Robot intelligent control method and device based on voice recognition

Abstract

The invention provides a robot intelligent control method and device based on voice recognition, and relates to the technical field of robots. And inputting the predicted waiting duration of the elevator of each floor and a set of room numbers in which the catering items need to be distributed into a pre-trained distribution strategy determination model, and determining a catering item distribution strategy with the minimum total duration after N distribution robots distribute all the catering items to be distributed, based on the catering article delivery strategy with the minimum total time length, catering article delivery instructions are sent to the N delivery robots respectively, catering articles associated with the catering article delivery instructions are sequentially taken out from at least one storage space of the catering article temporary storage cabinet based on the received catering article delivery instructions, and the catering articles associated with the catering article delivery instructions are taken out from the catering article temporary storage cabinet based on the catering article delivery instructions. And the at least one taken-out catering item is sequentially delivered to the guest room doorway corresponding to the at least one room number associated with the catering item delivery instruction by taking the elevator in sequence, so that the labor cost is saved, and the catering item delivery efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a robot intelligent control method and device based on speech recognition. Background Art

[0002] With the development of science and technology and society, intelligent robots have been used in hotels, which can provide delivery services to hotel guests, reduce the workload of hotel staff, and thus improve the hotel's intelligent service level.

[0003] At present, when hotel guests need to deliver food and beverage items purchased online, they can request robot service based on the voice of the telephone in the guest room. Then, the hotel front desk answers the guest's call, and the front desk staff places the food and beverage items delivered by the takeaway man on the robot. The front desk staff enters information such as the room number and issues delivery instructions to the robot, so that the robot performs the delivery service requested by the guest according to the instructions.

[0004] However, online food and beverage items are usually time-intensive (e.g., lunch takeout usually arrives at noon, afternoon tea usually arrives at 3:00-4:00 p.m.). At this time, if the hotel front desk is relied upon to cooperate with robots to perform the delivery service requested by the guests, the labor cost is still very high, the efficiency is low, and it takes a long time, resulting in a poor experience for hotel guests. Summary of the invention

[0005] The present application provides a robot intelligent control method and device based on voice recognition, which is used to solve the problem in the prior art that the labor cost of executing the delivery service requested by the guest is still very high, the efficiency is low, and it takes a long time, resulting in a poor experience for hotel guests.

[0006] In the first aspect, the present application provides a robot intelligent control method based on speech recognition, which is applied to a robot intelligent control device for speech recognition. The device includes a hotel server, N delivery robots, multiple guest room telephones, and a temporary storage cabinet for catering items. The temporary storage cabinet for catering items includes multiple storage spaces, each storage space includes a unique number, each delivery robot and each guest room telephone are respectively connected to the hotel server for communication. The method provided by the present application includes:

[0007] During the target period, the hotel server collects the voice information transmitted from multiple guest room phones to the front desk phone when they make calls to the front desk phone;

[0008] The hotel server determines, based on the voice information of the multiple guest room telephones, a set of identifiers of the guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items, where M and N are both positive integers, and M is greater than N;

[0009] The hotel server determines the guest identity information associated with the room phone number identifier of each room that needs the delivery robot to deliver food and beverage items;

[0010] The hotel server obtains order delivery completion information from the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the number of the storage space of the food and beverage item temporary storage cabinet to be delivered, and the room number carries the floor information;

[0011] The hotel server inputs the recorded hotel occupancy volume within the target period and the number of guest rooms that need to be delivered by the delivery robot into the pre-trained first elevator waiting time prediction model to determine the first reference waiting time for the elevator on each floor, wherein the elevator waiting time prediction model is obtained by inputting a plurality of first training samples into the first neural network for training, each first training sample including the hotel occupancy volume within the historical target period, the number of guest rooms that need to be delivered by the delivery robot in the history, and the corresponding historical waiting time for the elevator on each floor;

[0012] The hotel server inputs the actual waiting time of each floor of the elevator in the first two historical time periods equal to the length of the target time period and the actual waiting time of each floor of the elevator in the first historical time period equal to and continuous with the length of the target time period into the pre-trained second elevator waiting time prediction model to determine the second reference waiting time of each floor of the elevator, wherein the second elevator waiting time prediction model is obtained by inputting a plurality of second training samples into the long short-term memory model for training, each second training sample including the historical waiting time of each floor of the elevator in the first two historical time periods equal to the length of the historical target time period, the historical waiting time of each floor of the elevator in the first historical time period equal to and continuous with the length of the historical target time period, and the historical waiting time of each floor of the elevator in the historical target time period;

[0013] The hotel server determines the predicted waiting time for the elevator on each floor according to the first reference waiting time for the elevator on each floor and the second reference waiting time for the elevator on each floor;

[0014] The hotel server inputs the predicted waiting time of the elevator on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determines the food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered by N delivery robots, wherein the delivery strategy determination model is obtained by inputting a plurality of third training samples into the second neural network for training, and each third training sample includes the predicted waiting time of the elevator on each floor in history, the set of room numbers to which the food and beverage items need to be delivered in history, and the corresponding food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered in history;

[0015] The hotel server sends food item delivery instructions to the N delivery robots based on the food item delivery strategy with the shortest total delivery time, wherein each food item delivery instruction includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet and at least one room number to be delivered;

[0016] Based on the received food item delivery instructions, N delivery robots respectively take out food items associated with the food item delivery instructions from at least one storage space of the food item temporary storage cabinet in turn, and take the elevator in turn to deliver the at least one taken out food item to the door of the guest room corresponding to at least one room number associated with the food item delivery instruction.

[0017] In a possible implementation, the hotel server determines a set of identifications of the guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items based on the voice information of the multiple guest room telephones, including:

[0018] The hotel server converts the voice messages of multiple guest room phones into corresponding text messages;

[0019] For each text message, the hotel server compares the text message with each standard text message in a preset standard request text library for delivering catering items for similarity;

[0020] When the similarity between the text information and any standard text information is greater than a set similarity threshold, the hotel server determines that the guest room where the guest room phone number corresponding to the text information is located needs a delivery robot to deliver food and beverage items;

[0021] The hotel server aggregates the identifiers of multiple guest room telephone numbers that require the delivery robot to deliver food and beverage items, and obtains an identifier set of the guest room telephone numbers of M guest rooms that require the delivery robot to deliver food and beverage items.

