A processing method and system for automatically generating marketing selling points for agricultural products

Abstract

The application provides a processing method and system for automatically generating marketing selling points of agricultural products, belonging to the technical field of data processing, specifically comprising: determining available marketing selling points in agricultural products based on matching conditions, determining the marketing association type of a marketing account in the agricultural products based on the composition data of the available marketing selling points in the marketing selling points and the matching conditions of the available marketing selling points and different user portraits, determining the data processing method of the update processing of the marketing account for the user portrait according to the marketing association type of the marketing account in different agricultural products, and determining the generation processing method of the marketing selling points of the agricultural products based on the update results of the user portraits of different marketing accounts, thereby improving the identification processing efficiency of the marketing selling points.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a method and system for automatically generating marketing points for agricultural products. Background Technology

[0002] The accuracy of marketing selling points for agricultural products is crucial to their marketing effectiveness. To achieve accurate identification and processing of these selling points, patent application CN116797280A, "Method and Apparatus for Generating Advertising Copy," employs a selling point generation model to generate multiple corresponding marketing descriptions of selling points based on the product description text and images, constructing a set of marketing descriptions. Based on these multiple target selling point marketing descriptions, a generation prompt text is constructed, and a large language model is called to generate multiple corresponding advertising copy based on the generated prompt text. However, the following technical problems exist: In the marketing process, different marketing accounts correspond to different customer groups, resulting in varying levels of interest in different marketing selling points of agricultural products. Therefore, determining the push management strategy for agricultural products across different marketing accounts based on the matching of marketing selling points to the customer groups corresponding to the marketing accounts, thereby determining the degree of customer interest in marketing selling points and generating marketing selling points for agricultural products, has become an urgent technical problem to be solved.

[0003] Therefore, there is an urgent need for a method and system to automatically generate marketing selling points for agricultural products. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for automatically generating marketing points for agricultural products, which includes: S1 uses the analysis results of agricultural products as a basis to determine the matching situation between the marketing selling points of agricultural products and the user profiles of existing marketing accounts. When it is determined that there are no agricultural products with matching deviations among the agricultural products, proceed to the next step. Based on the matching situation, S2 determines the available marketing selling points in the agricultural products. Based on the composition data of the available marketing selling points in the marketing selling points, and combined with the matching situation of the available marketing selling points with different user profiles, S2 determines the marketing association type of the marketing account in the agricultural products. S3 determines the data processing method for updating user profiles of the marketing accounts based on the marketing association types of the marketing accounts in different agricultural products, and determines the method for generating marketing selling points of the agricultural products based on the update results of user profiles of different marketing accounts.

[0005] The beneficial effects of this invention are as follows: Based on the marketing association types of marketing accounts across different agricultural products, a data processing method for updating user profiles for marketing accounts was determined. This method determines the data processing method for updating user profiles for marketing accounts based on the degree of overlap between the marketing accounts and the marketing selling points of different agricultural products. This ensures the efficiency of updating user profiles for marketing accounts with a high degree of overlap, thus laying the foundation for timely and effective adjustments to the agricultural product generation and processing methods when the user profiles of marketing accounts change. In other words, it lays the foundation for adjusting marketing accounts that push marketing selling points of agricultural products.

[0006] Based on the updated user profiles of different marketing accounts, a method for generating and processing marketing points for agricultural products is determined. This takes into account the matching between the updated marketing accounts and the marketing points of agricultural products, thus avoiding the technical problem of poor accuracy caused by the inability to determine the timely and effective marketing point push strategy when the number of marketing accounts with a high degree of matching with the original marketing points changes significantly. This further improves the efficiency and reliability of marketing point identification and processing.

[0007] Furthermore, the analysis results of the agricultural products include the marketing selling points of the agricultural products.

[0008] Furthermore, the matching of the marketing selling points of the agricultural products with the user profiles of existing marketing accounts is determined based on whether the customers in the user profiles match the marketing selling points, specifically based on whether the customers in the user profiles pay attention to the marketing selling points.

[0009] It should be noted that whether the customers of the user profile pay attention to the marketing selling point is determined based on the proportion of the purchase quantity of the products of the marketing selling point by the customers of the user profile. If the proportion of the purchase quantity of the products of the marketing selling point by the customers of the user profile is less than a preset proportion threshold, it is determined that the customers of the user profile do not pay attention to the marketing selling point.

[0010] Furthermore, determining that there are no agricultural products with matching deviations among the agricultural products specifically includes: Based on the matching of user profiles of existing marketing accounts with the agricultural products, identify marketing accounts that match the marketing selling points of the agricultural products and use them as matching marketing accounts. Based on the matching marketing accounts for different agricultural products, determine whether there are any agricultural products with matching deviations among them; Furthermore, the method for determining the method for generating and processing the marketing selling points of the agricultural products is as follows: Based on the matching marketing accounts of the agricultural products, determine the matching marketing accounts of the agricultural products and the available marketing selling points matched by the matching marketing accounts; Based on the updated user profiles of different marketing accounts, identify the marketing accounts that match the marketing selling points of the agricultural products after the update. The method for generating marketing points for the agricultural product is determined by using the matching marketing account for the agricultural product and the available marketing points matched by the matching marketing account, combined with the updated marketing account that matches the marketing points of the agricultural product.

[0011] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for automatically generating marketing points for agricultural products when running the computer program.

