CN107832884A - A kind of method for recommending time for eating meals - Google Patents

A kind of method for recommending time for eating meals Download PDF

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CN107832884A
CN107832884A CN201711080633.8A CN201711080633A CN107832884A CN 107832884 A CN107832884 A CN 107832884A CN 201711080633 A CN201711080633 A CN 201711080633A CN 107832884 A CN107832884 A CN 107832884A
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time
dining
information
feasibility
mrow
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迟海鹏
张怀东
张京军
龚长华
邢希学
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Beijing Dynaflow Experiment Technology Co Ltd
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Beijing Dynaflow Experiment Technology Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/12—Hotels or restaurants

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Abstract

The invention discloses a kind of method for recommending time for eating meals.The method for recommending time for eating meals, including：Obtain the expectation meal time of user；Inquire any one of synthesis climatic information and generalized time information of the expectation meal time or more persons；According to any one of the comprehensive climatic information and the generalized time information or more persons, the feasibility for it is expected meal time is analyzed in the historical data；When the feasibility of the expectation meal time is less than default first threshold, it is pushed out recommending time for eating meals to user.The present invention greatly reduces the probability that user waits in line when reaching purpose dining room, so as to improve the usage experience of user on the basis of the expectation meal time of user is met as far as possible.

Description

Method for recommending dining time
Technical Field
The invention relates to the technical field of intelligent application, in particular to a method for recommending dining time.
Background
In life, people often experience the following situations: when arriving at a restaurant that wants to go, the restaurant needs to wait for a long time because it is too explosive. How to roughly determine whether the user can eat directly at the expected eating time without waiting when the user starts. Further, when the expected dining time of the user needs waiting on a large scale, the user is provided with a recommended dining time on the basis of meeting the expected dining time of the user as much as possible, so that the user can eat directly without waiting on a large scale when the recommended dining time arrives. How to properly solve the above problems is an urgent issue to be solved in the industry.
Disclosure of Invention
The invention provides a method for recommending dining time, which is used for greatly reducing the probability of queuing and waiting when a user arrives at a target restaurant on the basis of meeting the expected dining time of the user as much as possible, thereby improving the use experience of the user.
According to a first aspect of the embodiments of the present invention, there is provided a method for recommending dining time, including:
acquiring expected dining time of a user;
querying any one or more of comprehensive climate information and comprehensive time information of the expected dining time;
analyzing the feasibility of the expected meal time in historical data according to any one or more of the integrated climate information and the integrated time information;
and when the feasibility of the expected dining time is lower than a preset first threshold value, pushing out the recommended dining time to the user.
In one embodiment, the querying any one or more of the integrated climate information and integrated time information for the desired meal time comprises:
the comprehensive climate information comprises any one or more of weather information, temperature information, humidity information and air quality information, and any one or more of the weather information, the temperature information, the humidity information and the air quality information of the expected dining time is inquired;
the comprehensive time information comprises any one or more of date information, time information and holiday information, and any one or more of the date information, the time information and the holiday information of the expected dining time are inquired out, wherein the date information comprises year, month and day information, and the time information comprises hour, minute and second information.
In one embodiment, said analyzing viability of said desired meal time in historical data based on any one or more of said integrated climate information and said integrated time information comprises:
analyzing the feasibility of the expected dining time according to the comprehensive climate information in historical data;
multiplying the feasibility by a preset first weighting coefficient, and determining the processed feasibility as first weight data;
analyzing the feasibility of the expected dining time according to the comprehensive time information in historical data;
multiplying the feasibility by a preset second weighting coefficient, and determining the processed feasibility as second weight data;
analyzing the feasibility of the expected dining time according to the current dining condition, and determining the feasibility as third weight data;
and analyzing the feasibility of the expected dining time after weighting processing according to the first weight data, the second weight data and the third weight data.
