Dynamic display system about agricultural product retail monitoring

Abstract

The invention discloses a dynamic display system for agricultural product retail monitoring, and relates to the technical field of data monitoring. Comprising the steps of 1, capturing commodities and sales data of an e-commerce platform, 2, classifying related commodities according to classification of the commodities by the e-commerce platform in combination with classification standards of agricultural departments and statistical departments on agricultural products, distinguishing agricultural product classifications, defining the agricultural product classifications as first-level classifications, and defining second-level classifications as third-level classifications; 3, according to the first-level classification, the production places of the commodities, the delivery places of the commodities and the names of the commodities in combination with national geographical indication agricultural products and excellent agricultural product standards of all regions, classification with districts and counties + agricultural products as standard names is formed and defined as second-level classification, and 4, according to the second-level classification, the sales volume of each season of each kind of products in the preset age limit is counted, the proportion of the seasons is calculated, and the sales volume of each season is calculated according to the proportion of the seasons. And 5, dynamically displaying the obtained data of the dominant agricultural products and the data of the inferior agricultural products so as to guide related retail behaviors.

Description

Technical Field

[0001] This invention discloses a dynamic display system for monitoring agricultural product retail sales, which relates to the field of data monitoring technology. Background Technology

[0002] With the deepening development of e-commerce, online e-commerce retail of agricultural products has become an important channel for increasing farmers' income and for agricultural enterprises to expand their markets. However, agricultural practitioners, including individual farmers, agricultural cooperatives, and local agricultural departments, often face the dilemma of information asymmetry when formulating online sales strategies. The difficulty in comprehensively and accurately grasping the overall sales situation, competitive landscape, and time-varying sales patterns of specific agricultural products on major national e-commerce platforms is highly detrimental to their decision-making. Summary of the Invention

[0003] This invention addresses the problems of existing technologies by providing a dynamic display system for monitoring agricultural product retail. Through comparative analysis, it helps agricultural departments guide adjustments to the sales behavior of local agricultural products in e-commerce retail, or helps agricultural e-commerce enterprises adjust their own e-commerce sales strategies and behaviors.

[0004] The specific solution proposed in this invention is as follows: This invention provides a dynamic display method for monitoring agricultural product retail sales, comprising: Step 1: Scrape product and sales data from e-commerce platforms. Step 2: Based on the e-commerce platform's product classification and the agricultural and statistical departments' classification standards for agricultural products, classify the relevant products, distinguish agricultural product categories, and define them as primary categories. Step 3: Based on the primary classification, product origin, product shipping location, product name, and national geographical indication agricultural products standards, train a large-scale model to classify various products by region. The primary unit of classification is the district / county; if districts / counties cannot be distinguished, they are classified by prefecture-level cities, forming a classification based on the district / county + agricultural product name, defined as a secondary classification. Step 4: Based on the secondary categories, calculate the sales revenue of each product category for each quarter within a preset year, and determine the quarterly percentage. The quarters are calculated using the Chinese lunar calendar, with March-May as spring, June-August as summer, September-November as autumn, and December-February as winter, based on temperature. If the average percentage of a certain secondary category of agricultural products in a certain quarter within the preset year is greater than the dominance threshold, it is defined as a dominant agricultural product for that quarter. If the average percentage of a certain secondary category of agricultural products in a certain quarter within the preset year is lower than the disadvantage threshold, it is defined as a disadvantageous agricultural product for that quarter. Step 5: Dynamically display the obtained data on advantageous and disadvantageous agricultural products to guide relevant retail behaviors.

[0005] Furthermore, step 4 of the aforementioned dynamic display method for monitoring agricultural product retail uses the formula: quarterly sales / annual * 100 to calculate the quarterly percentage of each secondary category of products.

