A method, system and medium for intelligent configuration of transportation capacity based on logistics tracking

Abstract

The present invention relates to the field of logistics technology, and in particular to a method, medium, and system for intelligent capacity configuration based on logistics tracking. The intelligent capacity configuration method based on logistics tracking collects logistics data tables that have not been processed within a preset time period from the logistics library of each logistics transfer point, performs field processing on the logistics data tables to obtain logistics data files, and when there is logistics request information that needs to be allocated, extracts all fields of the logistics request information, identifies the matching fields with the data file, maps the logistics request information to each logistics data table in the data file based on the matching fields, calculates the matching degree between each logistics data table and the matching fields, and outputs the multiple logistics data tables with the highest matching degree in descending order of matching degree as capacity configuration strategies, thereby matching rapidly changing logistics conditions in real time and improving the flexibility of capacity scheduling.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a method for intelligently configuring transportation capacity based on logistics tracking, a computer-readable storage medium, and a logistics system. Background Art

[0002] The Southern Power Grid's unified logistics capacity platform is a modern logistics system characterized by a flat hierarchy, centralized dispatching, resource sharing, and internal and external collaboration. By integrating internal and external transport resources, it provides transportation process monitoring for the logistics and transportation sector, displaying driver and vehicle transport trajectories in real time, dynamically monitoring the entire process, and effectively controlling loading, unloading, and settlement processes. It also enables full-process tracking, recording, storage, and analysis of logistics information. Currently, the research and development of the logistics capacity platform focuses on in-transit trajectory monitoring and intelligent dispatching. However, due to the high degree of trajectory rigidity in the addressing algorithm, the overall logistics capacity dispatching flexibility is limited, making it difficult to adapt to rapidly changing logistics conditions in real time. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for intelligent configuration of transportation capacity based on logistics tracking, which can improve the flexibility of transportation capacity scheduling.

[0004] In order to solve the above technical problems, in a first aspect, the present invention provides a method for intelligently allocating transportation capacity based on logistics tracking, comprising the following steps:

[0005] The logistics data collection step collects the logistics data tables that have not been processed within a preset time period from the logistics library of each logistics transfer point according to the user's operation instructions, and synchronizes the collected multiple logistics data tables to form a logistics data file;

[0006] Obtain the current logistics request information and identify the matching fields between the current logistics request information and the data file. The matching fields include the logistics transfer points, transit time and carrying capacity on the logistics route.

[0007] Map the logistics request information to each logistics data table in the data file; calculate the matching degree of the matching fields;

[0008] The multiple logistics data tables with the highest matching degree are output as the transportation capacity allocation strategy according to the matching degree from high to low.

[0009] Furthermore, for each logistics data table and the matching fields of the current logistics request information and the data file, calculating the matching degree of the matching fields includes the following steps:

[0010] Calculate the average value of the indicator data of multiple matching fields;

[0011] Calculate the ratio of the respective indicator data of multiple matching fields to their average value;

[0012] The inverse of the ratio of the index data of each matching field is used as the weight to perform a weighted calculation on the matching degree between the current logistics request information and each logistics data table to obtain the weighted score of each logistics data table;

[0013] Sort multiple logistics data tables by matching degree based on weighted scores.

[0014] Furthermore, the greater the average daily logistics volume of the logistics transfer point, the higher the corresponding matching degree weight.

[0015] Furthermore, the smaller the density of logistics transfer points near the logistics transfer point, the higher the corresponding matching degree weight.

[0016] Furthermore, in the logistics data collection step, the length of the preset time period is adjusted according to the logistics volume of the target area. The greater the logistics volume, the shorter the preset time period.

[0017] Furthermore, the method further includes the following steps of adjusting the logistics volume of the target area:

[0018] The logistics data retrieval step is to retrieve the predicted logistics volume and the corresponding actual logistics volume in the target area historical database within a preset time period;

[0019] The logistics transfer point influence value calculation step includes obtaining storage points within a preset distance from the current logistics transfer point, identifying category information of these storage points, recording the number of storage points whose category information is in a preset storage category library as the logistics transfer point influence value of the current storage point, and using the logistics transfer point influence value to represent the degree to which the current logistics transfer point is influenced by the storage point;

[0020] Deviation logistics volume calculation step: calculating the impact value of the current logistics transfer point, determining the logistics deviation coefficient of the target area based on the predicted logistics volume and the actual logistics volume in the historical database, and taking the product of the logistics transfer point impact value and the logistics deviation coefficient as the deviation logistics volume;

[0021] Data filtering step, subtracting the deviation logistics flow from the predicted logistics data to obtain the corrected logistics data;

[0022] In the logistics volume output step, the predicted logistics data retrieved in the logistics data retrieval step is replaced by the corrected logistics data calculated in the data filtering step as the logistics volume of the target area.

