Collaborative production and delivery method and system of coffee machine and delivery robot

Abstract

The present application relates to the technical field of robot scheduling, and particularly relates to a coffee machine and delivery robot cooperative production and delivery method and system, the method comprising: in response to a code scanning order request, obtaining a beverage identifier of a beverage to be produced and a delivery location bound to a code scanning terminal used for ordering; predicting a production completion time of a production task according to a production time corresponding to the beverage identifier and a production queue state of the coffee machine; according to a first path time consumption of the delivery robot driving to a pickup location of the coffee machine and a current position of the delivery robot, making the delivery robot start according to a start timing, and making a deviation between a predicted arrival time of the delivery robot and the production completion time satisfy a preset convergence condition, and dispatching the delivery robot to drive to the pickup location according to the start timing; in response to an event representing the production completion, triggering the delivery robot waiting at the pickup location to pick up the beverage to be produced, and controlling the delivery robot to deliver the beverage to be produced to the delivery location.

Description

Technical Field

[0001] This invention relates to the field of robot scheduling technology, and in particular to a method and system for collaborative production and delivery of coffee using a coffee machine and a delivery robot. Background Technology

[0002] With the development of unmanned retail and service robot technologies, the "freshly made and delivered" service model, in office buildings, industrial parks, and other settings, is becoming increasingly popular. This model involves two physically independent pieces of equipment: the coffee machine responsible for making the drinks (which typically includes grinding, extraction, steam heating, and a robotic arm for dispensing the cup) and the delivery robot responsible for delivery. The two must work together precisely to complete an order.

[0003] However, existing technologies have the following problems: the preparation time for freshly made beverages is not a fixed value; it fluctuates dynamically depending on the beverage recipe (for example, the preparation time for an Americano and a latte can differ by up to 100%) and the current order queue status of the coffee machine. Furthermore, the delivery robot also requires a certain path travel time to reach the coffee machine's pickup position. Existing systems lack a collaborative mechanism to dynamically match the "completion time" with the "robot arrival time," and task coordination can only adopt one of two extreme approaches: firstly, a serial mode of "calling delivery after preparation is complete," where the coffee machine completes preparation... After the product is made, a delivery robot is called to pick it up. The robot is idle during the production process, and its one-way travel time (usually 2 to 4 minutes) is fully added to the user's waiting time, significantly lengthening the order fulfillment time. Secondly, the parallel mode of "dispatching a robot as soon as an order is placed" means that a robot is sent to the coffee machine to wait immediately after receiving an order. Due to the fluctuation of production time, the robot has to wait for several minutes on average, occupying the pickup position for a long time, blocking other robots and passageways. Moreover, if the robot is not in place after the beverage is dispensed, it will stay at the dispensing spout and cool down, resulting in a decrease in delivery quality.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for the collaborative production and delivery of coffee using a coffee machine and a delivery robot, thereby effectively solving the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a method for collaborative production and delivery of coffee using a coffee machine and a delivery robot, comprising the following steps:

[0007] In response to a scan-to-order request, the system obtains the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generates a production task and a delivery task, associates the production task and the delivery task as the same order, and sends the production task to the coffee machine to start making the beverage.

[0008] Based on the production time corresponding to the beverage identifier and the production queue status of the coffee machine, predict the completion time of the production task;

[0009] Based on the first path travel time of the delivery robot to the pickup position of the coffee machine and the current position of the delivery robot, the departure time of the delivery robot to the pickup position is determined, so that the deviation between the expected arrival time of the delivery robot according to the departure time and the completion time of the coffee machine meets the preset connection conditions, and the delivery robot is scheduled to travel to the pickup position to wait according to the departure time.

[0010] In response to an event indicating completion of preparation, the delivery robot waiting at the pickup location is triggered to pick up the prepared beverage and is controlled to deliver the beverage to the delivery location.

[0011] Furthermore, the preset connection condition is: the expected arrival time is not later than the production completion time, and the difference between the production completion time and the expected arrival time is less than or equal to a preset waiting threshold.

[0012] Furthermore, the production time is determined based on the recipe data corresponding to the beverage label and the historical production data of the coffee machine, and the historical production data is used to correct the baseline production time calibrated by the recipe data.

[0013] Furthermore, the delivery location bound to the scanning terminal is determined through a preset binding relationship, which records the correspondence between the terminal identifier of the scanning terminal and the delivery location; the delivery location is a pre-marked delivery point on the floor or in the area where the scanning terminal is deployed.

[0014] Furthermore, predicting the completion time of the production task includes: obtaining order information of each in-process order and each queued order in the production queue of the coffee machine; determining the pre-waiting time of the beverage to be produced based on the remaining working hours of each in-process order and the production working hours of each queued order; and determining the time corresponding to the sum of the pre-waiting time and the production working hours of the beverage to be produced as the production completion time.

[0015] Furthermore, the method also includes: during the process of the delivery robot traveling to the pickup location, updating the completion time according to the production progress reported by the coffee machine, and / or updating the estimated arrival time according to the location information reported by the delivery robot; when the updated deviation does not meet the preset connection conditions, adjusting at least one of the departure timing, the driving speed of the delivery robot, and the driving path.

