Service quality and efficiency monitoring method and system

Abstract

The invention relates to the field of data processing, in particular to a service quality and efficiency monitoring method and system. The method comprises the following steps: constructing a target service system corresponding to a target service; wherein the target service system at least comprises a plurality of service paths, each service path comprises a plurality of service nodes, the plurality of service nodes are respectively in one-to-one correspondence with a plurality of service processing stages in the corresponding service path, and a connection relationship among the plurality of service nodes is obtained based on a service interaction relationship in the corresponding service path; acquiring real-time monitoring data of a target service, and updating the target service system based on the real-time monitoring data; and performing service quality and efficiency optimization on each service path in the target service system. According to the method, the timeliness and accuracy of fault detection can be improved, and the automatic and systematic management of the operation and maintenance process is realized, so that the quality and efficiency of the whole network and service system are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for monitoring business quality and efficiency. Background Art

[0002] In the process of digital transformation, the integration and transformation of traditional and digital businesses inevitably bring challenges. Enterprises need to promote business innovation flexibly and efficiently while maintaining the steady development of traditional core businesses, and gradually achieve the re-upgrade of core businesses. This spiral iterative development model of "stability and innovation" not only promotes the continuous evolution and optimization of businesses, but also helps enterprises take the initiative in an era where opportunities and uncertainties coexist.

[0003] With the expansion of enterprise network scale and the surge in the number of application systems and terminals, the diversification trend of network technology and equipment has become increasingly obvious, and the dependence of various businesses of enterprises on computer networks, IT infrastructure platforms and operation and maintenance management has become increasingly deep. This also puts higher requirements on the availability and stability of network systems and business systems. However, the existing enterprise operation and maintenance methods mainly rely on manual management, which makes it difficult to detect faults in real time and often falls into passive "firefighting" management. Although enterprises have deployed various network security products, they lack effective means of monitoring business quality and efficiency, and cannot identify and handle violations in a timely manner. In addition, the operation and maintenance processes of enterprises lack systematic sorting and solidification, rely on manpower and paper forms for maintenance work, and lack automated management and maintenance systems, resulting in low efficiency and difficulty in coping with complex operation and maintenance needs.

[0004] Therefore, it is particularly urgent to design a technical solution to solve the bottleneck problems in the above operation and maintenance management. This solution not only needs to improve the timeliness and accuracy of fault detection, but also realize the automation and systematic management of the operation and maintenance process, so as to improve the quality and efficiency of the entire network and business system, and ensure that the enterprise moves forward steadily in the process of digital transformation. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a service quality and efficiency monitoring method and system to improve the timeliness and accuracy of fault detection, realize the automation and systematic management of operation and maintenance processes, thereby improving the quality and efficiency of the entire network and service system.

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring service quality and efficiency, the method comprising:

[0007] Constructing a target business system corresponding to the target business; wherein the target business system includes at least a plurality of business paths, each business path includes a plurality of business nodes, the plurality of business nodes correspond one-to-one to a plurality of business processing stages in the corresponding business path, and the connection relationship between the plurality of business nodes is obtained based on the business interaction relationship in the corresponding business path;

[0008] Acquire real-time monitoring data of a target business, and update the target business system based on the real-time monitoring data;

[0009] Optimize the service quality and efficiency of each service path in the target service system.

[0010] In a second aspect, an embodiment of the present application provides a business quality and efficiency monitoring system, the system comprising the following units, wherein:

[0011] A construction unit is configured to construct a target business system corresponding to the target business; wherein the target business system includes at least a plurality of business paths, each business path includes a plurality of business nodes, the plurality of business nodes correspond one-to-one to a plurality of business processing stages in the corresponding business path, and the connection relationship between the plurality of business nodes is obtained based on the business interaction relationship in the corresponding business path;

[0012] A monitoring unit, configured to obtain real-time monitoring data of a target business and update the target business system based on the real-time monitoring data;

[0013] The optimization unit is configured to monitor the service quality and efficiency of each service path in the target service system.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0015] at least one processor, memory, and input-output unit;

[0016] The memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the business quality and efficiency monitoring method of the first aspect.

[0017] In a fourth aspect, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes the business quality and efficiency monitoring method of the first aspect.

[0018] The beneficial effects of the present invention are as follows: a business quality and efficiency monitoring method and system are provided. In this technical solution, a target business system corresponding to the target business is constructed; wherein the target business system includes at least multiple business paths, each business path includes multiple business nodes, and the multiple business nodes correspond one-to-one to the multiple business processing stages in the corresponding business path, and the connection relationship between the multiple business nodes is obtained based on the business interaction relationship in the corresponding business path; the real-time monitoring data of the target business is obtained, and the target business system is updated based on the real-time monitoring data; the business quality and efficiency of each business path in the target business system is optimized. In this technical solution, by realizing real-time monitoring, dynamic optimization, efficient resource utilization and automated management, the reliance on manual intervention and human operation and maintenance is greatly reduced, and the quality and efficiency of the business system is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of a method for monitoring business quality and efficiency according to an embodiment of the present application;

[0020] Figure 2 It is a structural diagram of a business quality and efficiency monitoring system according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application;

[0022] Figure 4 It is a structural schematic diagram of a medium device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0024] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0025] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0026] The embodiment of the present application provides a method and system for monitoring business quality and efficiency. In this technical solution, a target business system corresponding to the target business is constructed; wherein the target business system includes at least multiple business paths, each business path includes multiple business nodes, and the multiple business nodes correspond to multiple business processing stages in the corresponding business path one by one, and the connection relationship between the multiple business nodes is obtained based on the business interaction relationship in the corresponding business path; the real-time monitoring data of the target business is obtained, and the target business system is updated based on the real-time monitoring data; the business quality and efficiency of each business path in the target business system is optimized.

