A night mail management method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a night mail receiving management method and device, electronic equipment and storage medium, belonging to the field of artificial intelligence. The method comprises: obtaining a target night mail receiving quantity of a target area and a first value of a night mail receiving feature; determining a first night mail receiving duration of the target area according to the target night mail receiving quantity, the first value of the night mail receiving feature and a night mail receiving relationship; the night mail receiving relationship is a target relationship between the target night mail receiving quantity, the night mail receiving feature and the night mail receiving duration; in the case that the first night mail receiving duration is greater than 0, determining a target night mail receiving duration of the target area according to the first night mail receiving duration. Embodiments of the present application determine the target night mail receiving duration in line with the actual situation of the target area based on the night mail receiving relationship, the target night mail receiving quantity and the first value of the night mail receiving feature of the target area, which can meet the night mail sending demand of part of customers while realizing fine management of express resource allocation.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, electronic device and storage medium for nighttime mail receiving management. Background Technology

[0002] With the fast pace of modern life, many people work or study during the day, and need to handle other matters such as sending packages and making deliveries at night. This means that traditional daytime delivery services can no longer fully meet the immediate needs of some people. Against this backdrop, adding nighttime pickup services has become a strategy to better meet the shipping needs of some customers who need to send packages at night. However, if nighttime pickup services are added indiscriminately to all areas around the clock, it may lead to resource waste in some areas, while areas with concentrated demand may face staff shortages.

[0003] Therefore, in order to effectively improve the operational efficiency of nighttime pickup services, it is necessary to adopt differentiated nighttime pickup business operation strategies based on the actual conditions of different regions, so as to meet the nighttime delivery needs of some customers while achieving refined management of express delivery resource allocation. Summary of the Invention

[0004] The main objective of this application is to propose a nighttime package collection management method, device, electronic device, and storage medium, which aims to meet the nighttime package sending needs of some customers while achieving refined management of express delivery resource allocation.

[0005] To achieve the above objectives, a first aspect of this application proposes a nighttime mail receiving management method, comprising: obtaining a target nighttime mail receiving volume for a target area and a first value of a nighttime mail receiving characteristic; the nighttime mail receiving characteristic being a feature affecting the nighttime mail receiving volume, and the first value being determined based on historical values ​​of the nighttime mail receiving characteristic in the target area during a first preset time period; determining a first nighttime mail receiving duration for the target area based on the target nighttime mail receiving volume, the first value of the nighttime mail receiving characteristic, and the nighttime mail receiving relationship; the nighttime mail receiving relationship being a target relationship between the nighttime mail receiving volume, the nighttime mail receiving characteristic, and the nighttime mail receiving duration; and determining a target nighttime mail receiving duration for the target area based on the first nighttime mail receiving duration when the first nighttime mail receiving duration is greater than 0.

[0006] In some embodiments, the nighttime package collection management method further includes: when the first nighttime package collection duration is less than or equal to 0, outputting the first nighttime package collection duration and a prompt message suggesting that the nighttime package collection service in the target area be turned off.

[0007] In some embodiments, obtaining the target nighttime mail volume for a target area includes: if the historical nighttime mail volume for the target area in a second preset time period is greater than 0, determining the target nighttime mail volume as the product of the first nighttime mail volume and 'a'; wherein, 'a' > 1; the first nighttime mail volume is determined based on the historical nighttime mail volume for the target area in the second preset time period; if the historical nighttime mail volume for the target area in the second preset time period is equal to 0, determining the target nighttime mail volume using the following formula:

[0008] Y = (b*X1) + (c*X2);

[0009] Wherein, Y is the target nighttime pickup volume, X1 is the annual individual pickup volume of the target region, X2 is the annual monthly pickup volume of the target region, b is the ratio of the nighttime individual pickup volume of all regions with nighttime pickup service to the total number of individual pickups of all regions, and c is the ratio of the nighttime monthly pickup volume of all regions to the total number of monthly pickups of all regions.

[0010] In some embodiments, before determining the first nighttime collection duration of the target area based on the target nighttime collection volume, the first value of the nighttime collection characteristic, and the nighttime collection relationship, the nighttime collection management method further includes: adjusting the linear relationship between the nighttime collection volume, the nighttime collection characteristic, and the nighttime collection duration based on the causal relationship between the changes in the nighttime collection volume and the nighttime collection duration, and determining the nighttime collection relationship.

[0011] In some embodiments, determining the first nighttime delivery duration of the target area based on the target nighttime delivery volume, the first value of the nighttime delivery feature, and the nighttime delivery relationship includes: inputting the target nighttime delivery volume and the first value of the nighttime delivery feature into a trained causal recognition model to obtain the first nighttime delivery duration; wherein the trained causal recognition model is a model that has learned the nighttime delivery relationship.

[0012] In some embodiments, before inputting the target nighttime mail receipt volume and the first value of the nighttime mail receipt characteristics into the trained causal recognition model to obtain the first nighttime mail receipt duration, the nighttime mail receipt management method further includes:

[0013] Multiple first training samples are obtained; each first training sample includes a second nighttime mail receipt volume of a sample region, a second nighttime mail receipt duration corresponding to the second nighttime mail receipt volume, and a second value of the nighttime mail receipt feature; the second value is determined based on the historical value of the nighttime mail receipt feature in a third preset time period in the sample region, and the second nighttime mail receipt volume is greater than 0; wherein, the sample regions included in each first training sample are different; a target mathematical model is established based on the multiple first training samples; the target mathematical model represents the linear relationship between the nighttime mail receipt volume, the nighttime mail receipt feature, and the nighttime mail receipt duration; the multiple second training samples and the target mathematical model are input into a preset causal recognition model, and the preset causal recognition model is trained until the training stop condition is met, thereby obtaining the trained causal recognition model.

[0014] In some embodiments, establishing a target mathematical model based on the plurality of first training samples includes: analyzing the plurality of first training samples using a linear regression model to obtain the target mathematical model; wherein, in the target mathematical model, the number of packages received at night is the dependent variable, and the duration of packages received at night and the characteristics of packages received at night are the independent variables.

[0015] In some embodiments, the nighttime pickup characteristics include the number of known competitors, the number of mails sent, and the number of nighttime pickup personnel.

[0016] To achieve the above objectives, a second aspect of this application provides a nighttime mail receiving management device, comprising: an acquisition module, a first duration module, and a second duration module; the acquisition module is used to acquire a target nighttime mail receiving volume in a target area and a first value of a nighttime mail receiving characteristic; the nighttime mail receiving characteristic is a feature affecting the nighttime mail receiving volume, and the first value is determined based on the historical value of the nighttime mail receiving characteristic in the target area during a first preset time period; the first duration module is used to determine a first nighttime mail receiving duration in the target area based on the target nighttime mail receiving volume, the first value of the nighttime mail receiving characteristic, and the nighttime mail receiving relationship; the nighttime mail receiving relationship is a target relationship between the nighttime mail receiving volume, the nighttime mail receiving characteristic, and the nighttime mail receiving duration; the second duration module is used to determine a target nighttime mail receiving duration in the target area based on the first nighttime mail receiving duration when the first nighttime mail receiving duration is greater than 0.

