Data processing method and device, computer equipment and computer readable storage medium

Abstract

The invention provides a data processing method and device, computer equipment and a computer readable storage medium, and the method comprises the steps: obtaining a basic logistics data set of a logistics supply chain which comprises at least one enterprise and logistics suppliers of each enterprise; carrying out a series of data analysis and processing on the basic logistics data set to obtain financing parameter data of the logistics supplier to each enterprise, stability parameter data between a node corresponding to the logistics supplier and a node corresponding to the enterprise, and importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise; and according to the financing parameter data, the stability parameter data and the importance parameter data, determining financing limit data of the logistics suppliers to each enterprise in the logistics supply chain, thereby realizing a technical effect of efficiently and accurately providing reliable financing reference data for the financing scheme of the logistics suppliers to each enterprise in the logistics supply chain.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method, device, computer equipment and computer-readable storage medium. Background Art

[0002] With the rapid development of science and technology, the application of technologies such as the Internet, big data, artificial intelligence, and blockchain is changing traditional finance.

[0003] Traditional corporate credit assessment still has many defects, such as the inability to fully reflect the actual situation of the enterprise, the difficulty in guaranteeing the data source, and the single analysis method. It is unable to efficiently and accurately provide reliable financing reference data for enterprises and logistics suppliers in the logistics supply chain.

[0004] Therefore, how to efficiently and accurately provide reliable financing reference data for the financing plans of logistics suppliers to various enterprises in the logistics supply chain is a technical problem that urgently needs to be solved in the current field of data processing technology. Summary of the invention

[0005] The present application provides a data processing method, apparatus, computer equipment and computer-readable storage medium, which aims to solve the problem of how to efficiently and accurately provide financing reference data for logistics suppliers to various enterprises in the logistics supply chain, which is a technical problem that urgently needs to be solved in the current field of data processing technology.

[0006] In one aspect, the present application provides a data processing method, the method comprising:

[0007] Acquire a basic logistics data set of a logistics supply chain, wherein the logistics supply chain includes at least one enterprise and a logistics supplier of each of the enterprises;

[0008] Performing feature extraction and supervised learning processing on the basic logistics data set to obtain financing parameter data of the logistics supplier for each of the enterprises;

[0009] Performing relationship construction processing on the basic logistics data set to obtain each logistics relationship network centered on each of the enterprises;

[0010] Perform feature mining on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise;

[0011] Performing network analysis on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise;

[0012] The financing amount data of the logistics supplier to each of the enterprises in the logistics supply chain is determined based on the financing parameter data, the stability parameter data and the importance parameter data.

[0013] In a possible implementation of the present application, determining the financing amount data of the logistics supplier to each of the enterprises in the logistics supply chain according to the financing parameter data, the stability parameter data and the importance parameter data includes:

[0014] Determine a financing ratio score based on the financing parameter data, the stability parameter data, the importance parameter data and a preset WSI model;

[0015] The financing ratio score is normalized to obtain normalized result data, and the normalized result data is used as the financing amount data of the logistics supplier in the logistics supply chain for each of the enterprises.

[0016] In a possible implementation of the present application, the basic logistics data set is subjected to feature extraction processing and supervised learning processing to obtain financing parameter data of the logistics supplier for each of the enterprises, including:

[0017] Obtaining historical financing parameter data of the logistics provider;

[0018] Determining historical financing characteristics of each of the enterprises based on the historical financing parameter data;

[0019] Performing feature extraction processing on the basic logistics data set to obtain a logistics data feature pool between the logistics supplier and each of the enterprises;

[0020] The logistics data feature pool and the historical financing features are subjected to supervised learning processing to obtain the financing parameter data of the logistics supplier for each of the enterprises.

[0021] In a possible implementation of the present application, the basic logistics data set is subjected to relationship building processing to obtain each logistics relationship network centered on each of the enterprises, including:

[0022] Converting the basic logistics data set into structured data of a preset structure type;

[0023] Performing relationship building processing on the structured data to obtain a global relationship network between each of the enterprises and the logistics suppliers;

[0024] The global relationship network is disconnected to obtain each logistics relationship network centered on each of the enterprises.

[0025] In a possible implementation of the present application, the structured data includes nodes and edges, the nodes are the enterprises or the logistics suppliers, and the edges are the collection and delivery behavior relationships between the nodes.

[0026] In a possible implementation of the present application, the nodes and edges of each logistics relationship network are subjected to feature mining processing to obtain stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise, including:

[0027] Obtain the edge attribute of each edge of each logistics relationship network and the node attribute of each node of each logistics relationship network;

[0028] Determine the relationship factor characteristics between each node according to the edge attributes and the node attributes;

[0029] Performing weight processing and normalization processing on the relationship factor features to obtain the relationship features between each node;

[0030] The stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined according to the relationship characteristics between each node.

[0031] In a possible implementation of the present application, the network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain the importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise, including:

[0032] Initialize the weight parameters of the nodes of each logistics relationship network to obtain the initial weight parameters of the nodes of each logistics relationship network;

[0033] Obtaining the node out-degree of each node in the logistics relationship network;

[0034] Based on the initial weight parameters of the nodes of each logistics relationship network, the node out-degree and a preset network analysis algorithm, the importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined.

