A react component rendering anomaly prediction and non-intrusive optimization suggestion method and system based on time sequence fingerprints

Abstract

The application provides a React component rendering anomaly prediction and non-intrusive optimization suggestion method and system based on time sequence fingerprints, and relates to the technical field of front-end framework development. The method collects rendering event data of the React component in the complete life cycle in real time without triggering the React component re-rendering, and constructs a rendering event sequence. According to a preset debounce delay time, the high-frequency rendering events in the rendering event sequence are merged, and an analysis task is triggered after the React component enters a rendering quiet period. Based on the rendering event sequence in the sliding window, the multi-dimensional behavior characteristics of the React component are calculated, the time sequence fingerprints are generated and evaluated, the anomaly score is calculated, and the current health status of the React component is determined. According to the current health status, the optimization suggestion is generated in combination with the component type characteristics of the React component. The application can realize early warning and active defense of the React component rendering anomaly.

Description

Technical Field

[0001] This invention relates to the field of front-end framework development technology, specifically to a method and system for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints. Background Technology

[0002] React, as a mainstream front-end development framework, often faces component rendering issues in complex application scenarios, including over-rendering, memory leaks, and performance degradation. These problems are often difficult to detect in advance and are only noticed when the application crashes or the user experience deteriorates significantly.

[0003] In the field of React front-end application development, component rendering performance monitoring and exception handling have always been key technical challenges affecting user experience. As the complexity of front-end applications continues to increase, the number of components and nesting levels are rising dramatically, making rendering exceptions increasingly prominent. However, existing technical solutions have significant shortcomings in terms of exception detection timing, monitoring intrusiveness, and the accuracy of recommendations, making it difficult to meet the performance assurance requirements of modern complex front-end applications.

[0004] Current technologies generally adopt a reactive design philosophy, only handling exceptions after they occur and lacking the ability to predict future exceptions based on historical data. Specifically, Error Boundary can only be triggered after a component throws a JavaScript exception and the entire component tree crashes, by which time the user has already perceived the application error. Performance monitoring tools like ReactDevTools Profiler can only collect and display historical rendering data, requiring developers to manually analyze it afterward. The entire front-end monitoring field lacks an exception early warning mechanism based on time-series analysis, failing to achieve proactive defense against potential problems.

[0005] Furthermore, existing self-healing mechanisms have fundamental flaws in their architectural design. The self-healing action itself triggers component re-rendering, creating an infinite loop of "detection → self-healing → rendering → detection." Simultaneously, the lack of debouncing and cooldown mechanisms causes the system to fall into an infinite loop in high-frequency scenarios, not only failing to fix the problem but also causing more severe performance degradation.

[0006] Furthermore, existing monitoring solutions generally employ an intrusive design, using APIs such as `useState` to trigger re-rendering during the tracing process, causing the monitoring behavior itself to become a performance bottleneck. According to the observer effect principle in quantum mechanics, the observation behavior itself interferes with the observed system, which is particularly evident in front-end performance monitoring scenarios.

[0007] Furthermore, even if existing performance analysis tools can identify performance issues, they can only provide general suggestions such as avoiding unnecessary re-rendering and using React.memo, lacking differentiated suggestions based on specific component types and performance metrics. All components, regardless of their functionalities, rendering characteristics, or exception types, receive the same optimization suggestions, which offers limited help to developers' actual optimization efforts. Summary of the Invention

[0008] The purpose of this invention is to propose a method and system for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints. By constructing temporal fingerprints of component rendering behavior and combining them with a multi-dimensional weighted anomaly scoring algorithm, accurate prediction of rendering anomalies is achieved. Simultaneously, a non-intrusive tracking design and anti-bouncing cooling mechanism are employed to fundamentally solve the problems of loop triggering and performance overhead. Finally, an intelligent and diversified suggestion generation algorithm provides targeted optimization suggestions for different types of components.

[0009] In a first aspect, the present invention proposes a method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints, the method comprising:

[0010] S1: Without triggering React component re-rendering, collect rendering event data of React component throughout its entire lifecycle in real time and construct a rendering event sequence; S2: Based on the preset anti-jitter delay time, merge the high-frequency rendering events in the rendering event sequence, and trigger the analysis task after the React component enters the rendering quiet period; S3: Calculate the multi-dimensional behavioral characteristics of the React component based on the rendering event sequence within the sliding window, and generate a temporal fingerprint; S4: Evaluate the time-series fingerprint, calculate the anomaly score, and determine the current health status of the React component; S5: Based on the current health status and the component type characteristics of the React component, generate targeted optimization suggestions, and limit the frequency of generating the optimization suggestions through a cooldown period control mechanism.

[0011] As a preferred option for the first aspect, in step S1, a reference object is used instead of a state object to store tracking data; a timestamp is recorded when the React component starts rendering, and the rendering time is calculated within the Effect cycle; The dimensions for collecting rendering event data include component identifier, event type, timestamp, rendering time, number of state changes, number of attribute changes, and a flag indicating whether it was triggered by the system.

[0012] As a preferred option in the first aspect, in step S2, an independent timer is maintained for each React component. When a new rendering event arrives, the existing timer is cleared and a new debouncing timer is reset. After the anti-shake timer reaches the preset analysis anti-shake time, the timing fingerprint calculation, anomaly prediction and suggestion generation logic are triggered sequentially.

[0013] As a preferred embodiment of the first aspect, in step S3, the time-series fingerprint includes the following multi-dimensional behavioral features: The average rendering interval reflects rendering frequency, the standard deviation of the rendering interval reflects rendering stability, the average rendering time reflects rendering performance, the standard deviation of the rendering time reflects performance fluctuations, the rendering frequency measures rendering density, and the trend slope reflecting the direction of performance change calculated based on regression analysis.

[0014] As a preferred embodiment of the first aspect, step S3 further includes: predicting the remaining time for the React component to fail based on the trend slope.

[0015] As a preferred embodiment of the first aspect, in step S4, the abnormality score in the range of 0-100 is output by weighted summation of five dimensions: rendering interval fluctuation, abnormal rendering time, abnormal rendering frequency, abnormal trend, and abnormal stability. Based on the comparison results between the abnormal score and the preset warning threshold and danger threshold, the current health status is divided into a healthy status, a warning status, or a danger status.

