the abnormal data flow can be synchronized to the log management module;

[0009] An alarm notification data stream integration module is used to utilize the data stream integration logic of the alarm notification module to form a variety of abnormal alarm triggering conditions;

[0010] The user portrait analysis data construction module is used to build a dynamic user portrait database based on user historical behavior data and combined with the portrait analysis algorithm.

[0011] As a preferred solution of the intelligent IVR multi-node monitoring system supporting high-availability services for power marketing described in the present invention, the operation steps of the system initialization module are as follows:

[0012] S11: In the initial stage of system construction, the basic architecture of the system is built by deploying various IVR functional modules, and deep connection is achieved with the existing power marketing system;

[0013] S12: Initialize the basic data of each IVR process node, including interface call parameters, node logic rules and flow chart design;

[0014] S13: Complete the initial import of the user portrait database, integrate user historical behavior data, and form a complete user information system;

[0015] S14: Establish a two-way data interaction channel with the business system to ensure that the query, update and data push in the subsequent IVR process can be seamlessly connected, providing a stable data foundation for the operation of the entire system.

[0016] As a preferred solution of the intelligent IVR multi-node monitoring system supporting high-availability services for power marketing described in the present invention, the functional modules of the IVR include:

[0017] The process control module is responsible for the main control logic of the IVR process and manages the calling relationship between nodes;

[0018] Data monitoring module, responsible for collecting data from each interface in real time and analyzing its status;

[0019] Exception handling module, used to provide fault tolerance mechanism and backup logic switching;

[0020] Alarm module, used to handle abnormal alarms and notify relevant personnel;

[0021] User portrait module, used to store and analyze user behavior data;

[0022] Microservice architecture is used to deploy each module independently as a service, and services communicate with each other through API interfaces.

[0023] As a preferred solution of the intelligent IVR multi-node monitoring system supporting high-availability services for power marketing described in the present invention, the operation steps of the node monitoring data flow configuration module are as follows:

[0024] S21: After the system is put into operation, the node monitoring module is started to cover all key nodes in the IVR process;

[0025] S22: By configuring real-time data flow monitoring logic, the system can periodically capture data flow information of each node, including key indicators such as interface response time, call success rate, and data validity;

[0026] S23: With the help of streaming analysis tools, the monitoring module can dynamically generate node operation status reports and store the results in real time in the monitoring database. The monitoring data provides the necessary analysis basis for subsequent exception processing and optimization, ensuring that the system runs efficiently and can quickly respond to sudden problems.

[0027] As a preferred solution of the intelligent IVR multi-node monitoring system supporting high-availability services for power marketing described in the present invention, the operation steps of the exception processing data flow optimization module are as follows:

[0028] S31: During the operation of the system, if a data flow of a node is abnormal, the system will immediately trigger the preset exception handling rule logic;

[0029] S32: Through the fault tolerance mechanism, the system automatically switches to the backup node or the default process to ensure that the user experience is not affected;

[0030] S33: After the exception processing is completed, the system will synchronize the processing records of its exception data flow to the log management module and generate a detailed exception report. Among them, by storing the processing records in real time in the log database for subsequent operation and maintenance personnel to review and analyze, it helps to further improve the exception handling process and improve the reliability of the system.

[0031] As a preferred solution of the intelligent IVR multi-node monitoring system supporting high-availability services for power marketing described in the present invention, the operation steps of the alarm notification data stream integration module are as follows:

[0032] S41: To improve the system abnormal response efficiency, configure the data flow integration logic of the alarm notification module. The alarm notification module will define multiple abnormal alarm triggering conditions based on the monitoring data flow;

[0033] S42: When an abnormal condition is triggered, the system will push the alarm data stream to the terminal device of the operation and maintenance personnel through the SMS gateway or email server to ensure that the abnormal information is obtained in the first time;

[0034] S43: The system will also synchronously store the alarm data stream into the alarm database to support historical alarm query and analysis; its integration mode can not only ensure the real-time nature of abnormal notification, but also provide data support for subsequent system optimization.

