Knowledge base dynamic optimization method based on candidate reply generation and gray verification mechanism

Abstract

The application discloses a knowledge base dynamic optimization method based on candidate reply generation and gray verification mechanism, relates to the field of knowledge base dynamic optimization, and first fuses and compiles user correction feedback and original knowledge points into a candidate version, and constructs a parallel database state with the original version. Subsequently, the user request is routed and shunted through a preset gray strategy, and the candidate reply is targeted in the controlled traffic. On this basis, the user interaction behavior is captured in real time by using the front-end burying, and the new and old versions are compared in performance based on objective data in combination with the routing log. Finally, the defeated version is automatically eliminated according to the performance evaluation result, the winner is established as the global main version, so that the knowledge base is safely controllable and the closed-loop optimization is data-driven.

Description

Technical Field

[0001] This application relates to the field of dynamic optimization of knowledge bases, and more specifically, to a dynamic optimization method for knowledge bases based on a candidate response generation and grayscale verification mechanism. Background Technology

[0002] With the rapid development of artificial intelligence technology, human-computer interaction systems, represented by intelligent question answering and chatbots, have been widely applied in customer service, information retrieval, and enterprise knowledge management. The core effectiveness of these systems highly depends on the accuracy, timeliness, and coverage of their underlying knowledge base. However, in practical applications, the maintenance and optimization of knowledge bases face significant challenges. Traditional knowledge base updates primarily rely on manual maintenance. When the system identifies unfavorable user feedback or finds that existing knowledge points cannot meet user queries, knowledge engineers need to manually analyze, revise, and publish. This model not only has a long response cycle and high labor costs, but the quality of revisions also heavily depends on personal experience, lacks objective data support, and is prone to subjective bias. More importantly, directly updating the main online version carries significant risks. If the new version has potential defects, it could lead to a large-scale decline in service quality and affect user experience.

[0003] To overcome the drawbacks of purely manual maintenance, existing technologies have begun to explore automated or semi-automated optimization paths. Some solutions utilize user feedback, such as likes and dislikes, to sort or tag knowledge points, assisting manual selection of optimization targets. However, this does not solve the automation challenge of content revision itself. Other cutting-edge explorations attempt to introduce generative language models to directly generate new responses based on user-provided correction suggestions. However, such methods have significant drawbacks: on the one hand, while generative models are powerful, the quality and stability of their output content cannot be fully guaranteed, potentially producing factual errors or misleading content that doesn't fit the specific context; on the other hand, and more critically, these methods typically lack a secure and effective online verification process. Deploying unverified generated content directly as a global version is tantamount to instantly exposing potential risks to all users, lacking necessary security sandboxes and data-driven decision-making mechanisms. This one-step update approach may not only fail to improve user satisfaction but could also degrade service quality by introducing new problems.

[0004] Therefore, how to build a method that can automatically generate high-quality candidate responses and perform A / B comparison verification in a low-volume, controllable online environment, and ultimately achieve knowledge base security, closed-loop, and dynamic optimization, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, according to one aspect of this application, a dynamic optimization method for a knowledge base based on a candidate response generation and grayscale verification mechanism is provided, comprising: Step 1: Perform feature fusion compilation on the obtained user correction feedback and the original knowledge points, and write the generated candidate version and the original knowledge points into the verification cache in the form of bidirectional pointer association to obtain the parallel database status; Step 2: Based on the preset canary release strategy, the user query requests and parallel database status are routed and version mapped to send targeted reply content to the corresponding user terminals, and the hit version tag and user identifier are serialized and encapsulated to generate routing logs. Step 3: Through the front-end tracking program, the user action elements triggered by the targeted reply content are detected, cleaned and filtered in real time to obtain the interaction behavior dataset; Step 4: Calculate performance metrics and perform asymmetric comparison verification on the interaction behavior dataset and routing logs to obtain performance evaluation results; Step 5: Based on the performance evaluation results, the losing version in the parallel database state is severed and physically erased, the winning version is identified as the global master version and disk fragmentation is reorganized to obtain a single-version knowledge base.

[0006] Compared with existing technologies, this application provides a dynamic optimization method for knowledge bases based on candidate response generation and gray-scale verification mechanisms. Addressing the technical problems of existing knowledge base updates relying on manual intervention, high risks associated with directly deploying automatically generated content, and the lack of effective online verification mechanisms, this method first integrates user feedback with the original knowledge points to compile candidate versions and constructs a parallel database state with the original version, thus replacing inefficient manual revisions with automation. Subsequently, a preset gray-scale strategy is used to route user requests, targeting candidate responses within controlled traffic to avoid the risk of widespread service degradation that might result from a direct full deployment. Furthermore, front-end tracking is used to capture user interaction behavior in real time, and combined with routing logs, a performance comparison of the old and new versions is performed based on objective data, effectively identifying and intercepting potential defects in the generated content. Finally, based on the performance evaluation results, the losing version is automatically eliminated, and the winner is established as the global master version, thereby achieving a secure, controllable, and data-driven closed-loop optimization of the knowledge base. Attached Figure Description

[0007] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0008] Figure 1 This is a flowchart of a knowledge base dynamic optimization method based on a candidate response generation and grayscale verification mechanism according to an embodiment of this application.

[0009] Figure 2 This is a data flow diagram of a knowledge base dynamic optimization method based on a candidate response generation and grayscale verification mechanism according to an embodiment of this application.

[0010] Figure 3 This is a flowchart of step one in the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to an embodiment of this application.

[0011] Figure 4 This is a flowchart of step three in the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to an embodiment of this application.

[0012] Figure 5 This is a schematic diagram of the data flow in step five of the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to an embodiment of this application. Detailed Implementation

[0013] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0014] To address the problems mentioned in the background section, this application proposes a dynamic optimization method for the knowledge base based on a candidate response generation and grayscale verification mechanism. Figure 1 This is a flowchart of a knowledge base dynamic optimization method based on a candidate response generation and grayscale verification mechanism according to an embodiment of this application. Figure 2 This is a data flow diagram illustrating the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanisms according to an embodiment of this application. Figure 1 and Figure 2As shown, the knowledge base dynamic optimization method based on candidate response generation and gray-scale verification mechanism according to the embodiments of this application includes: Step 1, performing feature fusion compilation on the obtained user correction feedback and the original knowledge points, and writing the generated candidate versions and the original knowledge points into the verification cache area in the form of bidirectional pointer association to obtain the parallel database state; Step 2, based on the preset gray-scale release strategy, performing traffic routing and version mapping on user query requests and parallel database states to send targeted response content to the corresponding user terminals, and serializing and encapsulating the hit version tags and user identifiers to generate routing logs; Step 3, through the front-end tracking program, performing real-time detection and cleaning filtering on user action elements triggered by the targeted response content to obtain an interaction behavior dataset; Step 4, performing performance index calculation and asymmetric comparison verification on the interaction behavior dataset and routing logs to obtain performance evaluation results; Step 5, based on the performance evaluation results, performing pointer severing and physical erasure on the losing versions in the parallel database state, confirming the winning version as the global master version and performing disk fragmentation reorganization to obtain a single-version knowledge base.