[0022] In a possible implementation, the hotel server determines a set of identifications of the guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items based on the voice information of the multiple guest room telephones, including:

[0023] The hotel server inputs the voice information of the multiple guest room telephones into a preset semantic recognition model to respectively recognize the semantics corresponding to the voice information of the multiple guest room telephones;

[0024] When the semantics corresponding to the voice information of any guest room phone indicate that a delivery robot is required to deliver food and beverage items, the hotel server determines that the guest room where the guest room phone corresponding to the voice information is located needs a delivery robot to deliver food and beverage items;

[0025] The hotel server aggregates the identifiers of multiple guest room telephone numbers that require the delivery robot to deliver food and beverage items, and obtains an identifier set of the guest room telephone numbers of M guest rooms that require the delivery robot to deliver food and beverage items.

[0026] In a possible implementation, the hotel server determines the predicted waiting time for the elevator on each floor according to the first reference waiting time for the elevator on each floor and the second reference waiting time for the elevator on each floor, including:

[0027] The hotel server is based on the formula T 11 =K1T1+K2T2, determine the predicted waiting time for the elevator on each floor, where T 11 is the predicted waiting time for the elevator on any floor, T1 is the first reference waiting time for the elevator on any floor, T2 is the second reference waiting time for the elevator on any floor, K1 is the first weighting coefficient, and K2 is the second weighting coefficient.

[0028] In a possible implementation, the delivery robot includes a manipulator and a placement tray, and the N delivery robots respectively take out the food and beverage items associated with the food and beverage item delivery instruction from at least one storage space of the food and beverage item temporary storage cabinet based on the received food and beverage item delivery instruction, and take the elevator in turn to deliver the at least one food and beverage item taken out to the door of the guest room corresponding to at least one room number associated with the food and beverage item delivery instruction, including:

[0029] For any one of the N delivery robots, receiving a food item delivery instruction;

[0030] Identify the number of at least one storage space of the food item temporary storage cabinet and at least one room number to be delivered in the food item delivery instruction;

[0031] Move to a side of the storage space corresponding to the number of at least one storage space;

[0032] Extending a mechanical arm to grab a food and beverage item to be delivered located in at least one storage space corresponding to the number, and placing at least one food and beverage item to be delivered on a placement tray;

[0033] Generate a navigation route based on the address of the catering item storage cabinet and the address associated with at least one room number to be delivered;

[0034] Based on the navigation route, carry the food and beverage items to be delivered and take the elevator to the door of at least one guest room associated with the room number to be delivered.

[0035] In a second aspect, the present application further provides a robot intelligent control device based on voice recognition, comprising a hotel server, N delivery robots, multiple guest room telephones, and a catering item temporary storage cabinet, wherein the catering item temporary storage cabinet comprises multiple storage spaces, each storage space comprises a unique number, each delivery robot and each guest room telephone are respectively connected to the hotel server for communication, wherein:

[0036] The hotel server is used to collect the voice information transmitted from multiple guest room phones to the front desk phone during the target period of time;

[0037] The hotel server is further used to determine, based on the voice information of the multiple guest room telephones, a set of identifiers of the guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items, wherein M and N are both positive integers, and M is greater than N;

[0038] The hotel server is further used to determine the guest identity information associated with the room phone identification of each room that needs the delivery robot to deliver food and beverage items;

[0039] The hotel server is also used to obtain order delivery completion information under the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the number of the storage space of the food and beverage item temporary storage cabinet to be delivered, and the room number carries the floor information;

[0040] The hotel server is further used to input the recorded hotel occupancy volume within the target time period and the number of guest rooms that need to be delivered with food and beverage items by the delivery robot into a pre-trained first elevator waiting time prediction model to determine a first reference waiting time for the elevator on each floor, wherein the elevator waiting time prediction model is obtained by inputting a plurality of first training samples into a first neural network for training, each first training sample including the hotel occupancy volume within the historical target time period, the number of guest rooms that need to be delivered with food and beverage items by the delivery robot in the history, and the corresponding historical waiting time for the elevator on each floor;

[0041] The hotel server is further used to input the actual waiting time of each floor of the elevator in the first two historical time periods equal to the length of the target time period, and the actual waiting time of each floor of the elevator in the first historical time period equal to and continuous with the length of the target time period, into a pre-trained second elevator waiting time prediction model to determine a second reference waiting time of each floor of the elevator, wherein the second elevator waiting time prediction model is obtained by inputting a plurality of second training samples into the long short-term memory model for training, and each second training sample includes the historical waiting time of each floor of the elevator in the first two historical time periods equal to the length of the historical target time period, the historical waiting time of each floor of the elevator in the first historical time period equal to and continuous with the length of the historical target time period, and the historical waiting time of each floor of the elevator in the historical target time period;

[0042] The hotel server is further used to determine the predicted waiting time of the elevator on each floor according to the first reference waiting time of the elevator on each floor and the second reference waiting time of the elevator on each floor;

[0043] The hotel server is further used to input the predicted waiting time of the elevator on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determine the food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered by N delivery robots, wherein the delivery strategy determination model is obtained by inputting a plurality of third training samples into the second neural network for training, and each third training sample includes the predicted waiting time of the elevator on each floor in history, the set of room numbers to which the food and beverage items need to be delivered in history, and the corresponding food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered in history;

[0044] The hotel server is further configured to send food item delivery instructions to the N delivery robots respectively based on the food item delivery strategy with the shortest total delivery time, wherein each food item delivery instruction includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet and at least one room number to be delivered;

[0045] The N delivery robots are respectively used to, based on the received food item delivery instructions, sequentially take out food items associated with the food item delivery instructions from at least one storage space of the food item temporary storage cabinet;

[0046] The N delivery robots are also used to take the elevator in turn to deliver at least one taken-out food and beverage item to the guest room door a corresponding to at least one room number associated with the food and beverage item delivery instruction.

[0047] In one possible implementation, a hotel server is specifically used to convert voice information of multiple guest room telephones into corresponding text information; for each text information, the text information is compared for similarity with each standard text information in a preset standard text library for requesting the delivery of food items; when the similarity between the text information and any standard text information is greater than a set similarity threshold, it is determined that the guest room where the guest room telephone corresponding to the text information is located needs a delivery robot to deliver food items; the identifiers of multiple guest room telephones that need a delivery robot to deliver food items are aggregated to obtain a set of identifiers of the guest room telephones of M guest rooms that need a delivery robot to deliver food items.

