Recommendation method of ecological products for karst desertification control based on digital platform

Abstract

The present invention relates to the technical field of commercial product recommendation, and in particular to a method for recommending ecological products for karst desertification control based on a digital platform. The method comprises: obtaining ecological product data and platform data of each ecological product, obtaining product categories according to product label classification, determining the fast consumption degree of ecological products according to sales volume and topic discussion, determining production and marketing parameters according to changes in product output and sales volume, determining the degree of emergence of ecological products as emerging products based on changes in production and marketing parameters, determining the exposure parameters of ecological products in combination with the degree of emergence and fast consumption of ecological products, and recommending products according to the exposure parameters of all ecological products. The present invention can avoid the disadvantageous position of emerging ecological products in product recommendations due to the influence of historical sales data, enhance the recommendation effect of digital platforms, and improve the reliability of digital platform recommendations.

Description

Technical Field

[0001] The present invention relates to the technical field of commercial product recommendation, and in particular to a method for recommending ecological products for karst desertification control based on a digital platform. Background Art

[0002] In the karst ecological zone, the fragile ecological environment is negatively coupled with the unreasonable living and production behaviors under the pressure of high agricultural population. The problem of rocky desertification is prominent and mainly occurs in rural areas, which seriously threatens the safety of the ecosystem. In the process of rocky desertification control, based on the inherent fragility of the karst natural resource environment, it is necessary to take into account the restoration and protection of the ecological environment. Therefore, new cash crops will be cultivated and introduced according to local conditions. On the premise of promoting the "greening" of the ecological environment and ensuring the support and regulation services of the ecosystem, material products will be produced to promote income growth, so as to better promote the realization of the value of ecological products and the revitalization of rural ecological industries. However, with the planting of new suitable crops, the promotion of new ecological products produced has become an important indicator, so that they can be recognized by the market more quickly, increase product popularity, and enhance the subsequent sustainable scientific management capabilities of the ecology.

[0003] In the relevant technology, a fixed recommendation strength is increased for the emerging products of the year. That is, when an ecological product is confirmed to be a new product of the year, a higher recommendation strength is used for recommendation. This method is too simple, which affects the recommendation effect of previous ecological products. The recommendation effect of the digital platform is poor, and the reliability of emerging ecological products when recommended on the digital platform is low. Summary of the invention

[0004] In order to solve the technical problems that the recommendation effect of digital platforms in related technologies is poor and the reliability of emerging ecological products is low when recommending them on digital platforms, the present invention provides a method for recommending ecological products for karst desertification control based on a digital platform. The technical solutions adopted are as follows:

[0005] The present invention proposes a method for recommending ecological products for karst desertification control based on a digital platform, the method comprising:

[0006] Obtaining ecological product data and platform data of each ecological product, wherein the ecological product data includes product labels and product output, and the platform data includes topic discussion and sales volume of each ecological product;

[0007] Classify all eco-products according to the product labels, determine the product category to which each eco-product belongs, and use any eco-product as a target product, and other eco-products in the product category to which the target product belongs as comparison products; determine the peak sales period of the target product according to the sales volume of the target product at different times, and determine the fast consumption degree of the target product by combining the topic discussion and peak sales period of the target product and all the comparison products;

[0008] Determine the production and marketing parameters of the target product in the current time period according to the changes in the product output and sales volume of the target product in the current time period and the previous time period, and determine the degree of emergence of the target product as an emerging product according to the changes in the production and marketing parameters of the target product and all the comparative products in different time periods;

[0009] Based on the target product's degree of emergence and fast-moving consumption, determine the target product's exposure parameters, and make product recommendations based on the exposure parameters of all ecological products.

[0010] Furthermore, the classifying of all ecological products according to the product labels to determine the product category to which each ecological product belongs includes:

[0011] Based on the keyword extraction algorithm, keywords are extracted from the product labels of each ecological product; the number of identical keywords corresponding to any two ecological products is taken as the similarity of the two ecological products;

[0012] Unsupervised data clustering is performed based on the similarity of all groups of ecological products to determine the product category to which each ecological product belongs.

[0013] Furthermore, determining the peak sales period of the target product according to the sales volume of the target product at different times includes:

[0014] Taking one year as a cycle, determine the sales volume of the target product in each month of the year, and use the average sales volume of all months as the sales base point of the target product;

[0015] The months when the sales volume is greater than the sales base point are regarded as the peak sales period.

