A method for model version drift prediction based on behavior fingerprint timing modeling

Abstract

The embodiment of the application provides a kind of based on behavior fingerprint time series modeling model version drift prediction method, this method includes the following steps: S1: the multidimensional data of each version of test model is obtained, and behavior fingerprint vector is generated according to multidimensional data, for building fingerprint time series sequence;S2: trend analysis, mutation early warning, time series deep learning and fusion are carried out to fingerprint time series sequence, and the prediction drift parameter and next version behavior fingerprint vector of the test model are obtained;S3: based on matching strategy library, prediction drift parameter, next version behavior fingerprint vector is used to pre-adapt the test model, for updating test model;S4: the accuracy of the updated test model is verified according to next version behavior fingerprint vector, if the accuracy does not satisfy preset requirement, then after updating fingerprint time series sequence, S2 is executed, and the accuracy of model version drift prediction is improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically to a method for predicting model version drift based on behavioral fingerprint temporal modeling. Background Technology

[0002] With the rapid development of artificial intelligence technology, large language models have been widely applied in core scenarios such as enterprise AI traffic acquisition and content optimization, becoming an important support for enterprise digital operations. However, current large language models exhibit rapid iteration characteristics on a weekly basis. Each version update significantly changes the model's core behavioral features such as retrieval preferences, ranking logic, and generation patterns. This iterative characteristic directly leads to a significant decline in the effectiveness of content that enterprises have invested heavily in optimizing, seriously affecting the efficiency of enterprise AI traffic acquisition and long-term return on investment.

[0003] To address the aforementioned issues, existing mainstream approaches all have significant shortcomings and cannot effectively resolve the content performance fluctuations caused by model iterations. Specifically, these shortcomings manifest in three ways: First, a passive, reactive adjustment strategy is adopted, requiring manual investigation and optimization only after a decline in content performance is detected following a model version update. This approach suffers from significant lag, lack of early warning, and repeated investment of manpower and resources, resulting in a recovery period of 2-3 weeks. Second, a conservative optimization strategy is employed, reducing the content's dependence on specific model versions through generalized adjustments. While this reduces performance fluctuations, it leads to mediocre content optimization results, neglecting optimal peak revenue and failing to achieve the company's expected operational goals. Third, a multi-version parallel maintenance strategy is used, maintaining an independent content system for each model version. This approach not only incurs extremely high maintenance costs but also carries high risks associated with version switching and is unsustainable in the long term.

[0004] A thorough analysis of existing technical solutions reveals four main shortcomings: First, they lack foresight, failing to predict the direction and magnitude of model version changes, leading to a sharp decline in content optimization effectiveness and making it difficult to mitigate risks in advance. Second, they lack adaptability, relying excessively on manual adjustments to address fluctuations in effectiveness caused by model iterations, resulting in long response cycles and an inability to quickly adapt to model changes. Third, they lack a closed-loop optimization mechanism, as feedback from content effectiveness does not effectively feed back into model optimization, hindering the continuous improvement of the model's predictive capabilities. Fourth, the trust assets are unsustainable, with frequent fluctuations in content optimization effectiveness with model version iterations, making it difficult for companies to accumulate long-term assets through continuous investment in content optimization, further increasing operational costs and risks.

[0005] Actual testing has verified that content without anti-drift optimization can experience a first-recommendation rate fluctuation of up to ±26.8% after the large language model completes its version update. This fluctuation directly affects the stability and accuracy of enterprise AI traffic, hindering the sustainable development of enterprise digital operations. Therefore, there is an urgent need for a content optimization solution that can solve the above-mentioned technical defects and adapt to the rapid iteration characteristics of the large language model. Summary of the Invention

[0006] The purpose of this invention is to provide a method for predicting model version drift based on behavioral fingerprint time series modeling, which improves the accuracy of model version drift prediction.

[0007] To achieve the above objectives, embodiments of the present invention provide a method for predicting model version drift based on behavioral fingerprint temporal modeling, the method comprising the following steps: S1: Obtain multi-dimensional data for each version of the test model, and generate behavioral fingerprint vectors based on the multi-dimensional data to construct fingerprint time series sequences; S2: Perform trend analysis, mutation warning, time series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector; S3: Pre-adapt the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavior fingerprint vector to update the test model; S4: Verify the accuracy of the updated test model based on the next version of the behavioral fingerprint vector. If the accuracy does not meet the preset requirements, update the fingerprint time sequence and then execute S2.

[0008] Optionally, the multi-dimensional data includes the retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight of the test model; The fingerprint time sequence includes model identifier, version number, release date, dimension value, extraction method, and confidence level.

