An AI engineering model pipeline management and automatic deployment method

Abstract

This invention relates to an AI engineering model pipeline management and automated deployment method, specifically in the field of AI engineering. By constructing a fully automated and standardized pipeline, models from different training frameworks can be uniformly accessed and evaluated. It utilizes a benchmark testing engine to perform comprehensive testing from functional verification to multi-stress scenario integrated testing, and automatically collects standardized performance data. Through deep aggregation and analysis of this data, it generates a comprehensive capability index, robustness score, and multi-dimensional capability profile, achieving a three-dimensional and quantitative evaluation of model performance and stability. Furthermore, leveraging meta-learning technology, the pipeline dynamically optimizes evaluation criteria based on the correlation between historical model performance and online business revenue, and performs value prediction for the current model. This provides objective, quantifiable, and closely aligned intelligent recommendation reports for model deployment decisions, significantly improving the efficiency, reliability, and scientific nature of model iteration and deployment.

Description

Technical Field

[0001] This invention relates to the field of AI engineering, and more specifically, to a method for pipeline management and automated deployment of AI engineering models. Background Technology

[0002] With the widespread application of deep learning technology, large internet companies and financial institutions have built centralized AI platforms to support core business scenarios such as recommendation systems, financial risk control, and intelligent marketing. In such platform systems, multiple algorithm teams often work in parallel to develop models for the same business goal, such as improving content click-through rates or optimizing credit approval accuracy. Teams may have different technology choices, such as using mainstream frameworks like PyTorch or TensorFlow, and writing independent model training and evaluation scripts based on their own understanding and habits. At the same time, business stakeholders have a continuous and urgent expectation for improving model performance, creating strong driving pressure. However, this collaborative model of parallel development and the demand for high efficiency lack a company-level, authoritative, and unified benchmark for measuring model performance and a testing environment.

[0003] Existing technical solutions typically rely on privately maintained test datasets, custom-implemented evaluation metrics, and localized validation processes maintained by each team. For example, for binary classification problems, some teams might use weighted F1 scores as the core metric to emphasize the ability to capture minority classes, while another team might insist on using the standard AUC area to measure overall ranking performance. In terms of stress testing, the conditions vary widely; for instance, there are no unified standards for simulated concurrent requests, noise levels in the input data, and distribution bias. This results in model performance reports being generated under different measurement systems and test benchmarks, lacking direct comparability. The core problem lies in the lack of an automated and standardized approach. Furthermore, a reproducible benchmarking engine can objectively evaluate any submitted model entity under consistent, recognized datasets, evaluation metrics, and stress testing scenarios. However, the inconsistency in these evaluation standards makes it difficult for technical decision-makers to select the optimal model based on fair and transparent horizontal comparisons. This may not only result in the online deployed model not being the actual optimal solution, leading to business results that fail to meet expectations, but it may also trigger local optimization competitions among teams around specific evaluation metrics, rather than focusing on solving real business challenges, causing serious internal consumption of R&D resources. In addition, the risk of deviation between offline evaluation and online real performance due to inconsistent testing conditions is also significantly increased, posing a hidden danger to system stability. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method for pipeline management and automatic deployment of AI engineering models, thereby resolving the issues raised in the background section.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: specifically, it includes the following steps: Step S1: In response to the model evaluation request, the models to be evaluated obtained based on different training frameworks are uniformly accessed through a predefined standardized interface encapsulation layer, so that each model to be evaluated can receive input data in a specified format; a structured evaluation profile is generated and initialized for each accessed model to be evaluated. Step S2: After the model to be evaluated is encapsulated, an automatic benchmark test engine is invoked to execute automated unit tests and batch tests on the model to be evaluated in sequence. The automated unit tests use test cases containing preset boundary conditions or outliers to verify the functional points of the model to be evaluated. The batch tests use a complete, standardized historical dataset with temporal relationships to verify the comprehensive performance of the model to be evaluated under various preset stress test scenarios. The raw performance data of the model to be evaluated calculated based on a unified evaluation index in the automated unit tests and batch tests are collected to form a raw data matrix and stored in the evaluation file corresponding to the model to be evaluated. Step S3: Read the original data matrix in the evaluation file of the model to be evaluated, apply a preset model comprehensive capability index formula to aggregate and calculate the original data matrix to obtain the model comprehensive capability index of the model to be evaluated; at the same time, calculate a model robustness score based on the performance data of the model to be evaluated under different stress test scenarios in the original data matrix, and combine the performance data of the model to be evaluated under various unified evaluation indicators to generate a multi-dimensional model capability profile. Step S4: Obtain the historical evaluation files of all evaluated models and their corresponding online real business revenue data. Use meta-learning algorithms to analyze the correlation between the model comprehensive capability index, the scores of each dimension in the model capability profile, and the online real business revenue recorded in each historical evaluation file. Based on the correlation, dynamically adjust the weight coefficients of each item in the model comprehensive capability index formula, and use the model comprehensive capability index formula with adjusted weight coefficients to re-evaluate the current model to be evaluated, generating a model decision recommendation report containing the expected business value score. In a preferred embodiment, the specific operation of uniformly accessing the models to be evaluated based on different training frameworks through a predefined standardized interface encapsulation layer in step S1 is as follows: Parse the model file of the model to be evaluated and extract the framework feature fingerprint from the header or metadata area of ​​the model file. Based on the extracted frame feature fingerprint, a frame adapter that matches the frame feature fingerprint is dynamically loaded from a pre-built frame adapter library. The model file of the model to be evaluated is loaded using the standardized encapsulation functions contained in the loaded framework adapter; the model file of the model to be evaluated is analyzed to obtain the tensor names, tensor shapes and tensor data types of the input tensors and output tensors defined therein; Based on the tensor names, tensor shapes, and tensor data types of the obtained input and output tensors, a wrapper object is created in memory, and the wrapper object conforms to the unified service interface specification. The encapsulated object provides standardized prediction methods to receive input data in a specified format and perform necessary data format conversion operations; Assign an object reference to the created encapsulated object, and store the object reference assigned to the encapsulated object in the structured evaluation file generated and initialized for the model to be evaluated, so that the structured evaluation file is uniquely associated with the encapsulated model object to be evaluated.

[0006] In a preferred embodiment, the process of generating and initializing a structured evaluation profile for each accessed model to be evaluated specifically involves: During the process of accessing the model to be evaluated through the standardized interface encapsulation layer, the model architecture and parameter feature extraction operations are performed simultaneously. Feature extraction operations obtain basic metadata of the model to be evaluated, including model name, model version, model creation time, and training framework identifier. Feature extraction operations obtain the computation graph topology features of the model to be evaluated. The computation graph topology features include the total number of nodes in the computation graph, the total number of edges connecting the nodes, the number of layers of the longest forward propagation path from input to output in the model, i.e., the maximum computation path depth, and the number of different operator types in the model. Based on the obtained computational graph topological features, a structural complexity metric value is calculated using a structural complexity metric function. The calculation process of the structural complexity metric function is as follows: First, obtain the total number of nodes and edges in the computational graph, as well as the number of different types of operators in the model. Second, calculate the logarithm of the total number of nodes and the logarithm of the total number of edges. When calculating the logarithm of the total number of nodes, add the total number of nodes to a first positive constant coefficient and take the logarithm. When calculating the logarithm of the total number of edges, add the total number of edges to a second positive constant coefficient and take the logarithm. Then, multiply the logarithm of the total number of edges by a first adjustment weight coefficient to obtain a first product. Add the first product to the logarithm of the total number of nodes to obtain an intermediate weighted sum. Next, divide the maximum computational path depth by the number of different types of operators to obtain a quotient. Multiply this quotient by a second adjustment weight coefficient to obtain a second product. Then, add the second product to a constant to obtain an adjustment factor. Finally, multiply the intermediate weighted sum by the adjustment factor to obtain the structural complexity metric value of the model. Create a structured evaluation file record with a predefined field structure; The fields recorded in the structured evaluation archive include: a model identifier field for globally uniquely identifying the model to be evaluated, a model instance reference field for storing object references corresponding to the encapsulated objects, and a model meta-information field for storing basic meta-information, computation graph topology features, and quantified values ​​of model structural complexity. The generated model identifier, object reference, extracted basic meta-information, computation graph topology features, and calculated model structural complexity quantification values ​​are respectively filled into the model identifier field, model instance reference field, and model meta-information field of the structured evaluation file record to complete the initialization of the structured evaluation file.