[0016] Furthermore, the topic discussion degree is a numerical indicator of the topic popularity at the current moment. The topic discussion degree and the peak sales period of the target product and all the comparison products are combined to determine the fast consumption degree of the target product, including:

[0017] The reciprocal of the number of months corresponding to the peak sales period of any ecological product is used as the first fast-moving consumer goods indicator;

[0018] Calculate the product of the topic discussion degree of any ecological product and the first fast-moving consumer goods index, and normalize the maximum and minimum values ​​as the fast-moving consumer goods characteristic value of the ecological product;

[0019] The average of the fast-consumption characteristic values ​​of the target product and all the comparison products is taken as the fast-consumption degree of the target product.

[0020] Furthermore, the production and sales parameters of the target product in the current time period are determined according to the change in the product output and sales volume of the target product in the current time period and the previous time period, including:

[0021] Calculate the difference between the product output of the target product in the current time period and the previous time period as the output gain indicator;

[0022] The production and marketing parameters of the target product in the current time period are determined based on the sales volume and the production gain index in the current time period, wherein the sales volume is positively correlated with the production and marketing parameters, and the gain index is negatively correlated with the production and marketing parameters.

[0023] Furthermore, the production and marketing parameters of the target product in the current time period are determined according to the sales volume and production gain index in the current time period, including:

[0024] The difference between the sales volume and the production gain index is calculated, and the maximum and minimum values ​​are normalized as the production and sales parameters of the target product in the current time period.

[0025] Furthermore, the determination of the degree to which the target product is an emerging product based on changes in production and sales parameters of the target product and all the comparison products in different time periods includes:

[0026] The production and sales change coordinate system is constructed with the time period as the horizontal axis and the production and sales parameters as the vertical axis;

[0027] Taking the production and sales parameters of the target product in different time periods as coordinate points, connecting the coordinate points in time series to obtain a production and sales change curve of the target product;

[0028] Determining the similarity between the target product and any comparative product according to the fluctuation difference between the production and sales change curves of the target product and the comparative product;

[0029] The opposite numbers of the mean values ​​of all curve similarities are normalized to the maximum and minimum values, which is used as the degree of emergence of the target product as an emerging product.

[0030] Further, according to the fluctuation difference between the production and sales change curves of the target product and any comparison product, determining the curve similarity between the target product and the comparison product includes:

[0031] Performing STL time series decomposition on the production and sales change curve to obtain a trend item as a product change trend curve of the target product;

[0032] Determine the curve similarity between the product change trend curve of the target product and the product change trend curve of each comparison product.

[0033] Furthermore, the exposure parameters of the target product are determined by combining the emerging degree and fast-moving consumption degree of the target product, including:

[0034] The product of the emerging degree and the fast-moving consumption degree of the target product is calculated, and the maximum and minimum values ​​are normalized as the exposure parameters of the target product.

[0035] Furthermore, the product recommendation based on the exposure parameters of all ecological products includes:

[0036] All ecological products are sorted from large to small according to the exposure parameters, and recommendation weights are assigned according to the sorting results. The higher the sorting result, the higher the recommendation weight value.

[0037] The present invention has the following beneficial effects:

[0038] In the embodiment of the present invention, ecological product data and platform data of each ecological product are obtained; then, the fast-consumption degree of each ecological product is determined through label classification, and specific analysis of topic discussion and sales peak periods is performed on different categories. The fast-consumption degree can effectively characterize the sales popularity of the ecological product at the current moment; then, the degree of emergence of the ecological product as an emerging product is determined through changes in product output and sales volume, and the degree of emergence characterizes the possibility that the ecological product is an emerging product; thus, the exposure parameters of the ecological product are determined in combination with the degree of emergence and the degree of fast-consumption, and product recommendations are made based on the exposure parameters, so that more reliable recommendations can be made in combination with the production and demand relationship of the ecological product itself at the current moment, avoiding the disadvantageous position of emerging ecological products in product recommendations due to the influence of historical sales data, enhancing the recommendation effect of the digital platform, and improving the reliability of emerging ecological products when recommending them on the digital platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1A flow chart of a method for recommending ecological products for karst desertification control based on a digital platform provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a method for recommending ecological products for karst desertification control based on a digital platform proposed by the present invention, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] The following is a detailed description of a specific scheme of a method for recommending ecological products for karst desertification control based on a digital platform provided by the present invention in conjunction with the accompanying drawings.

