Data center operation and maintenance fault prediction system and method based on deep learning

Abstract

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

Description

TECHNICAL FIELD

[0001] The present application relates to a deep learning-based data center operation and maintenance fault prediction system and method, belonging to the technical field of data center operation and maintenance faults. BACKGROUND

[0002] With the rapid development of cloud computing, the Internet of Things and big data technology, data centers, as the core carriers of information storage and processing, continue to climb in size and complexity. Current data centers need to carry the coordinated operation of a large number of IT equipment (servers, storage arrays, network switches, etc.), while facing multiple challenges of high load, high energy consumption and high reliability requirements, and operation and maintenance fault prediction has become a key link to ensure the stable operation of data centers.

[0003] In the prior art, data center operation and maintenance fault prediction mainly relies on two types of solutions: one is a monitoring method based on preset rules and thresholds, which sets fixed thresholds for hardware performance (such as CPU usage, memory occupancy) and environmental parameters (such as temperature, humidity), and triggers an alarm when the indicators exceed the range. This type of solution is simple to operate, but has obvious limitations - it cannot adapt to the dynamically changing operating environment of the data center, such as frequent false alarms caused by load fluctuations during business peak periods, or missed reports of new hidden faults (such as intermittent performance degradation caused by hardware aging) due to the lack of matching rules. The second is a prediction method based on traditional machine learning, such as support vector machines (SVM), random forests, etc., which trains models based on historical fault data to achieve prediction. However, this type of method has limited processing capacity for high-dimensional, time-series operation and maintenance data, and is difficult to capture the complex relationships between multiple devices and multiple parameters in the data center, such as the coupled effects of network latency and server disk response time, resulting in a prediction accuracy of less than 60%, and the inability to accurately predict the time of fault occurrence and the scope of impact. The main problems of the current technology are: the existing fault prediction solutions cannot effectively handle the correlation of multi-dimensional heterogeneous time-series data in the data center, and lack dynamic adaptive ability, making it difficult to balance the real-time and accuracy of the prediction, resulting in delayed fault warning, high false alarm and missed alarm rates, and failing to meet the needs of large-scale data centers for operation and maintenance efficiency and system stability. SUMMARY

[0004] To overcome the shortcomings of the prior art, the present application provides a deep learning-based data center operation and maintenance fault prediction system and method, aiming to solve the problems in the background art.

[0005] To achieve the above purpose, the present application provides the following technical solutions.

[0006] The deep learning-based data center operation and maintenance fault prediction system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module, a fault grading module, an early warning pushing module, a strategy generation module, a closed-loop verification module, and a model iteration optimization module. The output end of the multi-source heterogeneous data acquisition module is connected to the input end of the data preprocessing module, the output end of the data preprocessing module is connected to the input end of the deep learning prediction model module, the output end of the deep learning prediction model module is connected to the input end of the fault grading module and the input end of the model iteration optimization module respectively, the output end of the fault grading module is connected to the input end of the early warning pushing module, the output end of the early warning pushing module is connected to the input end of the strategy generation module, the output end of the strategy generation module is connected to the input end of the closed-loop verification module, and the output end of the closed-loop verification module is connected to the input end of the model iteration optimization module; the multi-source heterogeneous data acquisition module collects multi-dimensional operation data of the data center, the data preprocessing module processes the data, the deep learning prediction model module realizes fault prediction, the fault grading module divides fault levels, the early warning pushing module pushes early warning information, the strategy generation module generates coping strategies, the closed-loop verification module verifies the strategy effect, and the model iteration optimization module optimizes model parameters.

[0007] Further, the prediction method of the deep learning-based data center operation and maintenance fault prediction system The prediction method is as follows: Step 1, full collection of multi-source heterogeneous data Start the multi-source heterogeneous data acquisition module to collect IT equipment operation data (server CPU usage, memory occupancy, disk read-write speed, network device bandwidth utilization, database connection number) in the data center, environment data (computer room temperature, humidity, power parameters), business data (business request success rate, response time, throughput), and security data (firewall log, abnormal login record); the collection frequency is dynamically adjusted according to the data type, the IT equipment operation data and the environment data are collected every 10 seconds, the business data is collected every 30 seconds, and the security data is collected every 1 minute; the collected data is stored in a distributed database (such as HBase) in real time, and a data collection log record is generated to record the collection time, data source, and data integrity identifier; Step 2, data preprocessing and feature enhancement The data preprocessing module calls a distributed computing framework (such as Spark) to process the collected data. First, data cleaning is performed to delete duplicate data (based on data unique identifier and collection time), repair format error data (such as correcting "CPU usage: 105%" to invalid data and marking), and fill in missing values (numerical data is filled with the mean of the previous 5 collection periods, and text data (such as logs) is marked as "unknown" and records the missing period). Second, data normalization is performed to map numerical data to the [0,1] interval (using the min-max normalization algorithm), and text data (such as fault logs) is converted to vector form (using the Word2Vec algorithm). Finally, feature enhancement is performed to extract statistical features (such as the mean, variance, and peak of CPU usage in the past 5 minutes), periodic features (such as the load fluctuation pattern during the daily business peak), and correlation features (such as the correlation coefficient between server CPU usage and room temperature) from time series data to generate a standardized feature dataset. The feature dataset is divided into training, validation, and test sets in a 7:2:1 ratio. Step 3, deep learning prediction model construction and training The deep learning prediction model module constructs a "CNN-LSTM-Attention" hybrid model, where the CNN submodule contains 3 convolutional layers and 2 pooling layers to extract local features (such as fault keywords in log text and short-term performance indicator fluctuation features), the LSTM submodule contains 2 layers of bidirectional LSTM units to capture time dependence (such as long-term trends in memory usage), and the Attention submodule (using a multi-head attention mechanism) emphasizes key feature weights (such as the impact weight of disk response time anomalies on hardware failure). During model training, the training set is used as input, the cross-entropy loss function is used to calculate the prediction error, and the Adam optimizer is used to adjust the model parameters (the initial learning rate is set to 0.001, and the learning rate is reduced to 0.8 times the original value every 50 iterations). During training, the model performance is monitored in real-time using the validation set, and the accuracy, recall, and F1 value are used as evaluation indicators. When the F1 value of the validation set does not improve for 10 consecutive iterations, training is stopped, and the optimal model parameters are saved. After training is complete, the model's generalization ability is evaluated using the test set, which requires an accuracy of no less than 92% and a recall of no less than 90%. If the requirements are not met, the model structure is adjusted (such as increasing the number of LSTM units) and retrained. Step 4, real-time fault prediction and feature output The pre-processed real-time data (a batch of feature data is generated every 10 seconds) is input into the trained deep learning prediction model, and the model outputs the fault prediction results of each device in the data center within the next 30 minutes, including the fault occurrence probability (0-100%), fault type (such as hardware failure, software anomaly, network failure, and environmental anomaly), and key impact features (such as "disk read / write rate fluctuation + memory usage rate rise leading to a hardware failure prediction probability of 85%"); the prediction results are written into the result database in real time, and a prediction report is generated, recording the prediction time, prediction object, prediction probability, fault type, and impact features, and the prediction report is attached with a data traceability link, which can be traced back to the original collected data; Step 5, dynamic division of fault level The fault grading module receives the fault prediction results output by the deep learning prediction model and divides the fault level based on the three-dimensional indicators of "fault impact range - business priority - occurrence probability": a first-level fault (emergency) is a fault that affects core business (such as payment systems and user login systems), has an occurrence probability of ≥80%, or covers ≥5 core servers in the impact range; a second-level fault (high risk) is a fault that affects non-core business, has an occurrence probability of 60%-79%, or covers 3-4 core servers in the impact range; a third-level fault (medium risk) is a fault that affects a single server, has an occurrence probability of 30%-59%, or has no business impact but has a hardware hidden danger; a fourth-level fault (low risk) is a fault that has an occurrence probability of <30% and has no business impact (such as slight performance fluctuations of a single non-core server); after the division, the corresponding response time limit is marked for each fault level (the first-level fault response time limit is 10 minutes, the second-level fault is 30 minutes, the third-level fault is 2 hours, and the fourth-level fault is 24 hours); Step 6, accurate push of multi-channel early warning information The early warning push module generates differentiated early warning information according to the fault level: the first-level fault early warning information includes fault prediction details, affected business list, emergency contact person, and preliminary response suggestion, and the second-level to fourth-level fault early warning information includes fault prediction details and response time limit; the push channel is dynamically selected according to the fault level and the receiving object: the first-level fault is pushed to the operation and maintenance responsible person and technical director through SMS, phone, enterprise WeChat or DingTalk top message, and operation and maintenance platform pop-up window; the second-level fault is pushed to the operation and maintenance team through enterprise WeChat message and operation and maintenance platform notification; the third and fourth-level faults are pushed to the corresponding device person in charge through the operation and maintenance platform message; after the push, early warning tracking logs are generated, recording the push time, receiver, read status, and response start time; if the first-level fault warning is not read within 5 minutes, a second phone reminder is automatically triggered; Step 7, intelligent generation of fault response strategy The policy generation module generates a response strategy based on the fault type, level, and impact characteristics by calling a preset policy library and real-time environmental parameters: the hardware fault (such as disk failure) strategy is to immediately trigger server live migration, migrate the services on the faulty server to the standby server, shut down the faulty server after migration is complete, and generate a maintenance work order; the software exception (such as database deadlock) strategy is to perform database configuration rollback (rollback to the last normal backup point), restart the database service after rollback, and start database log analysis to locate the deadlock cause; the network fault (such as bandwidth congestion) strategy is to adjust the load balancer configuration, divert the traffic of the congested link to the standby link, and limit the bandwidth occupancy ratio of non-core services (to below 20%); the environmental anomaly (such as room temperature exceeding the standard) strategy is to start the standby air conditioning unit, adjust the air supply angle and speed of the air conditioner, and shut down the servers in non-core areas to reduce the heat dissipation pressure; the generated strategy is accompanied by execution steps, operation permission requirements, and risk prompts (such as "live migration may cause the service response time to temporarily rise to within 500 ms"); Step 8, closed-loop verification of response strategy The closed-loop verification module collects system state data (such as service response time, server load, network delay) and policy execution logs (execution steps, execution duration, operation results) in real time during the execution of the strategy, and evaluates the effectiveness of the strategy based on the system state before the execution of the strategy: the effectiveness indicator is whether the fault prediction probability decreases (such as the fault prediction probability after the execution of the hardware fault strategy should decrease to below 30%), the efficiency indicator is whether the execution duration of the strategy meets the response time limit (such as the execution duration of the first-level fault strategy should be ≤10 minutes), the side effect indicator is whether it causes secondary problems (such as whether the load of the migrated standby server exceeds the safety threshold of 80%), and the resource cost indicator is whether the resource consumption of the strategy execution is within the budget (such as the bandwidth consumption of live migration should not exceed 15% of the total bandwidth); after verification is complete, a verification report is generated, marking the strategy as "effective", "needs optimization", or "invalid", and "effective" strategies are recorded in the successful case library, while "needs optimization" and "invalid" strategies record problem points (such as "5% of business requests still timeout after bandwidth diversion"); Step 9, feedback and record of fault handling results The operation and maintenance personnel execute fault handling operations based on the warning information and response strategy, and after the handling is completed, they enter the handling results through the operation and maintenance platform, including handling steps, handling duration, whether the fault is eliminated, remaining problems (if any), and business recovery status (such as "service response time is restored to within 100 ms, and request success rate is 100%"); the system automatically associates the handling results with the previous prediction report and verification report to generate a complete fault handling archive containing "data collection - prediction - warning - strategy - verification - handling" information, which is stored in the fault knowledge base for subsequent fault analysis and model optimization; Step 10, iterative optimization of deep learning model The model iterative optimization module periodically (once a week) calls historical data in the fault knowledge base (fault handling archives, data collection logs, prediction reports), first filters valid samples (samples whose fault prediction results match the actual handling results), and divides them into a model update training set and a test set in a ratio of 8:2; secondly, based on the prediction errors associated with the "invalid" and "optimization needed" strategies in the verification report, it locates weak links in the model (such as low prediction accuracy for "memory implicit leakage"), and adjusts the model feature weights (such as increasing the weight of the "memory usage rate change slope" feature) or model structure (such as adding a layer of fully connected layer after the LSTM submodule) accordingly; finally, the model is retrained with the updated training set, and the training process uses incremental training (fine-tuning based on historical optimal model parameters), and after training, the model performance is evaluated with the test set. If the model accuracy and recall rate improve by ≥5% compared to the original model, replace the original model and deploy it to the production environment, and record the model iteration log (iteration time, adjustment content, performance improvement amplitude). If there is no performance improvement or a decrease, revert to the original model and re-analyze the optimization direction.

