CN113112052A - Early warning method and device for tableware user loss - Google Patents

Early warning method and device for tableware user loss Download PDF

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CN113112052A
CN113112052A CN202110270890.8A CN202110270890A CN113112052A CN 113112052 A CN113112052 A CN 113112052A CN 202110270890 A CN202110270890 A CN 202110270890A CN 113112052 A CN113112052 A CN 113112052A
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tableware
intelligent rental
rental cabinet
user
intelligent
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李军棉
周胡顺
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Guangdong Laigewan Network Technology Co ltd
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Guangdong Laigewan Network Technology Co ltd
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    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0645—Rental transactions; Leasing transactions
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    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
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Abstract

The invention discloses a method and a device for early warning loss of tableware users, wherein the method comprises the following steps: acquiring target data corresponding to the intelligent rental cabinet in the long section at the target, wherein the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet; analyzing target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet; and determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet. Therefore, the implementation of the invention can acquire the data of the intelligent rental cabinet, such as: tableware use data and the like are automatically analyzed, so that the potential tableware user loss risk can be predicted, a corresponding early warning strategy can be automatically determined according to the analyzed tableware user loss prediction result, the occurrence situation of tableware user loss can be reduced, and the popularization of tableware and the environmental protection are facilitated.

Description

Early warning method and device for tableware user loss
Technical Field
The invention relates to the technical field of big data analysis, in particular to a tableware user loss early warning method and device.
Background
With the more mature development of society, people pay more and more attention to environmental protection, and then a 'sharing concept' comes into play, for example: shared tableware, namely: when people need to use the tableware, the tableware is used in a renting mode and returned after being used, so that the use of the disposable lunch box is reduced, and the environment is protected.
In actual life, management personnel of the rental cabinet can store tableware in the storage space of the rental cabinet regularly or irregularly so that a user can obtain the tableware. When the tableware is needed to be used, the user can obtain the needed tableware from the storage space by opening the cabinet door of the storage space in the rental cabinet, so as to finish the obtaining and use of the tableware; when the tableware needs to be returned, the user can store the tableware to be returned in the storage space by opening the cabinet door of the vacant storage space in the rental cabinet, so that the returning of the tableware is completed, and the recycling of the tableware is realized. However, practice has found that the tableware users run away during the tableware putting and using process, which is not favorable for the popularization of the tableware and the environmental protection. Therefore, how to provide a technical scheme capable of reducing the loss of the tableware users so as to facilitate the popularization of the tableware and the environmental protection is very important.
Disclosure of Invention
The invention aims to provide a tableware user loss early warning method and device, which can reduce the occurrence of tableware user loss and is beneficial to popularization of tableware and environmental protection.
In order to solve the technical problem, a first aspect of the embodiments of the present invention discloses a method for warning loss of a tableware user, where the method includes:
acquiring target data corresponding to an intelligent rental cabinet in a long section during target, wherein the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet;
analyzing target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet;
and determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the tableware data corresponding to the intelligent rental cabinet includes tableware usage data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet;
wherein the tableware usage data of the intelligent rental cabinet comprises at least one of the current residual quantity of tableware of the intelligent rental cabinet, the tableware usage quantity of the intelligent rental cabinet, the tableware usage duration of the intelligent rental cabinet and the tableware usage evaluation data of the intelligent rental cabinet;
the tableware circulation data of the intelligent rental cabinet comprises at least one of tableware obtaining duration of the intelligent rental cabinet, sanitary data of a storage space of the intelligent rental cabinet and tableware distribution data of the intelligent rental cabinet.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the dish data corresponding to the intelligent rental cabinet includes dish data of at least one dish type;
and before analyzing the target data corresponding to the intelligent rental cabinet and obtaining the tableware user loss prediction result of the intelligent rental cabinet, the method further comprises the following steps:
determining whether there is a promotional demand for a target cutlery type, all of the cutlery types including the target cutlery type;
and when judging that the demand of the promotion demand of the tableware of the target tableware type does not exist, triggering and executing the operation of analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
As an optional implementation manner, in the first aspect of this embodiment of the present invention, the method further includes:
when the promotion requirement for the tableware of the target tableware type is judged to exist, screening target data corresponding to the target tableware type from the target data corresponding to the intelligent rental cabinet;
the analyzing of the target data corresponding to the intelligent rental cabinet to obtain the prediction result of the tableware user loss of the intelligent rental cabinet comprises:
and analyzing the target data corresponding to the target tableware type to obtain the tableware user loss prediction result of the target tableware type.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the analyzing the target data corresponding to the intelligent rental cabinet to obtain the prediction result of the loss of the dinnerware users of the intelligent rental cabinet includes;
