A method for mining and analyzing set-top box adaptation test data

Abstract

The application discloses a kind of set top box adaptation test data mining and analysis method, method includes: set top box adaptation test data acquisition, data space-time normalization, multi-modal feature engineering, multi-modal joint analysis modeling and deployment optimization.The application belongs to the field of data processing, specifically refers to a kind of set top box adaptation test data mining and analysis method, the scheme uses attention dynamic alignment layer to process nonlinear time delay, realizes non-uniform time delay modeling using feature space mapping and weighted reconstruction, and then optimizes time deviation and morphological difference through differentiable DTW joint optimization;Multi-modal joint analysis modeling is used, the multi-source heterogeneous data in set top box adaptation test data is processed by fusing specialized model, a chip perception meta-learning model is constructed, the decision robustness is improved by combining the optimized dynamic weighted voting mechanism, the full-automatic adaptation analysis of set top box test is realized, and the cost of manual tuning is significantly reduced.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically a method for mining and analyzing set-top box adaptation test data. Background Technology

[0002] The set-top box adaptation test data mining and analysis method is an intelligent compatibility assessment and optimization technology based on multi-source heterogeneous test data. Its core is to automatically identify compatibility issues of set-top boxes under different hardware and software combinations, quantitatively assess the stability of devices in complex environments, and provide early warnings of potential adaptation risks. However, existing set-top box adaptation test data mining and analysis methods suffer from several technical problems: dynamic latency during set-top box testing leads to spurious correlations; mismatched dimensions of multimodal data; high computational complexity makes real-time data processing difficult; and resource bottlenecks arise. Furthermore, the methods suffer from significant performance differences between different set-top box chips, making it impossible for traditional static models to adaptively adjust, resulting in low resource utilization. Additionally, multimodal data conflicts can lead to misjudgments with a single model, and insufficient fault mode coverage is a significant concern. Summary of the Invention

[0003] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a method for mining and analyzing set-top box adaptation test data. It addresses technical problems such as spurious correlations caused by dynamic latency during set-top box testing, mismatched dimensions of multimodal data, high computational complexity leading to difficulties in real-time data processing, and resource bottlenecks. The method employs data spatiotemporal normalization to identify data characteristics and latency features. It quickly estimates the global clock deviation through a basic alignment layer, uses an attention-based dynamic alignment layer to handle nonlinear latency, performs dynamic alignment reconstruction, and then jointly optimizes time deviation and morphological differences through differentiable DTW, supporting dynamic... Parameter adjustments reduce memory usage. Addressing the technical challenges of significant performance differences between set-top box chips, the inability of traditional static models to adapt, low resource utilization, multimodal data conflicts, and the potential for misjudgments with single models, as well as insufficient fault mode coverage, a multimodal joint analysis modeling approach is adopted. This approach integrates specialized models to process heterogeneous data in set-top box adaptation test data, constructs a chip-aware meta-learning model, calculates meta-loss functions, and integrates and optimizes multiple models through a dynamic weighted voting mechanism. This enhances decision robustness, enables fully automated adaptation analysis for set-top box testing, and significantly reduces manual tuning costs.

[0004] The technical solution adopted by this invention is as follows: This invention provides a method for mining and analyzing set-top box adaptation test data, which includes the following steps:

[0005] Step S1: Set-top box adaptation test data collection;

[0006] Step S2: Spatiotemporal normalization of data;

[0007] Step S3: Multimodal feature engineering;

[0008] Step S4: Multimodal joint analysis and modeling;

[0009] Step S5: Deployment optimization.

[0010] Further, in step S1, the set-top box adaptation test data is collected in real time through an embedded probe. The set-top box adaptation test data includes hardware performance data, video quality data, system interaction data, network transmission data, compatibility test data, stress test data, and log analysis data.

[0011] The hardware performance data includes CPU utilization, temperature sensor data, memory usage, GPU rendering frame rate, and video decoding latency.

[0012] The video quality data includes resolution adaptation, color accuracy, dynamic response, and stuttering rate;

[0013] The system interaction data includes user operation logs and interface response metrics;

[0014] The network transmission data includes real-time transmission quality and adaptive bitrate performance;

[0015] The compatibility test data includes peripheral compatibility matrix and DRM support status;

[0016] The pressure test data includes stability indicators and extreme temperature performance;

[0017] The log analysis data includes key error codes and kernel log characteristics.