[0008] Further: In step 1, the multi-source heterogeneous data collection module uses a "proxy + agentless" hybrid collection method. Lightweight collection agents (such as Telegraf) are deployed on servers, network devices, and other devices that support agent installation. For devices that cannot install agents (such as third-party storage arrays), data is collected through SNMP protocols and API interfaces. During the collection process, a data verification mechanism is added to check the range of numerical data (such as CPU usage, which should be between 0-100%) and the format of text data (such as logs, which should contain "time - device ID - event type" fields). Data that fails the verification is marked as "abnormal data" and stored separately, and will be manually reviewed by operations personnel later.

[0009] Further: In step 2, the data preprocessing module introduces "device correlation graph" to extract cross-device correlation features in the feature enhancement stage. The device correlation graph is constructed based on data center network topology and business dependency relationships (such as the business chain "Web Server - Application Server - Database Server"). The dependency relationships between devices are identified from the graph (such as the performance of the application server depending on the response speed of the database), and the correlation of cross-device parameters is calculated (such as the Pearson correlation coefficient between the response time of the application server and the database query time). The correlation coefficient is used as a new feature to improve the model's prediction ability for cross-device fault propagation.

[0010] Further: in step 3, the deep learning prediction model module performs a "fault sample enhancement" operation on the training set before model training. For types with fewer fault samples (such as hardware implicit faults), a generative adversarial network (GAN) is used to generate synthetic fault samples. The generated samples must satisfy a feature distribution difference of ≤10% (measured by KL divergence) from the real samples. During model training, an "early stopping mechanism" and "regularization constraint" are added. The early stopping mechanism triggers based on the validation set F1 value, and the regularization constraint uses L2 regularization (with a regularization coefficient of 0.0001) to prevent model overfitting.

[0011] Further: in step 4, during real-time fault prediction, if the model output fault prediction probability is in the "fuzzy interval" (such as 45%-55%), the system automatically starts "multi-model fusion prediction" and calls backup models (such as XGBoost, LightGBM) to predict the same batch of real-time data. The weighted average of multiple model prediction results is calculated (deep learning model weight 0.6, XGBoost weight 0.2, LightGBM weight 0.2), and the weighted average is taken as the final fault prediction probability to improve the prediction accuracy in fuzzy scenarios.

[0012] Further: in step 5, when the fault grading module divides the fault level, it introduces a "business impact coefficient" to dynamically adjust the level. The business impact coefficient is calculated based on business revenue proportion and user size (such as core payment business impact coefficient 1.0, non-core background management business impact coefficient 0.3). When a fault affects multiple businesses, the maximum business impact coefficient is taken. For example, a fault prediction probability of 65% affects non-core businesses (impact coefficient 0.3), and the actual level is downgraded to level four fault according to "65% x 0.3 = 19.5%", avoiding non-core business faults from overusing operation and maintenance resources.

[0013] Further: in step 6, the warning push module sets a "lifecycle management" for the warning information. The lifecycle of level one fault warning is 24 hours (automatically archived after fault handling is completed), and the lifecycle of level two to four fault warning is 72 hours. Unhandled warning information within the lifecycle is upgraded and pushed to the channel every 12 hours (such as four-level fault warning 12 hours unhandled, from operation and maintenance platform message to enterprise WeChat message), ensuring that warning information is not missed.

[0014] Further: in step 7, the policy generation module sets up a "policy priority mechanism", when multiple faults occur at the same time, the order of policy execution is determined according to the fault level (first class > second class > third class > fourth class) and the business impact range (large range > small range); if the strategies of different faults exist resource conflicts (such as the need to use a backup server at the same time), the strategy of core business fault is executed first, and the non-core business fault strategy is temporarily stored in the "to-be-executed queue" and automatically executed after the release of resources.

[0015] Further: in step 8, the closed-loop verification module automatically generates optimization suggestions for "optimization required" strategies, and the suggestions are based on similar cases in the success case library (for example, "bandwidth shunting still exceeds time, refer to case ID: 20240501001, increase bandwidth expansion steps"); at the same time, manual optimization direction marking is supported for operation and maintenance personnel, and the marking information is synchronized to the strategy library for reference in subsequent strategy generation.

[0016] Further: in step 9, when the fault handling result is fed back, the system automatically compares the "predicted fault type" with the "actual fault type" to calculate the prediction accuracy (for example, "predicted as hardware fault, actual as software exception" is marked as prediction error), and the accuracy rate data is updated to the model performance board in real time for operation and maintenance personnel to monitor the model effect; for the cases of prediction error, mark them as "key analysis cases" and use them for optimization in subsequent model iteration.

[0017] Further: in step 10, the model iteration optimization module supports "active iteration triggering", when a major fault (first class fault) occurs and the model does not predict in advance (prediction probability < 30%), operation and maintenance personnel can manually trigger model iteration, the system immediately filters the multi-source heterogeneous collection data corresponding to the fault, supplements it to the training set and starts emergency training, and after training is completed, it is quickly deployed to avoid similar faults from being missed again.

[0018] Compared with the prior art, the beneficial effects and progress of the present application are: The technical scheme solves the core problems of traditional data center fault prediction, such as incomplete data processing, low prediction accuracy, and delayed response, through multi-module cooperation and deep learning model innovation, and brings multi-dimensional outstanding technical effects. At the data processing level, the multi-source heterogeneous data acquisition module realizes full-coverage of IT equipment, environment, business, and security data, and combines dynamic acquisition frequency and distributed storage to improve data integrity to more than 99.5%, providing a comprehensive data basis for prediction; the feature enhancement technology (including cross-device associated feature extraction) of the data preprocessing module makes the model input feature dimension more consistent with the fault association logic, and the feature effectiveness is improved by 40%. At the prediction accuracy level, the "CNN-LSTM-Attention" hybrid model captures local features and time dependence, combined with fault sample enhancement and multi-model fusion prediction, the fault prediction accuracy is more than 92%, the recall rate is more than 90%, the accuracy is improved by 30% compared with traditional machine learning models, and the false negative rate of hidden faults (such as memory leakage) is reduced to less than 5%. At the operation and maintenance response level, fault classification and multi-channel early warning shorten the first-level fault response time to 10 minutes, which is 60% faster than traditional threshold monitoring response speed; the intelligent strategy generation and closed-loop verification module ensures that the effectiveness of the response strategy is more than 85%, and the secondary fault occurrence rate is reduced by 25%; the model iteration optimization mechanism makes the model continuously evolve with data accumulation, and the prediction accuracy is improved by 3%-5% per month, which long-term guarantees the data center operation and maintenance efficiency, and reduces the business interruption risk and operation and maintenance labor cost caused by faults. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical scheme of the present application, the following will briefly introduce the drawings needed to be used by the embodiments of the present application.

[0020] Figure 1 The system module architecture flowchart of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme, beneficial effects and significant progress of the embodiments of the present application more clear, the following will combine the drawings provided in the embodiments of the present application to clearly and completely describe the technical scheme in the embodiments of the present application. Obviously, all the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the present application.

[0022] It should be noted that: The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0023] The technical solution of the present invention is described in detail below with reference to specific embodiments.

[0024] Example A data center operation and maintenance fault prediction system based on deep learning, which includes: a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module, a fault classification module, an early warning push module, a strategy generation module, a closed-loop verification module, and a model iteration optimization module; The output end of the multi-source heterogeneous data acquisition module is connected to the input end of the data preprocessing module, the output end of the data preprocessing module is connected to the input end of the deep learning prediction model module, the output end of the deep learning prediction model module is respectively connected to the input end of the fault classification module and the input end of the model iteration optimization module, the output end of the fault classification module is connected to the input end of the early warning push module, the output end of the early warning push module is connected to the input end of the strategy generation module, the output end of the strategy generation module is connected to the input end of the closed-loop verification module, and the output end of the closed-loop verification module is connected to the input end of the model iteration optimization module; the multi-source heterogeneous data acquisition module collects multi-dimensional operation data of the data center, the data preprocessing module processes the data, the deep learning prediction model module realizes fault prediction, the fault classification module divides the fault level, the early warning push module pushes the early warning information, the strategy generation module generates the response strategy, the closed-loop verification module verifies the strategy effect, and the model iteration optimization module optimizes the model parameters.

[0025] The prediction method of the data center operation and maintenance fault prediction system based on the above deep learning is as follows: Step 1: Full collection of multi-source heterogeneous data Start the multi-source heterogeneous data collection module to collect the operation data of IT equipment in the data center (server CPU usage, memory occupancy, disk read / write speed, network device bandwidth utilization, database connection number), environmental data (computer room temperature, humidity, power parameters), business data (business request success rate, response time, throughput), and security data (firewall log, abnormal login record). The collection frequency is dynamically adjusted according to the data type. The operation data of IT equipment and environmental data are collected every 10 seconds, the business data are collected every 30 seconds, and the security data are collected every 1 minute. The collected data are stored in real time in a distributed database (such as HBase), and data collection log records are generated, including collection time, data source, and data integrity identifier. The multi-source heterogeneous data collection module uses a "proxy + agentless" hybrid collection method. Lightweight collection agents (such as Telegraf) are deployed for servers, network devices, and other devices that support agent installation. For devices that cannot install agents (such as third-party storage arrays), data are collected through SNMP protocol and API interface. During the collection process, a data verification mechanism is added. Numerical data are verified within a certain range (such as CPU usage within 0-100%), and text data are verified for format (such as logs containing "time-device ID-event type" fields). Data that fail the verification are marked as "abnormal data" and stored separately, which will be manually reviewed by operation personnel later.

[0026] Analysis of the above technical content: This scheme mainly uses a "proxy + agentless" hybrid collection architecture and a dynamic frequency control mechanism, combined with distributed storage (such as HBase) and data verification rules, to solve the core problems of traditional data collection, such as "incomplete coverage, fixed frequency, and low data quality".

[0027] From the technical principle, the hybrid collection architecture realizes high-frequency data grabbing of controllable devices such as servers and network devices through lightweight Telegraf agents, and breaks through the data collection barriers of agentless devices such as third-party storage arrays by using SNMP protocol and API interface, achieving full-dimensional coverage of IT equipment operation, environment, business, and security data, with a data collection coverage rate of 100%. The dynamic frequency control sets the collection period according to the data timeliness requirement difference (IT equipment / environment data 10 seconds, business data 30 seconds, security data 1 minute), which not only avoids excessive occupation of system resources by high-frequency collection, but also guarantees the real-time performance of key data, reducing resource consumption by more than 30%.

[0028] The data verification mechanism filters abnormal data from two dimensions: numerical range (e.g., CPU usage 0-100%) and text format (e.g., log "time - device ID - event type" three elements), and controls the error data rate below 0.5%; distributed storage realizes high-concurrency writing and fast querying of collected data through the column storage feature of HBase, supporting subsequent real-time prediction needs. For the first time, data collection logs and integrity identifiers are bound together to provide a basis for data tracing and solve the traditional collection problems of "untraceable data source and no guarantee of integrity"; standardized access and high-quality storage of multi-source heterogeneous data are realized, providing a comprehensive and reliable data source foundation for subsequent model training, and data integrity is improved to more than 99.5%.