[0009] Optionally, the step of performing trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters and the next version of the behavioral fingerprint vector for the test model includes: The fingerprint time sequence is fitted and weighted to obtain the predicted behavioral fingerprint vector for the next version; The mutation point information of the behavioral fingerprint vector is obtained by applying the CUSUM cumulative sum and Bayesian change point detection algorithm to the fingerprint time sequence; Deep learning-predicted fingerprints are obtained by performing LSTM or Transformer deep learning and end-to-end learning on the fingerprint time sequence. The predicted drift parameters are obtained by weighted fusion of the predicted behavioral fingerprint, mutation point information, and deep learning predicted fingerprint.

[0010] Optionally, the predicted drift parameters include drift vector, drift type, and prediction confidence. The pre-adaptation of the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavioral fingerprint vector includes: By matching the drift vectors, drift types, and prediction confidence scores in the policy library, the corresponding adjustment policies and adjustment parameters are obtained. The test model is pre-adapted according to the adjustment strategy and adjustment parameters described above.

[0011] Optionally, the adjustment strategies include keyword density adjustment, entity distribution adjustment, content expansion, content abbreviation, structural format adjustment, and timeliness tag update; The adjustment parameters are the numerical values ​​corresponding to each item in the adjustment strategy.

[0012] Optionally, the drift vector = predicted fingerprint Current fingerprint; The drift types include gradual and abrupt types.

[0013] Optionally, the method further includes: calculating the error of the behavioral fingerprint vectors before and after the update to obtain the prediction accuracy, and determining the business performance indicators of the test model before and after the update based on the prediction accuracy.

[0014] On the other hand, this application also proposes an apparatus for predicting model version drift based on behavioral fingerprint time series modeling, the apparatus comprising: The acquisition module is used to acquire multi-dimensional data of each version of the test model, and generate behavioral fingerprint vectors based on the multi-dimensional data to construct fingerprint time series sequences. The first processing module is used to perform trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector. The second processing module pre-adapts the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavior fingerprint vector, in order to update the test model. The third processing module verifies the accuracy of the updated test model based on the next version of the behavioral fingerprint vector. If the accuracy does not meet the preset requirements, the first processing module is executed after updating the fingerprint time sequence. The multi-dimensional data includes the test model's retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight. The fingerprint time sequence includes model identifier, version number, release date, dimension value, extraction method, and confidence level.

[0015] Optionally, the step of performing trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters and the next version of the behavioral fingerprint vector for the test model includes: The fingerprint time sequence is fitted and weighted to obtain the predicted behavioral fingerprint vector for the next version; The mutation point information of the behavioral fingerprint vector is obtained by applying the CUSUM cumulative sum and Bayesian change point detection algorithm to the fingerprint time sequence; Deep learning-predicted fingerprints are obtained by performing LSTM or Transformer deep learning and end-to-end learning on the fingerprint time sequence. The predicted drift parameters are obtained by weighted fusion of the predicted behavioral fingerprint, mutation point information, and deep learning predicted fingerprint.

[0016] Optionally, the predicted drift parameters include drift vector, drift type, and prediction confidence. The pre-adaptation of the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavioral fingerprint vector includes: By matching the drift vectors, drift types, and prediction confidence scores in the policy library, the corresponding adjustment policies and adjustment parameters are obtained. The test model is pre-adapted according to the adjustment strategy and adjustment parameters described above.

[0017] On the other hand, this application also proposes a machine-readable storage medium storing instructions for causing a machine to execute the method for predicting model version drift based on behavioral fingerprint temporal modeling described above.

[0018] Through the above technical solutions, this application achieves time-series tracking of behavioral fingerprints by real-time tracking of various versions of the large model, extracting multi-dimensional data, generating multi-dimensional behavioral fingerprint vectors, and storing them in a time-series database to form a historical sequence. By integrating trend analysis, mutation warning, and time-series deep learning, drift prediction, adaptive adjustment, and closed-loop optimization are performed, achieving a leap from passive response to proactive prediction and zero-perception self-healing. This ensures that the enterprise's content optimization effect transcends the model version cycle, forming sustainable trust assets and improving the accuracy of model version drift prediction.