[0015] Step one involves fusing and compiling the obtained user feedback and the original knowledge points using features, and then writing the generated candidate versions and the original knowledge points into a verification cache using a bidirectional pointer association to obtain the parallel database state. It is understandable that directly overwriting the online version with unverified content during the maintenance and updating of the knowledge base carries significant risks, potentially leading to a significant decline in service quality due to the introduction of inaccurate or unstable information. This risk is amplified, especially when new content is generated by a generative model, whose inherent illusion problem further exacerbates the issue. To mitigate the potential harm of such direct overwriting updates, this application establishes a secure and isolated sandbox environment through the feature fusion compilation and parallel state construction steps. This environment allows the newly generated candidate versions to coexist with the original knowledge points, rather than replacing them. This provides a technical foundation for subsequent gray-scale verification (i.e., small-scale A / B testing) without affecting the stability of the main service, ensuring that only the version that performs better after verification by real online user interaction data is ultimately solidified as the global master version, thus achieving secure, smooth, and data-driven dynamic optimization of the knowledge base.

[0016] Figure 3 This is a flowchart of step one in the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to an embodiment of this application. Figure 3As shown, in one possible implementation, step one includes: step one-one, based on a pre-trained text encoder, performing lexical mapping and high-dimensional semantic space projection on the user correction feedback and the original knowledge points respectively, and using a multilayer perceptron structure to perform cross-modal feature concatenation and nonlinear activation on the two semantic vectors to obtain a fused feature vector; step one-two, based on an autoregressive generative language model, performing initial latent state loading and bundle search condition decoding on the fused feature vector to obtain candidate versions; step one-three, using a graph database storage controller, performing node index allocation and bidirectional logical pointer association writing on the candidate versions and the original knowledge points to obtain a parallel database state.

[0017] The process is as follows: It begins by obtaining user feedback and the original knowledge point. After interacting with the Q&A service, if a user believes the returned knowledge point content is incorrect, outdated, or incomplete, they can submit a correction suggestion through the feedback entry embedded in the interface. For example, an original knowledge point about "how to reset a Model-X router" is: Press and hold the reset button for 10 seconds to restore factory settings. A user discovers that the firmware has been updated, changing the operation method, and submits a correction feedback: This method is incorrect; the latest firmware requires pressing and holding the button for 15 seconds to completely reset. In this case, the feedback text "The latest firmware requires pressing and holding the button for 15 seconds to completely reset" is the user correction feedback; while the original Q&A pair, "How to reset a Model-X router: Press and hold the reset button for 10 seconds to restore factory settings," is the original knowledge point. After obtaining these two input texts, the candidate version generation process begins.

[0018] The first stage of this process is feature fusion and compilation. This stage invokes a pre-trained text encoder, such as the BERT model based on the Transformer architecture. This model consists of several stacked encoder layers, each containing a multi-head self-attention mechanism module and a feedforward neural network module. When processing the input, the user's correction feedback and the original knowledge point are first lexicalized, that is, the text string is segmented into a sequence of words. For example, the original knowledge point may be segmented into ["press and hold", "reset", "button", "10", "seconds", ...]. Then, these word sequences are fed into the text encoder. The encoder captures the contextual dependencies of each word within its own text through the self-attention mechanism, and after multiple layers of nonlinear transformations, finally projects each text into a high-dimensional semantic space, generating a dense vector that can represent its overall semantics. Taking a 768-dimensional output space as an example, the original knowledge point is encoded into a 768-dimensional vector. User feedback was encoded into another 768-dimensional vector. Next, these two independent semantic vectors are fed into a multilayer perceptron structure for cross-modal feature concatenation and non-linear activation. This multilayer perceptron consists of an input layer, at least one hidden layer, and an output layer. Its weights and bias parameters are obtained through supervised training on a large amount of triplet-labeled data (original knowledge, correction feedback, and target fusion representation). During processing, the two 768-dimensional vectors are first processed at the input layer. and A concatenation operation is performed to form a 1536-dimensional combined vector. This aims to aggregate the semantic information of the original knowledge with the user's modification intent information within the same feature space. Subsequently, this combined vector flows through a hidden layer, where it is multiplied by the weight matrix, a bias vector is added, and it is processed by a non-linear activation function such as ReLU. This process effectively extracts and fuses the core correlated features from the two input sources while suppressing irrelevant noise, ultimately resulting in a 768-dimensional fused feature vector at the output layer. This vector retains the core content of the original knowledge points while incorporating the correction directions indicated in user feedback.

[0019] Obtain the fused feature vector Next, the candidate version generation stage begins. This stage utilizes an autoregressive generative language model, such as the GPT model based on the Transformer decoder architecture. This model also consists of multiple stacked decoder layers, each containing a masked multi-head self-attention mechanism, a cross-attention mechanism, and a feedforward network. First, the fused feature vector obtained in the previous step is... The initial hidden state of this generative language model is loaded, serving as a global context to guide the entire generation process. Subsequently, a bundle search conditional decoding strategy is employed to generate text. A bundle width is set. For example, 5. In the first time step of decoding, the model predicts the probability of all words in the vocabulary being used as starting words, and retains the one with the highest probability. Each word is used as the starting point for the candidate sequence. At each subsequent time step, the model will... as well as For each candidate sequence, the generated prefixes are used to predict the probability distribution of the next word, thus expanding each candidate sequence to all possible subsequent words. Then, from all the expanded new sequences, the one with the highest cumulative joint probability is selected again. A sequence of candidate texts is generated. This process is repeated until all candidate sequences have generated a terminator or reached a preset maximum length. The probability calculation followed by this process can be represented by the following natural language description: Target generated text sequence The conditional probability is given a fused feature vector. and historically generated word subsequences Under the combined conditions, the word units predicted by the model at the current time step t probability The cumulative product of . Its mathematical expression is: ,in, This represents the generation of sequences guided by fused feature vectors. The total probability, This is the total length of the sequence. The goal of the beam search algorithm is to find a sequence that maximizes this joint probability. Ultimately, from The sequence with the highest joint probability is selected from the candidate sequences, and its contained words are transcoded and concatenated into natural language text to obtain the candidate version. Using the previous example, the generated candidate version might be: Press and hold the reset button for 15 seconds to restore factory settings.

[0020] Finally, the generated candidate versions and the original knowledge points are written to the verification cache, forming a parallel database state. This operation is performed by the graph database storage controller. Graph databases such as Neo4j store data in the form of nodes and relationships. First, the storage controller writes the newly generated candidate version as an independent knowledge unit to the database and assigns it a unique node index, such as Node-ID:5001. At this point, the original knowledge point already exists as a node in the database, with its own index, such as Node-ID:1024. Next, to establish the relationship between the two, the controller creates two directed edges (logical pointers) in the graph database's mapping table: one from Node-ID:1024 to Node-ID:5001, and the other from Node-ID:5001 back to Node-ID:1024. This forms a bidirectional logical pointer relationship. These two pointers are appended with specific tags or attributes, such as type:'verification_pair', to indicate that they are a pair of mutually exclusive versions used for A / B testing. After the writing and association are completed, the current state of the database is the parallel database state, in which the original knowledge points and their candidate versions coexist and are related to each other.