[0018] Further, in step S2, the spatiotemporal normalization of the test data includes the following steps:

[0019] Step S21: Test environment feature identification. Identify key data features and latency characteristics in the set-top box test environment based on the set-top box adaptation test data.

[0020] Step S22: Set up a basic alignment layer to quickly estimate the global clock skew, provide an initial value for fine alignment, and limit the search range based on the maximum hardware clock skew.

[0021] Step S23: Set up an attention dynamic alignment layer to handle non-linear time delays. The formula used is as follows:

[0022] ;

[0023] In the formula, This represents the optimal time offset. Indicates global clock skew. This represents the i-th time point within the time window. This represents the j-th time point within the time window. This represents the eigenvector of the reference sequence at time t. Indicates the sequence to be aligned in The feature vector at time step, Indicates the sequence to be aligned in The feature vector at time t, where d represents the dimension of the feature vector, reflecting the complexity of the test data representation, and T represents the length of the time window. The squared norm is used to measure alignment error.

[0024] Dynamic alignment and reconstruction generate a sequence that best matches the reference data, which refers to the original standard test data sent from the test source, unprocessed by network transmission and set-top box. The formula used is as follows:

[0025] ;

[0026] In the formula, z represents the reconstructed sequence. i This represents the attention weight coefficient at the i-th time point;

[0027] Step S24: Calculate the spatiotemporal alignment error, perform differentiable DTW optimization, and achieve end-to-end differentiable morphological alignment. By minimizing the time offset and morphological differences between time series, the aligned set-top box adaptation test data is obtained. The formula used is as follows:

[0028] ;

[0029] ;

[0030] In the formula, E represents the spatiotemporal alignment error. Indicates the weight of time deviation. T1 represents the morphological similarity weight, and T1 represents the differentiable morphological alignment error. Let represent the DTW alignment path matrix, γ represent the smoothing parameter, and A represent all possible DTW alignment paths. Represents the point-to-point distance matrix. DTW represents the path cost, which is the cumulative distance of all points on the path.

[0031] Further, in step S3, the multimodal feature engineering includes the following steps:

[0032] Step S31: Perform basic feature extraction, mine basic features from the aligned set-top box adaptation test data, and generate higher-order features through combination and transformation;

[0033] Step S32: Feature selection, eliminating redundant features, unifying feature dimensions, performing feature scaling and encoding, standardizing all numerical features, and finally outputting the feature matrix.

[0034] Further, in step S4, the multimodal joint analysis modeling includes the following steps:

[0035] Step S41: Multimodal model division of labor. For different dimensions of set-top box adaptation test data, select specialized models, and each specialized model processes the set-top box adaptation test data to extract advanced features.

[0036] The core of the specialized model is to select a model based on data characteristics, including time series models, structured models, and unsupervised models;

[0037] For CPU temperature and frame rate sequences, a timing model is used to capture the timing degradation trend of hardware performance.

[0038] For hardware configurations, including chip models and memory size, a structured model is used to determine the compatibility of static configuration combinations.

[0039] An unsupervised model is used to detect unknown fault patterns based on network jitter patterns and anomaly logs.

[0040] Step S42: Multimodal data fusion, fusing high-level features to obtain a fused feature matrix;

[0041] Modal alignment: Input the feature matrices of each modality and ensure that the timestamps of the feature matrices are consistent;

[0042] Feature fusion integrates data features from different modalities into a unified representation. Within a modality, dimensionality is first reduced, and then cross-modal concatenation is performed. Intermodal correlations are captured through a cross-attention mechanism.