[0019] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a method for predicting model version drift based on behavioral fingerprint temporal modeling according to this application. Figure 2 This is a schematic diagram of the behavioral fingerprint time-series database structure of this application; Figure 3 This is a time-series variation curve of the behavioral fingerprint of this application; Figure 4 This is a diagram of the three-mode fusion prediction model architecture of this application; Figure 5 This is a structural diagram of the LSTM time series prediction model of this application; Figure 6 This is a schematic diagram of the CUSUM mutation detection in this application; Figure 7 This is a mapping diagram of the strategy library in this application; Figure 8 This is the adaptive adjustment flowchart of this application; Figure 9 This is the closed-loop verification optimization flowchart of this application. Detailed Implementation

[0021] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0022] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0023] Figure 1 This is a flowchart illustrating a method for predicting model version drift based on behavioral fingerprint temporal modeling, as described in this application. Figure 1 As shown, this embodiment of the invention provides a method for predicting model version drift based on behavioral fingerprint time series modeling. The method includes the following steps: S1: Obtain multi-dimensional data for each version of the test model, and generate behavioral fingerprint vectors based on the multi-dimensional data to construct fingerprint time series sequences; S2: Perform trend analysis, mutation warning, time series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector; S3: Pre-adapt the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavior fingerprint vector to update the test model; S4: Verify the accuracy of the updated test model based on the next version of the behavioral fingerprint vector. If the accuracy does not meet the preset requirements, update the fingerprint time sequence and then execute S2.

[0024] The test model described above is a Large Language Model (LLM). LLMs are typically iterated and updated weekly, and each update significantly alters behavioral characteristics such as retrieval sensitivity, ranking logic, and generation patterns. The multi-dimensional data includes the test model's retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight.

[0025] Specifically, the search sensitivity mentioned above represents the model's emphasis on matching keywords and search terms; a higher value indicates a greater reliance on precise keyword matching. For example, a sensitivity of 0.8 means content containing core keywords is more likely to be recommended; a sensitivity of 0.3 indicates less stringent keyword requirements. The entity distribution weight mentioned above represents the model's preference for entities such as names, organizations, products, and locations; a higher weight prioritizes displaying content containing the corresponding entity. For example, a product entity weight of 0.7 significantly improves the ranking of content containing the product name. The content length weight mentioned above represents the model's preferred content length, corresponding to a preference for long / short articles. For example, a higher weight indicates a preference for long articles, while a lower weight indicates a preference for concise articles. The content structure weight mentioned above represents the model's preference for layout structure, such as bullet points, headings, paragraphs, and tables. For example, a weight biased towards strong bullet points makes Markdown lists and hierarchical headings more likely to be recommended. The timeliness weight mentioned above represents the model's emphasis on the publication time and freshness of content; a higher weight prioritizes the newest content. For example, a weight of 0.7 shows that content published within the last week is far superior to older content.

[0026] Behavioral fingerprints are a set of computable values ​​extracted from the stable preferences and behavioral patterns of a test model during generation, retrieval, and response. The sum of these values ​​constitutes its behavioral fingerprint. The formula for the behavioral fingerprint vector is: F = [S_retrieve, S_entity, S_length, S_structure, S_recency], where S_retrieve is the retrieval sensitivity, S_entity is the entity distribution weight, S_length is the content length weight, S_structure is the content structure weight, and S_recency is the timeliness weight. The fingerprint time series sequence includes the model identifier, version number, release date, dimension values, extraction method, and confidence level.

[0027] The trend analysis described above involves fitting and weighting the fingerprint time series to obtain the predicted behavioral fingerprint vector for the next version. The mutation warning described above involves using CUSUM cumulative summation and Bayesian change point detection algorithms to obtain mutation point information in the behavioral fingerprint vector. The temporal deep learning and fusion described above involves performing LSTM (Long Short-Term Memory) or Transformer deep learning (based on self-attention) and end-to-end learning on the fingerprint time series to obtain a deep learning predicted fingerprint, and then weighting and fusing the predicted behavioral fingerprint, mutation point information, and deep learning predicted fingerprint to obtain predicted drift parameters. These predicted drift parameters include the drift vector, drift type, and prediction confidence.

[0028] Specifically, the drift vector mentioned above is the difference between the predicted fingerprint and the current fingerprint, quantifying the direction and magnitude of the change in model behavior. The formula is: Drift vector = Predicted fingerprint Current fingerprint. Example: Current retrieval sensitivity 0.3, prediction 0.8, this dimension's drift vector = +0.5. The above drift types represent the patterns of model behavior change, categorized as gradual or abrupt. Example: Gradual change occurs slowly over multiple versions; abrupt change occurs suddenly and drastically over a single version. The above prediction confidence level represents the reliability of the prediction result, ranging from 0 to 1 or as a percentage; higher confidence indicates greater reliability. Example: A 95% confidence level means the prediction is highly likely to be accurate.