[0021] Step two involves routing and version mapping user query requests and parallel database states based on a pre-defined canary release strategy. This routing maps the responses to the corresponding user terminals, and serializes and encapsulates the matched version tags and user identifiers to generate routing logs. Correspondingly, after generating candidate versions and constructing the parallel database states, the knowledge base contains both verified original versions and newly generated candidate versions with unknown potential. If all user requests are indiscriminately directed to the original version, the effectiveness of the candidate versions cannot be evaluated. Conversely, if all traffic is hastily switched to the candidate versions, the uncertainty of their content quality could lead to a large-scale decline in service quality. Therefore, a controllable online experimentation mechanism is urgently needed to accurately guide a small portion of user traffic to the candidate versions while ensuring that the majority of users still access the stable original version. This step, by establishing such a routing and version mapping mechanism, aims to securely and controllably deliver different versions of response content to real users and accurately record the key information of "which user received which version" for subsequent performance evaluation, thus providing a solid foundation for data-driven version iteration decisions.

[0022] In one possible implementation, step two includes: performing hash-based discrete mapping and grayscale flow determination on the user identifier in the user query request to obtain the flow determination result; and performing address lookup and version addressing on the parallel database status based on the flow determination result to obtain the address target text and routing metadata; using a question-and-answer context builder, performing placeholder filling and syntax polishing on the address target text to obtain the directed response content; and concatenating, transcoding, and hash binding on the routing metadata to obtain the routing log.

[0023] The processing is as follows: A user's query request is represented as a data packet containing multiple fields. This includes not only the text content of the query, such as how to reset the Model-X router, but also a unique user identifier, such as user-7A8B2E9F1D0C, and device fingerprint information. When this request arrives, it first enters the canary release strategy determination stage. This stage operates according to a preset canary release strategy, which is a key business parameter set by operations personnel based on risk assessment. For example, it might be set to allocate 20% of user traffic to candidate versions for testing. The canary release determination uses a salted hash modulo algorithm to ensure that the same user is consistently mapped to the same version, avoiding inconsistent user experience. This process first concatenates the user's unique identifier, user-7A8B2E9F1D0C, with a preset, non-public salt value (for example, a string of random characters, aBcDeF12345), forming the input string user-7A8B2E9F1D0C_aBcDeF12345. Then, a deterministic hash function such as SHA-256 is applied to calculate the concatenated string, generating a long hexadecimal string, which is then converted into a large integer. To map this large integer to a controlled range, a modulo operation is performed. The natural language description of this process is: the value of the target mapping address variable is determined by the modulo operation of the hash value of the user identifier included in the request with the set total number of global buckets. The expression is: In this formula, This represents the bucket number to which the user is assigned. It is the user's unique identifier user-7A8B2E9F1D0C. It has a salinity of aBcDeF12345. This represents the hash function and its conversion to an integer. It is the preset total number of global buckets, set to 100 for percentage calculations. For example, the hash integer obtained after calculating the user identifier and salt value, modulo 100, is... =15. Next, number this diversion bucket. Compare this to a canary release strategy. Let the traffic ratio of a canary release strategy be... =20%, then the threshold for traffic segmentation That is =0.2 × 100 = 20. The judgment rule is: when Less than If the user request is selected, it is routed to the candidate version; otherwise, it is routed to the original version. In this example, 15 < 20, so the result is that the user has hit the gray-scale strategy and should be shown the candidate version. This result constitutes the traffic splitting result. Based on this result, the address lookup table is read from the parallel database state generated in the previous step. The query logic finds the original knowledge point node Node-ID:1024 related to this query, and then finds the associated candidate version node Node-ID:5001 through its bidirectional logical pointer, and reads the knowledge content from it. The read text "Press and hold the reset button for 15 seconds to restore factory settings." is the addressing target text. At the same time as version addressing, the current timestamp such as 2026-03-23T10:00:00.123Z, the hit version identifier such as candidate_version, the corresponding node 5001, and the user identifier user-7A8B2E9F1D0C are packaged into a temporary memory object, which is the routing metadata.

[0024] After obtaining the target text, the process enters the question-and-answer context builder for processing. The question-and-answer context builder is a software module responsible for packaging the plain text knowledge content from the backend into a final response with contextual information suitable for frontend display. It contains a series of pre-defined interaction templates and polishing rules. For example, it selects different opening lines depending on whether the current dialogue is the first round of interaction. For the target text mentioned above, the builder might apply a general template, such as: Regarding your query '{query topic}', the answer is: {answer text}. It fills placeholders with the actual query topic "How to reset the Model-X router" and the retrieved answer text, and performs syntactic fine-tuning to ensure the sentences are fluent and natural. After processing, a structured data object is generated, such as a JSON string: {"status":"success","response":"Regarding your query 'How to reset the Model-X router', the answer is: Press and hold the reset button for 15 seconds to restore factory settings."}. This encapsulated final content, which can be directly sent to the user's terminal for rendering, is the targeted response content. This content is then sent to the user terminal that initiated the request via the communication interface.

[0025] Simultaneously with the delivery of the targeted response, the routing metadata generated in the previous step is sent to the log generation module. To ensure the accuracy of subsequent data analysis and the immutability of the data, a persistent and secure routing log needs to be generated. First, the various fields (timestamp, user identifier, version identifier) ​​in the routing metadata are concatenated and separated using a specific delimiter (such as |) to form a long string: 2026-03-23T10:00:00.123Z|user-7A8B2E9F1D0C|candidate_version. Then, to prevent log tampering, a message authentication code or digital signature is calculated for this string. For example, an HMAC-SHA256 algorithm is applied to this string using a secure key to generate a hash signature. Finally, the original concatenated string is concatenated with this signature again to form a complete log record, such as 2026-03-23T10:00:00.123Z|user-7A8B2E9F1D0C|candidate_version|{HMAC_SIGNATURE}. The calculated structured final log output stream is a holographic string generated by concatenating the three main parameters carried in the route metadata object—the request timestamp, user identifier, and trigger version identifier—using a delimiter concatenation operator, and then encoding them through serialization and security binding functions. This final persistent file stream is the route log, which is written to a dedicated log storage.

