Multilingual glossary driven product content consistency generation and cross platform alignment method

Abstract

This invention discloses a method for consistent product content generation and cross-platform alignment driven by a multilingual terminology database, belonging to the fields of computer data processing and artificial intelligence technology. It includes acquiring multilingual terminology data, domain ontology structure, and cross-platform field rules to construct a dynamic knowledge base; constructing a constraint generation context based on product content generation requests to obtain product content to be verified; calling the dynamic knowledge base for verification, generating violation location information to correct the product content to be verified, and outputting the verified product content; performing cross-platform adaptation rewriting operations on the verified product content according to the target publishing platform to generate platform-aligned product content, and simultaneously generating alignment explanation records and traceable audit logs. By constructing a dynamic knowledge base to drive constraint generation and post-generation verification correction of a large language model, and combining traceable log feedback for optimization, it is possible to achieve automated and efficient consistent generation and alignment of product content across multiple languages ​​and platforms.

Description

Technical Field

[0002] This invention relates to the fields of computer data processing and artificial intelligence, and in particular to a method for consistent generation and cross-platform alignment of product content driven by a multilingual terminology database. Background Technology

[0004] In the context of digital global trade, product content serves as the core medium connecting brands and consumers, and its quality directly impacts user experience and sales conversion. Product content typically involves multiple languages ​​and needs to be published on e-commerce platforms with varying architectures. This process involves significant data processing, text generation, and information management, representing a typical application of computer data processing technology in the e-commerce field.

[0005] In current technologies, processing cross-platform, multilingual product content primarily relies on a combination of human teams and content management systems. Operations personnel manually write or modify product descriptions based on glossaries, brand guidelines, and platform rules scattered across different documents or systems. In recent years, some companies have begun experimenting with using generalized large language models to assist in drafting initial drafts, followed by extensive manual review, fact-checking, terminology standardization, and formatting adjustments. These processes essentially apply discrete knowledge and rules to unstructured text processing.

[0006] However, the manual and simple template-based approach is inefficient and prone to inconsistencies, especially when dealing with massive amounts of products and rapidly changing platform rules, resulting in widespread content errors and information asynchrony. While directly applying general-purpose language models to generate content often results in factual errors, non-standard terminology, and an inability to automatically adapt to different platform field formats and limitations, due to the model's lack of in-depth domain knowledge and awareness of dynamic business rules. Consequently, the generated content still requires significant manual intervention to meet publishing standards, failing to establish an efficient and reliable automated process. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides a method for consistent product content generation and cross-platform alignment driven by a multilingual terminology database. By constructing a dynamic knowledge base to constrain and drive the large language model and perform post-generation verification and correction, and by combining traceable log feedback optimization, it can achieve automated generation and efficient alignment of consistent product content across multiple languages ​​and platforms.

[0009] The above objectives can be achieved through the following approach:

[0010] A method for consistent product content generation and cross-platform alignment driven by a multilingual terminology database includes: acquiring multilingual terminology data, domain ontology structure, and cross-platform field rules; integrating and structuring the multilingual terminology data, domain ontology structure, and cross-platform field rules to construct a dynamic knowledge base with a feedback interface; responding to product content generation requests, constructing a constraint generation context based on terminology constraints and entity attribute relationships in the dynamic knowledge base, and using the constraint generation context to drive a large language model to generate controlled key points of the original product content to obtain product content to be verified; calling the dynamic knowledge base to perform automated consistency verification on the product content to be verified, generating violation location information, and correcting the product content to be verified based on the violation location information, outputting verified product content; extracting corresponding platform field format constraints from the dynamic knowledge base according to the target publishing platform, performing cross-platform adaptation rewriting operations on the verified product content to generate platform-aligned product content, and simultaneously generating alignment explanation records; integrating the alignment explanation records with the platform-aligned product content to generate a traceable audit log, and updating the dynamic knowledge base based on the traceable audit log through the feedback interface.

[0011] Optionally, constructing a dynamic knowledge base with a feedback interface includes: parsing the multilingual terminology data, domain ontology structure, and cross-platform field rules to generate initial structured terminology entries, attribute relationship models, and platform field mapping tables; semantically associating the structured terminology entries based on the attribute relationship models to generate a set of domain terminology entities with domain context; associating the platform field mapping tables to attach cross-platform constraints to each entity in the set of domain terminology entities, forming an interconnected multidimensional constraint network; assigning rule weights to the constraint relationships in the multidimensional constraint network and setting the feedback interface; integrating the multidimensional constraint network with unit conversion rules and establishing index and query interfaces to construct a dynamic knowledge base.

[0012] Optionally, obtaining the product content to be verified includes: receiving a product content generation request and parsing the original product content highlights, language, and product category from the product content generation request; retrieving corresponding terminology constraints and attribute relationships from the dynamic knowledge base according to the language and product category to form a constraint instruction set; encapsulating the constraint instruction set and the original product content highlights into a structured prompt format to construct a constraint generation context; and performing text expansion and structured filling on the constraint generation context to output the product content to be verified.

[0013] Optionally, forming a constraint instruction set includes: extracting a category subgraph related to the product category from the dynamic knowledge base; and converting the rule nodes and relationship edges in the category subgraph into natural language condition prompts that distinguish between hard constraints and soft constraints according to preset rule weights, thereby forming a constraint instruction set.

[0014] Optionally, the output of the verified product content includes: matching and verifying the product content to be verified based on the multidimensional constraint network in the dynamic knowledge base, generating a verification result report that identifies all consistency differences; analyzing the verification result report, classifying and locating inconsistencies, and directly extracting or synthesizing deterministic repair fragments from the specific rule nodes that are violated to form violation location information, wherein the violation location information includes the identifier of the specific rule node that is violated; and performing targeted automatic correction on the product content to be verified based on the violation location information, thereby outputting the verified product content.

