A marketing content intelligent generation and optimization method for GEO

Abstract

This invention discloses a method for intelligent generation and optimization of marketing content for GEO (Generative Marketing Objects), specifically relating to the field of intelligent marketing content generation technology. It addresses the problem that existing marketing content generation processes often suffer from inconsistent constraints on product facts, activity rules, applicable scope, and timeliness boundaries, leading to issues such as fact drift, scope extrapolation, timeliness distortion, and mixing of content from multiple sources. By uniformly collecting, splitting, and constructing a marketing fact graph from product information, activity rules, applicable scenarios, regional rules, and question-and-answer records, and combining this with target fact subgraph extraction, candidate content skeleton generation, answer reorganization evaluation, and published content correction, the method achieves unified constraints on fact sources, applicable boundaries, timeliness relationships, and version relationships during the marketing content generation process. This effectively reduces fact drift, scope extrapolation, timeliness distortion, and content mixing, improving the consistency between generated content and the target question and publication scenario.

Description

Technical Field

[0001] This invention relates to the field of intelligent marketing content generation technology, specifically a method for intelligent generation and optimization of marketing content for GEO (Generation and Operation). Background Technology

[0002] Most existing marketing content generation technologies are based on a combination of product master data, activity configuration data, rule templates, and question-and-answer corpora. They generate marketing answers tailored to user question-and-answer scenarios through rule matching, content assembly, or model generation. In practical applications, these technologies typically handle tasks such as organizing product selling points, inserting activity information, and adjusting contextualized expressions, and are widely used in e-commerce guides, activity descriptions, customer service support, and content distribution. As generative question-and-answer capabilities are increasingly used in marketing scenarios, the generation process is no longer limited to single data tables or fixed templates, but is gradually shifting towards dynamic generation based on the aggregation of information from multiple sources, thus improving the flexibility of marketing answers.

[0003] However, in existing technologies, product information, activity rules, applicable scenarios, regional rules, and historical Q&A records are often scattered across different business systems. Field definitions, version definitions, and effective boundaries are not consistent. The generation process typically focuses more on whether an answer can be generated, lacking comprehensive management of the factual sources, applicable scope, timeliness, and version relationships behind the answer. This is especially true when generating marketing answers for specific questions. If conditions such as activity periods, regional restrictions, fulfillment boundaries, and applicable targets are not simultaneously constrained, the generated content is prone to mixing information from different times, regions, or sources, leading to issues such as fact drift, scope extrapolation, timeliness distortion, or omission of constraints. These problems are more pronounced in business environments with frequent changes to marketing activities, multiple versions of product rules, and simultaneous operation of Q&A across multiple channels, ultimately affecting the consistency between marketing answers and actual business rules.

[0004] Furthermore, existing technologies for utilizing external answers typically remain at the level of result monitoring, manual sampling, or simple error correction. Even when discrepancies arise between the actual external content and internal marketing facts, there is often a lack of a unified mechanism for fine-grained comparison and feedback correction around the factual items, resulting in a lack of a stable closed loop between the generation model, content skeleton rules, and published results. Therefore, existing technologies still need to further address a key issue: how to uniformly constrain product facts, activity conditions, applicable boundaries, timeliness, and version status during the generation of marketing content based on target questions, and how to continuously correct these discrepancies after publication by incorporating external answer deviations, thereby reducing inconsistencies between the generated marketing content and actual business rules. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for intelligent generation and optimization of marketing content for GEO, in order to solve the problems mentioned in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent generation and optimization of marketing content for GEOs, comprising:

[0007] S1. Collect product information, activity rules, applicable scenarios, regional rules, and Q&A records, and break them down into marketing fact units with factual subjects, attribute values, applicable scope, timeliness indicators, and source indicators;

[0008] S2. Construct a marketing fact graph based on the mutual exclusion, inheritance, and homology relationships among the marketing fact units, and mark conflict constraints and version constraints;

[0009] S3. Perform scenario identification on the target question, generate question-and-answer scenario identifiers, and extract the target fact subgraph that satisfies the conflict constraint and version constraint from the marketing fact graph based on the question-and-answer scenario identifiers;

[0010] S4. Generate candidate content skeletons with fact mapping identifiers based on the target fact subgraph, and input the candidate content skeletons into the engine answer proxy model to obtain the corresponding answer reorganization results;

[0011] S5. Based on the answer reorganization results, calculate the fact retention value, the limit loss value, and the citation risk value, perform expression perturbation search on the candidate content skeleton, and determine the target published content that meets the stability constraints.

[0012] S6. Collect external answers corresponding to the target published content, extract the factual items of the answers and compare them with the marketing fact graph, generate offset markers, and update the engine answer proxy model and candidate content skeleton generation rules based on the offset markers.

[0013] Furthermore, S1 includes:

[0014] Product information, activity rules, applicable scenarios, regional rules, and Q&A records should be aligned according to product association identifiers, source record time, approval effective time, and on-site listing time.

[0015] The aligned records are formatted, special symbols are cleaned up, synonyms are unified, fields are filled in, and sentences are segmented, with a single semantic fragment as the minimum merging boundary.

[0016] Marketing fact units are formed based on the factual subject, attribute value, scope of application, timeliness identifier, and source identifier in a single semantic fragment.

[0017] Furthermore, S2 includes:

[0018] The relationships between marketing fact units are determined in the order of mutual exclusion, inheritance, and homology.

[0019] Each marketing fact unit is used as a graph node, and mutually exclusive, inheritance, and homologous relationships that are determined to be valid are written as relation edges respectively;

[0020] Mark conflict constraints on the edges of mutual exclusion relationships, and mark version constraints on the edges of inheritance relationships.

[0021] Furthermore, S3 includes:

[0022] Based on the exclusion phrases, product association conditions, core phrase conditions of the question, restrictive phrase conditions, and scenario priority recorded in the scenario rule version, the question-and-answer scenario is determined;

[0023] And generate question-and-answer scenario identifiers based on the judgment results.

[0024] Furthermore, S3 also includes:

[0025] Based on the question-and-answer scenario identifier, the nodes in the marketing fact graph are filtered out according to the question-and-answer scenario name and attribute item mapping table;

[0026] Then, narrow the graph nodes according to the product association identifier, geographical origin, and time range corresponding to the question reception time, and check the conflict constraints and version constraints;

[0027] Retain the nodes that satisfy the constraints and their corresponding edges.

[0028] Furthermore, S4 includes:

[0029] Based on the question-and-answer scenario identifier, attribute item mapping table, and constraint verification results, content fragments are formed from the node number set and relation edge set of the target fact subgraph;

[0030] Given the existence of a main statement segment, the content segments are arranged in the order of main statement segment, conditional segment, supplementary segment, and limiting segment;

[0031] And write a corresponding fact mapping identifier for each content fragment.

[0032] Furthermore, S5 includes:

[0033] Based on the answer reconstruction results, perform back-pointing verification on the content fragments in the candidate content skeleton;

[0034] Based on the results of the back-pointing verification, a fact retention value, a limit for loss of information, and a commingling risk value are generated;

[0035] Each version of the stability rule records its own calculation results and whether the results are satisfied.

[0036] Furthermore, S5 also includes:

[0037] First, apply stability constraints to the original candidate content skeleton;

[0038] When the original candidate content skeleton does not meet the stability constraints, expression perturbation search is performed in the following order: fragment order adjustment, synonym replacement, conditional fragment fronting, restriction fragment fronting, supplementary fragment backing, and fragment merging and splitting, and the best candidate is retained within the same type of perturbation.

[0039] Then, the target content to be published is determined based on the results of the stability constraint judgment.

[0040] Furthermore, S6 includes:

[0041] After segmenting the external answer text by sentence, it is then grouped into answer fact items by fact subject, attribute item, attribute value, scope of application, and timeliness identifier.

[0042] Locate candidate nodes in the marketing fact map based on fact subjects and attribute items;

[0043] Then perform a comparison based on version status, attribute value, applicable scope, and expiration date.

[0044] An offset marker is generated based on the offset in the comparison results.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. By uniformly collecting, aligning, and splitting product information, activity rules, applicable scenarios, regional rules, and Q&A records, marketing fact units with factual subjects, attribute values, applicable scope, timeliness identifiers, and source identifiers are formed. Based on this, a marketing fact graph is constructed. Further, target fact subgraphs are extracted around the target question, and candidate content skeletons with fact mapping identifiers are generated. Then, the fact retention value, limitation loss value, and mixed content risk value are calculated in combination with the answer reorganization results to constrain and correct the published content. This achieves the goal of uniformly constraining the fact source, applicable boundaries, time validity, and version relationships in the marketing content generation process. It can effectively reduce fact drift, extrapolation of applicable scope, timeliness distortion, and mixed content from multiple sources in marketing answers, so that the generated marketing content maintains a higher consistency with the target question, marketing fact graph, and publishing scenario.

[0047] 2. By collecting external answers corresponding to the target published content, extracting factual items from the answers and comparing them with the marketing fact graph, offset markers are generated. Then, the engine answer proxy model and candidate content skeleton generation rules are updated based on the offset markers. This achieves the purpose of closed-loop correction and continuous optimization of the marketing content generation results. The system can promptly identify attribute value offsets, scope of application offsets, timeliness offsets, and new offsets in external answers, and feed these offsets back to subsequent batch tasks, thereby improving the ability of subsequent marketing content generation processes to correct deviations in actual external performance and adapt to rules. Attached Figure Description

[0048] Figure 1 A flowchart illustrating the overall process of intelligent generation and optimization of marketing content for GEO (Geographical Organization).

[0049] Figure 2 A schematic diagram illustrating the construction and constraint marking of a marketing fact graph;

[0050] Figure 3 This is a schematic diagram illustrating question-and-answer scenario recognition and target fact subgraph extraction.

[0051] Figure 4 A schematic diagram of the candidate content skeleton and fact mapping identifier;

[0052] Figure 5 This is a schematic diagram for stability assessment and expression perturbation search. Detailed Implementation

[0053] 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, and 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.

[0054] Example: Combined with Appendix Figures 1-5 This embodiment provides a method for intelligent generation and optimization of marketing content for GEOs, including:

[0055] GEO stands for Generative Engine Optimization, which refers to the technology of organizing and optimizing the factual information, applicable boundaries, timeliness, and version status of content for generative search engines or generative question-answering engines.

