A Service Access Intelligent Management Method and System Based on a Python Business Management Platform

Abstract

This invention discloses a service access intelligent management method and system based on a Python business management platform, relating to the field of industrial internet technology. The method includes: collecting fine-grained telemetry data associated with the business context; generating real-time status profiles for downstream service instances based on the data, including predictive future risk scores for specific business request types; automatically diagnosing whether service instances have silent faults or performance degradation using machine learning algorithms; automatically determining the business impact domain of diagnosed faults on specific business processes or key performance indicators by combining transaction context graphs and tracking data; and making intelligent decisions and executing them when receiving service access requests. This invention achieves intelligent risk perception and control of service access by providing refined and forward-looking assessment of service status and automated correlation analysis capabilities between hidden faults and their business impact, thus helping to improve the stability, reliability, and operational efficiency of the business platform.

Description

Technical Field

[0001] This invention relates to the field of industrial internet, and in particular to a service access intelligent management method and system based on a Python business management platform. Background Technology

[0002] Existing Business Management Platforms (BMPs) have limitations in service access management and health status monitoring. First, service access decisions are typically based on macro-level resource metrics or basic availability probes of target service instances, lacking in-depth assessments of the actual performance and potential risks of instances handling specific types of business loads. They also struggle to effectively detect the transmission risks caused by downstream dependency anomalies, easily leading to improper traffic allocation and impacting service quality. Second, traditional threshold-based monitoring systems have limited detection capabilities for non-catastrophic "silent failures" or "performance degradation." Furthermore, existing technologies lack effective means to automatically and accurately correlate and quantify the identified technical anomalies with their actual impact on specific business processes or key performance indicators (KPIs), hindering fault handling efficiency and risk-based operational decision-making. Summary of the Invention

[0003] This invention provides a service access intelligent management method and system based on a Python business management platform to solve the problems of insufficient service access decision information, delayed risk perception, difficulty in early detection of hidden faults, and difficulty in assessing business impact in the existing technology.

[0004] In a first aspect, this invention provides a service access intelligent management method based on a Python business management platform, the method comprising:

[0005] Collect fine-grained telemetry data from service instances within the platform and parse business requests to obtain business context information;

[0006] Based on the fine-grained telemetry data and the business context information, a real-time status profile for a specific business request type is generated for the downstream service instance. The real-time status profile includes a predictive future risk score for the service instance in handling the specific business request type.

[0007] Automatically diagnose whether the service instance has a non-crash silent failure or performance degradation, and automatically determine its business impact domain on specific business processes or metrics when identifying it;

[0008] When a service access request is received, the system makes an intelligent decision on the service access action based on the business context information of the request, the real-time status profile of the candidate downstream service instance for the request type, the predictive future risk score, the diagnosed silent fault information and its business impact domain.

[0009] Perform the service access action.

[0010] Secondly, this invention also provides a system for intelligent management of service access based on a Python business management platform, the system comprising:

[0011] The data acquisition and context association unit is configured to acquire fine-grained telemetry data, parse business requests to obtain business context information and inject tracking information, and build and maintain a transaction context graph rich in business semantics.

[0012] The status profile and risk prediction unit is configured to generate a real-time status profile that includes a predictive future risk score for handling specific business request types.

[0013] The silent fault diagnosis and impact domain location unit is configured to automatically diagnose whether a service instance has a silent fault or performance degradation, and automatically determine its business impact domain during identification.

[0014] The intelligent decision-making unit is configured to make decisions on service access actions based on the request context, real-time status profile, diagnostic information, and business impact domain.

[0015] The strategy execution unit is configured to execute the service access action instruction;

[0016] Federated learning support components are configured to support model training using a federated learning framework.

[0017] The digital twin simulation verification environment interface is used to connect or integrate digital twin environments for simulation verification of intervention strategies.

[0018] The technical solution provided in this application has at least the following technical effects or advantages:

[0019] Improved accuracy of service access decisions: By generating real-time status profiles for specific business request types and integrating predictive future risk scores, the ability and risk of downstream service instances to handle specific loads can be assessed more accurately, thereby reducing unreasonable traffic allocation.