[0007] In a preferred embodiment, step S2, which involves automatically invoking a benchmark testing engine to sequentially perform automated unit testing and batch testing on the model to be evaluated, is as follows: Based on the unique identifier of the model obtained from the evaluation file corresponding to the model to be evaluated, the evaluation file is retrieved from the global file registry. The reference of the encapsulated standardized model object is extracted from the model instance reference field of the evaluation file, and the tensor name, tensor shape and tensor data type description information of the input tensor and output tensor of the model to be evaluated are obtained from the model meta information field of the evaluation file. Based on the obtained input and output tensor description information, the corresponding standardized test case pool and a complete standardized historical dataset with temporal relationship are obtained from a centralized test resource library. By using a reference to a standardized model object and calling its standardized prediction interface, automated unit tests are first executed. The automated unit tests select test cases containing preset boundary conditions and outliers from the standardized test case pool and input them into the model, and verify the correctness of the model's output functionality. Batch testing is then performed, which inputs a standardized historical dataset into the model under various preset stress test scenarios. These preset stress test scenarios are defined by combining various basic stress dimension parameters. Each stress test scenario is determined by a specific set of dimension parameter values, including the distribution offset metric, the noise intensity, and the number of simulated concurrent request threads. For each stress test scenario determined by specific dimensional parameter values, a comprehensive scenario disturbance intensity value is calculated. The calculation process for the comprehensive scenario disturbance intensity value is as follows: the distribution offset metric is divided by a preset feature reference range value to obtain a first ratio; the noise intensity value is added to a baseline noise intensity value and the logarithm is taken to obtain a noise impact term; the number of concurrent request threads is divided by a baseline concurrency value to obtain a third ratio; then, the first ratio is multiplied by a first weighting coefficient, the noise impact term is multiplied by a second weighting coefficient, and the third ratio is multiplied by a third weighting coefficient; finally, the first ratio, the noise impact term, and the third ratio after being multiplied by the corresponding weighting coefficients are added together, and the result is the comprehensive scenario disturbance intensity value for that stress test scenario.

[0008] In a preferred embodiment, the specific operation of collecting the raw performance data of the model to be evaluated calculated based on a unified evaluation index in automated unit testing and batch testing, forming a raw data matrix, and storing it is as follows: After the automated unit test and the batch test under each stress test scenario are completed, the performance data of the model to be evaluated under the current test item are immediately calculated according to the predefined unified evaluation index. The unified evaluation index includes at least the accuracy index, recall index, inference latency index and throughput index. The performance data generated from each test execution is organized according to the test item identifier and evaluation index type to construct a structured three-dimensional raw data set. Among them, for batch testing, the performance data under each stress test scenario is treated as an independent test item and stored in association with the unique identifier of the stress test scenario and its calculated comprehensive disturbance intensity value. The entire set of three-dimensional raw data is recorded as a raw data matrix. Then, the raw data matrix is ​​serialized together with the metadata of this test execution. The metadata includes the test timestamp, the version identifier of the standardized historical dataset used, the specific dimensional parameter values ​​of each stress test scenario and the calculated comprehensive perturbation intensity value of the scenario. Finally, the serialized raw data matrix and metadata are stored in the dedicated raw data field of the evaluation file corresponding to the model to be evaluated, and the status of the evaluation file is marked as test completed.

[0009] In a preferred embodiment, step S3, which involves applying a preset model comprehensive capability index formula to aggregate and calculate the original data matrix to obtain the model comprehensive capability index of the model to be evaluated, specifically involves: First, the input data parsing and scenario alignment operation is performed: After receiving the notification that step S2 is completed, the corresponding evaluation file is retrieved according to the unique identifier of the model to be evaluated, and the original data matrix and its meta-information are deserialized from the original data fields of the evaluation file; the original data matrix is ​​parsed to identify the performance values ​​of various unified evaluation indicators of the model to be evaluated in various preset stress test scenarios, under scenarios with no additional pressure or with benchmark pressure parameters, as benchmark performance values, and the corresponding scenario performance values ​​under different stress test scenarios. Among them, different stress test scenarios are quantified and identified by their corresponding scenario comprehensive disturbance intensity values. Subsequently, based on the analyzed baseline performance values, scenario performance values, and overall scenario disturbance intensity values, aggregate calculations are performed: the baseline performance values ​​of each unified evaluation index are normalized and mapped to a value range of zero to one; a static weight is preset for each unified evaluation index, and the sum of the static weights of all indices is a constant of one; for each unified evaluation index, its performance retention under each stress test scenario is calculated. The performance retention is obtained by multiplying a first factor and a second factor. The first factor is the quotient obtained by dividing the performance value of the index under the current stress test scenario by its baseline performance value; the second factor is an exponential decay factor. The product of the natural constant e raised to the power of a negative attenuation coefficient and the overall perturbation intensity of the current stress test scenario; a scenario weight is preset for each stress test scenario, and the sum of the scenario weights of all stress test scenarios is a constant 1; for each unified evaluation index, its performance retention under all stress test scenarios is weighted and summed according to the corresponding scenario weight to obtain the average stress performance retention of the index; then, 1 is calculated and subtracted from the average stress performance retention to obtain a difference reflecting the degree of performance degradation; the products of the static weights of all unified evaluation indices and their normalized baseline performance values ​​are summed to obtain a baseline comprehensive performance value; Finally, the baseline performance composite value is multiplied by an exponential decay term to obtain the model composite capability index. The exponential decay term is a power of the natural constant e, with its exponent being the product of the negative global robustness sensitivity coefficient and a weighted decay degree. The weighted decay degree is the result of weighting and summing the performance decay differences of all unified evaluation indicators with their static weights as coefficients.

[0010] In a preferred embodiment, the specific process of calculating a model robustness score based on the performance data of the model to be evaluated under different stress test scenarios in the original data matrix is ​​as follows: Based on the performance retention of each unified evaluation index in each stress test scenario and the scenario weight of each stress test scenario, for each unified evaluation index, firstly, the weighted sum of its performance retention under different stress test scenarios is calculated with the corresponding scenario weight as the coefficient; then, the weighted sums of all unified evaluation indices are accumulated and divided by the total number of unified evaluation indices to obtain the first part of the basic value of the model robustness score; the process curve of the performance value of each unified evaluation index changing with the increase of the comprehensive perturbation intensity value of its corresponding stress test scenario is analyzed, and the smooth stability measure of this change process is calculated. The smooth stability measure is characterized by calculating the sample entropy of the process curve or the reciprocal of the slope of the fitted line; the smooth stability measures of all indices are averaged to obtain the second part of the stability index of the model robustness score; the first part of the basic value is added to the product of a preset stability weight coefficient multiplied by the second part of the stability index, and the final result is normalized to the interval between zero and one to obtain the model robustness score of the model to be evaluated.

[0011] In a preferred embodiment, the process of generating a multi-dimensional model capability profile specifically includes: Create a structured, multi-dimensional model capability profile, which includes at least the following dimensions: a comprehensive performance dimension for storing the overall model capability index; a core indicator dimension for storing the normalized baseline performance values ​​of various unified evaluation indicators; a robustness dimension for storing the final model robustness score; an efficiency dimension for storing the normalized inference latency and throughput values; a scenario adaptation spectrum dimension for displaying the relative retention rate of core indicators under multiple representative stress test scenarios in simplified table or vector form; and a resource consumption dimension for storing resource consumption data extracted from the original data matrix. The generated model comprehensive capability index, final model robustness score, and multi-dimensional model capability profile are stored in the evaluation result field of the evaluation file corresponding to the model to be evaluated.

[0012] In a preferred embodiment, step S4, which involves using a meta-learning algorithm to analyze the correlation between the model's comprehensive capability index, the scores of each dimension in the model's capability profile, and the actual online business revenue recorded in each historical evaluation file, specifically includes: Retrieve evaluation files of all completed historical models from the global file registry or associated persistent storage, and associate them with the quantitative value of the online real business revenue corresponding to each historical model. The quantitative value of online real business revenue includes the percentage increase in click-through rate, conversion rate gain, or revenue increase; normalize the quantitative value of online real business revenue for each historical model. For each historical model, the model comprehensive capability index is extracted from the evaluation result field of its evaluation file, and the scores of core indicator dimension, robustness dimension and efficiency dimension are extracted from its model capability profile. The extracted model comprehensive capability index and each score together constitute the offline evaluation feature vector of the historical model for meta-learning. Collect the offline evaluation feature vectors of all historical models, arrange them in rows to form a historical feature matrix, and collect the quantitative values ​​of the online real business revenue of all historical models after normalization to form a target vector. A meta-learning prediction model is used, with the historical feature matrix as input features and the target vector as the prediction target, to train the model and obtain the trained meta-learning prediction model. The meta-learning algorithm used in the meta-learning prediction model includes one of gradient boosting decision trees, neural networks with attention mechanisms, or Bayesian linear regression. After the meta-learning prediction model is trained, the feature importance analysis method is used to analyze the contribution of each feature dimension in the historical feature matrix to the predicted online real business revenue, resulting in a contribution vector. Each element in the contribution vector corresponds to the contribution quantification value of the model comprehensive capability index, core indicator dimension scores, robustness dimension score, and efficiency dimension score in the offline evaluation feature vector. The feature importance analysis method determines the contribution vector by analyzing the trained meta-learning prediction model, specifically by calculating the quantification value of the importance of each input feature to the prediction target.

