Hierarchical data hybrid storage method and system based on semantic vector fingerprint, and medium

Abstract

The application discloses a hierarchical data hybrid storage method and system based on a semantic vector fingerprint and a medium, relates to the field of hierarchical data hybrid storage, acquires an original capability document, calls a pre-trained language model to parse the capability document into a hierarchical list object, writes the fields of a discovery layer into a database flat column, serializes the hierarchical list object and writes it into a list large field, detects a version identifier of an embedding model, constructs a matching calibration matrix when the version identifier changes, performs orthogonal rotation alignment and residual correction based on singular value decomposition to obtain an affine alignment operator, performs a space migration transformation on a historical semantic vector, performs version determination to obtain a version determination result, performs version record updating, and outputs a capability list service view, which effectively guarantees the fault tolerance and integrity of the capability list service view output.

Description

Technical Field

[0001] This invention relates to the field of hierarchical data hybrid storage, specifically to a hierarchical data hybrid storage method, system, and medium based on semantic vector fingerprints. Background Technology

[0002] In capability list service platforms, numerous merchants submit raw capability documents describing their capabilities in natural language. The platform needs to transform these raw capability documents into structured list objects and store them in a database, providing a searchable and callable capability list service view. Typically, the raw capability documents are converted into hierarchical list objects, with fields used for retrieval and discovery organized hierarchically, and fields used for detailed invocation. Simultaneously, to identify whether the content has undergone substantial changes when merchants repeatedly update their capability documents, existing technologies commonly employ embedding models to generate semantic vectors for the list objects. These semantic vectors serve as content fingerprints, and the cosine similarity between two consecutive semantic vectors is calculated and compared with a threshold to determine whether an update is needed. This version control method, using semantic vectors as content fingerprints, can tolerate minor wording changes compared to character-by-character comparison and is widely used in content version management and near-duplicate deduplication scenarios.

[0003] However, during the long-term operation of the capability list service platform, maintenance personnel observed a set of phenomena that were significantly inconsistent with human intuition, and these phenomena only became apparent after a particular embedded model version upgrade. First, when merchants made only extremely minor formatting adjustments to the capability list, such as changing a single punctuation mark, the system incorrectly classified it as a substantial change and automatically generated a new version number; conversely, when merchants made significant substantial changes to the list content, the system classified it as a non-substantial change and skipped version generation. Second, this disconnect between the judgment result and the magnitude of the content change was not a consistent phenomenon since the system's launch, but only became apparent after a particular embedded model version upgrade. Third, and more seriously, when the embedded model on which the platform's underlying infrastructure relies is upgraded from a certain version to a new version, all historical capability lists may be mistakenly classified as substantial changes in a single submission comparison. This causes the platform to generate a massive number of invalid version numbers for the entire network's capability lists in a short period, rapidly exhausting storage resources and further triggering large-scale network outages for downstream users. Operations personnel find it difficult to understand why a routine operation like replacing the embedded model with a better-performing one would cause the entire version control mechanism to malfunction.

[0004] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0005] To address the technical problems raised in the background section, this invention is proposed. This invention provides a hierarchical data hybrid storage method, system, and medium based on semantic vector fingerprints.

[0006] This invention is achieved through the following technical solution: a hierarchical data hybrid storage method based on semantic vector fingerprints, comprising:

[0007] Obtain the original capability document, call the pre-trained language model to parse the capability document into a hierarchical list object, which includes a discovery layer and a detail layer. Write the fields of the discovery layer into a flat column in the database, serialize the hierarchical list object and write it into the large list field.

[0008] Based on the hierarchical list object, the version identifier of the embedded model is detected. When the version identifier changes, a pairing calibration matrix is ​​constructed. The pairing calibration matrix is ​​subjected to orthogonal rotation alignment and residual correction based on singular value decomposition to obtain an affine alignment operator. The affine alignment operator is used to perform a spatial transfer transformation on the historical semantic vector. The version determination is performed based on the transferred semantic vector to obtain the version determination result.

[0009] Based on the version determination result, the version record is updated. When responding to a read request, the large fields of the list are read first and field-level backfilling validation is performed. The capability list service view is then output.

[0010] Furthermore, the analysis steps for hierarchical list objects are as follows:

[0011] Obtain the original capability document submitted by the merchant, call the pre-trained language model interface to perform structured extraction on the original capability document according to the predefined list pattern definition, and generate a normalized list object;

[0012] Calculate the semantic vector of the current embedded model for the normalized list object as the current semantic vector fingerprint, and record the version identifier of the current embedded model and store it in the version identifier record.

[0013] Extract discovery layer fields from the normalized manifest object, write them into a flat column in the database and create an index, serialize the normalized manifest object as a whole and write it into the manifest large field, write the current semantic vector fingerprint into the version control table, and output the normalized manifest object as a hierarchical manifest object.

[0014] Furthermore, the analysis steps for the affine alignment operator are as follows:

[0015] When the version identifier changes, hierarchical sampling is performed from the version control table according to intent category and tag category to extract the calibration text set. For each text in the calibration text set, the old version embedding model and the new version embedding model are called to generate embedding vectors. The old version embedding vector and the new version embedding vector corresponding to the same text are vertically concatenated into column blocks. All column blocks are summarized to construct a paired calibration matrix.

[0016] An orthogonal rotation alignment based on singular value decomposition is performed on the paired calibration matrix, and the affine alignment operator is obtained by encapsulating it with low-rank residual correction and translation compensation.

[0017] Furthermore, the steps for obtaining the affine alignment operator by encapsulating it with low-rank residual correction and translation compensation are as follows:

[0018] Extract the old space submatrix and the new space submatrix from the paired calibration matrix respectively. Perform column mean centering on the old space submatrix and the new space submatrix respectively to obtain the centered old space submatrix and the centered new space submatrix. Calculate the transpose of the centered new space submatrix and the centered old space submatrix to obtain the cross covariance matrix.

[0019] Furthermore, the step of encapsulating the affine alignment operator with low-rank residual correction and translation compensation also includes:

[0020] Perform singular value decomposition on the cross covariance matrix, extract the left singular vector matrix and the right singular vector matrix, and calculate the transpose product of the left singular vector matrix and the right singular vector matrix to obtain the orthogonal rotation matrix.

[0021] The residual matrix is ​​obtained by performing a rotation transformation on the centered old space submatrix using an orthogonal rotation matrix and calculating the column-by-column difference between the rotated matrix and the centered new space submatrix.

[0022] Furthermore, the step of encapsulating the affine alignment operator with low-rank residual correction and translation compensation also includes:

[0023] The residual matrix is ​​subjected to truncated singular value decomposition to obtain a low-rank correction matrix. The orthogonal rotation matrix is ​​added to the low-rank correction matrix to obtain the composite transformation matrix. The old space mean vector and the new space mean vector recorded during column mean centering are combined to construct a complete affine transformation including translation compensation. The complete affine transformation is encapsulated as an affine alignment operator output.

[0024] Furthermore, the analysis steps for the version determination results are as follows:

[0025] A affine alignment operator is used to perform batch spatial migration transformation and normalization on all historical semantic vectors in the version control table. The cosine similarity between the current semantic vector fingerprint of the hierarchical list object and the semantic vector fingerprint of the previous version after migration is calculated. Version determination is performed based on the dynamic determination threshold to obtain the version determination result.

[0026] A validation subset is drawn from the calibration text set. The mean cosine similarity between the old version embedding vector and the corresponding new version embedding vector in the validation subset after migration is calculated as the alignment quality index. When the alignment quality index is lower than the preset alignment quality lower limit, the migration result of the affine alignment operator is abandoned, the semantic vector fingerprint is recalculated and overwritten.

[0027] Furthermore, the dynamic decision threshold is determined by coupling the mean of the alignment residuals of the affine alignment operator and the category coverage of the calibration text set.

[0028] Furthermore, the steps for outputting the capability list service view are as follows:

[0029] Receive the version determination result. If the version determination result indicates a substantial change, increment the revision number and write the new version record to the version control table. If the version determination result indicates a non-substantial change, only update the access timestamp.

[0030] When responding to a read request for a capability identifier, read the large fields of the list and perform deserialization. Perform non-empty checks on the key fields of the deserialization result. If a field is detected as missing, read data from the corresponding flat column and perform backfilling.

[0031] Furthermore, the steps for outputting the capability list service view also include:

[0032] Check if the details layer of the hierarchical list object contains valid data. If the details layer is not empty, mark the capability status as details ready. If the details layer is empty, mark the capability status as discoverable only. Output the capability list service view carrying the capability status markings.

[0033] A hierarchical hybrid data storage system based on semantic vector fingerprints includes:

[0034] The normalization access module is used to obtain the original capability document and call the pre-trained language model to parse the capability document into a hierarchical list object;

[0035] The hierarchical storage module is used to write fields from the discovery layer into flat columns in the database and serialize hierarchical list objects into large list fields.

[0036] The version control module is used to detect the version identifier of the embedded model based on the hierarchical list object. When the version identifier changes, a pairing calibration matrix is ​​constructed. The pairing calibration matrix is ​​subjected to orthogonal rotation alignment and residual correction based on singular value decomposition to obtain an affine alignment operator. The affine alignment operator is used to perform a spatial migration transformation on the historical semantic vector. The version determination is performed based on the migrated semantic vector to obtain the version determination result.

[0037] The read normalization module is used to perform version record updates based on the version determination results. When responding to a read request, it prioritizes reading the large fields of the list and performs field-level backfill validation, and outputs the capability list service view.

[0038] A hierarchical hybrid data storage medium based on semantic vector fingerprints, on which computer programs are stored.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention parses the original capability document into a hierarchical list object containing a discovery layer and a detail layer by calling a pre-trained language model. The fields of the discovery layer are written into a flat column in the database, and the hierarchical list object is serialized and written into a large list field, constructing a dual-track hybrid storage platform that balances conditional retrieval efficiency with the one-time complete restoration of large objects. To address the misalignment problem between old and new vector spaces caused by version upgrades of the embedded model, this application constructs a pairing calibration matrix when the version identifier changes, and performs orthogonal rotation alignment and residual correction based on singular value decomposition on this matrix to obtain an affine alignment operator. This operator is used to perform spatial migration transformation on the historical semantic vectors before version determination. This feature aligns the historical semantic vectors of the old version to the new vector space with high precision, restoring the true semantic distance meaning of cosine similarity, and solving the defect of batch misjudgment of list content due to cross-space comparison failure, resulting in the generation of massive invalid version numbers and exhausting system resources. When responding to read requests, the large field is read first and field-level backfill verification is performed, effectively ensuring the fault tolerance and integrity of the output capability list service view.

[0041] This application further achieves more accurate and highly available version control and storage effects: At the spatial mapping accuracy level, this application extracts a low-rank correction matrix by performing truncated singular value decomposition on the residual matrix, and constructs a complete affine transformation operator by combining translation compensation. This effectively filters out acquisition noise carried by small singular values ​​and accurately compensates for scale scaling and non-rigid distortion that cannot be covered by a single rigid rotation, improving the alignment accuracy of model transfer. At the version determination threshold level, this application introduces a dynamic determination threshold, which is coupled and determined by the mean of the alignment residual of the affine alignment operator and the category coverage of the calibration text set. This feature allows the determination line to adaptively fluctuate; that is, the threshold is relaxed to tolerate alignment errors when the alignment residual is large, and the threshold is tightened to prevent missed detection of real changes when the category coverage is insufficient, improving the robustness of the determination. An alignment quality indicator verification mechanism was constructed. When the indicator falls below the lower limit, the migration result is abandoned and a fallback path of full recalculation and coverage of the underlying layer is automatically triggered, thus safeguarding the bottom line of semantic comparison space consistency. Furthermore, when deserialization discovers that key fields are missing, the system reads data from the corresponding flat column to perform backfilling. The dual-track redundancy mechanism of hybrid storage is used to achieve high availability of system reads, ensuring the stable output of the capability list service view. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not drawn to scale according to the actual size, but are intended to show the main idea of ​​the present invention.

[0043] Figure 1This is a system block diagram of a hierarchical data hybrid storage system based on semantic vector fingerprints.

[0044] Figure 2 This is a flowchart of a hierarchical data hybrid storage method based on semantic vector fingerprints;

[0045] Figure 3 This is a diagram illustrating the failure principle across spatial comparisons.

[0046] Figure 4 This is a diagram of a hierarchical hybrid storage structure;

[0047] Figure 5 A diagram showing the construction of the paired calibration matrix;

[0048] Figure 6 This is a coupling adjustment diagram for dynamically determining the threshold. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in detail below.

[0050] Example 1

[0051] Before explaining each step, the essential reason for the apparent problem described in the background art should be explained so that those skilled in the art can understand the design basis of each step of this method. The essential reason for the aforementioned apparent problem is that the working logic of the version control step is to call the embedding model to generate a semantic vector for the currently submitted list content, then read the semantic vector stored in the previous version from the database, calculate the cosine similarity between the two and compare it with a judgment threshold. If the similarity is lower than the threshold, it is judged as a substantial change. This logical link involves three steps: calling the embedding model interface, the version control table and the similarity comparison, and implicitly assumes an undeclared premise, namely that the historical semantic vectors stored in the database and the newly generated semantic vector are in the same semantic vector space. Therefore, the cosine similarity between the two can stably measure the semantic differences between the texts. The training process of the embedding model determines the basis orientation and scale distribution of its output vector space. Even for the exact same text, different versions of the embedding model will have a systematic shift in the high-dimensional space, mainly orthogonal rotation, accompanied by scale scaling and slight non-rigid distortion. When an embedding model is replaced with a new version due to routine upgrades, the semantic vectors of the previous version stored in the database as historical snapshots are not automatically recalculated. Therefore, the semantic vectors generated by the new version of the embedding model and the historical snapshot vectors belong to two misaligned vector spaces. The calculated cosine similarity value at this time mainly reflects the offset between the two vector spaces, rather than the actual semantic differences between the text content. This results in a situation where content that is almost unchanged is judged as a substantial change, while content that has undergone significant changes is judged as a non-substantial change—a result decoupled from the magnitude of the change. Furthermore, when all historical snapshot vectors and the new version vectors are misaligned, the historical capability list is batch-wise misjudged as a substantial change, generating a massive number of invalid version numbers. Therefore, the core solution to this problem lies in migrating and aligning the historical semantic vectors from the old vector space to the new vector space when the embedding model version changes, and then performing cosine similarity comparison and version determination. Based on this, this invention designs a hierarchical data hybrid storage method based on semantic vector fingerprints, including steps S10, S20, and S30.

