A content e-commerce marketability diagnosis and multi-dimensional risk checking method and system

Abstract

The application discloses a content e-commerce salability diagnosis and multi-dimensional risk checking method and system. In view of the problems of difficult quantification of salability and non-preposition of hard risk, the method collects multi-source data in the product selection stage into a unified commodity feature object, and aggregates the single price, the decision cycle, the visual performance, the category preference, the market window and the collocation richness with configurable weights, and outputs the content form, the platform, the theme and the salability score. Then, according to the risk rule set, the certainty checking and grading are performed on each risk item of infringement, qualification, quality and regional marketability, and the highest one is taken as the comprehensive risk grade. The hard risk such as missing and blocking of qualification, high risk of infringement and target area ban is taken as preposition one-vote veto which is not entered into the score and is not compensated by high score, and if it is hit, it is determined to be collided and placed, and it is not entered into the downstream. Finally, the salability and risk conclusion are gathered into the diagnosis conclusion object. The salability is quantifiable, and the hard risk is prepositioned and intercepted.

Description

Technical Field

[0001] This invention relates to the fields of data processing and computer technology, specifically to a method and system for performing multi-dimensional quantitative diagnosis of marketability of candidate products and deterministic checks on multi-dimensional risks such as infringement, qualifications, quality, and regional salability of candidate products during the product selection stage of content e-commerce, thereby outputting structured product selection conclusions and recommendations; the marketability diagnosis and multi-dimensional risk checks are performed sequentially on the same batch of candidate products, and structured conclusions that can be consumed by downstream modules are generated at the granularity of each candidate product. Background Technology

[0002] In content e-commerce operations, before launching a campaign or live stream, operators need to determine from a large pool of candidate products whether a particular product is worth promoting, what content format, platform, and theme should be used, and whether the product carries any risk of being unlistable. This determination relies on the product's multidimensional attributes (such as average order value range, decision-making cycle, visual appeal, and category content preferences), external market conclusions (such as market opportunities, recommended price range, and window of opportunity), and related relationships (such as the richness of compatible products). It also requires verification of risk dimensions such as infringement, business qualifications, quality reputation, and cross-regional sales restrictions. How to uniformly quantify the aforementioned multi-source, heterogeneous data into comparable marketability scores during the product selection phase, and how to conduct definitive checks and proactive interception of risk items, has become a crucial technical problem that content e-commerce product selection must solve.

[0003] To complete the above judgments during the product selection phase, current practices often rely on the subjective experience of operations personnel for one-dimensional judgments, or handle product selection and risk verification separately using discrete manual processes. In summary, existing technologies have the following specific shortcomings: The first drawback is that product selection based solely on a single dimension or human experience lacks consistent quantification and the results are not reproducible. Current practices often determine a product's suitability for promotion based on only one dimension or the subjective experience of operations personnel. This approach fails to unify and weight multiple characteristics such as average order value, decision-making cycle, visual appeal, category content preference, market window, and product mix richness under a unified standard, resulting in a comparable marketability score. Furthermore, it does not generate clear recommendations regarding content format, platform, and theme. The judgment results for the same product differ among different people at different times, are not reproducible, and cannot be consistently ranked horizontally among a batch of candidate products.

[0004] The second drawback is that risk verification relies on manual, post-event discovery, which is uncertain and prone to omissions. Current practices often involve sporadic manual checks of business qualifications, infringement investigations, or responses to quality complaints only after products have been selected or even listed. There is a lack of a mechanism to conduct a systematic and definitive check of risk items such as infringement, qualifications, quality, and regional marketability during the product selection stage. Manual verification does not cover all risk dimensions and lacks a unified standard for hit determination and risk classification, resulting in high-risk products being missed and flowing into subsequent content production and distribution stages, leading to high post-event handling costs.

[0005] The third defect is the disconnect between marketability assessment and risk assessment, resulting in a lack of unified conclusions. In current practices, the evaluation of product quality and the verification of risks are often completed by different processes and personnel. The results of the two are independent and lack convergence on the same candidate product. It is impossible to produce a unified conclusion of "do it first, postpone it, don't touch it" based on the two, nor can it output consistent, directly consumable, structured product selection results to the downstream content production and product scheduling stages. This causes the product selection conclusion and the risk conclusion to be disconnected at the time of delivery.

[0006] Fourthly, incorporating risk into the scoring weighting results in hard risks being compensated for by high scores and thus overlooked. Some practices attempt to incorporate risk as a deduction item into the overall score for weighted evaluation. The consequence is that when a product's marketability score is high enough, the deductions for hard risks such as lack of qualifications, high risk of infringement, or prohibition in the target region, which should be grounds for rejection, are offset by high scores. This allows a legally prohibited product to be deemed acceptable due to its high overall score, thus implicitly allowing hard risks to pass. This "risk-benefit trade-off" approach cannot guarantee that hard risks are definitively intercepted in advance.

[0007] Therefore, the industry urgently needs a method and system that can perform a unified, multi-dimensional quantitative diagnosis of marketability for candidate products during the product selection stage, conduct rule-based deterministic checks and classifications of various risks, intercept hard risks as a pre-emptive veto that does not enter the scoring weighting, and converge marketability conclusions and risk conclusions into a unified, structured product selection conclusion and recommendation, so as to simultaneously obtain reproducible marketability quantification, deterministic risk interception, and consistent conclusion delivery during the product selection stage. Summary of the Invention

[0008] (a) The technical problem to be solved by the present invention The technical problems to be solved by this invention are: how to unify and weight the multi-source heterogeneous data of candidate products into comparable marketability scores under a unified standard during the product selection stage of content e-commerce, and output definitive recommendations on content format, platform, and theme; how to conduct definitive checks, hit determinations, and risk classifications on each of the multi-dimensional risk items of candidate products, such as infringement, qualifications, quality, and regional salability, in a rule-based manner; how to intercept hard risks as a pre-emptive veto to prevent them from entering the marketability score weighting, thus avoiding the omission of hard risks due to compensation by high marketability scores; and how to aggregate marketability diagnosis conclusions and multi-dimensional risk conclusions into unified structured product selection conclusions and suggestions for downstream modules to directly consume at the granular level of each product. The aforementioned technical problems are all technical problems of computer systems in normalizing, weighting, performing rule-based deterministic checks, and delivering structured data from multiple sources and heterogeneous sources. This invention solves these problems with a unified data structure and deterministic computation processing, achieving technical effects at the data processing level such as data comparability and normalization, reproducible and deterministic checks, and hard constraint pre-interception, rather than simply business management effects.

[0009] (II) Complete Technical Solution To address the aforementioned technical problems, the first aspect of this invention provides a method for diagnosing the marketability of content e-commerce and conducting multi-dimensional risk checks, comprising the following steps: S1, Feature Collection Stage: For each candidate product, the multi-dimensional features required for its marketability diagnosis are collected and organized into a unified product feature object. The multi-dimensional features include the average order value range from the product database, the decision cycle and category content preference from category configuration, the visual expressiveness from visual evaluation, the market window status from external market conclusions, and the richness of matching from product association relationships. Each feature is normalized to a unified value range according to its registered normalization caliber. In addition, a multi-dimensional risk check pre-set risk rule set is set. The risk rule set organizes qualification verification rules with category and target area as keys, and registers infringement classification threshold, quality risk threshold and regional marketability determination rules. Each risk item in the risk rule set registers an attribute of whether it is a blocking hard risk.

[0010] S2, the multi-dimensional diagnostic stage for marketability: For each candidate product, based on the product's characteristics, the corresponding features in each dimension are weighted and aggregated using configurable scoring weights in three sub-judgments: content format, platform, and theme. The content format sub-judgment calculates scores for multiple candidate content formats based on visual appeal, average order value fit, decision-making cycle fit, and category content preference, selecting the highest score as the recommended content format. The platform sub-judgment calculates scores for multiple candidate platforms by multiplying the platform's prior fit with the category by market window activity, selecting the highest score as the recommended platform. The theme sub-judgment maps the recommended content format and decision-making cycle using a rule table to obtain recommended themes. The content format sub-judgment score and the platform sub-judgment score are then weighted using configurable weights and superimposed with an upper limit correction term derived from the richness of matching options, resulting in a marketability score between the lower and upper limits of the value range. This marketability score, along with the recommended content format, recommended platform, and recommended theme, constitutes the multi-dimensional diagnostic indicator set for the candidate product.

