A deep learning model training optimization method based on multi-dimensional intelligent analysis

Abstract

This invention provides a deep learning model training optimization method based on multi-dimensional intelligent analysis. It achieves accurate quantitative judgment of training status based on statistical process control methods, realizes early warning of overfitting through multi-dimensional analysis, realizes deep understanding of learning dynamics by applying causal analysis and machine learning methods, establishes a multi-model integration framework to realize accurate prediction of training results, constructs a multi-dimensional risk assessment system to realize proactive management of training risks, and ensures the practical usability of optimization effect through training-inference correlation analysis.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and deep learning technology, and in particular to an intelligent analysis and optimization method for the training process of deep neural network models. Background Technology

[0002] With the rapid development of deep learning technology, computer vision model training has become a core part of AI application development. The training cost of large-scale models can reach millions of dollars, and resource waste due to training failures or poor performance is common. At the same time, model training relies heavily on engineers' experience and judgment, including hyperparameter settings, training status evaluation, problem diagnosis, and optimization strategy selection. This experience-driven approach leads to low efficiency in parameter tuning, poor repeatability, and unstable optimization results.

[0003] Existing visualization tools mainly include platforms such as TensorBoard, Weights & Biases, and Neptune. These tools can provide basic visualization functions such as loss curves and metric changes, and support real-time monitoring and multi-experiment comparisons. However, these tools only provide data display and lack intelligent analysis capabilities. They cannot automatically identify training problems or provide optimization suggestions, their analysis depth is insufficient to capture complex training dynamics, and they lack statistical analysis and process control capabilities. This forces engineers to still rely on experience for manual judgment, failing to fundamentally solve the problem of intelligent management of the training process.

[0004] Existing early stopping mechanisms are based on simple rules that validate the loss as no longer improving, with learning rate scheduling strategies including predefined rules such as Step Decay, Exponential Decay, and Cosine Annealing. While these methods are simple to implement and have low overhead, their judgment criteria are too simplistic and prone to misjudgment, failing to distinguish between temporary stagnation and true convergence, and lacking a deep understanding of training dynamics. More importantly, static rules cannot adapt to the actual training process, do not consider statistical significance, and may cause training to stop too early, resulting in performance loss, or be excessively prolonged, wasting computational resources.

[0005] Existing AutoML methods mainly include techniques such as Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO), such as Optuna, Ray Tune, and AutoKeras. These methods can automatically search for model architectures and hyperparameters, but they incur extremely high computational costs, requiring extensive trial and error. They focus on pre-training configuration search rather than dynamic optimization during training, lacking in-depth analysis and real-time optimization capabilities for the training process, and cannot provide early warnings or diagnosis of training problems. This makes AutoML methods ill-suited for handling dynamic changes and unexpected issues during training.

[0006] A comprehensive analysis of existing technologies reveals the following five core problems:

[0007] 1. Existing methods primarily rely on human experience to judge the training status, such as subjective judgments like "convergence means the loss has stopped decreasing," lacking a unified quantitative evaluation system. They focus only on whether convergence has occurred, neglecting the quality of convergence, relying solely on a single loss value while ignoring other important dimensions such as gradient, learning rate, and volatility, and failing to apply mature statistical process control methodologies. This leads to highly subjective judgments, susceptibility to misjudgments, and an inability to distinguish between "false convergence" (temporary stagnation) and "true convergence" (reaching the optimum), potentially causing training to stop prematurely or wasting resources.

[0008] 2. Current overfitting detection methods are mostly retrospective analyses, meaning that by the time overfitting is detected, the optimal intervention time has already passed. They can only qualitatively determine "whether overfitting exists," lacking precise quantification of the degree. Focusing only on the current state fails to capture development trends and speed, and the overall analysis ignores the differentiated performance of each network layer. This results in the inability to provide effective early warnings before overfitting occurs, difficulty in developing targeted mitigation strategies, inability to predict future risks, and insufficiently refined optimization strategies.

[0009] 3. Existing learning rate adjustments are mostly based on fixed rules, which cannot adapt to dynamic training processes and lack causal analysis. They cannot automatically identify different training stages, lack a comprehensive evaluation index system for training efficiency, and only monitor overall resource usage without in-depth analysis of resource consumption and optimization potential at each layer. This results in unsatisfactory learning rate adjustments, optimization strategies lacking specificity, inability to quantify optimization effects, and insufficient resource optimization.

[0010] 4. Existing methods cannot accurately predict the training time required, impacting resource scheduling and project planning. The final effect can only be known after training is complete, resulting in high trial-and-error costs. The prediction results lack confidence, leading to high decision-making risks. Furthermore, training optimization is separated from inference performance. This makes it impossible to make scientific predictions based on historical data, to predict the final performance of the model in advance, and to guarantee the reliability of the prediction results, potentially leading to the problem of "good training results but poor inference performance."

[0011] 5. Existing methods fail to identify training crashes and divergences in advance, wasting significant computational resources. They cannot promptly terminate meaningless training, focus only on single risk indicators, lack a systematic risk assessment framework, and lack statistical evaluation of the training process's stability. This results in a lack of risk warning mechanisms, incomplete risk assessment, and an inability to guarantee training repeatability.

[0012] Based on the above problem analysis, the field of computer vision model training urgently needs a new technical solution that can:

[0013] Establish a quantitative status assessment system: Introduce mature industrial statistical process control methodologies into training analysis to provide statistically significant quantitative assessments and scientific decision-making basis, thereby realizing the transformation from experience-driven to data-driven approaches.

[0014] Implement a predictive maintenance mechanism: Achieve early warning of training problems through trend analysis and pattern recognition, providing a warning time window of 5-10 epochs to support preventive intervention rather than post-event remediation.

[0015] Provides multi-dimensional integrated analysis capabilities: It conducts comprehensive analysis from multiple dimensions such as convergence, overfitting, learning dynamics, and risks, and establishes a comprehensive training status cognition system and optimization decision-making basis.

[0016] Supports refined analysis and optimization: multi-level analysis from the overall to the local level, identifies the differentiated characteristics and optimization potential of each level, and provides targeted optimization suggestions rather than general strategies.

[0017] Ensure consistency between training and inference performance: Establish a correlation mechanism between training metrics and inference performance to ensure that training optimization is beneficial to actual deployment results and achieve end-to-end optimization.

[0018] Forming an intelligent optimization closed loop: a complete closed loop from status analysis, problem early warning, risk assessment to optimization suggestion generation, achieving a high degree of automation, reducing human intervention, and reducing reliance on expert experience.

