Logistics enterprise knowledge base construction method and system based on big data

Abstract

The invention discloses a logistics enterprise knowledge base construction method and system based on big data, and relates to the technical field of data analysis, and the method comprises the steps: obtaining the historical logistics order information of an enterprise, carrying out the screening of the historical logistics order information of the enterprise based on a data analysis terminal, and determining the main cooperation clients of the logistics of the enterprise; the method comprises the following steps: analyzing and processing main cooperative clients of enterprise logistics to obtain core product information of the main cooperative clients; and based on the data analysis terminal, the core product information of the main cooperative clients is analyzed and processed, and warehouse position information of the core products is obtained. The logistics enterprise knowledge base is constructed according to the main cooperative clients of the enterprise logistics, the core product information of the main cooperative clients, the warehouse position information of the core products and the enterprise logistics charging standard, and after the product information is determined, the specific transportation cost and order information can be obtained only by inputting the product information into the knowledge base. The order information is prevented from being filled for many times, and the order delivery time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for constructing a logistics enterprise knowledge base based on big data. Background Art

[0002] A logistics enterprise (Logisticsenterprise) refers to an economic organization engaged in logistics activities, engaging in at least one business of transportation (including transportation agency, freight express) or warehousing, and being able to organize and manage basic functions such as transportation, storage, loading and unloading, packaging, circulation processing, and distribution in accordance with customer logistics needs, having an information management system that is suitable for its own business, and implementing independent accounting and independently assuming civil liability.

[0003] Existing logistics companies need to fill in order information when delivering logistics products. When the same customer sends multiple products, the order information needs to be filled in multiple times, which increases the delivery time of the order. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for constructing a logistics enterprise knowledge base based on big data are provided. This technical solution solves the problem proposed in the above background technology that existing logistics companies need to fill in order information when transporting logistics products. When the same customer sends multiple products, the order information needs to be filled in multiple times, which increases the delivery time of the order.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A method for constructing a logistics enterprise knowledge base based on big data, comprising: Obtain the company's historical logistics order information, filter and process the company's historical logistics order information based on the data analysis terminal, and determine the company's main logistics cooperation customers; Based on the data analysis terminal, analyze and process the main cooperative customers of the enterprise logistics to obtain the core product information of the main cooperative customers; Based on the data analysis terminal, analyze and process the core product information of major cooperative customers to obtain the warehouse location information of core products; Based on the data analysis terminal, the warehouse location information of core products is analyzed and processed to determine the enterprise logistics charging standards; The data analysis terminal constructs a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

[0006] Preferably, the obtaining of the enterprise's historical logistics order information, screening and processing the enterprise's historical logistics order information based on the data analysis terminal, and determining the enterprise's main logistics cooperation customers specifically include the following steps: Based on the data analysis terminal, historical order extraction and processing are performed on the enterprise data lake to obtain the enterprise's historical logistics order information; Based on the data analysis terminal, the company's historical logistics order information is retrieved and processed by parameters to obtain the sender customer information corresponding to the company's historical logistics order information; Based on the data analysis terminal, the sender customer information corresponding to the company's historical logistics order information is analyzed and processed to determine the company's main logistics cooperation customers.

[0007] Preferably, the step of analyzing and processing the mailing customer information corresponding to the historical logistics order information of the enterprise based on the data analysis terminal to determine the main cooperative customers of the enterprise logistics specifically includes the following steps: Based on the data analysis terminal, the information of the mailing customers corresponding to the historical logistics order information of the enterprise is counted and the number of times the customer mailed items was obtained; Based on the data analysis terminal, the customer's mailing times and the set mailing times threshold are compared and judged; If the customer's number of shipments is greater than or equal to the set shipment threshold, based on the data analysis terminal, the shipping customer information corresponding to the company's historical logistics order information is set as the company's main logistics cooperation customer; If the number of times a customer sends parcels is less than the set threshold, the sender customer information corresponding to the company's historical logistics order information does not meet the criteria for major cooperative customers, and the sender customer information corresponding to the company's historical logistics order information cannot become the logistics company's major cooperative customer.