[0011] S3, Multidimensional Risk Deterministic Check Phase: For each candidate product, deterministic checks are performed on each risk item (infringement, qualification, quality, and regional marketability) according to the aforementioned risk rule set, and a hit determination and risk classification are made: Infringement check: The infringement risk level is determined based on the similarity obtained from the search and the number of related lawsuits, according to the registered infringement classification threshold; Qualification check: The required qualification set is obtained by searching the product category and target region, and the missing qualification set is obtained by comparing it with the qualifications already possessed by the product, and the qualification verification status is determined based on whether there are any missing qualifications and whether the qualification rule is blocking; Quality check: The quality risk is determined based on the search score of negative review signals and defect keywords, according to the registered quality risk threshold; Regional marketability check: If the target region is restricted, it is determined whether the product is prohibited from sale in the target region; Then, the highest risk level of the above risk items is taken as the comprehensive risk level, where the missing qualification and blocking type, high quality risk, target region prohibition, and high infringement risk are all judged as high risk; Products with missing qualification and blocking type or a comprehensive risk level of high risk are placed as hit blocking type hard risk.

[0012] S4, Conclusion Aggregation and Recommendation Generation Stage: For each candidate product, a product matching score is calculated based on its marketability score and market window status using configurable weights. This matching score is then deducted according to the overall risk level, resulting in the product's final matching score. Next, based on whether a blocking hard risk is encountered and whether the product's matching score reaches the conclusion threshold, the product selection conclusion is determined as one of three: "Don't touch," "Do first," or "Postpone." Any product encountering a blocking hard risk is directly determined as "Don't touch," and its downstream entry flag is set to "No." This determination is not compensated by any marketability score or product matching score. For products with hard risks that trigger a blockade, the decision to proceed or postpone the process is made based on whether the product matching score reaches the conclusion threshold. Then, according to the differentiated sorting key corresponding to the type of operating entity to which the candidate products belong, the same batch of candidate products are sorted first by the priority of the product selection conclusion, and then by the dimensions of concern for that type, to obtain the sorting results of the batch diagnosis. The multidimensional diagnostic indicator set, multidimensional risk conclusion, product matching score, product selection conclusion and recommendation suggestions of each candidate product are gathered into the diagnostic conclusion object of that product, with each product as the granular output, thus forming a processing link of feature collection, marketability diagnosis, risk check and conclusion aggregation.

[0013] The second aspect of the present invention provides a content e-commerce marketability diagnosis and multi-dimensional risk inspection system, including a feature collection module (10), a marketability diagnosis module (20), a risk inspection module (30), a conclusion aggregation module (40), and a result output module (50), for performing the above method.

[0014] (iii) Technical features that distinguish it from existing technologies The technical features that distinguish this invention from the prior art are the combination of the following items, and each item can be found in the specific embodiments of this specification: The first distinguishing feature is that it aggregates the multi-source heterogeneous data required in the product selection stage into a unified product feature object, and uses configurable scoring weights to uniformly and weight the multi-dimensional features such as average order value, decision cycle, visual appeal, category content preference, market window and combination richness, and outputs comparable marketability scores and definitive recommendations for content format, platform and theme. In contrast, the existing approach relies on a single dimension or human experience to make judgments, which are inconsistent in quantitative standards, have unreproducible results, and lack clear format and platform recommendations.

[0015] The second distinguishing feature is that the risk items of infringement, qualification, quality and regional marketability are checked, hit the target and classified in a standardized manner during the product selection stage, and the highest level of each item is taken as the comprehensive risk level. In contrast, the existing risk verification relies on manual discovery after the fact, and the inspection is uncertain, incomplete in dimensions and inconsistent in terms of standards, which makes it easy to miss the detection.

[0016] The third distinguishing feature is that the marketability diagnosis conclusion and the multi-dimensional risk conclusion are aggregated into a unified diagnosis conclusion object on the same candidate product, and a unified product selection conclusion and a downstream entry indicator are generated based on the two. The structured results are output at the granularity of each product. In contrast, the existing practices are disconnected from product selection assessment and risk verification, which cannot produce a unified conclusion or deliver it to the downstream in a consistent manner.

[0017] The fourth distinguishing feature is that hard risks such as lack of qualifications and obstruction, high risk of infringement, and sales ban in the target area are treated as a prerequisite for exclusion from the marketability score weighting target and cannot be compensated by any marketability score or product matching score: any product that hits the obstruction type hard risk is directly judged as "don't touch" and the downstream entry mark is set to "no". The risk classification is determined independently of the marketability score. In contrast, the existing practice may incorporate the risk into the comprehensive score for weighting, so that hard risks are compensated by high marketability scores and thus hidden and allowed to pass.

[0018] The fifth distinguishing feature is that risk checks are performed sequentially after marketability diagnosis and before conclusion aggregation, so that risk conclusions are both independent items and coordinated with marketability conclusions in the conclusion aggregation stage. Furthermore, the same batch of candidate products is uniformly ranked horizontally according to the type of operating entity using a differentiated ranking key. In contrast, the existing practices lack both sequential coordination between the two and differentiated ranking of batch of candidate products under a unified standard.

[0019] (iv) Technical effects that are not available in the prior art resulting from the above distinguishing features. The combination of the above-mentioned distinguishing technical features produces the following technical effects that are not present in the prior art: Effect 1: Due to the inherent mechanism of distinguishing feature 1, the multi-source heterogeneous data in the product selection stage are normalized to a unified standard and aggregated into a marketability score by a defined weighting rule. Therefore, the same product will receive the same marketability score and recommendations based on form, platform, and theme under the same input. The judgment is reproducible and batches can be compared horizontally, overcoming the problems of inconsistent and unreproducible single-dimensional or experience-based judgments.

[0020] Secondly, due to the inherent mechanism of the second distinguishing feature, each risk item, including infringement, qualification, quality, and regional marketability, is checked, judged, and classified according to deterministic rules during the product selection stage. Risk discovery is transformed from manual ex-post to deterministic inspection before product selection, with comprehensive coverage and consistent standards, significantly reducing the probability of high-risk products being missed and flowing into subsequent stages.

[0021] Effect 3: Due to the inherent mechanism of the distinguishing feature 3, the marketability conclusion and risk conclusion are converged into a unified diagnostic conclusion object for the same product, and a unified product selection conclusion and downstream access indicator are generated. This makes product selection and risk control no longer separate. Downstream modules can directly consume consistent structured results at the granularity of each product, including negative marking of intercepted products.

[0022] Fourthly, due to the inherent mechanism of the fourth distinguishing feature, hard risks are deterministically blocked as a pre-emptive veto that does not enter the scoring weighting and cannot be compensated. Therefore, a product with missing qualifications and blocked, high risk of infringement, or prohibited from sale in the target area will not be judged as inferable because of its high marketability score or product matching score. This mechanism prevents hard risks from being missed due to high score compensation. At the same time, due to the fifth distinguishing feature, risk inspection and marketability diagnosis are carried out in a serial and coordinated manner, and batch candidates are consistently sorted using differentiated sorting keys, so that the conclusions are consistent and fit the focus dimensions of different types of operating entities.

[0023] In summary, the present invention employs technical means to solve technical problems and achieve measurable technical effects. The combination of the distinguishing technical features produces technical effects that are unpredictable by existing technologies, which are both uniformly quantified and marketable, deterministically check risks and intercept hard risks in advance, and deliver a consistent conclusion.

[0024] Furthermore, this invention is not a simple superposition of the aforementioned single-point technologies, but a collaborative closed loop: a unified product feature object provides a unified data contract for marketability diagnosis and risk inspection. Marketability diagnosis completes multi-dimensional quantification on this contract using defined weighted rules. Risk inspection verifies and classifies each item on the same batch of candidates using defined rules. The conclusions converge and then unify the marketability score, risk level, and hard risk indicators into a product selection conclusion and a downstream entry indicator—the four are interlinked, and the lack of any one of them will break the closed loop: without a unified feature object, multi-dimensional data cannot be quantified under a unified standard; without a defined risk inspection, risk discovery will be returned to manual and ex-post methods; without conclusion convergence, marketability and risk will act independently and cannot be delivered consistently; without hard risk pre-positioning, risks can be compensated for by benefits and allowed to proceed. It is particularly noteworthy that this invention treats hard risks such as lack of qualifications and obstruction, high risk of infringement, and sales ban in target areas as a prerequisite veto that prevents products from entering the marketability scoring weighting target and cannot be compensated by any marketability score or product matching score. This is contrary to the common practice in the field of incorporating various factors into a unified scoring weighting and balancing. Those skilled in the art usually tend to incorporate marketability, risk, cost, and benefit into a comprehensive scoring ranking. However, this invention insists on determining hard risks independently of the scoring, and if a risk is detected, the conclusion is immediately set as "don't touch" and the downstream entry indicator is set to "no". This overcomes the technical bias of "risk can be compromised with benefit". This layered design that places hard risks outside of the scoring is the core of this invention's collaborative closed loop, which ensures both marketability and hard risk identification and interception during the product selection stage. It is also the concentrated embodiment of the invention's creativity. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments are briefly introduced below.