[0035] As a preferred solution of the intelligent IVR multi-node monitoring system supporting high-availability services for power marketing described in the present invention, the operation steps of the user portrait analysis data construction module are as follows:

[0036] S51: Based on user historical behavior data and combined with portrait analysis algorithms, a dynamic user portrait database is constructed;

[0037] S52: The user portrait data stream is processed in real time to extract the user's behavior patterns, preferences and personalized needs, and output them as the basis for IVR process optimization.

[0038] Compared with existing technologies:

[0039] The core of the present invention is to comprehensively improve the high availability and intelligence level of power marketing services by building an intelligent and modular IVR multi-node monitoring system; its advantages are mainly reflected in: first, through the combination of node monitoring module and exception handling logic, the present invention can detect the operating status of each node in the IVR process in real time, and quickly switch to the backup node or default process when an abnormality occurs, ensuring that the system can maintain normal operation under any circumstances; this fault-tolerant mechanism significantly improves the stability of the service and reduces the impact of system failures on user experience; at the same time, through streaming analysis tools, a real-time monitoring capability of the IVR node interface data stream is established, including key parameters such as response time and data validity; the status report generated by the monitoring module provides data support for system optimization and problem troubleshooting, thereby realizing dynamic optimization of the IVR process and ensuring node operation efficiency and system health; secondly, the system has built-in exception handling rules Then, when a call failure or data anomaly occurs in the data stream, the processing logic can be automatically triggered and the recording and updating of the abnormal data can be completed; this highly intelligent exception handling capability significantly reduces the need for manual intervention, and at the same time provides a traceable exception handling log for system operation and maintenance; through the alarm notification module, the present invention can capture abnormal data streams in real time and push alarm information to the operation and maintenance personnel terminal; combined with the use of SMS gateways or email servers, it ensures that the operation and maintenance personnel are informed of abnormal conditions and take corresponding measures at the first time; at the same time, the alarm information is synchronously recorded in the database to facilitate subsequent problem analysis and service quality improvement; finally, a dynamic user portrait database is constructed using user historical behavior data and portrait analysis algorithms; user portraits not only support customized design of IVR processes, but also dynamically adjust service strategies to provide personalized services based on user needs; this precise service capability greatly improves user satisfaction and service efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] The present invention provides an intelligent IVR multi-node monitoring system that supports high-availability services for power marketing. Figure 1 ;

[0043] It includes: a system initialization module, which is used to initialize the data of the process nodes and load the user portrait database while deploying each functional module; a node monitoring data flow configuration module, which is used to use the node monitoring module and real-time data flow monitoring logic to regularly capture the data flow information of each node, and then dynamically generate a node operation status report with the help of a streaming analysis tool; an exception handling data flow optimization module, which is used to automatically switch to the backup node or the default process through a fault-tolerant mechanism when an exception occurs in the data flow of a certain node, and after the exception handling is completed, the processing record of the exception data flow can be synchronized to the log management module; an alarm notification data flow integration module, which is used to use the data flow integration logic of the alarm notification module to form a variety of abnormal alarm trigger conditions; a user portrait analysis data construction module, which is used to build a dynamic user portrait database based on user historical behavior data and combined with a portrait analysis algorithm.

[0044] The operating steps of the system initialization module are as follows:

[0045] S11: In the initial stage of system construction, the basic architecture of the system is built by deploying various IVR functional modules, and deep connection is achieved with the existing power marketing system;

[0046] S12: Initialize the basic data of each IVR process node, including interface call parameters, node logic rules and flow chart design;

[0047] S13: Complete the initial import of the user portrait database, integrate user historical behavior data, and form a complete user information system;

[0048] S14: Establish a two-way data interaction channel with the business system to ensure that the query, update and data push in the subsequent IVR process can be seamlessly connected, providing a stable data foundation for the operation of the entire system.