[0006] Furthermore, in step 4 of the dynamic display method for monitoring agricultural product retail, the advantage threshold is set to 40%. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is greater than 40%, it is defined as an advantageous agricultural product in that quarter. The disadvantage threshold is set to 10%. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 10%, it is defined as a disadvantageous agricultural product in that quarter. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 40% but greater than 10%, it is defined as a seasonal agricultural product, and product labels are formed by primary category, secondary category, advantageous agricultural products, disadvantaged agricultural products and seasonal agricultural products.

[0007] Furthermore, in step 5 of the aforementioned dynamic display method for monitoring agricultural product retail, when displaying dynamically, a primary category is selected, and secondary categories and product labels are displayed. At the same time, the monthly sales curves of each production area and the distribution on e-commerce platforms can be viewed.

[0008] This invention also provides a dynamic display system for monitoring agricultural product retail sales, including a data collection module, a classification module, and a display module. The data collection module captures product and sales data from e-commerce platforms. The classification module categorizes relevant products based on the e-commerce platform's product classification system, combined with the classification standards for agricultural products from agricultural and statistical departments. Agricultural products are then categorized and defined as primary categories. The classification module trains a large model to categorize various commodities by region based on primary classification, origin, shipping location, and product name, combined with national geographical indication agricultural products and local standards for excellent agricultural products. The primary unit of classification is the district / county; if districts / counties cannot be distinguished, cities / prefectures are used, resulting in classifications using the district / county + agricultural product name as the standard name, defined as secondary classifications. The classification module calculates the sales revenue of each product category for each quarter within a preset year based on its secondary classification, and also calculates the quarterly percentage. The quarters are calculated using the Chinese lunar calendar, with March to May as spring, June to August as summer, September to November as autumn, and December to February as winter, based on temperature. If the average percentage of a certain secondary category of agricultural products in a given quarter within the preset year is greater than a dominance threshold, it is defined as a dominant agricultural product for that quarter. Conversely, if the average percentage of a certain secondary category of agricultural products in a given quarter within the preset year is lower than a disadvantage threshold, it is defined as a disadvantageous agricultural product for that quarter. The display module dynamically presents the data on advantageous and disadvantageous agricultural products, thereby guiding relevant retail behaviors.

[0009] Furthermore, the classification module of the dynamic display system for monitoring agricultural product retail uses the formula: quarterly sales / annual * 100 to calculate the quarterly percentage of each secondary category of products.

[0010] Furthermore, the classification module of the dynamic display system for monitoring agricultural product retail sets the advantage threshold at 40%. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is greater than 40%, it is defined as an advantageous agricultural product in that quarter. The disadvantage threshold is set at 10%. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 10%, it is defined as a disadvantageous agricultural product in that quarter. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 40% but greater than 10%, it is defined as a seasonal agricultural product, and product labels are formed by primary category, secondary category, advantageous agricultural products, disadvantaged agricultural products and seasonal agricultural products.

[0011] Furthermore, when the display module of the dynamic display system for monitoring agricultural product retail is dynamically displayed, a primary category is selected to display secondary categories and product tags. At the same time, the monthly sales curves of each production area and the distribution on e-commerce platforms can be viewed.

[0012] The advantages of this invention are: High accuracy: By combining agricultural standard classification and geographical indication information, it has achieved accurate identification and origin traceability of agricultural products on e-commerce platforms, and the analysis results are more in line with agricultural realities.

[0013] Highly instructive: The innovative "strengths / weaknesses / balance" seasonal label system transforms complex data into clear decision-making criteria, directly pointing to adjustments in sales behavior. Good predictability: Trend analysis based on historical data helps predict future market trends and guides agricultural production to plan planting structure or market launch time in advance.

[0014] The process is comprehensive: from data collection, cleaning, and classification to analysis and visualization, a complete closed-loop solution has been formed, which is specifically designed to serve the agricultural e-commerce sector and has high practical value. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0016] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention. Example

[0017] This invention provides a dynamic display method for monitoring agricultural product retail sales, comprising: Step 1: Scrape product and sales data from e-commerce platforms.