[0026] Figure 1This is a schematic diagram of the overall process of the content e-commerce marketability diagnosis and multi-dimensional risk inspection method described in the embodiments of the present invention; Figure 2 This is a schematic diagram of the content e-commerce marketability diagnosis and multi-dimensional risk inspection system described in an embodiment of the present invention; Figure 3 This is a time sequence diagram illustrating a complete feature aggregation, marketability diagnosis, risk check, and conclusion convergence process of the system described in this embodiment of the invention. Figure 4 This is a schematic diagram illustrating the data flow between the steps of the method described in the embodiments of the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device described in an embodiment of the present invention.

[0027] Figure 2 The attached diagrams are labeled as follows: 10 - Feature aggregation module, 20 - Marketability diagnosis module, 30 - Risk check module, 40 - Conclusion aggregation module, 50 - Result output module; Figure 5 The reference numerals in the figures are: 501-processor, 502-memory, 503-bus, 504-communication interface. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Those skilled in the art, after reading this specification, can make equivalent substitutions, combinations, or improvements to the following embodiments based on the principles of the present invention. Any modifications or improvements that do not depart from the spirit and principles of the technical solution of the present invention should be covered within the scope of protection of the present invention. The rounded integer values ​​appearing below are merely illustrative values ​​for ease of understanding and are not actual production configurations, and do not constitute a limitation of the present invention. The collection and processing of data involved in the present invention are carried out under the premise of obtaining explicit authorization from the relevant subject. Sensitive fields are used after irreversible desensitization to remove personal identification identifiers, complying with the relevant provisions of the "Data Security Law of the People's Republic of China" and the "Personal Information Protection Law of the People's Republic of China".

[0029] I. System Overall Architecture and Responsibilities of Each Module See Figure 2The system in this embodiment of the invention serves as a product diagnosis and risk check component in the product selection stage of content e-commerce. It receives a batch of candidate products and their context, and outputs structured product selection conclusions at the granularity of each product. The system is logically divided into a feature collection module (10), a marketability diagnosis module (20), a risk check module (30), a conclusion aggregation module (40), and a result output module (50). The feature collection module (10) is responsible for collecting the multi-dimensional features required for marketability diagnosis for each candidate product and organizing them into a unified product feature object. At the same time, it pre-sets the risk rule set required for multi-dimensional risk checks. The marketability diagnosis module (20) is responsible for weighted aggregation of multi-dimensional features on the product feature object with configurable weights to obtain the recommended content format, recommended platform, recommended theme and marketability score. The risk check module (30) is responsible for performing deterministic checks, hit determination and risk classification on each risk item of infringement, qualification, quality and regional marketability according to the risk rule set, and marking whether the blocking hard risk is hit. The conclusion aggregation module (40) is responsible for aggregating the marketability conclusion and risk conclusion on the same product, calculating the product matching score, determining the product selection conclusion and sorting it differently according to the type of operating entity. The result output module (50) is responsible for outputting the diagnostic conclusion object at the granularity of each product.

[0030] The modules mentioned above are connected through controlled interfaces and structured intermediate results. Interface calls are implemented using a request-response method or a message channel method. The system can be implemented in software, hardware, or a combination of both, and can be distributed and deployed in a private cloud or server cluster. The system can also be implemented as an electronic device containing a processor (501), memory (502), bus (503), and communication interface (504). One unchanging boundary of responsibility for this system is: marketability scoring and risk classification are completed by a deterministic rule engine. Optional natural language interpretation only provides textual explanations of the calculated conclusions and does not participate in any numerical judgment. All confidential fields involving costs and break-even prices are desensitized before participating in any external text generation and are not restored.

[0031] See Figure 4 From an internal processing perspective, a complete diagnosis sequentially goes through stages such as feature aggregation, marketability diagnosis, risk check, conclusion aggregation, and result output. Since the risk check uses the candidate product set from the marketability diagnosis as input, and the conclusion aggregation uses both the marketability conclusion and the risk conclusion as input, there are data dependencies between stages. Therefore, the output of the previous stage is used as the input for the next stage, and structured intermediate results are passed sequentially between stages. The numerical determination processes for marketability diagnosis and risk check are both deterministic calculations without side effects. The same batch of candidate products can be processed in parallel on a product-by-product basis, and the results are reproducible under the same input.

[0032] II. Feature Collection and Commodity Feature Objects (Step S1) The product selection diagnosis of this invention takes a diagnosis request as input. The diagnosis request includes at least the type of operating entity, the main product category, the target region, the candidate product list, and the main target of this analysis. When the candidate product list is empty, the feature collection module (10) pulls a preset number of default candidate products from the product pool according to the main product category. When the target region is missing, a preset default region is used. The main target of this analysis is used to select the differentiated ranking weight in the conclusion aggregation stage. The type of operating entity is taken from enumerated literals such as brand self-operation, OEM operation, manufacturing, expert operation, and cross-regional operation. When it is missing, it reverts to the general type. If the main product category is missing, the verification fails and the upstream is required to complete it. The feature collection module (10) determines the data range and target region according to the diagnosis request, and then collects the product feature objects for each candidate product.

[0033] See Figure 1 and Figure 2 The feature collection module (10) collects a product feature object for each candidate product, which includes at least the following multi-dimensional features: average order value range, which comes from the price system of the product library. Confidential fields involving cost and break-even price are carried in the desensitized range and do not carry the original value during collection; decision cycle, which comes from the category configuration and identifies the purchase decision type of the category. The value is taken from the impulsive, rational and high decision type enumeration literal and mapped to a unified value range; visual expressiveness, which comes from the visual evaluation of the product material and is taken from a unified value range; category content preference, which comes from the category configuration and is carried by the distribution vector of the content form with the highest historical effect ranking of the category; market window status, which comes from the external market conclusion and is taken from the open, early, urgent and closed enumeration literal and converted into an activity coefficient according to the registration mapping; matching richness, which comes from the count of matching relationships of the product in the product association relationship and is converted to a unified value range according to the normalized upper limit of the registration. Each dimension of the feature is normalized according to its registered normalization caliber, so that the original dimensions of the heterogeneous source are unified into dimensionless values ​​that can be weighted and aggregated.

[0034] The feature collection module (10) also pre-sets a risk rule set required for multi-dimensional risk checks. The risk rule set is a data structure governed before release. It organizes qualification verification rules with category and target region as keys. Each qualification rule registers the necessary qualification set for the category in the target region, the issuing or competent authority, the missing risk level, and whether it is a blocking type. It also registers the infringement classification threshold, quality risk threshold, and regional marketability determination rules. Each risk item in the risk rule set registers an attribute of whether it is a blocking type hard risk, which is used to distinguish between soft risks that can be deducted and hard risks that cannot be compensated during the conclusion aggregation stage. The risk rule set is governed with the version number. The rule is updated by adding a new version and supports rollback to the previous version. It is read with a consistent version during runtime to avoid the participation of semi-updated configurations in the check.

[0035] As part of the boundary and anomaly handling in the data aggregation phase: when the source of a certain feature is unavailable or the data is outdated, a pre-defined degradation order is used to fall back—when market conclusions are missing or outdated, the system falls back to the nearest cache, similar category conclusions, and industry average, and the overall confidence of the product's diagnostic conclusion is lowered accordingly, with the degradation reason recorded in the alarm array; when visual assessment is unavailable, the visual performance feature takes the prior median of its category and is marked as degraded; when product association is unavailable, it is skipped along with richness-related derived items. All of the above degradations ensure that the diagnosis can still produce results, but the confidence reduction and alarm marking make the degradation observable and interpretable downstream, rather than silently replacing it with a default value.

[0036] III. Marketability Multidimensional Diagnosis (Step S2) See Figure 1 The marketability diagnosis module (20) performs multi-dimensional marketability diagnosis on the product feature object and outputs the recommended content format, recommended platform, recommended theme and marketability score. The diagnosis is organized into three sub-judgments. Each sub-judgment is deterministically weighted with configurable weights for the corresponding feature dimensions. The weights and prior tables are maintained by the data side by category and loaded into memory for lookup at runtime, without being hardcoded into the logic. The fit score of each sub-judgment and the final marketability score are all normalized to the same value range. In this embodiment, the value range is 0 to 100 (the following values ​​are only demonstration values ​​for easy understanding and are not actual production configurations, and do not constitute a limitation).