[0012] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0014] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 This is a flowchart of a method for automatically generating marketing points for agricultural products; Figure 2 This is a flowchart for identifying agricultural products that do not exhibit matching bias. Figure 3 This is a flowchart illustrating the method for determining the marketing association type of a marketing account for the agricultural product. Figure 4 This is a flowchart illustrating the method for determining the data processing approach used by marketing accounts to update user profiles. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] In this invention, based on the correlation between the marketing selling points of agricultural products and the user profiles of marketing accounts, marketing videos generated based on the marketing selling points of agricultural products are pushed and processed. The issues that users care about in the marketing selling points are identified, and then marketing plans are generated based on the issues they care about, thereby improving the efficiency and matching of marketing plan generation and processing.

[0017] Example 1 like Figure 1 As shown, this application provides a method for automatically generating marketing points for agricultural products, specifically including: S1 uses the analysis results of agricultural products as a basis to determine the matching situation between the marketing selling points of agricultural products and the user profiles of existing marketing accounts. When it is determined that there are no agricultural products with matching deviations among the agricultural products, proceed to the next step. Furthermore, the analysis results of the agricultural products include the marketing selling points of the agricultural products.

[0018] Specifically, marketing selling points refer to the unique advantages and core value points of agricultural products that can attract consumers to buy them and differentiate them from competitors.

[0019] In the examples provided: For organic vegetables, the marketing selling points are "green and organic," "no pesticide residues," and "health and wellness." This highlights its value in terms of health attributes. For premium gift-boxed rice, the marketing selling points are "holiday gifts," "high-end gifts," and "exquisite packaging." This highlights its value in terms of social and gift attributes. Marketing selling points serve as a bridge connecting products and customers and are the core theme of advertising and content creation.

[0020] Specifically, the matching of the marketing selling points of the agricultural products with the user profiles of existing marketing accounts is determined based on whether the customers in the user profiles match the marketing selling points, and more specifically, based on whether the customers in the user profiles pay attention to the marketing selling points.

[0021] User profiles are virtual avatar models representing a target customer group, built based on real data. They include demographic characteristics (such as age, gender, and region), interests, consumption behaviors, and pain points. For example, the user profile for account A might be: "Urban middle class aged 30-45, focusing on health and wellness..." This profile describes a group of people who pursue a high quality of life and physical health. The user profile for account B might be: "Corporate purchasing and sales personnel aged 35-50..." This profile describes a group of people with business dealings and gift-giving needs.

[0022] It should be noted that whether the customers of the user profile pay attention to the marketing selling point is determined based on the proportion of the purchase quantity of the products of the marketing selling point by the customers of the user profile. If the proportion of the purchase quantity of the products of the marketing selling point by the customers of the user profile is less than a preset proportion threshold, it is determined that the customers of the user profile do not pay attention to the marketing selling point.

[0023] Specifically, the purchase quantity percentage is a core quantitative metric in this matching method. It refers to the proportion of purchases made by a customer group with a specific user profile on products with a specific marketing focus, out of the total purchases made by that group. The calculation formula is: Purchase Quantity Percentage = (Purchase Quantity of Products with a Specific Marketing Focus by Customers of a User Profile / Total Purchase Quantity of Customers of a User Profile) * 100%.

[0024] Calculate the attention paid by user A to the "green and organic" selling point: (1,500 items / 10,000 items) * 100% = 15%, and calculate the attention paid by user B to the "organic" selling point: (200 items / 8,000 items) * 100% = 2.5%.

[0025] A preset quantity percentage threshold is a pre-defined critical value used to determine whether a purchase is "followed" or "not followed." It serves as a decision benchmark, and in this embodiment, it is set to 5%. The judgment rule is: if the purchase quantity percentage is >= 5%, it is determined as "followed"; if the purchase quantity percentage is < 5%, it is determined as "not followed." This threshold can be adjusted based on business experience, as it determines the strictness of the matching standard. Increasing the threshold makes the matching more stringent, while decreasing the threshold makes the matching more lenient.

[0026] Furthermore, such as Figure 2 As shown, determining that there are no agricultural products with matching deviations among the agricultural products specifically includes: Based on the matching of user profiles of existing marketing accounts with the agricultural products, identify marketing accounts that match the marketing selling points of the agricultural products and use them as matching marketing accounts. Matched marketing accounts refer to marketing accounts whose user profiles represent a customer group that has been confirmed by data to be interested in the marketing selling points of a certain agricultural product. Account A was identified as a matched marketing account for "green and organic", and Account B was identified as a matched marketing account for "holiday gifts".

[0027] Based on the matching marketing accounts for different agricultural products, determine whether there are any agricultural products with matching deviations among them; It is understandable that if different agricultural products have matching marketing accounts, then it is determined that there are no agricultural products with matching deviations among them.

[0028] "Mismatched agricultural products" refers to products for which no matching marketing account system exists that matches the main marketing selling points of the agricultural product in the user profile. In this example, after analysis, both "Organic Vegetables" and "Premium Gift Box Rice" found their respective matching marketing accounts. Therefore, in the conclusion of this example, there are no mismatched agricultural products.