In one embodiment, the pushing out of the recommended meal time to the user when the feasibility of the expected meal time is lower than a preset first threshold comprises:
when the feasibility of the expected dining time is lower than a preset first threshold, in the dining time with the feasibility higher than the preset first threshold, calculating the dining time with the minimum absolute value under the condition that the difference value with the expected dining time is a positive value, and confirming that the dining time is recommended after-push dining time;
when the feasibility of the expected dining time is lower than a preset first threshold value, in the dining time with the feasibility higher than the preset first threshold value, calculating the dining time with the minimum absolute value under the condition that the difference value with the expected dining time is a negative value, and confirming that the dining time is the recommended advanced dining time;
confirming that any one or more of the recommended meal-after time and the recommended meal-ahead time is the recommended meal time.
In one embodiment, further comprising:
recommending relevant takeaway information to the user when the recommended advanced dining time is earlier than the earliest dining time of the user and the recommended pushed dining time is later than the latest dining time of the user.
In one embodiment, further comprising:
selecting the dining data of the user in the last 28 days;
predicting the number of dining people at a certain moment today through the dining data of the users in the last 28 days;
if the predicted number of people having a meal at a certain moment today is larger than a threshold multiple of the number of people having a meal at a certain moment yesterday, relevant information that people having a high probability of waiting for a meal at the certain moment is issued to the user;
the formula for predicting a certain moment today is as follows:
D t ＝y 1 *W+y 2 *D+y 3 *Y ①
W＝0.4W 1 +0.3W 2 +0.2W 3 +0.1W 4 ②
D＝0.5*(0.4M 1xt *(T 1 +H 1 )+0.3M 2xt *(T 2 +H 2 )+
0.2M 3xt (T 3 +H 3 )+0.1M 4xt *(T 4 +H 4 )) ③
Y＝0.5*Y t *(T y +H y ) ④
wherein, the first and the second end of the pipe are connected with each other,
in the above formula, D t For predicting the number of dining people at time t of the day, y 1 、y 2 、y 3 Is a coefficient, and y 1 +y 2 +y 3 ＝1，y 1 Has a value range of [0.6,0.7 ]]，y 2 Has a value range of [0.1,0.2 ]]W is the first predicted number of dining people, D is the second predicted number of dining people, Y is the third predicted number of dining people, W 1 The average number of meals in the last week, W 2 The average number of meals in the last second week, W 3 Average number of meals in the last third week, W 4 The average number of recently available meals in the fourth week, M 1xt The number of dining people at the x day of the last week at the time of t, M 2xt The number of dining people at the time t on the x day of the last second week, M 3xt The number of people having a meal at the time of t on the x day of the last third week, M 4xt The number of the diners at the time T on the x th day in the last week, T 1 Is the weather scale factor, T, at the time of day x of the last week 2 Is the weather scale factor, T, at the time T of the x day of the last second week 3 At the x-th day t of the last third weekWeather scale factor, T 4 Is the weather scaling factor, H, at time t of the latest fourth day x 1 Is the holiday proportionality coefficient at time t of day x of the last week, H 2 Is the holiday scale factor, T, at time T on day x of the last second week 3 Is the holiday proportionality coefficient at time t for day x of the last third week, H 4 Is the holiday scale factor at time t of the latest fourth day x t The number of people who have a dinner at time T of yesterday, T y Is the weather scale factor of yesterday, H y Is the holiday scaling factor of yesterday.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating a method of recommending eating times in accordance with an exemplary embodiment of the present invention;
FIG. 2 is a flowchart illustrating a step S12 of a method of recommending eating times in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a flowchart illustrating a step S13 of a method for recommending eating times in accordance with an exemplary embodiment of the present invention;
FIG. 4 is a flowchart illustrating a step S14 of a method of recommending eating times in accordance with an exemplary embodiment of the present invention;
FIG. 5 is a flow chart illustrating a method of recommending eating times in accordance with another exemplary embodiment of the present invention;
fig. 6 is a flowchart illustrating a method of recommending eating times according to yet another exemplary embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
Fig. 1 is a flowchart illustrating a method for recommending eating time according to an exemplary embodiment, as shown in fig. 1, the method for recommending eating time includes the following steps S11-S14:
in step S11, a desired dining time of the user is acquired;
in step S12, any one or more of comprehensive climate information and comprehensive time information of the expected dining time is inquired;
analyzing feasibility of the expected dining time in historical data according to any one or more of the integrated climate information and the integrated time information in step S13;
in step S14, when the feasibility of the expected meal time is lower than a preset first threshold, the recommended meal time is pushed to the user.