[0026] Step three involves using front-end tracking to detect, clean, and filter user actions triggered by targeted responses in real time to obtain an interaction behavior dataset. Understandably, during the gray-scale verification phase, while the distribution mechanism ensures that different versions of knowledge content are accurately delivered to the corresponding user terminals, objectively and accurately measuring the actual performance of this content remains a core challenge in achieving closed-loop optimization of the knowledge base. Traditional feedback mechanisms often rely on users actively clicking "like" or "dislike," resulting in extremely sparse explicit feedback data that is heavily influenced by user subjective emotions, making it difficult to comprehensively reflect the true effectiveness of the content. To extract statistically significant quality indicators from massive amounts of real interactions, it is necessary to delve into the details of user-content interaction. By capturing users' micro-actions after receiving responses—such as reading time, page scrolling, text copying, follow-up questions, and conversation interruptions—a multi-dimensional evaluation vector can be constructed. This step introduces front-end tracking and data cleaning mechanisms to capture these raw signals that truly reflect user satisfaction. By eliminating mechanical noise and unintentional misoperations during the interaction process, fragmented action elements are transformed into structured, high-fidelity interactive datasets.

[0027] Figure 4This is a flowchart of step three in the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to an embodiment of this application. Figure 4 As shown, in one possible implementation, step three includes: step three-one, capturing and timing-packaging user interaction events triggered by targeted reply content in real time to obtain the original action stream; step three-two, based on anti-shake filtering rules and behavior mapping decision tree, removing high-frequency noise and calculating effective behavior in the original action stream to obtain an effective behavior primitive sequence; step three-three, performing time offset compensation alignment and structured encapsulation on the effective behavior primitive sequence to obtain an interaction behavior dataset.

[0028] The process is as follows: First, user interaction events triggered by the targeted reply content are captured and packaged in real time. When the client rendering engine receives and displays the targeted reply content to the user's view, the system automatically launches a front-end asynchronous detection script deeply bound to the lifecycle of that view—the SDK tracking program. During the initialization phase, this program subscribes to a series of specific underlying events from the event listener registry of the browser or mobile application. Taking the reply content on how to reset the Model-X router as an example, when the text is rendered on the screen, the tracking program becomes active and begins to detect and capture various user behaviors on the current session's DOM nodes in real time within a preset interaction time window. In this stage, the tracking program not only records macroscopic click events but also monitors subtle user actions with millisecond-level precision. For example, when a user is reading the core instruction to hold down the reset button for 15 seconds, the program tracks the position of the mouse hovering over that specific text area, the duration of the hover (e.g., 4500 milliseconds), and the subtle page swipes made by the user as they read. The listener will initially package and merge these mouse movements, clicks, hovers, and even pointer jitter caused by hesitation, according to their chronological order. Each event fragment will be appended with a local client-side timestamp. For example, if a user clicks the copy button 2 seconds after receiving a reply and then swipes up 5 seconds later, these scattered raw actions will be encapsulated into a binary stream with temporal characteristics, thus outputting a raw action stream carrying the local timestamp.

[0029] Subsequently, the raw motion stream is uploaded from the user terminal to the edge computing node, where it enters the core cleaning and filtering process. The cleaning engine first removes high-frequency noise from the raw motion stream based on preset anti-shake filtering rules and a behavior mapping decision tree. In real user interactions, there are numerous unconscious interference signals. The anti-shake filtering rules primarily target high-frequency noise, and their logic involves setting a time window threshold. For example, if two click events occur within 200 milliseconds at extremely close positions, the logic determines that the behavior is an unintentional double-click caused by finger tremors or a malfunctioning mouse button, and not a genuine business intent. That is, for any two adjacent event segments in a time series, if the absolute value of the timestamp difference is less than the set anti-shake step size, the latter event is discarded. After removing physical noise, the behavior mapping decision tree is invoked to perform a deep evaluation of the remaining signal. The behavior mapping decision tree is a multi-layered classification decision structure whose node parameters are obtained through supervised learning training on massive historical interaction samples. Each layer of nodes contains a series of decision criteria based on weights and biases. For example, in one layer of nodes, it determines the duration of a hovering event. .if Actions lasting less than a preset, extremely short duration threshold, such as 500 milliseconds, will be categorized as accidental touches and physically removed by the decision tree. Only when the dwell time exceeds a reading baseline threshold, dynamically calculated based on the number of characters in the text (e.g., a router reset command with 28 characters and a baseline reading time of 3000 milliseconds), will the action proceed to the next level of business logic. In this next level, the decision tree combines the attributes of the clicked DOM element to determine if the action points to genuine business logic. For example, clicking an element with the ID "like_btn" and performing a valid long press is considered a "like." After filtering by the decision tree, the originally chaotic action flow is reduced to a set of denoised, clean, and valid action primitives. This sequence is a subset matrix resulting from a filtering condition mapping of the original action flow set, ensuring that each extracted primitive represents a well-considered and effective user interaction.

[0030] To ensure the comparability of this behavioral data across users, the issue of inconsistent local clocks on the client side needs to be addressed. After reading the valid behavioral primitive sequence, the process enters the time offset compensation and structured encapsulation stage. Since the system clock of a user terminal may have a deviation of several minutes or even hours, directly using the local timestamp would lead to time sequence disorder of multi-user data. Therefore, the backend service asynchronously initiates a request to the high-availability time synchronization service clock to obtain a global absolute reference time. At this time, a time offset compensation amount is calculated. Its mathematical expression is: ,in, Represents the absolute standard time for time synchronization services. This represents the client's local time at the moment of sampling. Subsequently, this compensation amount is used to rewrite and align each scattered timestamp in the primitive sequence, originally measured using the local motherboard clock. For example, if the user's time is 1.5 seconds slower, all timestamps are shifted forward by 1.5 seconds. Based on this, all aligned primitive attributes are standardized and structurally encapsulated. Each interaction is mapped to a standard JSON schema, containing the user identifier, the corresponding preceding step's `user-7A8B2E9F1D0C`, the triggered version tag such as `candidate_version`, the behavior type such as `positive_click`, the behavior duration, and the synchronized global absolute timestamp. Finally, an end delimiter is added to the end of this data packet, and a sealed packet operation is performed, generating a complete interaction behavior dataset that is rigorously formatted, tamper-resistant, and can be directly read and consumed by mining algorithms.

[0031] Step four involves calculating performance metrics and performing asymmetric comparison verification on the interaction behavior dataset and routing logs to obtain performance evaluation results. It's understandable that after front-end tracking and acquiring a high-fidelity interaction behavior dataset, although a large amount of user action feedback has been accumulated, this data is still discrete and isolated. Simply counting clicks or dwell time cannot directly translate into an evaluation of the knowledge base quality. Furthermore, since gray-scale validation is conducted in a real production environment, feedback from different users on different versions is mixed together. To extract objective conclusions about version quality from these mixed raw signals, a strong correlation index based on routing records and behavior records needs to be established. Only through rigorous mathematical statistics and significance verification, performing asymmetric cross-validation of who saw what and who did what, can interference from sample randomness be eliminated. This step, through performance metric calculation and asymmetric comparison verification, aims to transform fragmented interaction behaviors into statistically significant quantitative indicators.