[0017] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0018] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0019] The nighttime parcel collection management method, apparatus, electronic device, and storage medium proposed in this application can accurately determine the first value of nighttime parcel collection characteristics that conforms to the actual situation of the target area based on the historical values ​​of nighttime parcel collection characteristics in the target area during a first preset time period. Furthermore, based on the target nighttime parcel collection volume of the target area, the first value of nighttime parcel collection characteristics that conforms to the actual situation of the target area, and the target relationship between nighttime parcel collection volume, nighttime parcel collection characteristics, and nighttime parcel collection duration, the first nighttime parcel collection duration of the target area is determined. This allows for accurate determination of the first nighttime parcel collection duration required for the target area to reach the target nighttime parcel collection volume, starting from the actual situation of the target area. Furthermore, by determining the target nighttime parcel collection duration of the target area based on the first nighttime parcel collection duration when the first nighttime parcel collection duration is greater than 0, the target nighttime parcel collection duration of the target area can be further accurately determined, thus enabling refined management of express delivery resource allocation while meeting the nighttime delivery needs of some customers. Attached Figure Description

[0020] Figure 1 This is a flowchart of the nighttime mail receiving management method provided in the embodiments of this application;

[0021] Figure 2 This is a flowchart of step S102 provided in the embodiments of this application;

[0022] Figure 3 This is a flowchart of the training causal recognition model provided in the embodiments of this application;

[0023] Figure 4 This is a flowchart of establishing the target mathematical model provided in the embodiments of this application;

[0024] Figure 5 This is a detailed process of the nighttime mail receiving management method provided in the embodiments of this application. Figure 1 ;

[0025] Figure 6 This is a detailed process of the nighttime mail receiving management method provided in the embodiments of this application. Figure 2 ;

[0026] Figure 7 This is a schematic diagram of the structure of the nighttime mail receiving management device provided in the embodiments of this application;

[0027] Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0031] Traditionally, logistics companies provide services during the daytime. To offer more comprehensive and detailed logistics services and meet the nighttime shipping needs of a wider range of users, while also considering the increased resource costs and vehicle scheduling adjustments required for different regional branches to arrange nighttime pickups, differentiated nighttime pickup management is necessary for different regions. Addressing the nighttime pickup needs of modern customers, this application provides a nighttime pickup management method, device, electronic device, and storage medium. Utilizing big data and artificial intelligence algorithms, it provides auxiliary decision-making for express delivery operations, achieving refined management of express delivery resource allocation while meeting the nighttime shipping needs of some customers.

[0032] The nighttime mail receiving management method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the nighttime mail receiving management method in this application embodiment is described.

[0033] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0034] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0035] The nighttime mail collection management method provided in this application relates to the field of artificial intelligence technology and can be applied to scenarios such as evaluating the nighttime mail collection service area to be opened, that is, the nighttime mail collection duration of the area to be opened for nighttime mail collection service from 0 to 1, and adjusting the nighttime mail collection duration of the area to be opened for nighttime mail collection service.

[0036] In assessing the nighttime collection duration of a pre-activated nighttime collection service area, the nighttime collection management method provided in this application embodiment can be used to determine the first nighttime collection duration of the pre-activated nighttime collection service area based on the target relationship between nighttime collection volume, nighttime collection characteristics, and nighttime collection duration, according to the target nighttime collection volume of the pre-activated nighttime collection service area and the first value of the nighttime collection characteristics of the pre-activated nighttime collection service area. If the first nighttime pickup duration is greater than 0, then the first nighttime pickup duration is feasible, indicating that the pre-activated nighttime pickup service area can achieve the target nighttime pickup volume and predetermined revenue target. This first nighttime pickup duration can be determined as the target nighttime pickup duration for the pre-activated nighttime pickup service area, and the target nighttime pickup duration for the pre-activated nighttime pickup service area is output so that managers can activate the nighttime pickup service for that area based on the target nighttime pickup duration. If the first nighttime pickup duration is less than or equal to 0, then the first nighttime pickup duration is not feasible, indicating that the pre-activated nighttime pickup service area cannot achieve the target nighttime pickup volume and predetermined revenue target. The first nighttime pickup duration and a prompt message suggesting that activating the nighttime pickup service for that area can be output so that managers can decide whether to activate the nighttime pickup service for that area. The nighttime pickup characteristics of the pre-activated nighttime pickup service area may include at least one of the following: the number of known competitors, the number of nighttime pickup personnel, and the area type number.

[0037] In scenarios involving adjusting the nighttime pickup duration in areas where nighttime pickup services have been activated, the nighttime pickup management method provided in this application can be utilized. Based on the target relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration, and according to the target nighttime pickup volume and a first value of the nighttime pickup characteristics of the activated nighttime pickup service area, a first nighttime pickup duration for the activated nighttime pickup service area is determined. If this first nighttime pickup duration is greater than 0, the first nighttime pickup duration is feasible, indicating that the activated nighttime pickup service area can achieve the target nighttime pickup volume and predetermined revenue target. Therefore, this first nighttime pickup duration can be determined as the target nighttime pickup duration for the activated nighttime pickup service area. Output the target nighttime pickup duration for areas where nighttime pickup service has been activated, so that managers of areas with activated nighttime pickup service can adjust the nighttime pickup duration based on the target nighttime pickup duration. Alternatively, the target nighttime pickup duration for areas with activated nighttime pickup service can be subtracted from the current nighttime pickup duration for those areas to determine and output the nighttime pickup duration deviation. This allows managers to determine whether to increase or decrease the nighttime pickup duration for areas with activated nighttime pickup service based on the deviation, and to adjust the nighttime pickup duration accordingly. If the first night's pickup duration is less than or equal to 0, the first night's pickup duration cannot be implemented, indicating that the area with the night pickup service enabled cannot achieve the target night pickup volume and predetermined revenue target. The system can output the first night's pickup duration and a suggestion to close the night pickup service in that area, allowing administrators to decide whether to close the service. The night pickup characteristics of the area with the night pickup service enabled may include at least one of the following: number of known competitors, number of shipments, number of customer complaints regarding shipping timeliness, number of pickups, number of customer complaints regarding pickup timeliness, total number of timeliness customer complaints, total timeliness customer complaint rate, number of individual shipments, insurance fees, number of shipping users, monthly settlement shipments, number of shipments from target customers, number of shipments from themed marketing campaigns, number of shipments from marketing coupon packages, number of shipments participating in promotional activities, number of shipments from marketing check-ins, number of discounted orders, number of new customer acquisitions, and night pickup staff shifts and area type code.

[0038] The nighttime email receiving management method provided in this application can be applied to electronic devices or the software of electronic devices. The electronic device can be a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, etc.; the server can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the nighttime email receiving management method, etc., but is not limited to the above forms.