[0027] In the embodiment of the present application, by obtaining the real-time monitoring data of the target business and updating the target business system based on these data, the system can maintain accuracy and effectiveness in a real-time changing environment. This real-time monitoring and dynamic update capability ensures that the business system can respond to various changes in a timely manner, including traffic fluctuations, system failures, or changes in the external environment, thereby improving the flexibility and adaptability of the business. By building a target business system, clarifying each business path and its corresponding business nodes and interactive relationships, the system can accurately identify key nodes and bottlenecks in the business process. Based on this structured path information, the system can optimize the quality and efficiency of the business in a targeted manner, thereby improving the efficiency and quality of the overall business process. This precise optimization avoids the waste of resources and unnecessary complexity caused by blindly adjusting the entire system in traditional methods. Through continuous monitoring and optimization of business paths, the system can quickly discover and deal with potential problems such as delays, errors, or excessive use of resources. This preventive and responsive optimization strategy can significantly improve the stability and reliability of the system, reduce the risk of business interruption, and thus ensure business continuity. By monitoring and optimizing the quality and efficiency of each business path, it is possible to effectively identify the irrationality of resource allocation and make adjustments. By optimizing resource allocation, computing resources, network bandwidth, storage and other resources can be used efficiently, reducing waste and lowering operating costs. This is especially important for resource-intensive businesses, as it can help companies reduce operating expenses while maintaining or improving business performance. Since the system can optimize business quality and efficiency in real time, companies can respond to customer needs and market changes more quickly in market competition and improve customer satisfaction. This agility and efficiency enables companies to maintain their leading position in the fierce market competition and enhance the overall competitiveness of their businesses. Through systematic business quality and efficiency monitoring and optimization, this method greatly reduces reliance on manual intervention and human operation and maintenance. Automated monitoring and optimization not only improves work efficiency, but also reduces the risk of human error, making the management of business systems easier and more reliable.

[0028] In the context of digital transformation, the embodiments of the present application provide technical support for the smooth transition of traditional businesses to digital businesses. By building and optimizing the target business system, enterprises can more effectively manage and monitor the operating status of their digital businesses, thereby promoting the continuous evolution and innovation of the business. In summary, the embodiments of the present application significantly improve the quality and efficiency of the business system by achieving real-time monitoring, dynamic optimization, efficient resource utilization and automated management, ensuring the stability, flexibility and efficiency of the enterprise in digital transformation and market competition.

[0029] The business quality and efficiency monitoring solution provided in the embodiments of the present application can also be executed by an electronic device, which can be a server, a server cluster, or a cloud server. The electronic device can also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a dedicated device (such as a dedicated terminal device with a business quality and efficiency monitoring system). These electronic devices can also be equipped with the chips introduced in the above embodiments. Alternatively, these electronic devices can also be installed with a service program for executing the business quality and efficiency monitoring solution.

[0030] Figure 1 A flowchart of a method for monitoring business quality and efficiency provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method comprises the following steps:

[0031] 101. Build a target business system corresponding to the target business;

[0032] 102, obtaining real-time monitoring data of a target business, and updating the target business system based on the real-time monitoring data;

[0033] 103. Optimize the service quality and efficiency of each service path in the target service system.

[0034] In an embodiment of the present application, the target business system includes at least multiple business paths, each business path includes multiple business nodes, the multiple business nodes correspond one-to-one to multiple business processing stages in the corresponding business path, and the connection relationship between the multiple business nodes is obtained based on the business interaction relationship in the corresponding business path.

[0035] Optionally, in 101, first clarify the business requirements of the system, identify the business processes, key nodes and expected results that need to be covered; then define multiple business paths, each path represents a complete business process, and further divide it into multiple business nodes, each node corresponding to a processing stage. Next, determine the connection relationship between the nodes, and establish the dependency and sequence of the nodes based on the interaction logic in the business process. Next, integrate all paths and nodes to establish a complete business system model, and display their connection relationships through visualization tools. Subsequently, develop the actual system and conduct comprehensive testing to ensure that each path and node can be executed correctly. After the system is deployed, real-time monitoring is carried out to ensure stable operation, and continuous optimization is carried out based on the monitoring data. This structured approach improves the design quality of the system and lays the foundation for maintenance, monitoring and optimization.

[0036] For example, the process of building the target business system corresponding to the target business in 101 can be divided into the following key steps. These steps will help design and implement an efficient and structured business system, in which each business path consists of multiple business nodes, and the connection relationship between these nodes is determined based on the interaction relationship in the business process. The following is an introduction to the detailed steps:

[0037] First, clarify the main functions and business requirements of the target business system to be achieved. This involves identifying the business processes, key nodes, and expected results that the business system needs to cover. For an airline's reservation and boarding management system, it is clear that the system needs to cover the entire process from flight inquiry, reservation, payment to boarding management.

[0038] Based on business needs, multiple business paths are defined, each of which represents a complete business process. The business path covers all stages of the business process from the starting point to the end point. For the airline booking process, the following business paths are defined:

[0039] Path 1: Flight query -> seat selection -> payment -> booking confirmation -> send e-ticket

[0040] Path 2: Boarding management -> Baggage check-in -> Security check -> Boarding -> Flight

[0041] Each business path is further divided into multiple business nodes. Each node represents a processing stage in the business process, usually corresponding to an independent operation or task. In the flight booking path, the business nodes may include:

[0042] Node 1: Flight query

[0043] Node 2: Seat Selection

[0044] Node 3: Payment

[0045] Node 4: Booking confirmation

[0046] Node 5: Sending electronic tickets

[0047] Analyze the interaction between nodes in the business process and determine the connections between nodes based on these interactions. The connection relationship reflects the dependency, sequence, and interaction logic between nodes. In the reservation process, the connection relationship may include:

[0048] Flight query -> seat selection (users query flights and select seats)

[0049] Seat selection -> payment (payment after selecting a seat)

[0050] Payment->Booking confirmation (booking is confirmed after successful payment)

[0051] Booking confirmation->send e-ticket (send e-ticket after booking confirmation)

[0052] Integrate the previously defined business paths, nodes, and connections into a unified business system model. This model should clearly show the nodes in each business path and their interrelationships. Create a complete flight booking management system model that contains all paths and nodes, and shows their connections through visualization tools (such as flowcharts and data flow diagrams). Further, optionally, develop an actual system based on the business system model and conduct a comprehensive test of the system to ensure that each business path and node can be executed correctly and that the connection relationship between nodes conforms to the business logic. For example, by simulating the booking process, test the function of each node, such as testing the correctness and efficiency of flight query, payment processing, and electronic ticket sending. Deploy the constructed business system to the actual environment and monitor it in real time to ensure that the system can run stably in actual use, and optimize it based on the monitoring data. Monitor the use of the flight booking system, such as the user's booking success rate, the frequency of payment problems, etc., to promptly discover and solve potential problems. Continuously optimize the system based on feedback and monitoring data from actual operation. Optimization may involve adjusting business paths, optimizing node functions, improving the connection relationship between nodes, etc. If it is found that the processing efficiency of a certain node is low (such as a payment node), the efficiency of the overall business path can be improved by optimizing the payment process, adding payment options or improving system performance.

