An order scheduling method suitable for robots

Abstract

This solution provides an order scheduling method suitable for robots, which includes the following steps: S4. Check the coordinates and inventory of all matching container machines according to the commodity information in the generated user order; the user behavior model determines the user's tolerance index according to the ordering user information in the generated user order; S5. Query the location, idle state information and elevator operation information of all robots from the digital twin map, and input the above information into the scheduling model, which outputs the delivery time sorting table of the robot and the corresponding elevator in combination with the inventory information in step S4; S6. The cloud server uses the order task distribution model, and determines the robot and the corresponding elevator that match the user according to the delivery time sorting table and the user's tolerance index, and issues a delivery task to the robot. This method can automatically judge the user's emotions, and timely and accurately schedule orders according to the user's characteristics and emotions, thereby improving the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of robot order allocation, and in particular to an order scheduling method suitable for robots. Background Art

[0002] Service robots are generally installed in customer service, hotels, beauty salons, hospitals, buildings, parks, exhibition halls, factories and other scenes with multimedia equipment. Not only can voice navigation and other functions be realized through these service robots, but also automatic shopping can be realized. For example, if a user wants to buy drinks, he places an order through the robot, and the background server controls the robot through scheduling to complete the delivery service. The participation of robots greatly saves labor costs.

[0003] However, the current robot order scheduling system generates an order task queue, and the system dispatches tasks in sequence according to the robot's idleness. When the number of robots is insufficient, tasks are easily held in the queue and cannot be processed in time. This method is inefficient and cannot call robots suitable for delivering the order task, which will cause waste of robot resources and insufficient utilization. In addition, it cannot automatically judge the user's emotions and cannot schedule according to user characteristics. For example, some users are more anxious, while others are not. At this time, the robot cannot accurately schedule orders according to user characteristics and emotions, which reduces user satisfaction.

[0004] Therefore, in response to this situation, there is an urgent need to design and produce an order scheduling method suitable for robots to solve the problem that robots in the existing technology cannot accurately schedule orders according to user characteristics and emotions, thereby reducing user satisfaction. Summary of the invention

[0005] The purpose of the present invention is to provide an order scheduling method suitable for a robot, which can automatically judge the user's emotions, and timely and accurately schedule orders according to the user's characteristics and emotions, thereby improving the user experience and saving labor costs.

[0006] The purpose of the present invention is achieved through the following technical solutions.

[0007] An order scheduling method suitable for a robot comprises the following steps:

[0008] S1. Perform three-dimensional mapping in the space where the robot is located to generate a digital twin map, match the digital twin map with the actual map, and mark the coordinate values ​​in the digital twin map in a coordinate manner;

[0009] S2. Upload the digital twin map to the cloud server and synchronize the information transmitted by the elevator's IoT module to the cloud server;

[0010] S3. Collect and store the behavior data of users in the space where the robot is located, and establish a user behavior model based on the behavior data;

[0011] S4. Check the coordinates of all matching container machines and the inventory status of these container machines according to the product information in the generated user order; input the ordering user information in the generated user order into the user behavior model, and determine the user's tolerance index according to the output result;

[0012] S5. Query the location, idle status information and elevator operation information of all robots from the digital twin map, and input the above information into the scheduling model. The scheduling model combines the coordinates of the matching container machines in step S4 and the inventory status of these container machines to output the delivery time ranking table of the robots and the corresponding elevators;

[0013] S6. The cloud server determines the robot and corresponding elevator that match the user through the order task distribution model, based on the delivery time sorting table output in step S5 and the tolerance index of the user determined in step S4, and issues a distribution task to the robot.

[0014] Preferably, after step S6, the method further comprises the following steps:

[0015] S7. While the robot is executing its tasks, the cloud server continuously performs dynamic analysis and adjustments to optimize the order task delivery model and the order delivery task.

[0016] Preferably, in step S1, the digital twin map is compared with the actual Figure 1 : 1 matches.

[0017] Preferably, in step S1, the coordinate values ​​marked in the digital twin map include the coordinate values ​​of the entrances and exits of each floor of the elevator.

[0018] Preferably, in step S5, the delivery time sorting table is sorted in order of delivery time from longest to shortest.