[0056] S1. Collect product information, activity rules, applicable scenarios, regional rules, and Q&A records, and break them down into marketing fact units with factual subjects, attribute values, applicable scope, timeliness indicators, and source indicators. The specific implementation is as follows:

[0057] The executing entity reads product information, activity rules, applicable scenarios, regional rules, and Q&A records in batches at the marketing content production site, and breaks them down into marketing fact units according to a unified recording standard. Product information is taken from the on-sale product archives and change registration records in the product master data warehouse, and is collected around the name, model, specifications, price range, applicable objects, usage restrictions, and after-sales commitments of individual products within the current effective period. The current effective period is fixed as the time range from the start time of the batch task back to the most recent effective listing record and forward to the delisting or expiration record. The name uses the full Chinese name, and the specifications use the approved and already listed on the product. The actual published text displayed on the details page uses prices in yuan and timestamps in Beijing time (millisecond level). Recording is event-triggered and supplemented with 5-minute cycle checks. Supplementation is limited to retrieving original records that failed to be successfully entered into the database at the time of the event trigger and supplementing newly generated but not yet entered records from the same batch before its launch. Speculative supplementation is not performed on prices, applicable scope, or source identifiers. The allowed time drift for a single field is no more than 1 second; records exceeding this tolerance retain their original values ​​but are marked as pending time synchronization verification. Activity rules are taken from the marketing activity configuration ledger and approval records, focusing on the activity name, start and end times, applicable products, target audience, and discount terms. The collection of items, collection restrictions, and fulfillment boundaries is based on the effective version corresponding to the approval time. Activity rules that have not been approved cannot proceed to this step. Applicable scenarios are taken from the operational configuration records on the product details page, historical advertising material filing records, and approved scenario descriptions in standard customer service Q&As. Scenario descriptions are limited to Chinese phrases that correspond to fixed consumption locations, fixed usage tasks, or fixed recommendation scenarios. Home, office, commuting, outdoor, and gifting are recorded as separate scenario items and are not mixed with audience tags. Regional rules are taken from the regional sales policy table, the shipping reach area table, and the compliance restriction table, focusing on the provincial, municipal, and district-level geographical scope and sales status. The following information is collected: delivery time, price difference criteria, and restrictive statements. Regional codes are based on administrative division codes. Missing codes will not be automatically filled in and will be transferred to the pending data entry. Question and answer records are taken from customer service reception logs, on-site question and answer logs, and publicly released marketing question and answer follow-up records. They are collected based on the user's actual question text, reply text, question time, product association identifier, and regional source. Only records that have completed de-identification and compliance review within 180 days before the start of the batch task are included. The 180 days are calculated by consecutive calendar days. Fields involving identity information, contact information, precise address, and payment information are deleted before entering this step, and only the de-identified reference identifier is retained.

[0058] To ensure consistency in subsequent segmentation, alignment will first be completed based on product association identifiers, source record times, approval effective times, and on-site listing times. Product association identifiers will be prioritized to determine the ownership of the same record; approval effective times will be used to determine version validity; and on-site listing times will only be used to fill time boundaries when the first two do not conflict. If the approval effective time is missing, the on-site listing time cannot substitute for the effective time of the activity rules. After alignment, the following steps will be performed sequentially: format standardization, special symbol cleanup, synonym unification, missing field completion, and sentence segmentation. Special symbol cleanup will only remove decorative symbols that do not carry attribute meaning; model connectors, price symbols, time separators, and region separators will be retained. Synonym unification will be based on an approved and effective thesaurus, with the thesaurus recording words... The entry must include the following: standard term, version number, effective date, and approval identifier. Each entry corresponds to only one standard term. In case of version conflict, the entry with the later effective date and that has already been approved will prevail. Completion is only allowed for one-way mapping between region name and region code, association between activity start and end time and approval number, and binding between product model and unique identifier in the product master file. Price, scope of application, and source identifier cannot be automatically completed. If any of these three types of fields are missing in a record, the process will stop. If the source exists but the fields are contradictory, the effective date is later than the expiration date, or different sources give mutually exclusive values ​​for the same field, the record will be moved to pending verification. If the source exists but the required field is missing, the record will be moved to pending supplementation. If the source cannot be associated, the record will be moved directly to pending verification. If the same record meets two conditions simultaneously, pending verification will take priority.

[0059] When splitting, the minimum merging boundary is a single semantic fragment. A single semantic fragment is defined as the shortest Chinese fragment that can uniquely correspond to one factual subject, one attribute value, one set of applicable scope, one set of time-limited identifier, and one source identifier after being separated from the original sentence. Periods and semicolons can be used for direct splitting, while commas are used only when they do not disrupt the overall expression of price ranges, time ranges, regions, model strings, and preferential conditions. If a factual subject, time-limited identifier, or source identifier is missing after splitting, the adjacent fragments are merged backward once. If the backward merging is still not valid, the adjacent fragments are merged backward once. If the backward merging is still not valid, the adjacent fragments are moved to the next verification stage.

[0060] Each source record containing independently valid content that can be verified through source document backtracking is broken down into marketing fact units, where the fact subject is fixed to a single product, single activity, or single scenario object. When the same source record involves both products and activities, a product fact subject is formed around the product, and an activity fact subject is formed around the activity. These are separated and not merged into the same marketing fact unit. When a question-and-answer record contains both a product association identifier and a clear activity name, both product and activity fact subjects should be generated, with the product fact subject written first and the activity fact subject written subsequently. Each should occupy an independent record. If a question-and-answer record does not contain a product name but has a product association identifier, the fact subject is determined by the product association identifier. If the determination fails, no marketing fact unit is formed.

[0061] The attribute value corresponds to a single attribute content that can be directly published or directly verified. It is allowed to use one of the following: text, numerical value, time period, or enumeration value. It is not allowed to write two independent restriction conditions in the same attribute value. Independent restriction conditions are limited to restrictions that constrain different boundary items and can still be established independently after being split. Geographic restrictions, time restrictions, population restrictions, and quantity restrictions are considered as independent restriction conditions. When there are unclear boundary expressions such as "some areas", "recently", "small quantity", or "limited quantity", it will be directly transferred to pending verification.

[0062] The scope of application corresponds to the range items that have been explicitly stated in the source record of the product set, the population set, the scenario set, and the region set. When multiple categories coexist, the intersection is recorded. If a certain category is missing, the field of that category is recorded as null and marked as not explicitly stated. When reading in the next stage, only the explicitly stated range will be effective. Null values ​​should not be interpreted as unlimited or automatically extrapolated. When the attribute value itself has a restriction condition, the scope of application and the restriction condition are intersected again.

[0063] The validity period identifier corresponds to the effective start time, the expiration end time, and the version number. The effective start time is taken first from the record time of approval and effectiveness, and secondly from the record time of product listing effectiveness. The expiration end time is taken first from the record time of cancellation, and secondly from the record time of expiration. If there is no expiration record, it is recorded as continuously valid, but this only means that no expiration record has been found up to the time the batch task was started.

[0064] The source identifier corresponds to the unique primary source number among the original record number, approval number, or log number. For product information, the product change record number is the primary source; for activity rules, the approval number is the primary source; for applicable scenarios, the operation configuration record number is the primary source; for regional rules, the regional policy record number is the primary source; and for Q&A records, the Q&A log number is the primary source. When merging multiple sources, the record number that directly determines the factual subject and attribute value is used as the primary source identifier.

[0065] To ensure order, idempotency, and deduplication, idempotent keys are generated within the same batch in a fixed order: source identifier, factual subject, attribute value, scope of application, timeliness identifier, and version number. Fields are formatted and normalized before being generated. If any field is empty, no idempotent key is generated, and the record is moved to pending verification. Duplicate arrivals are considered to be based on the consistency of all standardized fields, and no duplicate entries are made; only the arrival count and most recent arrival time are added. When an old version is still in effect, a new record can only be overwritten if there is approval evidence and the version number legally increments in the order of approval. Otherwise, the old version is retained for the next stage, and the new record is moved to pending verification.

[0066] The maximum delay for processing a single record is set to 300 milliseconds, the maximum concurrent task per batch is set to 32, and a single failed record will be retried twice with retry intervals of 500 milliseconds and 1500 milliseconds respectively. If the number of retries is exceeded, the record will be moved to the failure queue but will not block the entire batch of tasks.

[0067] After processing, each marketing fact unit is written into the marketing fact unit library in the form of a structured record. At the same time, the original source, rule version, parameter version, processing time, execution entity identifier, and status code are written into the evidence trace chain. The structured record must include at least the fact subject, attribute value, scope of application, timeliness identifier, source identifier, record status, creation time, and version number. It is available for the next stage to read through three types of controlled call interfaces: new submission, batch query, and source backtracking. The minimum set of status codes is fixed as success, missing field, missing source, version conflict, time mismatch, and duplicate arrival. The reasons for failure are fixed as missing field, missing source, version conflict, time mismatch, and duplicate arrival, and they correspond one-to-one with the status codes.

[0068] During on-site inspection, no fewer than 200 marketing fact units generated in each batch are randomly selected by product category for manual review. If the accuracy rate of fact subject positioning is less than 99%, the consistency rate of attribute value restoration is less than 98%, the timeliness mark matching rate is less than 99%, or the traceability rate of source mark does not reach 100%, the entire batch will be stopped from entering the next stage and returned to the queue for verification.

[0069] Preferably, in the e-commerce content production line for home appliances, based on products sold in the most recent 90 days, 5,000 original records are processed in a single batch, forming 4,860 marketing fact units. 48 are transferred to be verified, 19 are transferred to be supplemented, 112 are duplicates, and 300 are manually checked. The accuracy rate of fact subject positioning is 99.2%, the consistency rate of attribute value restoration is 98.8%, and the timeliness identifier matching rate is 99.4%. Alternatively, the marketing fact unit library can use relational storage to table by field or document storage to archive by record. As long as the field definitions of fact subject, attribute value, scope of application, timeliness identifier, and source identifier are consistent, and it is possible to trace back by source, lock by version, and call by batch, it is an equivalent implementation of this step.

[0070] S2. Construct a marketing fact graph based on the mutual exclusion, inheritance, and homology relationships among the marketing fact units, and mark conflict constraints and version constraints. The specific implementation is as follows:

[0071] The executing entity reads marketing fact units written to the marketing fact unit library in the previous stage and with a success status code in batches at the marketing content production site. It identifies the mutual exclusion, inheritance, and homology relationships between each marketing fact unit according to a unified relationship determination standard, and constructs a marketing fact graph based on this. Marketing fact units are taken from structured records under the same batch of tasks that have completed version locking and source tracking. The reading scope is limited to records written before the batch task creation time and not rolled back in the entire batch. The execution location is in the relationship building stage of the marketing content production line, the executing entity is the relationship building operation process corresponding to the batch task, and the effective time period is after the formation of the marketing fact unit and before the next stage's call. Reading is based on creation time. The version number and source identifier are aligned in order, and the fact subject, attribute item, attribute value, scope of application, time limit identifier, and source identifier are formatted in the same way as in the previous step. The attribute item is fixed as the attribute category identifier to which the attribute value belongs, which is taken from the approved field name or field code in the source record. The format is fixed, including removing leading and trailing spaces, unifying full-width and half-width characters, unifying time to millisecond timestamps, and unifying the use of regional codes for regional fields, with Chinese names as a supplement. No new fields are added or supplemented. If any marketing fact unit has missing fields, an unsuccessful status code, a contradictory time limit identifier, or an untraceable source identifier, the processing of that record is immediately stopped and it is transferred to the pending verification stage, without interrupting the processing of other records in the same batch.

[0072] In this step, the mutual exclusion relationship is fixed as follows: when two marketing fact units have a non-empty intersection of the same fact subject, the same attribute item, and the scope of application, and their timeliness identifiers overlap, the attribute values ​​cannot be simultaneously valid. For the same fact subject, the determination criterion is whether both the unique identifier of the primary source and the Chinese display name in the fact subject field are consistent; if they are different, the unique identifier of the primary source prevails. Overlapping timeliness identifiers are limited to situations where the effective intervals of the two marketing fact units have a non-zero length overlap; if they are only connected end-to-end without any overlap, it is not considered overlapping. Whether attribute values ​​cannot be simultaneously valid is determined in the following order: first, format unification; second, synonym unification; and third, attribute value comparison. Numerical attribute values ​​are uniformly compared to the smallest currency unit or the smallest unit of measurement. For units of measurement, textual representations that differ but have the same conversion result are considered identical. Time-based attribute values ​​are uniformly converted to start and end timestamps before comparison; those with consistent start and end boundaries are considered identical. Enumerated attribute values ​​are compared using the approved and effective standard enumerated values; those with consistent standard enumerated values ​​are considered identical. If, after format normalization and synonym normalization, they are still different, and only one approval record is retained as the currently valid record, they are considered mutually exclusive. If the approval records cannot be adjudicated, the coexistence of on-site display records will be used as a supplementary adjudication. The currently valid record is limited to the source record whose approval status is approved and whose timeliness identifier covers the creation time of the current batch of tasks. If, after synonym dictionary normalization, they point to the same standard expression, they must not be considered mutually exclusive.