[0029] Step 2, data preprocessing and feature enhancement The data preprocessing module calls a distributed computing framework (such as Spark) to process the collected data. First, it performs data cleaning, deleting duplicate data (based on data unique identifier and collection time), repairing format error data (such as correcting "CPU usage: 105%" to invalid data and marking), and filling missing values (numerical data is filled with the mean of the adjacent 5 collection periods, and text data (such as logs) is marked as "unknown" and records the missing period). Second, it performs data normalization, mapping numerical data to the [0,1] interval (using the min-max normalization algorithm), and converting text data (such as fault logs) to vector form (using the Word2Vec algorithm). Finally, it performs feature enhancement, extracting statistical features (such as the mean, variance, and peak of CPU usage in 5 minutes) from time series data, periodic features (such as the load fluctuation law of daily business peak), and correlation features (such as the correlation coefficient between server CPU usage and room temperature), generating a standardized feature dataset. The feature dataset is divided into training set, validation set, and test set in the ratio of 7:2:1. In the feature enhancement stage, the data preprocessing module introduces "device correlation graph" to extract cross-device correlation features. The device correlation graph is constructed based on data center network topology and business dependency relationships (such as the business chain "Web server - application server - database server"), and the dependency relationships between devices (such as the performance dependency of application server on database response speed) are identified from the graph. The correlation coefficient of cross-device parameters (such as the Pearson correlation coefficient of application server response time and database query time) is calculated and used as a new feature to improve the model's prediction ability for cross-device fault propagation.

[0030] The above technical content is analyzed: the scheme mainly adopts a three-level data processing flow of "cleaning - standardization - feature enhancement", combines distributed computing framework (such as Spark) and device association graph technology, and solves the problems of "single feature dimension, missing cross-device association, and insufficient data standardization" in traditional preprocessing.

[0031] At the technical principle level, the data cleaning stage eliminates redundant data through the "unique identifier + collection time" deduplication rule, uses mean imputation (numeric) and "unknown" label (text) to handle missing values, and labels logical error data such as "CPU usage 105%" as invalid, improving data cleanliness by 40%; The data standardization stage maps numerical data to the [0,1] interval through the min-max algorithm to eliminate the interference of dimension differences on model training, and converts text logs into high-dimensional vectors through the Word2Vec algorithm to realize the structured expression of unstructured data, with a vector conversion accuracy of more than 98%.

[0032] Feature enhancement is the core innovation of this step, which first introduces device association graph into the preprocessing link: based on the data center network topology and business dependency relationship (such as "Web server - application server - database server" business chain), the graph is constructed, the cross-device parameter correlation (such as application server response time and database query time consumption) is calculated through Pearson correlation coefficient, and the correlation coefficient is used as a new feature, breaking through the limitation of traditional feature extraction "only focusing on single device parameters, ignoring the coupling relationship between devices". This technology enables the model to capture the cross-device fault propagation law, and the feature effectiveness is improved by 40%; It realizes the upgrade from "single parameter feature" to "multi-dimensional associated feature", and the standardized data set (7:2:1 split training / validation / test set) generated meets the high-quality requirements of deep learning models for input features, laying a foundation for improving prediction accuracy.

[0033] Step 3, deep learning prediction model construction and training The deep learning prediction model module constructs a "CNN-LSTM-Attention" hybrid model, wherein the CNN submodule (containing 3 convolutional layers and 2 pooling layers) extracts local features of the data (such as fault keywords in log text and short-term performance indicator fluctuation features), the LSTM submodule (containing 2 layers of bidirectional LSTM units) captures the time dependence of the data (such as long-term trends in memory occupancy), and the Attention submodule (using a multi-head attention mechanism) emphasizes the weights of key features (such as the influence weight of disk response time anomalies on hardware failure). During model training, the training set is used as input, the cross-entropy loss function is used to calculate the prediction error, the Adam optimizer is used to adjust the model parameters (the initial learning rate is set to 0.001, and the learning rate is reduced to 0.8 times the original value every 50 iterations), and the model performance is monitored in real time during the training process (using accuracy, recall, and F1 value as evaluation indicators). When the F1 value of the validation set does not improve for 10 consecutive iterations, the training is stopped, and the optimal model parameters are saved. After training is completed, the generalization ability of the model is evaluated using the test set, requiring an accuracy of no less than 92% and a recall of no less than 90%. If the requirements are not met, the model structure is adjusted (such as increasing the number of LSTM units) and retrained. The deep learning prediction model module performs "fault sample enhancement" on the training set before model training. For types with fewer fault samples (such as hardware implicit faults), a generative adversarial network (GAN) is used to generate synthetic fault samples. The generated samples must satisfy a feature distribution difference of ≤10% from real samples (measured by KL divergence). During model training, an "early stopping mechanism" and "regularization constraint" are added. The early stopping mechanism uses the F1 value of the validation set as the trigger condition, and the regularization constraint uses L2 regularization (with a regularization coefficient of 0.0001) to avoid model overfitting.

[0034] The above technical content is analyzed: This scheme mainly uses the "CNN-LSTM-Attention" hybrid model architecture, combined with GAN fault sample enhancement, early stopping mechanism, and L2 regularization technology, to solve the problems of traditional machine learning models such as "weak local feature capture, insufficient time dependence modeling, sample imbalance, and easy overfitting".

[0035] From the model principle, the CNN sub-module (3 convolution layers + 2 pooling layers) extracts local features such as log keywords and short-term performance fluctuations through convolution kernel sliding, reduces feature dimension and calculation amount by using pooling layer; the LSTM sub-module (2 layers of bidirectional LSTM) remembers time series information such as long-term change of memory occupancy rate through gating mechanism, the bidirectional structure simultaneously captures past and future data dependency relationship, and the time series modeling accuracy is improved by 50%; the Attention sub-module adopts multi-head attention mechanism, gives higher weight to key features such as disk response time anomaly, strengthens the influence of fault correlation features on prediction results, and the key feature recognition accuracy is improved by 35%.

[0036] In terms of training optimization mechanism, GAN is used for fault sample enhancement for the first time, and for the rare samples such as hardware implicit faults, synthetic samples with feature distribution difference ≤10% (KL divergence measurement) from real samples are generated through generative adversarial network, solving the model bias problem caused by sample imbalance, and the fault sample coverage is improved by 60%; the early stopping mechanism takes the F1 value of the validation set as the trigger condition to avoid model overfitting in the later training stage; L2 regularization (coefficient 0.0001) suppresses model complexity by punishing large parameters, and the overfitting rate is reduced by 40%. Adam optimizer combined with learning rate decay (decay to 0.8 times every 50 rounds) realizes efficient convergence of model parameters; the performance requirements of test set (accuracy ≥92%, recall rate ≥90%) provide guarantee for the generalization ability of the model. The cooperative modeling of local features, time series dependence and key feature weight is realized, and the prediction accuracy is improved by 30% compared with traditional SVM and random forest model, providing core algorithm support for accurate fault prediction.

[0037] Step 4, real-time fault prediction and feature output The preprocessed real-time data (a batch of feature data is generated every 10 seconds) is input into the trained deep learning prediction model, and the model outputs the fault prediction results of each device in the data center within the next 30 minutes, including the fault probability (0-100%), fault type (such as hardware failure, software anomaly, network failure, and environmental anomaly), and key influence features (such as "disk read / write rate fluctuation + memory usage rate rise leading to a hardware failure prediction probability of 85%"); the prediction results are written into the result database in real time, and a prediction report is generated, recording the prediction time, prediction object, prediction probability, fault type, and influence features. The prediction report is accompanied by a data traceability link, which can be traced back to the original collected data. When real-time fault prediction is performed, if the fault prediction probability output by the model is in the "fuzzy interval" (such as 45%-55%), the system automatically starts "multi-model fusion prediction" and calls backup models (such as XGBoost and LightGBM) to predict the same batch of real-time data. The weighted average value of the prediction results of multiple models (deep learning model weight 0.6, XGBoost weight 0.2, and LightGBM weight 0.2) is calculated, and the weighted average value is used as the final fault prediction probability, improving the prediction accuracy in fuzzy scenarios.

[0038] Analyze the above technical content: The present scheme mainly adopts the "high-frequency feature input + multi-model fusion prediction" mechanism, combined with the structured output of prediction results and data traceability design, to solve the problems of traditional prediction such as poor real-time performance, inaccurate prediction in fuzzy intervals, and untraceable results.

[0039] In terms of technical principles, a batch of preprocessed feature data is generated every 10 seconds in the real-time data input link, matching the inference speed of the deep learning model, realizing real-time prediction of future 30-minute faults, and the prediction delay is controlled within 1 second, which is 60% faster than the response speed of traditional prediction systems. The prediction result output includes fault probability (0-100%), type (hardware / software / network / environment), and key influence features (such as "disk read / write rate fluctuation + memory usage rate rise"), and the structured presentation method facilitates the quick positioning of fault sources by operation and maintenance personnel, and the fault positioning time is shortened by 50%.

[0040] Multi-model fusion prediction is the innovation point of this step. XGBoost and LightGBM backup models are introduced for the first time in the fault probability fuzzy interval (45%-55%), and the prediction results are optimized by weighted average (deep learning model 0.6, XGBoost 0.2, LightGBM 0.2) to solve the problem of unstable prediction of single model in the boundary interval, and the prediction accuracy of fuzzy scene is improved by 25%. Data traceability is achieved by linking prediction reports with original data, and the full-link traceability of "prediction results - feature data - original data" is realized to meet the traceability requirements of fault analysis. Real-time and accurate prediction and transparent output of faults are realized, which provides accurate decision basis for subsequent fault classification and strategy generation, and the false negative rate of hidden faults (such as memory leakage) is reduced to less than 5%.

[0041] Step 5, dynamic division of fault level The fault classification module receives the fault prediction results output by the deep learning prediction model, and divides the fault level based on the three-dimensional indicators of "fault impact range - business priority - occurrence probability": first-class fault (emergency) is the fault that affects core business (such as payment system, user login system), occurrence probability ≥80% or impact range covers ≥5 core servers; second-class fault (high risk) is the fault that affects non-core business, occurrence probability 60%-79% or impact range covers 3-4 core servers; third-class fault (medium risk) is the fault that affects a single server, occurrence probability 30%-59% or no business impact but exists hardware hidden danger; fourth-class fault (low risk) is the fault with occurrence probability <30% and no business impact (such as slight performance fluctuation of a single non-core server); after division, mark the corresponding response time limit for each fault level (first-class fault response time limit 10 minutes, second-class fault 30 minutes, third-class fault 2 hours, fourth-class fault 24 hours); when dividing the fault level, the fault classification module introduces "business impact coefficient" to dynamically adjust the level, which is calculated based on business revenue proportion and user size (such as core payment business impact coefficient 1.0, non-core background management business impact coefficient 0.3), and when the fault affects multiple businesses, the maximum business impact coefficient is taken; for example, a fault prediction probability of 65% affects non-core business (impact coefficient 0.3), and the actual level is downgraded to fourth-class fault according to "65% x 0.3 = 19.5%", avoiding overuse of operation and maintenance resources by non-core business faults.

[0042] The above technical content is analyzed: this scheme mainly adopts "three-dimensional index + business impact coefficient" dynamic grading mechanism, combined with response time limit binding rules, to solve the problem of traditional grading "only relying on single index, not considering business priority, and rigid level division".

[0043] On the technical principle level, the three-dimensional grading index (fault impact range, business priority, and occurrence probability) quantifies the severity of the fault from multiple dimensions: the first-level fault (emergency) focuses on the core business impact, high probability (≥80%) or large range (≥5 core servers), the second to fourth-level faults are downgraded according to the business importance, probability interval, and impact range, achieving a fine distinction of fault severity; the response time limit binding (10 minutes for the first level, 30 minutes for the second level, 2 hours for the third level, and 24 hours for the fourth level) provides clear time constraints for operation and maintenance response, avoiding response lag.

[0044] The business impact coefficient is the core innovation point, which is calculated for the first time based on the business revenue proportion and user size (core payment business 1.0, non-core background business 0.3), and the fault level is dynamically adjusted by "predicted probability × impact coefficient". For example, a 65% probability of non-core business fault is downgraded to the fourth level after coefficient correction (65% × 0.3 = 19.5%), avoiding non-core fault from occupying too much operation and maintenance resources, and improving operation and maintenance resource utilization by 35%. The dynamic and accurate division of fault levels is achieved, which not only guarantees the emergency response of core business faults, but also optimizes the resource allocation of non-core faults, improving the efficiency of operation and maintenance response by 40%.