[0043] Step S43: Construct a chip-aware meta-learning model. This model learns both the task itself and how to quickly adapt to different hardware chips, thereby automatically achieving a balance between high performance and low power consumption on resource-constrained devices. The meta-learner simulates the deployment of specialized models in various hardware environments, calculates the minimized meta-learning loss function, and ensures that the model trained by the chip-aware meta-learning framework has strong cross-hardware adaptive capabilities in tasks. The formula used is as follows:

[0044] ;

[0045] In the formula, M represents the minimized meta-learning loss function, which is the joint optimization objective combining model performance and hardware efficiency. These represent the trainable parameters of a specialized model. By optimizing these parameters, the model can be adapted to chip constraints. Indicates task From task distribution The sampling task is used to ensure that meta-learning generalizes to diverse deployment environments. Indicates in the task upper model The prediction error G represents the parameters after rapid adaptation through meta-learning, which are updated via inner loop gradients to adapt to new tasks. arch This represents a hardware constraint mask, used to block operations that the chip does not support. Represents the regularization coefficient. The L2 norm penalty term for the parameters is used to suppress parameters that do not meet hardware constraints, thereby promoting structured pruning.

[0046] The formula used to update task parameters is as follows:

[0047] ;

[0048] In the formula, This represents the parameters of the specialized model after training and updating. This represents the learning rate, used to control the magnitude of parameter updates. This represents the gradient operator with respect to the parameters;

[0049] Step S44: Multi-model integration employs an optimized dynamic weighted voting mechanism. This involves introducing local confidence weights combined with the model's prediction confidence on the current test data, dynamically adjusting the weights to automatically reduce the weight of high-AUC models when prediction uncertainty arises, thus minimizing misjudgments. Models with significant discrepancies with other models are penalized to avoid collective misjudgments. Furthermore, models of different modalities are grouped and weighted to prevent single-modal dominance and improve the robustness of multi-modal predictions. The formula used is as follows:

[0050] ;

[0051] ;

[0052] In the formula, This represents the dynamic weights of the m-th model. C represents the balancing parameter, used to control the ratio of global AUC weights to local confidence weights. m This represents the prediction confidence of the m-th model for the current test data. This represents the sum of confidence scores for all models, used for normalization. Divergence is a parameter representing the divergence penalty, used to control the strength of the penalty for differences in predictions between models. mG represents the prediction difference of the m-th model, G represents the total number of modal groups, and γ represents the prediction difference of the m-th model. g Let m represent the preset weights of the g-th mode group, and m∈g represent the set of models belonging to the g-th mode group. represents the sum of dynamic weights of all models in group g, used for within-group normalization; FS represents the final ensemble score, used to predict set-top box compatibility risks. This represents the AUC score of the m-th model. Score represents the sum of the AUC scores of all models. m This represents the prediction output of the m-th model for the current data.

[0053] Further, in step S5, the deployment optimization includes the following steps:

[0054] Model deployment involves deploying the trained model to the set-top box automated testing environment to verify the set-top box adaptation fault detection rate, false alarm rate, and the improvement in testing efficiency. It also involves collecting misjudgment cases from actual operation, labeling them, and feeding them back into the training data. Furthermore, it involves supplementing the feature engineering with updated set-top box model parameters, regularly updating the model to adapt to new models and system versions, and continuously optimizing model adaptability.

[0055] The beneficial results achieved by the present invention using the above solution are as follows:

[0056] (1) To address the technical problems of false correlation caused by dynamic latency during set-top box testing, mismatch of multimodal data dimensions, high computational complexity leading to difficulty in real-time processing, and resource bottlenecks, data spatiotemporal normalization is adopted to identify data characteristics and latency features. The global clock deviation is quickly estimated through the basic alignment layer, and nonlinear latency is handled by the attention dynamic alignment layer. Dynamic alignment reconstruction is performed, and time deviation and morphological differences are jointly optimized through differentiable DTW. Dynamic parameter adjustment is supported and memory usage is reduced.

[0057] (2) To address the technical problems of large performance differences between different set-top box chips, the inability of traditional static models to adapt and adjust, low resource utilization, multimodal data conflicts, the ease with which a single model can lead to misjudgment, and insufficient coverage of fault modes, a multimodal joint analysis modeling is adopted. By integrating specialized models to process heterogeneous data in set-top box adaptation test data, a chip perception meta-learning model is constructed, a meta-loss function is calculated, and a dynamic weighted voting mechanism that integrates and optimizes multiple models is used to improve decision robustness, realize fully automatic adaptation analysis of set-top box testing, and significantly reduce the cost of manual optimization. Attached Figure Description

[0058] Figure 1 A flowchart illustrating a method for mining and analyzing set-top box adaptation test data provided by the present invention;

[0059] Figure 2 This is a flowchart illustrating step S2;

[0060] Figure 3 This is a flowchart illustrating step S3;

[0061] Figure 4 This is a flowchart illustrating step S4.

