CN114020234A - Artificial intelligent order urging method, equipment and medium - Google Patents
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Abstract
The invention provides an artificial intelligence order urging method, equipment and a medium in the technical field of catering information processing, wherein the method comprises the following steps: step S10, logging in the ordering system, and after receiving a triggered ordering instruction, sending dish data corresponding to the ordering instruction to the kitchen system by the ordering system; step S20, the kitchen system prints the received dish data through a printer and uploads the dish data to a server; step S30, the kitchen system receives a voice file generated by semantic conversion of the dish data by the server, and plays the voice file through a loudspeaker; and step S40, intelligently urging the order through the order urging instruction received by the ordering system, the login times of the ordering system, the standard dish making time or voice monitoring. The invention has the advantages that: the intelligent ordering is carried out on the kitchen, and the service satisfaction degree is greatly improved.
Description
Technical Field
The invention relates to the technical field of catering information processing, in particular to an artificial intelligence order prompting method, equipment and a medium.
Background
When the catering industry develops at a high speed, the problems in multiple aspects such as raw material cost rise, labor cost rise, rent cost rise, shortage of managed talents, difficulty in cost control and the like are increasingly highlighted, and the traditional management and operation modes are challenged seriously, so that self-service ordering and transportation come into play, convenience is brought to customers for ordering, the labor cost of operation is reduced, and the error caused by manual ordering is avoided.
However, after a customer places an order, when the time for making dishes is too long due to various situations, and the customer feeds back slow dishes to a waiter, the waiter may only answer the order in the mouth and does not actually urge the order to the back, or the waiter forgets to urge the order to the back due to busy, so that the customer cannot get back, and finally the customer is complained.
Therefore, how to provide an artificial intelligence order-urging method, equipment and medium to realize intelligent order-urging for a kitchen to improve the service satisfaction degree becomes a problem to be solved urgently.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an artificial intelligent order-urging method, equipment and medium, which can realize intelligent order-urging for a kitchen to improve the service satisfaction degree.
In a first aspect, the present invention provides an artificial intelligence order urging method, which comprises the following steps:
step S10, logging in the ordering system, and after receiving a triggered ordering instruction, sending dish data corresponding to the ordering instruction to the kitchen system by the ordering system;
step S20, the kitchen system prints the received dish data through a printer and uploads the dish data to a server;
step S30, the kitchen system receives a voice file generated by semantic conversion of the dish data by the server, and plays the voice file through a loudspeaker;
and step S40, intelligently urging the order through the order urging instruction received by the ordering system, the login times of the ordering system, the standard dish making time or voice monitoring.
Further, the step S10 is specifically:
logging in the ordering system through the tablet personal computer, the ordering screen or the ordering two-dimensional code, and automatically associating the current login account with the corresponding table number by the ordering system based on the table number associated with the tablet personal computer, the ordering screen or the ordering two-dimensional code in advance;
after receiving a triggered ordering instruction, the ordering system sends dish data corresponding to the ordering instruction to a kitchen system in real time; the dish data at least comprises a table number, a dish name, a dish quantity and ordering time.
Further, the step S20 is specifically:
and the kitchen system receives the dish data, prints the dish data through a pre-associated printer, and uploads the dish data to the server in real time.
Further, the step S30 is specifically:
the server receives the dish data, carries out semantic conversion on the dish data through a semantic conversion model created and trained by a neural network to generate a voice file, and sends the voice file to a kitchen system in real time;
and the kitchen system receives the voice file and plays the voice file through a pre-associated loudspeaker.