[0035] In another aspect, the present application provides a data processing device, the device comprising:

[0036] A first acquisition unit is used to acquire a basic logistics data set of a logistics supply chain, wherein the logistics supply chain includes at least one enterprise and a logistics supplier of each of the enterprises;

[0037] The first feature extraction unit and the first supervised learning unit are used to perform feature extraction processing and supervised learning processing on the basic logistics data set, respectively, to obtain financing parameter data of the logistics supplier for each of the enterprises;

[0038] A first relationship building unit, used for performing relationship building processing on the basic logistics data set to obtain each logistics relationship network centered on each of the enterprises;

[0039] A first feature mining unit is used to perform feature mining processing on the nodes and edges of each logistics relationship network to obtain stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise;

[0040] A first network analysis unit is used to perform network analysis processing on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise;

[0041] The first determination unit is used to determine the financing amount data of the logistics supplier in the logistics supply chain for each of the enterprises based on the financing parameter data, the stability parameter data and the importance parameter data.

[0042] In a possible implementation of the present application, the first determining unit is specifically configured to:

[0043] Determine a financing ratio score based on the financing parameter data, the stability parameter data, the importance parameter data and a preset WSI model;

[0044] The financing ratio score is normalized to obtain normalized result data, and the normalized result data is used as the financing amount data of the logistics supplier in the logistics supply chain for each of the enterprises.

[0045] In a possible implementation of the present application, the first feature extraction unit and the first supervised learning unit are specifically used to:

[0046] Obtaining historical financing parameter data of the logistics provider;

[0047] Determining historical financing characteristics of each of the enterprises based on the historical financing parameter data;

[0048] Performing feature extraction processing on the basic logistics data set to obtain a logistics data feature pool between the logistics supplier and each of the enterprises;

[0049] The logistics data feature pool and the historical financing features are subjected to supervised learning processing to obtain the financing parameter data of the logistics supplier for each of the enterprises.

[0050] In a possible implementation of the present application, the first relationship building unit is specifically used to:

[0051] Converting the basic logistics data set into structured data of a preset structure type;

[0052] Performing relationship building processing on the structured data to obtain a global relationship network between each of the enterprises and the logistics suppliers;

[0053] The global relationship network is disconnected to obtain each logistics relationship network centered on each of the enterprises.

[0054] In a possible implementation of the present application, the structured data includes nodes and edges, the nodes are the enterprises or the logistics suppliers, and the edges are the collection and delivery behavior relationships between the nodes.

[0055] In a possible implementation of the present application, the first feature mining unit is specifically used to:

[0056] Obtain the edge attribute of each edge of each logistics relationship network and the node attribute of each node of each logistics relationship network;

[0057] Determine the relationship factor characteristics between each node according to the edge attributes and the node attributes;

[0058] Performing weight processing and normalization processing on the relationship factor features to obtain the relationship features between each node;

[0059] The stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined according to the relationship characteristics between each node.

[0060] In a possible implementation of the present application, the first network analysis unit is specifically configured to:

[0061] Initialize the weight parameters of the nodes of each logistics relationship network to obtain the initial weight parameters of the nodes of each logistics relationship network;

[0062] Obtaining the node out-degree of each node in the logistics relationship network;

[0063] Based on the initial weight parameters of the nodes of each logistics relationship network, the node out-degree and a preset network analysis algorithm, the importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined.

[0064] On the other hand, the present application also provides a computer device, the computer device comprising:

[0065] one or more processors;

[0066] Memory; and

[0067] One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the data processing method.

[0068] On the other hand, the present application also provides a computer-readable storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the data processing method.

[0069] In an embodiment of the present application, a basic logistics data set of a logistics supply chain is obtained, wherein the logistics supply chain includes at least one enterprise and logistics suppliers of each enterprise; feature extraction processing and supervised learning processing are performed on the basic logistics data set to obtain financing parameter data of the logistics supplier for each enterprise; relationship construction processing is performed on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; feature mining processing is performed on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; according to the financing parameter data, stability parameter data and importance parameter data, the financing amount data of the logistics supplier for each enterprise in the logistics supply chain is determined, and in-depth data mining and analysis of the basic logistics data set of the logistics supply chain are achieved to obtain strongly correlated multi-angle data analysis results, so as to provide reliable technical support for the financing amount data of the logistics supplier for each enterprise in the logistics supply chain, thereby achieving the technical effect of efficiently and accurately providing reliable financing reference data for the financing plans of the logistics supplier for each enterprise in the logistics supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 is a scenario schematic diagram of a data processing system provided in an embodiment of the present application;

[0072] Figure 2 It is a schematic diagram of an embodiment of a data processing method provided in an embodiment of the present application;

[0073] Figure 3 is a schematic diagram of the structure of an embodiment of a data processing device provided in an embodiment of the present application;

[0074] Figure 4 It is a schematic diagram of the structure of an embodiment of a computer device provided in the embodiments of the present application. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0076] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0077] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0078] The embodiments of the present application provide a data processing method, apparatus, computer device, and computer-readable storage medium, which are described in detail below.