[0062] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0063] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0065] Example 1, see Figure 1 This invention provides a method for mining and analyzing set-top box adaptation test data, which includes the following steps:

[0066] Step S1: Set-top box adaptation test data collection;

[0067] Step S2: Data spatiotemporal normalization, identification of data characteristics and time delay characteristics, rapid estimation of global clock deviation through basic alignment layer, processing of nonlinear time delay through attention dynamic alignment layer, dynamic alignment reconstruction, and joint optimization of time deviation and morphological difference through differentiable DTW.

[0068] Step S3: Multimodal feature engineering, which involves extracting, combining, filtering, and standardizing features to generate the final feature matrix;

[0069] Step S4: Multimodal joint analysis and modeling. Specialized models are selected for different data types. Multimodal features are integrated into a unified representation to construct a chip-aware meta-learning model. Combined with an optimized dynamic weighted voting mechanism, decision robustness and fault detection coverage are improved.

[0070] Step S5: Deployment optimization.

[0071] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the set-top box adaptation test data is collected in real time through an embedded probe. The set-top box adaptation test data includes hardware performance data, video quality data, system interaction data, network transmission data, compatibility test data, stress test data, and log analysis data.

[0072] The hardware performance data includes CPU utilization, temperature sensor data, memory usage, GPU rendering frame rate, and video decoding latency.

[0073] The video quality data includes resolution adaptation, color accuracy, dynamic response, and stuttering rate;

[0074] The system interaction data includes user operation logs and interface response metrics;

[0075] The network transmission data includes real-time transmission quality and adaptive bitrate performance;

[0076] The compatibility test data includes peripheral compatibility matrix and DRM support status;

[0077] The pressure test data includes stability indicators and extreme temperature performance;

[0078] The log analysis data includes key error codes and kernel log characteristics.

[0079] Example 3, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S2, the test data is spatiotemporally normalized, including the following steps:

[0080] Step S21: Test environment feature identification. Identify key data features and latency characteristics in the set-top box test environment based on the set-top box adaptation test data.

[0081] Step S22: Set up a basic alignment layer to quickly estimate the global clock skew, provide an initial value for fine alignment, and limit the search range based on the maximum hardware clock skew.

[0082] Step S23: Set up an attention dynamic alignment layer to handle non-linear time delays. The formula used is as follows:

[0083] ;

[0084] In the formula, This represents the optimal time offset. Indicates global clock skew. This represents the i-th time point within the time window. This represents the j-th time point within the time window. This represents the eigenvector of the reference sequence at time t. Indicates the sequence to be aligned in The feature vector at time step, Indicates the sequence to be aligned in The feature vector at time t, where d represents the dimension of the feature vector, reflecting the complexity of the test data representation, and T represents the length of the time window. The squared norm is used to measure alignment error.

[0085] Dynamic alignment and reconstruction generate a sequence that best matches the reference data, which refers to the original standard test data sent from the test source, unprocessed by network transmission and set-top box. The formula used is as follows:

[0086] ;

[0087] In the formula, z represents the reconstructed sequence. i This represents the attention weight coefficient at the i-th time point;

[0088] Step S24: Calculate the spatiotemporal alignment error, perform differentiable DTW optimization, and achieve end-to-end differentiable morphological alignment. By minimizing the time offset and morphological differences between time series, the aligned set-top box adaptation test data is obtained. The formula used is as follows:

[0089] ;

[0090] ;

[0091] In the formula, E represents the spatiotemporal alignment error. Indicates the weight of time deviation. T1 represents the morphological similarity weight, and T1 represents the differentiable morphological alignment error. Let represent the DTW alignment path matrix, γ represent the smoothing parameter, and A represent all possible DTW alignment paths. Represents the point-to-point distance matrix. DTW represents the path cost, which is the cumulative distance of all points on the path.