[0013] In a preferred embodiment, the process of generating a model decision recommendation report containing an expected business value score specifically includes: Based on the contribution vector, the weight coefficients of each item in the formula of the model's comprehensive capability index are adjusted; the adjustment of the weight coefficients includes the adjustment of the static weights of each unified evaluation indicator, as well as the adjustment of the global robustness sensitivity coefficient. The adjustment process for the static weights of each unified evaluation indicator is as follows: For a given unified evaluation indicator, obtain its original static weight value before adjustment, and obtain the contribution quantification value corresponding to that indicator from the contribution vector; then, calculate the sum of the contribution quantification values ​​corresponding to all unified evaluation indicators; next, divide the contribution quantification value corresponding to the unified evaluation indicator by the sum to obtain a new weight ratio based on contribution normalization; finally, multiply the original static weight value by the difference between one and a preset learning rate to obtain the first product; multiply the new weight ratio based on contribution normalization by the learning rate to obtain the second product; add the first product and the second product to obtain the adjusted static weight of the unified evaluation indicator. The adjustment process for the global robustness sensitivity coefficient is as follows: First, obtain the contribution metric value corresponding to the robustness dimension from the contribution vector; then, calculate the sum of the contribution metric values ​​of all elements in the contribution vector; divide the contribution metric value corresponding to the robustness dimension by the sum to obtain a robustness correlation strength factor; next, calculate the difference between the robustness correlation strength factor and a preset robustness correlation benchmark strength; multiply the difference by a preset adjustment sensitivity coefficient to obtain a scaling factor; then, add the difference to the scaling factor to obtain a scaling coefficient; finally, multiply the original value of the global robustness sensitivity coefficient before adjustment by the scaling coefficient to obtain the adjusted global robustness sensitivity coefficient. The model comprehensive capability index of the current model to be evaluated is recalculated using the model comprehensive capability index formula that includes all adjusted static weights and adjusted global robustness sensitivity coefficients. The result is used as the adjusted model comprehensive capability index. Meanwhile, in accordance with the method of constructing offline evaluation feature vectors, the model comprehensive capability index of the current model to be evaluated and the scores of each dimension in the model capability profile are used to construct the offline evaluation feature vector corresponding to this evaluation, which is then input into the trained meta-learning prediction model to obtain the expected business value score of the model. Finally, a model decision recommendation report is generated, which includes at least: the adjusted model comprehensive capability index and expected business value score; a horizontal comparative analysis of the current model to be evaluated and the preset historical baseline model or candidate models in the same batch in terms of the adjusted model comprehensive capability index and expected business value score; a summary of the reasons for this weight adjustment based on contribution vector analysis; and deployment recommendations based on the expected business value score and the horizontal comparative analysis.

[0014] The beneficial effects of this invention are as follows: By constructing a fully automated and standardized pipeline, models from different training frameworks can be uniformly accessed and evaluated. It utilizes a benchmark testing engine to perform comprehensive testing from functional verification to multi-stress scenario integrated testing, and automatically collects standardized performance data. Through deep aggregation and analysis of this data, it generates a comprehensive capability index, robustness score, and multi-dimensional capability profile, achieving a three-dimensional and quantitative evaluation of model performance and stability. Furthermore, by leveraging meta-learning technology, the pipeline dynamically optimizes evaluation criteria based on the correlation between historical model performance and online business revenue, and performs value prediction for the current model. It provides objective, quantifiable, and closely aligned with business objectives intelligent recommendation reports for model deployment decisions, significantly improving the efficiency, reliability, and scientific nature of model iteration and deployment. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0018] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application. Example

[0019] This embodiment provides, for example Figure 1 The method for pipeline management and automated deployment of AI engineering models, as shown, includes the following steps: Step S1: In response to the model evaluation request, the models to be evaluated obtained based on different training frameworks are uniformly accessed through a predefined standardized interface encapsulation layer, so that each model to be evaluated can receive input data in a specified format; a structured evaluation file is generated and initialized for each accessed model to be evaluated, which is used to uniquely identify the model to be evaluated and record subsequent evaluation data. Step S2: After the model to be evaluated is encapsulated, an automatic benchmark test engine is invoked to execute automated unit tests and batch tests on the model to be evaluated in sequence. The automated unit tests use test cases containing preset boundary conditions or outliers to verify the functional points of the model to be evaluated. The batch tests use a complete, standardized historical dataset with temporal relationships to verify the comprehensive performance of the model to be evaluated under various preset stress test scenarios. The raw performance data of the model to be evaluated calculated based on a unified evaluation index in the automated unit tests and batch tests are collected to form a raw data matrix and stored in the evaluation file corresponding to the model to be evaluated. Step S3: Read the original data matrix in the evaluation file of the model to be evaluated, apply a preset model comprehensive capability index formula to aggregate and calculate the original data matrix to obtain the model comprehensive capability index of the model to be evaluated; at the same time, calculate a model robustness score based on the performance data of the model to be evaluated under different stress test scenarios in the original data matrix, and combine the performance data of the model to be evaluated under various unified evaluation indicators to generate a multi-dimensional model capability profile. Step S4: Obtain the historical evaluation files of all evaluated models and their corresponding online real business revenue data. Use meta-learning algorithms to analyze the correlation between the model comprehensive capability index recorded in each historical evaluation file, the scores of each dimension in the model capability profile, and the online real business revenue. Based on the correlation, dynamically adjust the weight coefficients of each item in the model comprehensive capability index formula, and use the model comprehensive capability index formula with adjusted weight coefficients to re-evaluate the current model to be evaluated, generating a model decision recommendation report containing the expected business value score.

[0020] In this embodiment, it is specifically necessary to explain the operation of unifying the access of the models to be evaluated obtained based on different training frameworks through a predefined standardized interface encapsulation layer in step S1 as follows: The model file of the model to be evaluated is parsed, and the framework feature fingerprint is extracted from the header or metadata area of ​​the model file. The framework feature fingerprint is used to identify the original training framework and framework version information used by the model to be evaluated. The framework feature fingerprint can be a structured string or vector, such as "PyTorch:1.12.0:TorchScript", which contains the framework name, major and minor version numbers and serialization format identifier in sequence. The extraction process is achieved by parsing the magic number of the model file, the metadata block at a specific offset or the embedded configuration file. Based on the extracted framework feature fingerprint, a framework adapter that matches the framework feature fingerprint is dynamically loaded from a pre-built framework adapter library. The framework adapter library is a series of pluggable components, each implemented for a specified "framework-version-format" combination. Dynamic loading means retrieving and instantiating the corresponding adapter class in the library based on the fingerprint keyword. For example, for the fingerprint "TensorFlow:2.9.0:SavedModel", the corresponding TF2SavedModelAdapter is loaded. The model file of the model to be evaluated is loaded through the standardized wrapper functions contained in the loaded framework adapter; the standardized wrapper functions are the core methods of the adapter, which call the API of the native framework (such as torch.jit.load or tf.saved_model.load) to deserialize the model file into a native model object in memory; The model file of the model to be evaluated is analyzed to obtain the tensor names, tensor shapes and tensor data types of the input and output tensors defined therein. This analysis is completed by querying the signature or computation graph definition of the loaded native model object. For example, relevant information is extracted from graph.inputs() / graph.outputs() of PyTorchJIT model or signature_def of TensorFlowSavedModel to form a structured list of input and output descriptions. Based on the tensor names, shapes, and data types of the obtained input and output tensors, a wrapper object is created in memory. The wrapper object conforms to the unified service interface specification. The unified service interface specification defines an abstract class or interface called UniformModel, which requires the implementation of a predict(input_dict) method. Creating a wrapper object is equivalent to instantiating a concrete subclass of UniformModel. This subclass holds the native model object and implements type checking and format mapping logic based on the aforementioned input and output descriptions. The encapsulated object provides a standardized prediction method. This method can receive input data in a specified format and convert the received call request into a forward inference computation call to the model logic loaded from the model file. During this process, necessary data format conversion operations are performed. The input data in the specified format is usually a dictionary, with the key being the tensor name and the value being a NumPy array that conforms to the expected shape and type. The format conversion operations include, but are not limited to: converting the input NumPy array into the framework's native tensor, adjusting the tensor memory layout, and handling data type mapping between different frameworks. The internal logic of the prediction method is as follows: 1) Validate the input dictionary according to the input description; 2) Perform format conversion; 3) Call the inference function of the native model; 4) Convert the output native tensor back to a NumPy array; 5) Organize the output into an output dictionary according to the output description and return it. Assign an object reference to the created encapsulated object and store the object reference in a designated field of the structured evaluation profile generated and initialized for the model to be evaluated. This ensures that the structured evaluation profile can be uniquely associated with the encapsulated model object to be evaluated. The object reference can be an object pointer, reference, or unique identifier in a programming language. In the evaluation profile, a field named model_instance_ref is set up to store this reference. This association ensures that in any subsequent step, the corresponding standardized model instance can be obtained through the evaluation profile to perform tests or inference. The process of generating and initializing a structured evaluation profile for each access model to be evaluated is as follows: During the process of accessing the model to be evaluated through the standardized interface encapsulation layer, the model architecture and parameter feature extraction operations are performed simultaneously. Feature extraction operations obtain basic metadata of the model, including model name, model version, model creation time, and training framework identifier. Some of this information comes from metadata embedded in the model file, some is attached when the user submits an evaluation request, and some is automatically generated by the system (such as creation time). Feature extraction operations obtain the computation graph topological features of the model. The computation graph topological features include the total number of nodes in the computation graph, the total number of edges connecting the nodes, the number of layers of the longest forward propagation path from input to output in the model, i.e., the maximum computation path depth, and the number of different operator types in the model. This is achieved by traversing the computation graph of the loaded native model. Nodes usually correspond to one operator, edges correspond to tensor flows, the maximum computation path depth can be calculated using a graph search algorithm, and the operator types are counted to include all unique operator types. Based on the obtained computational graph topological features, a structural complexity metric value is calculated using a structural complexity metric function. The calculation process of the structural complexity metric function is as follows: First, obtain the total number of nodes and edges in the computational graph, as well as the number of different types of operators in the model. Second, calculate the logarithm of the total number of nodes and the logarithm of the total number of edges. When calculating the logarithm of the total number of nodes, add the total number of nodes to a first normal coefficient and take the logarithm. When calculating the logarithm of the total number of edges, add the total number of edges to a second normal coefficient and take the logarithm. The first and second normal coefficients are extremely small positive numbers to prevent the logarithm from being zero. Then, multiply the logarithm of the total number of edges by a first adjustment weight coefficient to obtain a first product. Add the first product to the logarithm of the total number of nodes to obtain an intermediate weighted sum. Next, divide the maximum computational path depth by the number of different types of operators to obtain a quotient. Multiply this quotient by a second adjustment weight coefficient to obtain the second weighted sum. The second product is then added to a constant to obtain an adjustment factor. Finally, the intermediate weighted sum is multiplied by the adjustment factor to obtain the metric value of model structural complexity. This can be expressed as follows: Let the total number of nodes be V, the total number of edges be E, the maximum computational path depth be D, and the number of operator types be G. The metric value of structural complexity θ_s is calculated by the following formula: θ_s=[log(V+ε_v)+α*log(E+ε_e)]*[1+β*(D / G)], where ε_v and ε_e are the values ​​of the two nodes and the edge depth. _e represents the first and second positive constant coefficients, respectively, both of which are extremely small positive numbers (e.g., 1e-10) used to prevent taking the logarithm of zero; α is the first adjustment weight coefficient, used to balance the contribution of nodes and edges to complexity, with a typical value between 0.3 and 0.7; β is the second adjustment weight coefficient, used to adjust the amplification effect of model depth and heterogeneity on complexity, with a typical value between 0.05 and 0.2. This formula comprehensively considers model size, connection density, depth, and structural heterogeneity. Create a structured evaluation file record with a predefined field structure; The structured evaluation profile records the following fields: a model identifier field for globally uniquely identifying the model to be evaluated; a model instance reference field for storing object references corresponding to the encapsulated objects; a model meta-information field for storing basic meta-information, computation graph topology features, and quantifiable values ​​of model structural complexity; a raw data field reserved for storing raw performance data generated in subsequent evaluation steps; and an evaluation result field reserved for storing the final evaluation results. The evaluation profile can be implemented using JSON, YAML, or database records. For example, the initial state of a JSON-formatted evaluation profile can contain: {"model_id":"uuid-xxx","model_instance_ref":"obj-ref-xxx","meta":{"name":"","version":"",...",V":150,"E":300,"D":25,"G":12,"θ_s":8.76},"raw_data":{},"evaluation":{}}; The generated model identifier, object reference, extracted basic meta-information, computation graph topology features, and calculated model structural complexity quantification values ​​are respectively filled into the model identifier field, model instance reference field, and model meta-information field of the structured evaluation file record, completing the initialization of the structured evaluation file. The initialized structured evaluation file is used to record raw performance data and evaluation results in step S2, and its associated encapsulated object is used to execute automated unit tests and batch tests in step S2. After initialization, the evaluation file is stored in a global file registry center and indexed by model_id. The execution engine in step S2 retrieves the corresponding evaluation file from the registry center using the received model_id, then obtains the executable standardized model object from the model_instance_ref field of the file, and starts testing. The raw data generated by the test is filled back into the raw_data field of the file, realizing data flow and status recording between steps.