[0053] Through the above steps, when building the target business system, the complex business process can be effectively structured and modularized, and by defining clear business paths, nodes and connection relationships, accurate control and management of the business process can be achieved. This structured approach not only improves the design quality of the system, but also lays a good foundation for system maintenance, monitoring and optimization.

[0054] As an optional embodiment, in 102, updating the target business system based on the real-time monitoring data can be implemented as follows:

[0055] The real-time monitoring data is analyzed in real time through an automated evaluation model, and the analysis results are updated to the corresponding business nodes.

[0056] Among them, real-time monitoring data includes at least: business operation data, user behavior data, network traffic data, and database performance data. Specifically, business operation data includes the system's operating status, processing time, failure rate, etc., reflecting the health of the business process; user behavior data covers the user's click rate, page dwell time, conversion rate, etc., reflecting the user's behavior and interaction in the system; network traffic data involves bandwidth utilization, data transmission rate, network latency, etc., reflecting the system's performance at the network level; database performance data includes query response time, number of database connections, frequency and success rate of read and write operations, reflecting the database's workload and efficiency. These data together constitute a comprehensive monitoring of system performance and user experience.

[0057] In actual applications, the automated evaluation model will generate analysis results after parsing the data. These results can include alerts (such as the detection of performance bottlenecks), optimization suggestions (such as adjusting the load balancing strategy of network traffic), or direct operation instructions (such as increasing or decreasing the size of the database connection pool). For example, when an abnormal peak is detected in the network traffic data, the model may generate an analysis result and recommend temporarily increasing the bandwidth or diverting part of the traffic to a backup server.

[0058] This automated evaluation model acquires and preprocesses various real-time monitoring data through the data input layer, extracts key features from the data through the feature extraction layer, performs in-depth analysis on these features and generates optimization or prediction results through the analysis layer, and finally applies the results to the business system through the update layer to achieve dynamic adjustment and continuous optimization of the system. Through this multi-level architecture design, the target business system can maintain efficient and stable operation in a constantly changing environment.

[0059] For example, assuming that the automated evaluation model includes at least: a data input layer, a feature extraction layer, a parsing layer, and an update layer. Based on this, in 102, the real-time monitoring data is parsed in real time by the automated evaluation model, and the parsing results are updated to the corresponding business nodes, which can be implemented as follows:

[0060] Receiving the real-time monitoring data through the data input layer and preprocessing the real-time monitoring data;

[0061] Through the feature extraction layer, a multi-dimensional dynamic time window is used to extract the real-time monitoring features corresponding to each service path from the real-time monitoring data;

[0062] Through the parsing layer, multi-branch parsing is performed on the real-time monitoring features corresponding to each business path to obtain the real-time status prediction results corresponding to each business path; the real-time status prediction results at least include: predicted operation status, predicted interaction relationship, and abnormal operation status prediction results;

[0063] Through the update layer, the real-time status prediction results are updated to the corresponding business nodes respectively.

[0064] From a functional perspective, the data input layer is responsible for collecting and preprocessing real-time monitoring data from various data sources. These data sources include business operation data, user behavior data, network traffic data, database performance data, etc. The data input layer not only receives data in multiple formats, but also performs preliminary cleaning and conversion for subsequent processing. For example, the data input layer can collect data from multiple systems and sensors. Clean, standardize, and convert data to ensure data consistency and availability. Store the preprocessed data in a temporary database or cache for further analysis. This layer can collect data from database management systems, user behavior analysis platforms, and network monitoring tools, and perform format conversion and missing data filling.

[0065] The main task of the feature extraction layer is to extract useful features from the preprocessed data. These features are the basis for the model to analyze and make decisions. Feature extraction can include statistical analysis, pattern recognition, time series analysis and other methods. Identify key features from the input data, such as average response time, user click rate, packet loss rate, etc. Perform dimensionality reduction on the data, remove redundant information, and retain the most representative features to improve the processing efficiency of the model. Store the extracted features in a specific data structure for use by the parsing layer. For example, when monitoring network traffic, the feature extraction layer can extract the changing trend and peak of the packet transmission rate to identify potential network bottlenecks.

[0066] The parsing layer is the core part of the model, responsible for analyzing and interpreting the feature data provided by the feature extraction layer. The parsing layer usually contains a variety of algorithms and models, such as machine learning models, rule engines, and prediction models. It can identify potential problems, make optimization suggestions, or predict future system states based on set rules and patterns. Identify abnormal patterns in data, such as performance degradation trends, sudden changes in user behavior, etc. Predict future trends or potential problems based on existing data, such as resource exhaustion or user traffic surges. Generate optimization suggestions or automated decisions for business systems. For example, the parsing layer can use machine learning models to detect abnormal increases in database response time and predict whether this trend will continue, and then recommend increasing database resources.

[0067] The update layer is responsible for applying the results produced by the parsing layer to the target business system. Updates can include dynamic adjustments to configurations, reallocation of resources, changes to policies, and so on. The update layer is usually integrated with the system's management interface or automation tools to enable automated real-time updates. Based on the parsing results, automatically adjust the system configuration or update the parameters of the business nodes. After implementing the update, immediately monitor its effects and feed new data back to the data input layer to continuously optimize the system. Manage dynamic policies in the system to ensure that updates are executed correctly under different conditions. For example, if the parsing layer detects that database performance has degraded, the update layer may automatically increase the size of the database connection pool or adjust the query optimization strategy to improve overall performance.