[0048] In one possible implementation, a hotel server is specifically used to input voice information of multiple guest room telephones into a preset semantic recognition model to respectively identify the semantics corresponding to the voice information of the multiple guest room telephones; when the semantics corresponding to the voice information of any guest room telephone represents the need for a delivery robot to deliver food and beverage items, determine that the guest room where the guest room telephone corresponding to the voice information is located needs a delivery robot to deliver food and beverage items; aggregate the identifiers of multiple guest room telephones that need a delivery robot to deliver food and beverage items, and obtain a set of identifiers of the guest room telephones of M guest rooms that need a delivery robot to deliver food and beverage items.

[0049] In a possible implementation, the hotel server is specifically configured to calculate the 11 =K1T1+K2T2, determine the predicted waiting time for the elevator on each floor, where T 11 is the predicted waiting time for the elevator on any floor, T1 is the first reference waiting time for the elevator on any floor, T2 is the second reference waiting time for the elevator on any floor, K1 is the first weighting coefficient, and K2 is the second weighting coefficient.

[0050] In a possible implementation, each delivery robot includes a manipulator and a placement plate, and each delivery robot among the N delivery robots is used to receive a food item delivery instruction; identify the number of at least one storage space of a food item temporary storage cabinet and at least one room number to be delivered in the food item delivery instruction; move to one side of the storage space corresponding to the number of at least one storage space in sequence; extend the manipulator to grab the food item to be delivered in the at least one storage space corresponding to the number, and place the at least one food item to be delivered on the placement plate; generate a navigation route based on the address of the food item temporary storage cabinet and the address associated with at least one room number to be delivered; based on the navigation route, carry the food items to be delivered and take the elevator to move to the door of the guest room associated with at least one room number to be delivered in sequence.

[0051] The present application provides a robot intelligent control method and device based on voice recognition. The hotel server determines the identification set of the guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items based on the voice information of multiple guest room telephones.

[0052] The hotel server determines the guest identity information associated with the guest room phone identification for each guest room that needs the delivery robot to deliver food and beverage items. It can be understood that guests staying in the hotel need to register using their ID cards, so that the hotel server records the correspondence between the guest identity information and the room number of the guest staying. There is a correspondence between the room number and the guest room phone identification in the guest room, so the guest identity information associated with the guest room phone identification can be determined.

[0053] In this way, the hotel server can obtain the order delivery completion information under the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the storage space number of the food and beverage item temporary storage cabinet to be delivered, and the room number carries the floor information.

[0054] The hotel server inputs the recorded hotel occupancy volume during the target period and the number of guest rooms that need food and beverage items to be delivered by the delivery robot into the pre-trained first elevator waiting time prediction model to determine the first reference waiting time for the elevator on each floor. Since the elevator waiting time prediction model is obtained by inputting multiple first training samples into the first neural network for training, each first training sample includes the hotel occupancy volume during the historical target period, the number of guest rooms that need food and beverage items to be delivered by the delivery robot in the history, and the corresponding historical waiting time for the elevator on each floor. In this way, the first reference waiting time for the elevator on each floor is determined with high accuracy.

[0055] In addition, the hotel server also inputs the actual waiting time of each elevator floor in the first two historical time periods equal to the duration of the target time period, and the actual waiting time of each elevator floor in the first historical time period equal to and continuous with the duration of the target time period, into the pre-trained second elevator waiting time prediction model to determine the second reference waiting time of each elevator floor. Since the second elevator waiting time prediction model is obtained by inputting multiple second training samples into the long short-term memory model for training, each second training sample includes the historical waiting time of each elevator floor in the first two historical time periods equal to the duration of the historical target time period, the historical waiting time of each elevator floor in the first historical time period equal to and continuous with the duration of the historical target time period, and the historical waiting time of each elevator floor in the historical target time period. In this way, the second reference waiting time of each elevator floor is determined with high accuracy.

[0056] The hotel server determines the predicted waiting time for the elevator on each floor according to the first reference waiting time for the elevator on each floor and the second reference waiting time for the elevator on each floor. Since the accuracy of the first reference waiting time is high and the accuracy of the second reference waiting time is also high, the accuracy of the predicted waiting time for the elevator on each floor obtained based on the first reference waiting time and the second reference waiting time is also high.

[0057] The hotel server inputs the predicted waiting time of the elevators on each floor and the set of room numbers to which the food items need to be delivered into the pre-trained delivery strategy determination model, and determines the food item delivery strategy with the shortest total time after all the food items to be delivered are delivered by N delivery robots. Since the delivery strategy determination model is obtained by inputting multiple third training samples into the second neural network for training, each third training sample includes the predicted waiting time of the elevators on each floor in history, the set of room numbers to which the food items need to be delivered in history, and the corresponding food item delivery strategy with the shortest total time after all the food items to be delivered are delivered in history. In this way, the determined food item delivery strategy takes the shortest total time after all the food items to be delivered are delivered, and is particularly efficient; and since the time taken takes into account the elevator waiting time, the reliability is also high.

[0058] Based on the food item delivery strategy with the shortest total time, the hotel server sends food item delivery instructions to N delivery robots respectively, wherein each food item delivery instruction includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet and at least one room number to be delivered; based on the received food item delivery instructions, the N delivery robots take out the food items associated with the food item delivery instructions from at least one storage space of the food item temporary storage cabinet in turn, and take the elevator in turn to deliver the at least one food item taken out to the guest room door corresponding to the at least one room number associated with the food item delivery instruction. In this way, the delivery of the above-mentioned food items does not require any human intervention, which saves labor costs and further improves the delivery efficiency of food items. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0060] Figure 1 A schematic diagram of the architecture of a robot intelligent control device based on speech recognition provided in an embodiment of the present application;

[0061] Figure 2 A flowchart of a robot intelligent control method based on speech recognition provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments made by ordinary technicians in this field under the enlightenment of the embodiments belong to the scope of protection of the present application.

[0063] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0064] See also Figure 1 The embodiment of the present application provides a robot intelligent control method based on speech recognition, which is applied to a robot intelligent control device based on speech recognition. The device includes a hotel server 100, N delivery robots 103, multiple guest room telephones, and a catering item temporary storage cabinet 102. The catering item temporary storage cabinet 102 includes multiple storage spaces, each storage space includes a unique number, and each delivery robot 103 and each guest room telephone are respectively connected to the hotel server 100 for communication. Figure 2 As shown, the method provided in the embodiment of the present application includes:

[0065] S201: The hotel server 100 collects voice information transmitted from multiple guest room phones to the front desk phone when the guest room phones make calls to the front desk phone during a target period of time.