[0032] In one possible implementation, step four includes: based on a hash index table with user identifier and request timestamp as the joint primary key, performing association mapping and version labeling segmentation on the interaction behavior dataset and routing logs to obtain a grouped verification data matrix; based on the grouped statistical calculator, performing frequency accumulation and ratio calculation on like events, citation events and dialogue interruption events of each version slice in the grouped verification data matrix to obtain a multidimensional quantitative indicator set; and performing weighted fusion and significance comparison on the composite performance scores of the original version and candidate versions in the multidimensional quantitative indicator set to obtain the performance evaluation result.

[0033] The process is as follows: First, a high-performance index table is established to achieve data association mapping and physical partitioning. At this point, the backend simultaneously reads in two types of core data streams generated in the preceding steps: one is the routing log recording the correspondence between users and versions, and the other is the interaction behavior dataset recording the details of user operations. Because user requests and interactions experience time latency fluctuations in a high-concurrency environment, a hash index table needs to be established with a combined primary key of user identifier (e.g., user-7A8B2E9F1D0C) and request timestamp (e.g., 2026-03-23T10:00:00.123Z). The construction of this hash index table involves dynamic allocation of memory addresses. For each stub in the routing log, the user identifier string and high-precision timestamp string are extracted, concatenated, and hashed to map them to a unique memory offset address. The storage unit pointed to by this address stores the version tag matched by the request, such as candidate_version or original_version. Subsequently, the read-in interaction behavior dataset is traversed. Since the interaction data also carries the initial request timestamp and user ID that generated the behavior, the analysis engine can use the composite primary key formed by these two fields to perform precise searching in the hash index table. Through this mapping, each previously headless interaction primitive, such as a user clicking "like" at 4500 milliseconds, is given a clear version context identifier. Based on the established version identifier relationship, the mixed interaction records are physically segmented. This process is accomplished by sorting the data and pushing it into the corresponding multidimensional tensor slots. Specifically, two parallel storage matrices are initialized: one specifically stores interaction vectors associated with the original version, and the other specifically stores interaction vectors associated with candidate versions. For example, if there are 1000 valid interactions in this response to the Model-X router reset, of which 200 belong to candidate versions and 800 belong to the original version, then these 1000 records will enter two isolated storage spaces according to their respective versions. Finally, the resulting dataset supporting the equal-group control test is the grouped validation data matrix.

[0034] The process then proceeds to the metric calculation phase based on the grouped statistical calculator. The grouped statistical calculator is a specially designed parallel computing unit whose internal architecture includes a set of accumulator registers, division logic gates, and a feature concatenation module. When the grouped validation data matrix is ​​input into this operator, the statistical analyzer loads the original version data slice and the candidate version data slice respectively. Within their respective version scopes, the operator begins to accumulate the frequency of specific types of interaction events. Taking the candidate version as an example, the accumulator register will continuously count the total number of requests under that version as the denominator. And the total number of like events triggered in this version. Total number of referenced events This refers to the total number of events where users copied or forwarded replies, as well as the number of conversation interruptions. Based on this, the division logic gate calculates the ratio according to a preset logic execution ratio. For each type of behavioral event, it independently derives the like rate, citation rate, and interruption rate for each of the two versions. For example, the like rate... The calculation formula is: Similarly, citation rate It is important to note the conversation interruption rate. Defined as a negative indicator, this refers to the proportion of users who close the dialog box immediately after receiving a reply without conducting any further queries. After calculating each sub-indicator, the operator encapsulates these floating-point values ​​into a one-dimensional vector. For example, the feature vector of the original version might be [0.05, 0.08, 0.12], while the feature vector of the candidate version might be [0.08, 0.10, 0.09]. This combination of two sets of sub-indicator information constitutes the multi-dimensional quantitative indicator set, which holistically portrays the user response of the two versions across different dimensions.

[0035] Next, the multidimensional quantitative indicator set is fed into the core model of performance evaluation to perform weighted fusion and significance comparison. To obtain a comprehensive evaluation result, weights need to be assigned to each indicator. The weights are set based on business experience values; for example, to optimize satisfaction, the weight of the like rate is increased. Set to 0.5, citation rate weight Set it to 0.3, and weight the negative interruption rate. Set to -0.2. Composite performance score. The calculation uses a dot product fusion projection method, and its mathematical expression is: ,in, For the dimension of the indicator, Let be the calculated value of the i-th indicator. Using this formula, the composite performance score of the original version can be obtained. For example, the composite performance score of 0.5×0.05+0.3×0.08-0.2×0.12=0.025 and the candidate version. For example, 0.5×0.08+0.3×0.10-0.2×0.09=0.052. However, a mere numerical improvement is insufficient to prove that the candidate version is more universal, as this could be due to random errors caused by insufficient sample size. Therefore, this application introduces a two-sample Z-test algorithm model for significance comparison. This model verifies whether the difference between the two sets of data is statistically significant by calculating the difference in the comprehensive scores and their standard error. The specific judgment logic of the Z-test model is as follows: calculate the difference between the mean scores of the two versions. And combine the population variance of the two groups of samples , and the corresponding sample size , Calculate the test statistic. The natural language description of the calculation formula is as follows: The value equals the difference between the means of the two sample scores, divided by the joint standard error extracted from the variance and size of the two samples. Its mathematical expression is:

[0036] During the calculation process, the calculated result will be... The value was compared with the preset statistical confidence lower limit for preventing false positives. This threshold was set at 1.96, corresponding to a 95% confidence level. If >1.96, and the increase If the improvement exceeds a preset business threshold (e.g., an increase greater than 10%), the candidate version is considered a significant improvement over the original version. Once the calculation results support this conclusion, the evaluation model generates a Boolean value indicating the state is true, triggering key signals for subsequent physical storage reorganization and global version switching, ensuring that every update to the knowledge base has rigorous mathematical support. This Boolean value and its detailed description come with a detailed analysis report, including the improvement percentage, confidence interval, and comparative details of various sub-indicators. Conversely, if... If the value does not meet the standard, a false state variable is thrown. This single-state information carrier, which contains the Boolean decision result, the composite performance score, and details of the significance calculation, is the performance evaluation result.

[0037] In a possible preferred implementation, step four includes: based on a hash index table with user identifier and request timestamp as joint primary keys, performing association mapping and version labeling segmentation on the interaction behavior dataset and routing logs to obtain a grouped verification data matrix; based on a grouped statistical calculator, accumulating the frequency and calculating the ratio of like events, citation events, and dialogue interruption events for each version slice in the grouped verification data matrix to obtain a multidimensional quantitative indicator set; and performing dynamic Bayesian evaluation and posterior probability weighting on the composite performance scores of the original version and candidate versions in the multidimensional quantitative indicator set to obtain the performance evaluation result. Here, the implementation details of the first three steps in step four are the same as described above, so they are omitted. The following will provide a detailed explanation of the implementation of dynamic Bayesian evaluation and posterior probability weighting on the composite performance scores of the original version and candidate versions in the multidimensional quantitative indicator set to obtain the performance evaluation result.