[0016] As a preferred embodiment of the first aspect, in step S5, the cooling period control mechanism includes: Get the timestamp of the last time the React component generated a suggestion, and calculate the interval between it and the current time; If the interval is less than the preset cooling period, the generation of this suggestion will be skipped to prevent the detection loop caused by the re-rendering triggered by the optimization suggestion.

[0017] As a preferred embodiment of the first aspect, step S5, which involves generating targeted optimization suggestions, specifically includes: Component type identification is performed based on keywords in the component identifier; Threshold classification is determined based on specific indicator values; Variants of general component recommendations are selected using the hash value of the component identifier.

[0018] A second aspect of the present invention proposes a React component rendering anomaly prediction and non-intrusive optimization suggestion system based on temporal fingerprints, the system comprising: The non-intrusive rendering tracking module is used to collect rendering event data of React components without interfering with the normal rendering process of the components, construct rendering event sequences, and output them to the debouncing analysis and scheduling module. The anti-shake analysis and scheduling module is used to receive the rendering event sequence, merge the high-frequency rendering events, and trigger the analysis task during the quiet period of component rendering. The temporal fingerprint calculation module is used to calculate the multi-dimensional behavioral characteristics of the React component based on the analysis task, generate a temporal fingerprint that reflects the component's rendering behavior pattern, and evaluate its current health status. The cooling-off period control module is used to determine whether the recommended time interval requirement is met through a timestamp verification mechanism, so as to block the rendering detection loop caused by the feedback of optimization suggestions; The intelligent suggestion generation module is used to generate and output differentiated optimization suggestions based on the time sequence fingerprint, component type characteristics and the current health status when the cooling period requirements are met.

[0019] Beneficial Effects: This invention proposes a method and system for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints, offering a novel technical path and engineering solution for front-end performance optimization. This invention enables proactive prediction and early warning of React component rendering anomalies, transforming passive response into active defense. By separating cooldown period control and observation intervention architecture, the infinite loop problem is eliminated, ensuring long-term stable system operation. Employing useRef tracing and debouncing analysis mechanisms significantly reduces monitoring performance overhead with no perceptible impact on application performance. Automatic component type identification and differentiated suggestion generation are achieved, providing multiple targeted optimization strategies and significantly enhancing the practical value of the suggestions. All functionalities are implemented in pure TypeScript with zero external dependencies, making it easy to integrate into existing React projects. Attached Figure Description

[0020] Figure 1 This is a diagram illustrating the overall structure of the React component rendering anomaly prediction and non-intrusive optimization suggestion system based on time-series fingerprints in this embodiment. Detailed Implementation

[0021] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0022] This invention proposes a React component rendering anomaly prediction and non-intrusive optimization suggestion system based on temporal fingerprints, see [link to relevant documentation]. Figure 1As shown, the specific steps of this system in implementing the React component rendering anomaly prediction and non-intrusive optimization suggestion method based on time-series fingerprints are as follows: Step S1: Define the Non-Invasive Render Tracking (NIRT) module. The core objective of this module is to collect component rendering event data without interfering with the normal rendering process of the component. Traditional monitoring solutions use `useState` to store tracking data, causing the tracking process itself to trigger component re-rendering, creating a problem where the observer interferes with the observed system. This invention adopts the NIRT algorithm (Non-Invasive Render Tracking), using `useRef` instead of `useState` to achieve zero-re-rendering during the tracking process. This module achieves: 1. Zero-re-rendering tracking. All tracking data (rendering count, timestamp, state change count) is stored in `useRef`. The tracking logic only modifies `ref.current` and does not call any `setState` functions. 2. High-precision time measurement. `performance.now()` is used to synchronously record the time at the start of rendering, and the time is calculated in `useEffect` with sub-millisecond accuracy. 3. Coverage of the complete lifecycle. Covers three event types: MOUNT, UPDATE, and UNMOUNT.

[0023] Step S2: Define the Debounced Analysis Scheduling (DAS) module. The core objective of this module is to merge high-frequency rendering events to avoid triggering complex fingerprint calculations and anomaly predictions with each render. In high-frequency rendering scenarios (such as animation and real-time data updates), components may render more than 10 times within 100ms. If analysis is triggered with each render, it will cause significant performance overhead. This invention uses the DAS algorithm (Debounced Analysis Scheduling) to achieve batch processing of rendering events through a timer delay mechanism. This module implements: 1. Event merging strategy: Multiple consecutive rendering events are merged into one analysis, with the analysis trigger time being the delay time after the last render (default 100ms). 2. Timer reset mechanism: Each time a new event arrives, the previous timer is cleared and a new timer is set, ensuring that analysis is only triggered after the quiet period. 3. Component isolation: Each component maintains an independent timer, and the analysis of different components does not affect each other.

[0024] Step S3: Define the Time-Series Fingerprint Computation (TSF) module. The core objective of this module is to calculate the multi-dimensional behavioral characteristics of components based on the rendering event sequence within a sliding window, constructing a unique time-series fingerprint. Time-series fingerprinting is the core innovative concept of this invention; it abstracts the rendering behavior of components into quantifiable feature vectors, providing a data foundation for subsequent anomaly prediction. This invention employs the TSF algorithm (Time-Series Fingerprint Computation), using statistical analysis and linear regression methods to calculate six core dimensional features. This module implements: 1. The concept of time-series fingerprinting. It introduces the fingerprint concept into the field of React component monitoring for the first time; each component's rendering behavior has its unique fingerprint. 2. Multi-dimensional feature fusion. It integrates five dimensions: rendering interval, rendering time, rendering frequency, trend slope, and volatility. 3. The Multi-Weighted Anomaly Score (MWAS) algorithm calculates anomaly scores from 0 to 100 through weighted summation. 4. Trend prediction capability. It calculates the trend slope based on the least squares method to predict the direction of performance changes.

[0025] Step S4: Define the Cooldown-Controlled Suggestion (CCS) module. The core objective of this module is to prevent suggestions from being repeatedly triggered through a time-controlled mechanism, fundamentally solving the infinite loop problem of existing self-healing systems. In traditional self-healing systems, a self-healing action is triggered when an anomaly is detected. This action causes the component to re-render, which is then detected as a new event, triggering self-healing again, forming an infinite loop. This invention uses the CCS algorithm (Cooldown-Controlled Suggestion) to control the suggestion algorithm through a cooldown period. It uses the lastHealingTime timestamp and cooldown period checks to ensure that suggestions are not repeatedly generated within a specified time. This module implements: 1. Cooldown Mechanism: Each component maintains a lastHealingTime timestamp and does not generate new suggestions within the cooldown period (default 500ms). 2. Loop Blocking: Blocks the "detection → suggestion → rendering → detection" loop at the algorithm level. 3. Suggestion History Management: Maintains a history of suggestions, limiting the maximum number to prevent memory overflow. 4. Subscription Notification Mechanism: Notifies the UI layer to update suggestion display through a callback mechanism.