[0079] like Figure 1 As shown, Figure 1 1 is a schematic diagram of a data processing system provided in an embodiment of the present application. The data processing system may include a computer device 100, in which a data processing device is integrated, such as Figure 1 A computer device 100 in.

[0080] In the embodiment of the present application, the computer device 100 is mainly used to obtain a basic logistics data set of a logistics supply chain, which includes at least one enterprise and logistics suppliers of each enterprise; perform feature extraction processing and supervised learning processing on the basic logistics data set to obtain financing parameter data of the logistics supplier for each enterprise; perform relationship construction processing on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; perform feature mining processing on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; perform network analysis processing on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; determine the financing amount data of the logistics supplier for each enterprise in the logistics supply chain based on the financing parameter data, the stability parameter data and the importance parameter data.

[0081] In the embodiment of the present application, the computer device 100 may be a terminal or a server. When the computer device 100 is a server, it may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server constructed by a plurality of servers. The cloud server is constructed by a large number of computers or network servers based on cloud computing.

[0082] It is understandable that when the computer device 100 in the embodiment of the present application is a terminal, the terminal used may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, a medical auxiliary instrument, etc.

[0083] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution, and is not intended to limit the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the data processing system may also include one or more other computer devices, which are not specifically limited here.

[0084] In addition, if Figure 1As shown, the data processing system may also include a memory 200 for storing data, such as storing basic logistics data sets and data processing data of a logistics supply chain, such as data processing data when the data processing system is running.

[0085] It should be noted that Figure 1 The scenario diagram of the data processing system shown is merely an example. The data processing system and scenario described in the embodiments of the present application are intended to more clearly illustrate the technical solution of the embodiments of the present application, and do not constitute a limitation on the technical solution provided in the embodiments of the present application. A person of ordinary skill in the art will appreciate that with the evolution of the data processing system and the emergence of new business scenarios, the technical solution provided in the embodiments of the present application is equally applicable to similar technical problems.

[0086] Next, the data processing method provided by the embodiment of the present application is introduced.

[0087] In the data processing method of the present embodiment, a data processing device is used as an execution subject. For the sake of simplicity and ease of description, the execution subject will be omitted in the subsequent method embodiments. The data processing device is applied to a computer device. The method includes: obtaining a basic logistics data set of a logistics supply chain, which includes at least one enterprise and logistics suppliers of each enterprise; performing feature extraction and supervised learning processing on the basic logistics data set to obtain financing parameter data of the logistics supplier for each enterprise; performing relationship construction processing on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; performing feature mining processing on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; performing network analysis processing on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; determining the financing amount data of the logistics supplier for each enterprise in the logistics supply chain based on the financing parameter data, stability parameter data and importance parameter data.

[0088] See also Figures 2 to 4 , Figure 2 The following is a flow chart of an embodiment of a data processing method provided in an embodiment of the present application. The data processing method includes:

[0089] 201. Obtain basic logistics data sets for logistics supply chain.

[0090] The logistics supply chain includes at least one enterprise and each enterprise's logistics supplier. The logistics supplier provides corresponding products or services to the enterprises it serves to meet their business needs. Suppliers can provide a variety of products and services, such as but not limited to raw materials, parts, equipment, consulting services, human resources, etc.; generally, each enterprise has at least one logistics supplier.

[0091] The basic logistics data set is converted from the basic logistics data between at least one enterprise in the logistics supply chain and the logistics suppliers of each enterprise. The basic logistics data may include customer data, bill settlement data, relationship data between the enterprise and its logistics suppliers, logistics behavior data of logistics customers, and consignment data.

[0092] It should be noted that the acquisition, storage and use of the above-mentioned basic logistics data have been agreed upon by the relevant parties through relevant agreements, etc.

[0093] Optionally, the above-mentioned conversion method may include: desensitizing the basic logistics data to obtain desensitized available logistics data, and then performing data preprocessing and feature engineering on the available logistics data to obtain a processed logistics feature data set, and finally generate a basic logistics data set.

[0094] Optionally, the above-mentioned data preprocessing and feature engineering processing of available logistics data may include: aggregating the available logistics data according to different time slices and dimensions to generate corresponding logistics behavior characteristics, which include characteristics such as the type of consignment, the number of consignments, and the amount, and converting these logistics behavior characteristics into basic logistics factor characteristics; then filtering out non-B logistics behavior characteristics based on a preset screening strategy to retain as much B2B logistics behavior data as possible and reduce noise in graph mining; sorting out and constructing relationship data and basic industrial and commercial data between each enterprise and its logistics suppliers, mainly including cooperation data, capital flow data, business data, affiliation, etc., as well as public basic industrial and commercial data of enterprises, negative data, etc., performing necessary desensitization and data preprocessing to form a basic logistics data set.

[0095] The desensitization process may be performed using a preset encryption algorithm, such as md5.

[0096] B2B stands for "Business to Business" and refers to a business model in which one company sells products or provides services to another company. This transaction usually occurs between two companies, rather than between a business and a consumer. B2B transactions are very common in many industries and fields, including manufacturing, wholesale and retail, information technology, financial services, etc.; while non-B refers to other models that are not B2B models.