[0049] IVR functional modules include:

[0050] The process control module is responsible for the main control logic of the IVR process and manages the calling relationship between nodes;

[0051] Data monitoring module, responsible for collecting data from each interface in real time and analyzing its status;

[0052] Exception handling module, used to provide fault tolerance mechanism and backup logic switching;

[0053] Alarm module, used to handle abnormal alarms and notify relevant personnel;

[0054] User portrait module, used to store and analyze user behavior data;

[0055] Microservice architecture is used to deploy each module independently as a service, and services communicate with each other through API interfaces.

[0056] The operation steps of the node monitoring data flow configuration module are as follows:

[0057] S21: After the system is put into operation, the node monitoring module is started to cover all key nodes in the IVR process (such as gray control nodes, SMS sending nodes, user identity recognition nodes, etc.);

[0058] S22: By configuring real-time data flow monitoring logic, the system can periodically capture data flow information of each node, including key indicators such as interface response time, call success rate, and data validity;

[0059] S23: With the help of streaming analysis tools, the monitoring module can dynamically generate node operation status reports and store the results in real time in the monitoring database. The monitoring data provides the necessary analysis basis for subsequent exception processing and optimization, ensuring that the system runs efficiently and can quickly respond to sudden problems.

[0060] The operation steps of the exception handling data flow optimization module are as follows:

[0061] S31: During the operation of the system, if a node data flow is abnormal (such as interface call failure, timeout or data format error, etc.), the system will immediately trigger the preset exception handling rule logic;

[0062] S32: Through the fault tolerance mechanism, the system automatically switches to the backup node or the default process to ensure that the user experience is not affected;

[0063] S33: After the exception processing is completed, the system will synchronize the processing records of its exception data flow to the log management module and generate a detailed exception report. Among them, by storing the processing records in real time in the log database for subsequent operation and maintenance personnel to review and analyze, it helps to further improve the exception handling process and improve the reliability of the system.

[0064] The operation steps of the alarm notification data stream integration module are as follows:

[0065] S41: To improve the system abnormal response efficiency, configure the data flow integration logic of the alarm notification module. The alarm notification module will define multiple abnormal alarm triggering conditions (such as node timeout, data loss, call failure, etc.) based on the monitoring data flow;

[0066] S42: When an abnormal condition is triggered, the system will push the alarm data stream to the terminal device of the operation and maintenance personnel through the SMS gateway or email server to ensure that the abnormal information is obtained in the first time;

[0067] S43: The system will also synchronously store the alarm data stream into the alarm database to support historical alarm query and analysis; its integration mode can not only ensure the real-time nature of abnormal notification, but also provide data support for subsequent system optimization.

[0068] The operation steps of the user portrait analysis data construction module are as follows:

[0069] S51: Based on user historical behavior data and combined with portrait analysis algorithms, a dynamic user portrait database is constructed;

[0070] S52: User portrait data streams are processed in real time to extract user behavior patterns, preferences, and personalized needs, and output as the basis for IVR process optimization; for example, in the IVR process, user portrait data can support the customized design of the process and dynamically adjust the IVR service strategy for users. The continuous improvement of the user portrait database not only enhances the system's sensitivity to user needs, but also provides support for subsequent data analysis and business decision-making, ultimately achieving more intelligent and precise power marketing services.

[0071] Based on the above, in order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below in conjunction with implementation examples. The implementation examples described herein are only used to illustrate and explain the present invention, and are not intended to limit the present invention. The implementation examples are specifically as follows:

[0072] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below with reference to examples. The implementation examples described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0073] S1: System initialization

[0074] S11: Modular design and deployment

[0075] Decompose the system into multiple functional modules, including:

[0076] Process control module: responsible for the main control logic of the IVR process and the call relationship between management nodes;

[0077] Data monitoring module: responsible for real-time data collection of each interface and analysis of its status;

[0078] Exception handling module: provides fault tolerance mechanism and backup logic switching;

[0079] Alarm module: handles abnormal alarms and notifies relevant personnel;

[0080] User portrait module: store and analyze user behavior data;