[0052] like Figure 3 The diagram shown illustrates the failure principle of cross-space comparison.

[0053] Reference Figure 3The left box in the diagram represents the old vector space, containing an arrow pointing from the origin to the upper right with a solid dot at the end. This arrow represents the semantic vector fingerprint of the previous version stored in the old vector space. The right box represents the new vector space, also containing an arrow pointing from the origin to the upper right with a solid dot at the end. This arrow represents the current semantic vector fingerprint generated by the new version of the embedding model. Two unlabeled solid lines representing coordinate axes are also drawn in the new vector space, indicating that the coordinate basis of the new vector space has been shifted relative to the old vector space. The two boxes are connected by a horizontal dashed arrow. Above the dashed arrow is labeled "orthogonal rotation," and below it is labeled "scaling and distortion," indicating a systematic shift between the output vectors of the same text under the two embedding models, primarily characterized by orthogonal rotation, accompanied by scaling and mild non-rigid distortion. The text box at the bottom of the diagram indicates that the two vectors belong to two unaligned vector spaces. Cosine similarity reflects spatial offset rather than semantic difference. This shows that when the historical snapshot vector and the current vector are in two unaligned vector spaces, the cosine similarity between the two vectors mainly measures the offset between the two spaces rather than the actual semantic difference of the text. This leads to the counterintuitive phenomenon that minor changes to the content are judged as substantial changes and major changes to the content are judged as non-substantial changes.

[0054] Step S10: Obtain the original capability document, call the pre-trained language model to parse the capability document into a hierarchical list object, the hierarchical list object includes a discovery layer and a detail layer, write the fields of the discovery layer into a flat column of the database, and serialize the hierarchical list object into a large list field.

[0055] Specifically, step S10 transforms the unstructured capability document submitted by the merchant into a hierarchical list object that can be quickly retrieved and fully restored, and persists it using a hierarchical storage method. The hierarchical list object in step S10 includes two layers: a discovery layer and a details layer. The discovery layer contains the fields required for capability retrieval and discovery, while the details layer contains the complete fields required for capability invocation. Step S10 includes steps S11, S12, and S13.

[0056] Step S11: Obtain the original capability document submitted by the merchant, call the pre-trained language model interface to perform structured extraction on the original capability document according to the predefined list pattern definition, and generate a normalized list object. If the original capability document is missing detail layer data, perform empty placeholder processing on the detail layer.

[0057] Specifically, the original capability document refers to an unstructured document submitted by the merchant in the form of natural language text, describing the capabilities it provides to the outside world. Its data type is string, and it is input by the merchant through the capability input interface on the platform front end and transmitted to this step through the platform receiving module.

[0058] The predefined manifest pattern is pre-built and solidified during the platform deployment phase. After construction, the same predefined manifest pattern definition is reused for all original capability documents. The construction process consists of six steps. The first step is hierarchical division, dividing the manifest fields into a discovery layer and a detail layer based on the platform's two-stage usage scenario of first searching and discovering, then invoking and executing. The second step is determining the discovery layer fields, listing the fields necessary to support retrieval and deduplication, including at least the capability identifier, capability name, intent category, and tag category. The capability identifier is the unique primary key of the capability within the platform, the capability name is the capability abbreviation, the intent category is the usage category to which the capability belongs, and the tag category is the multi-dimensional tag for the capability. The third step is determining the detail layer fields, listing the fields necessary to support actual invocation, including at least invocation parameters, constraints, and invocation examples. The fourth step is defining field attributes, specifying the field name, meaning, value type, and whether it is required for each field. The value type can be string, number, boolean, or a list or nested object composed of the aforementioned types. The fifth step is to encode the data into a machine-readable template. Key-value pairs are encoded into a structured template description. The top level is distinguished by two keys: "Discovery Layer" and "Details Layer." Below each level key, the field name is used as the key, and the description object recording the meaning and value type of that field is used as the value. The sixth step is to store the structured template description in the platform configuration for each extraction. The predefined list pattern definition is the structured template description produced by the above six steps. Its data type is a pattern description object, containing two sets: discovery layer field descriptions and details layer field descriptions. Each set lists the field name, meaning, and value type of the corresponding level. The advantage of using the above structure in the predefined list pattern definition is that it simultaneously constrains the field set and field type of the extraction results. This ensures that original capability documents from different merchants with different expression styles fall into a unified field space after extraction, facilitating subsequent steps to store and retrieve data at fixed field positions.

[0059] Structured extraction is performed by the parsing module, which is a generative language model. The structure and training of the parsing module are described in this embodiment as follows: Structurally, the parsing module consists of three parts connected in sequence: an input embedding layer, N stacked decoder layers, and an output prediction layer. The input embedding layer segments the input text into a sequence of tokens using a tokenizer, maps each token to a d-dimensional token vector, and superimposes positional encodings of the same dimension to obtain a sequence of token vectors. The N stacked decoder layers each consist of a masked multi-head self-attention sublayer and a position-wise feedforward sublayer connected in series. These two sublayers are each externally connected to a residual and then subjected to layer normalization. The masked multi-head self-attention sublayer establishes contextual relationships with causal masks between tokens, and the position-wise feedforward sublayer performs two linear transformations and one non-linear activation on each token vector. The output prediction layer is a linear layer superimposed with a normalized exponential function, mapping the token vectors output by the final decoder layer to a probability distribution on the vocabulary, and generating the output text token by token based on this distribution. In this embodiment, the word vector dimension *d*, the number of decoder layers *N*, the number of self-attention heads *h*, and the dimension of the feedforward intermediate layer are the structural hyperparameters of the parsing module, set before training and remaining unchanged during training and inference. The training of the parsing module includes two stages. The first stage is self-supervised pre-training, using a large-scale general text corpus as training data. Each text is segmented into a word sequence, with all words preceding each position in the sequence as input and the true word at that position as the prediction target. The cross-entropy between the parsing module's predicted distribution and the true word is used as the loss function. A mini-batch stochastic gradient descent optimizer iteratively updates all network parameters of the parsing module until the loss converges, enabling the parsing module to learn general language rules. The second stage is instruction fine-tuning, constructing several samples composed of paired instruction texts and desired structured output texts as training data. With the instruction text as input and the desired structured output text as the target, mini-batch stochastic gradient descent is used to fine-tune the pre-trained parameters using word-by-word cross-entropy as the loss, enabling the parsing module to learn the ability to generate output conforming to a specified format based on the input instruction. The training parameters involved in the above two-stage training, including corpus size, batch size, learning rate, and number of iterations, are determined based on the size of the set structural hyperparameters and the amount of training data, aiming to ensure that the loss function converges on the validation set without overfitting. These parameters are calibrated by monitoring the validation set loss on candidate values. The parsing module obtained through the above two-stage training performs structured extraction of the input text according to the input instructions during the inference stage.

[0060] The method for organizing the predefined checklist pattern definition and the original capability document into the parsing module input is as follows: the input content is organized into a prompt text composed of an instruction segment and a processing segment concatenated sequentially, with the instruction segment first and the processing segment second. The instruction segment is written in three parts. The first part is the task description, stating that the parsing module should extract information from the processing text according to the field specifications given later and output structured results. The second part is the field specifications, listing each field in the discovery layer and detail layer of the predefined checklist pattern definition by field name, field meaning, value type, and whether it is required. The third part is the format constraints, requiring strict adherence to the above field names and value types, requiring the output to be a key-value pair structure consistent with the predefined checklist pattern definition hierarchy, requiring that fields not mentioned in the original capability document be filled with agreed-upon empty placeholder values ​​instead of omitting or arbitrarily creating them, and requiring that only the structured result itself be output without additional explanatory text. The processing segment is placed after the instruction segment with the original string text from the original capability document as is. The concatenated prompt text is submitted to the parsing module as a single inference input.

[0061] The processing chain for obtaining the normalized list object from the output of the parsing module consists of four steps, rather than directly using the output of the parsing module as the normalized list object. The first step is receiving the raw output. The parsing module performs semantic understanding and field attribution on the content to be processed according to the field specifications of the instruction segment. It extracts the scattered, colloquial ability descriptions from the original capability document and fills them into each field, outputting a structured text representing the key-value pair structure, which is recorded as the model's raw output. The second step is format parsing. The model's raw output is parsed using the key-value pair format to obtain an intermediate object with field names as keys and extracted values ​​as values. If parsing fails, the inference input is resubmitted according to a preset retry strategy. If it still fails, the original capability document is marked as an extraction anomaly and submitted for manual review, without proceeding to subsequent steps. The third step is field validation and type normalization. This involves verifying whether the intermediate object contains all the fields specified in the discovery layer and detail layer field descriptions. Missing fields are filled with agreed-upon placeholder values. The actual value type is compared to the specified value type for each field. For inconsistencies, type conversion is performed according to the specified value type. Fields that cannot be converted are set to agreed-upon placeholder values. Redundant fields beyond those specified in the intermediate object are deleted, and each field is assigned to a discovery layer sub-object or a detail layer sub-object according to its hierarchy. The fourth step is the output object. After the above validation and normalization, the resulting object is completely consistent with the predefined list pattern definition in terms of field composition, field hierarchy, and value type. This object is then output as a normalized list object. This establishes an inherent connection between the parsing module and the service scenario of this capability list. Specifically, the input to the parsing module is constrained by the predefined list pattern definition to include field descriptions for the discovery layer and the detail layer. The output of the parsing module, after field validation and type normalization, is constrained to structured fields at two levels: the discovery layer and the detail layer. This reduces the free-flowing natural language into a normalized list object with fixed fields and a strict hierarchy. The normalized list object is a structured object after field validation and type normalization. Its data type is a nested object containing two sub-objects: the discovery layer and the detail layer. The field composition of each sub-object in the discovery layer and the detail layer corresponds one-to-one with the field descriptions of the corresponding levels in the predefined list pattern definition.

[0062] The criterion for determining missing detail layer data is as follows: count the number of required fields in the detail layer sub-objects of the normalized list object whose values ​​are not empty placeholders. When this number equals zero, the original capability document is determined to have missing detail layer data. The method for handling empty placeholders in the detail layer is to uniformly set the value of each detail layer field in the detail layer sub-object to a pre-defined empty placeholder value. This empty placeholder value is a specific marker value that distinguishes it from the normal value type, used in subsequent steps to indicate that the field currently does not have valid data and is not a normal empty string. The physical reason for performing empty placeholder handling instead of directly discarding the detail layer is that the platform allows merchants to first submit capability documents containing only discovery layer information for retrieval and discovery, and then complete the detail layer information later. Directly discarding the detail layer would disrupt the fixed two-layer structure of the hierarchical list object, making it impossible for subsequent steps to access data using fixed field positions. Therefore, it is necessary to preserve the structural skeleton of the detail layer with empty placeholder values.

[0063] The entities being processed are natural language descriptions of merchant capabilities, characterized by free expression, widespread field omissions, and severe synonym-heterogeneity. A predefined list pattern is injected into the input as a target structural constraint. Based on the principle that sequence-to-sequence modeling, under sufficient contextual constraints, can map the input semantics to an output conforming to the target structure, the parsing module output is forced to align to the fixed field space of the discovery and detail layers. Missing detail layer values ​​are filled with specific markers that distinguish them from normal values, preserving both the structural skeleton and the information dimension of field validity. The resulting normalized list object has fixed fields and a unified hierarchy, becoming the unified starting point for subsequent steps S12 (vectorized concatenation), S13 (layered database storage), and S33 (state determination), eliminating processing errors accumulated and amplified in subsequent stages due to structural inconsistencies from the source.

[0064] Step S12: Calculate the semantic vector of the current embedded model for the normalized list object as the current semantic vector fingerprint, and record the version identifier of the current embedded model and store it in the version identifier record.

[0065] Specifically, the current semantic vector fingerprint is calculated as follows: following the predefined field order specified in the list pattern definition, the values ​​of each field in the discovery layer sub-object and the detail layer sub-object of the normalized list object are concatenated sequentially to form a fingerprint source text. This fingerprint source text is then submitted to the current embedding model, which outputs a fixed-dimensional real vector. Each component of this real vector is divided by its Euclidean norm to achieve normalization, resulting in the current semantic vector fingerprint. Under normal circumstances, the output vector of the current embedding model has a non-zero Euclidean norm. If a zero vector appears, it is considered an embedding model call exception, and no further processing is performed.

[0066] Vectorization is performed by the embedding module, which is a text embedding model. The structure and training of the embedding module are described below. Structurally, the embedding module consists of three parts connected in sequence: an input embedding layer, an M-layer stacked encoder layer, and a pooling layer. The input embedding layer segments the input text into a sequence of tokens using a tokenizer, mapping each token to a d-dimensional token vector and superimposing positional encodings of the same dimension. The M-layer encoder layers are stacked in sequence, each consisting of a multi-head self-attention sublayer and a position-wise feedforward sublayer connected in series. These two sublayers are each externally connected to residuals and then subjected to layer normalization. The multi-head self-attention sublayer establishes bidirectional contextual relationships between tokens without causal masks. The position-wise feedforward sublayer performs a nonlinear transformation on each token vector, and after being extracted layer by layer by the M layers, a set of d-dimensional hidden layer representations of the same length as the number of tokens is obtained. The pooling layer reduces this number of hidden layer representations, which varies with the input, to a single d-dimensional vector according to a preset aggregation method and outputs it as the embedding vector of the text. The aggregation method is average pooling, which calculates the arithmetic mean of the hidden layer representations of all tokens component by component. In this embodiment, the word vector dimension *d*, the number of encoder layers *M*, and the number of self-attention heads *h* are structural hyperparameters of the embedding module, set before training and remaining unchanged during training and inference. The word vector dimension *d* is the dimension of the embedding vector. During training, the embedding module is trained using a contrastive learning approach. The training data consists of paired text samples, where two semantically similar texts form a positive sample pair, and two semantically unrelated texts form a negative sample pair. Each text is encoded into an embedding vector using the aforementioned structure. A contrastive loss function is used as the optimization objective. This function increases the similarity between the two embedding vectors of a positive sample pair and decreases the similarity between the two embedding vectors of a negative sample pair. A mini-batch stochastic gradient descent optimizer iteratively updates all network parameters of the embedding module until the loss converges, thus ensuring that the embedding vectors of semantically similar texts are close to each other, and the embedding vectors of semantically dissimilar texts are far apart. The training parameters involved above, such as sample size, batch size, learning rate, and number of iterations, are determined by monitoring the validation set loss on candidate values ​​to ensure that the contrastive loss function converges on the validation set without overfitting. The embedding module obtained through the above training maps any input text into a d-dimensional embedding vector during the inference phase.