[0068] As an optional embodiment, in the above steps, the real-time monitoring features corresponding to each service path are subjected to multi-branch analysis through the analysis layer to obtain the real-time status prediction results corresponding to each service path, which can be implemented as at least one of the following:

[0069] The first branch model is used to predict the future changes of the business based on the time series characteristics of the operating status in each business path, and the prediction results of the future operating status corresponding to each business path are obtained;

[0070] The second branch model is used to identify the dependency characteristics in each business path to predict the structural change, and the structural change prediction results corresponding to each business path are obtained;

[0071] The third branch model is used to perform abnormal prediction on the business path characteristics in each business path, and obtain the abnormal operation prediction results corresponding to each business path.

[0072] For example, in this system, the parsing layer performs multi-branch parsing on the real-time monitoring features of each service path and applies three different branch models to obtain the real-time status prediction results of each service path. The following is a detailed introduction to the three branch models:

[0073] The first branch model: prediction of operating status time series characteristics. This model focuses on analyzing and predicting the operating status time series characteristics in each business path. Time series characteristics include changing trends, periodic patterns, and abnormal fluctuations in historical data. By modeling these time series data, the model can predict the operating status of the business path at a future time point.

[0074] Identify and predict future trends of key indicators in business paths, such as response time, processing efficiency, etc. By identifying abnormal changes in time series, predict abnormal conditions or performance bottlenecks that may occur in the future. Estimate the possible operating status of business paths in different scenarios to help the system schedule resources or adjust policies in advance. For example, predict whether the processing time of a business process will increase significantly during future peak periods, so that measures can be taken in advance to avoid performance degradation.

[0075] Second branch model: dependency feature prediction. This model is mainly used to identify and analyze dependency features in each business path, and predict structural changes based on these features. Dependency features include interdependencies between business paths, resource sharing, bottleneck links, etc. By analyzing these features, the model can predict structural changes in business paths, such as load changes at key nodes or the need to restructure dependency paths. Identify the dependencies between various links in the business path and determine the key paths and weak links. Predict changes in business path structure caused by changes in external conditions or internal adjustments, such as load transfer at a certain node or redistribution of dependencies. Based on the structural prediction results, make recommendations for optimizing business path dependencies to avoid single point failures or resource contention. For example, in the case of a surge in user traffic, predict whether the load of a database node will become a bottleneck, and recommend adding backup nodes or adjusting query allocation strategies.

[0076] The third branch model: business path feature anomaly prediction. The third branch model focuses on anomaly prediction of features in each business path. It uses abnormal patterns in historical data and real-time data to predict abnormal operation of business paths. These features include abnormal performance indicators, operational errors, resource exhaustion, etc. The model identifies the precursors of anomalies and issues warnings or takes preventive measures in advance.

[0077] Identify potential abnormal patterns in business paths, such as sudden performance degradation, abnormal response time growth, etc. Predict possible abnormal situations in advance and notify relevant systems or operation and maintenance personnel through early warning mechanisms. Automatically trigger response strategies based on abnormal prediction results, such as adding redundant resources, reallocating tasks, or automating fault handling. For example, predict that a critical business path may suffer performance degradation due to sudden traffic, and initiate response measures in advance, such as adding computing resources or enabling load balancing.

[0078] These three branch models work together to fully cover the future operating states, structural changes, and abnormal situations that may occur in the business path. By analyzing and predicting the timing characteristics, dependency characteristics, and business path characteristics respectively, the system can detect potential problems in advance, optimize resource allocation, and ensure the stable operation of business processes. This multi-branch analysis strategy improves the system's prediction accuracy and response capabilities, allowing it to remain efficient and reliable in complex and changing environments.

[0079] As an optional embodiment, it is assumed that the automated assessment model also includes: a monitoring alarm layer. Based on this structure, the third branch model is used to perform abnormal prediction on the service path characteristics in each service path, and after obtaining the abnormal operation prediction results corresponding to each service path, it also includes:

[0080] Determine whether the abnormal operation prediction result exceeds a predetermined threshold or reaches a predetermined abnormal condition; if the abnormal operation prediction result exceeds a predetermined threshold or reaches a predetermined abnormal condition, check the business nodes of the current business path one by one and perform feature abnormality analysis to obtain a preliminary fault node; analyze the interaction logs between the preliminary fault node and the surrounding nodes, and use the business node that has abnormal data flow interaction with at least two surrounding nodes as a faulty business node; and / or, perform a time series retrospective analysis on the historical operation characteristics of the preliminary fault node to identify whether the preliminary fault node has abnormal data fluctuations, and use the business node with abnormal data fluctuations as a faulty business node; issue an abnormal warning to the faulty business node, and generate a corresponding fault handling strategy.

[0081] Specifically, in this optional embodiment, it is assumed that the automated evaluation model also includes a monitoring alarm layer. After the third branch model is used to predict the anomaly of the characteristics of each business path, the layer can further analyze the anomaly and generate a processing strategy. After the analysis layer uses the third branch model to predict the anomaly of each business path, the system will first determine whether these prediction results exceed the predetermined threshold or reach the set abnormal condition. If the prediction result shows that a business path is abnormal and exceeds the set threshold, the system will take further action. Assuming that the response time of a business path suddenly increases significantly and exceeds the set 5-second threshold, the system will consider this as a potential abnormal situation and trigger the subsequent abnormal analysis process. Once an abnormal situation is found, the system will check each business node of the business path one by one, and determine the preliminary fault node through feature abnormal analysis. This step is intended to narrow the scope of the fault and determine the node that may have problems. On an e-commerce platform, assuming that an order processing path is abnormal, the system will check each node on the path one by one, such as user verification node, payment node, order generation node, etc., and finally find that the processing time of the payment node has increased significantly. For the identified preliminary fault node, the system will further analyze the interaction log between it and the surrounding nodes to check whether there is an abnormal data flow. If abnormal data flow interactions are found with at least two surrounding nodes, the node will be confirmed as a faulty business node. Assuming that the interaction logs between the payment node and the order generation node and the user verification node show that the packet transmission error rate increases significantly when the anomaly occurs, the system confirms the payment node as a faulty business node. The system can also perform a time series review of the historical operating characteristics of the preliminary faulty node to see if there are abnormal data fluctuations. If the node has similar abnormal fluctuations in history, it is further confirmed that it is a faulty node. When reviewing the historical data of the payment node, the system found that the node had also experienced a similar surge in processing time during the previous high traffic period, confirming that this is a persistent problem and marking it as a faulty business node. For confirmed faulty business nodes, the system will generate an abnormal warning and notify the relevant operation and maintenance personnel. At the same time, the system will automatically generate a fault handling strategy, such as temporarily switching to a backup node, increasing processing resources, or adjusting the load balancing configuration. For the abnormality of the payment node, the system immediately triggers an early warning notification to the operation and maintenance team, and recommends starting the backup payment node, and generates a report to recommend future optimization of resource allocation for the payment process.