[0039] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0040] Figure 1 This is an optional flowchart of the nighttime mail receiving management method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S103.

[0041] Step S101: Obtain the target nighttime package volume in the target area, and the first value of the nighttime package volume characteristic; the nighttime package volume characteristic is the feature that affects the nighttime package volume, and the first value is determined based on the historical value of the nighttime package volume characteristic in the target area during a first preset time period;

[0042] In practice, the target area can be either the area where nighttime pickup service is pre-activated or the area where nighttime pickup service is already activated. This application does not limit the definition of the area. In some embodiments, the area can be understood as a geographical region; in other embodiments, the area can be understood as the service area corresponding to the outlet providing nighttime pickup service. For example, the target area can be the service area corresponding to the target courier outlet that pre-activates or has already activated nighttime pickup service. In these embodiments, the courier outlets that pre-activate or have already activated nighttime pickup service can be referred to as area of ​​interest (AOI) outlets, and the service area corresponding to the courier outlets that pre-activate or have already activated nighttime pickup service can be referred to as the AOI area.

[0043] The target nighttime pickup volume can be a preset value or a value determined based on the historical nighttime pickup volume of the target area in the second preset period. The target nighttime pickup volume can be used to represent the nighttime pickup volume required to achieve the predetermined revenue target. The first preset period and the second preset period can be the same period or different periods, which can be determined according to the actual situation.

[0044] Nighttime pickup characteristics may include one or more characteristics that affect the nighttime pickup volume. In some embodiments, nighttime pickup characteristics may include the number of known competitors, the number of shipments, and the number of nighttime pickup personnel.

[0045] It is known that the number of competitors has a significant negative impact on nighttime parcel collection volume; as the number of known competitors increases, the nighttime parcel collection volume decreases significantly. Conversely, the number of parcels sent and the number of nighttime parcel collectors each have a significant positive impact on nighttime parcel collection volume; as the number of parcels sent and the number of nighttime parcel collectors increase, respectively, the nighttime parcel collection volume increases significantly. Therefore, based on the known number of competitors, the number of parcels sent, and the number of nighttime parcel collectors, the target nighttime parcel collection duration that best suits the actual conditions of the target area can be accurately determined.

[0046] In other embodiments, nighttime pickup characteristics may include at least one of the following: known competitor quantity, mail sending volume, mail sending timeliness customer complaint volume, pickup volume, pickup customer complaint volume, pickup timeliness customer complaint volume, total timeliness customer complaint volume, total timeliness customer complaint rate, individual order volume, insurance fee, number of mail sending users, monthly settlement volume, target customer volume, themed marketing volume, marketing coupon package volume, volume of volume participating in the "spend more, save more" activity, marketing check-in volume, discount order volume, new customer acquisition volume, number of nighttime pickup personnel, and region type number. The number of nighttime pickup personnel can be determined based on the number of pickup personnel shift pairs at the AOI branch, which can also be called the AOI shift pair quantity; the region type number can also be called the AOI scene type number, which can represent the type to which the region belongs.

[0047] In practice, nighttime pickup characteristics of the target area can be selected based on the actual situation of the target area, from dimensions such as competitors, customer complaints, types of logistics shipments, types of activities involved in logistics shipments, and regional types. Furthermore, for each selected nighttime pickup characteristic, based on the historical value of the nighttime pickup characteristic in the target area during the first preset time period, the first value of the nighttime pickup characteristic that conforms to the actual situation of the target area can be accurately determined. Thus, based on the first value of the nighttime pickup characteristic of the target area, the target nighttime pickup duration that conforms to the actual situation of the target area can be accurately determined. This allows for the refined management of express delivery resource allocation while meeting the nighttime delivery needs of some customers.

[0048] In practice, for each nighttime delivery feature, the average value of the nighttime delivery feature among its historical values ​​over a first preset time period in the target area can be determined as the first value of the nighttime delivery feature; alternatively, the maximum value of the nighttime delivery feature among its historical values ​​over a first preset time period in the target area can be determined as the first value of the nighttime delivery feature. This application does not limit the method of determining the first value of the nighttime delivery feature based on its historical values ​​over a first preset time period in the target area; the specific implementation can be determined based on actual circumstances.

[0049] Step S102: Based on the target nighttime pickup volume, the first value of the nighttime pickup characteristics, and the nighttime pickup relationship, determine the first nighttime pickup duration for the target area; the nighttime pickup relationship is the target relationship between the nighttime pickup volume, the nighttime pickup characteristics, and the nighttime pickup duration.

[0050] In practice, the above target relationship can be a linear relationship between the number of packages received at night, the characteristics of packages received at night, and the duration of packages received at night; or it can be a relationship determined by adjusting the above linear relationship based on the causal relationship between the changes in the number of packages received at night and the duration of packages received at night. For specific implementation details, please refer to the relevant descriptions below, which will not be described here.

[0051] Optionally, the nighttime receipt relationship is a pre-stored relationship between the nighttime receipt volume, nighttime receipt characteristics, and nighttime receipt duration. In some embodiments, the target nighttime receipt volume and the first value of the nighttime receipt characteristics of the target area can be substituted into the pre-stored nighttime receipt relationship to obtain the first nighttime receipt duration of the target area. In other embodiments, the target nighttime receipt volume and the first value of the nighttime receipt characteristics of the target area can be input into a model that has learned the nighttime receipt relationship to obtain the first nighttime receipt duration of the target area.

[0052] Step S103: If the first night's pickup duration is greater than 0, determine the target night's pickup duration for the target area based on the first night's pickup duration.

[0053] Specifically, if the first night's package collection duration is greater than 0, the first night's package collection duration can be directly determined as the target night's package collection duration. Alternatively, the first night's package collection duration can be compared with the current night's package collection duration in the target area. If the deviation is outside a preset range, the first night's package collection duration can be determined as the target night's package collection duration; if the deviation is within a preset range, the current night's package collection duration can be determined as the target night's package collection duration, thus avoiding adjustment of the target area's night's package collection duration. In practice, if the target area is a pre-activated night's package collection service area, the current night's package collection duration in the target area can be 0. Alternatively, the relationship between the night's package collection duration and the target coefficient can be used to determine the target night's package collection duration based on the first night's package collection duration and the target coefficient. In practice, the target coefficient and the relationship between the night's package collection duration and the target coefficient can be determined according to the actual situation. The target coefficient and the relationship between the night's package collection duration and the target coefficient are used to determine a target night's package collection duration that better reflects the actual situation of the target area. This application does not limit the method of determining the target nighttime collection time for the target area based on the first nighttime collection time. In actual implementation, the target nighttime collection time can be flexibly determined based on the actual situation of the target area according to the first nighttime collection time.

[0054] Furthermore, after determining the target nighttime pickup duration for the target area, the target nighttime pickup duration for the target area can be output. This allows managers in the target area to activate or adjust the nighttime pickup service based on the target nighttime pickup duration, thereby improving the efficiency and accuracy of management decisions and ultimately enhancing the user experience.

