Logistics transportation route data analysis system
By applying the AI prediction model generated by convolutional neural networks in the logistics transportation system, evaluate the busyness of the logistics route and dynamically allocate logistics vehicles, solve the deviation of logistics transportation companies in vehicle distribution, and achieve more efficient logistics and transportation. manage.
Patent Information
- Application Number
- CN202411471651.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2024-07-19
- Filing Date
- 2024-10-22
- Publication Date
- 2025-01-24
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
When distributing logistics vehicles, logistics and transportation companies rely on historical data and manual experience, resulting in deviations in the allocation of logistics vehicles, which cannot accurately meet the needs of different logistics routes.
A logistics transportation route data analysis system is designed, which uses a convolutional neural network to perform a preset number of learning operations to generate an AI prediction model, which is used to evaluate the busyness of logistics routes and is based on various configuration data and multiple routes. Data, dynamically allocate the number of daily logistics vehicles.
Through intelligent evaluation and dynamic allocation, it can more accurately meet the needs of each logistics route, improve the efficiency of logistics and transportation, and make full use of limited logistics vehicle resources.
Smart Images
Figure CN119359205A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of logistics and transportation, and in particular to a logistics and transportation route data analysis system. Background Art
[0002] Each mode of transport and means of transport have their own characteristics, and different types of goods have different requirements for transportation. Reasonable selection of the mode of transport is an important part of rationally organizing transportation, ensuring transportation quality, and improving transportation efficiency. The choice of mode of transport is to choose from railway, road, aviation, waterway, pipeline transportation or combined transportation, and make a decision by evaluating the freight rates and service levels of different modes.
[0003] Since transportation costs account for an important proportion of total logistics costs, and the freight rates of different modes of transportation vary greatly, freight rates are one of the five most important factors in choosing a mode of transportation. However, the mode of transportation with the lowest transportation cost usually leads to an increase in the costs of other parts of the logistics system, so it is difficult to ensure the lowest cost of the entire logistics system. Therefore, although freight rates are a factor that affects decision-making, they are by no means the only factor. Enterprises must consider the quality of transportation services and the impact of such services on the cost of the entire logistics system. The quality of transportation services usually includes transportation time, transportation reliability, transportation safety, and service ease.
[0004] In actual operation, a logistics transportation company may be responsible for the logistics transportation of multiple logistics routes at the same time. However, since the logistics quantity of each logistics route cannot be determined in advance, the logistics transportation company can only roughly determine the logistics data based on historical data and manual experience, and then dynamically allocate a relatively limited number of logistics vehicles to multiple logistics routes on this basis. Obviously, this allocation mechanism has an allocation deviation in the number of logistics vehicles due to unreliable basic data. Summary of the invention
[0005] In order to solve the above problems, the present invention provides a logistics transportation route data analysis system, which performs a preset number of learning operations on a convolutional neural network to obtain a convolutional neural network that has undergone a preset number of learning operations and outputs it as an AI prediction model. The longer the route length of the set logistics route, the larger the value of the preset number, so that artificial intelligence models with different structures for evaluating the busyness of logistics routes are designed for different logistics routes, and then the daily logistics quantity of the set logistics route is evaluated based on the various configuration data of the set logistics route and multiple route data of the set logistics route using the AI prediction model, and the daily number of logistics vehicles allocated to the set logistics route is determined based on the daily logistics quantity of the set logistics route, so as to complete the dynamic allocation of the daily number of logistics vehicles for each logistics route based on the evaluation result of the busyness of each logistics route, while taking into account the logistics transportation needs, and making full use of limited logistics vehicle resources.
[0006] According to the present invention, a logistics transportation route data analysis system is provided, the system comprising: A data capture device, used to obtain various configuration data of a set logistics route, wherein the set logistics route is a logistics route between a first city and a second city, and the various configuration data of the set logistics route include the number of permanent residents in the first city, the area occupied by the first city, the number of permanent residents in the second city, and the area occupied by the second city; A route analysis device, used for obtaining a plurality of route data of the set logistics route, wherein the plurality of route data of the set logistics route are the route length, the number of intermediate cities and the average number of residents in the intermediate cities of the set logistics route; A target device is constructed to perform a preset number of learning operations on the convolutional neural network to obtain a convolutional neural network that has undergone the preset number of learning operations and output it as an AI prediction model; A logistics prediction device, connected to the data capture device, the route analysis device and the target building device respectively, for synchronously inputting various configuration data of the set logistics route and multiple route data of the set logistics route into the AI prediction model, and executing the AI prediction model to obtain the daily logistics quantity of the set logistics route output by the AI prediction model; An allocation execution device, connected to the logistics prediction device, for determining the number of daily logistics vehicles allocated to the set logistics route based on the daily logistics quantity of the set logistics route; Wherein, determining the daily number of logistics vehicles allocated to the set logistics route based on the daily logistics quantity of the set logistics route includes: the determined daily number of logistics vehicles allocated to the set logistics route is monotonically positively correlated with the daily logistics quantity of the set logistics route; Among them, the target building device is used to perform a preset number of learning operations on the convolutional neural network to obtain a convolutional neural network that has undergone a preset number of learning operations and output it as an AI prediction model, including: the longer the route length of the set logistics route, the larger the value of the preset number.