inputting target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis;
and obtaining an analysis result output by the user loss prediction model as a tableware user loss prediction result of the intelligent rental cabinet.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, before inputting the target data corresponding to the intelligent rental cabinet into the determined user churn prediction model for analysis, the method further includes:
judging whether the data characteristics of the target data corresponding to the intelligent rental cabinet are matched with the data characteristics matched with the determined user loss prediction model;
and when the intelligent rental cabinets are matched with the intelligent rental cabinets, triggering and executing the operation of inputting the target data corresponding to the intelligent rental cabinets into the determined user loss prediction model for analysis.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, before determining, according to the dinnerware user churn prediction result of the intelligent rental cabinet, an early warning policy matching the dinnerware user churn prediction result, the method further includes:
judging whether the intelligent rental cabinet has a corresponding strategy to be executed, wherein the strategy to be executed is a strategy related to at least one of tableware storage of the intelligent rental cabinet, cleaning of a storage space of the intelligent rental cabinet and promotion content of the tableware of the intelligent rental cabinet;
when the strategy to be executed does not exist, triggering and executing the operation of determining the early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet;
and when the strategy to be executed is judged to exist, determining a target early warning strategy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result of the intelligent rental cabinet and the strategy to be executed.
As an alternative implementation, in the first aspect of the embodiments of the present invention, the cutlery usage data of the intelligent rental locker further includes cutlery user data of at least one cutlery type and a cutlery storage space of each of the cutlery types, wherein the cutlery user data of each of the cutlery types includes an average height of the cutlery user of that cutlery type;
wherein, according to the tableware user loss prediction result of the intelligent rental cabinet, determining an early warning strategy matched with the tableware user loss prediction result, comprises:
when the prediction result of the tableware user loss of the intelligent rental cabinet is used for indicating that the tableware storage space of the tableware type corresponding to the intelligent rental cabinet is not matched with the average height of the tableware user of the tableware type, screening the storage space matched with the average height of the tableware user of the tableware type from the storage space of the intelligent rental cabinet according to the average height of the tableware user of each tableware type;
the method comprises the steps of obtaining a space identifier uniquely corresponding to a storage space matched with the average height of each tableware user of each tableware type, and generating a tableware storage indication according to the space identifier uniquely corresponding to the storage space matched with the average height of each tableware user of each tableware type, wherein the tableware storage indication comprises the space identifier uniquely corresponding to the storage space matched with the average height of each tableware user of each tableware type, and the tableware storage indication is used for indicating that each tableware of the tableware types is stored in the storage space matched with the average height of the tableware user of the tableware type.
The second aspect of the embodiment of the invention discloses a warning device for tableware user loss, which comprises:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring target data corresponding to an intelligent rental cabinet in a long section when a target is reached, and the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet;
the analysis module is used for analyzing target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet;
and the determining module is used for determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the tableware data corresponding to the intelligent rental cabinet includes tableware use data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet;
wherein the tableware usage data of the intelligent rental cabinet comprises at least one of the current residual quantity of tableware of the intelligent rental cabinet, the tableware usage quantity of the intelligent rental cabinet, the tableware usage duration of the intelligent rental cabinet and the tableware usage evaluation data of the intelligent rental cabinet;
the tableware circulation data of the intelligent rental cabinet comprises at least one of tableware obtaining duration of the intelligent rental cabinet, sanitary data of a storage space of the intelligent rental cabinet and tableware distribution data of the intelligent rental cabinet.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the dish data corresponding to the intelligent rental cabinet includes dish data of at least one dish type;
and, the apparatus further comprises:
the first judgment module is used for judging whether promotion requirements for tableware of a target tableware type exist or not before the analysis module analyzes target data corresponding to the intelligent rental cabinet and obtains a tableware user loss prediction result of the intelligent rental cabinet, wherein all the tableware types comprise the target tableware type; and when the demand for promotion of the tableware of the target tableware type does not exist, triggering the analysis module to execute the operation of analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the apparatus further includes:
the screening module is used for screening target data corresponding to the target tableware type from the target data corresponding to the intelligent rental cabinet when the first judging module judges that the promotion requirement for the tableware of the target tableware type exists;
the method for obtaining the tableware user loss prediction result of the intelligent rental cabinet by analyzing the target data corresponding to the intelligent rental cabinet by the analysis module specifically comprises the following steps:
and analyzing the target data corresponding to the target tableware type to obtain the tableware user loss prediction result of the target tableware type.