[0044] It should be noted that in the embodiment of the present invention, rocky desertification refers to the negative coupling of the fragile natural environment superimposed on the irrational social and economic activities under the pressure of high agricultural population in the tropical-subtropical karst landform area, resulting in serious soil erosion, resulting in surface soil loss, bedrock exposure, land loss of agricultural utilization value and ecological environment degradation, and the evolution process of the surface showing a desert-like landscape of rocks gradually exposed, which is an extreme form of land degradation. Based on the inherent fragility of the karst natural resource environment, in the process of rocky desertification control, it is necessary to take into account the win-win development of ecological economy. Therefore, in combination with the karst ecosystem environment and location characteristics, new adaptive species or crops are introduced to promote the coordinated development of ecological economy. For example, in the process of moderate-intensity rocky desertification control in the karst plateau canyon, new species are adapted to the dry and hot valley climate and have the characteristics of stone, calcium, drought resistance, etc. Tropical fruits. However, due to the lack of past sales category data, there is no regional popularity, the market dissemination and recommendation of ecological product information is weak, it is difficult to carry out market promotion, and it is difficult to carry out on a large scale, which makes the realization and premium increase of high-quality ecological products at a disadvantage. Therefore, by building a digital platform, we can provide a more easily controllable product promotion platform and effectively promote new ecological products.

[0045] In the related art, the promotion process of the digital platform is based on historical data such as historical exposure for recommendation. In this case, due to the lack of relevant data information of the emerging ecological products, they are naturally at a disadvantage during promotion and cannot keep up with the complex and changing market and production needs. Therefore, in the embodiments of the present invention, by constructing an ecological product recommendation method, more accurate and effective product promotion can be carried out on the digital platform.

[0046] See also Figure 1 , which shows a flow chart of a method for recommending ecological products for karst desertification control based on a digital platform provided by an embodiment of the present invention, the method comprising:

[0047] S101: Obtain ecological product data and platform data of each ecological product, wherein the ecological product data includes product labels and product output, and the platform data includes topic discussion and sales volume of each ecological product.

[0048] Among them, ecological product data is the data information of the ecological product itself, specifically including product labels and product output. The product label is the text label of the product itself. For example, for pitaya, the corresponding label can be specifically "food, fruit, red dragon fruit, high anthocyanin content, vitamins", etc. The product output is the production volume of the product in the corresponding period. In the embodiment of the present invention, one month can be specifically used as a time period and one year can be used as a cycle to determine the production quality of pitaya every month in a year as the product output.

[0049] Among them, platform data refers to the relevant data information of ecological products in the digital platform. In the embodiment of the present invention, the platform data includes the topic discussion degree and sales volume of each ecological product. Among them, the topic discussion degree is the numerical representation of the topic popularity at the current moment. It is a specific value. It can be used as the topic discussion degree based on the number of click queries of the corresponding ecological product in the digital platform, or it can be combined with a variety of heat analysis models to integrate and analyze to obtain the topic popularity information, and perform numerical processing to obtain the topic discussion degree. Among them, the sales volume is the sales volume of the product in the corresponding period. It can be understood that the period corresponding to the product output and sales volume is the same.

[0050] S102: Classify all ecological products according to product labels, determine the product category to which each ecological product belongs, take any ecological product as the target product, and other ecological products in the product category to which the target product belongs as comparison products; determine the peak sales period of the target product based on the sales volume of the target product at different times, and determine the fast consumption degree of the target product based on the topic discussion and sales peak period of the target product and all comparison products.

[0051] Among them, the product category refers to the type of product. It should be noted that due to the variety of ecological products, in order to facilitate specific analysis, it is impossible to strictly classify them according to larger categories such as fruits, vegetables, and flowers. A more adaptive classification standard is needed. Therefore, in the embodiment of the present invention, the product labels of all ecological products are combined for specific category classification.

[0052] Furthermore, in some embodiments of the present invention, all ecological products are classified according to product labels to determine the product category to which each ecological product belongs, including: performing keyword extraction on the product label of each ecological product based on a keyword extraction algorithm; taking the number of identical keywords corresponding to any two ecological products as the similarity between the two ecological products; and performing unsupervised data clustering based on the similarities of all groups of ecological products to determine the product category to which each ecological product belongs.