[0026] Step S5: Define the Intelligent Diversified Suggestion Generation (IDSG) module. The core objective of this module is to generate differentiated optimization suggestions based on component type, anomaly type, and specific metric values, avoiding generic, one-size-fits-all suggestions. Traditional performance tools can only provide general suggestions like using React.memo, lacking specificity. This invention employs the IDSG algorithm (Intelligent Diversified Suggestion Generation), using a triple mechanism of component type identification, metric threshold judgment, and ID hash diversification to generate accurate, personalized suggestions. This module implements: 1. Component Type Recognition (CTR): Inferring the component type based on keywords in the component ID (e.g., "User", "Product", "List"). 2. Metric Threshold Classification: Providing different levels of suggestions based on specific metric values, such as frequency > 12 times / second. 3. ID Hash Diversification (IDH): For generic components, using the component ID hash value to select suggestion variants, ensuring different suggestions for different components. 4. Coverage of six optimization strategies. MEMO_OPTIMIZATION, CALLBACK_OPTIMIZATION, STATE_BATCHING, RENDER_THROTTLE, COMPONENT_SPLIT, LAZY_LOADING. These optimization suggestions are then displayed through the developer console. In a further embodiment, the specific details of step S1 are disclosed: Step S1.1: Data Structure Definition This invention designs a complete and original data structure system to support the entire process of time-series fingerprint analysis and anomaly prediction. These data structures form the foundation of this technical solution and have the following characteristics: 1. Specifically designed for time-series fingerprint analysis. Existing technologies lack a time-series fingerprint data model for React component rendering behavior; this technical solution innovatively defines a complete type system. 2. Hierarchical structure design. From the bottom-level rendering events (RenderEvent), to the middle-level time-series fingerprints (TimeSeriesFingerprint), and then to the top-level optimization suggestions (HealingAction), a clear data flow path is formed. 3. Complete state machine design. By defining event types, health states, and suggestion types through enumeration types, the predictability and type safety of system behavior are ensured.

[0027] Step S1.1.1: Define the rendering event type enumeration class RenderEventType. Define 5 component lifecycle events, among which RECOVERY is unique to this technical solution. This type is used in the Non-Intrusive Render Tracking Module (NIRT) to identify the type of each rendering event. Define various events of the component lifecycle in the RenderEventType enumeration class.

[0028] Step S1.1.2: Define the component health status enumeration HealthStatus. Define four health levels to form a complete state machine. This type is used in the Multidimensional Weighted Anomaly Scoring Module (MWAS) to represent the current health status of the component. Define the four health levels of the component in the HealthStatus enumeration class.

[0029] Step S1.1.3: Define the optimization suggestion type enumeration class HealingActionType. Define 6 optimization strategies to cover common React performance optimization scenarios. This type is used in the Intelligent Suggestion Generation Module (IDSG) to identify the specific type of suggestion. Define the six optimization strategies in the HealingActionType enumeration class.

[0030] Step S1.1.4: Define the RenderEvent interface. Design purpose: The RenderEvent interface is the fundamental data unit for time-series fingerprint analysis, used to record complete information about each component rendering. Existing technologies, such as ReactDevTools, only record rendering time, while this technical solution innovatively designs 7-dimensional event attributes, providing a data foundation for subsequent multi-dimensional analysis. The `stateChangeCount` and `propsChangeCount` fields are original designs. Existing technologies cannot distinguish the reason for rendering triggers; this technical solution uses these two fields to accurately record the trigger source of rendering. The `isHealingTriggered` field, a unique flag in this technical solution, is used to distinguish between normal rendering and system-triggered rendering, and is a key design to solve the problem of cyclic triggering. Use cases include: creating and recording in the Non-Intrusive Render Tracing Module (NIRT); caching to the event queue in the Debouncing Analysis and Scheduling Module (DAS); and using it as input to calculate fingerprint features in the Time-Series Fingerprint Calculation Module (TSF). The basic data unit for time-series fingerprints is defined in the RenderEvent interface.

[0031] Step S1.1.5: Define the TimeSeriesFingerprint interface. Design Purpose: The TimeSeriesFingerprint interface is the core innovative data structure of this invention, used to represent the digital fingerprint of component rendering behavior. Similar to how human fingerprints uniquely identify individuals, time-series fingerprints can uniquely identify and track the rendering behavior patterns of components. A six-dimensional feature vector is designed: `avgRenderInterval`, `stdRenderInterval`, `avgRenderDuration`, `stdRenderDuration`, `renderFrequency`, and `trendSlope`, which together constitute a multi-dimensional feature representation of component rendering behavior. Anomaly score calculation: The `anomalyScore` field is calculated using a Multidimensional Weighted Assay (MWAS) algorithm and is a core indicator for judging the health status of components. Fault prediction: The `predictedTimeToFailure` field is the most innovative design of this technical solution, predicting the time when a component may fail based on the trend slope, achieving the core goal of pre-failure prediction. Use cases: Creating and updating time-series fingerprints in the Time-Series Fingerprint Calculation Module (TSF); calculating anomaly scores and health status in the Multidimensional Weighted Anomaly Scoring Module (MWAS); serving as the basis for generating recommendations in the Intelligent Suggestion Generation Module (IDSG); and displaying component health dashboards and anomaly alerts in the UI layer. The data structure of the Time-Series Fingerprint interface is defined within the Time-Series Fingerprint interface.

[0032] Step S1.1.6: Define the system configuration interface SystemConfig. Design Purpose: The SystemConfig interface defines adjustable parameters for the system, allowing for flexible adjustments based on different application scenarios. These parameters are the optimal default values ​​summarized from extensive experiments and practice in this technical solution. Original Design: `healingCooldown`, a parameter unique to this invention, defaults to 500ms and is a key configuration for solving the cyclic triggering problem. `analysisDebounce`, a parameter unique to this technical solution, defaults to 100ms and is used to control the delay time of the Debounce Analysis Scheduling Module (DAS). `windowSize` controls the event range for time-series fingerprint calculation, affecting the smoothness and response speed of the fingerprint. Use Cases: Loading configuration during system initialization; reading corresponding parameters during the runtime of each module; supporting dynamic adjustment at runtime to meet the needs of different scenarios. The adjustable parameters of the system are defined in the SystemConfig interface.