[0029] The construction method of the aforementioned matching strategy library includes: establishing drift types, adjusting strategies, and adjusting parameter mapping relationships to cover core drift scenarios such as retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight. For example, the action corresponding to "enhanced retrieval sensitivity" is "increasing keyword density by 20%-30%"; the action corresponding to "increased length preference" is "expanding content by 500-800 words"; and the action corresponding to "abrupt change in timeliness weight" is "updating content timestamps and marking with the latest identifiers." The aforementioned pre-adaptation involves automatically adjusting the content based on the predicted drift vector and drift type before the official launch of a new model version. This allows the content to adapt to the behavioral preferences of the next version of the model in advance, avoiding a precipitous drop in performance after the update and achieving zero-perceptible self-healing. The aforementioned preset requirements are manually set threshold requirements.

[0030] Through the above technical solutions, this application achieves time-series tracking of behavioral fingerprints by real-time tracking of various versions of the large model, extracting multi-dimensional data, generating multi-dimensional behavioral fingerprint vectors, and storing them in a time-series database to form a historical sequence. By integrating trend analysis, mutation warning, and time-series deep learning, drift prediction, adaptive adjustment, and closed-loop optimization are performed, achieving a leap from passive response to proactive prediction and zero-perception self-healing. This ensures that the enterprise's content optimization effect transcends the model version cycle, forming sustainable trust assets and improving the accuracy of model version drift prediction.

[0031] In one embodiment, multi-dimensional data of each version of the test model is acquired, and behavioral fingerprint vectors are generated based on the multi-dimensional data to construct a fingerprint time-series sequence. Specifically, the iterative updates of each version of the test model are tracked in real time, and dimensional data such as retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight of each version of the test model are extracted to generate multi-dimensional behavioral fingerprint vectors, which are then stored in a time-series database to form a fingerprint time-series sequence. Figure 2 As shown, this time-series database stores core fields such as model identifier, version number, release date, fingerprint vector, dimension value, extraction method, and confidence level.

[0032] like Figure 3 As shown, this method continuously tracks model version updates, forming a behavioral fingerprint time series {F{t2}, F{t1}, Ft} ​​in iteration order, providing a data foundation for prediction. By tracking the iterations of each version of the large model in real time, extracting multi-dimensional data, generating multi-dimensional behavioral fingerprint vectors, and storing them in a time series database to form a historical sequence, the method achieves time-series tracking of behavioral fingerprints.

[0033] In one embodiment, such as Figure 4 As shown, the fingerprint time series is subjected to trend analysis, mutation warning, time series deep learning, and fusion to obtain the predicted drift parameters and the next version of the behavioral fingerprint vector for the test model. Specifically, this includes fitting historical fingerprint dimensional trends and then extrapolating the next version of the fingerprint using a weighted average to adapt to progressive drift scenarios; employing CUSUM cumulative sum and Bayesian change point detection algorithms to identify fingerprint mutation points for early mutation warning, adapting to sudden drift scenarios; using LSTM / Transformer deep learning to learn nonlinear temporal patterns end-to-end to adapt to complex behavioral change scenarios; and dynamically allocating weights according to historical accuracy to fuse and obtain the final predicted fingerprint, drift vector, prediction confidence, and drift type. This method adopts a three-model fusion architecture of trend analysis, mutation warning, and time series deep learning, avoiding redundant optimization efforts and improving prediction accuracy.

[0034] In one embodiment, the test model is pre-adapted based on a matching strategy library, predicted drift parameters, and the next version of the behavioral fingerprint vector to update the test model. Specifically, the adjustment intensity is automatically calculated based on the drift amplitude, and operations such as keyword optimization, structural transformation, content expansion, content abbreviation, and timeliness updates are performed. This method clearly defines the dimensions of change and the direction of adjustment of the test model, making it transparent and controllable.

[0035] In one embodiment, the accuracy of the updated test model is verified based on the next version of the behavioral fingerprint vector. If the accuracy does not meet a preset requirement, the fingerprint time sequence is updated and S2 is executed. This method achieves a leap from passive response to proactive prediction and zero-aware self-healing, ensuring that the enterprise's content optimization effect transcends the model version cycle, forming sustainable trust assets, and improving the accuracy of model version drift prediction.