[0073] In this step, the inheritance relationship is fixed and limited to two marketing fact units forming a succession around the same fact subject and the same attribute item. The subsequent marketing fact unit continues or replaces the previous marketing fact unit based on the approval record, listing change record, or resale and re-issuance record. Version continuation is limited to the attribute value remaining unchanged and the time stamp being consecutive. Version replacement is limited to the attribute value changing and the subsequent one replacing the previous one and continuing to be effective. The succession is limited to the starting point of the subsequent one being equal to the end point of the previous one's expiration, or later than the end point of the previous one's expiration, with an interval not exceeding one batch task cycle. If the interval exceeds this, an inheritance relationship cannot be established. If the approval basis is missing, the version number is backwards, or the source link is discontinuous, an inheritance relationship cannot be established. When the version number is a text version identifier, the order of approval effective time is used as the sole basis for incremental determination.

[0074] In this step, the same-origin relationship is fixed as two marketing fact units originating from the same primary source identifier, or originating from different source identifiers but being merged into the same primary business item in the order of approval number, product change order number, operation configuration order number, and Q&A follow-up task number. The fact subject, attribute items, and attribute values ​​are the same after normalization. Time-of-use identifiers are allowed to have inclusion or complete overlap. The primary business item is limited to a single business document item that can directly determine the basis for the establishment of the fact subject and attribute values. When multiple documents coexist, the document that is hit first in the above order is the only primary business item. Among them, the approval number, product change order number, operation configuration order number, and Q&A follow-up task number are all necessary document candidates in the merging of primary business items. If the previous document is not hit, the next document in the next order will continue to be searched until it is hit and then the subsequent search will stop. Source notes, manual annotations, and display snapshots are only used as auxiliary documents and are not used as the basis for determining the primary business item. If the source link cannot be merged in this order, it shall not be recorded as a same-origin relationship.

[0075] The order of relationship determination is fixed as follows: first determine mutually exclusive relationships, then inheritance relationships, and finally homology relationships. Once a pair of marketing fact units is determined to be mutually exclusive, inheritance and homology relationships will not be determined. If all necessary conditions for mutual exclusion are not met, inheritance relationship determination will proceed. If all necessary conditions for mutual exclusion and inheritance are not met, homology relationship determination will proceed. If determination cannot be completed solely due to missing fields or broken source links, the process will proceed directly to pending verification. Only one type of relationship record is retained for the same pair of marketing fact units, and homology relationships are no longer retained after inheritance is established.

[0076] When constructing a marketing fact graph, each marketing fact unit is treated as a graph node. Established mutually exclusive, inheritance, and homologous relationships are written as different relationship edges into the graph record area. Each graph node retains at least the node number, fact subject, attribute item, attribute value, scope of application, timeliness identifier, source identifier, and version number. Each relationship edge retains at least the starting node number, ending node number, relationship type, relationship determination basis, rule version number, and creation time. The relationship determination basis includes at least the set of source identifiers, the set of approval record numbers, the timeliness interval, and the rule version number. Node numbers are generated in a fixed order based on the source identifier, fact subject, attribute item, attribute value, timeliness identifier, and version number. Relationship edges generate idempotent keys in a fixed order based on the starting node number, ending node number, and relationship type. Fields are formatted uniformly before generation. Time fields are standardized to milliseconds, and geographic fields use geographic codes. Chinese names are only used for display. If an idempotent key already exists, it is not repeated; only the arrival count and most recent arrival time are added.

[0077] After completing the marketing fact graph construction, continue to mark conflict constraints and version constraints on the established relationship edges. Conflict constraints are only marked on mutually exclusive relationship edges, and are only established when the intersection of applicable scopes is not empty. The intersection of applicable scopes is calculated sequentially in the order of product set, audience set, scenario set, and region set. If a certain type of scope item is not explicitly stated to be empty, skip that type and continue to calculate the intersection of subsequent explicitly stated scope items. If a certain type of scope item is explicitly stated and the intersection result is empty, immediately stop subsequent intersection calculations and determine that the intersection is empty. The recorded content includes at least the conflict start time, conflict end time, conflict scope item, and conflict version constraint. The conflict resolution criteria are as follows: the conflict scope item only records the scope items that are explicitly stated and whose intersection is not empty; null values ​​shall not be interpreted as unlimited. Version constraints are only marked on inheritance relationship edges, and the recorded content includes at least the previous version node number, the subsequent version node number, the succession method, the version effective order, and the overriding permission flag. When a version continues, the overriding permission flag is marked as prohibiting overriding. When a version is replaced, it is marked as allowing overriding only when the approval basis exists and the version number legally increases in the approval effective order; otherwise, it is marked as prohibiting overriding. The same-source relationship edge is not marked with conflict constraints and version constraints, but only retains the same-source identifier and the source tracing basis.

[0078] To ensure continuous on-site operations, the observation window for relationship determination in this step is fixed at 30 days prior to the batch task creation time to the batch task creation time. Marketing fact units that cross the observation window but are still in a continuously valid state as of the batch task creation time will only participate in inheritance relationship determination when there is an approval link connection with the node within the observation window. They will not participate in mutual exclusion relationship determination or same-origin relationship determination. The continuously valid state is limited to the expiration endpoint in the time-limited identifier being empty and no revocation record being found as of the batch task creation time.

[0079] A single batch of concurrent tasks can be set to 16 paths, and the maximum delay for determining a single relationship can be set to 200 milliseconds. A single failed task will be retried twice, with retry intervals of 500 milliseconds and 1500 milliseconds respectively. After the number of retries is exceeded, the pair of nodes will be written to the failure queue and the reason for failure will be recorded. This will not block the continued execution of the entire batch of tasks. The reasons for failure are fixed as node missing, source chain broken, timeliness conflict, version rollback, and duplicate arrival. Among them, source chain broken is limited to the missing or inconsistent necessary documents currently hit in the main business item merging sequence with the previous confirmed documents. Relationship conflict is limited to the same pair of nodes being repeatedly determined as different relationship types under the same rule version. When a relationship conflict occurs, the first and last arrival records are not retained. The corresponding relationship that has been written is immediately marked as invalid and a trace is retained, which is not available for the next step. At the same time, the entire pair of nodes is transferred to the pending verification.

[0080] The completed marketing fact graph is written into the graph storage area in the form of structured records. It is available for the next stage to read through three types of controlled call interfaces: batch query, query by fact subject, and node backtracking. The returned content includes at least node records, relationship edge records, conflict constraints, and version constraints. The minimum set of status codes is fixed as success, missing node, relationship conflict, broken source link, version rollback, and duplicate arrival. The reasons for failure are fixed as missing node, relationship conflict, broken source link, version rollback, and duplicate arrival, and correspond one-to-one with the status codes.

[0081] During on-site inspection, no less than 200 new relation edges are extracted from each batch of newly written relation edges according to relation type for manual review. The consistency rate of relation type determination is no less than 99%, the accuracy rate of conflict constraint marking is no less than 99%, the accuracy rate of version constraint marking is no less than 99%, and the source traceability completeness rate reaches 100%. If any indicator is not met, the entire batch is stopped from entering the next stage and is returned to the queue for verification.

[0082] Preferably, in the e-commerce content production line for home appliances, 4860 marketing fact units are read in a single batch, forming 4860 graph nodes and 9120 relationship edges within a 30-day observation window. Among these, there are 640 mutually exclusive relationship edges, 2180 inheritance relationship edges, 6300 homologous relationship edges, 640 conflict constraint markers, 2180 version constraint markers, and 300 manually checked. The consistency rate of relationship type determination is 99.1%, the accuracy rate of conflict constraint marking is 99.3%, and the accuracy rate of version constraint marking is 99.4%. Alternatively, the graph storage area can adopt relational storage with separate tables for nodes and relationships, or document storage with grouped archiving by fact subject. As long as the field definitions of marketing fact units, mutually exclusive relationships, inheritance relationships, homologous relationships, conflict constraints, and version constraints are consistent, and it is possible to trace back by node, deduplicate by relationship, and call by batch, it is an equivalent implementation of this step.

[0083] S3. Perform scenario identification on the target question, generate question-and-answer scenario identifiers, and extract the target fact subgraph that satisfies the conflict constraint and version constraint from the marketing fact graph based on the question-and-answer scenario identifiers. The specific implementation is as follows:

[0084] The executing entity receives the target question at the marketing content production site and immediately identifies its execution scenario. It then generates a question-and-answer scenario identifier and extracts the target fact subgraph from the marketing fact graph based on this identifier, ensuring it meets conflict and version constraints. The target question is taken from text records that have undergone anonymization and compliance review, found in the site's Q&A entry point, customer service assistance response entry point, activity page Q&A component, or external content access gateway. Before entering this step, only the question text, receiving time, channel source, geographic source, product association identifier, and batch task number are retained. The execution location is in the question arrangement stage of the marketing content production line. The executing entity is the question parsing process bound to the current batch task, and its effect occurs after the marketing fact graph is built but before the candidate content skeleton is generated. When reading the target question, it is first aligned according to the batch task number, receiving time, and channel source completion order, then format normalization is performed before noise cleanup. Format normalization includes removing leading and trailing spaces. The process standardizes full-width and half-width characters, time expressions to standard time phrases, and regional abbreviations to standard regional names. Noise cleanup only removes decorative symbols, consecutive repeated characters, and meaningless emoticons that do not carry semantic meaning; it does not remove product model numbers, price symbols, time separators, or regional names. This step does not speculate on or supplement question text, regional source, or product association identifiers. If a product association identifier is missing but the question text matches only one product after item-by-item comparison with the full product name, approved abbreviation, and unique model number, the process continues for that product. If zero or multiple product association identifiers are matched, the process is moved to pending verification. When a regional source is missing, it is simply recorded as an empty value and retained for continued execution; it cannot be interpreted as nationwide. Question record numbers with the same number in the same batch of tasks are recorded as duplicate arrivals and are not written repeatedly. Question record numbers with different numbers in the same batch of tasks but whose question text, channel source, receiving time, and product association identifier are completely identical after standardization are also recorded as duplicate arrivals, with only the arrival count and most recent arrival time added.

[0085] In this step, scene recognition is fixed to determine the question-and-answer scene to which the question belongs based on the normalized question text, product association identifier, geographical origin, and channel origin of the target question. The question-and-answer scene is the business scenario category directly corresponding to the on-site marketing response, and it is fixed to adopt 6 basic scenarios: recommendation, price comparison, adaptation, activity description, parameter verification, and after-sales explanation. The scene name is taken from the scene rule version that has been approved and locked by batch task. Even if the scene rule version is updated during the batch task execution, the locked version of the current batch shall not be changed. The scene rule version shall at least record the scene name, trigger phrase, exclusion phrase, effective time, version number, and the necessary condition set corresponding to the scene. The necessary condition set is fixed to be composed of one or more items selected from product association conditions, core phrase conditions, and restrictive phrase conditions, and is locked along with the scene rule version.

[0086] During execution, the decision is made in a fixed order: exclusion phrase, product association identifier, core question phrase, restrictive phrase, and scenario priority. The exclusion phrase is the complete phrase matching rule; if it matches, the corresponding scenario is immediately invalidated. The core question phrase is the merged category identifier, limited to six categories: price, usage, comparison, discount, parameters, and after-sales service. The restrictive phrase is the approved complete phrase set. A question-and-answer scenario is only considered to have been matched if all necessary conditions for a given scenario are met and the exclusion phrase is invalidated. When the same target question matches multiple question-and-answer scenarios, the scenario with the highest priority is retained in the following order: parameter verification, activity description, price comparison, compatibility, recommendation, and after-sales explanation. Questions that do not match any question-and-answer scenarios are moved to pending verification.