[0019] Preferably, in step S1, real-time spatial environment data of the robot is captured by networked sensors, and the spatial environment data of the robot is transmitted back to the hub, and the hub generates a digital twin map based on the spatial environment data of the robot.

[0020] Preferably, the sensor is mounted on the robot.

[0021] The beneficial effects of the present invention are:

[0022] The present solution provides an order scheduling method suitable for robots, which includes the following steps: S4. Check the coordinates of all matching container machines and the inventory of these container machines according to the commodity information in the generated user order; input the ordering user information in the generated user order into the user behavior model, and judge the user's tolerance index according to the output result; S5. Query the location, idle state information and elevator operation information of all robots from the digital twin map, and input the above information into the scheduling model, and the scheduling model combines the coordinates of the matching container machines in step S4 and the inventory of these container machines to output the delivery time sorting table of the robot and the corresponding elevator; S6. The cloud server uses the order task distribution model, and according to the delivery time sorting table output in step S5 and the tolerance index of the user determined in step S4, it determines the robot and the corresponding elevator that match the user, and sends the distribution task to the robot. Therefore, this method can automatically judge the user's emotions, and timely and accurately schedule orders according to the user's characteristics and emotions, improve the user experience, and save labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of an order scheduling method applicable to a robot provided in this embodiment;

[0024] Figure 2 This is a flow chart of an optimal order scheduling method suitable for robots provided in this embodiment. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] like Figure 1 As shown, the present solution provides an order scheduling method suitable for robots, comprising the steps of:

[0027] S1. Conduct three-dimensional mapping in the space where the robot is located to generate a digital twin map. The digital twin map is matched with the actual map, and coordinate values ​​are marked in the digital twin map in a coordinate manner.

[0028] In this embodiment, as a preferred solution, the digital twin map is Figure 1 : 1 matches.

[0029] In this embodiment, as a preferred solution, the coordinate values ​​marked in the digital twin map include the coordinate values ​​of the entrances and exits of each floor of the elevator. Of course, the coordinate values ​​marked in the digital twin map are not limited to the coordinate values ​​of the entrances and exits of each floor of the elevator, but also include the coordinate values ​​of other key locations.

[0030] In this embodiment, as a preferred solution, real-time spatial environment data of the robot is captured through networked sensors, and the spatial environment data of the robot is transmitted back to the hub, and the hub generates a digital twin map based on the spatial environment data of the robot.

[0031] The hub is generally installed on a robot, but is not limited thereto, and the hub may also be set on other devices other than the robot.

[0032] As a preferred solution, the sensor is installed on the robot. Of course, it is not limited to this, and the sensor can also be installed on a physical device other than the robot.

[0033] This solution mainly involves the popularity and cost-effectiveness of sensors, the network bandwidth problem during transmission, and the operation that some collection points with too much data need sensors to perform calculations and processing before saving. That is, the collected data is partially deleted and some data is retained.

[0034] S2. Upload the digital twin map to the cloud server, and synchronize the information transmitted by the elevator's IoT module to the cloud server. That is, synchronize the information related to the elevator to the cloud server, such as the weight that the elevator can carry, the elevator usage rate, which time period is relatively idle, which time period has a large passenger volume, which floor the elevator can stop, and other related information.

[0035] S3. Collect and store the behavior data of users in the space where the robot is located, and establish a user behavior model based on the behavior data. For example, collect some user behavior data, such as which customers are more impatient, which customers are slower, and the urgency of customers to purchase goods within a certain period of time, etc., and establish a user behavior model through these user data.

[0036] S4. The user places an order through an external device, usually by clicking on the robot screen, and the robot generates an order based on the information entered by the user. User orders generally include product information and ordering user information, but of course, it is not limited to this information. According to the product information in the generated user order, check the coordinates of all matching container machines and the inventory status of these container machines. For example, which container machines have these products and how much inventory is left, and find out the corresponding numbers and inventory information of these container machines. At the same time, the ordering user information in the generated user order is input into the user behavior model, and the tolerance index of the user is judged based on the output result.