[0016] Furthermore, the method also includes: acquiring the location information and status information of each of the multiple candidate delivery robots, the status information including battery status and vehicle status; and determining the delivery robot to perform the delivery task from the multiple candidate delivery robots based on the first path time and the status information corresponding to each candidate delivery robot.

[0017] Furthermore, the coffee machine includes a robotic arm and the retrieval station; the event indicating completion of preparation includes a positioning detection signal triggered when the robotic arm places the prepared beverage to be prepared at the retrieval station; triggering the delivery robot to retrieve the prepared beverage to be prepared includes: sending a retrieval command to the delivery robot, the retrieval command instructing the delivery robot's platform to align with the retrieval station and carry the beverage to be prepared; and controlling the delivery robot to leave the retrieval station in response to a carrying confirmation signal from the platform.

[0018] Furthermore, the method also includes: when the delivery robot encounters an obstruction event during driving or waiting and the expected obstruction duration exceeds a preset obstruction threshold, re-determining the target delivery robot to perform the delivery task from candidate delivery robots other than the delivery robot, and updating the departure time; or, when the production queue status indicates that the production completion time is delayed, notifying the delivery robot to extend its waiting time at the pickup position or drive to the avoidance position to avoid the obstruction.

[0019] Furthermore, the pickup location includes multiple waiting stations; scheduling the delivery robot to travel to the pickup location to wait includes: assigning a target waiting station to the delivery robot based on the occupancy status of each waiting station and the expected arrival time of the delivery robot, so that the delivery robot waits to pick up the goods at the target waiting station.

[0020] Furthermore, the method also includes: determining the estimated delivery time from the completion of preparation to the completion of delivery; when the estimated delivery time exceeds the preset quality time limit corresponding to the beverage to be prepared, generating a scheduling instruction to expedite delivery and / or a prompt message to indicate the quality status of the beverage; the preset quality time limit is determined according to the category of the beverage to be prepared.

[0021] Furthermore, when there are multiple coffee machines, the method further includes: predicting the completion time of each coffee machine, and jointly determining the target coffee machine to perform the production task and the target delivery robot to perform the delivery task based on the path time of each delivery robot to the pickup position of each coffee machine, so as to minimize the order fulfillment time.

[0022] The present invention also includes a collaborative coffee-making and delivery device for a coffee machine and a delivery robot, applied to the method described above, the device comprising:

[0023] The order management module is used to respond to a scan-to-order request, obtain the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generate a production task and a delivery task, associate the production task and the delivery task as the same order, and send the production task to the coffee machine to start making.

[0024] The completion time prediction module is used to predict the completion time of the production task based on the production time corresponding to the beverage identifier and the production queue status of the coffee machine.

[0025] The robot scheduling module is used to determine the departure time of the delivery robot to the pickup position of the coffee machine based on the first path time taken by the delivery robot to travel to the pickup position of the coffee machine and the current position of the delivery robot, so that the deviation between the expected arrival time of the delivery robot according to the departure time and the completion time of the production meets the preset connection conditions, and to schedule the delivery robot to travel to the pickup position to wait according to the departure time.

[0026] The collaborative handover module is used to respond to an event indicating that the preparation is complete, trigger the delivery robot waiting at the pickup location to pick up the prepared beverage, and control the delivery robot to deliver the beverage to the delivery location.

[0027] Furthermore, the device also includes a dynamic rescheduling module; the dynamic rescheduling module is used to update the completion time based on the production progress reported by the coffee machine and / or update the expected arrival time based on the location information reported by the delivery robot during the process of the delivery robot traveling to the pickup location. When the updated deviation does not meet the preset connection conditions, at least one of the departure time, the driving speed of the delivery robot and the driving path is adjusted, or the target delivery robot to perform the delivery task is re-determined from the candidate delivery robots, and the robot scheduling module is triggered to update the scheduling.

[0028] The present invention also includes a collaborative production and delivery system of a coffee machine and a delivery robot, the system comprising a barcode scanning terminal, a coffee machine, a delivery robot, and the device described above;

[0029] The scanning terminal is used to generate the scanning order request in response to the scanning operation. The scanning order request carries the beverage identifier of the beverage to be made and the terminal identifier of the scanning terminal.

[0030] The coffee machine is used to prepare the beverage to be prepared according to the issued preparation task, and place the prepared beverage to be prepared in the retrieval position when the preparation is completed.

[0031] The delivery robot is used to travel to the pickup location according to the departure time, wait in response to the pickup command, pick up the prepared beverage, and deliver the beverage to the delivery location.

[0032] The present invention also includes a computer device comprising a processor and a memory coupled to the processor and the memory, the memory storing program instructions which, when executed by the processor, implement the method described above.

[0033] The present invention also includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0034] The present invention also includes a computer program product comprising a computer program that, when run on a computer, causes the computer to perform the methods described above.

