Enterprise marketing recommendation method and device based on urban industrial chain integration

Abstract

The present application relates to the field of enterprise marketing management, and discloses an enterprise marketing recommendation method and device based on the integration of urban industrial chains, the method comprising: based on the target enterprise CRM of the target enterprise, adding the target enterprise node, the target enterprise customer node and the target enterprise customer relationship edge in the urban industrial graph database; through the supply relationship identification model, according to the first target enterprise customer relationship edge, determining one or more similar supply chain edges of the first target enterprise customer relationship edge in the urban industrial graph database, and determining the two supplier nodes corresponding to each similar supply chain edge; determining the similar supplier node corresponding to the target enterprise customer node connected to the first target enterprise customer relationship edge in the two supplier nodes corresponding to each similar supply chain edge, and taking the supplier corresponding to the similar supplier node as the enterprise marketing target of the target enterprise. The present application can improve the direction guidance effect of enterprises in promoting TOB business through the integration of urban industrial chains.

Description

Technical Field

[0001] The present application relates to the technical field of enterprise marketing management, and more specifically, to an enterprise marketing recommendation method and device based on the integration of urban industrial chains. Background Art

[0002] In order to better manage and maintain the relationship between the enterprise and its customers, enterprises centrally manage the relationship between the enterprise and its customers through Customer Relationship Management (CRM). Enterprise CRM includes functions such as customer data management, sales management, customer service and marketing. CRM tools are designed to improve customer satisfaction and loyalty, increase sales performance, and optimize the interaction between the enterprise and its customers. Through data analysis of the enterprise and its customers, the enterprise can better understand customer needs and provide personalized services to customers.

[0003] Patent CN116610803B (application number: CN202310882881.3) provides an information management method for the best enterprises in the industrial chain based on big data. By obtaining the text description of the TOB business to be promoted and the target enterprise information, the text description of the TOB business to be promoted is semantically understood to obtain the business semantic expression feature vector, and then the target enterprise information is semantically understood to obtain the target enterprise semantic expression feature vector, and then the business semantic expression feature vector and the target enterprise semantic expression feature vector are semantically associated and encoded to obtain the enterprise-business semantic matching feature matrix, and finally, based on the enterprise-business semantic matching feature matrix, it is determined whether to push the TOB business to be promoted to the target enterprise. The method in patent CN116610803B improves the matching efficiency of enterprise partners through enterprise-business semantic matching, but has poor directional guidance for enterprises when promoting TOB businesses. Summary of the invention

[0004] The purpose of this application is to provide a method and device for enterprise marketing recommendation based on the integration of urban industrial chains, which solves the technical problem of poor directional guidance for enterprises when promoting TOB business, and achieves the technical effect of improving the directional guidance effect of enterprises when promoting TOB business.

[0005] An enterprise marketing recommendation method based on the integration of urban industrial chains is provided in an embodiment of the present application. The method includes: based on the urban industrial chain, constructing an urban industrial graph database including supplier nodes and supply chain edges, and based on the target enterprise CRM of the target enterprise, adding target enterprise nodes, target enterprise customer nodes and target enterprise customer relationship edges in the urban industrial graph database; wherein the supplier node represents the supplier in the urban industrial chain, the supply chain edge represents the supply relationship between two suppliers in the urban industrial chain, the target enterprise node represents the target enterprise, the target enterprise customer node represents the customer of the target enterprise, and the target enterprise customer relationship edge represents the relationship between the target enterprise and the customer of the target enterprise; through a supply relationship identification model, according to the first target enterprise customer relationship edge, determining one or more similar supply chain edges of the first target enterprise customer relationship edge in the urban industrial graph database, and determining two supplier nodes corresponding to each similar supply chain edge; wherein the first target enterprise customer relationship edge connects the target enterprise and a target enterprise customer node respectively; determining the similar supplier node corresponding to the target enterprise customer node connected by the first target enterprise customer relationship edge in the two supplier nodes corresponding to each similar supply chain edge, and taking the supplier corresponding to the similar supplier node as the enterprise marketing target of the target enterprise.

[0006] In one possible implementation, based on the first target enterprise-customer relationship edge, one or more similar supply chain edges of the first target enterprise-customer relationship edge are determined in the city industry graph database, including: clustering all supply chain edges and all target enterprise-customer relationship edges to obtain multiple supply chain edge categories; wherein each supply chain edge category includes multiple supply chain edges and at least one target enterprise-customer relationship edge, and clustering is performed based on the attributes of two supplier nodes corresponding to each supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise-customer relationship edge, and the attributes of the target enterprise-customer relationship edge; in each supply chain edge category, based on the first target enterprise-customer relationship edge in the supply chain edge category, one or more similar supply chain edges of the first target enterprise-customer relationship edge are determined in each supply chain edge category.

