Distribution robot control method and device and robot

Abstract

The invention provides a distribution robot control method and device and a robot, and relates to the technical field of robot distribution. The method comprises the steps that delivery areas of a robot at least comprise a first area and a second area, and the density of waybills with delivery addresses located in the first area is different from that of waybills in the second area within a period of time; determining a relation attribute between the number of the waybills delivered by the robot and the first time period, wherein the delivery addresses of the waybills are located in the first area and the second area in the first time period; according to the current or future time period, the number of robots which are distributed in the first area and the second area and wait for receiving the waybill delivery task is determined, or the number of robots which are dispatched from one area to another area is determined, the density degree of the waybills in the first area / or the second area is related to the number of the waybills in the first area / or the second area and the relation attribute of the first time period.

Description

Technical Field

[0001] The present disclosure relates to the field of robot delivery technology, and in particular to a delivery robot control method, device, and robot. Background Art

[0002] The allocation of delivery robots across different regions primarily relies on fixed scheduling rules or manual intervention, failing to fully account for the dynamic changes and density of delivery orders within each region, resulting in inefficient delivery. For example, during peak shopping season, certain hotspots may experience a severe shortage of delivery robots due to a surge in orders, while other areas may have idle robots. This unbalanced resource allocation not only increases delivery times but also reduces customer satisfaction. Summary of the Invention

[0003] The present disclosure provides a delivery robot control method, device, and robot, which improve the robot's delivery efficiency at least to a certain extent.

[0004] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0005] According to one aspect of the present disclosure, a delivery robot control method is provided, including: the robot's delivery area includes at least a first area and a second area, and the density of waybills with delivery addresses located in the first area and the second area over a period of time is different; determining the relationship attribute between the number of waybills delivered by the robot with delivery addresses located in the first area and the second area within the first time period and the first time period; determining the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determining the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

[0006] In one embodiment of the present disclosure, the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or the number of robots dispatched from one area to another area is determined based on the time period, including: in the first time period, there are a first number of robots waiting in the first area, and a second number of robots waiting in the second area; in the second time period, the density of waybills in the second area is greater than the warehouse acceptance capacity of the second number of robots, or it is estimated that the amount of waybills undertaken by the second number of robots has a risk of delivery timeout; based on the waybill completion rate of the robots in the first area in the second time period, the number of robots dispatched from the first area to the second area to wait for the receipt of waybills from the second area is determined.

[0007] In one embodiment of the present disclosure, the method further includes: obtaining shopping order information and an estimated delivery time period of a shopping order placed on a shopping platform whose delivery address is located in the first area or the second area during a predetermined period; determining the number of robots assigned to the first area / or the second area to wait for receiving delivery orders to perform delivery tasks, or determining the number of robots transferred from one area to support delivery tasks in another area, based on the shopping order information and the estimated delivery time period of the orders placed on the shopping platform, wherein the robot delivery order corresponding to the area is at least partially associated with the shopping order of the area.

[0008] In one embodiment of the present disclosure, the method includes: a delivery waybill is created by a delivery-related person depositing goods into a robot and entering a consignee's delivery address, or is created based on order information that the consignee location is within the robot's delivery area.

[0009] In one embodiment of the present disclosure, the method also includes: the delivery address of the shopping order has a first address segment, the delivery address of the robot delivery waybill has a second address segment, and the area indicated by the second address segment is a subset of the area indicated by the first address segment; based on the first address segment of the shopping order, the number of orders whose delivery addresses are located in the first area or the second area and are to be delivered within a preset time period is determined, wherein part of the orders are delivered by the robot to the address indicated by the second address segment; based on the number of orders to be delivered by the robot in the first area or the second area, the number of robots waiting in the first area or the second area is determined.

[0010] In one embodiment of the present disclosure, the method further includes: determining a regional delivery type of an area, the regional delivery type being determined by a category attribute based on the area for describing the business purpose of the area; determining the order density of the area based on the regional delivery type related to the category attribute of the business purpose of the area, and based on the relationship attribute between the number of waybills delivered by robots whose delivery addresses are located in the area during the first time period and the first time period.

[0011] In one embodiment of the present disclosure, the method also includes: determining an order holding time threshold corresponding to an area based on the order density of the area; for the robot in the area, when the robot is not fully loaded, controlling the robot to take over the waybills to be allocated in the area until the robot is fully loaded or the deadline for delivery exceeds the order holding delivery time, and controlling the robot to start delivering the waybills that have been taken over, wherein the deadline for delivery is related to the latest delivery time of the orders that have been taken over, and the order holding delivery time is related to the order holding time threshold corresponding to the area.

[0012] In one embodiment of the present disclosure, the method also includes: determining the deadline for delivery from the latest delivery time of each waybill that the robot has already accepted in order from the latest delivery time; calculating the delivery time of each waybill accepted by the robot, and determining the delivery time of the waybill based on the order acceptance time and delivery time of each waybill and the order holding time threshold corresponding to the area.