[0007] It can be seen that the present invention has at least the following three important inventive concepts: Firstly: a preset number of learning operations are performed on the convolutional neural network to obtain a convolutional neural network that has undergone the preset number of learning operations and output it as an AI prediction model. The longer the route length of the logistics route is, the larger the value of the preset number is, so as to design different structures of artificial intelligence models for evaluating the busyness of logistics routes for different logistics routes; Secondly: based on the various configuration data of the set logistics route and the multiple route data of the set logistics route, an AI prediction model is used to evaluate the daily logistics quantity of the set logistics route, and based on the daily logistics quantity of the set logistics route, the daily logistics vehicle quantity allocated to the set logistics route is determined, wherein the daily logistics vehicle quantity allocated to the set logistics route is determined to be monotonically positively correlated with the daily logistics quantity of the set logistics route, thereby completing the dynamic allocation of the daily logistics vehicle quantity of each logistics route based on the evaluation result of the busyness of each logistics route, while taking into account the logistics transportation needs, making full use of limited logistics vehicle resources; Again: Specifically, the logistics route is set as the logistics route between the first city and the second city, and the various configuration data of the logistics route include the number of permanent residents in the first city, the area of the first city, the number of permanent residents in the second city and the area of the second city; the multiple route data of the logistics route are set as the route length of the set logistics route, the number of intermediate cities and the average number of residents in the intermediate cities, thereby completing the targeted selection of multiple basic data for intelligent evaluation. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 Schematic diagram of the internal structure of a logistics transportation route data analysis system according to the first embodiment of the present invention.
[0009] Figure 2 Schematic diagram of the internal structure of a logistics transportation route data analysis system according to the second embodiment of the present invention.
[0010] Figure 3 Schematic diagram of the internal structure of a logistics transportation route data analysis system according to the third embodiment of the present invention. DETAILED DESCRIPTION
[0011] The following is a detailed description of an embodiment of the logistics transportation route data analysis system of the present invention with reference to the accompanying drawings.
[0012] Figure 1 This is a schematic diagram of the internal structure of a logistics transportation route data analysis system according to a first embodiment of the present invention, wherein the system comprises: A data capture device, used to obtain various configuration data of a set logistics route, wherein the set logistics route is a logistics route between a first city and a second city, and the various configuration data of the set logistics route include the number of permanent residents in the first city, the area occupied by the first city, the number of permanent residents in the second city, and the area occupied by the second city; For example, multiple different content capture components are selected to respectively capture the number of permanent residents in the first city, the area of the first city, the number of permanent residents in the second city, and the area of the second city; A route analysis device, used for obtaining a plurality of route data of the set logistics route, wherein the plurality of route data of the set logistics route are the route length, the number of intermediate cities and the average number of residents in the intermediate cities of the set logistics route; A target device is constructed to perform a preset number of learning operations on the convolutional neural network to obtain a convolutional neural network that has undergone the preset number of learning operations and output it as an AI prediction model; A logistics prediction device, connected to the data capture device, the route analysis device and the target building device respectively, for synchronously inputting various configuration data of the set logistics route and multiple route data of the set logistics route into the AI prediction model, and executing the AI prediction model to obtain the daily logistics quantity of the set logistics route output by the AI prediction model; An allocation execution device, connected to the logistics prediction device, for determining the number of daily logistics vehicles allocated to the set logistics route based on the daily logistics quantity of the set logistics route; Wherein, determining the daily number of logistics vehicles allocated to the set logistics route based on the daily logistics quantity of the set logistics route includes: the determined daily number of logistics vehicles allocated to the set logistics route is monotonically positively correlated with the daily logistics quantity of the set logistics route; The target construction device is used to perform a preset number of learning operations on the convolutional neural network to obtain a convolutional neural network that has undergone a preset number of learning operations and output it as an AI prediction model, including: the longer the route length of the set logistics route, the greater the value of the preset number; Among them, the route analysis device is used to obtain multiple route data of the set logistics route, and the multiple route data of the set logistics route are the route length, the number of intermediate cities and the average number of residents in the intermediate cities of the set logistics route, including: the intermediate city of the set logistics route is a city between the first city and the second city.
