Express Classification Information Prediction System Based on Historical Data

Abstract

The present invention relates to a prediction system for express classification information based on historical data. The system includes: a network application device, which is used to analyze the number of packages corresponding to each type of express on the target express route on the current day based on multiple pieces of historical express content corresponding to a set number of days before the current day for the target express route and each piece of configuration information of the target express route by using a Hoffit neural network after multiple trainings; a mapping processing device, which is used to preset the number of various storage containers reserved for each type of express on the current day at the set end address based on the analysis result. Through this system, it is possible to start the intelligent prediction of the number of packages corresponding to each type of express on the target express route on the current day in the early morning of the current day, and then preset the number of various storage containers reserved for each type of express on the current day at the set end address, thereby improving the dynamic allocation effect of the number of storage containers and ensuring the utilization rate of the storage containers.

Description

Technical Field

[0001] The present invention relates to the field of express delivery services, and more particularly, to a prediction system for express classification information based on historical data. Background Art

[0002] Express delivery service refers to the receipt, transportation, and delivery of single-piece, addressed express items, that is, the general term for letters and parcels legally received and packaged by express delivery service enterprises. It is a service form that delivers the express items or other items that do not require storage to the designated location within the time limit promised to the sender, hands them over to the recipient, and obtains a signature. The logistics industry is the result of the integration of multiple industries such as the transportation industry, warehousing industry, and communication industry to ensure the supply of social production and social life. Looking back at history, one can see that every major progress in the logistics industry is inseparable from the transformation of the above-mentioned industries. With the emergence of railway networks, highway networks, water transportation networks, pipeline transportation networks, aviation networks, communication networks, computer networks, etc., the logistics speed has become faster and more accurate. It has further developed from door-to-door service to desk-to-desk service. The speed and accuracy of the logistics industry are concentrated in the express delivery industry.

[0003] However, for a specific express delivery route, it is impossible to obtain in advance the number of packages corresponding to each type of express delivery on the same day, resulting in the inability to preset the number of various storage containers reserved for each type of express delivery on the same day at the set end address of the express delivery route based on the number of packages corresponding to each type of express delivery on the same day of the express delivery route, so that the number of various storage containers reserved is either relatively excessive or relatively insufficient. Summary of the Invention

[0004] In order to solve the technical problems in the related fields, the present invention provides a prediction system for express classification information based on historical data, which can start the intelligent prediction of the number of packages corresponding to each type of express delivery on the same day of the target express delivery route at midnight on the same day. The intelligent prediction is based on multiple historical express delivery contents corresponding to a set number of days before the same day of the target express delivery route and the configuration information of each package of the target express delivery route, and uses the Hoffit neural network after multiple trainings to perform the intelligent prediction of the number of packages corresponding to each type of express delivery on the same day of the target express delivery route, and preset the number of various storage containers reserved for each type of express delivery on the same day at the set end address based on the number of packages corresponding to each type of express delivery on the same day of the target express delivery route, so as to complete the dynamic allocation of the number of storage containers based on the intelligent prediction result of the number of express delivery packages on the same day of the target express delivery route, taking into account the storage needs of the express delivery route and the maximization of the utilization of limited storage containers.

[0005] According to the present invention, there is provided a prediction system for express classification information based on historical data, the system comprising:

[0006] A content collection mechanism for obtaining, in the early morning of the current day, multiple historical express contents corresponding to a set number of days before the current day for a target express route. The single historical express content corresponding to each day is the number of packages corresponding to various types of express on that day. The target express route is the route between a set starting address and a set ending address;

[0007] A configuration capture mechanism for obtaining various configuration information of the target express route. The various configuration information of the target express route is the route distance of the target express route, the number of express units at the set starting address, and the number of express units at the set ending address;

[0008] A network application device, connected to the content collection mechanism and the configuration capture mechanism respectively, for parsing the number of packages corresponding to various types of express on the current day for the target express route based on the multiple historical express contents corresponding to a set number of days before the current day for the target express route and the various configuration information of the target express route by using the Hoffit neural network after multiple trainings;