[0081] Using the microservice architecture, each module is independently deployed as a service. Services communicate with each other through API interfaces. The service scheduling delay time includes interface delay, network delay, data transmission time, etc. Start the microservice container environment and deploy each environment;

[0082] S12: Process node data initialization

[0083] Initialize the node, which requires configuring the basic parameters of the node, including node ID, interface, node priority, etc., and use the node to build a node call graph to ensure the fault tolerance of the process;

[0084] S13: User portrait database loading

[0085] In order to analyze data using the user portrait function, the user portrait database is loaded when the system is initialized, and the user historical behavior data is exported from the system;

[0086] For user portrait data, we combine statistical methods to capture user behavior characteristics, write user behavior feature vectors into the database, start the user portrait module, load the portrait data, and provide support for subsequent data analysis;

[0087] S2: Node monitoring data flow configuration

[0088] S21: Monitoring module initialization

[0089] The monitoring module mainly covers interface status detection, data capture, exception capture, status report generation services, etc. The monitoring module includes data collection layer, status analysis layer, storage and display layer;

[0090] The data collection layer of the monitoring module mainly communicates with the IVR process node through the API interface to capture indicator data such as interface response time, data status and call success rate;

[0091] The status analysis layer of the monitoring module mainly uses the collected data for real-time analysis to determine the node operation status;

[0092] The storage display layer of the monitoring module mainly provides storage analysis results, generates node status reports, and displays real-time feedback.

[0093] S22: Node data flow configuration

[0094] Configure a unique API address for each node and define input and output parameters to ensure that the interface call data is structured;

[0095] During this period, the node response status is monitored to analyze whether there are any abnormalities, including timeout abnormalities and data abnormalities. If the response time is less than the threshold and the data is valid, it is normal. Otherwise, the node is abnormal.

[0096] S3: Optimizing data flow for abnormal data

[0097] S31: Abnormal data collection

[0098] In order to collect abnormal data, the present invention adopts a data collection module; in order to initialize the data collection module, the present invention deploys an abnormal monitoring module to monitor the interface call of the IVR process node in real time; at the same time, the interface call status and data valid representation are captured;

[0099] When the interface returns any status (timeout status, data format error, call failure status), the system will perform exception processing on the abnormal data flow;

[0100] First, classify the abnormal status and set the exception handling priority for each node according to the business importance. When an abnormality occurs in a node, a fault-tolerant mechanism is adopted to switch to a backup node or perform deep repair on the node.

[0101] At the same time, in order to ensure subsequent statistics, this system records abnormal data in the daily module, including node ID, timestamp, abnormal type, abnormal status code, abnormal number, etc., and stores the logs in the back-end database;

[0102] S31: Abnormal data flow optimization

[0103] For abnormal data streams, the system optimizes node configuration based on historical abnormal data, including dynamic optimization of node thresholds:

[0104] T threshold-new =T average +σ

[0105] Among them, T threshold old-new is the optimized response time threshold, T average is the historical average response time, σ is the standard deviation of the response time;

[0106] S4: Alarm notification data stream integration

[0107] S41: Alarm notification module

[0108] Alarm notification is necessary for exception handling. In order to capture the abnormal state of the system and pass the information to the operation and maintenance personnel, the alarm notification module has the functions of triggering alarms, sending notifications and logging for the results of abnormal detection.

[0109] The alarm notification module receives and parses abnormal data flow records from the monitoring module, including response time, error code, etc.; determines whether to generate an alarm based on the alarm trigger conditions, and sends the alarm information;

[0110] The alarm rule is that when the node abnormality rate exceeds the threshold, an alarm is triggered. At the same time, the abnormal word alarm of the node will directly trigger an alarm; the logic is:

[0111]

[0112] Among them, A trigger For warning signs, E rate is the abnormal rate, T timeout is the response time threshold;

[0113] S42: Abnormal data alarm

[0114] When the system monitors abnormal data flow, in addition to the fault tolerance mechanism, it is also necessary to trigger an alarm for the occurrence of abnormal data flow. The alarm level is defined according to the frequency and severity of the abnormality. When the abnormality rate of a node exceeds the threshold, multiple channels of alarms can be issued according to the system settings, including but not limited to SMS, email, system notification, etc. Among them, the notification priority configuration usually gives priority to high-risk node abnormalities.