[0066] The target time period may be a concentrated delivery time period for food and beverage items, for example, the target time period may be 11:30-13:00 noon. The voice message may be, but is not limited to, "Please help me pick up the takeaway", "Hello, please pick up the food for me", "Send me the meal", etc., which are not limited here.

[0067] S202: The hotel server 100 determines, based on the voice information of the multiple guest room telephones, a set of identifications of the guest room telephones of the M guest rooms 104 to which the delivery robot 103 is required to deliver food and beverage items.

[0068] Wherein, M and N are both positive integers, and M is greater than N. Generally, for cost considerations, the number of delivery robots 103 in hotels is generally small. For example, the number N of delivery robots 103 is generally less than 5, while the number of food and beverage items delivered by the delivery robots 103 is usually large, generally greater than 20. In this case, it is necessary to reasonably determine the delivery strategy of the food and beverage items to minimize the total time spent.

[0069] For example, the specific implementation of S202 may include but is not limited to the following two methods:

[0070] The first one:

[0071] Step 1: The hotel server 100 converts the voice messages of multiple guest room telephones into corresponding text messages.

[0072] Step 2: The hotel server 100 compares each text message with each standard text message in a preset standard text library for requesting the delivery of catering items for similarity.

[0073] Step 3: When the similarity between the text information and any standard text information is greater than a set similarity threshold, the hotel server 100 determines that the guest room 104 where the guest room phone number corresponding to the text information is located needs the delivery robot 103 to deliver food and beverage items.

[0074] Step 4: The hotel server 100 collects the identifiers of the multiple guest room telephone numbers that require the delivery robot 103 to deliver food and beverage items, and obtains an identifier set of the guest room telephone numbers of the M guest rooms 104 that require the delivery robot 103 to deliver food and beverage items.

[0075] Second type:

[0076] Step A: The hotel server 100 inputs the voice information of the multiple guest room telephones into a preset semantic recognition model to respectively recognize the semantics corresponding to the voice information of the multiple guest room telephones.

[0077] Step B: When the semantics corresponding to the voice information of any guest room phone indicates that the delivery robot 103 is required to deliver food and beverage items, the hotel server 100 determines that the guest room 104 where the guest room phone corresponding to the voice information is located needs the delivery robot 103 to deliver food and beverage items.

[0078] Step C: the hotel server 100 collects the identifiers of the telephone numbers of the guest rooms that need the delivery robot 103 to deliver food and beverage items, and obtains an identifier set of the telephone numbers of the M guest rooms 104 that need the delivery robot 103 to deliver food and beverage items.

[0079] S203: The hotel server 100 determines the guest identity information associated with the identification of the room phone number of each guest room 104 that requires the delivery robot 103 to deliver food and beverage items.

[0080] S204: The hotel server 100 obtains order delivery completion information from the online shopping application account associated with the guest identity information.

[0081] The order delivery completion information includes the room number to which the food and beverage items need to be delivered and the number of the storage space of the food and beverage items to be delivered in the food and beverage item temporary storage cabinet 102 , and the room number carries the floor information.

[0082] S205: The hotel server 100 inputs the recorded hotel occupancy volume during the target period and the number of guest rooms 104 that require the delivery robot 103 to deliver food and beverage items into the pre-trained first elevator 103 waiting time prediction model to determine the first reference waiting time for the elevator 103 on each floor.

[0083] Among them, the elevator 103 waiting time prediction model is obtained by inputting multiple first training samples into the first neural network for training, each first training sample includes the hotel occupancy volume in the historical target period, the number of guest rooms 104 that historically required the delivery robot 103 to deliver food and beverage items, and the corresponding historical waiting time of the elevator 103 on each floor.

[0084] S206: The hotel server 100 inputs the actual waiting time of the elevator 103 on each floor in the first two historical time periods equal to the length of the target time period and the actual waiting time of the elevator 103 on each floor in the first historical time period equal to and continuous with the length of the target time period into the pre-trained second elevator 103 waiting time prediction model to determine the second reference waiting time of the elevator 103 on each floor.

[0085] Among them, the second elevator 103 waiting time prediction model is obtained by inputting multiple second training samples into the long short-term memory model for training, and each second training sample includes the historical waiting time of the elevator 103 on each floor in the first two historical time periods equal to the duration of the historical target time period, the historical waiting time of the elevator 103 on each floor in the first historical time period equal to and continuous with the duration of the historical target time period, and the historical waiting time of the elevator 103 on each floor in the historical target time period.

[0086] S207: The hotel server 100 determines the predicted waiting time of the elevator 103 on each floor according to the first reference waiting time of the elevator 103 on each floor and the second reference waiting time of the elevator 103 on each floor.

[0087] For example, the hotel server 100 can calculate the value of the formula T 11 =K1T1+K2T2, determine the predicted waiting time of the elevator 103 on each floor, where T 11 is the predicted waiting time of the elevator 103 on any floor, T1 is the first reference waiting time of the elevator 103 on any floor, T2 is the second reference waiting time of the elevator 103 on any floor, K1 is the first weighting coefficient, and K2 is the second weighting coefficient. For example, K1 may be equal to 0.5, and K2 may be equal to 0.5.

[0088] S208: The hotel server 100 inputs the predicted waiting time of the elevator 103 on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determines the food and beverage item delivery strategy with the shortest total time after the N delivery robots 103 have delivered all the food and beverage items to be delivered.

[0089] Among them, the distribution strategy determination model is obtained by inputting multiple third training samples into the second neural network for training, each third training sample includes the predicted waiting time of the elevator 103 on each floor in history, the set of room numbers to which the catering items need to be delivered in history, and the corresponding catering item delivery strategy with the shortest total time after all the catering items to be delivered have been delivered in history.

[0090] For example, the food delivery strategy with the shortest total time includes but is not limited to assigning the delivery room numbers of the same floor to the same delivery robot 103, assigning the delivery room numbers of adjacent floors to the same delivery robot 103, etc., which are not limited here.