[0055] This application embodiment, based on the historical values ​​of nighttime pickup characteristics in a target area during a first preset time period, can accurately determine the first value of nighttime pickup characteristics that conforms to the actual situation of the target area. Furthermore, based on the target nighttime pickup volume of the target area, the first value of nighttime pickup characteristics that conforms to the actual situation of the target area, and the target relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration, the first nighttime pickup duration of the target area is determined. This allows for accurate determination of the first nighttime pickup duration required for the target area to reach the target nighttime pickup volume, starting from the actual situation of the target area. Furthermore, by determining the target nighttime pickup duration of the target area based on the first nighttime pickup duration when the first nighttime pickup duration is greater than 0, the target nighttime pickup duration of the target area can be further accurately determined, thus enabling refined management of express delivery resource allocation while meeting the nighttime shipping needs of some customers.

[0056] Among these, the known number of competitors in the above-mentioned nighttime pickup characteristics can be the number of known competitors that offer nighttime pickup services; the number of pickup customer complaints can be the total number of customer complaints caused by nighttime pickup timeliness and other nighttime pickup reasons; wherein, customer complaints caused by other nighttime pickup reasons can include at least one of customer complaints caused by nighttime pickup attitude and customer complaints caused by nighttime pickup fees; the total timeliness customer complaint volume can be the total number of complaints caused by nighttime pickup timeliness and nighttime shipping timeliness; the total timeliness customer complaint rate can be the ratio of the total number of complaints caused by timeliness reasons to the total number of customer complaints for nighttime pickup services; the target customer parcel volume can be the number of logistics parcels of the target customer picked up at night; wherein, the target customer can be a customer whose individual parcel volume is higher than a first preset value. It can also be for customers whose monthly parcel volume exceeds the second preset value; the theme marketing parcel volume can be the number of parcels collected at night that participate in theme marketing activities; the marketing coupon parcel volume can be the number of parcels collected at night that use marketing coupon packages; the full-gift activity participation parcel volume can be the number of parcels collected at night that participate in the full-gift activity; the marketing check-in parcel volume can be the number of parcels collected at night that participate in the marketing check-in activity; the discount parcel volume can be the number of parcels collected at night that enjoy discounts; the new customer acquisition volume can be the number of new customers using the night parcel collection service; the area type number is a number corresponding to the type of the area, and each number is used to represent the type of the area corresponding to the number; optionally, the type of the area includes at least one of residential area, industrial area, central business district, school, commercial area, professional market, remote town and mountain area, and mixed commercial and residential area.

[0057] When the target nighttime parcel volume is a value determined based on the historical nighttime parcel volume of the target area during a second preset time period, obtaining the target nighttime parcel volume of the target area in step S101 includes:

[0058] If the historical nighttime package volume of the target area during the second preset period is greater than 0, the product of the first nighttime package volume and a is determined as the target nighttime package volume; where a > 1; the first nighttime package volume is determined based on the historical nighttime package volume of the target area during the second preset period.

[0059] If the historical nighttime parcel volume in the target area during the second preset time period is equal to 0, the target nighttime parcel volume is determined using the following formula:

[0060] Y = (b*X1) + (c*X2);

[0061] Where Y is the target nighttime pickup volume, X1 is the annual single-item pickup volume of the target region, also known as the annual target AOI region single-item pickup volume, X2 is the annual monthly pickup volume of the target region, also known as the annual target AOI region monthly pickup volume, b is the ratio of the nighttime single-item pickup volume of all regions with nighttime pickup service to the total single-item pickup volume of all regions, also known as the average nighttime single-item pickup volume of all AOI regions, and c is the ratio of the nighttime monthly pickup volume of all regions to the total monthly pickup volume of all regions, also known as the average nighttime monthly pickup volume of all AOI regions.

[0062] The annual volume of individual shipments received in the target area can be the ratio of the total number of individual shipments received in the target area over the past i years to i; the annual monthly shipment volume can be the ratio of the total number of monthly shipments received in the target area over the past j years to j; where i and j are integers greater than or equal to 1; i and j can be equal or unequal.

[0063] In practice, the average of the historical nighttime package receipts for the target area during the second preset time period can be used as the first nighttime package receipt; alternatively, the maximum value among the historical nighttime package receipts for the target area during the second preset time period can be used as the first nighttime package receipt. This application does not limit the method of determining the first nighttime package receipt based on the historical nighttime package receipts for the target area during the second preset time period; the specific implementation can be determined based on actual circumstances.

[0064] Optionally, a = 110%; if the historical nighttime receipts in the target area during the second preset period are greater than 0, the target nighttime receipts can be determined using formula (1):

[0065] Target nighttime pickup volume = First nighttime pickup volume × 110%; (1)

[0066] Optionally, b = 0.03493, c = 0.00863; if the historical nighttime mail volume in the target area during the second preset time period is equal to 0, the target nighttime mail volume can be determined by formula (2):

[0067] Y=(0.03493*X1)+(0.00863*X2); (2)

[0068] Where the historical nighttime parcel volume during the second preset period is greater than 0, the target area can be an area where nighttime parcel collection service has been activated; where the historical nighttime parcel volume during the second preset period is equal to 0, the target area can be an area where nighttime parcel collection service has been activated, or an area where nighttime parcel collection service is to be activated. This application embodiment does not limit the second preset period; the second preset period can be determined according to actual circumstances.

[0069] This application embodiment, based on the historical nighttime pickup volume of the target area during a second preset period, determines the target nighttime pickup volume by multiplying the first nighttime pickup volume by 'a', or by using the ratio 'b' of the nighttime pickup volume of individual parcels in all areas with nighttime pickup services to the total number of individual parcels in all areas, and the ratio 'c' of the nighttime pickup volume of monthly parcels in all areas to the total number of monthly parcels in all areas. The target nighttime pickup volume for the target area is determined using the formula: Target Nighttime Pickup Volume for Target Area = (b * Annual Individual Parcel Pickup Volume of Target Area) + (c * Annual Monthly Parcel Pickup Volume of Target Area). This avoids overestimating or underestimating the nighttime pickup capacity of the target area, thus ensuring that the target nighttime pickup volume aligns with the actual situation of the target area. Based on the target nighttime pickup volume, the target nighttime pickup duration can be further accurately determined, thus enabling refined management of express delivery resource allocation while meeting the nighttime shipping needs of some customers.

[0070] In some embodiments, before determining the first nighttime receipt duration of the target area based on the target nighttime receipt volume, the first value of the nighttime receipt characteristics, and the nighttime receipt relationship in step S102 above, the nighttime receipt management method provided in this application embodiment further includes:

[0071] Based on the causal relationship between the changes in nighttime parcel volume and nighttime parcel duration, the linear relationship between nighttime parcel volume, nighttime parcel characteristics, and nighttime parcel duration is adjusted to obtain the nighttime parcel relationship.