[0021] In this embodiment, it is specifically necessary to explain the following steps in step S2: Automatically calling a benchmark testing engine to sequentially perform automated unit tests and batch tests on the model to be evaluated is as follows: Based on the unique model identifier obtained from the evaluation file corresponding to the model to be evaluated, the evaluation file is retrieved from the global file registry. References to encapsulated standardized model objects are extracted from the model instance reference field of the evaluation file, and descriptions of the input and output tensors of the model to be evaluated, including tensor names, shapes, and data types, are obtained from the model metadata field. Based on the obtained input and output tensor descriptions, a corresponding standardized test case pool and a complete, time-series standardized historical dataset are obtained from a centralized test resource library. The standardized test case pool is a collection of test cases corresponding to the input tensor description format, prepared for automated unit testing. The standardized historical dataset... The centralized test resource repository is a versioned data storage service. The standardized test case pool is maintained by a dedicated test data management module. It pre-defines various test case templates, including numerical boundary conditions, string anomalies, image noise, and missing value imputation, based on different types of model tasks (such as classification, regression, and object detection) and input specifications. It can also dynamically generate specific test cases that conform to the format based on the specific input tensor description during evaluation. The standardized historical dataset is a golden dataset exported from production environment logs after desensitization, cleaning, and deduplication. Its version number is managed separately from the data version used for model training to ensure the stability of the evaluation benchmark. By referencing standardized model objects and calling their standardized prediction interfaces, automated unit tests are first executed. These tests select test cases containing preset boundary conditions and outliers from a standardized test case pool and input them into the model, verifying the functional correctness of the model's output. Functional correctness verification includes: for classification models, checking whether the preset catch-all class or confidence level is lower than the safety threshold is output under abnormal inputs; for regression models, checking whether the output value is within a reasonable numerical range; and for models involving numerical operations, verifying whether their handling logic for boundary conditions such as division by zero and overflow meets expectations. The verification rules exist in the form of configurable assertions. Batch testing is then performed, inputting a standardized historical dataset into the model under various pre-defined stress test scenarios. These scenarios are defined by combining multiple basic stress dimension parameters, including dimensions for simulating input data distribution shifts, dimensions for injecting noise into the input data, and dimensions for simulating high-concurrency request loads. Each stress test scenario is determined by a specific set of dimension parameter values, including a distribution shift metric, a noise intensity value, and a simulated number of concurrent request threads. Stress test scenarios are predefined as a series of configuration files; for example, a scenario named "High-Noise Concurrency" might be defined as: {"shift_type":"covariate","shift_magnitude":0.1,"noise_type":"gaussian","noise_sigma":0.05,"concurrency":50}. Distribution shifts can be simulated using algorithms, such as applying KL divergence or Wasserstein distance constraints to the test data. Noise injection involves directly superimposing random distributions onto the tensor data. Concurrency loads are simulated using a thread pool to handle multiple simultaneous prediction requests. For each stress test scenario determined by specific dimensional parameter values, a comprehensive scenario disturbance intensity value is calculated. The calculation process is as follows: divide the distribution offset metric by a preset feature reference range value to obtain a first ratio; add a baseline noise intensity value to the noise intensity value and take the logarithm to obtain a noise impact term; divide the number of concurrent request threads by a baseline concurrency value to obtain a third ratio; then, multiply the first ratio by a first weighting coefficient, multiply the noise impact term by a second weighting coefficient, and multiply the third ratio by a third weighting coefficient. The sum of the first, second, and third weighting coefficients is a constant. Finally, add the first ratio, the noise impact term, and the third ratio after multiplying by their respective weighting coefficients. The result is the comprehensive scenario disturbance intensity of that stress test scenario. The scene comprehensive disturbance intensity value is used to quantify the severity of the corresponding stress test scene; the feature reference range value is set to 1.0 (corresponding to normalized feature), the baseline noise intensity value is 0.01, the baseline concurrency value is 10, the first weight coefficient, the second weight coefficient, and the third weight coefficient can be preset to 0.4, 0.3, and 0.3 respectively. For a specific scene parameter: distribution offset metric value 0.15, noise intensity value 0.08, concurrency 25, then its scene comprehensive disturbance intensity value is calculated as: Ψ=0.4(0.15 / 1.0)+0.3log(1+0.08 / 0.01)+0.3(25 / 10)≈0.06+0.3log(9)+0.75≈0.06+0.66+0.75=1.47. This value will be recorded and associated with the test results under this scene. The specific steps for collecting and storing the raw performance data of the model to be evaluated, calculated based on a unified evaluation metric during automated unit testing and batch testing, to form a raw data matrix are as follows: After the automated unit tests and batch tests under each stress test scenario are completed, the performance data of the model to be evaluated under the current test item are immediately calculated according to the predefined unified evaluation metrics. The unified evaluation metrics include at least the precision metric, recall metric, inference latency metric, and throughput metric. The calculation is performed in real time. For example, precision and recall are calculated in batch tests by comparing the model's prediction results with the real labels in the standardized historical dataset; inference latency is measured by a high-precision timer to measure the time taken for a single prediction; throughput is calculated by counting the number of prediction requests successfully completed per unit time. The calculation logic of all metrics is encapsulated in a unified metric calculator. The performance data generated from each test execution is organized according to the test item identifier and evaluation metric type to construct a structured three-dimensional raw data set. The first dimension of the three-dimensional raw data set represents different test items, the second dimension represents different evaluation metrics, and the third dimension stores the corresponding specific performance data values. For batch testing, the performance data under each stress test scenario is treated as an independent test item and stored in association with the unique identifier of that stress test scenario and its calculated comprehensive perturbation intensity value. In terms of data structure, a dictionary nested dictionary or similar structure can be used, for example: raw_data["batch_test"]["scene_high_noise_concurrent"]={"psi":1.47,"metrics":{"accuracy":0.892,"latency_ms":45.2,"throughput_rps":221.5}}. The results of automated unit tests can be organized under another key, such as `raw_data["unit_test"]; The entire set of original 3D data is recorded as a raw data matrix. Then, the raw data matrix, along with the metadata of this test execution, is serialized. The metadata includes the test timestamp, the version identifier of the standardized historical dataset used, the specific dimensional parameter values ​​of each stress test scenario, and the calculated comprehensive perturbation intensity value of the scenario. The serialization format can be JSON, Protocol Buffers, etc. The metadata and the raw data matrix are packaged together to form a complete test report data package. This data package is self-describing and contains all the environment and parameter information required to reproduce this test. Finally, the serialized raw data matrix and metadata are stored in the dedicated raw data field of the evaluation file corresponding to the model to be evaluated, and the status of the evaluation file is marked as "test completed". At the same time, a notification is triggered to the subsequent processing flow to complete the persistence of test result data and status transition. The storage operation is atomic. The raw_data field in the evaluation file is updated to the serialized data packet, and the status field is updated from "under testing" to "test completed". The update of the status mark will automatically trigger an event or message. The event carries the model_id and notifies the subsequent service (i.e. the execution engine of step S3) that it has subscribed to the "test completed" event to start processing this model to be evaluated. This ensures loose coupling and automated connection between the various links in the pipeline.