[0029] Counterexample: Suppose there is an agricultural product whose selling point is "industrial raw material" or "textile fiber". If the purchase volume of all marketing accounts' users (such as health-conscious individuals or gift buyers) for this selling point is extremely low (e.g., all <1%), then the agricultural product is a mismatched product. In this case, the data processing method used by the marketing accounts to update user profiles should be the second preset processing method, that is, to update the user profiles of the marketing accounts in real time, thereby identifying the marketing accounts that are associated with the agricultural product's marketing selling point and quickly implementing targeted push notifications.

[0030] Based on the matching situation, S2 determines the available marketing selling points in the agricultural products. Based on the composition data of the available marketing selling points in the marketing selling points, and combined with the matching situation of the available marketing selling points with different user profiles, S2 determines the marketing association type of the marketing account in the agricultural products. Specifically, the available marketing selling points of the agricultural products are those with matching marketing accounts. That is, if users with user profiles of marketing accounts are interested in the marketing selling points of the agricultural products, then the marketing selling points of the agricultural products are determined to be available marketing selling points of the agricultural products.

[0031] Scene setting: Agricultural Product C: Premium Highland Blueberries, Total Marketing Selling Points (4): Selling Point A: High anthocyanin content (core health selling point), Selling Point B: Originating from the pollution-free snowy plateau (origin selling point), Selling Point C: Freshly picked in season (freshness selling point), Selling Point D: High-end dessert ingredient (processing / gift selling point).

[0032] Existing marketing account pool: Account 1, Account 2, Account 3.

[0033] Preset thresholds: Preset marketing selling point quantity threshold: 2 (i.e., matching more than 2 selling points can be directly considered as strong association), Preset account quantity threshold: 2 (i.e., if only 1 account matches a certain selling point, then the account is highly scarce), Preset factor threshold: 5 (used for final comprehensive judgment).

[0034] Specifically, such as Figure 3 As shown, the method for determining the marketing association type of the marketing account in the agricultural product is as follows: S21 uses the composition data of the available marketing points of the agricultural product in the marketing points to determine the composition ratio of the available marketing points of the agricultural product in the marketing points, and uses it as the available composition ratio. In the above steps, the available composition ratio is calculated, and it is checked whether there is at least one user profile interested in each marketing selling point (i.e., matching marketing accounts). Assuming the check results are: Selling point A (anthocyanins): matched by "Account 1" -> available, Selling point B (highland origin): matched by "Account 2" -> available, Selling point C (fresh in season): matched by "Account 1" and "Account 2" -> available, Selling point D (dessert ingredient): matched by "Account 3" -> available; The available marketing points are: A, B, C, and D. The quantity is 4, and the total number of marketing points is 4. Therefore, the available percentage is calculated as: (Number of available marketing points / Total number of marketing points) = 4 / 4 = 100%.

[0035] Available Selling Points: These are the selling points of an agricultural product that have at least one target customer group interested in them (i.e., a matching marketing account). They are considered "effective" selling points. Available Selling Point Ratio: This is the proportion of available selling points to all selling points. It reflects the overall "market acceptance" or "selling point effectiveness" of the agricultural product. A higher ratio indicates that the product is more likely to find its target audience through existing channels.

[0036] This step provides a macro-level assessment of the agricultural product's marketing potential. A 100% usable composition ratio means that all selling points of the blueberry have market value, with no "ineffective selling points," laying a solid foundation for subsequent account-level fine-tuning.

[0037] S22 determines the matching marketing account for the available marketing points based on the matching results between the available marketing points and different user profiles; In the above steps, the matching marketing accounts for available marketing points are determined. Based on historical data (such as purchase ratio analysis), the matching accounts for each available marketing point are determined: Matching marketing account for marketing point A (anthocyanins): Account 1; Matching marketing account for marketing point B (highland origin): Account 2; Matching marketing accounts for marketing point C (fresh in season): Account 1 and Account 2; Matching marketing account for marketing point D (dessert ingredients): Account 3.

[0038] Matching marketing accounts: This refers to marketing accounts whose user profiles have been confirmed by data to be interested in a specific marketing selling point. This is a "selling point-account" pairing relationship. This step builds a clear "resource map," indicating which account(s) should carry and promote each selling point, which is the foundation for achieving precise targeting.

[0039] S23 determines the marketing association type of the marketing account for the agricultural product based on the available marketing selling points, available composition ratios, and matching marketing accounts for different available marketing selling points in the marketing account.

[0040] It should be noted that when the user profile of the marketing account does not match the marketing selling points of the agricultural product, the marketing association type of the marketing account with the agricultural product is determined to be unrelated. In another embodiment, S231: If the user profile of the marketing account matches the marketing selling points of the agricultural product, obtain the number of marketing selling points matched in the agricultural product, determine whether the number of marketing selling points matched in the agricultural product is greater than a preset marketing selling point number threshold, if yes, determine that the marketing association type of the marketing account in the agricultural product is a strong association type, if no, proceed to the next step. Specifically, taking "Account 1" as an example, let's analyze its association with "Premium Highland Blueberries". S231: Preliminary judgment of the number of matches: Which selling points of blueberries does the user profile of "Account 1" match? The check found that it matches selling point A (anthocyanins) and selling point C (fresh in season). The number of matches is 2. The number of matches (2) = the preset marketing selling point number threshold (2)? The rule is that only when it is "greater than" the threshold is it judged as a strong association. The condition is not met, so proceed to the next step.