Further, in step S40, the intelligent order prompting performed by the order prompting instruction received by the ordering system specifically includes:
logging in a food ordering system, checking the names of the dishes which are ordered through the historical record of the ordering instruction, selecting the names of the dishes which need to be ordered, generating the ordering instruction based on the table number, the names of the dishes and the ordering time, and sending the ordering instruction to a kitchen system;
the kitchen system uploads the order prompting instruction to a server, the server receives the order prompting instruction, semantic conversion is carried out on the order prompting instruction through a semantic conversion model established and trained by a neural network to generate first order prompting voice data, and the first order prompting voice data are sent to the kitchen system in real time;
and the kitchen system receives the first order voice data and plays the first order voice data through a pre-associated loudspeaker.
Further, in step S40, the intelligent ordering by the number of times of login of the ordering system specifically includes:
the ordering system presets a threshold value of the number of times, judges whether the number of times of logging in again of the consumer exceeds the threshold value of the number of times after the ordering instruction is triggered, and if not, continuously monitors the number of times of logging in; if yes, then:
and sending the corresponding table number to a kitchen system, uploading the table number to a server by the kitchen system for semantic conversion to generate second order voice data, receiving the second order voice data returned by the server, and playing the second order voice data through a loudspeaker.
Further, in step S40, the intelligent order prompting performed according to the standard dish making duration specifically includes:
the server prestores the standard dish making duration corresponding to each dish name and acquires the dish output record of the kitchen system in real time;
and calculating the making time corresponding to the dish name of each table number which does not have dish making in real time based on the dish making record and the dish data, generating third order voice data based on the making time and the standard making time of the dish, sending the third order voice data to a kitchen system for playing, and updating the standard making time of the dish based on the making time.
Further, in step S40, the performing intelligent order prompting through voice monitoring specifically includes:
the method comprises the following steps that a tablet personal computer or a meal ordering screen monitors voice chat records of a consumer in real time, and the voice chat records are uploaded to a server through a kitchen system;
the server presets a plurality of order-prompting keywords, judges whether the received voice chat records contain one of the order-prompting keywords in real time, generates fourth order-prompting voice data based on a table number pre-associated with a tablet personal computer or a ordering screen if the received voice chat records contain one of the order-prompting keywords, and sends the fourth order-prompting voice data to a kitchen system for playing.
In a second aspect, the present invention provides an artificial intelligence order urging device, comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor executes the program to implement the method of the first aspect.
In a third aspect, the present invention provides an artificial intelligence invoicing medium having a computer program stored thereon, which when executed by a processor, performs the method of the first aspect.
One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:
intelligent ordering is carried out through an ordering instruction received by the ordering system, the login times of the ordering system, the dish standard making time or voice monitoring, namely the ordering is carried out when the ordering instruction of a consumer is received, the order is carried out when the repeated login times of the ordering system of the consumer exceeds a preset time threshold value, the making time of dishes exceeds the making time of the dish standard, the order is carried out when the voice chatting record contains preset order keywords, multi-dimensional intelligent ordering is carried out, the dish making sequence of the kitchen is dynamically adjusted, the situation that a server does not actually carry out the ordering of the kitchen or forgets to carry out the ordering of the kitchen is avoided in the prior art, and dish data and ordering voice data are all played through a loudspeaker, so that a cook can efficiently receive information and timely adjust the dish making sequence, and finally intelligent ordering of the kitchen is carried out, the requirements of consumers are met as much as possible, and the service satisfaction is greatly improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
The invention will be further described with reference to the following examples with reference to the accompanying drawings.
FIG. 1 is a flow chart of an artificial intelligence invoicing method of the present invention.
Fig. 2 is a schematic structural diagram of an artificial intelligence order-urging device of the invention.
Fig. 3 is a schematic structural diagram of an artificial intelligence order-urging medium of the present invention.
Detailed Description
The embodiment of the application provides an artificial intelligence order-urging method, equipment and medium, so that intelligent order-urging is carried out on a kitchen, and the service satisfaction degree is improved.
The technical scheme in the embodiment of the application has the following general idea: the order-prompting instruction received by the ordering system, the login times of the ordering system, the standard making time of dishes or the voice monitoring are used for carrying out multidimensional intelligent order prompting, the dish making sequence of the kitchen is dynamically adjusted, so that the intelligent order prompting is carried out on the kitchen, and the service satisfaction is improved.