[0008] Preferably, the step of analyzing and processing the main cooperative customers of the enterprise logistics based on the data analysis terminal to obtain the core product information of the main cooperative customers specifically includes the following steps: The data analysis terminal performs product information retrieval and processing on the historical logistics order information of the enterprise based on the main cooperative customers of the enterprise logistics, and obtains the product information of the main cooperative customers of the enterprise logistics; Based on the data analysis terminal, the product information of the main logistics cooperation customers of the enterprise is counted and processed to determine the number of products shipped by the main logistics cooperation customers of the enterprise; Based on the data analysis terminal, the quantity of products shipped by the main logistics cooperation customers of the enterprise is analyzed and processed to determine the core product information of the main logistics cooperation customers of the enterprise.

[0009] Preferably, the step of analyzing and processing the quantity of products shipped by the main cooperative customers of the enterprise logistics based on the data analysis terminal to determine the core product information of the main cooperative customers of the enterprise logistics specifically includes the following steps: Based on the data analysis terminal, the product shipment quantity of the main logistics cooperation customers of the enterprise is compared and judged with the set product shipment quantity threshold; If the number of products shipped by the main cooperative customers of the enterprise logistics is greater than or equal to the set product shipping quantity threshold, based on the data analysis terminal, the product corresponding to the number of products shipped by the main cooperative customers of the enterprise logistics is set as the core product information of the main cooperative customers of the enterprise logistics; If the number of products shipped by the main cooperative customers of the enterprise logistics is less than the set product shipment quantity threshold, the number of products shipped by the main cooperative customers of the enterprise logistics does not meet the evaluation criteria of core products, and the number of products shipped by the main cooperative customers of the enterprise logistics cannot become the core product information of the main cooperative customers of the enterprise logistics.

[0010] Preferably, the step of analyzing and processing the core product information of the main cooperative customers based on the data analysis terminal to obtain the warehouse location information of the core products specifically includes the following steps: Based on the data analysis terminal, perform information retrieval processing on the database of major cooperative customers to obtain product-related information of major cooperative customers; Based on the data analysis terminal, the product-related information of the main cooperative customers is processed and the warehouse location information of the main cooperative customers is determined; The data analysis terminal screens and processes the warehouse location information of major cooperative customers based on the core product information of major cooperative customers, and determines the warehouse location information of core products.

[0011] Preferably, the step of analyzing and processing the warehouse location information of the core products based on the data analysis terminal to determine the enterprise logistics charging standard specifically includes the following steps: Based on the data analysis terminal, information retrieval and processing are performed on the enterprise data lake to obtain the enterprise logistics reachable destination and enterprise logistics billing standards; Based on the data analysis terminal, the location analysis of the enterprise logistics reachable destination and the warehouse location information of core products is carried out to determine the logistics route data; Based on the data analysis terminal, the enterprise logistics billing standards and logistics distance data are calculated and processed to determine the enterprise logistics charging standards.

[0012] Preferably, the data analysis terminal constructs a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards, and obtaining the enterprise logistics knowledge base specifically includes the following steps: Based on the data analysis terminal, enterprise logistics is set as the backbone of the knowledge base; Based on the data analysis terminal, analyze and process the main cooperative customers of the enterprise logistics and determine the number of the main cooperative customers of the enterprise logistics; Based on the data analysis terminal, the number of the main cooperative customers of the enterprise logistics is set as the branch node of the knowledge base trunk; Based on the data analysis terminal, the main cooperative customers of the enterprise logistics are set as the branch entities of the knowledge base trunk; The data analysis terminal extends the branch entities of the knowledge base trunk according to the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

[0013] Preferably, the data analysis terminal performs extension processing on the branch body of the knowledge base trunk according to the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards, and obtaining the enterprise logistics knowledge base specifically includes the following steps: Based on the data analysis terminal, analyze and process the core product information of major cooperative customers to determine the number of core products; Based on the data analysis terminal, the number of core products is set as the node of the branch body of the knowledge base trunk; Based on the data analysis terminal, the core product information of the main cooperative customers is set as the branches of the branch body of the knowledge base trunk; Based on the data analysis terminal, the warehouse location information of the core product is set as the node of the branch of the branch body; The data analysis terminal extends the nodes of the branches of the branch subject according to the enterprise logistics charging standard to obtain the leaves of the branches of the branch subject; The data analysis terminal constructs the knowledge base according to the knowledge base trunk, branch nodes of the knowledge base trunk, branch subjects of the knowledge base trunk, nodes of branch subjects of the knowledge base trunk, branches of branch subjects of the knowledge base trunk, nodes of branches of branch subjects and leaves of branches of branch subjects to obtain the enterprise logistics knowledge base.