[0007] In another possible implementation, all supply chain edges and all target enterprise customer relationship edges are clustered to obtain multiple supply chain edge categories, including: using a weight allocation model, according to the attributes of the target enterprise, different weights are assigned to the attributes of the two supplier nodes corresponding to the supply chain edges, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to the target enterprise customer relationship edges, and the attributes of the target enterprise customer relationship edges; all supply chain edges and all target enterprise customer relationship edges are clustered according to the weights corresponding to the attributes of the two supplier nodes corresponding to the supply chain edges, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edges, to obtain multiple supply chain edge categories.

[0008] In another possible implementation, the method also includes: when the number of supply chain edges in the first supply chain edge category is greater than the preset number of supply chain edges, determining the first marketing method corresponding to the target enterprise and the first supply chain edge category through the enterprise marketing method recommendation model according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge; wherein the first marketing method includes one or more of a multimodal model application, an AIGC application, a metaverse application, and a data assetization application; and performing enterprise marketing on suppliers corresponding to multiple supplier nodes in the first supply chain edge category through the first marketing method.

[0009] In another possible implementation, the method also includes: after performing enterprise marketing for a preset time period on suppliers corresponding to multiple supplier nodes in a first supply chain edge category through a first marketing method, obtaining first marketing revenue data corresponding to the first supply chain edge category, and determining a first marketing revenue indicator based on the first marketing revenue data; when the first marketing revenue indicator is less than the preset first marketing revenue indicator, correcting the model through the enterprise marketing method, and determining a second marketing method corresponding to the target enterprise and the first supply chain edge category based on the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge and the first marketing revenue data; performing enterprise marketing on suppliers corresponding to multiple supplier nodes in the first supply chain edge category through the second marketing method.

[0010] In another possible implementation, the first marketing revenue data includes the marketing time sequence order quantity corresponding to the first supply chain edge category, and the model is corrected through the enterprise marketing method. According to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge and the first marketing revenue data, the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined, including: correcting the model through the enterprise marketing method, according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge, the preset time period and the marketing time sequence order quantity, the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined.

[0011] An embodiment of the present application also provides an enterprise marketing recommendation device based on the integration of urban industrial chains, including a unit for executing any of the methods described above.

[0012] An embodiment of the present application also provides an enterprise marketing recommendation device based on the integration of urban industrial chains, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the methods described above is implemented.

[0013] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0014] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of any of the methods described above when executed by a processor.

[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0016] The embodiment of the present application provides an enterprise marketing recommendation method based on the integration of urban industrial chains, and the method includes: based on the urban industrial chain, constructing an urban industrial graph database including supplier nodes and supply chain edges, and based on the target enterprise CRM of the target enterprise, adding the target enterprise node, the target enterprise customer node and the target enterprise customer relationship edge in the urban industrial graph database; wherein the supplier node represents the supplier in the urban industrial chain, the supply chain edge represents the supply relationship between two suppliers in the urban industrial chain, the target enterprise node represents the target enterprise, the target enterprise customer node represents the customer of the target enterprise, and the target enterprise customer relationship edge represents the relationship between the target enterprise and the customer of the target enterprise; through the supply relationship identification model, according to the first target enterprise customer relationship edge, one or more similar supply chain edges of the first target enterprise customer relationship edge are determined in the urban industrial graph database, and two supplier nodes corresponding to each similar supply chain edge are determined; wherein the first target enterprise customer relationship edge connects the target enterprise and a target enterprise customer node respectively; in the two supplier nodes corresponding to each similar supply chain edge, the similar supplier node corresponding to the target enterprise customer node connected by the first target enterprise customer relationship edge is determined, and the supplier corresponding to the similar supplier node is used as the enterprise marketing target of the target enterprise. The enterprise marketing recommendation method based on the integration of urban industrial chains in the embodiment of the present application utilizes the data in the urban industrial chain, integrates the enterprise customer relationships in the urban industrial chain and the target enterprise CRM, and mines potential enterprise customer relationships in the urban industrial chain. This improves the directional guidance effect of enterprise marketing for the target enterprise in the urban industrial chain and improves the efficiency of enterprise marketing for the target enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 A flowchart of an enterprise marketing recommendation method based on urban industrial chain integration provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of adding a target enterprise node, a target enterprise customer node, and a target enterprise customer relationship edge in a city industry graph database in an embodiment of the present application;

[0020] Figure 3 A flowchart of another enterprise marketing recommendation method based on city industrial chain integration provided in an embodiment of the present application;

[0021] Figure 4 A schematic diagram of the workflow of another enterprise marketing method recommendation model provided in an embodiment of the present application;

[0022] Figure 5 A logical structure diagram of an enterprise marketing recommendation device based on city industrial chain integration provided in an embodiment of the present application;

[0023] Figure 6 A schematic diagram of the physical structure of an enterprise marketing recommendation device based on the integration of urban industrial chains provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0025] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0027] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] In the prior art, when recommending TOB services to enterprises, the matching efficiency of enterprise partners is improved through enterprise-business semantic matching, but the direction of guiding enterprises in promoting TOB services is poor.