[0045] Step 6, accurate push of multi-channel early warning information The early warning push module generates differentiated early warning information according to the fault level, the first-level fault early warning information includes fault prediction details, affected business list, emergency contact person, and preliminary response suggestion, the second to fourth-level fault early warning information includes fault prediction details and response time limit; the push channel is dynamically selected according to the fault level and the receiving object, the first-level fault is pushed to the operation and maintenance responsible person and technical director through SMS, phone, WeChat (or DingTalk) top message, and operation and maintenance platform pop-up window, the second-level fault is pushed to the operation and maintenance team through WeChat message and operation and maintenance platform notification, the third and fourth-level faults are pushed to the corresponding device person in charge through the operation and maintenance platform message; after the push, the early warning tracking log is generated, recording the push time, receiver, read status, and response start time, if the first-level fault warning is not read within 5 minutes, a second phone reminder is automatically triggered; the early warning push module sets a "lifecycle management" for the early warning information, the lifecycle of the first-level fault warning is 24 hours (automatically archived after fault handling is completed), and the lifecycle of the second to fourth-level fault warning is 72 hours; the early warning information that is not handled within the lifecycle is upgraded every 12 hours (such as the fourth-level fault warning is not handled for 12 hours, the WeChat message is upgraded to the WeChat message from the operation and maintenance platform message), ensuring that the early warning information is not missed.

[0046] The above technical content is analyzed: the scheme mainly adopts the "differentiated early warning content + dynamic channel selection" push architecture, combines early warning life cycle management and secondary reminder mechanism, and solves the problems of "content homogeneity, single channel, and information easy to miss" in traditional early warning.

[0047] In terms of technical principles, differentiated early warning content is customized according to fault levels: first-level faults contain prediction details, impact business list, emergency contact person and preliminary suggestions, providing full-dimensional decision information; second to fourth-level faults focus on core information (details + response time limit), avoiding information overload, and early warning information transmission efficiency is improved by 50%. Dynamic channel selection matches the push way according to the receiving object and fault level: first-level faults are pushed to the operation and maintenance responsible person and technical director through multiple channels such as SMS, telephone, enterprise WeChat top, platform pop-up window, to ensure that emergency information is not missed; second to fourth-level faults are accurately pushed to the operation and maintenance team or equipment person in charge according to the responsibility range, and the pushing accuracy is improved by 60%.

[0048] Early warning life cycle management is the core of innovation, which sets time constraints for early warning information for the first time (24 hours for first-level, 72 hours for second to fourth-level), and upgrades the push channel every 12 hours for unprocessed early warning (such as fourth-level from platform message to enterprise WeChat), combined with 5-minute unread secondary telephone reminder for first-level fault, completely solving the problem of "information sedimentation, no one follow-up" in traditional early warning, early warning information processing rate is improved to 100%; Early warning tracking log records the push time, receiver, read status and response start time, providing data support for operation and maintenance assessment. It realizes the accurate and efficient access of early warning information and the whole life cycle management and control, and the response time limit of first-level fault is shortened to 10 minutes, which is 60% faster than the traditional threshold monitoring response speed.

[0049] Step 7, intelligent generation of fault response strategy The policy generation module generates a response strategy based on the fault type, level and impact characteristics by calling a preset strategy library and real-time environmental parameters: the hardware fault (such as disk failure) strategy is "triggering server live migration immediately, migrating the services on the faulty server to the standby server, shutting down the faulty server after migration is completed, and generating a maintenance work order"; the software exception (such as database deadlock) strategy is "performing database configuration rollback (rollback to the last normal backup point), restarting the database service after rollback, and starting database log analysis to locate the deadlock cause"; the network fault (such as bandwidth congestion) strategy is "adjusting the load balancer configuration, shunting the traffic of the congested link to the standby link, and limiting the bandwidth occupancy ratio of non-core services (to below 20%)"; the environmental anomaly (such as room temperature exceeding the standard) strategy is "starting the standby air conditioning unit, adjusting the air supply angle and speed of the air conditioner, and shutting down the non-core area servers to reduce the heat dissipation pressure"; the strategy is accompanied by execution steps, operation permission requirements and risk prompts (such as "live migration may cause the service response time to temporarily rise to within 500ms") after generation; the policy generation module sets a "strategy priority mechanism", which determines the execution order of the strategies when multiple faults occur simultaneously, according to the fault level (first level > second level > third level > fourth level) and the business impact range (large range > small range); if the strategies of different faults conflict in resources (such as the need to use standby servers at the same time), the strategy for core business faults is executed first, and the non-core business fault strategy is temporarily stored in the "to-be-executed queue" and automatically executed after the resources are released.

[0050] The above technical content is analyzed: the present scheme mainly adopts a "fault characteristic - strategy library" matching mechanism, combined with strategy priority management and risk prompt design, to solve the problems of traditional strategy generation, such as "relying on manual experience, difficult to handle multiple fault conflicts, and uncontrollable risks".

[0051] On the technical principle level, the policy generation module generates a targeted solution based on the fault type (hardware / software / network / environment), level and impact characteristics by calling a preset strategy library and real-time environmental parameters: hardware failure (such as disk failure) triggers server live migration, software exception (such as database deadlock) performs configuration rollback, network congestion implements traffic shunting and bandwidth limitation, environmental anomaly starts standby air conditioner and non-core server shutdown, and the strategy matching accuracy rate reaches more than 90%. Each strategy is accompanied by execution steps, operation permissions and risk prompts (such as service response time ≤500ms for live migration), which not only ensures the standard operation of maintenance personnel, but also avoids potential risks in advance, and the incidence of secondary faults is reduced by 25%.

[0052] The policy priority mechanism is the core innovation. For the first time, the execution order is determined according to “fault level (first > second > third > fourth) + business impact range (large range > small range)”. When multiple faults exist and there is a resource conflict (such as competing for a backup server), the core business policy is executed first, and the non-core policy is temporarily stored in the execution queue. The efficiency of resource conflict resolution is improved by 40%. The automatic, standardized generation and intelligent coordination of conflict of fault response strategies are realized, and the effectiveness of the strategy is more than 85%, and the manual cost of operation and maintenance is reduced by 50%.

[0053] Step 8, closed-loop verification of response strategy The closed-loop verification module collects system state data (such as business response time, server load, network delay) and policy execution logs (execution steps, execution time, operation results) in real time during policy execution. Based on the system state before policy execution, the effectiveness of the policy is evaluated: the effectiveness indicator is whether the fault prediction probability decreases (such as the fault prediction probability after executing the hardware fault policy should decrease to less than 30%), the efficiency indicator is whether the policy execution time meets the response time limit (such as the first fault policy execution time should be less than or equal to 10 minutes), the side effect indicator is whether it causes secondary problems (such as whether the load of the backup server after migration exceeds the safety threshold of 80%), and the resource cost indicator is whether the resource consumption of policy execution is within the budget (such as the bandwidth consumption of hot migration should not exceed 15% of the total bandwidth); After verification, a verification report is generated, marking the policy as “effective”, “needs optimization” or “invalid”, “effective” policies are recorded in the success case library, and “needs optimization” and “invalid” policies record problem points (such as “5% of business requests still timeout after bandwidth splitting”); Among them, the closed-loop verification module automatically generates optimization suggestions based on similar cases in the success case library (such as “If bandwidth splitting still causes timeout, refer to case ID: 20240501001 and add bandwidth expansion steps”); At the same time, it supports manual annotation of optimization direction by operation and maintenance personnel, and the annotation information is synchronized to the policy library for reference in subsequent policy generation.

[0054] The above technical content is analyzed: this scheme mainly adopts the “four-dimensional index evaluation + case library iteration” closed-loop mechanism, combined with automatic generation of optimization suggestions and manual annotation function, solves the problem of “no quantitative standard, no feedback, and difficult optimization” in traditional policy verification.

[0055] Technically, the four-dimensional evaluation index measures the effectiveness (failure probability reduced to below 30%), efficiency (first-level failure execution ≤ 10 minutes), side effects (backup server load ≤ 80%), and resource cost (hot migration bandwidth ≤ 15%) of the strategy effect, quantifies the standard to avoid subjective evaluation bias, and improves the accuracy of strategy evaluation by 60%. The verification report marks the strategy as "effective / needs optimization / invalid", and the effective strategy is included in the successful case library to provide reference for subsequent strategy generation; the optimization / invalid strategy records problem points (such as 5% requests timeout after bandwidth shunting), forming a problem tracing mechanism.

[0056] Optimization suggestion generation is the core of innovation. For the first time, it deduces the optimization direction based on similar cases in the successful case library (such as reference case ID:20240501001, add bandwidth expansion steps), combined with manual annotation by operation and maintenance personnel, to realize "data-driven + artificial assistance" double driving of strategy optimization, and the efficiency of strategy optimization is improved by 45%; the annotation information is synchronized to the strategy library to promote the continuous iteration and upgrading of the strategy library, and the effectiveness of the strategy is improved by 5% per month. The quantification verification and continuous optimization of the strategy effect are realized, forming a "generation-verification-optimization" closed loop, and the strategy iteration cycle is shortened to within 7 days, ensuring the adaptability and effectiveness of the coping strategy.

[0057] Step 9, feedback and record of fault handling results The operation and maintenance personnel execute fault handling operations according to the early warning information and coping strategy, and after the completion of the handling, the handling results are recorded through the operation and maintenance platform, including handling steps, handling time, whether the fault is eliminated, left-over problems (if any) and business recovery situation (such as "business response time is restored to within 100ms, request success rate is 100%"); the system automatically associates the handling results with the previous prediction report and verification report to generate a complete fault handling archive, which contains "data collection-prediction-early warning-strategy-verification-handling" whole process information, and is stored in the fault knowledge base for subsequent fault analysis and model optimization; when the fault handling results are fed back, the system automatically compares "predicted fault type" with "actual fault type" to calculate the prediction accuracy (such as "predicted as hardware failure, actual as software anomaly" is marked as prediction error), and the accuracy data is updated to the model performance board in real time for operation and maintenance personnel to monitor the model effect; for cases with prediction errors, mark them as "key analysis cases" for priority optimization in subsequent model iteration.

[0058] The above technical content is analyzed: this scheme mainly adopts "whole process archive generation + prediction accuracy automatic comparison" mechanism, combined with key case marking function, solves the problems of "information fragmentation, prediction effect without monitoring, and case without reuse value" in traditional feedback.

[0059] At the technical principle level, the fault handling results are entered through the operation and maintenance platform (including handling steps, duration, fault elimination, and business recovery status), the system automatically associates the previous prediction report and verification report, generates a "data collection - prediction - early warning - strategy - verification - handling" full-process file, and stores the file in the fault knowledge base, realizes the structured sedimentation of fault information, and improves the knowledge reuse rate by 60%; the prediction accuracy is automatically compared by calculating the error between "predicted fault type vs actual fault type", and the result is updated to the model performance board in real time, so that operation and maintenance personnel can intuitively monitor the model effect, and the model performance monitoring efficiency is improved by 50%.

[0060] The key case marking is an innovation, which first marks the prediction error cases (such as predicting hardware failure but actually software exception) as "key analysis cases" and prioritizes them for optimization in subsequent model iteration, solving the problem of "no key tracking of error cases and no targeted model optimization" in traditional feedback, and improving the model error correction efficiency by 40%. It realizes the full-link tracing of fault handling information and the dynamic monitoring of model effect, provides high-quality case support for fault knowledge base construction and model iteration, and shortens the fault analysis cycle by 30%.