[0013] According to one aspect of the present disclosure, a delivery robot control device is provided, including: the robot's delivery area includes at least a first area and a second area, and the density of waybills with delivery addresses located in the first area and the second area over a period of time is different; a first determination module is configured to determine the relationship attribute between the number of waybills delivered by the robot with delivery addresses located in the first area and the second area within the first time period and the first time period; a second determination module is configured to determine the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determine the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

[0014] According to one aspect of the present disclosure, a robot is provided, wherein the delivery area of the robot includes at least a first area and a second area, and the density of waybills with delivery addresses located in the first area and the waybills in the second area over a period of time is different; the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period; the robot obtains the relationship attribute between the number of waybills delivered by the robot with delivery addresses located in the first area and the second area within the first time period and the first time period; the number of robots in the first area or the second area is determined by the current or future time period.

[0015] In an embodiment of the present disclosure, the number of robots assigned to the first area and the second area waiting to receive delivery orders is determined based on the current or future time period, or the number of robots dispatched from one area to another area is determined. The density of waybills in the first area and / or the second area is related to the relationship attribute between the number of waybills in the first area and / or the second area and the first time period. Over a period of time, the density of waybills with delivery addresses in the first area and the second area is different. The above-mentioned technical means are used to solve the problem in the prior art of the mismatch between the number of delivery robots in different areas and actual needs, resulting in low delivery efficiency, and then dispatch delivery robots between different areas to improve delivery efficiency.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0018] Figure 1 A schematic diagram showing the structure of a delivery robot control system in an embodiment of the present disclosure.

[0019] Figure 2 A flow chart of a delivery robot control method in an embodiment of the present disclosure is shown.

[0020] Figure 3 A flow chart of a delivery robot allocation method in an embodiment of the present disclosure is shown.

[0021] Figure 4 A flowchart of a method for calculating regional order density in an embodiment of the present disclosure is shown.

[0022] Figure 5 A schematic diagram of a delivery robot control device in an embodiment of the present disclosure is shown.

[0023] Figure 6 A schematic diagram of an electronic device provided in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0025] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0026] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0027] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0028] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0029] It should be pointed out that, in the absence of conflict, the embodiments of the present disclosure and the technical features therein may be combined with each other.

[0030] It should be noted that robot delivery waybills, delivery waybills, and waybills are synonymous. Waybills refer to orders for robot delivery. Shopping orders and orders are synonymous; orders refer to orders placed by users on shopping platforms. Waybills are part of an order. The second time period is later than the first, for example, the first period is the current time period, while the second period is in the future. Robots and delivery robots are synonymous.

[0031] The specific implementation of the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0032] Figure 1 A schematic diagram showing the structure of a delivery robot control system in an embodiment of the present disclosure is shown. The system can apply the delivery robot control method or delivery robot control device in various embodiments of the present disclosure.

[0033] like Figure 1 As shown, the system architecture may include a robot 101 and a control center 102. The robot 101 may be any robot for delivering items, and the control center 102 may be any server.

[0034] In which, an application can be installed in the control center 102 to perform: the robot's delivery area includes at least a first area and a second area, and the density of waybills with delivery addresses in the first area and the second area is different over a period of time; determining the relationship attributes between the number of waybills delivered by the robot with delivery addresses in the first area and the second area in the first time period and the first time period; determining the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determining the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of waybills in the first area / or the second area is related to the relationship attributes between the number of waybills in the first area / or the second area and the first time period.

[0035] The robot 101 and the control center 102 are connected via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0036] Optionally, the above-mentioned wireless network or wired network uses standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network). In some embodiments, technologies and / or formats including Hypertext Markup Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged over the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec), etc. can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0037] Figure 2 A flow chart of a delivery robot control method according to an embodiment of the present disclosure is shown. Figure 2 As shown, the following steps are included:

[0038] The robot's delivery area includes at least a first area and a second area, and over a period of time, the density of waybills with delivery addresses in the first area is different from that of waybills with delivery addresses in the second area;

[0039] A robot's delivery area includes multiple zones. These zones are the geographical areas within which the robot performs delivery services, such as the first and second zones. The waybill density of the first zone refers to the number of waybill orders per unit time within the first zone. The waybill density of the second zone refers to the number of waybill orders per unit time within the second zone. Over a period of time, the density corresponding to the first zone may differ from the density corresponding to the second zone.

[0040] S201, determining a relationship attribute between the number of waybills whose delivery addresses are located in the first area and the second area and the first time period;

[0041] The relationship attribute between the number of waybills delivered by the robot whose delivery address is located in the first area during the first time period and the first time period is the number of waybills in the first area during the first time period and its trend over time.

[0042] The relationship attribute between the number of waybills delivered by the robot whose delivery address is located in the second area during the first time period and the first time period is the number of waybills in the second area during the first time period and its trend over time.

[0043] Analyze the number of shipping orders in different regions over a period of time and their trends over time. This technology enhances understanding of distribution needs in each region and improves the accuracy of resource allocation.