[0020] It enhances the ability to identify hidden faults at an early stage: by using machine learning diagnostic technology, it helps to identify “silent faults” or “performance degradation” in the early stages that are difficult to detect by conventional monitoring, creating conditions for timely intervention.

[0021] It enables automated correlation and assessment of technical failures and business impacts: by combining transaction context graphs and tracking data to locate the business impact domain, it can correlate and quantify the impact of underlying technical issues on specific business processes or KPIs, providing a basis for prioritizing failure handling and risk control.

[0022] It improves the overall stability and management intelligence of the system: integrating deep state perception, risk prediction, accurate diagnosis and business impact assessment into the closed-loop control of service access helps to enhance the system's operational resilience and improve the intelligence of operation and maintenance management. Attached Figure Description

[0023] Figure 1 This is a flowchart of the intelligent service access management method based on the Python business management platform of the present invention;

[0024] Figure 2 Architecture diagram for integrating services from a Python-based business management platform into an intelligent management system. Detailed Implementation

[0025] This invention relates to a service access intelligent management method and system based on a Python business management platform to solve the technical problems in the prior art, such as insufficient service access decision information, delayed risk perception, difficulty in early detection of hidden faults, and difficulty in assessing business impact.

[0026] The above technical solutions will now be described in detail with reference to the accompanying drawings and specific embodiments to provide a better understanding of them. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments used only to explain the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Furthermore, it should be noted that, for ease of description, only the parts related to the present invention are shown in the drawings, not all of them.

[0027] Example 1

[0028] like Figures 1-2 As shown, the service access intelligent management method and system based on the Python business management platform of the present invention can be deployed as a core support system in a BMP environment. Its logical architecture typically includes the following core functional units working in tandem:

[0029] 1. Data Acquisition and Contextualization Unit: Responsible for building the data foundation required for analysis and decision-making. The key lies in the comprehensiveness, fine granularity, and semantic relevance of the data.

[0030] Fine-grained telemetry data acquisition: Metric collection: Configure a standard metric collection client (e.g., integrating OpenTelemetrySDK) in the BMP microservice (which can be built based on a Python Web framework such as FastAPI). In addition to collecting system resource metrics, key performance metrics must be collected separately for each service instance handling different business request types (uniquely identified by API endpoints, etc.) through application-layer monitoring methods (such as middleware). The collected metrics must at least cover: multiple statistical quantiles of processing latency (including P95 or P99 values ​​to reflect the long tail), error rate information (distinguishing important error categories), internal service status metrics (such as real-time length of critical queues, waiting time), and interaction performance metrics for downstream dependencies (such as database interaction time, connection pool utilization). All metrics are strictly associated with the service instance ID and business request type ID through tags and transmitted to the central time-series data storage system.

[0031] Structured Logs: Implement structured logging (preferably JSON format). Log records include standard metadata and TraceID and SpanID for link association. Important contextual information during business execution (such as user ID and order ID) is recorded as structured fields. Log streams are aggregated into a central log management system.

[0032] Distributed Tracing and Context Injection: The entire system implements distributed tracing based on OpenTelemetry. Automated instrumentation and manual event tracking are used. At the service entry point, business context information is extracted and injected. The parsed context information (including request type, business ID, priority, etc.) is appended to the current tracing span as propagable tracing attributes (Baggage or SpanTags) using the OpenTelemetry API, ensuring correct propagation across service boundaries. Tracing data is exported to the backend system.

[0033] Business events: Capture business status change events by subscribing to the message bus. The event message includes the associated business ID and TraceID.

[0034] Transaction Context Graph (RBTCG) Construction and Maintenance: Backend data processing services (which can be based on technologies such as Apache Flink or Python Faust) consume the integrated telemetry data.

[0035] Based on the call chain structure and Span attributes (including injection context) in the tracking data, and combined with externally provided or dynamically discovered business process mapping information, a Transaction Context Graph (RBTCG) containing business semantics is constructed and incrementally updated in real time. Nodes in the graph represent service instances (with additional state information), and edges represent call relationships (with additional interaction statistics and context), clearly mapping technical interactions and business processes. Graph data is stored in suitable systems such as graph databases.