[0023] Furthermore, the category information of the storage point includes one or more of storage scale, item category, storage conversion rate, and average daily logistics volume.

[0024] Furthermore, the preset range refers to the coverage range between multiple preset logistics transfer points adjacent to the current logistics transfer point.

[0025] In a second aspect, the present invention provides a computer-readable storage medium having an executable computer program stored thereon, which, when executed, implements the above-mentioned method for intelligent configuration of transportation capacity based on logistics tracking.

[0026] In a third aspect, the present invention provides a logistics system comprising a processor and a computer-readable storage medium, wherein the computer-readable storage medium is the computer-readable storage medium as described above, and the processor executes the computer program in the computer-readable storage medium to implement the intelligent capacity configuration method based on logistics tracking.

[0027] Beneficial effect: The intelligent capacity configuration method based on logistics tracking collects logistics data tables that have not been processed within a preset time period from the logistics library of each logistics transfer point, performs field processing on the logistics data tables to obtain logistics data files, and when there is logistics request information that needs to be allocated, extracts all fields of the logistics request information, identifies its matching fields with the data file, maps the logistics request information to each logistics data table in the data file with this matching field, calculates the matching degree between each logistics data table and the matching field, and outputs the multiple logistics data tables with the highest matching degree in descending order of matching degree as capacity configuration strategies, matches the rapidly changing logistics situation in real time, and improves the flexibility of capacity scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the method for intelligent configuration of transportation capacity based on logistics tracking of the present invention. DETAILED DESCRIPTION

[0029] The present invention is further described in detail below in conjunction with specific embodiments.

[0030] This embodiment provides a logistics system, comprising a capacity processing module, a logistics generation module, and a logistics distribution module. The capacity processing module is responsible for processing demand data for generating logistics. For example, demand data includes vehicle data reported by vehicle owners and item data reported by business parties. Vehicle data includes vehicle carrying capacity and driving routes, while item data includes logistics origin and destination, item category, and volume. The capacity processing module creates a logistics request based on the business party's demand data, performs pre-processing operations such as collecting and filtering the demand data, and generates pre-processed data, which it then sends to the logistics generation module. The logistics generation module processes the pre-processed data output by the capacity processing module using a rule configuration submodule. This rule configuration submodule may include configuration information for generating various logistics, which is used to filter and determine the pre-processed data. For example, if the pre-processed data includes transportation time information and order time, and if, based on the configuration information, the transportation time information satisfies the order time, the rule configuration submodule determines that the logistics can be allocated to the relevant vehicle owner, the capacity processing module forwards the logistics request to the logistics distribution module, which then sends the logistics request to the vehicle owner and prompts him or her to process it. The logistics system also includes a processor and a computer-readable storage medium. The computer-readable storage medium stores an executable logistics capacity configuration program (i.e., a computer program). The processor of the electronic terminal runs the logistics capacity configuration program to achieve the following. Figure 1 The intelligent capacity configuration method based on logistics tracking is shown.

[0031] The following details the implementation process of the intelligent capacity allocation method based on logistics tracking.

[0032] The logistics data collection step, based on user instructions, collects unprocessed logistics data tables from the logistics database at each logistics transfer point within a preset time period and synchronizes these multiple collected logistics data tables into a logistics data file. The duration of the preset time period is adjusted based on the logistics volume of the target area; the greater the logistics volume, the shorter the preset time period.

[0033] The logistics volume of the target area is specifically:

[0034] The logistics data retrieval step is to retrieve the predicted logistics volume and the corresponding actual logistics volume in the target area historical database within a preset time period;

[0035] The logistics transfer point impact value calculation step includes obtaining storage points within a preset distance from the current logistics transfer point, identifying the category information of these storage points, recording the number of storage points whose category information is in a preset storage category library as the logistics transfer point impact value of the current storage point, and using the logistics transfer point impact value to represent the degree to which the current logistics transfer point is affected by the storage point; the storage point category information includes storage scale, item category, storage conversion rate, and average daily logistics volume;

[0036] Deviation logistics volume calculation step: calculating the impact value of the current logistics transfer point, determining the logistics deviation coefficient of the target area based on the predicted logistics volume and the actual logistics volume in the historical database, and taking the product of the logistics transfer point impact value and the logistics deviation coefficient as the deviation logistics volume;

[0037] Data filtering step, subtracting the deviation logistics flow from the predicted logistics data to obtain the corrected logistics data;

[0038] In the logistics volume output step, the predicted logistics data retrieved in the logistics data retrieval step is replaced by the corrected logistics data calculated in the data filtering step as the logistics volume of the target area.

[0039] Obtain the current logistics request information and identify the matching fields between the current logistics request information and the data file. The matching fields include the logistics transfer points, transit time and carrying capacity on the logistics route.