[0067] The embedding module's ability to output a fixed-dimensional real vector for any input text stems from its pooling layer. The number of tokens after word segmentation varies with text length, resulting in a variable-length (d-dimensional) hidden representation of each token obtained through the encoder layer. The pooling layer then reduces this variable-length vector group to a single d-dimensional vector using a fixed aggregation method. The length of this single d-dimensional vector is determined solely by the structural hyperparameter d and remains constant regardless of the number of input tokens. Therefore, for input text of any length and content, the embedding module outputs a real vector with a constant dimension of d. This constant dimension d is the inherent output dimension of the embedded vector. This step obtains a fixed-dimensional real vector without setting any output dimension, and all semantic vector fingerprints generated on the same platform with consistent dimensions during periods when the embedding model version remains unchanged can be directly compared. Normalization is then performed on the output vector by dividing each component by the vector's Euclidean norm, ensuring that the normalized vector has a Euclidean norm of one, falling on the unit hypersphere. This establishes the intrinsic connection between the embedding module and the service scenario of this capability list. Specifically, the input to the embedding module is constrained to be fingerprint source text concatenated according to the predefined field order defined in the list pattern. The output of the embedding module is constrained to be a fixed-length real-number vector of constant dimension d, normalized and lying on a unit hypersphere. This allows the semantic similarity between any two capability lists to be directly measured by cosine similarity. Here, the current semantic vector fingerprint refers to the normalized semantic vector, which is a fixed-length real-number vector. Its physical meaning is the position coordinate of the fingerprint source text in the semantic vector space defined by the current embedding model. After normalization, its values ​​lie on a unit hypersphere, making the cosine similarity between any two semantic vector fingerprints equal to their inner product.

[0068] The method for recording the version identifier of the current embedded model is as follows: when calling the interface of the current embedded model, the field value identifying the model version is read from the response metadata returned by the interface, and this field value is used as the version identifier of the current embedded model. The version identifier is a string that uniquely identifies the version of the embedded model. Its data type is string, and it is provided by the embedded model service in the interface response. Its physical meaning is the version identity of the embedded model used to generate the semantic vector. The version identifier record is a record table that appends the version identifiers of the embedded models used in each call in chronological order. Its data structure is a sequence of string records with timestamps. Each record contains the capability identifier, the version identifier, and the timestamp at the time of appending. The method for associating the version identifier of the current embedded model with the version identifier record is to append the version identifier of the current embedded model, the capability identifier to which the current semantic vector fingerprint belongs, and the current timestamp as a single record to the version identifier record. The physical reason for storing version identifiers in association rather than just semantic vectors is that subsequent steps need to determine whether the version identifiers of two adjacent records in the version identifier record are consistent to determine whether the embedding model has undergone a version change. If the version identifier is not recorded, it is impossible to perceive whether the vector space has been migrated, which is the root cause of the problem described in the background technology.

[0069] The entity being processed is the semantic content of the capabilities carried by the normalized list object, and its representation coordinate system is uniquely determined by the invoked embedding model. This step is based on the semantic representation theory of vector space, that is, semantically similar texts are mapped to adjacent positions by the embedding model, and semantically dissimilar texts are mapped to distant positions. Specifically, the fingerprint source text is mapped into a semantic vector through the embedding module, and normalized to fall on a unit hypersphere. The modulus is removed and only the direction is retained, so that the cosine similarity degenerates into an inner product, which facilitates direct comparison of subsequent steps S13 and S23 on a unified scale. The key to this step is to record the version identifier simultaneously: the version identifier is the only observable basis for judging whether the vector space has migrated. If only the semantic vector is stored as in the conventional approach, the system will not know that the historical vector is in an invalid old space after the embedding model is upgraded, which is the source of the hidden danger of cross-space comparison failure mentioned in the background technology. Therefore, step S12 simultaneously solidifies the semantic content and the model version identity, providing two indispensable types of input for the spatial alignment and version determination in the subsequent step S20.

[0070] like Figure 4 The diagram shown is a hierarchical hybrid storage structure.

[0071] Reference Figure 4The top box in the diagram represents a hierarchical list object, divided into two columns by a horizontal line. The upper column is labeled "Discovery Layer," and the lower column is labeled "Details Layer." A dotted line separates the two columns, indicating that the hierarchical list object contains two levels: the Discovery Layer and the Details Layer. Two arrows extend downwards from the hierarchical list object, pointing to the storage table box at the bottom: the left arrow, labeled "Discovery Layer Fields," points to a flat column in the left part of the storage table, composed of several small squares, with "Index Creation" below it, indicating that each field in the Discovery Layer is written to its corresponding independent column and indexed. The right arrow, labeled "Complete Serialization," points to a large list field in the right part of the storage table, labeled "Complete Object," indicating that the normalized list object is serialized into a byte sequence and written to a single large field. A vertical dotted line separates the flat column area and the large field area within the storage table, indicating that they belong to the same storage table.

[0072] Step S13: Extract the discovery layer field from the normalized list object, write it into a flat column in the database and create an index, serialize the normalized list object as a whole and write it into the list large field, write the current semantic vector fingerprint into the version control table, commit within the same database transaction to ensure the consistency between the flat column and the list large field, and output the normalized list object as the hierarchical list object; wherein, when the version identifier record indicates that the embedded model version has not changed, directly calculate the cosine similarity between the current semantic vector fingerprint and the semantic vector fingerprint of the previous version in the database and perform regular version determination, skipping the spatial alignment process in step S20.

[0073] Specifically, the normalized list object and the current semantic vector fingerprint are used as inputs to perform hierarchical hybrid database storage. The method for extracting discovery layer fields and writing them to the database flat columns is as follows: according to the discovery layer field descriptions in the predefined list pattern definition, values ​​are extracted field by field from the discovery layer sub-objects of the normalized list object, and the value of each discovery layer field is written to a corresponding independent column in the database. This independent column is the database flat column. A database flat column refers to a storage structure in a relational database table where each column corresponds to a discovery layer field, and the column only stores the scalar value of that field. Its advantage lies in supporting indexing by field and efficient retrieval based on column conditions. The method for creating indexes on the database flat columns is as follows: database indexes are created for discovery layer field columns that at least include intent category columns and tag category columns, enabling the capability list service view to quickly filter capabilities by intent category or tag category. The method for serializing and writing the normalized list object into the list large field is as follows: the normalized list object is encoded into a byte sequence according to the agreed serialization format, and the byte sequence is written into a single large field in the storage table. The storage table refers to the relational database table that stores the discovery layer database flat columns and the list large field. The database flat columns into which the discovery layer fields are written and the list large field written in this step belong to the same storage table. The list large field refers to the large object column in the storage table used to store variable-length byte sequences. The list large field completely preserves all fields and their hierarchical structure of the discovery layer and detail layer of the normalized list object, so that the complete object can be restored without relying on the flat columns when reading. The method for writing the current semantic vector fingerprint into the version control table is as follows: the current semantic vector fingerprint, along with its capability identifier, corresponding version identifier, revision number, fingerprint source text, and timestamp, is written into the version control table as a record. The version control table is a two-dimensional table that organizes and stores historical semantic vector fingerprints and their metadata by capability identifier and revision number. It and the storage table are two independent database tables. The storage table stores the discovery layer flat columns and list large fields, while the version control table stores the semantic vector fingerprints and their metadata. Its data structure is a two-dimensional table in which each row contains capability identifier, revision number, version identifier, semantic vector fingerprint, fingerprint source text, and timestamp. The fingerprint source text field stores the fingerprint source text concatenated in step S12 when generating the semantic vector fingerprint of that row, which is read in subsequent steps S21 sampling and S24 full recalculation.

[0074] To ensure consistency between the flat columns and the large list field, the following method is used within the same database transaction: The three write operations—writing to the database flat columns, writing to the large list field, and writing to the version control table—are incorporated into the same database transaction. The transaction is committed only after all writes are successful; if any write fails, the entire transaction is rolled back. The physical reason for using the same transaction instead of separate commits is that the database flat columns and the large list field represent the same normalized list object on two heterogeneous storage tracks. If committed separately, system interruptions between commits could lead to inconsistencies, such as the flat columns being updated while the large list field remains unupdated. This would cause the capabilities retrieved by the flat columns to contradict those reconstructed by the large list field. Therefore, strong transaction atomicity constraints are necessary to ensure that both operations are either simultaneously active or simultaneously inactive. After the commit is complete, the normalized list object is output as a hierarchical list object for use in subsequent steps.

[0075] This step also includes a conditional flow control logic. The condition for determining whether the embedded model version has changed is as follows: Read the version identifiers of the last two records in the version identifier record arranged by timestamp. If the version identifier of the last record is exactly equal to the version identifier of the previous record, the embedded model version is determined to have not changed; if they are not equal, the embedded model version is determined to have changed. When there is only one record in the version identifier record, the embedded model version is determined to have not changed, and the normal version determination is directly performed. When there is no historical record of the capability identifier in the version control table, the current submission is determined to be the initial version submission of the capability identifier. The current submission is directly identified as a substantial change, and the current semantic vector fingerprint is used as the initial historical snapshot of the capability identifier. Cosine similarity comparison is not performed, and the process directly proceeds to step S30 to perform version record update. When it is determined that the embedded model version has not changed and the version control table already contains a historical record of the capability identifier, since the current semantic vector fingerprint and the semantic vector fingerprint of the previous version in the database are in the same vector space, no spatial alignment is required, and the regular version determination is performed directly: the semantic vector fingerprint of the previous version corresponding to the capability identifier is read from the version control table, the cosine similarity between the current semantic vector fingerprint and the semantic vector fingerprint of the previous version is calculated, and the cosine similarity is compared with the basic determination threshold. The basic determination threshold is explained in step S23. When the current cosine similarity is less than the basic determination threshold, it is determined to be a substantial change; when it is greater than or equal to the basic determination threshold, it is determined to be a non-substantial change. This determination result is output as the version determination result for use in step S30, thereby skipping the spatial alignment process in step S20. When it is determined that the embedded model version has changed, the comparison is not performed directly in this step, but instead, spatial alignment is performed in step S20 before determination. Let the current semantic vector fingerprint be a vector. The semantic vector fingerprint of the previous version was a vector. Both dimensions are Since step S12 has already processed the vector with vector Normalized cosine similarity is equal to the dot product of two vectors, calculated using the following formula:

[0076]

[0077] The principle behind this formula stems from the relationship between the dot product of two vectors and the angle between them. That is, the dot product of two vectors equals the product of their magnitudes multiplied by the cosine of the angle between them. Therefore, the cosine of the angle equals the dot product divided by the product of their magnitudes, and this cosine is the cosine similarity. Since the vectors... with vector Both have been normalized and have a magnitude of one; the product of their magnitudes is one, and the cosine similarity degenerates into the inner product of the two vectors. In the formula, The first semantic vector fingerprint Each component originates from the normalized result of the embedded module output vector in step S12. The semantic vector fingerprint of the previous version Each component originates from the historical records stored in the version control table; The semantic vector fingerprint dimension is equal to the inherent output dimension d of the embedding module described in step S12. That is, the semantic vector fingerprint dimension referred to in subsequent steps of this specification and the inherent output d of the embedding module described in step S12 are the same value, and this value will be uniformly referred to as the semantic vector fingerprint dimension thereafter. The physical consistency of this formula lies in the following: when the directions of two vectors are completely consistent, the cosine of the included angle is one, and the similarity is maximum; when the directions are completely opposite, the cosine of the included angle is negative one, and the similarity is minimum; when the directions are orthogonal, the similarity is zero, which is consistent with the physical expectation that semantically similar vectors are adjacent. Since step S12 has normalized the vectors, the similarity value falls within the range of negative one to one, making it easy to compare with the threshold.

[0078] This scenario presents two representations of the same capability semantic content on two different storage media, along with their semantic vector fingerprints. This scenario is characterized by three features: read-heavy and write-light characteristics, fixed retrieval fields but variable-length content, and the potential for interrupted write operations. A dual-track hybrid storage approach is adopted, where the discovery layer is distributed to flat columns in the database, and the entire content is serialized to a large list field. Based on the principle that fixed-length indexable fields are suitable for conditional retrieval, and variable-length objects are suitable for overall reconstruction, retrieval by intent category and tag category uses the indexed flat columns, while the complete content is reconstructed from the large field in one step, ensuring that both types of access are handled appropriately. Furthermore, the three write operations are constrained by the same database transaction, and the atomicity of the transaction ensures that the flat columns and large fields do not exhibit inconsistencies where one has been updated while the other has not, even under any interruption. When the version remains unchanged, direct comparison within the same space is performed; when the version changes, the process switches to step S20 for spatial alignment, directly addressing the underlying reason revealed by the background technology: cosine similarity only has semantic distance significance when two vectors reside in the same vector space. If all submissions are directly compared, the comparison results will be systematically distorted after the embedded model upgrade, as historical and new vectors belong to heterogeneous spaces. Thus, step S13 not only establishes a hybrid storage base that supports efficient retrieval and complete restoration, but also precisely restricts expensive space migration to a few occasions where model upgrades actually occur through version diversion, thereby suppressing the generation of invalid versions from the source.

[0079] Step S20: Based on the hierarchical list object, detect the version identifier of the embedded model. When the version identifier changes, construct a pairing calibration matrix. Perform orthogonal rotation alignment and residual correction based on singular value decomposition on the pairing calibration matrix to obtain an affine alignment operator. Use the affine alignment operator to perform a spatial migration transformation on the historical semantic vector. Perform version determination based on the migrated semantic vector to obtain the version determination result.

[0080] Specifically, step S20 involves, when the embedding model version changes, first migrating and aligning the historical semantic vectors stored in the old vector space to the new vector space defined by the new version of the embedding model, and then performing version determination within the same space to restore the semantic distance meaning of cosine similarity. The criteria for detecting the embedding model version identifier and determining whether it has changed are the same as in step S13: comparing the version identifier strings of the last two records in the version identifier record for equality. If they are equal, there is no change; otherwise, there is a change. When it is determined that the embedding model version has not changed, step S20 is not triggered, and the version determination result is directly obtained from the regular version determination path of step S13. When it is determined that the embedding model version has changed, step S20 is triggered, and step S20 includes steps S21, S22, S23, and S24.

