Pre-positioning algorithm for abnormal fault of data interface program based on AI

Abstract

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of program anomaly positioning, and in particular to a pre-positioning algorithm for abnormal faults in data interface programs based on AI. Background Art

[0002] With the rapid development of information technology, data interfaces play a key role in modern software systems. Data interface programs support core businesses in multiple fields such as finance, medical care, and e-commerce by enabling data exchange between different systems or modules. However, the problem of abnormal failures in data interface programs has become increasingly prominent, and their impact on system stability and business continuity cannot be ignored. Traditional anomaly detection and fault handling methods usually rely on rule engines or manual analysis, which are difficult to cope with the increasingly complex data flows and abnormal patterns in modern systems.

[0003] In data interface programs, abnormal failures may be caused by various factors such as data format mismatch, interface call timeout, field loss, etc. These problems may not only lead to data transmission errors, but also trigger chain reactions, affecting the stability of the entire system. Especially in the face of high concurrency and massive data scenarios, traditional methods often have problems such as low detection efficiency, difficult positioning, and high false alarm rate. In addition, the fault repair process usually requires a lot of manual intervention, which is inefficient and costly.

[0004] The current technologies used for data interface program anomaly detection and processing mainly face the following challenges: 1. Traditional methods find it difficult to capture weak abnormal signals from massive data, resulting in high missed and false alarm rates, which increases the difficulty of troubleshooting; 2. The source of abnormal signals is often hidden in complex data streams and causal networks. Existing methods lack efficient positioning algorithms, and the fault positioning process is time-consuming and labor-intensive; 3. Most systems only provide abnormality reports, and subsequent repair work still requires manual intervention, which increases the time and cost of fault handling; 4. As system and business requirements change, traditional methods lack continuous learning and optimization mechanisms, making it difficult to cope with new scenarios and new problems; 5. Existing technologies have limited visualization and collaboration support functions, and fault analysis and processing between teams often lack a unified platform and tools, affecting overall efficiency.

[0005] To solve the above problems, we provide an AI-based pre-location algorithm for abnormal faults in data interface programs. Summary of the invention

[0006] In view of the above-mentioned shortcomings of the prior art, the first purpose of the present invention is to provide an AI-based pre-location algorithm for abnormal faults in data interface programs to solve the problems in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An AI-based pre-location algorithm for abnormal faults in a data interface program includes the following steps:

[0009] S1, data flow pre-analysis;

[0010] S2, abnormal signal capture;

[0011] S3, anomaly clustering and feature extraction;

[0012] S4, interface behavior modeling;

[0013] S5, abnormal source tracing and location;

[0014] S6, adaptive repair suggestion generation;

[0015] S7. Feedback loop and continuous learning;

[0016] S8. Visualization and report generation.

[0017] The present invention is further configured as follows: in the step S1, data stream pre-analysis:

[0018] S1.1. Automatically classify the interface data streams through the Gaussian Mixture Model clustering algorithm to separate the real-time streams and batch streams. During the classification process, the correlation between the field and the target category is calculated using mutual information and stored in the association matrix.

[0019] S1.2. Use kernel density estimation to analyze data stream distribution and generate distribution probability density graphs. By estimating the changes in probability density values ​​in different time windows, outliers in data distribution and the tendency of distribution changes can be accurately detected.

[0020] S1.3, calculate the embedded features of each field through the Laplace score, select the field with the highest score as the key field, and further introduce the recursive feature elimination algorithm;

[0021] S1.4. Use regular expression parsing combined with syntax tree generation to check whether the data conforms to the protocol definition, construct a finite state machine to verify the protocol interaction status, and return the event sequence that violates the rules;

[0022] S1.5. Use a bilateral median filter to remove abnormal data peaks, and define the noise threshold in combination with the IQR method. The cleaned data is further verified by drawing a box plot to verify the noise filtering effect.