[0082] By integrating the monitoring alarm layer and combining it with the third branch model, the system can not only predict abnormal situations in the business path, but also automatically determine whether further action is needed. When a serious abnormality is detected, the system can intelligently troubleshoot the faulty node, confirm the root cause of the problem through interactive log analysis and time series review analysis, and finally generate an early warning and formulate a response strategy. This process significantly improves the system's automated fault handling capabilities, ensuring that business processes can quickly resume normal operation when problems occur.

[0083] As an optional embodiment, in 103, optimizing the service quality and efficiency of each service path in the target service system may be implemented as follows:

[0084] Through the quality and efficiency monitoring module, the quality and efficiency of each business path in the target business system is evaluated to obtain the quality and efficiency optimization processing strategy corresponding to each business path; the quality and efficiency optimization processing strategy corresponding to each business path is executed to achieve automatic quality and efficiency optimization of each business path.

[0085] In this optional embodiment, the introduction of the quality and efficiency optimization module enables the system to automatically evaluate and optimize the quality and efficiency of the business path. First, a quality and efficiency monitoring module is introduced, which is specifically used to evaluate the quality and efficiency (quality and efficiency) of each business path in the target business system. By monitoring key performance indicators (such as response time, success rate, resource utilization, etc.), the quality and efficiency monitoring module can identify the business path that needs to be optimized. For example, in an e-commerce platform, the quality and efficiency monitoring module monitors the order processing path and finds that the order processing time of the path is longer than expected, and the order success rate has slightly decreased, indicating that the path has quality and efficiency problems. After identifying the business path with quality and efficiency problems, the system conducts an in-depth evaluation through the quality and efficiency monitoring module to analyze the performance bottleneck or inefficient link of the path. According to the evaluation results, the system generates corresponding quality and efficiency optimization processing strategies. These strategies may include adjusting resource allocation, optimizing process logic, reducing unnecessary steps, etc. For example, for the quality and efficiency problems of the order processing path, the system found that the payment verification step takes too long after analysis and has become a bottleneck. Therefore, the quality and efficiency monitoring module generates an optimization strategy that recommends parallel processing of the payment verification step and optimizes database queries to speed up processing. The system executes the quality and efficiency optimization processing strategy corresponding to each business path to achieve automatic quality and efficiency optimization of each business path. This means that the system can automatically adjust and improve according to the preset optimization strategy without manual intervention. The system automatically adjusted the payment verification step in the order processing path to parallel processing and optimized the database index. After implementing these optimizations, the order processing time was significantly reduced and the order success rate was also improved. After the optimization strategy is executed, the system will continue to monitor the performance of the business path to ensure that the optimization effect is maintained. If new quality and efficiency issues are detected, the system can further adjust the strategy to form a closed-loop optimization process. After the optimization measures are implemented, the quality and efficiency monitoring module continues to track the performance of the order processing path to confirm that the improvement of the payment verification step is effective and no new bottlenecks are introduced. If the processing time is found to increase again in the future, the system can automatically adjust the strategy and continue to optimize.

[0086] By introducing the quality and efficiency monitoring module, the system can automatically evaluate and optimize the quality and efficiency of each business path. When quality and efficiency problems are found, the system will generate and execute corresponding optimization processing strategies to continuously improve the performance of the business path. This automated quality and efficiency optimization mechanism not only improves the operating efficiency of the system, but also reduces the need for manual intervention, allowing the business system to operate more intelligently and efficiently.

[0087] Specifically, in the above steps, the quality and efficiency of each business path in the target business system is evaluated to obtain the quality and efficiency optimization processing strategy corresponding to each business path, which can be implemented as follows: setting the reward optimization function and optimization target corresponding to the target business system; the optimization target includes at least: success rate, response time, and resource utilization; according to the reward optimization function and the optimization target, each business path is adaptively optimized and evaluated, and the dynamic resource configuration information corresponding to each business path is determined.

[0088] Further optionally, the reward optimization function is expressed as:

[0089]

[0090] Where R(Pi) represents the comprehensive reward value of the ith service path, ws is the weight coefficient of the success rate Si of the ith service path, αs is the adjustment parameter of the success rate Si of the ith service path, wr is the weight coefficient of the response time Ri of the ith service path, αr is the adjustment parameter of the response time Ri of the ith service path, e -β·Ri It is used to reflect the negative impact of response time on the reward value, β is the attenuation speed adjustment factor, wu is the weight coefficient of the resource utilization Ui of the i-th business path, αu is the adjustment parameter of the resource utilization Ui of the i-th business path, and yu is the ideal parameter value of resource utilization, (Ui-yu) 2 It is used to represent the deviation between the ideal parameter value and the predicted resource utilization Ui.

[0091] First, you need to set a reward optimization function and a clear optimization goal for the target business system. The optimization goals usually include key performance indicators such as success rate, response time, and resource utilization. The reward optimization function is used to evaluate the overall performance of the system under different optimization goals. By calculating the value of this function, the system can determine whether the current configuration is optimal. In a cloud service platform, the optimization goals may include the following:

[0092] Success rate: The success rate of user request processing should be maintained above 99%.

[0093] Response time: The average response time for user requests should be kept below 100 milliseconds.

[0094] Resource utilization: Server resource utilization should reach more than 80% to avoid idle or overloaded resources.