[0022] In this embodiment, it is specifically necessary to explain that in step S3, the process of applying a preset model comprehensive capability index formula to aggregate and calculate the original data matrix to obtain the model comprehensive capability index of the model to be evaluated is as follows: First, the input data parsing and scenario alignment operations are performed: After receiving the notification that step S2 is complete, the corresponding evaluation file is retrieved based on the unique identifier of the model to be evaluated, and the original data matrix and its meta-information are deserialized from the original data fields of the evaluation file; the original data matrix is ​​parsed to identify the performance values ​​of various unified evaluation indicators of the model to be evaluated in various preset stress test scenarios, under scenarios without additional pressure or with baseline pressure parameters, as the baseline performance values, and the corresponding scenario performance values ​​under different stress test scenarios. Among them, different stress test scenarios are quantified and identified by their corresponding scenario comprehensive perturbation intensity values; the meta-information of the stress test scenario defines which scenario is the "baseline". Usually, a scenario in which all pressure dimension parameters are zero (or preset minimum value) is designated as the baseline scenario. The baseline performance value is the evaluation result of the model under this scenario. The scenario performance value and the scenario comprehensive perturbation intensity value Ψ_k correspond one-to-one, forming the core data pair for evaluating the robustness of the model; Subsequently, based on the parsed baseline performance values, scenario performance values, and overall scenario disturbance intensity values, aggregate calculations are performed: The baseline performance values ​​of each unified evaluation indicator are normalized, mapping them to a numerical range of zero to one; the normalization process can use Min-Max normalization, pre-setting the theoretical maximum and minimum values ​​for each indicator type. For example, the theoretical range for precision A is [0,1], and the theoretical range for latency L can be set to [0, an acceptable maximum latency threshold, such as 1000ms]; a static weight is preset for each unified evaluation indicator, and the sum of the static weights of all indicators is a constant; the static weight reflects business priority. For example, in a search ranking model, the precision weight might be... The weights are set to 0.6 for recall, 0.3 for latency, and 0.1 for overall model performance evaluation. For each unified evaluation metric, its performance retention rate under each stress test scenario is calculated. This retention rate is obtained by multiplying the first factor by the second factor. The first factor is the quotient of the metric's performance value in the current stress test scenario divided by its baseline performance value, used to measure absolute performance retention. The second factor is an exponential decay factor, which is a power of the natural constant e, with a negative exponent, multiplied by the overall scenario perturbation intensity value of the current stress test scenario. The decay factor is a predefined positive constant. For each stress test scenario... Let a scenario weight be defined, and the sum of the scenario weights for all stress test scenarios be a constant. For each unified evaluation metric, its performance retention across all stress test scenarios is weighted and summed according to the corresponding scenario weight to obtain the average stress performance retention of that metric. Then, the average stress performance retention is subtracted from the calculated value to obtain a difference reflecting the degree of performance degradation. The products of the static weights of all unified evaluation metrics and their normalized baseline performance values ​​are summed to obtain a comprehensive baseline performance value. Normalization can be performed using minimum-maximum normalization or a Sigmoid function mapping based on business experience. Static weights, scenario weights, degradation coefficients, and global robustness sensitivity coefficients can all be used. By setting presets in the configuration file, for example, for classification tasks, the static weight of the precision metric can be set to 0.6 and the recall metric to 0.4; for stress scenarios, the scenario weight can be set according to the estimated occurrence frequency, such as 0.5 for common noise scenarios, 0.3 for extreme distribution offset scenarios, and 0.2 for high concurrency scenarios. The decay coefficient is used to adjust the impact of scenario severity on the expected performance retention, and can be preset to 0.1, which means that for every increase of 1 in the overall perturbation intensity value of the scenario, there is an acceptable performance retention rate with an exponential decay tolerance of about 10%. The global robustness sensitivity coefficient controls the model's tolerance to overall performance fluctuations under stress, and can be preset to 0.5. The larger this value is, the more sensitive the overall index is to performance decay. Finally, the baseline performance composite value is multiplied by an exponential decay term to obtain the model composite capability index. The exponential decay term is a power of the natural constant e, with its exponent being the product of a negative global robustness sensitivity coefficient and a weighted decay degree. The global robustness sensitivity coefficient is a predefined constant greater than zero. The weighted decay degree is the result of weighting and summing the differences in performance decay degrees of all unified evaluation indicators with their static weights as coefficients. The design effect of this formula is that the model composite capability index is jointly determined by the baseline performance composite value and a decay factor based on performance decay under pressure. If the model maintains good performance under various pressure scenarios (average pressure performance retention is close to 1), the weighted decay degree is small, the exponential decay term is close to 1, and the model composite capability index mainly reflects its baseline performance. If the model's performance decays significantly under pressure, the weighted decay degree is large, the exponential decay term is much less than 1, which will significantly reduce the model composite capability index, thus integrating performance and robustness in a single index. The specific process for calculating a model robustness score based on the performance data of the model to be evaluated under different stress test scenarios in the original data matrix is ​​as follows: Based on the performance retention of each unified evaluation index under each stress test scenario and the scenario weights of each stress test scenario, for each unified evaluation index, first calculate the weighted sum of its performance retention under different stress test scenarios with the corresponding scenario weight as the coefficient; then, sum the weighted sums of all unified evaluation indices and divide by the total number of unified evaluation indices to obtain the first part of the basic value of the model robustness score; analyze the process curve of the performance value of each unified evaluation index changing with the increase of the comprehensive perturbation intensity value of its corresponding stress test scenario, and calculate the smoothness stability measure of this change process. The smoothness stability measure is characterized by calculating the sample entropy of the process curve or the reciprocal of the slope of the fitted line; average the smoothness stability measures of all indices to obtain the first part of the model robustness score. The model robustness score is calculated by multiplying the first part of the base value by a preset stability weight coefficient and the second part of the stability index, and then normalizing the final result to the range of zero to one. The first part of the base value reflects the average performance retention level of the model under stress, while the second part of the stability index further characterizes the performance degradation pattern: a low sample entropy value or a small absolute value of the slope of the fitted line (i.e., a large reciprocal) indicates that the performance degradation process with increasing stress is smooth and predictable, rather than fluctuating drastically. This smooth degradation pattern is generally considered more robust. The stability weight coefficient is used to adjust the contribution of stability to the total score and can be preset to 0.2. Finally, the model robustness score is a value between 0 and 1. The higher the value, the more robust and predictable the model's behavior under stress. The process of generating a multi-dimensional model capability profile is as follows: Create a structured, multi-dimensional model capability profile, which includes at least the following dimensions: a comprehensive performance dimension for storing the overall model capability index; a core indicator dimension for storing the normalized baseline performance values ​​of various unified evaluation metrics; a robustness dimension for storing the final model robustness score; an efficiency dimension for storing the normalized inference latency and throughput values; a scenario adaptation spectrum dimension for displaying the relative retention rate of core metrics under multiple representative stress test scenarios in simplified table or vector form; and a resource consumption dimension for storing resource consumption data extracted from the original data matrix. The scenario adaptation spectrum dimension can select stress test scenarios corresponding to three representative scenarios with low, medium, and high comprehensive perturbation intensity values, and record the retention rate of core metrics (such as precision and recall) relative to their baseline performance values ​​under these scenarios. The resource consumption dimension can include peak memory usage and average CPU utilization during model inference. In a specific implementation, the model capability profile can be a JSON object whose structure directly maps to the above dimensions. The generated model comprehensive capability index, final model robustness score, and multi-dimensional model capability profile are stored in the evaluation result field of the evaluation file corresponding to the model to be evaluated. After storage, the evaluation file contains complete information from the original test data to the highly condensed evaluation conclusion. The model comprehensive capability index and model capability profile in this evaluation result field are the direct inputs and core basis for the subsequent steps S4 to perform meta-learning analysis, dynamically adjust weights, and generate decision recommendation reports.