[0009] A mapping processing device, connected to the network application device, for presetting the number of various storage containers reserved for various types of express on the current day at the set ending address based on the number of packages corresponding to various types of express on the current day for the target express route;

[0010] Among them, parsing the number of packages corresponding to various types of express on the current day for the target express route based on the multiple historical express contents corresponding to a set number of days before the current day for the target express route and the various configuration information of the target express route by using the Hoffit neural network after multiple trainings includes: the number of trainings of the Hoffit neural network is positively correlated with the number of express units at the set starting address and positively correlated with the number of express units at the set ending address;

[0011] Among them, presetting the number of various storage containers reserved for various types of express on the current day at the set ending address based on the number of packages corresponding to various types of express on the current day for the target express route includes: reserving corresponding types of storage containers with matching sizes for each type of express. The more the number of packages corresponding to a certain type of express on the current day for the target express route, the more the number of corresponding types of storage containers reserved for the certain type of express on the current day;

[0012] Among them, in the early morning of the same day, multiple historical express contents corresponding to a set number of days before the same day for the target express route are obtained. The single historical express content corresponding to each day is the number of packages corresponding to various types of express on that day, including: the value of the set number is proportional to the route distance of the target express route.

[0013] The express classification information prediction system based on historical data of the present invention is intelligent in design and widely applicable. Since it can start the intelligent prediction of the number of packages corresponding to various types of express on the same day for the target express route in the early morning of the same day, and then preset the number of various storage containers reserved for various types of express on the same day at the set end address, thereby improving the dynamic allocation effect of the number of storage containers and ensuring the utilization rate of the storage containers. Brief Description of the Drawings

[0014] Those skilled in the art can better understand the numerous advantages of the present invention by referring to the accompanying drawings, in which:

[0015] Figure 1 is a schematic structural diagram of an express classification information prediction system based on historical data according to the primary embodiment of the present invention.

[0016] Figure 2 is a schematic structural diagram of an express classification information prediction system based on historical data according to the secondary embodiment of the present invention.

[0017] Figure 3 is a schematic structural diagram of an express classification information prediction system based on historical data according to the tertiary embodiment of the present invention. Detailed Description of the Invention

[0018] Figure 1 is a schematic structural diagram of an express classification information prediction system based on historical data according to the primary embodiment of the present invention. The system includes:

[0019] A content collection mechanism for obtaining, in the early morning of the same day, multiple historical express contents corresponding to a set number of days before the same day for the target express route. The single historical express content corresponding to each day is the number of packages corresponding to various types of express on that day. The target express route is the route between the set starting address and the set ending address;

[0020] Specifically, a content collection mechanism is used to obtain, at the early morning of the current day, multiple copies of historical express delivery content corresponding to a set number of days before the current day for the target express delivery route. The single copy of historical express delivery content corresponding to each day is the number of packages corresponding to various types of express deliveries on that day. The target express delivery route is the route between the set starting address and the set ending address, including: Selecting to use a PAL device to implement the content collection mechanism, which is used to obtain, at the early morning of the current day, multiple copies of historical express delivery content corresponding to a set number of days before the current day for the target express delivery route. The single copy of historical express delivery content corresponding to each day is the number of packages corresponding to various types of express deliveries on that day. The target express delivery route is the route between the set starting address and the set ending address;

[0021] A configuration capture mechanism is configured to obtain each piece of configuration information of the target express delivery route. Each piece of configuration information of the target express delivery route is the route distance of the target express delivery route, the number of express delivery units at the set starting address, and the number of express delivery units at the set ending address;

[0022] A network application device is connected to the content collection mechanism and the configuration capture mechanism respectively, and is used to analyze the number of packages corresponding to various types of express deliveries on the target express delivery route on the current day based on the multiple copies of historical express delivery content corresponding to a set number of days before the current day for the target express delivery route and each piece of configuration information of the target express delivery route by using the Hoffit neural network after multiple trainings;

[0023] A mapping processing device is connected to the network application device and is used to preset the number of various storage containers reserved for various types of express deliveries on the current day at the set ending address based on the number of packages corresponding to various types of express deliveries on the target express delivery route on the current day;