[0035] The beneficial effects of this invention are as follows: Through a closed-loop collaborative scheme of "order binding to scanning location, prediction of production completion time, inferring robot departure time based on path time, and triggering pickup and handover with production completion event as synchronization point," the arrival time of the delivery robot and the production completion time are dynamically aligned under the condition of production time fluctuation, realizing "pickup upon arrival" and significantly shortening the order fulfillment time. The production process and the robot's travel process are carried out in parallel with dynamic alignment of arrival time, reducing the average order fulfillment time from 7.6 minutes in the serial call mode to 5.4 minutes. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0038] Figure 2 This is an overall flowchart of the collaborative production and delivery method in Embodiment 2 of the present invention;

[0039] Figure 3 This is a schematic diagram of the collaborative manufacturing and delivery system in Embodiment 2 of the present invention;

[0040] Figure 4 This is a schematic diagram illustrating the principle of predicting the completion time and determining the departure time in Embodiment 2 of the present invention;

[0041] Figure 5 This is a schematic diagram of the timing of the handover of goods at the pick-up location in Embodiment 2 of the present invention;

[0042] Figure 6 This is a schematic diagram of the dynamic rescheduling process in Embodiment 3 of the present invention;

[0043] Figure 7 This is a schematic diagram of the collaborative manufacturing and delivery device in Embodiment 4 of the present invention.

[0044] Figure 8 This is a schematic diagram of the computer device in Embodiment 5 of the present invention. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0046] Example 1:

[0047] like Figure 1 As shown: A method for collaborative coffee making and delivery using a coffee machine and a delivery robot, comprising the following steps:

[0048] In response to a scan-to-order request, the system obtains the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generates a production task and a delivery task, associates the production task and the delivery task with the same order, and sends the production task to the coffee machine to start making the beverage.

[0049] Based on the production time corresponding to the beverage label and the production queue status of the coffee machine, predict the completion time of the production task;

[0050] Based on the first path travel time of the delivery robot to the coffee machine's pickup location and the delivery robot's current position, determine the departure time for the delivery robot to go to the pickup location, so that the deviation between the expected arrival time of the delivery robot according to the departure time and the completion time of the product meets the preset connection conditions, and schedule the delivery robot to go to the pickup location to wait according to the departure time.

[0051] In response to an event indicating completion of preparation, the system triggers a delivery robot waiting at the pickup location to pick up the prepared beverage and controls the robot to deliver the beverage to the delivery location.

[0052] By employing a closed-loop collaborative solution that "binds orders to barcode scanning locations, predicts production completion times, uses path time to determine robot departure times, and triggers pickup and handover based on production completion events," the arrival time of the delivery robot is dynamically aligned with the production completion time, even under fluctuations in production duration. This achieves "pickup upon arrival," significantly shortening order fulfillment time. With the production process and robot movement occurring in parallel and their arrival times dynamically aligned, the average order fulfillment time has been reduced from 7.6 minutes in a serial call mode to 5.4 minutes.

[0053] The preset connection conditions are: the expected arrival time is no later than the production completion time, and the difference between the production completion time and the expected arrival time is less than or equal to the preset waiting threshold.

[0054] In this embodiment, the production time is determined based on the recipe data corresponding to the beverage label and the historical production data of the coffee machine. The historical production data is used to correct the baseline production time calibrated by the recipe data.

[0055] As a preferred embodiment of the above, the delivery location bound to the scanning terminal is determined by a preset binding relationship, which records the correspondence between the terminal identifier of the scanning terminal and the delivery location; the delivery location is a pre-marked delivery point on the floor or in the area where the scanning terminal is deployed.

[0056] The process of predicting the completion time of a production task includes: obtaining order information for each in-process order and each queued order in the coffee machine's production queue; determining the pre-processing waiting time for the beverage to be produced based on the remaining processing time for each in-process order and the production time for each queued order; and determining the time corresponding to the sum of the pre-processing waiting time and the production time of the beverage to be produced as the production completion time.

[0057] As a preferred embodiment of the above, the method further includes: updating the completion time of the coffee machine according to the production progress reported by the coffee machine, and / or updating the estimated arrival time according to the location information reported by the delivery robot during the process of the delivery robot traveling to the pickup location; when the updated deviation does not meet the preset connection conditions, adjusting at least one of the departure time, the driving speed of the delivery robot and the driving path.

[0058] As a preferred embodiment of the above, the method further includes: acquiring the location information and status information of each of the multiple candidate delivery robots, the status information including battery status and vehicle status; and determining the delivery robot to perform the delivery task from the multiple candidate delivery robots based on the first path time and status information corresponding to each candidate delivery robot.

[0059] In this embodiment, the coffee machine includes a robotic arm and a retrieval station; events indicating completion of preparation include a positioning detection signal triggered when the robotic arm places the prepared beverage into the retrieval station; triggering the delivery robot to retrieve the prepared beverage includes: sending a retrieval command to the delivery robot, the retrieval command instructing the delivery robot's platform to align with the retrieval station and carry the beverage; and controlling the delivery robot to move away from the retrieval station in response to a carrying confirmation signal from the platform.

[0060] As a preferred embodiment of the above, the method further includes: when the delivery robot encounters an obstruction event during driving or waiting and the expected obstruction duration exceeds a preset obstruction threshold, re-determining the target delivery robot to perform the delivery task from candidate delivery robots other than the delivery robot, and updating the departure time; or, when the production queue status indicates that the production completion time is delayed, notifying the delivery robot to extend its waiting time at the pickup location or drive to the avoidance location to avoid the obstruction.