[0015] Optionally, generating a verification result report that identifies all consistency differences includes: mapping entities, attributes, and values ​​in the product content to be verified to nodes and edges to construct a temporary subgraph; performing graph matching between the temporary subgraph and the multidimensional constraint network in the dynamic knowledge base to verify whether the existence of nodes and the relationship between edges conform to a preset definition; and performing logical reasoning on nodes or edges that fail to match or have inconsistencies by traversing the path of the multidimensional constraint network to identify logical conflicts and generate a verification result report.

[0016] Optionally, the step of generating platform-aligned product content and simultaneously generating alignment explanation records includes: querying and extracting platform field mapping rules corresponding to the target publishing platform identifier from the dynamic knowledge base; applying the platform field mapping rules to the corresponding fields of the verified product content, and generating a rewrite operation instruction set that includes content rewriting, format conversion, and unit conversion operations by comparing differences; executing the rewrite operation instruction set one by one to generate platform-aligned product content; and simultaneously generating an atomic record unit for each executed instruction, which contains a key-value pair structure including the original value, the modified value, and the rule ID key on which it is based, and assembling all atomic record units together into a serialized alignment explanation record.

[0017] Optionally, generating a traceable audit log and updating the dynamic knowledge base based on the traceable audit log through the feedback interface includes: obtaining the alignment explanation record and associating the verified product content with the platform-aligned product content to form a version change unit; attaching external metadata including an operation timestamp, operator identification, and generation source to the version change unit, and generating a unique corresponding digital digest or hash checksum based on the content, thereby encapsulating the content alignment operation into a structured audit log entry; appending the audit log entry to a preset audit record repository to construct a traceable audit log; analyzing the traceable audit log and adjusting the rule weights or update constraints in the dynamic knowledge base through the feedback interface.

[0018] Optionally, the step of analyzing the traceable audit logs and adjusting the rule weights or updating constraint relationships in the dynamic knowledge base through the feedback interface includes: performing aggregate analysis on the traceable audit logs to identify and analyze violating rules or model errors; adjusting the rule weights of the corresponding rule nodes in the dynamic knowledge base through the feedback interface based on the analysis results of the violating rules; and optimizing the corresponding constraint injection method in the dynamic knowledge base through the feedback interface based on the analysis results of the model errors.

[0019] Based on the same inventive concept, this invention also provides a multilingual terminology database-driven product content consistency generation and cross-platform alignment system. The system comprises: a dynamic knowledge governance module, used to acquire multilingual terminology data, domain ontology structure, and cross-platform field rules, and to integrate and structure the multilingual terminology data, domain ontology structure, and cross-platform field rules to construct a dynamic knowledge base with a feedback interface; and a controlled generation module, used to respond to product content generation requests, construct a constraint generation context based on the terminology constraints and entity attribute relationships in the dynamic knowledge base, and use the constraint generation context to drive a large language model to generate key points of the original product content in a controlled manner, obtaining the product content to be verified. The consistency verification module is used to call the dynamic knowledge base to perform automatic consistency verification on the product content to be verified, generate violation location information, correct the product content to be verified based on the violation location information, and output the verified product content. The platform alignment module is used to extract the corresponding platform field format constraints from the dynamic knowledge base according to the target publishing platform, perform cross-platform adaptation rewriting operation on the verified product content, generate platform-aligned product content, and synchronously generate alignment explanation records. The audit log module is used to integrate the alignment explanation records and the platform-aligned product content to generate traceable audit logs, and update the dynamic knowledge base based on the traceable audit logs through the feedback interface.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] 1. This invention constructs a dynamic knowledge base as a unified and computable center of rules and facts, ensuring that product content follows the same standard throughout the entire process of generation, verification and adaptation. This fundamentally guarantees the high consistency and accuracy of product terminology, attribute descriptions and compliance requirements across different language versions and multiple publishing platforms, avoiding information silos and content contradictions and factual errors caused by manual operation.

[0022] 2. This invention combines the creative capabilities of a large language model with the deterministic constraints of a knowledge base, enabling automated and controlled generation from core points to a complete product page. Through automated verification, correction, and platform alignment processes, it reduces the heavy workload of manual writing, review, and format adjustment, shortens the product cycle from information preparation to multi-platform release, and significantly improves the overall efficiency and scalability of content production.

[0023] 3. This invention provides a clear and complete chain of evidence for each automated content modification and adaptation operation by generating alignment interpretation records and traceable audit logs, meeting enterprises' needs for compliance auditing and quality traceability of content changes. Simultaneously, the feedback mechanism based on audit logs enables the knowledge base to self-optimize according to actual application effects, forming a continuously improving closed-loop system and enhancing the adaptability of content strategies to market changes.

[0024] 4. This invention uses a multi-dimensional constraint network to precisely guide and rigorously verify the output of a large language model, effectively suppressing its tendency to produce factual illusions or deviate from business norms in professional fields. This enables artificial intelligence technology to be applied more safely and reliably to commercial content production scenarios with strict requirements for accuracy and consistency. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating the method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database, according to an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the structure of the multilingual terminology database-driven product content consistency generation and cross-platform alignment method according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Reference Figure 1 One embodiment of the present invention proposes a method for consistent generation and cross-platform alignment of product content driven by a multilingual terminology database. By constructing a dynamic knowledge base to constrain and drive the large language model and perform post-generation verification and correction, and combining traceable log feedback optimization, it is possible to achieve automated generation and efficient alignment of product content across multiple languages ​​and platforms.

[0032] The method described in this embodiment specifically includes:

[0033] Acquire multilingual terminology data, domain ontology structure, and cross-platform field rules, and integrate and structure the multilingual terminology data, domain ontology structure, and cross-platform field rules to construct a dynamic knowledge base with a feedback interface;

[0034] In response to the product content generation request, a constraint generation context is constructed based on the term constraints and entity attribute relationships in the dynamic knowledge base. The constraint generation context is then used to drive the large language model to generate the key points of the original product content in a controlled manner, resulting in the product content to be verified.

[0035] The dynamic knowledge base is invoked to perform automatic consistency verification on the content of the product to be verified, generate violation location information, and correct the content of the product to be verified based on the violation location information, and output the verified product content.