[0087] When generating question-and-answer scenario identifiers, a structured record is written in a fixed order, consisting of batch task number, target question record number, question-and-answer scenario name, and scenario rule version number. The generation time is only used for record keeping and does not participate in uniqueness determination. If the question text, scenario name, and scenario rule version number are completely consistent after being normalized, they will not be written repeatedly.

[0088] When extracting the target fact subgraph based on the question-and-answer scenario identifier, the graph nodes, relationship edges, conflict constraints, and version constraints are read from the marketing fact graph that matches the current batch task number and has a successful status code. First, nodes are filtered according to the mapping table between the question-and-answer scenario name and attribute item, and then the scope is further narrowed according to the product association identifier, geographical source, and the timeliness range corresponding to the question reception time. The attribute item mapping table is locked by batch task. The recommended scenario corresponds only to the product's main attribute, applicable scenario, and basic rights attribute items. The price comparison scenario corresponds only to the price, discount, and fulfillment boundary attribute items. The adaptation scenario corresponds only to the applicable audience, applicable task, and restriction attribute items. The activity description scenario corresponds only to the activity name, activity period, redemption conditions, and fulfillment boundary attribute items. The parameter verification scenario corresponds only to the specifications, capabilities, time conditions, and restriction description attribute items. The after-sales explanation scenario corresponds only to the after-sales commitment, return and exchange scope, and timeliness description attribute items. No new graph nodes may be added during the filtering process, and the fact subject, attribute item, attribute value, applicable scope, timeliness identifier, and source identifier may not be rewritten.

[0089] In this step, satisfying conflict constraints and version constraints is fixed as follows: nodes entering the target fact subgraph must meet the screening conditions of question-and-answer scenario name, product association identifier, geographical origin, and timeliness range with the question reception time as the sole point of reference, and must not be within the scope of conflict constraints valid for the current question. When inheritance relationships exist, only nodes allowed to enter are retained based on version constraints. The question reception time must fall within the question observation window from 7 days prior to the batch task creation time to that creation time. Subsequent timeliness range checks, conflict constraint checks, and version constraint checks will all use the question reception time as the sole point of reference, and will not use the batch task creation time again. Conflict constraint determination is based on the product set. The system checks each item in the following order: population set, scenario set, region set, and time interval. If a previous item is found to be unsatisfactory, the subsequent checks stop. Conflict removal is only triggered when the conflict scope item intersects with the current question's product association identifier, question-and-answer scenario name, region source, and question reception time. Version constraint determination checks each item in the following order: continuation method, version effective order, and coverage permission flag. If a previous item is found to be unsatisfactory, the subsequent checks stop. For nodes with the coverage permission flag set to allow coverage, only the later version node is retained. For nodes with the coverage permission flag set to prohibit coverage, the previous version node is retained, and the later version node is removed. After a node is removed due to conflict or version, its corresponding relationship edge is no longer retained.

[0090] After extraction, the resulting target fact subgraph is written to the subgraph record area in structured record form. The target fact subgraph is fixed as a connected subgraph composed of retained nodes and their existing relation edges that have not been removed, and is not allowed to contain isolated nodes. If the retained nodes are not connected after being filtered by scenario, they are split into multiple connected components according to the fact subject, and only the connected component that directly corresponds to the product association identifier or the unique product name that has been matched in the target question is retained. When there are multiple connected components that directly correspond to the target question, the connected component with the most nodes is retained. If the number of nodes is the same, the connected component with the most relation edges is retained. If the number of nodes and relation edges are the same, the connected component with the earliest creation time is retained, and the remaining connected components are not included in the target fact subgraph. The constraint verification result includes at least conflict constraint verification passed or conflict removed, version constraint verification passed or version removed, and the corresponding adjudication basis number. The subgraph record retains at least the subgraph number and the question and answer. The system includes scene identifiers, node number sets, relation edge sets, constraint verification results, rule version numbers, and creation times. It provides three controlled call interfaces for the next stage: query by question record number, query by question-and-answer scene identifier, and backtracking by subgraph number. The minimum set of status codes is fixed as success, missing question, scene mismatch, missing graph, conflict removal, and version removal. If the target fact subgraph has been successfully generated but contains some removed nodes, it still returns success, and the removed nodes and their reasons are recorded in the constraint verification results. Conflict removal is returned only when all candidate nodes are conflict removed and the target fact subgraph cannot be formed; version removal is returned only when all candidate nodes are version removed and the target fact subgraph cannot be formed. When both conflict removal and version removal cause the failure to form the target fact subgraph, conflict removal takes priority. The reasons for failure are fixed as missing question, scene mismatch, missing graph, conflict removal, and version removal, and each corresponds to a status code.

[0091] To ensure continuous operation on-site, this step only performs scene recognition within the observation window of questions from 7 days prior to the batch task creation time to the creation time. Target questions that cross the observation window will not enter this step. The upper limit of the execution delay for a single target question can be set to 150 milliseconds, and the number of concurrent tasks in a single batch can be set to 32. A single failed task will be retried twice, with retry intervals of 500 milliseconds and 1500 milliseconds, respectively. After the number of retries is exceeded, the question will be written to the failure queue and the reason for failure will be recorded, without blocking the continued execution of the entire batch of tasks. The reasons for failure are fixed as missing question, scene mismatch, missing graph, inconsistent rule version, and duplicate arrival.

[0092] During on-site inspection, no less than 200 entries are manually reviewed from each batch of newly generated question-and-answer scenario identifiers and target fact subgraphs, stratified by question-and-answer scenario. The consistency rate of question-and-answer scenario identification is no less than 99%, the accuracy rate of target fact subgraph node retention is no less than 99%, the accuracy rate of conflict constraint verification is no less than 99%, and the accuracy rate of version constraint verification is no less than 99%. If any indicator is not met, the entire batch is stopped from proceeding to the next stage and is returned to the queue for pending verification.

[0093] Preferably, in the e-commerce content production line for home appliances, a single batch receives 5200 target questions, and within a 7-day observation window, 5036 question-and-answer scenario identifiers are generated, including 1180 recommendation scenarios, 940 price comparison scenarios, 860 adaptation scenarios, 1120 activity description scenarios, 706 parameter verification scenarios, and 230 after-sales explanation scenarios. 164 were not matched and were transferred for further review. Further, 5036 target fact subgraphs are extracted, with an average of 12 nodes and 21 relationship edges retained per subgraph. 286 nodes were removed due to conflict constraints, and 412 nodes were removed due to version constraints. 30 nodes were manually sampled. 0 records, 99.2% consistency rate in question-and-answer scenario recognition, 99.1% accuracy rate in retaining target fact subgraph nodes, 99.3% accuracy rate in conflict constraint verification, and 99.4% accuracy rate in version constraint verification; alternatively, the subgraph record area can be stored separately in relational storage by subgraph, node association table, and relation association table, or archived by question record number in document storage. As long as the field definitions of the target question, question-and-answer scenario identifier, marketing fact graph, conflict constraint, version constraint, and target fact subgraph are consistent, and it is possible to trace back by question, call by subgraph, and lock by version, it is an equivalent implementation of this step.

[0094] S4. Generate candidate content skeletons with fact mapping identifiers based on the target fact subgraph, and input the candidate content skeletons into the engine answer proxy model to obtain the corresponding answer reorganization results. The specific implementation is as follows:

[0095] The execution entity reads the target fact subgraph that has been written into the subgraph record area in the previous stage and has a success status code at the marketing content production site. According to the skeleton generation rules locked by the current batch task, it first forms a candidate content skeleton with a fact mapping identifier, and then sends the candidate content skeleton into the engine's answer proxy model to obtain the answer reorganization result corresponding to the candidate content skeleton. The target fact subgraph is located before the skeleton arrangement stage of the marketing content production line. It is read by the skeleton arrangement process bound to the current batch task in the order of question record number, question-and-answer scenario identifier, and subgraph number. The reading scope is limited to subgraph records that have been written to the database before the batch task creation time and have not been rolled back in the entire batch. During reading, the question-and-answer subgraph is read first. The scene identifier, node number set, relation edge set, and constraint verification results are formatted in a consistent manner with the previous steps. The format standardization includes removing leading and trailing spaces, unifying full-width and half-width characters, unifying time to millisecond-level timestamps, and using regional codes for the region field, supplemented by Chinese names. No additions or supplements are made to the node numbers, relation edges, constraint verification results, and rule version numbers in the target fact subgraph. If any target fact subgraph has a missing subgraph number, an empty node number set, a relation edge set that is inconsistent with the node number set, a missing constraint verification result, or a rule version number that is inconsistent with the version locked in the current batch of tasks, the processing of that item will be stopped immediately and the item will be transferred to the pending verification stage.

[0096] The skeleton generation rules are fixed and include fragment classification rules, fragment sorting rules, rules for stopping the writing of subsequent fragments, and fragment adjudication priority. All of these are locked according to the skeleton rule version number and written into the evidence trace chain when the batch task is created. Even if the rules are updated during the execution of the batch task, the locked version of the current batch must not be changed.

[0097] In this step, the candidate content skeleton is fixedly defined as a structured record consisting of a content fragment sequence, a fact mapping identifier set, a skeleton rule version number, and a skeleton number. The content fragment sequence includes at least one or more of the following: main statement fragment, conditional fragment, supplementary fragment, and restrictive fragment. The main statement fragment corresponds to the direct answer required by the target question and contains at least a factual subject and a target attribute value. The conditional fragment corresponds to the effective period, applicable region, applicable population, or eligibility conditions. The supplementary fragment corresponds to the performance boundary, after-sales commitment, or additional benefits. The restrictive fragment corresponds to inapplicable situations, conflict exclusion content, or version restriction content. When the same semantic content meets multiple fragment conditions, it is classified in the order of restrictive fragment, conditional fragment, main statement fragment, and supplementary fragment.

[0098] Each content fragment is obtained from the node number set and relation edge set of the target fact subgraph, arranged sequentially according to the question-and-answer scenario identifier, attribute item mapping table, and constraint verification results. The fact subject, attribute value, scope of application, and time limit identifier in the node fields are written into the fragment text in the order of locked fields. Relation edges are only used to determine the connection order between multiple nodes and the attachment position of the restriction clauses, and do not form fragment text separately. When multiple nodes form a content fragment, irrelevant nodes are first filtered out according to the question-and-answer scenario identifier, then nodes that have been removed due to conflicts or version are removed according to the constraint verification results, and finally, the fragment text is arranged in the order of node creation time from earliest to latest. Multiple nodes under the same attribute item can be merged into the same fragment, but if removing any node would cause the fragment to lose one of the fact subject, target attribute value, scope of application, or time limit boundary, the node is retained in the minimum node set.

[0099] In this step, the fact mapping identifier is fixed as the corresponding mark between each content fragment and the minimum set of nodes and relation edges that actually participate in forming that content fragment. It is written in the form of "fragment number + node number set + relation edge number set + skeleton rule version number" and is written immediately after the fragment is formed. A content fragment can correspond to multiple nodes and multiple relation edges, and a node can also correspond to multiple content fragments, but each fragment number corresponds to only one set of fact mapping identifiers. If the fragment texts within the same candidate content skeleton are completely identical but the set of node numbers or the set of relation edge numbers are different, different fact mapping identifiers must be written separately. If the fragment texts are different but the fact sources are completely identical, they should be written separately according to different fragment numbers and cannot be reused.