[0033] Step S1.2: Define the Non-Invasive RenderTracking (NIRT) module; Step S1.2.1: Define the non-intrusive rendering tracking method `useRenderTracking`. This method is the core hook, and its core technology lies in using `useRef` exclusively, without triggering any state changes during the tracking process. The input parameter is the component's unique identifier, and the optional parameter is a custom timing fingerprint engine instance `customEngine`.

[0034] Step S1.2.2: Obtain the temporal fingerprint engine instance in the `useRenderTracking` method. A custom temporal fingerprint engine is preferred; otherwise, a global temporal fingerprint singleton is used.

[0035] Step S1.2.3: Use `useRef` to store the tracking data in the `useRenderTracking` method. The tracking process does not trigger additional rendering, achieving the design goal that the observer does not affect the observed system.

[0036] A. Use useRef to record the number of renderings.

[0037] B. Use useRef to record the last rendering time.

[0038] C. Use useRef to record the number of state changes.

[0039] D. Use useRef to record the number of times Props are changed.

[0040] E. Use useRef to record whether the component is mounted.

[0041] F. Use useRef to record the rendering start time for calculating rendering time.

[0042] G. Record the time at the start of rendering and execute synchronously before JSX returns.

[0043] Step S1.2.4: Define the component mounting and unmounting handling method `useEffect` in the `useRenderTracking` method. The dependency arrays are `componentId` and `engine`, and the method is re-executed only when either `componentId` or `engine` changes.

[0044] A. Mark the component as mounted in the component mounting and unmounting handling method useEffect so that subsequent rendering events will be recorded.

[0045] B. In the component mount and unmount handling method `useEffect`, construct the mount event object `mountEvent`. Define all necessary monitoring fields in the mount event object `mountEvent`.

[0046] C. In the component mounting and unmounting handling method useEffect, the mounting event is recorded in the timing fingerprint engine.

[0047] Step S1.2.5: Define a cleanup method `return` when the component is unloaded in the component mounting and unmounting handling method `useEffect`.

[0048] A. When a component is unloaded, the unmount event object `unmountEvent` is constructed in the cleanup method `return`.

[0049] B. Perform cleanup in the unmount event object unmountEvent.

[0050] C. Record the uninstallation event in the cleanup method's return value when the component is uninstalled.

[0051] D. When a component is unloaded, clear all data of the component in the cleanup method's return value to release memory.

[0052] Step S1.2.6: Define the rendering tracing method useEffect. It is executed on every render, but does not trigger a new render.

[0053] A. Increment the rendering counter in the `useEffect` method of rendering tracing. This only modifies the `ref` and does not trigger a re-render.

[0054] B. Calculate the rendering time in the useEffect method, from the start of rendering to the execution of Effect.

[0055] C. In the render tracking method useEffect, construct the render event object event. Note: Do not call any setState.

[0056] D. Set the rendering event field in the rendering event object (event).

[0057] E. In the `useEffect` method of the rendering tracing function, the rendering events are recorded to the timing fingerprint engine. The engine uses a debouncing mechanism internally, so analysis is not triggered immediately.

[0058] F. Update the last rendering time in the render tracking method useEffect.

[0059] G. Reset the change counter in the render tracking method useEffect to prepare for the next render.

[0060] Step S1.2.7: Define the callback function `trackStateChange` in the `useRenderTracking` method to track state changes. This function increments the state change count, modifies only the `ref`, and does not trigger a re-render.

[0061] Step S1.2.8: Define the callback function `trackPropsChange` in the `useRenderTracking` method to track Props changes. This function is used to increment the Props change count. It only modifies the `ref` and does not trigger a re-render.

[0062] Step S1.2.9: Return the tracking tool in the render tracking method useRenderTracking. This includes: the state change tracking function trackStateChange, the props change tracking function trackPropsChange, and the current render count (read-only).

[0063] In a further embodiment, specific details of step S2 are disclosed: Step S2.1: Define the DAS (Debounced Analysis Scheduling) module.

[0064] By defining a debouncing analysis scheduler, analysis calculations are avoided on every render. Core technology: using setTimeout for delayed analysis, merging consecutive renders into a single analysis.

[0065] Step S2.1.1: Define the DAS anti-shake scheduler scheduleAnalysis, with the component identifier componentId as the input parameter.

[0066] Step S2.1.2: Clear the existing timer (if it exists) of the component in the dejitter scheduler scheduleAnalysis.

[0067] Step S2.1.3: Set a new anti-shake timer in the anti-shake scheduler scheduleAnalysis, with a default anti-shake delay of 100ms.

[0068] Step S2.1.4: Perform fingerprint calculation in the new debounce timer to generate the timing fingerprint of the component.

[0069] Step S2.1.5: Perform anomaly prediction in the new anti-shake timer, calculate the anomaly probability and predict the failure time.

[0070] Step S2.1.6: Check in the new debounce timer whether optimization suggestions need to be generated (with cool-down period check).

[0071] Step S2.1.7: Clear the timer reference in the new debounce timer to allow garbage collection.

[0072] Step S2.1.8: Store timer references in the debounce scheduler scheduleAnalysis so that they can be canceled later.

[0073] In a further embodiment, specific details of step S3 are disclosed: Step S3.1: Define the Time-Series Fingerprint Computation (TSF) module.

[0074] By analyzing the task to calculate the multi-dimensional behavioral characteristics of React components, a temporal fingerprint reflecting the component's rendering behavior pattern is generated, and its current health status is evaluated.

[0075] Step S3.1.1: Define the computeFingerprint algorithm for calculating the temporal fingerprint. This algorithm is the key innovative algorithm of this invention.

[0076] Step S3.1.2: Obtain the historical event history of the component in the computeFingerprint algorithm.

[0077] Step S3.1.3: Take the most recent windowSize events (recentEvents) from the computeFingerprint algorithm to calculate the sliding window.

[0078] Step S3.1.4: Calculate the rendering interval sequence intervals in the computeFingerprint algorithm.

[0079] A. Initialize the rendering interval sequence intervals.

[0080] B. Iterate through the event pairs recentEvents and calculate the time interval between adjacent events.

[0081] C. Calculate the time difference (in milliseconds) between the current event and the previous event during the traversal.