[0019] The present invention aims to solve the above-mentioned technical problems by proposing a deep learning model training optimization method based on multi-dimensional intelligent analysis. Summary of the Invention

[0020] The purpose of this invention is to provide a deep learning model training optimization method based on multi-dimensional intelligent analysis, which overcomes the problems in the existing technology, such as lack of quantitative standards for judging the training state, delayed overfit detection, lack of scientific basis for training efficiency optimization, lack of prediction and planning capabilities, and lack of risk identification and management.

[0021] Specifically, this invention aims to achieve the following technical objectives: to achieve accurate quantitative judgment of training status based on statistical process control methods; to achieve early warning of overfitting through multi-dimensional analysis; to achieve deep understanding of learning dynamics by applying causal analysis and machine learning methods; to establish a multi-model integration framework to achieve accurate prediction of training results; to construct a multi-dimensional risk assessment system to achieve proactive management of training risks; and to ensure the practical usability of optimization effects through training-inference correlation analysis.

[0022] This invention proposes a deep learning model training optimization method based on multi-dimensional intelligent analysis. This method organically combines statistical process control methods with multi-dimensional intelligent analysis to achieve comprehensive intelligent monitoring, precise analysis and dynamic optimization of the computer vision model training process.

[0023] The method of the present invention includes the following steps: First, multi-dimensional indicator data during the training process are collected in real time through a data collection module; second, the collected data is subjected to quality control and anomaly detection using a statistical process control framework; then, eight intelligent analysis engines are executed in parallel to perform multi-dimensional analysis; further, all analysis results are integrated through a decision fusion module to generate an interpretable analysis report; finally, a targeted optimization strategy is generated based on the fusion decision and real-time adjustments are performed.

[0024] The core algorithmic innovation of this invention includes three key aspects:

[0025] (1) Quantitative evaluation method for training state based on statistical process control

[0026] Unlike existing technologies that rely on subjective experience to judge training status, this invention is the first to systematically introduce the Statistical Process Control (SPC) method into deep learning training analysis. The method includes constructing a moving range control chart (MR-chart) to monitor the stability of the training process, applying hypothesis testing to assess the statistical significance of the convergence trend, and calculating a process capability index to evaluate training quality. This statistical process control method achieves a fundamental shift in judging training status from subjective qualitative assessment to objective quantitative analysis.

[0027] (2) Multi-dimensional fusion training dynamic analysis framework

[0028] This invention innovatively constructs an eight-dimensional fusion analysis framework, including convergence, overfitting, learning dynamics, prediction information, risk level, loss landscape, optimization suggestions, and training-inference consistency. This multi-dimensional analysis framework captures the complex features of the training process from different perspectives, enabling a more comprehensive and accurate understanding of the training state and identification of potential problems compared to single-dimensional analysis.

[0029] (3) Predictive maintenance mechanism based on trend extrapolation

[0030] This invention designs a predictive maintenance mechanism based on trend extrapolation. By calculating the first and second derivatives of loss and performance metrics, and combining this with a multi-model ensemble prediction framework, it achieves early warning of training problems. This predictive maintenance mechanism shifts from passive response to proactive prevention, providing a sufficient time window for problem intervention.

[0031] The system of this invention consists of the following core modules: a data collection module responsible for real-time acquisition of multi-dimensional indicators during the training process; a statistical process control framework for data quality control and anomaly detection; eight parallel intelligent analysis engines each executing specific analysis tasks; a hierarchical fine-grained analysis module providing differentiated monitoring of network layers; a training-inference correlation analysis module establishing a correlation model between training indicators and inference performance; and a decision fusion module integrating all analysis results to generate optimization decisions. These modules form a complete closed-loop iterative mechanism from data acquisition to optimization execution.

[0032] The convergence analysis engine is used to determine the convergence status of model training and to evaluate convergence quality and stability.

[0033] One of the key innovations of this invention lies in establishing a three-dimensional evaluation framework of "statistical significance, trend stability, and fluctuation convergence." For the evaluation of statistical significance, a linear regression model L(t) = β0 + β1t + ε is used. t To analyze the trend of the loss sequence, the p-value of the slope β1 is calculated through hypothesis testing. Convergence is determined when |β1| < 0.001 and p-value > 0.05; convergence is determined when β1 < -0.001 and p-value < 0.05; and divergence is determined when β1 > 0.001 and p-value < 0.05. For trend stability assessment, the sliding window variance Var_w(t) = (1 / w)Σ(L) is calculated. i -L - _w(t)) 2 The rate of change was analyzed, and the stability of the training was quantified using the stability index Stability = exp(-λ × |dVar / dt|). For fluctuation convergence assessment, a moving range control chart was constructed to detect runaway patterns and identify anomalies such as single-point exceedances, continuous increases and decreases, and deviations.

[0034] The proposed three-dimensional evaluation framework breaks through the limitations of traditional single-indicator judgment and improves the accuracy and reliability of convergence judgment through multi-dimensional cross-validation.

[0035] The overfitting detection and early warning engine is used to identify overfitting phenomena and provide early warnings of risks.

[0036] One of the key innovations of this invention lies in establishing a dynamic analysis model based on the evolution of the generalization gap. The generalization gap (gap(t) = L_val(t) - L_train(t)) and the relative gap (relative_gap(t) = gap(t) / L_train(t)) are calculated using the first derivative speed = d(gap) / dt and the second derivative acceleration = d 2 (gap) / dt 2The evolution trend is analyzed. The method achieves a four-level quantitative assessment of overfitting severity: no overfitting (relative_gap < 0.05 and speed ≤ 0), slight overfitting (0.05 ≤ relative_gap < 0.15 and speed > 0), moderate overfitting (0.15 ≤ relative_gap < 0.3), and severe overfitting (relative_gap ≥ 0.3 or acceleration > 0).

[0037] The trend-based extrapolation-based early warning mechanism uses the physical model gap_pred(t+k) = gap(t) + speed×k + 0.5×acceleration×k 2 The system predicts the gap change over the next k steps and triggers an early warning when the predicted value exceeds a threshold and the confidence level meets the requirements. This early warning mechanism can provide early warning of overfitting risks and offer sufficient time for intervention measures.

[0038] The learning dynamics deep analysis engine is used to analyze the learning rate effect and identify the training phase.

[0039] In terms of learning rate effectiveness analysis, this invention innovatively introduces a causal analysis method. The correlation between learning rate and loss improvement is quantified by calculating the Pearson correlation coefficient corr(LR, ΔL), and the lag effect lag_corr(k) = corr(LR[t], ΔL[t+k]) is analyzed to identify the delayed impact of learning rate adjustment. The efficiency ratio efficiency = |ΔL| / |ΔLR| is calculated to evaluate the adjustment effect. The method establishes a four-level evaluation standard: excellent (|corr|>0.6 and efficiency>1.2), good (|corr|>0.4 and efficiency>0.8), average (|corr|>0.2 and efficiency>0.5), and poor (other cases).

