Method for site selection of rural e-commerce logistics distribution center

Abstract

The invention discloses a rural e-commerce logistics distribution center site selection method, and relates to the technical field of e-commerce logistics. The method comprises the following steps: determining a service target and principle; collecting corresponding basic information based on the service target; according to the basic information and the principle, a site selection and preferential selection model is constructed and trained, site selection prediction is carried out by using the site selection and preferential selection model, and an optimal rural e-commerce logistics distribution center site selection is determined; the site selection and preferential selection model is a multi-objective optimization model which is constructed based on a neural network and considers cost, service and environment. According to the invention, the site selection efficiency of the rural e-commerce logistics distribution center can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce logistics, and in particular to a method for site selection for rural e-commerce logistics distribution centers. Background Art

[0002] The site selection of rural e-commerce logistics distribution centers is of great significance for reducing logistics costs, improving distribution efficiency, and promoting the development of rural e-commerce. Site selection decisions need to consider a variety of factors, including geographical location, transportation network, population and economic data, and market demand. These factors jointly affect the planning and layout of logistics distribution centers, which are directly related to the overall operation of the logistics network and the growth of rural e-commerce. However, in current technology, traditional site selection models may unilaterally pursue the minimization of logistics costs while ignoring the level of logistics service, and the efficiency of traditional site selection models is not high. Summary of the invention

[0003] The purpose of the present invention is to provide a method for site selection of rural e-commerce logistics distribution centers, which can improve the efficiency of site selection of rural e-commerce logistics distribution centers.

[0004] To achieve the above object, the present invention provides at least the following solutions:

[0005] A method for selecting a location for a rural e-commerce logistics distribution center, comprising:

[0006] Determine service objectives and principles;

[0007] Collect corresponding basic information based on the service target; the basic information includes geographical information of rural areas, transportation network information, population and economic data, and market demand;

[0008] A site selection optimization model is constructed and trained based on the basic information and the principles, and the site selection optimization model is used to perform site selection prediction to determine the optimal location for the rural e-commerce logistics distribution center; the site selection optimization model is a multi-objective optimization model based on a neural network that considers cost, service and environment.

[0009] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0010] The present invention discloses a method for selecting a site for a rural e-commerce logistics distribution center, the method comprising determining service objectives and principles; collecting corresponding basic information based on the service objectives; constructing and training a site selection optimization model based on the basic information and the principles, and using the site selection optimization model to perform site selection prediction to determine the optimal site selection for a rural e-commerce logistics distribution center; the site selection optimization model is a multi-objective optimization model based on a neural network that considers cost, service and environment. The present invention can improve the efficiency of site selection for a rural e-commerce logistics distribution center. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0012] Figure 1 The present invention is a flow chart of the method for selecting a site for a rural e-commerce logistics distribution center. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] The purpose of the present invention is to provide a method for site selection of rural e-commerce logistics distribution centers, which can improve the efficiency of site selection of rural e-commerce logistics distribution centers.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown, the present invention provides a method for selecting a site for a rural e-commerce logistics distribution center, comprising:

[0017] Step 100: Determine service objectives and principles; the service objectives include the main service area and target customer group of the logistics distribution center; the principles include the cost-effectiveness principle and the service efficiency principle.

[0018] Step 200: Collecting corresponding basic information based on the service target; the basic information includes geographical information of rural areas, transportation network information, population and economic data, and market demand;

[0019] Step 300: Construct and train a site selection optimization model based on the basic information and the principles, and use the site selection optimization model to perform site selection prediction to determine the optimal location for the rural e-commerce logistics distribution center; the site selection optimization model is a multi-objective optimization model based on a neural network that considers cost, service and environment.

[0020] As a specific implementation of step 300, the training process of the site selection model includes:

[0021] Acquire training data; the training data includes input data and corresponding logistics distribution center locations;

[0022] Build a pre-trained network based on LSTM network and CNN network;

[0023] The training data is input into the pre-trained network, and parameter optimization is performed with the goal of minimizing the loss between the prediction result output by the pre-trained network and the location of the logistics distribution center, and the trained pre-trained network is determined as the location optimization model. The loss function used in the training process is the cross entropy loss.

[0024] Before inputting the training data into the pre-training network, the method further includes: sequentially cleaning and formatting the training data.

[0025] The cleaning process is as follows: interpolating, deleting or marking missing values ​​in the training data and then manually supplementing them to obtain preprocessed data, and merging or deleting outliers and duplicate data entries in the data to obtain cleaned data.

[0026] The processing process of the format conversion is: performing unified dimension processing on the data processed last time, and performing unified format processing on the data from different data sources to obtain data after format conversion.

[0027] In addition, the results of each output of the site selection model, i.e., the optimal location of the rural e-commerce logistics distribution center, are summarized and visualized in the form of a map.

[0028] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0029] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for selecting a site for a rural e-commerce logistics distribution center, characterized in that: include: Determine service objectives and principles; Collect corresponding basic information based on the service target; The basic information includes geographical information of rural areas, transportation network information, population and economic data, and market demand; A site selection optimization model is constructed and trained based on the basic information and the principles, and the site selection optimization model is used to perform site selection prediction to determine the optimal location for the rural e-commerce logistics distribution center; the site selection optimization model is a multi-objective optimization model based on a neural network that considers cost, service and environment.

2. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 1, characterized in that: The service objectives include the main service areas and target customer groups of the logistics distribution center; the principles include the cost-effectiveness principle and the service efficiency principle.

3. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 1, characterized in that: The training process of the site selection model includes: Acquire training data; the training data includes input data and corresponding logistics distribution center locations; Build a pre-trained network based on LSTM network and CNN network; The training data is input into the pre-trained network, parameters are optimized with the goal of minimizing the loss between the prediction result output by the pre-trained network and the location of the logistics distribution center, and the trained pre-trained network is determined as the location optimization model.

4. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 3 is characterized in that: The loss function used in the training process is cross entropy loss.

5. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 3 is characterized in that: Before inputting the training data into the pre-training network, the method further includes: sequentially cleaning and formatting the training data.

6. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 5, characterized in that: The cleaning process is as follows: The missing values ​​in the training data are interpolated, deleted or marked and then manually supplemented to obtain preprocessed data, and outliers and duplicate data entries in the data are merged or deleted to obtain cleaned data.

7. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 5, characterized in that: The processing process of the format conversion is as follows: The data processed last time are processed in a unified dimension, and the data from different data sources are processed in a unified format to obtain data after format conversion.

8. The method for selecting a location for a rural e-commerce logistics distribution center according to claim 1, characterized in that: Also includes: The optimal locations for rural e-commerce logistics distribution centers output multiple times by the site selection model are summarized and visualized in the form of a map.

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