[0053] Among them, the keyword extraction algorithm can be specifically, for example, the word2vec natural language processing algorithm, through which the product label is further extracted with keywords, so that the ecological product is characterized by multiple keywords, that is, each ecological product corresponds to multiple keywords, and similarity analysis can be performed through the keywords.

[0054] In an embodiment of the present invention, the number of the same keywords corresponding to any two ecological products is taken as the similarity of the two ecological products. That is, the more keywords there are, the more similar they are, and the more they can represent the same category. Therefore, the clustering distance is determined according to the similarity of all groups of ecological products. That is, the higher the similarity, the closer the distance between the two ecological products when clustered. A large model is constructed for unsupervised data clustering to determine the product category to which each ecological product belongs.

[0055] The unsupervised data clustering may specifically include density clustering, such as k-means clustering, which is not limited.

[0056] Of course, in some other embodiments of the present invention, classification can also be performed directly based on similarity, and two ecological products with a similarity greater than a preset similarity threshold can be regarded as the same product category. In this way, all groups of ecological products are traversed to achieve product classification and obtain different product categories. There is no restriction on this.

[0057] After determining the product category, for the convenience of analysis, any ecological product can be used as the target product, and other ecological products in the product category to which the target product belongs can be used as comparison products.

[0058] Specifically, the present invention takes one month as a period and one year as a cycle, and then specifically analyzes the sales volume of the target product in different periods, so as to determine the peak sales period.

[0059] Furthermore, according to the sales volume of the target product at different times, the peak sales period of the target product is determined, including: taking one year as a cycle, determining the sales volume of the target product in each month of the year, and taking the average sales volume of all months as the sales base point of the target product; and taking the month with sales volume greater than the sales base point as the peak sales period.

[0060] It should be noted that due to the different demands for different ecological products, for example, the demand for pitaya and grain crops is not at the same level, therefore, it is necessary to analyze each ecological product separately. Specifically, the average sales volume of all months is used as the sales base point of the target product, and based on the sales base point, the months greater than the sales base point are regarded as the peak sales period.

[0061] It is understandable that the sales base point can accurately represent the sales of ecological products, and the more values ​​there are during the peak sales period, the greater the demand for this type of ecological product and the less affected it is by timeliness.

[0062] For example, if a certain ecological product is only popular for 1 to 2 months in a year, and the sales volume is low in other months, the sales volume in these two months will increase the sales base point, resulting in the final sales peak period only including these two months. In other words, because the sales fluctuation is too large, there are fewer months in the sales peak period.

[0063] The sales volume fluctuates slightly from month to month. When the sales volume is balanced each month, the mean is usually in the middle state, which means that there are more months with peak sales periods.

[0064] When sales suddenly decrease and then quickly recover, as represented by a sudden drop in sales in two months, the drop in these two months indicates that there is a problem with the corresponding product, but because of the high sales before, there is still a high sales prospect. At this time, there are more months with peak sales.

[0065] Fast-moving consumer goods usually last for a short time, and when sales suddenly increase, there are fewer months with peak sales. Therefore, the number of months with peak sales can be used to make a specific analysis of whether an ecological product is a fast-moving consumer product.

[0066] Furthermore, in some embodiments of the present invention, the fast-consumption degree of the target product is determined in combination with the topic discussion degree and the peak sales period of the target product and all the comparison products, including: taking the inverse of the number of months corresponding to the peak sales period of any ecological product as the first fast-consumption indicator; calculating the product of the topic discussion degree of any ecological product and the first fast-consumption indicator, and normalizing the maximum and minimum values ​​as the fast-consumption characteristic value of the ecological product; taking the average of the fast-consumption characteristic values ​​of the target product and all the comparison products as the fast-consumption degree of the target product.

[0067] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0068] Combined with the above analysis, the fewer months with peak sales, the more likely the ecological product is a fast-moving consumer product, and the higher its fast-moving consumer degree. Fast-moving consumer products have the effect of mutual influence among similar products, such as seasonal vegetables. Therefore, the average of the fast-moving consumer characteristic values ​​of the target product and all the comparison products is used as the fast-moving consumer degree of the target product. By analyzing the target product and all the comparison products, we can avoid inaccurate calculation results of the fast-moving consumer degree due to abnormal sales of the target product within the period, and improve the objectivity of the fast-moving consumer degree.

[0069] S103: Determine the production and marketing parameters of the target product in the current time period based on the changes in the output and sales volume of the target product in the current time period and the previous time period, and determine the degree of emergence of the target product as an emerging product based on the changes in the production and marketing parameters of the target product and all comparison products in different time periods.