[0013] Figure 2 Schematic diagram of the internal structure of a logistics transportation route data analysis system according to the second embodiment of the present invention.
[0014] and Figure 1 different, Figure 2 The logistics transportation route data analysis system in the system can also include the following components: A fault self-detection device, connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device, respectively, for measuring the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively; Wherein, the fault self-checking device is respectively connected with the logistics prediction device, the data capture device, the route analysis device and the target construction device, and is used to respectively measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device, including: the fault self-checking device includes a plurality of fault self-checking units, which are respectively connected with the logistics prediction device, the data capture device, the route analysis device and the target construction device, so as to respectively measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device; Wherein, the fault self-checking device comprises a plurality of fault self-checking units, which are respectively connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device to respectively measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device, respectively, including: the internal structures of the plurality of fault self-checking units respectively used by the logistics prediction device, the data capture device, the route analysis device and the target construction device are the same; And wherein, the fault self-detection device includes multiple fault self-detection units, which are used to connect with the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively, so as to respectively measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively, and also includes: the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively use multiple fault self-detection units to perform internal self-detection operations.
[0015] Figure 3 Schematic diagram of the internal structure of a logistics transportation route data analysis system according to the third embodiment of the present invention.
[0016] and Figure 1 different, Figure 3 The logistics transportation route data analysis system in the system can also include the following components: A signal identification device is arranged near the logistics prediction device, the data capture device, the route analysis device and the target construction device and is respectively connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device, and is used for real-time measurement of the computing utilization percentage of each operator of the logistics prediction device, the data capture device, the route analysis device and the target construction device.
[0017] Next, the specific structure of the logistics transportation route data analysis system of the present invention will be further described.
[0018] In the logistics transportation route data analysis system according to any embodiment of the present invention: The logistics prediction device, the data capture device, the route analysis device and the target construction device all include a signal input unit and a signal output unit, and the signal input unit and the signal output unit both include a ground terminal.
[0019] In the logistics transportation route data analysis system according to any embodiment of the present invention: The logistics prediction device, the data capture device, the route analysis device and the target construction device are respectively implemented by different CPLD chips, and the CPLD chip is designed by VHDL language.
[0020] And in the logistics transportation route data analysis system according to any embodiment of the present invention: The logistics prediction device, the data capture device, the route analysis device and the target construction device are each provided with a plurality of heat dissipation holes on their respective shells, and the plurality of heat dissipation holes of any device of the logistics prediction device, the data capture device, the route analysis device and the target construction device are evenly distributed on their shells; Among them, the logistics prediction device, the data capture device, the route analysis device and the target construction device are each provided with a plurality of heat dissipation holes on their respective shells, and the plurality of heat dissipation holes of any one of the logistics prediction device, the data capture device, the route analysis device and the target construction device are evenly distributed on their shells, including: the aperture values of each of the plurality of heat dissipation holes of any one of the logistics prediction device, the data capture device, the route analysis device and the target construction device are the same.
[0021] In addition, in the logistics transportation route data analysis system, the route analysis device is used to obtain multiple route data of the set logistics route. The multiple route data of the set logistics route are the route length, the number of intermediate cities and the average number of residents in the intermediate cities of the set logistics route, and also include: the average number of residents in the intermediate cities of the set logistics route is the arithmetic mean of the number of permanent residents corresponding to each intermediate city of the set logistics route.
[0022] The logistics and transportation route data analysis system of the present invention is used to address the technical problem in the prior art that logistics and transportation companies that are responsible for multiple logistics routes at the same time have deviations in the allocation of the number of logistics vehicles. By adopting an AI prediction model with targeted structural design, the daily logistics quantity of the set logistics route is intelligently evaluated based on the various configuration data of the set logistics route and multiple route data of the set logistics route, and then the daily number of logistics vehicles allocated to the set logistics route is determined, thereby helping logistics and transportation companies to complete the dynamic allocation of the daily number of logistics vehicles for each logistics route.
[0023] Although the present invention has been fully described by way of example, it should be understood that various changes and modifications are obvious to those skilled in the art. Therefore, unless such changes and modifications depart from the protection scope of the present invention, they should be understood to be included in the present invention.