[0030] Based on the above reasons, an embodiment of the present application provides an enterprise marketing recommendation method based on the integration of urban industrial chains, and the method includes: based on the urban industrial chain, constructing an urban industrial graph database including supplier nodes and supply chain edges, and based on the target enterprise CRM of the target enterprise, adding target enterprise nodes, target enterprise customer nodes and target enterprise customer relationship edges in the urban industrial graph database; wherein, the supplier node represents the supplier in the urban industrial chain, the supply chain edge represents the supply relationship between two suppliers in the urban industrial chain, the target enterprise node represents the target enterprise, the target enterprise customer node represents the customer of the target enterprise, and the target enterprise customer relationship edge represents the relationship between the target enterprise and the customer of the target enterprise; through the supply relationship identification model, according to the first target enterprise customer relationship edge, one or more similar supply chain edges of the first target enterprise customer relationship edge are determined in the urban industrial graph database, and two supplier nodes corresponding to each similar supply chain edge are determined; wherein the first target enterprise customer relationship edge connects the target enterprise and a target enterprise customer node respectively; in the two supplier nodes corresponding to each similar supply chain edge, the similar supplier node corresponding to the target enterprise customer node connected by the first target enterprise customer relationship edge is determined, and the supplier corresponding to the similar supplier node is used as the enterprise marketing target of the target enterprise. The enterprise marketing recommendation method based on the integration of urban industrial chains in the embodiment of the present application utilizes the data in the urban industrial chain, integrates the enterprise customer relationships in the urban industrial chain and the target enterprise CRM, and mines potential enterprise customer relationships in the urban industrial chain. This improves the directional guidance effect of enterprise marketing for the target enterprise in the urban industrial chain and improves the efficiency of enterprise marketing for the target enterprise.

[0031] In some scenarios, a corporate marketing recommendation method based on the integration of urban industrial chains in an embodiment of the present application can be applied to the daily marketing of an enterprise. By mining the enterprise marketing goals in the urban industrial chain and combining the urban industrial chain, the marketing effect of the enterprise is improved and the efficiency of the enterprise marketing is improved.

[0032] The following is a detailed description of an enterprise marketing recommendation method based on the integration of urban industrial chains provided in an embodiment of the present application with reference to specific examples.

[0033] Figure 1 A flowchart of a method for recommending enterprise marketing based on the integration of urban industrial chains provided in an embodiment of the present application is shown in FIG. Figure 1As shown, the method includes S110 to S130, and S110 to S130 are described in detail below.

[0034] S110. Based on the city industrial chain, a city industrial graph database including supplier nodes and supply chain edges is constructed. Based on the target enterprise CRM of the target enterprise, target enterprise nodes, target enterprise customer nodes and target enterprise customer relationship edges are added to the city industrial graph database. Among them, the supplier node represents the supplier in the city industrial chain, the supply chain edge represents the supply relationship between two suppliers in the city industrial chain, the target enterprise node represents the target enterprise, the target enterprise customer node represents the customer of the target enterprise, and the target enterprise customer relationship edge represents the relationship between the target enterprise and the customer of the target enterprise.

[0035] Figure 2 This is a schematic diagram of adding a target enterprise node, a target enterprise customer node, and a target enterprise customer relationship edge in the city industry graph database in an embodiment of the present application, such as Figure 2 As shown, when the embodiment of the present application is working, it can utilize the supply chain data in the urban industrial chain. The urban industrial chain is the connection and interactive relationship between related industries in a specific city or region. The urban industrial chain includes various production links from raw material procurement, production, processing to sales, involving multiple business parties such as enterprises, governments, suppliers, and customers. The urban industrial chain aims to optimize resource allocation, improve industrial efficiency, and promote economic development. For example, the city's automobile industry chain includes automobile manufacturers, parts suppliers, sales network providers, and maintenance service providers. The urban industrial chain includes multiple supplier nodes and multiple supply chain edges. The supplier nodes represent different suppliers, and the supply chain edges represent the supply relationships between different suppliers. By utilizing the data in the urban industrial chain, potential supply relationships can be mined to improve the marketing accuracy of the enterprise.

[0036] like Figure 2 As shown in the figure, based on the city industrial chain, we can build a city industrial graph database including supplier nodes and supply chain edges. Figure 2 The graph database in includes a first supplier node, a second supplier node, a third supplier node, and a fourth supplier node. Figure 2 The supply chain edges in include the first supply chain edge, the second supply chain edge and the third supply chain edge. There is a first supply chain edge representing the supply chain relationship between the first supplier node and the second supplier node, a second supply chain edge representing the supply chain relationship between the second supplier node and the third supplier node, and a third supply chain edge representing the supply chain relationship between the third supplier node and the fourth supplier node.

[0037] When forming the urban industrial graph database, the attributes of the supplier nodes and the attributes of the supply chain edges can be added. The attributes of the supplier nodes include information of the suppliers in multiple different dimensions, and the attributes of the supply chain edges include information of different dimensions of the supply chain edges.