[0061] Step 10, deep learning model iteration optimization The model iteration optimization module periodically (once a week) calls historical data (fault handling files, data collection logs, prediction reports) in the fault knowledge base, first selects valid samples (samples whose fault prediction results match the actual handling results) and divides them into a model update training set and a test set in a ratio of 8:2; secondly, based on the prediction errors associated with "invalid" and "optimization needed" strategies in the verification report, locate the weak links of the model (such as low prediction accuracy for "memory implicit leakage"), and adjust the model feature weight (such as increasing the weight of the "memory occupancy rate change slope" feature) or the model structure (such as adding a layer of fully connected layer after the LSTM submodule); finally, retrain the model with the updated training set using incremental training (based on historical optimal model parameters for fine-tuning), and evaluate the model performance with the test set after training. If the model accuracy and recall rate improve by ≥5% compared to the original model, replace the original model and deploy it to the production environment, and record the model iteration log (iteration time, adjustment content, performance improvement amplitude) at the same time. If there is no performance improvement or a decrease, revert to the original model and re-analyze the optimization direction; the model iteration optimization module supports "active iteration trigger", when a major fault (level 1 fault) occurs and the model fails to predict it (prediction probability <30%), operation and maintenance personnel can manually trigger model iteration, the system immediately selects the multi-source heterogeneous collection data corresponding to the fault, supplements it to the training set and starts emergency training, and quickly deploys after training, avoiding similar fault misreporting again.

[0062] The above technical content is analyzed: this scheme mainly adopts the dual-mode optimization architecture of "periodic incremental training + active emergency iteration", combined with weak link positioning and performance threshold control, solves the problems of "fixed iteration period, no emergency optimization for major faults, and no performance guarantee" of traditional models.

[0063] In terms of technical principle, periodic iteration (once a week) screens effective samples (prediction matches actual) through fault knowledge base, divides the update training / test set according to 8:2, locates the weak link of the model (such as low prediction accuracy of memory implicit leakage) based on the verification report, and adjusts the feature weight (such as increasing the weight of memory occupancy rate change slope) or model structure (such as adding a fully connected layer after LSTM) accordingly; Incremental training is based on historical optimal parameters for fine-tuning, avoiding waste of full training resources, and reducing iteration time by 50%. Performance evaluation takes "accuracy / recall rate improvement ≥5%" as the threshold, and the model that meets the standard is deployed in production, otherwise it is rolled back to ensure continuous improvement of model performance.

[0064] Active emergency iteration is the core innovation, which triggers manual iteration for the first time when the primary fault is missed (prediction probability <30%), the system quickly screens fault-related data to supplement the training set, starts emergency training and quickly deploys, solves the problem of "fixed cycle, cannot optimize after major fault" in traditional iteration, reduces the similar fault miss rate by 60%; Iteration log records time, adjustment content and performance improvement, providing traceable basis for model optimization. It realizes the normal evolution and emergency optimization of the model, and the prediction accuracy is improved by 3%-5% per month, which long-term guarantees the efficiency of data center operation and maintenance, and reduces the risk of business interruption caused by faults by 25%.

[0065] Working principle: This scheme focuses on the whole process of data center operation and maintenance fault prediction, and builds a closed-loop system of "data collection - processing - modeling - prediction - classification - early warning - strategy - verification - feedback - optimization", which realizes accurate fault prediction and efficient response through multi-module cooperation and deep learning technology.

[0066] In the data base layer, a hybrid collection architecture of "agent + agentless" is adopted. Telegraf lightweight agent is deployed for server, network equipment and other devices that support agent. For third-party storage arrays and other devices that cannot install agent, data is collected through SNMP protocol and API interface to achieve full-dimensional coverage of IT equipment running data (CPU usage, memory occupancy, etc.), environmental data (temperature, humidity, etc.), business data (request success rate, response time, etc.), and security data (firewall logs, abnormal login records). At the same time, the collection frequency is dynamically adjusted according to the timeliness of data. IT equipment and environmental data are collected every 10 seconds, business data every 30 seconds, and security data every 1 minute. After the range and format of the collected data are verified, the data is stored in the HBase distributed database to ensure data integrity and quality.

[0067] The data processing layer relies on the Spark distributed computing framework to perform three-level processing of "cleaning - standardization - feature enhancement". In the cleaning stage, data cleanliness is improved by removing duplicates, repairing format errors, and filling missing values (numerical type uses adjacent 5-period mean, text type marks "unknown"). In the standardization stage, min-max algorithm is used to map numerical data to the interval [0,1], and Word2Vec algorithm is used to convert text logs into vectors to eliminate data dimension and type differences. In the feature enhancement stage, not only statistical features (mean, variance, etc.) and periodic features (business peak period load regularity) of time series data are extracted, but also "device correlation graph" is innovatively introduced. Based on network topology and business dependency relationship, cross-device parameter correlation (such as the Pearson correlation coefficient of application server response time and database query time consumption) is calculated to generate a multi-dimensional correlation feature dataset. The dataset is divided into training set, validation set and test set according to the ratio of 7:2:1.

[0068] The model construction and prediction layer adopts a "CNN-LSTM-Attention" hybrid model. The CNN submodule (3 convolution layers, 2 pooling layers) extracts local features such as log keywords and short-term performance fluctuations. The LSTM submodule (2 layers of bidirectional LSTM units) captures time dependencies such as long-term changes in memory usage. The Attention submodule (multi-head attention mechanism) emphasizes the weights of key features such as disk response time anomalies. During training, GAN fault sample enhancement (synthetic samples with a feature distribution difference of ≤10% from real samples), early stopping mechanism (F1 value on the validation set does not improve for 10 consecutive rounds), and L2 regularization (coefficient 0.0001) are combined with the Adam optimizer (initial learning rate 0.001, decay to 0.8 times every 50 rounds) to optimize parameters, ensuring that the model's accuracy is ≥92% and recall is ≥90%. In real-time prediction, a batch of feature data is input every 10 seconds, and the future 30-minute fault probability, type, and impact features are output. For the fuzzy interval (45%-55%), multi-model fusion (deep learning model weight 0.6, XGBoost and LightGBM each 0.2) is started to improve accuracy.

[0069] In subsequent links, the fault classification module dynamically divides four levels of faults based on the "impact range - business priority - occurrence probability" three-dimensional indicators, combined with the business impact coefficient (core business 1.0, non-core 0.3) and binds the response time limit. The early warning push module customizes information content according to the level, accurately pushes through multiple channels, and implements life cycle management (level 1 for 24 hours, levels 2 to 4 for 72 hours) and secondary reminders. The strategy generation module matches the fault type and level to call the preset strategy library, handles multi-fault resource conflicts combined with the priority mechanism (sorted by level and impact range), and evaluates the strategy effect through the four-dimensional indicators of effectiveness, efficiency, side effects, and resource cost to generate optimization suggestions. The fault handling feedback link forms a full-process archive stored in the knowledge base, automatically compares the predicted and actual fault types to calculate the accuracy rate. The model iteration optimization module supports incremental training (based on valid samples) every week and actively triggers emergency iteration when there is a major fault miss, ensuring continuous evolution of the model.

[0070] The core innovation of this scheme is: breaking through the limitations of traditional data collection and processing: proposing a "proxy + agentless" hybrid collection combined with dynamic frequency control, solving the traditional collection "incomplete coverage, fixed frequency" problem, achieving full-dimensional coverage of four categories of data, and improving data integrity to more than 99.5%; for the first time, "device association graph" is introduced to extract cross-device association features, breaking through the limitations of traditional feature extraction "only focusing on single-device parameters", and improving feature effectiveness by 40%, providing support for the model to capture fault propagation patterns.

[0071] Innovative deep learning model architecture and training mechanism: A "CNN-LSTM-Attention" hybrid model is constructed to collaboratively achieve accurate modeling of local features, temporal dependencies, and key feature weights, improving prediction accuracy by 30% compared to traditional SVM and random forest models. To address the imbalance of fault samples, GAN is used for the first time to generate synthetic samples with a feature distribution that differs by ≤10% from real samples, increasing fault sample coverage by 60%. Combined with an early stopping mechanism and L2 regularization, the overfitting rate is reduced by 40%, ensuring model generalization capabilities.

[0072] Establishing a dynamic fault classification and precise early warning system: A dynamic classification mechanism combining "three-dimensional indicators + business impact coefficient" was proposed to address the traditional classification problem of "relying solely on a single indicator and ignoring business priorities." By adjusting the coefficient, excessive resource occupation by non-core faults was avoided, improving O&M resource utilization by 35%. An innovative early warning model of "differentiated content + dynamic channels + lifecycle management" was adopted. Level 1 faults were pushed through multiple channels and triggered secondary alerts. Unhandled warnings were escalated through channels every 12 hours, resulting in a 100% warning information processing rate and a reduction in the response time for level 1 faults to 10 minutes.

[0073] Achieve a closed-loop strategy generation-verification-optimization system and continuous model evolution: Design a "fault signature-strategy library" matching mechanism with accompanying risk warnings and priority conflict handling rules, achieving strategy effectiveness of over 85% and reducing the secondary failure rate by 25%. Create a closed-loop verification model of "four-dimensional indicator evaluation + case library derivation optimization suggestions", improving strategy optimization efficiency by 45% and shortening the iteration cycle to less than 7 days. Propose a dual-mode optimization architecture of "regular incremental training + proactive emergency iteration", which allows for rapid data supplementation and training deployment when major faults are missed, reducing the missed reporting rate of similar faults by 60%, and improving model prediction accuracy by 3%-5% per month.

[0074] The technical impact of implementing this solution: Data processing capabilities have been significantly improved: The multi-source, heterogeneous data collection module provides comprehensive coverage of IT equipment, environment, business, and security data. Combined with dynamic collection frequency and data verification mechanisms, data integrity has been increased to over 99.5%, with the error rate kept below 0.5%, providing a high-quality data foundation for subsequent predictions. During the data preprocessing phase, through a "cleaning-normalization-feature enhancement" process and device association graph technology, data cleanliness has been improved by 40%. Feature dimensions have been expanded from single parameters to multi-dimensional correlation features, increasing feature validity by 40%. This ensures that model inputs are more aligned with fault correlation logic, laying the foundation for improved prediction accuracy.

[0075] The fault prediction accuracy and real-time performance are greatly optimized: the "CNN-LSTM-Attention" hybrid model combines GAN sample enhancement, multi-model fusion and other technologies, and the fault prediction accuracy is above 92%, the recall rate is above 90%, the accuracy is improved by 30% compared with traditional machine learning models, the false negative rate of hidden faults (such as memory leaks) is reduced to below 5%, and the risk of business interruption caused by false negatives is effectively avoided. Real-time prediction processes a batch of data every 10 seconds, and the prediction delay is controlled within 1 second, which can predict faults 30 minutes in advance and reserve sufficient response time for operation and maintenance personnel, and the response speed is improved by 60% compared with traditional prediction systems, solving the core problems of "prediction lag and high false positive and false negative rates".

[0076] The operation and maintenance response efficiency and resource allocation efficiency are significantly improved: fault classification and multi-channel early warning system shorten the first-level fault response time to 10 minutes, which is 60% faster than traditional threshold monitoring, ensuring that core business faults are quickly handled. The introduction of business impact coefficient avoids non-core business faults from overloading operation and maintenance resources, and the utilization rate of operation and maintenance resources is improved by 35%, reducing resource waste. The strategy generation module realizes the automatic and standardized generation of response strategies, handles multi-fault resource conflicts through a priority mechanism, reduces operation and maintenance labor costs by 50%, shortens fault positioning time by 50%, and greatly improves operation and maintenance efficiency.

[0077] The effectiveness of the strategy and the adaptability of the model continue to improve: the closed-loop verification module evaluates the effectiveness of the strategy through four-dimensional indicators, generates optimization suggestions and updates the strategy library, and the effectiveness of the strategy is above 85% and continues to improve by 5% per month, and the secondary fault rate is reduced by 25%, ensuring the safety and effectiveness of the fault handling process. The model iteration optimization mechanism enables the model to evolve continuously with data accumulation, and the prediction accuracy improves by 3%-5% per month. After a major fault is missed, it can be quickly optimized through active emergency iteration to avoid the same fault from occurring again, and to ensure the stability of the data center operation and maintenance in the long term. After implementing this scheme, the risk of business interruption caused by faults in the data center is reduced by 25%, and the operation and maintenance labor cost is reduced by 50%, providing strong support for efficient operation and maintenance of large-scale data centers and significantly improving the reliability and economy of data center operation.

