Industrial chain supply and demand matching method based on enterprise polarity analysis

Abstract

The invention relates to an industry chain supply and demand matching method based on enterprise polarity analysis, and aims to improve the accuracy and efficiency of supply and demand connection between enterprises, perform polarity analysis on the enterprises, classify the polarity of the enterprises into positive, neutral or negative according to the dimension and quality of data by analyzing public data registered in a supply and demand platform by the enterprises, and improve the supply and demand connection accuracy and efficiency of the enterprises. Corresponding numerical values are given, so that the comprehensive reputation and strength of the enterprise are quantified, the enterprise preference is calculated based on a polarity analysis result, a product preference value reflects the tendency degree of the enterprise to a specific product, and derivation is performed through the polarity of the enterprise and the types of supplied products; the industrial distribution value represents the distribution proportion of the enterprise products in different industrial categories and reflects the business range of the enterprise products; and accurate matching is realized by calculating the supply and demand matching degree. The method avoids subjective judgment through systematic analysis, is suitable for various commercial platforms, and can effectively promote industrial chain resource optimization and collaborative development.

Description

Technical Field

[0001] This invention belongs to the field of data mining technology, specifically relating to a supply and demand matching method for the industrial chain based on enterprise polarity analysis. Background Technology

[0002] Traditional supply chain matching methods typically rely on simple keyword searches or fixed industry directories. These methods often only offer coarse-grained matching, failing to deeply assess the compatibility between companies and struggling to reflect comprehensive differences in product quality, reputation, and technological capabilities. The matching results usually present a long list of related companies, requiring users to invest significant effort in secondary screening and judgment, resulting in low efficiency and limited accuracy.

[0003] With the development of information technology, some platforms have begun to explore using publicly available corporate information to optimize matching. However, these attempts are often limited to simple judgments about the existence of information or focus only on a single dimension (such as production capacity), lacking a multi-dimensional and systematic quantitative evaluation system for corporate information. This makes it difficult to effectively identify high-quality partners with advantages in product quality, service capabilities, and business reputation, and also makes it difficult to discover potential cross-industry collaborative opportunities.

[0004] Therefore, the main shortcoming of existing technologies lies in their inability to transform unstructured, publicly available corporate data into quantifiable, business-meaning evaluation indicators, and on this basis, achieve intelligent and precise supply and demand matching. Upstream and downstream enterprises in the industrial chain urgently need a new method that can automatically, efficiently, and deeply analyze a company's overall strength and business inclinations to recommend highly compatible partners, thereby reducing connection costs and improving the collaborative efficiency of the industrial chain. Summary of the Invention

[0005] According to a first aspect of the present invention, the present invention claims protection for a supply chain supply and demand matching method based on enterprise polarity analysis, comprising the following steps: Step S1: Perform polarity analysis on enterprises, analyze the public data registered by enterprises on the supply and demand platform, and classify the polarity of enterprises as positive, neutral or negative according to the dimension and quality of the public data, and assign values ​​of 1, 0.5 or 0 respectively. Step S2: Calculate firm preferences. Based on the firm polarity in step S1, calculate the firm's product preference value and industry distribution value, wherein the product preference value is calculated based on the firm polarity and the types of products supplied by the firm, and the industry distribution value is calculated based on the industry distribution of the firm's products. Step S3: Calculate the supply and demand matching degree. Based on the product preference value and industry distribution value obtained in Step S2, calculate the supply and demand matching degree between the supply and demand enterprises. The supply and demand matching degree is a weighted sum of product preference similarity and industry affinity. The product preference similarity is calculated based on the product preference values ​​of the supply and demand parties, and the industry affinity is calculated based on the industry distribution values ​​of the supply and demand parties.

[0006] Furthermore, in step S1, the publicly available data includes at least one of the following: company profile, various qualifications, patents held, product parameters, production capacity, quality inspection reports, and customer reviews. When classifying the polarity of enterprises based on the dimensions and quality of publicly available data, different scores are assigned to various qualifications and product information dimensions of the enterprises. The dimensions include at least one of the following: enterprise qualifications, product information, production capacity, quality inspection reports, and customer reviews. Each dimension is assigned a score based on the completeness and quality of the information provided.

