Robot intelligent scheduling and management and control system and method for hospital delivery business

Abstract

The invention discloses a robot intelligent scheduling and management and control system and method for hospital delivery services, and relates to the technical field of realizing material delivery services by using robots. The scheduling platform determines the execution category according to the attribute of the transport work order, and when the execution category is robot execution, the scheduling platform assigns the transport work order to the robot according to an order insertion, sequential and / or proximity strategy; a shortest-time-consumption planning strategy is adopted, and the path of the robot assigned to convey the work order is planned; and when executing the work order, the robot interacts with the scheduling platform in real time and reports the task completion condition. The scheduling strategy of the transport work order is further optimized, the transport efficiency and the load rate of the transport robot are improved, and the scheduling platform can master the information of each node in the transport path in real time, so that reference is provided for subsequent planning of the transport path, and the transport efficiency of the transport robot is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of utilizing robots to realize material transportation services, and in particular to a robot intelligent dispatching and control system and method for hospital transportation services. Background Art

[0002] At present, hospitals have gradually started to use robots to transport goods. The current robot dispatch system is based on cloud services, and the system cannot be interconnected with the hospital's existing systems. In the use of the dispatch system, the system administrator manually assigns robots or personnel to handle operational operations. When the business is complex, it is difficult for manpower to make accurate predictions and there is a waste of personnel. The dispatch system uses a static route mechanism, and human intervention is required in the event of emergencies on the hospital route, resulting in relatively high operating costs.

[0003] For example, the invention patent application with application publication number CN114442631A and application publication date May 6, 2022, and titled "Intelligent Scheduling System and Method for Hospital Material Transport Robots", is a component module of the transport system, including a scheduling platform and a robot cluster; the scheduling platform is used to manage transport work orders, calculate the transport path based on the transport work order, and determine whether to implement an automatic transport solution. When the automatic transport solution is implemented, the appropriate robot in the robot cluster is scheduled to complete the transport work order; the robot cluster completes the transport task based on the transport work order received by the automatic transport solution, and feeds back to the scheduling platform after the transport task is completed. This invention supports asynchronous scheduling of multiple robots and parallelization of multiple transport tasks by establishing a transport planning system and a transport robot cluster, matching appropriate robots according to specific rules, planning the shortest path, maximizing the robot's transport capacity, and reasonably realizing scheduling and transport.

[0004] The above-mentioned dispatching strategy of the transport robot in the prior art still has some shortcomings, which are specifically reflected in the following aspects: (1) In the prior art, the delivery route is calculated based on the delivery work order to determine whether to perform an automatic delivery solution, which will cause the delivery work orders to wait in line, resulting in unreasonable allocation of delivery work orders and failure to handle emergency work orders in a timely manner; (2) The existing technology constructs the shortest path for the delivery task based on a two-dimensional delivery network. This method can improve the delivery efficiency of the delivery robot to a certain extent. However, the path conditions of each node on the delivery path and between each node vary greatly. From the perspective of the shortest distance alone, the delivery efficiency of the delivery robot cannot be effectively improved.

[0005] Therefore, there is an urgent need to improve the intelligent scheduling system and method of the delivery robot in the above-mentioned prior art to further optimize the scheduling strategy of the delivery robot, improve the delivery efficiency of the delivery robot, and improve its intelligence level. Summary of the invention

[0006] In order to overcome the defects and deficiencies in the above-mentioned prior art, the present invention provides a robot intelligent scheduling and control system and method for hospital transportation business. The invention purpose of the present invention is to optimize the robot scheduling strategy, improve the transportation efficiency and load rate of the transportation robot, and optimize the transportation business management. The human-machine collaborative scheduling system for hospital transportation business of the present invention includes a scheduling platform and a robot cluster. The scheduling platform is used to manage the transportation work order, and the execution category is determined according to the attributes of the transportation work order. When the execution category is robot execution, the scheduling platform assigns the transportation work order to the robot according to the insertion order, sequential and / or nearby strategy; the scheduling platform adopts the shortest time-consuming planning strategy to plan the path of the robot assigned to the transportation work order; after the planning is completed, the planning path information and the transportation work order information are sent to the robot so that the robot executes the transportation work order; when the robot executes the transportation work order, it moves according to the planning path information, and each time it passes a node of the planning path, the robot reports the execution status of the node to the scheduling platform, and executes the next node according to the instructions of the scheduling platform until the transportation work order is completed. Compared with the prior art, the present invention further optimizes the scheduling strategy of delivery work orders, improves the delivery efficiency and load rate of the delivery robot, and the scheduling platform can grasp the information of each node in the delivery path in real time, so as to provide a reference for the planning of subsequent delivery paths, thereby further improving the delivery efficiency of the delivery robot.

[0007] In order to solve the above problems existing in the prior art, the present invention is implemented through the following technical solutions.

[0008] A first aspect of the present invention provides a robot intelligent dispatching and control system for hospital transportation services, the system comprising a dispatching platform and a robot cluster, the dispatching platform being used to manage transportation work orders; The scheduling platform determines the execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include an initial execution category and a transport work order time limit; the initial execution category includes manual delivery, robot delivery, and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; When the dispatching platform determines that the execution category is robot delivery according to the attributes of the delivery work order, the dispatching platform screens the robot availability and obtains a list of available robots; the dispatching platform arranges the delivery work orders to be dispatched in reverse order according to the delivery work order time limit and obtains a list of delivery work orders to be executed; The dispatching platform matches the delivery orders with available robots based on the delivery order list and the available robot list. The undispatched delivery orders in the delivery order list that have not been matched with a robot in this round will wait for dispatch in the next round. When matching the delivery work order with the robot, the dispatching platform matches them in turn according to the order insertion strategy, the sequential strategy and the nearest strategy; for the delivery work order and the robot matched by the nearest strategy, the dispatching platform plans the path from the current position of the robot to the destination using the shortest time planning strategy; the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot; The robot receives the delivery work order information and the planned path information, and walks in the order of nodes in the planned path information according to its own positioning; if the task of the robot node is only the walking task, it will continue to execute the next node in the order of nodes in the planned path information; if the task of the robot node includes other tasks besides the walking task, the robot will report the task completion status of the node after completing the task corresponding to the node until the delivery work order is completed.

[0009] Further preferably, when the scheduling platform matches delivery work orders and available robots based on the delivery work order list and the available robot list, if the number of delivery work orders to be dispatched in the delivery work order list is greater than the number of robots in the available robot list, the scheduling platform adopts the scheduling strategy of robot selection for delivery work orders for matching; if the number of delivery work orders to be dispatched in the delivery work order list is less than the number of robots in the available robot list, the scheduling platform adopts the scheduling strategy of robot selection for delivery work orders for matching.

[0010] Further preferably, the scheduling strategy for the robot to select the delivery work order refers to matching the delivery work orders in the delivery work order list in sequence according to the order of the robots arranged in the available robot list. Specifically, there are m available robots in the available robot list, and there are n delivery work orders to be dispatched in the delivery work order list, m<n; according to the order of the robots in the available robot list, each robot is matched with the n delivery work orders to be dispatched, and when the robot matches a delivery work order from the n delivery work orders to be dispatched, the delivery work order matched to the robot is removed from the delivery work order list, and the next robot is matched with nb delivery work orders, b represents the number of delivery work orders that have been matched to the robot.

[0011] Further preferably, the scheduling strategy for selecting robots based on the delivery work orders refers to matching the available robots arranged in the available robot list in sequence according to the order of the delivery work orders arranged in the delivery work order list. Specifically, there are n delivery work orders to be dispatched in the delivery work order list, and there are m available robots in the available robot list, where n<m; each delivery work order is matched with m robots according to the order of the delivery work orders in the delivery work order list, and when a delivery work order is matched to a robot, the delivery work order is removed from the delivery work order list.

[0012] Further preferably, the predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time taken for the currently planned robot to pass through an area that overlaps in time and space with robots with other planned paths; the historical deviation time refers to the deviation value between the actual time taken for the robot to pass a node and the predicted time taken for the robot to pass the node.

[0013] More preferably, the scheduling platform records the predicted time and actual time when the robot passes through a node, and counts deviation values ​​according to the conflict type when the robot passes through the node, specifically including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

[0014] More preferably, the calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, It represents the average speed of the robot in the current plan to go straight; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; Indicates the total number of turns in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

[0015] More preferably, the weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

[0016] More preferably, the insertion order strategy means that if the starting station of the transport work order and the starting station of the robot are the same node, the transport work order is assigned to the robot, and the robot matching the insertion order strategy is removed from the list of available robots.

[0017] Further preferably, the sequential strategy means that when the destination site of the delivery work order of the robot is the same node as the destination site of the delivery work order to be assigned, and the current robot has more than one free cabinet, the delivery work order is dispatched to the robot, and the robot matching the sequential strategy is removed from the list of available robots.

[0018] More preferably, the proximity strategy is to obtain the current position of each available robot in the current available robot list; obtain the starting points of all the delivery work orders to be dispatched in the current delivery work order list, and obtain the node path matrix of each robot reaching the starting point of each delivery work order, denoted as A, , where m represents the number of available robots and n represents the number of delivery orders. Represents the node path from the current position of the mth robot to the starting point of the nth delivery work order; using the shortest time calculation method, calculate the time of each node path in the matrix A respectively, and get the time matrix C. The minimum value in the time matrix C is the node path with the least time. Associate the delivery work order and robot corresponding to the node path, and dispatch the delivery work order to the robot.