Example one
The embodiment provides an artificial intelligence invoicing method, as shown in fig. 1, including the following steps:
step S10, logging in the ordering system, and after receiving a triggered ordering instruction, sending dish data corresponding to the ordering instruction to the kitchen system by the ordering system;
step S20, the kitchen system prints the received dish data through a printer and uploads the dish data to a server;
step S30, the kitchen system receives a voice file generated by semantic conversion of the dish data by the server, and plays the voice file through a loudspeaker;
step S40, intelligent order urging is carried out through the order urging instruction received by the ordering system, the login times of the ordering system, the standard dish making time or voice monitoring; namely a consumer initiative invoicing method and three automatic invoicing methods.
The step S10 specifically includes:
logging in the ordering system through the tablet personal computer, the ordering screen or the ordering two-dimensional code, and automatically associating the current login account with the corresponding table number by the ordering system based on the table number associated with the tablet personal computer, the ordering screen or the ordering two-dimensional code in advance;
after receiving a triggered ordering instruction, the ordering system sends dish data corresponding to the ordering instruction to a kitchen system in real time; the dish data at least comprises a table number, a dish name, a dish quantity and ordering time.
The step S20 specifically includes:
and the kitchen system receives the dish data, prints the dish data through a pre-associated printer, and uploads the dish data to the server in real time.
The step S30 specifically includes:
the server receives the dish data, carries out semantic conversion on the dish data through a semantic conversion model created and trained by a neural network to generate a voice file, and sends the voice file to a kitchen system in real time;
the kitchen system receives the voice file, plays the voice file through the pre-associated loudspeaker, and plays the voice file through the loudspeaker, so that the condition that a cook is making dishes and is inconvenient to check the data of the printed dishes is effectively avoided.
For example, the method plays' one fish boiled in single water at the 10 th table, please make the fish by the big kitchen, and the chef makes the corresponding dish according to the printed dish data and the played voice file.
In step S40, the intelligent order prompting performed by the order prompting instruction received by the ordering system specifically includes:
logging in a food ordering system, checking the names of the dishes which are ordered through the historical record of the ordering instruction, selecting the names of the dishes which need to be ordered, generating the ordering instruction based on the table number, the names of the dishes and the ordering time, and sending the ordering instruction to a kitchen system;
the kitchen system uploads the order prompting instruction to a server, the server receives the order prompting instruction, semantic conversion is carried out on the order prompting instruction through a semantic conversion model established and trained by a neural network to generate first order prompting voice data, and the first order prompting voice data are sent to the kitchen system in real time;
the kitchen system receives the first order voice data and plays the first order voice data through a pre-associated loudspeaker; for example, play the 10 th table of boiled fish, the guest officer's order, speed up the production ".
In step S40, the intelligent ordering by the login frequency of the ordering system specifically includes:
the ordering system presets a threshold value of the number of times, judges whether the number of times of logging in again of the consumer exceeds the threshold value of the number of times after the ordering instruction is triggered, and if not, continuously monitors the number of times of logging in; if yes, then:
and sending the corresponding table number to a kitchen system, uploading the table number to a server by the kitchen system for semantic conversion to generate second order voice data, receiving the second order voice data returned by the server, and playing the second order voice data through a loudspeaker. And if the number of times of logging in the ordering system again exceeds the threshold value, automatically judging that the consumer has subconscious invoicing, and automatically invoicing.
In the step S40, the intelligent order prompting performed according to the standard dish making duration specifically includes:
the server prestores the standard dish making duration corresponding to each dish name and acquires the dish output record of the kitchen system in real time;
calculating the making time corresponding to the dish name of each table number which does not have dish serving in real time based on the dish serving record and the dish data, generating third order voice data based on the making time and the standard making time of the dish, sending the third order voice data to a kitchen system for playing, and updating the standard making time of the dish based on the making time; the manufacturing time length is the manufacturing ending time minus the manufacturing starting time; by updating the standard cooking time of the dishes, the characteristic library is closer to the industry standard, so that the accuracy of the estimation of the dish serving time is improved.