[0097] Among them, the above-mentioned aggregation processing of available logistics data generates corresponding logistics behavior characteristics, which mainly focuses on the behavioral information of sending and receiving parcels. The atomic indicators include consignment, amount, number of pieces, weight, etc.; the main derivative logics are as follows: 1) Time feature derivation: extract date-related features from the time of sending (receiving) parcels, such as working days, holidays, years and time window aggregation, such as the number of parcels sent in the past 6 months; 2) Based on numerical statistical aggregation: for example, ratios, average piece cost, differences, such as how many more parcels are sent this month compared to last month; there are many more, such as text feature extraction, category feature extraction (consignment), geographic information extraction (receiving and sending addresses), cross-combination, etc.

[0098] The above preset screening strategies specifically include the following:

[0099] The main purpose of screening is to distinguish between toC and toB data. The so-called "toC" (i.e. to Consumer) and "toB" (i.e. to Business) refer to two different types of markets in terms of marketing direction. Among them, "toC" is a market specifically for consumers, while "ToB" is a market for corporate / business customers.

[0100] Therefore, the main purpose of screening is to distinguish the types of data required by these two markets. Depending on the marketing strategy and target audience, different data needs to be collected to meet their specific needs.

[0101] For example, if a company wants to sell products to consumers, then they need to conduct consumer research, such as investigating consumer behavior, preferences, purchasing decision factors, etc., in order to understand consumer needs and preferences. On the contrary, if a B2B (business to business) company sells products, it needs to understand more about the needs of corporate customers, such as corporate size, budget, purchasing process, purchasing decision factors, etc.

[0102] The specific screening methods can adopt the following two methods: one is a screening method based on the model, and the second is a screening method based on strategy rules.

[0103] First of all, both of the above methods require the subject of the waybill to be determined. Here, the subject can be a contact mobile phone (desensitized).

[0104] For the first method, after obtaining authorization from the target object, the target object's object information is collected, including enterprises and individuals. Then, the basic logistics factor features constructed in the previous article are used to perform traditional machine learning supervised algorithms, and then a model that can identify ToB and ToC is obtained, and then some waybills with unknown labels are classified. Finally, some waybills with a high probability of being ToC items are filtered out.

[0105] For the second method, the policy rule-based method may include: based on the company's settlement information, such as the settlement account or card number between the logistics company, finding the ToB items through the settlement information (desensitizing the entire process); or, based on the sender and recipient addresses, standardization, judging whether they are in an office area, business area, non-residential area, etc. (desensitizing).

[0106] 202. Perform feature extraction and supervised learning on the basic logistics data set to obtain the financing parameter data of logistics suppliers for each enterprise;

[0107] Optionally, the basic logistics data set is subjected to feature extraction processing and supervised learning processing to obtain financing parameter data of the logistics supplier for each of the enterprises, including: obtaining historical financing parameter data of the logistics supplier; determining historical financing characteristics of each of the enterprises based on the historical financing parameter data; performing feature extraction processing on the basic logistics data set to obtain a logistics data feature pool between the logistics supplier and each of the enterprises; performing supervised learning processing on the logistics data feature pool and the historical financing characteristics to obtain financing parameter data of the logistics supplier for each of the enterprises.

[0108] Optionally, feature extraction processing is performed on the basic logistics data set to obtain a logistics data feature pool between the logistics supplier and each of the enterprises, including: further feature fusion, feature combination and other operations are performed on the basic physical data set to form corresponding high-order features, thereby obtaining a logistics data feature pool between the logistics supplier and each of the enterprises.

[0109] 203. Perform relationship construction processing on the basic logistics data set to obtain each logistics relationship network centered on each enterprise;

[0110] Optionally, the relationship building processing is performed on the basic logistics data set to obtain logistics relationship networks centered on each of the enterprises, including: converting the basic logistics data set into structured data of a preset structure type; performing relationship building processing on the structured data to obtain a global relationship network between each of the enterprises and the logistics suppliers; and disconnecting the global relationship network to obtain logistics relationship networks centered on each of the enterprises.

[0111] The structured data includes nodes and edges, the nodes are the enterprises or the logistics suppliers, and the edges are the collection and delivery behavior relationships between the nodes.

[0112] Optionally, the structured data is processed for relationship building to obtain a global relationship network between each of the enterprises and the logistics suppliers, including: determining the type and attributes between each node and edge; quantifying the relationships between the nodes and edges based on the logistics data feature pool mentioned above to obtain quantitative data; then, based on the type and attributes between each node and edge and the quantitative data, the structured data is processed for relationship building to obtain a global relationship network between each of the enterprises and the logistics suppliers.

[0113] Among them, the network disconnection processing is to split the global relationship network into a logistics relationship network with a single enterprise as the core.

[0114] It is understandable that before the network disconnection process, the entire global relationship network is similar to a complete large network, and after the network disconnection process, this complete large network is split into multiple small networks.

[0115] Optionally, the global relationship network is disconnected to obtain logistics relationship networks centered on each of the enterprises, including: based on a preset disconnection strategy, the global relationship network is disconnected to obtain logistics relationship networks centered on each of the enterprises.