[0023] The present invention is further configured as follows: in the step S2, abnormal signal capture:

[0024] S2.1. Based on the dynamic upper and lower limit algorithm of the sliding window, the maximum and minimum values ​​and standard deviation (σ) of the data in the window are calculated in real time. If the new data point exceeds the range of [mean±3σ], it is marked as abnormal;

[0025] S2.2. Use the dynamic time warping algorithm to calculate the time alignment distance D between the current data sequence and the historical template sequence DTW , set the threshold T DTW , when D DTW >T DTW When , the judgment mode deviates;

[0026] S2.3, forecast time series data through the autoregressive integrated moving average (ARIMA) model, calculate the residual sequence ∈t between the true value and the predicted value, and if ∈t exceeds the set confidence interval, an abnormal event is captured;

[0027] S2.4. Use Granger causality test to analyze the temporal causal relationship between multiple data streams, draw the correlation heat map of multidimensional time series, and locate the interactive impact of abnormal events;

[0028] S2.5. Construct a weighted voting model to aggregate abnormal labels from multi-dimensional signals, use logistic regression to weight the labels, and output a unified abnormal probability distribution P (anomaly ).

[0029] The present invention is further configured as follows: in the step S3, abnormal clustering and feature extraction:

[0030] S3.1. Clustering outliers by density peak clustering. This algorithm generates a hierarchical structure of outlier distribution based on local density ρi and distance δi, thus forming anomaly categories.

[0031] S3.2, set a time window [t-Δt, t+Δt] near the outlier point, extract context data, and use the TF-IDF algorithm to represent the context as a feature vector matrix;

[0032] S3.3, combine t-SNE and principal component analysis dimensionality reduction methods to embed high-dimensional features into two-dimensional or three-dimensional space, draw visualization graphs of abnormal clustering, and calculate feature contribution rates after dimensionality reduction to verify information integrity;

[0033] S3.4. Use the random forest classifier to preliminarily label the clustering results and optimize the label assignment rules based on domain knowledge;

[0034] S3.5. By calculating the cosine similarity Sim(vcurrent, vhistory) between the current abnormal feature vector and the records in the historical abnormal library, similar historical abnormal events are matched.

[0035] The present invention is further configured as follows: in the step S4, interface behavior modeling:

[0036] S4.1. Use Petri nets to model the interface logic process, define the event-triggered transfer relationship, and generate a state transfer matrix to describe the constraint rules of the interface behavior;

[0037] S4.2. Establishing the state transition probability model P(S) based on Markov chain t+1 |S t ), predict the next possible state of the interface, verify the model, and ensure the coverage of the training data;

[0038] S4.3, introduce the Apriori algorithm to mine frequent itemsets from historical data and extract rules with high support and confidence as the benchmark of interface behavior;

[0039] S4.4. Use the Transformer supervised learning model to optimize the interface behavior prediction model and maximize the generalization ability of normal behavior;

[0040] S4.5. Implement an online learning mechanism to update the interface behavior model parameters in real time, enhance the model adaptability, and verify the effectiveness of the new parameters for anomaly detection through a feedback loop.

[0041] The present invention is further configured as follows: in the step S5, abnormality tracing and location:

[0042] S5.1. Use the topological sorting algorithm to analyze the dependency chain of interface calls, check layer by layer from upstream interfaces to downstream interfaces, build a priority matrix based on the number of calls and error frequencies, and identify high-risk nodes;

[0043] S5.2. Use time-series playback technology to restore the complete data stream when the anomaly occurs to the problem node, use the backtracking sliding window to gradually adjust the window size Δt, and locate the time point when the anomaly breaks out;

[0044] S5.3. Construct a causal diagram based on the structural equation model, calculate the causal strength between variables through the path coefficient, generate the minimum cut set, and determine the key path leading to the anomaly;

[0045] S5.4, divide the data stream into multiple subsets according to the attributes, analyze the abnormal distribution within the partitions through the mean shift algorithm, and express the results in a partition weight matrix to indicate the concentrated area of ​​the problem;

[0046] S5.5. Construct multiple prediction models, calculate the error matrix of each model, and select the best model to locate the root cause of the fault by cross-validation.