[0072] In practice, the causal relationship between the changes in the number of packages received at night and the duration of packages received at night can be determined by learning the impact of the changes in the number of packages received at night on the duration of packages received at night. Therefore, in some embodiments, the causal relationship can also be understood as the causal relationship between the changes in the number of packages received at night and the duration of packages received at night.

[0073] In practical implementation, the linear relationship between nighttime package volume, nighttime package characteristics, and nighttime package duration can be obtained by linearly fitting the data based on the historical nighttime package volume, nighttime package characteristics, and nighttime package duration of multiple AOOI regions during the second preset time period. Alternatively, a linear regression model can be used to analyze the historical nighttime package volume, nighttime package characteristics, and historical nighttime package duration of multiple AOOI regions during the second preset time period to obtain the linear relationship between the data. This application does not limit the method for determining the linear relationship between nighttime package volume, nighttime package characteristics, and nighttime package duration; the specific implementation can be determined based on the actual situation.

[0074] In some embodiments, the linear relationship between nighttime package volume, nighttime package characteristics, and nighttime package duration may not accurately represent the actual relationship between them. Further adjustments can be made based on the causal relationship between nighttime package volume and nighttime package duration, adjusting the coefficient or proportion of at least one of these three factors in the linear relationship to obtain a nighttime package relationship that accurately represents the actual relationship between them. Alternatively, based on the causal relationship between nighttime package volume and nighttime package duration, a polynomial transformation can be performed on the linear relationship between them to obtain a nighttime package relationship that accurately represents the actual relationship between them. The specific implementation can be determined based on actual circumstances, and this application does not limit this approach.

[0075] This application's embodiments, based on the causal relationship between changes in nighttime pickup volume and nighttime pickup duration, adjust the linear relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration to obtain nighttime pickup relationships. This improves the accuracy of determining nighttime pickup relationships, and further improves the accuracy of determining target nighttime pickup durations that conform to the actual situation of the target area based on nighttime pickup relationships. Thus, it can achieve refined management of express delivery resource allocation while meeting the nighttime delivery needs of some customers.

[0076] Optionally, the above model is a causal identification model. Please refer to [reference needed]. Figure 2 In some embodiments, step S102 above, which involves determining the first nighttime collection duration of the target area based on the target nighttime collection volume, a first value of the nighttime collection characteristics, and the nighttime collection relationship, includes:

[0077] Step S201: Input the target nighttime package collection volume and the first value of the nighttime package collection characteristics into the trained causal recognition model to obtain the first nighttime package collection duration;

[0078] Among them, the causal recognition model that has been trained is a model that has learned the relationship of receiving packages at night.

[0079] This application embodiment utilizes a causal recognition model that has been trained to learn the relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration. The first values ​​of the target nighttime pickup volume and nighttime pickup characteristics are input into the trained causal recognition model to obtain the first nighttime pickup duration of the target area. This can improve the efficiency and accuracy of determining the first nighttime pickup duration of the target area, and further improve the efficiency and accuracy of determining the target nighttime pickup duration that conforms to the actual situation of the target area based on the first nighttime pickup duration. Thus, it can achieve refined management of express delivery resource allocation while meeting the nighttime delivery needs of some customers.

[0080] Please refer to Figure 3 Before inputting the target nighttime package volume and the first value of the nighttime package characteristics into the trained causal recognition model to obtain the first nighttime package duration, the nighttime package management method provided in this application embodiment further includes:

[0081] Step S301: Obtain multiple first training samples; each first training sample includes the second nighttime receipt volume of a sample region, the second nighttime receipt duration corresponding to the second nighttime receipt volume, and a second value of the nighttime receipt feature; the second value is determined based on the historical value of the nighttime receipt feature in the sample region corresponding to the first training sample in the third preset time period, and the second nighttime receipt volume is greater than 0; wherein, each first training sample includes a different sample region;

[0082] Step S302: Based on multiple first training samples, establish a target mathematical model; the target mathematical model represents the linear relationship between nighttime package volume, nighttime package characteristics, and nighttime package duration;

[0083] Step S303: Input multiple second training samples and the target mathematical model into the preset causal recognition model, train the preset causal recognition model until the training stopping condition is met, and obtain the trained causal recognition model.

[0084] In some embodiments, step S302 above, which involves establishing a target mathematical model based on multiple first training samples, includes:

[0085] For each first training sample, perform the following steps:

[0086] The first training sample is input into the preset mathematical model. Based on the second value of the second night's package collection volume and night's package collection characteristics, the preset mathematical model is used to obtain the estimated night's package collection time corresponding to the second night's package collection volume.

[0087] Based on the estimated nighttime collection duration corresponding to the second nighttime collection volume and the second nighttime collection duration corresponding to the second nighttime collection volume, determine the first loss function value of the preset mathematical model;

[0088] If the first loss function value does not meet the training stopping condition of the preset mathematical model, the preset mathematical model parameters are adjusted to obtain the updated mathematical model. The updated mathematical model is then trained using the next training sample until the training stopping condition is met, and the trained mathematical model is obtained.

[0089] The trained mathematical model is then identified as the target mathematical model.

[0090] Optionally, the target mathematical model is a model that has learned the linear relationship between the number of packages received at night, the characteristics of packages received at night, and the duration of packages received at night; that is, the linear relationship between the number of packages received at night, the characteristics of packages received at night, and the duration of packages received at night can be a linear relationship between the number of packages received at night, the characteristics of packages received at night, and the duration of packages received at night.

[0091] Optionally, the second training sample can be a training sample that is exactly the same as, partially the same as, or completely different from the first training sample. Each second training sample may include the third night's mail receipt volume of a sample region, the third night's mail receipt duration corresponding to the third night's mail receipt volume, and a third value of the night's mail receipt feature; each second training sample corresponds to a different sample region; the third value is determined based on the historical value of the night's mail receipt feature in a fourth preset time period in the sample region, and the third night's mail receipt volume is greater than 0.

[0092] Optionally, step S303 above: inputting multiple second training samples and the target mathematical model into a preset causal recognition model, training the preset causal recognition model until the training stopping condition is met, and obtaining a trained causal recognition model, including:

[0093] For each second training sample, perform the following steps:

[0094] The second training sample is input into the preset causal recognition model. Based on the third value of the third nighttime package collection volume and the nighttime package collection characteristics, the preset causal recognition model obtains the estimated nighttime package collection duration corresponding to the third nighttime package collection volume.

[0095] Based on the estimated nighttime collection duration corresponding to the third nighttime collection volume and the third nighttime collection duration corresponding to the third nighttime collection volume, determine the second loss function value of the preset causal identification model;

[0096] If the second loss function value does not meet the training stopping condition of the preset causal identification model, the preset causal identification model is adjusted to obtain an updated causal identification model. The updated causal identification model is then trained using the next training sample until the training stopping condition is met, resulting in a trained causal identification model.