[0037] (I) Content Format Determination. For various candidate content formats such as short video product recommendations, live streaming e-commerce, and text / image notes, the four factors of visual appeal, average order value suitability, decision-making cycle suitability, and category content preference are weighted and summed according to the registered weights of each format. The sum is then normalized to a unified value range to obtain the suitability score for each format. The content format with the highest suitability score is recommended. In this embodiment, the weights of the four factors for a certain content format can be 0.4, 0.35, 0.15, and 0.1 respectively (the sum of the four weights is 1) (the following values ​​are only demonstration values ​​for ease of understanding and are not actual production configurations, and do not constitute a limitation). The adaptation of average order value (AOV) varies depending on the content format: for live-streaming e-commerce, the higher the AOV, the higher the adaptation degree through a monotonically increasing mapping; for text and image notes, the lower the AOV, the higher the adaptation degree through a monotonically decreasing mapping; for short video product seeding, the AOV adaptation takes a constant independent of the AOV (as a demonstration value, this embodiment takes the constant as 0.5), meaning that the AOV is not sensitive to it (the following values ​​are only demonstration values ​​for ease of understanding, not actual production configurations, and do not constitute limitations). The monotonically changing mapping is a general form of bounded monotonic function, and its inflection point position and steepness are configurable parameters, not written into fixed calibration values.

[0038] (II) Platform Sub-determination. For each platform in the candidate platform set, the platform's fit score is obtained by multiplying its prior fit with the product category by the activity coefficient calculated from the market window status. The platform with the highest fit score is selected as the recommended platform. The prior fit is registered in a two-dimensional prior table of platform and category, with values ​​between 0 and 1. The activity coefficient is calculated from the market window status according to the registration mapping, ensuring high activity when the window is open or early, and low activity when it is urgent or closed, thus positively correcting the platform fit score within a window with better market opportunities. As a demonstration value, this embodiment can set the prior fit of a platform to a product category to 0.8, and set the activity coefficients for the market window status as open, early, urgent, and closed to 1.0, 0.8, 0.5, and 0.2, respectively (the following values ​​are only demonstration values ​​for ease of understanding, not actual production configurations, and do not constitute limitations).

[0039] (III) Topic Determination. Based on the predetermined recommended content format and the product's decision-making cycle, recommended topics are obtained through a topic rule table. The topic values ​​are derived from the enumerated literals of evaluation, scenario, comparison, and product recommendation. The topic rule table records the content format and decision-making cycle as the key and the recommended topic as the value. For example, when short video product recommendation is paired with a rational decision-making cycle, it is mapped to the comparison category. The rule table is maintained by the data side and obtained by looking up the table at runtime, and is determined to be a deterministic mapping.

[0040] (iv) Marketability Score Aggregation. The marketability score is obtained by weighting the matching score of recommended content format (obtained from the content format sub-judgment) and the matching score of recommended platform (obtained from the platform sub-judgment) with configurable weights, and then superimposed with an upper limit correction term derived from the richness of pairings. This aggregation yields the marketability score, which is ultimately tailored to the upper limit of a unified value range. The correction term is obtained by multiplying the richness of pairings by a registered upper limit correction coefficient, and its contribution to the marketability score does not exceed the registered upper limit correction, thus ensuring that products with rich pairing relationships receive a bounded positive boost without overshadowing the main product. The marketability score, together with the recommended content format, recommended platform, and recommended theme, constitutes a multidimensional diagnostic indicator set for the candidate product. As a demonstration value, in this embodiment, the weights of the content form sub-judgment score and the platform sub-judgment score can be set to 0.4 and 0.35 respectively (the sum of the two does not exceed 1), the upper limit of the matching correction can be set to 10 points, and the matching richness can be counted according to the matching relationship, divided by a registration upper limit (in this embodiment, the upper limit is 20) and truncated to 1 (the following values ​​are only demonstration values ​​for easy understanding, not actual production configurations, and do not constitute a limitation).

[0041] The marketability rating is a deterministic hard calculation. The optional natural language interpretation is only generated after the rating and recommendation have been calculated. It uses the rating and basic product information as input to generate a readable explanation of why the recommendation is made. The interpretation does not participate in any numerical judgment, and confidential fields involving cost and break-even price are all entered into the interpretation generation as placeholders and are not restored. When the external reasoning ability on which the interpretation generation depends is unavailable, the interpretation is directly output using the rating table and fixed wording and marked as downgraded. The numerical conclusion is not affected.

[0042] IV. Multidimensional Risk Certainty Check and Classification (Step S3) See Figure 1 and Figure 3 The risk check module (30) takes the same batch of candidate products from the marketability diagnosis stage as input, and performs deterministic checks, hit determinations, and risk classifications on the four risk items of each candidate product: infringement, qualification, quality, and regional marketability, according to the risk rule set. The risk check is completed by the deterministic rule engine, and each risk level takes values ​​from three levels: high, medium, and low, enumerating literals.

[0043] (I) Infringement Inspection. Similarity and related litigation counts are obtained by searching for the product's search characteristics in the infringement search source. The search result cache is checked first; if the cache is not hit or expired, a search is initiated and the cache is refilled. The infringement risk level is determined according to the registered infringement classification thresholds: similarity higher than the high-risk upper threshold or related litigation count higher than the high-risk count threshold is considered high-risk; similarity between the medium-risk lower threshold and the high-risk upper threshold or related litigation count within the medium-risk count range is considered medium-risk; the rest are low-risk. The infringing document identifiers and abstract fragments are retained as citation evidence along with the conclusion for post-event verification and interpretation, avoiding arbitrary generation. The thresholds are configurable relative thresholds, registered in the configuration but not written with fixed values, and satisfy the relative relationship that the high-risk upper threshold is higher than the medium-risk lower threshold. As demonstration values, this embodiment can set the high-risk similarity upper threshold to 0.85 and the medium-risk lower threshold to 0.7, and the high-risk related litigation count threshold to 3 and the medium-risk lower threshold to 1 (the following values ​​are only demonstration values ​​for ease of understanding, not actual production configurations, and do not constitute limitations).

[0044] (II) Qualification Check. Based on the product category and target region, the required qualification set is retrieved from the qualification verification rule database. This set is then compared with the product's existing qualification set to determine the missing qualification set. If a corresponding qualification rule exists and the missing qualification set is not empty, the qualification verification status is "missing." If a rule exists and there are no missing qualifications, the product is compliant. If no corresponding rule exists, meaning there are no mandatory qualifications for this category in this region, the qualification is not applicable. When the qualification verification status is "missing" and the qualification rule is registered as a blocking type, it is determined to be a blocking type hard risk, and the product cannot enter the downstream content production and ranking process.

[0045] (III) Quality Inspection. Negative review signals are used to perform semantic retrieval on a quality signal source to obtain negative review scores. Defect keywords are aggregated and judged according to the registered quality risk threshold: when the negative review score exceeds the quality risk threshold or the defect keywords match registered high-risk terms, a high-risk quality marker is set; otherwise, it is not high-risk. The quality risk threshold is registered in the configuration; as a demonstration value, this embodiment uses a quality risk threshold of 0.6 under the normalization of negative review scores to 0 to 1 (the following values ​​are only demonstration values ​​for ease of understanding, not actual production configurations, and do not constitute a limitation).

[0046] (iv) Regional marketability check. Only when the target region is restricted, the sales restriction relationship of the product in the target region is used to determine whether the product is prohibited from sale or requires additional qualifications in the target region: when there is a restriction relationship of type prohibition, it is judged as prohibited from sale in the target region; when there is a restriction relationship of type requiring qualifications, it is marked as requiring additional qualifications to be sold; when the product association relationship is unavailable, it degenerates to using the redundant field of restricted region of the product itself for quick filtering, and marks the confidence level as decreased.

[0047] (V) Comprehensive Risk Level and Hard Risk Labeling. The highest risk level determined for each of the above-mentioned risk items (infringement, qualification, quality, and regional marketability) is taken as the comprehensive risk level: Qualification deficiency and blocking type, high quality risk, target region sales ban, and high infringement risk are all considered high risk, and the comprehensive risk level is raised accordingly. Any entity with qualification deficiency and blocking type, or a comprehensive risk level of high risk, is designated as a blocking type hard risk. This label will be used as an uncompensable pre-emptive veto during the conclusion aggregation stage. As a boundary measure, when the infringement retrieval source times out and the cache is unavailable, the infringement risk level is set to unknown and marked as requiring manual review, rather than being allowed by default as low risk, thus ensuring stricter handling when data is insufficient.

[0048] V. Conclusion aggregation and structured suggestion generation (step S4) See Figure 3 and Figure 4 The conclusion aggregation module (40) aggregates the marketability diagnosis conclusion and the multidimensional risk conclusion on the same candidate product, produces a unified product selection conclusion, product matching score and recommendation suggestion, and performs differentiated sorting on the same batch of candidate products.