[0038] It is understandable that when performing weighted fusion of the composite performance scores of the original and candidate versions in a multidimensional quantitative indicator set, simply assigning preset static weights to different indicators for linear weighting has significant limitations. Static weights are mainly subjectively set based on engineers' prior knowledge and cannot adaptively reflect the dynamic changes in the true confidence level of different indicators during the verification period. For example, a like rate calculated based on 10,000 user interactions has a much higher statistical reliability than a like rate calculated based on only 100 interactions, but in a static weight model, their contributions to the final score may be exactly the same, which is unreasonable. In addition, traditional ratio calculations only provide a point estimate, ignoring the statistical uncertainty caused by the sparsity of the data behind this estimate. Especially in the early stages of gray-scale verification, when the data volume is small and the indicators fluctuate drastically, over-reliance on unstable point estimates for decision-making can easily lead to incorrect version selection. Therefore, to improve the accuracy and reliability of performance evaluation calculations and ensure that the final version confirmation and resource cleanup operations are based on scientific and objective data, this application preferably introduces a dynamic Bayesian evaluation and posterior probability weighting method. The core purpose of this method is to move away from fixed weights and allow the weight of each indicator to be dynamically determined by its own data credibility. By utilizing Bayesian inference, combined with prior knowledge and real observational data, a complete posterior probability distribution is constructed for each indicator. This distribution itself contains a measure of the uncertainty of the indicator's true value, thus automatically giving higher weights to indicators with high confidence (large data volume, stable performance) in the final decision, while suppressing those with lower confidence, thereby avoiding the risk of misjudgment caused by random fluctuations in early data.

[0039] Specifically, in one possible implementation, dynamic Bayesian evaluation and posterior probability weighting are performed on the composite performance scores of the original and candidate versions in the multidimensional quantitative index set to obtain the performance evaluation results, including: The process calculates the posterior probability of each metric to be evaluated in a multidimensional quantitative metric set to obtain a posterior probability set. This process upgrades the evaluation of metrics from a single, uncertain point estimate, such as a simple like rate = 5%, to a statistical model that can fully describe the true probability behind it. Bayesian inference allows us to combine pre-experiment beliefs (prior) with observed data during the experiment to obtain a more comprehensive and reasonable updated post-experiment belief (posterior). Thus, each metric to be evaluated, such as like rate and citation rate, is no longer an isolated value, but a probability distribution, such as a Beta distribution. The shape of this distribution intuitively reflects our degree of confidence in the true value of the metric. For binary events like like rate and citation rate, where the user's behavior is either like or not like, the Beta distribution can be used as the conjugate prior of its probability. First, a prior Beta distribution is set for each metric. If there is no prior knowledge, the morphological parameters are taken. , This corresponds to a uniform distribution, meaning that before observing the data, the true probability of the indicator is considered to be equally likely between 0 and 1, without any bias. Then, the observed data is extracted from the grouped validation data matrix generated in the previous step.

[0040]

[0041] in, For the i-th index in the posterior probability set, when observed... Sub-events and Posterior probability distribution after a successful event. Let be the unknown true probability value behind the i-th indicator, such as the actual like rate. Let be the number of times the target event occurs for the i-th metric, i.e., the number of successes. denoted as the total number of observations for the i-th indicator. To indicate that it conforms to a distribution of ... It follows a Beta distribution, consisting of two morphological parameters. and definition. and The morphological parameters of the prior distribution. Specifically, taking the grayscale verification of "how to reset the Model-X router" in the previous steps as an example. The original version was displayed 800 times ( =800), received 40 likes ( =40). The candidate version was displayed 200 times ( =200), received 16 likes ( =16). Assume unbiased priors. , The posterior distribution parameters of the original version's like rate are: =1+40=41, =1 + 800 - 40 = 761. The posterior distribution parameters of the candidate version's like rate are: =1 + 16 = 17, =1+200-16=185. The output posterior probability set contains these two Beta distributions: Beta(41,761) and Beta(17,185).

[0042] The dynamic confidence weight set is calculated for each indicator to be evaluated based on the posterior probability set. This step aims to quantify the uncertainty inherent in the posterior distribution obtained in the previous step into a specific numerical weight. The variance of a distribution is a standard statistic for measuring its uncertainty; the larger the variance, the more sparse and wide the distribution, indicating higher uncertainty. Therefore, the weight should be inversely proportional to the variance. A dynamic, data-driven weight is generated for each indicator. The larger the amount of data and the more stable the results of an indicator, the smaller its posterior distribution variance will be, resulting in a higher dynamic confidence weight. This ensures that more reliable evidence has a greater influence in the final decision. Specifically, using the variance calculation formula of the Beta distribution, the variance of each distribution in the posterior probability set is calculated, and then the reciprocal of this variance is taken as its dynamic confidence weight.

[0043]

[0044] in, is the dynamic confidence weight of the i-th indicator in the dynamic confidence weight set. Let be the variance of the posterior probability distribution of the i-th index. , Let be the parameters of the posterior distribution of the i-th indicator, calculated in the previous step. Specifically, the weighting of the original version's like rate is calculated as follows: =(41+761) 2 ×(41+761+1)) / (41×761)≈16553.73; Weighting calculation of candidate version like rate: =(17+185) 2 ×(17+185+1)) / (17×185)≈2633.77. It can be seen that since the original version has a much larger amount of data (800 times) than the candidate version (200 times), its calculated weight is significantly higher, which is consistent with intuition.

[0045] The posterior probability set and dynamic confidence weight set are used to calculate the posterior expected value and composite performance score to obtain the total composite performance score. This step transforms the evaluation result of each indicator (a probability distribution) into a single performance score (posterior expected value), and combines it with its confidence weight to form a composite performance score that represents the overall performance of the version. This produces a comprehensive, confidence-weighted final score for each version (original version and candidate version). This score is more scientific than a simple summation of indicators because it internalizes the uncertainty of the data. First, the expected value (mean) of each posterior distribution is calculated as the best point estimate for that indicator. Then, this expected value is multiplied by its corresponding dynamic weight and business-oriented sign, and finally, the weighted scores of all indicators are summed. Posterior expected value: Then calculate the composite performance score:

[0046] in, Let be the posterior expected value of the i-th indicator, i.e., its performance score. This is the overall composite performance score for the version. This represents the total number of indicators involved in the evaluation. This is the business-oriented symbol for the i-th metric. For positive metrics like like rate, it's +1; for negative metrics (like conversation interruption rate), it's -1. For example, besides like rate, there's also a negative metric, interruption rate (sign=-1). The original version's interruption data is k=80, n=800, and the candidate version is k=10, n=200. Original version: Expected like rate. =41 / (41+761)≈0.051; Post-interruption rate parameter =81, =721->Expected Value ≈0.101, weight ≈8843.42; Total Score =(+1)×16553.73×0.051+(-1)×8845.42×0.101≈-46.95. Candidate Version: Expected Like Rate =17 / (17+185)≈0.0842; Post-interruption rate parameter =11, =191->Expected Value ≈0.054, weight ≈3942.51; Total Score =(+1)×2633.77×0.0842+(-1)×3942.51×0.0545≈+6.96. The extremely large difference in scores presented here (+6.96 and -46.95) illustrates the cumulative net efficiency amplifier effect of this weighted summation formula in a gray-scale environment: Because the original version (hold for 10 seconds) had a very poor interruption rate, it was continuously causing negative user experience damage under the large base traffic of up to 800 times. The huge prior statistical weights amplified its business volume many times over, ultimately leading to a serious negative score warning value. On the contrary, the candidate version (hold for 15 seconds), although it only operated in its small traffic verification sandbox of 200 times, benefited from its excellent positive feedback conversion rate (higher likes and extremely low interruptions), and had begun to accumulate substantial net positive experience assets within the same verification period, thus achieving a positive score against the trend.