In one embodiment, in life, people often experience a situation where they need to wait long when arriving at a restaurant they want to go because the restaurant is too hot. How to determine whether the user can directly eat at the expected dining time of the user without waiting when the user starts can be roughly determined. Further, when the expected dining time of the user needs waiting on a large scale, the user is provided with a recommended dining time on the basis of meeting the expected dining time of the user as much as possible, so that the user can eat directly without waiting on a large scale when the recommended dining time arrives. The technical scheme in the embodiment can properly solve the problems.
The expected dining time of the user is obtained, the number of dining people of the user is also required to be obtained, and whether the demand of a bunk is required is also obtained.
Then, any one or more of the integrated climate information and the integrated time information of the expected dining time are inquired. Further, the integrated climate information comprises any one or more of weather information, temperature information, humidity information and air quality information, and any one or more of the weather information, the temperature information, the humidity information and the air quality information of the expected dining time are inquired. The integrated time information comprises any one or more of date information, time information and holiday information, and any one or more of the date information, the time information and holiday information of the expected dining time are inquired out, wherein the date information comprises year, month and day information, and the time information comprises hour, minute and second information.
The feasibility of the desired meal time is analyzed in historical data from any one or more of the integrated climate information and the integrated time information. Further, in historical data, the feasibility of the expected dining time is analyzed according to the comprehensive climate information; multiplying the feasibility by a preset first weighting coefficient, and determining the processed feasibility as first weight data; analyzing the feasibility of the expected dining time according to the comprehensive time information in historical data; multiplying the feasibility by a preset second weighting coefficient, and determining the feasibility after the processing as second weight data; analyzing the feasibility of the expected dining time according to the current dining condition, and determining the feasibility as third weight data; and analyzing the feasibility of the expected dining time after weighting processing according to the first weight data, the second weight data and the third weight data.
And when the feasibility of the expected dining time is lower than a preset first threshold value, pushing out the recommended dining time to the user. Further, when the feasibility is lower than the preset first threshold, the recommended eating time is pushed out to the user, including when the feasibility of the expected eating time is lower than the preset first threshold, in the eating time with the feasibility higher than the preset first threshold, calculating the eating time with the minimum absolute value in the case that the difference value with the expected eating time is a positive value, and confirming that the eating time is the recommended post-push eating time. And when the feasibility of the expected dining time is lower than a preset first threshold value, calculating the dining time with the minimum absolute value when the difference value with the expected dining time is a negative value in the dining times with the feasibility higher than the preset first threshold value, and confirming that the dining time is the recommended advanced dining time. Confirming that any one or more of the recommended meal time after push and the recommended meal time before push is the recommended meal time.
The method also comprises recommending relevant takeaway information to the user if the recommended meal advance time is earlier than the earliest meal time of the user and the recommended meal delay time is later than the latest meal time of the user.
According to the technical scheme, on the basis that expected dining time of the user is met as far as possible, the probability of queuing when the user arrives at the destination restaurant is greatly reduced, and therefore use experience of the user is improved.
In one embodiment, as shown in FIG. 2, step S12 includes the following steps S21-S22:
in step S21, the integrated climate information includes any one or more of weather information, temperature information, humidity information, and air quality information, and any one or more of the weather information, temperature information, humidity information, and air quality information of the expected dining time is queried;
in step S22, the integrated time information includes any one or more of date information, time information, and holiday information, and any one or more of date information, time information, and holiday information of the desired dining time are queried, where the date information includes year, month, and day information, and the time information includes hour, minute, and second information.