[0091] S209: The hotel server 100 sends food and beverage item delivery instructions to the N delivery robots 103 respectively based on the food and beverage item delivery strategy with the shortest total time.

[0092] Each food item delivery instruction includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet 102 and at least one room number to be delivered.

[0093] S210: N delivery robots 103 take out food items associated with the food item delivery instruction from at least one storage space of the food item temporary storage cabinet 102 in turn based on the received food item delivery instruction, and take the elevator 103 in turn to deliver the at least one taken out food item to the door of the guest room 104 corresponding to at least one room number associated with the food item delivery instruction.

[0094] Exemplarily, the delivery robot 103 includes a manipulator and a placement tray, and S202 can be specifically implemented as follows:

[0095] For any one of the N delivery robots 103 , a food item delivery instruction is received.

[0096] The number of at least one storage space of the food item temporary storage cabinet 102 and at least one room number to be delivered in the food item delivery instruction are identified.

[0097] Move in sequence to one side of the storage space corresponding to the number of at least one storage space of the food item temporary storage cabinet 102 .

[0098] The robot arm is extended to grab the food and beverage item to be delivered in at least one storage space corresponding to the number, and the at least one food and beverage item to be delivered is placed on the placement plate.

[0099] For example, if at least one storage space is numbered "001", "002" and "003", then the robot moves to one side of the storage space numbered "001" of the food item temporary storage cabinet 102, extends the robot arm to place the food items in the storage space numbered "001" on the placement plate; then moves to one side of the storage space numbered "002" of the food item temporary storage cabinet 102, extends the robot arm to place the food items in the storage space numbered "002" on the placement plate. Finally, it moves to one side of the storage space numbered "003" of the food item temporary storage cabinet 102, extends the robot arm to place the food items in the storage space numbered "003" on the placement plate.

[0100] A navigation route is generated based on the address of the food storage cabinet 102 and the address associated with at least one room number to be delivered. Based on the navigation route, the food items to be delivered are taken to the door of the guest room 104 associated with at least one room number to be delivered by elevator 103 in sequence.

[0101] Still see Figure 1 , the embodiment of the present application also provides a robot intelligent control device based on voice recognition. It should be noted that the basic principle and technical effect of the robot intelligent control device based on voice recognition provided by the embodiment of the present invention are the same as those of the above-mentioned embodiment. For the sake of brief description, for parts not mentioned in the embodiment of the present invention, please refer to the corresponding content in the above-mentioned embodiment. The device includes a hotel server 100, N delivery robots 103, multiple guest room telephones and a catering item temporary storage cabinet 102. The catering item temporary storage cabinet 102 includes multiple storage spaces, each storage space includes a unique number, each delivery robot 103 and each guest room telephone are respectively communicated with the hotel server 100, wherein,

[0102] The hotel server 100 is used to collect voice information transmitted from multiple guest room phones to the front desk phone when the multiple guest room phones make calls to the front desk phone during the target period;

[0103] The hotel server 100 is further used to determine, based on the voice information of the multiple guest room telephones, a set of identifications of the guest room telephones of the M guest rooms 104 that require the delivery robot 103 to deliver food and beverage items, wherein M and N are both positive integers, and M is greater than N;

[0104] The hotel server 100 is further used to determine the guest identity information associated with the identification of the room phone of each room 104 that needs the delivery robot 103 to deliver food and beverage items;

[0105] The hotel server 100 is also used to obtain order delivery completion information under the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the number of the storage space of the food and beverage item temporary storage cabinet 102 to be delivered, and the room number carries the floor information;

[0106] The hotel server 100 is further used to input the recorded hotel occupancy volume within the target time period and the number of guest rooms 104 that need the delivery robot 103 to deliver food and beverage items into the pre-trained first elevator 103 waiting time prediction model to determine the first reference waiting time of the elevator 103 on each floor, wherein the elevator 103 waiting time prediction model is obtained by inputting multiple first training samples into the first neural network for training, each first training sample including the hotel occupancy volume within the historical target time period, the number of guest rooms 104 that need the delivery robot 103 to deliver food and beverage items in the history, and the corresponding historical waiting time of the elevator 103 on each floor;

[0107] The hotel server 100 is further used to input the actual waiting time of the elevator 103 on each floor in the first two historical time periods equal to the duration of the target time period, and the actual waiting time of the elevator 103 on each floor in the first historical time period equal to and continuous with the duration of the target time period, into the pre-trained second elevator 103 waiting time prediction model, and determine the second reference waiting time of the elevator 103 on each floor, wherein the second elevator 103 waiting time prediction model is obtained by inputting a plurality of second training samples into the long short-term memory model for training, and each second training sample includes the historical waiting time of the elevator 103 on each floor in the first two historical time periods equal to the duration of the historical target time period, the historical waiting time of the elevator 103 on each floor in the first historical time period equal to and continuous with the duration of the historical target time period, and the historical waiting time of the elevator 103 on each floor in the historical target time period;

[0108] The hotel server 100 is further used to determine the predicted waiting time of the elevator 103 on each floor according to the first reference waiting time of the elevator 103 on each floor and the second reference waiting time of the elevator 103 on each floor;

[0109] The hotel server 100 is further used to input the predicted waiting time of the elevator 103 on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determine the food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered by the N delivery robots 103, wherein the delivery strategy determination model is obtained by inputting a plurality of third training samples into the second neural network for training, and each third training sample includes the predicted waiting time of the elevator 103 on each floor in history, the set of room numbers to which the food and beverage items need to be delivered in history, and the corresponding food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered in history;

[0110] The hotel server 100 is further configured to send food item delivery instructions to the N delivery robots 103 respectively based on the food item delivery strategy with the shortest total delivery time, wherein each food item delivery instruction includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet 102 and at least one room number to be delivered;

[0111] The N delivery robots 103 are respectively used to sequentially take out food items associated with the food item delivery instructions from at least one storage space of the food item temporary storage cabinet 102 based on the received food item delivery instructions;

[0112] The N delivery robots 103 are also used to take the elevator 103 in turn to deliver at least one taken-out food and beverage item to the door of the guest room 104 corresponding to at least one room number associated with the food and beverage item delivery instruction.