[0014] Furthermore, a system for constructing a logistics enterprise knowledge base based on big data is proposed, which is used to implement the above-mentioned method for constructing a logistics enterprise knowledge base based on big data, including: Data analysis terminal, which is used to analyze and process the company's historical logistics order information, determine the company's main logistics cooperation customers, the core product information of the main cooperation customers, the warehouse location information of the core products and the company's logistics charging standards, and is used to control the data transmission and information interaction between various modules; Enterprise data lake, which is used to store the enterprise's historical logistics order information, the enterprise's logistics reachable destinations, and the enterprise's logistics billing standards; A data classification module, wherein the data classification module classifies the enterprise's historical logistics order information and obtains the sender customer information corresponding to the enterprise's historical logistics order information; A first judgment module, which compares and judges the number of times a customer sends parcels and a set threshold of the number of times a customer sends parcels, and determines the main cooperative customers of the logistics enterprise; A second judgment module, which compares and judges the quantity of products shipped by the main cooperative customers of the enterprise logistics and the set product shipment quantity threshold to determine the core product information of the main cooperative customers of the enterprise logistics; A warehouse location determination module, wherein the warehouse location determination module screens the warehouse location information of the main cooperative customers based on the core product information of the main cooperative customers, and determines the warehouse location information of the core products; A charging standard determination module, which calculates and processes the enterprise logistics billing standard and logistics distance data to determine the enterprise logistics charging standard; A knowledge base construction module is used to construct a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

[0015] Compared with the prior art, the present invention provides a method and system for constructing a logistics enterprise knowledge base based on big data, which has the following beneficial effects: The present invention firstly screens and processes the historical logistics order information of the enterprise to determine the main cooperative customers of the enterprise logistics, thereby reducing the computing resources required for building a knowledge base. Then, the main cooperative customers of the enterprise logistics are analyzed to determine the core product information of the main cooperative customers and the warehouse location information of the core products. The warehouse location information of the core products is then analyzed to determine the enterprise logistics charging standards. Finally, a logistics enterprise knowledge base is constructed based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards. After the core product information is determined, it is only necessary to input the core product information into the knowledge base to obtain the specific transportation cost and order information, thereby avoiding filling in the order information for multiple times and shortening the delivery time of the order. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of steps S100-S500 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 2 This is a flow chart of steps S101-S103 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 3 This is a flow chart of steps S1031-S1034 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 4 This is a flow chart of steps S201-S203 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 5 This is a flow chart of steps S2031-S2033 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 6 This is a flow chart of steps S301-S303 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 7 This is a flow chart of steps S401-S403 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Figure 8 This is a flow chart of steps S501-S505 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Fig. 9 This is a flow chart of steps S5051-S5056 in a method for constructing a logistics enterprise knowledge base based on big data proposed by the present invention; Fig.10 This is a structural block diagram of a system for building a logistics enterprise knowledge base based on big data proposed by the present invention. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 As shown, a method for constructing a logistics enterprise knowledge base based on big data includes: S100, obtaining the historical logistics order information of the enterprise, screening and processing the historical logistics order information of the enterprise based on the data analysis terminal, and determining the main cooperative customers of the enterprise logistics; S200, based on the data analysis terminal, analyze and process the main cooperative customers of the enterprise logistics to obtain the core product information of the main cooperative customers; S300, based on the data analysis terminal, analyze and process the core product information of the main cooperative customers to obtain the warehouse location information of the core products; S400, based on the data analysis terminal, analyze and process the warehouse location information of core products to determine the enterprise logistics charging standards; S500, the data analysis terminal constructs a knowledge base based on the enterprise logistics major cooperative customers, the core product information of the major cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base; Those skilled in the art will understand that a logistics company will cooperate with multiple companies, some of which deliver their products more frequently and some of which deliver their products less frequently. However, in order to avoid filling out order information multiple times, the logistics company builds a knowledge base related to the cooperating companies. If a knowledge base is also built for companies with fewer product deliveries, the computing resources and data storage space required for the knowledge base will increase. Therefore, the company's main logistics cooperation customers are screened out and a knowledge base is built for them to avoid filling out order information multiple times and calculating transportation costs multiple times, which indirectly shortens the delivery time of the products.