[0019] Further preferably, the robot receives the delivery work order information and the planned path information, and walks in sequence according to the order of nodes in the planned path information based on its own positioning; If the robot node's task is only walking, the robot will report the time it takes to complete the node to the scheduling platform and continue to execute the next node according to the node sequence in the planned path information; The dispatching platform obtains the position of the robot in real time and determines whether the next node executed by the robot is a traffic control node. When the robot is within a traffic control node and within the set threshold time before reaching the traffic control node, the robot applies to the dispatching platform for traffic control node resources. If there is no robot in the traffic control node, the dispatching platform issues an instruction to the robot to pass through the traffic control node, and the robot passes through the traffic control node according to the instruction of the dispatching platform. If there are other robots passing through the traffic control node, the dispatching platform continues to apply for traffic control node resources for the robot until the application is successful. The dispatching platform issues the successfully applied traffic control node to the robot, and the robot passes according to the instruction issued by the dispatching platform. The dispatching platform determines whether the next node executed by the robot is the elevator area. Within the set threshold time before the robot reaches the elevator area, the dispatching platform obtains the real-time information of the elevator operation in the elevator area, and obtains the elevator number of the robot's fastest floor change, generates a position node and an elevator command, and sends it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the command issued by the dispatching platform; If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

[0020] More preferably, the traffic control node includes any one or more combinations of intersections, narrow alleys, elevator cars, workstations and charging piles.

[0021] More preferably, the set threshold time is 20s.

[0022] Further preferably, the scheduling platform determines the execution category based on the attributes of the delivery work order. Specifically, if the initial execution category in the attributes of the delivery work order is manual delivery, the delivery work order is directly converted to manual delivery; if the initial execution category in the attributes of the delivery work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of this type of delivery for the delivery work order with this initial execution category by calculating the robot load.

[0023] More preferably, the calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders whose initial execution categories in the attributes of the delivery order are robot delivery and manual / robot delivery, and M represents the total number of available robots. When M=0, W=-1 is set. When the value of W is not -1 or is less than or equal to the set load threshold, the scheduling platform will determine the delivery order that meets the conditions as robot delivery, otherwise it will be determined as manual delivery.

[0024] More preferably, the load threshold is W=3.

[0025] Further preferably, when the execution time of a delivery work order delivered by the robot has exceeded a set execution time threshold and has not yet been assigned to a specific robot for execution, the scheduling platform determines the execution category of the delivery work order as manual delivery.

[0026] More preferably, when the execution time of the transport work order is T, the execution time threshold is set to T / 2.

[0027] Further preferably, the dispatching platform obtains the situation of the robot executing the delivery work order in real time. When the robot fails, the robot reports the fault information to the dispatching platform, and the dispatching platform sends an alarm to the dispatching platform administrator. After receiving the alarm, the dispatching platform administrator checks the current status of the robot through the monitoring interface. The dispatching platform administrator prompts the robot to continue to execute the delivery work order by remote restart, remote modification of configuration items to adapt to the current abnormal scenario of the robot and / or switching to remote manual operation mode; if the robot still cannot continue to execute the delivery work order, the dispatching platform converts the delivery work order of the robot to manual delivery.

[0028] Further preferably, the scheduling platform performs robot availability screening, specifically, the scheduling platform obtains the power, online status, idle status, robot cabinet idle status and / or fault status of each robot in the robot cluster, screens the available robots, and obtains a list of available robots.

[0029] Further preferably, the dispatching platform obtains a three-dimensional map of the target hospital and loads it as a three-dimensional topological map, wherein each node in the three-dimensional topological map includes roads, departments, corridors, elevators and access control; the three-dimensional topological map contains horizontal and vertical distance values ​​of the target hospital space; the dispatching platform plans the path from the current position of the robot to the destination based on the three-dimensional topological map using the shortest time-consuming planning strategy.

[0030] Further preferably, the dispatching platform is deployed in the hospital intranet area, and the dispatching platform is provided with an API interface, which is used to connect the dispatching platform with the hospital's existing management system; the robot cluster is arranged in a public network, and the robots in the robot cluster establish a communication connection with the dispatching platform through a wireless communication network.

[0031] Further preferably, the hospital's existing management system includes any one or more combinations of an HIS system, a digital twin system, an information push system, a BI analysis system, a task scheduling system, an operation and maintenance management system, and an AI big data system.

[0032] The robot establishes communication connections with the elevator and access control via the Lora communication protocol to call the elevator and control the access control.

[0033] A second aspect of the present invention provides a robot intelligent scheduling and control method based on the robot intelligent scheduling and control system for hospital transportation services described in the first aspect, the method comprising the following steps: S1. Determine an execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include an initial execution category and a transport work order time limit; the initial execution category includes manual delivery, robot delivery, and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; S2. When the determined execution category is robot delivery, robot availability screening is performed to obtain a list of available robots; the delivery work orders to be dispatched are sorted in reverse order according to the delivery work order time limit to obtain a list of delivery work orders to be executed; S3. Match the delivery work order with the available robots according to the delivery work order list and the available robot list. The undispatched delivery work orders in the delivery work order list that are not matched to the robot in this round are returned to step S1 for processing; when matching the delivery work order with the robot, the order insertion strategy, the sequential strategy and the nearest strategy are sequentially matched. For the delivery work order and the robot matched by the nearest strategy, the path from the current position of the robot to the destination is planned by the shortest time planning strategy; the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot; S4. The robot receives the delivery work order information and the planned path information, and walks in the order of nodes in the planned path information according to its own positioning; if the task of the robot node is only the walking task, it continues to execute the next node in the order of nodes in the planned path information; if the task of the robot node includes other tasks besides the walking task, the robot reports the task completion status of the node after completing the task corresponding to the node until the delivery work order is completed.

[0034] Further preferably, in step S3, when matching the delivery work orders and the available robots according to the delivery work order list and the available robot list, if the number of delivery work orders to be dispatched in the delivery work order list is greater than the number of robots in the available robot list, the scheduling strategy of selecting the delivery work orders by the robot is adopted for matching; if the number of delivery work orders to be dispatched in the delivery work order list is less than the number of robots in the available robot list, the scheduling strategy of selecting the robot by the delivery work order is adopted for matching.

[0035] Further preferably, the scheduling strategy for the robot to select the delivery work order refers to matching the delivery work orders in the delivery work order list in sequence according to the order of the robots arranged in the available robot list. Specifically, there are m available robots in the available robot list, and there are n delivery work orders to be dispatched in the delivery work order list, m<n; according to the order of the robots in the available robot list, each robot is matched with the n delivery work orders to be dispatched, and when the robot matches a delivery work order from the n delivery work orders to be dispatched, the delivery work order matched to the robot is removed from the delivery work order list, and the next robot is matched with nb delivery work orders, b represents the number of delivery work orders that have been matched to the robot.

[0036] Further preferably, the scheduling strategy for selecting robots based on the delivery work orders refers to matching the available robots arranged in the available robot list in sequence according to the order of the delivery work orders arranged in the delivery work order list. Specifically, there are n delivery work orders to be dispatched in the delivery work order list, and there are m available robots in the available robot list, where n<m; each delivery work order is matched with m robots according to the order of the delivery work orders in the delivery work order list, and when a delivery work order is matched to a robot, the delivery work order is removed from the delivery work order list.

[0037] Further preferably, in step S3, the predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulation speed; the predicted traffic conflict time refers to the time taken for the currently planned robot to pass through an area that overlaps in time and space with robots with other planned paths; the historical deviation time refers to the deviation value between the actual time taken for the robot to pass a node and the predicted time taken for the robot to pass the node.

[0038] More preferably, the statistics of historical deviation time are to record the predicted time and actual time when the robot passes through a node, and to count the deviation values ​​according to the conflict types when the robot passes through the node, specifically including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

[0039] More preferably, the calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, It represents the average speed of the robot in the current plan to go straight; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; Indicates the total number of turns in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

[0040] More preferably, the weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

[0041] More preferably, the insertion order strategy means that if the starting station of the transport work order and the starting station of the robot are the same node, the transport work order is assigned to the robot, and the robot matching the insertion order strategy is removed from the list of available robots.

[0042] Further preferably, the sequential strategy means that when the destination site of the delivery work order of the robot is the same node as the destination site of the delivery work order to be assigned, and the current robot has more than one free cabinet, the delivery work order is dispatched to the robot, and the robot matching the sequential strategy is removed from the list of available robots.

[0043] More preferably, the proximity strategy is to obtain the current position of each available robot in the current available robot list; obtain the starting points of all the delivery work orders to be dispatched in the current delivery work order list, and obtain the node path matrix of each robot reaching the starting point of each delivery work order, denoted as A, , where m represents the number of available robots and n represents the number of delivery orders. Represents the node path from the current position of the mth robot to the starting point of the nth delivery work order; using the shortest time calculation method, calculate the time of each node path in the matrix A respectively, and get the time matrix C. The minimum value in the time matrix C is the node path with the least time. Associate the delivery work order and robot corresponding to the node path, and dispatch the delivery work order to the robot.

[0044] More preferably, in step S4, the robot receives the delivery work order information and the planned path information, and walks in sequence according to the order of nodes in the planned path information based on its own positioning; The robot's position is obtained in real time, and it is determined whether the next node executed by the robot is a traffic control node. When the robot is within a traffic control node and within the set threshold time before reaching the traffic control node, the robot applies for traffic control node resources. If there is no robot in the traffic control node, an instruction to pass the traffic control node is issued to the robot, and the robot passes the traffic control node according to the instruction. If there are other robots passing the traffic control node, the robot continues to apply for traffic control node resources until the application is successful, and then the traffic control node with successful application is issued to the robot, and the robot passes according to the instruction issued. Determine whether the next node executed by the robot is the elevator area. Within the set threshold time before the robot reaches the elevator area, obtain the real-time information of the elevator operation in the elevator area, and obtain the elevator number of the robot's fastest floor change, generate a position node and an elevator command, and send it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the issued command; If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

[0045] More preferably, the traffic control node includes any one or more combinations of intersections, narrow alleys, elevator cars, workstations and charging piles.