[0097] Optionally, the third value of the nighttime receipt feature is determined based on the historical values ​​of the nighttime receipt feature in the sample region corresponding to the second training sample during a third preset time period. This application does not limit the third or fourth preset time period; these periods can be determined according to actual circumstances. Any two of the first, second, third, and fourth preset time periods can be the same or different time periods.

[0098] Optionally, the trained causal recognition model is a model obtained by learning the causal relationship between the variation range of nighttime package volume, nighttime package characteristics, and nighttime package duration, based on the linear relationship between nighttime package volume, nighttime package characteristics, and nighttime package duration. That is, the target relationship between the above-mentioned nighttime package volume, nighttime package characteristics, and nighttime package duration can be the nighttime package characteristic relationship obtained by adjusting the linear relationship between the above-mentioned nighttime package volume, nighttime package characteristics, and nighttime package duration based on the causal relationship between the variation range of nighttime package volume and nighttime package duration.

[0099] It is worth mentioning that when the number of training samples is small, such as when the proportion of AOI outlets that have opened nighttime pickup services is only a few per thousand among all AOI outlets, determining the nighttime pickup characteristic relationship solely by learning the linear relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration may face the problem of insufficient fitting. However, the embodiment of this application can, on the basis of learning the linear relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration, further obtain the causal relationship between the changes in nighttime pickup volume and the changes in nighttime pickup duration by learning the influence of the changes in nighttime pickup volume on the changes in nighttime pickup duration. This causal relationship is then used to adjust the linear relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration to obtain the nighttime pickup relationship. In this way, even with a small number of training samples, the relationship between nighttime pickup volume, nighttime pickup characteristics, and nighttime pickup duration can be fully fitted, resulting in an accurate nighttime pickup characteristic relationship. Thus, by using the trained causal recognition model, the target nighttime collection time required for the target area to reach the target nighttime collection volume can be accurately assessed based on the first value of the nighttime collection characteristics of the target area. In actual implementation, the target nighttime collection volume is generally the current nighttime collection volume that has not yet been reached. Therefore, using the trained causal recognition model to determine the target nighttime collection time is equivalent to using the trained causal recognition model to perform counterfactual modeling to determine the target nighttime collection time.

[0100] This application embodiment obtains multiple first training samples, establishes a target mathematical model based on these samples, and trains a pre-set causal identification model by inputting multiple second training samples and the target mathematical model into it. This ensures the effectiveness of the causal identification model training and improves the efficiency and accuracy of determining the target nighttime pickup time that conforms to the actual situation of the target area using the trained causal identification model. This allows for the refined management of express delivery resource allocation while meeting the nighttime delivery needs of some customers.

[0101] Please refer to Figure 4In other embodiments, step S302 above, which involves establishing a target mathematical model based on multiple first training samples, includes:

[0102] Step S401: Analyze multiple first training samples using a linear regression model to obtain the target mathematical model;

[0103] Optionally, in the target mathematical model, the number of packages received at night is the dependent variable, and the duration and characteristics of packages received at night are the independent variables.

[0104] Specifically, the second nighttime mail volume in the first training sample can be used as the dependent variable, and the second nighttime mail collection duration and the second value of the nighttime mail collection feature corresponding to the second nighttime mail volume in the first training sample can be used as independent variables. A linear regression model can be used to analyze the linear relationship between the second nighttime mail volume, the second nighttime mail collection duration corresponding to the second nighttime mail volume, and the second value of the nighttime mail collection feature, so as to obtain a target mathematical model that can characterize the linear relationship between the nighttime mail volume, the nighttime mail collection duration, and the nighttime mail collection feature.

[0105] This application embodiment analyzes multiple first training samples using a linear regression model to obtain an accurate target mathematical model. Based on the target mathematical model, the training efficiency and effectiveness of the causal identification model can be improved. This, in turn, improves the efficiency and accuracy of using the trained causal identification model to determine the target nighttime pickup time that conforms to the actual situation of the target area. Thus, it is possible to achieve refined management of express delivery resource allocation while meeting the nighttime delivery needs of some customers.

[0106] In some embodiments, the nighttime mail receiving management method provided in this application further includes:

[0107] If the first night's pickup duration is less than or equal to 0, output the first night's pickup duration and a prompt message suggesting that the night pickup service in the target area be turned off.

[0108] Specifically, if the first night's pickup duration is less than or equal to 0, the nighttime pickup duration required for the target area to reach the target nighttime pickup volume is not feasible, indicating that the target area cannot reach the target nighttime pickup volume. In this case, the first night's pickup duration can be output, along with a suggestion to disable the nighttime pickup service in the target area. This allows the target area's administrators to disable the nighttime pickup service based on the first night's pickup duration and the suggestion. It's worth noting that for areas with pre-activated nighttime pickup services, the suggestion to disable the target area's nighttime pickup service can be used to instruct that the nighttime pickup service in that area not to be activated. Conversely, for areas with activated nighttime pickup services, the suggestion to disable the target area's nighttime pickup service can be used to instruct that the activated nighttime pickup service in that area be disabled.

[0109] This application embodiment outputs the first night's package collection duration and a prompt message suggesting the shutdown of the nighttime package collection service in the target area when the first night's package collection duration is less than or equal to 0. This allows the managers of the target area to improve the efficiency and accuracy of their decisions regarding the nighttime package collection service based on the first night's collection duration and the suggestion, thereby enhancing the user experience.

[0110] Figure 5 This is a flowchart illustrating the nighttime mail receiving management method provided in this application embodiment. The nighttime mail receiving management method provided in this application embodiment may include, but is not limited to, steps S501 to S507.

[0111] Step S501: Obtain the current nighttime collection duration, target nighttime collection volume, and first value of nighttime collection characteristics for the target area;

[0112] Step S502: Using the trained causal recognition model, determine the first nighttime collection duration of the target area based on the target nighttime collection volume and the first value of the nighttime collection characteristics;

[0113] Step S503: Determine whether the first night's package collection time is greater than 0;

[0114] If the first night's pickup duration is greater than 0, proceed to step S504: determine the target night's pickup duration for the target area based on the first night's pickup duration;

[0115] If the duration of the first night is less than or equal to 0, proceed to step S505: output the duration of the first night's mail collection, and a prompt message suggesting that the nighttime mail collection service in the target area be turned off;

[0116] Step S506: Determine the difference between the target nighttime pickup duration of the target area and the current nighttime pickup duration of the target area as the nighttime pickup duration deviation;

[0117] Step S507: Output the nighttime pickup time deviation and the corresponding prompt information.

[0118] Optionally, if the target area has enabled nighttime mail collection service, the current nighttime mail collection duration of the target area is the current nighttime mail collection duration being performed in the target area; if the target area is a pre-enabled nighttime mail collection service area, the current nighttime mail collection duration of the target area is 0.