[0018] Step 2: Based on the e-commerce platform's product classification and the agricultural and statistical departments' classification standards for agricultural products, classify the relevant products, distinguish agricultural product categories, and define them as primary categories, such as apples, green onions, and radishes.

[0019] Step 3: Based on the primary classification, origin, shipping location, and product name, combined with national geographical indication agricultural products and local standards for excellent agricultural products, train a large model to classify various products by region. The main unit of classification is districts and counties. If districts and counties cannot be distinguished, they are classified by prefecture-level cities, forming a classification with district / county + agricultural product as the standard name, which is defined as a secondary classification, such as apples from region A, scallions from region XX, apples from region B, etc. This scope will be broader than that of geographical indication agricultural products.

[0020] Step 4: Based on the secondary categories, calculate the sales revenue of each product category for each quarter within the preset year, and calculate the quarterly percentage. Use the formula: Quarterly sales revenue / Annual revenue * 100 to calculate the quarterly percentage of each secondary category of products. The quarterly calculation uses the Chinese lunar calendar. Based on temperature, spring is defined as March-May, summer as June-August, autumn as September-November, and winter as December-February. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset year exceeds a dominance threshold, it is defined as a dominant agricultural product for that quarter. Conversely, if the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset year falls below a disadvantage threshold, it is defined as a disadvantageous agricultural product for that quarter. Specifically, the dominance threshold is set at 40%; if the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset year is greater than 40%, it is defined as a dominant agricultural product for that quarter. The disadvantage threshold is set at 10%; if the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset year is less than 10%, it is defined as a disadvantageous agricultural product for that quarter. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 40% but greater than 10%, it is defined as a seasonal agricultural product, and product labels are formed by primary category, secondary category, advantageous agricultural products, disadvantaged agricultural products and seasonal agricultural products.

[0021] For example, using data from the past three years to calculate the average percentage of a certain secondary category of agricultural products in a given quarter, if the percentage in a certain quarter is greater than 40%, it is defined as a fruit of that quarter. For example, if the percentage in the second quarter (June-August) exceeds 40%, the agricultural product is defined as a summer agricultural product. If there is no such characteristic, and the percentage in a certain quarter is less than 10%, it is defined as a disadvantageous agricultural product of that quarter. For example, if the percentage of an agricultural product does not exceed 40% in any quarter, and the percentage in March-May is less than 10%, it is defined as a disadvantageous spring agricultural product. Otherwise, it is defined as an all-season agricultural product, forming a product label. For example, calculating the average percentage of "apples from region A" in each quarter over the past three years, it is found that the average percentage in autumn (September-November) is 55% (>40%), so it is labeled as an "autumn advantage". Calculating the percentage of "apples from region C", it is found that the percentage of apples sold in spring (March-May) reaches 45%, so it is labeled as a "spring advantage". However, the sales of apples from a certain new production area do not exceed 40% in any quarter, and the percentage in spring is only 8% (<10%), so it is labeled as a "spring disadvantage", otherwise it is an all-season fruit.

[0022] Step 5: Dynamically display the obtained data on advantageous and disadvantageous agricultural products to guide relevant retail behaviors.

[0023] When displaying dynamic information, selecting a primary category will show secondary categories and product tags. Users can also view monthly sales curves and e-commerce platform distribution for each production area. For example, agricultural clients in a specific region can view the apple category to see which regions primarily sell apples on e-commerce platforms, such as apples from region A, region B, and region C. They can then see tags for these categories, such as apples from region A being year-round apples, apples from region B being autumn apples, and apples from region C being spring apples. They can also see related seasonal sales figures, monthly sales figures, and major sales platforms, thus identifying sales opportunities or guiding local farmers and agricultural enterprises to adjust their practices. For instance, if the spring market is dominated by apples from region C, while local apples are available in autumn, they face fierce competition from apples from region A. Therefore, policy recommendations could include: a) guiding farmers to partially switch to early-maturing varieties to try and capture the spring market; b) collaborating with e-commerce companies to strengthen marketing in autumn, highlighting the unique characteristics of local apples and differentiating them from apples from region A; c) to address the disadvantage in spring sales by developing cold storage technology or apple processing industries to achieve off-season sales. Example