[0092] By performing the above operations, data spatiotemporal normalization is adopted to identify data characteristics and latency features. The global clock deviation is quickly estimated through the basic alignment layer, and the nonlinear latency is handled by the attention dynamic alignment layer. Dynamic alignment reconstruction is performed, and the time deviation and morphological differences are jointly optimized through differentiable DTW. Dynamic parameter adjustment is supported, memory usage is reduced, and the technical problems of false correlation caused by dynamic latency, mismatch of multimodal data dimensions, high computational complexity leading to difficulty in real-time processing, and resource bottlenecks during set-top box testing are solved.

[0093] Example 4, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S3, the multimodal feature engineering includes the following steps:

[0094] Step S31: Perform basic feature extraction, mine basic features from the aligned set-top box adaptation test data, and generate higher-order features through combination and transformation;

[0095] Step S32: Feature selection, eliminating redundant features, unifying feature dimensions, performing feature scaling and encoding, standardizing all numerical features, and finally outputting the feature matrix.

[0096] Example 5, see Figure 1 and Figure 4 This embodiment is based on the above embodiment. In step S4, the multimodal joint analysis modeling includes the following steps:

[0097] Step S41: Multimodal model division of labor. For different dimensions of set-top box adaptation test data, select specialized models, and each specialized model processes the set-top box adaptation test data to extract advanced features.

[0098] The core of the specialized model is to select a model based on data characteristics, including time series models, structured models, and unsupervised models; data from different sources have completely different structures and characteristics, and need to be processed using different specialized models.

[0099] For CPU temperature and frame rate sequences, a timing model is used to capture the timing degradation trend of hardware performance.

[0100] For hardware configurations, including chip models and memory size, a structured model is used to determine the compatibility of static configuration combinations.

[0101] An unsupervised model is used to detect unknown fault patterns based on network jitter patterns and anomaly logs.

[0102] Step S42: Multimodal data fusion, fusing high-level features to obtain a fused feature matrix;

[0103] Modal alignment: Input the feature matrices of each modality and ensure that the timestamps of the feature matrices are consistent;

[0104] Feature fusion integrates data features from different modalities into a unified representation. Within a modality, dimensionality is first reduced, and then cross-modal concatenation is performed. Intermodal correlations are captured through a cross-attention mechanism.

[0105] Step S43: Construct a chip-aware meta-learning model. This model learns both the task itself and how to quickly adapt to different hardware chips, thereby automatically achieving a balance between high performance and low power consumption on resource-constrained devices. The meta-learner simulates the deployment of specialized models in various hardware environments, calculates the minimized meta-learning loss function, and ensures that the model trained by the chip-aware meta-learning framework has strong cross-hardware adaptive capabilities in tasks. The formula used is as follows:

[0106] ;

[0107] In the formula, M represents the minimized meta-learning loss function, which is the joint optimization objective combining model performance and hardware efficiency. These represent the trainable parameters of a specialized model. By optimizing these parameters, the model can be adapted to chip constraints. Indicates task From the task distribution The sampling task is used to ensure that meta-learning generalizes to diverse deployment environments. Indicates in the task upper model The prediction error G represents the parameters after rapid adaptation through meta-learning, which are updated via inner loop gradients to adapt to new tasks. arch This represents a hardware constraint mask, used to block operations that the chip does not support. Represents the regularization coefficient. The L2 norm penalty term for the parameters is used to suppress parameters that do not meet hardware constraints, thereby promoting structured pruning.