[0116] The network disconnection strategy may include the following three strategies:

[0117] Strategy 1: First, take each enterprise as the target enterprise to construct the logistics network, specifically including removing the remaining core enterprise nodes and the relationships associated with them. However, the "removal" here does not mean direct physical deletion, but adjusting the weights of these nodes and relationship edges to 0, and then constructing the graph to ensure that it will not affect the PageRank contribution value of other nodes, and output the logistics relationship network based on the target enterprise;

[0118] Strategy 2: Take the target enterprise as the starting point in the global relationship network, retain the nodes and relationship edges within the N-degree range, and adjust the weights of the nodes and relationship edges outside the N-degree range to 0, and then build the logistics relationship network of the core target enterprise;

[0119] Strategy 3: Use clustering algorithms to cluster nodes in the network and gather nodes related to the target enterprise node into the same cluster. For example, K-means or spectral clustering algorithms. Then, treat each cluster as a subgraph, thereby obtaining multiple subgraphs. See which cluster the target enterprise node falls into, and then use that cluster as the logistics relationship network of the target enterprise. Similarly, adjust the weights of nodes and relationship edges outside the cluster to 0.

[0120] 204. Perform feature mining on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise;

[0121] Optionally, the nodes and edges of each logistics relationship network are subjected to feature mining processing to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise, including: obtaining edge attributes of each edge of each logistics relationship network and node attributes of each node of each logistics relationship network; determining the relationship factor characteristics between each node according to the edge attributes and the node attributes; performing weight processing and normalization processing on the relationship factor characteristics to obtain the relationship characteristics between each node; and determining the stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise according to the relationship characteristics between each node.

[0122] Among them, the edge attributes of each edge of each logistics relationship network include time attributes (cooperation time, frequency), quantity attributes, weight attributes and value attributes; the node attributes of each node of the logistics relationship network include out-degree and in-degree attributes.

[0123] Optionally, determining the relationship factor characteristics between each node based on the edge attributes and the node attributes includes: performing feature mining processing on the edge attributes and the node attributes, using the edge attributes and the node attributes as factor characteristics for measuring the strength of the node relationship, so as to determine the relationship factor characteristics between each node.

[0124] Optionally, the relationship factor features are weighted and normalized to obtain the relationship features between each node, including: weighting the relationship factor features based on a preset expert experience strategy to obtain corresponding scores; and then normalizing the scores to obtain the relationship features between each node.

[0125] Optionally, stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise is determined based on the relationship characteristics between each node, including determining the stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise based on the strength of the relationship characteristics between each node (the normalized score).

[0126] 205. Perform network analysis on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise;

[0127] Optionally, the nodes and edges of each logistics relationship network are subjected to network analysis processing to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise, including: initializing weight parameters of the nodes of each logistics relationship network to obtain initial weight parameters of the nodes of each logistics relationship network; obtaining the node out-degree of each node in the logistics relationship network; and determining the importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise based on the initial weight parameters of the nodes of each logistics relationship network, the node out-degree and a preset network analysis algorithm.

[0128] Optionally, the weight parameters of the nodes of each logistics relationship network are initialized to obtain the initial weight parameters of the nodes of each logistics relationship network, including: initializing a weight for the nodes of each logistics relationship network according to the node attributes of the nodes of each logistics relationship network to obtain the initial weight parameters of the nodes of each logistics relationship network.

[0129] Among them, the node attributes of the nodes of each logistics relationship network include enterprise scale attributes and logistics behavior richness attributes.

[0130] Optionally, obtaining the node out-degree of each node in the logistics relationship network includes: obtaining the node out-degree of each node in the logistics relationship network from the basic logistics data set.

[0131] Optionally, based on the initial weight parameters of the nodes of each logistics relationship network, the node out-degree and a preset network analysis algorithm, the importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise are determined, including: inputting the initial weight parameters of the nodes of each logistics relationship network and the node out-degree into a preset network analysis algorithm, and calculating the importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise.

[0132] The calculation formula of the network analysis algorithm is as follows:

[0133]

[0134] Among them, PR(X) represents the PageRank value of enterprise node X; PR(T n ) represents the PageRank value of a certain enterprise node pointing to node X; C(T n ) represents the out-degree of a certain enterprise node pointing to node X (i.e., how many enterprise nodes the package is sent to); D represents the damping coefficient.

[0135] After calculating the PageRank value of node X, the PageRank value of each node X is normalized to obtain a normalized result. Finally, the normalized result is used as the importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise.

[0136] 206. Based on the financing parameter data, stability parameter data and importance parameter data, determine the financing quota data of the logistics supplier to each enterprise in the logistics supply chain.

[0137] Among them, the financing amount data is used to measure the financing-related parameters of logistics suppliers to each enterprise in the logistics supply chain, which can be the financing ratio or financing amount.

[0138] Optionally, when the financing amount data is used for the financing ratio, the financing amount data of the logistics supplier for each of the enterprises in the logistics supply chain is determined based on the financing parameter data, the stability parameter data and the importance parameter data, including: determining a financing ratio score based on the financing parameter data, the stability parameter data, the importance parameter data and a preset WSI model; normalizing the financing ratio score to obtain normalized result data, and using the normalized result data as the financing amount data of the logistics supplier for each of the enterprises in the logistics supply chain.