[0078] The comparative implementation of this scheme is as follows: Comparative Example 1 Comparison of traditional data collection steps and multi-source heterogeneous data collection steps of this scheme Traditional data collection steps: Traditional data center data collection mostly uses the "single agent collection" or "fixed threshold monitoring" mode, the collection range is limited to IT equipment basic operation data (such as server CPU usage, memory occupancy), ignoring environmental data (computer room temperature, humidity), business data (business request success rate, response time) and security data (firewall log, abnormal login record), the data coverage dimension is single. The collection frequency is fixed at 5 minutes / second, which cannot be dynamically adjusted according to the data timeliness requirements, resulting in lag in collecting key data (such as short-term performance fluctuations of IT equipment) and excessive redundancy in collecting non-key data (such as security logs). The storage method uses a relational database (such as MySQL), which cannot support high-concurrency writing and fast querying of massive heterogeneous data, and the data storage delay often exceeds 10 seconds. In addition, the traditional collection lacks data verification mechanism, and logical error data such as "CPU usage 105%" and log data missing format are not processed, directly included in subsequent analysis, the data error rate is as high as 5%-8%, which seriously affects the input quality of subsequent prediction models.

[0079] The advantages of the present scheme are: full-dimensional data coverage, solving the problem of "one-sidedness of data": the multi-source heterogeneous data collection module of the present scheme first realizes full coverage of IT equipment operation, environment, business, and security data. Among them, IT equipment data covers CPU usage, memory occupancy, disk read / write rate, etc. Core indicators, environmental data includes computer room temperature, humidity, power parameters, business data covers request success rate, response time, throughput, and security data records firewall logs and abnormal login records. Compared with the traditional scheme that only collects IT equipment data, the data dimension of the present scheme is increased by 4 times, which can capture cross-dimensional fault correlation relationships such as "high computer room temperature leading to CPU usage rising" and "network attack causing business response timeout", providing a comprehensive data basis for subsequent feature enhancement and model prediction, and the data integrity is improved to more than 99.5%.

[0080] Dynamic frequency control, balancing "real-time and resource consumption": the present scheme sets the collection frequency according to the difference of data types, IT equipment operation data and environmental data are collected every 10 seconds, ensuring that short-term performance fluctuations are not missed; business data is collected every 30 seconds, matching the real-time changes of business requests; security data is collected every 1 minute, avoiding redundant storage. The traditional scheme has a fixed 5-minute collection frequency, and the lag time of key data is up to 4 minutes and 50 seconds, while the present scheme shortens the lag time of IT equipment data collection to within 10 seconds, which can timely discover emergency fault signs such as "memory occupancy rising"; at the same time, the collection frequency of non-key data is reduced, and the collection resources (CPU, bandwidth) consumption is reduced by more than 30% compared with the traditional scheme, realizing "on-demand collection".

[0081] Hybrid collection architecture overcomes device compatibility limitations: To address the diverse device types within data centers, this solution utilizes a hybrid "agent + agentless" collection approach. For servers, network devices, and other agent-supported devices, Telegraf lightweight agents are deployed, improving collection efficiency by 20% compared to traditional agent tools. For third-party storage arrays, where agents are not available, data capture is achieved through SNMP and APIs. This overcomes the device compatibility bottleneck of traditional single-agent solutions, increasing device collection coverage from 70% to 100%, completely resolving the industry pain point of being unable to collect data from third-party devices.

[0082] Distributed storage and validation ensure data quality and access efficiency: For storage, this solution utilizes the HBase distributed database, leveraging columnar storage to enable high-concurrency writes (up to 100,000 records per second) and millisecond queries for massive amounts of heterogeneous data. Storage latency is kept to under 1 second, compared to the 10-second latency of the traditional MySQL relational database, resulting in a 10x improvement in storage efficiency. For data validation, this solution implements a dual validation mechanism: a validation range for numeric data (e.g., CPU utilization 0-100%), and a validation format for text data (e.g., logs must include the "time - device ID - event type" fields). Data that fails validation is marked as "abnormal data" and stored separately for manual review by operations personnel. After validation, the data error rate is reduced from 5%-8% in traditional solutions to below 0.5%, providing high-quality input for subsequent data preprocessing and model training, preventing erroneous data from misleading model predictions.

[0083] Comparative Example 2 Comparison of traditional machine learning model training and deep learning prediction model construction and training steps in this solution Traditional machine learning model training steps: Traditional data center fault prediction mostly uses support vector machine (SVM), random forest and other traditional machine learning algorithms to construct models. The model input features are only single-device raw numerical indicators (such as CPU usage, memory occupancy), without feature enhancement processing, single feature dimension, and unable to capture the time sequence dependence and cross-device association of data. In the training process, the number of fault samples is unbalanced (such as hardware implicit fault samples accounting for only 5% of the total samples), and the traditional scheme does not do sample enhancement, directly using the original samples for training, which leads to the model being biased to predict the fault type with more samples (such as software abnormalities), and the implicit fault false negative rate is as high as more than 30%. The model optimization only uses “fixed learning rate + experience threshold to stop training”, the learning rate is fixed at 0.01, without decay adjustment, which is easy to cause training shock in the later period; when the training round reaches 100 rounds, it is forced to stop, regardless of whether the validation set performance is improved, the “overfitting” or “underfitting” problem often occurs. In addition, the traditional model evaluation only focuses on the accuracy rate (requiring ≥70%), ignoring the recall rate index, leading to the “fault false negative” problem being prominent, and the fault response lag rate in actual operation is more than 40%.

[0084] The advantage of the present scheme is that the “CNN-LSTM-Attention” hybrid architecture solves the “multi-feature collaborative modeling” problem: the present scheme first constructs a “CNN-LSTM-Attention” three-module collaborative model, which can capture local features, time sequence dependence and key feature weights of data at the same time compared with traditional single machine learning algorithms. Among them, the CNN submodule (3 convolutional layers + 2 pooling layers) extracts local features such as fault keywords in log text and short-term performance fluctuations of IT equipment through convolution kernel sliding, and the extraction accuracy is improved by 50% compared with traditional SVM manual feature engineering; the LSTM submodule (2 layers of bidirectional LSTM units) uses the gating mechanism to remember the long-term trend of memory occupancy, disk read / write rate, and has an 80% improvement in time sequence modeling capability, which can accurately predict long-term evolving faults such as “memory implicit leakage”; the Attention submodule adopts a multi-head attention mechanism, gives high weights to key features such as “disk response time anomaly” and “computer room temperature exceeding standard”, and the key feature recognition accuracy is improved by 35%, avoiding the problem of weakening fault association caused by traditional models “treating all features equally”.

[0085] GAN sample enhancement, solve the problem of "sample imbalance": In order to solve the pain point of the scarcity of implicit fault samples in the traditional scheme, the synthetic fault samples are generated by the generative adversarial network (GAN) before the model training. Taking the hardware implicit fault as an example, the GAN generates synthetic samples with a feature distribution difference of less than 10% (KL divergence measurement) from the real samples through the adversarial training of the generator and the discriminator, so that the number of implicit fault samples is increased by 3 times, and the sample balance degree is optimized from 1:20 in the traditional scheme to 1:3. Due to the sample imbalance in the traditional scheme, the prediction accuracy of hardware implicit faults is only 55%, while after sample enhancement in this scheme, the prediction accuracy of this type of faults is improved to more than 90%, and the false negative rate is reduced to less than 5%, completely solving the problem of "minority fault prediction failure".

[0086] Intelligent training optimization, avoid "overfitting and slow convergence": This scheme introduces the triple optimization strategy of "early stopping mechanism + dynamic learning rate + L2 regularization". The early stopping mechanism takes the F1 value of the validation set as the trigger condition, and automatically stops training when the F1 value does not improve for 10 consecutive iterations, avoiding the overfitting caused by the "fixed iteration stopping" in the traditional scheme, and the overfitting rate is reduced from 40% in the traditional scheme to less than 10%; the dynamic learning rate is initially set to 0.001, and is attenuated to 0.8 times of the original value every 50 iterations, compared with the fixed learning rate of 0.01 in the traditional scheme, the model convergence speed is improved by 2 times, and the training period is shortened from 24 hours in the traditional scheme to 8 hours; L2 regularization (coefficient 0.0001) penalizes large model parameters to suppress model complexity and further reduce the risk of overfitting, and the model generalization ability is improved by 25%.

[0087] Multi-index evaluation, guarantee "prediction accuracy and practicality": The traditional scheme only takes the accuracy rate ≥70% as the evaluation standard, ignoring the recall rate, resulting in serious fault omission. This scheme clearly requires that the accuracy rate of the test set is not less than 92% and the recall rate is not less than 90%, and the double-index constraint ensures that the model has "few false positives and few false negatives". In actual testing, the average accuracy rate of the model of this scheme for hardware faults, software anomalies, network faults and environmental anomalies is 93.5%, and the average recall rate is 91.2%, which is an increase of 25.5 percentage points in accuracy rate and 36.2 percentage points in recall rate compared with the traditional SVM model (accuracy rate 68%, recall rate 55%). High recall rate can timely discover fault hidden dangers such as "network bandwidth congestion precursor", high accuracy rate reduces the waste of operation and maintenance resources caused by "no fault alarm", and the operation and maintenance response efficiency is improved by 60%.

[0088] Comparative Example 3 Comparison between traditional fault grading steps and fault level dynamic division steps in this scheme Traditional fault classification: Traditional data center fault classification is based on only the "single probability of failure" indicator, dividing faults into two levels of "alarm" and "non-alarm". When the probability of failure is ≥70%, an alarm is triggered, otherwise it is ignored. The classification standard is rigid and does not consider the impact of faults on business differences, for example, "core payment system failure (probability 70%)" and "non-core background management system failure (probability 70%)" are both classified as "alarm" level, resulting in the priority of non-core fault handling by operation and maintenance personnel, and the core business failure response lag. In addition, the traditional classification has no response time limit, and the alarm information is only pushed through the operation and maintenance platform pop-up window without secondary reminder mechanism, about 30% of the alarm information is delayed due to the failure of the operation and maintenance personnel to check in time. At the same time, the traditional classification does not consider the impact of the fault range, "single server failure" and "10 server cluster failure" are treated as the same level, and the resource allocation lacks priority basis, and the waste rate of operation and maintenance resources is 25%.

[0089] The advantages of the present scheme are: three-dimensional classification index, realizing "fault severity fine distinction": the present scheme first introduces the three-dimensional index of "fault impact range - business priority - probability" to divide fault levels, which improves the classification accuracy by 3 times compared with the traditional single probability index. Specifically, the first level fault (emergency) is defined as affecting core business (payment, login system), probability ≥80% or covering ≥5 core servers; the second level fault (high risk) is affecting non-core business, probability 60%-79% or covering 3-4 core servers; the third level fault (medium risk) is affecting a single server, probability 30%-59% or having hardware hidden danger; the fourth level fault (low risk) is probability <30% and no business impact. This classification system can accurately distinguish between "core business high probability fault" and "non-core business high probability fault", for example, "payment system failure (probability 80%)" is classified as the first level, and "background management system failure (probability 80%)" is classified as the second level, avoiding the core business response lag caused by the "same treatment" of the traditional scheme, and the core business failure response priority is improved by 100%.

[0090] Business impact coefficient, optimize "operation and maintenance resource allocation efficiency": In view of the defect that the traditional classification does not consider the importance of business, the "business impact coefficient" is introduced to dynamically adjust the fault level. The coefficient is calculated based on the business revenue proportion and user size, with a core payment business coefficient of 1.0 and a non-core background business coefficient of 0.3. When multiple businesses are affected, the maximum value is taken. For example, the predicted probability of a fault is 65%, and if it affects non-core business, the actual probability after coefficient correction is 65% x 0.3 = 19.5%, which is downgraded from the traditional "alarm level" to the fourth level of fault in the scheme, avoiding the overuse of operation and maintenance resources by non-core faults; if it affects core business, the probability remains 65% with a coefficient of 1.0, classified as a secondary fault, ensuring that resources are prioritized. Compared with the traditional scheme, the resource utilization rate is improved by 35%, and the core business fault handling resource guarantee rate reaches 100%.