[0044] S202, based on the current or future time period, determines the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determines the number of robots dispatched from one area to another area, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

[0045] Adjusting the number of robots in different areas based on current or future time periods: Based on the number of delivery orders in different areas, their trends over time, and the density of delivery orders in different areas, the number of robots waiting to receive delivery orders in each area can be dynamically adjusted or robots can be dispatched across different areas during specific time periods. This improves response speed and service flexibility. Through rational robot scheduling, sufficient robots are available to complete delivery tasks in high-demand areas while avoiding resource waste in low-demand areas. For example, in a complex building, floors 1 to 3 are a shopping mall (including restaurants, retail, etc.), and floors 4 and above are offices / office areas. The shopping mall is the first area, and the office area is the second area. During the day, the first and second areas experience peak delivery order times, resulting in different numbers of delivery robots required. The commercial area experiences peak order times on weekends and after 6:00 PM. The office area experiences peak order times during lunch from 11:30 AM to 2:00 PM and afternoon tea from 4:00 PM to 6:00 PM. Here are the robot deployment strategies based on peak delivery order times:

[0046] During the lunch rush hour in the first area (office area), from 11:30 AM to 2:00 PM, the number of robots in the first area (office area) needs to be increased to handle the large number of takeout orders. Therefore, robots are transferred from the second area (commercial area) to the first area (office area), bringing the total number of robots to the first area (office area). This third number is calculated based on the lunch demand in the first area (office area).

[0047] During the afternoon tea time in the first area (office area) from 4:00 PM to 6:00 PM, demand in the first area (office area) remains high. Therefore, robots are transferred from the second area (commercial area) to the first area (office area), allocating the fourth number of robots to the first area (office area). This fourth number is calculated based on demand in the first area (office area).

[0048] During a specific time period, from 7:00 PM to 9:00 PM, activity in the first area (office area) decreases while demand for dinner and shopping increases in the second area (commercial area). Therefore, robots are redeployed from the first area (office area) to the second area (commercial area), bringing the fifth number of robots to the first area (office area). This fifth number is calculated based on demand for dinner and shopping in the first area (office area) and the second area (commercial area).

[0049] All day on weekends: Given the surge in customer traffic in the second area (commercial area) on weekends, the vast majority of robots should be deployed in the second area (commercial area), with only a few robots remaining in the first area (office area) on standby to handle sudden demands.

[0050] In a hospital campus, there are outpatient departments, inpatient departments, and a health and wellness center. The outpatient department is considered the first zone, and the inpatient department and health and wellness center are considered the second zone. The following describes the peak delivery periods and robot deployment strategies at different times:

[0051] During a certain period of time, 07:30-09:00: the second area (breakfast for the inpatient department and the health care center) is the peak delivery period, so robots are allocated from the first area (outpatient department) to the second area (breakfast for the inpatient department and the health care center) to meet the needs of the second area (breakfast for the inpatient department and the health care center).

[0052] During a certain period of time, from 11:30 to 14:00, the first area (outpatient department) experiences a peak in terms of lunchtime medical consultations and medication pickup, as well as meal ordering. Therefore, robots are transferred from the second area (inpatient department and breakfast at the health care center) to the first area (outpatient department) to meet the needs of the first area (outpatient department).

[0053] During a certain period of time, from 18:00 to 20:00, the second area (breakfast for the inpatient department and the health care center) is at its peak for dinner or supper delivery. Therefore, robots are transferred from the first area (outpatient department) to the second area (breakfast for the inpatient department and the health care center) to meet the needs of the second area (breakfast for the inpatient department and the health care center).

[0054] This is because a single area encompasses a complex of diverse functional areas, such as commercial districts, office areas, hospital outpatient and inpatient departments, and wellness centers. These diverse areas exhibit distinct order peaks at different times, exhibiting distinct peaks and troughs and a "tidal" distribution of delivery orders. Since the density of delivery orders requiring robot delivery fluctuates over time in each area, and peak hours often stagger or partially overlap, efficient utilization of limited robot resources is crucial. To maximize robot resource utilization and service responsiveness, it is necessary to deeply analyze and understand the correlation between delivery order volume, time, and region—in other words, to establish a dynamic relationship between "time, region, and delivery order density." This dynamic relationship enables intelligent scheduling and predictive deployment of robots across different areas, optimizing resource allocation and improving overall delivery efficiency.

[0055] In one embodiment of the present disclosure, the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or the number of robots dispatched from one area to another area is determined based on the time period, including: in the first time period, there are a first number of robots waiting in the first area, and a second number of robots waiting in the second area; in the second time period, the density of waybills in the second area is greater than the warehouse acceptance capacity of the second number of robots, or it is estimated that the amount of waybills undertaken by the second number of robots has a risk of delivery timeout; based on the waybill completion rate of the robots in the first area in the second time period, the number of robots dispatched from the first area to the second area to wait for the receipt of waybills from the second area is determined.