In one embodiment, any one or more of weather information, temperature information, humidity information, and air quality information for the desired meal time is queried. In the case of heavy rain or heavy snow, the number of people at a restaurant will fall. In the case of a cold temperature, the number of people having a meal in a hot pot restaurant will increase. When the temperature is high, the outgoing will of people will be reduced, and the number of people having meals in the restaurant will be slightly reduced. Especially, under the severe condition of haze, the outgoing willingness of people is sharply reduced, and the dining number of the dining room is reduced to a greater extent.
In addition, the number of the people having dinner in the restaurant regularly changes in the date information and the time information, the number of the people having dinner in the restaurant regularly fluctuates on weekends and weekdays, and the number of the people having dinner in the dining room reaches a peak. The major holidays in the holiday information will cause the diner number of restaurants near famous tourist attractions to greatly increase, and the minor holidays will cause the diner number of restaurants near minor attractions around a major city to obviously increase. And the number of people gathering food in the restaurant is greatly increased in special festivals such as spring festival.
In one embodiment, as shown in FIG. 3, step S13 includes the following steps S31-S36:
in step S31, analyzing feasibility of the expected dining time according to the comprehensive climate information in historical data;
in step S32, multiplying the feasibility by a preset first weighting coefficient, and determining the feasibility after the processing as first weight data;
in step S33, analyzing feasibility of the expected dining time according to the comprehensive time information in historical data;
in step S34, multiplying the feasibility by a preset second weighting coefficient, and determining the processed feasibility as second weight data;
in step S35, analyzing feasibility of the expected dining time according to the current dining condition, and determining the feasibility as a third weight data;
in step S36, after weighting processing is performed on the first weight data, the second weight data, and the third weight data, feasibility of the expected meal time is analyzed.
In one embodiment, the historical data of a specific restaurant is used, wherein the recent historical data in the historical data takes a larger weight, and the future historical data takes a smaller weight. Inquiring dinning people number data matched with the comprehensive climate information of the expected dining time in the historical data, carrying out comprehensive evaluation on the dinning people number data to obtain the feasibility of the expected dining time in the comprehensive climate information, multiplying the feasibility by a preset first weighting coefficient, and determining the feasibility after the processing as first weight data.
The influence of the comprehensive time information on the number of people having a meal is also considered, and the number of people having a meal matched with the comprehensive time information of the expected meal time is inquired from the historical data. Specifically, the number of dining people data matched with the monthly, daily, hour, minute and second data in the expected dining time phase is considered, the number of dining people data is comprehensively evaluated, the feasibility of the expected dining time in the aspect of the comprehensive time information is obtained, the feasibility is multiplied by a preset second weighting coefficient, and the feasibility after the processing is confirmed to be second weight data.
The time when the expected dining time of the user is obtained, that is, the number of people having a meal and the number of people queuing in the restaurant at the current time, needs to be considered, so that the feasibility of the expected dining time is analyzed, and the feasibility is confirmed to be third weight data.
And finally, analyzing the feasibility of the expected meal time after weighting processing according to the first weight data, the second weight data and the third weight data.
For example, the first weight data is analyzed to be 80% according to the comprehensive climate information, the second weight data is analyzed to be 95% according to the comprehensive time information, the third weight data is analyzed to be 100% according to the current dining condition, and after weighting processing, the feasibility of the expected dining time is analyzed to be 93%.
Besides, the feasibility of each time point in a period before or after the expected dining time of the user is included, so that the reference information is provided for the user. For example, the expected meal time of the user is 12: 50. 11, 55, 12.
In one embodiment, as shown in FIG. 4, step S14 includes the following steps S41-S43:
in step S41, when the feasibility of the expected dining time is lower than a preset first threshold, calculating a dining time with a smallest absolute value when a difference value with the expected dining time is a positive value, among the dining times with feasibility higher than the preset first threshold, and confirming that the dining time is a recommended post-dining time;
in step S42, when the feasibility of the expected dining time is lower than a preset first threshold, calculating a dining time with a minimum absolute value when the difference value with the expected dining time is a negative value in the dining times with feasibility higher than the preset first threshold, and confirming that the dining time is a recommended advanced dining time;
in step S43, it is confirmed that any one or more of the recommended meal behind time and the recommended meal ahead time is the recommended meal time.