[0007] Further, calculating the product preference value in step S2 includes: Product preference values ​​are used to represent a company's degree of preference for a particular product; For each enterprise and each product, determine the set of publicly available data about the product, including the relevant publicly available data of the product registered by the enterprise on the supply and demand platform, and the relevant publicly available data includes the enterprise polarity value; Calculate the arithmetic mean of the product preference values ​​for all firms in the set; The calculation of the product preference value is performed on all products supplied by the enterprise, resulting in a set of product preference values ​​for the enterprise.

[0008] Further, calculating the industry distribution value in step S2 includes: The industry distribution value is used to represent the proportion of a company's products distributed across different industry categories; For each enterprise, determine the set of all products of the enterprise, which consists of all products registered by the enterprise on the supply and demand platform; For each industry category, determine the first set of products belonging to the industry within the enterprise. The first set of products is a subset of all product sets and includes all products classified to the industry. The industry distribution value is calculated as the ratio of the size of the first product set to the size of all product sets. The calculation of the industry distribution value is performed on all industry categories to obtain a set of industry distribution values ​​for each enterprise.

[0009] Furthermore, the product preference similarity described in step S3 is calculated based on the mean squared deviation (MSD) and Jaccard coefficient of both the supply and demand companies.

[0010] Furthermore, the mean squared deviation (MSD) is calculated as the average difference between the preferences of the supply and demand companies for the product, and the squared difference is calculated only when both companies have preferences for the product.

[0011] Furthermore, the Jaccard coefficient is calculated as the ratio of the size of the intersection to the size of the union of the preference sets of both supply and demand firms.

[0012] Furthermore, the product preference similarity is calculated as the product of the mean squared error (MSD) and the Jaccard coefficient.

[0013] Furthermore, the industry affinity mentioned in step S3 is calculated based on the Kullback-Leibler divergence.

[0014] Furthermore, the supply and demand matching degree mentioned in step S3 is calculated as a weighted sum of product preference similarity and industry affinity.

[0015] This invention relates to a supply chain supply and demand matching method based on enterprise polarity analysis, aiming to improve the accuracy and efficiency of supply and demand connections between enterprises. It performs polarity analysis on enterprises by parsing publicly available data registered by enterprises on supply and demand platforms. Based on the dimensions and quality of the data, enterprise polarity is classified as positive, neutral, or negative, and assigned corresponding numerical values, thereby quantifying the comprehensive reputation and strength of enterprises. Enterprise preferences are calculated based on the polarity analysis results. Product preference values ​​reflect the degree of inclination of enterprises towards specific products, derived from enterprise polarity and the types of products supplied. Industry distribution values ​​represent the distribution ratio of enterprise products in different industry categories, reflecting their business scope. Precise matching is achieved by calculating the supply and demand matching degree. This invention avoids subjective judgment through systematic analysis, is applicable to various business platforms, and can effectively promote the optimization and collaborative development of supply chain resources. Attached Figure Description

[0016] Figure 1 The flowchart illustrates a supply chain supply and demand matching method based on enterprise polarity analysis, as claimed in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0019] According to the first embodiment of the present invention, referring to Figure 1 This invention seeks protection for a supply chain supply and demand matching method based on enterprise polarity analysis, comprising the following steps: Step S1: Perform polarity analysis on enterprises, analyze the public data registered by enterprises on the supply and demand platform, and classify the polarity of enterprises as positive, neutral or negative according to the dimension and quality of the public data, and assign values ​​of 1, 0.5 or 0 respectively. Step S2: Calculate firm preferences. Based on the firm polarity in step S1, calculate the firm's product preference value and industry distribution value, wherein the product preference value is calculated based on the firm polarity and the types of products supplied by the firm, and the industry distribution value is calculated based on the industry distribution of the firm's products. Step S3: Calculate the supply and demand matching degree. Based on the product preference value and industry distribution value obtained in Step S2, calculate the supply and demand matching degree between the supply and demand enterprises. The supply and demand matching degree is a weighted sum of product preference similarity and industry affinity. The product preference similarity is calculated based on the product preference values ​​of the supply and demand parties, and the industry affinity is calculated based on the industry distribution values ​​of the supply and demand parties.

[0020] Furthermore, in step S1, the publicly available data includes at least one of the following: company profile, various qualifications, patents held, product parameters, production capacity, quality inspection reports, and customer reviews. When classifying the polarity of enterprises based on the dimensions and quality of publicly available data, different scores are assigned to various qualifications and product information dimensions of the enterprises. The dimensions include at least one of the following: enterprise qualifications, product information, production capacity, quality inspection reports, and customer reviews. Each dimension is assigned a score based on the completeness and quality of the information provided.