[0108] The formula used to update task parameters is as follows:

[0109] ;

[0110] In the formula, This represents the parameters of the specialized model after training and updating. This represents the learning rate, used to control the magnitude of parameter updates. This represents the gradient operator with respect to the parameters;

[0111] Step S44: Multi-model integration employs an optimized dynamic weighted voting mechanism. This involves introducing local confidence weights combined with the model's prediction confidence on the current test data, dynamically adjusting the weights to automatically reduce the weight of high-AUC models when prediction uncertainty arises, thus minimizing misjudgments. Models with significant discrepancies with other models are penalized to avoid collective misjudgments. Furthermore, models of different modalities are grouped and weighted to prevent single-modal dominance and improve the robustness of multi-modal predictions. The formula used is as follows:

[0112] ;

[0113] ;

[0114] In the formula, This represents the dynamic weights of the m-th model. C represents the balancing parameter, used to control the ratio of global AUC weights to local confidence weights. m This represents the prediction confidence of the m-th model for the current test data. This represents the sum of confidence scores for all models, used for normalization. Divergence is a parameter representing the divergence penalty, used to control the strength of the penalty for differences in predictions between models. m G represents the prediction difference of the m-th model, G represents the total number of modal groups, and γ represents the prediction difference of the m-th model. g Let m represent the preset weights of the g-th mode group, and m∈g represent the set of models belonging to the g-th mode group. represents the sum of dynamic weights of all models in group g, used for within-group normalization; FS represents the final ensemble score, used to predict set-top box compatibility risks. This represents the AUC score of the m-th model. Score represents the sum of the AUC scores of all models. m This represents the prediction output of the m-th model for the current data.

[0115] By performing the above operations, multimodal joint analysis and modeling are adopted. By integrating specialized models to process heterogeneous data in set-top box adaptation test data, a chip-aware meta-learning model is constructed, a meta-loss function is calculated, and a dynamic weighted voting mechanism that integrates and optimizes multiple models is used to improve decision robustness. This enables fully automated adaptation analysis for set-top box testing, significantly reducing manual tuning costs. It solves the technical problems of large performance differences between different set-top box chips, the inability of traditional static models to adapt and adjust, low resource utilization, multimodal data conflicts, the tendency of a single model to lead to misjudgment, and insufficient fault mode coverage.

[0116] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the deployment optimization includes the following steps:

[0117] Model deployment involves deploying the trained model to the set-top box automated testing environment to verify the set-top box adaptation fault detection rate, false alarm rate, and the improvement in testing efficiency. It also involves collecting misjudgment cases from actual operation, labeling them, and feeding them back into the training data. Furthermore, it involves supplementing the feature engineering with updated set-top box model parameters, regularly updating the model to adapt to new models and system versions, and continuously optimizing model adaptability.

[0118] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0119] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0120] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for mining and analyzing set-top box adaptation test data, characterized in that: The method includes the following steps: Step S1: Set-top box adaptation test data acquisition, real-time acquisition of set-top box adaptation test data through embedded probes; Step S2: Data spatiotemporal normalization. Identify the set-top box test environment based on the set-top box adaptation test data. Estimate the global clock deviation through the basic alignment layer. Use the attention dynamic alignment layer to handle nonlinear delays. Perform dynamic alignment reconstruction. Then, jointly optimize the time deviation and form difference through differentiable DTW to obtain the aligned set-top box adaptation test data. Step S3: Multimodal feature engineering, extracting basic features from the aligned set-top box adaptation test data, generating higher-order features, performing feature selection, and outputting the feature matrix; Step S4: Multimodal joint analysis and modeling, select specialized models for different data types, input feature matrices, integrate features from different modalities into a unified representation through feature fusion, construct a chip-aware meta-learning model, calculate the meta-loss function, and integrate multiple models and optimize a dynamic weighted voting mechanism; Step S5: Deployment optimization; In step S2, the spatiotemporal normalization of the data includes the following steps: Step S21: Test environment feature identification. Identify key data features and latency characteristics in the set-top box test environment based on the set-top box adaptation test data. Step S22: Set the base alignment layer; Step S23: Set up an attention dynamic alignment layer to handle non-linear time delays. The formula used is as follows: ； In the formula, This represents the optimal time offset. Indicates global clock skew. This represents the i-th time point within the time window. This represents the j-th time point within the time window. This represents the eigenvector of the reference sequence at time t. Indicates the sequence to be aligned in The feature vector at time step, Indicates the sequence to be aligned in The feature vector at time t, where d represents the dimension of the feature vector, reflecting the complexity of the data representation, and T represents the length of the time window. The squared norm is used to measure alignment error. Dynamic alignment and reconstruction generate the sequence that best matches the reference data, using the following formula: ； In the formula, z represents the reconstructed sequence. i This represents the attention weight coefficient at the i-th time point; Step S24: Calculate the spatiotemporal alignment error, perform differentiable DTW optimization, and achieve end-to-end differentiable morphological alignment. By minimizing the time offset and morphological differences between time series, the aligned set-top box adaptation test data is obtained. The formula used is as follows: ； ； In the formula, E represents the spatiotemporal alignment error. Indicates the weight of time deviation. T1 represents the morphological similarity weight, and T1 represents the differentiable morphological alignment error. Let represent the DTW alignment path matrix, γ represent the smoothing parameter, and A represent all possible DTW alignment paths. Represents the point-to-point distance matrix. DTW represents the path cost and the time warped distance.