[0036] 2. The Status Profiling and Risk Prediction Unit extracts structured, decision-oriented, in-depth service status information from massive, heterogeneous raw telemetry data, and provides forward-looking risk assessments by combining machine learning technology.

[0037] Real-time status profile generation for specific business request types:

[0038] Data processing flow: This function is performed through a real-time or near-real-time data processing pipeline (implemented based on a stream processing framework such as Apache Flink, or using a Python stream processing library). This pipeline continuously consumes fine-grained telemetry data streams associated with the business context, provided by the data acquisition and context association unit.

[0039] Core aggregation and profile building logic: For each service instance within the system monitoring scope, and for each type of business request defined as critical that instance handles, this processing pipeline performs the following operations:

[0040] Within a preset time window (e.g., the last 5 minutes), filter and aggregate all fine-grained performance metric data points related to the specific service instance and the specific business request type.

[0041] Based on aggregated data, independently calculate and generate a multi-dimensional performance profile describing the current state of the service instance when handling a specific business request type. This performance profile must include the following key statistical dimensions: key statistics on processing latency distribution (covering high quantiles such as P95 or P99), error rate statistics (distinguishing important error categories), quantitative indicators of the status of key internal queues (such as queue length or waiting time), and performance characteristics of downstream dependent calls when handling this request type (such as interaction latency). Where appropriate, related resource consumption pattern characteristics may also be included.

[0042] While generating this performance profile, two other important analysis results are dynamically queried and integrated: first, any identified silent fault status identifiers and their severity levels for this service instance that may affect the current business request type, derived from the silent fault diagnosis and impact domain location unit; second, a predictive future risk score for this service instance that handles this business request type, generated by the risk prediction module within this unit.

[0043] Profile Structure and Storage: The final generated real-time status profile is a structured data object that encapsulates comprehensive status information about a specific service instance. Crucially, it can distinguish and store independent performance profiles, risk scores, and fault markers for the instance when handling different types of business requests. This profile data needs to be stored in a data storage system that supports low latency and high concurrency access (e.g., using an in-memory database like Redis) so that the intelligent decision-making unit can quickly and in real-time obtain the status input required for decision-making.

[0044] Predictive future risk score generation: Utilizing machine learning techniques to predict the probability of a specific service instance experiencing significant performance degradation or failure, or other predefined "negative events," when processing a specific type of business request within a predefined short time window (e.g., the next 5 to 15 minutes). Specifically, this includes:

[0045] Feature engineering: Constructing a feature set for the predictive model input. The core input is historical multivariate time-series data related to the target service instance and the target business request type (primarily extracted from performance indicator sequences in historical real-time status profile records). To improve prediction accuracy, auxiliary features are typically introduced. These features may include the global resource utilization time series of the service instance, the health status or risk score time series of its important downstream dependent services, recent environmental change event markers (e.g., deployment, configuration modification), and indicator sequences reflecting the overall system load or specific business activity status.

[0046] Model Training and Application: Select a suitable machine learning model for handling complex temporal dependencies (e.g., deep learning models based on recurrent neural networks or Transformer architectures, or ensemble models such as gradient boosting trees after good feature engineering) for training. Training is a supervised learning task requiring a large amount of labeled historical data. Rigorous time-series cross-validation methods must be employed during training, and potential data imbalance issues must be addressed. For scenarios with data privacy constraints, a federated learning framework is recommended for distributed training, building a global prediction model by training model replicas locally and aggregating model parameter updates through a central coordinator.

[0047] Online Inference and Integration: Deploying the optimal model as a stable, low-latency online prediction service. This service receives the latest input feature vectors in real time, performs inference, and calculates a predictive future risk score representing the probability of risk. This score is immediately updated in the real-time status profile record of the corresponding service instance and the corresponding business request type, becoming an integral part of the profile.

[0048] 3. The silent fault diagnosis and impact domain location unit uses data analysis technology to automatically discover hidden system problems that are difficult to detect by conventional monitoring methods, and establishes a correlation between them and the actual business impact.