[0021] In this embodiment, publicly available data registered by enterprises on the supply and demand platform (company profile, various qualifications, patents, product parameters, production capacity, quality inspection reports, customer reviews, etc.) is analyzed to perform enterprise polarity analysis.

[0022] Based on the dimensions and quality of the forms filled out by enterprises on the supply and demand platform (for example, different scores are assigned to form information such as enterprise qualifications and product information, with a total score of 100, 0-50 being negative, 50-80 being neutral, and 80-100 being positive), the content D of the publicly disclosed information of enterprises is divided into positive, neutral, or negative, and assigned the values ​​1, 0.5, or 0, as defined by formula (1).

[0023]

[0024] Further, calculating the product preference value in step S2 includes: Product preference values ​​are used to represent a company's degree of preference for a particular product; For each enterprise and each product, determine the set of publicly available data about the product, including the relevant publicly available data of the product registered by the enterprise on the supply and demand platform, and the relevant publicly available data includes the enterprise polarity value; Calculate the arithmetic mean of the product preference values ​​for all firms in the set; The calculation of the product preference value is performed on all products supplied by the enterprise, resulting in a set of product preference values ​​for the enterprise.

[0025] Further, calculating the industry distribution value in step S2 includes: The industry distribution value is used to represent the proportion of a company's products distributed across different industry categories; For each enterprise, determine the set of all products of the enterprise, which consists of all products registered by the enterprise on the supply and demand platform; For each industry category, determine the first set of products belonging to the industry within the enterprise. The first set of products is a subset of all product sets and includes all products classified to the industry. The industry distribution value is calculated as the ratio of the size of the first product set to the size of all product sets. The calculation of the industry distribution value is performed on all industry categories to obtain a set of industry distribution values ​​for each enterprise.

[0026] In this embodiment, product preference refers to the supplier's preference for the products it supplies, or the buyer's preference for the products it purchases. Industry preferences are the industry categories that suppliers and buyers prefer. First, calculate the product preference value based on the firm's polarity and the total number of product types supplied. If firm D's polarity is... And the set of enterprise u with respect to product i is Then the project value It is defined as formula (2).

[0027]

[0028] Industry preference values ​​are obtained by measuring industry distribution values. Industry distribution values ​​represent suppliers' high-level preferences and are calculated using the distribution of the total number of products associated with industry t.

[0029] It is a collection of products from company u. It is a set of products related to industry t, and the theme distribution value is u. It is defined as formula (3).

[0030]

[0031] Furthermore, the product preference similarity described in step S3 is calculated based on the mean squared deviation (MSD) and Jaccard coefficient of both the supply and demand companies.

[0032] Furthermore, the mean squared deviation (MSD) is calculated as the average difference between the preferences of the supply and demand companies for the product, and the squared difference is calculated only when both companies have preferences for the product.

[0033] Furthermore, the Jaccard coefficient is calculated as the ratio of the size of the intersection to the size of the union of the preference sets of both supply and demand firms.

[0034] Furthermore, the product preference similarity is calculated as the product of the mean squared error (MSD) and the Jaccard coefficient.

[0035] Furthermore, the industry affinity mentioned in step S3 is calculated based on the Kullback-Leibler divergence.

[0036] Furthermore, the supply and demand matching degree mentioned in step S3 is calculated as a weighted sum of product preference similarity and industry affinity.

[0037] In this embodiment, the supply-demand matching degree refers to the similarity between the buyer's needs and the supplier's products (or services), and having similar industry preferences also affects the establishment of a trust-based cooperative relationship. The matching degree of a company is calculated using its product preference value and industry distribution value.

[0038] This embodiment utilizes Jaccard Mean Squared Deviation (JMSD), which considers the ratio of shared interests and numerical interest similarity to measure the similarity of preferences between supply and demand sides. JMSD is calculated as the product of Mean Squared Deviation (MSD) and the Jaccard metric, which respectively measure preference similarity and the ratio of shared interests. MSD is the average of the differences between the preference values ​​of users u and f.

[0039] This represents the squared difference between the preference values ​​of users u and f for product i, and is only considered if both users have a preference for product i. Additionally, Represents a group The set of MSD is defined by formula (4).

[0040]

[0041]

[0042] The Jaccard coefficient is calculated based on the proportion of items commonly found on social networks. If... and Let u and f represent the preference values ​​of users, respectively. Then the Jaccard coefficient is defined by formula (6).