[0041] Number of matching marketing selling points: This refers to the number of all marketing selling points related to the agricultural product that the user profile of a particular marketing account is simultaneously interested in (matched with). It measures the "breadth" of the connection between the account and the product, serving as a quick filtering mechanism. If an account covers most of the product's core selling points, it is naturally a strongly relevant account, requiring no complex calculations.

[0042] S232 Based on the matching marketing accounts of the agricultural product in different available marketing outlets, determine the total number of matching marketing accounts in different available marketing outlets, and determine whether the total number of matching marketing accounts in different available marketing outlets is less than a preset account number threshold. If so, determine that the marketing association type of the marketing account in the agricultural product is a strong association type. If not, proceed to the next step. In the above steps, we will determine the scarcity of accounts. We will check whether the total number of marketing accounts matched by "Account 1" is scarce. It is matched with selling point A and selling point C. The total number of marketing accounts matched by selling point A is 1 (only itself), and the total number of marketing accounts matched by selling point C is 2 (it and "Account 2"). Judgment: Does there exist a matching selling point whose total number of matching accounts is not less than the preset account number threshold (2)? The number of matching accounts (2) of selling point C is not less than the threshold (2), so the condition is not met, proceed to the next step.

[0043] Total number of matching marketing accounts: This refers to the total number of marketing accounts interested in a specific marketing selling point. It measures the "scarcity" or "competitiveness" of the marketing channel for that selling point. The fewer the accounts, the higher the scarcity; this step assesses the "irreplaceability" of the accounts. If an account is the "only" spokesperson for a particular selling point, even if it covers only a few selling points, its association strength is very high.

[0044] S233 Based on the number of marketing points matched by the marketing account in the agricultural product, determine the proportion of the available marketing points in the agricultural product and use it as the matching proportion. Determine whether there is a marketing account with a higher matching proportion than the marketing account. If yes, proceed to the next step. If no, determine that the marketing association type of the marketing account in the agricultural product is a strong association type. In the above steps, a relative advantage assessment is performed. The matching ratio of "Account 1" is calculated. The number of matching selling points = 2 (A, C), the total number of available marketing selling points = 4 (A, B, C, D), and the matching ratio = 2 / 4 = 50%. Determine: Is there an account with a higher matching ratio? Account 2: Matches selling points B and C, with a matching ratio of 50%. Account 3: Matches only selling point D, with a matching ratio of 25%.

[0045] Conclusion: Other accounts with the same matching percentage (50%) exist, not that "no such accounts exist". Therefore, the condition is not met, proceed to the next step.

[0046] Match Ratio: This refers to the proportion of selling points matched by a particular marketing account out of all available selling points for that agricultural product. It measures the account's "relative breadth of coverage" in the marketing of that product, judged from the perspective of "relative ranking." If an account has the most comprehensive coverage, then it is naturally strongly associated. This avoids arbitrarily judging one of multiple accounts as strongly associated when their performance is similar.

[0047] S234 determines the marketing selling points matched by the marketing account in the agricultural product, and determines the comprehensive association factor of the marketing account based on the number of marketing accounts matched by the matched marketing selling points, and determines the marketing association type of the marketing account based on the comprehensive association factor.

[0048] It is understood that the overall correlation factor of the marketing account is determined based on the number of marketing points matched by the marketing account in the agricultural product and the number of marketing accounts matched by the matched marketing points. The more marketing points matched and the fewer marketing accounts matched in different matched marketing points, the greater the overall correlation factor of the marketing account.

[0049] In the above steps, a comprehensive correlation factor is calculated. The number of matched marketing points (N_match) is 2(A, C). The scarcity score of the matched points is calculated: Generally, the fewer the total number of accounts that match a point, the higher the value of that point to the accounts that match it. We can represent this relationship using reciprocals: Scarcity score of point A = 1 / total number of accounts that match point A = 1 / 1 = 1, Scarcity score of point C = 1 / total number of accounts that match point C = 1 / 2 = 0.5. The comprehensive correlation factor is calculated: The scarcity scores of all matched points of the account are added together. The comprehensive correlation factor = Score of point A + Score of point C = 1 + 0.5 = 1.5.

[0050] Comprehensive Relevance Factor: A comprehensive quantitative indicator that considers both the "breadth" (quantity) and "depth" (scarcity / uniqueness) of the match between the account and the product's selling points. The higher the value, the greater the marketing value of the account for that agricultural product, and the stronger the correlation. This is the final scientific quantitative assessment. It solves the problem of balancing "breadth" and "scarcity" in the previous steps. A high factor indicates a large number of matching selling points and few competing accounts for those points.

[0051] At this time, the comprehensive correlation factor (1.5) is not greater than the preset factor threshold (2), and it is determined that the marketing correlation type of account 1 with "premium plateau blueberry" is a weak correlation type.

[0052] Through step-by-step analysis of Account 1, it was ultimately determined to be a related channel. This means that while it is one of the effective promotional channels for blueberries, it is not the most core or indispensable channel. When allocating marketing resources, it can be used as a supplementary or testing channel.

[0053] Furthermore, the marketing association type of the marketing account is determined based on the comprehensive association factors, specifically including: When the comprehensive correlation factor is greater than the preset factor threshold, the marketing association type of the marketing account in the agricultural product is determined to be a strong correlation type; if the comprehensive correlation factor is not greater than the preset factor threshold, the marketing association type of the marketing account in the agricultural product is determined to be a weak correlation type.