[0139] Optionally, based on the financing parameter data, the stability parameter data, the importance parameter data and a preset WSI model, a financing ratio score is determined, including: inputting the financing parameter data of the logistics supplier for each of the enterprises, the stability parameter data and the importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprises into the preset WSI model, and outputting the financing ratio score.

[0140] Among them, the WSI model can map the financing parameter data, the stability parameter data, and the importance parameter data to obtain corresponding scores. The dimensional indicators of the WSI model come from the three result indicators of financing willingness financing parameter data W (willingness), stability parameter data S (Stability), and importance parameter data I (Importance).

[0141] Optionally, when the financing amount data is used for the financing amount, after the normalized result data is calculated as above, the financing amount data of the logistics supplier in the logistics supply chain for each of the enterprises will be calculated based on the normalized result data and the preset total amount to be financed. The calculation method can be to multiply the normalized result data by the preset total amount to be financed.

[0142] In an embodiment of the present application, a basic logistics data set of a logistics supply chain is obtained, wherein the logistics supply chain includes at least one enterprise and logistics suppliers of each enterprise; feature extraction processing and supervised learning processing are performed on the basic logistics data set to obtain financing parameter data of the logistics supplier for each enterprise; relationship construction processing is performed on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; feature mining processing is performed on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; according to the financing parameter data, stability parameter data and importance parameter data, the financing amount data of the logistics supplier for each enterprise in the logistics supply chain is determined, and in-depth data mining and analysis of the basic logistics data set of the logistics supply chain are achieved to obtain strongly correlated multi-angle data analysis results, so as to provide reliable technical support for the financing amount data of the logistics supplier for each enterprise in the logistics supply chain, thereby achieving the technical effect of efficiently and accurately providing reliable financing reference data for the financing plans of the logistics supplier for each enterprise in the logistics supply chain.

[0143] In order to better implement the data processing method in the embodiment of the present application, based on the data processing method, a data processing device is also provided in the embodiment of the present application, such as Figure 3 As shown, the data processing device 300 includes:

[0144] A first acquisition unit 301 is used to acquire a basic logistics data set of a logistics supply chain, wherein the logistics supply chain includes at least one enterprise and a logistics supplier of each enterprise;

[0145] The first feature extraction unit 302 and the first supervised learning unit 303 are used to perform feature extraction processing and supervised learning processing on the basic logistics data set, respectively, to obtain the financing parameter data of the logistics supplier for each of the enterprises;

[0146] A first relationship building unit 304 is used to perform relationship building processing on the basic logistics data set to obtain each logistics relationship network centered on each enterprise;

[0147] The first feature mining unit 305 is used to perform feature mining processing on the nodes and edges of each logistics relationship network to obtain stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise;

[0148] The first network analysis unit 306 is used to perform network analysis on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise;

[0149] The first determination unit 307 is used to determine the financing amount data of the logistics supplier to each of the enterprises in the logistics supply chain according to the financing parameter data, the stability parameter data and the importance parameter data.

[0150] In some embodiments of the present application, the first determining unit 307 is specifically configured to:

[0151] Determine a financing ratio score based on the financing parameter data, the stability parameter data, the importance parameter data and a preset WSI model;

[0152] The financing ratio score is normalized to obtain normalized result data, and the normalized result data is used as the financing amount data of the logistics supplier in the logistics supply chain for each of the enterprises.

[0153] In some embodiments of the present application, the first feature extraction unit 302 and the first supervised learning unit 303 are specifically used to:

[0154] Obtaining historical financing parameter data of the logistics provider;

[0155] Determining historical financing characteristics of each of the enterprises based on the historical financing parameter data;

[0156] Performing feature extraction processing on the basic logistics data set to obtain a logistics data feature pool between the logistics supplier and each of the enterprises;

[0157] The logistics data feature pool and the historical financing features are subjected to supervised learning processing to obtain the financing parameter data of the logistics supplier for each of the enterprises.

[0158] In some embodiments of the present application, the first relationship building unit 304 is specifically used to:

[0159] Converting the basic logistics data set into structured data of a preset structure type;

[0160] Performing relationship building processing on the structured data to obtain a global relationship network between each of the enterprises and the logistics suppliers;

[0161] The global relationship network is disconnected to obtain each logistics relationship network centered on each of the enterprises.

[0162] In some embodiments of the present application, the structured data includes nodes and edges, the nodes are the enterprises or the logistics suppliers, and the edges are the collection and delivery behavior relationships between the nodes.

[0163] In some embodiments of the present application, the first feature mining unit 305 is specifically used to:

[0164] Obtain the edge attribute of each edge of each logistics relationship network and the node attribute of each node of each logistics relationship network;

[0165] Determine the relationship factor characteristics between each node according to the edge attributes and the node attributes;

[0166] Performing weight processing and normalization processing on the relationship factor features to obtain the relationship features between each node;

[0167] The stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined according to the relationship characteristics between each node.