[0040] In terms of training phase identification, this invention is the first to apply the K-means clustering algorithm to achieve automatic identification of the training phase. A feature matrix F = [loss value, loss change rate, learning rate, volatility] is constructed, standardized, and then subjected to k=4 clustering analysis. The Viterbi algorithm is applied to smooth the phase transitions. This method can automatically identify four phases: the initial exploration phase, the rapid decline phase, the fine-tuning phase, and the convergence and stabilization phase, without manual annotation, providing a basis for phase-specific optimization.

[0041] The multi-model integrated predictive analytics engine is used to predict training trajectories and final performance.

[0042] This invention overcomes the limitations of single-model prediction by constructing a model that includes linear models (y = ax + b) and multinomial models (y = ax + b). 2An ensemble prediction framework combining the y=ae^(-bx)+c, exponential, and random forest models was developed. The performance of each model was evaluated using cross-validation (k=5), and R0 was calculated. 2 Indicators such as score, MAE, and RMSE are used to select the optimal model using the AIC / BIC criteria. The method not only provides point prediction L_future = best_model.predict(t+1:t+N), but also quantifies the prediction uncertainty through confidence interval CI = prediction ± 1.96 × √(variance).

[0043] In terms of final performance estimation, the engine uses a logarithmic or exponential function to fit the performance convergence curve, identifies the saturation point to estimate asymptotic values, and makes corrections based on overfitting detection results to improve prediction reliability.

[0044] The multi-dimensional risk assessment engine is used to identify and warn of training risks.

[0045] This invention establishes a multi-dimensional risk assessment system encompassing loss divergence risk R1, gradient explosion risk R2, learning rate mismatch risk R3, and training stagnation risk R4. A weighted average risk score is calculated using the formula R_total = w1×R1 + w2×R2 + w3×R3 + w4×R4 (default weights w1 = 0.3, w2 = 0.3, w3 = 0.2, w4 = 0.2). A three-tiered risk classification is then implemented: low risk (R_total < 0.3) requires continued training; medium risk (0.3 ≤ R_total < 0.7) requires close monitoring; and high risk (R_total ≥ 0.7) requires intervention.

[0046] The risk assessment engine sets a duration threshold to avoid false alarms. When a high-risk state continues for more than a preset number of epochs, an early warning is triggered and a detailed risk report is generated.

[0047] The loss landscape analysis engine is used to analyze the geometric features of the loss function and guide the selection of optimization algorithms.

[0048] This invention is the first to introduce geometric feature analysis of the loss function into training optimization. This is achieved by calculating an approximation of the Hessian matrix. Perform PCA decomposition Principal components are extracted, and the loss profile is analyzed by sampling along the principal component directions. Three types of geometric features are extracted: curvature κ = λmax / λmin indicates the optimization difficulty, flatness = √(Σλ) i 2 ) / λmax indicates generalization, and anisotropy = λmax / λ_min indicates the adaptive learning rate requirement.

[0049] The geometric feature analysis provides a theoretical basis for the selection of optimization algorithms. For example, high curvature regions require smaller learning rates, flat regions have better generalization, and high anisotropy requires an adaptive optimizer.

[0050] The intelligent optimization suggestion generation engine is used to generate targeted parameter adjustment suggestions and optimization strategies.

[0051] This invention breaks through the limitations of traditional fixed rules and establishes an intelligent suggestion generation framework based on problem pattern recognition and effect-parameter mapping models. It identifies three typical problem patterns: oscillation mode (frequent fluctuations in loss, excessively large learning rate, suggesting a reduction to a certain percentage of the original learning rate), stagnation mode (long-term lack of improvement in loss, excessively small learning rate, suggesting an increase in the learning rate or the adoption of a learning rate restart strategy), and divergence mode (rapidly increasing loss, severely excessive learning rate, suggesting a significant reduction in the learning rate and the application of gradient pruning).

[0052] The engine establishes an effect-parameter mapping model `mapping_model`: (current parameters, training state) → (optimized effect), training a regression model based on historical data. It not only provides numerical adjustment suggestions `new_lr = current_lr × adjustment_factor`, but also recommends optimization strategies based on the training phase and estimates the optimization effect using `mapping_model.evaluate(new_lr)`.

[0053] In terms of regularization optimization, a mapping relationship between the degree of overfitting and the regularization strength is established. Based on mild, moderate and severe overfitting, suggestions for quantization adjustment of dropout and weight_decay are given, and adaptive adjustments are made considering model complexity.

[0054] The training-inference correlation analysis engine is used to establish a correlation model between training metrics and inference performance.

[0055] This invention fills the technical gap between training optimization and inference performance. It calculates the correlation matrix between training metrics T = {accuracy_train, loss_train, convergence_speed, ...} and inference metrics I = {accuracy_infer, latency_infer, memory_usage, ...}, and analyzes the lag effect considering the time difference between model storage and deployment.

[0056] The engine constructs a performance transfer model I_pred = f(T), using multiple regression or neural network modeling. This is achieved through R... 2 The scoring system employs a three-level consistency assessment: strong consistency (corr > 0.8 and R0.05). 2(> 0.7) indicates that training optimization directly benefits inference, medium consistency (0.5 < corr ≤ 0.8 and 0.4 < R 2 ≤ 0.7) indicates partial benefit, and weak consistency (corr ≤ 0.5 or R 2 ≤ 0.4) requires re-evaluation of the optimization strategy. Adjust the optimization suggestions based on the consistency level, and generate inference-friendly training strategies including model compression, architecture adjustment, deployment environment adaptation, etc.

[0057] The hierarchical fine-grained analysis module is used for differential monitoring and optimization of network levels, which is an optional enhanced function of the present invention.

[0058] This module monitors indicators such as the activation value distribution (mean, standard deviation, quantile), gradient flow (gradient norm, gradient direction), parameter update ratio, resource consumption (memory, computational amount), etc. of each network layer. Identifying hierarchical anomalies includes: vanishing gradients (the gradient norm of a certain layer is significantly lower than the average), exploding gradients (the gradient norm of a certain layer is significantly higher than the average), activation saturation (activation values are concentrated in the extreme value region), and dead neurons (the proportion of neurons with a constant activation value of 0 exceeds the set threshold).

[0059] The module provides differential optimization suggestions for different layers. For example, for shallow layers, it suggests the learning rate multiplier and initialization method, for deep layers, it suggests residual connections and normalization strategies, and for the output layer, it suggests the loss function weight and regularization strength. This module breaks through the limitations of traditional overall monitoring and realizes refined analysis and optimization of network levels.

[0060] Based on the multi-dimensional analysis results, the present invention generates targeted optimization strategies and performs iterative improvement to form a complete closed-loop control mechanism.