[0054] S3 determines the data processing method for updating user profiles of the marketing accounts based on the marketing association types of the marketing accounts in different agricultural products, and determines the method for generating marketing selling points of the agricultural products based on the update results of user profiles of different marketing accounts.

[0055] Specifically, such as Figure 4 As shown, the method for determining the data processing method for updating user profiles using the marketing account is as follows: S31 uses the marketing association type of the marketing account in different agricultural products to determine the agricultural products with weak association type and strong association type with the marketing account, and regards them as weak association agricultural products and strong association agricultural products. Weakly related agricultural products (1): Product D1 (instant oatmeal): Available marketing weight factor = 1 / 2 = 0.5, (Total selling points: convenient breakfast, high dietary fiber; Available selling points: convenient breakfast); Preset threshold: Preset threshold for the number of related agricultural products (T_num) = 3, Preset threshold for the related factor (T_factor) = 2.5.

[0056] In the above steps, identifying the set of related agricultural products is explained as follows: This step involves classifying all agricultural products promoted by Account 2 into two categories based on historical data: "strongly related" and "weakly related." Strongly related agricultural products are those that highly match the account's user profile. These represent the account's core strengths and reflect the user's most core and stable needs.

[0057] Weakly correlated agricultural products: Products with a generally low match to the account's user profile. These reflect secondary or potential user needs, but are not the account's strongest areas of expertise; this forms the basis for strategy formulation. By dividing the business into "core-periphery" segments, data is provided to guide whether to adopt an "expansion" or "focus" strategy.

[0058] The result of this example is: Strongly associated set = {Product C1}, Weakly associated set = {Product D1}.

[0059] S32 uses the proportion of available marketing points in the weakly correlated agricultural products to the total number of marketing points in the weakly correlated agricultural products as the basis for the available marketing weight factor, and uses the proportion of available marketing points in the strongly correlated agricultural products to the total number of marketing points in the strongly correlated agricultural products as the basis for the available marketing weight factor. In the above steps, the available marketing weight factor is calculated for each agricultural product identified in S31.

[0060] Available Marketing Weighting Factor: An indicator that measures the overall effectiveness of the selling points of a single agricultural product. The calculation formula is: Factor = Number of Available Marketing Selling Points for the Product / Total Number of Marketing Selling Points for the Product. This factor introduces a "quality" dimension. A strongly correlated product with a factor of 1.0 (all selling points are effective) has a far higher strategic value than a strongly correlated product with a factor of 0.5 (half of its selling points are ineffective). This ensures that subsequent decisions consider not only quantity but also business quality.

[0061] In this example, the product C1 factor is approximately 0.67 (2 / 3), and the product D1 factor is approximately 0.5 (1 / 2).

[0062] S33 determines the data processing method for updating user profiles for the marketing account based on the data of weakly correlated and strongly correlated agricultural products, and in combination with the available marketing weight factors of the weakly correlated and strongly correlated agricultural products.

[0063] Specifically, based on the data of weakly correlated and strongly correlated agricultural products, and in conjunction with the available marketing weight factors for the weakly correlated and strongly correlated agricultural products, the data processing method for updating user profiles for the marketing account is determined, including: S331 Based on the data of weakly correlated agricultural products and strongly correlated agricultural products, determine the total quantity of weakly correlated agricultural products and strongly correlated agricultural products, and determine whether the marketing account has weakly correlated agricultural products or strongly correlated agricultural products. If yes, proceed to the next step; if no, determine that the data processing method for updating user profiles of the marketing account is the preset processing method. In the above steps, the existence check determines whether the marketing account has at least one strongly or weakly related agricultural product. Existence check: This is the "qualification review" step of the process. An account without any related products (new account or extremely inactive account) cannot be used for effective strategy analysis.

[0064] The preset processing method is based on the observation results of comment users in the marketing videos of the marketing account. Whenever the number of new comment users exceeds a preset threshold for the number of comment users, such as more than 200, the user profile of the marketing account is updated.

[0065] In this example, it is determined that account 2 has a strongly associated agricultural product C1 and a weakly associated agricultural product D1, and the process proceeds to S332; S332 determines whether the total number of weakly related agricultural products and strongly related agricultural products is greater than the preset threshold for the number of related agricultural products. If yes, the data processing method for updating user profiles for the marketing account is determined to be the second preset processing method. If no, proceed to the next step. In the above steps, the business breadth judgment determines whether the total number of associated agricultural products exceeds a preset threshold. Business breadth refers to the total number of agricultural products associated with an account, which directly reflects the breadth of its business scope.

[0066] The second preset processing method is to perform real-time analysis of the comments from users on the marketing videos in the marketing account and dynamically update the user profile of the marketing account.

[0067] The total number of related agricultural products = 1 (strong correlation) + 1 (weak correlation) = 2. In this example, is 2 > T_num (3)? No, no, the business breadth is within a controllable range. The process enters S333.

[0068] S333 determines whether the marketing account has a strong association with agricultural products. If so, proceed to the next step. If not, determine that the data processing method for updating the user profile of the marketing account is the preset processing method. In the above steps, a core advantage check is performed to determine whether the marketing account has at least one strongly associated agricultural product.

[0069] In this example, the assessment is: Account 2 has a strong correlation with agricultural product C1. Decision: Yes, it possesses a core advantage. Proceed to S334.