[0049] (I) Product Matching Score Calculation. A base score is obtained by weighting the product's marketability score (based on configurable weights) and the market window score (based on configurable weights). Then, a graded deduction is applied to the base score based on the overall risk level—the higher the risk level, the greater the deduction, ensuring the deduction is not negative, thus obtaining the product matching score. The market window score is set to a higher value when the market window is open or in its early stages, and a lower value otherwise. The graded deduction has three levels: high, medium, and low, each corresponding to a registered deduction amount, with the high-risk deduction being greater than the medium-risk deduction, and the medium-risk deduction not less than the low-risk deduction. As a demonstration, this embodiment uses marketability score weights of 0.7 and market window score weights of 0.3 (the sum of the two is 1), a market window score of 80 points when open or in its early stages, and 40 points otherwise. The deduction amounts for the high, medium, and low levels are 40, 20, and 10 points respectively (arranged from largest to smallest). (The following values ​​are only demonstration values ​​for ease of understanding and are not actual production configurations; they do not constitute a limitation.)

[0050] (II) Product Selection Conclusion Judgment and Pre-emptive Veto of Hard Risks. Based on whether a product hits a blocking hard risk and whether its product matching score reaches the conclusion threshold, the product selection conclusion is determined as: Do Not Touch, Proceed First, or Postpone. For products hitting a blocking hard risk, they are directly judged as Do Not Touch, and their downstream entry flag is set to No. This judgment is made independently of the marketability score and product matching score and is not compensated by any difference in marketability or product matching score. For products not hitting a blocking hard risk, the conclusion threshold is then determined based on whether the product matching score reaches it; those reaching it are judged as Proceed First, and those not reaching it are judged as Postpone. In this embodiment, the conclusion threshold can be set to 60 points, meaning that products with a matching score of 60 points are judged as Proceed First, and those below 60 points are judged as Postpone (the following values ​​are only demonstrative values ​​for ease of understanding and are not actual production configurations, and do not constitute a limitation). Therefore, hard risk, as a prerequisite veto that is not included in the scoring weighting target and cannot be compensated for by revenue, ensures that a product with missing qualifications and blocked, high risk of infringement, or prohibited from sale in the target area will not be judged as eligible for promotion due to a high marketability score or product matching score.

[0051] (III) Differentiated Sorting. For the same batch of candidate products, they are first sorted according to the priority of the selection conclusion, with priority given to those that are done first over those that are postponed, and those that are postponed over those that should be avoided. Within the same selection conclusion, they are then sorted according to the differentiated sorting key corresponding to the type of operating entity to which the candidate products belong: for influencer-based entities, they are sorted in the order of commission from high to low, return rate from low to high, and influencer matching degree from high to low; for manufacturing entities, they are sorted in the order of profit margin from high to low and competition degree from low to high; for cross-regional operating entities, they are sorted in the order of regional marketability compliance first, and then by product matching score; for other general types, they are directly sorted by product matching score. All sorting keys and conclusion thresholds are registered in the configuration, and the corresponding sorting key is selected by the type of operating entity.

[0052] (iv) Diagnostic Conclusion Objects and Structured Output. The results output module (50) aggregates the multidimensional diagnostic indicator set, multidimensional risk conclusions, product matching score, product selection conclusions, downstream access flags, and recommendations for each candidate product into a diagnostic conclusion object for that product, outputting it at the granularity of each product. Products that are identified as hit due to blocking hard risks are also output and marked negatively with the downstream access flag, allowing downstream users to exclude them and avoid them being mistakenly included in subsequent stages. The diagnostic conclusion object can also carry the overall confidence level and alarm array, making the degradation and data freshness observable to downstream users.

[0053] VI. Continuous Iteration and Weight Recalibration of Product Pool Scores Furthermore, the system of this invention may also include a continuously iterative, self-optimizing closed loop: after candidate products generate selection conclusions through the aforementioned diagnostic link and are delivered to downstream content production and distribution, the downstream links, after completing their execution and attribution, provide feedback on the actual effects of each product. This feedback includes at least the product's actual conversion rate, whether it hits the bestseller list, and its contribution to user dwell time. This system uses this feedback to drive the iterative update of the product pool score and score weights, forming a closed loop of product selection prediction, actual feedback, and score and weight recalibration. This ensures that the product pool score is no longer static after a single calibration but continuously self-calibrates based on the actual feedback from downstream users.

[0054] Specifically, for each returned product, the iterative closed loop first loads the product selection conclusions generated by the system during this campaign, including the selection conclusions, product matching scores, and marketability diagnostics. It then reconciles the predicted selection conclusions with the actual conversion results and records this in the audit log. Next, based on the actual conversion rate, hit rate of best-selling products, and dwell time contribution, it rewrites the product's rating dimensions in the product pool, ensuring that the product pool ratings gradually approach actual marketability performance based on real-world data. The returned feedback uses event identifiers for idempotent deduplication, is periodically rewritten in batch buffers, and is on a non-critical path, thus not blocking the main product selection process.

[0055] When the deviation between the predicted product selection conclusion and the actual result accumulates to exceed a preset drift threshold within an observation window, an iterative closed-loop system generates a recalibration suggestion for the scoring weights. The weights involved include the content form sub-judgment weight and the platform's prior adaptability, etc. This suggestion does not directly rewrite the operating logic, but is implemented according to the version after being reviewed through a configuration gray-scale process and supports rollback. This makes the weight updates controllable, traceable, and rollbackable, avoiding scoring weight fluctuations caused by abnormal feedback in a single batch. As a demonstration value, this embodiment can take the drift threshold as 0.15 and the observation window as one natural week (the following values ​​are only demonstration values ​​for easy understanding, not actual production configurations, and do not constitute limitations).

[0056] It should be noted that the recalibration of the product pool selection score and the calibration of the market window forecast are independent of each other, and are applied to the individual product selection score and market opportunity forecast respectively, without overlapping with each other; moreover, the recalibration is always based on the premise that hard risks are determined independently of the score, and the weight iteration only applies to the soft marketability and matching scores, without changing the veto status of hard risks.

[0057] VII. Exception and Boundary Handling (i) Market conclusions are missing or outdated: The conclusions are rolled back in the order of the most recent cache, similar category conclusions, and industry average. The relevant features of the market window are taken from the rollback source, and the overall confidence level is adjusted down and the source of the downgrade is recorded in the alarm array.

[0058] (ii) Product association is unavailable: The regional salability check is degraded to using the redundant fields of the restricted region of the product for quick filtering, combined with richness-related derived items to skip and mark the confidence level as decreased.

[0059] (iii) Timeout of infringement search source: Prioritize cached search results; if the cache is also unavailable, set the infringement risk level to unknown and mark it as requiring manual review, instead of allowing it by default as low risk.

[0060] (iv) Visual assessment or external reasoning is not available: visual expressiveness is taken as the prior median of the category and downgraded; readable interpretation is given directly with a scoring table and fixed wording, and numerical judgment is not affected.

[0061] (v) Confidential fields fail to be desensitized: Fields involving cost and break-even price will be refused entry into any external text generation when desensitization fails. Instead, they will be directly output using templates without bypassing desensitization, thus ensuring that confidential information is not leaked.

[0062] 8. Performance, Timeout, and Batch Concurrency Budget To ensure that product selection diagnosis is completed within an acceptable timeframe, this invention sets timeout budgets and concurrency limits for each stage, all registered in the configuration rather than written to fixed values. Each stage—feature aggregation, marketability diagnosis, risk check, and conclusion aggregation—has its own timeout. Sub-steps relying on external search sources, such as infringement retrieval, have separate, shorter external call timeouts and are coupled with search result caching; a cache hit eliminates the need for external calls. Diagnosis of batch candidate products is processed in parallel on a product-by-product basis with a parallelism limit to avoid excessive concurrency on external search sources and external inference capabilities. As a demonstration, this embodiment sets the timeout for a single external infringement search in risk check to 5 seconds, the search result cache validity period to 7 days, and the batch parallelism limit to a preset positive integer, ensuring that the overall diagnostic latency budget, after being synchronized with upstream market conclusions, does not exceed a preset limit (the following values ​​are merely demonstrative values ​​for ease of understanding, not actual production configurations, and do not constitute limitations).

[0063] When a stage times out or its dependencies become unavailable, the system rolls back and marks it as downgraded according to the aforementioned degradation order, instead of waiting indefinitely or experiencing a complete interruption. When the external inference capability becomes unavailable due to its requests exceeding capacity, the explanation sub-step uses a scoring table with fixed wording, and the visual evaluation sub-step uses the category prior median and marks it as downgraded. When the external retrieval source is rate-limited, it uses a preset backoff retry method, and if the retry limit is exceeded, the corresponding risk item is marked as unknown and awaits manual confirmation. In this way, the system can still produce usable and observable conclusions in a downgraded manner when some dependencies are abnormal, and the downgrade process is observable downstream by lowering the overall confidence level and alarm array.