[0168] In some embodiments of the present application, the first network analysis unit 306 is specifically used to:

[0169] Initialize the weight parameters of the nodes of each logistics relationship network to obtain the initial weight parameters of the nodes of each logistics relationship network;

[0170] Obtaining the node out-degree of each node in the logistics relationship network;

[0171] Based on the initial weight parameters of the nodes of each logistics relationship network, the node out-degree and a preset network analysis algorithm, the importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined.

[0172] In the embodiment of the present application, a first acquisition unit 301 is used to acquire a basic logistics data set of a logistics supply chain, wherein the logistics supply chain includes at least one enterprise and a logistics supplier of each of the enterprises; a first feature extraction unit 302 and a first supervised learning unit 303 are used to perform feature extraction processing and supervised learning processing on the basic logistics data set, respectively, to obtain financing parameter data of the logistics supplier for each of the enterprises; a first relationship construction unit 304 is used to perform relationship construction processing on the basic logistics data set, to obtain logistics relationship networks centered on each of the enterprises; a first feature mining unit 305 is used to perform feature mining processing on the nodes and edges of each logistics relationship network, to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; the first network The analysis unit 306 is used to perform network analysis on the nodes and edges of each logistics relationship network to obtain the importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; the first determination unit 307 is used to determine the financing amount data of the logistics supplier for each of the enterprises in the logistics supply chain based on the financing parameter data, the stability parameter data and the importance parameter data, to achieve in-depth data mining and analysis of the basic logistics data set of the logistics supply chain, to obtain strongly correlated multi-angle data analysis results, and to provide reliable technical support for the financing amount data of the logistics supplier for each of the enterprises in the logistics supply chain, thereby achieving the technical effect of efficiently and accurately providing reliable financing reference data for the financing plans of the logistics supplier for each enterprise in the logistics supply chain.

[0173] In addition to the above-described data processing method and device, the embodiment of the present application further provides a computer device, which integrates any data processing device provided in the embodiment of the present application, and the computer device includes:

[0174] one or more processors;

[0175] Memory; and

[0176] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the processor to perform the operations of any method described in any of the above data processing method embodiments.

[0177] The present application also provides a computer device that integrates any data processing device provided in the present application. Figure 4 As shown, it shows a schematic diagram of the structure of the computer device involved in the embodiment of the present application, specifically:

[0178] The computer device may include one or more processing core processors 401, one or more computer readable storage medium storage units 402, a power supply 403, an input unit 404 and other components. Those skilled in the art will understand that Figure 4 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0179] The processor 401 is the control center of the computer device. It uses various interfaces and lines to connect various parts of the entire computer device. By running or executing software programs and / or modules stored in the storage unit 402 and calling data stored in the storage unit 402, it executes various functions of the computer device and processes data, thereby monitoring the computer device as a whole. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 401.

[0180] The storage unit 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the storage unit 402. The storage unit 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the storage unit 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the storage unit 402 may also include a memory controller to provide the processor 401 with access to the storage unit 402.

[0181] The computer device also includes a power supply 403 for supplying power to various components. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 403 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0182] The computer device may further include an input unit 404, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0183] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiment of the present application, the processor 401 in the computer device will load the executable file corresponding to the process of one or more application programs into the storage unit 402 according to the following instructions, and the processor 401 will run the application program stored in the storage unit 402, thereby realizing various functions, as follows:

[0184] A basic logistics data set of a logistics supply chain is obtained, and the logistics supply chain includes at least one enterprise and logistics suppliers of each enterprise; feature extraction processing and supervised learning processing are performed on the basic logistics data set to obtain financing parameter data of logistics suppliers for each enterprise; relationship construction processing is performed on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; feature mining processing is performed on the nodes and edges of each logistics relationship network to obtain stability parameter data between nodes corresponding to the logistics supplier and nodes corresponding to the enterprise; network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain importance parameter data between nodes corresponding to the logistics supplier and nodes corresponding to the enterprise; and financing amount data of logistics suppliers for each enterprise in the logistics supply chain are determined based on the financing parameter data, stability parameter data and importance parameter data.

[0185] In an embodiment of the present application, a basic logistics data set of a logistics supply chain is obtained, wherein the logistics supply chain includes at least one enterprise and logistics suppliers of each enterprise; feature extraction processing and supervised learning processing are performed on the basic logistics data set to obtain financing parameter data of the logistics supplier for each enterprise; relationship construction processing is performed on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; feature mining processing is performed on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; according to the financing parameter data, stability parameter data and importance parameter data, the financing amount data of the logistics supplier for each enterprise in the logistics supply chain is determined, and in-depth data mining and analysis of the basic logistics data set of the logistics supply chain are achieved to obtain strongly correlated multi-angle data analysis results, so as to provide reliable technical support for the financing amount data of the logistics supplier for each enterprise in the logistics supply chain, thereby achieving the technical effect of efficiently and accurately providing reliable financing reference data for the financing plans of the logistics supplier for each enterprise in the logistics supply chain.