[0070] Pre-set handling method: This is the strategy triggered when an account lacks a core strength. It's typically an "exploratory" update designed to find and establish the account's core positioning through extensive testing and the introduction of new tags. This step identifies whether the account's "core base" is solid. An account without strongly related products means its user profile lacks a solid foundation; the current strategy should be the (pre-set handling method), not optimization or adjustment.

[0071] S334 Based on the available marketing weight factors of the strongly associated agricultural products and the number of strongly associated agricultural products of the marketing account, determine the agricultural product association factor between the marketing account and the strongly associated agricultural products, and determine whether the agricultural product association factor between the marketing account and the strongly associated agricultural products is greater than the preset association factor threshold. If yes, determine that the data processing method for updating the user profile of the marketing account is the second preset processing method. If no, proceed to the next step. In the above steps, the strength of the core advantage is assessed, and the overall strength of the account in the strongly related fields is calculated and judged to be strong enough.

[0072] Agricultural Product Association Factor (Strong Association): This metric specifically measures an account's overall strength in areas of strong association. The calculation formula is: Strong Association Factor = Σ (Available Marketing Weight Factor for each strongly associated agricultural product). It aggregates the "quality score" of all core products.

[0073] Strong correlation factor = Product C1 factor (0.67) = 0.67. In this example, is the strong correlation factor (0.67) greater than T_factor (2.5)? No, the core advantage strength is insufficient. The process proceeds to the final step S335.

[0074] S335 determines a comprehensive correlation factor based on the sum of agricultural product correlation factors between strongly correlated agricultural products and agricultural product correlation factors between weakly correlated agricultural products, and determines a data processing method for updating user profiles for the marketing account based on the comprehensive correlation factor.

[0075] It is understood that the data processing method for determining the marketing account used for updating user profiles based on the aforementioned comprehensive correlation factors specifically includes: When the comprehensive correlation factor is greater than the preset correlation factor threshold, the data processing method for updating user profiles using the marketing account is determined to be the second preset processing method; if the comprehensive correlation factor is not greater than the preset correlation factor threshold, the data processing method for updating user profiles using the marketing account is determined to be the preset processing method.

[0076] In the above steps, a comprehensive strength assessment and final decision are made, taking into account the account's overall performance in both core (strong correlation) and peripheral (weak correlation) areas. The agricultural product correlation factor (weak correlation) is an indicator that measures the account's overall performance in weak correlation areas. The calculation formula is: Weak correlation factor = Σ (Available marketing weight factor for each weakly correlated agricultural product).

[0077] Comprehensive Correlation Factor: The ultimate indicator for measuring the quality and breadth of the account's overall business layout. The calculation formula is: Comprehensive Correlation Factor = Strong Correlation Factor + Weak Correlation Factor. This is the final and most comprehensive evaluation. In this example, the calculation is: Weak Correlation Factor = Product D1 Factor (0.5) = 0.5, Comprehensive Correlation Factor = Strong Correlation Factor (0.67) + Weak Correlation Factor (0.5) = 1.17. Is Comprehensive Correlation Factor (1.17) > T_factor (2.5)? No. Since the Comprehensive Correlation Factor is not greater than the preset threshold, the data processing method for updating the user profile for Account 2 is determined to be the [Preset Processing Method].

[0078] Specifically, the method for determining the method for generating and processing the marketing selling points of the agricultural products is as follows: S41 Based on the matching marketing account of the agricultural product, determine the matching marketing account of the agricultural product and the available marketing selling points matched by the matching marketing account; S42 determines the marketing account that matches the marketing selling points of the agricultural product after the update based on the updated user profiles of different marketing accounts; S43 uses the matching marketing account of the agricultural product and the available marketing points matched by the matching marketing account, and combines the updated marketing account that matches the marketing points of the agricultural product to determine the method for generating and processing the marketing points of the agricultural product.

[0079] Step Explanation: This stage is the data collection and problem initialization stage. S41: Obtain the basic data before the update, that is, the existing matching accounts of agricultural product Z and their recognized selling points (available marketing selling points). S42: Obtain the new data generated after the user profile is updated, that is, the marketing accounts on the new matching. S43: Combine the old and new data as input for decision-making.

[0080] Matching Marketing Accounts: Accounts whose user profiles already matched certain selling points of agricultural product Z before the update. These represent stable, historically proven marketing channels. Updated Matching Marketing Accounts: New accounts that match the selling points of agricultural product Z after the user profile update, representing new market trends and potential opportunities. Business Significance: Comparing and analyzing the "historical foundation" with the "latest market signals" is key to making forward-looking decisions.

[0081] Suppose we focus on an apple product called Agricultural Product Z and develop a selling point strategy for it. The total marketing selling points for Agricultural Product Z are (4): Selling Point 1: Organically grown (safe), Selling Point 2: Rich in vitamins (healthy), Selling Point 3: Sweet, crisp and juicy (taste), Selling Point 4: Holiday gift box (gift).

[0082] Initial state (before user profile update): Available marketing selling points (2): Selling point 1 (sweet, crisp and juicy), Selling point 2 (rich in vitamins), (that is, only these two selling points have found matching marketing accounts), Matching marketing accounts: Account M (matching selling point 1), Account N (matching selling point 2).

[0083] Input for this example: Old data: Available selling points = {1, 2}; Matching accounts = {M, N}.