[0024] This invention also provides a dynamic display system for monitoring agricultural product retail sales, including a data collection module, a classification module, and a display module. The data collection module captures product and sales data from e-commerce platforms. The classification module categorizes relevant products based on the e-commerce platform's product classification system, combined with the classification standards for agricultural products from agricultural and statistical departments. Agricultural products are then categorized and defined as primary categories. The classification module trains a large model to categorize various commodities by region based on primary classification, origin, shipping location, and product name, combined with national geographical indication agricultural products and local standards for excellent agricultural products. The primary unit of classification is the district / county; if districts / counties cannot be distinguished, cities / prefectures are used, resulting in classifications using the district / county + agricultural product name as the standard name, defined as secondary classifications. The classification module calculates the sales revenue of each product category for each quarter within a preset year based on its secondary classification, and also calculates the quarterly percentage. The quarters are calculated using the Chinese lunar calendar, with March to May as spring, June to August as summer, September to November as autumn, and December to February as winter, based on temperature. If the average percentage of a certain secondary category of agricultural products in a given quarter within the preset year is greater than a dominance threshold, it is defined as a dominant agricultural product for that quarter. Conversely, if the average percentage of a certain secondary category of agricultural products in a given quarter within the preset year is lower than a disadvantage threshold, it is defined as a disadvantageous agricultural product for that quarter. The display module dynamically presents the data on advantageous and disadvantageous agricultural products, thereby guiding relevant retail behaviors.

[0025] The information interaction and execution process between the modules in the above system are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description in the method embodiment of the present invention, and will not be repeated here.

[0026] Similarly, the advantages of the system of the present invention are: High accuracy: By combining agricultural standard classification and geographical indication information, it has achieved accurate identification and origin traceability of agricultural products on e-commerce platforms, and the analysis results are more in line with agricultural realities.

[0027] Highly instructive: The innovative "strengths / weaknesses / balance" seasonal label system transforms complex data into clear decision-making criteria, directly pointing to adjustments in sales behavior. Good predictability: Trend analysis based on historical data helps predict future market trends and guides agricultural production to plan planting structure or market launch time in advance.

[0028] The process is comprehensive: from data collection, cleaning, and classification to analysis and visualization, a complete closed-loop solution has been formed, which is specifically designed to serve the agricultural e-commerce sector and has high practical value.

[0029] It should be noted that not all steps and modules in the above processes and system structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structures described in the above embodiments can be physical or logical structures. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0030] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A dynamic visualization method for monitoring agricultural product retail sales, characterized by: include: Step 1: Scrape product and sales data from e-commerce platforms. Step 2: Based on the e-commerce platform's product classification and the agricultural and statistical departments' classification standards for agricultural products, classify the relevant products, distinguish agricultural product categories, and define them as primary categories. Step 3: Based on the primary classification, product origin, product shipping location, product name, and national geographical indication agricultural products standards, train a large-scale model to classify various products by region. The primary unit of classification is the district / county; if districts / counties cannot be distinguished, they are classified by prefecture-level cities, forming a classification based on the district / county + agricultural product name, defined as a secondary classification. Step 4: Based on the secondary categories, calculate the sales revenue of each product category for each quarter within a preset year, and determine the quarterly percentage. The quarters are calculated using the Chinese lunar calendar, with March-May as spring, June-August as summer, September-November as autumn, and December-February as winter, based on temperature. If the average percentage of a certain secondary category of agricultural products in a certain quarter within the preset year is greater than the dominance threshold, it is defined as a dominant agricultural product for that quarter. If the average percentage of a certain secondary category of agricultural products in a certain quarter within the preset year is lower than the disadvantage threshold, it is defined as a disadvantageous agricultural product for that quarter. Step 5: Dynamically display the obtained data on advantageous and disadvantageous agricultural products to guide relevant retail behaviors.