[0061] The optimization strategy includes three aspects: targeted data augmentation at the data level, robust regularization term at the loss level L_total = L_original + λ·Σw(pattern)·L_robust(pattern), and model structure adjustment at the architecture level. After the optimization is executed, multi-dimensional analysis is performed again, and the improvement effect is evaluated by comparing the analysis indicators before and after optimization. If significant problems still exist, continue iterative optimization until the expected goal is achieved.

[0062] Compared with the prior art, by organically combining statistical process control methods with multi-dimensional intelligent analysis, the present invention establishes a complete technical link from data-driven analysis to scientific decision optimization, and has the following significant technical advantages and beneficial effects:

[0063] 1. This invention employs a quantitative evaluation mechanism based on statistical process control, which offers higher accuracy compared to traditional empirical judgment methods. It uses a linear regression model and hypothesis testing to assess the statistical significance of the convergence trend, avoiding misjudgments caused by random fluctuations; it captures the volatility evolution of the loss sequence through sliding window variance analysis to identify "false convergence" phenomena; and it automatically identifies runaway modes using a moving range control chart. This three-dimensional evaluation framework offers higher scientific rigor and reliability compared to traditional single-indicator judgments, significantly improving accuracy and avoiding resource waste caused by premature training termination or excessive training extension.

[0064] 2. This invention, based on a dynamic model of generalization gap evolution analysis, achieves a qualitative breakthrough in predictive maintenance compared to traditional post-event detection methods. This invention analyzes the evolutionary trend using first and second derivatives, applies a physical model to extrapolate the trend, and predicts future overfitting states, achieving a four-level quantitative assessment of severity. Traditional methods are mostly post-event analyses, while this invention's predictive early warning mechanism provides an early warning window, creating conditions for the implementation of intervention measures such as data augmentation and regularization adjustments, enabling engineers to take preventative measures before overfitting actually occurs.

[0065] 3. This invention, through intelligent parameter adjustment and targeted optimization, improves model performance while shortening training time compared to traditional methods. It avoids overtraining through precise convergence judgment, prevents ineffective training through early warning, and accelerates the convergence process through intelligent learning rate adjustment, saving a significant proportion of training time on average. Furthermore, through multi-dimensional optimization measures such as overfitting prevention, learning rate optimization, and loss landscape guidance, this invention improves model performance in various computer vision tasks, demonstrating that it not only increases efficiency but also enhances training quality.

[0066] 4. The multi-model ensemble prediction framework of this invention provides accurate prediction capabilities for training results compared to traditional methods. This invention constructs an ensemble prediction framework comprising multiple models, adaptively selecting the optimal model through cross-validation, providing not only point predictions but also confidence intervals to quantify uncertainty. For final performance prediction, a function is applied to fit the convergence curve and identify saturation points, with corrections made based on overfitting detection results to improve prediction reliability. This prediction capability has significant application value for resource scheduling, project management, and cost estimation.

[0067] 5. The multi-dimensional risk assessment system of this invention has higher risk identification accuracy and lower false positive rate compared to traditional methods. This invention establishes an assessment system encompassing multiple risk categories, implementing a three-level risk classification through weighted synthesis, and setting duration thresholds to avoid false positives. The multi-dimensional fusion, compared to single-indicator judgment, can more comprehensively and accurately identify training risks, and the hierarchical management mechanism provides differentiated response strategies, significantly reducing resource waste and improving training success rate.

[0068] 6. The training-inference correlation analysis mechanism of this invention fills the technical gap of the disconnect between training optimization and inference performance. This invention constructs a quantitative correlation model between training metrics and inference performance, implements a three-level consistency evaluation, and generates inference-friendly training suggestions by adjusting optimization strategies based on the consistency level. The correlation analysis ensures that the training optimization strategy not only improves training metrics but also benefits the inference performance after the model is actually deployed. This invention's method can identify the problem of "good training but poor inference" and provides inference-friendly suggestions such as model compression, architecture adjustment, and deployment environment adaptation.

[0069] 7. The method of this invention has good versatility and scalability in the field of computer vision. The core method of this invention is independent of specific vision tasks; it can be adapted to different tasks simply by configuring corresponding performance indicators and evaluation criteria. This invention is applicable to various computer vision tasks such as object detection, image classification, semantic segmentation, and instance segmentation, supports models of different sizes, and is framework-independent. This invention adopts a modular design, with each analysis engine working independently yet collaboratively. New analysis modules can be selectively enabled or expanded according to actual needs.

[0070] 8. This invention not only provides optimization suggestions but also a detailed and interpretable analysis report. The analysis report includes a wealth of dimensions such as quantitative evaluation results of the training status, statistical basis of the analysis, predictive information, targeted optimization suggestions, and hierarchical analysis information. This interpretable report helps engineers deeply understand the training process and the causes of problems, providing a scientific basis for optimization decisions, avoiding blind parameter tuning, and meeting the requirements of industrial applications for traceability of the model training process.

[0071] 9. This invention achieves a high degree of automation in the training process, significantly reducing the need for manual intervention. The eight intelligent analysis engines of this invention work in parallel to automatically perform multi-dimensional analysis; the statistical process control framework automatically detects abnormal patterns; the predictive early warning mechanism automatically identifies potential risks; and the intelligent optimization suggestion engine automatically generates parameter adjustment schemes. This automated process eliminates the need for continuous manual monitoring and experience-based judgment during training, greatly reducing the need for manual intervention. By shortening training time, automating management, and intelligent optimization, it reduces costs and improves efficiency, making it of significant value to organizations that frequently perform model training.

[0072] The core technological innovations of this invention are reflected in the following aspects:

[0073] 1) This invention is the first to systematically apply industrial statistical process control methods to deep learning training analysis, establish a scientific evaluation system from experience-driven to data-driven, and realize a fundamental transformation of training status judgment from subjective qualitative to objective quantitative.

[0074] 2) This invention constructs an eight-dimensional integrated intelligent analysis framework, including convergence, overfitting, learning dynamics, prediction, risk, loss landscape, optimization suggestions, training-inference correlation, etc. Compared with traditional single-dimensional analysis methods, it has more comprehensive cognitive capabilities and more accurate decision-making basis.

[0075] 3) This invention establishes a predictive maintenance mechanism based on trend extrapolation, which realizes early warning of training problems through first-order and second-order derivative analysis, transforming passive response into active prevention, and significantly improving training efficiency and success rate.

[0076] 4) This invention innovatively introduces a causal analysis method to evaluate the learning rate effect, applies a clustering algorithm to achieve automatic identification during the training phase, constructs a multi-model integrated prediction framework to achieve high-precision prediction, designs a hierarchical fine-grained analysis module to achieve differentiated optimization of network layers, and establishes a training-inference correlation analysis mechanism to ensure the practical usability of the optimization effect.