[0037] For example, five tolerance indexes are set: 1, 2, 3, 4, and 5. 1 represents that the user's tolerance is relatively poor and he can accept receiving the goods within 5 minutes. 2 represents that the user's tolerance is relatively poor and he can accept receiving the goods within 7 minutes. 3 represents that the user's tolerance is medium and he can accept receiving the goods within 9 minutes. 4 represents that the user's tolerance is relatively good and he can accept receiving the goods within 11 minutes. 5 represents that the user's tolerance is very good and he can accept receiving the goods within 15 minutes.

[0038] S5. Query the location, idle status information and elevator operation information of all robots from the digital twin map, and input the above information into the scheduling model. The scheduling model combines the coordinates of the matching container machines in step S4 and the inventory status of these container machines to output the delivery time sorting table of the robots and the corresponding elevators.

[0039] For example, the time when robot No. 1 completes the delivery corresponding to elevator No. 1 is t1, the time when robot No. 2 completes the delivery corresponding to elevator No. 3 is t2, the time when robot No. 3 completes the delivery corresponding to elevator No. 5 is t3, etc. And the delivery times t1, t2, and t3 are sorted.

[0040] In this embodiment, the delivery time sorting table is sorted according to the order of delivery time from long to short. Of course, it is not limited to this, and the delivery time can also be sorted according to the order of delivery time from short to long.

[0041] S6. The cloud server determines the robot and corresponding elevator that match the user through the order task distribution model, based on the delivery time sorting table output in step S5 and the tolerance index of the user determined in step S4, and issues a distribution task to the robot.

[0042] This method can automatically judge the user's emotions, and schedule orders in a timely and accurate manner according to the user's characteristics and emotions, thereby improving the user experience and saving labor costs.

[0043] In this embodiment, as a preferred solution, Figure 2 As shown, after step S6, the following steps are also included:

[0044] S7. During the robot's task execution, the cloud server continuously and dynamically analyzes and adjusts to optimize the order task delivery model and the order delivery task. Through this step, order scheduling can be carried out more timely and accurately, improving product delivery efficiency and user experience.

[0045] Of course, the above are only typical examples of the present invention. In addition, the present invention may also have many other specific implementations. All technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention.

Claims

1. An order scheduling method suitable for robots, characterized in that: Includes steps: S1. Perform three-dimensional mapping in the space where the robot is located to generate a digital twin map, match the digital twin map with the actual map, and mark the coordinate values ​​in the digital twin map in a coordinate manner; S2. Upload the digital twin map to the cloud server and synchronize the information transmitted by the elevator's IoT module to the cloud server; S3. Collect and store the behavior data of users in the space where the robot is located, and establish a user behavior model based on the behavior data; S4. Check the coordinates of all matching container machines and the inventory status of these container machines according to the product information in the generated user order; input the ordering user information in the generated user order into the user behavior model, and determine the user's tolerance index according to the output result; S5. Query the location, idle status information and elevator operation information of all robots from the digital twin map, and input the above information into the scheduling model. The scheduling model combines the coordinates of the matching container machines in step S4 and the inventory status of these container machines to output the delivery time ranking table of the robots and the corresponding elevators; S6. The cloud server determines the robot and corresponding elevator that match the user through the order task distribution model, based on the delivery time sorting table output in step S5 and the tolerance index of the user determined in step S4, and issues a distribution task to the robot.

2. The order scheduling method applicable to robots according to claim 1, characterized in that: After step S6, the method further includes the following steps: S7. While the robot is executing its tasks, the cloud server continuously performs dynamic analysis and adjustments to optimize the order task delivery model and the order delivery task.

3. The order scheduling method applicable to robots according to claim 1, characterized in that: In step S1, the digital twin map is matched 1:1 with the actual map.

4. The order scheduling method applicable to robots according to claim 1, characterized in that: In step S1, the coordinate values ​​marked in the digital twin map include the coordinate values ​​of the entrances and exits of each floor of the elevator.

5. The order scheduling method applicable to robots according to claim 1, characterized in that: In step S5, the delivery time sorting table is sorted in order of delivery time from longest to shortest.

6. The order scheduling method applicable to robots according to claim 1, characterized in that: In step S1, the real-time spatial environment data of the robot is captured through networked sensors, and the spatial environment data of the robot is transmitted back to the hub. The hub generates a digital twin map based on the spatial environment data of the robot.

7. The order scheduling method applicable to robots according to claim 6, characterized in that: The sensor is mounted on the robot.

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