[0040] Map the logistics request information to each logistics data table in the data file.

[0041] Calculate the matching degree of the matching fields:

[0042] Calculate the average value of the indicator data of multiple matching fields;

[0043] Calculate the ratio of the respective indicator data of multiple matching fields to their average value;

[0044] The inverse of the ratio of the index data of each matching field is used as the weight to perform a weighted calculation on the matching degree between the current logistics request information and each logistics data table to obtain the weighted score of each logistics data table;

[0045] Among them, the greater the average daily logistics volume of the logistics transfer point, the higher the corresponding matching degree weight; the smaller the density of logistics transfer points near the logistics transfer point, the higher the corresponding matching degree weight.

[0046] Sort multiple logistics data tables by matching degree based on weighted scores.

[0047] The intelligent capacity configuration method based on logistics tracking in this embodiment finally outputs multiple logistics data tables with the highest matching degree in descending order of matching degree as capacity configuration strategies, which can match rapidly changing logistics conditions in real time and improve the flexibility of capacity scheduling.

[0048] The above is only an embodiment of the invention and does not limit the scope of patent protection. Those skilled in the art can make non-substantial changes or substitutions based on the invention and still fall within the scope of patent protection.

Claims

1. A method for intelligent capacity configuration based on logistics tracking, characterized by The steps include: The logistics data collection step collects the logistics data tables that have not been processed within a preset time period from the logistics library of each logistics transfer point according to the user's operation instructions, and synchronizes the collected multiple logistics data tables to form a logistics data file; Adjust the length of the preset time period according to the logistics volume of the target area. The greater the logistics volume, the shorter the preset time period; Obtaining current logistics request information, and identifying matching fields between the current logistics request information and the data file, wherein the matching fields include logistics transfer points, transit time, and carrying volume on the logistics route; Mapping the logistics request information to each logistics data table of the data file; calculating the matching degree of the matching fields; and matching fields of each logistics data table and the current logistics request information with the data file, including the following steps: Calculating the average value of the respective indicator data of the multiple matching fields; Calculating an index ratio between the index data of each of the plurality of matching fields and the average value thereof; The inverse of the ratio of the index data of each matching field is used as the weight to perform a weighted calculation on the matching degree between the current logistics request information and each logistics data table to obtain the weighted score of each logistics data table; sorting the plurality of logistics data tables according to the degree of matching according to the weighted scores; Output the multiple logistics data tables with the highest matching degree in descending order of matching degree as the transportation capacity allocation strategy; It also includes the following steps to adjust the logistics volume of the target area: The logistics data retrieval step is to retrieve the predicted logistics volume and the corresponding actual logistics volume in the target area historical database within a preset time period; The logistics transfer point influence value calculation step includes obtaining storage points within a preset distance from the current logistics transfer point, identifying category information of these storage points, recording the number of storage points whose category information is in a preset storage category library as the logistics transfer point influence value of the current storage point, and using the logistics transfer point influence value to represent the degree to which the current logistics transfer point is influenced by the storage point; a deviation logistics volume calculation step, calculating the impact value of the current logistics transfer point, determining the logistics deviation coefficient of the target area based on the predicted logistics volume and the actual logistics volume in the historical database, and taking the product of the logistics transfer point impact value and the logistics deviation coefficient as the deviation logistics volume; a data filtering step of subtracting the deviation logistics flow from the predicted logistics flow to obtain the deviation-corrected logistics data; In the logistics volume output step, the predicted logistics volume retrieved in the logistics data retrieval step is replaced by the corrected logistics data calculated in the data filtering step as the logistics volume of the target area.

2. The method for intelligent transportation capacity allocation based on logistics tracking according to claim 1, characterized in that: The greater the average daily logistics volume of the logistics transfer point, the higher the corresponding matching degree weight.

3. The method for intelligent transportation capacity allocation based on logistics tracking according to claim 1, characterized in that: The smaller the density of logistics transfer points near the logistics transfer point, the higher the corresponding matching degree weight.

4. The method for intelligent transport capacity allocation based on logistics tracking according to claim 1, characterized in that: The category information of the storage point includes one or more of storage scale, item category, storage conversion rate, and average daily logistics volume.

5. The method for intelligent transportation capacity allocation based on logistics tracking according to claim 1, characterized in that: The preset range refers to the coverage range between multiple preset logistics transfer points adjacent to the current logistics transfer point.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it can implement: the intelligent transportation capacity configuration method based on logistics tracking as described in any one of claims 1 to 5.

7. A logistics system comprising a processor, characterized in that The computer-readable storage medium according to claim 6 is further included, and the computer program on the computer-readable storage medium can be executed by a processor.

Images