[0077] 5) This invention forms a complete closed-loop mechanism from data acquisition, quality control, multi-dimensional analysis, decision fusion to optimized execution, realizing highly automated intelligent training management and providing industrial-grade reliability and efficiency assurance for computer vision model training. Attached Figure Description

[0078] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0079] Figure 1 This is a schematic diagram of the overall system architecture of the present invention, showing the relationship and data flow between the data collection module, the statistical process control framework, eight parallel intelligent analysis engines, the hierarchical fine-grained analysis module, the decision fusion module, and the optimization execution module;

[0080] Figure 2 This is a schematic diagram of the convergence analysis process of the present invention, which shows the workflow of the three-dimensional convergence evaluation framework constructed based on linear regression analysis, sliding window analysis of variance and statistical process control chart;

[0081] Figure 3 This is a schematic diagram of the overfitting detection and early warning process of the present invention, which shows the workflow of calculating the generalization gap, first derivative and second derivative to classify the severity into four levels, and realizing early warning based on trend extrapolation.

[0082] Figure 4 This is a schematic diagram of the learning rate effect analysis process of the present invention, which shows the workflow of comprehensively evaluating the learning rate effect through Pearson correlation analysis, lag effect analysis and efficiency ratio calculation;

[0083] Figure 5 This is a schematic diagram of the training phase identification process of the present invention, which shows the workflow of automatically identifying the training phase based on the K-means clustering algorithm and the Viterbi algorithm for smoothing.

[0084] Figure 6 This is a schematic diagram of the multi-model ensemble prediction process of the present invention, which shows the workflow of constructing an ensemble prediction framework that includes linear models, multinomial models, exponential models and random forest models, selecting the optimal model through cross-validation and generating confidence intervals.

[0085] Figure 7 This is a schematic diagram of the risk assessment process of the present invention, which shows the workflow of weighted comprehensive risk scoring and three-level risk classification based on four dimensions: loss divergence, gradient explosion, learning rate mismatch, and training stagnation.

[0086] Figure 8 This is a schematic diagram of the intelligent optimization suggestion generation process of the present invention, which shows the workflow of generating learning rate adjustment, regularization optimization and optimizer strategy suggestions through problem pattern recognition and effect-parameter mapping model;

[0087] Figure 9 This is a schematic diagram of the training-inference correlation analysis process of the present invention, which shows the workflow of establishing the correlation between training indicators and inference performance and implementing a three-level consistency evaluation through correlation analysis, lag effect analysis and performance transfer modeling. Detailed Implementation

[0088] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0089] Example 1: Training and Optimization of YOLO Object Detection Model

[0090] Application scenario description

[0091] Task: Object detection in autonomous driving scenarios using YOLOv8. Dataset: 50,000 traffic scene images, including vehicles, pedestrians, traffic signs, etc. Model: YOLOv8-medium, approximately 25M parameters. Hardware: Single NVIDIA RTX 4090 GPU (24GB VRAM). Training framework: PyTorch 2.1

[0092] Implementation steps

[0093] Step 1: Data Preparation and Configuration

[0094] 1.1 Data Collection Configuration

[0095]

[0096]

[0097] 1.2 Early Warning Threshold Setting

[0098]

[0099] Step 2: Convergence Analysis Implementation

[0100] 2.1 Linear Regression Analysis

[0101]

[0102]

[0103]

[0104] 2.2 Sliding Window Analysis of Variance

[0105]

[0106] 2.3 Statistical Process Control Chart

[0107]

[0108]

[0109]

[0110] Implementation Results (Epoch 30):

[0111] Convergence analysis results:

[0112] Status: Converging

[0113] -Slope: -0.0235

[0114] -p-value:0.0023

[0115] - Confidence level: 99.77%

[0116] -R 2 0.92

[0117] Stability analysis results:

[0118] - Current variance: 0.0012

[0119] - Rate of change of variance: 0.00015

[0120] -Stability index: 0.85

[0121] - Assessment: Highly stable

[0122] SPC control chart:

[0123] - Control limits: CL = 0.032, UCL = 0.105

[0124] -Out of Control Mode: None

[0125] Process capability: Qualified

[0126] RTX 4090 GPU utilization: 92%, VRAM usage: 18.5GB / 24GB

[0127] Overall assessment: Training is progressing well; continue with the current strategy.

[0128] Step 3: Overfitting Detection and Early Warning

[0129] 3.1 Calculation of generalization gap

[0130]

[0131]

[0132]

[0133] Implementation Results (Epoch 45):

[0134] Overfitting detection results:

[0135] Current status:

[0136] - Level: Slight overfitting

[0137] Severity: 1

[0138] - Generalization gap: 0.025

[0139] - Relative gap: 11.2%

[0140] - Growth rate: 0.003 / epoch

[0141] -Acceleration: 0.0001 / epoch 2

[0142] Warning information:

[0143] Warning: Moderate overfitting is expected after 5 epochs (Epoch 50).

[0144] -Prediction gap: 0.041

[0145] -Predicted relative gap: 18.5%

[0146] - Confidence level: 75%

[0147] Recommended measures:

[0148] 1. Added dropout: 0.3 → 0.4

[0149] 2. Increase weight_decay: 1e-4 → 5e-4

[0150] 3. Enhance data augmentation strength

[0151] 4. Consider early stopping preparations

[0152] Step 4: Learning Dynamic Analysis

[0153] 4.1 Evaluation of Learning Rate Effectiveness

[0154]

[0155]

[0156]

[0157] 4.2 Identification during the training phase

[0158]

[0159]

[0160] Implementation Results (Epoch 60):

[0161] Learning rate effectiveness evaluation:

[0162] - Correlation coefficient: 0.68

[0163] -p-value: 0.0008 (highly significant)

[0164] -Optimal lag: 1 epoch

[0165] - Efficiency ratio: 1.45

[0166] Rating: Excellent

[0167] The current learning rate strategy is effective and should be maintained.

[0168] Training phase identification:

[0169] -Current Stage: Fine-tuning Period

[0170] -Stage distribution:

[0171] *Initial exploration phase: Epoch 1-15

[0172] *Rapid decline phase: Epoch 16-45

[0173] *Fine-tuning period: Epoch 46-60

[0174] *Convergence and stabilization period: Expected Epoch 70+

[0175] Targeted recommendations:

[0176] - We are currently in the fine-tuning phase; it is recommended to reduce the learning rate to 1e-4.