In one embodiment, the recommended meal time is pushed to the user when the feasibility of the desired meal time is below a preset first threshold. The method is divided into two cases to be processed according to whether the user prefers to have a meal in advance or whether the user prefers to have a meal after the meal is returned.
And when the user prefers to have a meal after the meal is returned, calculating the meal time with the minimum absolute value under the condition that the difference value with the expected meal time is a positive value, and confirming that the meal time is the recommended meal after-push meal time.
When the user prefers to eat in advance, the eating time with the minimum absolute value under the condition that the difference value with the expected eating time is a negative value is calculated, and the eating time is confirmed to be the recommended advanced eating time.
And according to the preference of the user, confirming that the recommended after-dinner time or the recommended before-dinner time is the recommended dinner time.
When the user prefers to move back as well as to move ahead, the recommended dining time is the dining time with the smallest absolute value when the difference value from the expected dining time is a positive value.
In one embodiment, as shown in fig. 5, the following step S51 is further included:
in step S51, in a case where the recommended advanced dining time is earlier than the earliest dining time of the user and the recommended delayed dining time is later than the latest dining time of the user, relevant take-out information is recommended to the user.
In one embodiment, in some cases, the user's desired meal time is 12 00, the earliest meal time is 11:30, and the latest meal time is 12. If the recommended post-meal time obtained in the previous embodiment is 12. Then the recommended lead time is earlier than the user's earliest meal time and the recommended post meal time is later than the user's latest meal time. Under the condition, the relevant take-out information is pushed to the user, and the user is prevented from waiting too long after arriving at a restaurant.
In one embodiment, as shown in FIG. 6, the method further comprises the following steps S61-S63:
in step S61, the user dining data of the last 28 days is selected;
in step S62, the number of people eating at a certain moment today is predicted according to the last 28 days of user eating data;
in step S63, if the predicted number of people having meals today at a certain time is greater than a threshold multiple of the number of people having meals yesterday at a certain time, relevant information that waiting for meals at the certain time has a high probability is issued to the user.
In one embodiment, the last 28 days of user dining data are selected;
predicting the number of dining people at a certain moment today through the dining data of the users in the last 28 days;
if the predicted number of people having a meal at a certain moment today is larger than a threshold multiple of the number of people having a meal at a certain moment yesterday, relevant information that people having a high probability of waiting for a meal at the certain moment is issued to the user;
the formula for predicting a certain moment today is as follows:
D t ＝y 1 *W+y 2 *D+y 3 *Y ①
W＝0.4W 1 +0.3W 2 +0.2W 3 +0.1W 4 ②
D＝0.5*(0.4M 1xt *(T 1 +H 1 )+0.3M 2xt *(T 2 +H 2 )+
0.2M 3xt (T 3 +H 3 )+0.1M 4xt *(T 4 +H 4 )) ③
Y＝0.5*Y t *(T y +H y ) ④
wherein the content of the first and second substances,
in the above formula, D t For predicting the number of dining people at time t of the day, y 1 、y 2 、y 3 Is a coefficient, and y 1 +y 2 +y 3 ＝1，y 1 Has a value range of [0.6,0.7 ]]，y 2 Has a value range of [0.1,0.2 ]]W is the first predicted number of dining people, D is the second predicted number of dining people, Y is the third predicted number of dining people, W 1 The average number of meals in the last week, W 2 The average number of meals in the last second week, W 3 Average number of meals in the last third week, W 4 Is the most importantAverage number of dining people near the fourth week, M 1xt The number of dining people at the x day of the last week at time t, M 2xt The number of dining people at the time t on the x day of the last second week, M 3xt The number of people having a meal at the time of t on the x day of the last third week, M 4xt The number of people having a meal at the time T on the xth day of the week T 1 Is the weather scale factor, T, at the time of day x of the last week 2 Is the weather scale factor, T, at the time of T on the x days of the last second week 3 Is the weather scale factor, T, at time T of the x-th day of the last third week 4 Is the weather scale factor at the time of t on the latest fourth day x 1 Is the holiday proportionality coefficient at time t of day x of the last week, H 2 Is the holiday proportionality coefficient at time T on day x of the latest second week, T 3 Is the holiday proportionality coefficient at time t for day x of the last third week, H 4 Is the holiday proportionality coefficient at time t of the latest x-th day of the week, Y t The number of dining people at T time of yesterday, T y Is the weather scale factor of yesterday, H y Yesterday's holiday scaling factor.