[0095] In this way, the reward optimization function can be defined as a comprehensive function that combines these optimization objectives to balance the various performance indicators.

[0096] On a cloud service platform, suppose the system detects that the response time of a certain service path has increased and is affecting the user experience. In order to optimize the response time, the system may automatically adjust resource allocation and allocate more computing resources to this service path. At the same time, the system may reduce resource consumption on other non-critical service paths to keep resource utilization within an ideal range.

[0097] After completing the adaptive optimization evaluation, the system will determine the dynamic resource configuration information for each business path. This includes the amount of CPU, memory, network bandwidth and other resources allocated to each path, as well as their dynamic adjustment schemes under different conditions. Dynamic resource configuration information enables the system to adjust resource allocation in real time according to actual business needs, ensuring that each path can operate within the optimization target range.

[0098] For example, if the system finds that the success rate of a business path drops to 95% and the response time increases to 150 milliseconds when processing requests during peak hours, the system will automatically increase the number of server instances for the path or migrate it to hardware with higher performance. These dynamically adjusted resource configuration information ensures that the path can maintain a high success rate and a short response time even during peak hours.

[0099] Specifically, in 103, the dynamic resource configuration information includes at least: computing resource allocation, network resource allocation, load balancing strategy, task scheduling information, elastic configuration strategy, cache strategy, secure access strategy, and fault-tolerant recovery strategy.

[0100] For example, in an airline system, the optimization of dynamic resource configuration information can greatly improve the efficiency and reliability of multiple business paths such as flight scheduling, passenger reservations, and baggage management. The following is a detailed example of how to apply these dynamic resource configuration information in an airline:

[0101] Computing resource allocation refers to the dynamic allocation of server CPU, memory and other resources to meet the needs of airlines' business paths. During peak periods, such as holidays, when a large number of passengers visit the airline's website to book tickets at the same time, the system can automatically increase the computing resources allocated to the booking system to ensure a fast response in the booking process and avoid system crashes or response delays.

[0102] Network resource allocation includes dynamic adjustment of bandwidth and network channels to meet the data transmission requirements of different business paths. When airlines update flight schedules, the system needs to quickly transmit scheduling information to multiple airports and flights. The system can prioritize allocating more network bandwidth to the scheduling system to ensure timely data transmission and avoid flight delays or untimely information transmission.

[0103] Load balancing strategies are used to distribute task loads among multiple servers to prevent a single server from being overloaded and ensure system stability. When processing passenger boarding, the system needs to process a large amount of boarding information and security check data at the same time. Load balancing strategies can evenly distribute these tasks to different servers to ensure that each server is not overloaded, thereby ensuring that passengers can board quickly.

[0104] Task scheduling information refers to how to arrange and manage the execution order and priority of different business tasks to improve overall efficiency. When the airline system processes baggage management and passenger information at the same time, the system can give priority to passenger information of the upcoming flight and postpone the baggage management task appropriately to ensure that passengers can board the plane on time.

[0105] The elastic configuration strategy allows the system to automatically increase or decrease resource configuration according to actual business needs, ensuring flexibility in resource utilization. During off-peak hours, when airline website traffic is low, the system can automatically reduce the number of servers allocated to the reservation system to save resources. During peak hours, resource configuration is increased to ensure system stability during high traffic.

[0106] The cache strategy refers to storing frequently used data in the cache to speed up data access and reduce database query pressure. In the flight query system, frequently used flight information (such as flight schedules for popular routes) can be cached to speed up query speed and reduce the burden on the database.

[0107] Security access policies are used to manage and protect system access rights to prevent unauthorized access or data leakage. For passengers' personal information and payment information, airline systems will implement strict security access policies, allowing only verified users to access these sensitive data, and monitor abnormal access behaviors in real time to prevent data leakage.

[0108] Fault-tolerant recovery strategies ensure that when a system fails, it can be quickly restored and business interruption is minimized. If an airline's passenger information system fails, the fault-tolerant recovery strategy automatically switches to the backup system to ensure that passenger boarding and information processing are not affected. At the same time, the system will record the failure and try to automatically repair it to ensure service continuity.

[0109] By optimizing these dynamic resource configuration information, airlines can achieve more efficient operation management in different business paths. For example, timely increase resource allocation during peak hours to meet passenger demand, reduce resource waste during off-peak hours, or improve the response speed of flight queries through caching strategies. Secure access and fault-tolerant recovery strategies ensure the security and stability of the system, prevent data leakage and ensure rapid recovery of the system in the event of a failure. In this way, airlines can improve overall operational efficiency, enhance passengers' service experience, and effectively manage resources.

[0110] In an embodiment of the present application, a target business system corresponding to the target business is constructed; wherein the target business system includes at least multiple business paths, each business path includes multiple business nodes, the multiple business nodes correspond one-to-one to multiple business processing stages in the corresponding business path, and the connection relationship between the multiple business nodes is obtained based on the business interaction relationship in the corresponding business path; real-time monitoring data of the target business is obtained, and the target business system is updated based on the real-time monitoring data; and the business quality and efficiency of each business path in the target business system is optimized. In an embodiment of the present application, by realizing real-time monitoring, dynamic optimization, efficient resource utilization and automated management, the reliance on manual intervention and human operation and maintenance is greatly reduced, and the quality and efficiency of the business system is significantly improved.

[0111] In another embodiment of the present application, a business quality and efficiency monitoring system is also provided. Figure 2 The business quality and efficiency monitoring system includes the following units:

[0112] A construction unit is configured to construct a target business system corresponding to the target business; wherein the target business system includes at least a plurality of business paths, each business path includes a plurality of business nodes, the plurality of business nodes correspond one-to-one to a plurality of business processing stages in the corresponding business path, and the connection relationship between the plurality of business nodes is obtained based on the business interaction relationship in the corresponding business path;

[0113] A monitoring unit, configured to obtain real-time monitoring data of a target business and update the target business system based on the real-time monitoring data;

[0114] The optimization unit is configured to monitor the service quality and efficiency of each service path in the target service system.

[0115] Further optionally, the monitoring unit updates the target business system based on the real-time monitoring data, and is configured to:

[0116] The real-time monitoring data is analyzed in real time through an automated evaluation model, and the analysis results are updated to the corresponding business nodes; wherein the real-time monitoring data includes at least: business operation data, user behavior data, network traffic data, and database performance data.