[0019] Reference Figure 2 As shown, obtaining the historical logistics order information of the enterprise, filtering and processing the historical logistics order information of the enterprise based on the data analysis terminal, and determining the main cooperative customers of the enterprise logistics specifically include the following steps: S101. Based on the data analysis terminal, historical order extraction and processing are performed on the enterprise data lake to obtain the enterprise's historical logistics order information; S102, based on the data analysis terminal, the historical logistics order information of the enterprise is retrieved and processed by parameters to obtain the mailing customer information corresponding to the historical logistics order information of the enterprise; S103, based on the data analysis terminal, analyzing and processing the mailing customer information corresponding to the enterprise's historical logistics order information to determine the enterprise's main logistics cooperation customers; In this embodiment, in order to distinguish the main cooperative customers of the enterprise logistics, the historical logistics order information of the enterprise is analyzed and counted, and the customers with the highest number of shipments are set as the main cooperative customers of the enterprise logistics. A knowledge base is built for the main cooperative customers of the enterprise logistics, which shortens the time for them to fill in order information and calculate transportation costs, thereby indirectly shortening the product delivery time.

[0020] Reference Figure 3 As shown, based on the data analysis terminal, the information of the mailing customers corresponding to the historical logistics order information of the enterprise is analyzed and processed, and the main cooperative customers of the enterprise logistics are determined, which specifically includes the following steps: S1031. Based on the data analysis terminal, count the mailing customer information corresponding to the historical logistics order information of the enterprise to obtain the number of times the customer mailed the mail; S1032, based on the data analysis terminal, comparing and judging the number of times the customer sends mail and the set number of times of sending mail; S1033. If the number of times the customer sends parcels is greater than or equal to the set number of times threshold, based on the data analysis terminal, the information of the sender customer corresponding to the historical logistics order information of the enterprise is set as the main cooperative customer of the enterprise logistics; S1034. If the number of times the customer sends parcels is less than the set number of times, the information of the customer sending parcels corresponding to the historical logistics order information of the enterprise does not meet the criteria for judging a major cooperative customer, and the information of the customer sending parcels corresponding to the historical logistics order information of the enterprise cannot become a major cooperative customer of the logistics enterprise; In this embodiment, some companies deliver products less frequently, while some companies deliver products more frequently. Therefore, customers are screened by the number of times they send parcels and by setting a threshold for the number of times they send parcels, and the main cooperative customers of the logistics company are determined. Then, a knowledge base is constructed for the main cooperative customers of the logistics company, which reduces the computing resources required to construct the knowledge base and, at the same time, reduces the storage space occupied by data.

[0021] Reference Figure 4 As shown, based on the data analysis terminal, the main cooperative customers of the enterprise logistics are analyzed and processed, and the core product information of the main cooperative customers is obtained, which specifically includes the following steps: S201, the data analysis terminal performs product information retrieval processing on the historical logistics order information of the enterprise based on the main cooperative customers of the enterprise logistics, and obtains the product information of the main cooperative customers of the enterprise logistics; S202, based on the data analysis terminal, counting and processing the product information of the main cooperative customers of the enterprise logistics, and determining the quantity of products shipped by the main cooperative customers of the enterprise logistics; S203. Based on the data analysis terminal, the quantity of products shipped by the main cooperative customers of the enterprise logistics is analyzed and processed to determine the core product information of the main cooperative customers of the enterprise logistics.