[0177] -Increase training stability and reduce randomness

[0178] Step 5: Predictive Analysis

[0179] 5.1 Multi-model ensemble prediction

[0180]

[0181]

[0182]

[0183]

[0184] Implementation Results (Epoch 60):

[0185] Predictive analysis results:

[0186] -Optimal Model: exponential

[0187] - Cross-validation R 2 0.91

[0188] Loss trajectory prediction (future 20 epochs):

[0189] Epoch 61-80:[0.185,0.178,0.172,...,0.142]

[0190] Training completion estimate:

[0191] -Expected convergence epoch: 78

[0192] -Expected final loss: 0.142 ± 0.018

[0193] -Expected final mAP: 0.805 ± 0.025

[0194] - Remaining training time: Approximately 2.5 hours

[0195] Recommendation: Continue training until Epoch 78, which is expected to achieve the target performance.

[0196] Step 6: Optimization Suggestion Generation and Execution

[0197] Based on the above analysis, the system generates the following optimization suggestions at Epoch 50:

[0198] Intelligent Optimization Recommendation Report - Epoch 50

[0199] == ...

[0200] Issues detected:

[0201] 1. Slight overfitting trend (expected to worsen after 5 epochs)

[0202] 2. Training enters the fine-tuning phase.

[0203] Optimization suggestions:

[0204] [High Priority]

[0205] 1. Learning rate adjustment

[0206] -Current value: 1e-3

[0207] - Recommended value: 5e-4

[0208] - Reason for adjustment: Entering a fine-tuning phase, a smaller learning rate is required.

[0209] - Expected results: Improved stability and convergence quality.

[0210] 2. Regularization Enhancement

[0211] -Dropout: 0.3 → 0.4 (+0.1)

[0212] -Weight Decay:1e-4→5e-4(+4e-4)

[0213] - Reason for adjustment: To prevent overfitting

[0214] - Expected result: Reduce generalization gap by 8-12%

[0215] [Medium Priority]

[0216] 3. Data Augmentation

[0217] - Add MixUp or CutMix

[0218] - Strength modulus: 0.3

[0219] - Expected outcome: Further enhance generalization ability

[0220] [Low priority]

[0221] 4. Early Stopping Preparation

[0222] - Set patience=10

[0223] - Monitoring metric: val_mAP

[0224] - Save the best model

[0225] Expected overall results:

[0226] Overfitting risk: -60%

[0227] -Final performance: +8-12% mAP

[0228] Training time: -15% (early convergence)

[0229] Follow up on the results of the optimization:

[0230]

[0231] Summary of Implementation Results

[0232] Quantitative indicators

[0233]

[0234] Key success factors

[0235] 1. Early warning: Successfully warned of overfitting at Epoch 45, providing a 5-epoch adjustment window.

[0236] 2. Precise Adjustment: Parameter adjustment based on quantitative analysis, rather than trial and error based on experience.

[0237] 3. Stage Identification: Accurately identify the training stage and optimize the strategy accordingly.

[0238] 4. Accurate prediction: The final performance prediction error is only 5.8%.

[0239] Resource saving

[0240] Computational cost: Saves approximately 42 hours of GPU training time

[0241] Labor costs: Reduce manual monitoring and parameter tuning time by 90%.

[0242] Example 2: Semantic Segmentation Model Training Optimization

[0243] Application scenario description

[0244] Task: Semantic segmentation of autonomous driving scenarios using a DeepLabV3+ model. Dataset: Cityscapes dataset (5000 finely annotated city street view images, 19 categories). Model: DeepLabV3+ with ResNet-101, approximately 58M parameters. Hardware: Single NVIDIA RTX 4090 GPU (24GB VRAM). Training framework: PyTorch 2.1

[0245] Special challenges faced in semantic segmentation training (compared to object detection):

[0246] 1. Category Imbalance: Major categories such as roads and buildings have a high proportion, while minor categories (traffic signs, pedestrians, etc.) have a low proportion.

[0247] 2. High memory consumption: High-resolution input and intensive prediction require a large amount of video memory.

[0248] 3. Multi-scale problems: These require processing objects of different scales simultaneously.

[0249] 4. Boundary accuracy: The quality of the segmentation boundary directly affects the practical application effect.

[0250] Implementation steps

[0251] Step 1: Category-level performance monitoring

[0252] To address the class imbalance problem in semantic segmentation, a class-level monitoring system is established:

[0253] Category-level monitoring configuration

[0254]

[0255] Class imbalance detection:

[0256]

[0257]

[0258] Implementation Results (Epoch 25):

[0259] Category-level performance analysis:

[0260] Main categories (high percentage):

[0261] -road: IoU 0.94, convergence rate 0.008 / epoch√

[0262] -building: IoU 0.89, convergence rate 0.006 / epoch√

[0263] -vegetation: IoU 0.87, convergence rate 0.005 / epoch√

[0264] Medium category:

[0265] -car: IoU 0.82, convergence rate 0.004 / epoch

[0266] -sidewalk: IoU 0.79, convergence rate 0.003 / epoch

[0267] -fence: IoU 0.71, convergence rate 0.002 / epoch

[0268] Minority categories (low percentage):

[0269] -traffic_sign:IoU 0.58, convergence rate 0.001 / epoch

[0270] -pole: IoU 0.55, convergence rate 0.0008 / epoch

[0271] -person: IoU 0.68, convergence rate 0.0015 / epoch

[0272] Issues detected:

[0273] Severe class imbalance: Training in a few classes is significantly lagging.

[0274] suggestion:

[0275] 1. Increase the loss weights for a minority of classes (currently all are 1.0).

[0276] 2. Use Focal Loss to mitigate class imbalance

[0277] 3. Add data augmentation to small target categories.

[0278] 4. RTX 4090 VRAM usage: 21.2GB / 24GB

[0279] Step 2: Multi-scale performance analysis

[0280] To address the multi-scale problem of semantic segmentation, the training effect of objects at different scales is analyzed:

[0281]

[0282]

[0283]

[0284] Implementation Results (Epoch 30):

[0285] Multi-scale performance analysis:

[0286] Large-scale objects (>10000 pixels):

[0287] - Average IoU: 0.88

[0288] - Boundary IoU: 0.82

[0289] - Number of objects: 2340

[0290] -Evaluation: Excellent√

[0291] Medium-scale objects (1000-10000 pixels):

[0292] - Average IoU: 0.74

[0293] - Boundary IoU: 0.68

[0294] - Number of objects: 4580

[0295] - Assessment: Good

[0296] Small-scale objects (<1000 pixels):

[0297] - Average IoU: 0.52

[0298] - Boundary IoU: 0.38

[0299] - Number of objects: 1820

[0300] - Assessment: Needs improvement

[0301] Scale variance: 0.078 (significant)

[0302] Issues detected:

[0303] Small-scale objects exhibit poor performance and low boundary precision.

[0304] suggestion:

[0305] 1. Add multi-scale training strategies

[0306] 2. Increase input resolution (currently 1024×512)

[0307] 3. Use the boundary refinement module

[0308] 4. Add Crop enhancement to small goals.