[0056] A robot's cargo hold capacity refers to the maximum quantity or volume of cargo it can handle. This capacity limits the number of orders that can be handled in a single delivery. The number of robots that should be deployed can be estimated based on the robot's cargo hold capacity and the order volume. For example, if a robot has N cargo hold slots and can handle N orders at full capacity, and the order volume is M, then M divided by N robots should be deployed.

[0057] Delivery overtime risk refers to the risk that a waybill may not be delivered within the promised time, based on factors such as the current number of waybill volumes, the robot's delivery capabilities, and traffic conditions in the area.

[0058] In this embodiment, the number of robots assigned to each area waiting to receive waybill delivery tasks, or the number of robots dispatched from one area to another area is determined according to the time period. Specifically: in the first time period, there are a specific number of robots waiting in the first area and the second area respectively to receive waybill delivery tasks. When entering the second time period, if the density of waybills in the second area exceeds the warehouse acceptance capacity of the second number of robots, or if there is an estimated risk of delivery timeout, the robot allocation needs to be re-evaluated. This technical means enhances the understanding of actual needs in different areas, improves the effective utilization of robot resources, and improves overall delivery efficiency. Based on the estimated waybill completion rate of the robots in the first area in the second time period, it is decided to dispatch a certain number of robots from the first area to the second area to ensure that the second area has sufficient resources to cope with peak demand.

[0059] In an optional embodiment, in addition to scheduling robots based on order density and delivery delay risk, strategies can also be adjusted based on real-time traffic data and weather forecasts. For example, if delivery delays in a certain area are predicted due to inclement weather, the number of robots deployed in that area can be increased in advance, or delivery routes can be temporarily adjusted. This enhances the system's flexibility and adaptability, improves its ability to respond to emergencies, and ultimately improves customer satisfaction.

[0060] Figure 3 A flow chart of a distribution robot allocation method according to an embodiment of the present disclosure is shown. Figure 3 As shown, the following steps are included:

[0061] S301, obtaining shopping order information and an estimated delivery time period for an order placed on a shopping platform with a delivery address located in a first region or a second region during a scheduled time period;

[0062] S302, based on the shopping order information placed on the shopping platform and the expected delivery time period, determine the number of robots assigned to the first area / or the second area to wait for receiving delivery waybills to perform delivery tasks, or determine the number of robots transferred from one area to support delivery tasks in another area, wherein the robot delivery waybills corresponding to the area are at least partially associated with the shopping orders of the area.

[0063] Shopping order information refers to the detailed record generated after the user completes the order process on the shopping platform. This information includes but is not limited to: order time, which is the specific time when the user completes the purchase behavior; delivery address, which refers to the specific location where the goods need to be delivered; product details, including information such as the type, quantity and characteristics of the purchased goods; and user information. The scheduled time period refers to the time range pre-set in the shopping platform, which is used to analyze and predict user ordering behavior and delivery needs. The expected delivery time period refers to the time range required to complete the delivery task calculated based on the shopping order information. The robot delivery waybill corresponding to the area is at least partially associated with the shopping order of the area, which can be that the robot delivery waybill corresponding to a certain area at least partially overlaps with the shopping order of the area.

[0064] In actual delivery scenarios, user orders often target a larger area, such as a campus or complex. While these orders are typically placed within this larger area, not all are ultimately delivered by robots. Data analysis can be used to estimate which orders are likely to be converted into robot delivery orders in the future, such as orders with delivery addresses specific to a particular building or even a specific floor. Take a typical weekday as an example: between 8:00 AM and 10:00 AM, a large number of coffee orders are concentrated at a specific location in the first or second area. At this point, actual robot delivery orders have not yet been generated (because some robot delivery orders are not created until the goods arrive at the building). This disclosed embodiment eliminates the need to wait until 3:00 PM for the goods to arrive at the building only to discover that there are insufficient robots waiting on-site, by which time dispatch is too late. As long as a large number of orders at the target building are detected around 10:00 AM and are about to enter the meal preparation and delivery process, robots can be deployed in advance to areas expected to experience a peak in orders, even if the merchants have not yet prepared the food and the riders have not yet delivered the goods to the building's front desk, thus preparing for the surge in orders.

[0065] In this embodiment, the system first obtains shopping order information and estimated delivery times for each order placed by a user on the shopping platform within a predetermined time period, with the delivery address located in each region. Next, based on this shopping order information and estimated delivery times, the system determines the number of robots to be assigned to each region to receive delivery orders and perform delivery tasks, or the number of robots to be transferred from one region to another. This technical approach enhances the ability to predict delivery demand within different regions, improves the efficiency of dynamic allocation of robot resources, and increases the overall delivery service responsiveness.

[0066] For example, during an e-commerce promotion, orders were collected from the shopping platform for delivery within the next two hours (predetermined timeframe) to areas one and two. Based on the estimated delivery times for these orders, it was predicted that demand in area one would be low, while the second area, with a high concentration of orders, might experience delivery delays. Therefore, a decision was made to reassign some robots originally assigned to the first area to meet peak demand in the second area. This dynamic adjustment not only alleviated pressure on area two but also avoided wasting resources in area one.