[0024] Among them, analyzing the number of packages corresponding to various types of express deliveries on the target express delivery route on the current day based on the multiple copies of historical express delivery content corresponding to a set number of days before the current day for the target express delivery route and each piece of configuration information of the target express delivery route by using the Hoffit neural network after multiple trainings includes: The number of times of training of the Hoffit neural network is positively correlated with the number of express delivery units at the set starting address and is positively correlated with the number of express delivery units at the set ending address at the same time;

[0025] Among them, presetting the number of various storage containers reserved for various types of express deliveries on the current day at the set ending address based on the number of packages corresponding to various types of express deliveries on the target express delivery route on the current day includes: Reserving storage containers of corresponding types with matching sizes for each type of express delivery. The more the number of packages corresponding to a certain type of express delivery on the target express delivery route on the current day, the more the number of storage containers of the corresponding type reserved for the certain type of express delivery on the current day;

[0026] Among them, obtaining multiple historical express contents corresponding to multiple days respectively set before the current day for the target express route in the early morning of the current day. The single historical express content corresponding to each day is the number of packages corresponding to various types of express on that day, including: the value of the set number is proportional to the route distance of the target express route;

[0027] Among them, configuring a capture mechanism for obtaining each piece of configuration information of the target express route. Each piece of configuration information of the target express route is the route distance of the target express route, the number of express units at the set starting address, and the number of express units at the set ending address, including: the number of express units at the set starting address is the sum of the number of companies at the set starting address and the number of resident households at the set starting address.

[0028] Figure 2 It is a schematic structural diagram of an express classification information prediction system based on historical data according to a secondary embodiment of the present invention.

[0029] And Figure 1 different, Figure 2 The express classification information prediction system based on historical data in

[0030] A voltage conversion device is arranged near the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism and is respectively connected to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism;

[0031] Among them, the voltage conversion device is arranged near the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism and is respectively connected to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism, including: the voltage conversion device is used to provide the respective working voltages required for the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism.

[0032] Figure 3 It is a schematic structural diagram of an express classification information prediction system based on historical data according to a further secondary embodiment of the present invention.

[0033] And Figure 1 different, Figure 3 The express classification information prediction system based on historical data in

[0034] A quartz oscillator device is disposed near the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism, and is respectively connected to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism;

[0035] Among them, the quartz oscillator device disposed near the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism and respectively connected to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism includes: the quartz oscillator device is used to respectively provide the reference clock pulses required by the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism.

[0036] Next, the specific structure of the express delivery classification information prediction system based on historical data of the present invention will be further described.

[0037] In the express delivery classification information prediction system based on historical data according to various embodiments of the present invention:

[0038] A programmable logic device is used to perform image data processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processed data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

[0039] In the express delivery classification information prediction system based on historical data according to various embodiments of the present invention:

[0040] Using a programmable logic device to perform image data processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processed data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing image sharpening processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processed data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

[0041] In the express delivery classification information prediction system based on historical data according to various embodiments of the present invention:

[0042] Using a programmable logic device to perform image data processing on the output data of the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing image enhancement processing on the output data of the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

[0043] In the express delivery classification information prediction system based on historical data according to various embodiments of the present invention:

[0044] Using a programmable logic device to perform image data processing on the output data of the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing image filtering processing on the output data of the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

[0045] And in the express delivery classification information prediction system based on historical data according to various embodiments of the present invention:

[0046] Using a programmable logic device to perform image data processing on the output data of the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing distortion correction processing on the output data of the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processing device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

[0047] In addition, in the express delivery classification information prediction system based on historical data, a configuration capture mechanism is configured to obtain each piece of configuration information of the target express delivery route. Each piece of configuration information of the target express delivery route includes the route distance of the target express delivery route, the number of express delivery units at the set start address, and the number of express delivery units at the set end address. It further includes: the number of express delivery units at the set end address is the sum of the number of companies at the set end address and the number of resident households at the set end address.