[0070] Among them, emerging products are new products introduced to facilitate the control of karst desertification. This type of product usually has a large planting volume but a small sales volume. In order to analyze this type of product, the degree of emergence can be set to characterize the emerging situation of the target product.

[0071] After analyzing emerging products, we learned that emerging products usually have higher output but lower sales. The underlying reason is that the emerging ecological products are planted to better control the deterioration of the desertification environment. Therefore, the planting value of emerging ecological products will be increased year by year, but the market recognition of emerging products still needs time, that is, their sales changes are small, while the sales and output of normal ecological products are in line with market laws. The output changes and sales of emerging products are quite different from those of other ecological products. Therefore, the changes in product output and sales are analyzed through production and marketing parameters.

[0072] Furthermore, in some embodiments of the present invention, production and marketing parameters of the target product in the current time period are determined based on changes in product output and sales volume of the target product in the current time period and the previous time period, including: calculating the difference in product output of the target product in the current time period and the previous time period as a production gain indicator; determining the production and marketing parameters of the target product in the current time period based on the sales volume and the production gain indicator in the current time period, wherein the sales volume is positively correlated with the production and marketing parameters, and the gain indicator is negatively correlated with the production and marketing parameters.

[0073] Among them, the output gain index is the growth index of product output. The current time period can be this year, and the previous time period can be last year, that is, the output difference between this year and last year. As the output gain index, the production and sales parameters are analyzed through sales volume and output gain indicators.

[0074] Among them, the positive correlation indicates that the dependent variable increases as the independent variable increases, that is, the larger the sales volume, the larger the value of the production and sales parameter, while the negative correlation indicates that the dependent variable decreases as the independent variable increases, that is, the larger the gain index, the smaller the value of the production and sales parameter.

[0075] Furthermore, in some embodiments of the present invention, the production and marketing parameters of the target product in the current time period are determined based on the sales volume and the production gain index in the current time period, including: calculating the difference between the sales volume and the production gain index, and normalizing the maximum and minimum values ​​as the production and marketing parameters of the target product in the current time period.

[0076] Among them, the maximum and minimum value normalization can be based on the sum of the target product's own product output and sales volume as the denominator, and the difference between the sales volume and the output gain index as the numerator, thereby performing normalization processing. Of course, the maximum value can also be selected from the sales volume as the denominator, and the difference between the sales volume and the output gain index as the numerator. Normalization eliminates the gap in inconsistent output and sales volume corresponding to different target products and improves the reliability of production and marketing parameters.

[0077] Furthermore, in some embodiments of the present invention, the degree to which the target product is an emerging product is determined based on changes in production and marketing parameters of the target product and all comparative products in different time periods, including: constructing a production and marketing change coordinate system with the time period as the horizontal coordinate and the production and marketing parameters as the vertical coordinate; using the production and marketing parameters of the target product in different time periods as coordinate points, and connecting the coordinate points in time series to obtain a production and marketing change curve of the target product; determining the curve similarity between the target product and any comparative product based on the fluctuation difference between the production and marketing change curves of the target product and the comparative product; and normalizing the opposites of the mean values ​​of all curve similarities to the maximum and minimum values, as the degree to which the target product is an emerging product.

[0078] Among them, the production and sales change coordinate system can be specifically, for example, a two-dimensional rectangular coordinate system. The curve is constructed through the coordinate system to obtain the production and sales change curve. The production and sales change curve can represent the overall fluctuation. The fluctuation is compared with the comparison product. When the overall gap is large, it can be said that the target product itself has a large difference from the comparison product. The probability that the target product is an emerging product is higher, and the value of the degree of emergence is larger.

[0079] Among them, curve similarity refers to the similarity between the target product and the comparison product in the production and sales change curve.

[0080] Furthermore, in some embodiments of the present invention, the curve similarity between the target product and any comparison product is determined based on the fluctuation difference between the production and sales change curves of the target product and the comparison product, including: performing STL time series decomposition on the production and sales change curve to obtain trend items as the product change trend curve of the target product; determining the curve similarity between the product change trend curve of the target product and the product change trend curve of each comparison product.

[0081] Among them, STL time series decomposition is a data decomposition algorithm well known to those skilled in the art. Through STL time series decomposition, the curve is divided into trend terms, periodic terms and residual terms, and the trend terms are used as the product change trend curve of the target product. The product change trend curve represents the product change trend. It can be understood that the change trends of emerging products and other products are significantly different, and the similarity is low.