[0113] In one possible implementation, the hotel server 100 is specifically used to convert voice information of multiple guest room telephones into corresponding text information; for each text information, the text information is compared for similarity with each standard text information in a preset standard text library for requesting the delivery of food items; when the similarity between the text information and any standard text information is greater than a set similarity threshold, it is determined that the guest room 104 where the guest room telephone corresponding to the text information is located needs the delivery robot 103 to deliver food items; the identifiers of multiple guest room telephones that need the delivery robot 103 to deliver food items are aggregated to obtain a set of identifiers of the guest room telephones of M guest rooms 104 that need the delivery robot 103 to deliver food items.

[0114] In one possible implementation, the hotel server 100 is specifically used to input voice information of multiple guest room telephones into a preset semantic recognition model to respectively identify the semantics corresponding to the voice information of the multiple guest room telephones; when the semantics corresponding to the voice information of any guest room telephone represents the need for the delivery robot 103 to deliver food and beverage items, determine that the guest room 104 where the guest room telephone corresponding to the voice information is located needs the delivery robot 103 to deliver food and beverage items; aggregate the identifiers of multiple guest room telephones that need the delivery robot 103 to deliver food and beverage items, and obtain a set of identifiers of the guest room telephones of M guest rooms 104 that need the delivery robot 103 to deliver food and beverage items.

[0115] In a possible implementation manner, the hotel server 100 is specifically configured to calculate the 11 =K1T1+K2T2, determine the predicted waiting time of the elevator 103 on each floor, where T 11 is the predicted waiting time of the elevator 103 on any floor, T1 is the first reference waiting time of the elevator 103 on any floor, T2 is the second reference waiting time of the elevator 103 on any floor, K1 is the first weighting coefficient, and K2 is the second weighting coefficient.

[0116] In one possible embodiment, each delivery robot 103 includes a manipulator and a placement plate, and each delivery robot 103 among the N delivery robots 103 is used to receive a food item delivery instruction; identify the number of at least one storage space of the food item temporary storage cabinet 102 and at least one room number to be delivered in the food item delivery instruction; move to one side of the storage space corresponding to the number of at least one storage space; extend the manipulator to grab the food item to be delivered in the at least one storage space corresponding to the number, and place the at least one food item to be delivered on the placement plate; generate a navigation route based on the address of the food item temporary storage cabinet 102 and the address associated with at least one room number to be delivered; based on the navigation route, carry the food items to be delivered and take the elevator 103 in sequence to move to the door of the guest room 104 associated with at least one room number to be delivered.

[0117] The present application provides a robot intelligent control method based on voice recognition, in which a hotel server 100 determines a set of identifications of guest room telephones of M guest rooms 104 to which a delivery robot 103 is required to deliver food and beverage items based on voice information of multiple guest room telephones.

[0118] The hotel server 100 determines the guest identity information associated with the identification of the guest room phone for each guest room 104 to which the delivery robot 103 delivers food and beverage items. It can be understood that guests staying in the hotel need to register using their ID cards, so that the hotel server 100 records the correspondence between the guest identity information and the room number where the guest is staying, and there is a correspondence between the room number and the identification of the guest room phone in the guest room 104, so the guest identity information associated with the identification of the guest room phone can be determined.

[0119] In this way, the hotel server 100 can obtain the order delivery completion information under the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the storage space number of the food and beverage items to be delivered in the food and beverage temporary storage cabinet 102, and the room number carries the floor information.

[0120] The hotel server 100 inputs the recorded hotel occupancy volume within the target period and the number of guest rooms 104 that need the delivery robot 103 to deliver food and beverage items into the pre-trained first elevator 103 waiting time prediction model to determine the first reference waiting time for each floor elevator 103. Since the elevator 103 waiting time prediction model is obtained by inputting multiple first training samples into the first neural network for training, each first training sample includes the hotel occupancy volume within the historical target period, the number of guest rooms 104 that need the delivery robot 103 to deliver food and beverage items in the history, and the corresponding historical waiting time for each floor elevator 103. In this way, the first reference waiting time for each floor elevator 103 is determined with high accuracy.

[0121] In addition, the hotel server 100 also inputs the actual waiting time of each elevator 103 in the first two historical time periods equal to the duration of the target time period, and the actual waiting time of each elevator 103 in the first historical time period equal to and continuous with the duration of the target time period, into the pre-trained second elevator 103 waiting time prediction model to determine the second reference waiting time of each elevator 103. Since the second elevator 103 waiting time prediction model is obtained by inputting a plurality of second training samples into the long short-term memory model for training, each second training sample includes the historical waiting time of each elevator 103 in the first two historical time periods equal to the duration of the historical target time period, the historical waiting time of each elevator 103 in the first historical time period equal to and continuous with the duration of the historical target time period, and the historical waiting time of each elevator 103 in the historical target time period. In this way, the second reference waiting time of each elevator 103 is determined with high accuracy.

[0122] The hotel server 100 determines the predicted waiting time of the elevator 103 on each floor according to the first reference waiting time of the elevator 103 on each floor and the second reference waiting time of the elevator 103 on each floor. Since the accuracy of the first reference waiting time is high and the accuracy of the second reference waiting time is also high, the accuracy of the predicted waiting time of the elevator 103 on each floor obtained based on the first reference waiting time and the second reference waiting time is also high.

[0123] The hotel server 100 inputs the predicted waiting time of the elevator 103 on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determines the food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered by the N delivery robots 103. Since the delivery strategy determination model is obtained by inputting multiple third training samples into the second neural network for training, each third training sample includes the predicted waiting time of the elevator 103 on each floor in history, the set of room numbers to which the food and beverage items need to be delivered in history, and the corresponding food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered are delivered in history. In this way, the determined food and beverage item delivery strategy will minimize the total time after all the food and beverage items to be delivered are delivered, and the efficiency is particularly high.

