Logistics resource allocation system

Abstract

The invention relates to a logistics resource allocation system which comprises a first capturing part, a second capturing part, an object training mechanism, an information analysis mechanism, a resource allocation mechanism and the like. The logistics resource allocation system is intelligent in design and stable in operation.

Description

Technical Field

[0001] The present invention relates to the field of logistics resource management, and in particular to a logistics resource allocation system. Background Art

[0002] Smart shared logistics refers to the integration of the two concepts of intelligence and sharing into the modern logistics operation system, and the coupling and connection between the related elements of the smart logistics intelligent technology system and the shared logistics sharing interactive mechanism under the action of coupling force mechanisms such as cost reduction and efficiency improvement, so as to promote the mutual adaptation, coupling and coordination, and mutual complementation of the main functional links of the logistics system, and ultimately achieve a new logistics operation model with efficient and intelligent logistics operation processes, highly shared logistics resources, and comprehensive transformation and upgrading of logistics system functions.

[0003] A major feature of smart shared logistics is the sharing of logistics service resources, including the sharing of logistics service resources between different logistics companies and the sharing of logistics service resources between the same e-commerce company in different regions and at different times. However, since it is impossible to reliably analyze the sales information corresponding to the time intervals set by the e-commerce application in the target city on the same day, it is impossible to configure the corresponding number of logistics service resources in advance for the time interval set in the target city on the same day, such as the corresponding number of delivery personnel, the corresponding number of sorting personnel and the corresponding number of servers in advance, making it difficult to implement the goal of sharing logistics service resources between the same e-commerce company in different regions and at different times. Summary of the invention

[0004] In order to solve the technical problems in the related fields, the present invention provides a logistics resource allocation system, which can configure the corresponding logistics service resource quantity for the set time interval of the target city on the same day in advance for the set e-commerce application based on the respective sold information corresponding to the set time interval of the target city on the same day, thereby configuring the corresponding logistics service resource quantity in advance based on the predicted sales data of the future time interval, thereby improving the utilization rate of logistics service resources, wherein, for intelligently predicting the respective sold information corresponding to the set time interval of the e-commerce application on the same day in the target city, a targeted convolutional neural network model is introduced, the convolutional neural network model is a convolutional neural network after completing multiple training operations, and the number of training operations is proportional to the number of each category of goods sold by the set e-commerce application in the target city, thereby completing the customization of the structure of the convolutional neural network model, and for intelligently predicting the respective sold information corresponding to the set time interval of the e-commerce application on the same day in the target city, sufficient and comprehensive multiple basic data are introduced, including the set service data of the e-commerce application in the target city and the set sold information corresponding to the same set time interval of the e-commerce application in the past days in the target city, thereby ensuring the reliability and stability of the intelligent prediction results.

[0005] According to the present invention, a logistics resource allocation system is provided, the system comprising: The first capturing component is used to obtain various service data of the set e-commerce application in the target city, wherein the various service data of the set e-commerce application in the target city are the saleable quantities of each category of goods sold by the set e-commerce application in the target city and the total number of each category of goods sold by the set e-commerce application in the target city; The second capturing component is used to obtain the sales information corresponding to the same set time interval of the past days of the set e-commerce application in the target city, and the single sales information corresponding to the same set time interval of each past day is the sales quantity corresponding to each category of goods sold by the set e-commerce application in the set time interval of the day; The object training mechanism is used to train the convolutional neural network multiple times to obtain the convolutional neural network after completing the multiple training operations, and output the convolutional neural network after completing the multiple training operations as a convolutional neural network model; an information analysis mechanism, connected to the first capture component, the second capture component and the object training mechanism respectively, for inputting the duration of the set time interval, the urban area of ​​the target city, various service data of the set e-commerce application in the target city and various pieces of sold information corresponding to the same set time interval of the set e-commerce application in the target city on previous days in parallel into the convolutional neural network model, and running the convolutional neural network model to obtain various pieces of sold information corresponding to the set time interval of the set e-commerce application in the target city on the current day output by the convolutional neural network model; A resource allocation mechanism, connected to the information analysis mechanism, configured to configure in advance the corresponding number of logistics service resources for the e-commerce application in the set time interval of the target city on the same day based on each piece of sold information corresponding to the set time interval of the e-commerce application in the target city on the same day; Wherein, the number of corresponding logistics service resources configured in advance for the set time interval of the e-commerce application in the target city on the day based on the respective pieces of sold information corresponding to the set time interval of the e-commerce application in the target city on the day includes: the number of corresponding logistics service resources configured in advance includes the number of corresponding delivery personnel, the number of corresponding sorting personnel and the number of corresponding servers; Among them, the object training mechanism is used to train the convolutional neural network multiple times to obtain the convolutional neural network after completing multiple training operations, and output the convolutional neural network after completing multiple training operations as the convolutional neural network model, including: the number of training operations is proportional to the number of each category of goods sold by the set e-commerce application in the target city.