[0061] The pickup location includes multiple waiting stations; scheduling delivery robots to drive to the pickup location to wait includes: assigning a target waiting station to the delivery robot based on the occupancy status of each waiting station and the expected arrival time of the delivery robot, so that the delivery robot can wait for pickup at the target waiting station.

[0062] As a preferred embodiment of the above, the method further includes: determining the estimated delivery time from the completion of production to the completion of delivery; when the estimated delivery time exceeds the preset quality time limit corresponding to the beverage to be produced, generating a scheduling instruction for speeding up delivery and / or a prompt message for indicating the quality status of the beverage; the preset quality time limit is determined according to the category of the beverage to be produced.

[0063] In this embodiment, when there are multiple coffee machines, the method further includes: predicting the completion time of each coffee machine, and jointly determining the target coffee machine for making the coffee and the target delivery robot for delivering the coffee based on the path time of each delivery robot traveling to the pickup position of each coffee machine, so as to minimize the order fulfillment time.

[0064] This embodiment also includes a collaborative coffee-making and delivery device combining a coffee machine and a delivery robot, applied to the method described above. The device includes:

[0065] The order management module is used to respond to the scan-to-order request, obtain the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generate the production task and delivery task and associate the production task and delivery task into the same order, and send the production task to the coffee machine to start making.

[0066] The completion time prediction module is used to predict the completion time of the production task based on the production time corresponding to the beverage identifier and the production queue status of the coffee machine.

[0067] The robot scheduling module is used to determine the departure time of the delivery robot to the pickup position of the coffee machine based on the first path time of the delivery robot and the current position of the delivery robot, so that the deviation between the expected arrival time of the delivery robot according to the departure time and the completion time of the production meets the preset connection conditions, and the delivery robot is scheduled to travel to the pickup position to wait according to the departure time.

[0068] The collaborative handover module is used to respond to events indicating that the beverage preparation is complete, triggering the delivery robot waiting at the pickup location to pick up the prepared beverage, and controlling the delivery robot to deliver the beverage to the delivery location.

[0069] As a preferred embodiment of the above, the device further includes a dynamic rescheduling module; the dynamic rescheduling module is used to update the completion time according to the production progress reported by the coffee machine and / or update the estimated arrival time according to the location information reported by the delivery robot during the process of the delivery robot traveling to the pickup position; when the updated deviation does not meet the preset connection conditions, at least one of the departure time, the driving speed of the delivery robot and the driving path is adjusted, or the target delivery robot to perform the delivery task is re-determined from the candidate delivery robots, and the robot scheduling module is triggered to update the scheduling.

[0070] This embodiment also includes a collaborative production and delivery system of a coffee machine and a delivery robot. The system includes a barcode scanning terminal, a coffee machine, a delivery robot, and the device described above.

[0071] The barcode scanning terminal is used to generate a barcode scanning order request in response to the barcode scanning operation. The barcode scanning order request carries the beverage identifier of the beverage to be prepared and the terminal identifier of the barcode scanning terminal.

[0072] A coffee machine is used to prepare beverages according to the assigned preparation task and place the prepared beverages in the retrieval position when the preparation is complete.

[0073] Delivery robots are used to travel to the pickup location according to the departure time, wait in response to the pickup instruction, pick up the prepared beverages, and deliver the beverages to the delivery location.

[0074] Example 2:

[0075] The method in this embodiment is as follows: Figure 2 As shown, the collaborative production and delivery system of this embodiment is deployed in, as Figure 3 The T2 office building in a certain technology park shown includes: a collaborative server 10, deployed in the park's server room, communicating with various devices via a wireless network; three barcode scanning terminals 20, deployed in the elevator lobbies on the 1st, 3rd, and 5th floors respectively, with each terminal identifier pre-bound to a delivery location 60 on its respective floor (e.g., the 5th-floor barcode scanning terminal is bound to the 5th-floor tea room delivery point, and the binding relationship is recorded in the binding relationship table of the collaborative server 10); and one coffee machine 30, deployed in the lobby on the basement level (B1), including a robotic arm 31 and a coffee dispenser. The coffee machine 30 consists of a cup holder 32, a retrieval station 33, two waiting stations 34, and a positioning detection sensor 35 (pressure sensor). The entire machine is composed of grinding, extraction, steam heating and other production units and a robotic arm 31. The robotic arm 31 is used to pick up and put down cups, inject milk foam and place the finished product in the retrieval station 33. There are 3 delivery robots 40 (R1, R2 and R3). Each robot includes a mobile chassis 41, a platform 42 and an alignment mark 43 set on the edge of the platform 42. The platform 42 has four cup holders and is equipped with a weight sensor.

[0076] A user ordered an oat latte by scanning a QR code at a terminal on the 5th floor elevator lobby. The process included the following steps:

[0077] Step S100: Order generation and binding. The collaborative server 10 responds to the scan-to-order request, parses the beverage identifier (oat latte, number LAT-OAT) and the terminal identifier of the scanning terminal 20 used to place the order from the request, queries the binding relationship table to obtain the corresponding delivery location 60 (5th floor tea room delivery point), generates a production task and a delivery task, and associates them as the same order; simultaneously, the production task is sent to the coffee machine 30, which adds the order to the production queue and begins production according to the queue order.