[0082] D. Add the interval to the rendering interval sequence intervals during the traversal.

[0083] Step S3.1.5: Calculate the statistical characteristics of the rendering interval in the computeFingerprint algorithm.

[0084] A. Calculate the average rendering interval avgInterval, which reflects the rendering frequency.

[0085] B. Calculate the standard deviation of the rendering interval, stdInterval, to reflect rendering stability.

[0086] Step S3.1.6: Calculate the statistical characteristics of the rendering time sequence in the computeFingerprint algorithm.

[0087] A. Extract all rendering times.

[0088] B. Calculate the average rendering time (avgDuration) to reflect rendering performance.

[0089] C. Calculate the standard deviation of rendering time, stdDuration, to reflect performance fluctuations.

[0090] Step S3.1.7: Calculate the rendering frequency in the computeFingerprint algorithm.

[0091] A. Calculate the total duration of the time window, timeWindow, which is the time of the last event minus the time of the first event.

[0092] B. Calculate the number of renders per second, i.e., the rendering frequency.

[0093] Step S3.1.8: Calculate the trend slope in the computeFingerprint algorithm and use the least squares linear regression to calculate the trend of rendering time.

[0094] Step S3.1.9: Calculate the anomaly score by integrating multiple dimensions in the computeFingerprint algorithm. Add the following feature fields to the anomaly score: Average rendering interval: avgInterval Standard deviation of rendering interval: stdInterval Average rendering time: avgDuration Rendering time standard deviation: stdDuration Rendering frequency: frequency Trend slope: slope Step S3.1.10: Determine the health status in the computeFingerprint algorithm; A. Initialize the health status (healthStatus).

[0095] B. If the abnormal score exceeds 60 points, it is marked as a dangerous state.

[0096] C. If the abnormal score exceeds 25 points, mark it as a warning state.

[0097] D. If the abnormal score is below 25, it is marked as a healthy state.

[0098] Step S3.1.11: Predict the failure time predictedTimeToFailure in the computeFingerprint algorithm.

[0099] Step S3.1.12: Construct and store the temporal fingerprint in the computeFingerprint algorithm.

[0100] A. Construct a time-series fingerprint.

[0101] B. Add the following fields to the time-series fingerprint.

[0102] Component identifier. componentId, Actual window size. `windowSize: recentEvents.length` Average rendering interval. avgRenderInterval: avgInterval, Render interval standard deviation. stdRenderInterval: stdInterval, Average rendering time. avgRenderDuration: avgDuration, Standard deviation of rendering time. stdRenderDuration: stdDuration, Rendering frequency. `renderFrequency`: frequency, Anomaly score. Trend slope. (trendSlope: slope) Update timestamp. `lastUpdated: Date.now()`, Health status. Predicted failure time. (predictedTimeToFailure) C. Store the time-series fingerprints and store the fingerprints in the fingerprint map.

[0103] Step S3.2: Define the anomaly score calculation algorithm: calculateAnomalyScore. It performs a weighted evaluation based on five dimensions and outputs an anomaly score from 0 to 100. The input parameters are: average rendering interval, standard deviation of rendering interval, average rendering time, standard deviation of rendering time, rendering frequency, and trend slope.

[0104] Step S3.2.1: Define the weights for each dimension in the anomaly score calculation algorithm, `calculateAnomalyScore`. These weights can be adjusted according to the actual application scenario. Settings: Rendering interval volatility weight 20%, rendering time anomaly weight 25%, rendering frequency anomaly weight 20%, trend anomaly weight 20%, stability anomaly weight 15%.

[0105] Step S3.2.2: Calculate the dimension 1 score, i.e. the rendering interval volatility score, in the anomaly score calculation algorithm calculateAnomalyScore.

[0106] A. Calculate the coefficient of variation (CV). The formula is: Coefficient of variation (CV) = Standard deviation / Mean, which reflects the degree of relative fluctuation.

[0107] B. Calculate the coefficient of variation score (intervalScore). The formula is: map the coefficient of variation to a score between 0 and 100 (CV). 2. Maximum 100).

[0108] Step S3.2.3: Calculate the dimension 2 score, i.e. the rendering time anomaly score durationScore, in the anomaly score calculation algorithm calculateAnomalyScore.

[0109] A. Set the normal rendering time threshold to 16ms, which corresponds to 60fps.

[0110] B. Calculate the percentage of values ​​exceeding the normal range, durationRatio.

[0111] C. Calculate the rendering time anomaly score (durationScore). Map this to a score between 0 and 100; any time exceeding this range will be considered. 50, maximum 100.

[0112] Step S3.2.4: Calculate the dimension 3 score, i.e. the rendering frequency anomaly score, in the anomaly score calculation algorithm calculateAnomalyScore.

[0113] A. Set the normal rendering frequency threshold to 5 times / second.

[0114] B. Calculate the rendering frequency anomaly score (frequencyScore), which increases rapidly when it exceeds the threshold.

[0115] Step S3.2.5: Calculate the dimension 4 score, i.e., the trend anomaly score, in the anomaly score calculation algorithm, `calculateAnomalyScore`. A positive slope indicates a performance degradation trend, meaning that rendering time is gradually increasing.

[0116] Step S3.2.6: Calculate the dimension 5 score, i.e. the stability anomaly score, in the anomaly score calculation algorithm calculateAnomalyScore.

[0117] A. Calculate the coefficient of variation (durationCV) of rendering time. The coefficient of variation of rendering time reflects the stability of rendering performance.

[0118] B. Calculate the stability anomaly score (stabilityScore). Map it to a score of 0-100, CV. 1.5, maximum 100.

[0119] Step S3.2.7: Calculate the total weighted anomaly score (totalScore) in the anomaly score calculation algorithm (calculateAnomalyScore).

[0120] Step S3.2.8: Return the final anomaly score in the anomaly score calculation algorithm, which is limited to the range of 0-100.

[0121] In a further embodiment, specific details of step S4 are disclosed: Step S4.1: Define the Cooldown-Controlled Suggestion (CCS) module. Core method: Check and generate optimization suggestions with cooldown control. Key technology: The cooldown mechanism fundamentally solves the infinite loop problem.

[0122] Step S4.1.1: Define the CCS cooling-off period check and suggestion generation algorithm checkAndGenerateSuggestion, with the component identifier componentId as the input parameter.

[0123] Step S4.1.2: Obtain the timing fingerprint of the component.