[0082] Among them, the curve similarity is the similarity of two curves. In the embodiment of the present invention, the curve similarity function can be directly called for calculation, or the dtw values ​​of the two curves can be calculated, and the opposite numbers of the dtw values ​​are normalized to the maximum and minimum values ​​as the curve similarity. There is no limitation on this.

[0083] The smaller the mean similarity between the target product and all the comparison products, the greater the difference between the target product and the comparison products, that is, the more likely the target product is an emerging product. The opposite numbers of all the curve similarity means are normalized to the maximum and minimum values ​​to obtain the degree of emergence of the target product as an emerging product.

[0084] S104: Determine the exposure parameters of the target product based on its degree of emergence and fast-moving consumption, and make product recommendations based on the exposure parameters of all ecological products.

[0085] Among them, the higher the degree of emergence, the more likely the target product is an emerging product. At this time, more exposure is needed to increase sales; the higher the value of the fast-moving consumer goods degree, the more popular the target product is at the current moment, and the popularity dissipates quickly. In order to seize this opportunity, it is necessary to increase the exposure. Therefore, the product of the degree of emergence and the degree of fast-moving consumer goods of the target product can be calculated, and the maximum and minimum values ​​can be normalized as the exposure parameters of the target product.

[0086] In the embodiment of the present invention, the exposure parameter indicates the exposure amount of the digital platform to the target product. The larger the value is, the more exposure the target product needs.

[0087] Furthermore, in some embodiments of the present invention, product recommendations are made based on the exposure parameters of all ecological products, including: sorting all ecological products from large to small according to the exposure parameters, and assigning recommendation weights based on the sorting results, wherein the higher the sorting result, the higher the recommendation weight value.

[0088] Since the larger the exposure parameter value is, the more the target product needs to be recommended and exposed on the platform, all ecological products can be sorted from large to small according to the exposure parameter, and the recommendation weight can be assigned according to the sorting result. For example, the recommendation weight can be set to 1-10, and different recommendation weights correspond to different recommendation levels. The larger the value of the recommendation weight is, the greater the recommendation strength of the digital platform, such as pop-up ads based on the digital platform, homepage recommendations, etc. Therefore, the higher the exposure parameter is in the ranking, the more likely it is to be assigned a 10-level weight, thereby greatly improving the exposure strength.

[0089] Based on the calculated exposure parameters and combined with the original product recommendation mechanism of the digital platform, optimal recommendations are made. This will prevent emerging ecological products from being at a disadvantage when recommending products due to the influence of historical sales data, thereby improving the sales of emerging products and enhancing the reliability of product recommendations.

[0090] In the embodiment of the present invention, ecological product data and platform data of each ecological product are obtained; then, the fast-consumption degree of each ecological product is determined through label classification, and specific analysis of topic discussion and sales peak periods is performed on different categories. The fast-consumption degree can effectively characterize the sales popularity of the ecological product at the current moment; then, the degree of emergence of the ecological product as an emerging product is determined through changes in product output and sales volume, and the degree of emergence characterizes the possibility that the ecological product is an emerging product; thus, the exposure parameters of the ecological product are determined in combination with the degree of emergence and the degree of fast-consumption, and product recommendations are made based on the exposure parameters, so that more reliable recommendations can be made in combination with the production and demand relationship of the ecological product itself at the current moment, thereby avoiding the disadvantageous position of emerging ecological products in product recommendations due to the influence of historical sales data, improving the sales of emerging ecological products, and enhancing the reliability of product recommendations.