[0124] Based on the food item delivery strategy with the shortest total time, the hotel server 100 sends food item delivery instructions to the N delivery robots 103, wherein each food item delivery instruction includes the number of the food item to be delivered in at least one storage space of the food item temporary storage cabinet 102 and at least one room number to be delivered; the N delivery robots 103 take out the food item associated with the food item delivery instruction from at least one storage space of the food item temporary storage cabinet 102 in turn based on the received food item delivery instruction, and take the elevator 103 in turn to deliver the at least one food item taken out to the door of the guest room 104 corresponding to the at least one room number associated with the food item delivery instruction. In this way, the delivery of the above-mentioned food items does not require any human intervention, which saves labor costs and further improves the delivery efficiency of food items.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A robot intelligent control method based on speech recognition, characterized in that: A robot intelligent control device for speech recognition, the device comprising a hotel server, N delivery robots, a plurality of guest room telephones and a temporary storage cabinet for food and beverage items, the temporary storage cabinet for food and beverage items comprising a plurality of storage spaces, each of the storage spaces comprising a unique number, each of the delivery robots and each of the guest room telephones being respectively connected to the hotel server for communication, the method comprising: The hotel server collects voice information transmitted from the guest room phones to the front desk phone during the target period of time; The hotel server determines, based on the voice information of the plurality of guest room telephones, a set of identifications of guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items, wherein M and N are both positive integers, and M is greater than N; The hotel server determines the guest identity information associated with the identification of the guest room telephone number of each guest room that requires the delivery robot to deliver food and beverage items; The hotel server obtains order delivery completion information under the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the number of the storage space of the food and beverage items to be delivered in the food and beverage item temporary storage cabinet, and the room number carries floor information; The hotel server inputs the recorded hotel occupancy volume within the target period and the number of guest rooms that need the delivery robot to deliver food and beverage items into a pre-trained first elevator waiting time prediction model to determine a first reference waiting time for the elevator on each floor, wherein the elevator waiting time prediction model is obtained by inputting a plurality of first training samples into a first neural network for training, each of the first training samples including the hotel occupancy volume within the historical target period, the number of guest rooms that historically need the delivery robot to deliver food and beverage items, and the corresponding historical waiting time for the elevator on each floor; The hotel server inputs the actual waiting time of each floor of the elevator in the first two historical time periods equal to the duration of the target time period, and the actual waiting time of each floor of the elevator in the first historical time period equal to and continuous with the duration of the target time period into the pre-trained second elevator waiting time prediction model, and determines the second reference waiting time of each floor of the elevator, wherein the second elevator waiting time prediction model is obtained by inputting a plurality of second training samples into the long short-term memory model for training, and each of the second training samples includes the historical waiting time of each floor of the elevator in the first two historical time periods equal to the duration of the historical target time period, the historical waiting time of each floor of the elevator in the first historical time period equal to and continuous with the duration of the historical target time period, and the historical waiting time of each floor of the elevator in the historical target time period; The hotel server determines the predicted waiting time for the elevator on each floor according to the first reference waiting time for the elevator on each floor and the second reference waiting time for the elevator on each floor; The hotel server inputs the predicted waiting time of the elevator on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determines the food and beverage item delivery strategy with the shortest total time after the N delivery robots have delivered all the food and beverage items to be delivered, wherein the delivery strategy determination model is obtained by inputting a plurality of third training samples into the second neural network for training, each of the third training samples including the predicted waiting time of the elevator on each floor in history, the set of room numbers to which the food and beverage items need to be delivered in history, and the corresponding food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered have been delivered in history; The hotel server sends food item delivery instructions to the N delivery robots respectively based on the food item delivery strategy with the shortest total time, wherein each of the food item delivery instructions includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet and at least one room number to be delivered; The N delivery robots respectively take out food items associated with the food item delivery instruction from at least one storage space of the food item temporary storage cabinet based on the received food item delivery instruction, and take the elevator in turn to deliver the at least one taken out food item to the door of the guest room corresponding to at least one room number associated with the food item delivery instruction.

2. The method according to claim 1, characterized in that The hotel server determines, based on the voice information of the plurality of guest room telephones, a set of identifications of the guest room telephones of the M guest rooms to which the delivery robot is required to deliver food and beverage items, including: The hotel server converts the voice information of the plurality of guest room telephones into corresponding text information; The hotel server compares each text message with each standard text message in a preset standard text library for requesting the delivery of catering items for similarity; When the similarity between the text information and any standard text information is greater than a set similarity threshold, the hotel server determines that the guest room where the guest room phone number corresponding to the text information is located needs the delivery robot to deliver food and beverage items; The hotel server aggregates the identifiers of multiple guest room telephone numbers that require the delivery robot to deliver food and beverage items, and obtains an identifier set of the guest room telephone numbers of M guest rooms that require the delivery robot to deliver food and beverage items.

3. The method according to claim 1, characterized in that The hotel server determines, based on the voice information of the plurality of guest room telephones, a set of identifications of the guest room telephones of the M guest rooms to which the delivery robot is required to deliver food and beverage items, including: The hotel server inputs the voice information of the plurality of guest room telephones into a preset semantic recognition model to respectively recognize the semantics corresponding to the voice information of the plurality of guest room telephones; The hotel server determines that the guest room where the guest room phone corresponding to the voice information is located needs the delivery robot to deliver food and beverage items when the semantics corresponding to the voice information of any guest room phone indicates that the delivery robot needs to deliver food and beverage items; The hotel server aggregates the identifiers of multiple guest room telephone numbers that require the delivery robot to deliver food and beverage items, and obtains an identifier set of the guest room telephone numbers of M guest rooms that require the delivery robot to deliver food and beverage items.

4. The method according to claim 1, characterized in that: The hotel server determines the predicted waiting time of the elevator on each floor according to the first reference waiting time of the elevator on each floor and the second reference waiting time of the elevator on each floor, including: The hotel server is based on the formula T 11 =K1T1+K2T2, determine the predicted waiting time for the elevator on each floor, where T 11 is the predicted waiting time for the elevator on any floor, T1 is the first reference waiting time for the elevator on any floor, T2 is the second reference waiting time for the elevator on any floor, K1 is the first weighting coefficient, and K2 is the second weighting coefficient.

5. The method according to claim 1, characterized in that The delivery robot comprises a manipulator and a placement plate, and the N delivery robots respectively take out the food and beverage items associated with the food and beverage item delivery instruction from at least one storage space of the food and beverage item temporary storage cabinet based on the received food and beverage item delivery instruction, and take the elevator in turn to deliver the at least one food and beverage item taken out to the door of the guest room corresponding to at least one room number associated with the food and beverage item delivery instruction, including: For any one of the N delivery robots, receiving a food item delivery instruction; Identifying the number of at least one storage space of the food item temporary storage cabinet and at least one room number to be delivered in the food item delivery instruction; Move to one side of the storage space corresponding to the number of the at least one storage space; Extending a mechanical arm to grab a food item to be delivered that is located in at least one storage space corresponding to the number, and placing at least one food item to be delivered on the placement tray; Generate a navigation route based on the address of the food item temporary storage cabinet and the address associated with at least one of the room numbers to be delivered; Based on the navigation route, the food and beverage items to be delivered are taken to the door of at least one guest room associated with the room number to be delivered.