[0006] It can be seen that the present invention has at least the following significant substantive features: Substantive feature 1: For a set e-commerce application, the corresponding number of logistics service resources is configured in advance for the set time interval of the target city on the same day based on the sales information corresponding to each set time interval of the target city on the same day, thereby configuring the corresponding number of logistics service resources in advance based on the predicted sales data of the future time interval, thereby improving the utilization rate of logistics service resources; Substantive feature 2: In order to intelligently predict the sales information corresponding to the time intervals set by the e-commerce application in the target city on the same day, a specially designed convolutional neural network model is introduced. The convolutional neural network model is a convolutional neural network that has completed multiple training operations, and the number of training operations is proportional to the number of each category of goods sold by the e-commerce application in the target city, thereby completing the customization of the structure of the convolutional neural network model; Substantive feature three: In order to set the sales information corresponding to the set time period of the e-commerce application in the target city on the same day for intelligent prediction, sufficient and comprehensive basic data are introduced, including the service data of the e-commerce application in the target city and the sales information corresponding to the same set time period of the e-commerce application in the target city on the past days, thereby ensuring the reliability and stability of the intelligent prediction results.

[0007] The logistics resource allocation system of the present invention is intelligently designed and stable in operation. Since it can use a convolutional neural network model to intelligently analyze the sales information corresponding to the set time interval of the target city on the same day for the set e-commerce application, and then configure the matching logistics service resource quantity in advance, the goal of sharing logistics service resources between the same e-commerce in different regions and at different times can be achieved. 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 FIG. 4 is an internal structural diagram of a logistics resource allocation system according to a first embodiment of the present invention.

[0009] Figure 2 FIG. 4 is an internal structural diagram of a logistics resource allocation system according to a second embodiment of the present invention.

[0010] Figure 3 FIG. 4 is an internal structural diagram of a logistics resource allocation system according to a third embodiment of the present invention. DETAILED DESCRIPTION

[0011] The implementation scheme of the logistics resource allocation system of the present invention will be described in detail below with reference to the accompanying drawings.

[0012] Figure 1 The internal structure diagram of the logistics resource allocation system according to the first embodiment of the present invention is shown, and the system includes: The first capturing component is used to obtain various service data of the set e-commerce application in the target city, wherein the various service data of the set e-commerce application in the target city are the saleable quantities of each category of goods sold by the set e-commerce application in the target city and the total number of each category of goods sold by the set e-commerce application in the target city; Specifically, the first capture component is used to obtain various service data of the set e-commerce application in the target city, and the various service data of the set e-commerce application in the target city are the saleable quantities corresponding to each category of goods sold by the set e-commerce application in the target city and the total number of each category of goods sold by the set e-commerce application in the target city, including: the first capture component includes a first acquisition unit and a second acquisition unit, which are used to respectively obtain the saleable quantities corresponding to each category of goods sold by the set e-commerce application in the target city and the total number of each category of goods sold by the set e-commerce application in the target city; The second capturing component is used to obtain the sales information corresponding to the same set time interval of the past days of the set e-commerce application in the target city, and the single sales information corresponding to the same set time interval of each past day is the sales quantity corresponding to each category of goods sold by the set e-commerce application in the set time interval of the day; The object training mechanism is used to train the convolutional neural network multiple times to obtain the convolutional neural network after completing the multiple training operations, and output the convolutional neural network after completing the multiple training operations as a convolutional neural network model; an information analysis mechanism, connected to the first capture component, the second capture component and the object training mechanism respectively, for inputting the duration of the set time interval, the urban area of ​​the target city, various service data of the set e-commerce application in the target city and various pieces of sold information corresponding to the same set time interval of the set e-commerce application in the target city on previous days in parallel into the convolutional neural network model, and running the convolutional neural network model to obtain various pieces of sold information corresponding to the set time interval of the set e-commerce application in the target city on the current day output by the convolutional neural network model; A resource allocation mechanism, connected to the information analysis mechanism, configured to configure in advance the corresponding number of logistics service resources for the e-commerce application in the set time interval of the target city on the same day based on each piece of sold information corresponding to the set time interval of the e-commerce application in the target city on the same day; Wherein, the number of corresponding logistics service resources configured in advance for the set time interval of the e-commerce application in the target city on the day based on the respective pieces of sold information corresponding to the set time interval of the e-commerce application in the target city on the day includes: the number of corresponding logistics service resources configured in advance includes the number of corresponding delivery personnel, the number of corresponding sorting personnel and the number of corresponding servers; The object training mechanism is used to train the convolutional neural network multiple times to obtain the convolutional neural network after completing the multiple training operations, and output the convolutional neural network after completing the multiple training operations as the convolutional neural network model, including: the number of training operations is proportional to the number of commodities of each category sold by the set e-commerce application in the target city; Among them, the number of corresponding logistics service resources configured in advance for the e-commerce application in the set time interval on the day of the target city based on the respective pieces of sold information corresponding to the set time interval on the day of the target city also includes: the more the respective pieces of sold information corresponding to the set time interval on the day of the target city the e-commerce application is, the more the number of corresponding logistics service resources configured in advance is; Among them, the more copies of sold information corresponding to the set time intervals of the e-commerce application in the target city on the day, the more corresponding logistics service resources are configured in advance, including: the more copies of sold information corresponding to the set time intervals of the e-commerce application in the target city on the day, the more corresponding delivery personnel are configured in advance, the more corresponding sorting personnel are configured in advance, and the more corresponding servers are configured in advance.