[0078] Step S200: Predict the completion time. The collaborative server 10 has pre-set recipe data corresponding to each beverage identifier. The recipe data is labeled with baseline processing times: Americano 60 seconds (grinding 12 seconds + extraction 25 seconds + cup assembly 23 seconds), Latte 95 seconds (grinding 18 seconds + extraction 28 seconds + milk steaming 35 seconds + assembly 14 seconds), and the baseline processing time for Oatmeal Latte is the same as for Latte, 95 seconds. Simultaneously, the collaborative server 10 records the historical production data of the coffee machine 30 and performs regression correction on the baseline processing times, resulting in a corrected production time of 102 seconds for the Oatmeal Latte (the historical average is 7 seconds higher than the baseline processing time, mainly due to the higher viscosity of oatmeal milk causing a slower milk steaming process). At the time of order placement, the production queue status is: 1 Americano in production (40 seconds remaining), 1 Latte in queue (95 seconds processing time), and the Oatmeal Latte for this order is third in line. Figure 4As shown, the predicted completion time is: pre-waiting time = remaining time of orders in production 40 seconds + time of queued orders 95 seconds = 135 seconds, plus the time of this order's production 102 seconds, that is, the completion time T is 237 seconds after the order is placed.

[0079] Step S300: Departure Timing and Scheduling. Delivery robot R2 is currently located in the charging area on the 3rd floor. Based on the building map and real-time elevator status, the collaborative server 10 estimates the first path travel time T2 to reach the pickup location 33 of coffee machine 30 to be 158 seconds (including approximately 40 seconds of elevator waiting time). If it departs immediately, R2's estimated arrival time will be 158 seconds after the order is placed, which is 79 seconds earlier than T2 (237 seconds), exceeding the preset waiting threshold of 30 seconds. This deviation does not meet the preset connection conditions. Figure 4 As shown, the collaborative server 10 calculates the departure time according to the formula t_departure = T_complete - T_route - T_early (T_early is a 20-second error margin), which means it will depart after a delay of 59 seconds. At this time, the estimated arrival time of R2 is 217 seconds after the order is placed, which is 20 seconds earlier than the completion time. The deviation meets the preset connection condition that "the estimated arrival time is not later than the completion time and the difference does not exceed 30 seconds". The collaborative server 10 issues a scheduling instruction to R2 according to this departure time. R2 departs 59 seconds after the order is placed and travels to the waiting workstation 34 of the pickup location 33 to wait (the waiting workstation is allocated according to the occupancy status, and in this example, workstation 1 is allocated).

[0080] Step S400, Synchronization Point Handover and Delivery. After the robotic arm 31 completes the preparation of the oat latte, it places the coffee cup at the pickup location 33 237 seconds after the order is placed. The arrival detection sensor 35 detects the cup's arrival (pressure change of approximately 320 grams) and generates an arrival detection signal, which is reported to the collaborative server 10. This arrival detection signal signifies the completion of the preparation. Figure 5 As shown, in response to the event, the collaborative server 10 sends a pickup command to R2. R2 aligns itself with the alignment marker 43 of its own platform 42 based on the alignment marker 33 of the pickup location 33 (alignment accuracy ±5 mm), and places the coffee cup on the cup position of the platform 42. After the weight sensor of the platform 42 detects a weight change of +320 grams, it generates a pickup confirmation signal and reports it. In response to the pickup confirmation signal, the collaborative server 10 controls R2 to leave the pickup location 33 and navigate to the delivery location 60 (5th floor tea room delivery point), with a path taking 71 seconds. After R2 arrives at the delivery location, it pushes a pickup code to the user terminal. After the user verifies the pickup, the order is completed.

[0081] In this embodiment, the total fulfillment time from order placement to user pickup is 237 seconds (production) + 8 seconds (handover) + 71 seconds (delivery) + 15 seconds (pickup confirmation) = 331 seconds, approximately 5.5 minutes. In comparison, the total fulfillment time for the serial call scheme (calling the nearest R3 after production, with a path time of 142 seconds) is 237 + 142 + 8 + 71 + 15 = 473 seconds, approximately 7.9 minutes. This invention shortens the time by approximately 30%. R2's actual waiting time at the pickup location is 20 seconds, achieving "pickup upon arrival"; while in the order-on-delivery mode, the robot will wait for more than 79 seconds and occupy the waiting station throughout the process.

[0082] 30 consecutive days of operational statistics show that: the average order fulfillment time decreased from 7.6 minutes in the serial call mode to 5.4 minutes (a decrease of 28.9%); the average waiting time for pickup locations decreased from 163 seconds in the order-on-delivery mode to 22 seconds (a decrease of 86.5%); the beverage output temperature reached 62℃, and the delivery temperature compliance rate (≥50℃) increased from 82.6% to 96.3%; during peak hours (11:00-14:00), the capacity of a single coffee machine increased from 18 orders per hour to 27 orders per hour (an increase of 50%), and the peak fulfillment success rate increased from 91.2% to 98.4%.

[0083] It should be noted that the above order of steps does not constitute a limitation on the present invention. Those skilled in the art can adjust the execution order of each step according to the actual application scenario. For example, the prediction of the completion time of step S200 can also be performed before the production task is issued.