[0023] In this embodiment, it is specifically necessary to explain the process in step S4, which involves using a meta-learning algorithm to analyze the correlation between the model's comprehensive capability index, the scores of each dimension in the model's capability profile, and the actual online business revenue recorded in each historical evaluation file. The system retrieves evaluation files for all completed historical models from the global file registry or its associated persistent storage, and associates these files with the quantified value of the actual online business revenue for each historical model. This quantified value includes percentage increase in click-through rate, conversion rate gain, or revenue increment. The system then normalizes the quantified value of the actual online business revenue for each historical model. This normalization process involves adding one to the quantified value and taking the natural logarithm to compress its numerical range. Adding one to the quantified value and taking the logarithm helps handle situations where revenue might be zero or negative (which may occur in A / B testing), and it maps revenue data, which might otherwise exhibit a long-tail distribution, to a range closer to a normal distribution, thus improving the training stability of the meta-learning model. For example, a historical model with a 2% increase in click-through rate has a normalized value of log(1+0.02)≈0.0198; a model that brings a 100,000 yuan revenue increment has a normalized value of log(1+100000). However, the revenue increment is usually first divided by a large baseline value (such as average daily revenue) for scaling before taking the logarithm. For each historical model, the overall model capability index is extracted from the evaluation result field of its evaluation file, and the scores for the core indicator dimension, robustness dimension, and efficiency dimension are extracted from its model capability profile. The extracted overall model capability index and each score are combined to form the offline evaluation feature vector for meta-learning of the historical model. The offline evaluation feature vector is a one-dimensional array. For example, for a binary classification model, its feature vector may be: [overall model capability index, precision score, recall score, robustness score, latency score, throughput score]. Among them, precision and recall in the core indicator dimension are two independent scores. All scores have been normalized before forming the vector to ensure consistency of units. The offline evaluation feature vectors of all historical models are collected, arranged in rows to form a historical feature matrix, and the quantified values ​​of the online real business benefits of all historical models after normalization are collected to form a target vector. Each row of the historical feature matrix corresponds to a historical model, each column corresponds to an offline evaluation feature, and the target vector is a column vector that corresponds one-to-one with the rows of the historical feature matrix. This dataset constitutes the training sample set for meta-learning analysis. A meta-learning prediction model is used, with the historical feature matrix as input features and the target vector as the prediction target, for training to obtain the trained meta-learning prediction model. The meta-learning algorithm used in the meta-learning prediction model includes one of gradient boosting decision trees, neural networks with attention mechanisms, or Bayesian linear regression. This meta-learning prediction model is used to learn the mapping relationship from offline evaluation features to online real business benefits. Gradient boosting decision trees (such as XGBoost, LightGBM) are chosen because they can handle non-linear relationships between features well and provide feature importance; attention mechanism neural networks are suitable for capturing complex interactions between features; Bayesian linear regression can provide an estimate of the uncertainty of the prediction. In practice, it can be selected according to the amount of historical data and the feature dimension. The goal of model training is to minimize the error between the predicted business benefits and the actual normalized business benefits, such as mean squared error. After the meta-learning prediction model is trained, feature importance analysis is used to analyze the contribution of each feature dimension in the historical feature matrix to the prediction of real online business revenue, resulting in a contribution vector. Each element in the contribution vector corresponds to the contribution quantification value of the model's comprehensive capability index, core indicator dimension scores, robustness dimension score, and efficiency dimension score in the offline evaluation feature vector. The feature importance analysis method determines the contribution vector by analyzing the trained meta-learning prediction model. Specifically, it calculates the quantification value of the importance of each input feature to the prediction target. The importance quantification value is a non-negative value. The larger the value, the stronger the prediction ability and the closer the correlation of the corresponding input feature to the real online business revenue in history. The methods used to calculate the importance quantification value include SHAP value analysis, the model's built-in feature importance scoring method, or model weight analysis. SHAP values ​​are based on game theory and can provide a consistent and reliable allocation of feature contributions. For example, by calculating the SHAP values ​​of all historical samples and averaging the absolute values, the global importance of each feature can be obtained. The contribution vector is the basis for subsequent dynamic adjustment of weights, and it quantifies the historical correlation strength between each offline evaluation metric and the final business success. The process of generating a model-based decision recommendation report that includes a score of expected business value is as follows: Based on the contribution vector, the weight coefficients of each item in the formula of the model's comprehensive capability index are adjusted; the adjustment of the weight coefficients includes the adjustment of the static weights of each unified evaluation indicator, as well as the adjustment of the global robustness sensitivity coefficient. The adjustment process for the static weights of each unified evaluation indicator is as follows: For a given unified evaluation indicator, obtain its original static weight value before adjustment, and obtain the quantified value of the contribution corresponding to that indicator from the contribution vector; then, calculate the sum of the quantified values ​​of the contribution corresponding to all unified evaluation indicators; next, divide the quantified value of the contribution corresponding to the unified evaluation indicator by the sum to obtain a new weight ratio based on contribution normalization; finally, multiply the original static weight value by the difference between one and a preset learning rate to obtain the first product; multiply the new weight ratio based on contribution normalization by the learning rate to obtain... The second product; add the first product and the second product together, and the result is the static weight after the unified evaluation index is adjusted; the learning rate is a predefined value, which is greater than zero and less than or equal to one, and is used to control the magnitude of the adjustment; the learning rate can be set to 0.3, which means that 30% of the new weight comes from the historical contribution and 70% inherits the original weight, thus achieving a balance between adapting to historical patterns and maintaining the stability of the evaluation standard. For example, if the original precision weight is 0.6 and its historical contribution ratio is 0.8, then the new weight = 0.6(1-0.3) + 0.8*0.3 = 0.42 + 0.24 = 0.66; The adjustment process for the global robustness sensitivity coefficient is as follows: First, obtain the contribution metric value corresponding to the robustness dimension from the contribution vector; then, calculate the sum of the contribution metric values ​​of all elements in the contribution vector; divide the contribution metric value corresponding to the robustness dimension by the sum to obtain a robustness correlation strength factor; next, calculate the difference between the robustness correlation strength factor and a preset robustness correlation baseline strength; multiply the difference by a preset adjustment sensitivity coefficient to obtain a scaling factor; then, add the difference to the scaling factor to obtain the scaling coefficient; finally, multiply the original value of the global robustness sensitivity coefficient before adjustment by the scaling coefficient to obtain the adjusted global robustness sensitivity coefficient. Robustness sensitivity coefficient; Robustness correlation benchmark strength is a preset benchmark ratio value used for comparison, and adjustment sensitivity coefficient is a predefined constant used to control the adjustment range; Robustness correlation benchmark strength can be set to 0.15, indicating that under equilibrium conditions, the expected contribution of robustness features to business revenue is 15%, and adjustment sensitivity coefficient κ can be set to 0.5. If the currently calculated robustness correlation strength factor is 0.25 (higher than the benchmark), then the scaling factor = 1 + 0.5(0.25 - 0.15) = 1.05. If the original sensitivity coefficient λ is 0.5, then after adjustment λ' = 0.5 * 1.05 = 0.525, which increases the weight of the consideration of model robustness; The model comprehensive capability index of the current model to be evaluated is recalculated using a formula that includes all adjusted static weights and adjusted global robustness sensitivity coefficients. The result is used as the adjusted model comprehensive capability index. It uses a new weight system that has been "calibrated" with historical business revenue to re-evaluate the current model. The adjusted model comprehensive capability index is theoretically more correlated with online business revenue. Meanwhile, following the method of constructing offline evaluation feature vectors, the model comprehensive capability index and the scores of each dimension in the model capability profile of the current model to be evaluated are used to construct the offline evaluation feature vector corresponding to this evaluation. This vector is then input into the trained meta-learning prediction model to obtain the expected business value score of the model. The expected business value score is a quantitative prediction of the online performance of the current model by the meta-learning prediction model. It combines offline evaluation indicators with historical experience, providing a more forward-looking evaluation for decision-making than simply model performance. Finally, a model decision recommendation report is generated. This report includes at least: the adjusted model comprehensive capability index and expected business value score; a horizontal comparative analysis of the current model to be evaluated and the preset historical baseline model or candidate models in the same batch in terms of the adjusted model comprehensive capability index and expected business value score; a summary of the reasons for the weight adjustment based on contribution vector analysis; and deployment recommendations based on the expected business value score and horizontal comparative analysis. The model decision recommendation report is presented in the form of a structured document (such as HTML or PDF) or database record. The deployment recommendations can be automatically generated based on the threshold set by the expected business value score. For example, a score greater than 0.8 is "strongly recommended for deployment", 0.6-0.8 is "recommended for deployment and monitoring", and less than 0.6 is "not recommended". The horizontal comparative analysis can use tables or charts to clearly show the advantages and disadvantages of the current model relative to the benchmark or competitors. The report is ultimately stored in association with the evaluation file of the current model to be evaluated, serving as the final deliverable of the pipeline output.