[0047] The present invention is further configured as follows: in the step S6, generating the adaptive repair suggestion:

[0048] S6.1. Based on abnormal features, use gradient boosting decision tree to explore the limitations of existing rules and optimize the threshold range Δτ of the rules through back propagation;

[0049] S6.2. Combine the pre-trained large language model, input the abnormal context to generate code repair suggestions, perform static analysis on the generated code, and verify its grammatical and logical correctness;

[0050] S6.3. Build a discrete event simulation model of the interface call process, run the process under optimized conditions, and record performance indicators such as throughput and latency after simulation;

[0051] S6.4. Evaluate the dependency between the interface and external services through deep graph analysis and calculate the dependency strength D between services dependency And generate impact path diagrams to identify dependency bottlenecks;

[0052] S6.5. Generate a repair script using automated operation and maintenance tools and run it in a test environment. Verify the repair success rate and abnormal recurrence through script log analysis.

[0053] The present invention is further configured as follows: in step S7, feedback loop and continuous learning:

[0054] S7.1. Collect feedback information of false positives or false negatives through user behavior tracking, verify the accuracy of feedback through A / B testing, and use Bayesian updating to optimize the posterior probability distribution of the detection model;

[0055] S7.2. Use ensemble learning to retrain the problem attribution model, adjust the influence weights of different features in the model through weighted voting, and output a new feature importance ranking for the next round of model improvement;

[0056] S7.3. Introduce a transfer learning algorithm to migrate the labeled abnormal data features to the new scene model, and adapt to the feature differences of different scenes by freezing the weights of the convolutional network layer by layer;

[0057] S7.4. Use unsupervised learning (autoencoder) to detect new abnormal events, automatically record their features into the abnormal pattern library, and synthesize more similar abnormal data to expand the sample through generative adversarial networks;

[0058] S7.5. Build a streaming learning framework to receive new data and update model weights in real time, avoid overfitting through the window mechanism, and ensure the stable performance of the model in dynamic scenarios.

[0059] The present invention is further configured as follows: in the step S8, visualization and report generation:

[0060] S8.1. Build a dynamic anomaly topology graph based on Graphviz, where nodes represent anomalies and edges represent causal relationships. Combined with the force-guided layout algorithm, adjust the graph in real time to highlight key anomalies.

[0061] S8.2. Use the interactive charting tool (Plotly) to draw the abnormal timeline, mark the abnormal level and event description at each time point, and embed the slider function to support users to replay the entire process of the abnormality;

[0062] S8.3. Build a real-time monitoring dashboard, integrate multi-dimensional data such as CPU utilization, memory usage, and interface call latency, and use Kafka streaming and Elasticsearch real-time query to support dynamic updates;

[0063] S8.4. Convert the anomaly analysis results into a text report using natural language generation technology, including anomaly feature description, root cause location process, and repair suggestion key content;

[0064] S8.5. Integrate exception analysis tools into the collaboration platform (JIRA), support distributed team collaborative review of reports, and optimize team processing flows through task allocation algorithms.

[0065] Beneficial Effects

[0066] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0067] 1. Through the steps of data stream classification, key field extraction and behavior modeling, the system of the present invention can accurately capture abnormal signals in massive data. At the same time, the abnormal pattern is intuitively displayed through cluster analysis and dimensionality reduction technology, which lays a reliable foundation for subsequent positioning and repair. In addition, topological sorting, causal network reasoning and other methods are used to quickly locate the source of the abnormality. Combined with sliding window and time series analysis, the false alarm rate and missed alarm rate of the positioning process are effectively reduced, greatly improving the efficiency of problem solving.

[0068] 2. The present invention realizes dynamic optimization and closed-loop processing of the system through adaptive repair and continuous learning mechanisms. It combines gradient boosting decision trees, pre-trained models to generate patch codes, and automated repair tools to greatly reduce the cost of manual intervention. At the same time, through feedback loop mechanisms and transfer learning technology, the system can continuously adapt to new scenarios and new problems to ensure continuous improvement of detection and repair capabilities. Combined with rich visualization functions and collaborative support platforms, the present invention not only provides users with intuitive exception analysis and reporting, but also improves the efficiency of collaborative processing between teams, ultimately achieving efficient, accurate, and intelligent exception fault management. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] The present invention will be further described below in conjunction with the embodiments.

[0071] Example 1

[0072] The present invention provides a technical solution: a pre-location algorithm for abnormal faults in data interface programs based on AI, wherein in step S1, data flow pre-analysis:

[0073] S1.1. Automatically classify the interface data streams through the Gaussian Mixture Model clustering algorithm to separate the real-time streams and batch streams. During the classification process, the correlation between the field and the target category is calculated using mutual information and stored in the association matrix.