[0046] More preferably, the set threshold time is 20s.

[0047] Further preferably, the execution category is determined according to the attributes of the delivery work order. Specifically, if the initial execution category in the attributes of the delivery work order is manual delivery, the delivery work order is directly converted to manual delivery; if the initial execution category in the attributes of the delivery work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of this type of delivery for the delivery work order with this initial execution category by calculating the robot load.

[0048] More preferably, the calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders whose initial execution categories in the attributes of the delivery order are robot delivery and manual / robot delivery, and M represents the total number of available robots. When M=0, W=-1 is set. When the value of W is not -1 or is less than or equal to the set load threshold, the scheduling platform will determine the delivery order that meets the conditions as robot delivery, otherwise it will be determined as manual delivery.

[0049] More preferably, the load threshold is W=3.

[0050] Further preferably, when the execution time of a delivery work order delivered by the robot has exceeded a set execution time threshold and has not yet been assigned to a specific robot for execution, the execution category of the delivery work order is determined to be manual delivery.

[0051] More preferably, when the execution time of the transport work order is T, the execution time threshold is set to T / 2.

[0052] More preferably, when the robot executes a delivery work order, the status of the robot's execution of the delivery work order is obtained in real time. When the robot fails, the robot reports the failure information and sends an alarm to the administrator. The administrator can view the current status of the robot through the monitoring interface. The administrator can prompt the robot to continue to execute the delivery work order by remote restart, remote modification of configuration items to adapt to the robot's current abnormal scenario and / or switching to remote manual operation mode; if the robot still cannot continue to execute the delivery work order, the delivery work order of the robot is converted to manual delivery.

[0053] Further preferably, in step S2, the robot availability screening specifically refers to obtaining the power, online status, idle status, robot cabinet idle status and / or fault status of each robot in the robot cluster, screening the available robots, and obtaining a list of available robots.

[0054] A third aspect of the present invention provides a robot intelligent scheduling and control method based on the robot intelligent scheduling and control system for hospital transportation services described in the first aspect, the method comprising: The shortest time planning strategy is adopted to plan the path from the current position of the robot to the destination. The shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot; the predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time taken by the currently planned robot to pass through the area that overlaps with other robots with planned paths in time and space; the historical deviation time refers to the deviation value between the actual time taken by the robot to pass a node and the predicted time taken by the robot to pass the node.

[0055] Further preferably, the predicted time and actual time consumed by the robot to pass a certain node are recorded, and deviation values ​​are counted separately according to the conflict types when the robot passes through the node, specifically including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

[0056] More preferably, the calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, It represents the average speed of the robot in the current plan to go straight; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; Indicates the total number of turns in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

[0057] More preferably, the weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

[0058] A fourth aspect of the present invention provides a robot intelligent scheduling and control method based on the robot intelligent scheduling and control system for hospital transportation services described in the first aspect, the method comprising: The robot receives the delivery work order information and the planned path information, and walks in sequence according to the node order in the planned path information based on its own positioning; If the robot node's task is only walking, the robot will report the time it takes to complete the node to the scheduling platform and continue to execute the next node according to the node sequence in the planned path information; The dispatching platform obtains the position of the robot in real time and determines whether the next node executed by the robot is a traffic control node. When the robot is within a traffic control node and within the set threshold time before reaching the traffic control node, the robot applies to the dispatching platform for traffic control node resources. If there is no robot in the traffic control node, the dispatching platform issues an instruction to the robot to pass through the traffic control node, and the robot passes through the traffic control node according to the instruction of the dispatching platform. If there are other robots passing through the traffic control node, the dispatching platform continues to apply for traffic control node resources for the robot until the application is successful. The dispatching platform issues the successfully applied traffic control node to the robot, and the robot passes according to the instruction issued by the dispatching platform. The dispatching platform determines whether the next node executed by the robot is the elevator area. Within the set threshold time before the robot reaches the elevator area, the dispatching platform obtains the real-time information of the elevator operation in the elevator area, and obtains the elevator number of the robot's fastest floor change, generates a position node and an elevator command, and sends it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the command issued by the dispatching platform; If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

[0059] Further preferably, the dispatching platform obtains the situation of the robot executing the delivery work order in real time. When the robot fails, the robot reports the fault information to the dispatching platform, and the dispatching platform sends an alarm to the dispatching platform administrator. After receiving the alarm, the dispatching platform administrator checks the current status of the robot through the monitoring interface. The dispatching platform administrator prompts the robot to continue to execute the delivery work order by remote restart, remote modification of configuration items to adapt to the current abnormal scenario of the robot and / or switching to remote manual operation mode; if the robot still cannot continue to execute the delivery work order, the dispatching platform converts the delivery work order of the robot to manual delivery.

[0060] More preferably, the traffic control node includes any one or more combinations of intersections, narrow alleys, elevator cars, workstations and charging piles.

[0061] More preferably, the set threshold time is 20s.

[0062] A fifth aspect of the present invention provides a robot intelligent scheduling and control method based on the robot intelligent scheduling and control system for hospital transportation services described in the first aspect, the method comprising: Determine the execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include the initial execution category and the time limit of the transport work order; the initial execution category includes manual delivery, robot delivery, and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; when the initial execution category of the transport work order is manual delivery, the transport work order is executed by manual delivery; if the initial execution category in the attributes of the transport work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of the transport of this type of initial execution category by calculating the robot load; The calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders whose initial execution categories in the attributes of the delivery order are robot delivery and manual / robot delivery, and M represents the total number of available robots. When M=0, W=-1 is set. When the value of W is not -1 or is less than or equal to the set load threshold, the scheduling platform will determine the delivery order that meets the conditions as robot delivery, otherwise it will be determined as manual delivery.

[0063] Further preferably, the load threshold is W=3.

[0064] Further preferably, when the execution time of a delivery work order delivered by the robot has exceeded a set execution time threshold and has not yet been assigned to a specific robot for execution, the scheduling platform determines the execution category of the delivery work order as manual delivery.

[0065] More preferably, when the execution time of the transport work order is T, the execution time threshold is set to T / 2.

[0066] Compared with the prior art, the beneficial technical effects brought by the present invention are as follows: 1. Compared with the prior art, the present invention determines the execution category according to the attributes of the delivery work order, specifically, determines the execution category according to the initial execution category of the delivery work order and the time limit of the delivery work order. Compared with the prior art method of only judging based on the delivery path, the scheduling system and scheduling method of the present invention can greatly improve the delivery accuracy rate.

[0067] 2. The present invention combines the number of transport work orders and the number of available robots when distributing transport work orders, and matches them in sequence according to the order insertion strategy, the sequential strategy and the nearest strategy, to ensure that the transport work orders can be promptly dispatched to the corresponding robots and that the transport work orders can be executed in a timely manner. The introduction of the order insertion strategy and the sequential strategy also greatly improves the utilization rate of the robots. The order insertion strategy, the sequential strategy and the nearest strategy are matched in sequence, and transport work orders that can be inserted are preferred, followed by sequential transport work orders. When neither is satisfied, the nearest allocation method is considered to match the transport work orders with the robots, and the path of the transport station is shortened as much as possible, and the execution efficiency of the transport work orders is ensured based on time.

[0068] 3. Compared with the prior art, the present invention adopts the shortest time planning strategy to plan the robot's path, combines historical path information, takes into account traffic conflicts and historical time deviation values, and makes the predicted shortest time more accurate and more referenceable. The shortest time planning strategy can improve the robot's transportation efficiency compared with the shortest path planning strategy of the prior art. The shortest path does not necessarily mean the shortest time. The scheduling system and method of the present invention greatly improve the robot's transportation efficiency.

[0069] 4. Compared with the prior art, the robot of the present invention will report the completion of its node task and robot status information every time it passes a node, so that the robot's transportation status can be grasped in real time, and the information fed back by the robot can also be used as a reference for the shortest time-consuming path when the transportation work order is dispatched, so as to further optimize the planned path and improve the robot's transportation efficiency. When an emergency occurs, it can also be handled in time.

[0070] 5. During path planning, the present invention configures different node tasks for different nodes and reasonably optimizes the scheduling strategy, so that the scheduling platform can make real-time adjustments to the path planning in subsequent scheduling tasks based on the node feedback content of other robots.

[0071] 6. This system provides a fully automated and intelligent solution for hospital transportation services based on human-machine collaboration; the solution includes the hospital's existing system docking, human-machine collaborative scheduling, cluster scheduling of multiple types of robots working together, and local intelligent functions of robots.

[0072] 7. This system provides a standard docking API, which can quickly integrate the hospital's existing information system to achieve rapid delivery of transportation services within the hospital. Efficiency is improved, and strategies such as shortest path scheduling, nearby robot selection, task insertion, task boarding, and robot shortest time are provided to increase the robot's load rate and reduce the path walking time. Availability is improved, the system supports human-machine diversion and human-machine collaboration, supports task cancellation, automatic task transfer, automatic detour for equipment failures, abnormal path re-planning and other strategies. The system dynamically handles exceptions, and scenes that cannot be handled will be quickly sent to operators through information. Regular system maintenance and fault prediction can greatly improve the stability of the robot.

[0073] 8. Scalability is improved, supporting new business scenarios, configuring mutually exclusive logic of tasks, supporting new types of robots, configuring corresponding business capabilities and local docking of robots, and realizing a system that can be expanded for various transportation scenarios.