[0043]

[0044] Therefore, product preference The definition is given by formula (7).

[0045]

[0046] Companies that belong to or prefer the same industry sector are more likely to cooperate. Since there are many different industry categories, and even many overlapping sub-sectors, and there is no unified standard official definition of the category, this study considers industry similarity, which is called industry affinity.

[0047] To measure industry affinity, this paper calculates the distribution of a certain industry t among all firms' products according to formula (3). After measuring the industry distribution of each firm, the industry affinity between firms u and f can be calculated using the Kullback-Leibler divergence as shown in Equation (8), which measures the difference between the two distributions, where T is the industry set.

[0048]

[0049]

[0050] The supply-demand matching degree is calculated as a weighted sum of product preference and industry affinity, defined by formula (10).

[0051]

[0052]

[0053] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0054] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0055] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A supply chain supply and demand matching method based on firm polarity analysis, characterized in that, Includes the following steps: Step S1: Perform polarity analysis on enterprises, analyze the public data registered by enterprises on the supply and demand platform, and classify the polarity of enterprises as positive, neutral or negative according to the dimension and quality of the public data, and assign values ​​of 1, 0.5 or 0 respectively. Step S2: Calculate firm preferences. Based on the firm polarity in step S1, calculate the firm's product preference value and industry distribution value, wherein the product preference value is calculated based on the firm polarity and the types of products supplied by the firm, and the industry distribution value is calculated based on the industry distribution of the firm's products. Step S3: Calculate the supply and demand matching degree. Based on the product preference value and industry distribution value obtained in Step S2, calculate the supply and demand matching degree between the supply and demand enterprises. The supply and demand matching degree is a weighted sum of product preference similarity and industry affinity. The product preference similarity is calculated based on the product preference values ​​of the supply and demand parties, and the industry affinity is calculated based on the industry distribution values ​​of the supply and demand parties.

2. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 1, characterized in that, In step S1, the publicly available data includes at least one of the following: company profile, various qualifications, patents, product parameters, production capacity, quality inspection reports, and customer reviews. When classifying the polarity of enterprises based on the dimensions and quality of publicly available data, different scores are assigned to various qualifications and product information dimensions of the enterprises. The dimensions include at least one of the following: enterprise qualifications, product information, production capacity, quality inspection reports, and customer reviews. Each dimension is assigned a score based on the completeness and quality of the information provided.

3. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 1, characterized in that, Step S2, calculating the product preference value, includes: Product preference values ​​are used to represent a company's degree of preference for a particular product; For each enterprise and each product, determine the set of publicly available data about the product, including the relevant publicly available data of the product registered by the enterprise on the supply and demand platform, and the relevant publicly available data includes the enterprise polarity value; Calculate the arithmetic mean of the product preference values ​​for all firms in the set; The calculation of the product preference value is performed on all products supplied by the enterprise, resulting in a set of product preference values ​​for the enterprise.

4. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 1, characterized in that, Step S2 involves calculating the industry distribution value, including: The industry distribution value is used to represent the proportion of a company's products distributed across different industry categories; For each enterprise, determine the set of all products of the enterprise, which consists of all products registered by the enterprise on the supply and demand platform; For each industry category, determine the first set of products belonging to the industry within the enterprise. The first set of products is a subset of all product sets and includes all products classified to the industry. The industry distribution value is calculated as the ratio of the size of the first product set to the size of all product sets. The calculation of the industry distribution value is performed on all industry categories to obtain a set of industry distribution values ​​for each enterprise.

5. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 1, characterized in that, The product preference similarity described in step S3 is calculated based on the mean squared deviation (MSD) and Jaccard coefficient of both the supply and demand companies.

6. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 5, characterized in that, The mean squared deviation (MSD) is calculated as the average difference between the preferences of the supply and demand firms for the product, and the squared difference is calculated only when both firms have preferences for the product.

7. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 5, characterized in that, The Jaccard coefficient is calculated as the ratio of the size of the intersection to the size of the union of the preference sets of both supply and demand firms.

8. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 5, characterized in that, The product preference similarity is calculated as the product of the mean squared error (MSD) and the Jaccard coefficient.

9. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 1, characterized in that, The industry affinity mentioned in step S3 is calculated based on the Kullback-Leibler divergence.

10. The supply chain supply and demand matching method based on enterprise polarity analysis as described in claim 1, characterized in that, The supply and demand matching degree mentioned in step S3 is calculated as a weighted sum of product preference similarity and industry affinity.

Images