[0309] Step 3: Generate Adaptive Optimization Strategy

[0310] Based on the above analysis, the system generates targeted optimization suggestions at Epoch 35:

[0311]

[0312]

[0313] Optimization suggestions generated (Epoch 35):

[0314] Intelligent Optimization Recommendation Report - Epoch 35

[0315] == ...

[0316] The core issue detected:

[0317] 1. Training lag in minority classes (traffic_sign, pole, person)

[0318] 2. Insufficient segmentation accuracy for small-scale objects.

[0319] 3. Training enters the fine-tuning phase.

[0320] Optimization strategy:

[0321] [High Priority]

[0322] 1. Category weight rebalancing

[0323] -traffic_sign:1.0→3.5

[0324] -pole:1.0→4.0

[0325] -person: 1.0 → 2.5

[0326] - Expected results: IoU increase of 15-20% in a few categories

[0327] 2. Multi-scale training

[0328] - Input scale: [768×384, 1024×512, 1280×640, 1536×768]

[0329] - Random selection strategy

[0330] - Expected results: A small target of increasing IoU by 10-15%

[0331] [Medium Priority]

[0332] 3. Adaptive learning rate decay

[0333] -Current: 1e-3

[0334] - Adjustment: 5e-4 (Cosine)

[0335] - Expected result: Improved convergence quality

[0336] 4. Boundary Refinement

[0337] - Increase the boundary loss weight: 0.3

[0338] -Use boundary awareness module

[0339] RTX 4090 compatibility optimization:

[0340] - Mixed Precision Training (FP16) reduces GPU memory usage

[0341] Gradient Checkpointing saves 30% of video memory.

[0342] -Supports larger batch sizes: 8→12

[0343] Summary of Implementation Results

[0344] Quantitative indicators

[0345] After implementing the optimization strategy (Epochs 36-65), the results on the Cityscapes validation set are as follows:

[0346]

[0347]

[0348] Key improvements

[0349] 1. Category balancing optimization: Through category-level monitoring and weight adjustment, performance of a few categories was improved by 13.8%.

[0350] 2. Multi-scale adaptation: The multi-scale training strategy improves the segmentation accuracy of small objects by 12.4%.

[0351] 3. Improved Boundary Quality: Boundary perception optimization improved boundary accuracy by 11.3%.

[0352] 4. Training efficiency: Early identification of convergence trends saves 55 epochs of training time.

[0353] RTX 4090 optimization effects

[0354] VRAM optimization: VRAM usage is reduced to 16.8GB after mixed-precision training, leaving ample margin.

[0355] Throughput improvement: Batch size increased from 8 to 12, training speed improved by 35%.

[0356] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A deep learning model training optimization method based on multi-dimensional intelligent analysis, characterized in that, Includes the following steps: Step S1: Collect multi-dimensional data in real time during the training process of the deep learning model. The multi-dimensional data includes training loss, validation loss, learning rate, performance metrics, gradient information, layer activation values, resource consumption data, and inference performance metrics. Step S2: Perform statistical process control analysis on the collected data, construct a moving range control chart and detect runaway modes, and evaluate the stability of the training process through process capability analysis; Step S3: Perform convergence analysis on the training loss sequence based on linear regression analysis and sliding window variance analysis, and combine statistical significance test to determine the convergence status of model training; Step S4: Calculate the generalization gap between training loss and validation loss, analyze the evolution trend of the gap and its first and second derivatives, classify and evaluate the severity of overfitting, and implement overfitting early warning based on trend extrapolation. Step S5: Analyze the correlation between learning rate and training effect, use K-means clustering method to identify the training stage, and evaluate the adaptability of learning rate; Step S6: Predict the training loss trajectory, final performance, and training time based on the multi-model ensemble prediction framework; Step S7: Identify training failure risk and resource waste risk based on multi-dimensional statistical analysis, calculate comprehensive risk score and classify risk level; Step S8: Generate optimization suggestions based on problem pattern recognition and effect-parameter mapping, including learning rate adjustment suggestions and regularization parameter optimization suggestions; Step S9: Establish a correlation model between the training process and inference performance, analyze the correlation between training metrics and inference metrics, evaluate the consistency level, and generate an inference-friendly training optimization strategy. Step S10: Integrate all analysis results to generate the final optimization decision and control instructions.

2. The deep learning model training optimization method based on multi-dimensional intelligent analysis according to claim 1, characterized in that, The statistical process control analysis in step S2 includes: (1) Construct a moving range control chart: Calculate the moving range MRᵢ=|L i -L i-1 |, Determine the centerline CL_MR=mean(MR), and determine the upper control limit UCL_MR=3.267×CL_MR and the lower control limit LCL_MR=0; (2) Detection of runaway modes: Identify runaway modes such as a single point exceeding the 3σ control limit, 7 consecutive points being monotonous, and 8 consecutive points being on the same side of the center line; (3) Calculate the process capability index Cpk, assess process stability, and trigger corresponding optimization measures based on the runaway mode.

3. The method according to claim 1, characterized in that, The convergence analysis in step S3 includes: (1) The linear regression model L(t) = β0 + β1t + ε is adopted. t Analyze the trend of the loss sequence, and calculate the slope β1 and significance p-value; (2) Calculate the sliding window variance Var_w(t)=(1 / w)Σ i＝t-m+1 t (L i -L - _w(t)) 2 The stability index is evaluated as Stability = exp(-λ × |dVar / dt|). (3) Comprehensive judgment of convergence status: when β1<-0.001 and p-value<0.05, it is judged as converging; when |β1|<0.001 and p-value>0.05, it is judged as converged; when β1>0.001 and p-value<0.05, it is judged as diverging.

4. The method according to claim 1, characterized in that, The overfitting detection in step S4 includes: (1) Calculate the generalization gap gap(t) = L_val(t) - L_train(t) and the relative gap relative_gap(t) = gap(t) / L_train(t); (2) Calculate the first derivative speed = d(gap) / dt and the second derivative acceleration = d 2 (gap) / dt 2 ; (3) The severity of overfitting is classified into four levels: when relative_gap < 0.05 and speed ≤ 0, there is no overfitting; when 0.05 ≤ relative_gap < 0.15 and speed > 0, it is slightly overfitting; when 0.15 ≤ relative_gap < 0.3, it is moderate overfitting; when relative_gap ≥ 0.3 or acceleration > 0, it is severely overfitting. (4) Based on trend extrapolation, gap_pred(t+k) = gap(t) + speed × k + 0.5 × acceleration × k 2 An alert is triggered when gap_pred(t+k) > threshold and confidence > 0.

8.