[0081] like Figure 5 The diagram shown is a construction diagram of the paired calibration matrix.

[0082] Reference Figure 5 The diagram shows a box on the left representing the same fingerprint source text. Two arrows radiate from this box: the upper arrow points to the box labeled with the old version of the embedding model, which then points to the right via an arrow labeled with the old version of the embedding vector; the lower arrow points to the box labeled with the new version of the embedding model, which also points to the right via an arrow labeled with the new version of the embedding vector. This indicates that two embedding vectors are generated for the same fingerprint source text using both the old and new versions of the embedding model. The diagram on the right shows a pairing calibration matrix, divided into upper and lower parts by a horizontal dashed line. The upper part labels the old version of the embedding vector, and the lower part labels the new version of the embedding vector. Vertical dividing lines within the matrix further divide the matrix into columns, indicating that the old and new version embedding vectors of the same text are vertically concatenated into a single column. The upper half stores the old spatial coordinates, and the lower half stores the new spatial coordinates. The text below the diagram indicates that the old and new coordinates of the same text are aligned in the same column, and the text is sampled hierarchically according to intent category and tag category. This means that each column of the matrix corresponds to the same text and its old and new spatial coordinates are aligned in the same column. Furthermore, the calibration text set is sampled hierarchically according to intent category and tag category to ensure that each semantic region is covered.

[0083] Step S21: When the version identifier changes, perform hierarchical sampling from the version control table according to intent category and tag category, extract the calibration text set, call the old version embedding model and the new version embedding model to generate embedding vectors for each text in the calibration text set, vertically concatenate the old version embedding vector and the new version embedding vector corresponding to the same text into column blocks, and summarize all column blocks to construct a paired calibration matrix.

[0084] Specifically, step S21 is triggered when the embedded model version changes. Its function is to collect a batch of paired samples that simultaneously possess both old and new spatial coordinates, serving as the basis for solving the mapping relationship between the two vector spaces. The stratified sampling method based on intent category and label category is as follows: using the combination of intent category and label category carried by each historical record in the version control table as the stratification basis, all historical records in the version control table are divided into several layers according to the combination of intent category and label category. Within each layer, sampling quotas are allocated according to the proportion of records in that layer to the total number of records. A corresponding number of historical records' fingerprint source texts are randomly selected from each layer. These fingerprint source texts are read from the fingerprint source text field of the version control table. The fingerprint source texts extracted from each layer are summarized to form a calibration text set. The calibration text set refers to the text set used to solve the spatial mapping relationship. Its data structure is a set of fingerprint source texts, and each fingerprint source text is the fingerprint source text concatenated in step S12 when the historical record is entered into the database. The physical reason for using stratified sampling by intent category and label category instead of simple random sampling is that the capability lists of different intent categories and label categories are distributed in different regions in the vector space. If simple random sampling is used, some categories may be absent from the calibration text set, resulting in inaccurate mapping relationships in the spatial regions where these absent categories are located. Therefore, stratified sampling must be used to ensure that each category is covered in the calibration text set.

[0085] The sampling quantity, i.e., the number of text entries in the calibration text set, is obtained as follows: The calibration text set is divided into two parts: a training subset and a validation subset. The training subset is used for solving the affine alignment operator in steps S221 to S223, and the validation subset is used for evaluating the alignment quality in step S24. The sampling quantity mentioned in this step specifically refers to the number of text entries in the training subset used for solving. The sampling quantity is equal to three times the dimension of the semantic vector fingerprint. The determination of this multiple is based on the following: In the subsequent step S221, a square matrix with a dimension equal to the semantic vector fingerprint dimension will be constructed from the training subset as the cross covariance matrix. The rank of the cross covariance matrix depends on the rank of the sample matrix after the training subset is centered. Column mean centering makes the sum of each column after centering zero vector. A linear constraint is introduced so that the effective rank of the centered training subset is at most one less than the number of samples. In order to make the cross covariance matrix full rank and thus ensure that the subsequent singular value decomposition can be solved stably, the number of samples must not be less than the semantic vector fingerprint dimension plus one. On this basis, in order to suppress the influence of the sampling noise of a single sample on the stability of the singular value decomposition result, so that there are three mutually independent constraint samples on average in each dimension, the number of samples is taken as three times the semantic vector fingerprint dimension. In addition to the training subset mentioned above, this step also reserves a portion of samples according to intent category and label category as the verification subset of step S24. The number of samples in the verification subset is not included in the sampling quantity. The number of samples in each intent category and label category is not less than the minimum number of samples in the verification subset division described in step S24. Each sample in the verification subset also completes the collection of old version embedding vector and new version embedding vector in this step. Therefore, the total number of samples actually extracted from the version control table in this step is equal to the sum of the sampling quantity and the number of samples in the verification subset.

[0086] The method for generating embedding vectors for each text in the calibration text set by calling the old version embedding model and the new version embedding model respectively is as follows: the old version embedding model refers to the embedding model corresponding to the previous record in the version identifier record, and the new version embedding model refers to the embedding model corresponding to the current version identifier; both the old version embedding model and the new version embedding model are embedding modules with the structure described in step S12. They have the same input embedding layer, M-layer encoder layer and pooling layer structure and the same semantic vector fingerprint dimension. The only difference is that they are two sets of network parameters obtained by the embedding module before and after the embedding model version upgrade. When the embedding model version is upgraded, the platform retains the network parameters of the old version embedding model before the upgrade. Therefore, for the same text, the network parameters of the old version embedding model can be loaded to generate an old version embedding vector, and the network parameters of the new version embedding model can be loaded to generate a new version embedding vector. For each text in the calibration text set, the old version embedding model is called first to generate an old version embedding vector, and then the new version embedding model is called to generate a new version embedding vector. The dimension of both vectors is equal to the semantic vector fingerprint dimension. Here, the old version embedding vector refers to the fixed-length real-number vector output by the old version embedding model for the text, and the new version embedding vector refers to the fixed-length real-number vector output by the new version embedding model for the text. Both the old and new version embedding vectors are normalized as described in step S12, that is, each component is divided by the Euclidean norm of the vector to make it fall on the unit hypersphere. The method of vertically concatenating the old and new version embedding vectors corresponding to the same text into a column block is as follows: the old version embedding vector of the text is placed in the upper half and the new version embedding vector is placed in the lower half, and they are concatenated end to end along the column direction to form a column vector with a length equal to twice the dimension of the semantic vector fingerprint. This column vector is the column block corresponding to the text. The method of summarizing all column blocks to construct the paired calibration matrix is ​​as follows: the column blocks corresponding to all texts in the calibration text set are arranged horizontally side by side as columns of the matrix to form a matrix with the number of rows equal to twice the dimension of the semantic vector fingerprint and the number of columns equal to the number of samples. This matrix is ​​the paired calibration matrix. A pairing calibration matrix is ​​a two-dimensional real matrix in which the upper half stores the old version of the embedding vector, the lower half stores the new version of the embedding vector, and each column corresponds to the same text. Structurally, it aligns the old and new spatial coordinates of the same text in the same column, thus fully depicting the pairing positional relationship of the same batch of texts in the two vector spaces.

[0087] The same batch of capability list texts has two sets of position coordinates under the old and new versions of the embedding model, which exhibit a systematic offset with highly consistent offset patterns across different texts. The principle behind simultaneously calling the old and new versions of the embedding model for the same text and aligning the two sets of coordinates in the same column of the pairing calibration matrix is ​​paired sample estimation. That is, to estimate the transformation between two coordinate systems, paired observations of the same group of entities in both coordinate systems must be obtained. Therefore, the two embedding vectors of the same text in the old and new spaces are used as paired observations of the same entity, allowing subsequent step S22 to inversely solve for the spatial transformation. Layered sampling based on intent category and tag category ensures that paired observations cover the entire semantic region, making the inversely solved transformation valid across the entire space. If only the new version of the embedding model is used to recalculate each historical text, the new model needs to be called once for each massive historical record, resulting in extremely high costs.

[0088] The paired calibration matrix aligns the old and new spatial coordinates of the same text in the same column, allowing step S221 to directly extract sub-matrices from its upper and lower halves and calculate the cross covariance matrix. Hierarchical sampling ensures category coverage, maintaining the accuracy of the obtained affine alignment operator across all intent and label category regions. The sampling quantity is constrained to three times the dimension of the semantic vector fingerprint, ensuring that the total number of calls to the old and new version embedding models is only proportional to the dimension and independent of the massive amount of historical data. This replaces the high cost of recalculating every single data entry with dual-model encoding of a small number of paired samples. Furthermore, the category coverage collected in this step will be transformed into category coverage in step S23 to participate in the determination of the dynamic judgment threshold, allowing the statistical confidence level in the sampling stage to directly affect the leniency or strictness of the final judgment.

[0089] Step S22: Perform orthogonal rotation alignment based on singular value decomposition on the paired calibration matrix, and encapsulate it with low-rank residual correction and translation compensation to obtain an affine alignment operator.

[0090] Specifically, step S22 takes the pairing calibration matrix as input. Its function is to solve for an affine alignment operator that can map any vector in the old space to the new space from the pairing relationship between the old and new space coordinates described by the pairing calibration matrix. Step S22 decomposes the offset between vector spaces, which is mainly orthogonal rotation accompanied by scaling and slight non-rigid distortion, into orthogonal rotation components and low-rank residual correction components, solves them separately, and encapsulates them into a complete affine alignment operator by combining translation compensation. Step S22 includes steps S221, S222, and S223.

[0091] Step S221: Extract the old space submatrix and the new space submatrix from the paired calibration matrix respectively, perform column mean centering on the old space submatrix and the new space submatrix respectively to obtain the centered old space submatrix and the centered new space submatrix, and calculate the cross covariance matrix by the transpose of the centered new space submatrix and the centered old space submatrix.

[0092] Specifically, the old and new spatial sub-matrices are extracted from the paired calibration matrix as follows: all rows of the upper half of the paired calibration matrix are taken to form the old spatial sub-matrice, and all rows of the lower half of the paired calibration matrix are taken to form the new spatial sub-matrice. The number of rows in both the old and new spatial sub-matrices is equal to the semantic vector fingerprint dimension, and the number of columns is equal to the number of samples. Each column of the old spatial sub-matrice is the old version embedding vector of the corresponding text, and each column of the new spatial sub-matrice is the new version embedding vector of the corresponding text.

[0093] The method for performing column mean centering on the old space submatrix is ​​as follows: First, calculate the arithmetic mean vector of all columns of the old space submatrix, that is, add the corresponding components of each column of the old space submatrix component by component and divide by the number of samples to obtain the old space mean vector; then subtract the old space mean vector from each column of the old space submatrix to obtain the centered old space submatrix. The method for performing column mean centering on the new space submatrix is ​​the same: first, calculate the average of each component to obtain the new space mean vector, then subtract the new space mean vector from each column of the new space submatrix to obtain the centered new space submatrix. Here, the old space mean vector refers to the arithmetic mean vector of all columns of the old space submatrix, and its physical meaning is the geometric center of the calibration text set in the old vector space; the new space mean vector refers to the arithmetic mean vector of all columns of the new space submatrix, and its physical meaning is the geometric center of the calibration text set in the new vector space. The mathematical purpose of performing column mean centering is to separate the overall translation components of the two spaces from the coordinates, so that the rotation components can be solved only in terms of the orientation structure. The separated old space mean vector and new space mean vector are reserved for step S223 to construct translation compensation.

[0094] The cross covariance matrix is ​​calculated by multiplying the centered new space submatrix by the transpose of the centered old space submatrix; the product is the cross covariance matrix. The cross covariance matrix is ​​a square matrix with both rows and columns equal to the semantic vector fingerprint dimension. Its element in the i-th row and j-th column measures the degree of co-variance between the i-th coordinate component of the new space and the j-th coordinate component of the old space on the calibration text set, thus characterizing the linear relationship between the coordinate axes of the new and old spaces. The mathematical origin of this construction method—right multiplication of the centered new space submatrix by the transpose of the centered old space submatrix—is the standard solution to the orthogonal Prouke problem. Its purpose is to prepare for extracting the optimal orthogonal rotation matrix from this relationship structure through singular value decomposition.

[0095] The calibration text set consists of two point clouds in the old and new vector spaces. These two point clouds are highly similar in shape but exhibit overall translation and rotation, with corresponding points paired one-to-one. This step first performs column mean centering on both point clouds to remove translation, then calculates the cross covariance matrix to extract rotational correlations. The principle is based on orthogonal Pluke analysis theory, whereby the optimal orthogonal transformation, after removing the centroids of the two paired point clouds, minimizes the sum of squared point-by-point distances between one set of point clouds and the other set. This optimal orthogonal transformation can be given by the singular value decomposition of the cross covariance matrices of the two centered point clouds. The reason for centering before calculating the cross covariance matrix is ​​that if the translation components are not removed first, they will contaminate the estimation of the rotational components. The cross covariance matrix precisely gathers translation-independent directional correlations into a compact matrix. If rotation is directly calculated from the original paired coordinates without first centering, the overall positional deviation of the two point clouds will be incorrectly factored into the rotation estimation, causing the obtained rotational components to deviate from the true directional relationship.

[0096] Since the cross covariance matrix already encapsulates the pure directional correlation between the old and new spatial coordinate axes, step S222 can directly perform singular value decomposition on it to obtain the optimal orthogonal rotation matrix in one step, without iterative optimization. Simultaneously, the output mean vectors of the old and new spaces allow step S223 to construct translation compensation and reconstruct the complete affine transformation including translation, ensuring the correct placement of the geometric center of the migrated vector. This step decouples the translation and rotation components, so that the estimation of the rotation component is no longer affected by the overall positional deviation between the two spaces, thereby improving the alignment accuracy of the affine alignment operator. Thus, step S221 reduces the high-dimensional space alignment problem to a compact problem solvable by a single standard singular value decomposition, providing a key intermediate quantity for low-overhead, high-precision spatial migration of massive historical semantic vectors.