[0115] If it is not confirmed at the same time, the system will choose to resend the notification, which is the timeout resend alarm mechanism;

[0116] S5: User portrait analysis data construction

[0117] S51: User portrait data collection

[0118] For user portrait data, the data source is clearly defined, including basic user information, consumption behavior, historical behavior records, etc. of the power marketing system; data collection is synchronized from the business system to the portrait construction module through the API interface;

[0119] After collecting user data, data preprocessing is performed on it. First, data cleaning is to remove missing and abnormal data in the data, unify the data format, and normalize the numerical data to align the data in the same space. The normalization formula is:

[0120]

[0121] Where x is the original value, x new is the standard value;

[0122] S52: User feature extraction

[0123] Design user characteristics based on business needs. The characteristics include consumption frequency, payment timeliness, electricity usage preferences, etc. The characteristics are as follows:

[0124] U vector ={F i},i∈[1,n]

[0125] Among them, F i is the i-th feature;

[0126] S53: Portrait model construction

[0127] Clustering the user feature vectors using an aggregation algorithm combined with an unsupervised learning algorithm to divide the user groups, and store the feature vectors and clustering results in a database;

[0128] in,

[0129]

[0130] In order to predict the results after segmentation, this system uses supervised learning algorithms, such as decision tree algorithms, to classify users and build prediction models. The input is feature vectors and the output is user labels.

[0131] S54: Optimization of image data application

[0132] Use user portrait data stream as input for marketing optimization, support optimization of IVR process based on user portrait, and provide personalized services such as real-time adjustment of electricity fee reminders and power outage notifications;

[0133] In order to support the dynamic optimization function, the portrait model is optimized based on user feedback and actual application effects, and the feature extraction rules and classification algorithms are adjusted. The specific evaluation indicators are:

[0134]

[0135] Among them, TP is the number of correctly classified positive samples, FP is the number of incorrectly classified positive samples, and FN is the number of incorrectly classified negative samples.

[0136] In summary, the present invention aims to comprehensively optimize the monitoring capability, abnormal response mechanism and intelligent service level of the IVR system through technological innovation, and promote the efficient operation of the power marketing business. The system tracks the dynamic status of key nodes through a real-time monitoring module, and realizes rapid response and fault repair in combination with an abnormal handling module, thereby significantly improving the high availability and service stability of the system. At the same time, the system introduces a user portrait analysis function to optimize the interactive process design and intelligent service capabilities to ensure that customers' needs in different scenarios can be quickly responded to and accurately met. In addition, the system also provides intuitive system status display and operation tools for operation and maintenance personnel by constructing a visual operation and maintenance interface, further improving management efficiency and reducing operation and maintenance complexity. By deeply integrating microservice architecture and cloud technology, the system can meet the growing needs of high-concurrency scenarios in the field of power marketing, and provide strong technical support for key business assurance of enterprises in digital transformation.

[0137] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An intelligent IVR multi-node monitoring system supporting high-availability services for power marketing, characterized in that: include: The system initialization module is used to initialize the data of the process nodes and load the user portrait database while deploying each functional module; The node monitoring data flow configuration module is used to regularly capture the data flow information of each node using the node monitoring module and real-time data flow monitoring logic, and then dynamically generate node operation status reports with the help of streaming analysis tools; The exception processing data flow optimization module is used to automatically switch to the backup node or the default process through the fault tolerance mechanism when an exception occurs in the data flow of a certain node, and after the exception processing is completed, the processing record of the abnormal data flow can be synchronized to the log management module; An alarm notification data stream integration module is used to utilize the data stream integration logic of the alarm notification module to form a variety of abnormal alarm triggering conditions; The user portrait analysis data construction module is used to build a dynamic user portrait database based on user historical behavior data and combined with the portrait analysis algorithm.