[0117] Further optionally, the automated evaluation model at least includes: a data input layer, a feature extraction layer, a parsing layer, and an update layer; the monitoring unit performs real-time parsing of the real-time monitoring data through the automated evaluation model and updates the parsing results to the corresponding business node, and is configured as follows:

[0118] Receiving the real-time monitoring data through the data input layer and preprocessing the real-time monitoring data;

[0119] Through the feature extraction layer, a multi-dimensional dynamic time window is used to extract the real-time monitoring features corresponding to each service path from the real-time monitoring data;

[0120] Through the parsing layer, multi-branch parsing is performed on the real-time monitoring features corresponding to each business path to obtain the real-time status prediction results corresponding to each business path; the real-time status prediction results at least include: predicted operation status, predicted interaction relationship, and abnormal operation status prediction results;

[0121] Through the update layer, the real-time status prediction results are updated to the corresponding business nodes respectively.

[0122] Further optionally, the monitoring unit performs multi-branch analysis on the real-time monitoring features corresponding to each service path through the analysis layer to obtain the real-time status prediction result corresponding to each service path, and is configured as follows:

[0123] The first branch model is used to predict the future changes of the business based on the time series characteristics of the operating status in each business path, and the prediction results of the future operating status corresponding to each business path are obtained;

[0124] The second branch model is used to identify the dependency characteristics in each business path to predict the structural change, and the structural change prediction results corresponding to each business path are obtained;

[0125] The third branch model is used to perform abnormal prediction on the business path characteristics in each business path, and obtain the abnormal operation prediction results corresponding to each business path.

[0126] Further optionally, the automated assessment model further includes: a monitoring alarm layer; a monitoring unit, which adopts a third branch model to perform abnormal prediction on the service path characteristics in each service path, and after obtaining the abnormal operation prediction result corresponding to each service path, is further configured as follows:

[0127] Determining whether the abnormal operation condition prediction result exceeds a predetermined threshold or reaches a predetermined abnormal condition;

[0128] If the abnormal operation prediction result exceeds the predetermined threshold or reaches the predetermined abnormal condition, then

[0129] Check the business nodes one by one on the current business path and perform feature anomaly analysis to obtain preliminary fault nodes;

[0130] Analyze the interaction logs between the preliminary faulty node and surrounding nodes, and identify the service node that has abnormal data flow interaction with at least two surrounding nodes as the faulty service node; and / or,

[0131] Conduct a time series review analysis of the historical operation characteristics of the preliminary faulty node to identify whether the preliminary faulty node has abnormal data fluctuations, and identify the business node with abnormal data fluctuations as the faulty business node;

[0132] Provide abnormal warning for faulty business nodes and generate corresponding fault handling strategies.

[0133] Further optionally, the optimization unit performs service quality and efficiency optimization on each service path in the target service system and is configured to:

[0134] Through the quality and efficiency monitoring module, the quality and efficiency of each business path in the target business system is evaluated to obtain the quality and efficiency optimization processing strategy corresponding to each business path;

[0135] Execute the quality and efficiency optimization processing strategy corresponding to each business path to achieve automatic quality and efficiency optimization of each business path.

[0136] Further optionally, the optimization unit performs quality and efficiency evaluation on each service path in the target service system to obtain a quality and efficiency optimization processing strategy corresponding to each service path, and is configured as follows:

[0137] Setting a reward optimization function and an optimization target corresponding to the target business system; the optimization target at least includes: success rate, response time, and resource utilization;

[0138] According to the reward optimization function and the optimization target, each business path is adaptively optimized and evaluated, and the dynamic resource configuration information corresponding to each business path is determined; wherein the reward optimization function is expressed as: Where R(Pi) represents the comprehensive reward value of the ith service path, ws is the weight coefficient of the success rate Si of the ith service path, αs is the adjustment parameter of the success rate Si of the ith service path, wr is the weight coefficient of the response time Ri of the ith service path, αr is the adjustment parameter of the response time Ri of the ith service path, e -β·Ri It is used to reflect the negative impact of response time on the reward value, β is the attenuation speed adjustment factor, wu is the weight coefficient of the resource utilization Ui of the i-th business path, αu is the adjustment parameter of the resource utilization Ui of the i-th business path, and yu is the ideal parameter value of resource utilization, (Ui-yu) 2 Used to represent the deviation between the ideal parameter value and the predicted resource utilization Ui;

[0139] The dynamic resource configuration information includes at least: computing resource allocation, network resource allocation, load balancing strategy, task scheduling information, elastic configuration strategy, cache strategy, secure access strategy, and fault-tolerant recovery strategy.

[0140] The system can implement various steps in the above method embodiment, which will not be expanded here.

[0141] In the embodiments of the present application, the timeliness and accuracy of fault detection can be improved, and the automation and systematic management of the operation and maintenance process can be achieved, thereby improving the quality and efficiency of the entire network and business system.

[0142] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 5102, and a computer program 511 stored in the memory 510 and executable on the processor 5102. When the processor 5102 executes the computer program 511, the following steps are implemented: constructing a target business system corresponding to the target business; wherein the target business system includes at least multiple business paths, each business path includes multiple business nodes, and the multiple business nodes correspond one-to-one to multiple business processing stages in the corresponding business path, and the connection relationship between the multiple business nodes is obtained based on the business interaction relationship in the corresponding business path; obtaining real-time monitoring data of the target business, and updating the target business system based on the real-time monitoring data; optimizing the business quality and efficiency of each business path in the target business system.

[0143] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a computer program 611 is stored. When the computer program 611 is executed by a processor, the following steps are implemented: constructing a target business system corresponding to the target business; wherein the target business system includes at least multiple business paths, each business path includes multiple business nodes, and the multiple business nodes correspond one-to-one to multiple business processing stages in the corresponding business path, and the connection relationship between the multiple business nodes is obtained based on the business interaction relationship in the corresponding business path; obtaining real-time monitoring data of the target business, and updating the target business system based on the real-time monitoring data; optimizing the business quality and efficiency of each business path in the target business system.