[0097] Step S222: Perform singular value decomposition on the cross covariance matrix to extract the left singular vector matrix and the right singular vector matrix. Calculate the transpose product of the left singular vector matrix and the right singular vector matrix to obtain an orthogonal rotation matrix. Use the orthogonal rotation matrix to perform a rotation transformation on the centered old space submatrix. Calculate the column-by-column difference between the rotated matrix and the centered new space submatrix to obtain the residual matrix.

[0098] Specifically, the singular value decomposition (SVD) of the cross covariance matrix is ​​performed as follows: the cross covariance matrix is ​​decomposed into the product of the left singular vector matrix, the singular value diagonal matrix, and the transpose of the right singular vector matrix. This decomposition is the standard singular value decomposition in linear algebra. The left singular vector matrix is ​​an orthogonal square matrix formed by arranging the left singular vectors of the cross covariance matrix column-wise. The right singular vector matrix is ​​an orthogonal square matrix formed by arranging the right singular vectors of the cross covariance matrix column-wise. The singular value diagonal matrix is ​​a diagonal square matrix with the singular values ​​of the cross covariance matrix arranged in descending order on the diagonal and zero elements on the off-diagonal. The row and column dimensions of all three are equal to the semantic vector fingerprint dimension.

[0099] The orthogonal rotation matrix is ​​calculated by multiplying the left singular vector matrix by the transpose of the right singular vector matrix; the product is the orthogonal rotation matrix. An orthogonal rotation matrix is ​​a square matrix whose number of rows and columns are equal to the semantic vector fingerprint dimension, whose columns are mutually orthogonal, and whose magnitude is one. Physically, it represents the optimal orthogonal transformation that ensures the old spatial orientation structure best coincides with the new spatial orientation structure after rotation. The mathematical origin of constructing the orthogonal rotation matrix by multiplying the left singular vector matrix by the transpose of the right singular vector matrix is ​​the closed-form solution to the orthogonal Prouk problem. This is based on the fact that the solution to the orthogonal Prouk problem is precisely the product of the left and right singular vector matrices in the singular value decomposition of the cross covariance matrix. This construction ensures that the sum of squared column-wise distances between the centered old space submatrix and the centered new space submatrix after transformation by this orthogonal rotation matrix reaches its minimum under the orthogonal transformation constraint. The direction of matrix multiplication is involved here; the orthogonal rotation matrix is ​​calculated using the following formula:

[0100]

[0101] in It is a left singular vector matrix. It is a right singular vector matrix. for transpose, For each of the four elements, the row and column dimensions are equal to the semantic vector fingerprint dimension; For matrix and Determinant of the product, This is a sign function, whose value is positive one when the determinant is positive and negative one when it is negative; This is a diagonal square matrix where the first d-1 elements are all one, and the last element is the symbol value. Its dimension is equal to the dimension of the semantic vector fingerprint. The purpose of introducing this diagonal symbol matrix is ​​to ensure... The determinant of a matrix is ​​always equal to positive one, thus making For a valid rotation matrix that does not involve mirror flips: when When the value is positive 1, the diagonal matrix degenerates into an identity matrix. It equals the product of the left singular vector matrix and the transpose of the right singular vector matrix; when When the value is negative one, the diagonal matrix takes the negative of the last column of the left singular vector matrix to avoid sign flipping in individual dimensions during embedding model version iterations. This degenerates into a mirror transformation, leading to the erroneous flipping and migration of historical semantic vectors. The closed-form solution to the orthogonal Pluke problem shows that the optimal orthogonal rotation matrix is ​​determined only by the left and right singular vector matrices and the aforementioned determinant sign, and is independent of the specific singular values ​​of the singular value diagonal matrix. Therefore, the construction of the orthogonal rotation matrix does not involve the singular value diagonal matrix.

[0102] The rotation transformation of the centered old space submatrix using an orthogonal rotation matrix is ​​as follows: The orthogonal rotation matrix is ​​left-multiplied by the centered old space submatrix, and the product is denoted as the rotated matrix. The rotated matrix is ​​the matrix obtained by rotating each column of the centered old space submatrix using the orthogonal rotation matrix. Its row and column dimensions are the same as those of the centered old space submatrix. Physically, it represents the predicted position of the calibrated text set's centered coordinates in the new space after applying only the optimal rotation. The residual matrix is ​​calculated as follows: Each column of the rotated matrix is ​​subtracted from the corresponding column of the centered new space submatrix, and the residual matrix is ​​obtained by subtracting each column. The residual matrix is ​​a matrix with the same dimensions as the centered new space submatrix, and each column is equal to the corresponding column of the rotated matrix minus the corresponding column of the centered new space submatrix. Physically, it represents the remaining deviation between the predicted position and the actual position in the new space after applying only the optimal orthogonal rotation. Each column of the residual matrix adopts the sign convention of subtracting the true value in the new space from the predicted value after rotation. That is, each column represents the deviation of the rotated predicted position from the true position in the new space. Therefore, in the subsequent step S223, the negative of the low-rank residual approximation matrix is ​​taken to construct the compensation direction. The existence of the residual matrix indicates that the offset between the two vector spaces includes scaling and slight non-rigid distortion components in addition to orthogonal rotation. This part of the deviation cannot be covered by pure rotation and needs to be compensated by low-rank correction in step S223.

[0103] The entity processed in step S222 is the condensed cross covariance matrix of the directional correlation between two vector spaces and the centralized coordinates of the calibration text set. Its directional correlation can be orthogonally decomposed, and non-rigid deviations remain after pure rotation. The left and right singular vector matrices are extracted from the cross covariance matrix through singular value decomposition and multiplied to obtain the orthogonal rotation matrix. The principle is based on the closed form of the orthogonal Prouk problem, namely, the optimal orthogonal transformation that minimizes the sum of squared point-by-point distances between the two sets of centralized paired point clouds under orthogonal transformation constraints. This is equal to the product of the transposes of the left and right singular vector matrices in the singular value decomposition of the cross covariance matrix, aligning the old spatial directional structure to the new space with minimal error. The reason for calculating the residual matrix is ​​that the optimal rotation can only eliminate the rigid rotation component in the offset; the scaling and non-rigid distortion brought about by incremental training of the embedded model will remain as residuals. If only the orthogonal rotation matrix is ​​used as the final transformation without considering the residuals, the vectors after migration will still systematically deviate from their true positions in these directions.

[0104] Since the orthogonal rotation matrix is ​​the closed-form optimal solution to the orthogonal Prouke problem, it allows the old spatial vectors to be aligned to the new spatial orientation structure to the greatest extent possible after rotation, providing the main transformation for subsequent migration. The output residual matrix measures the non-rigid deviations remaining after rotation column by column, enabling step S223 to extract the systematic low-rank correction components from it and superimpose them with the orthogonal rotation matrix, incorporating scale scaling and slight distortion into the alignment. Thus, the spatial offset is decomposed into two parts: an analytically solvable rotation subject and a low-rank approximation residual supplement. The overall alignment accuracy is significantly higher than that of simple rigid body rotation, and the alignment subject and residual characterization are obtained simultaneously in a single singular value decomposition throughout the process, without the need for iterative optimization.

[0105] Step S223: Perform truncated singular value decomposition on the residual matrix to retain the components corresponding to the first few singular values ​​to obtain a low-rank correction matrix. Add the orthogonal rotation matrix and the low-rank correction matrix to obtain a composite transformation matrix. Combine the old space mean vector and the new space mean vector recorded during column mean centering to construct a complete affine transformation including translation compensation. Encapsulate the complete affine transformation as the output of the affine alignment operator.

[0106] Specifically, the method for performing truncated singular value decomposition on the residual matrix to retain the components corresponding to the first few singular values ​​is as follows: First, perform singular value decomposition on the residual matrix to obtain a sequence of singular values ​​arranged in descending order and their corresponding left and right singular vectors; then, starting from the largest singular value, accumulate the squares of the singular values ​​in descending order. Stop when the proportion of the accumulated value to the sum of the squares of all singular values ​​is not less than the energy retention ratio threshold for the first time. The number of singular values ​​accumulated at this point is recorded as the truncated retention rank; reconstruct the residual matrix by retaining the first truncated retention rank singular values ​​and their corresponding left and right singular vectors to obtain a low-rank residual approximation matrix. The energy retention ratio threshold is a parameter used to define how much residual energy to retain. Its physical meaning is the proportion of the bias energy carried by the retained low-rank components to the total bias energy of the residual matrix, and its value range is greater than zero and not greater than one. The energy retention ratio threshold is obtained as follows: In the target deployment scenario, residual matrices corresponding to real embedded model version upgrades are collected multiple times. For each residual matrix, its singular value descending sequence is calculated. The decay pattern from large to small in this sequence is observed to locate the inflection point where the singular values ​​transition from rapid decline to slow and stable. Specifically, the inflection point is determined by successively calculating the absolute difference between two adjacent singular values. When this absolute difference is first less than 5% of the largest singular value, the corresponding singular value position is determined as the inflection point. The proportion of the sum of squares of singular values ​​before the inflection point to the sum of squares of all singular values ​​is calculated, and this proportion is used as the energy retention ratio threshold. This method is based on the fact that the systematic components corresponding to scaling and non-rigid distortion in the residual matrix are concentrated in the first few larger singular values, while the collected noise is dispersed as a large number of smaller singular values. The energy before the inflection point corresponds to the systematic components, and the energy after the inflection point corresponds to the noise. Using the energy proportion at the inflection point as the threshold can precisely retain the systematic components while discarding the noise components, avoiding overfitting to noise.

[0107] Specifically, the low-rank correction matrix is ​​calculated as follows: the low-rank correction matrix is ​​equal to the negative of the low-rank residual approximation matrix multiplied by the Moore-Penrose pseudoinverse of the centered old space submatrix. Since the centered old space submatrix is ​​invertible when its rows are full rank, its product with its transpose is also invertible. Therefore, the low-rank correction matrix is ​​calculated using the following formula:

[0108]

[0109] in It is a low-rank correction matrix, a square matrix with dimensions equal to the number of rows and columns of the semantic vector fingerprint. It is a low-rank residual approximation matrix with dimensions of semantic vector fingerprint dimension rows and sampling number column; For the centered old space submatrix, the dimension and same; for transpose; Let f(x) be a square matrix of rows and columns representing the semantic vector fingerprint. It is invertible when the submatrix of the centered old space has full row rank, and its inverse matrix is ​​denoted as f(x). ; The Moore-Penrose pseudoinverse of the centered old space submatrix is ​​used to extrapolate the residual structure defined on the calibration sample column into a linear operator that can act on any vector in the old space. The low-rank correction matrix is ​​a square matrix with both rows and columns equal to the dimension of the semantic vector fingerprint. Physically, it is a linear operator that compensates for scaling and slightly non-rigid distortion components not covered by the orthogonal rotation matrix. The mathematical purpose of taking the negative and multiplying by the pseudoinverse is that each column of the residual matrix equals the corresponding column of the rotated matrix minus the corresponding column of the centered new space submatrix. To ensure that the composite transformation applied to the centered old space submatrix cancels out this residual and falls onto the centered new space submatrix, a linear operator that acts on the old space vector and produces the negative of the residual needs to be constructed. Therefore, the negative of the low-rank residual approximation matrix is ​​taken and extrapolated through the pseudoinverse to obtain this linear operator. The composite transformation matrix is ​​calculated by adding the orthogonal rotation matrix and the low-rank correction matrix element by element to obtain the composite transformation matrix. The composite transformation matrix is ​​a square matrix whose number of rows and columns are equal to the dimension of the semantic vector fingerprint. Its physical meaning is a linear transformation matrix that simultaneously contains the rotation subject and the low-rank residual compensation. After the vector of the centered old space is transformed by the composite transformation matrix, it approximately falls to the corresponding position in the centered new space.

[0110] Specifically, the method for constructing a complete affine transformation including translation compensation by combining the mean vector of the old space and the mean vector of the new space is as follows: All semantic vectors mentioned in this step are column vectors. For any semantic vector in the old space, first subtract the mean vector of the old space from the semantic vector to obtain its centered coordinates. Then, multiply the centered coordinates by the composite transformation matrix to obtain its centered predicted coordinates in the new space. Finally, add the centered predicted coordinates to the mean vector of the new space to obtain the migration coordinates of the semantic vector in the new space. This complete affine transformation is expressed by the following formula:

[0111]

[0112] in These are semantic vectors in the old space, and they are column vectors. It is a composite transformation matrix; The mean vector of the old space; The mean vector of the new space; The translation coordinates of the semantic vector in the new space are defined as follows: Subtracting the mean vector of the old space and adding the mean vector of the new space constitutes the translation compensation. Physically, this compensation restores the deviation between the geometric centers of the two spaces removed during centering in step S221, ensuring the geometric center of the translation coordinates correctly falls on the geometric center of the new space. The complete affine transformation including translation compensation is encapsulated as an affine alignment operator output. The affine alignment operator is an operator composed of a composite transformation matrix, the mean vector of the old space, and the mean vector of the new space. It can act on any semantic vector in the old space and output its new space translation coordinates. Its data structure is a composite structure containing a square matrix and two vectors.

[0113] The residuals, including the non-rigid biases remaining after rotation and alignment, and the geometric centers of the two vector spaces, show that the systematic and noise components in the residuals are separable on the singular value spectrum, and the geometric centers of the two spaces exhibit definite translations. A truncated singular value decomposition is performed on the residual matrix, retaining only the first few components. This is based on the low-rank approximation theory, where the optimal low-rank approximation of a matrix is ​​reconstructed from its first few largest singular values ​​and corresponding singular vectors. This extracts the systematic scaling and non-rigid distortions carried by large singular values ​​in the residuals and filters out the acquisition noise carried by small singular values, resulting in a low-rank correction matrix that compensates for both systematic biases and overfitting noise. The Moore-Penrose pseudo-inverse of the centered old space submatrix is ​​used to extrapolate the residuals on the samples to a full-space linear operator. This is because the pseudo-inverse provides the least-squares linear mapping that best matches the sample residuals, allowing the correction to be extended to any historical semantic vector outside the calibration text set. Superimposed translation compensation then replenishes the definite geometric center biases stripped away during the centered stage, ensuring the absolute position of the migrated coordinates is correct.