[0036] Based on the target publishing platform, the corresponding platform field format constraints are extracted from the dynamic knowledge base, and cross-platform adaptation rewriting is performed on the verified product content to generate platform-aligned product content, and alignment explanation records are generated simultaneously.

[0037] The alignment interpretation records are integrated with the product content aligned with the platform to generate a traceable audit log, and the dynamic knowledge base is updated based on the traceable audit log through the feedback interface.

[0038] Optionally, constructing a dynamic knowledge base with a feedback interface includes:

[0039] The multilingual terminology data, domain ontology structure, and cross-platform field rules are parsed to generate initial structured terminology entries, attribute relationship models, and platform field mapping tables.

[0040] Based on the attribute relationship model, the structured term entries are semantically associated to generate a set of domain term entities with domain context;

[0041] By associating the platform field mapping table, cross-platform constraints are attached to each entity in the domain terminology entity set to form an interconnected multi-dimensional constraint network.

[0042] Assign rule weights to the constraint relationships in the multidimensional constraint network and set the feedback interface;

[0043] By integrating the multidimensional constraint network and unit conversion rules, and establishing indexes and query interfaces, a dynamic knowledge base is constructed.

[0044] Specifically, a data parsing and integration pipeline is initiated. This pipeline receives three heterogeneous data source inputs: multilingual terminology data consisting of CSV or Excel files containing product terminology and its multilingual translations; a domain ontology structure consisting of OWL or RDF files defining product category hierarchy and attribute relationships; and structured definition files conforming to the corresponding data exchange protocol standards, such as JSONSchema files based on the ECMA-404 standard or other serialization formats, describing the field requirements and formats of different e-commerce platforms, forming cross-platform field rules. The system uses dedicated parsers, such as ETL scripts for CSV and RDFLib libraries for OWL, to convert these input data into three standardized intermediate data structures: structured terminology entries, attribute relationship models, and platform field mapping tables. Among them, the domain ontology structure is an engineered form of knowledge representation that precisely defines entities, attributes, and their relationships within a specific domain in a graph-like manner. For example, it specifies that the entity "mobile phone" must contain attributes such as "screen size" and "processor model."

[0045] The system performs semantic association operations to assign domain context to isolated terms, forming a coherent knowledge network. This step uses an attribute relation model as its framework and leverages entity linking techniques from natural language processing to map and connect terms in structured term entries to corresponding nodes in the model. For example, the system identifies the term "Kirin 9000" and links it to the attribute node "processor model" in the attribute relation model, thereby generating a set of domain term entities. Subsequently, the system performs constraint overlay, integrating platform rules into the domain knowledge. By traversing the platform field mapping table, the system attaches platform-specific constraints for each entity, such as Amazon's 200-character limit for title length, as attributes to the corresponding nodes in the domain term entity set, ultimately forming a multi-dimensional constraint network that integrates domain knowledge, multilingual expressions, and multi-platform rules.

[0046] To achieve dynamic adjustment and priority differentiation of rules, the system quantifies and weights each constraint relationship in the multidimensional constraint network. The rule weight is a quantitative indicator, typically set between 0 and 1, used to identify the degree of enforceability of the rule. A weight of 1.0 represents a hard constraint that must be followed, while lower weights represent soft constraints that can be handled flexibly. The weight is calculated based on a weighted summation model, as shown in the following formula:

[0047] ,

[0048] in, This represents the final weight of rule r. It is an authoritative score of the source of the rules, obtained through a preset scoring table. For example, legal and regulatory sources are 1.0, and platform mandatory policies are 0.8. It is a user feedback rating from the subsequent business loop, with an initial value of 1.0, which is dynamically adjusted by the feedback interface. This is a business impact factor, assessed by business experts based on the potential losses caused by violations, with a value range of 0 to 1. a, b, and c are three hyperparameters representing the importance of each factor, summing to 1; for example, a=0.5, b=0.3, c=0.2. Simultaneously, the system establishes a standard application programming interface (API) based on the HTTP / HTTPS protocol, such as a RESTful API or GraphQL interface, as a feedback interface. This allows external operations or automated analysis results to call this interface to update specific rules. value.

[0049] To construct the knowledge base, the system integrates a pre-built unit conversion rule library, such as lookup tables containing conversion relationships between inches and centimeters, and pounds and kilograms, with a multidimensional constraint network. The entire network structure data is then loaded into the graph database Neo4j or the search engine Elasticsearch to establish efficient indexing and query interfaces. This ensures that the system can perform constraint-based retrieval based on conditions such as product category and target platform, ultimately forming a dynamic knowledge base that can be invoked in real-time by upper-layer applications.

[0050] Optionally, obtaining the content of the product to be verified includes:

[0051] Receive a product content generation request, and parse the original product content highlights, language, and product category from the product content generation request;

[0052] Based on the language and product category, the corresponding terminology constraints and attribute relationships are retrieved from the dynamic knowledge base to form a constraint instruction set;

[0053] The constraint instruction set and the original product content points are encapsulated in a structured prompt format to construct a constraint generation context;

[0054] The constraint generation context is expanded and structured to output the product content to be verified.

[0055] Specifically, the process of generating product content to be verified involves automatically expanding discrete core product information into a structurally complete product description text that conforms to preliminary specifications using a large language model. This process begins with receiving a product content generation request, which is typically a JSON object passed through an API interface. The system then initiates a parsing process to precisely extract three key parameters: the original product content essentials that form the basis of content creation, usually represented as a string array, such as ["A17Pro chip", "6.7-inch ProMotion display"]; the language identifier that determines the language of the output text, such as "zh-CN" or "en-GB"; and the product category used to locate relevant rules in a dynamic knowledge base. This category can be a hierarchical path string such as "electronic products / mobile phones / smartphones" or a unique category ID.

[0056] After successfully parsing the input parameters, the system performs constraint retrieval and construction operations. A set of rules and instructions is tailored for this generation task from the dynamic knowledge base. The system uses the parsed language and product category as a composite query key to send a request to the query interface of the dynamic knowledge base. This query aims to retrieve all relevant terminology constraints and attribute relationships. Terminology constraints are rules that ensure the accuracy and compliance of word usage, such as lists of prohibited words or standard wording for brand terms. Attribute relationships define which technical specifications and their logical relationships should be included under this product category. The system integrates these scattered rules and transforms them into a format that can be understood by a large language model, forming a constraint instruction set.