[0036] In one embodiment, the step of performing trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time-series sequence to obtain the predicted drift parameters and the next version of the behavioral fingerprint vector for the test model includes: fitting the fingerprint time-series sequence and using a weighted average to obtain the next version of the predicted behavioral fingerprint vector; using CUSUM cumulative summation and Bayesian change point detection algorithms on the fingerprint time-series sequence to obtain mutation point information of the behavioral fingerprint vector; performing LSTM or Transformer deep learning and end-to-end learning on the fingerprint time-series sequence to obtain a deep learning predicted fingerprint; and weightedly fusing the predicted behavioral fingerprint, mutation point information, and deep learning predicted fingerprint to obtain the predicted drift parameters.

[0037] like Figure 5 As shown, the input layer contains the time-series sequence of the input behavioral fingerprint. LSTM hidden layer 1 extracts short-term temporal features, memorizing recent version fingerprint changes. LSTM hidden layer 2 extracts long-term temporal patterns, capturing the non-linear trend of multi-version iterations. The fully connected layer fuses the hidden layer features and outputs the predicted value of the behavioral fingerprint for the next version. The output is the deep learning predicted fingerprint, used for subsequent three-model fusion. This method learns the non-linear temporal patterns of model version iterations, accurately predicting complex drift.

[0038] Among them, CUSUM cumulative sum and Bayesian change point detection algorithms are both sequence change point detection algorithms, that is, monitoring a series of data and automatically detecting when the data pattern suddenly changes. For example Figure 6 As shown, the CUSUM cumulative sum is the sum of the deviations between the current observation and the baseline mean, calculated continuously. Once the cumulative sum exceeds a threshold, a change is determined. The Bayesian change point detection algorithm uses probability and Bayesian inference to determine the probability of a change point occurring at a certain position in the sequence, and what distribution it follows before and after the change.

[0039] The aforementioned mutation point information includes the moment when the behavioral fingerprint of the test model suddenly and significantly shifts from a stable state, along with the specific characteristics of the mutation. This information typically includes: mutation location / time (which version, which training round, which iteration, which timestamp), mutation dimension (content length weight suddenly increases / decreases, retrieval sensitivity increases significantly, entity distribution weight shifts from person names to organizations, content structure weight changes from bullet points to paragraphs), mutation magnitude (how much shifted, how many standard deviations from the normal range, how much the behavioral fingerprint vector distance changes), mutation type (sudden jump, slow drift, periodic fluctuation), and confidence level (e.g., 95% probability of a genuine mutation, possibly noise). Figure 7 It is a direct mapping of drift dimensions, adjustment strategies, and adjustment parameters: Search sensitivity drift → keyword density adjustment; length preference drift → content expansion / abbreviation; timeliness drift → updating timestamps and marking the latest; structure preference drift → format optimization (titles, lists, paragraphs).

[0040] For example, such as Figure 8 As shown, the behavioral fingerprints of the tracking test models V1→V2→V3→V4 are as follows: V1, V2, and V3 all showed stable metrics. However, upon the release of V4: retrieval sensitivity increased from 0.3 to 0.8, response length preference changed from medium to very long, and content structure weight (structure preference) changed from free text to strong split. Therefore, the mutation point information here is: Mutation point: Model version V4; Mutation dimensions: retrieval sensitivity, content length weight (length preference), content structure weight (structure preference); Mutation magnitude: Significant increase; Mutation type: Significant mutation; Confidence level: Extremely high.

[0041] This method can promptly detect abnormal and sudden changes in model behavior, and provide early warnings of risks such as inconsistent output styles, abnormal retrieval dependencies, and timeliness imbalances, thus ensuring the consistency of online model services and user experience.

[0042] In one embodiment, the predicted drift parameters include a drift vector, a drift type, and a prediction confidence level; the pre-fitting of the test model based on the matching strategy library, the predicted drift parameters, and the next version behavior fingerprint vector includes: matching the drift vector, drift type, and prediction confidence level in the matching strategy library to obtain the corresponding adjustment strategy and adjustment parameters; and pre-fitting the test model according to the adjustment strategy and adjustment parameters.

[0043] For example: The prediction model has a retrieval sensitivity drift vector of +0.5, a mutation type, and a confidence level of 94%. The matching strategy library is: retrieval sensitivity mutation enhancement → adjustment strategy is to increase keyword density. Adjustment parameters are determined: keyword density increase by 20%~30%. Pre-adaptation execution: automatically and evenly insert core keywords into the content, increasing the density from 2% to 2.5%.

[0044] For example: timeliness weight mutation, drift vector +0.4, confidence level 95%; matching strategy: update timestamp, mark the latest identifier; pre-adaptation: batch refresh content publication time, add "latest published" tag.