2. According to claim 1, an intelligent IVR multi-node monitoring system supporting high-availability services for power marketing, characterized in that: The operating steps of the system initialization module are as follows: S11: In the initial stage of system construction, the basic architecture of the system is built by deploying various IVR functional modules, and deep connection is achieved with the existing power marketing system; S12: Initialize the basic data of each IVR process node, including interface call parameters, node logic rules and flow chart design; S13: Complete the initial import of the user portrait database, integrate user historical behavior data, and form a complete user information system; S14: Establish a two-way data interaction channel with the business system to ensure that the query, update and data push in the subsequent IVR process can be seamlessly connected, providing a stable data foundation for the operation of the entire system.

3. According to claim 2, an intelligent IVR multi-node monitoring system supporting high-availability services for power marketing is characterized in that: The IVR functional modules include: The process control module is responsible for the main control logic of the IVR process and manages the calling relationship between nodes; Data monitoring module, responsible for collecting data from each interface in real time and analyzing its status; Exception handling module, used to provide fault tolerance mechanism and backup logic switching; Alarm module, used to handle abnormal alarms and notify relevant personnel; User portrait module, used to store and analyze user behavior data; Microservice architecture is used to deploy each module independently as a service, and services communicate with each other through API interfaces.

4. The intelligent IVR multi-node monitoring system supporting high-availability services for power marketing according to claim 1, characterized in that: The operation steps of the node monitoring data flow configuration module are as follows: S21: After the system is put into operation, the node monitoring module is started to cover all key nodes in the IVR process; S22: By configuring real-time data flow monitoring logic, the system can periodically capture data flow information of each node, including key indicators such as interface response time, call success rate, and data validity; S23: With the help of streaming analysis tools, the monitoring module can dynamically generate node operation status reports and store the results in real time in the monitoring database. The monitoring data provides the necessary analysis basis for subsequent exception processing and optimization, ensuring that the system runs efficiently and can quickly respond to sudden problems.

5. The intelligent IVR multi-node monitoring system supporting high-availability services for power marketing according to claim 1 is characterized in that: The operation steps of the exception handling data flow optimization module are as follows: S31: During the operation of the system, if a data flow of a node is abnormal, the system will immediately trigger the preset exception handling rule logic; S32: Through the fault tolerance mechanism, the system automatically switches to the backup node or the default process to ensure that the user experience is not affected; S33: After the exception processing is completed, the system will synchronize the processing records of its exception data flow to the log management module and generate a detailed exception report. Among them, by storing the processing records in real time in the log database for subsequent operation and maintenance personnel to review and analyze, it helps to further improve the exception handling process and improve the reliability of the system.

6. The intelligent IVR multi-node monitoring system supporting high-availability services for power marketing according to claim 1, characterized in that: The operation steps of the alarm notification data stream integration module are as follows: S41: To improve the system abnormal response efficiency, configure the data flow integration logic of the alarm notification module. The alarm notification module will define multiple abnormal alarm triggering conditions based on the monitoring data flow; S42: When an abnormal condition is triggered, the system will push the alarm data stream to the terminal device of the operation and maintenance personnel through the SMS gateway or email server to ensure that the abnormal information is obtained in the first time; S43: The system will also synchronously store the alarm data stream into the alarm database to support historical alarm query and analysis; its integration mode can not only ensure the real-time nature of abnormal notification, but also provide data support for subsequent system optimization.

7. The intelligent IVR multi-node monitoring system supporting high-availability services for power marketing according to claim 1, characterized in that: The operation steps of the user portrait analysis data construction module are as follows: S51: Based on user historical behavior data and combined with portrait analysis algorithms, a dynamic user portrait database is constructed; S52: The user portrait data stream is processed in real time to extract the user's behavior patterns, preferences and personalized needs, and output them as the basis for IVR process optimization.

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