[0013] Figure 2 FIG. 4 is an internal structural diagram of a logistics resource allocation system according to a second embodiment of the present invention.

[0014] exist Figure 2 In, with Figure 1 Differently, the logistics resource allocation system shown in the second embodiment of the present invention may also include: a heat analysis device, connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, respectively, for measuring the instantaneous internal heat of each of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism; Wherein, the heat analysis device is connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, and is used to measure the instantaneous internal heat of each of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, including: the heat analysis device includes a plurality of heat measurement units, which are used to be connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the measurement of the instantaneous internal heat of each of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively; Wherein, the heat analysis device comprises a plurality of heat measurement units, which are used to be connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the respective measurement of the instantaneous internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, including: the plurality of heat measurement units are a plurality of heat sensors, which are used to be connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the respective measurement of the instantaneous internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively; Wherein, the plurality of heat measurement units are a plurality of heat sensors, which are used to be connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the respective measurement of the instant internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, including: the internal structures of the plurality of heat sensors are the same; Among them, the multiple heat measurement units are multiple heat sensors, which are used to connect with the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the separate measurement of the instant internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, and also include: the multiple heat sensors have the same heat measurement upper limit value and heat measurement lower limit value.

[0015] Figure 3 FIG. 4 is an internal structural diagram of a logistics resource allocation system according to a third embodiment of the present invention.

[0016] exist Figure 3 In, with Figure 1 Differently, the logistics resource allocation system shown in the third embodiment of the present invention may also include: an embedded processor, connected to the information parsing mechanism, the first capture component, the second capture component and a plurality of heat measurement units of the object training mechanism, respectively, for realizing on-site configuration of the real-time working mode of the information parsing mechanism, the first capture component, the second capture component and the object training mechanism; Wherein, the embedded processor is respectively connected to the information analysis mechanism, the first capture component, the second capture component and the plurality of heat measurement units of the object training mechanism, and is used to implement the on-site configuration of the real-time working mode of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, including: the embedded processor is a processor with an ARM 13 core; And wherein, the embedded processor is respectively connected to multiple heat measurement units of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, and is used to realize the on-site configuration of the real-time working mode of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, including: the embedded processor has built-in registers, timers and static storage devices.

[0017] In addition, in the logistics resource allocation system, the duration of the set time interval, the urban area of ​​the target city, the various service data of the set e-commerce application in the target city, and the sold information corresponding to the same set time interval of the set e-commerce application in the target city in the past days are input into the convolutional neural network model in parallel, and the convolutional neural network model is run to obtain the sold information corresponding to the set time interval of the set e-commerce application in the target city on the day output by the convolutional neural network model, including: the sold information corresponding to the set time interval of the set e-commerce application in the target city on the day is the sold quantities corresponding to each category of goods sold by the set e-commerce application within the set time interval of the day.

[0018] In the above, although the present disclosure discusses the embodiments of the present invention, the present invention is not limited thereto but various modifications and changes may be made by those skilled in the art to which the present disclosure pertains without departing from the spirit and scope of the present disclosure as claimed in the appended claims.