[0067] In an optional embodiment, in addition to scheduling robots based on shopping order information and expected delivery times, predictions can also be made based on historical user order data and behavioral patterns. For example, by analyzing user ordering habits within a specific time period (e.g., concentrated orders during peak hours), delivery demand can be estimated in advance and robot resource allocation adjusted. This approach enhances the precise understanding of user needs, improves delivery flexibility, and enhances the customer experience.

[0068] In one embodiment of the present disclosure, a delivery waybill is created by a delivery-related person depositing goods into a robot and inputting a consignee's delivery address, or is created based on order information that the consignee's location is within the robot's delivery area.

[0069] Delivery personnel are the staff or service robots responsible for loading goods into the robot's cargo hold and entering the consignee's delivery address to create a delivery order. The consignee's delivery address is the specific location where the goods need to be delivered, which determines the delivery zone to which the delivery order belongs.

[0070] In this embodiment, delivery waybills can be created in two ways. The first way is for the delivery-related personnel to deposit the goods into the robot warehouse and manually enter the consignee's delivery address to generate a delivery waybill. This method is suitable for non-automated order processing scenarios or situations that require manual intervention. The second way is to automatically generate a delivery waybill based on the order information where the delivery location is located in the robot delivery area. This method is usually directly connected to the order system of the shopping platform, which can achieve a more efficient waybill creation process. Through the above technical means, the flexibility of delivery waybill creation is enhanced, the efficiency of waybill generation is improved, and the adaptability of the overall delivery system is improved.

[0071] In one embodiment of the present disclosure, the delivery address of a shopping order has a first address segment, and the delivery address of a robot delivery order has a second address segment, and the area indicated by the second address segment is a subset of the area indicated by the first address segment; the number of orders whose delivery addresses are located in the first area or the second area and are to be delivered within a preset time period is determined based on the first address segment of the shopping order, wherein part of the orders are delivered by robots to the addresses indicated by the second address segment; the number of robots waiting in the first area or the second area is determined based on the number of orders to be delivered by robots in the first area or the second area.

[0072] The first address segment refers to the broader delivery address range in a shopping order, which may cover multiple blocks or a larger geographic area. The second address segment refers to the more specific delivery address range in a robot delivery order, which is usually a subset of the first address segment, such as within a residential complex or building.

[0073] Because only a portion of orders located in a large area (such as an office park) will eventually be converted into delivery tasks completed by robots. By analyzing historical data, we can derive the average robot delivery conversion rate for the area - that is, what proportion of orders will eventually land in which building, floor, and room, and have the robot complete the last 100 meters of delivery. In order to allocate robot resources more accurately, it is necessary to estimate the conversion rate from user orders to the final generation of robot waybills. The conversion rate can be estimated by analyzing the first field (such as the delivery area) and the second field (such as the building number, floor, and room number), thereby constructing a "funnel-shaped" conversion model.

[0074] For example: when it is detected that a certain office park has generated 1,000 beverage orders on the same day, it is estimated based on historical data that a certain proportion of the converted orders will be completed by robot delivery, and the current maximum carrying capacity of the robots in the area is not enough to support the estimated robot delivery orders. In this case, we cannot wait until the goods are actually delivered to the front desk of the building to discover the lack of transportation capacity. Because many waybills are currently created after the riders deliver the goods to the front desk of the building or the designated pickup point, this "relay delivery" model is prone to concentrated outbreaks of waybills, which puts great pressure on on-site scheduling. Therefore, the embodiment of the present disclosure begins to predict the number of robot waybills during the order payment stage, and deploys robots in advance accordingly. Through the above-mentioned technical means, the problem of insufficient transportation capacity during peak periods can be effectively avoided, and the overall delivery efficiency and service stability can be improved.

[0075] In an optional embodiment, in addition to scheduling deliveries based on the first and second address segments, the strategy can also be adjusted based on real-time traffic conditions. For example, during peak hours or when encountering temporary traffic restrictions, delivery routes and robot allocation can be dynamically adjusted based on the latest traffic information, prioritizing routes with better traffic conditions or deploying more robots to less-affected areas. This approach enhances delivery adaptability and flexibility, improves the ability to respond to emergencies, and improves overall delivery efficiency and service levels.

[0076] Figure 4 A flow chart showing a method for calculating regional order density in an embodiment of the present disclosure is shown. Figure 4 As shown, the following steps are included:

[0077] S401, determining a regional distribution type for a region, where the regional distribution type is determined by a category attribute based on the region that is used to describe the regional business purpose;

[0078] S402, determining the order density of the area based on the regional delivery type related to the category attribute of the business purpose of the area, the number of waybills delivered by robots whose delivery addresses are located in the area during the first time period, and the relationship attribute of the first time period.