Namely, the making time length exceeds the standard making time length of dishes, and third invoicing voice data is generated based on the overtime dish name and the corresponding table number.
In step S40, the intelligent order prompting through voice monitoring specifically includes:
the method comprises the following steps that a tablet personal computer or a meal ordering screen monitors voice chat records of a consumer in real time through a sound pickup, and the voice chat records are uploaded to a server through a kitchen system;
the server presets a plurality of order-prompting keywords, judges whether the received voice chat records contain one of the order-prompting keywords in real time, generates fourth order-prompting voice data based on a table number pre-associated with a tablet personal computer or a ordering screen if the received voice chat records contain one of the order-prompting keywords, and sends the fourth order-prompting voice data to a kitchen system for playing. In specific implementation, whether the voice chat records contain the order-prompting keywords or not can be directly judged, or whether the voice chat records contain the order-prompting keywords or not can be judged after the voice chat records are converted into characters; the order-urging keywords are, for example, "so slow", "starved", etc.
Based on the same inventive concept, the application provides an electronic device embodiment corresponding to the first embodiment, which is detailed in the second embodiment.
Example two
The embodiment provides an artificial intelligence order-urging device, as shown in fig. 2, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, any one of the embodiments may be implemented.
Since the electronic device described in this embodiment is a device used for implementing the method in the first embodiment of the present application, based on the method described in the first embodiment of the present application, a specific implementation of the electronic device in this embodiment and various variations thereof can be understood by those skilled in the art, and therefore, how to implement the method in the first embodiment of the present application by the electronic device is not described in detail herein. The equipment used by those skilled in the art to implement the methods in the embodiments of the present application is within the scope of the present application.
Based on the same inventive concept, the application provides a storage medium corresponding to the third embodiment.
EXAMPLE III
The present embodiment provides an artificial intelligence invoicing medium, as shown in fig. 3, on which a computer program is stored, and when the computer program is executed by a processor, any one of the embodiments may be implemented.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
intelligent ordering is carried out through an ordering instruction received by the ordering system, the login times of the ordering system, the dish standard making time or voice monitoring, namely the ordering is carried out when the ordering instruction of a consumer is received, the order is carried out when the repeated login times of the ordering system of the consumer exceeds a preset time threshold value, the making time of dishes exceeds the making time of the dish standard, the order is carried out when the voice chatting record contains preset order keywords, multi-dimensional intelligent ordering is carried out, the dish making sequence of the kitchen is dynamically adjusted, the situation that a server does not actually carry out the ordering of the kitchen or forgets to carry out the ordering of the kitchen is avoided in the prior art, and dish data and ordering voice data are all played through a loudspeaker, so that a cook can efficiently receive information and timely adjust the dish making sequence, and finally intelligent ordering of the kitchen is carried out, the requirements of consumers are met as much as possible, and the service satisfaction is greatly improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although specific embodiments of the invention have been described above, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, and that equivalent modifications and variations can be made by those skilled in the art without departing from the spirit of the invention, which is to be limited only by the appended claims.
Claims (10)
1. An artificial intelligence order urging method is characterized in that: the method comprises the following steps:
step S10, logging in the ordering system, and after receiving a triggered ordering instruction, sending dish data corresponding to the ordering instruction to the kitchen system by the ordering system;
step S20, the kitchen system prints the received dish data through a printer and uploads the dish data to a server;
step S30, the kitchen system receives a voice file generated by semantic conversion of the dish data by the server, and plays the voice file through a loudspeaker;
and step S40, intelligently urging the order through the order urging instruction received by the ordering system, the login times of the ordering system, the standard dish making time or voice monitoring.