[0038] like Figure 2 As shown, the target enterprise CRM includes the target enterprise and the target enterprise's target enterprise customers. Based on the target enterprise CRM of the target enterprise, target enterprise nodes, target enterprise customer nodes and target enterprise customer relationship edges can be added to the city industry graph database. Since the target enterprise customers of the target enterprise and the suppliers in the city supply chain may overlap or not overlap, by adding target enterprise nodes, target enterprise customer nodes and target enterprise customer relationship edges to the city industry graph database, the target enterprise customers of the target enterprise itself and the data in the city industry chain can be uniformly represented.

[0039] Similarly, in the urban industrial graph database, the attributes of the target enterprise customer node and the attributes of the target enterprise customer relationship edge corresponding to each target enterprise customer relationship edge can be added.

[0040] like Figure 2 As shown, the target enterprise is connected to the first target enterprise customer node through the first target enterprise customer relationship edge, connected to the second target enterprise customer node through the second target enterprise customer relationship edge, and connected to the third target enterprise customer node through the third target enterprise customer relationship edge. At the same time, the first target enterprise customer node is also the second supplier node in the urban industrial chain, the second target enterprise customer node is also the fourth supplier node in the urban industrial chain, and the third target enterprise customer node does not constitute a supplier node in the urban industrial chain. Through the integration of the urban industrial chain and the CRM information of the target enterprise, the target enterprise customer node can be accurately located in the urban industrial chain, avoiding the duplication of the supplier node and the target enterprise customer node in the urban industrial chain, and achieving the purpose of deduplication in the graph database.

[0041] In the graph database, the supplier node represents the supplier in the city's industrial chain, the supply chain edge represents the supply relationship between two suppliers in the city's industrial chain, the target enterprise node represents the target enterprise, the target enterprise customer node represents the customer of the target enterprise, and the target enterprise customer relationship edge represents the relationship between the target enterprise and the target enterprise's customers. The graph database can clearly represent and save unstructured data.

[0042] S120, using the supply relationship identification model, according to the first target enterprise customer relationship edge, determine one or more similar supply chain edges of the first target enterprise customer relationship edge in the city industry graph database, and determine two supplier nodes corresponding to each similar supply chain edge. The first target enterprise customer relationship edge connects the target enterprise and one target enterprise customer node respectively.

[0043] When making a marketing plan, the first target enterprise customer relationship edge may be selected first. The first target enterprise customer relationship edge connects the target enterprise and a target enterprise customer node respectively, and marketing is carried out for the first target enterprise customer relationship edge.

[0044] When working, through the supply relationship identification model, according to the customer relationship edge of the first target enterprise, one or more similar supply chain edges of the customer relationship edge of the first target enterprise are determined in the urban industrial graph database. One or more similar supply chain edges are supply chains with similar supply relationships, and then one or more similar supply chain edges can be used as potential developable supply chains for the target enterprise, so that precise marketing can be carried out.

[0045] After obtaining one or more similar supply chain edges, two supplier nodes corresponding to each similar supply chain edge can be determined. The supplier nodes are the targets for the target enterprise to conduct marketing.

[0046] Exemplarily, the supply relationship identification model may be a deep learning model trained using a data set consisting of multiple similar supply chain edges and enterprise-customer relationship edges.

[0047] S130. Determine a similar supplier node corresponding to the target enterprise customer node connected to the first target enterprise customer relationship edge among the two supplier nodes corresponding to each similar supply chain edge, and use the supplier corresponding to the similar supplier node as the enterprise marketing target of the target enterprise.

[0048] After obtaining the two supplier nodes corresponding to each similar supply chain edge, in order to determine the marketing target, the similar supplier node corresponding to the target enterprise customer node connected to the first target enterprise customer relationship edge can be determined among the two supplier nodes corresponding to each similar supply chain edge. The similar supplier node is a supplier node that is similar to the target enterprise customer node connected to the first target enterprise customer relationship edge. Then, the supplier corresponding to the similar supplier node can be used as the corporate marketing target of the target enterprise for precise marketing.

[0049] The beneficial effect brought about by the above-mentioned implementation method is that, first, one or more similar supply chain edges of the first target enterprise customer relationship edge are determined in the urban industrial graph database, the similar supply chain edges are determined, and then the similar supplier nodes corresponding to the target enterprise customer nodes connected by the first target enterprise customer relationship edge are determined among the two supplier nodes corresponding to each similar supply chain edge, and the similar suppliers of the target enterprise customer nodes connected by the first target enterprise customer relationship edge are determined. Through two steps, the marketing target of the target enterprise is accurately identified, thereby improving the marketing accuracy and marketing efficiency.

[0050] The beneficial effect brought about by the above-mentioned implementation method is that the graph database can clearly represent and save the unstructured data in the urban industrial chain and the target enterprise CRM, which is convenient for the enterprise to carry out marketing plans based on the industrial chain data. At the same time, it can accurately locate the target enterprise customer nodes in the urban industrial chain, avoiding the duplication of supplier nodes and target enterprise customer nodes in the urban industrial chain, and achieving the purpose of deduplication in the graph database.