[0057] The system then enters the constraint generation context construction phase, creating a structured and comprehensive prompt to achieve precise control over the output of the large language model. The system encapsulates the constraint instruction set generated in the previous step with the original product content points, organizing them into a structured prompt format. This format is not a simple text concatenation, but a hierarchical input structure, such as a JSON object containing specific key-value pairs. This object might include an "instructions" field to store the constraint instruction set and a "key_features" field to store the original product content points. This structured encapsulation, i.e., the constraint generation context, effectively guides the large language model to distinguish between instructions and content materials, significantly improving its ability to comply with constraints.

[0058] The system submits the constructed constraint generation context to the pre-configured large language model service to perform the actual text creation. When calling the model API, the system sets a series of generation parameters, such as setting the temperature parameter in a low range of 0.2 to 0.4 to ensure the determinism and factual accuracy of the content, and setting the max_tokens parameter according to the estimated length. After receiving the constraint generation context, the large language model performs text expansion and structured completion tasks, that is, expanding the original product content key points into fluent descriptive paragraphs, and filling in structured fields such as titles and selling points according to the requirements of the constraint instruction set. The complete content generated by the model is a preliminary product copy containing information such as titles, descriptions, and specifications. The system outputs this as the product content to be verified, so as to enter the subsequent automated consistency verification process.

[0059] Optionally, the set of constraint formation instructions includes:

[0060] Extract category subgraphs related to the product category from the dynamic knowledge base;

[0061] The rule nodes and relation edges in the category subgraph are transformed into natural language condition prompts that distinguish between hard and soft constraints according to preset rule weights, thereby forming a constraint instruction set.

[0062] Specifically, the constraint instruction set transforms the graph-structured rules stored in the dynamic knowledge base into a sequence of natural language instructions that the large language model can directly understand and execute. Upon receiving a product category, the system initiates a ranged traversal query to the graph query interface of the dynamic knowledge base. This query starts with the node corresponding to the product category and performs a breadth-first search within the multi-dimensional constraint network. The search depth is limited to a preset parameter, typically 2 to 3 hops, to ensure that only directly and closely related rules are retrieved, avoiding the introduction of irrelevant constraints. The query result is a serialized graph data structure, namely a category subgraph, which contains all rule nodes, entity attribute nodes, and relation edges defining the logical connections between them related to the product category.

[0063] The system initiates a rule transformation process to translate the symbolic rules in the category subgraph into mandatory natural language conditional prompts based on their importance level. The system iterates through each rule node and relation edge in the category subgraph, reading its preset rule weight value. A hard constraint threshold parameter is set, for example, 0.9. When a rule's weight value is greater than or equal to this threshold, the rule is determined to be a hard constraint; conversely, if its weight is in a lower range, such as between 0.5 and 0.9, it is determined to be a soft constraint. For hard constraints, the system calls a preset instruction template, such as "Must comply with: [rule content]", to transform it into a mandatory instruction, such as "Must comply with: Product title length must not exceed 150 characters". For soft constraints, another template is used, such as "Suggested to include: [rule content]", to generate a suggestive prompt, such as "Suggested to include: Highlight battery life". By performing this classification and transformation operation on all rule elements in the category subgraph, the system generates a series of priority-based natural language conditional prompts. Ultimately, these prompts are integrated into an ordered list or structured object, forming the final set of constraint instructions, which is used to subsequently build the constraint generation context that drives the large language model to generate content.

[0064] Optionally, the output verified product content includes:

[0065] Based on the multidimensional constraint network in the dynamic knowledge base, the content of the product to be verified is matched and verified, and a verification result report that identifies all consistency differences is generated.

[0066] Analyze the verification result report, classify and locate the inconsistencies, and directly extract or synthesize deterministic repair fragments from the specific rule nodes that were violated to form violation location information, wherein the violation location information includes the identifier of the specific rule node that was violated;

[0067] Based on the violation location information, the content of the product to be verified is automatically corrected at specific points, thereby outputting the verified product content.

[0068] Specifically, after output verification, the initially generated text of the product content is precisely compared with a dynamic knowledge base. This automatically identifies and corrects all inconsistencies, ensuring that the final product description conforms to preset business and platform specifications. The process begins with a matching and verification procedure, which performs a deep scan of the input product content based on a multidimensional constraint network in the dynamic knowledge base. The system utilizes an entity-relation extraction component from natural language processing to parse the terms, attributes, and their corresponding values ​​in the product content. This extracted information is then compared with the nodes and relationships in the multidimensional constraint network. All detected inconsistencies, such as incorrect terminology, attribute values ​​exceeding preset ranges, or violations of logical relationships between entities, are accurately recorded and summarized into a structured verification result report.

[0069] The system analyzes the verification report, transforming the original error log into directly executable correction instructions. The system iterates through each inconsistency entry in the report, categorizing and locating the inconsistencies based on a predefined remediation strategy library. For example, for inappropriate terminology, the remediation strategy is to directly replace it with standard terms from the knowledge base; for incorrect numerical unit formats, the system calls the unit conversion module to generate a corrected remediation fragment. Crucially, the system attaches a clear, specific rule node identifier to the remediation suggestion generated for each inconsistency. This information collectively constitutes a detailed violation location report. This report precisely indicates the location of the content to be corrected in the original text, the nature of the error, and the corrected fragment to be replaced.

[0070] Based on this violation location information, the system performs targeted automated correction operations on the product content to be verified. This is a deterministic text editing process, rather than relying on secondary generation from a large language model. The system parses the violation location information line by line and performs replacement, deletion, or insertion operations on the corresponding text fragments in the original text. For example, it corrects "A17pro" in the text to "A17Pro" based on the rule node ID "BRAND_TERM_042". After all correction operations are completed, the system outputs a verified product content that conforms to the constraints of the dynamic knowledge base in terms of content and format, providing a high-quality input for the subsequent cross-platform adaptation process.