[0144] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for monitoring business quality and efficiency, characterized in that: The method comprises: Constructing a target business system corresponding to the target business; wherein the target business system includes at least a plurality of business paths, each business path includes a plurality of business nodes, the plurality of business nodes correspond one-to-one to a plurality of business processing stages in the corresponding business path, and the connection relationship between the plurality of business nodes is obtained based on the business interaction relationship in the corresponding business path; Acquire real-time monitoring data of a target business, and update the target business system based on the real-time monitoring data; Optimize the service quality and efficiency of each service path in the target service system.

2. The method for monitoring business quality and efficiency according to claim 1, characterized in that: The updating of the target business system based on the real-time monitoring data includes: The real-time monitoring data is analyzed in real time through an automated evaluation model, and the analysis results are updated to the corresponding business nodes; wherein the real-time monitoring data includes at least: business operation data, user behavior data, network traffic data, and database performance data.

3. The method for monitoring business quality and efficiency according to claim 2, characterized in that: The automated evaluation model at least includes: a data input layer, a feature extraction layer, a parsing layer, and an update layer; The real-time analysis of the real-time monitoring data by the automated evaluation model and updating the analysis results to the corresponding business nodes include: Receiving the real-time monitoring data through the data input layer and preprocessing the real-time monitoring data; Through the feature extraction layer, a multi-dimensional dynamic time window is used to extract the real-time monitoring features corresponding to each service path from the real-time monitoring data; Through the parsing layer, multi-branch parsing is performed on the real-time monitoring features corresponding to each business path to obtain the real-time status prediction results corresponding to each business path; the real-time status prediction results at least include: predicted operation status, predicted interaction relationship, and abnormal operation status prediction results; Through the update layer, the real-time status prediction results are updated to the corresponding business nodes respectively.

4. The method for monitoring business quality and efficiency according to claim 3, characterized in that: The analysis layer performs multi-branch analysis on the real-time monitoring features corresponding to each service path to obtain the real-time status prediction results corresponding to each service path, including: The first branch model is used to predict the future changes of the business based on the time series characteristics of the operating status in each business path, and the prediction results of the future operating status corresponding to each business path are obtained; The second branch model is used to identify the dependency characteristics in each business path to predict the structural change, and the structural change prediction results corresponding to each business path are obtained; The third branch model is used to perform abnormal prediction on the business path characteristics in each business path, and obtain the abnormal operation prediction results corresponding to each business path.

5. The method for monitoring business quality and efficiency according to claim 4, characterized in that: The automated assessment model also includes: a monitoring alarm layer; The method of using the third branch model to perform abnormal prediction on the service path characteristics in each service path and obtain the abnormal operation prediction result corresponding to each service path also includes: Determining whether the abnormal operation condition prediction result exceeds a predetermined threshold or reaches a predetermined abnormal condition; If the abnormal operation prediction result exceeds the predetermined threshold or reaches the predetermined abnormal condition, then Check the business nodes one by one on the current business path and perform feature anomaly analysis to obtain preliminary fault nodes; Analyze the interaction logs between the preliminary faulty node and surrounding nodes, and identify the service node that has abnormal data flow interaction with at least two surrounding nodes as the faulty service node; and / or, Conduct a time series review analysis of the historical operation characteristics of the preliminary faulty node to identify whether the preliminary faulty node has abnormal data fluctuations, and identify the business node with abnormal data fluctuations as the faulty business node; Provide abnormal warning for faulty business nodes and generate corresponding fault handling strategies.

6. The method for monitoring business quality and efficiency according to claim 1, characterized in that: The optimizing the service quality and efficiency of each service path in the target service system includes: Through the quality and efficiency monitoring module, the quality and efficiency of each business path in the target business system is evaluated to obtain the quality and efficiency optimization processing strategy corresponding to each business path; Execute the quality and efficiency optimization processing strategy corresponding to each business path to achieve automatic quality and efficiency optimization of each business path.

7. The method for monitoring business quality and efficiency according to claim 6, characterized in that: The quality and efficiency evaluation of each business path in the target business system to obtain a quality and efficiency optimization processing strategy corresponding to each business path includes: Setting a reward optimization function and an optimization target corresponding to the target business system; the optimization target at least includes: success rate, response time, and resource utilization; According to the reward optimization function and the optimization target, each business path is adaptively optimized and evaluated, and the dynamic resource configuration information corresponding to each business path is determined; wherein the reward optimization function is expressed as: Where R(Pi) represents the comprehensive reward value of the ith service path, ws is the weight coefficient of the success rate Si of the ith service path, αs is the adjustment parameter of the success rate Si of the ith service path, wr is the weight coefficient of the response time Ri of the ith service path, αr is the adjustment parameter of the response time Ri of the ith service path, e -β·Ri It is used to reflect the negative impact of response time on the reward value, β is the attenuation speed adjustment factor, wu is the weight coefficient of the resource utilization Ui of the i-th business path, αu is the adjustment parameter of the resource utilization Ui of the i-th business path, and yu is the ideal parameter value of resource utilization, (Ui-yu) 2 Used to represent the deviation between the ideal parameter value and the predicted resource utilization Ui; The dynamic resource configuration information includes at least: computing resource allocation, network resource allocation, load balancing strategy, task scheduling information, elastic configuration strategy, cache strategy, secure access strategy, and fault-tolerant recovery strategy.

8. A business quality and efficiency monitoring system, characterized in that: The system comprises the following units, wherein: A construction unit is configured to construct a target business system corresponding to the target business; wherein the target business system includes at least a plurality of business paths, each business path includes a plurality of business nodes, the plurality of business nodes correspond one-to-one to a plurality of business processing stages in the corresponding business path, and the connection relationship between the plurality of business nodes is obtained based on the business interaction relationship in the corresponding business path; A monitoring unit, configured to obtain real-time monitoring data of a target business and update the target business system based on the real-time monitoring data; The optimization unit is configured to monitor the service quality and efficiency of each service path in the target service system.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the business quality and efficiency monitoring method described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the business quality and efficiency monitoring method according to any one of claims 1 to 7 is implemented.

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