[0051] In some implementations, in the above S120, based on the first target enterprise customer relationship edge, one or more similar supply chain edges of the first target enterprise customer relationship edge are determined in the city industry graph database, including S121 to S122. S121 to S122 are described in detail below.

[0052] S121. Cluster all supply chain edges and all target enterprise customer relationship edges to obtain multiple supply chain edge categories. Each supply chain edge category includes multiple supply chain edges and at least one target enterprise customer relationship edge, and clustering is performed according to the attributes of two supplier nodes corresponding to each supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge.

[0053] In the urban industrial graph database, all supply chain edges and all target enterprise customer relationship edges can be clustered to obtain multiple supply chain edge categories. Each supply chain edge category is similar to one or more supply chain edges and one or more target enterprise customer relationship edges, which facilitates the subsequent determination of one or more similar supply chain edges of the first target enterprise customer relationship edge.

[0054] After obtaining multiple supply chain edge categories, each supply chain edge category includes multiple supply chain edges and at least one target enterprise customer relationship edge, so that one or more similar supply chain edges of the first target enterprise customer relationship edge can be more accurately determined according to the supply chain edge category.

[0055] When clustering supply chain edges, clustering can be performed based on the attributes of the two supplier nodes corresponding to each supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge, so as to achieve accurate clustering of the supply chain edges.

[0056] Exemplarily, when clustering all supply chain edges and all target enterprise customer relationship edges, the distances between different supply chain edges and target enterprise customer relationship edges may be calculated by a K-means algorithm for clustering.

[0057] S122. Determine one or more similar supply chain edges of the first target enterprise-customer relationship edge in the supply chain edge category including the first target enterprise-customer relationship edge.

[0058] After obtaining multiple supply chain edge categories, when determining one or more similar supply chain edges of the first target enterprise customer relationship edge, the supply chain edge category including the first target enterprise customer relationship edge can be determined first, and then one or more similar supply chain edges of the first target enterprise customer relationship edge can be determined in the supply chain edge category including the first target enterprise customer relationship edge. Different supply chain edge categories characterize the similarities of different supply chains. Determining one or more similar supply chain edges of the first target enterprise customer relationship edge from different supply chain categories with similarities can help enterprises formulate personalized service or product strategies and improve customer satisfaction and loyalty.

[0059] The beneficial effect brought about by the above-mentioned implementation method is that by clustering to obtain supply chain edge categories, one or more similar supply chain edges of the first target enterprise customer relationship edges are determined from different supply chain categories with similarities, which can help enterprises formulate personalized service or product strategies and improve customer satisfaction and loyalty.

[0060] The beneficial effect brought about by the above-mentioned implementation method is that, by clustering, supply chain edge categories are obtained, and one or more similar supply chain edges of the first target enterprise customer relationship edge are determined from different supply chain categories with similarities, thereby reducing the number of subsequent calculations for determining one or more similar supply chain edges of the first target enterprise customer relationship edge and reducing the amount of calculation.

[0061] The beneficial effect brought about by the above-mentioned implementation method is that clustering is performed from multiple angles, including the attributes of the two supplier nodes corresponding to each supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge. This can improve the accuracy and scientificity of supply chain edge clustering and improve marketing efficiency.

[0062] In some implementations, in the above S121, all supply chain edges and all target enterprise customer relationship edges are clustered to obtain multiple supply chain edge categories, including S121a to S121b. S121a to S121b are described in detail below.

[0063] S121a. Through the weight allocation model, according to the attributes of the target enterprise, different weights are assigned to the attributes of the two supplier nodes corresponding to the supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to the target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge.

[0064] When clustering, a weight distribution model can be used to assign different weights to the attributes of the two supplier nodes corresponding to the supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to the target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge according to the attributes of the target enterprise, so as to achieve clustering according to different weights.

[0065] Exemplarily, the attributes of the target enterprise may include attributes such as business type, market positioning, size and capabilities, geographic location, financial status and supply chain strategy. Business type includes the business field of the target enterprise (such as manufacturing, retail, service, etc.) which will affect its business needs. For example, a manufacturing enterprise may pay more attention to the production capacity and delivery punctuality of the supplier, while a retail enterprise may pay more attention to the distribution capacity and inventory management of the supplier. Market positioning includes the market positioning of the target enterprise (such as high-end market, mass market) which determines the requirements for the quality, price and service of the supplier. For example, enterprises in the high-end market may pay more attention to the quality and innovation ability of the supplier.

[0066] S121b. Cluster all supply chain edges and all target enterprise customer relationship edges according to the attributes of the two supplier nodes corresponding to the supply chain edges, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to each target enterprise customer relationship edge, and the weights corresponding to the attributes of the target enterprise customer relationship edges, to obtain multiple supply chain edge categories.