[0071] Optionally, generating a verification result report that identifies all consistency differences includes:

[0072] The entities, attributes, and values ​​in the product content to be verified are mapped to nodes and edges to construct a temporary subgraph;

[0073] The temporary subgraph is graph matched with the multidimensional constraint network in the dynamic knowledge base to verify whether the existence of nodes and the relationship between edges conform to the preset definition.

[0074] By traversing the path of the multidimensional constraint network, logical reasoning is performed on nodes or edges that fail to match or have inconsistencies to identify logical conflicts and generate a verification result report.

[0075] Specifically, the verification result report, which identifies all consistency discrepancies, quantifies and locates all non-compliant aspects in the product content to be verified through multi-level structured comparison and logical reasoning. The first step in this process is to construct a temporary subgraph. The system launches an information extraction module that applies deep learning-based entity and relation extraction technology to parse the product content to be verified, identify all mentioned entities, such as product model, attributes like screen size, and specific values, such as 6.7 inches, and map them into nodes and edges in a graph structure. For example, "iPhone 15 Pro has a 6.7-inch screen" will be transformed into a temporary subgraph containing three nodes "iPhone 15 Pro", "screen size", and "6.7 inches" and two relation edges ("iPhone 15 Pro", has_attribute, "screen size") and ("screen size", has_value, "6.7 inches").

[0076] The system performs graph matching to verify the factual and structural compliance of the content. It aligns the temporary subgraph generated in the previous step with the multidimensional constraint network in the dynamic knowledge base. This process employs an optimized subgraph isomorphism algorithm, such as VF2++, to verify whether each node in the temporary subgraph has a corresponding, authoritatively verified node in the multidimensional constraint network, and whether the edges between nodes conform to the preset domain ontology definition. For example, it checks whether the node "iPhone15Pro" exists and verifies that a valid "has_attribute" relationship does exist between "iPhone15Pro" and "screen size". For node existence verification, the system uses a fuzzy matching mechanism based on word vector cosine similarity, setting a similarity threshold, such as 0.9, to tolerate slight spelling variations.

[0077] The system uses path traversal of a multidimensional constraint network to perform logical reasoning and uncover deep-seated errors that appear compliant but actually contain inherent logical contradictions. For nodes or edges that fail to match or are found to be inconsistent in graph matching, the system uses the inconsistency point as a starting point to perform a depth-first path search in the multidimensional constraint network to trace the fundamental rule violated. For example, if the content describes a mobile phone as having a "body made of titanium alloy" and "supporting wireless charging," but the "titanium alloy" node in the knowledge base is connected to the "blocking wireless signals" node through an "influence" relationship, and then to the constraint node "does not support wireless charging," the system can identify this logical conflict by traversing this path. All identified node existence errors, relationship inconsistencies, and logical conflicts are recorded, along with the specific violation path and rule ID, and finally compiled into a detailed, structured verification result report, providing accurate input for subsequent automated corrections.

[0078] Optionally, the process of generating aligned product content and simultaneously generating alignment explanation records includes:

[0079] Query and extract platform field mapping rules corresponding to the target publishing platform identifier from the dynamic knowledge base;

[0080] The platform field mapping rules are applied to the corresponding fields of the verified product content. By comparing the differences, a rewrite operation instruction set containing content rewriting, format conversion and unit conversion operations is generated.

[0081] Execute the rewrite operation instruction set one by one to generate platform-aligned product content;

[0082] Simultaneously, for each executed instruction, an atomic record unit containing a key-value pair structure including the original value, the modified value, and the rule ID key on which it is based is generated. All atomic record units are then assembled into a serialized, aligned, and interpreted record.

[0083] Specifically, the generated platform-aligned product content and alignment explanation records automatically adapt standardized product content to target platforms with different publishing specifications, and generate visual, traceable change evidence for each adaptation modification. Starting with the target publishing platform identifier, such as "AMZN_DE" or "EBAY_US", the system uses this identifier as the query key to send a request to the dynamic knowledge base's query interface, retrieving all platform field mapping rules associated with that platform. These rules are stored in structured data formats such as JSON or YAML, defining in detail the content requirements for each field, such as a maximum title length of 80 characters and weight units in kilograms (kg).

[0084] After obtaining the adaptation rules, the system launches a comparison and instruction generation engine to transform the declarative platform rules into an executable set of rewrite operation instructions. This engine iterates through the verified product content field by field, comparing it with the platform's field mapping rules. For example, if the weight field of the verified product content is "2.5 lbs," while the target platform's rules require the unit to be "kg" with two decimal places, the engine will generate a unit conversion instruction. If the title length exceeds the platform's limit, a content rewrite instruction will be generated, which may include logic for intelligent truncation or keyword retention. All detected differences are transformed into specific instructions containing the operation type, target field, and execution parameters, and assembled into a rewrite operation instruction set according to a preset execution order.

[0085] The system sequentially executes each instruction in the rewrite operation instruction set to generate platform-aligned product content. This process is deterministic; the execution of each instruction corresponds to a specific text or data transformation function. Simultaneously, to ensure traceability of changes, the system synchronously generates an atomic record unit for each successfully executed instruction. This is a data structure containing explicit key-value pairs, which must record three core pieces of information: the original value of the modified field, the new value generated after the instruction is executed, and the rule ID that triggered the modification. For example, a record unit might be {"field":"weight","original_value":"2.5lbs","modified_value":"1.13kg","rule_id":"AMZN_DE_WEIGHT_UNIT"}. All atomic record units generated during the adaptation process are collected and compiled into a complete array or list, forming a detailed alignment explanation record, which is output along with the final platform-aligned product content.

[0086] Optionally, generating a traceable audit log and updating the dynamic knowledge base based on the traceable audit log through the feedback interface includes:

[0087] Obtain the alignment explanation record and associate the verified product content with the platform-aligned product content to form a version change unit;

[0088] The version change unit is appended with external metadata including operation timestamp, operator identity identifier and generation source, and a unique corresponding digital digest or hash check code is generated based on the content, thereby encapsulating the content alignment operation into a structured audit log entry.