[0124] Step S4.1.3: If the fingerprint does not exist or is in a healthy state, no suggestion needs to be generated.

[0125] Step S4.1.4: Cooling-off period check A. Get the last suggested time and the current time.

[0126] B. If it is within the cooldown period (default 500ms), skip this suggestion generation.

[0127] Step S4.1.5: Obtain the prediction result.

[0128] Step S4.1.6: If there is no prediction result or no recommended action, skip this step.

[0129] Step S4.1.7: Update the last trigger time and reset the cooldown period.

[0130] Step S4.1.8: Obtain the highest priority suggestion.

[0131] Step S4.1.9: Add to suggestion history.

[0132] Step S4.1.10: Limit the number of historical records to prevent memory overflow.

[0133] Step S4.1.11: Notify all subscribed callback functions to push the suggestion to subscribers.

[0134] In a further embodiment, the specific details of step S5 are disclosed: Step S5.1: Define the Intelligent Diversified Suggestion Generation (IDSG) module. This algorithm is an auxiliary method used to generate intelligent and diverse optimization suggestions. Core technology: Generate targeted suggestions based on component type and specific indicator values.

[0135] Step S5.1.1: Define the intelligent suggestion generation implementation algorithm, with the input parameters being the temporal fingerprint of the component and the predicted anomaly type.

[0136] Step S5.1.2: Initialize the suggestion array.

[0137] Step S5.1.3: Obtain the current timestamp now, which is used to generate a unique ID.

[0138] Step S5.1.4: Obtain the component identifier componentId.

[0139] Step S5.1.5: Analyze the component type characteristics, infer the component type based on the component ID, and use it to generate targeted suggestions.

[0140] Step S5.1.6: Extract the following key metrics for customized suggestions: rendering frequency; average rendering time; anomaly score; trend slope.

[0141] Step S5.1.7: Generate intelligent suggestions based on the anomaly type (anomalyType) and component characteristics (isUserCard, isProductList, general components).

[0142] Step S5.1.8: Determine if the scene is over-rendered.

[0143] Step S5.1.8.1: If the component is detected to be of type user card isUserCard, add the following suggestions to the suggestions: Recommended type: Batch status processing. actionType: HealingActionType.STATE_BATCHING Suggested reason: `User card rendering frequency ${frequency.toFixed(1)} times / second, status updates are too frequent`.

[0144] Specific suggestions: `It is recommended to split user data into static information and dynamic status, and cache the static part using React.memo.`

[0145] Danger level: High. Priority: 'HIGH'.

[0146] A. Continue to provide further suggestions based on the rendering frequency. If the rendering frequency is >12, it indicates ultra-high frequency rendering. Add the following suggestions to the suggestions list: Recommended type: Render throttling. actionType: HealingActionType. RENDER_THROTTLE, Suggested reason: `The rendering frequency has reached ${frequency.toFixed(1)} times / second, exceeding the user's perceived threshold.`

[0147] Specific recommendations: Use `useDeferredValue` to delay updates to non-critical states to avoid blocking user interaction.

[0148] Danger level: High. Priority: 'HIGH'.

[0149] B. Continue to provide further suggestions based on rendering frequency. If the rendering frequency is <12, it indicates medium-frequency rendering. Add the following suggestions to the suggestions list: Recommended type: Callback optimization. actionType: HealingActionType. CALLBACK_OPTIMIZATION, Suggested reason: `There may be uncached event handlers.`

[0150] Specific suggestions: Check if event handlers such as onClick are using useCallback caching.

[0151] Danger level: Medium. Priority: MEDIUM.

[0152] Step S5.1.8.2: If the component is detected to be of type product list isProductList, add the following suggestions to the suggestions: Recommended type: Memo optimization. actionType: HealingActionType. MEMO_OPTIMIZATION Suggested reason: The list component renders at a frequency of ${frequency.toFixed(1)} times per second, and price updates trigger a full re-render.

[0153] Specific suggestions: `It is recommended to use a virtual list (react-window) to optimize the rendering of long lists, rendering only the visible area.`

[0154] Danger level: High. Priority: 'HIGH'.

[0155] Step S5.1.8.3: If a common component is detected, first define several suggestion variants. Three basic suggestions are defined in the common variants suggestionVariants: { suggestion: `It is recommended to use useTransition to mark non-urgent updates, with the current rendering frequency being ${frequency.toFixed(1)} times / second`, type: HealingActionType.RENDER_THROTTLE}, { suggestion: 'It is recommended to check the useEffect dependency array to avoid unnecessary side effects being triggered', type: HealingActionType.CALLBACK_OPTIMIZATION}, { suggestion: 'It is recommended to use React.memo in conjunction with a custom comparison function to precisely control the re-rendering conditions', type: HealingActionType.MEMO_OPTIMIZATION}; A. Use component ID hashing to select suggested variants to ensure consistent suggestions for the same component but different suggestions for different components.

[0156] B. Execute `suggestions.push({…})` to add the following suggestions to the suggestions list: Recommended type. actionType: selectedVariant.type, Suggested reason: `Rendering frequency abnormal: ${frequency.toFixed(1)} times / second`.

[0157] Specific suggestions. Suggestion: `selectedVariant.suggestion`.

[0158] Danger level: High. Priority: 'HIGH'.

[0159] Step S5.1.9: Determine if the scene is slow rendering.

[0160] Step S5.1.9.1: If severely slow rendering is detected, i.e., average rendering time (avgDuration) > 100, add the following suggestion to the suggestions: Recommended type: Component split. actionType: HealingActionType.COMPONENT_SPLIT Reason suggested: Rendering time of ${avgDuration.toFixed(0)}ms severely impacts user experience.

[0161] Specific recommendations. Suggestion: Immediately split the components and move complex calculations to Web Workers.

[0162] Danger level: High. Priority: 'HIGH'.

[0163] Step S5.1.9.2: If moderately slow rendering is detected, i.e., average rendering time (avgDuration) > 50, add the following suggestion to the suggestions: Recommended type: Lazy loading. actionType: HealingActionType.LAZY_LOADING Suggested reason: `Rendering time ${avgDuration.toFixed(0)} ms, close to the performance warning line`.

[0164] Specific suggestions: `It is recommended to use React.lazy lazy loading for content not displayed on the first screen to reduce the initial rendering burden.`

[0165] Danger level: Medium. Priority: MEDIUM.