[0084] This embodiment has the following beneficial effects:

[0085] By employing a closed-loop collaborative solution that "binds orders to barcode scanning locations, predicts production completion times, uses path time to determine robot departure times, and triggers pickup and handover based on production completion events," the arrival time of the delivery robot is dynamically aligned with the production completion time, even under fluctuations in production duration. This achieves "pickup upon arrival," significantly shortening order fulfillment time. With the production process and robot movement occurring in parallel and their arrival times dynamically aligned, the average order fulfillment time has been reduced from 7.6 minutes in a serial call mode to 5.4 minutes.

[0086] Robot waiting time and pickup location occupancy have been significantly reduced: Robots travel to pickup locations based on the predicted completion time, reducing the average waiting time at pickup locations from 163 seconds in the order-on-delivery mode to 22 seconds, a decrease of 86.5%, thus avoiding pickup location congestion.

[0087] Improved beverage delivery quality: Beverages are picked up and delivered immediately after being made, the time window from preparation to delivery is controllable, and the delivery temperature compliance rate has increased from 82.6% to 96.3%.

[0088] Peak capacity increase: Faster turnover of pickup locations and parallel scheduling of multiple orders have increased the peak capacity of a single coffee machine from 18 orders per hour to 27 orders per hour.

[0089] Strong adaptability to abnormal scenarios: Abnormalities such as production delays and robot path obstruction are handled through dynamic rescheduling in a closed loop, increasing the fulfillment success rate from 91.2% to 98.4% during peak periods.

[0090] Example 3:

[0091] This embodiment verifies the portability of the method of the present invention under multiple coffee machines, multiple concurrent orders, and abnormal scenarios. For example... Figure 3 , Figure 6 As shown, this embodiment is deployed on the B1 food court floor of a shopping mall, including two coffee machines 30 (machine A and machine B, spaced approximately 120 meters apart), four delivery robots 40, and eight distributed barcode scanning terminals 20. The collaborative server 10 is deployed in the mall's edge server room.

[0092] The differences from Example 2 are as follows: First, multiple coffee machines are selected jointly. When order D (ice Americano) is placed during peak hours, the collaborative server 10 predicts that machine A will complete its order 310 seconds after the order is placed (due to the longer queue) and machine B will complete its order 185 seconds after the order is placed. Simultaneously, it estimates the path time for candidate robot R1 to machine A's pickup location to be 210 seconds and to machine B's pickup location to be 175 seconds. After joint evaluation, the "machine B + R1" combination is selected (estimated arrival time 165 seconds, arriving 20 seconds earlier to meet the connection conditions), resulting in the shortest fulfillment time. Second, waiting workstations are dynamically allocated. When two robots arrive at the same pickup location one after the other, workstation 1 and workstation 2 are allocated according to the occupancy status of waiting workstation 34. If the later-arriving robot is expected to wait more than 60 seconds, it will move to a passing position to avoid blocking the passage. Third, abnormal dynamic rescheduling is implemented. First, R4 encountered a temporary barrier during its journey, with an estimated delay of 120 seconds, exceeding the preset delay threshold of 60 seconds. The collaborative server 10 re-selected R1 from the idle candidate robots to perform the delivery task and updated the departure time, ensuring the order fulfillment was unaffected. Second, an abnormal temperature in the milk steaming unit of machine A caused a 35-second delay for orders in progress. The collaborative server 10 updated the completion time based on the production progress reported by coffee machine 30, notified R3, which was already waiting at the workstation, to extend its wait by 20 seconds, and simultaneously postponed the departure time of the next two orders. The entire process was conducted without human intervention. Fourth, quality time limit control. The preset quality time limit for frozen products is 8 minutes (preset by category). When the predicted delivery time for a certain frozen drink order exceeded the limit by 9.2 minutes, the collaborative server 10 generated an accelerated scheduling instruction for elevator priority access and pushed beverage quality status information to the scanning terminal 20.

[0093] In this embodiment, 94 orders were completed in 2 hours during peak hours, with a fulfillment success rate of 98.4%, which verifies the portability of the method of the present invention for multi-machine multi-order concurrency and abnormal scenarios.

[0094] Example 4:

[0095] like Figure 7 As shown, this embodiment provides a collaborative production and delivery device for a coffee machine and a delivery robot. The device is deployed on a collaborative server 10 and includes: an order management module 61, a completion time prediction module 62, a robot scheduling module 63, a collaborative handover module 64, and a dynamic rescheduling module 65.

[0096] The order management module 61 is used to respond to the scan-to-order request, obtain the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generate the production task and delivery task and associate the two as the same order, and send the production task to the coffee machine to start production; the order management module 61 also maintains a binding relationship table between the scan terminal and the delivery location.

[0097] The completion time prediction module 62 is used to predict the completion time of the production task based on the production time corresponding to the beverage identifier and the production queue status of the coffee machine. The production time is obtained by correcting the baseline production time marked by the recipe data with historical production data.

[0098] The robot scheduling module 63 is used to determine the departure time based on the first path time of the delivery robot to the coffee machine pickup position and the current position of the delivery robot, so that the deviation between the expected arrival time and the completion time meets the preset connection conditions, and to schedule the delivery robot to the waiting position of the pickup position according to the departure time; the robot scheduling module 63 is also used to determine the delivery robot to perform the delivery task based on the position information, power status and vehicle status of each candidate delivery robot when there are multiple candidate delivery robots.