[0067] After adjusting the different weights that affect clustering, all supply chain edges and all target enterprise-customer relationship edges can be clustered according to the weights corresponding to the attributes of the two supplier nodes corresponding to the supply chain edges, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to each target enterprise-customer relationship edge, and the attributes of the target enterprise-customer relationship edges, to obtain multiple supply chain edge categories.

[0068] It should be noted that the above S121a to S121b can be repeatedly executed multiple times. For the same target enterprise, after assigning different weights to the attributes of the two supplier nodes corresponding to the supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge, and clustering all supply chain edges and all target enterprise customer relationship edges, it can be ensured that the multiple supply chain edge categories obtained by clustering meet marketing needs with different emphases, thereby improving the marketing effect.

[0069] The beneficial effect brought about by the above-mentioned implementation method is that, according to the attributes of the target enterprise, different weights are assigned and clustered to the attributes of the two supplier nodes corresponding to the supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to the target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge, which can adapt to the usage needs of different types of target enterprises and improve the adaptability of this method.

[0070] The beneficial effect brought about by the above-mentioned implementation method is that, during use, the above-mentioned process can be repeated, and different weights can be assigned to the attributes of the two supplier nodes corresponding to the supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to the target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge. The marketing needs of the target enterprise can be extracted from different angles, which can further improve the marketing efficiency.

[0071] Figure 3 A flowchart of another enterprise marketing recommendation method based on city industrial chain integration provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the above method further includes S210 to S220, and S210 to S220 are described in detail below.

[0072] S210. When the number of supply chain edges in the first supply chain edge category is greater than the preset number of supply chain edges, the first marketing method corresponding to the target enterprise and the first supply chain edge category is determined through the enterprise marketing method recommendation model according to the attributes of the two supplier nodes corresponding to all supply chain edges in the first supply chain edge category, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to all target enterprise customer relationship edges, and the attributes of the target enterprise customer relationship edges. The first marketing method includes one or more of a multimodal model application, an AIGC application, a metaverse application, and a data assetization application.

[0073] After obtaining multiple supply chain edge categories, when the number of supply chain edges in the first supply chain edge category is greater than the preset number of supply chain edges, it means that the multiple supply chain edges in the first supply chain edge category may include more potential marketing targets, and a wide range of marketing methods can be further adopted to further market and improve marketing efficiency.

[0074] Exemplarily, the preset number of supply chain edges may be 100, 500 or 1000.

[0075] Figure 4 A schematic diagram of the workflow of another enterprise marketing method recommendation model provided in the embodiment of the present application, such as Figure 4 As shown, in further marketing, the enterprise marketing method recommendation model can be used to determine the first marketing method corresponding to the target enterprise and the first supply chain edge category according to the attributes of the two supplier nodes corresponding to all supply chain edges in the first supply chain edge category, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to all target enterprise customer relationship edges, and the attributes of the target enterprise customer relationship edges. The attributes of the two supplier nodes corresponding to all supply chain edges in the first supply chain edge category, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to all target enterprise customer relationship edges, and the attributes of the target enterprise customer relationship edges represent the cluster characteristics of the supply chain relationships in the first supply chain category. Through appropriate marketing methods, potential marketing targets in the first supply chain category can be efficiently marketed.

[0076] Exemplarily, the first marketing method includes one or more of multimodal model application, AIGC (Artificial Intelligence Generated Content) application, metaverse application and data assetization application.

[0077] When training a corporate marketing method recommendation model, a data set can be formed and trained by using the attributes of the two supplier nodes corresponding to all supply chain edges in the sample supply chain edge category, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to all target enterprise customer relationship edges, the attributes of the target enterprise customer relationship edges, and the sample marketing methods corresponding to the sample supply chain edge category.

[0078] S220. Conduct enterprise marketing for suppliers corresponding to multiple supplier nodes in the first supply chain edge category through a first marketing method.

[0079] When conducting enterprise marketing, after obtaining the first marketing method corresponding to the first supply chain category, enterprise marketing can be conducted on suppliers corresponding to multiple supplier nodes in the first supply chain edge category through the first marketing method to achieve extensive marketing.

[0080] The beneficial effect brought about by the above implementation method is that when multiple supply chain edges in the first supply chain edge category may include more potential marketing targets, the corresponding marketing method is determined according to the characteristics of the first supply chain edge category, thereby improving marketing efficiency.

[0081] The beneficial effect brought about by the above-mentioned implementation method is that when determining the marketing method, the scientific nature and marketing efficiency of the marketing method are improved according to the marketing characteristics in multimodal model application, AIGC application, metaverse application and data assetization application.

[0082] In some implementations, the above method further includes S310 to S330, and S310 to S330 are described in detail below.

[0083] S310. After conducting enterprise marketing for suppliers corresponding to multiple supplier nodes in a first supply chain edge category through a first marketing method for a preset time period, obtain first marketing revenue data corresponding to the first supply chain edge category, and determine a first marketing revenue indicator based on the first marketing revenue data.