[0049] Automated Silent Fault Diagnosis: Designed to automatically and early detect non-catastrophic, potentially long-term, anomalous behavior patterns in service instances. Implemented by a background analytics service, it continuously processes fine-grained telemetry data. To ensure high accuracy and low false alarm rate, a multi-model fusion strategy is preferred, which integrates the outputs of at least two anomaly detection algorithms based on different technical principles. These algorithms may include the following technical categories:

[0050] (a) Deep learning-based reconstruction model approach: Applying methods such as deep autoencoders to analyze the reconstruction error of multidimensional time series index data, abnormal data usually cause the reconstruction error to deviate significantly from the normal range.

[0051] (b) Methods based on statistical process control (SPC) theory: Applying multivariate SPC control charts (e.g., Hotelling T2 charts) or univariate SPC rules (e.g., CUSUM chart rules) to detect small but statistically significant persistent shift patterns in key indicator sequences.

[0052] (c) Log data pattern mining method: Apply online log clustering algorithm (e.g., Drain algorithm) to extract log templates, and then monitor the abnormal patterns of the log template sequence (e.g., use hidden Markov model or RNN to analyze the transition probability) or the distribution drift of numerical parameters within the template.

[0053] Fusion Decision: By using predefined fusion logic (e.g., weighted voting or meta-learning models), abnormal signals from different algorithms are comprehensively evaluated, and a silent fault diagnosis conclusion with high confidence is output. This conclusion (including fault type, confidence level, evidence, etc.) is recorded and updated in the real-time status profile of the affected instance.

[0054] Business Influence Domain Positioning:

[0055] Triggering condition: Automatically starts after the system confirms a high-confidence silent fault diagnosis result.

[0056] Further, the analysis process involves: based on the fault instance identifier and occurrence time window, filtering all relevant tracing links from the distributed tracing data store, especially those links that might exhibit anomalies (such as high latency or errors) when flowing through the fault instance; utilizing a pre-built transaction context graph containing business semantics. First, the node representing the fault instance is located on this graph. Then, a graph path analysis algorithm is executed, typically combined with the filtered anomaly tracing link information, to perform reverse tracing. This tracing process aims to identify a list of all upstream business transaction types potentially affected by the fault and the end-to-end business processes to which they belong.

[0057] Key Performance Indicator (KPI) Impact Assessment: Obtain historical time-series data for predefined business KPIs related to the affected business processes identified in the previous step. Using rigorous statistical correlation analysis or causal inference methods, assess whether there is a statistically significant association between the failure event and the observable changes in these KPIs within the time period of the failure, and quantify the extent of this impact as much as possible (e.g., estimate the percentage decrease in KPIs).

[0058] Report generation: The results of the entire analysis process—including fault details, scope of technical impact, list of affected business transactions / processes, and (if significantly related) quantified business KPI impact assessment—are integrated to generate a structured business impact domain report.

[0059] 4. The intelligent decision-making unit is responsible for receiving and understanding all the in-depth insight information provided by the aforementioned units, and formulating and outputting optimal service access routing decisions or proactive risk intervention strategies accordingly: responding to service access requests or internally triggered intervention needs in real time. It queries and obtains the latest and complete real-time status profile of relevant service instances (including predicted risks and diagnostic information), understands the business context of the current request, combines relevant business impact domain reports, and then generates control instructions through internal decision-making logic.

[0060] The process of making intelligent routing decisions:

[0061] Input Acquisition: When it is necessary to select a target from a set of candidate downstream service instances for an incoming service request (whose business context is known and whose request type is determined), the decision unit first concurrently queries to obtain the latest real-time status profile (RTORSP) of these instances, focusing on extracting their predictive future risk score (T-PHRS) for the current request type and silent fault diagnosis markers for any activities.

[0062] Evaluation and selection logic: Construct and apply an internal evaluation function or decision strategy to determine the routing priority of each candidate instance. This evaluation process must take into account the following key input factors:

[0063] (a) Predictive risk: The predictive future risk score is used as the core consideration, and generally the lower the risk score, the better.