[0114] The affine alignment operator, by simultaneously encapsulating rotation, low-rank residual compensation, and translation compensation, enables any historical semantic vector in the old space to be accurately transferred to the new vector space defined by the new version of the embedding model. As a reusable operator output, step S23 can perform a one-time batch transfer of all historical semantic vectors at the cost of only one matrix operation without calling the new version of the embedding model line by line. Because the low-rank correction filters out acquisition noise, the operator maintains stable accuracy on vectors outside the calibration text set. Thus, the fundamental dilemma in the background technology—where historical and new vectors belong to heterogeneous spaces due to embedding model upgrades, leading to cross-space comparison failures—is transformed into a regular comparison within the same space, eliminating the batch generation of invalid versions at its source. Furthermore, the alignment accuracy of the operator, in the form of the average alignment residual, participates in the determination of the dynamic judgment threshold in step S23, allowing the alignment quality to be directly fed back to the judgment leniency.

[0115] like Figure 6 The diagram shown is a coupling adjustment diagram for the dynamic determination threshold.

[0116] Reference Figure 6 The diagram illustrates a cosine similarity axis with a horizontal arrow. The left end is labeled -1, and the right end is labeled 1. The right end is represented by a horizontal line indicating the positive region, and the arrowhead is labeled with the cosine similarity. A solid circle marks the base threshold on the axis, with its position indicated by a solid vertical line. To the right of the base threshold, a hollow circle marks the dynamic threshold, with its position indicated by a dashed vertical line. This dynamic threshold is obtained by adjusting the base threshold. Above the axis, a larger mean alignment residual is indicated by an arrow pointing to the left, indicating a more lenient direction. This means that a larger mean alignment residual shifts the dynamic threshold towards lower similarity. Below the axis, a lower category coverage is indicated by an arrow pointing to the right, indicating a more conservative direction. This means that a lower category coverage shifts the dynamic threshold towards higher similarity. The lower left corner of the diagram is marked as "less than the threshold" and "substantial change," while the lower right corner is marked as "not less than the threshold" and "non-substantial change." This indicates that when the current cosine similarity is less than the dynamic judgment threshold, it is judged as a substantial change, and when it is not less than the dynamic judgment threshold, it is judged as a non-substantial change.

[0117] Step S23: Perform batch spatial migration transformation and normalization on all historical semantic vectors in the version control table using the affine alignment operator, calculate the cosine similarity between the current semantic vector fingerprint of the hierarchical list object and the migrated semantic vector fingerprint of the previous version, and perform version determination based on a dynamic determination threshold to obtain the version determination result; wherein, the dynamic determination threshold is determined by coupling the mean of the alignment residual of the affine alignment operator and the category coverage of the calibration text set. The larger the mean of the alignment residual, the more relaxed the dynamic determination threshold is adjusted, and the lower the category coverage, the more conservative the dynamic determination threshold is adjusted.

[0118] Specifically, the method of performing batch spatial migration transformation and normalization on all historical semantic vectors in the version control table using the affine alignment operator is as follows: For each historical semantic vector stored in the version control table, the complete affine transformation described in step S223 is performed sequentially to obtain its new spatial migration coordinates. Then, each component of the new spatial migration coordinates is divided by the Euclidean norm of the migration coordinates to complete normalization, resulting in the migrated previous version semantic vector fingerprint corresponding to the historical semantic vector. The migrated previous version semantic vector fingerprint is temporarily stored in memory. After step S24 completes the alignment quality verification and confirms that the alignment is qualified, it is uniformly written into the version control table to overwrite the original semantic vector fingerprint of the record. If step S24 determines that the alignment is unqualified, the migration result in memory is discarded, and the process of full recalculation described in step S24 is initiated. Here, the migrated previous version semantic vector fingerprint refers to the vector obtained after the historical semantic vector is migrated and normalized by the affine alignment operator, which falls on the unit hypersphere of the vector space of the new version embedding model. The derivation that the magnitude of the migrated coordinates after normalization is one is as follows: Let the migrated coordinates be vectors y, and let their Euclidean norm be a non-zero positive number. The normalization result is y divided by its Euclidean norm. Then the Euclidean norm of the normalized result is equal to the Euclidean norm of y divided by the Euclidean norm of y, and its value is always equal to one. Therefore, the magnitude of the previous version of the semantic vector fingerprint after migration is one, and it is located on the same unit hypersphere as the current semantic vector fingerprint. The magnitudes of the two are consistent, which provides the basis for directly calculating the dimensions and magnitude of cosine similarity.

[0119] The cosine similarity between the current semantic vector fingerprint and the transferred previous version semantic vector fingerprint is calculated using the cosine similarity formula given in step S13. Simply replace vector b in the formula with the transferred previous version semantic vector fingerprint. The resulting cosine similarity is denoted as the current cosine similarity. Since both the current semantic vector fingerprint and the transferred previous version semantic vector fingerprint are normalized and reside in the same new version embedding model vector space, their cosine similarity restores the semantic distance meaning. Its value falls between -1 and 1, approaching 1 for cases where the text semantics are similar.

[0120] Specifically, the determination of the dynamic judgment threshold is expressed step by step by splitting intermediate variables according to the rules. First, two input quantities are defined: the mean of the alignment residuals and the class coverage. The mean of the alignment residuals refers to the average of the relative residuals of orthogonal rotation alignment on the training subset. It is used to measure the degree of deviation between the old space and the new space that needs to be compensated by low-rank correction. Its physical meaning is the level of relative deviation that remains after applying only orthogonal rotation and needs to be compensated by low-rank correction. The larger the mean of the alignment residuals, the more serious the non-rigid distortion and the less reliable the overall alignment. It should be noted that the mean of the alignment residuals represents the intensity of non-rigid deviation before low-rank correction. It is only used as a surrogate quantity to measure the degree of distortion introduced by this embedding model version upgrade to adjust the judgment strictness. The actual alignment effect after the affine alignment operator applies low-rank correction and translation compensation is independently evaluated by the alignment quality index in step S24. The two are in different stages of the alignment process and have different measurement objects. Mean of alignment residuals Calculate using the following formula:

[0121]

[0122] The reason for using a formula here is that the calculation requires summing the relative residuals of all samples in the training subset, and the summed term itself is an independent operation of a norm ratio. A verbal description cannot determine the boundaries of the scope of each calculation, hence the need for a formula. The formula is constructed based on the principle that each column of the residual matrix measures the absolute deviation of the corresponding sample after rotation correction. Dividing this by the norm of the sample's new centered spatial coordinates yields the relative deviation unaffected by the sample's own scale. Taking the arithmetic mean of the relative deviations of all samples gives the overall mean of the alignment residuals. In the formula, The number of samples for the training subset is equal to three times the dimension of the semantic vector fingerprint, which is the number of samples actually involved in solving steps S221 to S223. The residual matrix output in step S222 is the first... List, The first digit of the centered new space submatrix output in step S221 List; The Euclidean norm of a vector. When the coordinates of a sample are centered in a new spatial space... When the Euclidean norm approaches zero, skip the sample and do not include it in the summation; accordingly, Subtract one to avoid the relative residuals diverging as the denominator approaches zero. The physical consistency of this formula lies in the fact that the relative deviation is a dimensionless ratio, and the mean of the alignment residuals obtained by averaging them is also a dimensionless ratio, with a range of non-negative real numbers. The larger the value, the worse the alignment, which is consistent with the physical expectation of using residuals to measure alignment quality.

[0123] Category coverage refers to the ratio of the number of intent category and tag category combinations actually covered by the calibration text set to the total number of intent category and tag category combinations existing in the version control table. Physically, it represents the degree of coverage of the entire semantic region by the calibration sampling. Its value ranges from greater than zero to no greater than one; a larger value indicates more complete coverage and higher statistical confidence of the sampling. Category coverage equals the number of different intent category and tag category combinations appearing in the calibration text set divided by the number of different intent category and tag category combinations appearing in the version control table. Furthermore, category coverage gap is defined as one minus category coverage. Its physical meaning is the proportion of semantic regions not covered by the calibration sampling, with a value range greater than or equal to zero and less than one.

[0124] Before determining the dynamic judgment threshold, the basic judgment threshold is first determined. The basic judgment threshold refers to the cosine similarity threshold used to distinguish between substantive and non-substantive changes under the ideal situation of completely reliable alignment and complete category coverage. Its physical meaning is the semantic similarity boundary that distinguishes the two types of changes. The basic judgment threshold is obtained using a reverse calculation method constrained by the false positive rate: multiple sets of capability list submission sample pairs, manually confirmed as non-substantive changes, are collected as positive class samples, and multiple sets of capability list submission sample pairs, manually confirmed as substantive changes, are collected as negative class samples. Here, the positive class refers to samples whose content has not undergone substantial change and should be judged as non-substantive changes, and the negative class refers to samples whose content has undergone substantial change and should be judged as substantive changes. The current cosine similarity of each sample pair is calculated, and the mean and standard deviation of the current cosine similarity of the positive class samples and the negative class samples are statistically analyzed. When the current cosine similarity distributions of both classes of samples approximately follow a normal distribution, the basic judgment threshold is calculated using the following formula:

[0125]

[0126] in Based on the threshold; and These are the mean and standard deviation of the current cosine similarity for positive class samples, respectively. and These represent the mean and standard deviation of the current cosine similarity for the negative class samples, respectively. This formula gives the similarity value that makes the standardized distances from the threshold equal for both approximately normal distributions. Specifically, at this value, the ratio of the deviation of the positive class mean from the threshold to its standard deviation is equal to the ratio of the deviation of the negative class mean from the threshold to its standard deviation, thus achieving a balance in the misclassification tail probabilities of both classes under the weighted average of standard deviations. For example, under the condition that the two sample distributions are approximately normal and partially overlap, the basic judgment threshold is taken from the value obtained by the above formula.

[0127] Specifically, the dynamic judgment threshold is calculated by adding two adjustments to the basic judgment threshold. The residual adjustment term equals the residual weight coefficient multiplied by the mean of the alignment residuals, and the coverage adjustment term equals the coverage weight coefficient multiplied by the category coverage gap. The dynamic judgment threshold equals the sum of the basic judgment threshold and the coverage adjustment term minus the residual adjustment term. If the above result exceeds the range of zero to one, it is truncated to the boundary of the range, i.e., zero is taken when the result is less than zero and one is taken when the result is greater than one, to ensure that the comparison between the dynamic judgment threshold and cosine similarity has practical significance. Among them, the residual weight coefficient is a weighting factor that converts the mean of the alignment residuals into the threshold reduction magnitude, and its physical meaning is the threshold relaxation amount corresponding to a unit of alignment residual; the coverage weight coefficient is a weighting factor that converts the category coverage gap into the threshold increase magnitude, and its physical meaning is the threshold tightening amount corresponding to a unit of coverage gap. The residual weight coefficient and coverage weight coefficient are obtained through an on-site calibration process: In the target deployment scenario, several sets of submitted samples with known real change types and different average alignment residuals and different category coverages are constructed. For each set of samples, a dynamic judgment threshold is calculated using candidate residual weight coefficients and coverage weight coefficients, and version judgment is performed. The proportion of samples whose version judgment results are inconsistent with the real change type is statistically analyzed. With the goal of minimizing this inconsistency proportion, the values ​​of the residual weight coefficient and coverage weight coefficient are searched and determined among the candidate values. The larger the average alignment residual, the larger the residual adjustment term and the lower the dynamic judgment threshold. This means that only when the current cosine similarity is lower will it be judged as a substantial change, i.e., the judgment is adjusted in a more lenient direction. The reason is that when the alignment is less reliable, the cosine similarity itself contains more alignment error. Relaxing the threshold can avoid mistaking alignment error as substantial change. The larger the category coverage gap, the larger the coverage adjustment term and the higher the dynamic judgment threshold. This means that it is easier to judge as a substantial change, i.e., the judgment is adjusted in a more conservative direction. The reason is that when the coverage is insufficient, the migration results have low confidence in the uncovered area. Tightening the threshold can avoid missing real substantial changes.

[0128] Specifically, before performing the final version comparison, the comparability of the two compared values ​​is verified. The two compared values ​​are the current cosine similarity and the dynamic judgment threshold. In terms of physical comparability, the current cosine similarity measures the closeness of the fingerprint directions of two semantic vectors, while the dynamic judgment threshold is a boundary set on the same cosine similarity metric. The basic judgment threshold is obtained statistically from the current cosine similarity samples, and both adjustments are converted in units of cosine similarity; therefore, they belong to the same metric dimension. Regarding numerical magnitude matching, the current cosine similarity value falls between -1 and 1. The basic judgment threshold is obtained by weighted averaging of the current cosine similarity of the real samples, and therefore must fall between the means of the two classes of samples, thus falling between -1 and 1. The residual adjustment term and the coverage adjustment term, after being calibrated by the residual weight coefficient and the coverage weight coefficient, are constrained to ensure that the dynamic judgment threshold does not exceed the range of zero to one. Therefore, the ranges of the two values ​​overlap, making the comparison meaningful for practical judgment. Regarding consistency in boundary behavior, when the mean alignment residual approaches zero and the category coverage gap approaches zero, the dynamic judgment threshold approaches the basic judgment threshold. When the mean alignment residual increases, the dynamic judgment threshold decreases; when the category coverage gap increases, the dynamic judgment threshold increases. In extreme cases, the magnitude relationship is consistent with the expectation of relaxation or tightening. All three validations passed, and comparisons can be performed. The judgment criteria for version determination are: when the current cosine similarity is less than the dynamic judgment threshold, it is determined as a substantial change; when the current cosine similarity is greater than or equal to the dynamic judgment threshold, it is determined as a non-substantial change. That is, when the current cosine similarity is exactly equal to the dynamic judgment threshold, it is classified as a non-substantial change. A conservative attribution of not generating a new version is adopted for boundary cases to suppress invalid versions, which is consistent with the conservative attribution principle when determining the basic judgment threshold. This judgment conclusion is recorded as the version judgment result output.

[0129] Step S23 processes the coordinates of the current capability list and the previous version's capability list in the new version's embedded model vector space, as well as the mean of the alignment residuals and the class coverage, which characterize the alignment reliability and sampling confidence. These two values ​​fluctuate with the model upgrade magnitude and uneven class distribution. The historical semantic vector is transferred and normalized using an affine alignment operator, so that it is located on the same unit hypersphere as the current semantic vector fingerprint. Then, cosine similarity is used as a measure. Based on the property that the cosine similarity on the unit hypersphere is equal to the inner product, the two are guaranteed to have the same dimensionality and can be directly compared. The dynamic decision threshold is coupled with the mean of the alignment residuals and the class coverage because the reliability of cosine similarity itself depends on the transfer accuracy and sampling coverage. A fixed threshold cannot reflect this fluctuation: when the mean of the alignment residuals is large, the cosine similarity contains more alignment errors, so the threshold is lowered to tolerate the errors; when the class coverage is low, the transfer confidence of the uncovered areas is low, so the threshold is raised to prevent missed judgments, thus making the decision line adaptively move with the alignment and sampling states.