[0074] S1.2. Use kernel density estimation to analyze data stream distribution and generate distribution probability density graphs. By estimating the changes in probability density values ​​in different time windows, outliers in data distribution and the tendency of distribution changes can be accurately detected.

[0075] S1.3, calculate the embedded features of each field through the Laplace score, select the field with the highest score as the key field, and further introduce the recursive feature elimination algorithm;

[0076] S1.4. Use regular expression parsing combined with syntax tree generation to check whether the data conforms to the protocol definition, construct a finite state machine to verify the protocol interaction status, and return the event sequence that violates the rules;

[0077] S1.5. Use bilateral median filter to remove abnormal data peaks, combine IQR method to define noise threshold, and draw box plots to further verify the noise filtering effect of the cleaned data;

[0078] In the step S3, abnormal clustering and feature extraction:

[0079] S3.1. Clustering outliers by density peak clustering. This algorithm generates a hierarchical structure of outlier distribution based on local density ρi and distance δi, thus forming anomaly categories.

[0080] S3.2, set a time window [t-Δt, t+Δt] near the outlier point, extract context data, and use the TF-IDF algorithm to represent the context as a feature vector matrix;

[0081] S3.3, combine t-SNE and principal component analysis dimensionality reduction methods to embed high-dimensional features into two-dimensional or three-dimensional space, draw visualization graphs of abnormal clustering, and calculate feature contribution rates after dimensionality reduction to verify information integrity;

[0082] S3.4. Use the random forest classifier to preliminarily label the clustering results and optimize the label assignment rules based on domain knowledge;

[0083] S3.5, by calculating the cosine similarity Sim(vcurrent,vhistory) between the current abnormal feature vector and the records in the historical abnormal library, matching similar historical abnormal events;

[0084] In the step S4, interface behavior modeling:

[0085] S4.1. Use Petri nets to model the interface logic process, define the event-triggered transfer relationship, and generate a state transfer matrix to describe the constraint rules of the interface behavior;

[0086] S4.2. Establishing the state transition probability model P(S) based on Markov chain t+1 |S t ), predict the next possible state of the interface, verify the model, and ensure the coverage of the training data;

[0087] S4.3, introduce the Apriori algorithm to mine frequent itemsets from historical data and extract rules with high support and confidence as the benchmark of interface behavior;

[0088] S4.4. Use the Transformer supervised learning model to optimize the interface behavior prediction model and maximize the generalization ability of normal behavior;

[0089] S4.5. Implement an online learning mechanism to update the interface behavior model parameters in real time, enhance the model adaptability, and verify the effectiveness of the new parameters for anomaly detection through a feedback loop.

[0090] In this embodiment, data preprocessing technology is used to classify, analyze distribution, and filter noise on the data stream to ensure high-quality data input; then, clustering algorithms and feature extraction methods are used to identify abnormal patterns and features and construct abnormal context information; finally, Petri nets, Markov chains and other technologies are used to establish the logic and behavior model of the interface, providing a reliable reference standard for subsequent abnormal location and repair. This step lays the data foundation and behavior modeling framework for the entire abnormality detection process, ensuring that the system's abnormality analysis is scientific and complete.

[0091] Example 2

[0092] The present invention provides a technical solution: a pre-positioning algorithm for abnormal faults in data interface programs based on AI, in the step S2, abnormal signal capture:

[0093] S2.1. Based on the dynamic upper and lower limit algorithm of the sliding window, the maximum and minimum values ​​and standard deviation (σ) of the data in the window are calculated in real time. If the new data point exceeds the range of [mean±3σ], it is marked as abnormal;

[0094] S2.2. Use the dynamic time warping algorithm to calculate the time alignment distance D between the current data sequence and the historical template sequence DTW , set the threshold T DTW , when D DTW >T DTW When , the judgment mode deviates;

[0095] S2.3, forecast time series data through the autoregressive integrated moving average (ARIMA) model, calculate the residual sequence ∈t between the true value and the predicted value, and if ∈t exceeds the set confidence interval, an abnormal event is captured;