2. The method for mining and analyzing set-top box adaptation test data according to claim 1, characterized in that: In step S4, the multimodal joint analysis modeling includes the following steps: Step S41: Multimodal model division of labor. For different dimensions of set-top box adaptation test data, select specialized models, and each specialized model processes the set-top box adaptation test data to extract advanced features. Step S42: Multimodal data fusion, fusing high-level features to obtain a fused feature matrix; Step S43: Construct a chip-aware meta-learning model. The meta-learner simulates the deployment of specialized models under different hardware environments and calculates the minimized meta-learning loss function. The formula used is as follows: ； In the formula, M represents the minimized meta-learning loss function. Represents the trainable parameters of the specialization model. Indicates task From task distribution The sampling task is used to ensure that meta-learning generalizes to diverse deployment environments. Indicates in the task upper model The prediction error, G arch This represents a hardware constraint mask, used to block operations that the chip does not support. Represents the regularization coefficient. The L2 norm penalty term for the parameter is used to suppress parameters that do not meet hardware constraints; The formula used to update task parameters is as follows: ； In the formula, This represents the parameters of the specialized model after training and updating. This represents the learning rate, used to control the magnitude of parameter updates. This represents the gradient operator with respect to the parameters; Step S44: Multi-model integration, implementing an optimized dynamic weighted voting mechanism, introducing local confidence weights combined with the model's prediction confidence of the current test data, dynamically adjusting the weights, using the following formula: ； ； In the formula, This represents the dynamic weights of the m-th model. C represents the balancing parameter used to control the ratio of global AUC weights to local confidence weights. m This represents the prediction confidence of the m-th model for the current test data. This represents the sum of confidence scores for all models, used for normalization. Divergence is a parameter representing the divergence penalty, used to control the strength of the penalty for differences in predictions between models. m G represents the prediction difference of the m-th model, G represents the total number of modal groups, and γ represents the prediction difference of the m-th model. g Let m represent the preset weights of the g-th mode group, and m∈g represent the set of models belonging to the g-th mode group. represents the sum of dynamic weights of all models in group g, used for within-group normalization; FS represents the final ensemble score, used to predict set-top box compatibility risks. This represents the AUC score of the m-th model. Score represents the sum of the AUC scores of all models. m This represents the prediction output of the m-th model for the current data.

3. The method for mining and analyzing set-top box adaptation test data according to claim 1, characterized in that: In step S1, the set-top box adaptation test data is collected in real time through an embedded probe. The set-top box adaptation test data includes hardware performance data, video quality data, system interaction data, network transmission data, compatibility test data, stress test data, and log analysis data.

4. The method for mining and analyzing set-top box adaptation test data according to claim 1, characterized in that: In step S3, the multimodal feature engineering includes the following steps: Step S31: Perform basic feature extraction, mine basic features from the aligned set-top box adaptation test data, and generate higher-order features through combination and transformation; Step S32: Feature selection, eliminating redundant features, unifying feature dimensions, performing feature scaling and encoding, standardizing all numerical features, and finally outputting the feature matrix.

5. The method for mining and analyzing set-top box adaptation test data according to claim 1, characterized in that: In step S5, the deployment optimization specifically involves model deployment, which involves deploying the trained model to the set-top box automated testing environment to verify the model's effectiveness, collecting misjudgment cases from actual operation, labeling them, feeding them back to the training data, and supplementing the updated set-top box model parameters into the feature engineering, and updating the model regularly.

Images