[0119] Optionally, if the target area is an area where nighttime pickup service has been activated, and the nighttime pickup duration deviation determined in step S506 is less than 0, then in step S507, while outputting the nighttime pickup duration deviation, a prompt message suggesting shortening the current nighttime pickup duration of the target area by a first duration can also be output, where the first duration is the absolute value of the nighttime pickup duration deviation; if the nighttime pickup duration deviation determined in step S506 is greater than 0, then in step S507, while outputting the nighttime pickup duration deviation, a prompt message suggesting extending the current nighttime pickup duration of the target area by a second duration can also be output, where the second duration is the absolute value of the nighttime pickup duration deviation; if the nighttime pickup duration deviation determined in step S506 is equal to 0, then in step S507, while outputting the nighttime pickup duration deviation, a prompt message suggesting not adjusting the nighttime pickup duration of the target area can also be output.

[0120] Optionally, if the target area is a pre-activated nighttime pickup service area, and the nighttime pickup duration deviation determined in step S506 is less than 0, then in step S507, while outputting the nighttime pickup duration deviation, a prompt message suggesting to disable the nighttime pickup service in the target area can also be output, where the first duration is the absolute value of the nighttime pickup duration deviation; if the nighttime pickup duration deviation determined in step S506 is greater than 0, then in step S507, while outputting the nighttime pickup duration deviation, a prompt message suggesting to set the current nighttime pickup duration in the target area to the second duration can also be output, where the second duration is the absolute value of the nighttime pickup duration deviation; if the nighttime pickup duration deviation determined in step S506 is equal to 0, then in step S507, while outputting the nighttime pickup duration deviation, a prompt message suggesting to disable the nighttime pickup service in the target area can also be output.

[0121] Steps S501-S505 described above can be referred to the above embodiments, and will not be repeated here.

[0122] This application embodiment, when the first night's package collection time is greater than 0, determines the target night's package collection time for the target area based on the first night's package collection time, and determines the difference between the target night's package collection time for the target area and the current night's package collection time for the target area as the night's package collection time deviation. It also outputs the night's package collection time deviation and the corresponding prompt information regarding the activation of the night's package collection service in the target area. This can improve the efficiency and accuracy of management personnel in making decisions regarding the night's package collection service in the target area, thereby improving the user experience.

[0123] Figure 6 This is another specific flowchart of the nighttime mail receiving management method provided in the embodiments of this application. The nighttime mail receiving management method provided in the embodiments of this application may include, but is not limited to, a training phase and a prediction phase.

[0124] During the training phase, the second values ​​of nighttime package collection duration, nighttime package collection volume, and nighttime package collection characteristics of multiple AOOI regions can be obtained first. Then, a linear regression model can be used to analyze the second values ​​of nighttime package collection duration, nighttime package collection volume, and nighttime package collection characteristics of multiple AOOI regions to obtain the target mathematical model. Next, the target mathematical model can be used to train the causal recognition model to obtain the trained causal recognition model.

[0125] In the prediction stage, the target nighttime receipt volume of the target aoi region can first be determined based on the historical nighttime receipt volume of the target aoi region during a preset time period. Optionally, if the historical nighttime receipt volume of the target aoi region during the preset time period is greater than 0, the target nighttime receipt volume is determined using the above formula (1); if the historical nighttime receipt volume of the target region during the preset time period is equal to 0, the target nighttime receipt volume is determined using the above formula (2). Then, the target nighttime receipt volume of the target aoi region and the first value of the nighttime receipt feature can be input into the trained causal recognition model to obtain the first nighttime receipt duration of the target aoi region. Next, the prediction result of the nighttime receipt duration of the target aoi region can be output based on the constraint rules: If the first night's message receiving duration is greater than 0, the difference between the first night's message receiving duration of the target AOI region and the current night's message receiving duration of the target AOI region is calculated to obtain the night's message receiving duration deviation of the target AOI region. This deviation is then output so that the administrators of the target AOI region can activate the night's message receiving service or adjust the night's message receiving duration based on this deviation. If the first night's message receiving duration is less than or equal to 0, the first night's message receiving duration of the target AOI region is output, along with a prompt message suggesting that the night's message receiving service of the target AOI region be deactivated. This allows administrators to decide whether to deactivate or deactivate the night's message receiving service of the target AOI region.

[0126] In practice, the average of the actual nighttime receipt times of the target AOI region during the fifth preset time period can be determined as the current nighttime receipt time of the target AOI region. Alternatively, the maximum value of the actual nighttime receipt times of the target AOI region during the fifth preset time period can be determined as the current nighttime receipt time of the target AOI region. This application embodiment does not limit the method for determining the current nighttime receipt time of the target AOI region, and the specific implementation can be determined based on the actual situation. The fifth preset time period and any of the above-mentioned first preset time period, second preset time period, third preset time period and fourth preset time period can be the same time period or different time periods, which can be determined according to the actual situation.

[0127] The specific implementation process of each step in this embodiment is the same as that in the above embodiment, and will not be repeated here.

[0128] Please see Figure 7 This application embodiment also provides a nighttime package receiving management device 700, which can implement the above-mentioned nighttime package receiving management method. The device includes: an acquisition module 701, a first duration module 702, and a second duration module 703.

[0129] The acquisition module 701 is used to acquire the target nighttime package volume in the target area and the first value of the nighttime package volume feature; the nighttime package volume feature is the feature that affects the nighttime package volume, and the first value is determined based on the historical value of the nighttime package volume feature in the target area during a first preset time period.

[0130] The first duration module 702 is used to determine the first nighttime collection duration of the target area based on the target nighttime collection volume, the first value of the nighttime collection characteristics, and the nighttime collection relationship; the nighttime collection relationship is the target relationship between the nighttime collection volume, the nighttime collection characteristics, and the nighttime collection duration.

[0131] The second duration module 703 is used to determine the target nighttime collection duration for the target area based on the first nighttime collection duration when the first nighttime collection duration is greater than 0.

[0132] The specific implementation of this nighttime mail receiving management device is basically the same as the specific implementation of the above-described nighttime mail receiving management method, and will not be repeated here.

[0133] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described nighttime package receiving management method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0134] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0135] The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0136] The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the nighttime mail receiving management method of the embodiments of this application.

[0137] The 803 input / output interface is used to implement information input and output.

[0138] The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0139] Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804);

[0140] The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0141] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described nighttime mail receiving management method.