[0186] To this end, an embodiment of the present application provides a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. The computer-readable storage medium stores a plurality of instructions, which can be loaded by a processor to execute the steps in any data processing method provided in the embodiment of the present application. For example, the instruction can execute the following steps:

[0187] A basic logistics data set of a logistics supply chain is obtained, and the logistics supply chain includes at least one enterprise and logistics suppliers of each enterprise; feature extraction processing and supervised learning processing are performed on the basic logistics data set to obtain financing parameter data of logistics suppliers for each enterprise; relationship construction processing is performed on the basic logistics data set to obtain logistics relationship networks centered on each enterprise; feature mining processing is performed on the nodes and edges of each logistics relationship network to obtain stability parameter data between nodes corresponding to the logistics supplier and nodes corresponding to the enterprise; network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain importance parameter data between nodes corresponding to the logistics supplier and nodes corresponding to the enterprise; and financing amount data of logistics suppliers for each enterprise in the logistics supply chain are determined based on the financing parameter data, stability parameter data and importance parameter data.

[0188] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0189] The above is a detailed introduction to a data processing method, device, computer equipment and computer-readable storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire a basic logistics data set of a logistics supply chain, wherein the logistics supply chain includes at least one enterprise and a logistics supplier of each of the enterprises; Performing feature extraction and supervised learning processing on the basic logistics data set to obtain financing parameter data of the logistics supplier for each of the enterprises; Performing relationship construction processing on the basic logistics data set to obtain each logistics relationship network centered on each of the enterprises; Perform feature mining on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; Performing network analysis on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; The financing amount data of the logistics supplier to each of the enterprises in the logistics supply chain is determined based on the financing parameter data, the stability parameter data and the importance parameter data.

2. The data processing method according to claim 1, characterized in that: Determining the financing amount data of the logistics supplier to each of the enterprises in the logistics supply chain according to the financing parameter data, the stability parameter data and the importance parameter data includes: Determine a financing ratio score based on the financing parameter data, the stability parameter data, the importance parameter data and a preset WSI model; The financing ratio score is normalized to obtain normalized result data, and the normalized result data is used as the financing amount data of the logistics supplier in the logistics supply chain for each of the enterprises.

3. The data processing method according to claim 1, characterized in that: The basic logistics data set is subjected to feature extraction and supervised learning processing to obtain the financing parameter data of the logistics supplier for each of the enterprises, including: Obtaining historical financing parameter data of the logistics provider; Determining historical financing characteristics of each of the enterprises based on the historical financing parameter data; Performing feature extraction processing on the basic logistics data set to obtain a logistics data feature pool between the logistics supplier and each of the enterprises; The logistics data feature pool and the historical financing features are subjected to supervised learning processing to obtain the financing parameter data of the logistics supplier for each of the enterprises.

4. The data processing method according to claim 1, characterized in that: The relationship building process is performed on the basic logistics data set to obtain each logistics relationship network centered on each of the enterprises, including: Converting the basic logistics data set into structured data of a preset structure type; Performing relationship building processing on the structured data to obtain a global relationship network between each of the enterprises and the logistics suppliers; The global relationship network is disconnected to obtain each logistics relationship network centered on each of the enterprises.

5. The data processing method according to claim 4, characterized in that: The structured data includes nodes and edges, the nodes are the enterprises or the logistics suppliers, and the edges are the collection and delivery behavior relationships between the nodes.

6. The data processing method according to claim 1, characterized in that: The feature mining process is performed on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise, including: Obtain the edge attribute of each edge of each logistics relationship network and the node attribute of each node of each logistics relationship network; Determine the relationship factor characteristics between each node according to the edge attributes and the node attributes; Performing weight processing and normalization processing on the relationship factor features to obtain the relationship features between each node; The stability parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined according to the relationship characteristics between each node.

7. The data processing method according to claim 1, characterized in that: The network analysis processing is performed on the nodes and edges of each logistics relationship network to obtain the importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise, including: Initialize the weight parameters of the nodes of each logistics relationship network to obtain the initial weight parameters of the nodes of each logistics relationship network; Obtaining the node out-degree of each node in the logistics relationship network; Based on the initial weight parameters of the nodes of each logistics relationship network, the node out-degree and a preset network analysis algorithm, the importance parameter data between the node corresponding to the logistics supplier and the node corresponding to the enterprise are determined.

8. A data processing device, characterized in that: The device comprises: A first acquisition unit is used to acquire a basic logistics data set of a logistics supply chain, wherein the logistics supply chain includes at least one enterprise and a logistics supplier of each of the enterprises; The first feature extraction unit and the first supervised learning unit are used to perform feature extraction processing and supervised learning processing on the basic logistics data set, respectively, to obtain financing parameter data of the logistics supplier for each of the enterprises; A first relationship building unit, used for performing relationship building processing on the basic logistics data set to obtain each logistics relationship network centered on each of the enterprises; A first feature mining unit is used to perform feature mining processing on the nodes and edges of each logistics relationship network to obtain stability parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; A first network analysis unit is used to perform network analysis processing on the nodes and edges of each logistics relationship network to obtain importance parameter data between the nodes corresponding to the logistics supplier and the nodes corresponding to the enterprise; The first determination unit is used to determine the financing amount data of the logistics supplier to each of the enterprises in the logistics supply chain according to the financing parameter data, the stability parameter data and the importance parameter data.

9. A computer device, characterized in that: The computer device comprises: one or more processors; Memory; and One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the data processing method according to any one of claims 1 to 7.

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