[0047] The overall composite performance score is determined to obtain the performance evaluation result. This step is the endpoint of the decision-making process, requiring a clear conclusion based on the previously calculated total score, which can be executed by subsequent procedures. The final output is a clear Boolean or enumerated performance evaluation result, such as determining whether the candidate version wins or loses, directly guiding subsequent database operations such as pointer severing and physical erasure. The overall composite performance scores of the original version and the candidate version are compared. In the example above, (+6.96) (-46.95), initially indicating that the candidate version performs better. To ensure that this advantage is not due to randomness, a more in-depth statistical comparison of the posterior distributions of the two versions can be performed, such as calculating... (The probability that the candidate version is better than the original version) is calculated, and a confidence threshold (such as 95%) is set. Once the comparison result exceeds the threshold, the candidate version is finally determined to win, and a performance evaluation result carrying the truth value is generated.

[0048] Step five: Based on the performance evaluation results, pointer severing and physical erasure are performed on the losing versions in the parallel database state. The winning version is confirmed as the global master version and undergoes disk fragmentation and reorganization to obtain a single-version knowledge base. Correspondingly, after the knowledge base completes gray-scale verification and produces definite performance evaluation results, the parallel database state of the original version and candidate versions coexisting is maintained. This state is a temporary structure designed to isolate the verification environment. Although its existence ensures security during testing, if maintained for a long time, it will not only redundantly occupy valuable storage and computing resources, but also increase the logical complexity of subsequent query routing due to the existence of multiple versions of knowledge entry points, violating the core principle of the knowledge base providing a unique and accurate answer. In order to solidify the optimization results verified by online data and return the knowledge base to an efficient, concise, monolithic authoritative state, a final adjudication and cleanup process needs to be executed. This step aims to make irreversible structural adjustments to the state of the parallel database based on the final conclusions of the performance evaluation. Its core purpose is to completely eliminate the poorly performing and losing versions from the logical and physical levels through a series of atomic operations such as cutting pointers and erasing data, while officially establishing the winning version as the globally unique master version, and defragmenting the storage space, ultimately completing the transformation of the knowledge base from the experimental state to the production stable state.

[0049] Figure 5 This is a schematic diagram of the data flow in step five of the knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to an embodiment of this application. Figure 5As shown, in one possible implementation, step five includes: based on the Boolean step value carried by the performance evaluation result, determining the winner and loser of candidate versions and original knowledge points in the parallel database state to obtain a set of confirmed versions; forcibly severing the bidirectional logical pointers between the loser and winner versions in the set of confirmed versions, and performing zero-byte stream overwriting and erasing on the physical storage block occupied by the loser version to obtain an intermediate discrete database; performing displacement compaction integration on the intermediate discrete database and binding it with the trunk pointer of the winner version to obtain a single-version knowledge base.

[0050] The process is as follows: This begins with intercepting and parsing the performance evaluation results output from the previous step. The performance evaluation result is a structured data object whose core payload is a Boolean step value, which is the final determination of whether the performance difference between the candidate version and the original version is statistically significant. In a specific scenario, regarding the knowledge point of how to reset the Model-X router, since the candidate version holding down the button for 15 seconds scores significantly better on user interaction data than the original version holding down the button for 10 seconds, the Z-test result exceeds the preset confidence lower bound. Therefore, the Boolean step value carried in the performance evaluation result is true, which can be represented as 1 in binary. After receiving this evaluation result with a true Boolean value, the decision controller triggers a preset decision route. This route logic interprets the true value as an instruction for the candidate version to win. Accordingly, the controller performs a weighting determination on the two related nodes in the current parallel database state: the node representing the candidate version, whose unique identifier in the graph database is Node-ID:5001, is marked as the winning version in its metadata state; simultaneously, the node representing the original knowledge point, Node-ID:1024, is marked as the losing version in its state. Conversely, if the received boolean value is false, the decision route will perform the opposite operation, marking the original version as the winner and the candidate version as the loser. After marking, the two node references carrying the winner and loser identity labels are encapsulated into an immutable fixed binary tuple feature item. This data structure is the set of rights-determined versions.

[0051] Next, the database's storage controller extracts and parses this set of authorized versions, initiating a pointer severing and physical erasure procedure. First, within the graph database's memory-mapped space, the controller precisely locates the bidirectional logical parallel association pointer established during the gray-scale verification, based on the two node IDs provided in the set: Node-ID:5001 and Node-ID:1024. This pointer is crucial for maintaining the logical coexistence of the two versions. Once located, the controller performs an atomic forced severing operation, completely removing the pointer from the logical graph. The atomicity of this operation ensures that no intermediate states occur in a high-concurrency environment; the severing operation either succeeds completely or is not executed at all, thus avoiding the risk of data inconsistency. After the pointer is severed, the defeated version Node-ID:1024 becomes an isolated node in the knowledge graph's logical hierarchy, inaccessible through normal query paths. After logical isolation is complete, to thoroughly release physical resources and prevent obsolete old data from being accidentally revived due to low-level operations such as database snapshot rollback (i.e., the data ghost phenomenon), the controller initiates a high-priority I / O overwrite request to the physical storage area where the failed version resides. It queries the underlying file allocation table to obtain the addresses of all physical storage block segments occupied by the data content, indexes, and metadata of node-ID:1024. Subsequently, a low-level hardware access process is scheduled, generating a continuous stream of zero bytes, such as a long string of hexadecimal 0x00, and thoroughly overwrites the target physical segments block by block. This process erases all binary traces on the disk related to the old knowledge of holding down the reset button for 10 seconds. After this step, redundant content in the original parallel database state has been removed, but due to the physical erasure operation, some discontinuous memory gaps and storage fragments remain inside the database file. This database state is called an intermediate discrete database.