6. A robot intelligent control device based on speech recognition, characterized in that: It includes a hotel server, N delivery robots, multiple guest room telephones and a catering item temporary storage cabinet, wherein the catering item temporary storage cabinet includes multiple storage spaces, each storage space includes a unique number, each delivery robot and each guest room telephone are respectively connected to the hotel server for communication, wherein: The hotel server is used to collect voice information transmitted from the guest room phones to the front desk phone during the target period of time when the guest room phones make calls to the front desk phone; The hotel server is further used to determine, based on the voice information of the plurality of guest room telephones, a set of identifiers of guest room telephones of M guest rooms that require the delivery robot to deliver food and beverage items, wherein M and N are both positive integers, and M is greater than N; The hotel server is further configured to determine the guest identity information associated with the identification of the guest room telephone of each guest room that requires the delivery robot to deliver food and beverage items; The hotel server is further used to obtain order delivery completion information under the online shopping application account associated with the guest identity information, wherein the order delivery completion information includes the room number to which the food and beverage items need to be delivered and the number of the storage space of the food and beverage items to be delivered in the food and beverage item temporary storage cabinet, and the room number carries floor information; The hotel server is further used to input the recorded hotel occupancy volume within the target time period and the number of guest rooms that need the delivery robot to deliver food and beverage items into a pre-trained first elevator waiting time prediction model to determine a first reference waiting time for the elevator on each floor, wherein the elevator waiting time prediction model is obtained by inputting a plurality of first training samples into a first neural network for training, each of the first training samples including the hotel occupancy volume within the historical target time period, the number of guest rooms that historically need the delivery robot to deliver food and beverage items, and the corresponding historical waiting time for the elevator on each floor; The hotel server is further used to input the actual waiting time of each floor of the elevator in the first two historical time periods equal to the duration of the target time period, and the actual waiting time of each floor of the elevator in the first historical time period equal to and continuous with the duration of the target time period, into a pre-trained second elevator waiting time prediction model to determine a second reference waiting time for each floor of the elevator, wherein the second elevator waiting time prediction model is obtained by inputting a plurality of second training samples into a long short-term memory model for training, and each of the second training samples includes the historical waiting time of each floor of the elevator in the first two historical time periods equal to the duration of the historical target time period, the historical waiting time of each floor of the elevator in the first historical time period equal to and continuous with the duration of the historical target time period, and the historical waiting time of each floor of the elevator in the historical target time period; The hotel server is further used to determine the predicted waiting time of the elevator on each floor according to the first reference waiting time of the elevator on each floor and the second reference waiting time of the elevator on each floor; The hotel server is further used to input the predicted waiting time of the elevator on each floor and the set of room numbers to which the food and beverage items need to be delivered into the pre-trained delivery strategy determination model, and determine the food and beverage item delivery strategy with the shortest total time after the N delivery robots have delivered all the food and beverage items to be delivered, wherein the delivery strategy determination model is obtained by inputting a plurality of third training samples into the second neural network for training, each of the third training samples including the predicted waiting time of the elevator on each floor in history, the set of room numbers to which the food and beverage items need to be delivered in history, and the corresponding food and beverage item delivery strategy with the shortest total time after all the food and beverage items to be delivered have been delivered in history; The hotel server is further configured to send food item delivery instructions to the N delivery robots respectively based on the food item delivery strategy with the shortest total time, wherein each of the food item delivery instructions includes the number of at least one storage space of the food item to be delivered in the food item temporary storage cabinet and at least one room number to be delivered; The N delivery robots are respectively used to, based on the received food item delivery instruction, sequentially take out food items associated with the food item delivery instruction from at least one storage space of the food item temporary storage cabinet; The N delivery robots are also used to take the elevator in turn to deliver at least one taken-out food and beverage item to the door of the guest room corresponding to at least one room number associated with the food and beverage item delivery instruction.

7. The device according to claim 6, characterized in that The hotel server is specifically used to convert the voice information of multiple guest room telephones into corresponding text information; for each text information, the text information is compared with each standard text information in a preset standard text library for requesting the delivery of food and beverage items; when the similarity between the text information and any standard text information is greater than a set similarity threshold, it is determined that the guest room where the guest room telephone corresponding to the text information is located needs the delivery robot to deliver food and beverage items; the identifiers of multiple guest room telephones that need the delivery robot to deliver food and beverage items are aggregated to obtain a set of identifiers of the guest room telephones of M guest rooms that need the delivery robot to deliver food and beverage items.

8. The device according to claim 6, characterized in that The hotel server is specifically used to input the voice information of multiple guest room phones into a preset semantic recognition model to respectively identify the semantics corresponding to the voice information of multiple guest room phones; when the semantics corresponding to the voice information of any guest room phone represents the need for the delivery robot to deliver food and beverage items, determine that the guest room where the guest room phone corresponding to the voice information is located needs the delivery robot to deliver food and beverage items; summarize the identifiers of multiple guest room phones that need the delivery robot to deliver food and beverage items, and obtain a set of identifiers of the guest room phones of M guest rooms that need the delivery robot to deliver food and beverage items.

9. The device according to claim 6, characterized in that The hotel server is specifically used to calculate the 11 =K1T1+K2T2, determine the predicted waiting time for the elevator on each floor, where T 11 is the predicted waiting time for the elevator on any floor, T1 is the first reference waiting time for the elevator on any floor, T2 is the second reference waiting time for the elevator on any floor, K1 is the first weighting coefficient, and K2 is the second weighting coefficient.

10. The device according to claim 6, characterized in that Each of the delivery robots includes a manipulator and a placement plate, and each of the N delivery robots is used to receive a food item delivery instruction; identify the number of at least one storage space of the food item temporary storage cabinet and at least one room number to be delivered in the food item delivery instruction; move to one side of the storage space corresponding to the number of the at least one storage space in sequence; extend the manipulator to grab the food item to be delivered in the at least one storage space corresponding to the number, and place at least one food item to be delivered on the placement plate; generate a navigation route based on the address of the food item temporary storage cabinet and the address associated with at least one room number to be delivered; Based on the navigation route, the food and beverage items to be delivered are taken to the door of at least one guest room associated with the room number to be delivered.

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