[0096] S2.4. Use Granger causality test to analyze the temporal causal relationship between multiple data streams, draw the correlation heat map of multidimensional time series, and locate the interactive impact of abnormal events;

[0097] S2.5. Construct a weighted voting model to aggregate abnormal labels from multi-dimensional signals, use logistic regression to weight the labels, and output a unified abnormal probability distribution P (anomaly );

[0098] In the step S5, abnormality tracing and location:

[0099] S5.1. Use the topological sorting algorithm to analyze the dependency chain of interface calls, check layer by layer from upstream interfaces to downstream interfaces, build a priority matrix based on the number of calls and error frequencies, and identify high-risk nodes;

[0100] S5.2. Use time-series playback technology to restore the complete data stream when the anomaly occurs to the problem node, use the backtracking sliding window to gradually adjust the window size Δt, and locate the time point when the anomaly breaks out;

[0101] S5.3. Construct a causal diagram based on the structural equation model, calculate the causal strength between variables through the path coefficient, generate the minimum cut set, and determine the key path leading to the anomaly;

[0102] S5.4, divide the data stream into multiple subsets according to the attributes, analyze the abnormal distribution within the partitions through the mean shift algorithm, and express the results in a partition weight matrix to indicate the concentrated area of ​​the problem;

[0103] S5.5. Construct multiple prediction models, calculate the error matrix of each model, and select the best model to locate the root cause of the fault by cross-validation.

[0104] In this embodiment, potential anomalies are captured through signal detection methods such as sliding windows and dynamic time warping, and then deep tracing is performed through methods such as causal networks, topological sorting, and partition positioning. The anomaly detection part analyzes abnormal signals in the data stream in real time, extracts features, and marks problem points; the anomaly tracing part accurately identifies the root cause of the problem through time backtracking and dependency analysis. The integrated work chain can not only detect problems in real time, but also clarify the source and scope of the problem, providing precise direction for subsequent repair measures. Its beneficial effects are reflected in the rapid detection of anomalies and efficient location of problems, significantly reducing problem handling time and resource waste.

[0105] Example 3

[0106] The present invention provides a technical solution: a pre-positioning algorithm for abnormal faults in data interface programs based on AI, wherein in step S6, generating adaptive repair suggestions:

[0107] S6.1. Based on abnormal features, use gradient boosting decision tree to explore the limitations of existing rules and optimize the threshold range Δτ of the rules through back propagation;

[0108] S6.2. Combine the pre-trained large language model, input the abnormal context to generate code repair suggestions, perform static analysis on the generated code, and verify its grammatical and logical correctness;

[0109] S6.3. Build a discrete event simulation model of the interface call process, run the process under optimized conditions, and record performance indicators such as throughput and latency after simulation;

[0110] S6.4. Evaluate the dependency between the interface and external services through deep graph analysis and calculate the dependency strength D between services dependency And generate impact path diagrams to identify dependency bottlenecks;

[0111] S6.5. Generate a repair script using automated operation and maintenance tools and run it in a test environment. Verify the success rate of the repair and the recurrence of the anomaly through script log analysis.

[0112] In step S7, feedback loop and continuous learning:

[0113] S7.1. Collect feedback information of false positives or false negatives through user behavior tracking, verify the accuracy of feedback through A / B testing, and use Bayesian updating to optimize the posterior probability distribution of the detection model;

[0114] S7.2. Use ensemble learning to retrain the problem attribution model, adjust the influence weights of different features in the model through weighted voting, and output a new feature importance ranking for the next round of model improvement;

[0115] S7.3. Introduce a transfer learning algorithm to migrate the labeled abnormal data features to the new scene model, and adapt to the feature differences of different scenes by freezing the weights of the convolutional network layer by layer;

[0116] S7.4. Use unsupervised learning (autoencoder) to detect new abnormal events, automatically record their features into the abnormal pattern library, and synthesize more similar abnormal data to expand the sample through generative adversarial networks;

[0117] S7.5. Build a streaming learning framework to receive new data and update model weights in real time, avoid overfitting through the window mechanism, and ensure the stable performance of the model in dynamic scenarios;

[0118] In the step S8, visualization and report generation:

[0119] S8.1. Build a dynamic anomaly topology graph based on Graphviz, where nodes represent anomalies and edges represent causal relationships. Combined with the force-guided layout algorithm, adjust the graph in real time to highlight key anomalies.