[0064] (b) Business context-dependent risk tolerance: Determine the level of tolerance for potential risks based on the business attributes of the request itself (e.g., is it a critical transaction or a background task?). High-priority or critical business operations usually require routing to instances with very low risk.

[0065] (c) Real-time performance and load: Refer to the current performance metrics of instances handling similar requests reflected in the real-time status profile (e.g., lower latency and lower error rate are better) and the current overall load status (e.g., CPU, queue length, and a preference for selecting relatively idle instances to achieve load balancing).

[0066] (d) Silent Fault Impact Mitigation: If an instance has a silent fault, its business impact domain report should be used to determine whether the fault is related to the current request's business process. If it is related, the instance's priority should be significantly reduced or a penalty factor should be applied; if it is not related, the impact will be minimal.

[0067] Decision generation: Based on the results of the above comprehensive evaluation (e.g., by calculating scores or ranking by cost), combined with preset routing rules that include risk thresholds (e.g., if the predictive future risk score exceeds a certain value, it will not be selected), or by using a trained reinforcement learning model (which can learn the optimal decision strategy based on real-time status and historical feedback) to dynamically adjust the routing decision, the final decision is made on which specific instance to route the request to, or on a scheme that includes multiple instances and their corresponding traffic allocation ratios.

[0068] Proactive intervention decisions are triggered by high-severity silent fault diagnosis reports and their business impact domain assessment results.

[0069] Strategy selection: Based on the predefined strategy library and the current system status, select appropriate intervention measures (such as alarms, isolation, traffic switching, service degradation, triggering self-healing, etc.).

[0070] Safety Validation: For proposed automated intervention strategies with potentially wide-ranging impacts, it is recommended or mandatory to first conduct simulation validation in a digital twin environment. This environment must be able to simulate the behavior of the real system and execute the strategy in the simulation to evaluate its expected effects and potential side effects (e.g., whether it conforms to the "minimum blast radius" principle). The simulation results serve as a key basis for guiding the final decision on whether to implement the intervention in the production environment.

[0071] 5. The strategy execution unit is responsible for accurately translating decision instructions into operations on the underlying system.

[0072] Operations such as route updates, instance status changes, configuration modifications, and alarm notifications are performed by calling the standardized management interfaces (APIs) of various infrastructure components (API gateway, service mesh, container platform, configuration center, alarm system, etc.). The reliability, idempotency, and security of the execution process must be ensured, and detailed operation audit logs must be recorded.

[0073] The system further incorporates a closed-loop feedback mechanism. By continuously measuring the actual effects of decision execution (performance, changes in business metrics, etc.) and feeding this information back to relevant intelligent analysis units (prediction, diagnosis) and decision-making units, it drives model retraining, adaptive parameter adjustment, or iterative strategy optimization, ensuring the long-term effectiveness and intelligence level of the system.

[0074] This invention provides a practical and feasible intelligent management method and system for service access through a detailed explanation of the aforementioned system architecture, implementation details of key functional units, and their interactions. Its core technological contribution lies in achieving deep state awareness and forward-looking risk prediction for specific business requests, automated silent fault diagnosis and accurate assessment of business impact, and intelligent, risk-aware closed-loop control based on these deep insights. This specification fully discloses the technology using a Python-based technology stack and cloud-native environment as examples; its principles and methods can be extended to other technology platforms. Implementing this invention is expected to significantly improve the stability, performance, and operational intelligence level of distributed business management platforms.

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

[0076] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A service access intelligent management method based on a Python business management platform, characterized in that: include: Collect fine-grained telemetry data from service instances within the platform and parse business requests to obtain business context information; Based on the fine-grained telemetry data and the business context information, a real-time status profile for a specific business request type is generated for the downstream service instance. The real-time status profile includes a predictive future risk score for the service instance's handling of the specific business request type, wherein the predictive future risk score is generated through machine learning technology. An ensemble learning or multi-model fusion strategy is adopted, combining autoencoder-based reconstruction error analysis, statistical process control-based control chart rule analysis, and at least two algorithms based on log template vectorization and cluster drift detection to jointly analyze fine-grained time-series indicators and log streams for the request type. This is to automatically diagnose whether the service instance has non-collapse-related silent failures or performance degradation, and to automatically determine its business impact domain on specific business processes or indicators during identification. When a service access request is received, the system makes an intelligent decision on the service access action based on the business context information of the request, the real-time status profile of the candidate downstream service instance for the request type, the predictive future risk score, the diagnosed silent fault information and its business impact domain. Perform the service access action.