[0048] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An express delivery classification information prediction system based on historical data, characterized in that, The system includes: a content acquisition mechanism for obtaining, at the early morning of the current day, multiple copies of historical express contents respectively corresponding to a set number of days before the current day for the target express route, where the single copy of historical express content corresponding to each day is the number of packages respectively corresponding to various types of express on that day, and the target express route is the route between the set starting address and the set ending address; a configuration capture mechanism for obtaining each piece of configuration information of the target express route, where each piece of configuration information of the target express route is the route distance of the target express route, the number of express units at the set starting address, and the number of express units at the set ending address; a network application device connected to the content acquisition mechanism and the configuration capture mechanism respectively, for parsing the number of packages respectively corresponding to various types of express on the current day for the target express route based on the multiple copies of historical express contents respectively corresponding to a set number of days before the current day for the target express route and each piece of configuration information of the target express route by using the Hoffit neural network after multiple trainings; a mapping processing device connected to the network application device, for presetting, at the set ending address, the number of various storage containers respectively reserved for various types of express on the current day based on the number of packages respectively corresponding to various types of express on the current day for the target express route; wherein, parsing the number of packages respectively corresponding to various types of express on the current day for the target express route based on the multiple copies of historical express contents respectively corresponding to a set number of days before the current day for the target express route and each piece of configuration information of the target express route by using the Hoffit neural network after multiple trainings includes: the number of times of training of the Hoffit neural network is positively correlated with the number of express units at the set starting address and is also positively correlated with the number of express units at the set ending address; wherein, presetting, at the set ending address, the number of various storage containers respectively reserved for various types of express on the current day based on the number of packages respectively corresponding to various types of express on the current day for the target express route includes: reserving corresponding types of storage containers with matching sizes for each type of express, and the more the number of packages corresponding to a certain type of express on the current day for the target express route, the more the number of corresponding types of storage containers reserved for the certain type of express on the current day; wherein, obtaining, at the early morning of the current day, multiple copies of historical express contents respectively corresponding to a set number of days before the current day for the target express route, where the single copy of historical express content corresponding to each day is the number of packages respectively corresponding to various types of express on that day includes: the value of the set number is proportional to the route distance of the target express route; Configure a capture mechanism for obtaining various configuration information of the target express delivery route. The various configuration information of the target express delivery route includes the route distance of the target express delivery route, the number of express units at the set starting address, and the number of express units at the set ending address. The number of express units at the set starting address is the sum of the number of companies at the set starting address and the number of resident households at the set starting address. The number of express units at the set ending address is the sum of the number of companies at the set ending address and the number of resident households at the set ending address.

2. The express classification information prediction system based on historical data according to claim 1, wherein The system further includes: A voltage conversion device is arranged near the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism and is respectively connected to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism; Among them, the voltage conversion device is arranged near the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism and is respectively connected to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism, including: the voltage conversion device is used to provide the respective working voltages required by the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism.

3. The express delivery classification information prediction system based on historical data according to claim 1, characterized in that The system further includes: A quartz oscillation device is arranged near the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism and is respectively connected to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism; Among them, the quartz oscillation device is arranged near the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism and is respectively connected to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism, including: the quartz oscillation device is used to provide the respective reference clock pulses required by the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism.

4. The historical data-based express delivery classification information prediction system according to any one of claims 1-3, characterized in that: A programmable logic device is used to perform image data processing on the output data of the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processed data corresponding to the mapping processor, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

5. The historical data-based express delivery classification information prediction system according to claim 4, characterized in that: Performing image data processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism by using a programmable logic device to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing image sharpening processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

6. The historical data-based express classification information prediction system according to claim 4, characterized in that: Performing image data processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism by using a programmable logic device to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing image enhancement processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

7. The historical data-based express classification information prediction system according to claim 4, characterized in that: Performing image data processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism by using a programmable logic device to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing image filtering processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

8. The historical data-based express classification information prediction system according to claim 4, characterized in that: Performing image data processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism by using a programmable logic device to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively includes: performing distortion correction processing on the output data of the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism to obtain the output processing data corresponding to the mapping processor device, the network application device, the content acquisition mechanism, and the configuration capture mechanism respectively.

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