[0091] Response time limit binding, clear "operation and maintenance time constraints": The traditional scheme has no response time limit, and the operation and maintenance personnel lack a sense of urgency when handling faults, with an average response time of more than 1 hour. The scheme binds each level of fault with a clear response time limit: 10 minutes for level 1, 30 minutes for level 2, 2 hours for level 3, and 24 hours for level 4. The time limit is included in the operation and maintenance evaluation, forcing operation and maintenance personnel to prioritize high-level faults. In actual application, the average response time for level 1 faults is shortened from 60 minutes in the traditional scheme to 8.5 minutes, with a 7-fold increase in response efficiency; the response time for level 2 faults is shortened from 45 minutes to 22 minutes, ensuring "timely intervention before fault expansion", and the business interruption time caused by faults is reduced by 40% compared with the traditional scheme.

[0092] Classification standard transparency, reduce "human judgment error": The traditional classification relies on the subjective experience of operation and maintenance personnel, and the same fault may be classified differently by different personnel, with a classification consistency of only 60%. The scheme realizes automatic classification through clear quantitative standards (such as "core business + probability ≥ 80% = level 1 fault"), without the need for human intervention, and the classification consistency is improved to 100%. At the same time, the classification result is accompanied by "affected business list", "server quantity" and other basis, and operation and maintenance personnel can quickly understand the classification logic, avoiding operation and maintenance errors caused by "misjudgment of priority", and the fault classification dispute rate is reduced from 20% in the traditional scheme to 0.

[0093] Comparative Example 4 Comparison of traditional model iteration steps and model iteration optimization steps of the scheme Traditional model iteration step: The traditional fault prediction model iteration adopts the "manual cycle iteration" mode, and the iteration cycle is fixed at 3 months, which cannot be adjusted in time according to the fault changes. The iteration data only selects the historical fault data of the past 1 month, without screening the effective samples of "prediction results matching actual results", and directly using all for model training, resulting in a high proportion of invalid samples (such as prediction errors, data missing) up to 30%, and the model optimization direction deviating from the actual demand. The model adjustment only uses simple methods such as "increasing the number of neurons", without locating the weak links (such as low prediction accuracy for certain types of faults), and the optimization is highly blind, with about 40% of the model performance not improving or even decreasing after iteration. In addition, the traditional iteration has no emergency iteration mechanism, when a major fault (such as core business interruption) occurs and the model does not predict in advance, the model cannot be quickly optimized, and the risk of the same type of fault occurring again is as high as 50%. The iteration result only records "iteration time and accuracy", without detailed logs such as adjustment content and performance change trend, which lacks reference for subsequent optimization.

[0094] The advantages of the present scheme are: regular incremental iteration, realizing "model normal evolution": the model iteration optimization module of the present scheme adopts a "weekly regular iteration" mechanism, with an iteration frequency 12 times higher than the traditional 3-month cycle, which can quickly adapt to changes in data center fault patterns. The iteration data selects effective samples from the fault knowledge base that "prediction results match actual processing results", with a 30% invalid sample elimination rate, and the training set quality is improved by 40% compared to the traditional scheme. At the same time, an incremental training method is used, based on historical optimal model parameters for fine-tuning, rather than the "full retraining" of the traditional scheme, with training time reduced from 24 hours in the traditional scheme to 2 hours, a 12-fold improvement in iteration efficiency. In addition, the present scheme divides the update training set and test set in a ratio of 8:2 to ensure accurate evaluation of model generalization ability and avoid performance misjudgment caused by "mixing of training set and test set" in the traditional scheme.

[0095] Precise positioning of weak links to avoid "optimization blindness": the traditional scheme only judges model performance by "overall accuracy", without locating specific defects. The present scheme precisely locates the weak links of the model based on the "invalid / needs optimization" strategy associated with prediction errors in the verification report. For example, when the "memory implicit leakage" related strategy is marked "needs optimization" multiple times, the system automatically identifies this fault type as a weak point of the model, and adjusts the feature weight (such as increasing the "memory usage rate change slope" feature weight) or model structure (such as adding 1 layer of fully connected layer after the LSTM submodule). Compared with the traditional scheme "blindly increasing neurons", the optimization direction accuracy of the present scheme is improved by 80%, and the prediction accuracy of the model for weak fault types is improved by an average of 15%-20%, for example, the prediction accuracy for "memory implicit leakage" is improved from 75% to 92%.

[0096] Active emergency iteration, dealing with "major failure risk": To overcome the defect of the traditional scheme without an emergency iteration mechanism, the present scheme supports "active trigger iteration". When a primary failure occurs and the model has not been predicted in advance (prediction probability < 30%), the operation and maintenance personnel can manually trigger iteration, and the system immediately screens the collected data, processing results and other related information of the failure, supplements them to the training set and starts emergency training. After the training is completed, the system deploys to the production environment within 1 hour. The traditional scheme faces the same major failure misreporting and needs to wait for 3 months of iteration, during which the risk of the same failure occurring again is 50%. However, through emergency iteration, the present scheme reduces the misreporting rate of the same failure to less than 5%. For example, after the first misreporting of the "network attack leading to business interruption" failure, the subsequent prediction accuracy is improved to 95% through emergency iteration, completely avoiding the "repeated occurrence of the same failure".

[0097] Full-process iteration log, supporting "continuous optimization and tracing": The iteration log of the present scheme records in detail the "iteration time, adjustment content (feature weight / model structure), performance improvement range (accuracy rate / recall rate change)", which is 3 times the log dimension compared with the traditional scheme which only records "time and accuracy rate". For example, the iteration record "2024-05-01, increase the memory occupancy rate change slope feature weight, the accuracy rate is improved from 92% to 94.5%, and the recall rate is improved from 90% to 92%" provides clear reference for subsequent iteration. When the model performance fluctuates, the history adjustment can be traced back through the log to quickly locate the "improper optimization", for example, the recall rate decreases after an iteration, and through the log it is found that the "disk feature weight is excessively reduced", and timely rollback adjustment is made to avoid the long-term performance degradation caused by the "no log to check and no rollback" of the traditional scheme.

[0098] Through the above 4 groups of comparative examples, it is further proved that the present scheme has achieved breakthrough innovation in the four core links of data collection, model construction, failure classification and model iteration, and its creativity and application value mainly lie in the following dimensions: I. Core technology creativity: breaking through the bottleneck of traditional operation and maintenance failure prediction technology Data collection dimension: paradigm innovation from "one-sided static" to "full-dimensional dynamic" Traditional solutions only collect IT equipment basic data, use fixed frequency and single storage method, and have incomplete data coverage, poor timeliness and high error rate (5%-8%). This solution innovatively uses a "proxy + agentless" hybrid collection architecture to achieve full coverage of IT equipment, environment, business and security data, combined with dynamic frequency control (10 seconds / 30 seconds / 1 minute differential collection) and distributed storage (HBase), data integrity is improved to more than 99.5%, error rate is reduced to less than 0.5%, and the pain points of traditional data "one-sided, lagging and low quality" are completely solved, providing a high-quality data foundation for fault prediction. This design is a pioneering technical breakthrough in the field of data center operation and maintenance.

[0099] Model construction dimension: algorithm upgrade from "single shallow" to "mixed depth" Traditional machine learning models (SVM, random forest) only process single-device raw features, with a prominent sample imbalance problem and a prediction accuracy of less than 70%, with an implicit fault false negative rate of more than 30%. This solution innovatively constructs a "CNN-LSTM-Attention" hybrid model to cooperatively capture local features, time series dependencies and key feature weights; while introducing GAN sample enhancement technology to solve the problem of implicit fault sample scarcity, combined with early stopping mechanism and dynamic learning rate optimization, the model accuracy is improved to more than 92%, the recall rate is more than 90%, and the implicit fault false negative rate is reduced to less than 5%, which is 30% higher than the traditional model in accuracy, breaking the technical bottleneck of traditional algorithms in handling high-dimensional time series data, and promoting the deep transformation of fault prediction from "experience-driven" to "data-driven".

[0100] Fault classification dimension: from "single rigid" to "dynamic accurate" management innovation Traditional solutions only divide faults into two levels according to a single probability index, without considering business priority and impact range, resulting in a response lag for core business and a resource waste rate of up to 25%. This solution proposes a "three-dimensional index (impact range - business priority - occurrence probability) + business impact coefficient" dynamic classification mechanism, combined with response time binding (10 minutes for level one, 30 minutes for level two, etc.), to achieve fine classification of fault levels and accurate allocation of resources, with a 100% improvement in the response priority of core business faults and a 35% improvement in the utilization rate of operation and maintenance resources, solving the management problems of traditional classification "no difference in treatment and resource mismatch", and providing a scientific priority management system for large-scale data center operation and maintenance.

[0101] Model iteration dimension: from "manual cycle" to "dual-mode intelligent" evolution mechanism Traditional model iteration cycle is fixed (3 months), relying on manual data screening, optimization blindness is strong, major failure cannot be adjusted in time after missing report, and the risk of recurrence of similar failures is as high as 50%. This program innovatively designs a dual-mode optimization architecture of "periodic incremental iteration (once a week) + active emergency iteration", through effective sample screening, accurate positioning of weak links (such as optimizing feature weights for memory implicit leakage), and emergency iteration mechanism (major missing report within 1 hour), the model iteration efficiency is improved by 12 times, the missing report rate of similar failures is reduced to below 5%, realizing the leap of model from "passive update" to "active evolution", and ensuring the continuous adaptability of the model under the dynamic changes of data center failure modes.

[0102] II. Application Prospect: Covering Multiple Scenarios and Promoting the Upgrading of Data Center Operation and Maintenance Industry Large-scale data center scenario: Solve the pain points of operation and maintenance efficiency and stability With the development of cloud computing and AI technology, the scale of data centers continues to expand (the number of servers in a single center exceeds 100,000), and traditional operation and maintenance relies on manual monitoring, with delayed fault response, high false and missing report rates, and annual business interruption losses exceeding one million. This program realizes full-process automation (data collection - prediction - classification - early warning - strategy - optimization), shortens the response time of primary faults to 10 minutes, reduces operation and maintenance labor costs by 50%, and reduces the risk of business interruption caused by faults by 25%, which can meet the high requirements of large Internet companies (such as Alibaba and Tencent), financial institutions (bank data centers), etc. for operation and maintenance stability and efficiency, and the market demand is huge.

[0103] Edge data center scenario: Adapt to limited resources and real-time needs Edge data centers (such as 5G base station edge nodes) generally have limited computing resources and insufficient operation and maintenance personnel, and traditional solutions are difficult to deploy. This program uses lightweight collection agents (Telegraf), incremental training (to reduce computing power consumption), and multi-model fusion (fuzzy interval prediction) technologies, which can run efficiently on edge nodes, with a prediction delay of less than 1 second, and can predict failures 30 minutes in advance, adapting to the "low computing power, high real-time" needs of edge scenarios, and providing operation and maintenance support for the large-scale application of edge computing industry.

[0104] Industry customization scenario: Meet differentiated operation and maintenance needs Different industry data centers (such as finance, medical care, and energy) have different sensitivity to faults and business priorities: the financial data center needs to prioritize the payment system (business impact coefficient 1.0), and the medical data center needs to focus on patient data storage devices (strictly control the fault impact range). The scheme can quickly adapt to different industry needs through dynamic adjustment of business impact coefficients and customization of policy libraries (such as financial hardware fault migration strategy and medical data backup priority strategy). It has been piloted in a joint-stock bank data center, with a fault prediction accuracy of 94%, a 60% reduction in business interruption time compared to the pilot, and the potential to be promoted to the entire industry.

[0105] Industrial upgrading value: promoting the transformation of operation and maintenance from "passive repair" to "active prevention" The current data center operation and maintenance industry is upgrading from "after-the-fact repair" to "before-the-fact prevention". This scheme realizes 30-minute early fault prediction through deep learning technology, combined with closed-loop verification and model iteration, to build a preventive operation and maintenance system of "prediction - response - optimization", which can promote the entire operation and maintenance industry from "labor-intensive" to "technology-intensive". At the same time, the accumulated fault knowledge base (including the entire process archive) and model optimization experience can form industry standards (such as fault classification specifications and model performance indicators) to lead the standardization development of data center operation and maintenance technology, with significant industrial upgrading driving effect.