[0130] The version determination result is based on the cosine similarity of normalized vectors within the same vector space, ensuring that the determination result is consistent with the actual changes in the capability list. This corrects the counterintuitive phenomenon in the background technology where minor content changes were judged as substantial changes and major content changes as non-substantial changes. The mean of alignment residuals is coupled into the dynamic determination threshold, automatically widening the threshold when the alignment residuals are high, avoiding misjudging alignment errors as substantial changes. Category coverage is coupled into the threshold, automatically tightening it when sampling is insufficient, avoiding missing true changes in insufficiently sampled areas. Therefore, version determination neither artificially inflates versions due to alignment errors nor misses versions due to insufficient sampling. The situation in the background technology where massive historical lists were misjudged and a massive number of invalid version numbers were generated instantaneously after model upgrades is completely suppressed. This result is then used by step S31 to determine whether to increment the revision number.

[0131] Step S24: Extract a verification subset from the calibration text set, calculate the mean cosine similarity between the old version embedding vector and the corresponding new version embedding vector in the verification subset after migration as the alignment quality index. When the alignment quality index is lower than the preset alignment quality lower limit, discard the migration result of the affine alignment operator, call the new version embedding model to recalculate the semantic vector fingerprint for all historical fingerprint source texts in the version control table and overwrite it.

[0132] Specifically, step S24 independently verifies the alignment effect of the affine alignment operator and triggers a fallback path of full recalculation at the underlying level when alignment fails. The verification subset consists of samples that were reserved hierarchically from the version control table by intent and label categories during sampling in step S21, and which are not involved in the solution processes of steps S221 to S223. The number of these samples is not included in the sampling count, but the number of samples in each intent and label category is no less than the minimum number of samples required for the verification subset. The verification subset is independent of the solution process, thus avoiding overestimation of alignment quality due to self-verification on the samples used for solution. The old and new version embedding vectors of each sample in the verification subset reuse the results collected and normalized in step S21, eliminating the need to repeatedly call the embedding model. The verification subset refers to a subset of samples in the calibration text set that do not participate in the affine alignment operator solution and are dedicated to evaluating alignment quality. Its data structure is a sample subset, with each sample carrying both its old and new version embedding vectors. The validation subset partitioning must ensure that it contains at least the minimum number of samples required for each intent category and label category. The minimum number of samples required for the validation subset partitioning is determined as follows: An upper limit for the sampling standard error of the alignment quality metric is set. Based on the statistical relationship that the sampling standard error of the sample mean equals the sample standard deviation divided by the square root of the sample size, the minimum number of samples required to ensure that the sampling standard error does not exceed this upper limit is calculated. This minimum number of samples is taken as the minimum number of samples required for the validation subset partitioning. The required sample standard deviation is estimated using the standard deviation of the cosine similarity of all sample pairs in the calibration text set as a priori estimate. When this prior estimate is unavailable, a conservative upper bound estimate of 0.5 is used. The upper limit for the sampling standard error is a preset percentage of the allowable error range of the alignment quality metric.

[0133] The alignment quality index is calculated as follows:

[0134]

[0135] The principle behind this formula is that ideal alignment should ensure that the orientation of the old version embedding vector after migration is highly consistent with that of the corresponding new version embedding vector, i.e., the cosine similarity should approach one. The average cosine similarity of all samples in the validation subset yields the overall degree to which the alignment approximates the ideal. In the formula... To align quality metrics, To verify the sample size of the subset; To verify the subset number The old version of the embedding vector after sample transfer To verify the subset number The new version of the embedding vector for each sample is derived from step S21; The cosine similarity is defined in step S13. This is the result of normalizing the old version's embedded vector after affine alignment operator transfer. The new version of the embedding vector, which has been normalized in step S21, can be directly used with the cosine similarity formula expressed by the inner product as described in step S13. The physical consistency of this formula lies in the fact that both the old and new version embedding vectors after migration lie on the unit hypersphere, the cosine similarity value falls between -1 and 1, the better the alignment, the closer the mean is to 1, and the alignment quality index, as the mean, also falls between -1 and 1, with consistent dimensions and magnitude.

[0136] The alignment quality lower bound is obtained using the median method based on sample statistics: multiple sets of successfully aligned validation cases are collected as positive class samples, and multiple sets of unaligned validation cases are collected as negative class samples. Here, the positive class refers to successfully aligned cases where the migrated vectors can be used normally for version determination, and the negative class refers to unaligned cases where the migrated vectors are no longer usable and must be recalculated. For each set of cases, its alignment quality index is calculated, and the lower bound of the distribution of the alignment quality index for positive class samples and the upper bound of the distribution of the alignment quality index for negative class samples are statistically analyzed. The median between the two is taken as the alignment quality lower bound. The lower bound of the distribution of the alignment quality index for positive class samples is taken as the 10th percentile of the alignment quality index for positive class samples, and the upper bound of the distribution of the alignment quality index for negative class samples is taken as the 90th percentile of the alignment quality index for negative class samples. The statistical rationale for selecting the 10th and 90th percentiles is that the 10th to 90th percentiles cover approximately 80% of the main interval of the sample distribution, and approximately 10% of extreme values ​​at each end are excluded, thus representing the typical alignment quality of each type of sample with a robust distribution boundary without being affected by individual abnormal cases. When the alignment quality metric is exactly equal to the lower limit of alignment quality, it is classified as the side of successful alignment, that is, no rollback is triggered, so as to avoid frequent and expensive full recalculation near the boundary.

[0137] Specifically, when the alignment quality index is below the lower limit, the affine alignment operator is deemed to have failed, the migration result temporarily stored in memory is discarded, and a full recalculation and overwrite is performed at the underlying level. When the alignment quality index is not below the lower limit, the alignment is deemed successful, and the migrated semantic vector fingerprints of the previous version temporarily stored in memory are uniformly written to the version control table to overwrite the original semantic vector fingerprints of each record. The physical reason for discarding the migration result and performing a full recalculation is that when the alignment quality index is below the lower limit, it indicates that the offset between the two vector spaces has exceeded the range that can be characterized by orthogonal rotation plus low-rank correction, that is, there is a strong nonlinear distortion that the affine alignment operator cannot cover. In this case, continuing to use the migration result will distort the migrated semantic vector fingerprint itself, thereby polluting the version determination, so the migration result must be discarded. The method for recalculating semantic vector fingerprints and overwriting them by calling the new version embedding model to rewrite the fingerprint source text of all historical records in the version control table is as follows: traverse each historical record in the version control table, extract the fingerprint source text from the fingerprint source text field of that record, call the new version embedding model to generate a new semantic vector for the fingerprint source text in the manner described in step S12, that is, after the new version embedding model outputs a real number vector with a dimension equal to the semantic vector fingerprint dimension, divide each component of the vector by the Euclidean norm of the vector to complete the normalization, use the normalization result to overwrite the semantic vector fingerprint field of the record, and update the version identifier of the record to the current version identifier. Although full recalculation has a high calling cost, it directly reconstructs all historical semantic vector fingerprints in the new space using the new version embedding model, fundamentally ensuring the same spatiality of subsequent comparisons, serving as a fallback path in case of alignment failure. Since the version determination result of step S23 is based on the distorted semantic vector fingerprint of the previous version after migration, and its reliability has been determined to be unqualified by this step, after completing the above full recalculation and overwrite, the version determination needs to be re-executed to replace the distorted determination result of step S23: read the semantic vector fingerprint of the previous version corresponding to the capability identifier from the version control table after overwrite. This vector has been reconstructed in the new space by the new version embedding model and is in the same vector space as the current semantic vector fingerprint. Calculate the cosine similarity between the current semantic vector fingerprint and the semantic vector fingerprint of the previous version, and compare the cosine similarity with the basic determination threshold. When the cosine similarity is less than the basic determination threshold, it is determined to be a substantial change; when it is greater than or equal to the basic determination threshold, it is determined to be a non-substantial change. The conclusion of this re-determination is used as the final version determination result output by this step, replacing the version determination result output by step S23 for use by step S30. The reason for using a basic decision threshold instead of a dynamic decision threshold here is that the full recalculation has ensured that the historical semantic vector and the current semantic vector are precisely in the new vector space, and there is no uncertainty caused by alignment residuals and insufficient sampling coverage. Therefore, there is no need to add residual adjustment terms and coverage adjustment terms.

[0138] The affine alignment operator's transfer performance on independent validation samples varies with the degree of nonlinear distortion between the two spaces, and self-validation can overestimate the alignment quality. An independent validation subset, separate from the solution process, is drawn from the calibration text set to evaluate alignment quality. Based on the statistical proposition of separating training and validation—evaluating on samples involved in the fitting process will overestimate performance due to overfitting—evaluation on independent samples not involved in the fitting process is necessary to obtain an unbiased estimate, ensuring the alignment quality metric accurately reflects the generalization alignment performance of the affine alignment operator on unknown samples. Using an alignment quality metric below the lower limit as a backoff trigger condition is because this metric measures the directional consistency between the transferred vector and the true new space vector. When it falls below the boundary obtained from the statistics of successful and failed alignment samples, it indicates that the strong nonlinear distortion in the residuals has exceeded the characterization capability of low-rank correction. Continuing to use the transfer results at this point will pollute the version judgment, therefore the transfer must be abandoned and the entire dataset recalculated.

[0139] Because the alignment quality metric is calculated on an independent validation subset, the evaluation of the generalization effect of the affine alignment operator remains objective, unaffected by overfitting of the solution samples. When the metric is not lower than the lower limit, low-cost batch migration results are used, enabling most version upgrades to complete historical vector alignment with a single matrix operation. When the metric is lower than the lower limit, it automatically reverts to a full recalculation and overwrite using the new version's embedding model, ensuring that correct co-space semantic vector fingerprints are still obtained even in scenarios with strong nonlinear distortions. Thus, regardless of whether the spatial offset is a mild affine offset or a severe nonlinear distortion, the historical semantic vector fingerprint on which version determination depends always resides in the same vector space as the current fingerprint, guaranteeing the absolute validity of the comparison. The fast migration path and the full recalculation path are automatically selected based on the alignment quality, saving the cost of embedding model calls in most cases and maintaining the bottom line of judgment correctness in extreme cases, preventing the generation of invalid versions in batches due to alignment distortion from the source.

[0140] Step S30: Based on the version determination result, perform version record update. When responding to a read request, prioritize reading the large fields of the list and perform field-level backfill verification, and output the capability list service view.

[0141] Specifically, step S30 determines how to update the version record based on the version determination result and reorganizes the layered list object in a fault-tolerant manner when providing the capability list service view externally. Step S30 receives the version determination result and the layered list object stored in the database in step S13 as input. When the embedded model version has not changed, the version determination result is output by step S13. When the embedded model version has changed and the alignment is qualified in step S24, the version determination result is output by step S23. When the embedded model version has changed but the alignment is unqualified in step S24 and a full recalculation is triggered, the version determination result is obtained by re-determining after a full recalculation in step S24. Step S30 includes steps S31, S32, and S33.

[0142] Step S31: Receive the version determination result. If the version determination result indicates a substantial change, increment the revision number and write the new version record into the version control table. If the version determination result indicates a non-substantial change, only update the access timestamp.

[0143] Specifically, the version determination result output in steps S23, S24, or S13 is used as input. The determination condition is directly taken from the version determination result: when the version determination result indicates a substantial change, the current revision number of the capability identifier is incremented by one to obtain a new revision number, and a new version record containing the capability identifier, the new revision number, the current version identifier, the current semantic vector fingerprint, the current fingerprint source text, and the current timestamp is written into the version control table. The current fingerprint source text is the fingerprint source text concatenated in step S12 and written in step S13, to ensure that each record in the version control table completely contains the fingerprint source text field for sampling in step S21 and full recalculation in step S24 when the embedded model version changes; when the version determination result indicates a non-substantial change, the revision number is not incremented, and no new version record is written, but only the access timestamp of the record corresponding to the capability identifier is updated to the current timestamp. The revision number is a monotonically increasing integer that identifies the sequential order of versions under the same capability identifier. Physically, it represents the version number of the capability list. The revision number is set to one when the initial version record of the capability identifier is written, and is incremented by one with each subsequent substantive change. The access timestamp is the time value that records the most recent submission or access to the capability list, representing its activity level. The physical reason for incrementing the revision number only on substantive changes, rather than with every submission, is that the version number semantically identifies the substantial evolution of the content. Non-substantive changes do not alter the content's essence; incrementing the revision number for non-substantive changes would generate a large number of redundant versions unrelated to the actual content evolution. Therefore, non-substantive changes only update the access timestamp to record activity without polluting the version sequence.

[0144] Step S31 processes the version evolution sequence of the capability list. In this scenario, version number resources are limited, and downstream calling networks establish calling relationships based on version numbers. The version determination result is used as the sole basis for whether to increment the revision number. Based on the principle that the version number should correspond one-to-one with the substantial content evolution—each increment of the version number should correspond to a meaningful content change; otherwise, the version number will lose its function of identifying evolution—the revision number is only incremented when a substantial change is determined, ensuring that the version sequence strictly corresponds to the substantial content evolution. For non-substantial changes, only the access timestamp is updated. Since activity and version evolution are two types of orthogonal information, using the access timestamp to carry the activity both preserves this information and does not interfere with the version sequence.

[0145] New version records are added only when there are substantial changes, ensuring that the version sequence strictly corresponds to the actual content evolution. Downstream calling networks do not need to rebuild calling relationships for non-substantial changes such as format tweaks. Non-substantial changes only update the access timestamp, allowing activity information to be retained without consuming version number resources. Combining the judgment result corrected in step S23, step S31 further ensures, from the implementation stage, that the situation of generating a massive number of invalid version numbers and rapidly exhausting storage resources in the background technology will no longer occur. The version control table always accumulates version records with semantic value, and provides an accurate index for locating the correct version in steps S32 and S33.

[0146] Step S32: When responding to a read request for the capability identifier, the large fields of the list are read first and deserialized. Non-empty checks are performed on the key fields of the deserialization result. If a field is detected to be missing, data is read from the corresponding flat column and backfilled.