[0022] Reference Figure 5 As shown, based on the data analysis terminal, the number of products shipped by the main cooperative customers of the enterprise logistics is analyzed and processed, and the core product information of the main cooperative customers of the enterprise logistics is determined, which specifically includes the following steps: S2031. Based on the data analysis terminal, compare and judge the quantity of products shipped by the main logistics cooperation customers of the enterprise and the set product shipment quantity threshold; S2032. If the quantity of products shipped by the main cooperative customer of the enterprise logistics is greater than or equal to the set product shipment quantity threshold, based on the data analysis terminal, the product corresponding to the quantity of products shipped by the main cooperative customer of the enterprise logistics is set as the core product information of the main cooperative customer of the enterprise logistics; S2033. If the number of products shipped by the main cooperative customers of the enterprise logistics is less than the set product shipment quantity threshold, the number of products shipped by the main cooperative customers of the enterprise logistics does not meet the evaluation criteria of core products, and the number of products shipped by the main cooperative customers of the enterprise logistics cannot become the core product information of the main cooperative customers of the enterprise logistics; In this embodiment, the main cooperative customers of the enterprise logistics have a variety of products, some of which are popular, while some are not. The popular products will be purchased in large quantities and will be shipped multiple times. Therefore, the product information of the main cooperative customers of the enterprise logistics is screened to determine the core product information of the main cooperative customers of the enterprise logistics. When the core products of the main cooperative customers of the enterprise logistics need to be transported, only part of the order information needs to be filled in through the knowledge base, and the other part of the order information is automatically filled in and generated by the knowledge base, which shortens the time for filling in the order information.

[0023] Reference Figure 6 As shown, based on the data analysis terminal, the core product information of the main cooperative customers is analyzed and processed, and the warehouse location information of the core products is obtained, which specifically includes the following steps: S301, based on the data analysis terminal, perform information retrieval processing on the database of major cooperative customers to obtain product-related information of the major cooperative customers; S302, based on the data analysis terminal, traverse and process the data related to the products of the main cooperative customers to determine the warehouse location information of the main cooperative customers; S303, the data analysis terminal screens the warehouse location information of the main cooperative customers based on the core product information of the main cooperative customers, and determines the warehouse location information of the core products; In this embodiment, since the main cooperative customers of the enterprise logistics will produce multiple products, warehouses will be established in different cities. Therefore, the warehouses are screened based on the core product information of the main cooperative customers to determine the warehouse location information of the core products.

[0024] Reference Figure 7 As shown, based on the data analysis terminal, the warehouse location information of the core products is analyzed and processed to determine the enterprise logistics charging standards, which specifically includes the following steps: S401. Based on the data analysis terminal, information retrieval and processing are performed on the enterprise data lake to obtain the enterprise logistics reachable destination and the enterprise logistics billing standard; S402, based on the data analysis terminal, performing location analysis on the reachable destination of the enterprise logistics and the warehouse location information of the core products to determine the logistics route data; S403, based on the data analysis terminal, calculating and processing the enterprise logistics billing standard and logistics distance data to determine the enterprise logistics charging standard; In this embodiment, after the warehouse location information of the core product is determined, a location analysis is performed on the accessible destination of the enterprise logistics and the warehouse location information of the core product to determine the distance required for the enterprise logistics to each city. Then, the distance required for the enterprise logistics to each city and the enterprise logistics billing standards are calculated to determine the enterprise logistics charging standards. The enterprise logistics charging standards are then included in the knowledge base. When it is necessary to calculate the enterprise logistics charging standards, you only need to enter the destination city into the knowledge base to get the corresponding logistics costs, which reduces the time for calculating the logistics costs.

[0025] Reference Figure 8 As shown, the data analysis terminal constructs a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards. The specific steps of obtaining the enterprise logistics knowledge base are as follows: S501. Based on the data analysis terminal, the enterprise logistics is set as the knowledge base backbone; S502. Analyze and process the main cooperative customers of the enterprise logistics based on the data analysis terminal to determine the number of the main cooperative customers of the enterprise logistics; S503. Based on the data analysis terminal, the number of the main cooperative customers of the enterprise logistics is set as a branch node of the knowledge base trunk; S504. Based on the data analysis terminal, the main cooperative customers of the enterprise logistics are set as the branch subjects of the knowledge base trunk; S505. The data analysis terminal extends the branch body of the knowledge base trunk according to the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