[0166] Step S5.1.10: Determine if the rendering scene is unstable. Execute `suggestions.push({…})` to add the following suggestions to the suggestions list: Recommended type: Batch status processing. actionType: HealingActionType.STATE_BATCHING Suggested reason: `Rendering interval fluctuation: standard deviation ${fingerprint.stdRenderInterval.toFixed(0)}ms`.

[0167] Specific suggestions: Use `unstable_batchedUpdates` for batch updates to reduce rendering jitter.

[0168] Danger level: Medium. Priority: MEDIUM.

[0169] Step S5.1.11: Identify performance degradation scenarios. Add the following suggestions to the suggestions list: Recommended type: Memo optimization. actionType: HealingActionType.MEMO_OPTIMIZATION Suggested reason: `Performance degradation slope: ${trendSlope.toFixed(3)}, expected to reach the critical point after ${fingerprint.predictedTimeToFailure} ms`.

[0170] Specific recommendations: Use React Profiler to analyze rendering bottlenecks and check for computationally intensive operations.

[0171] Danger level: High. Priority: HIGH.

[0172] Step S5.1.12: Determine if an unknown abnormal scenario occurs. Add the following suggestions to the suggestions list: Recommended type: Memo optimization. actionType: HealingActionType.RENDER_THROTTLE.

[0173] Suggested reason: `anomaly score:${anomalyScore.toFixed(1)}`.

[0174] Specific suggestions: Use React DevTools Profiler to record the rendering process and identify hotspots.

[0175] Danger level: Low. Priority: LOW.

[0176] Step S5.1.13: Return the suggestions.

[0177] Step S5.2: Define the component ID hash calculation method hashComponentId.

[0178] Step S5.2.1: Initialize the hash value in the hash calculation method hashComponentId.

[0179] Step S5.2.2: Iterate through the component IDs and convert them into numbers.

[0180] Step S5.2.3: Obtain the ASCII code (char) of the character during the traversal.

[0181] Step S5.2.4: During the traversal, convert the hash value into an integer hash value using a string hash algorithm.

[0182] Step S5.2.5: Convert the hash value to a 32-bit integer during the traversal.

[0183] Step S5.2.6: In the hash calculation method hashComponentId, return the hash value of the defined component ID, ensuring that it is a positive integer.

[0184] The following detailed implementation example uses an e-commerce application. This application comprises two core components: user information cards and product lists. During promotional events, frequent price updates and inventory changes cause over-rendering of these components, resulting in page lag reported by users.

[0185] Step 1: Import the Hooks provided by the timing fingerprint system.

[0186] Import the Hooks provided by the timing fingerprint system, namely: non-intrusive rendering tracking Hook, timing fingerprint acquisition Hook, and optimization suggestion acquisition Hook.

[0187] Step 2: Define the UserInfoCard component, with the username userId as the input parameter.

[0188] Step 2.1: Define a unique identifier for the component to distinguish different component instances.

[0189] Step 2.2: Enable non-intrusive rendering tracking. This hook uses useRef internally and will not trigger additional re-rendering.

[0190] Step 2.3: Obtain the component's timing fingerprint, refreshed every 500ms, to display the component's health status on the UI.

[0191] Step 2.4: Obtain optimization suggestions (latestSuggestion) for this component. The system will generate targeted suggestions based on the component type (user information card component).

[0192] Step 2.5: Define the component's business state, userInfo.

[0193] Step 2.6: Simulate real-time updates of user points in a promotional activity scenario. Before the state update, call the tracking function to record the state change, and then update the points. The dependency of useEffect is trackStateChange, which tracks the state change of the user information card component, updating every 100ms to simulate high-frequency state changes.

[0194] Step 2.7: Display the health status of the user information card component: Health status: {fingerprint.healthStatus} ; Anomaly score: {fingerprint.anomalyScore.toFixed(1)} ; Rendering frequency: {fingerprint.renderFrequency.toFixed(1)} times / second ; Step 2.8: Optimization suggestions for the user information card display component: Optimization suggestions: ; {latestSuggestion.suggestion} ; Reason: {latestSuggestion.reason}; Step 3: Define the ProductList component.

[0195] Step 3.1: Define the component's unique identifier, componentId.

[0196] Step 3.2: Enable non-intrusive rendering tracing.

[0197] Step 3.3: Obtain the time-series fingerprint.

[0198] Step 3.4: Obtain optimization suggestions (latestSuggestion).

[0199] Step 3.5: Define the product list data.

[0200] Step 3.6: Simulate real-time price fluctuations in a promotional activity scenario. The dependency of useEffect is trackStateChange, which tracks the state change of the product list component and updates every 120ms to simulate high-frequency state changes.

[0201] Step 3.7: Display the health status of the product list component: Anomaly score: {fingerprint.anomalyScore.toFixed(1)} ; Rendering frequency: {fingerprint.renderFrequency.toFixed(1)} times / second ; Step 3.8: Optimization suggestions for the product list component: {latestSuggestion.suggestion} ; Step 4: Running results.

[0202] Step 4.1: UserInfoCard component: When updating scores frequently, the system detects that the rendering frequency reaches 10 times / second, the abnormal score rises to 35 points (warning level), and generates the suggestion: "It is recommended to split user data into static information and dynamic status, and use React.memo to cache the static part."

[0203] Step 4.2: ProductList component: When prices fluctuate frequently, the system detects that the rendering frequency reaches 8.3 times / second and generates the suggestion: "It is recommended to use a virtual list (react-window) to optimize the rendering of long lists and only render the visible area."

[0204] Step 4.3: After developer optimization: After optimizing the code according to the suggestions, the rendering frequency of the two components was reduced to 2 times / second and 1 time / second respectively, and the error scores were reduced to below 10 points (healthy level), and the page lag problem was solved.

[0205] In summary, as described in the above embodiments, this invention discloses a method and system for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints. Addressing the shortcomings of existing technologies, such as Error Boundary only allowing for reactive responses, DevToolsProfiler requiring manual analysis, and the infinite loop inherent in self-healing mechanisms, this invention proposes five core technological innovations: 1. Non-Intrusive Rendering Tracing (NIRT). It is the first to use useRef instead of useState to store tracing data. The tracing process itself does not trigger any component re-rendering, realizing the design principle that the observer does not affect the observed system, and fundamentally solving the intrusive problem of traditional monitoring solutions.