[0099] The collaborative handover module 64 is used to respond to events indicating the completion of preparation (such as a pickup location arrival detection signal), trigger the delivery robot waiting at the pickup location to pick up the prepared beverage, and control the delivery robot to deliver the beverage to the delivery location.

[0100] The dynamic rescheduling module 65 is used to update the completion time based on the production progress reported by the coffee machine and the estimated arrival time based on the location information reported by the delivery robot during the delivery robot's operation. When the deviation no longer meets the preset connection conditions, it adjusts at least one of the departure time, driving speed and driving path, or re-determines the target delivery robot when the robot is blocked beyond a threshold and triggers the robot scheduling module 63 to update the scheduling.

[0101] The connections between the modules are as follows: the output of the order management module 61 is connected to the input of the completion time prediction module 62; the output of the completion time prediction module 62 is connected to the input of the robot scheduling module 63; the output of the robot scheduling module 63 is connected to the input of the collaborative handover module 64; and the output of the collaborative handover module 64 is connected to the communication interfaces of the coffee machine 30 and the delivery robot 40 via a wireless network. The input of the dynamic rescheduling module 65 is connected to the status reporting interfaces of the coffee machine 30 and the delivery robot 40, and the output of the dynamic rescheduling module 65 is connected to both the completion time prediction module 62 and the robot scheduling module 63. Each module can be implemented by a processor deployed on the collaborative server 10 executing computer programs stored in its memory.

[0102] Alternative Solution Examples:

[0103] This embodiment provides several alternative implementations of the present invention, all of which fall within the protection scope of the present invention.

[0104] Alternative Solution 1 (Cup Dispensing Method Alternative): The main solution is for the finished beverage to be placed at the picking position 33 by the robotic arm 31; alternatively, a cup dispensing slide can be used to directly slide the cup from the dispensing position 32 to the picking position 33, or a turntable buffer position can be used as an equivalent implementation of the picking position 33, and the arrival detection sensor 35 can be replaced with a photoelectric sensor or a vision detection unit accordingly.

[0105] Alternative Solution 2 (Deployment Form Alternative): The collaborative server 10 can be deployed on a cloud server for multiple buildings to share, or it can be deployed on a building edge gateway; when the coffee machine 30 and the delivery robot 40 come from different suppliers and use different communication protocols, their access can be achieved by combining a cross-protocol task collaborative architecture (protocol adapter and unified message bus). The method steps of this invention are not affected by the differences in device protocols on the collaborative side.

[0106] Alternative Solution 3 (Category Expansion Alternative): The production end of this invention is not limited to coffee machines, but can also be other fresh-making equipment such as tea machines and light meal machines. The delivery robot 40's platform 42 can be equipped with an insulated compartment to extend the quality time limit. The preset quality time limit is marked separately according to the category (hot drinks, iced drinks, light meals).

[0107] Example 5:

[0108] Please see Figure 8 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0109] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0110] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0111] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0112] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0113] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0114] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0115] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0116] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0117] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0118] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for collaborative coffee making and delivery using a coffee machine and a delivery robot, characterized in that, Includes the following steps: In response to a scan-to-order request, the system obtains the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generates a production task and a delivery task, associates the production task and the delivery task as the same order, and sends the production task to the coffee machine to start making the beverage. Based on the production time corresponding to the beverage identifier and the production queue status of the coffee machine, predict the completion time of the production task; Based on the first path travel time of the delivery robot to the pickup position of the coffee machine and the current position of the delivery robot, the departure time of the delivery robot to the pickup position is determined, so that the deviation between the expected arrival time of the delivery robot according to the departure time and the completion time of the coffee machine meets the preset connection conditions, and the delivery robot is scheduled to travel to the pickup position to wait according to the departure time. In response to an event indicating completion of preparation, the delivery robot waiting at the pickup location is triggered to pick up the prepared beverage and is controlled to deliver the beverage to the delivery location.

2. The method according to claim 1, characterized in that, The preset connection condition is that the expected arrival time is not later than the production completion time, and the difference between the production completion time and the expected arrival time is less than or equal to a preset waiting threshold.

3. The method according to claim 2, characterized in that, The production time is determined based on the recipe data corresponding to the beverage label and the historical production data of the coffee machine. The historical production data is used to correct the baseline production time calibrated by the recipe data.

4. The method according to claim 1, characterized in that, The delivery location bound to the scanning terminal is determined by a preset binding relationship, which records the correspondence between the terminal identifier of the scanning terminal and the delivery location. The delivery location is a pre-marked delivery point on the floor or in the area where the scanning terminal is deployed.

5. The method according to claim 1, characterized in that, The method of predicting the completion time of the production task includes: obtaining the order information of each in-process order and each queued order in the production queue of the coffee machine; determining the pre-waiting time of the beverage to be produced based on the remaining working hours of each in-process order and the production working hours of each queued order; and determining the time corresponding to the sum of the pre-waiting time and the production working hours of the beverage to be produced as the production completion time.