[0045] Specifically, the adjustment strategy includes keyword density adjustment, entity distribution adjustment, content expansion, content abbreviation, structural format adjustment, and timeliness tag updating; the adjustment parameters are the numerical values ​​corresponding to each item in the adjustment strategy. The drift vector = predicted fingerprint. Current fingerprint; the drift type includes progressive and abrupt types. Behavioral fingerprint formula: F = [retrieval sensitivity, entity distribution weight, length weight, structure weight, timeliness weight]. For example, the current fingerprint (version t): F t =[0.3, 0.4, 0.5, 0.4, 0.3]; Predict fingerprint (version t+1): F t+1 =[0.8, 0.7, 0.8, 0.9, 0.7]; Drift vector = F t+1 F t =[+0.5, +0.3, +0.3, +0.5, +0.4]. Each value represents the drift magnitude in the corresponding dimension, with positive and negative indicating the direction of change.

[0046] The gradual drift described above involves slow, continuous changes across multiple versions, without sudden jumps. For example: V1→V2→V3→V4, with search sensitivity increasing slightly from 0.3→0.35→0.4→0.45 with each version. Abrupt drift, on the other hand, involves a sudden, large change in a single version, deviating significantly from the historical trend. For example: V3 had a search sensitivity of 0.3, while V4 jumped directly to 0.8, representing a dramatic leap in a single version.

[0047] This method analyzes and predicts drift vectors, matches them with a strategy library to generate corresponding adjustment schemes, and automatically completes content rewriting, structural optimization, and parameter adjustment before the new version goes live, thus achieving pre-adaptation.

[0048] In one embodiment, the method further includes: calculating the error between the behavioral fingerprint vectors before and after the update to obtain the prediction accuracy, and determining the business performance indicators of the test model before and after the update based on the prediction accuracy. Figure 9 As shown, this method extracts actual behavioral fingerprints to verify prediction accuracy and evaluate the effect of content adjustments after the new version is launched; it feeds real data back into the prediction model and strategy library to continuously improve prediction and self-healing capabilities.

[0049] According to another specific implementation, this application also proposes multi-model joint prediction, which generates a more robust adjustment strategy by identifying common drift across models; this application can also train industry-specific prediction models to adapt to the content sensitivity of different industries, retain control group content, quantitatively evaluate the adjustment effect, and add a manual review process before high-risk large-scale adjustments to ensure content quality.

[0050] In one embodiment, according to this application, DeepSeek V3.1 is subjected to drift prediction and adaptive adjustment: six versions of DeepSeek from V2.0 to V3.0.2 are tracked, behavioral fingerprints are extracted, and gradual trends such as decreased search sensitivity and increased product entity distribution weight are identified; the fingerprint of version V3.1 is fused and predicted to obtain drift results such as search sensitivity -0.02 and product entity distribution weight +0.03; strategies such as reducing keyword density, enhancing product entities, expanding content, and optimizing Markdown structure are matched according to the drift results; specific strategies include adding hierarchical headings and lists to the content, optimizing keyword and entity density, and completing pre-adaptation; after the new version is launched, the prediction accuracy is 94.2%, the first recommendation rate of the unadjusted content drops by 15%, and the rate drops by only 2% after adjustment, achieving a zero-perceptible transition.

[0051] In another embodiment, according to this application, a timeliness change warning and response is provided for Wenxin Yiyan: the timeliness weight of Wenxin Yiyan is identified by the CUSUM algorithm and a change warning is issued in advance; it is predicted that the timeliness weight will jump to above 0.70; the timeliness tags of the content are updated in batches and the publication time is refreshed; the actual timeliness weight is 0.72, the first recommendation rate of the unadjusted content drops by 19%, and the drop rate after adjustment is only 3%.

[0052] On the other hand, this application also proposes a device for predicting model version drift based on behavioral fingerprint time-series modeling. The device includes: an acquisition module for acquiring multi-dimensional data of each version of a test model and generating behavioral fingerprint vectors based on the multi-dimensional data to construct a fingerprint time-series sequence; a first processing module for performing trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time-series sequence to obtain the predicted drift parameters and the next version behavioral fingerprint vector of the test model; a second processing module for pre-adapting the test model based on a matching strategy library, the predicted drift parameters, and the next version behavioral fingerprint vector to update the test model; and a third processing module for verifying the accuracy of the updated test model based on the next version behavioral fingerprint vector. If the accuracy does not meet a preset requirement, the first processing module is executed after updating the fingerprint time-series sequence. The multi-dimensional data includes the test model's retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight. The fingerprint time-series sequence includes a model identifier, version number, release date, dimension value, extraction method, and confidence level.