[0079] Regional delivery types categorize a specific area based on order volume and distribution. These types include high and concentrated volume, high and dispersed volume, low and concentrated volume, and low and dispersed volume. Category attributes describe the business use of an area, such as commercial, residential, or industrial areas. Specifically, these areas could include postpartum care centers, office buildings, or residential communities.

[0080] In this embodiment, the regional delivery type of a region is first determined. The regional delivery type is determined by the category attribute based on the region that is used to describe the business purpose of the region. For example, a commercial area may be characterized by a large number and concentration, while a suburban residential area may be a small number and dispersed type. Then, based on the regional delivery type related to the category attribute of the business purpose of the region, the order density of the region is determined based on the relationship attribute between the number of waybills delivered by robots whose delivery addresses are located in the region during the first time period and the first time period. Through the above technical means, the understanding of the distribution needs in different regions is enhanced, the accuracy of resource allocation is improved, and the overall distribution efficiency and service quality are improved.

[0081] In an optional embodiment, in addition to scheduling robots based on category attributes and order density, the strategy can also be adjusted to account for holiday factors. For example, during holidays, certain areas with a normally low and dispersed number of orders may experience a high and concentrated number of orders due to events like family gatherings. In this case, historical data can be used to predict this change and increase the number of robots deployed in these areas in advance to meet the temporary increase in delivery demand.

[0082] In one embodiment of the present disclosure, the order holding time threshold corresponding to an area is determined based on the order density of the area; for the robots in the area, when the robots are not fully loaded, the robots are controlled to take over the waybills to be assigned in the area until the robots are fully loaded or the deadline for delivery exceeds the order holding delivery time, and the robots are controlled to start delivering the accepted waybills, wherein the deadline for delivery is related to the latest delivery time of the orders that have been taken over, and the order holding delivery time is related to the order holding time threshold corresponding to the area.

[0083] The hold time threshold is a time limit set based on the order density of a region. It controls the maximum waiting time for a robot to begin delivery after accepting an order. The delivery deadline is the latest delivery time specified for all orders the robot has accepted, ensuring that all goods are delivered within the customer's desired timeframe. The hold time is a time point determined based on the hold time threshold for that region. Robots should begin deliveries before this time point to avoid delays.

[0084] In this embodiment, the order holding time threshold corresponding to an area is first determined based on the order density of the area. For the robots in the area, when the robots are not fully loaded, the robots are controlled to continue to accept the waybills to be allocated in the area until the robots reach their maximum carrying capacity or the current time exceeds the predetermined order holding delivery time. Once one of these two conditions is met, that is, the robot is fully loaded or the delivery deadline exceeds the order holding delivery time, the robot will be instructed to start executing the delivery task of the accepted waybills. Through the above technical means, the flexible control of the delivery rhythm in different areas is enhanced, the efficiency of robot use is improved, and the timeliness and customer satisfaction of the overall delivery service are improved.

[0085] In one embodiment of the present disclosure, the delivery deadline is determined from the latest delivery time of each waybill that the robot has already handled in order from the latest delivery time; the delivery time of each waybill handled by the robot is calculated, and the delayed delivery time is determined based on the order acceptance time and delivery time of each waybill and the delayed delivery time threshold corresponding to the area.

[0086] Orders handled by the delivery robot are sorted by the latest delivery time, starting with the latest delivery time. The latest delivery time of the first-ranked order is used as the delivery deadline. The delivery time for each waybill is the time required to deliver each waybill. The target time for each order is calculated by adding the order acceptance time and delivery time for each robot, along with the corresponding order hold time threshold for that area. The target time with the highest value is used as the hold time.

[0087] Based on the same inventive concept, the present disclosure also provides a delivery robot control device, as shown in the following embodiment. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0088] Figure 5 A schematic diagram of a delivery robot control device according to an embodiment of the present disclosure is shown. Figure 5 As shown, the delivery robot control device may include:

[0089] The robot's delivery area includes at least a first area and a second area, and over a period of time, the density of waybills with delivery addresses in the first area is different from that of waybills with delivery addresses in the second area;

[0090] The first determining module 501 is configured to determine a relationship attribute between the number of waybills whose delivery addresses are located in the first area and the second area and the first time period;

[0091] The second determination module 502 is configured to determine the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determine the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

[0092] According to the technical solution provided by the embodiment of the present disclosure, the delivery area of the robot includes at least a first area and a second area, and the density of waybills with delivery addresses in the first area and the second area is different over a period of time; the relationship attribute between the number of waybills delivered by the robot with delivery addresses in the first area and the second area and the first time period is determined; based on the current or future time period, the number of robots assigned to the first area and the second area waiting to receive delivery tasks for waybills is determined, or the number of robots dispatched from one area to another area is determined, wherein the density of waybills in the first area and / or the second area is related to the relationship attribute between the number of waybills in the first area and / or the second area and the first time period. Through the above technical means, the problem of low delivery efficiency caused by the mismatch between the number of delivery robots in different areas and actual needs in the prior art is solved, and delivery robots can be dispatched between different areas to improve delivery efficiency.