[0024] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0025] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0026] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0027] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0028] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0029] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0030] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An AI engineering model pipeline management and automatic deployment method, characterized in that, Specifically, the steps include the following: Step S1: In response to the model evaluation request, the models to be evaluated obtained based on different training frameworks are uniformly connected through a predefined standardized interface encapsulation layer, so that each model to be evaluated can receive input data in a specified format; a structured evaluation profile is generated and initialized for each connected model to be evaluated. Step S2: After the model to be evaluated is encapsulated, an automatic benchmark test engine is invoked to execute automated unit tests and batch tests on the model to be evaluated in sequence. The automated unit tests use test cases containing preset boundary conditions or outliers to verify the functional points of the model to be evaluated. The batch tests use a complete, standardized historical dataset with temporal relationships to verify the comprehensive performance of the model to be evaluated under various preset stress test scenarios. The raw performance data of the model to be evaluated calculated based on a unified evaluation index in the automated unit tests and batch tests are collected to form a raw data matrix and stored in the evaluation file corresponding to the model to be evaluated. Step S3: Read the original data matrix in the evaluation file of the model to be evaluated, apply a preset model comprehensive capability index formula to aggregate and calculate the original data matrix to obtain the model comprehensive capability index of the model to be evaluated; at the same time, calculate a model robustness score based on the performance data of the model to be evaluated under different stress test scenarios in the original data matrix, and combine the performance data of the model to be evaluated under various unified evaluation indicators to generate a multi-dimensional model capability profile. Step S4: Obtain the historical evaluation files of all evaluated models and their corresponding online real business revenue data. Use meta-learning algorithms to analyze the correlation between the model comprehensive capability index recorded in each historical evaluation file, the scores of each dimension in the model capability profile, and the online real business revenue. Based on the correlation, dynamically adjust the weight coefficients of each item in the model comprehensive capability index formula, and use the model comprehensive capability index formula with adjusted weight coefficients to re-evaluate the current model to be evaluated, generating a model decision recommendation report containing the expected business value score.

2. The AI engineering model pipeline management and automatic deployment method of claim 1, wherein: In step S1, the specific operation of uniformly accessing the models to be evaluated based on different training frameworks through a predefined standardized interface encapsulation layer is as follows: Parse the model file of the model to be evaluated and extract the framework feature fingerprint from the header or metadata area of ​​the model file. Based on the extracted frame feature fingerprint, a frame adapter that matches the frame feature fingerprint is dynamically loaded from a pre-built frame adapter library. The model file of the model to be evaluated is loaded using the standardized encapsulation functions contained in the loaded framework adapter; the model file of the model to be evaluated is analyzed to obtain the tensor names, tensor shapes and tensor data types of the input tensors and output tensors defined therein; Based on the tensor names, tensor shapes, and tensor data types of the obtained input and output tensors, a wrapper object is created in memory, and the wrapper object conforms to the unified service interface specification. The encapsulated object provides standardized prediction methods to receive input data in a specified format and perform necessary data format conversion operations; Assign an object reference to the created encapsulated object, and store the object reference assigned to the encapsulated object in the structured evaluation file generated and initialized for the model to be evaluated, so that the structured evaluation file is uniquely associated with the encapsulated model object to be evaluated.

3. The AI engineering model pipeline management and automatic deployment method of claim 2, wherein: The process of generating and initializing a structured evaluation profile for each access model to be evaluated is as follows: During the process of accessing the model to be evaluated through the standardized interface encapsulation layer, the model architecture and parameter feature extraction operations are performed simultaneously. Feature extraction operations obtain basic metadata of the model to be evaluated, including model name, model version, model creation time, and training framework identifier. Feature extraction operations obtain the computation graph topology features of the model to be evaluated. The computation graph topology features include the total number of nodes in the computation graph, the total number of edges connecting the nodes, the number of layers of the longest forward propagation path from input to output in the model, i.e., the maximum computation path depth, and the number of different operator types in the model. Based on the obtained computational graph topological features, a structural complexity metric value is calculated using a structural complexity metric function. The calculation process of the structural complexity metric function is as follows: First, obtain the total number of nodes and edges in the computational graph, as well as the number of different types of operators in the model. Second, calculate the logarithm of the total number of nodes and the logarithm of the total number of edges. When calculating the logarithm of the total number of nodes, add the total number of nodes to a first positive constant coefficient and take the logarithm. When calculating the logarithm of the total number of edges, add the total number of edges to a second positive constant coefficient and take the logarithm. Then, multiply the logarithm of the total number of edges by a first adjustment weight coefficient to obtain a first product. Add the first product to the logarithm of the total number of nodes to obtain an intermediate weighted sum. Next, divide the maximum computational path depth by the number of different types of operators to obtain a quotient. Multiply this quotient by a second adjustment weight coefficient to obtain a second product. Then, add the second product to a constant to obtain an adjustment factor. Finally, multiply the intermediate weighted sum by the adjustment factor to obtain the structural complexity metric value of the model. Create a structured evaluation file record with a predefined field structure; The fields recorded in the structured evaluation archive include: a model identifier field for globally uniquely identifying the model to be evaluated, a model instance reference field for storing object references corresponding to the encapsulated objects, and a model meta-information field for storing basic meta-information, computation graph topology features, and quantified values ​​of model structural complexity. The generated model identifier, object reference, extracted basic meta-information, computation graph topology features, and calculated model structural complexity quantification values ​​are respectively filled into the model identifier field, model instance reference field, and model meta-information field of the structured evaluation file record to complete the initialization of the structured evaluation file.

4. The method for pipeline management and automatic deployment of AI engineering models according to claim 3, characterized in that: In step S2, the specific operation of automatically calling a benchmark testing engine to sequentially perform automated unit testing and batch testing on the model to be evaluated is as follows: Based on the unique identifier of the model obtained from the evaluation file corresponding to the model to be evaluated, the evaluation file is retrieved from the global file registry. The reference of the encapsulated standardized model object is extracted from the model instance reference field of the evaluation file, and the tensor name, tensor shape and tensor data type description information of the input tensor and output tensor of the model to be evaluated are obtained from the model meta information field of the evaluation file. Based on the obtained input and output tensor description information, the corresponding standardized test case pool and a complete standardized historical dataset with temporal relationship are obtained from a centralized test resource library. By using a reference to a standardized model object and calling its standardized prediction interface, automated unit tests are first executed. The automated unit tests select test cases containing preset boundary conditions and outliers from the standardized test case pool and input them into the model, and verify the correctness of the model's output functionality. Batch testing is then performed, which inputs a standardized historical dataset into the model under various preset stress test scenarios. These preset stress test scenarios are defined by combining various basic stress dimension parameters. Each stress test scenario is determined by a specific set of dimension parameter values, including the distribution offset metric, the noise intensity, and the number of simulated concurrent request threads. For each stress test scenario determined by specific dimensional parameter values, a scenario comprehensive disturbance intensity value is calculated. The calculation process of the scenario comprehensive disturbance intensity value is as follows: divide the distribution offset metric by a preset feature reference range value to obtain the first ratio. The noise intensity value is added to a reference noise intensity value and then the logarithm is taken to obtain the noise impact term; Divide the number of concurrent request threads by a baseline concurrent number to obtain the third ratio. Next, the first ratio is multiplied by a first weighting coefficient, the noise impact term is multiplied by a second weighting coefficient, and the third ratio is multiplied by a third weighting coefficient. Finally, the first ratio, the noise impact term, and the third ratio after being multiplied by their respective weighting coefficients are added together, and the result is the overall disturbance intensity value of the stress test scenario.

5. The method for pipeline management and automatic deployment of AI engineering models according to claim 4, characterized in that: The specific steps for collecting and storing the raw performance data calculated based on a unified evaluation metric for the model to be evaluated in automated unit testing and batch testing, forming a raw data matrix, are as follows: After the automated unit test and the batch test under each stress test scenario are completed, the performance data of the model to be evaluated under the current test item are immediately calculated according to the predefined unified evaluation index. The unified evaluation index includes at least the accuracy index, recall index, inference latency index and throughput index. The performance data generated from each test execution is organized according to the test item identifier and evaluation index type to construct a structured three-dimensional raw data set. Among them, for batch testing, the performance data under each stress test scenario is treated as an independent test item and stored in association with the unique identifier of the stress test scenario and its calculated comprehensive disturbance intensity value. The entire set of three-dimensional raw data is recorded as a raw data matrix. Then, the raw data matrix is ​​serialized together with the metadata of this test execution. The metadata includes the test timestamp, the version identifier of the standardized historical dataset used, the specific dimensional parameter values ​​of each stress test scenario and the calculated comprehensive perturbation intensity value of the scenario. Finally, the serialized raw data matrix and metadata are stored in the dedicated raw data field of the evaluation file corresponding to the model to be evaluated, and the status of the evaluation file is marked as test completed.