[0100] Combination Figure 4 To facilitate understanding of the correspondence between the target fact subgraph, candidate content skeleton, and fact mapping identifier, the following is now presented: Figure 4 The content shown is further explained below:

[0101] Let's start with the target fact subgraph on the left. In the target fact subgraph, the central node is F1: Product (Mobile Phone A). Around this product node, there are multiple fact nodes associated with it. The first is F2: Brand (Brand X), indicating the brand fact corresponding to phone A; the second is F3: Attribute (8GB large memory), indicating the attribute fact corresponding to phone A; the third is F4: Activity (Limited-time Sale), connected to the product node through the "Participate" relationship in the diagram, indicating that phone A participates in a limited-time sale activity; the fourth is F5: Applicable Scenarios (Student Use), indicating the applicable scenario corresponding to phone A; the fifth is F6: Applicable Audience (University Students), continuing from F5, indicating that this applicable scenario further corresponds to the applicable audience of university students; the sixth is F7: Applicable Region (A Certain Region), marked "Restricted Region" in the diagram, indicating that phone A is geographically restricted by the fact that it is applicable in a certain region; the seventh is F8: Regional Rules (Requires Verification of University Student Identity), continuing downward from F7, and marked "Comply with Rules," indicating that when purchasing in a certain region, the regional rule of "Requires Verification of University Student Identity" must be followed; the eighth is F9: Related Questions and Answers (Installment Payment: Not Supported), indicating that installment payment is not supported in the questions and answers related to this product, activity, and regional restrictions. In this way, the target fact subgraph on the left organizes the factual content such as products, brands, attributes, activities, applicable scenarios, applicable audiences, applicable regions, regional rules, and related questions and answers within the same graph structure.

[0102] Then we move on to the candidate content skeleton in the middle. The candidate content skeleton consists of four types of fragments. The first type is the main statement fragment, whose text content is "Mobile phone A has 8GB of memory and is from brand X." This fragment corresponds to the facts of the product, brand, and attributes, directly stating the core product information. The second type is the conditional fragment, whose text content is "Participate in the limited-time flash sale now." This fragment corresponds to the fact of the event, explaining the current event conditions. The third type is the supplementary fragment, whose text content is "Very suitable for university students, meeting their daily study needs." This fragment corresponds to the facts of the applicable scenarios and target audience, supplementing the applicable target and usage context. The fourth type is the restriction fragment, whose text content is "Purchase is limited to a certain region and requires verification of university student identity. Installment payments are not supported." This fragment corresponds to the applicable region, regional rules, and related question and answer facts, expressing the regional restrictions on purchase, identity verification requirements, and payment restrictions. The meaning of this middle part is that the different fact nodes and relationships in the target fact subgraph on the left are organized and arranged into several directly expressible content fragments, thus forming the candidate content skeleton.

[0103] Finally, we enter the fact mapping identifier on the right. The fact mapping identifier indicates which fact nodes and relationships correspond to each content fragment on the left. For the main statement fragment, the right side provides M1, written in the diagram as "Identifier for the corresponding main statement fragment: {F1, F2, F3, R (Brand), R (Attribute)}", indicating that the main statement fragment corresponds to the product node F1, brand node F2, attribute node F3, and the brand and attribute relationships. For the conditional fragment, the right side provides M2, written in the diagram as "Identifier for the corresponding conditional fragment: {F4, R (Participation)}", indicating that the conditional fragment corresponds to the activity node F4 and the participation relationship. For the supplementary fragment, the right side provides M3, written in the diagram as "Identifier for the corresponding supplementary fragment: {F5, F6, R (Applicable Scenarios), R (Applicable Audience)}", indicating that the supplementary fragment corresponds to the applicable scenario node F5, the applicable audience node F6, and the applicable scenario and applicable audience relationships. For the restricted fragment, M4 is given on the right, and in the diagram it is written as "Identifier of the corresponding restricted fragment: {F7, F8, F9, R (Restricted Region), R (Comply with Rules), R (Related Questions and Answers)}", indicating that the restricted fragment corresponds to the applicable region node F7, the regional rule node F8, and the related question and answer node F9, as well as the relationship between the restricted region, the relationship between complying with rules, and the relationship between related questions and answers. In this way, the complete process that the whole diagram wants to express is: first, organize the product-related facts in the target fact subgraph; then organize these facts into main statement fragments, conditional fragments, supplementary fragments, and restricted fragments; finally, write the corresponding fact mapping identifier for each fragment, thereby corresponding the content fragments with their source fact nodes and relational content one by one.

[0104] When forming the candidate content skeleton, it is arranged in a fixed order of main statement fragment, conditional fragment, supplementary fragment, and restriction fragment. The main statement fragment must exist. If the main statement fragment is not formed, it will be directly transferred to the waiting for supplementation and returned as a skeleton missing, and no further fragments will be formed. If the main statement fragment has been formed but the conditional fragment, supplementary fragment, or restriction fragment is missing, the formed fragment will be retained and execution will continue, and the missing fragment type will be recorded in the fact mapping identifier. When multiple main statement fragments can be formed within the target fact subgraph, a unique main statement fragment will be retained in the order of the smallest node set, the largest number of retained relation edges, and the earliest creation time.

[0105] In this step, the engine answer proxy model is fixed as a controlled model entity that performs answer reconstruction simulation on the candidate content skeleton. Its physical location is in the answer reconstruction simulation stage of the marketing content production line. When called, it only receives the content fragment sequence, question-and-answer scenario identifier, fact mapping identifier set, skeleton rule version number, and model version number from the candidate content skeleton, and does not receive the full text record of the original target fact subgraph. The model version number and skeleton rule version number are verified separately. If either version number is inconsistent with the version locked in the current batch task, the submission is stopped and either "model version inconsistency" or "skeleton rule version inconsistency" is returned respectively. Before submission, the fragment sequence number, fact mapping identifier, and question-and-answer scenario identifier are aligned in the order of completion. Then, it is checked whether the fragment sequence number is continuous, whether the number of fact mapping identifiers is consistent with the number of fragments, and whether the set of fact mapping identifiers is consistent with the current skeleton record after ignoring the internal arrangement order of the set. If they are inconsistent, a unified return message "mapping inconsistency" is returned. Under the same batch of tasks, if the candidate content skeleton text, question-and-answer scenario identifier, fact mapping identifier set, and model version number are completely consistent after ignoring the internal order of the set, it is recorded as a duplicate arrival and is not submitted repeatedly.

[0106] In this step, the answer reorganization result is fixedly limited to the reorganized text record and its corresponding fragment source backtracking record returned by the engine's answer proxy model based on the candidate content skeleton. It includes at least the reorganized text, the order of reorganized fragments, the fragment merging status, the fragment deletion status, and the corresponding fact mapping identifier backreference set. All of the above fields are mandatory fields. If any field is missing, it shall not be counted as successful, and the reorganization result shall be returned as missing. Among them, the fragment merging status is limited to the record in which two or more original fragments are merged into one continuous fragment in the reorganized text. The fragment deletion status is limited to the record in which the original fragment as a whole does not enter the reorganized text or only retains part of the semantic content. Partial retention is also counted as deletion. The corresponding fact mapping identifier backreference set is limited to the set that can be backreferenced from the reorganized text fragment to the original fragment fact mapping identifier. Fragments that are not backreferenced at all or only backreferenced to some nodes but fail to backreference to the corresponding fragment sequence number are all counted as fragments that are not backreferenced.

[0107] Upon completion, the candidate content skeleton and the answer reconstruction results are written to the skeleton record area and the reconstruction result record area respectively in structured record form. The skeleton record retains at least the skeleton number, question record number, question-answer scenario identifier, fragment sequence number set, fact mapping identifier set, skeleton rule version number, and creation time. The reconstruction result record retains at least the reconstruction result number, skeleton number, reconstructed text, reconstructed fragment order, fragment merging status, fragment deletion status, corresponding fact mapping identifier backreference set, model version number, and creation time. These records are provided to the next stage through three controlled call interfaces: query by question record number, query by skeleton number, and backtrack by reconstruction result number. The minimum set of status codes is fixed as: success, missing subgraph, missing skeleton, inconsistent mapping, inconsistent skeleton rule version, inconsistent model version, and missing reconstruction result. If a candidate content skeleton has been successfully formed and the answer reorganization result text exists, but some fragments have been deleted or some fragments have not been referenced, the result will still be returned as successful, and the corresponding fragments will be recorded in the fragment deletion situation and reference set. If the reorganized text is empty, the result will be returned as missing and the result will not be returned as missing skeleton. If the main statement fragment has not been formed, the arrangement is stopped before the main statement fragment is formed, or the candidate content skeleton has not been formed, the result will be returned as missing skeleton. If the fragment sequence number is not continuous before being sent to the model, the number of fact mapping identifiers is inconsistent with the number of fragments, or the set of fact mapping identifiers is inconsistent with the skeleton record, the result will be returned as inconsistent mapping. The reasons for failure are fixed as missing subgraph, missing skeleton, inconsistent mapping, inconsistent skeleton rule version, inconsistent model version, missing reorganization result, and duplicate arrival, and each of these corresponds to a status code.

[0108] To ensure continuous operation on-site, this step is only performed on the target fact subgraphs within the question observation window from 7 days prior to the batch task creation time to the creation time of that time. The upper limit for the formation delay of a single candidate content skeleton can be set to 120 milliseconds, and the upper limit for the return delay of the answer reorganization result after a single item is sent to the engine's answer proxy model can be set to 300 milliseconds. The number of concurrent tasks in a single batch can be set to 24 paths. A single failed task will be retried twice, with retry intervals of 500 milliseconds and 1500 milliseconds, respectively. After the number of retries is exceeded, the record will be written to the failure queue and the reason for failure will be recorded, without blocking the continued execution of the entire batch of tasks. The reasons for failure are fixed as follows: missing subgraph, missing skeleton, inconsistent mapping, inconsistent skeleton rule version, inconsistent model version, missing reorganization result, and duplicate arrival, and each is associated with a status code.

[0109] During on-site inspection, no fewer than 200 items are manually reviewed from each batch of newly written candidate content skeletons and answer reorganization results, stratified by question and answer scenario. The consistency rate of candidate content skeleton fragment order is no less than 99%, the accuracy rate of fact mapping identifier correspondence is no less than 99%, and the completeness rate of answer reorganization result return is no less than 99%. If any indicator is not met, the entire batch is stopped from entering the next stage and is returned to the queue for verification.

[0110] Preferably, in the e-commerce content production line for home appliances, 5036 target fact subgraphs are read in a single batch, forming 5036 candidate content skeletons. On average, each candidate content skeleton contains one main statement fragment, two conditional fragments, one supplementary fragment, and one limiting fragment, with an average of 16 sets of fact mapping identifiers. These are further fed into the engine's answer proxy model to obtain 5036 answer reorganization results, including 412 fragment mergings, 286 fragment deletions, 79 fragments without reciprocal references, and 300 manually checked. The consistency rate of the candidate content skeleton fragment order is [not specified]. The accuracy rate is 99.2%, the fact mapping identifier accuracy rate is 99.3%, and the answer reconstruction result retrieval completeness rate is 99.1%. Alternatively, the skeleton record area and the reconstruction result record area can be stored separately in relational storage by skeleton table, fragment table and retrieval table, or archived by question record number in document storage. As long as the field scope of the target fact subgraph, candidate content skeleton, fact mapping identifier, engine answer proxy model and answer reconstruction result is consistent, and it is possible to trace back by question, call by skeleton and lock by version, it is an equivalent implementation of this step.