[0064] IX. Determinism and Verifiability Guarantees In this invention, both the marketability score and multidimensional risk classification are completed by a deterministic rule engine. The numerical judgment process is a pure computation without side effects: for the same product feature object and the same version of the weight table, prior table, and risk rule set, repeated execution yields completely consistent marketability scores, risk levels, and product selection conclusions. Therefore, the diagnostic results are reproducible and regressible. Based on this, assertion test cases can be programmed with fixed inputs and expected outputs for key judgments. When the weight table or rule set changes, assertion regression can detect unexpected deviations, avoiding undetected judgment drift introduced by weight or rule updates.

[0065] The optional natural language interpretation and numerical judgment are strictly separated in terms of responsibilities: the interpretation only provides textual explanations of the already calculated scores, risk levels and product selection conclusions. If the generation of the interpretation is abnormal, such as the unavailability of external reasoning capabilities, it will only trigger the scoring table with fixed wording and mark downgrades, without affecting any numerical judgments and product selection conclusions; thus, the determinism of the numerical link does not change due to fluctuations in the availability of the interpretation link.

[0066] 10. Alternative Implementation Methods (a) Alternative integrated form: The system of the present invention can be deployed horizontally in the product selection process as an independent product selection diagnostic component, or it can be embedded in the operation platform as a library or service; each module can be merged or split according to function, and the module division is only an example.

[0067] (ii) Substitution of marketability aggregation factors: In addition to content format score, platform score and matching correction aggregation, marketability score can also include decision cycle score, category preference score and other configurable weighting factors. However, hard risk is always the first-hand veto factor, rather than a weighting item that can be compensated by high score.

[0068] (iii) Alternatives to risk inspection criteria: infringement classification can adopt multiple configurable criteria such as similarity, related litigation count, or a combination of the two; quality inspection can adopt negative review search scoring, defect keyword hit, or a combination of the two; qualification and regional marketability inspection rules can be configurable and expanded according to product category and region.

[0069] (iv) Replacement of sorting and conclusion thresholds: Differentiated sorting keys, product selection conclusion thresholds and deduction amounts for each level can all be configured; the upper limit of parallelism for batch diagnosis can be configured.

[0070] (v) Substitution of feature sources: Each source of multidimensional features can be replaced with an equivalent data source according to the deployment environment. As long as the normalization caliber is consistent, it will not affect the certainty of marketability aggregation and risk check.

[0071] XI. Example 1: Marketability Diagnosis, Risk Assessment, and Preliminary Conclusions for Conventional Candidate Products The following demonstrates the complete supply chain for a typical candidate product. The values ​​below are for illustrative purposes only and do not represent actual production configurations; they are not intended to be limiting.

[0072] Feature aggregation (S1): The feature aggregation module (10) aggregates the product features of a candidate product—the average order value range is the median range, the decision cycle is rational, the visual expressiveness is high, the category content preference is short video, the market window status is open, and the matching richness is moderate; and loads the qualification rules, infringement classification threshold and quality risk threshold of the category in the target area.

[0073] Marketability Diagnosis (S2): In the content form sub-judgment of the marketability diagnosis module (20), short video seeding, live streaming and text notes are weighted and scored respectively. Due to the high visual expressiveness and the category preference for short videos, short video seeding has the highest suitability score and is selected as the recommended content form. In the platform sub-judgment, the platform and category prior are multiplied by the activity coefficient calculated by the open window (the open window is 1.0 in this example) and the platform with the highest score is selected as the recommended platform. The theme sub-judgment is to obtain the comparative theme by mapping short video seeding and rational type through the rule table. The marketability score is obtained by weighting the content form score and platform score according to their respective weights and superimposing the upper limit correction item derived from the richness of matching. The weighted aggregation is used to obtain a higher marketability score. In this example, it is 82 points (value range 0 to 100).

[0074] Risk Check (S3): The risk check module (30) performs four types of checks on the product: infringement search similarity of about 0.4 and related litigation count of 0, both below the medium risk threshold, indicating low risk of infringement; the product has the necessary qualifications by category and region and all of them are in compliance with the qualifications; the negative review search score is about 0.2, below the quality risk threshold of 0.6, indicating non-high risk; the target area is not restricted, indicating it is salable. The highest level of the four items is taken as the overall risk level of low, and no blocking hard risk is hit.

[0075] Conclusion aggregation (S4): The conclusion aggregation module (40) weights the marketability score of 82 and the open window score of 80 with weights of 0.7 and 0.3 respectively to obtain the base score. Since the overall risk is low, no deduction is made, and the product matching score is 81. The product does not hit the hard risk and the product matching score of 81 reaches the conclusion threshold of 60, so it is judged to be done first. The result output module (50) aggregates its multidimensional diagnostic indicator set, risk conclusion, product matching score and first-do conclusion into a diagnostic conclusion object output and sets the downstream entry flag to yes.

[0076] 12. Example 2: Pre-emptive veto for hard-risk products with missing qualifications The following demonstrates the handling of a hit-blocking type hard risk commodity. The following values ​​are for illustrative purposes only and do not represent actual production configurations, nor do they constitute limitations.

[0077] Marketability Diagnosis (S2): A candidate product has high visual appeal and an open market window. The marketability diagnosis module (20) calculates a high marketability score (88 points in this example) and recommends content format, platform, and theme. In other words, the product performs well in terms of marketability.

[0078] Risk check (S3): The risk check module (30) finds the set of required qualifications according to the product category and target area. The comparison finds that the product is missing the mandatory qualification, the missing qualification set is not empty, and the qualification rule is registered as blocking type. Therefore, the qualification verification status is judged as missing and the blocking type attribute is judged as high risk. The comprehensive risk level is taken as the highest of each item as high, and the product is set as hitting the blocking type hard risk.

[0079] Conclusion Convergence (S4): Although the product has a high marketability score (88 points, and its product matching score can reach 85 points if hard risks are not considered), the conclusion convergence module (40) directly judges its selection conclusion as "don't touch" and sets the downstream entry flag as "no" because it hits the blocking hard risk. This judgment is not compensated by its high marketability score or product matching score. The result output module (50) still outputs the product as the diagnostic conclusion object and marks it negatively with the downstream entry flag as "no", so that the downstream can exclude it from content production and product ranking. It can be seen that hard risks, as a pre-vote veto that do not enter the score weighting, prevent high marketability score products from being secretly released due to lack of qualifications.

[0080] Thirteen, Example 3: Regional Sellability Check and Differentiated Ranking of Candidate Products Across Regions The following demonstrates the diagnosis and ranking of candidate products for a batch of cross-regional operating entities. The values ​​below are for illustrative purposes only and do not represent actual production configurations, nor do they constitute limitations.

[0081] Regional marketability check (S3): For a batch of candidate products of cross-regional business entities, the risk check module (30) determines the sales restriction relationship of the product to the target area in the regional marketability check one by one - those with sales restriction relationship are judged to be prohibited from sale in the target area, high risk is calculated and hit the blocking hard risk, those with qualification restriction relationship are marked as requiring additional qualification to be sold, and those without restriction relationship are judged to be sold.

[0082] Differentiated sorting (S4): The conclusion aggregation module (40) first divides the products into three layers according to the priority of the product selection conclusion: first-to-do, postpone, and avoid. Among them, those products that are prohibited from sale in the target area fall into the avoid layer. Within the same conclusion layer, since the operating entity is a cross-regional business, it is sorted according to the regional salability compliance priority and then according to the differentiated sorting key of the product matching score, so that compliant products with high matching scores are ranked first. Thus, the same batch of candidate products are consistently sorted horizontally under the same standard, and hard-risk products that are prohibited from sale in the region are intercepted in advance in the avoid layer.

[0083] XIV. Example 4: Parallel Diagnosis and Confidence Downgrading Labeling of Batch Candidate Products The following demonstrates the diagnosis and downgrade process for a batch of candidate products. The values ​​below are for illustrative purposes only and do not represent actual production configurations; they are not intended to be limiting.

[0084] Batch parallel diagnostics (S2, S3): For a batch of candidate products, the numerical determination of marketability diagnosis and risk check are both deterministic calculations without side effects. Therefore, they are processed in parallel on a product-by-product basis. Each product does not share variable states, but only shares read-only weight tables, prior tables and risk rule sets. The results can be reproduced under the same input.

[0085] Downgrade labeling (S1, S4): In this batch of diagnoses, if the conclusion of an external market is outdated, the feature collection module (10) will backtrack to retrieve the market window features in the order of the closest cache, similar categories, and industry average, and downgrade the overall confidence of the corresponding product's diagnosis conclusion and record the downgrade source in the alarm array; if the visual evaluation of a product is unavailable, its visual performance will be taken from the prior median of its category and downgraded. The conclusion aggregation module (40) will carry the downgraded overall confidence and alarm array in the output diagnosis conclusion object, so that the downstream can identify which conclusions in this batch have been downgraded based on the confidence and alarm, instead of being silently replaced by the default value.