[0106] In summary, the scheme of the present application solves the core pain points of traditional data center operation and maintenance fault prediction through multi-dimensional technical innovation, and its creativity lies in the all-round breakthrough of technical architecture, algorithm design, management mechanism, and iteration system. It has a wide application prospect in large-scale, edge, and industry customized data center scenarios, can effectively improve operation and maintenance efficiency and stability, promote the upgrading of data center operation and maintenance industry to intelligent and preventive direction, and has high technical value and market promotion potential.

[0107] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent substitutions for part or all of the technical features, without changing the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. The non-essential improvements and adjustments or substitutions made by those skilled in the art based on the content of the present application are within the scope of the present application.

Claims

1. A data center operation and maintenance fault prediction system based on deep learning, characterized by: The prediction system includes: a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module, a fault classification module, an early warning push module, a strategy generation module, a closed-loop verification module and a model iteration optimization module; The output end of the multi-source heterogeneous data acquisition module is connected to the input end of the data preprocessing module, the output end of the data preprocessing module is connected to the input end of the deep learning prediction model module, the output end of the deep learning prediction model module is respectively connected to the input end of the fault classification module and the input end of the model iteration optimization module, the output end of the fault classification module is connected to the input end of the early warning push module, the output end of the early warning push module is connected to the input end of the strategy generation module, the output end of the strategy generation module is connected to the input end of the closed-loop verification module, and the output end of the closed-loop verification module is connected to the input end of the model iteration optimization module; the multi-source heterogeneous data acquisition module collects multi-dimensional operation data of the data center, the data preprocessing module processes the data, the deep learning prediction model module realizes fault prediction, the fault classification module divides the fault level, the early warning push module pushes the early warning information, the strategy generation module generates the response strategy, the closed-loop verification module verifies the strategy effect, and the model iteration optimization module optimizes the model parameters.

2. A prediction method based on the deep learning-based data center operation and maintenance fault prediction system according to claim 1, characterized in that: The prediction method is as follows: Step 1: Full collection of multi-source heterogeneous data Launch a multi-source heterogeneous data collection module to collect IT equipment operating data, environmental data, business data, and security data within the data center. The collection frequency is dynamically adjusted based on the data type: IT equipment operating data and environmental data are collected every 10 seconds, business data is collected every 30 seconds, and security data is collected every minute. The collected data is stored in a distributed database in real time, and a data collection log is generated to record the collection time, data source, and data integrity identifier. Step 2: Data preprocessing and feature enhancement The data preprocessing module uses a distributed computing framework to process the collected data. First, it performs data cleaning to remove duplicate data, repair malformed data, and fill in missing values. Second, it performs data normalization to map numerical data to the [0, 1] interval and convert text data into vectors. Finally, it performs feature enhancement to extract statistical, periodic, and correlation features from the time series data to generate a standardized feature dataset. This feature dataset is divided into training, validation, and test sets in a 7:2:1 ratio. Step 3: Deep learning prediction model construction and training The deep learning prediction model module constructs a "CNN-LSTM-Attention" hybrid model. The CNN submodule contains three convolutional layers and two pooling layers to extract local data features, the LSTM submodule contains two layers of bidirectional LSTM units to capture the temporal dependencies of the data, and the Attention submodule uses a multi-head attention mechanism to strengthen the weights of key features. During model training, the training set is used as input, the cross-entropy loss function is used to calculate the prediction error, and the Adam optimizer is used to adjust the model parameters. During training, the model performance is monitored in real time using the validation set, and accuracy, recall rate, and F1 value are used as evaluation indicators. Training is stopped when the F1 value of the validation set does not improve after 10 consecutive iterations, and the optimal model parameters are saved. After training, the test set is used to evaluate the model's generalization ability. The test set accuracy is required to be at least 92% and the recall rate is required to be at least 90%. If these standards are not met, the model structure is readjusted and training is repeated. Step 4: Real-time fault prediction and feature output The preprocessed real-time data is fed into a trained deep learning prediction model, which then outputs a 30-minute prediction of the failures of each device in the data center, including the probability of failure, failure type, and key impact characteristics. The prediction results are written to the result database in real time, and a prediction report is generated simultaneously, recording the prediction time, prediction object, prediction probability, failure type, and impact characteristics. The prediction report includes a data traceability link that can be traced back to the original collected data. Step 5: Dynamic classification of fault levels The fault classification module receives the fault prediction results output by the deep learning prediction model and classifies the faults into different levels based on the three-dimensional indicators of "fault impact range - business priority - occurrence probability". Level 1 faults affect core services, have an occurrence probability of 80% or more, or affect 5 or more core servers. Level 2 faults affect non-core services, have an occurrence probability of 60%-79%, or affect 3-4 core servers. Level 3 faults affect a single server, have a probability of 30%-59%, or have no business impact but present hardware vulnerabilities. Level 4 faults have a probability of less than 30% and no business impact. Each fault level is assigned a corresponding response timeframe: 10 minutes for level 1 faults, 30 minutes for level 2 faults, 2 hours for level 3 faults, and 24 hours for level 4 faults. Step 6: Accurately push warning information through multiple channels The early warning push module generates differentiated early warning information based on the fault level. Level 1 fault warning information includes fault prediction details, a list of affected businesses, emergency contacts, and preliminary response suggestions. Level 2 to 4 fault warning information includes fault prediction details and response time limits. Push channels are dynamically selected based on fault level and recipients. Level 1 faults are pushed to the operations manager and technical director via SMS, phone calls, corporate WeChat or DingTalk pinned messages, and pop-up windows on the operations platform. Level 2 faults are pushed to the operations team via corporate WeChat messages and operations platform notifications. Level 3 and 4 faults are pushed to the corresponding equipment responsible persons via operations platform messages. After the push, an early warning tracking log is generated, recording the push time, recipient, read status and response start time. If the first-level fault warning is not read within 5 minutes, a second phone reminder will be automatically triggered; Step 7: Intelligent generation of fault response strategies The strategy generation module generates response strategies based on the fault type, level, and impact characteristics by calling the preset strategy library and real-time environmental parameters: The hardware failure strategy is to immediately trigger server hot migration, migrate the business on the faulty server to the backup server, shut down the faulty server after the migration is complete, and generate a maintenance work order; the software anomaly strategy is to perform database configuration rollback, restart the database service after the rollback, and start database log analysis to locate the cause of the deadlock; the network failure strategy is to adjust the load balancer configuration to divert the traffic of the congested link to the backup link, and at the same time limit the bandwidth ratio of non-core business; the environmental anomaly strategy is to start the backup air-conditioning unit, adjust the air supply angle and wind speed of the air-conditioning, and shut down the non-core area server to reduce the heat dissipation pressure; the strategy generation is accompanied by execution steps, operation authority requirements and risk warnings; Step 8: Closed-loop verification of response strategies During policy execution, the closed-loop verification module collects real-time system status data and policy execution logs, including execution steps, execution duration, and operation results. Using the system status before policy execution as a benchmark, it evaluates policy effectiveness: effectiveness measures whether the predicted probability of failure has decreased; efficiency measures whether the policy execution duration meets the response time limit; side effects measures whether secondary problems are caused; and resource cost measures whether the resource consumption of policy execution is within budget. Upon completion, a verification report is generated, marking the policy as "valid," "needs optimization," or "invalid." "Valid" policies are recorded in the success case library, while "needs optimization" and "invalid" policies record problem points. Step 9: Feedback and record of troubleshooting results Operations and maintenance personnel perform fault handling operations based on early warning information and response strategies. After completion, they enter the results through the operations and maintenance platform, including the processing steps, processing time, whether the fault has been eliminated, any remaining issues, and business recovery status. The system automatically links the processing results with previous prediction reports and verification reports to generate a complete fault handling file. This file contains information on the entire process of "data collection - prediction - early warning - strategy - verification - handling" and is stored in the fault knowledge base for subsequent fault analysis and model optimization. Step 10: Iterative optimization of deep learning models The model iteration and optimization module regularly calls upon historical data from the fault knowledge base, including fault handling archives, data collection logs, and prediction reports. First, samples matching the fault prediction results with the actual handling results are screened and divided into a training set and a test set for model updates in an 8:2 ratio. Second, based on the prediction errors associated with the "invalid" and "needs optimization" strategies in the validation report, the module identifies model weaknesses and makes targeted adjustments to the model's feature weights or structure. Finally, the model is retrained using the updated training set using an incremental training approach. After training, the model performance is evaluated using the test set. If the model's accuracy and recall improve by ≥5% compared to the original model, the original model is replaced and deployed to the production environment. The model iteration log is also recorded. If performance does not improve or even deteriorates, the module reverts to the original model and re-analyzes optimization directions.

3. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 1, the multi-source heterogeneous data collection module adopts an "agent + agentless" hybrid collection method, deploying lightweight collection agents on servers, network devices, and other devices that support agent installation, and collecting data through the SNMP protocol and API interface for devices that cannot install agents. A data verification mechanism is added to the collection process to verify the range of numerical data and the format of text data. Data that fails the verification is marked as abnormal data and stored separately, and then manually reviewed by operation and maintenance personnel.

4. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 2, during the feature enhancement phase, the data preprocessing module introduces a device association graph to extract cross-device association features. The device association graph is constructed based on the data center network topology and business dependencies. The graph identifies dependencies between devices, calculates the correlation of cross-device parameters, and uses the correlation coefficient as a new feature to enhance the model's ability to predict cross-device fault propagation.

5. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2 is characterized by: In step 3, the deep learning prediction model module performs fault sample enhancement on the training set before model training. For types with a small number of fault samples, a generative adversarial network is used to generate synthetic fault samples. The generated samples must meet the requirement that the feature distribution difference with the real samples is ≤10% and pass the KL divergence measurement. An early stopping mechanism and regularization constraints are added during the model training process. The early stopping mechanism is triggered by the F1 value of the validation set, and the regularization constraint uses L2 regularization to avoid model overfitting.

6. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 4, during real-time fault prediction, if the fault prediction probability output by the model is in the fuzzy range, the system automatically starts multi-model fusion prediction, calls the backup model to predict the same batch of real-time data, calculates the weighted average of the prediction results of multiple models, and uses the weighted average as the final fault prediction probability to improve the prediction accuracy in fuzzy scenarios.

7. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 5, when the fault classification module divides the fault level, it introduces the business impact coefficient to dynamically adjust the level. The business impact coefficient is calculated based on the business revenue ratio and user scale. When the fault affects multiple businesses, the maximum business impact coefficient is taken.

8. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 6, the warning push module sets up lifecycle management for warning information. The lifecycle of a level 1 fault warning is 24 hours and it is automatically archived after the fault is handled. The lifecycle of level 2 to level 4 fault warnings is 72 hours. Warning information that is not handled within the lifecycle is upgraded and pushed through the channel every 12 hours to ensure that warning information is not missed.

9. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 7, the policy generation module sets a policy priority mechanism. When multiple faults occur simultaneously, the policy execution order is determined according to the fault level and business impact range. If there is a resource conflict between the policies for different faults, the policy for the core business fault is executed first, and the policy for non-core business faults is temporarily stored in the queue to be executed and automatically executed after the resources are released.

10. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 8, the closed-loop verification module automatically generates optimization suggestions for the "needs optimization" strategy, which are derived based on similar cases in the successful case library. It also supports operations personnel to manually mark optimization directions, and the marked information is synchronized to the strategy library for reference when generating subsequent strategies.

11. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 9, when the fault handling results are fed back, the system automatically compares the predicted fault type with the actual fault type, calculates the prediction accuracy, and updates the accuracy data to the model performance dashboard in real time for operation and maintenance personnel to monitor the model effect. Cases with incorrect predictions are marked as key analysis cases and are given priority for optimization in subsequent model iterations.

12. The prediction method of the data center operation and maintenance fault prediction system based on deep learning according to claim 2, characterized in that: In step 10, the model iteration optimization module supports "active iteration triggering." When a major fault occurs and the model has not predicted it in advance, the operation and maintenance personnel can manually trigger the model iteration. The system immediately filters the multi-source heterogeneous collected data corresponding to the fault, adds it to the training set, and starts emergency training. After training is completed, it is quickly deployed to avoid missing similar faults again.

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