[0089] The audit log entries are appended to a preset audit record repository to build a traceable audit log;

[0090] The traceable audit logs are analyzed, and the rule weights or update constraints in the dynamic knowledge base are adjusted through the feedback interface.

[0091] Specifically, generating traceable audit logs and updating the dynamic knowledge base solidifies each content adaptation operation into an immutable record. These records form a feedback loop, continuously optimizing the accuracy and timeliness of the knowledge base. The process begins with integrating data to form a version change unit. The system obtains the alignment explanation record output by the platform alignment module, which is structured data containing details of all atomic modifications. Simultaneously, the system associates the verified product content before modification with the platform-aligned product content after modification, generating a precise difference view through a version control system, such as Git's diff mechanism. These two parts of data are encapsulated together to form a detailed version change unit, describing the entire process of an adaptation operation: "where it came from, why it changed, and how it changed."

[0092] The system appends metadata to the version change unit, encapsulating it into a structured audit log entry. This provides sufficient contextual information and data integrity proof for the change record, meeting compliance audit requirements. The system automatically captures and appends a series of external metadata, including millisecond-accurate operation timestamps, the identity of the user or automated system performing the operation, and the batch number or request ID identifying the source of this content generation task. Next, the system uses a cryptographic hash function such as SHA-256 to calculate the combination of the external metadata and the version change unit, generating a unique corresponding digital digest or hash checksum. This metadata, hash checksum, and version change unit are serialized together into a standard format log object, such as JSON or Avro, forming a complete audit log entry with verifiable authenticity.

[0093] The system appends this structured audit log entry to a pre-defined audit record repository. This repository typically uses a database or log storage system that supports append-only writes, such as Apache Kafka, a dedicated log management service, or a database with write-time integrity verification, combined with the aforementioned hash checksum mechanism to ensure the integrity and post-event verifiability of the log records. Over time, all written entries together constitute a traceable audit log. Finally, the system initiates a background analysis task to analyze these audit logs periodically or in real-time. The goal of this analysis task is to adjust the dynamic knowledge base through a feedback interface. For example, by statistically analyzing the frequency of specific rule IDs appearing in alignment explanation records and the types of modifications they result in, the system can identify systematic biases in rules or model-generated content that frequently cause problems. The analysis results trigger calls to the feedback interface, adjusting the rule weights of corresponding rule nodes in the dynamic knowledge base via API requests, or updating the constraint injection method used to drive the large language model, thereby achieving self-learning and iterative optimization of the knowledge base.

[0094] Optionally, analyzing the traceable audit logs and adjusting the rule weights or update constraints in the dynamic knowledge base through the feedback interface includes:

[0095] The traceable audit logs are aggregated and analyzed to identify and analyze violations of rules or model errors.

[0096] Based on the analysis results of the violation rules, the rule weights of the corresponding rule nodes in the dynamic knowledge base are adjusted through the feedback interface;

[0097] Based on the analysis results of the model errors, the corresponding constraint injection method in the dynamic knowledge base is optimized through the feedback interface.

[0098] Specifically, the system analyzes traceable audit logs and adjusts the dynamic knowledge base through feedback interfaces to establish an automated, data-driven closed loop, enabling the knowledge base to self-correct and evolve based on actual application results. This process is initiated by a periodic aggregation analysis task, typically configured as a batch job executed daily at midnight. This task scans all log entries within a specified time window in the audit log repository. The system performs aggregation queries, grouping and counting logs by the ID of the violation rule and the type of model error, thereby identifying frequently occurring violation location information. For example, it discovers that the rule with the rule ID "TITLE_WORD_COUNT_MAX_60" has been triggered for correction more than 500 times in the past 24 hours, indicating that this rule may be a common violation point, or that the large language model has a systemic problem of excessively long titles when generating them.

[0099] Based on the analysis results of violation rules obtained from aggregate analysis, the system will trigger automatic adjustments to the rule weights, dynamically adjusting the enforcement force of the rules through quantitative methods. When the violation frequency of a certain soft constraint rule... Exceeding the preset dynamic threshold When this happens, the system will initiate an update request through the feedback interface. Rule weight The adjustment follows an incremental update algorithm, the formula of which is:

[0100] ,

[0101] in, These are the adjusted weights of the new rules. This represents the current weights. k is a learning rate hyperparameter, typically set between 0.01 and 0.05, used to control the step size of the adjustment. This refers to the actual frequency of violations of the rule within the analysis period, while This represents the system's expected acceptable frequency of rule violations. This formula ensures that when the actual frequency of violations exceeds the expected frequency, the rule weight will increase accordingly, thereby imposing stronger constraints on it in subsequent controlled generation tasks.

[0102] Based on the analysis of model errors, the system optimizes the constraint injection method, improves communication with the large language model, and resolves systemic generation biases caused by ambiguous or incomplete instructions. When analysis reveals that a certain type of content, such as the technical specifications of a specific brand, repeatedly contains factual errors or style inconsistencies, the system locates the constraint instruction set template used to generate that content. Through the feedback interface, the system does not modify rule weights but updates or refines the natural language prompt templates associated with that rule node stored in the dynamic knowledge base. For example, the simple instruction "describe processor performance" is optimized to "describe the performance of the A17Pro chip in concise and professional language, including its core count and main technical features, such as improved ray tracing performance," thus providing more accurate and contextual guidance to the large language model and improving the quality of generated content from the source.

[0103] Based on the same inventive concept, such as Figure 2 As shown, the present invention also provides a product content consistency generation and cross-platform alignment system driven by a multilingual terminology database, characterized in that the system includes:

[0104] The dynamic knowledge governance module is used to acquire multilingual terminology data, domain ontology structure and cross-platform field rules, and to integrate and structure the multilingual terminology data, domain ontology structure and cross-platform field rules to build a dynamic knowledge base with feedback interface;

[0105] The controlled generation module is used to respond to product content generation requests. Based on the term constraints and entity attribute relationships in the dynamic knowledge base, it constructs a constraint generation context and uses the constraint generation context to drive the large language model to generate the key points of the original product content in a controlled manner, thereby obtaining the product content to be verified.