[0142] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0143] The nighttime parcel collection management method, device, electronic device, and storage medium provided in this application have the following advantages: First, by selecting nighttime parcel collection characteristics of a target area from dimensions such as competitors, customer complaints, parcel types, parcel participation activities, and regional types, and for each nighttime parcel collection characteristic, determining a first value of the nighttime parcel collection characteristic of the target area based on the historical value of the nighttime parcel collection characteristic in the target area during a first preset period, the first value of the nighttime parcel collection characteristic that conforms to the actual situation of the target area can be comprehensively and accurately determined. Second, based on the historical nighttime parcel collection volume of the target area during a second preset period, the target nighttime parcel collection volume is determined by multiplying the first nighttime parcel collection volume by 'a', or by using the ratio 'b' of the nighttime parcel collection volume of individual parcels in all areas where nighttime parcel collection services are available to the total number of individual parcels in all areas, and the ratio 'c' of the nighttime parcel collection volume of monthly parcels in all areas to the total number of monthly parcels in all areas, the target nighttime parcel collection volume of the target area is determined by the formula: Target Nighttime Parcel Collection Volume of the Target Area = (b * Annual Individual Parcel Collection Volume of the Target Area) + (c * Annual Monthly Parcel Collection Volume of the Target Area). The nighttime pickup volume helps avoid overestimating or underestimating the nighttime pickup capacity of the target area, thus determining a target nighttime pickup volume that aligns with the actual situation of the target area. Thirdly, by analyzing multiple first training samples using a linear regression model, a target mathematical model is obtained. Furthermore, by inputting multiple second training samples and the target mathematical model into a pre-set causal identification model, the pre-set causal identification model is trained, resulting in a trained causal identification model, ensuring the effectiveness of the training. In summary, by using the trained causal identification model, based on the target nighttime pickup volume and the first value of the nighttime pickup characteristics of the target area, the first nighttime pickup duration required to reach the target nighttime pickup volume can be accurately determined from the actual situation of the target area. Furthermore, by determining the target nighttime pickup duration based on the first nighttime pickup duration when it is greater than 0, the target nighttime pickup duration for the target area can be further accurately determined, aligning with the actual situation of the target area. This allows for refined management of express delivery resource allocation while meeting the nighttime shipping needs of some customers.

[0144] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0145] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0146] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0148] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0149] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0151] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0153] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0154] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for managing nighttime package receipts, characterized in that, include: Obtain the target nighttime parcel collection volume in the target area, and a first value of the nighttime parcel collection characteristic; the nighttime parcel collection characteristic is a feature that affects the nighttime parcel collection volume, and the first value is determined based on the historical value of the nighttime parcel collection characteristic in the target area during a first preset time period; Based on the target nighttime package volume, the first value of the nighttime package feature, and the nighttime package relationship, the first nighttime package duration for the target area is determined; the nighttime package relationship is the target relationship between the nighttime package volume, the nighttime package feature, and the nighttime package duration. If the first nighttime message collection duration is greater than 0, the target nighttime message collection duration for the target area is determined based on the first nighttime message collection duration.

2. The nighttime package receiving management method as described in claim 1, characterized in that, The nighttime mail receiving management method also includes: If the first nighttime package collection duration is less than or equal to 0, output the first nighttime package collection duration and a prompt message suggesting that the nighttime package collection service in the target area be turned off.

3. The nighttime package receiving management method as described in claim 1, characterized in that, The acquisition of the target nighttime package volume for the target area includes: If the historical nighttime package volume of the target area during the second preset time period is greater than 0, the product of the first nighttime package volume and a is determined as the target nighttime package volume; wherein a > 1; the first nighttime package volume is determined based on the historical nighttime package volume of the target area during the second preset time period. If the historical nighttime parcel volume in the target area during the second preset time period is equal to 0, the target nighttime parcel volume is determined using the following formula: Y = (b*X1) + (c*X2); Wherein, Y is the target nighttime pickup volume, X1 is the annual individual pickup volume of the target region, X2 is the annual monthly pickup volume of the target region, b is the ratio of the nighttime individual pickup volume of all regions with nighttime pickup service to the total number of individual pickups of all regions, and c is the ratio of the nighttime monthly pickup volume of all regions to the total number of monthly pickups of all regions.

4. The nighttime package receiving management method as described in claim 1, characterized in that, Before determining the first nighttime collection duration of the target area based on the target nighttime collection volume, the first value of the nighttime collection characteristic, and the nighttime collection relationship, the nighttime collection management method further includes: Based on the causal relationship between the changes in the number of packages received at night and the duration of packages received at night, the linear relationship between the number of packages received at night, the characteristics of packages received at night, and the duration of packages received at night is adjusted to obtain the relationship of packages received at night.

5. The nighttime package receiving management method as described in claim 1, characterized in that, The step of determining the first nighttime delivery duration of the target area based on the target nighttime delivery volume, the first value of the nighttime delivery characteristic, and the nighttime delivery relationship includes: The target number of packages received at night and the first value of the nighttime package receipt feature are input into the trained causal recognition model to obtain the first nighttime package receipt duration. The causal recognition model that has been trained is a model that has learned the nighttime package receiving relationship.

6. The nighttime package receiving management method as described in claim 5, characterized in that, Before inputting the target nighttime mail receipt volume and the first value of the nighttime mail receipt characteristics into the trained causal recognition model to obtain the first nighttime mail receipt duration, the nighttime mail receipt management method further includes: Multiple first training samples are obtained; each first training sample includes a second nighttime mail volume of a sample region, a second nighttime mail volume corresponding to the second nighttime mail volume, and a second value of the nighttime mail volume feature; the second value is determined based on the historical value of the nighttime mail volume feature in the sample region during a third preset time period, and the second nighttime mail volume is greater than 0; wherein, each first training sample includes a different sample region; Based on the multiple first training samples, a target mathematical model is established; the target mathematical model represents the linear relationship between the nighttime parcel collection volume, the nighttime parcel collection characteristics, and the nighttime parcel collection duration. Multiple second training samples and the target mathematical model are input into a preset causal identification model, and the preset causal identification model is trained until the training stop condition is met, thus obtaining the trained causal identification model.

7. The nighttime package receiving management method as described in claim 6, characterized in that, The step of establishing a target mathematical model based on the plurality of first training samples includes: The target mathematical model is obtained by analyzing the multiple first training samples using a linear regression model. In the target mathematical model, the number of packages received at night is the dependent variable, and the duration of packages received at night and the characteristics of packages received at night are the independent variables.

8. The nighttime package receiving management method as described in claim 1, characterized in that, The nighttime mail collection characteristics include the number of known competitors, the number of mails sent, and the number of nighttime mail collection personnel.

9. A nighttime package receiving and management device, characterized in that, include: Acquisition module, first duration module, second duration module; The acquisition module is used to acquire the target nighttime package volume in the target area, and a first value of the nighttime package volume characteristic; the nighttime package volume characteristic is a feature that affects the nighttime package volume, and the first value is determined based on the historical value of the nighttime package volume characteristic in the target area during a first preset time period; The first duration module is used to determine the first nighttime delivery duration of the target area based on the target nighttime delivery volume, the first value of the nighttime delivery feature, and the nighttime delivery relationship; the nighttime delivery relationship is the target relationship between the nighttime delivery volume, the nighttime delivery feature, and the nighttime delivery duration. The second duration module is used to determine the target nighttime collection duration for the target area based on the first nighttime collection duration when the first nighttime collection duration is greater than 0.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the nighttime mail receiving management method as described in any one of claims 1-8.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the nighttime mail receiving management method as described in any one of claims 1-8.

Images