2. The artificial intelligence invoicing method of claim 1, wherein: the step S10 specifically includes:
logging in the ordering system through the tablet personal computer, the ordering screen or the ordering two-dimensional code, and automatically associating the current login account with the corresponding table number by the ordering system based on the table number associated with the tablet personal computer, the ordering screen or the ordering two-dimensional code in advance;
after receiving a triggered ordering instruction, the ordering system sends dish data corresponding to the ordering instruction to a kitchen system in real time; the dish data at least comprises a table number, a dish name, a dish quantity and ordering time.
3. The artificial intelligence invoicing method of claim 1, wherein: the step S20 specifically includes:
and the kitchen system receives the dish data, prints the dish data through a pre-associated printer, and uploads the dish data to the server in real time.
4. The artificial intelligence invoicing method of claim 1, wherein: the step S30 specifically includes:
the server receives the dish data, carries out semantic conversion on the dish data through a semantic conversion model created and trained by a neural network to generate a voice file, and sends the voice file to a kitchen system in real time;
and the kitchen system receives the voice file and plays the voice file through a pre-associated loudspeaker.
5. The artificial intelligence invoicing method of claim 1, wherein: in step S40, the intelligent order prompting performed by the order prompting instruction received by the ordering system specifically includes:
logging in a food ordering system, checking the names of the dishes which are ordered through the historical record of the ordering instruction, selecting the names of the dishes which need to be ordered, generating the ordering instruction based on the table number, the names of the dishes and the ordering time, and sending the ordering instruction to a kitchen system;
the kitchen system uploads the order prompting instruction to a server, the server receives the order prompting instruction, semantic conversion is carried out on the order prompting instruction through a semantic conversion model established and trained by a neural network to generate first order prompting voice data, and the first order prompting voice data are sent to the kitchen system in real time;
and the kitchen system receives the first order voice data and plays the first order voice data through a pre-associated loudspeaker.
6. The artificial intelligence invoicing method of claim 1, wherein: in step S40, the intelligent ordering by the login frequency of the ordering system specifically includes:
the ordering system presets a threshold value of the number of times, judges whether the number of times of logging in again of the consumer exceeds the threshold value of the number of times after the ordering instruction is triggered, and if not, continuously monitors the number of times of logging in; if yes, then:
and sending the corresponding table number to a kitchen system, uploading the table number to a server by the kitchen system for semantic conversion to generate second order voice data, receiving the second order voice data returned by the server, and playing the second order voice data through a loudspeaker.
7. The artificial intelligence invoicing method of claim 1, wherein: in the step S40, the intelligent order prompting performed according to the standard dish making duration specifically includes:
the server prestores the standard dish making duration corresponding to each dish name and acquires the dish output record of the kitchen system in real time;
and calculating the making time corresponding to the dish name of each table number which does not have dish making in real time based on the dish making record and the dish data, generating third order voice data based on the making time and the standard making time of the dish, sending the third order voice data to a kitchen system for playing, and updating the standard making time of the dish based on the making time.
8. The artificial intelligence invoicing method of claim 1, wherein: in step S40, the intelligent order prompting through voice monitoring specifically includes:
the method comprises the following steps that a tablet personal computer or a meal ordering screen monitors voice chat records of a consumer in real time, and the voice chat records are uploaded to a server through a kitchen system;
the server presets a plurality of order-prompting keywords, judges whether the received voice chat records contain one of the order-prompting keywords in real time, generates fourth order-prompting voice data based on a table number pre-associated with a tablet personal computer or a ordering screen if the received voice chat records contain one of the order-prompting keywords, and sends the fourth order-prompting voice data to a kitchen system for playing.
9. An artificial intelligence invoicing apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of any one of claims 1 to 8.
10. An artificial intelligence invoicing medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method of any one of claims 1 to 8.
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