[0086] XV. Example 5: Serial coordination and unified conclusion delivery of marketability diagnosis and risk assessment The following demonstrates the process of sequentially and collaboratively producing a unified conclusion from marketability diagnosis and risk assessment. The values ​​below are for illustrative purposes only and do not represent actual production configurations, nor do they constitute limitations.

[0087] Serial Collaboration (S2 to S3 to S4): The marketability diagnosis module (20) first calculates the marketability conclusion of each product on the candidate product set and writes it into the intermediate results; the risk check module (30) calculates the risk conclusion of each product with the same candidate product set as input and writes it into the intermediate results; the conclusion aggregation module (40) then aggregates the marketability conclusion and the risk conclusion together as input for each product to form a diagnosis conclusion object. Since the risk check takes the candidate set of marketability diagnosis as input and the conclusion aggregation takes both as input, there is a data dependency between the three stages, so they are executed serially, with the output of the previous stage as the input of the next stage.

[0088] Unified Delivery (S4): The result output module (50) outputs diagnostic conclusions for each product at the granular level. These conclusions include the recommended content format, recommended platform, recommended theme, marketability score, comprehensive risk level, qualification verification status, quality risk mark, regional marketability conclusion, product matching score, product selection conclusion, and downstream access indicator. This allows downstream modules to obtain consistent marketability and risk conclusions for each product at once, without having to piece them together from separate product selection evaluation and risk verification processes.

[0089] XVI. Example 6: Product Pool Scoring Iteration and Weight Recalibration Driven by Downstream Actual Effect Feedback The following demonstrates the process of downstream performance feedback driving scoring iteration. The values ​​below are for illustrative purposes only and do not represent actual production configurations, nor do they constitute limitations.

[0090] Feedback Feedback: After a batch of candidate products is diagnosed and product selection conclusions are generated, they are delivered to downstream platforms for deployment. After deployment and attribution are completed, the downstream platforms feed back the actual conversion rate of each product, whether it hit the best-selling product, and the contribution of user dwell time. The iterative closed loop loads the product selection conclusions at that time onto each product, reconciles the predictions at that time (e.g., judged to be prioritized, product matching score of 81 points) with the actual conversion results, and records them in the audit log.

[0091] Scoring Rewrite and Weight Recalibration: The iterative closed-loop system rewrites the scoring dimensions of each product in the product pool based on actual conversion rates and other operational facts. Products predicted to be prioritized in this batch but with good actual conversions have their scores increased, while products predicted to be prioritized but with significantly lower actual conversions have their scores decreased. When the cumulative deviation between prediction and actual conversions within the observation window exceeds the drift threshold of 0.15, a scoring weight recalibration suggestion is generated. After configuration and gray-scale review, the new version takes effect and retains rollback capability. Thus, the marketability diagnosis of subsequent batches continuously self-calibrates based on real-world feedback, while weight updates are fully controllable, traceable, and rollback-capable, and hard risks are unaffected by weight iterations.

[0092] XVII. Data Structures and Flow To facilitate implementation by those skilled in the art, the organization of the main data structures is given below, while the specific field names and carrying methods are configurable implementation details.

[0093] (i) Product Feature Object: The product identifier is used as the primary key, and the fields include average order value range, decision cycle, visual appeal, category content preference distribution, market window status, and combination richness. Each dimension is normalized according to the registration normalization standard. This object provides a unified data contract for marketability diagnosis and risk inspection.

[0094] (ii) Risk rule set: The qualification verification rules are organized by product category and target region. Each rule includes the set of required qualifications, the issuing authority, the level of missing risk and whether it is a blocking type; infringement classification threshold, quality risk threshold and regional salesability determination rules are also registered; the rules are governed with the version number and support rollback.

[0095] (III) Multidimensional diagnostic indicator set: It consists of recommended content format, recommended platform, recommended topic, detailed breakdown of each content format and marketability score, and serves as the output of marketability diagnosis.

[0096] (iv) Multidimensional risk conclusions: These consist of comprehensive risk level, infringement risk level, qualification verification status, quality risk markers, regional marketability conclusions, whether blocking hard risks are hit, and evidence cited for infringement, and serve as the output of risk checks.

[0097] (v) Diagnostic conclusion objects: consisting of product identification, operating entity type, multi-dimensional diagnostic indicator set, multi-dimensional risk conclusion, product matching score, product selection conclusion, downstream access indicator, recommendation suggestions, overall confidence level and alarm array, output at the granularity of each product and can be stored in the database.

[0098] XVIII. System Implementation Examples and Electronic Device and Storage Media Implementation Examples Based on the same inventive concept as the methods described above, embodiments of the present invention provide a content e-commerce marketability diagnosis and multi-dimensional risk inspection system, see [link to documentation]. Figure 2The system includes a feature collection module (10), a marketability diagnosis module (20), a risk check module (30), a conclusion aggregation module (40), and a result output module (50). The feature collection module (10) is used to collect multi-dimensional features for each candidate product and organize them into a product feature object, and to pre-set a risk rule set. The marketability diagnosis module (20) is used to weight and aggregate multi-dimensional features on the product feature object with configurable weights to obtain the recommended content format, recommended platform, recommended theme, and marketability score. The risk check module (30) is used to perform deterministic checks, hit determination, and risk classification on each risk item of infringement, qualification, quality, and regional marketability according to the risk rule set, and to mark whether the blocking hard risk is hit. The conclusion aggregation module (40) is used to aggregate the marketability conclusion and risk conclusion, calculate the product matching score, determine the product selection conclusion with hard risk as a prerequisite veto, and sort the results according to the operating entity type. The result output module (50) is used to output the diagnostic conclusion object at the granularity of each product. The modules described above are used to execute the corresponding steps of the aforementioned methods. The modules collaborate with each other through a controlled interface. The module division is only an example and can be merged or split according to function.

[0099] See Figure 3 In this invention, the various functional modules coordinate with each other in a timely manner when executing the above method; see also Figure 4 In this invention, the output of the previous step is used as the input of the next step, and structured intermediate results are passed sequentially between steps to form a complete processing link.

[0100] See Figure 5 The present invention also provides an electronic device, including a memory (502) and a processor (501). The memory (502) stores a computer program, and the processor (501) executes the computer program to implement the above-mentioned content e-commerce marketability diagnosis and multi-dimensional risk inspection method. The electronic device may also include a bus (503) and a communication interface (504). The processor (501) and the memory (502) are coupled via the bus (503) and communicate with the outside via the communication interface (504). The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned content e-commerce marketability diagnosis and multi-dimensional risk inspection method. The specific models, algorithms, and parameters involved in the present invention are all configurable implementation details and can be implemented using technologies known in the art. All modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention fall within the protection scope of the present invention.