2. The dynamic display method for monitoring agricultural product retail sales according to claim 1, characterized in that: Step 4: Use the formula: Quarterly sales / Annual sales * 100 to calculate the quarterly percentage of each secondary product category.

3. A dynamic display method for monitoring agricultural product retail sales according to claim 1 or 2, characterized in that, in step 4, the advantage threshold is set to 40%; if the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is greater than 40%, it is defined as an advantageous agricultural product in that quarter; and the disadvantage threshold is set to 10%; if the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 10%, it is defined as a disadvantageous agricultural product in that quarter. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 40% but greater than 10%, it is defined as a seasonal agricultural product, and product labels are formed by primary category, secondary category, advantageous agricultural products, disadvantaged agricultural products and seasonal agricultural products.

4. The dynamic display method for monitoring agricultural product retail sales according to claim 1, characterized in that: When displaying dynamically in step 5, select the primary category to display the secondary categories and product tags. You can also view the monthly sales curves of each production area and the distribution on e-commerce platforms.

5. A dynamic display system for monitoring agricultural product retail sales, characterized by: It includes a data collection module, a classification module, and a display module. The data collection module captures product and sales data from e-commerce platforms. The classification module categorizes relevant products based on the e-commerce platform's product classification system, combined with the classification standards for agricultural products from agricultural and statistical departments. Agricultural products are then categorized and defined as primary categories. The classification module trains a large model to categorize various commodities by region based on primary classification, origin, shipping location, and product name, combined with national geographical indication agricultural products and local standards for excellent agricultural products. The primary unit of classification is the district / county; if districts / counties cannot be distinguished, cities / prefectures are used, resulting in classifications using the district / county + agricultural product name as the standard name, defined as secondary classifications. The classification module calculates the sales revenue of each product category for each quarter within a preset year based on its secondary classification, and also calculates the quarterly percentage. The quarters are calculated using the Chinese lunar calendar, with March to May as spring, June to August as summer, September to November as autumn, and December to February as winter, based on temperature. If the average percentage of a certain secondary category of agricultural products in a given quarter within the preset year is greater than a dominance threshold, it is defined as a dominant agricultural product for that quarter. Conversely, if the average percentage of a certain secondary category of agricultural products in a given quarter within the preset year is lower than a disadvantage threshold, it is defined as a disadvantageous agricultural product for that quarter. The display module dynamically presents the data on advantageous and disadvantageous agricultural products, thereby guiding relevant retail behaviors.

6. A dynamic display system for monitoring agricultural product retail sales according to claim 5, characterized in that: The categorization module uses the formula: Quarterly sales / Annual sales * 100 to calculate the quarterly percentage of each secondary category of products.

7. A dynamic display system for monitoring agricultural product retail sales according to claim 5, characterized in that: The classification module sets the advantage threshold to 40%. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is greater than 40%, it is defined as an advantageous agricultural product for that quarter. The disadvantage threshold is set to 10%. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 10%, it is defined as a disadvantageous agricultural product for that quarter. If the average proportion of a certain secondary category of agricultural products in a certain quarter within a preset period is less than 40% but greater than 10%, it is defined as a seasonal agricultural product, and product labels are formed by primary category, secondary category, advantageous agricultural products, disadvantaged agricultural products and seasonal agricultural products.

8. A dynamic display system for monitoring agricultural product retail sales according to claim 5, characterized in that: When the display module is dynamically presented, selecting the primary category will display the secondary categories and product tags. At the same time, you can view the monthly sales curves of each production area and the distribution on e-commerce platforms.

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