[0074] 9. Self-learning ability reduces project operating costs. The system can be connected to AI platforms and data models. The system supports data regression based on historical data on deviations in scenarios such as paths, elevator ride time, task dispatching, task execution, robot maintenance, etc. It supports early dispatch of tasks and predicts obstacles, robot load conditions, road congestion, and elevator congestion.

[0075] 10. The system effectively improves the hospital's information and intelligent level, and can effectively reduce the management difficulties, low efficiency and traceability difficulties caused by manual transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A framework diagram of the robot intelligent dispatching and control system for hospital transportation services of the present invention; Figure 2 This is a functional architecture diagram of the dispatching platform in the robot intelligent dispatching and control system for hospital transportation services of the present invention; Figure 3 A schematic diagram of the deployment structure of the dispatching platform in the robot intelligent dispatching and control system for hospital transportation services of the present invention; Figure 4 An interactive flow chart of medicine transportation for the robot intelligent dispatching and control system of the hospital transportation business of the present invention; Figure 5 A flow chart of the human-machine collaborative automated scheduling method for hospital transportation services of the present invention; Figure 6 The transport data of a robot that is scheduled using the scheduling system and method of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Example 1 As a preferred embodiment of the present invention, refer to the attached specification Figure 1 ,This embodiment presents a robot intelligent dispatching and control system dedicated to hospital transportation services. The system is mainly composed of two core parts: a dispatching platform and a robot cluster.

[0079] The dispatching platform is responsible for managing delivery orders, and its operation process is rigorous and sophisticated. First of all, for each delivery order, the dispatching platform will focus on the attributes of the delivery order to determine the execution category. The delivery order attributes here cover two key factors: the initial execution category and the delivery order time limit. Among them, the initial execution category is further divided into three categories: manual delivery, robot delivery, and manual / robot delivery. However, the final execution category determined after comprehensive judgment will only fall into one of the two categories of manual delivery and robot delivery.

[0080] As an example, the properties of a delivery work order are generated when the delivery work order is created. Its properties are a field for configuring the delivery work order. When configuring the running work order, the initial execution category can be determined based on the nature of the items being delivered, the time limit requirements, etc. For example, if the items being delivered are medicines and are required to be delivered within 60 minutes, the initial execution category can be configured as manual / robot delivery. If the items being delivered are inspection materials and are required to be delivered within 10 minutes, the initial execution category can be configured as manual delivery.

[0081] As another example, when creating a delivery work order, the initial execution category of the delivery work order can be automatically configured according to the task type of the delivery work order, and the manual delivery category, the robot delivery category, or the manual / robot delivery category can be selected according to the task type of the delivery work order.

[0082] The dispatching platform determines whether to deliver the goods manually or by robot based on the initial execution category and time limit of the delivery work order. For delivery work orders with an initial execution category of manual delivery, they will be directly delivered by manual delivery. For delivery work orders with an initial execution category of manual / robot delivery and robot delivery, the dispatching platform determines based on the robot's load, the urgency of the delivery work order, etc. When the robot cannot meet the time limit requirements, the delivery work order will be transferred to manual delivery and executed manually.

[0083] When the execution category is determined to be robot delivery based on the attributes of the delivery work order, the scheduling platform immediately starts the robot availability screening process and sorts out the list of robots currently available. At the same time, the scheduling platform will sort the delivery work orders to be dispatched in reverse order according to the delivery work order time limit, and then generate a list of delivery work orders to be executed to ensure that work orders with urgent time limits can be processed first.

[0084] Subsequently, the dispatching platform starts matching the delivery work orders with the available robots, and matches each work order in the delivery work order list with the robots in the available robot list one by one. If, after a round of matching, some undispatched delivery work orders fail to find the corresponding robots, then these unmatched work orders will be postponed to the next round of dispatching process.

[0085] In the actual matching of delivery orders and robots, the scheduling platform will proceed in an orderly manner according to the order insertion strategy, sequential strategy and proximity strategy. In particular, for the combination of delivery orders and robots matched with the proximity strategy, the scheduling platform will enable the shortest time planning strategy and carefully plan the optimal path for the robot from its current location to the destination. The shortest time mentioned here comprehensively considers the three key indicators of predicted length time, predicted traffic conflict time and historical time deviation value, and strives to make the planned path take the shortest time. The final generated path information not only includes the details of the path nodes, but also details the task information corresponding to each path node. This information will be sent to the corresponding robot together with the details of the delivery order.

[0086] After receiving the delivery work order information and the planned path information, the robot immediately moves forward steadily according to the node sequence set in the planned path information based on its own precise positioning system. If the robot's task at a certain node is just walking, it will advance to the next node in the established order without stopping; if the node task involves other matters besides walking, the robot will first complete the task corresponding to the node with all its strength, and then report the completion status of the node task, repeating the cycle until the entire delivery work order is successfully completed.

[0087] Example 2 As another high-quality embodiment of the present invention, this embodiment, based on the solid foundation of Embodiment 1, provides a more in-depth and detailed supplement and explanation of the technical solution.

[0088] In the scheduling process of this embodiment, when the scheduling platform starts matching the delivery work order list with the available robot list, it will first compare the numbers of the two. If the number of delivery work orders to be dispatched is greater than the number of available robots, the scheduling platform will enable the robot selection scheduling strategy for delivery work orders; conversely, if the number of delivery work orders to be dispatched is less than the number of available robots, the delivery work order selection scheduling strategy will be adopted.

[0089] The scheduling strategy for selecting the delivery work order by the robot refers to matching the delivery work orders in the delivery work order list in sequence according to the order of the robots arranged in the available robot list. This strategy matches the robots with the delivery work orders one by one according to the established order of arrangement of the available robot list.

[0090] Specifically, there are m available robots in the available robot list, and there are n delivery work orders to be dispatched in the delivery work order list, where m<n.

[0091] One example is that starting from the first robot in the list of available robots, it will go through all n delivery orders to be dispatched and try to complete the match. Once a match is successful, the corresponding delivery order will be removed from the list. The next robot only needs to match the remaining nb delivery orders, where b represents the number of delivery orders that have been matched to the robot.

[0092] Another example is to match the robots from the available robot list with the shipping work orders in the shipping work order list, that is, to classify the shipping work orders in the shipping work order list into the available robots.

[0093] On the one hand, it makes full use of the advantages of robot resource redundancy and minimizes robot idle time through orderly allocation, thereby improving overall scheduling efficiency. On the other hand, it fixes the robot matching order, reduces the complexity of the system matching logic, reduces computing resource consumption, and makes the scheduling process more stable and efficient.

[0094] The scheduling strategy for selecting robots based on delivery work orders refers to matching the available robots arranged in the available robot list in sequence according to the order of delivery work orders arranged in the delivery work order list. Specifically, there are n delivery work orders to be dispatched in the delivery work order list, and there are m available robots in the available robot list, where n<m; each delivery work order is matched with m robots according to the order of delivery work orders in the delivery work order list, and when a delivery work order is matched to a robot, the delivery work order is removed from the delivery work order list. The adoption of this strategy has unique technical advantages: first, it gives priority to work orders, which meets some scenarios with extremely high requirements for the timeliness of work orders, and ensures that emergency work orders can lock the execution robot as soon as possible; second, the simple and intuitive matching sequence is easy to implement and maintain the system, which reduces the probability of errors caused by complex matching logic and ensures the reliability of the scheduling system.

[0095] As an implementation method of this embodiment, the scheduling platform matches in sequence according to the order insertion strategy, the sequential strategy and the nearest strategy; wherein, the order insertion strategy means that if the starting station of the transport work order and the starting station of the robot are the same node, the transport work order will be assigned to the robot, and the robot matching the order insertion strategy will be removed from the list of available robots.

[0096] The sequential strategy means that when the destination site of a delivery order is the same node as the destination site of the delivery order to be assigned, and the current robot has more than one free cabinet, the delivery order is dispatched to the robot, and the robot matching the sequential strategy is removed from the list of available robots.

[0097] The proximity strategy is to obtain the current position of each available robot in the current available robot list; obtain the starting points of all the delivery orders to be dispatched in the current delivery order list, and obtain the node path matrix of each robot reaching the starting point of each delivery order, denoted as A, , where m represents the number of available robots and n represents the number of delivery orders. Represents the node path from the current position of the mth robot to the starting point of the nth delivery work order; using the shortest time calculation method, calculate the time of each node path in the matrix A respectively, and get the time matrix C. The minimum value in the time matrix C is the node path with the least time. Associate the delivery work order and robot corresponding to the node path, and dispatch the delivery work order to the robot.

[0098] Example 3 As another preferred embodiment of the present invention, this embodiment further supplements and elaborates on the technical solution of the present invention on the basis of the above-mentioned embodiment 1 or embodiment 2.

[0099] In this embodiment, for the delivery work orders and robots matched with the proximity strategy, the scheduling platform adopts the shortest time planning strategy to plan the path from the current location of the robot to the destination; the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot.

[0100] The predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time it takes for the currently planned robot to pass through an area that overlaps with other robots with planned paths in time and space; the historical deviation time refers to the deviation between the actual time it takes for the robot to pass a node and the predicted time it takes for the robot to pass the node.