[0091] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for recommending ecological products for karst desertification control based on a digital platform, characterized in that: The method comprises: Obtaining ecological product data and platform data of each ecological product, wherein the ecological product data includes product labels and product output, and the platform data includes topic discussion and sales volume of each ecological product; Classify all eco-products according to the product labels, determine the product category to which each eco-product belongs, and use any eco-product as a target product, and other eco-products in the product category to which the target product belongs as comparison products; determine the peak sales period of the target product according to the sales volume of the target product at different times, and determine the fast consumption degree of the target product by combining the topic discussion and peak sales period of the target product and all the comparison products; Determine the production and marketing parameters of the target product in the current time period according to the changes in the product output and sales volume of the target product in the current time period and the previous time period, and determine the degree of emergence of the target product as an emerging product according to the changes in the production and marketing parameters of the target product and all the comparative products in different time periods; Determine the exposure parameters of the target product based on its emerging degree and fast-moving consumption degree, and make product recommendations based on the exposure parameters of all ecological products; Determine the peak sales period of the target product according to the sales volume of the target product at different times, including: Taking one year as a cycle, determine the sales volume of the target product in each month of the year, and use the average sales volume of all months as the sales base point of the target product; The month in which the sales volume is greater than the sales base point is regarded as the peak sales period; The topic discussion degree is a numerical indicator of the topic popularity at the current moment. The fast consumption degree of the target product is determined by combining the topic discussion degree and the peak sales period of the target product and all the comparison products, including: The reciprocal of the number of months corresponding to the peak sales period of any ecological product is used as the first fast-moving consumer goods indicator; Calculate the product of the topic discussion degree of any ecological product and the first fast-moving consumer goods index, and normalize the maximum and minimum values ​​as the fast-moving consumer goods characteristic value of the ecological product; The average of the fast-consumption characteristic values ​​of the target product and all the comparison products is taken as the fast-consumption degree of the target product.

2. The method for recommending ecological products for karst desertification control based on a digital platform according to claim 1, characterized in that: The method of classifying all ecological products according to the product labels and determining the product category to which each ecological product belongs includes: Based on the keyword extraction algorithm, keywords are extracted from the product labels of each ecological product; the number of identical keywords corresponding to any two ecological products is taken as the similarity of the two ecological products; Unsupervised data clustering is performed based on the similarity of all groups of ecological products to determine the product category to which each ecological product belongs.

3. The method for recommending ecological products for karst desertification control based on a digital platform according to claim 1, characterized in that: Determining the production and sales parameters of the target product in the current time period according to the change in the product output and sales volume of the target product in the current time period and the previous time period includes: Calculate the difference between the product output of the target product in the current time period and the previous time period as the output gain indicator; The production and marketing parameters of the target product in the current time period are determined based on the sales volume and the production gain index in the current time period, wherein the sales volume is positively correlated with the production and marketing parameters, and the gain index is negatively correlated with the production and marketing parameters.

4. The method for recommending ecological products for karst desertification control based on a digital platform as claimed in claim 3, characterized in that: Determining the production and marketing parameters of the target product in the current time period according to the sales volume and production gain indicators in the current time period includes: The difference between the sales volume and the production gain index is calculated, and the maximum and minimum values ​​are normalized as the production and sales parameters of the target product in the current time period.

5. The method for recommending ecological products for karst desertification control based on a digital platform according to claim 1, characterized in that: Determining the degree to which the target product is an emerging product based on changes in production and sales parameters of the target product and all the comparison products in different time periods includes: The production and sales change coordinate system is constructed with the time period as the horizontal axis and the production and sales parameters as the vertical axis; Taking the production and sales parameters of the target product in different time periods as coordinate points, connecting the coordinate points in time series to obtain a production and sales change curve of the target product; Determining the similarity between the target product and any comparative product according to the fluctuation difference between the production and sales change curves of the target product and the comparative product; The opposite numbers of the mean values ​​of all curve similarities are normalized to the maximum and minimum values, which is used as the degree of emergence of the target product as an emerging product.

6. The method for recommending ecological products for karst desertification control based on a digital platform as claimed in claim 5, characterized in that: Determining the similarity between the target product and any comparative product according to the fluctuation difference between the production and sales change curves of the target product and the comparative product includes: Performing STL time series decomposition on the production and sales change curve to obtain a trend item as a product change trend curve of the target product; Determine the curve similarity between the product change trend curve of the target product and the product change trend curve of each comparison product.

7. The method for recommending ecological products for karst desertification control based on a digital platform according to claim 1, characterized in that: The exposure parameters of the target product are determined by combining the emerging degree and fast-moving consumption degree of the target product, including: The product of the emerging degree and the fast-moving consumption degree of the target product is calculated, and the maximum and minimum values ​​are normalized as the exposure parameters of the target product.

8. The method for recommending ecological products for karst desertification control based on a digital platform according to claim 1, characterized in that: The product recommendations based on the exposure parameters of all ecological products include: All ecological products are sorted from large to small according to the exposure parameters, and recommendation weights are assigned according to the sorting results. The higher the sorting result, the higher the recommendation weight value.

Images