[0093] In one embodiment, the second determination module 502 is also configured to determine the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determine the number of robots dispatched from one area to another area according to the time period in an embodiment of the present disclosure, including: in the first time period, there are a first number of robots waiting in the first area, and a second number of robots waiting in the second area; in the second time period, the density of waybills in the second area is greater than the warehouse acceptance capacity of the second number of robots or it is estimated that the amount of waybills undertaken by the second number of robots has the risk of delivery timeout; based on the waybill completion rate of the robots in the first area in the second time period, determine the number of robots transferred from the first area to the second area to wait for the allocation of waybills from the second area.

[0094] In one embodiment, the second determination module 502 is further configured to obtain shopping order information and an estimated delivery period for orders placed on the shopping platform during a predetermined period, the delivery address of which is located in the first area or the second area; based on the shopping order information and the estimated delivery period placed on the shopping platform, determine the number of robots assigned to the first area / or the second area to wait for delivery orders to perform delivery tasks, or determine the number of robots transferred from one area to support delivery tasks in another area, wherein the robot delivery order corresponding to the area is at least partially associated with the shopping order of the area.

[0095] In one embodiment, the first determination module 501 is further configured to create a delivery waybill by a delivery-related person depositing the goods into the robot and entering the consignee's delivery address, or to create it based on order information that the consignee location is within the robot's delivery area.

[0096] In one embodiment, the second determination module 502 is further configured such that the delivery address of the shopping order has a first address segment, the delivery address of the robot delivery waybill has a second address segment, and the area indicated by the second address segment is a subset of the area indicated by the first address segment; the number of orders whose delivery addresses are located in the first area or the second area and are to be delivered within a preset time period is determined based on the first address segment of the shopping order, wherein part of the orders are delivered by the robot to the address indicated by the second address segment; the number of robots waiting in the first area or the second area is determined based on the number of orders to be delivered by the robot in the first area or the second area.

[0097] In one embodiment, the second determination module 502 is further configured to determine the regional delivery type of a region, where the regional delivery type is determined by a category attribute based on the region that is used to describe the business purpose of the region; the order density of the region is determined based on the regional delivery type related to the category attribute of the business purpose of the region, and the relationship attribute between the number of waybills delivered by robots whose delivery addresses are located in the region during the first time period and the first time period.

[0098] In one embodiment, the second determination module 502 is further configured to determine the order holding time threshold corresponding to an area based on the order density of the area; for the robot in the area, when the robot is not fully loaded, the robot is controlled to accept the waybills to be allocated in the area until the robot is fully loaded or the delivery deadline exceeds the order holding delivery time, and the robot is controlled to start delivering the accepted waybills, wherein the delivery deadline is related to the latest delivery time of the orders that have been accepted, and the order holding delivery time is related to the order holding time threshold corresponding to the area.

[0099] In one embodiment, the second determination module 502 is further configured to determine the delivery deadline from the latest delivery time of each waybill that the robot has already accepted in order from the latest delivery time; calculate the delivery time of each waybill accepted by the robot, and determine the delivery time of the waybill based on the order acceptance time and delivery time of each waybill and the order holding time threshold corresponding to the area.

[0100] In one embodiment, a delivery robot control device is provided, including: the robot's delivery area includes at least a first area and a second area, and the density of waybills with delivery addresses located in the first area and the second area over a period of time is different; a first determination module is configured to determine the relationship attribute between the number of waybills delivered by the robot with delivery addresses located in the first area and the second area within the first time period and the first time period; a second determination module is configured to determine the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determine the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

[0101] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0102] Refer to the following Figure 6 hereinafter, an electronic device 600 according to this embodiment of the present disclosure is described. Figure 6 The electronic device 600 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0103] like Figure 6 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, the aforementioned at least one processing unit 610, the aforementioned at least one storage unit 620, and a bus 630 connecting different system components (including storage unit 620 and processing unit 610).

[0104] The storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps described in the "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can execute the following steps of the above-mentioned method embodiment: the delivery area of the robot includes at least a first area and a second area, and the density of the waybills with delivery addresses in the first area and the waybills in the second area over a period of time is different; determine the relationship attribute between the number of waybills delivered by the robot with delivery addresses in the first area and the second area during the first time period and the first time period; determine the number of robots assigned to the first area and the second area waiting to receive the waybill delivery tasks, or determine the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of the waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

[0105] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0106] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0107] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0108] The electronic device 600 can also communicate with one or more external devices 640 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0109] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0110] In the disclosed exemplary embodiments, a computer-readable storage medium is also provided. The computer-readable storage medium may be a readable signal medium or a readable storage medium.

[0111] In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of various exemplary implementations of the present disclosure described in the above "Specific Implementation Methods" section of this specification.

[0112] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] In the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0115] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, and the like, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0116] The present disclosure provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the delivery robot control method provided in any of the various optional embodiments of the present disclosure.