[0101] The dispatching platform records the predicted time and actual time taken by the robot to pass a certain node, and counts the deviation values ​​according to the conflict types when the robot passes through the node, including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

[0102] The calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, It represents the average speed of the robot in the current plan to go straight; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; Indicates the total number of turns in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

[0103] The weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

[0104] Example 4 As another preferred embodiment of the present invention, this embodiment is based on the above-mentioned embodiment 1, embodiment 2 or embodiment 3, and further supplements and explains the technical solution of the present invention in detail. Figure 4 As shown, the robot receives the delivery work order information and the planned path information, and walks in sequence according to the node order in the planned path information based on its own positioning; If the robot node's task is only walking, the robot will report the time it takes to complete the node to the scheduling platform and continue to execute the next node according to the node sequence in the planned path information; The dispatching platform obtains the position of the robot in real time and determines whether the next node executed by the robot is a traffic control node. When the robot is at a traffic control node and the robot is within the set threshold time (20s) before reaching the traffic control node, the robot applies for traffic control node resources from the dispatching platform. If there is no robot in the traffic control node, the dispatching platform sends an instruction to the robot to pass through the traffic control node, and the robot passes through the traffic control node according to the instruction of the dispatching platform. If there are other robots passing through the traffic control node, the dispatching platform continues to apply for traffic control node resources for the robot until the application is successful. The dispatching platform sends the successfully applied traffic control node to the robot, and the robot passes according to the instruction issued by the dispatching platform. The dispatching platform determines whether the next node executed by the robot is the elevator area. Within the set threshold time (20s) before the robot reaches the elevator area, the dispatching platform obtains the real-time information of the elevator operation in the elevator area, and obtains the elevator number of the robot's fastest floor change, generates a position node and an elevator command, and sends it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the command issued by the dispatching platform. If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

[0105] Refer to the instruction manual Figure 4As shown in the figure, it shows the complete process of issuing tasks from the HIS system, the scheduling platform planning tasks and issuing instructions, the robot executing tasks and interacting with the elevator, and finally feeding back the task status information to the HIS system. The various parts transmit and interact with each other through HTTP and MQ to ensure the smooth progress of the transportation task. Specifically, the HIS system issues a transportation task to go to Department A to pick up items and transport them to B1 and B2 to the scheduling platform through the HTTP protocol. After receiving the task from the HIS system, the scheduling platform plans to go to Department A (authentication m) to transport to Department B (authentication n1 place, and Department A (authentication m) to transport to Department B (authentication n2 place). As an example, the plan is that robot R passes through nodes N1, N2 and other nodes to M1 node to perform vehicle traffic control tasks, and then passes through nodes Nm1, Nm2 and other nodes to Nmk Change the floor, and after changing the floor, pass the subsequent node Nxx, etc. When robot R moves several nodes to a distance of 20s from the M1 node, it applies to the dispatching platform for the execution of the traffic control strategy through the MQ communication method. After the traffic control is passed, the dispatching platform sends a pass instruction to the robot, and the robot executes the subsequent nodes after receiving the pass instruction. When the robot is 20s away from the elevator area, it applies to the dispatching platform for floor change. The dispatching platform obtains the real-time information of the elevator according to the floor change passage strategy, selects the appropriate elevator, and plans the subsequent node path of the robot, such as planning the floor change-related nodes Nm1, Nm2 and other nodes to the Nmk node to execute the floor change instruction; robot R executes the floor change instruction and moves to the waiting point to wait for the elevator. The elevator side executes instructions such as calling the elevator. After the elevator arrives, the robot executes floor change instructions such as entering and exiting the elevator and changing floors. After completing the floor change, robot R sends the status of completing the floor change instruction to the dispatching platform, and the dispatching platform further plans the subsequent For example, robot R passes through nodes m1, m2 and other nodes to node M2 ​​to perform the task of releasing goods, and then passes through nodes mm1, mm2 and other nodes to node M3 to perform the task of picking up goods. Taking the robot moving to node M2 ​​to pick up goods as an example, after the robot moves to node M2, it reports the robot status information to the dispatching platform, and the dispatching platform uploads the robot status information and the delivery work order information to the HIS system for information update. At the same time, the user authenticates the robot through his smart terminal, such as swiping a card, voice password, etc. The robot performs light and screen display reminders. After successful authentication, the robot opens the cabinet door and reports the cabinet information associated with the delivery work order to the dispatching platform. The robot plays the voice of releasing goods and reminds by means of lights, screen displays, etc. After the delivery is completed, the cabinet door is closed; the process of the robot moving to point M3 to pick up goods is the same as above. After completing the pickup, it moves to point B1 to release the goods, and then moves to point B2 to release the goods, and reports the status information of the delivery work order in real time until the delivery work order is completed.

[0106] As an example of this embodiment, the traffic control node includes any one or more combinations of intersections, narrow alleys, elevator cars, workstations and charging piles.

[0107] Example 5 As another preferred embodiment of the present invention, this embodiment is a further detailed supplement and elaboration of the technical solution of the present invention on the basis of the above-mentioned embodiment 1, embodiment 2, embodiment 3 or embodiment 4.

[0108] Based on the above embodiment, the scheduling platform determines the execution category according to the attributes of the transport work order. Specifically, if the initial execution category in the attributes of the transport work order is manual delivery, the transport work order is directly converted to manual delivery; if the initial execution category in the attributes of the transport work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of this type of transportation for the transport work order with this initial execution category by calculating the robot load.

[0109] The calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders with the initial execution categories of robot delivery and manual / robot delivery in the attributes of the delivery order, and M represents the total number of available robots; when M=0, W=-1 is set; when the value of W is not-1 or is less than or equal to the set load threshold, the scheduling platform determines the delivery order that meets the conditions as robot delivery, otherwise it is determined as manual delivery. The load threshold is W=3, which is set based on experience.

[0110] Furthermore, when the execution time of the delivery work order delivered by the robot has exceeded the set execution time threshold and has not been assigned to a specific robot for execution, the scheduling platform determines the execution category of the delivery work order as manual delivery. When the execution time of the delivery work order is T, the execution time threshold is set to T / 2.

[0111] The dispatching platform obtains the status of the robot executing the delivery work order in real time. When a robot fails, the robot reports the failure information to the dispatching platform, and the dispatching platform sends an alarm to the dispatching platform administrator. After receiving the alarm, the dispatching platform administrator checks the current status of the robot through the monitoring interface. The dispatching platform administrator prompts the robot to continue to execute the delivery work order by remote restart, remote modification of configuration items to adapt to the current abnormal scenario of the robot and / or switching to remote manual operation mode; if the robot still cannot continue to execute the delivery work order, the dispatching platform will convert the delivery work order of the robot to manual delivery.

[0112] Specifically, after the robot captures the abnormality, it reports the fault through VDA5050; the dispatching platform configures the abnormal alarm mechanism, supports the configuration of the alarm duration for enterprise WeChat group, SMS, and phone notifications, and the content of the notification includes the robot ID, robot floor and nearby departments, fault error code, fault description, fault duration, etc. After receiving the alarm information, the system administrator can view more detailed information about the current robot through the monitoring interface, including a 2D visualization interface, the status information of each sensor, and support for opening the camera to view the current image and video information of the robot. The system administrator can restore each sensor by restarting the robot remotely with one click, and can remotely modify the configuration items to adapt to the current abnormal scene. The robot supports remote switching of manual and automatic modes. In manual mode, the interface supports remote control of the robot, supports continuous walking, turning, jogging walking, and inputting the end point coordinates. After reaching the target point, the robot is switched to automatic mode, and the system resets the position and task according to the new coordinates of the robot, and the task can be continued. After the above operations, if the robot still cannot continue to execute the delivery work order, the dispatching platform will convert the delivery work order of the robot to manual delivery.

[0113] Example 6 As another preferred embodiment of the present invention, this embodiment further supplements and elaborates on the technical solution of the present invention on the basis of the above-mentioned embodiment 1, embodiment 2, embodiment 3, embodiment 4 or embodiment 6.

[0114] The scheduling platform performs robot availability screening, specifically, the scheduling platform obtains the power, online status, idle status, robot cabinet idle status and / or fault status of each robot in the robot cluster, screens the available robots, and obtains a list of available robots.

[0115] The dispatching platform obtains the three-dimensional map of the target hospital and loads it into a three-dimensional topological map. The nodes in the three-dimensional topological map include roads, departments, corridors, elevators and access control; the three-dimensional topological map contains the horizontal and vertical distance values ​​of the target hospital space; based on the three-dimensional topological map, the dispatching platform uses the shortest time-consuming planning strategy to plan the path from the robot's current position to the destination.

[0116] Refer to the instruction manual Figure 3 As shown, the dispatching platform is deployed in the intranet area of ​​the hospital, and the dispatching platform is provided with an API interface, which is used to connect the dispatching platform with the existing hospital management system; the robot cluster is arranged in a public network, and the robots in the robot cluster establish a communication connection with the dispatching platform through a wireless communication network. Figure 3As shown in the figure, the scheduling platform is deployed in the hospital intranet area and connected to the hospital's existing management system through the registration center module. The hospital's existing management system includes digital twin system, information push system, BI analysis system, task scheduling system, operation and maintenance system, AI big data system and HIS system, etc. It relies on the technical base, data base and AI base of the Tiansuo PaaS platform as data support. Furthermore, a DMZ (Demilitarized Zone) area is set between the hospital intranet area and the public network, and an Nginx server is deployed in the DMZ area as a bridge for data interaction between the hospital intranet and the public network. The scheduling platform interacts with the Nginx server deployed in the DMZ area through MQ (Message Queue) in json format. The Nginx server deployed in the DMZ area interacts with the robot cluster in the public network through MQ (Message Queue) in json format. The protocol used for data interaction is MQTT (Message Queuing Telemetry Transport). The robot uses 4G or 5G networks for data transmission, and the robot communicates with the elevator and access control using Lora (Long Range) wireless communication technology.