E.g. when calculating D t >1.25Y t In this case, the user is issued with the related information that waiting for dining occurs at a high probability at the certain time.
According to the embodiment, the influence of weather factors and holiday factors on the number of dining people is particularly considered through the number of dining people of the week history data and the calendar history data in the history data, and the consistency of the number of dining people at a certain moment predicted by the technical scheme in the embodiment and the actual situation is remarkably 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been 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.
The above embodiments can be freely combined. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (6)

1. A method of recommending eating times, comprising:
acquiring expected dining time of a user;
querying any one or more of comprehensive climate information and comprehensive time information of the expected dining time;
analyzing the feasibility of the expected meal time in historical data according to any one or more of the integrated climate information and the integrated time information;
and when the feasibility of the expected dining time is lower than a preset first threshold value, pushing out the recommended dining time to the user.
2. The method of claim 1, wherein the querying out any one or more of integrated climate information and integrated time information for the desired meal time comprises:
the comprehensive climate information comprises any one or more of weather information, temperature information, humidity information and air quality information, and any one or more of the weather information, the temperature information, the humidity information and the air quality information of the expected dining time is inquired;
the comprehensive time information comprises any one or more of date information, time information and holiday information, and any one or more of the date information, the time information and the holiday information of the expected dining time are inquired out, wherein the date information comprises year, month and day information, and the time information comprises hour, minute and second information.
3. The method of claim 2, wherein said analyzing the feasibility of said desired meal time in historical data from any one or more of said integrated climate information and said integrated time information comprises:
analyzing the feasibility of the expected dining time according to the comprehensive climate information in historical data;
multiplying the feasibility by a preset first weighting coefficient, and determining the processed feasibility as first weight data;
analyzing the feasibility of the expected dining time according to the comprehensive time information in historical data;
multiplying the feasibility by a preset second weighting coefficient, and determining the processed feasibility as second weight data;
analyzing the feasibility of the expected dining time according to the current dining condition, and determining the feasibility as third weight data;
and analyzing the feasibility of the expected dining time after weighting processing according to the first weight data, the second weight data and the third weight data.
4. The method of claim 1, wherein pushing out a recommended meal time to a user when the feasibility of the desired meal time is below a preset first threshold comprises:
when the feasibility of the expected dining time is lower than a preset first threshold value, in the dining time with the feasibility higher than the preset first threshold value, calculating the dining time with the minimum absolute value under the condition that the difference value between the calculated dining time and the expected dining time is a positive value, and confirming that the dining time is recommended post-dining time;
when the feasibility of the expected dining time is lower than a preset first threshold value, calculating the dining time with the minimum absolute value under the condition that the difference value with the expected dining time is a negative value in the dining times with the feasibility higher than the preset first threshold value, and confirming that the dining time is recommended advanced dining time;
confirming that any one or more of the recommended meal-after time and the recommended meal-ahead time is the recommended meal time.
5. The method of any of claims 1 to 4, further comprising:
recommending relevant takeaway information to the user when the recommended advanced dining time is earlier than the earliest dining time of the user and the recommended pushed dining time is later than the latest dining time of the user.