[0084] Furthermore, based on the matching marketing accounts for the agricultural products and the available marketing points matched by those accounts, and in conjunction with the updated marketing accounts matching the marketing points of the agricultural products, a method for generating and processing the marketing points of the agricultural products is determined, specifically including: S431 obtains the number of available marketing points for the agricultural product, and determines whether the number of available marketing points for the agricultural product is greater than the preset marketing point number threshold. If so, it is determined that the method for generating marketing points for the agricultural product is that regardless of whether the updated marketing account matches the marketing points of the agricultural product that are outside the available marketing points, it is not necessary to perform the identification process of marketing points other than available marketing points. If not, proceed to the next step. In the above steps, a sufficiency assessment of selling points can be used to determine whether there are enough selling points currently being verified for the agricultural product. This involves using marketing videos on existing marketing accounts to highlight the user's focus on the selling points, such as organic farming, including how to verify organic status, whether there are corresponding testing reports, and whether there are relevant planting videos. Based on the user's focus, marketing videos can be generated specifically for the selling points.

[0085] Number of available marketing selling points: This refers to the number of selling points currently undergoing validity verification in the marketing account. If the product already has a large number of marketing selling points (exceeding the threshold), to ensure the effectiveness and consistency of the verification process, even if new marketing accounts match the marketing selling points of agricultural products, there is no need to conduct user attention type verification for the new marketing selling points. Priority should be given to maintaining existing successful models to avoid blindly chasing new trends and dispersing resources. This is a "conservative" strategy.

[0086] In this example, the following judgment is made: Is the number of available marketing points (2) greater than T_available (3)? No, the number of available marketing points is insufficient. The process proceeds to S432.

[0087] S432 determines the identification matching ratio based on the proportion of available marketing selling points in the agricultural product, and judges whether the identification matching ratio of the agricultural product is greater than the preset matching ratio threshold. If so, it is determined that the method for generating marketing selling points of the agricultural product is that regardless of whether the updated marketing account matches the marketing selling points of the agricultural product that are outside the available marketing selling points, it is not necessary to perform the identification processing of marketing selling points other than available marketing selling points. If not, proceed to the next step. In the steps above, the selling point coverage assessment determines whether the proportion of effective selling points among all selling points is sufficiently high, identifying the matching ratio: number of available marketing selling points / total number of marketing selling points. This measures the success rate of the product's pre-set selling point strategy.

[0088] Even if the absolute number of available selling points is small, if a large portion of the marketing selling points have already been validated historically, their usability can still be effectively verified. In this case, a conservative approach can be taken, avoiding easy changes. This assesses the quality of the selling point strategy, not just its quantity. The matching ratio is 2 / 4 = 50%. In this example, is 50% > T_ratio (60%)? No, the selling point coverage is insufficient. The process proceeds to S433.

[0089] S433 takes the marketing account that matches the marketing selling points of the agricultural product after the update as the updated matching marketing account, and determines the method for generating the marketing selling points of the agricultural product based on the matching situation between the updated matching marketing account and the agricultural product excluding available marketing selling points, as well as the matching marketing account of the agricultural product.

[0090] Furthermore, based on the matching status of the updated marketing accounts and the agricultural products (excluding available marketing points), and the matching marketing accounts of the agricultural products, a method for generating marketing points for the agricultural products is determined, specifically including: S433 & S434: New Trend Significance Assessment, Step-by-Step Explanation: Focus on previously unverified selling points (i.e., "marketing selling points other than available marketing selling points") and determine if a sufficient number of new accounts are starting to pay attention to them. Marketing selling points other than available marketing selling points refer to those selling points in the product definition that haven't been matched by any accounts before and are considered "invalid" or "with unknown potential." These are the targets for this decision on whether they need to be "regenerated" (i.e., re-discovered and verified). This is a crucial step in determining whether market trends have changed. If a large number of new accounts are simultaneously focusing on a previously unpopular selling point, it likely indicates a change in consumer trends and the emergence of new market opportunities.

[0091] S434 obtains the number of updated matching marketing accounts that match the available marketing points, and determines whether the number of updated matching marketing accounts that match the available marketing points exceeds the preset account number threshold. If so, the marketing points of the updated matching marketing accounts that match the available marketing points are generated and processed, i.e., identified, to determine whether they are marketing points that users are interested in. If not, proceed to the next step. Scenario 1 (Needs to be generated and processed): Assume the data: After the user profile is updated, 3 newly updated matching marketing accounts (Accounts P, Q, R) start to focus on Selling Point 3 (Sweet, crispy and juicy (taste)), and is the number of updated accounts matching the new selling point (Selling Point 3) greater than T_new (2)? Yes. Decision: Yes, the new trend is very significant. The process ends here. Generate and process Selling Point 3 (Sweet, crispy and juicy (taste)), that is, perform identification processing among the updated matching marketing accounts to determine the issues concerned in the marketing selling points, so as to generate marketing videos targeted.

[0092] S435 Based on the matching marketing accounts of the agricultural product, determine the matching situation between the matching marketing accounts and the available marketing selling points after the update, and judge whether the number of matching marketing accounts matching the available marketing selling points after the update is less than the preset marketing account number threshold. If so, for the marketing selling points with matching updated matching marketing accounts except the available marketing selling points, perform generation processing, that is, perform identification processing to determine whether it is a marketing selling point that users are interested in. If not, determine that the generation processing method of the marketing selling points of the agricultural product is that regardless of whether the updated marketing accounts match the marketing selling points outside the available marketing selling points of the agricultural product, no identification processing of the marketing selling points outside the available marketing selling points needs to be performed.