[0120] S8.2. Use the interactive charting tool (Plotly) to draw the abnormal timeline, mark the abnormal level and event description at each time point, and embed the slider function to support users to replay the entire process of the abnormality;

[0121] S8.3. Build a real-time monitoring dashboard, integrate multi-dimensional data such as CPU utilization, memory usage, and interface call latency, and use Kafka streaming and Elasticsearch real-time query to support dynamic updates;

[0122] S8.4. Convert the anomaly analysis results into a text report using natural language generation technology, including anomaly feature description, root cause location process, and repair suggestion key content;

[0123] S8.5. Integrate exception analysis tools into the collaboration platform (JIRA), support distributed team collaborative review of reports, and optimize team processing flows through task allocation algorithms.

[0124] In this embodiment, repair suggestions and execution repair plans are generated through gradient boosting decision trees, automated operation and maintenance tools, and optimized patch codes are generated in combination with pre-trained models. After repair, the anomaly detection model is continuously optimized through user feedback, integrated learning, and transfer learning, so that the system has the ability to adapt and continuously learn. At the same time, the entire anomaly detection, positioning, and repair process is visualized in the form of dynamic graphs, timelines, dashboards, and analysis reports, providing users with intuitive analysis results and collaborative support. Its beneficial effects are to improve the accuracy of anomaly detection and repair efficiency, reduce the occurrence of repeated problems, and provide efficient tool support for team collaboration.

[0125] In actual use, the present invention realizes comprehensive preprocessing of data streams and abstract modeling of interface behaviors through data analysis and modeling. The specific process includes using Gaussian Mixture Model to classify data streams into real-time streams and batch streams, drawing distribution probability graphs in combination with kernel density estimation, and detecting outliers and distribution change trends. At the same time, key fields are extracted by Laplace scores and recursive feature elimination to ensure that the core data dimensions of the analysis are highly representative. Further combined with regular parsing and finite state machines to verify data protocol consistency, clean noise and draw box plots to verify data quality. On this basis, clustering algorithms and feature extraction techniques are used to identify abnormal patterns (such as density peak clustering and TF-IDF vectorization), and dimensionality reduction algorithms (such as t-SNE and PCA) are used to intuitively display abnormal category distribution. Finally, the interface logic and behavior rules are modeled through technologies such as Petri nets, Markov chains, and Transformer to form a high-dimensional understanding of the interface, laying the model foundation for subsequent positioning and repair.

[0126] In the anomaly capture stage, the dynamic upper and lower limit algorithm based on the sliding window monitors the data fluctuation range in real time, combines dynamic time warping (DTW) to identify pattern deviation events, and predicts time series anomalies through the ARIMA model. The Granger causality test is further used to analyze the temporal association between data, generate an interactive influence heat map, and aggregate the anomaly marks of multi-dimensional signals through the weighted voting model to output the anomaly probability distribution. In the anomaly location stage, the topological sorting algorithm is used to analyze the interface dependency from the call chain, and the time series playback technology is used to restore the context data of the anomaly, and the sliding window is adjusted to gradually narrow the location range. Combined with the structural equation model to generate a causal network, the path coefficient is calculated to determine the key impact path, and the problem concentration area is identified based on the partition weight matrix. Finally, the error matrix is ​​used to compare multiple prediction models, and the best model is selected to assist in tracing the source, completing efficient and accurate anomaly location.

[0127] Through gradient boosting decision trees, we analyze the limitations of existing rules and optimize anomaly detection thresholds. We combine pre-trained large language models to generate patch codes for abnormal contexts and verify their logical correctness. We use discrete event simulation technology to test the performance of the repair process, and combine deep graph analysis to generate impact path graphs to identify dependency bottlenecks. After repair, we adjust the model weights through user feedback combined with Bayesian updating and ensemble learning, further introduce transfer learning to adapt to new scenarios, and expand the abnormal pattern library through unsupervised learning. In terms of visualization, dynamic anomaly topology maps, timelines, and real-time monitoring dashboards intuitively display the anomaly detection and repair process. Combined with natural language generation technology, detailed analysis reports are generated to support team optimization workflows on distributed collaboration platforms. The entire stage effectively improves detection and repair efficiency and achieves closed-loop processing of abnormal problems.