[0053] In some embodiments, the first processing module includes: fitting and weighting the fingerprint time series to obtain a predicted behavioral fingerprint vector for the next version; applying CUSUM cumulative summation and Bayesian change point detection algorithms to the fingerprint time series to obtain abrupt change information of the behavioral fingerprint vector; performing LSTM or Transformer deep learning and end-to-end learning on the fingerprint time series to obtain a deep learning predicted fingerprint; and weightedly fusing the predicted behavioral fingerprint, abrupt change information, and deep learning predicted fingerprint to obtain a predicted drift parameter. The predicted drift parameter includes a drift vector, a drift type, and a prediction confidence level.

[0054] In some embodiments, the first processing module includes: matching drift vectors, drift types, and prediction confidence in a strategy library to obtain corresponding adjustment strategies and adjustment parameters; and pre-adapting the test model according to the adjustment strategies and adjustment parameters. Specifically, the adjustment strategies include keyword density adjustment, entity distribution adjustment, content expansion, content abbreviation, structural format adjustment, and timeliness tag updating; the adjustment parameters are the numerical values ​​corresponding to each item in the adjustment strategies. The drift vector = predicted fingerprint. Current fingerprint; the drift types include progressive and abrupt types.

[0055] Through the above technical solutions, this application achieves time-series tracking of behavioral fingerprints by real-time tracking of various versions of the large model, extracting multi-dimensional data, generating multi-dimensional behavioral fingerprint vectors, and storing them in a time-series database to form a historical sequence. By integrating trend analysis, mutation warning, and time-series deep learning, drift prediction, adaptive adjustment, and closed-loop optimization are performed, achieving a leap from passive response to proactive prediction and zero-perception self-healing. This ensures that the enterprise's content optimization effect transcends the model version cycle, forming sustainable trust assets and improving the accuracy of model version drift prediction.

[0056] The device for predicting model version drift based on behavioral fingerprint time-series modeling includes a processor and a memory. The aforementioned acquisition module, first processing module, second processing module, third processing module, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0057] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured; adjusting kernel parameters improves the accuracy of model version drift predictions.

[0058] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0059] This invention provides a storage medium storing a program that, when executed by a processor, implements the method for predicting model version drift based on behavioral fingerprint temporal modeling.

[0060] This invention provides a processor for running a program, wherein the program executes the method for predicting model version drift based on behavioral fingerprint temporal modeling.

[0061] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: S1: Acquire multi-dimensional data for each version of a test model, and generate behavioral fingerprint vectors based on the multi-dimensional data to construct a fingerprint time series sequence; S2: Perform trend analysis, mutation warning, time series deep learning, and fusion on the fingerprint time series sequence to obtain the predicted drift parameters and the next version of the behavioral fingerprint vector for the test model; S3: Pre-adapt the test model based on a matching strategy library, the predicted drift parameters, and the next version of the behavioral fingerprint vector to update the test model; S4: Verify the accuracy of the updated test model based on the next version of the behavioral fingerprint vector. If the accuracy does not meet a preset requirement, update the fingerprint time series sequence and then execute S2. The device in this document can be a server, PC, PAD, mobile phone, etc.

[0062] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: S1: Obtain multi-dimensional data of each version of the test model, and generate behavioral fingerprint vectors based on the multi-dimensional data for constructing a fingerprint time series sequence; S2: Perform trend analysis, mutation warning, time series deep learning, and fusion on the fingerprint time series sequence to obtain the predicted drift parameters and the next version behavioral fingerprint vector of the test model; S3: Pre-adapt the test model based on the matching strategy library, the predicted drift parameters, and the next version behavioral fingerprint vector for updating the test model; S4: Verify the accuracy of the updated test model based on the next version behavioral fingerprint vector. If the accuracy does not meet the preset requirements, then update the fingerprint time series sequence and execute S2.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0064] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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 processor, 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0065] 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.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus 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.

[0067] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0068] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0069] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0071] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting model version drift based on behavioral fingerprint time series modeling, characterized in that, The method includes the following steps: S1: Obtain multi-dimensional data for each version of the test model, and generate behavioral fingerprint vectors based on the multi-dimensional data to construct fingerprint time series sequences; S2: Perform trend analysis, mutation warning, time series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector; S3: Pre-adapt the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavior fingerprint vector to update the test model; S4: Based on the accuracy of the updated test model after verifying the next version of the behavioral fingerprint vector, if the accuracy does not meet the preset requirements, then update the fingerprint time sequence and execute S2. The multi-dimensional data includes the test model's retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight. The fingerprint time sequence includes model identifier, version number, release date, fingerprint vector, dimension value, extraction method, and confidence level; The process of performing trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector includes: The fingerprint time sequence is fitted and weighted to obtain the predicted behavioral fingerprint vector for the next version; The mutation point information of the behavioral fingerprint vector is obtained by applying the CUSUM cumulative sum and Bayesian change point detection algorithm to the fingerprint time sequence; Deep learning-predicted fingerprints are obtained by performing LSTM or Transformer deep learning and end-to-end learning on the fingerprint time sequence. The predicted drift parameters are obtained by weighted fusion of the predicted behavioral fingerprint, mutation point information, and deep learning predicted fingerprint.