2. The service access intelligent management method based on a Python business management platform as described in claim 1, characterized in that, The fine-grained telemetry data acquisition step collects and associates P95 or P99 latency, error rate, internal queue length, database interaction time, cache interaction time, connection pool usage, and log stream containing a tracking ID injected with the business context information when the same service instance processes different request types.

3. The service access intelligent management method based on a Python business management platform as described in claim 2, characterized in that, The business context information parsing step involves injecting the parsed business context information into the tags of the distributed tracing information, and constructing and maintaining a transaction context graph containing business semantics based on the tracing information and business process information. The transaction context graph containing business semantics includes nodes representing service instances and edges representing call relationships, and the nodes and edges are attached to the business context and tracing information.

4. The service access intelligent management method based on a Python business management platform as described in claim 3, characterized in that, The step of generating a real-time status profile utilizes the association information of the transaction context graph containing business semantics to aggregate the fine-grained telemetry data related to the specific request type, independently calculates and stores the current performance profile and resource consumption pattern for each key request type of each service instance, and marks the diagnosed silent fault states and the predicted type-specific risk scores that affect the request type.

5. The intelligent service access management method based on a Python business management platform as described in claim 4, characterized in that, The step of automatically determining the business impact domain involves, after identifying the silent fault, using fault instance identifiers and time windows to perform graph traversal or impact propagation algorithms on the transaction context graph containing business semantics, combining distributed tracing data for reverse tracing, identifying the affected upstream service call chain and business transaction type list, and quantifying or evaluating the degree of impact of the fault on key performance indicators of the business by associating changes in business event data, and generating a business impact domain report.

6. The intelligent service access management method based on a Python business management platform as described in claim 5, characterized in that, The steps of the intelligent decision-making service access action include, when processing a service access request, querying the real-time status profile and predictive future risk score of all candidate downstream instances for the request type, and combining the business context of the request with the preset routing strategy, or using a reinforcement learning model to dynamically adjust the routing weights and decide on the routing target or traffic allocation ratio.

7. The intelligent service access management method based on a Python business management platform as described in claim 6, characterized in that, The predictive future risk score generation or the machine learning model training in the silent fault diagnosis employs a federated learning framework, training model replicas in multiple service clusters or tenant local environments, and aggregating model parameters to construct a global model.

8. The intelligent service access management method based on a Python business management platform as described in claim 7, characterized in that, The method further includes a digital twin environment that simulates the behavior of a business management platform based on a real-time transaction context graph containing business semantics, a state profile, and a predictive or diagnostic model. When an intervention strategy for an identified silent fault is generated, the intervention strategy is first executed and evaluated in the digital twin environment, and the simulation results are used to decide whether to execute the intervention in the production environment.

9. A system for intelligent service access management based on a Python business management platform, the system being configured to execute the intelligent service access management method based on a Python business management platform as described in claim 8, characterized in that, The system includes: The data acquisition and context association unit is configured to acquire fine-grained telemetry data, parse business requests to obtain business context information and inject tracking information, and build and maintain a transaction context graph rich in business semantics. The status profile and risk prediction unit is configured to generate a real-time status profile that includes a predictive future risk score for handling specific business request types. The silent fault diagnosis and impact domain location unit is configured to automatically diagnose whether a service instance has a silent fault or performance degradation, and automatically determine its business impact domain during identification. The intelligent decision-making unit is configured to make decisions on service access actions based on the request context, real-time status profile, diagnostic information, and business impact domain. The strategy execution unit is configured to execute the service access action instruction; Federated learning support components are configured to support model training using a federated learning framework. The digital twin simulation verification environment interface is used to connect or integrate digital twin environments for simulation verification of intervention strategies.

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