6. The method of any of claims 1 to 5, further comprising:
selecting the dining data of the user in the last 28 days;
predicting the number of dining people at a certain moment today through the dining data of the users in the last 28 days;
if the predicted number of the diner people at a certain moment today is more than a threshold multiple of the number of the diner people at a certain moment yesterday, relevant information that diner waiting for diner at the certain moment is probably generated is issued to the user;
the formula for predicting a certain moment today is as follows:
D t ＝y 1 *W+y 2 *D+y 3 *Y ①
W＝0.4W 1 +0.3W 2 +0.2W 3 +0.1W 4 ②
D＝0.5*(0.4M 1xt *(T 1 +H 1 )+0.3M 2xt *(T 2 +H 2 )+0.2M 3xt (T 3 +H 3 )+0.1M 4xt *(T 4 +H 4 )) ③
Y＝0.5*Y t *(T y +H y ) ④
wherein the content of the first and second substances,
<mrow> <msub> <mi>H</mi> <mn>4</mn> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>H</mi> <mrow> <mn>4</mn> <mi>x</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mi>H</mi> <mi>T</mi> <mi>t</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0.8</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>H</mi> <mrow> <mn>4</mn> <mi>x</mi> <mi>t</mi> </mrow> </msub> <mo>&amp;NotEqual;</mo> <mi>H</mi> <mi>T</mi> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> <msub> <mi>H</mi> <mi>y</mi> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>N</mi> <mi>H</mi> <mi>t</mi> <mo>=</mo> <mi>Y</mi> <mi>H</mi> <mi>t</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>N</mi> <mi>H</mi> <mi>t</mi> <mo>&amp;NotEqual;</mo> <mi>N</mi> <mi>H</mi> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
in the above formula, D t For the predicted number of dining people at time t of the day, y 1 、y 2 、y 3 Is a coefficient, and y 1 +y 2 +y 3 ＝1，y 1 Has a value range of [0.6,0.7 ]]，y 2 Has a value range of [0.1,0.2 ]]W is the first predicted number of dining people, D is the second predicted number of dining people, Y is the third predicted number of dining people, W 1 The average number of meals in the last week, W 2 The average number of meals in the recent second week, W 3 Average number of meals in the last third week, W 4 The average number of meals in the nearest week, M 1xt The number of dining people at the x day of the last week at time t, M 2xt The number of dining people at the time t on the x day of the last second week, M 3xt The number of people having a meal at the time of t on the x day of the last third week, M 4xt The number of people having a meal at the time T on the xth day of the week T 1 Is the weather scale factor, T, at the time of T on the x day of the last week 2 Is the weather scale factor, T, at the time T of the x day of the last second week 3 Is the weather scale factor, T, at the time of T on the x day of the last third week 4 Is the weather scale factor at the time of t on the latest fourth day x 1 Is the holiday proportionality coefficient at time t of day x of the last week, H 2 Is the holiday scale factor, T, at time T on day x of the last second week 3 Is the holiday proportionality coefficient at time t for the x day of the last third week, H 4 Is the holiday scale factor at time t of the latest fourth day x t The number of people who have a dinner at time T of yesterday, T y Is the weather scale factor of yesterday, H y Is the holiday scaling factor of yesterday.
CN201711080633.8A 2017-11-06 2017-11-06 A kind of method for recommending time for eating meals Pending CN107832884A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108922020A (en) * 2018-05-17 2018-11-30 刘文尧 A kind of warm-served food Intelligent Machining vending machine based on wireless network
CN109902859A (en) * 2019-01-26 2019-06-18 美味不用等（上海）信息科技股份有限公司 Queuing peak period predictor method based on big data and machine learning algorithm
CN113592183A (en) * 2021-08-05 2021-11-02 杭州企智互联科技有限公司 Dining peak prediction method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108922020A (en) * 2018-05-17 2018-11-30 刘文尧 A kind of warm-served food Intelligent Machining vending machine based on wireless network
CN109902859A (en) * 2019-01-26 2019-06-18 美味不用等（上海）信息科技股份有限公司 Queuing peak period predictor method based on big data and machine learning algorithm
CN109902859B (en) * 2019-01-26 2023-03-24 美味不用等（上海）信息科技股份有限公司 Queuing peak period estimation method based on big data and machine learning algorithm
CN113592183A (en) * 2021-08-05 2021-11-02 杭州企智互联科技有限公司 Dining peak prediction method and device

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