Claims
1. A logistics transportation route data analysis system, characterized in that: The system comprises: A data capture device, used to obtain various configuration data of a set logistics route, wherein the set logistics route is a logistics route between a first city and a second city, and the various configuration data of the set logistics route include the number of permanent residents in the first city, the area occupied by the first city, the number of permanent residents in the second city, and the area occupied by the second city; A route analysis device, used for obtaining a plurality of route data of the set logistics route, wherein the plurality of route data of the set logistics route are the route length, the number of intermediate cities and the average number of residents in the intermediate cities of the set logistics route; A target device is constructed to perform a preset number of learning operations on the convolutional neural network to obtain a convolutional neural network that has undergone the preset number of learning operations and output it as an AI prediction model; A logistics prediction device, connected to the data capture device, the route analysis device and the target building device respectively, for synchronously inputting various configuration data of the set logistics route and multiple route data of the set logistics route into the AI prediction model, and executing the AI prediction model to obtain the daily logistics quantity of the set logistics route output by the AI prediction model; An allocation execution device, connected to the logistics prediction device, for determining the number of daily logistics vehicles allocated to the set logistics route based on the daily logistics quantity of the set logistics route; Wherein, determining the daily number of logistics vehicles allocated to the set logistics route based on the daily logistics quantity of the set logistics route includes: the determined daily number of logistics vehicles allocated to the set logistics route is monotonically positively correlated with the daily logistics quantity of the set logistics route; Among them, the target building device is used to perform a preset number of learning operations on the convolutional neural network to obtain a convolutional neural network that has undergone a preset number of learning operations and output it as an AI prediction model, including: the longer the route length of the set logistics route, the larger the value of the preset number.
2. The logistics transportation route data analysis system according to claim 1, characterized in that: A route analysis device is used to obtain multiple route data of the set logistics route, wherein the multiple route data of the set logistics route are the route length, the number of intermediate cities and the average number of residents in the intermediate cities of the set logistics route, including: the intermediate city of the set logistics route is a city between the first city and the second city.
3. The logistics transportation route data analysis system according to claim 2, characterized in that: The system comprises: A fault self-detection device, connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device, respectively, for measuring the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively; Among them, the fault self-detection device is respectively connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device, and is used to respectively measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device. The fault self-detection device includes multiple fault self-detection units, which are respectively connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device, so as to respectively measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device.
4. The logistics transportation route data analysis system according to claim 3, characterized in that: The fault self-detection device includes multiple fault self-detection units, which are used to connect to the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively to measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively. The internal structures of the multiple fault self-detection units used by the logistics prediction device, the data capture device, the route analysis device and the target construction device are the same.
5. The logistics transportation route data analysis system according to claim 4, characterized in that: The fault self-detection device includes multiple fault self-detection units, which are used to connect to the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively to measure the current fault codes inside the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively. It also includes: the logistics prediction device, the data capture device, the route analysis device and the target construction device respectively use multiple fault self-detection units to perform internal self-detection operations.
6. The logistics transportation route data analysis system according to claim 2, characterized in that: The system further comprises: A signal identification device is arranged near the logistics prediction device, the data capture device, the route analysis device and the target construction device and is respectively connected to the logistics prediction device, the data capture device, the route analysis device and the target construction device, and is used for real-time measurement of the computing utilization percentage of each operator of the logistics prediction device, the data capture device, the route analysis device and the target construction device.
7. The logistics transportation route data analysis system according to any one of claims 2 to 6, characterized in that: The logistics prediction device, the data capture device, the route analysis device and the target construction device all include a signal input unit and a signal output unit, and the signal input unit and the signal output unit both include a ground terminal.
8. The logistics transportation route data analysis system according to any one of claims 2 to 6, characterized in that: The logistics prediction device, the data capture device, the route analysis device and the target construction device are respectively implemented by different CPLD chips, and the CPLD chip is designed by VHDL language.
9. The logistics transportation route data analysis system according to any one of claims 2 to 6, characterized in that: The logistics prediction device, the data capture device, the route analysis device and the target construction device are each provided with a plurality of heat dissipation holes on their respective shells, and the plurality of heat dissipation holes of any device of the logistics prediction device, the data capture device, the route analysis device and the target construction device are evenly distributed on their shells; Among them, the logistics prediction device, the data capture device, the route analysis device and the target construction device are each provided with a plurality of heat dissipation holes on their respective shells, and the plurality of heat dissipation holes of any one of the logistics prediction device, the data capture device, the route analysis device and the target construction device are evenly distributed on their shells, including: the aperture values of each of the plurality of heat dissipation holes of any one of the logistics prediction device, the data capture device, the route analysis device and the target construction device are the same.