5. The method according to claim 1, characterized in that, The learning dynamics analysis in step S5 includes: (1) Calculate the Pearson correlation coefficient corr(LR, ΔL) between the learning rate and the rate of change of loss, analyze the lag effect lag_corr(k)=corr(LR[t],ΔL[t+k]), and calculate the efficiency ratio efficiency=|ΔL| / |ΔLR|; (2) The K-means clustering method was used to perform 4-cluster analysis on the feature matrix F = [loss value, loss change rate, learning rate, volatility] to identify the initial exploration period, rapid decline period, fine-tuning period and convergence stabilization period; (3) Comprehensive evaluation of learning rate adaptability: when |corr|>0.6 and efficiency>1.2, the rating is excellent; when |corr|>0.4 and efficiency>0.8, the rating is good; when |corr|>0.2 and efficiency>0.5, the rating is average; otherwise, the rating is poor.

6. The method according to claim 1, characterized in that, The predictive analysis in step S6 and the risk assessment in step S7 include: (1) Construct a candidate model set including linear model, multinomial model, exponential model and random forest, evaluate the performance of each model using cross-validation, and select R... 2 The model with the highest score is used for prediction; (2) Calculate the risk of loss divergence R1, gradient explosion R2, learning rate mismatch R3 and training stagnation R4, and obtain the comprehensive risk score R_total=w1R1+w2R2+w3R3+w4R4 by weighted summation; (3) Risk level classification: when R_total<0.3, it is low risk; when 0.3≤R_total<0.7, it is medium risk; when R_total≥0.7, it is high risk. When the high risk state lasts for more than 3 epochs, an early warning is triggered.

7. The method according to claim 1, characterized in that, The optimization suggestion generation in step S8 includes: (1) Identify problem patterns: When the loss curve fluctuates frequently, it is identified as an oscillation pattern, indicating that the learning rate is too large; when the loss does not improve for a long time, it is identified as a stagnation pattern, indicating that the learning rate is too small; when the loss increases rapidly, it is identified as a divergence pattern, indicating that the learning rate is seriously too large. (2) Establish an effect-parameter mapping model to generate learning rate optimization suggestions, including numerical adjustment new_lr = current_lr × adjustment_factor and policy adjustment; (3) Generate regularization optimization suggestions based on the degree of overfitting: For slight overfitting, increase dropout to 0.1-0.2 and weight_decay to 1e-4; for moderate overfitting, increase dropout to 0.2-0.4 and weight_decay to 5e-4; for severe overfitting, increase dropout to 0.4-0.6 and weight_decay to 1e-3.

8. The method according to claim 1, characterized in that, The training-inference association analysis in step S9 includes: (1) Calculate the correlation coefficient corr(T, I) between the training index T and the inference index I, and analyze the lag effect; (2) Establish a performance migration prediction model I_pred=f(T); (3) Conduct consistency evaluation: When corr > 0.8 and R 2 > 0.7, it is strongly consistent, and training optimization directly benefits inference; when 0.5 < corr ≤ 0.8 and 0.4 < R 2 ≤ 0.7, it is moderately consistent, and training optimization partially benefits inference; when corr ≤ 0.5 or R 2 ≤ 0.4, it is weakly consistent, and the optimization strategy needs to be re-evaluated; (4) Adjust and optimize the strategy based on the consistency level to generate inference-friendly training suggestions.

9. The method according to claim 1, further comprising a hierarchical fine-grained analysis step: (1) Monitor the activation value distribution, gradient flow, parameter update ratio and resource consumption of each network layer; (2) Identify hierarchical anomalies: When the gradient norm of a certain layer is <0.01×average, it is identified as gradient vanishing; when the gradient norm of a certain layer is >100×average, it is identified as gradient explosion; when the activation values ​​are concentrated in the extreme value region, it is identified as activation saturation; when the proportion of neurons with constant activation values ​​of 0 is >20%, it is identified as dead neurons. (3) Provide differentiated optimization suggestions for different layers: adjust the learning rate factor and initialization method for shallow layers; optimize residual connections and normalization strategies for deep layers; adjust the weight of loss function and regularization intensity for output layers.

10. A deep learning model training and optimization system based on multi-dimensional intelligent analysis, characterized in that, include: The data collection module is used to collect multi-dimensional data in real time during the training process of deep learning models. The multi-dimensional data includes training loss, validation loss, learning rate, performance indicators, gradient information, layer activation values, resource consumption data, and inference performance indicators. The statistical process control module, connected to the data collection module, is used to perform statistical process control analysis on the collected data, including control chart construction, process capability analysis, and runaway mode detection. The convergence analysis module, connected to the data collection module, is used to determine the convergence state of the model training based on linear regression analysis, sliding window analysis of variance, and statistical significance test. An overfitting detection module, connected to the data collection module, is used to assess and classify the severity of overfitting, and to provide early warning based on generalization gap evolution analysis and trend extrapolation. The learning dynamic analysis module, connected to the data collection module, is used to analyze the learning rate effect and identify the training phase, based on correlation analysis and K-means clustering. The predictive analysis module, connected to the data collection module, is used to predict the training loss trajectory, final performance, and training duration, based on a multi-model integrated prediction framework. The risk assessment module, connected to the data collection module, is used to identify training failure risk and resource waste risk based on multi-dimensional statistical analysis. The optimization suggestion generation module is connected to the statistical process control module, convergence analysis module, overfit detection module, learning dynamics analysis module, predictive analysis module, and risk assessment module. It is used to generate parameter adjustment suggestions based on problem pattern recognition and effect-parameter mapping. The training inference correlation analysis module is connected to the data collection module and is used to establish a correlation model between the training process and inference performance to ensure that training optimization is beneficial to inference performance. The decision fusion module, connected to the optimization suggestion generation module and the training inference correlation analysis module, is used to generate optimization decisions by integrating all analysis results, including multi-source information fusion, confidence assessment, and interpretability generation.

11. The system according to claim 10, further comprising a hierarchical fine-grained analysis module connected to the data collection module and the optimization suggestion generation module, for monitoring the activation value distribution, gradient flow and resource consumption of each network layer, identifying hierarchical anomalies and providing hierarchical-specific optimization suggestions.

12. The system according to claim 10, characterized in that, The statistical process control module, convergence analysis module, overfit detection module, learning dynamics analysis module, predictive analysis module, and risk assessment module are implemented through a parallel computing architecture, which can perform multi-dimensional analysis simultaneously and output analysis results in real time, supporting streaming processing and incremental updates.

13. The system according to claim 10, further comprising a visualization report module connected to the decision fusion module, for generating a visualization report including analysis result charts, prediction information, optimization suggestions and risk assessment, supporting HTML interactive reports, PDF static reports, JSON structured data and real-time dashboard output formats.

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