6. The method for pipeline management and automatic deployment of AI engineering models according to claim 5, characterized in that: In step S3, the process of applying a preset model comprehensive capability index formula to aggregate and calculate the original data matrix to obtain the model comprehensive capability index of the model to be evaluated is as follows: First, the input data parsing and scenario alignment operation is performed: After receiving the notification that step S2 is completed, the corresponding evaluation file is retrieved according to the unique identifier of the model to be evaluated, and the original data matrix and its meta-information are deserialized from the original data fields of the evaluation file; the original data matrix is ​​parsed to identify the performance values ​​of various unified evaluation indicators of the model to be evaluated in various preset stress test scenarios, under scenarios with no additional pressure or with benchmark pressure parameters, as benchmark performance values, and the corresponding scenario performance values ​​under different stress test scenarios. Among them, different stress test scenarios are quantified and identified by their corresponding scenario comprehensive disturbance intensity values. Subsequently, based on the analyzed baseline performance values, scenario performance values, and overall scenario disturbance intensity values, aggregate calculations are performed: the baseline performance values ​​of each unified evaluation index are normalized and mapped to a value range of zero to one; a static weight is preset for each unified evaluation index, and the sum of the static weights of all indices is a constant of one; for each unified evaluation index, its performance retention under each stress test scenario is calculated. The performance retention is obtained by multiplying a first factor and a second factor. The first factor is the quotient obtained by dividing the performance value of the index under the current stress test scenario by its baseline performance value; the second factor is an exponential decay factor. The product of the natural constant e raised to the power of a negative attenuation coefficient and the overall perturbation intensity of the current stress test scenario; a scenario weight is preset for each stress test scenario, and the sum of the scenario weights of all stress test scenarios is a constant 1; for each unified evaluation index, its performance retention under all stress test scenarios is weighted and summed according to the corresponding scenario weight to obtain the average stress performance retention of the index; then, 1 is calculated and subtracted from the average stress performance retention to obtain a difference reflecting the degree of performance degradation; the products of the static weights of all unified evaluation indices and their normalized baseline performance values ​​are summed to obtain a baseline comprehensive performance value; Finally, the baseline performance composite value is multiplied by an exponential decay term to obtain the model composite capability index. The exponential decay term is a power of the natural constant e, with its exponent being the product of the negative global robustness sensitivity coefficient and a weighted decay degree. The weighted decay degree is the result of weighting and summing the performance decay differences of all unified evaluation indicators with their static weights as coefficients.

7. The method for pipeline management and automatic deployment of AI engineering models according to claim 6, characterized in that: The specific process for calculating a model robustness score based on the performance data of the model to be evaluated under different stress test scenarios in the original data matrix is ​​as follows: Based on the performance retention of each unified evaluation index in each stress test scenario and the scenario weight of each stress test scenario, for each unified evaluation index, first calculate the weighted sum of its performance retention in different stress test scenarios with the corresponding scenario weight as the coefficient; then, sum the weighted sums of all unified evaluation indices and divide by the total number of unified evaluation indices to obtain the first part of the basic value of the model robustness score; analyze the process curve of the performance value of each unified evaluation index changing with the increase of the comprehensive perturbation intensity value of its corresponding stress test scenario, and calculate the smooth stability measure of this change process. The smooth stability measure is characterized by calculating the sample entropy of the process curve or the reciprocal of the slope of the fitted line; average the smooth stability measures of all indices to obtain the second part of the stability index of the model robustness score. The first part of the basic value is added to the product of a preset stability weight coefficient and the second part of the stability index. The final result is then normalized to the range of zero to one to obtain the model robustness score of the model to be evaluated.

8. The method for pipeline management and automatic deployment of AI engineering models according to claim 7, characterized in that: The process of generating a multi-dimensional model capability profile is as follows: Create a structured, multi-dimensional model capability profile, which includes at least the following dimensions: a comprehensive performance dimension for storing the overall model capability index; a core indicator dimension for storing the normalized baseline performance values ​​of various unified evaluation indicators; a robustness dimension for storing the final model robustness score; an efficiency dimension for storing the normalized inference latency and throughput values; a scenario adaptation spectrum dimension for displaying the relative retention rate of core indicators under multiple representative stress test scenarios in simplified table or vector form; and a resource consumption dimension for storing resource consumption data extracted from the original data matrix. The generated model comprehensive capability index, final model robustness score, and multi-dimensional model capability profile are stored in the evaluation result field of the evaluation file corresponding to the model to be evaluated.

9. The method for pipeline management and automatic deployment of AI engineering models according to claim 8, characterized in that: In step S4, the process of using meta-learning algorithms to analyze the correlation between the model's comprehensive capability index, the scores of each dimension in the model's capability profile, and the actual online business revenue recorded in each historical evaluation file is as follows: Retrieve evaluation files of all historical models that have been evaluated from the global file registry or its associated persistent storage, and associate them with the quantitative value of the online real business revenue corresponding to each historical model. The quantitative value of online real business revenue includes the percentage increase in click-through rate, conversion rate gain, or revenue increase. The quantitative values ​​of the actual online business revenue for each historical model are normalized. For each historical model, the model comprehensive capability index is extracted from the evaluation result field of its evaluation file, and the scores of core indicator dimension, robustness dimension and efficiency dimension are extracted from its model capability profile. The extracted model comprehensive capability index and each score together constitute the offline evaluation feature vector of the historical model for meta-learning. Collect the offline evaluation feature vectors of all historical models, arrange them in rows to form a historical feature matrix, and collect the quantitative values ​​of the online real business revenue of all historical models after normalization to form a target vector. A meta-learning prediction model is used, with the historical feature matrix as input features and the target vector as the prediction target, to train the model and obtain the trained meta-learning prediction model. Meta-learning prediction models use one of the following meta-learning algorithms: gradient boosting decision tree, neural network with attention mechanism, or Bayesian linear regression. After the meta-learning prediction model is trained, the feature importance analysis method is used to analyze the contribution of each feature dimension in the historical feature matrix to the predicted online real business revenue, resulting in a contribution vector. Each element in the contribution vector corresponds to the contribution quantification value of the model comprehensive capability index, core indicator dimension scores, robustness dimension score, and efficiency dimension score in the offline evaluation feature vector. The feature importance analysis method determines the contribution vector by analyzing the trained meta-learning prediction model, specifically by calculating the quantification value of the importance of each input feature to the prediction target.

10. The method for pipeline management and automatic deployment of AI engineering models according to claim 9, characterized in that: The process of generating a model-based decision recommendation report that includes a score of expected business value is as follows: Based on the contribution vector, the weight coefficients of each item in the formula of the model's comprehensive capability index are adjusted; the adjustment of the weight coefficients includes the adjustment of the static weights of each unified evaluation indicator, as well as the adjustment of the global robustness sensitivity coefficient. The adjustment process for the static weights of each unified evaluation indicator is as follows: For a given unified evaluation indicator, obtain its original static weight value before adjustment, and obtain the contribution quantification value corresponding to that indicator from the contribution vector; then, calculate the sum of the contribution quantification values ​​corresponding to all unified evaluation indicators; next, divide the contribution quantification value corresponding to the unified evaluation indicator by the sum to obtain a new weight ratio based on contribution normalization; finally, multiply the original static weight value by the difference between one and a preset learning rate to obtain the first product; multiply the new weight ratio based on contribution normalization by the learning rate to obtain the second product; add the first product and the second product to obtain the adjusted static weight of the unified evaluation indicator. The adjustment process for the global robustness sensitivity coefficient is as follows: First, obtain the contribution metric value corresponding to the robustness dimension from the contribution vector; then, calculate the sum of the contribution metric values ​​of all elements in the contribution vector; divide the contribution metric value corresponding to the robustness dimension by the sum to obtain a robustness correlation strength factor; next, calculate the difference between the robustness correlation strength factor and a preset robustness correlation benchmark strength; multiply the difference by a preset adjustment sensitivity coefficient to obtain a scaling factor; then, add the difference to the scaling factor to obtain a scaling coefficient; finally, multiply the original value of the global robustness sensitivity coefficient before adjustment by the scaling coefficient to obtain the adjusted global robustness sensitivity coefficient. The model comprehensive capability index of the current model to be evaluated is recalculated using the model comprehensive capability index formula that includes all adjusted static weights and adjusted global robustness sensitivity coefficients. The result is used as the adjusted model comprehensive capability index. Meanwhile, in accordance with the method of constructing offline evaluation feature vectors, the model comprehensive capability index of the current model to be evaluated and the scores of each dimension in the model capability profile are used to construct the offline evaluation feature vector corresponding to this evaluation, which is then input into the trained meta-learning prediction model to obtain the expected business value score of the model. Finally, a model decision recommendation report is generated, which includes at least the adjusted model comprehensive capability index and expected business value score. A comparative analysis of the current model to be evaluated with the preset historical baseline model or candidate models in the same batch on the adjusted model comprehensive capability index and expected business value score; a summary of the reasons for this weight adjustment based on contribution vector analysis; and deployment recommendations based on expected business value score and comparative analysis.

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