[0111] S5. Based on the answer reorganization results, calculate the fact retention value, the limit loss value, and the citation risk value; perform an expression perturbation search on the candidate content skeleton to determine the target publication content that satisfies the stability constraints. The specific implementation is as follows:

[0112] The executing entity reads the answer reorganization result that has been written into the reorganization result record area in the previous stage and has a success status code at the marketing content production site. It then calculates the fact retention value, loss limit value, and mixed-index risk value sequentially according to the stability assessment rules locked in the current batch task. Next, it performs an expression perturbation search around the candidate content skeleton to finally determine the target release content that meets stability constraints. The answer reorganization result is taken from the reorganization result record area bound to the current batch task. The reading scope is limited to records that have been completed and stored before the batch task creation time and have not been rolled back in the entire batch. The execution location is the stable release assessment stage in the marketing content production line. The executing entity is the stability assessment operation process bound to the current batch task, and the time period is after the answer reorganization result is formed and the target release content is written into the release record area. Previously, during the reading process, the question record number, skeleton number, and reconstructed result number were aligned in order. Then, the reconstructed text, reconstructed fragment order, fragment merging status, fragment deletion status, and corresponding fact mapping identifier reference set were formatted in the same way as the previous steps. The format standardization included removing leading and trailing spaces, unifying full-width and half-width characters, unifying time to millisecond-level timestamps, and using regional codes for the region field with Chinese names as supplementary display. No new fields were added or supplemented. If any answer reconstructed result had a missing reconstructed result number, missing skeleton number, missing corresponding fact mapping identifier reference set, model version number inconsistent with the current batch task locked version, or skeleton rule version number inconsistent with the current batch task locked version, the process for that result was immediately stopped and moved to the pending verification stage.

[0113] The fixed stability assessment rules include the calculation method for fact retention value, the calculation method for limit loss value, the calculation method for mixed index risk value, the search order for expression perturbation, the stability constraint threshold, and the selection rules for target published content. All of these are locked according to the stability rule version number and written into the evidence trace chain when the batch task is created.

[0114] The fact retention value is fixed based on the total number of fragments in the candidate content skeleton and the number of retained fragments that can still be referenced back to the original fragment by the corresponding fact mapping identifier and maintain consistency between the fact subject and the target attribute value. Consistency of fact subject is limited to the main fact node not being replaced. Consistency of target attribute value is limited to the following: numerical attribute values ​​are identical after conversion to the same minimum unit of measurement; enumerable attribute values ​​are identical after standard enumeration normalization; time attribute values ​​are identical after time boundary normalization; and text attribute values ​​are identical in text content and the defined object after format normalization and synonym normalization. The restriction loss value is fixed based solely on the conditional fragments and restriction fragments in the candidate content skeleton as the total number of constraint fragments. Fragments that do not appear at all in the answer reorganization result, only retain partial semantic content, or retain text but cannot be referenced back to the original fragment number are recorded as lost. Conditional fragments lacking any element of time period, region, population, or receiving conditions are recorded as partially missing semantic content. Restriction fragments... A segment lacking any element of the restriction object or restriction boundary is considered as having partial semantic content missing. The mixed index risk value is fixed based on whether a single recombined segment in the answer recombination result simultaneously points back to two or more sets of distinct fact mapping identifiers. Distinctness is limited to any one of the following: the segment sequence number, node number set, relation edge number set, or version number corresponding to the fact mapping identifier. If only one set of fact mapping identifiers is pointed back, or although multiple sets are pointed back but the above items are completely consistent, it is recorded as no mixed index. The aforementioned fact retention value, restriction loss value, and mixed index risk value are all based on the ratio formed by the corresponding numerator and denominator, and are directly compared with the current batch task locking threshold. The stability assessment record simultaneously writes the numerator, denominator, ratio, whether the result is satisfied, rule version number, and creation time. The aforementioned three calculations are performed in the order of fact retention value, restriction loss value, and mixed index risk value. If the previous item cannot be calculated, the subsequent calculations are stopped and a unified evaluation inconsistency is returned.

[0115] Expression perturbation search is a fixed-term process that involves performing controlled expression transformations on the main statement fragment, conditional fragment, supplementary fragment, and limiting fragment already formed in the candidate content skeleton, and obtaining corresponding answer reorganization results one by one. The controlled expression transformations are limited to six categories: fragment order adjustment, synonym replacement, conditional fragment fronting, limiting fragment fronting, supplementary fragment backing, and fragment merging and splitting. Among them, fronting is defined as adjusting the corresponding fragment to a preset order before the main statement fragment, and backing is defined as adjusting the corresponding fragment to a preset order after the main statement fragment and conditional fragment. Neither of these changes alters the factual mapping of the fragments. The set of knowledge is taken from the version of the perturbation rule locked in the current batch of tasks; the synonym replacement is only performed in the preset replacement order within the scope of the approved and effective synonym dictionary, and the fact subject and target attribute value must not be changed; the fragment order adjustment allows cross-category movement and the fragment number is rearranged in the new order after adjustment; fragment merging and splitting are only allowed to be performed within the same set of fact mapping identifiers. After merging, the new fragment inherits the fact mapping identifiers of the original participating fragments and is recorded in the union mode. After splitting, each new fragment inherits the subset of the fact mapping identifier to which the original fragment number belongs. They must not be merged across different sets of fact mapping identifiers.

[0116] Before performing expression perturbation search, stability constraints are first applied to the fact retention value, restriction loss value, and citation risk value corresponding to the original candidate content skeleton. If the original candidate content skeleton meets the stability constraints, its corresponding text is directly identified as the target published content and will not be included in subsequent perturbation searches. If the original candidate content skeleton does not meet the stability constraints, it is then expanded in the order of fragment order adjustment, synonym replacement, conditional fragment fronting, restriction fragment fronting, supplementary fragment backing, and fragment merging and splitting. After all candidates in the same perturbation category have been traversed, the best candidate in that category is retained in the order of highest fact retention value, lowest restriction loss value, lowest citation risk value, and earliest creation time. When the best candidate in a certain category meets the stability constraints, subsequent category perturbations are stopped and the search is no longer continued.

[0117] The stability constraint is fixed as follows: the fact retention value is not lower than the lower limit of the lock, the loss value is not higher than the upper limit of the lock, and the mixed-reference risk value is not higher than the upper limit of the lock, while satisfying the constraint.

[0118] When determining the target content to be published, the process is carried out in the following order: first, the original candidate content skeleton; second, the perturbation category; and third, the best candidate of the same category is compared. If none of the perturbed candidate content skeletons meet the stability constraints, the original candidate content skeleton text is retained and marked as pending verification in the publication record only if the main statement fragment of the original candidate content skeleton is complete and all three values ​​have been calculated. The pending verification publication must include at least the pending verification mark, the original skeleton number, and the unmet stability constraints, and must not be directly adopted in the next stage.

[0119] Upon completion, the fact-reserved value, the limit loss value, the cross-reference risk value, and the target published content are written into the stability assessment record area and the publication record area in structured record form, respectively. The minimum set of status codes is fixed as success, missing reorganization result, inconsistent assessment, inconsistent perturbation rule version, inconsistent stability rule version, and duplicate arrival. If any of the fact-reserved value, the limit loss value, or the cross-reference risk value cannot be calculated, an inconsistent assessment will be returned. If the original candidate content skeleton does not meet the stability constraints and the candidate content skeleton still does not meet the stability constraints after all perturbations, but is allowed to be published under verification, success will still be returned and marked as published under verification. The reasons for failure are fixed as missing reorganization result, inconsistent assessment, inconsistent perturbation rule version, inconsistent stability rule version, and duplicate arrival, and correspond one-to-one with the status codes.

[0120] To ensure continuous operation on-site, this step is only performed on the answer recombination results in the question observation window from 7 days before the batch task creation time to the creation time of that time. The upper limit of the single stability assessment latency can be set to 150 milliseconds, the upper limit of the single expression perturbation search and obtaining the final target published content latency can be set to 500 milliseconds, the single batch concurrent tasks can be set to 24 paths, and a single failure will be retried twice, with retry intervals of 500 milliseconds and 1500 milliseconds respectively. After the number of retries is exceeded, the record will be written to the failure queue and the reason for failure will be recorded, without blocking the continued execution of the entire batch of tasks. The reasons for failure are fixed as missing recombination results, inconsistent assessment, inconsistent perturbation rule version, inconsistent stability rule version, and duplicate arrival, and correspond one-to-one with the status code.

[0121] During on-site inspection, no fewer than 200 entries are manually reviewed from each batch of newly written target release content and stability assessment records, categorized by question and answer scenario. The consistency rate of factual retained values, the consistency rate of restricted missing values, the consistency rate of mixed index risk values, and the consistency rate of target release content selection must be no less than 99%. If any indicator is not met, the entire batch is stopped from proceeding to the next stage and is returned to the queue for verification.

[0122] Preferably, in the e-commerce content production line for home appliances, 5036 answer reconstruction results are read in a single batch, and stability assessment is completed for all 5036 results. Among these, 4682 results have a fact retention value that meets the lower limit of the lock, 241 results have a loss limit value that exceeds the upper limit of the lock, and 113 results have a mixed-index risk value that exceeds the upper limit of the lock. After further performing expression perturbation search, 5036 target content items are determined. Among these, 1460 results directly meet the stability constraints of the original candidate content skeleton, 980 results meet the stability constraints by adjusting the fragment order, 842 results meet the stability constraints by replacing synonyms, 616 results meet the stability constraints by placing conditional fragments before the content, 348 results meet the stability constraints by placing restrictive fragments before the content, and 246 results meet the stability constraints by placing supplementary fragments after the content. Of the fragments merged and split, 188 met the stability constraints, while 356 remained marked as pending verification and release. A manual check of 300 fragments showed a 99.3% consistency rate for factual retention values, a 99.1% consistency rate for loss limit values, a 99.2% consistency rate for mixed-reference risk values, and a 99.0% consistency rate for target release content selection. Alternatively, the stability assessment record area and release record area can use relational storage to separate the data into assessment, disturbance, and release tables, or document storage to archive data by question record number. As long as the field definitions for answer reorganization results, factual retention values, loss limit values, mixed-reference risk values, candidate content skeletons, expression disturbance search, stability constraints, and target release content are consistent, and backtracking by question, calling by release, and locking by version are possible, this step is considered an equivalent implementation.

[0123] S6. Collect external answers corresponding to the target published content, extract factual items from the answers and compare them with the marketing fact graph, generate offset markers, and update the engine's answer proxy model and candidate content skeleton generation rules based on the offset markers. The specific implementation is as follows:

[0124] The execution entity reads target published content written to the publication record area and with a success status code in batches at the marketing content production site. It then continuously collects external answers within a 30-day observation window following the writing of the target published content, based on the target question, question-and-answer scenario identifier, publication number, model version number, and skeleton rule version number. Subsequently, it extracts answer fact items from the external answers, compares these with the marketing fact graph, generates offset markers, and updates the engine's answer proxy model and candidate content skeleton generation rules based on these offset markers. External answers are taken from external Q&A page re-collection records, external answer interface feedback records, or external display crawling records corresponding to the target published content. The re-collection records are limited to those created by re-acquiring externally displayed answers according to publication number, source identifier, and re-collection time; they are only used to retain the actual display content of the external answers at the corresponding time point and are not used to replace the original publication records. This execution location is the publication re-collection stage in the marketing content production line, and the execution entity is the re-collection and update operation process bound to the current batch task, with a fixed collection rhythm. The data is collected once every 4 hours on the first day after release, and once daily thereafter. If the text from the same source remains unchanged after three consecutive recollections, collection from that source will cease, but collection from other sources will not stop. During collection, the data is first aligned in the order of release number, question record number, source identifier, and recollection time. Then, the format of the external answer text, recollection time, source identifier, and external display position is normalized. The external display position is only used as a trace field and is not involved in the extraction of answer fact items, graph comparison, or offset mark determination. The format normalization is fixed, including removing leading and trailing spaces, unifying full-width and half-width characters, unifying the time to millisecond-level timestamps, and using the region code as the primary field for the region field, supplemented by the Chinese name. No inferences are made to complete the external answer text, source identifier, and recollection time. If the source identifier, recollection time, and external answer text are completely identical after normalization under the same release number, it is considered a duplicate arrival and will not be written repeatedly. If any external answer has a missing release number, missing external answer text, missing recollection time, untraceable source identifier, or version number that is inconsistent with the version locked in the current batch task, the processing of that entry will be stopped immediately and the entry will be transferred to pending verification.