6. The method according to claim 5, characterized in that, The method further includes: updating the completion time based on the production progress reported by the coffee machine during the process of the delivery robot traveling to the pickup location, and / or updating the estimated arrival time based on the location information reported by the delivery robot; when the updated deviation does not meet the preset connection conditions, adjusting at least one of the departure timing, the driving speed of the delivery robot, and the driving path.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: acquiring the location information and status information of each of the multiple candidate delivery robots, the status information including battery status and vehicle status; and determining the delivery robot to perform the delivery task from the multiple candidate delivery robots based on the first path time and the status information corresponding to each candidate delivery robot.

8. The method according to claim 1, characterized in that, The coffee machine includes a robotic arm and the retrieval station; the event indicating completion of preparation includes a positioning detection signal triggered when the robotic arm places the prepared beverage to be prepared at the retrieval station; triggering the delivery robot to retrieve the prepared beverage to be prepared includes: sending a retrieval command to the delivery robot, the retrieval command instructing the delivery robot's platform to align with the retrieval station and carry the beverage to be prepared; and controlling the delivery robot to move away from the retrieval station in response to a carrying confirmation signal from the platform.

9. The method according to claim 8, characterized in that, The method further includes: when the delivery robot encounters an obstruction event during driving or waiting and the expected obstruction duration exceeds a preset obstruction threshold, re-determining the target delivery robot to perform the delivery task from candidate delivery robots other than the delivery robot, and updating the departure time; or, when the production queue status indicates that the production completion time is delayed, notifying the delivery robot to extend its waiting time at the pickup position or drive to the avoidance position to avoid the obstruction.

10. The method according to claim 1, characterized in that, The pickup location includes multiple waiting stations; scheduling the delivery robot to drive to the pickup location to wait includes: assigning a target waiting station to the delivery robot according to the occupancy status of each waiting station and the expected arrival time of the delivery robot, so that the delivery robot waits to pick up the goods at the target waiting station.

11. The method according to claim 1, characterized in that, The method further includes: determining the estimated delivery time from the completion of preparation to the completion of delivery; when the estimated delivery time exceeds the preset quality time limit corresponding to the beverage to be prepared, generating a scheduling instruction to speed up delivery and / or a prompt message to indicate the quality status of the beverage; the preset quality time limit is determined according to the category of the beverage to be prepared.

12. The method according to claim 1, characterized in that, When there are multiple coffee machines, the method further includes: predicting the completion time of each coffee machine, and jointly determining the target coffee machine to perform the production task and the target delivery robot to perform the delivery task based on the path time of each delivery robot to the pickup position of each coffee machine, so as to minimize the order fulfillment time.

13. A collaborative coffee-making and delivery device combining a coffee machine and a delivery robot, characterized in that, The apparatus, used in any one of claims 1 to 12, comprises: The order management module is used to respond to a scan-to-order request, obtain the beverage identifier of the beverage to be made and the delivery location bound to the scan terminal used to place the order, generate a production task and a delivery task, associate the production task and the delivery task as the same order, and send the production task to the coffee machine to start making. The completion time prediction module is used to predict the completion time of the production task based on the production time corresponding to the beverage identifier and the production queue status of the coffee machine. The robot scheduling module is used to determine the departure time of the delivery robot to the pickup position of the coffee machine based on the first path time taken by the delivery robot to travel to the pickup position of the coffee machine and the current position of the delivery robot, so that the deviation between the expected arrival time of the delivery robot according to the departure time and the completion time of the production meets the preset connection conditions, and to schedule the delivery robot to travel to the pickup position to wait according to the departure time. The collaborative handover module is used to respond to an event indicating that the preparation is complete, trigger the delivery robot waiting at the pickup location to pick up the prepared beverage, and control the delivery robot to deliver the beverage to the delivery location.

14. The apparatus according to claim 13, characterized in that, The device further includes a dynamic rescheduling module; the dynamic rescheduling module is used to update the completion time based on the production progress reported by the coffee machine and / or update the estimated arrival time based on the location information reported by the delivery robot during the process of the delivery robot traveling to the pickup location. When the updated deviation does not meet the preset connection conditions, the module adjusts at least one of the departure time, the driving speed of the delivery robot, and the driving path, or re-determines the target delivery robot to perform the delivery task from the candidate delivery robots and triggers the robot scheduling module to update the scheduling.

15. A collaborative coffee-making and delivery system combining a coffee machine and a delivery robot, characterized in that, The system includes a barcode scanning terminal, a coffee machine, a delivery robot, and the device as described in claim 13 or 14; The scanning terminal is used to generate the scanning order request in response to the scanning operation. The scanning order request carries the beverage identifier of the beverage to be made and the terminal identifier of the scanning terminal. The coffee machine is used to prepare the beverage to be prepared according to the issued preparation task, and place the prepared beverage to be prepared in the retrieval position when the preparation is completed. The delivery robot is used to travel to the pickup location according to the departure time, wait in response to the pickup command, pick up the prepared beverage, and deliver the beverage to the delivery location.

16. A computer device, characterized in that, The method includes a processor and a memory, the processor and the memory being coupled together, the memory storing program instructions, which, when executed by the processor, implement the method of any one of claims 1 to 12.

17. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method of any one of claims 1 to 12.

18. A computer program product, characterized in that, The computer program product includes a computer program that, when run on a computer, causes the computer to perform the method of any one of claims 1 to 12.

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