[0147] Specifically, step S32 receives a read request for the capability identifier as input, and its function is to restore a complete and usable hierarchical list object in a fault-tolerant manner. The method of prioritizing reading the large list field and performing deserialization is as follows: based on the capability identifier in the read request, locate the corresponding record in the storage table. The large list field is located in the storage table. Prioritize reading the large list field of this record, and reverse-decode the byte sequence stored in the large list field according to the serialization format in step S13 to restore it to a hierarchical list object. This decoding process is deserialization. The method of performing non-empty checks on the key fields of the deserialization result is as follows: check each predefined key field in the deserialization result. The key fields at least include the capability identifier, capability name, intent category, and tag category of the discovery layer. The non-empty check condition is that the value of the key field is neither missing nor an empty placeholder value; when the value of a key field is missing or an empty placeholder value, the field is determined to be missing. If a field is detected as missing, the method for backfilling by reading data from the corresponding flat column is as follows: For each critical field determined to be missing, its scalar value is read from the database flat column corresponding to that field in the storage table, and this value is written to the corresponding field position in the deserialization result to complete the backfilling. The physical reason for backfilling from the flat column instead of directly reporting an error is that: as a variable-length object, the large fields in the list may become unavailable in extreme cases due to storage media damage or deserialization decoding anomalies. However, the critical fields of the discovery layer are also redundantly stored in the database flat column in scalar form. Therefore, the field can be read from the flat column to repair the deserialization result, ensuring that the read request can still return usable discovery layer information.

[0148] The same capability list is represented in two separate storage tracks: a large field in the list and a flat column in the database. In this scenario, the large field restores complete content but is occasionally unavailable, while the flat column has limited fields but is stable and indexable. Prioritizing reading the large field and backfilling from the flat column when missing information is found, based on the fault tolerance principle of redundant storage—when the same information is redundantly stored on two independent storage paths, a failure in one can be compensated by the other—the dual storage of key fields in the discovery layer on both the large field and the flat column serves as redundancy. Using non-empty and non-empty placeholder values ​​in key fields as the condition for backfilling is because empty placeholder values ​​represent the actual state that the merchant has not yet filled in and should not be masked by backfilling; only missing information requires backfilling.

[0149] The hierarchical list object, after backfilling verification, is preferentially taken from the complete large fields of the list, ensuring that read requests in most cases restore the complete object containing the discovery and detail layers in one go. When key fields are missing during the deserialization of large fields, backfilling is performed from flat columns, preventing read requests from failing due to occasional anomalies in a single storage track, and ensuring that the discovery layer information is always visible. Backfilling only targets missing fields and does not overwrite empty placeholder values, ensuring that the actual state of the merchant that has not yet been filled in is accurately preserved. Thus, high availability for reads is achieved through dual-track redundancy. Even if the anomaly described in the background affects the large fields of individual records, it will not lead to an interruption of external services. This object is then used by step S33 to determine the capability status.

[0150] Step S33: Check whether the details layer of the hierarchical list object contains valid data. If the details layer is not empty, mark the capability status as details ready. If the details layer is empty, mark the capability status as discoverable only. Output the capability list service view carrying the capability status mark.

[0151] Specifically, step S33 receives the backfilled and validated hierarchical list object output from step S32 as input. Its function is to mark the capability status and assemble the external capability list service view based on the valid data of the detail layer. The criterion for checking whether the detail layer of the hierarchical list object contains valid data is as follows: count the number of fields in the detail layer of the hierarchical list object whose values ​​are neither missing nor empty placeholders, and record this as the valid field count of the detail layer. When the valid field count of the detail layer is greater than zero, the detail layer is determined to be non-empty, i.e., containing valid data. When the valid field count of the detail layer is equal to zero, the detail layer is determined to be empty, i.e., not containing valid data. The capability status is marked as follows: when the detail layer is determined to be non-empty, the capability status is marked as "details ready," indicating that the capability can be discovered and has complete call details; when the detail layer is determined to be empty, the capability status is marked as "discoverable only," indicating that the capability can be retrieved and discovered but does not yet have call details. The capability status flag is an enumerated flag attached to the capability list service view to characterize the current availability of the capability. Its value is either "details ready" or "discoverable only," serving as the basis for downstream callers to determine whether the capability can be directly invoked. The capability list service view carrying the capability status flag is output by assembling the discovery layer fields, detail layer fields, and capability status flags of the backfilled and validated hierarchical list object into a capability list service view and returning it externally. The capability list service view is a structured view object provided externally, carrying capability discovery information, capability invocation information, and capability status flags. Its data structure is a composite object containing discovery layer information, detail layer information, and capability status flags.

[0152] Step S33 processes the completeness of the details layer data in the capability list. In this scenario, the details layer allows for missing data to be added later, and downstream callers need to know in advance whether the capability can be directly invoked. The determination of the capability status is based on whether the count of valid fields in the details layer is greater than zero. According to the design that the empty placeholder value and valid data can be distinguished, that is, when the missing details layer is empty in step S11, a specific flag value is used to distinguish it from the normal value, so that the valid data and the unfilled state can be strictly distinguished at the data level. Therefore, the number of non-empty placeholder value fields is counted to accurately determine whether the details layer actually contains valid data. The availability is distinguished by two flags: "details ready" and "only discoverable". This is because downstream users should only search and display capabilities that can only be discovered, and only capabilities that are ready can be invoked.

[0153] The output capability list service view, carrying capability status flags, accurately distinguishes between two states: "details ready" and "discoverable only." This allows downstream callers to determine whether a capability has complete call details before making a call, avoiding invalid calls to capabilities without details. Since the flags are derived based on a definite mathematical condition—the count of valid fields in the details layer—capabilities containing only discovery layer information can still be retrieved and discovered normally without being misjudged as unavailable. Thus, the business requirement of supplementing missing details at the later stage and the requirement of completeness verification before downstream calls are simultaneously satisfied. As the final output of this method, the capability list service view integrates the content consistency, version correctness, and high availability ensured by the preceding steps into a stable and usable service capability. The various apparent problems described in the background technology are resolved one by one at each stage of the data flow.

[0154] Example 2

[0155] The hierarchical data hybrid storage system based on semantic vector fingerprints includes a normalization access module, which is used to obtain the original capability documents and call a pre-trained language model to parse the capability documents into hierarchical list objects.

[0156] The hierarchical storage module is used to write fields from the discovery layer into flat columns in the database and serialize hierarchical list objects into large list fields.

[0157] The version control module is used to detect the version identifier of the embedded model based on the hierarchical list object. When the version identifier changes, a pairing calibration matrix is ​​constructed. The pairing calibration matrix is ​​subjected to orthogonal rotation alignment and residual correction based on singular value decomposition to obtain an affine alignment operator. The affine alignment operator is used to perform a spatial migration transformation on the historical semantic vector. The version determination is performed based on the migrated semantic vector to obtain the version determination result.

[0158] The read normalization module is used to perform version record updates based on the version determination results. When responding to a read request, it prioritizes reading the large fields of the list and performs field-level backfill validation, and outputs the capability list service view.

[0159] Example 3

[0160] The storage medium can be a tangible medium capable of storing program code, such as a read-only memory, random access memory, disk, optical disk, or solid-state storage. When the processor reads and executes the computer program from the computer-readable storage medium, it sequentially completes the capability document access and hierarchical database writing in step S10, the cross-version space alignment and version determination in step S20, the version record update and reading reorganization in step S30, and all the sub-steps described in steps S11 to S13, S21 to S24, and S31 to S33, thereby achieving all the technical effects of the aforementioned method at the program product level.

[0161] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0162] The foregoing description is illustrative of the invention and should not be construed as limiting it. Although several exemplary embodiments of the invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the invention. Therefore, all such modifications are intended to be included within the scope of the invention as defined in the claims. It should be understood that the foregoing description is illustrative of the invention and should not be construed as limiting it to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.

Claims

1. A hierarchical data hybrid storage method based on semantic vector fingerprints, characterized in that, include: Obtain the original capability document, call the pre-trained language model to parse the capability document into a hierarchical list object, which includes a discovery layer and a detail layer. Write the fields of the discovery layer into a flat column in the database, serialize the hierarchical list object and write it into the large list field. Based on the hierarchical list object, the version identifier of the embedded model is detected. When the version identifier changes, a pairing calibration matrix is ​​constructed. The pairing calibration matrix is ​​subjected to orthogonal rotation alignment and residual correction based on singular value decomposition to obtain an affine alignment operator. The affine alignment operator is used to perform a spatial transfer transformation on the historical semantic vector. The version determination is performed based on the transferred semantic vector to obtain the version determination result. Based on the version determination result, the version record is updated. When responding to a read request, the large fields of the list are read first and field-level backfilling validation is performed. The capability list service view is then output.

2. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 1, characterized in that, The analysis steps for hierarchical inventory objects are as follows: Obtain the original capability document submitted by the merchant, call the pre-trained language model interface to perform structured extraction on the original capability document according to the predefined list pattern definition, and generate a normalized list object; Calculate the semantic vector of the current embedded model for the normalized list object as the current semantic vector fingerprint, and record the version identifier of the current embedded model and store it in the version identifier record. Extract discovery layer fields from the normalized manifest object, write them into a flat column in the database and create an index, serialize the normalized manifest object as a whole and write it into the manifest large field, write the current semantic vector fingerprint into the version control table, and output the normalized manifest object as a hierarchical manifest object.

3. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 1, characterized in that, The analysis steps for the affine alignment operator are as follows: When the version identifier changes, hierarchical sampling is performed from the version control table according to intent category and tag category to extract the calibration text set. For each text in the calibration text set, the old version embedding model and the new version embedding model are called to generate embedding vectors. The old version embedding vector and the new version embedding vector corresponding to the same text are vertically concatenated into column blocks. All column blocks are summarized to construct a paired calibration matrix. An orthogonal rotation alignment based on singular value decomposition is performed on the paired calibration matrix, and the affine alignment operator is obtained by encapsulating it with low-rank residual correction and translation compensation.

4. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 3, characterized in that, The steps to obtain the affine alignment operator by encapsulating it with low-rank residual correction and translation compensation are as follows: Extract the old space submatrix and the new space submatrix from the paired calibration matrix respectively. Perform column mean centering on the old space submatrix and the new space submatrix respectively to obtain the centered old space submatrix and the centered new space submatrix. Calculate the transpose of the centered new space submatrix and the centered old space submatrix to obtain the cross covariance matrix.

5. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 4, characterized in that, The steps for obtaining the affine alignment operator by encapsulating low-rank residual correction and translation compensation also include: Perform singular value decomposition on the cross covariance matrix, extract the left singular vector matrix and the right singular vector matrix, and calculate the transpose product of the left singular vector matrix and the right singular vector matrix to obtain the orthogonal rotation matrix. The residual matrix is ​​obtained by performing a rotation transformation on the centered old space submatrix using an orthogonal rotation matrix and calculating the column-by-column difference between the rotated matrix and the centered new space submatrix.

6. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 5, characterized in that, The steps for obtaining the affine alignment operator by encapsulating low-rank residual correction and translation compensation also include: The residual matrix is ​​subjected to truncated singular value decomposition to obtain a low-rank correction matrix. The orthogonal rotation matrix is ​​added to the low-rank correction matrix to obtain the composite transformation matrix. The old space mean vector and the new space mean vector recorded during column mean centering are combined to construct a complete affine transformation including translation compensation. The complete affine transformation is encapsulated as an affine alignment operator output.

7. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 3, characterized in that, The analysis steps for version determination results are as follows: A affine alignment operator is used to perform batch spatial migration transformation and normalization on all historical semantic vectors in the version control table. The cosine similarity between the current semantic vector fingerprint of the hierarchical list object and the semantic vector fingerprint of the previous version after migration is calculated. Version determination is performed based on the dynamic determination threshold to obtain the version determination result. A validation subset is drawn from the calibration text set. The mean cosine similarity between the old version embedding vector and the corresponding new version embedding vector in the validation subset after migration is calculated as the alignment quality index. When the alignment quality index is lower than the preset alignment quality lower limit, the migration result of the affine alignment operator is abandoned, the semantic vector fingerprint is recalculated and overwritten.

8. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 7, characterized in that, The dynamic decision threshold is determined by coupling the mean of the alignment residuals of the affine alignment operator and the category coverage of the calibration text set.

9. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 1, characterized in that, The steps to output the capability list service view are as follows: Receive the version determination result. If the version determination result indicates a substantial change, increment the revision number and write the new version record to the version control table. If the version determination result indicates a non-substantial change, only update the access timestamp. When responding to a read request for a capability identifier, read the large fields of the list and perform deserialization. Perform non-empty checks on the key fields of the deserialization result. If a field is detected as missing, read data from the corresponding flat column and perform backfilling.

10. The hierarchical data hybrid storage method based on semantic vector fingerprints according to claim 9, characterized in that, The steps for outputting the capability list service view also include: Check if the details layer of the hierarchical list object contains valid data. If the details layer is not empty, mark the capability status as details ready. If the details layer is empty, mark the capability status as discoverable only. Output the capability list service view carrying the capability status markings.

11. A hierarchical data hybrid storage system based on semantic vector fingerprints, characterized in that, The hierarchical data hybrid storage method based on semantic vector fingerprints according to any one of claims 1-10 includes: The normalization access module is used to obtain the original capability document and call the pre-trained language model to parse the capability document into a hierarchical list object; The hierarchical storage module is used to write fields from the discovery layer into flat columns in the database and serialize hierarchical list objects into large list fields. The version control module is used to detect the version identifier of the embedded model based on the hierarchical list object. When the version identifier changes, a pairing calibration matrix is ​​constructed. The pairing calibration matrix is ​​subjected to orthogonal rotation alignment and residual correction based on singular value decomposition to obtain an affine alignment operator. The affine alignment operator is used to perform a spatial migration transformation on the historical semantic vector. The version determination is performed based on the migrated semantic vector to obtain the version determination result. The read normalization module is used to perform version record updates based on the version determination results. When responding to a read request, it prioritizes reading the large fields of the list and performs field-level backfill validation, and outputs the capability list service view.

12. A hierarchical data hybrid storage medium based on semantic vector fingerprints, on which computer programs are stored, characterized in that: When the computer program is executed by the processor, it implements the hierarchical data hybrid storage method based on semantic vector fingerprints as described in any one of claims 1 to 10.

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