[0117] Refer to the instruction manual Figure 2 As shown, the dispatching platform is a comprehensive management platform for transportation services, which can be connected with the existing hospital management systems, such as HIS systems, digital twin systems, information push systems, BI analysis systems, task scheduling systems, operation and maintenance management systems, and AI big data systems. Its corresponding applications can apply for Web applications, App applications, robot Pad applications, mobile terminal applications, and large-screen displays in the monitoring center. The functional modules that can be implemented include task management, equipment management, map management, path planning, traffic control, protocol docking, task dispatching strategies, idle strategies, task site strategies, charging strategies, dynamic planning, equipment simulation, and statistics. The data modules that are connected include map topology modules, task modules, robot type modules, strategy configuration modules, and statistics modules. The map topology module obtains the three-dimensional map of the target hospital and loads it into a three-dimensional topology map. Each node in the three-dimensional topology map includes roads, departments, corridors, elevators, and access control; the three-dimensional topology map contains the horizontal and vertical distance values ​​of the target hospital space. The connected devices include robot clusters, elevators, cameras, access control, etc. The robot clusters include integrated cabinet robots, AMR robots, robotic arms, collaborative robots, cleaning robots, etc.

[0118] The robot establishes communication connections with the elevator and access control through the Lora communication protocol to call the elevator and control the access control.

[0119] Example 7 As another preferred embodiment of the present invention, this embodiment is a robot intelligent dispatching and control method of the robot intelligent dispatching and control system for hospital transportation services shown in the above-mentioned embodiments 1 to 6. Figure 5 As shown, the method comprises the following steps: S1. Determine an execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include an initial execution category and a transport work order time limit; the initial execution category includes manual delivery, robot delivery, and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; If the initial execution category in the properties of the delivery work order is manual delivery, the delivery work order will be directly converted to manual delivery; if the initial execution category in the properties of the delivery work order is robot delivery and manual / robot delivery, the scheduling platform will determine the execution category of this type of delivery for the delivery work order with this initial execution category by calculating the robot load.

[0120] The calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders whose initial execution categories are robot delivery and manual / robot delivery in the attributes of the delivery order, and M represents the total number of available robots; when M=0, set W=-1; when the value of W is not -1 or is less than or equal to the set load threshold, the scheduling platform will determine the delivery order that meets the conditions as robot delivery, otherwise it will be determined as manual delivery. The load threshold is W=3, which is determined based on experience. When the execution time of a delivery order delivered by a robot has exceeded the set execution time threshold and has not yet been assigned to a specific robot for execution, the execution category of the delivery order is determined to be manual delivery. When the execution time of the delivery order is T, set the execution time threshold to T / 2.

[0121] S2. When the execution category determined is robot delivery, perform robot availability screening to obtain a list of available robots; for example, obtain the power, online status, idle status, robot cabinet idle status and / or fault status of each robot in the robot cluster, screen available robots, and obtain a list of available robots. Arrange the delivery work orders to be dispatched in reverse order according to the delivery work order time limit to obtain a list of delivery work orders to be executed; S3, matching the delivery work orders with the available robots according to the delivery work order list and the available robot list, and returning to step S1 for processing the undispatched delivery work orders in the delivery work order list that have not been matched with a robot in this round; If the number of delivery orders to be dispatched in the delivery order list is greater than the number of robots in the available robot list, the scheduling strategy of robot selection for delivery orders is used for matching; if the number of delivery orders to be dispatched in the delivery order list is less than the number of robots in the available robot list, the scheduling strategy of delivery order selection for robots is used for matching.

[0122] Among them, the scheduling strategy for the robot to select the delivery work order refers to matching the delivery work orders in the delivery work order list in sequence according to the order of the robots arranged in the available robot list. Specifically, there are m available robots in the available robot list, and there are n delivery work orders to be dispatched in the delivery work order list, m<n; according to the order of the robots in the available robot list, each robot is matched with the n delivery work orders to be dispatched. When the robot matches a delivery work order from the n delivery work orders to be dispatched, the delivery work order matched to the robot is removed from the delivery work order list, and the next robot is matched with nb delivery work orders, b represents the number of delivery work orders that have been matched to the robot.

[0123] The scheduling strategy for selecting robots based on delivery work orders means matching the available robots arranged in the available robot list in sequence according to the order of the delivery work orders arranged in the delivery work order list. Specifically, there are n delivery work orders to be dispatched in the delivery work order list, and there are m available robots in the available robot list, where n<m; each delivery work order is matched with m robots according to the order of the delivery work orders in the delivery work order list. When a delivery work order is matched to a robot, the delivery work order is removed from the delivery work order list.

[0124] When matching the delivery work order with the robot, the matching is carried out in sequence according to the insertion strategy, the sequential strategy and the proximity strategy. For the delivery work order and the robot matched by the proximity strategy, the shortest time planning strategy is used to plan the path from the current position of the robot to the destination; the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot.

[0125] The insertion strategy means that if the starting station of the transport work order and the starting station of the robot are the same node, the transport work order will be assigned to the robot, and the robot matching the insertion strategy will be removed from the list of available robots. The sequential strategy means that if the destination station of the transport work order and the destination station of the transport work order to be assigned are the same node, and the current robot has more than one free cabinet, the transport work order will be dispatched to the robot, and the robot matching the sequential strategy will be removed from the list of available robots. The proximity strategy means obtaining the current position of each available robot in the current list of available robots; obtaining the starting points of all transport work orders to be dispatched in the current list of transport work orders, and obtaining the node path matrix of each robot reaching the starting point of each transport work order, denoted as A, , where m represents the number of available robots and n represents the number of delivery orders. Represents the node path from the current position of the mth robot to the starting point of the nth delivery work order; using the shortest time calculation method, calculate the time of each node path in the matrix A respectively, and get the time matrix C. The minimum value in the time matrix C is the node path with the least time. Associate the delivery work order and robot corresponding to the node path, and dispatch the delivery work order to the robot.

[0126] The predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time it takes for the currently planned robot to pass through an area that overlaps with other robots with planned paths in time and space; the historical deviation time refers to the deviation between the actual time it takes for the robot to pass a node and the predicted time it takes for the robot to pass the node.

[0127] The statistics of historical deviation time are to record the predicted time and actual time when the robot passes through a node, and to count the deviation values ​​according to the conflict types when the robot passes through the node, including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

[0128] The calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, It represents the average speed of the robot in the current plan to go straight; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; Indicates the total number of turns in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

[0129] The weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

[0130] S4. The robot receives the delivery work order information and the planned path information, and walks in the order of nodes in the planned path information according to its own positioning; if the task of the robot node is only the walking task, it continues to execute the next node in the order of nodes in the planned path information; if the task of the robot node includes other tasks besides the walking task, the robot reports the task completion status of the node after completing the task corresponding to the node until the delivery work order is completed.

[0131] Specifically, the robot receives the delivery work order information and the planned path information, and walks in sequence according to the node order in the planned path information based on its own positioning; The robot's position is obtained in real time, and it is determined whether the next node executed by the robot is a traffic control node (such as any one or more combinations of intersections, narrow alleys, elevators, workstations and charging piles). When the robot is at a traffic control node and the robot is within the set threshold time (20s) before reaching the traffic control node, the robot applies for traffic control node resources. If there is no robot in the traffic control node, an instruction to pass the traffic control node is issued to the robot, and the robot passes the traffic control node according to the instruction. If there are other robots passing through the traffic control node, the robot continues to apply for traffic control node resources until the application is successful, and then the traffic control node with successful application is issued to the robot, and the robot passes according to the instruction issued. Determine whether the next node executed by the robot is the elevator area. Within the set threshold time (20s) before the robot reaches the elevator area, obtain the real-time information of the elevator operation in the elevator area, and obtain the elevator number of the robot's fastest floor change, generate a position node and elevator command, and send it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the issued command; If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

[0132] When the robot is executing a delivery work order, the status of the robot's execution of the delivery work order is obtained in real time. When the robot fails, the robot reports the failure information and sends an alarm to the administrator. The current status of the robot can be viewed through the monitoring interface. The administrator can prompt the robot to continue to execute the delivery work order by remotely restarting, remotely modifying configuration items to adapt to the robot's current abnormal scenario and / or switching to remote manual operation mode. If the robot still cannot continue to execute the delivery work order, the delivery work order of the robot will be transferred to manual delivery.

[0133] Example 8 As another preferred embodiment of the present invention, this embodiment is a robot intelligent scheduling and control method of the robot intelligent scheduling and control system for hospital transportation services shown in the above-mentioned embodiments 1 to 6, the method includes adopting the shortest time planning strategy to plan the path from the current position of the robot to the destination, the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the transportation work order information and the planned path information are sent to the robot; the predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time taken for the currently planned robot to pass through the area that overlaps with other robots with planned paths in time and space; the historical deviation time refers to the deviation value between the actual time taken for the robot to pass a node and the predicted time taken for the robot to pass the node.