[0084] During the marketing process, after a preset period of time, enterprise marketing can be carried out on suppliers corresponding to multiple supplier nodes in a first supply chain edge category through a first marketing method. In order to verify the marketing effect of the first marketing method, first marketing revenue data corresponding to the first supply chain edge category can be obtained. The first marketing revenue data represents the marketing effect corresponding to the first marketing method.

[0085] After the first marketing revenue data is obtained, a first marketing revenue index may be determined according to the first marketing revenue data, where the first marketing revenue index represents an evaluation value of the first marketing revenue data.

[0086] Exemplarily, the preset time period may be 30 days or 120 days.

[0087] Exemplarily, the first marketing revenue data may be the number of orders marketed in the first supply chain edge category. When determining the first marketing revenue indicator based on the first marketing revenue data, the ratio of the number of orders to the sum of all supply chain edges and all target enterprise customer relationship edges in the first supply chain edge category may be used as the first marketing revenue data.

[0088] S320. When the first marketing revenue indicator is less than the preset first marketing revenue indicator, the model is corrected through the enterprise marketing method, and the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge and the first marketing revenue data.

[0089] When evaluating the marketing effectiveness, when the first marketing revenue indicator is less than the preset first marketing revenue indicator, it means that the marketing effect of the first marketing method is not good. The model can be further corrected through the enterprise marketing method. According to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge and the first marketing revenue data, the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined. The second marketing method is a second marketing method corrected by combining the first marketing revenue data and the characteristics of all supply chain edges and all target enterprise customer relationship edges in the first supply chain edge category.

[0090] Exemplarily, the first marketing revenue data may include the marketing order volume and specific order-taking suppliers in the first supply chain edge category. The characteristics and order volume of the order-taking suppliers reflect the supplier data that failed to place orders using the first marketing method. The first marketing revenue data can improve the marketing effect of the second marketing method.

[0091] S330. Conduct enterprise marketing for suppliers corresponding to multiple supplier nodes in the first supply chain edge category through a second marketing method.

[0092] After obtaining the second marketing method, enterprise marketing can be performed on suppliers corresponding to multiple supplier nodes in the first supply chain edge category through the second marketing method to ensure marketing efficiency.

[0093] The beneficial effect brought about by the above-mentioned implementation method is that when the marketing effect of the first marketing method that only reflects the characteristics of all supply chain edges and all target enterprise customer relationship edges in the first supply chain edge category is not good, marketing can be carried out by combining the first marketing revenue data and the characteristics of all supply chain edges and all target enterprise customer relationship edges in the first supply chain edge category and the second marketing method to ensure marketing efficiency.

[0094] In some implementations, the first marketing revenue data includes the marketing time sequence order quantity corresponding to the first supply chain edge category. In the above S320, the model is corrected through the enterprise marketing method, and the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge, and the first marketing revenue data, including: correcting the model through the enterprise marketing method, and determining the second marketing method corresponding to the target enterprise and the first supply chain edge category according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge, the preset time period and the marketing time sequence order quantity.

[0095] When collecting the first marketing revenue data, the marketing time sequence order volume corresponding to the first supply chain edge category can be collected. The marketing time sequence order volume corresponds to the marketing effect of the first marketing method in terms of time sequence within the preset marketing time period. Therefore, the first marketing method can be adjusted according to the marketing time sequence order volume.

[0096] Exemplarily, the marketing sequence order volume can be 2 orders within the first week of the preset time period, 5 orders within the second week, 15 orders within the third week, and 30 orders within the fourth week through the first marketing method.

[0097] When adjusting the first marketing method, the model can be corrected through the enterprise marketing method according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge, the preset time period and the marketing sequence order volume. The marketing sequence order volume represents the marketing effect in actual marketing. By combining the actual marketing effect, the second marketing method corresponding to the target enterprise and the first supply chain edge category can be determined.

[0098] The beneficial effect brought about by the above-mentioned implementation method is that when the marketing effect of the first marketing method which only reflects the characteristics of all supply chain edges and all target enterprise customer relationship edges in the first supply chain edge category is not good, the second marketing method can be re-determined by combining the first marketing revenue data and the characteristics of all supply chain edges and all target enterprise customer relationship edges in the first supply chain edge category, the preset time period and the marketing sequence order volume within the preset time period, thereby improving the scientific nature of the revised marketing method and ensuring the efficiency of marketing.

[0099] An embodiment of the present application also provides an enterprise marketing recommendation device based on the integration of urban industrial chains, including a unit for executing any of the methods described above.

[0100] Figure 5 A logical structure diagram of a business marketing recommendation device based on city industry chain integration provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the device 1 of this embodiment includes a processing unit 11, a storage unit 12 and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12 and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects brought about by the embodiment of the present application have been described in the above method and will not be repeated here.

[0101] An embodiment of the present application also provides an enterprise marketing recommendation device based on the integration of urban industrial chains, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the methods described above is implemented.