[0026] Reference Fig. 9 As shown, the data analysis terminal extends the branch body of the knowledge base trunk according to the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards. The specific steps of obtaining the enterprise logistics knowledge base are as follows: S5051. Analyze and process the core product information of major cooperative customers based on the data analysis terminal to determine the number of core products; S5052. Based on the data analysis terminal, set the number of core products as nodes of the branch body of the knowledge base trunk; S5053. Based on the data analysis terminal, the core product information of the main cooperative customers is set as the branches of the branch body of the knowledge base trunk; S5054. Based on the data analysis terminal, the warehouse location information of the core product is set as a node of the branch of the branch body; S5055. The data analysis terminal performs extension processing on the nodes of the branches of the branch subject according to the enterprise logistics charging standard to obtain leaves of the branches of the branch subject. S5056. The data analysis terminal constructs a knowledge base according to the knowledge base trunk, the branch nodes of the knowledge base trunk, the branch subjects of the knowledge base trunk, the nodes of the branch subjects of the knowledge base trunk, the branches of the branch subjects of the knowledge base trunk, the nodes of the branches of the branch subjects, and the leaves of the branches of the branch subjects, to obtain the enterprise logistics knowledge base; In this embodiment, the enterprise logistics is set as the trunk of the tree, that is, the trunk of the knowledge base, and then the number of the main cooperative customers of the enterprise logistics is set as the branch nodes of the trunk of the tree, and the main cooperative customers of the enterprise logistics are the branch bodies on the trunk of the tree, and then the number of core products of the main cooperative customers of the enterprise logistics is set as the nodes of the branch bodies, and the core product information is the branches of the branch bodies, and then the warehouse location information of the core products is set as the nodes of the branches, and the small branches on the nodes of the branches are extended according to the logistics route data to form leaves, and the leaves are the terminals for product delivery, and the length of the small branches is the enterprise logistics charging standard. When the customer needs to deliver the product, he only needs to enter the product name and terminal into the knowledge base, and a certain path in the knowledge base can be lit up, that is, a certain branch, branch and leaf are lit up, and then the data on the path is filled in the order information, avoiding filling in the order information multiple times. At the same time, it also avoids calculating the transportation costs multiple times, indirectly reducing the delivery time of the product.

[0027] Reference Fig.10 As shown, a system for constructing a logistics enterprise knowledge base based on big data is used to implement the above-mentioned method for constructing a logistics enterprise knowledge base based on big data, including: Data analysis terminal, which is used to analyze and process the company's historical logistics order information, determine the company's main logistics cooperation customers, the core product information of the main cooperation customers, the warehouse location information of the core products and the company's logistics charging standards, and is used to control the data transmission and information interaction between various modules; Enterprise data lake, which is used to store the enterprise's historical logistics order information, the enterprise's logistics reachable destinations, and the enterprise's logistics billing standards; A data classification module, wherein the data classification module classifies the enterprise's historical logistics order information and obtains the sender customer information corresponding to the enterprise's historical logistics order information; A first judgment module, which compares and judges the number of times a customer sends parcels and a set threshold of the number of times a customer sends parcels, and determines the main cooperative customers of the logistics enterprise; A second judgment module, which compares and judges the quantity of products shipped by the main cooperative customers of the enterprise logistics and the set product shipment quantity threshold to determine the core product information of the main cooperative customers of the enterprise logistics; A warehouse location determination module, wherein the warehouse location determination module screens the warehouse location information of the main cooperative customers based on the core product information of the main cooperative customers, and determines the warehouse location information of the core products; A charging standard determination module, which calculates and processes the enterprise logistics billing standard and logistics distance data to determine the enterprise logistics charging standard; A knowledge base construction module is used to construct a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for constructing a logistics enterprise knowledge base based on big data, characterized in that: include: Obtain the company's historical logistics order information, filter and process the company's historical logistics order information based on the data analysis terminal, and determine the company's main logistics cooperation customers; Based on the data analysis terminal, analyze and process the main cooperative customers of the enterprise logistics to obtain the core product information of the main cooperative customers; Based on the data analysis terminal, analyze and process the core product information of major cooperative customers to obtain the warehouse location information of core products; Based on the data analysis terminal, the warehouse location information of core products is analyzed and processed to determine the enterprise logistics charging standards; The data analysis terminal constructs a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

2. According to the method for constructing a logistics enterprise knowledge base based on big data according to claim 1, it is characterized in that: The obtaining of the enterprise's historical logistics order information, screening and processing the enterprise's historical logistics order information based on the data analysis terminal, and determining the enterprise's main logistics cooperation customers specifically include the following steps: Based on the data analysis terminal, historical order extraction and processing are performed on the enterprise data lake to obtain the enterprise's historical logistics order information; Based on the data analysis terminal, the company's historical logistics order information is retrieved and processed by parameters to obtain the sender customer information corresponding to the company's historical logistics order information; Based on the data analysis terminal, the sender customer information corresponding to the company's historical logistics order information is analyzed and processed to determine the company's main logistics cooperation customers.