[0106] The consistency verification module is used to call the dynamic knowledge base to perform automatic consistency verification on the content of the product to be verified, generate violation location information, correct the content of the product to be verified based on the violation location information, and output the verified product content.

[0107] The platform alignment module is used to extract the corresponding platform field format constraints from the dynamic knowledge base according to the target publishing platform, perform cross-platform adaptation rewriting operation on the verified product content, generate platform-aligned product content, and simultaneously generate alignment explanation records.

[0108] The audit log module is used to integrate the alignment explanation records with the product content aligned with the platform, generate traceable audit logs, and update the dynamic knowledge base based on the traceable audit logs through the feedback interface.

[0109] Using the launch of the Apex-1 smartphone as an example, this project required the simultaneous release of product content in English and German, conforming to the specifications of each platform, across multiple e-commerce platforms.

[0110] The system integrates three types of data sources: a glossary containing terms related to "Apex-1" and its components, such as "Quantum-Z processor" and "Lumina screen" in multiple languages, including English, German, and Chinese; a domain ontology (OWL file) that defines the entity attribute relationships under the "smartphone" category, such as "must include" processor model and "recommended to include" battery capacity; and cross-platform field rules (JSON file) that describe the field requirements for each platform.

[0111] To prioritize rules, the system assigns quantitative weights to them. For example, the rule "R-Legal-001: Titles are prohibited from using superlative words such as 'best' and 'first'" is based on an authoritative rating system. The rule is set at 1.0, and the business impact factor is specified. Its weight is 1.0. It is set to 1.0 as a hard constraint. For the rule "R-Suggest-005: Header suggestion includes screen size", its... The business recommendation is 0.6, and the business impact factor is... The score is 0.7, which is the feedback rating. The initial value is 1.0. Based on the weighting formula... Its initial weights are calculated as follows: As a soft constraint, the knowledge network, which integrates rule weights and a unit conversion table, was ultimately loaded into the Neo4j graph database to construct a dynamic knowledge base.

[0112] The system received a request to generate product content, parsed the original product content key points as [“Quantum-Z processor”, “6.8-inch Lumina screen”, “5000mAh battery”, “titanium alloy frame”], the target language as “en-US”, and the product category as “smartphone”. The system then extracted weights from the dynamic knowledge base based on the category. Hard constraints, such as "R-Legal-001", and weights Soft constraints, such as "R-Suggest-005", form a constraint instruction set. This instruction set, along with the original key points, is encapsulated into a structured hint and submitted to a large language model with a temperature set to 0.2. The model generates the following product content to be validated: "The best new Apex-1 phone with the quantum-z processor. It has a large 6.8-inch Lumina screen and an impressive 5000mah battery."

[0113] The system parses the content to be verified, constructs a temporary subgraph, and performs graph matching with a dynamic knowledge base. Multiple inconsistencies were identified during the matching process:

[0114] 1. The content "best" violates the hard constraint rule "R-Legal-001". .

[0115] 2. The term “quantum-z” does not match the capitalization of the standard term “Quantum-Z” in the knowledge base, violating rule “TERM_CASE_012”.

[0116] 3. The format of the numerical value and unit "6.8 inch" does not conform to the standard format "6.8-inch" defined by the knowledge base, violating rule "UNIT_FORMAT_004".

[0117] 4. The unit “mah” does not conform to the standard term “mAh”, violating rule “UNIT_CASE_007”.

[0118] The system generates violation location information and performs automatic point-to-point correction, outputting the verified product information: "The powerful new Apex-1 phone with the Quantum-Z processor. It has a large 6.8-inch Lumina screen and an impressive 5000mAh battery."

[0119] The target platform is a German website. The system extracts the platform's field mapping rules from the knowledge base: the title must be at most 150 characters long and must be in German; the weight unit must be grams (g). The validated input contains an English title and "weight: 0.485lbs" from the specifications. The system generates a rewrite instruction set: 1. Translate the title and description into German; 2. Truncate the German title to within 150 characters; 3. Convert the weight "0.485lbs" to grams. The system executes the instructions, first calling the translation engine, then converting the weight to "220g". Simultaneously, it generates an atomic record unit for the weight conversion operation: {"field":"weight","original_value":"0.485lbs","modified_value":"220g","rule_id":"AMZN_DE_WEIGHT_UNIT"}. All record units are assembled into an aligned interpretation record.

[0120] The aforementioned alignment explanation records were integrated with the platform-aligned content, and metadata such as operation timestamps were added to generate traceable audit logs. After one week of operation, the backend analysis task aggregated and analyzed the audit logs, finding that the rule "TERM_CASE_012" (term case sensitivity) was triggered and corrected 3,500 times out of 10,000 content generation operations, indicating a high frequency of violations. Based on the analysis results, the system identified the problem as a model error and optimized the constraint injection method related to "processor" in the dynamic knowledge base through the feedback interface. The original prompt "describe the processor model" was optimized to "precisely describe the processor model using the official standard name '[processor_name]', paying attention to capitalization".

[0121] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0122] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for consistent product content generation and cross-platform alignment driven by a multilingual terminology database, characterized in that: The method includes: Acquire multilingual terminology data, domain ontology structure, and cross-platform field rules, and integrate and structure the multilingual terminology data, domain ontology structure, and cross-platform field rules to construct a dynamic knowledge base with a feedback interface; In response to the product content generation request, a constraint generation context is constructed based on the term constraints and entity attribute relationships in the dynamic knowledge base. The constraint generation context is then used to drive the large language model to generate the key points of the original product content in a controlled manner, resulting in the product content to be verified. The dynamic knowledge base is invoked to perform automatic consistency verification on the content of the product to be verified, generate violation location information, and correct the content of the product to be verified based on the violation location information, and output the verified product content. Based on the target publishing platform, the corresponding platform field format constraints are extracted from the dynamic knowledge base, and cross-platform adaptation rewriting is performed on the verified product content to generate platform-aligned product content, and alignment explanation records are generated simultaneously. The alignment interpretation records are integrated with the product content aligned with the platform to generate a traceable audit log, and the dynamic knowledge base is updated based on the traceable audit log through the feedback interface.

2. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 1, characterized in that, The construction of a dynamic knowledge base with a feedback interface includes: The multilingual terminology data, domain ontology structure, and cross-platform field rules are parsed to generate initial structured terminology entries, attribute relationship models, and platform field mapping tables. Based on the attribute relationship model, the structured term entries are semantically associated to generate a set of domain term entities with domain context; By associating the platform field mapping table, cross-platform constraints are attached to each entity in the domain terminology entity set to form an interconnected multi-dimensional constraint network. Assign rule weights to the constraint relationships in the multidimensional constraint network and set the feedback interface; By integrating the multidimensional constraint network and unit conversion rules, and establishing indexes and query interfaces, a dynamic knowledge base is constructed.

3. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 2, characterized in that, The obtained product content to be verified includes: Receive a product content generation request, and parse the original product content highlights, language, and product category from the product content generation request; Based on the language and product category, the corresponding terminology constraints and attribute relationships are retrieved from the dynamic knowledge base to form a constraint instruction set; The constraint instruction set and the original product content points are encapsulated in a structured prompt format to construct a constraint generation context; The constraint generation context is expanded and structured to output the product content to be verified.

4. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 3, characterized in that, The set of constraint formation instructions includes: Extract category subgraphs related to the product category from the dynamic knowledge base; The rule nodes and relation edges in the category subgraph are transformed into natural language condition prompts that distinguish between hard and soft constraints according to preset rule weights, thereby forming a constraint instruction set.

5. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 3, characterized in that, The output verified product content includes: Based on the multidimensional constraint network in the dynamic knowledge base, the content of the product to be verified is matched and verified, and a verification result report that identifies all consistency differences is generated. Analyze the verification result report, classify and locate the inconsistencies, and directly extract or synthesize deterministic repair fragments from the specific rule nodes that were violated to form violation location information, wherein the violation location information includes the identifier of the specific rule node that was violated; Based on the violation location information, the content of the product to be verified is automatically corrected at specific points, thereby outputting the verified product content.

6. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 5, characterized in that, The generated verification result report, which identifies all consistency differences, includes: The entities, attributes, and values ​​in the product content to be verified are mapped to nodes and edges to construct a temporary subgraph; The temporary subgraph is graph matched with the multidimensional constraint network in the dynamic knowledge base to verify whether the existence of nodes and the relationship between edges conform to the preset definition. By traversing the path of the multidimensional constraint network, logical reasoning is performed on nodes or edges that fail to match or have inconsistencies to identify logical conflicts and generate a verification result report.

7. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 5, characterized in that, The generated platform aligns the product content and simultaneously generates an alignment explanation record, including: Query and extract platform field mapping rules corresponding to the target publishing platform identifier from the dynamic knowledge base; The platform field mapping rules are applied to the corresponding fields of the verified product content. By comparing the differences, a rewrite operation instruction set containing content rewriting, format conversion and unit conversion operations is generated. Execute the rewrite operation instruction set one by one to generate platform-aligned product content; Simultaneously, for each executed instruction, an atomic record unit containing a key-value pair structure including the original value, the modified value, and the rule ID key on which it is based is generated. All atomic record units are then assembled into a serialized, aligned, and interpreted record.

8. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 7, characterized in that, The step of generating a traceable audit log and updating the dynamic knowledge base based on the traceable audit log through the feedback interface includes: Obtain the alignment explanation record and associate the verified product content with the platform-aligned product content to form a version change unit; The version change unit is appended with external metadata including operation timestamp, operator identity identifier and generation source, and a unique corresponding digital digest or hash check code is generated based on the content, thereby encapsulating the content alignment operation into a structured audit log entry. The audit log entries are appended to a preset audit record repository to build a traceable audit log; The traceable audit logs are analyzed, and the rule weights or update constraints in the dynamic knowledge base are adjusted through the feedback interface.

9. The method for generating consistent product content and achieving cross-platform alignment driven by a multilingual terminology database according to claim 8, characterized in that, The step of analyzing the traceable audit logs and adjusting the rule weights or update constraints in the dynamic knowledge base through the feedback interface includes: The traceable audit logs are aggregated and analyzed to identify and analyze violations of rules or model errors. Based on the analysis results of the violation rules, the rule weights of the corresponding rule nodes in the dynamic knowledge base are adjusted through the feedback interface; Based on the analysis results of the model errors, the corresponding constraint injection method in the dynamic knowledge base is optimized through the feedback interface.

10. A product content consistency generation and cross-platform alignment system driven by a multilingual terminology database, characterized in that: The system includes: The dynamic knowledge governance module is used to acquire multilingual terminology data, domain ontology structure and cross-platform field rules, and to integrate and structure the multilingual terminology data, domain ontology structure and cross-platform field rules to build a dynamic knowledge base with feedback interface; The controlled generation module is used to respond to product content generation requests. Based on the term constraints and entity attribute relationships in the dynamic knowledge base, it constructs a constraint generation context and uses the constraint generation context to drive the large language model to generate the key points of the original product content in a controlled manner, thereby obtaining the product content to be verified. The consistency verification module is used to call the dynamic knowledge base to perform automatic consistency verification on the content of the product to be verified, generate violation location information, correct the content of the product to be verified based on the violation location information, and output the verified product content. The platform alignment module is used to extract the corresponding platform field format constraints from the dynamic knowledge base according to the target publishing platform, perform cross-platform adaptation rewriting operation on the verified product content, generate platform-aligned product content, and simultaneously generate alignment explanation records. The audit log module is used to integrate the alignment explanation records with the product content aligned with the platform, generate traceable audit logs, and update the dynamic knowledge base based on the traceable audit logs through the feedback interface.

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