[0125] In this step, the answer fact item is fixedly defined as the smallest fact record obtained by segmenting the external answer text into sentences and then merging them item by item according to the fact subject, attribute item, attribute value, scope of application, and timeliness identifier. Sentence segmentation is fixed to be performed sequentially using periods, semicolons, line breaks, and question-and-answer separators with independent factual expression. After segmentation, each sentence segment is merged around only one fact subject and one attribute item to form an answer fact item. Sentence segments that cannot be uniquely merged do not form answer fact items. Each answer fact item must contain both a fact subject and an attribute item; if either is missing, an answer fact item cannot be formed. If there are multiple attribute items in the same sentence segment, it is split into multiple answer fact items. Time conditions and geographical conditions are attached to the answer fact item that is closest to them and has a direct semantic correspondence. If time conditions or geographical conditions cannot be uniquely attached to a single answer fact item, they cannot be repeatedly attached across answer fact items, and the corresponding content is only retained in the original external answer text trace. The following information is not included in the answer fact item record: The fact subject is taken from the standard description of the same product, activity, or scenario object as the target published content in the external answer. The standard description is fixed and taken from the Chinese display name of the fact subject of the corresponding node in the marketing fact graph and its main source unique identifier. In case of conflict between the two, the main source unique identifier shall prevail. The attribute item is taken from the approved and effective field name or field code. The attribute value is normalized according to the numerical, enumeration, time, and text types. The scope of application is recorded according to the explicitly stated scope of the product set, audience set, scenario set, and region set. The time limit is recorded according to the effective start point and the expiration end point. The scope of application is only recorded when it appears explicitly in the external answer. "Nationwide free shipping" is recorded as an explicitly stated region set. "Some regions" is not recorded in the scope of application due to unclear boundaries. If the external answer only contains vague words and the fact subject, attribute item, or attribute value cannot be located, no answer fact item will be formed and the fact item will be returned as missing.

[0126] In this step, the comparison is fixed as a process of verifying each answer fact item against the nodes, relationship edges, conflict constraints, and version constraints in the marketing fact graph. During execution, candidate nodes are first located by fact subject and attribute item, then compared item by item in the order of version status, attribute value, applicable scope, and timeliness identifier. If the version status does not meet the requirements, further comparison is not performed. When multiple candidate nodes exist, a unique candidate node is retained in the following order: the valid version corresponding to the current data collection time takes priority, the latest creation time takes priority, and the smallest node number takes priority. If a unique candidate node cannot be retained even after following the aforementioned order, the answer fact item only returns a mismatch and does not proceed to offset marker generation or any update record writing process. Attribute value comparison is limited to: numerical attribute values ​​being identical after conversion to the same minimum unit of measurement; enumerated attribute values ​​being identical after standard enumeration normalization; and time-related attribute values ​​being identical after conversion to the same minimum unit of measurement. After time boundary normalization, the text content and the defined object are the same; after format normalization and synonym normalization, the text attribute values ​​are the same; the scope of application comparison is limited to the explicitly stated scope in the external answer that does not exceed the allowed scope in the marketing fact graph; the timeliness identifier comparison is limited to the time boundary corresponding to the external answer not falling into the expired interval and not violating the version constraint, and the version constraint verification uses the re-collection time as the only time point; if the answer fact item has a corresponding node in the marketing fact graph and all comparisons are successful, it is recorded as consistent; if there is a corresponding fact subject and attribute item but any attribute value, scope of application or timeliness identifier is not successful, it is recorded as offset; if there is no corresponding fact subject or attribute item, it is recorded as new; the same answer fact item can only produce 1 comparison conclusion. New additions will not enter the offset judgment. When the candidate node cannot be uniquely judged, only the comparison inconsistency is returned, no offset mark is generated, and no update record is written.

[0127] In this step, the offset marker is fixed as a structured marker indicating the deviation of the answer fact item from the marketing fact graph. It includes at least the offset type, corresponding publication number, corresponding answer fact item number, corresponding graph node number set, offset position, rule version number, and creation time. The offset type is fixed to include attribute value offset, scope of application offset, time-sensitive offset, new offset, and mixed offset. Attribute value offset is limited to situations where the fact subject and attribute item are the same but the attribute values ​​are different; scope of application offset is limited to situations where the attribute values ​​are the same but the scope of application exceeds the graph's allowed range; and time-sensitive offset is limited to situations where the attribute values ​​are the same. However, if the timeliness indicator falls into the invalidation range or violates version constraints, the new offset is limited to the absence of a corresponding fact subject or attribute item in the marketing fact graph. The mixed offset is limited to the existence of two or more offset types for the same answer fact item. When the same answer fact item hits multiple offset subtypes at the same time, only one mixed offset mark is generated, and each subtype is recorded within the mark. Individual subtype marks are no longer generated repeatedly. Under the same batch of tasks, if the offset type, answer fact item number, graph node number set and rule version number are completely consistent after being unified, they are not written repeatedly. Only the arrival count and the most recent arrival time are added.

[0128] When updating the engine answer proxy model and candidate content skeleton generation rules based on offset markers, the order is fixed: first update the engine answer proxy model, then update the candidate content skeleton generation rules. The model update record must include at least the publication number, external answer text, target publication content text, offset marker, fact mapping identifier set, answer fact item set, model version number, rule version number, and creation time. The external answer text and target publication content text are used to maintain the comparison before and after the update. The offset marker is used to identify the deviation type. The fact mapping identifier set and answer fact item set are used to limit the fact source boundary corresponding to the update record. A model update record cannot be formed if any of these fields are missing. The skeleton rule update record must include at least the publication number, offset type, fragment position, fragment type, fact mapping identifier set, perturbation method, skeleton rule version number, and creation time. Only when the fragment position, fragment type, and perturbation method corresponding to the offset marker can be located is a new update record generated. When writing candidate content skeleton generation rule update records, if any of the offset markers corresponding to the fragment position, fragment type, or perturbation method cannot be located, only the model update record is retained, and no skeleton rule update record is formed. If the writing of the previous type of update record fails, the writing of the next type of update record is stopped, and an update inconsistency is returned uniformly. If the previous type of update record has been successfully written, but the next type of update record has not yet started or has failed to be written, the previous type of written record is retained but uniformly marked as an ineffective update record, and an update inconsistency is still returned overall. If both types of update records are successfully written, they are recorded as pending update records. They are excluded when called in the current batch of tasks. Only pending update records that have been approved before the creation of the next batch of tasks can form a new model version record and a new skeleton rule version record and take effect when the next batch of tasks is created. The current batch of tasks cannot read or call the new model version record and new skeleton rule version record formed by the above pending update records from creation to completion.

[0129] Upon completion, the external answer, answer fact items, offset markers, model update records, and skeleton rule update records are written into the back-collection record area, fact item record area, offset record area, model update record area, and skeleton rule update record area, respectively, in structured record format. These records are then available for the next batch of tasks to read via three controlled call interfaces: query by release number, query by offset marker number, and backtrack by version number. The minimum set of status codes is fixed as follows: success, missing external answer, missing fact item, mismatch, mismatch model version, mismatch rule version, mismatch update, and duplicate arrival. An empty external answer text indicates a missing external answer; failure to form an answer fact item indicates a missing fact item; a formed answer fact item that cannot be verified due to incomplete fields or the inability to uniquely adjudicate candidate nodes indicates a mismatch; and failure to write either the model update record or the skeleton rule update record indicates a mismatch update. The reasons for failure are fixed as missing external answer, missing fact item, mismatch, mismatch model version, mismatch rule version, mismatch update, and duplicate arrival, each corresponding to a status code.

[0130] To ensure continuous on-site operation, this step is only performed within a 30-day observation window after the target published content is written. The upper limit of the delay for a single external answer collection and format normalization can be set to 150 milliseconds, the upper limit of the delay for a single answer fact item extraction and graph comparison can be set to 300 milliseconds, the upper limit of the delay for a single offset marker generation and update record writing can be set to 300 milliseconds, the single batch concurrent task can be set to 24 paths, a single failure will be retried twice, with retry intervals of 500 milliseconds and 1500 milliseconds respectively. After the number of retries is exceeded, the record will be written to the failure queue and the reason for failure will be recorded, without blocking the continued execution of the entire batch of tasks. The reasons for failure are fixed as missing external answers, missing fact items, inconsistency in comparison, inconsistency in model version, inconsistency in rule version, inconsistency in update, and duplicate arrival, and correspond one-to-one with status codes.

[0131] During on-site inspection, no fewer than 200 records are manually reviewed from each batch of newly written offset markers and update records, categorized by question-and-answer scenario. The consistency rate of the extracted factual items in the answer must be no less than 99%, the consistency rate of the graph comparison must be no less than 99%, the consistency rate of the offset marker generation must be no less than 99%, and the consistency rate of the update record association must be no less than 99%. If any of these indicators are not met, the entire batch will be stopped from entering the next batch of tasks and will be rolled back to the queue for verification.

[0132] Preferably, in the e-commerce content production line for home appliances, 5036 pieces of target published content are read in a single batch, 4820 external answers are collected within a 30-day observation window, and 18640 answer fact items are extracted. Among them, 17102 are consistent with the marketing fact graph, and 1538 offset markers are generated, including 612 attribute value offsets, 284 applicable scope offsets, 193 timeliness offsets, 321 new offsets, and 128 mixed offsets. Further, 1538 update records are written to the engine answer proxy model, of which 74 are marked as ineffective update records. 1176 update records are written to the candidate content skeleton generation rule update record. These 1176 records can be located based on their corresponding fragment position, fragment type, and perturbation method. The remaining 362 records are not written to the candidate content skeleton generation rule update record because at least one of these three factors cannot be located. These 362 records are retained. In the offset record area and model update record area, the data serves as a record for subsequent version revisions, but does not participate in the generation of the current batch of skeleton rule versions. A manual check of 300 records showed a 99.2% consistency rate for extracting answer fact items, a 99.1% consistency rate for graph comparison, a 99.3% consistency rate for offset marker generation, and a 99.0% consistency rate for update record association. Alternatively, the back-collection record area, fact item record area, offset record area, model update record area, and skeleton rule update record area can be separated into relational storage tables or archived by publication number using document storage. As long as the field definitions of the target publication content, external answers, answer fact items, marketing fact graph, offset markers, engine answer proxy model, and candidate content skeleton generation rules are consistent, and backtracking by publication, calling by offset, and locking by version are possible, this step is an equivalent implementation.