3. According to the method for constructing a logistics enterprise knowledge base based on big data according to claim 2, it is characterized in that: The data analysis terminal is used to analyze and process the mailing customer information corresponding to the historical logistics order information of the enterprise, and determine the main cooperative customers of the enterprise logistics, which specifically includes the following steps: Based on the data analysis terminal, the information of the mailing customers corresponding to the historical logistics order information of the enterprise is counted and the number of times the customer mailed items was obtained; Based on the data analysis terminal, the customer's mailing times and the set mailing times threshold are compared and judged; If the customer's number of shipments is greater than or equal to the set shipment threshold, based on the data analysis terminal, the shipping customer information corresponding to the company's historical logistics order information is set as the company's main logistics cooperation customer; If the number of times a customer sends parcels is less than the set threshold, the sender customer information corresponding to the company's historical logistics order information does not meet the criteria for major cooperative customers, and the sender customer information corresponding to the company's historical logistics order information cannot become the logistics company's major cooperative customer.

4. According to the method for constructing a logistics enterprise knowledge base based on big data in claim 1, it is characterized in that: The method of analyzing and processing the main cooperative customers of the enterprise logistics based on the data analysis terminal and obtaining the core product information of the main cooperative customers specifically includes the following steps: The data analysis terminal performs product information retrieval and processing on the historical logistics order information of the enterprise based on the main cooperative customers of the enterprise logistics, and obtains the product information of the main cooperative customers of the enterprise logistics; Based on the data analysis terminal, the product information of the main logistics cooperation customers of the enterprise is counted and processed to determine the number of products shipped by the main logistics cooperation customers of the enterprise; Based on the data analysis terminal, the quantity of products shipped by the main logistics cooperation customers of the enterprise is analyzed and processed to determine the core product information of the main logistics cooperation customers of the enterprise.

5. According to the method for constructing a logistics enterprise knowledge base based on big data according to claim 4, it is characterized in that: The method of analyzing and processing the quantity of products shipped by the main cooperative customers of the enterprise logistics based on the data analysis terminal to determine the core product information of the main cooperative customers of the enterprise logistics specifically includes the following steps: Based on the data analysis terminal, the product shipment quantity of the main logistics cooperation customers of the enterprise is compared and judged with the set product shipment quantity threshold; If the number of products shipped by the main cooperative customers of the enterprise logistics is greater than or equal to the set product shipping quantity threshold, based on the data analysis terminal, the product corresponding to the number of products shipped by the main cooperative customers of the enterprise logistics is set as the core product information of the main cooperative customers of the enterprise logistics; If the number of products shipped by the main cooperative customers of the enterprise logistics is less than the set product shipment quantity threshold, the number of products shipped by the main cooperative customers of the enterprise logistics does not meet the evaluation criteria of core products, and the number of products shipped by the main cooperative customers of the enterprise logistics cannot become the core product information of the main cooperative customers of the enterprise logistics.

6. The method for constructing a logistics enterprise knowledge base based on big data according to claim 1 is characterized in that: The step of analyzing and processing the core product information of the main cooperative customers based on the data analysis terminal to obtain the warehouse location information of the core products specifically includes the following steps: Based on the data analysis terminal, perform information retrieval processing on the database of major cooperative customers to obtain product-related information of major cooperative customers; Based on the data analysis terminal, the product-related information of the main cooperative customers is processed and the warehouse location information of the main cooperative customers is determined; The data analysis terminal screens and processes the warehouse location information of major cooperative customers based on the core product information of major cooperative customers, and determines the warehouse location information of core products.