[0128] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pre-location algorithm for abnormal faults in data interface programs based on AI, characterized in that: The following steps are involved: S1, data flow pre-analysis; S2, abnormal signal capture; S3, anomaly clustering and feature extraction; S4, interface behavior modeling; S5, abnormal source tracing and location; S6, adaptive repair suggestion generation; S7. Feedback loop and continuous learning; S8. Visualization and report generation.

2. According to the AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1, it is characterized by: In the step S1, data stream pre-analysis: S1.

1. Automatically classify the interface data streams through the Gaussian Mixture Model clustering algorithm to separate the real-time streams and batch streams. During the classification process, the correlation between the field and the target category is calculated using mutual information and stored in the association matrix. S1.

2. Use kernel density estimation to analyze data stream distribution and generate distribution probability density graphs. By estimating the changes in probability density values ​​in different time windows, outliers in data distribution and the tendency of distribution changes can be accurately detected. S1.3, calculate the embedded features of each field through the Laplace score, select the field with the highest score as the key field, and further introduce the recursive feature elimination algorithm; S1.

4. Use regular expression parsing combined with syntax tree generation to check whether the data conforms to the protocol definition, construct a finite state machine to verify the protocol interaction status, and return the event sequence that violates the rules; S1.

5. Use a bilateral median filter to remove abnormal data peaks, and define the noise threshold in combination with the IQR method. The cleaned data is further verified by drawing a box plot to verify the noise filtering effect.

3. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In the step S2, abnormal signal capture: S2.

1. Based on the dynamic upper and lower limit algorithm of the sliding window, the maximum and minimum values ​​and standard deviation (σ) of the data in the window are calculated in real time. If the new data point exceeds the range of [mean±3σ], it is marked as abnormal; S2.

2. Use the dynamic time warping algorithm to calculate the time alignment distance D between the current data sequence and the historical template sequence DTW , set the threshold T DTW , when D DTW >T DTW When , the judgment mode deviates; S2.3, forecast time series data through the autoregressive integrated moving average (ARIMA) model, calculate the residual sequence ∈t between the true value and the predicted value, and if ∈t exceeds the set confidence interval, an abnormal event is captured; S2.

4. Use Granger causality test to analyze the temporal causal relationship between multiple data streams, draw the correlation heat map of multidimensional time series, and locate the interactive impact of abnormal events; S2.

5. Construct a weighted voting model to aggregate abnormal labels from multi-dimensional signals, use logistic regression to weight the labels, and output a unified abnormal probability distribution P (anomaly ).

4. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In the step S3, abnormal clustering and feature extraction: S3.

1. Clustering outliers by density peaks. This algorithm generates a hierarchical structure of outlier distribution based on local density ρi and distance δi, thereby forming anomaly categories. S3.2, set a time window [t-Δt, t+Δt] near the outlier point, extract context data, and use the TF-IDF algorithm to represent the context as a feature vector matrix; S3.3, combine t-SNE and principal component analysis dimensionality reduction methods to embed high-dimensional features into two-dimensional or three-dimensional space, draw visualization graphs of abnormal clustering, and calculate feature contribution rates after dimensionality reduction to verify information integrity; S3.

4. Use the random forest classifier to preliminarily label the clustering results and optimize the label assignment rules based on domain knowledge; S3.

5. By calculating the cosine similarity Sim(vcurrent, vhistory) between the current abnormal feature vector and the records in the historical abnormal library, similar historical abnormal events are matched.

5. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In the step S4, interface behavior modeling: S4.

1. Use Petri nets to model the interface logic process, define the event-triggered transfer relationship, and generate a state transfer matrix to describe the constraint rules of the interface behavior; S4.

2. Establishing the state transition probability model P(S) based on Markov chain t+1 |S t ), predict the next possible state of the interface, verify the model, and ensure the coverage of the training data; S4.3, introduce the Apriori algorithm to mine frequent itemsets from historical data and extract rules with high support and confidence as the benchmark of interface behavior; S4.