[0133] In the operational scenario shown in this embodiment: taking the e-commerce marketing content production line for home appliances as an example, a platform launched an air fryer in East China. The product name registered in the product file is "Household Smart Air Fryer Model A", and the product model is "A6-East China Version". The approved release text on the product details page specifies its capacity, power, applicable family size, support for 7-day no-reason return and exchange, and next-day delivery service in East China. At the same time, the marketing activity configuration ledger has a "Weekend Kitchen Renovation" activity. The activity rules state that the product can receive a coupon of 80 RMB off for purchases over 500 RMB from 6 PM on Friday to midnight on Sunday. The applicable users are real-name authenticated users of the platform, and the applicable regions are Shanghai, Jiangsu, Zhejiang, and Anhui.

[0134] When a batch task is started, the system first reads product information, activity rules, applicable scenarios, regional rules, and Q&A records from the product master data warehouse, marketing activity configuration ledger, regional sales policy table, operation configuration records, and customer service standard Q&A logs. It then breaks these down into marketing fact units according to a unified recording standard. For example, for "Air Fryer Model A", it forms a capacity fact unit, a power fact unit, a promotional activity fact unit, an East China next-day delivery fact unit, a 7-day no-reason return and exchange fact unit, and a "home cooking" applicable scenario fact unit. Each fact unit specifies the subject, attribute value, scope of application, timeliness identifier, and source identifier.

[0135] Subsequently, the system constructs a marketing fact graph based on the mutual exclusion, inheritance, and homology relationships between the various marketing fact units. For example, it establishes an inheritance relationship between the old promotional fact unit "spend 500 and get 50 off" and the new promotional fact unit "spend 500 and get 80 off". It retains fact units such as "next-day delivery in East China" and "free shipping nationwide" that do not conflict in different regions. It establishes a homology relationship between the fact units "support 7-day no-reason return" from the product details page and customer service standard Q&A under the same approval number. The system also marks the version constraints corresponding to the effective period of the promotion and the conflict constraints corresponding to different applicable regions in the graph.

[0136] Subsequently, when a user in Shanghai submits the target question "Is there a promotion for this air fryer now? Can it be delivered to Shanghai tomorrow?" through the in-site Q&A portal at 10:00 AM on Saturday, the system performs scenario recognition on the target question. Based on "Is there a promotion?", it matches the scenario of the promotion description; based on "Can it be delivered tomorrow?", it matches the relevant conditions of the fulfillment boundary. Then, it generates the Q&A scenario identifier corresponding to the promotion description according to the predetermined priority. It also extracts the target fact subgraph that meets the conflict constraints and version constraints from the marketing fact graph, retains the activity name node, activity time period node, redemption condition node, and East China next-day delivery node that are directly related to the current question, and removes expired old activity nodes and nodes from other regions that are not applicable to Shanghai.

[0137] Next, the system generates candidate content skeletons with fact mapping identifiers based on the target fact subgraph. For example, it forms segments such as "Currently available for the weekend kitchen renovation event," "A coupon for 80 RMB off orders over 500 RMB is available from 6 PM Friday to midnight Sunday," "Next-day delivery is supported in Shanghai," and "Specific details are subject to the rules displayed at the time of ordering." Each segment is assigned a corresponding fact mapping identifier. The candidate content skeletons are then fed into the engine's answer proxy model to obtain the corresponding answer reorganization result. If the answer reorganization result merges "80 RMB off coupon" and "Next-day delivery in Shanghai" into the same continuous segment, the system continues to calculate the fact retention value, the limitation of loss value, and the confounding risk value based on the answer reorganization result. The fact retention value reflects whether the activity amount, time range, and fulfillment boundaries are completely retained, while the limitation of loss value reflects whether the "Shanghai area is suitable" rule. Whether constraints such as "region" and "activity period limited to weekends" have been removed, the aliasing risk value reflects whether activity information and performance information have been improperly mixed after reorganization; if the initial answer reorganization result has a high restriction loss value, such as omitting the activity period of "Friday 18:00 to Sunday 24:00", the system performs expression perturbation search around the candidate content skeleton, moves the condition fragments or restriction fragments to the front, and sends them back to the engine answer proxy model for reorganization until the target published content is obtained with a fact retention value not lower than the lower limit of the lock, and restriction loss value and aliasing risk value not higher than the upper limit of the lock, such as "The current air fryer A is eligible to participate in the weekend kitchen renovation activity, the activity period is from Friday 18:00 to Sunday 24:00, and you can receive a coupon of 80 off when you meet the conditions; next-day delivery is supported in Shanghai, the details are subject to the following order page display.

[0138] After the target content is written into the publication record area, the system continuously collects the corresponding external answers according to the locked collection rhythm within the subsequent 30-day observation window. For example, if the external generated answer page returns "This air fryer is currently on sale with a discount of 80 RMB off for purchases over 500 RMB, delivery to Shanghai the next day, and free shipping nationwide" during the recollection, the system extracts factual items such as "discount of 80 RMB off for purchases over 500 RMB", "delivery to Shanghai the next day", and "free shipping nationwide" from the external answer and compares them item by item with the marketing fact graph. It identifies that "free shipping nationwide" is inconsistent with the existing marketing fact graph's factual system, which only applies to next-day delivery in East China and does not explicitly state free shipping nationwide, thus generating an applicable scope offset mark. If the external answer omits the activity period, it will form a timeliness offset or a mixed offset.

[0139] Based on the generated offset markers, the system writes the external answer text, the target published content text, the offset type, the fact mapping identifier set, and the answer fact item set into the engine answer proxy model update record area. At the same time, it writes locatable issues such as "the condition fragment position in the activity description scenario is too late, which may lead to the missing activity period" and "when the performance boundary description and the regional description are adjacent, it may induce nationwide extrapolation" into the candidate content skeleton generation rule update record area. In the current batch, only traces are left and the locked version is not immediately overwritten. After the next batch of tasks is created and approved, a new model version and a new skeleton rule version are formed, thereby realizing the closed-loop operation of marketing content generation, answer reorganization, external answer collection, offset identification, and version update.

[0140] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0141] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0142] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., UHT), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0143] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0145] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0147] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0149] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent generation and optimization of marketing content for GEOs, characterized in that, include: S1. Collect product information, activity rules, applicable scenarios, regional rules, and Q&A records, and break them down into marketing fact units with factual subjects, attribute values, applicable scope, timeliness indicators, and source indicators; S2. Construct a marketing fact graph based on the mutual exclusion, inheritance, and homology relationships among the marketing fact units, and mark conflict constraints and version constraints; S3. Perform scenario identification on the target question, generate question-and-answer scenario identifiers, and extract the target fact subgraph that satisfies the conflict constraint and version constraint from the marketing fact graph based on the question-and-answer scenario identifiers; S4. Generate candidate content skeletons with fact mapping identifiers based on the target fact subgraph, and input the candidate content skeletons into the engine answer proxy model to obtain the corresponding answer reorganization results; S5. Based on the answer reorganization results, calculate the fact retention value, the limit loss value, and the citation risk value, perform expression perturbation search on the candidate content skeleton, and determine the target published content that meets the stability constraints. S6. Collect external answers corresponding to the target published content, extract the factual items of the answers and compare them with the marketing fact graph, generate offset markers, and update the engine answer proxy model and candidate content skeleton generation rules based on the offset markers.

2. The intelligent generation and optimization method for marketing content for GEOs according to claim 1, characterized in that, S1 includes: Product information, activity rules, applicable scenarios, regional rules, and Q&A records should be aligned according to product association identifiers, source record time, approval effective time, and on-site listing time. The aligned records are formatted, special symbols are cleaned up, synonyms are unified, fields are filled in, and sentences are segmented, with a single semantic fragment as the minimum merging boundary. Marketing fact units are formed based on the factual subject, attribute value, scope of application, timeliness identifier, and source identifier in a single semantic fragment. 3.The GEO-oriented marketing content intelligent generation and optimization method of claim 1, wherein S2 include: The relationships between marketing fact units are determined in the order of mutual exclusion, inheritance, and homology. Each marketing fact unit is used as a graph node, and mutually exclusive, inheritance, and homologous relationships that are determined to be valid are written as relation edges respectively; Mark conflict constraints on the edges of mutual exclusion relationships, and mark version constraints on the edges of inheritance relationships.

4. The intelligent generation and optimization method for marketing content for GEO as described in claim 1, characterized in that, S3 includes: Based on the exclusion phrases, product association conditions, core phrase conditions of the question, restrictive phrase conditions, and scenario priority recorded in the scenario rule version, the question-and-answer scenario is determined; And generate question-and-answer scenario identifiers based on the judgment results.

5. The method for intelligent generation and optimization of marketing content for GEO according to claim 4, characterized in that, S3 also includes: Based on the question-and-answer scenario identifier, the nodes in the marketing fact graph are filtered out according to the question-and-answer scenario name and attribute item mapping table; Then, narrow the graph nodes according to the product association identifier, geographical origin, and time range corresponding to the question reception time, and check the conflict constraints and version constraints; Retain the nodes that satisfy the constraints and their corresponding edges.

6. The method for intelligent generation and optimization of marketing content for GEO according to claim 1, wherein S4 include: Based on the question-and-answer scenario identifier, attribute item mapping table, and constraint verification results, content fragments are formed from the node number set and relation edge set of the target fact subgraph; Given the existence of a main statement segment, the content segments are arranged in the order of main statement segment, conditional segment, supplementary segment, and limiting segment; And write a corresponding fact mapping identifier for each content fragment.

7. The method for intelligent generation and optimization of marketing content for GEO according to claim 1, wherein S5 include: The answer reorganization result should include at least the reorganized text, the order of the reorganized fragments, the fragment merging status, the fragment deletion status, and the corresponding fact mapping identifier retracement set; The corresponding fact mapping identifier back-reference set is limited to the set that can be back-referenced from the reconstructed text fragment to the original fragment fact mapping identifier. Fragments that are not back-referenced at all or only back-referenced to some nodes but fail to back-reference to the corresponding fragment number are all recorded as fragments that are not back-referenced. The fact retention value is fixed based on the total number of all fragments in the candidate content skeleton and the number of retained fragments that can still be pointed back to the original fragment by the corresponding fact mapping identifier and maintain the consistency of the fact subject and target attribute value. The missing value is fixed and only the condition fragments and constraint fragments in the candidate content skeleton are used as the total number of constraint fragments. Fragments that do not appear at all in the answer reorganization result, only retain some semantic content, or have text retention but fail to point back to the original fragment number are recorded as missing. The risk value of mixed indexing is fixed based on whether a single recombined fragment in the answer recombining result simultaneously points back to two or more sets of different fact mapping identifiers. The difference is limited to any one of the fragment sequence number, node number set, relation edge number set, or version number corresponding to the fact mapping identifier being different. Each version of the stability rule records its own calculation results and whether the results are satisfied.

8. The method of claim 7, wherein the method further comprises: S5 also includes: First, apply stability constraints to the original candidate content skeleton; When the original candidate content skeleton does not meet the stability constraints, expression perturbation search is performed in the following order: fragment order adjustment, synonym replacement, conditional fragment fronting, restriction fragment fronting, supplementary fragment backing, and fragment merging and splitting, and the best candidate is retained within the same type of perturbation. Then, the target content to be published is determined based on the results of the stability constraint judgment.

9. The intelligent generation and optimization method for marketing content for GEO as described in claim 1, characterized in that, S6 include: After segmenting the external answer text by sentence, it is then grouped into answer fact items by fact subject, attribute item, attribute value, scope of application, and timeliness identifier. Locate candidate nodes in the marketing fact map based on fact subjects and attribute items; Then perform a comparison based on version status, attribute value, applicable scope, and expiration date. An offset marker is generated based on the offset in the comparison results.

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