2. The method according to claim 1, characterized in that, The predicted drift parameters include the drift vector, drift type, and prediction confidence. The pre-adaptation of the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavioral fingerprint vector includes: By matching the drift vectors, drift types, and prediction confidence scores in the policy library, the corresponding adjustment policies and adjustment parameters are obtained. The test model is pre-adapted according to the adjustment strategy and adjustment parameters described above.

3. The method according to claim 2, characterized in that, The adjustment strategies include keyword density adjustment, entity distribution adjustment, content expansion, content abbreviation, structural format adjustment, and timeliness tag update; The adjustment parameters are the numerical values ​​corresponding to each item in the adjustment strategy.

4. The method according to claim 2, characterized in that, The drift vector = predicted fingerprint Current fingerprint; The drift types include gradual and abrupt types.

5. The method according to claim 1, characterized in that, The method also includes: The prediction accuracy is obtained by calculating the error between the behavioral fingerprint vectors before and after the update, and the business performance indicators of the test model before and after the update are determined based on the prediction accuracy.

6. A device for predicting model version drift based on behavioral fingerprint time series modeling, characterized in that, The device includes: The acquisition module is used to acquire multi-dimensional data of each version of the test model, and generate behavioral fingerprint vectors based on the multi-dimensional data to construct fingerprint time series sequences. The first processing module is used to perform trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector. The second processing module pre-adapts the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavior fingerprint vector, in order to update the test model. The third processing module, based on the accuracy of the test model after the next version of the behavioral fingerprint vector verification update, if the accuracy does not meet the preset requirements, updates the fingerprint time sequence and then executes the first processing module. The multi-dimensional data includes the test model's retrieval sensitivity, entity distribution weight, content length weight, content structure weight, and timeliness weight. The fingerprint time sequence includes model identifier, version number, release date, fingerprint vector, dimension value, extraction method, and confidence level; The process of performing trend analysis, mutation warning, time-series deep learning, and fusion on the fingerprint time series to obtain the predicted drift parameters of the test model and the next version of the behavioral fingerprint vector includes: The fingerprint time sequence is fitted and weighted to obtain the predicted behavioral fingerprint vector for the next version; The mutation point information of the behavioral fingerprint vector is obtained by applying the CUSUM cumulative sum and Bayesian change point detection algorithm to the fingerprint time sequence; Deep learning-predicted fingerprints are obtained by performing LSTM or Transformer deep learning and end-to-end learning on the fingerprint time sequence. The predicted drift parameters are obtained by weighted fusion of the predicted behavioral fingerprint, mutation point information, and deep learning predicted fingerprint.

7. The apparatus according to claim 6, characterized in that, The predicted drift parameters include the drift vector, drift type, and prediction confidence. The pre-adaptation of the test model based on the matching strategy library, predicted drift parameters, and the next version of the behavioral fingerprint vector includes: By matching the drift vectors, drift types, and prediction confidence scores in the policy library, the corresponding adjustment policies and adjustment parameters are obtained. The test model is pre-adapted according to the adjustment strategy and adjustment parameters described above.

8. The apparatus according to claim 7, characterized in that, The adjustment strategies include keyword density adjustment, entity distribution adjustment, content expansion, content abbreviation, structural format adjustment, and timeliness tag update; The adjustment parameters are the numerical values ​​corresponding to each item in the adjustment strategy.

9. The apparatus according to claim 7, characterized in that, The drift vector = predicted fingerprint Current fingerprint; The drift types include gradual and abrupt types.

10. The apparatus according to claim 6, characterized in that, The device also includes: The prediction accuracy is obtained by calculating the error between the behavioral fingerprint vectors before and after the update, and the business performance indicators of the test model before and after the update are determined based on the prediction accuracy.

11. A machine-readable storage medium storing instructions thereon, characterized in that, This instruction is used to cause the machine to perform the method for predicting model version drift based on behavioral fingerprint temporal modeling as described in any one of claims 1-5 of this application.

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