[0134] Further preferably, the predicted time and actual time consumed by the robot to pass a certain node are recorded, and deviation values ​​are counted separately according to the conflict types when the robot passes through the node, specifically including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

[0135] More preferably, the calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, It represents the average speed of the robot in the current plan to go straight; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; Indicates the total number of turns in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

[0136] The weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

[0137] Example 9 As another preferred embodiment of the present invention, this embodiment is a robot intelligent scheduling and control method of the robot intelligent scheduling and control system for hospital transportation services shown in the above-mentioned embodiments 1 to 6, the method comprising the robot receiving transportation work order information and planned path information, and walking in sequence according to the node order in the planned path information according to its own positioning; If the robot node's task is only walking, the robot will report the time it takes to complete the node to the scheduling platform and continue to execute the next node according to the node sequence in the planned path information; The dispatching platform obtains the position of the robot in real time and determines whether the next node executed by the robot is a traffic control node (such as an intersection, narrow alley, elevator car, workstation and charging pile, etc.). When the robot is close to the next node and it is within the set threshold time (20s) before the robot reaches the traffic control node, the robot applies for traffic control node resources from the dispatching platform. If there is no robot in the traffic control node, the dispatching platform sends an instruction to the robot to pass the traffic control node, and the robot passes the traffic control node according to the instruction of the dispatching platform. If there are other robots passing through the traffic control node, the dispatching platform continues to apply for traffic control node resources for the robot until the application is successful. The dispatching platform sends the successfully applied traffic control node to the robot, and the robot passes according to the instruction issued by the dispatching platform. The dispatching platform determines whether the next node executed by the robot is the elevator area. Within the set threshold time (20s) before the robot reaches the elevator area, the dispatching platform obtains the real-time information of the elevator operation in the elevator area, and obtains the elevator number of the robot's fastest floor change, generates a position node and an elevator command, and sends it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the command issued by the dispatching platform. If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

[0138] Further preferably, the dispatching platform obtains the situation of the robot executing the delivery work order in real time. When the robot fails, the robot reports the fault information to the dispatching platform, and the dispatching platform sends an alarm to the dispatching platform administrator. After receiving the alarm, the dispatching platform administrator checks the current status of the robot through the monitoring interface. The dispatching platform administrator prompts the robot to continue to execute the delivery work order by remote restart, remote modification of configuration items to adapt to the current abnormal scenario of the robot and / or switching to remote manual operation mode; if the robot still cannot continue to execute the delivery work order, the dispatching platform converts the delivery work order of the robot to manual delivery.

[0139] Example 10 As another preferred embodiment of the present invention, this embodiment is a robot intelligent scheduling and control method of the robot intelligent scheduling and control system for hospital transportation services shown in the above-mentioned embodiments 1 to 6, the method comprising determining the execution category according to the attributes of the transportation work order, the attributes of the transportation work order comprising the initial execution category and the transportation work order time limit; the initial execution category comprises manual delivery, robot delivery and manual / robot delivery; the execution category determined according to the attributes of the transportation work order comprises manual delivery or robot delivery; when the initial execution category of the transportation work order is manual delivery, the transportation work order is executed by manual delivery; if the initial execution category in the attributes of the transportation work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of this type of transportation for the transportation work order of this type of initial execution category by calculating the robot load; The calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders with the initial execution categories of robot delivery and manual / robot delivery in the attributes of the delivery order, and M represents the total number of available robots; when M=0, W=-1 is set; when the value of W is not-1 or is less than or equal to the set load threshold, the scheduling platform determines the delivery order that meets the conditions as robot delivery, otherwise it is determined as manual delivery. The load threshold is W=3.

[0140] Further preferably, when the execution time of the delivery work order delivered by the robot has exceeded the set execution time threshold and has not been assigned to a specific robot for execution, the scheduling platform determines the execution category of the delivery work order as manual delivery. When the execution time of the delivery work order is T, the execution time threshold is set to T / 2.

[0141] Refer to the instruction manual Figure 6 As shown, the delivery data of the robot scheduled by the scheduling system and method shown in the above embodiments 1-10 is shown. The number of delivery work orders and the number of available robots are combined, and the order insertion strategy, the sequential strategy and the nearest strategy are matched in sequence to ensure that the delivery work order can be distributed to the corresponding robot in time and the delivery work order can be executed in time. The introduction of the order insertion strategy and the sequential strategy also greatly improves the utilization rate of the robot. The order insertion strategy, the sequential strategy and the nearest strategy are matched in sequence, and the delivery work order that can be inserted is preferred, followed by the sequential delivery work order. When both are not satisfied, the nearest allocation method is considered to match the delivery work order with the robot, and the path itinerary executed by the delivery station is shortened as much as possible. The execution efficiency of the delivery work order is ensured based on time. The shortest time-consuming planning strategy is used to plan the path of the robot, combined with the historical path information, considering the traffic conflict and the historical time-consuming deviation value, so that the predicted shortest time is more accurate and more referenceable. The shortest time-consuming planning strategy can improve the delivery efficiency of the robot compared with the shortest path planning strategy of the prior art.

Claims

1. A robot intelligent dispatching and control system for hospital transportation services, the system comprising a dispatching platform and a robot cluster, the dispatching platform being used to manage transportation work orders; characterized in that: The scheduling platform determines the execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include an initial execution category and a transport work order time limit; the initial execution category includes manual delivery, robot delivery, and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; When the dispatching platform determines that the execution category is robot delivery according to the attributes of the delivery work order, the dispatching platform screens the robot availability and obtains a list of available robots; the dispatching platform arranges the delivery work orders to be dispatched in reverse order according to the delivery work order time limit and obtains a list of delivery work orders to be executed; The dispatching platform matches the delivery orders with available robots based on the delivery order list and the available robot list. The undispatched delivery orders in the delivery order list that have not been matched with a robot in this round will wait for dispatch in the next round. When matching the delivery work order with the robot, the dispatching platform matches them in turn according to the order insertion strategy, the sequential strategy and the nearest strategy; for the delivery work order and the robot matched by the nearest strategy, the dispatching platform plans the path from the current position of the robot to the destination using the shortest time planning strategy; the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot; The robot receives the delivery work order information and the planned path information, and walks in the order of nodes in the planned path information according to its own positioning; if the task of the robot node is only the walking task, it will continue to execute the next node in the order of nodes in the planned path information; if the task of the robot node includes other tasks besides the walking task, the robot will report the task completion status of the node after completing the task corresponding to the node until the delivery work order is completed.

2. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 1, characterized in that: When the scheduling platform matches delivery work orders and available robots based on the delivery work order list and the available robot list, if the number of delivery work orders to be dispatched in the delivery work order list is greater than the number of robots in the available robot list, the scheduling platform adopts the scheduling strategy of robot selection for delivery work orders for matching; if the number of delivery work orders to be dispatched in the delivery work order list is less than the number of robots in the available robot list, the scheduling platform adopts the scheduling strategy of delivery work orders to select robots for matching.

3. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 2, characterized in that: The scheduling strategy for the robot to select the delivery work order refers to matching the delivery work orders in the delivery work order list in sequence according to the order of the robots arranged in the available robot list. Specifically, there are m available robots in the available robot list, and there are n delivery work orders to be dispatched in the delivery work order list, where m<n; according to the order of the robots in the available robot list, each robot is matched with the n delivery work orders to be dispatched. When the robot matches a delivery work order from the n delivery work orders to be dispatched, the delivery work order matched to the robot is removed from the delivery work order list, and the next robot is matched with nb delivery work orders, where b represents the number of delivery work orders that have been matched to the robot.

4. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 2, characterized in that: The scheduling strategy for selecting robots based on delivery work orders refers to matching the available robots arranged in the available robot list in sequence according to the order of the delivery work orders arranged in the delivery work order list. Specifically, there are n delivery work orders to be dispatched in the delivery work order list, and there are m available robots in the available robot list, where n<m; each delivery work order is matched with m robots according to the order of the delivery work orders in the delivery work order list. When a delivery work order is matched to a robot, the delivery work order is removed from the delivery work order list.

5. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4, characterized in that: The insertion order strategy means that if the starting station of the delivery work order and the starting station of the robot are the same node, the delivery work order will be assigned to the robot, and the robot matching the insertion order strategy will be removed from the list of available robots.

6. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4, characterized in that: The sequential strategy means that when the destination site of a delivery order is the same node as the destination site of the delivery order to be assigned, and the current robot has more than one free cabinet, the delivery order is dispatched to the robot, and the robot matching the sequential strategy is removed from the list of available robots.

7. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 1, characterized in that: The proximity strategy is to obtain the current position of each available robot in the current available robot list; obtain the starting points of all the delivery orders to be dispatched in the current delivery order list, and obtain the node path matrix of each robot reaching the starting point of each delivery order, denoted as A, , where m represents the number of available robots and n represents the number of delivery orders. Represents the node path from the current position of the mth robot to the starting point of the nth delivery work order; using the shortest time calculation method, calculate the time of each node path in the matrix A respectively, and get the time matrix C. The minimum value in the time matrix C is the node path with the least time. Associate the delivery work order and robot corresponding to the node path, and dispatch the delivery work order to the robot.

8. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time it takes for the currently planned robot to pass through an area that overlaps with other robots with planned paths in time and space; the historical deviation time refers to the deviation between the actual time it takes for the robot to pass a node and the predicted time it takes for the robot to pass the node.

9. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 8, characterized in that: The dispatching platform records the predicted time and actual time taken by the robot to pass a certain node, and counts the deviation values ​​according to the conflict types when the robot passes through the node, including the deviation value when there is no traffic conflict when the robot passes through the node, the deviation value when there is an opposite conflict when the robot passes through the node, and the deviation value when there is a following vehicle conflict when the robot passes through the node.

10. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 8, characterized in that: The calculation formula for the shortest time consumption is as follows: ; In the formula, T represents the shortest time, and n represents the number of areas included in the robot's planned path, which includes floors and elevators; To predict the duration of traffic conflicts, the calculation formula is: ; In the formula, Indicates the remaining path length of the currently planned robot to reach the traffic conflict node, , , …, They represent the remaining path lengths of other robots 1, 2, ..., m other than the currently planned robot to reach the traffic conflict node, Indicates the average speed of the robot's straight movement in the current plan; It indicates the time consumption of prediction length, and the calculation formula is: ; In the formula, s represents the total length of the path currently planned by the robot in the area, represents the average speed of the robot's straight movement in the current plan; r represents the turning constant, if there is a turn, r=1, if there is no turn, r=0; θ represents the total turning degree in the area; Indicates the average angular velocity of the robot's current planned turn; Represents the historical time deviation value, and the calculation formula is: ; In the formula, Indicates the amount of historical time-consuming data. Indicates Historical time consumption data; Represents the weight coefficient.

11. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 10, characterized in that: The weight coefficient , is the activation function Determined, where the value of x uses the variance of historical time-consuming data, that is , , weight coefficient The value of falls in the interval (0,1). The closer it is to 1, the greater the fluctuation of historical time consumption data. The historical time consumption data is not referenceable. The weight coefficient ,like The closer it is to 0, the more stable the historical time consumption data is. .

12. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The robot receives the delivery work order information and the planned path information, and walks in sequence according to the node order in the planned path information based on its own positioning; If the robot node's task is only walking, the robot will report the time it takes to complete the node to the scheduling platform and continue to execute the next node according to the node sequence in the planned path information; The dispatching platform obtains the position of the robot in real time and determines whether the next node executed by the robot is a traffic control node. When the robot is within a traffic control node and within the set threshold time before reaching the traffic control node, the robot applies to the dispatching platform for traffic control node resources. If there is no robot in the traffic control node, the dispatching platform issues an instruction to the robot to pass through the traffic control node, and the robot passes through the traffic control node according to the instruction of the dispatching platform. If there are other robots passing through the traffic control node, the dispatching platform continues to apply for traffic control node resources for the robot until the application is successful. The dispatching platform issues the successfully applied traffic control node to the robot, and the robot passes according to the instruction issued by the dispatching platform. The dispatching platform determines whether the next node executed by the robot is the elevator area. Within the set threshold time before the robot reaches the elevator area, the dispatching platform obtains the real-time information of the elevator operation in the elevator area, and obtains the elevator number of the robot's fastest floor change, generates a position node and an elevator command, and sends it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the command issued by the dispatching platform; If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

13. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The scheduling platform determines the execution category based on the attributes of the delivery work order. Specifically, if the initial execution category in the attributes of the delivery work order is manual delivery, the delivery work order is directly converted to manual delivery; if the initial execution category in the attributes of the delivery work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of this type of delivery for the delivery work order with this initial execution category by calculating the robot load.

14. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 13, characterized in that: The calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders whose initial execution categories in the attributes of the delivery order are robot delivery and manual / robot delivery, and M represents the total number of available robots. When M=0, W=-1 is set. When the value of W is not -1 or is less than or equal to the set load threshold, the scheduling platform will determine the delivery order that meets the conditions as robot delivery, otherwise it will be determined as manual delivery.

15. The robot intelligent dispatching and control system for hospital transportation services according to claim 13, characterized in that: When the execution time of a delivery work order delivered by a robot has exceeded the set execution time threshold and has not yet been assigned to a specific robot for execution, the scheduling platform determines the execution category of the delivery work order as manual delivery.

16. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The dispatching platform obtains the status of the robot executing the delivery work order in real time. When a robot fails, the robot reports the failure information to the dispatching platform, and the dispatching platform sends an alarm to the dispatching platform administrator. After receiving the alarm, the dispatching platform administrator checks the current status of the robot through the monitoring interface. The dispatching platform administrator prompts the robot to continue to execute the delivery work order by remote restart, remote modification of configuration items to adapt to the current abnormal scenario of the robot and / or switching to remote manual operation mode; if the robot still cannot continue to execute the delivery work order, the dispatching platform will convert the delivery work order of the robot to manual delivery.

17. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The scheduling platform performs robot availability screening, specifically, the scheduling platform obtains the power, online status, idle status, robot cabinet idle status and / or fault status of each robot in the robot cluster, screens the available robots, and obtains a list of available robots.

18. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The dispatching platform obtains the three-dimensional map of the target hospital and loads it into a three-dimensional topological map. The nodes in the three-dimensional topological map include roads, departments, corridors, elevators and access control; the three-dimensional topological map contains the horizontal and vertical distance values ​​of the target hospital space; based on the three-dimensional topological map, the dispatching platform uses the shortest time-consuming planning strategy to plan the path from the robot's current position to the destination.

19. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The dispatching platform is deployed in the intranet area of ​​the hospital. The dispatching platform is provided with an API interface, which is used to connect the dispatching platform with the existing management system of the hospital; the robot cluster is arranged in a public network, and the robots in the robot cluster establish a communication connection with the dispatching platform through a wireless communication network.

20. The robot intelligent dispatching and control system for hospital transportation services as claimed in claim 19, characterized in that: The hospital's existing management system includes any one or more combinations of the HIS system, digital twin system, information push system, BI analysis system, task scheduling system, operation and maintenance management system, and AI big data system.

21. The robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 4 or 7, characterized in that: The robot establishes communication connections with the elevator and access control via the Lora communication protocol to call the elevator and control the access control.

22. A robot intelligent dispatching and control method of a robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 21, characterized in that: The method comprises the following steps, S1. Determine an execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include an initial execution category and a transport work order time limit; the initial execution category includes manual delivery, robot delivery, and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; S2. When the determined execution category is robot delivery, robot availability screening is performed to obtain a list of available robots; the delivery work orders to be dispatched are sorted in reverse order according to the delivery work order time limit to obtain a list of delivery work orders to be executed; S3. Match the delivery work order with the available robots according to the delivery work order list and the available robot list. The undispatched delivery work orders in the delivery work order list that are not matched to the robot in this round are returned to step S1 for processing; when matching the delivery work order with the robot, the order insertion strategy, the sequential strategy and the nearest strategy are sequentially matched. For the delivery work order and the robot matched by the nearest strategy, the path from the current position of the robot to the destination is planned by the shortest time planning strategy; the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot; S4. The robot receives the delivery work order information and the planned path information, and walks in the order of nodes in the planned path information according to its own positioning; if the task of the robot node is only the walking task, it continues to execute the next node in the order of nodes in the planned path information; if the task of the robot node includes other tasks besides the walking task, the robot reports the task completion status of the node after completing the task corresponding to the node until the delivery work order is completed.

23. A robot intelligent dispatching and control method of a robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 21, characterized in that: The method adopts the shortest time planning strategy to plan the path from the current position of the robot to the destination, and the shortest time includes the predicted length time, the predicted traffic conflict time and the historical time deviation value; the planned path information includes the path node information and the task information corresponding to each path node, and the delivery work order information and the planned path information are sent to the robot; the predicted length time refers to the ratio of the road length in the robot's walking path to the robot's simulated speed; the predicted traffic conflict time refers to the time taken by the currently planned robot to pass through the area that overlaps with other robots with planned paths in time and space; the historical deviation time refers to the deviation value between the actual time taken by the robot to pass a node and the predicted time taken by the robot to pass another node.

24. A robot intelligent dispatching and control method of a robot intelligent dispatching and control system for hospital transportation services according to any one of claims 1 to 21, characterized in that: The method is that the robot receives the delivery work order information and the planned path information, and walks in sequence according to the node order in the planned path information based on its own positioning; If the robot node's task is only walking, the robot will report the time it takes to complete the node to the scheduling platform and continue to execute the next node according to the node sequence in the planned path information; The dispatching platform obtains the position of the robot in real time and determines whether the next node executed by the robot is a traffic control node. When the robot is within a traffic control node and within the set threshold time before reaching the traffic control node, the robot applies to the dispatching platform for traffic control node resources. If there is no robot in the traffic control node, the dispatching platform issues an instruction to the robot to pass through the traffic control node, and the robot passes through the traffic control node according to the instruction of the dispatching platform. If there are other robots passing through the traffic control node, the dispatching platform continues to apply for traffic control node resources for the robot until the application is successful. The dispatching platform issues the successfully applied traffic control node to the robot, and the robot passes according to the instruction issued by the dispatching platform. The dispatching platform determines whether the next node executed by the robot is the elevator area. Within the set threshold time before the robot reaches the elevator area, the dispatching platform obtains the real-time information of the elevator operation in the elevator area, and obtains the elevator number of the robot's fastest floor change, generates a position node and an elevator command, and sends it to the robot. The robot goes to the corresponding elevator position node and waits for the elevator according to the command issued by the dispatching platform; If the node where the robot is walking is the destination for picking up goods, after the robot arrives at the destination node, the robot interface switches to the authentication interface. After the user completes the authentication, the robot interface displays the target department and the list of cabinet doors that can be clocked in. After the user selects the corresponding cabinet door and places the items, the robot reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform; If the node where the robot is walking is the destination for cargo delivery, after the robot arrives at the destination node, the robot interface switches to the authentication interface, opens the corresponding cabinet door after the user completes the authentication, and reports the task completion information, waybill status information and vehicle status information of the node to the dispatching platform after the user completes the pickup.

25. A robot intelligent dispatching and control method of a robot intelligent dispatching and control system for hospital transportation services based on any one of claims 1 to 21, characterized in that: The method is to determine the execution category according to the attributes of the transport work order, wherein the attributes of the transport work order include the initial execution category and the time limit of the transport work order; the initial execution category includes manual delivery, robot delivery and manual / robot delivery; the execution category determined according to the attributes of the transport work order includes manual delivery or robot delivery; when the initial execution category of the transport work order is manual delivery, the transport work order is executed by manual delivery; if the initial execution category in the attributes of the transport work order is robot delivery and manual / robot delivery, the scheduling platform determines the execution category of the transport of this type of initial execution category by calculating the robot load; the calculation process of the robot load is as follows: W=N / M, where W represents the robot load, N represents the total number of delivery orders whose initial execution categories in the attributes of the delivery order are robot delivery and manual / robot delivery, and M represents the total number of available robots. When M=0, W=-1 is set. When the value of W is not -1 or is less than or equal to the set load threshold, the scheduling platform will determine the delivery order that meets the conditions as robot delivery, otherwise it will be determined as manual delivery.

Images