[0093] In the above steps, when the new trend is not significant, turn back to check whether the original "basic market" (that is, the original matching marketing accounts) is still stable. The stability of the basic market: It is measured by checking whether the original matching marketing accounts still match the original available marketing selling points after the user profile is updated.

[0094] This is a risk control step. If the users in the original matching marketing accounts all start to leave (for example, the accounts that originally promoted "organic farming" are no longer interested), it means that the product may not be able to effectively identify the concerns of users in the original matching marketing accounts.

[0095] After the update, the original matching marketing accounts M and N still match Selling Point 1 and Selling Point 2. That is, the number of matching marketing accounts matching the available selling points after the update = 2, and is 2 < T_old (1)? No, the basic market is very stable. Therefore, determine that the generation processing method of the marketing selling points of Agricultural Product Z is: regardless of whether the updated marketing accounts match the marketing selling points outside the available marketing selling points of the agricultural product, no identification processing of the marketing selling points outside the available marketing selling points needs to be performed.

[0096] This process ensures that adjustments to agricultural product marketing strategies are always based on solid data analysis, enabling both the agility to seize new opportunities and the robustness to determine whether existing marketing selling points can effectively identify and address user concerns, thus achieving a balance between risk and return.

[0097] Example 2 Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for automatically generating marketing points for agricultural products when running the computer program.

[0098] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0099] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0100] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for automatically generating marketing points for agricultural products, characterized in that, Specifically, it includes: Based on the analysis results of agricultural products, determine the matching situation between the marketing selling points of agricultural products and the user profiles of existing marketing accounts. If it is determined that there are no agricultural products with matching deviations based on the matching situation, proceed to the next step. Based on the matching results, the available marketing selling points in the agricultural products are determined. Based on the composition data of the available marketing selling points in the marketing selling points, and combined with the matching results of the available marketing selling points with different user profiles, the marketing association type of the marketing account in the agricultural products is determined. Based on the marketing association types of the marketing accounts in different agricultural products, a data processing method for updating user profiles of the marketing accounts is determined. Based on the update results of user profiles of different marketing accounts, a method for generating marketing selling points of the agricultural products is determined.

2. The method for automatically generating marketing points for agricultural products as described in claim 1, characterized in that, The analysis results of the agricultural products include their marketing selling points.

3. The method for automatically generating marketing points for agricultural products as described in claim 1, characterized in that, The matching degree between the marketing selling points of the agricultural products and the user profiles of existing marketing accounts is determined based on whether the customers in the user profiles match the marketing selling points.

4. The method for automatically generating marketing points for agricultural products as described in claim 1, characterized in that, Whether the customers in the user profile pay attention to the marketing selling points is determined based on the proportion of purchases made by the customers in the user profile of the products related to the marketing selling points.

5. The method for automatically generating marketing points for agricultural products as described in claim 1, characterized in that, Determining that there are no agricultural products with matching deviations among the agricultural products specifically includes: Based on the matching of user profiles of existing marketing accounts with the agricultural products, identify marketing accounts that match the marketing selling points of the agricultural products and use them as matching marketing accounts. Based on the matching marketing accounts for different agricultural products, determine whether there are any agricultural products with matching deviations.

6. The method for automatically generating marketing points for agricultural products as described in claim 5, characterized in that, If different agricultural products all have matching marketing accounts, then it is determined that there are no agricultural products with matching deviations among them.

7. The method for automatically generating marketing points for agricultural products as described in claim 1, characterized in that, The available marketing selling points of the agricultural products are those that have matching marketing accounts. That is, if users with user profiles of marketing accounts are interested in the marketing selling points of the agricultural products, then the marketing selling points of the agricultural products are determined to be available marketing selling points of the agricultural products.

8. The method for automatically generating marketing points for agricultural products as described in claim 1, characterized in that, The method for determining the method for generating and processing the marketing selling points of the agricultural products is as follows: Based on the matching marketing accounts of the agricultural products, determine the matching marketing accounts of the agricultural products and the available marketing selling points matched by the matching marketing accounts; Based on the updated user profiles of different marketing accounts, identify the marketing accounts that match the marketing selling points of the agricultural products after the update. The method for generating marketing points for the agricultural product is determined by using the matching marketing account for the agricultural product and the available marketing points matched by the matching marketing account, combined with the updated marketing account that matches the marketing points of the agricultural product.

9. The method for automatically generating marketing points for agricultural products as described in claim 8, characterized in that, The method for generating marketing points for the agricultural product is determined by using the matching marketing accounts for the agricultural product and the available marketing points matched by those accounts, combined with the updated marketing accounts that match the marketing points of the agricultural product. This method specifically includes: The number of available marketing points for the agricultural product is obtained. If the number of available marketing points for the agricultural product is greater than a preset threshold, then the method for generating marketing points for the agricultural product is that regardless of whether the updated marketing account matches the marketing points of the agricultural product that are outside the available marketing points, it is not necessary to perform the identification process of marketing points other than available marketing points.

10. A computer system, comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a processing method for automatically generating marketing points for agricultural products as described in any one of claims 1-9.

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