[0102] Figure 6 A schematic diagram of the physical structure of an enterprise marketing recommendation device based on the integration of urban industrial chains provided in an embodiment of the present application is shown as follows: Figure 6 As shown, the device 2 of this embodiment includes: at least one processor 20 ( Figure 6 Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, the steps in any of the above-mentioned method embodiments are implemented. The beneficial effects brought about by the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0103] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0104] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0105] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0106] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0108] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0109] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A corporate marketing recommendation method based on the integration of urban industrial chains, characterized in that: The method comprises: Based on the city industrial chain, an urban industrial graph database including supplier nodes and supply chain edges is constructed. Based on the target enterprise CRM of the target enterprise, target enterprise nodes, target enterprise customer nodes and target enterprise customer relationship edges are added to the urban industrial graph database; wherein the supplier node represents the supplier in the urban industrial chain, the supply chain edge represents the supply relationship between two suppliers in the urban industrial chain, the target enterprise node represents the target enterprise, the target enterprise customer node represents the customer of the target enterprise, and the target enterprise customer relationship edge represents the relationship between the target enterprise and the customer of the target enterprise; Through the supply relationship identification model, according to the first target enterprise customer relationship edge, one or more similar supply chain edges of the first target enterprise customer relationship edge are determined in the city industry graph database, and two supplier nodes corresponding to each similar supply chain edge are determined; wherein the first target enterprise customer relationship edge connects the target enterprise and a target enterprise customer node respectively; Determine a similar supplier node corresponding to the target enterprise customer node connected by the first target enterprise customer relationship edge among the two supplier nodes corresponding to each similar supply chain edge, and use the supplier corresponding to the similar supplier node as the enterprise marketing target of the target enterprise; According to the first target enterprise customer relationship edge, determining one or more similar supply chain edges of the first target enterprise customer relationship edge in the city industry graph database includes: Clustering all supply chain edges and all target enterprise customer relationship edges to obtain multiple supply chain edge categories; wherein each supply chain edge category includes multiple supply chain edges and at least one target enterprise customer relationship edge, and clustering is performed according to the attributes of two supplier nodes corresponding to each supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge; In each supply chain edge category, one or more similar supply chain edges of the first target enterprise-customer relationship edge are determined in each supply chain edge category according to the first target enterprise-customer relationship edge in the supply chain edge category.

2. The method according to claim 1, characterized in that Cluster all supply chain edges and all target enterprise customer relationship edges to obtain multiple supply chain edge categories, including: Through the weight allocation model, according to the attributes of the target enterprise, different weights are assigned to the attributes of the two supplier nodes corresponding to the supply chain edge, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to the target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge; According to the attributes of the two supplier nodes corresponding to the supply chain edges, the attributes of the supply chain edges, the attributes of the target enterprise customer nodes corresponding to each target enterprise customer relationship edge, and the weights corresponding to the attributes of the target enterprise customer relationship edges, all supply chain edges and all target enterprise customer relationship edges are clustered to obtain multiple supply chain edge categories.

3. The method according to claim 2, characterized in that The method further comprises: When the number of supply chain edges in the first supply chain edge category is greater than the preset number of supply chain edges, the first marketing method corresponding to the target enterprise and the first supply chain edge category is determined through the enterprise marketing method recommendation model according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, and the attributes of the target enterprise customer relationship edge; wherein the first marketing method includes one or more of a multimodal model application, an AIGC application, a metaverse application, and a data assetization application; Enterprise marketing is performed on suppliers corresponding to multiple supplier nodes in the first supply chain edge category through a first marketing method.

4. The method according to claim 3, characterized in that The method further comprises: After performing enterprise marketing for a preset period of time on suppliers corresponding to a plurality of supplier nodes in a first supply chain edge category through a first marketing method, first marketing revenue data corresponding to the first supply chain edge category is obtained, and a first marketing revenue indicator is determined according to the first marketing revenue data; When the first marketing revenue indicator is less than the preset first marketing revenue indicator, the model is corrected through the enterprise marketing method, and the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge and the first marketing revenue data; Enterprise marketing is performed on suppliers corresponding to multiple supplier nodes in the first supply chain edge category through a second marketing method.

5. The method according to claim 4, characterized in that The first marketing revenue data includes the marketing time series order quantity corresponding to the first supply chain edge category. The model is corrected by the enterprise marketing method. According to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge and the first marketing revenue data, the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined, including: The model is modified by the enterprise marketing method, and the second marketing method corresponding to the target enterprise and the first supply chain edge category is determined according to the attributes of the two supplier nodes corresponding to each supply chain edge in the first supply chain edge category, the attributes of the supply chain edge, the attributes of the target enterprise customer node corresponding to each target enterprise customer relationship edge, the attributes of the target enterprise customer relationship edge, the preset time period and the marketing sequence order quantity.

6. An enterprise marketing recommendation device based on the integration of urban industrial chains, characterized in that: The method comprises means for performing the method according to any one of claims 1 to 5.

7. An enterprise marketing recommendation device based on the integration of urban industrial chains, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images