[0117] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0118] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0119] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0120] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope of the present disclosure being indicated by the appended claims.

Claims

1. A dispatching control method for a delivery robot, characterized in that: include: The robot's delivery area includes at least a first area and a second area, and over a period of time, the density of waybills with delivery addresses in the first area is different from that of waybills with delivery addresses in the second area; Determine a relationship attribute between the number of waybills whose delivery addresses are located in the first area and the second area and the first time period; Based on the current or future time period, determine the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determine the number of robots dispatched from one area to another area, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

2. The method according to claim 1, characterized in that Determining the number of robots allocated to the first area and the second area to wait for receiving delivery tasks for waybills, or determining the number of robots dispatched from one area to another area, based on the time period, includes: During the first period, a first number of robots are waiting in the first area, and a second number of robots are waiting in the second area; During the second period, the density of waybills in the second area exceeds the handling capacity of the warehouse of the second number of robots, or the number of waybills handled by the second number of robots is estimated to be at risk of delivery delay; The number of robots to be transferred from the first area to the second area to wait for receiving the waybill allocation of the second area is determined based on the estimated completion rate of the waybill of the robots in the first area in the second time period.

3. The method according to claim 1, characterized in that The method further comprises: Obtaining shopping order information and an estimated delivery time period for an order placed on the shopping platform during a predetermined period, the delivery address of which is located in the first region or the second region; Based on the shopping order information placed on the shopping platform and the expected delivery time period, the number of robots assigned to the first area / or the second area to wait for receiving delivery orders to perform delivery tasks is determined, or the number of robots transferred from one area to support delivery tasks in another area is determined, wherein the robot delivery order corresponding to the area is at least partially associated with the shopping order of the area.

4. The method according to claim 3, characterized in that The method comprises: The delivery waybill is created by the delivery-related personnel depositing the goods into the robot and entering the consignee's delivery address, or is created based on order information that the consignee location is within the robot's delivery area.

5. The method according to claim 3, characterized in that The method further comprises: The delivery address of the shopping order has a first address segment, and the delivery address of the robot delivery waybill has a second address segment, and the area indicated by the second address segment is a subset of the area indicated by the first address segment; Determining, based on the first address segment of the shopping order, a number of orders whose delivery addresses are located in the first area or the second area and are to be delivered within a preset time period, wherein a portion of the orders are delivered by a robot to the address indicated by the second address segment; The number of robots waiting in the first area or the second area is determined according to the number of orders to be delivered by the robots in the first area or the second area.

6. The method according to claim 1, characterized in that The method further comprises: Determine a regional delivery type for a region, where the regional delivery type is determined by a category attribute based on the region and used to describe the regional business purpose; The order density of the area is determined based on the regional delivery type related to the category attribute of the area for business purposes, and the relationship attribute between the number of waybills delivered by robots whose delivery addresses are located in the area during the first time period and the first time period.

7. The method according to claim 1, characterized in that The method further comprises: Determine the order suppression time threshold for a region based on the order density of that region; For the robots in this area, when the robots are not fully loaded, the robots are controlled to take over the waybills to be assigned in this area until the robots are fully loaded or the deadline for delivery exceeds the order retention delivery time, and the robots are controlled to start delivering the waybills that have been taken over, wherein the deadline for delivery is related to the latest delivery time of the orders that have been taken over, and the order retention delivery time is related to the order retention time threshold corresponding to the area.

8. The method according to claim 7, characterized in that The method further comprises: Determine the delivery deadline from the latest delivery time of each waybill already handled by the robot in descending order of the latest delivery time; Calculate the delivery time of each waybill undertaken by the robot, and determine the delivery time based on the order acceptance time and delivery time of each waybill and the order holding time threshold corresponding to the area.

9. A delivery robot control device, characterized in that: include: The robot's delivery area includes at least a first area and a second area, and over a period of time, the density of waybills with delivery addresses in the first area is different from that of waybills with delivery addresses in the second area; A first determining module is configured to determine a relationship attribute between a number of waybills whose delivery addresses are located in a first area and a second area and the first time period; The second determination module is configured to determine the number of robots assigned to the first area and the second area waiting to receive waybill delivery tasks, or determine the number of robots dispatched from one area to another area based on the current or future time period, wherein the density of waybills in the first area / or the second area is related to the relationship attribute between the number of waybills in the first area / or the second area and the first time period.

10. A robot, characterized in that: The robot's delivery area includes at least a first area and a second area, and over a period of time, the density of waybills with delivery addresses in the first area is different from that of waybills with delivery addresses in the second area; The density of waybills in the first area / or the second area is related to a relationship attribute between the number of waybills in the first area / or the second area and the first time period; The robot obtains a relationship attribute between the number of waybills whose delivery addresses are located in the first area and the second area delivered by the robot in the first time period and the first time period; The number of the robots in the first area or the second area is determined by a current or future time period.

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