[0206] 2. De-jitter Analysis and Scheduling (DAS). A 100ms de-jitter timer mechanism is introduced. Each rendering event is only recorded in the event queue, and multiple consecutive renderings are merged into a single analysis calculation. This reduces computational overhead by 90% in high-frequency rendering scenarios, significantly reducing the impact of the monitoring system on application performance. For example, when a product list component renders 10 times consecutively within 80ms, the traditional solution would trigger 10 analysis calculations. The DAS algorithm of this invention merges these 10 renderings into one analysis, with the analysis trigger time being 100ms after the last rendering, reducing computational overhead by 90%. Because 80ms is close to the threshold of human visual perception, setting it to 100ms, which is exactly equal to the de-jitter time, might not stably trigger anomaly detection in simulating real-world anomaly scenarios. This provides an effective comparison with the 100ms de-jitter mechanism, verifying the effectiveness of the system design and demonstrating the exquisite balance between the de-jitter mechanism and anomaly detection.

[0207] 3. Cooldown Control (CCS). A 500ms cooldown mechanism is introduced, maintaining a lastHealingTime timestamp for each component. No new suggestions are generated during the cooldown period, fundamentally solving the problem of the infinite loop of the existing self-healing mechanism: detection → self-healing → rendering → detection.

[0208] 4. Separation of Observation and Intervention Architecture. The system acts as a pure observer, responsible only for monitoring, analyzing, and generating optimization suggestions, without directly interfering with the component rendering process. Suggestions are presented to developers through the UI, allowing them to decide whether to adopt them, thus completely avoiding side effects caused by system intervention.

[0209] 5. Intelligent Diversified Suggestion Generation (IDSG). Component types (user cards / product lists / general components) are identified by component ID keywords, and differentiated suggestions are generated based on specific anomaly metric values. For general components, ID hashing is used to select suggestion variants, ensuring that different components receive targeted optimization guidance and avoiding generic suggestions.

[0210] Compared with existing technologies, this invention achieves an architectural upgrade from invasive self-healing to non-invasive suggestion, and for the first time applies temporal fingerprint analysis technology to the field of React component rendering anomaly prediction. Actual testing has verified that applying this system can significantly improve the average fault prediction time, reduce most computational overhead through a debouncing mechanism, eliminate infinite loops through a cooldown period mechanism, and provide intelligent suggestions that cover different differentiated optimization strategies.

[0211] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for predicting rendering anomalies and providing non-intrusive optimization suggestions for React components based on temporal fingerprints, characterized in that, Includes the following steps: S1: Without triggering React component re-rendering, collect rendering event data of React component throughout its entire lifecycle in real time and construct a rendering event sequence; S2: Based on the preset anti-jitter delay time, merge the high-frequency rendering events in the rendering event sequence, and trigger the analysis task after the React component enters the rendering quiet period; S3: Calculate the multi-dimensional behavioral characteristics of the React component based on the rendering event sequence within the sliding window, and generate a temporal fingerprint; S4: Evaluate the time-series fingerprint, calculate the anomaly score, and determine the current health status of the React component; S5: Based on the current health status and the component type characteristics of the React component, generate targeted optimization suggestions, and limit the frequency of generating the optimization suggestions through a cooldown period control mechanism.

2. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 1, characterized in that, In step S1, a reference object is used instead of a state object to store tracking data; a timestamp is recorded when the React component starts rendering, and the rendering time is calculated within the Effect cycle; The dimensions for collecting rendering event data include component identifier, event type, timestamp, rendering time, number of state changes, number of attribute changes, and a flag indicating whether it was triggered by the system.

3. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 1, characterized in that, In step S2, an independent timer is maintained for each React component. When a new rendering event arrives, the existing timer is cleared and a new debouncing timer is reset. After the anti-shake timer reaches the preset analysis anti-shake time, the timing fingerprint calculation, anomaly prediction and suggestion generation logic are triggered sequentially.

4. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 1, characterized in that, In step S3, the time-series fingerprint includes the following multi-dimensional behaviors. feature: The average rendering interval reflects rendering frequency, the standard deviation of the rendering interval reflects rendering stability, the average rendering time reflects rendering performance, the standard deviation of the rendering time reflects performance fluctuations, the rendering frequency measures rendering density, and the trend slope reflecting the direction of performance change calculated based on regression analysis.

5. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 4, characterized in that, Step S3 further includes: predicting the remaining time for the React component to fail based on the trend slope.

6. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 1, characterized in that, In step S4, the five dimensions of rendering interval fluctuation, rendering time anomaly, rendering frequency anomaly, trend anomaly and stability anomaly are weighted and summed to output the anomaly score in the range of 0-100. Based on the comparison results between the abnormal score and the preset warning threshold and danger threshold, the current health status is divided into a healthy status, a warning status, or a danger status.

7. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 1, characterized in that, In step S5, the cooling period control mechanism includes: Get the timestamp of the last time the React component generated a suggestion, and calculate the interval between it and the current time; If the interval is less than the preset cooling period, the generation of this suggestion will be skipped to prevent the detection loop caused by the re-rendering triggered by the optimization suggestion.

8. The method for predicting React component rendering anomalies and providing non-intrusive optimization suggestions based on temporal fingerprints according to claim 2, characterized in that, In step S5, generating targeted optimization suggestions specifically includes: Component type identification is performed based on keywords in the component identifier; Threshold classification is determined based on specific indicator values; Variants of general component recommendations are selected using the hash value of the component identifier.

9. A React component rendering anomaly prediction and non-intrusive optimization suggestion system based on temporal fingerprints, used to execute the React component rendering anomaly prediction and non-intrusive optimization suggestion method based on temporal fingerprints as described in any one of claims 1 to 8, characterized in that, The system includes: The non-intrusive rendering tracking module is used to collect rendering event data of React components without interfering with the normal rendering process of the components, construct rendering event sequences, and output them to the debouncing analysis and scheduling module. The anti-shake analysis and scheduling module is used to receive the rendering event sequence, merge the high-frequency rendering events, and trigger the analysis task during the quiet period of component rendering. The temporal fingerprint calculation module is used to calculate the multi-dimensional behavioral characteristics of the React component based on the analysis task, generate a temporal fingerprint that reflects the component's rendering behavior pattern, and evaluate its current health status. The cooling-off period control module is used to determine whether the recommended time interval requirement is met through a timestamp verification mechanism, so as to block the rendering detection loop caused by the feedback of optimization suggestions; The intelligent suggestion generation module is used to generate and output differentiated optimization suggestions based on the time sequence fingerprint, component type characteristics and the current health status when the cooling period requirements are met.

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