Claims

1. A method for diagnosing the marketability of content e-commerce and conducting multi-dimensional risk assessment, characterized in that, Includes the following steps: S1, Feature Collection Stage: For each candidate product, multi-dimensional features required for marketability diagnosis are collected and organized into a unified product feature object. These multi-dimensional features include the average order value range from the product database, decision-making cycle and category content preferences from category configuration, visual expressiveness from visual evaluation, market window status from external market conclusions, and the richness of product associations. Each feature is normalized to a unified value range according to its registered normalization caliber. Furthermore, a pre-set risk rule set for multi-dimensional risk checks is established. This risk rule set organizes qualification verification rules using category and target region as keys, and registers infringement classification thresholds, quality risk thresholds, and regional marketability determination rules. Then, the registration of each risk item is centralized to determine whether it is a blocking hard risk; S2, the marketability multi-dimensional diagnosis stage, for each candidate product, according to the product feature object, the corresponding feature of each dimension is weighted and aggregated with configurable scoring weights in the three sub-judgments of content form, platform and theme: the content form sub-judgment calculates the scores of multiple candidate content forms according to visual expressiveness, average order value fit, decision cycle fit and category content preference, and takes the one with the highest score as the recommended content form; the platform sub-judgment calculates the scores of multiple candidate platforms by multiplying the prior fit between the platform and the category by the activity coefficient converted from the market window status, and takes the one with the highest score as the recommended platform; the theme sub-judgment calculates the scores according to the recommended content form and decision cycle through the theme rule table. The recommended topics are mapped to obtain the following: the matching score of the recommended content format and the matching score of the recommended platform are weighted by configurable weights and superimposed with an upper limit correction term derived from the richness of matching, and the marketability score between the lower and upper limits of the value range is obtained. The marketability score, together with the recommended content format, recommended platform, and recommended topics, constitutes the multidimensional diagnostic indicator set of the candidate product; S3, multidimensional risk deterministic check stage, for each candidate product, according to the risk rule set, deterministic checks are performed on each risk item of infringement, qualification, quality, and regional marketability, and a hit determination and risk classification are made: the infringement check determines the infringement risk level according to the similarity obtained from the search and the number of related lawsuits according to the infringement classification threshold, and the qualification check... The system retrieves the set of essential qualifications by product category and target region, compares these with the product's existing qualifications to obtain the set of missing qualifications, and determines the qualification verification status based on the presence or absence of missing qualifications and whether the qualification rule is blocking. For quality checks, the system uses negative review signals and defect keywords to determine whether the product is at high risk according to the stated quality risk threshold. For regional marketability checks, the system determines whether the product is prohibited from sale in the target region if restrictions apply. The highest risk level among the above risk items is then taken as the comprehensive risk level, where missing qualifications and blocking, high quality risk, prohibited sale in the target region, and high infringement risk are all classified as high risk. Products with missing qualifications and blocking or a high comprehensive risk level are designated as hard-risk products that have hit the blocking type.S4, Conclusion Aggregation and Recommendation Generation Stage: For each candidate product, a base score is calculated based on its marketability rating and market window status using configurable weights. This base score is then graded and deducted according to the overall risk level to obtain the product matching score. Next, based on whether a blocking hard risk is hit and whether the product matching score reaches the conclusion threshold, the product selection conclusion is determined as "Don't touch," "Do first," or "Postpone." Products hitting a blocking hard risk are directly judged as "Don't touch" and their downstream entry flag is set to "No." This judgment is not compensated by any marketability rating or product matching score. Products not hitting a blocking hard risk... For products with high risk, the decision to proceed or postpone is made based on whether the product matching score reaches the conclusion threshold. Then, according to the differentiated sorting key corresponding to the type of operating entity to which the candidate products belong, the same batch of candidate products are first sorted by the priority of the selection conclusion, and then by the dimensions of concern for that type, resulting in a batch diagnostic ranking. The multi-dimensional diagnostic indicator set, multi-dimensional risk conclusion, product matching score, selection conclusion, and recommendation suggestions for each candidate product are aggregated into a diagnostic conclusion object for that product, outputting at the granular level of each product. This forms a processing chain of feature aggregation, marketability diagnosis, risk check, and conclusion aggregation.

2. The method according to claim 1, characterized in that, The average order value range mentioned in S1 is derived from the pricing system of the product database. Confidential fields involving costs and break-even prices are anonymized during aggregation, without carrying their original values. The decision cycle is enumerated using literal values ​​for impulsive, rational, and high-decisive types, and mapped to a unified value range. The category content preference is carried by the distribution vector of the content form with the highest historical performance ranking for that category. The market window status is enumerated using literal values ​​for open, early, urgent, and closed, and converted into the activity coefficient according to the registration mapping, ensuring that the activity level is higher when the window is open or early than when the window is urgent or closed. The combination richness is calculated by dividing the number of possible combinations of the product in the product association relationship by a registration normalization upper limit and truncating it to the upper limit of the value range.

3. The method according to claim 1, characterized in that, The average order value (AOV) matching for content format determination in S2 varies depending on the content format: for live-streaming e-commerce, the higher the AOV, the higher the AOV matching through a bounded monotonically increasing mapping; for text and image notes, the lower the AOV, the higher the AOV matching through a bounded monotonically decreasing mapping; for short video product recommendations, the AOV matching takes a neutral constant; the inflection point position and steepness of the bounded monotonically mapping are configurable parameters; and the topic rule table in S2 uses a binary tuple of content format and decision cycle as the key, and records recommended topics with values ​​taken from evaluation, scenario, comparison, and product recommendation enumeration literals as values, and retrieves recommended topics by looking up the table at runtime.

4. The method according to claim 1, characterized in that, The upper limit correction term derived from the richness of combination as described in S2 is obtained by multiplying the richness of combination by a registered upper limit correction coefficient, and its contribution to the marketability score does not exceed the registered upper limit correction. Furthermore, the marketability score is a deterministic hard calculation. The marketability score and basic product information are used as input to generate a readable explanation of the recommendation conclusion through external reasoning. The readable explanation does not participate in any numerical judgment. Confidential fields involving cost and break-even price are all entered into the explanation generation as placeholders and are not restored. When external reasoning is unavailable, the explanation is directly output with a score table and fixed wording and downgraded. The numerical conclusion is not affected.

5. The method according to claim 1, characterized in that, The infringement check described in S3 first checks the cached search results; if the cache is not hit or expired, a search is initiated and the cache is refilled. The infringement risk level is determined by the infringement classification threshold as follows: those with similarity higher than the upper threshold of high risk or the number of related lawsuits higher than the high risk count threshold are judged as high risk; those with similarity between the lower threshold of medium risk and the upper threshold of high risk or the number of related lawsuits within the medium risk count range are judged as medium risk; the rest are low risk, and the upper threshold of high risk is higher than the lower threshold of medium risk. The infringing document identifier and abstract fragment are retained as citation evidence along with the conclusion. When the infringement search source times out and the cache is also unavailable, the infringement risk level is set to unknown and marked as requiring manual review, rather than being allowed by default as low risk.

6. The method according to claim 1, characterized in that, The determination of the qualification verification status described in S3 is as follows: when there is a corresponding qualification rule and the missing qualification set is not empty, it is considered missing; when there is a corresponding qualification rule and the missing qualification set is empty, it is considered compliant; when there is no corresponding qualification rule, it is considered inapplicable. When the qualification verification status is missing and the qualification rule is registered as a blocking type, the product is marked as hitting a blocking type hard risk. In addition, the regional sellability check described in S3 is based on the sales restriction relationship of the product to the target region in the product association relationship. When there is a restriction relationship of type prohibition, it is judged as prohibition of sales in the target region and the product is marked as hitting a blocking type hard risk. When there is a restriction relationship of type requiring qualification, it is marked as requiring additional qualification to be sold. When the product association relationship is unavailable, it degenerates into filtering with the redundant field of the restricted region that comes with the product and marking a decrease in confidence.

7. The method according to claim 1, characterized in that, The base score mentioned in S4 is obtained by weighting the product's marketability score with a configurable weight and the window score calculated from the market window status with a configurable weight. The window score is higher when the market window is open or early than when it is urgent or closed. The graded deduction of the base score based on the comprehensive risk level is divided into three levels: high, medium, and low, each with a corresponding registered deduction amount. The deduction amount for the high-risk level is greater than that for the medium-risk level, and the deduction amount for the medium-risk level is not less than that for the low-risk level. After the deduction, the product matching score is not negative. The sum of the marketability score weight and the window score weight is one.

8. The method according to claim 1, characterized in that, The differentiated sorting described in S4 first prioritizes the selection conclusions of candidate products, giving priority to those selected first over those temporarily suspended, and then to those temporarily suspended over those that should be avoided. Within the same selection conclusion, the candidate products are then sorted according to the differentiated sorting key corresponding to the type of operating entity to which they belong. Specifically, for influencer-operated entities, the sorting is done in the order of commission from high to low, return rate from low to high, and influencer matching degree from high to low. For manufacturing entities, the sorting is done in the order of profit margin from high to low and competition degree from low to high. For cross-regional operating entities, the sorting is done in the order of regional salability compliance first, and then by product matching score. For other general types, the sorting is done directly by product matching score. Furthermore, products that are deemed to be excluded due to hitting the blocking hard risk are also output at the product level and negatively marked as whether they can enter the downstream market.

9. A content e-commerce marketability diagnosis and multi-dimensional risk inspection system, characterized in that, The system includes a feature aggregation module, a marketability diagnosis module, a risk check module, a conclusion aggregation module, and a result output module. The feature aggregation module aggregates multi-dimensional features for each candidate product and organizes them into a unified product feature object. It also pre-sets qualification verification rules based on category and target region, and registers infringement classification thresholds, quality risk thresholds, regional marketability judgment rules, and a risk rule set indicating whether each risk item is a blocking hard risk attribute. The marketability diagnosis module weights and aggregates multi-dimensional features on the product feature object with configurable weights to obtain recommended content format, recommended platform, recommended theme, and marketability score. The risk check module checks for infringement, qualification, and marketability based on the risk rule set. Each risk item related to quality and regional marketability undergoes a deterministic check, hit determination, and risk classification. The highest risk level for each item is taken as the comprehensive risk level, and it is marked whether a blocking hard risk is hit. The conclusion aggregation module is used to calculate the product matching score based on the marketability score and market window status, and to perform graded deductions according to the comprehensive risk level. Hitting a blocking hard risk is a prerequisite veto judgment for product selection that cannot be compensated by the marketability score or product matching score. The same batch of candidate products is sorted according to the differentiated sorting key of the operating entity type. The result output module is used to output diagnostic conclusion objects at the granularity of each product. Each module is used to execute the corresponding steps of the method described in any one of claims 1 to 8.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of any one of claims 1 to 8.

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