4. Use the Transformer supervised learning model to optimize the interface behavior prediction model and maximize the generalization ability of normal behavior; S4.

5. Implement an online learning mechanism to update the interface behavior model parameters in real time, enhance the model adaptability, and verify the effectiveness of the new parameters for anomaly detection through a feedback loop.

6. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In the step S5, abnormality tracing and location: S5.

1. Use the topological sorting algorithm to analyze the dependency chain of interface calls, check layer by layer from upstream interfaces to downstream interfaces, build a priority matrix based on the number of calls and error frequencies, and identify high-risk nodes; S5.

2. Use time-series playback technology to restore the complete data stream when the anomaly occurs to the problem node, use the backtracking sliding window to gradually adjust the window size Δt, and locate the time point when the anomaly breaks out; S5.

3. Construct a causal diagram based on the structural equation model, calculate the causal strength between variables through the path coefficient, generate the minimum cut set, and determine the key path leading to the anomaly; S5.4, divide the data stream into multiple subsets according to the attributes, analyze the abnormal distribution within the partitions through the mean shift algorithm, and express the results in a partition weight matrix to indicate the concentrated area of ​​the problem; S5.

5. Construct multiple prediction models, calculate the error matrix of each model, and select the best model to locate the root cause of the fault by cross-validation.

7. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In the step S6, generating the adaptive repair suggestion: S6.

1. Based on abnormal features, use gradient boosting decision tree to explore the limitations of existing rules and optimize the threshold range Δτ of the rules through back propagation; S6.

2. Combine the pre-trained large language model, input the abnormal context to generate code repair suggestions, perform static analysis on the generated code, and verify its grammatical and logical correctness; S6.

3. Build a discrete event simulation model of the interface call process, run the process under optimized conditions, and record performance indicators such as throughput and latency after simulation; S6.

4. Evaluate the dependency between the interface and external services through deep graph analysis and calculate the dependency strength D between services dependency And generate impact path diagrams to identify dependency bottlenecks; S6.

5. Generate a repair script using automated operation and maintenance tools and run it in a test environment. Verify the repair success rate and abnormal recurrence through script log analysis.

8. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In step S7, feedback loop and continuous learning: S7.

1. Collect feedback information of false positives or false negatives through user behavior tracking, verify the accuracy of feedback through A / B testing, and use Bayesian updating to optimize the posterior probability distribution of the detection model; S7.

2. Use ensemble learning to retrain the problem attribution model, adjust the influence weights of different features in the model through weighted voting, and output a new feature importance ranking for the next round of model improvement; S7.

3. Introduce a transfer learning algorithm to migrate the labeled abnormal data features to the new scene model, and adapt to the feature differences of different scenes by freezing the weights of the convolutional network layer by layer; S7.

4. Use unsupervised learning (autoencoder) to detect new abnormal events, automatically record their features into the abnormal pattern library, and synthesize more similar abnormal data to expand the sample through generative adversarial networks; S7.

5. Build a streaming learning framework to receive new data and update model weights in real time, avoid overfitting through the window mechanism, and ensure the stable performance of the model in dynamic scenarios.

9. The AI-based pre-location algorithm for abnormal faults in data interface programs according to claim 1 is characterized in that: In the step S8, visualization and report generation: S8.

1. Build a dynamic anomaly topology graph based on Graphviz, where nodes represent anomalies and edges represent causal relationships. Combined with the force-guided layout algorithm, adjust the graph in real time to highlight key anomalies. S8.

2. Use the interactive charting tool (Plotly) to draw the abnormal timeline, mark the abnormal level and event description at each time point, and embed the slider function to support users to replay the entire process of the abnormality; S8.

3. Build a real-time monitoring dashboard, integrate multi-dimensional data such as CPU utilization, memory usage, and interface call latency, and use Kafka streaming and Elasticsearch real-time query to support dynamic updates; S8.

4. Convert the anomaly analysis results into a text report using natural language generation technology, including anomaly feature description, root cause location process, and repair suggestion key content; S8.

5. Integrate exception analysis tools into the collaboration platform (JIRA), support distributed team collaborative review of reports, and optimize team processing flows through task allocation algorithms.