Claims

1. A logistics resource allocation system, characterized in that: The system comprises: The first capturing component is used to obtain various service data of the set e-commerce application in the target city, wherein the various service data of the set e-commerce application in the target city are the saleable quantities of each category of goods sold by the set e-commerce application in the target city and the total number of each category of goods sold by the set e-commerce application in the target city; The second capturing component is used to obtain the sales information corresponding to the same set time interval of the past days of the set e-commerce application in the target city, and the single sales information corresponding to the same set time interval of each past day is the sales quantity corresponding to each category of goods sold by the set e-commerce application in the set time interval of the day; The object training mechanism is used to train the convolutional neural network multiple times to obtain the convolutional neural network after completing the multiple training operations, and output the convolutional neural network after completing the multiple training operations as a convolutional neural network model; an information analysis mechanism, connected to the first capture component, the second capture component and the object training mechanism respectively, for inputting the duration of the set time interval, the urban area of ​​the target city, various service data of the set e-commerce application in the target city and various pieces of sold information corresponding to the same set time interval of the set e-commerce application in the target city on previous days in parallel into the convolutional neural network model, and running the convolutional neural network model to obtain various pieces of sold information corresponding to the set time interval of the set e-commerce application in the target city on the current day output by the convolutional neural network model; A resource allocation mechanism, connected to the information analysis mechanism, configured to configure in advance the corresponding number of logistics service resources for the e-commerce application in the set time interval of the target city on the same day based on each piece of sold information corresponding to the set time interval of the e-commerce application in the target city on the same day; Wherein, the number of corresponding logistics service resources configured in advance for the set time interval of the e-commerce application in the target city on the day based on the respective pieces of sold information corresponding to the set time interval of the e-commerce application in the target city on the day includes: the number of corresponding logistics service resources configured in advance includes the number of corresponding delivery personnel, the number of corresponding sorting personnel and the number of corresponding servers; Among them, the object training mechanism is used to train the convolutional neural network multiple times to obtain the convolutional neural network after completing multiple training operations, and output the convolutional neural network after completing multiple training operations as the convolutional neural network model, including: the number of training operations is proportional to the number of each category of goods sold by the set e-commerce application in the target city.

2. The logistics resource allocation system according to claim 1, characterized in that: The number of corresponding logistics service resources configured in advance for the e-commerce application in the set time interval on the day of the target city based on the respective pieces of sold information corresponding to the set time interval on the day of the target city by the e-commerce application also includes: the more the respective pieces of sold information corresponding to the set time interval on the day of the target city by the e-commerce application are, the more the number of corresponding logistics service resources configured in advance is; Among them, the more copies of sold information corresponding to the set time intervals of the e-commerce application in the target city on the day, the more corresponding logistics service resources are configured in advance, including: the more copies of sold information corresponding to the set time intervals of the e-commerce application in the target city on the day, the more corresponding delivery personnel are configured in advance, the more corresponding sorting personnel are configured in advance, and the more corresponding servers are configured in advance.

3. The logistics resource allocation system according to claim 2, characterized in that: The system further comprises: a heat analysis device, connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, respectively, for measuring the instantaneous internal heat of each of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism; Among them, the thermal analysis device is respectively connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, and is used to respectively measure the instantaneous internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, including: the thermal analysis device includes a plurality of heat measurement units, which are respectively connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, so as to complete the respective measurements of the instantaneous internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism.

4. The logistics resource allocation system as claimed in claim 3, characterized in that: The thermal analysis device includes a plurality of thermal measurement units, which are used to be respectively connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism to complete the respective measurements of the instantaneous internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism. The plurality of thermal measurement units are a plurality of thermal sensors, which are used to be respectively connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism to complete the respective measurements of the instantaneous internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism.

5. The logistics resource allocation system according to claim 4, characterized in that: The multiple heat measurement units are multiple heat sensors, which are used to be connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the separate measurement of the real-time internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, including: the internal structures of the multiple heat sensors are the same.

6. The logistics resource allocation system according to claim 5, characterized in that: The multiple heat measurement units are multiple heat sensors, which are used to be connected to the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, so as to complete the separate measurement of the instant internal heat of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism respectively, and also include: the multiple heat sensors have the same heat measurement upper limit value and heat measurement lower limit value.

7. The logistics resource allocation system according to claim 3, characterized in that: The system further comprises: An embedded processor is respectively connected to multiple heat measurement units of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, and is used to realize on-site configuration of the real-time working mode of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism.

8. The logistics resource allocation system according to claim 7, characterized in that: An embedded processor is respectively connected to the information analysis mechanism, the first capture component, the second capture component and multiple heat measurement units of the object training mechanism, and is used to realize on-site configuration of the real-time working mode of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, including: the embedded processor is a processor with an ARM 13 core.

9. The logistics resource allocation system according to claim 7, characterized in that: An embedded processor is respectively connected to the information analysis mechanism, the first capture component, the second capture component and multiple heat measurement units of the object training mechanism, and is used to realize on-site configuration of the real-time working mode of the information analysis mechanism, the first capture component, the second capture component and the object training mechanism, including: the embedded processor has built-in registers, timers and static storage devices.

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