7. The method for constructing a logistics enterprise knowledge base based on big data according to claim 1 is characterized in that: The above-mentioned steps of analyzing and processing the warehouse location information of the core products based on the data analysis terminal and determining the enterprise logistics charging standards specifically include the following steps: Based on the data analysis terminal, information retrieval and processing are performed on the enterprise data lake to obtain the enterprise logistics reachable destination and enterprise logistics billing standards; Based on the data analysis terminal, the location analysis of the enterprise logistics reachable destination and the warehouse location information of core products is carried out to determine the logistics route data; Based on the data analysis terminal, the enterprise logistics billing standards and logistics distance data are calculated and processed to determine the enterprise logistics charging standards.

8. The method for constructing a logistics enterprise knowledge base based on big data according to claim 1 is characterized in that: The data analysis terminal constructs a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards. The acquisition of the enterprise logistics knowledge base specifically includes the following steps: Based on the data analysis terminal, enterprise logistics is set as the backbone of the knowledge base; Based on the data analysis terminal, analyze and process the main cooperative customers of the enterprise logistics and determine the number of the main cooperative customers of the enterprise logistics; Based on the data analysis terminal, the number of the main cooperative customers of the enterprise logistics is set as the branch node of the knowledge base trunk; Based on the data analysis terminal, the main cooperative customers of the enterprise logistics are set as the branch entities of the knowledge base trunk; The data analysis terminal extends the branch entities of the knowledge base trunk according to the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

9. The method for constructing a logistics enterprise knowledge base based on big data according to claim 8 is characterized in that: The data analysis terminal performs extension processing on the branch body of the knowledge base trunk according to the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards, and obtains the enterprise logistics knowledge base, which specifically includes the following steps: Based on the data analysis terminal, analyze and process the core product information of major cooperative customers to determine the number of core products; Based on the data analysis terminal, the number of core products is set as the node of the branch body of the knowledge base trunk; Based on the data analysis terminal, the core product information of the main cooperative customers is set as the branches of the branch body of the knowledge base trunk; Based on the data analysis terminal, the warehouse location information of the core product is set as the node of the branch of the branch body; The data analysis terminal extends the nodes of the branches of the branch subject according to the enterprise logistics charging standard to obtain the leaves of the branches of the branch subject; The data analysis terminal constructs the knowledge base according to the knowledge base trunk, branch nodes of the knowledge base trunk, branch subjects of the knowledge base trunk, nodes of branch subjects of the knowledge base trunk, branches of branch subjects of the knowledge base trunk, nodes of branches of branch subjects and leaves of branches of branch subjects to obtain the enterprise logistics knowledge base.

10. A system for constructing a logistics enterprise knowledge base based on big data, used to implement a method for constructing a logistics enterprise knowledge base based on big data as described in any one of claims 1 to 9, characterized in that: include: Data analysis terminal, which is used to analyze and process the company's historical logistics order information, determine the company's main logistics cooperation customers, the core product information of the main cooperation customers, the warehouse location information of the core products and the company's logistics charging standards, and is used to control the data transmission and information interaction between various modules; Enterprise data lake, which is used to store the enterprise's historical logistics order information, the enterprise's logistics reachable destinations, and the enterprise's logistics billing standards; A data classification module, wherein the data classification module classifies the enterprise's historical logistics order information and obtains the sender customer information corresponding to the enterprise's historical logistics order information; A first judgment module, which compares and judges the number of times a customer sends parcels and a set threshold of the number of times a customer sends parcels, and determines the main cooperative customers of the logistics enterprise; A second judgment module, which compares and judges the quantity of products shipped by the main cooperative customers of the enterprise logistics and the set product shipment quantity threshold to determine the core product information of the main cooperative customers of the enterprise logistics; A warehouse location determination module, wherein the warehouse location determination module screens the warehouse location information of the main cooperative customers based on the core product information of the main cooperative customers, and determines the warehouse location information of the core products; A charging standard determination module, which calculates and processes the enterprise logistics billing standard and logistics distance data to determine the enterprise logistics charging standard; A knowledge base construction module is used to construct a knowledge base based on the main cooperative customers of the enterprise logistics, the core product information of the main cooperative customers, the warehouse location information of the core products and the enterprise logistics charging standards to obtain the enterprise logistics knowledge base.

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