[0052] Finally, to restore the knowledge base to its optimal performance and structurally unified final form, this intermediate discrete database is scanned and processed. The underlying fragmentation and reassembly algorithm is triggered. This algorithm first scans the entire database file or memory space, accurately identifying all blank memory fragments and discrete data pointer segments caused by the previous physical erasure operation. Next, the algorithm performs an offset compaction operation. Taking the winning version Node-ID:5001 core business node and its possible derived knowledge graph topology, such as links to other related setup questions, as a whole data block, the algorithm calculates an optimal migration path to offset this data block on the physical storage medium, precisely filling the gaps left by the previous erasure operation. This offset splicing operation makes the physically scattered data continuous again, greatly improving the I / O efficiency of future data read operations. After the physical storage base address becomes continuous, the final and most crucial step is performed: updating the global service backbone mapping table. Before this, all queries about how to reset the Model-X router pass through the grayscale gateway, where the routing logic determines which version to return. Now, this gray-scale routing logic has been completely bypassed. A globally unique, permanent direct access pointer is issued to the winning version feature segment after rectification and alignment, namely the data represented by Node-ID:5001. This new trunk pointer is written into the trunk mapping table of the system service, replacing the original entry that pointed to the gray-scale logic or the old version. This write operation marks the complete end of the gray-scale verification cycle. From now on, any query on this topic will directly and definitively resolve to the verified, unique answer: "Press and hold the reset button for 15 seconds to restore factory settings." After this series of integration and binding operations, the knowledge base is finally solidified into an indivisible, single-version knowledge base with stable data and structure.

[0053] In summary, the knowledge base dynamic optimization method based on the candidate response generation and gray-scale verification mechanism of this application is explained. Addressing the technical problems of existing knowledge base updates relying on manual intervention, the high risk of directly deploying automatically generated content, and the lack of effective online verification mechanisms, this method first integrates user correction feedback with the original knowledge points to compile a candidate version, and then constructs a parallel database state with the original version, thereby replacing inefficient manual revisions with an automated mechanism. Subsequently, user requests are routed and distributed through a preset gray-scale strategy, and candidate responses are targeted and deployed within controlled traffic, avoiding the risk of large-scale service degradation that might result from a direct full deployment. Furthermore, front-end tracking is used to capture user interaction behavior in real time, and combined with routing logs, a performance comparison of the old and new versions is performed based on objective data, thereby effectively identifying and intercepting potential defects in the generated content. Finally, the losing version is automatically eliminated based on the performance evaluation results, and the winner is established as the global master version, thus achieving a secure, controllable, and data-driven closed-loop optimization of the knowledge base.

Claims

1. A dynamic optimization method for a knowledge base based on candidate response generation and gray-scale verification mechanisms, characterized in that, include: Step 1: Perform feature fusion compilation on the obtained user correction feedback and the original knowledge points, and write the generated candidate version and the original knowledge points into the verification cache area in the form of bidirectional pointer association to obtain the parallel database status; Step 2: Based on the preset canary release strategy, the user query requests and parallel database status are routed and version mapped to send targeted reply content to the corresponding user terminals, and the hit version tag and user identifier are serialized and encapsulated to generate routing logs. Step 3: Through the front-end tracking program, the user action elements triggered by the targeted reply content are detected, cleaned and filtered in real time to obtain the interaction behavior dataset; Step 4: Calculate performance metrics and perform asymmetric comparison verification on the interaction behavior dataset and routing logs to obtain performance evaluation results; Step 5: Based on the performance evaluation results, the losing version in the parallel database state is severed and physically erased, the winning version is identified as the global master version and disk fragmentation is reorganized to obtain a single-version knowledge base.

2. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 1, characterized in that, Step one includes: Based on a pre-trained text encoder, the user correction feedback and the original knowledge points are respectively lexicalized and projected into a high-dimensional semantic space. The two semantic vectors are then concatenated across modal features and nonlinearly activated through a multilayer perceptron structure to obtain a fused feature vector. Based on an autoregressive generative language model, the fused feature vector is loaded with an initial hidden state and subjected to bundle search conditional decoding to obtain candidate versions. The graph database storage controller allocates node indexes and writes bidirectional logical pointer associations between candidate versions and original knowledge points to obtain the parallel database state.

3. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 1, characterized in that, Step two includes: The user identifier in the user query request is hashed and discretely mapped and grayscale flow is determined to obtain the flow determination result. The flow determination result is then used to perform address lookup and version addressing on the parallel database status to obtain the address target text and routing metadata. By using a question-and-answer context builder, placeholders are filled and syntax is refined on the target text to obtain the targeted response content; The routing metadata is concatenated, transcoded, and hashed to obtain the routing log.

4. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 1, characterized in that, Step three includes: Real-time capture and time-series packaging of user interaction events triggered by targeted replies to obtain the raw action flow; Based on anti-shake filtering rules and behavior mapping decision trees, high-frequency noise is removed and effective behavior is reduced from the original action stream to obtain an effective behavior primitive sequence. The effective behavioral primitive sequences are time-off offset-compensated, aligned, and structured to obtain an interactive behavior dataset.

5. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 1, characterized in that, Step four includes: Based on a hash index table with user ID and request timestamp as the joint primary key, the interaction behavior dataset and routing log are associated and mapped and version-labeled to obtain a grouped verification data matrix; Based on the grouped statistical calculation unit, the frequency accumulation and ratio calculation of like events, citation events and dialogue interruption events of each version slice in the grouped verification data matrix are performed to obtain a multi-dimensional quantitative indicator set; We perform weighted fusion and significance comparison on the composite performance scores of the original and candidate versions of the multidimensional quantitative index set to obtain the performance evaluation results.

6. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 1, characterized in that, Step four includes: Based on a hash index table with user ID and request timestamp as the joint primary key, the interaction behavior dataset and routing log are associated and mapped and version-labeled to obtain a grouped verification data matrix; Based on the grouped statistical calculation unit, the frequency accumulation and ratio calculation of like events, citation events and dialogue interruption events of each version slice in the grouped verification data matrix are performed to obtain a multi-dimensional quantitative indicator set; Dynamic Bayesian evaluation and posterior probability weighting are performed on the composite performance scores of the original and candidate versions of the multidimensional quantitative index set to obtain the performance evaluation results.

7. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 6, characterized in that, Dynamic Bayesian evaluation and posterior probability weighting are performed on the composite efficacy scores of the original and candidate versions in the multidimensional quantitative index set to obtain the efficacy evaluation results, including: Calculate the posterior probability of each indicator to be evaluated in the multidimensional quantitative indicator set to obtain the posterior probability set; The dynamic confidence weight of each indicator to be evaluated is calculated based on the posterior probability set to obtain the dynamic confidence weight set; The posterior expected value and composite efficacy score are calculated from the posterior probability set and the dynamic confidence weight set to obtain the total composite efficacy score. The overall composite performance score is determined to obtain the performance evaluation result.

8. The knowledge base dynamic optimization method based on candidate response generation and grayscale verification mechanism according to claim 1, characterized in that, Step five includes: Based on the Boolean step value carried by the performance evaluation results, the candidate versions and original knowledge points in the parallel database state are judged to determine the winner and loser to obtain the set of confirmed versions. Forcefully sever the bidirectional logical pointers between the defeated and winning versions in the set of confirmed versions, and perform zero-byte stream overwrite and erasure on the physical storage blocks occupied by the defeated versions to obtain an intermediate discrete database. The intermediate discrete database is compacted and integrated with the backbone pointer of the winning version to obtain a single-version knowledge base.

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