As an alternative implementation manner, in the second aspect of the embodiment of the present invention, the analysis module includes:
the analysis submodule is used for inputting the target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis;
and the acquisition submodule is used for acquiring an analysis result output by the user loss prediction model and taking the analysis result as a tableware user loss prediction result of the intelligent rental cabinet.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the analysis module further includes a judgment sub-module, where:
the judging submodule is used for judging whether the data characteristics of the target data corresponding to the intelligent rental cabinet are matched with the data characteristics matched with the determined user loss prediction model or not before the analysis submodule inputs the target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis;
the analysis submodule is specifically configured to:
and when the judgment sub-module judges that the target data are matched, inputting the target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the apparatus further includes:
the second judging module is used for judging whether a corresponding strategy to be executed exists in the intelligent rental cabinet before the determining module determines an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet, wherein the strategy to be executed is a strategy related to at least one of tableware storage of the intelligent rental cabinet, cleaning of a storage space of the intelligent rental cabinet and popularization content of tableware of the intelligent rental cabinet; when the strategy to be executed does not exist, triggering the determining module to execute the operation of determining the early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet;
the determining module is further configured to determine a target early warning policy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result of the intelligent rental cabinet and the policy to be executed when the second determining module determines that the policy to be executed exists.
As an alternative implementation, in the second aspect of the embodiment of the present invention, the cutlery usage data of the intelligent rental locker further includes cutlery user data of at least one cutlery type and a cutlery storage space of each of the cutlery types, wherein the cutlery user data of each of the cutlery types includes an average height of the cutlery user of that cutlery type;
the mode of the determining module for determining the early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet is specifically as follows:
when the prediction result of the tableware user loss of the intelligent rental cabinet is used for indicating that the tableware storage space of the tableware type corresponding to the intelligent rental cabinet is not matched with the average height of the tableware user of the tableware type, screening the storage space matched with the average height of the tableware user of the tableware type from the storage space of the intelligent rental cabinet according to the average height of the tableware user of each tableware type;
the method comprises the steps of obtaining a space identifier uniquely corresponding to a storage space matched with the average height of each tableware user of each tableware type, and generating a tableware storage indication according to the space identifier uniquely corresponding to the storage space matched with the average height of each tableware user of each tableware type, wherein the tableware storage indication comprises the space identifier uniquely corresponding to the storage space matched with the average height of each tableware user of each tableware type, and the tableware storage indication is used for indicating that each tableware of the tableware types is stored in the storage space matched with the average height of the tableware user of the tableware type.
The third aspect of the invention discloses another early warning device for the loss of tableware users, which comprises:
a storage storing executable program code;
a processor coupled to the depository;
the processor calls the executable program code stored in the storage device to execute the early warning method for the loss of the tableware users disclosed by the first aspect of the invention.
The fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, the computer instructions are used for executing the early warning method for tableware user loss disclosed in the first aspect of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a method and a device for early warning loss of tableware users, wherein the method comprises the steps of obtaining target data corresponding to an intelligent rental cabinet in a long period when a target is obtained, wherein the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet; analyzing target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet; and determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet. Therefore, the embodiment of the invention can be implemented by acquiring the data of the intelligent rental cabinet, such as: the tableware use data, the tableware circulation data and the like are automatically analyzed, the prediction of potential tableware user loss risks is facilitated, a corresponding early warning strategy is automatically determined according to analyzed tableware user loss prediction results, the occurrence situation of tableware user loss can be reduced, the popularization of tableware and the environment protection are facilitated, the throwing of tableware is optimized according to the early warning strategy, the thrown tableware is the tableware required by a user, and the use viscosity of the tableware is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart illustrating a method for warning loss of a tableware user according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating another warning method for the loss of tableware users according to the embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an early warning device for the loss of users of tableware according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of another tableware user loss warning device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of another warning device for tableware user loss according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may alternatively include other steps or elements not listed or inherent to such process, method, product, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention discloses a tableware user loss early warning method and device, which can acquire data of an intelligent rental cabinet, such as: the tableware use data, the tableware circulation data and the like are automatically analyzed, the prediction of potential tableware user loss risks is facilitated, a corresponding early warning strategy is automatically determined according to analyzed tableware user loss prediction results, the occurrence situation of tableware user loss can be reduced, the popularization of tableware and the environment protection are facilitated, the throwing of tableware is optimized according to the early warning strategy, the thrown tableware is the tableware required by a user, and the use viscosity of the tableware is improved. The following are detailed below.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for warning loss of a tableware user according to an embodiment of the present invention. The early warning method for tableware user loss described in fig. 1 may be applied to a server of an intelligent rental cabinet, where the server of the intelligent rental cabinet includes a local server or a cloud server, and the embodiment of the present invention is not limited thereto. It should be noted that the intelligent rental cabinet can be understood as any one of intelligent rental devices in any shape, such as an intelligent rental bucket and an intelligent rental cabinet, the embodiment of the present invention is not limited, and "rental" can be understood as meaning of sharing, using, acquiring, renting, borrowing, and the like. As shown in fig. 1, the method for warning the loss of the tableware user may include the following operations:
101. and acquiring target data corresponding to the intelligent rental cabinet in the long section at the target, wherein the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet.
In the embodiment of the present invention, optionally, the tableware data corresponding to the intelligent rental cabinet may include tableware use data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet. The tableware use data of the intelligent rental cabinet can comprise at least one of the current residual quantity of tableware of the intelligent rental cabinet, the tableware use quantity of the intelligent rental cabinet, the tableware use duration of the intelligent rental cabinet and the tableware use evaluation data of the intelligent rental cabinet. The remaining quantity of the current tableware in the intelligent rental cabinet can be understood as the remaining quantity of the tableware currently stored in the storage space of the intelligent rental cabinet, and can also be understood as the remaining quantity of the tableware currently stored in the storage space of the intelligent rental cabinet and not reserved. The using quantity of the tableware in the intelligent rental cabinet can be the total quantity of the tableware acquired in the intelligent rental cabinet in the long period at the target time, and can also be understood as the total acquiring times of all the tableware in the intelligent rental cabinet in the long period at the target time.
In an embodiment of the present invention, optionally, the tableware circulation data of the intelligent rental cabinet may include at least one of a tableware obtaining duration of the intelligent rental cabinet, hygiene data of a storage space of the intelligent rental cabinet, and tableware distribution data of the intelligent rental cabinet. The tableware obtaining duration of the intelligent rental cabinet comprises the average obtaining duration of each piece of tableware obtained in the intelligent rental cabinet within the long section at the target and/or the total obtaining duration of all pieces of tableware obtained in the intelligent rental cabinet within the long section at the target; the sanitary data of the storage space of the intelligent rental cabinet comprise at least one of cleaning data, disinfection data, environmental dryness and environmental temperature of the storage space of the intelligent rental cabinet; the tableware distribution data of the intelligent rental cabinet is used for indicating that the tableware required by the user is delivered to the storage space corresponding to the intelligent rental cabinet in time (the delivery time is earlier than the time specified by the user) or is not delivered to the geographical position specified by the user in time.
In the embodiment of the invention, optionally, the intelligent rental cabinet has a corresponding management platform. Further optionally, the target data corresponding to the intelligent rental cabinet further includes, but is not limited to, at least one of a tableware user type of the intelligent rental cabinet, interaction data of the user with a manager of the management platform (e.g., tableware type consultation interaction data, etc.), tableware cost pre-stored on the management platform by the user, and promotion activities for tableware of the intelligent rental cabinet. The promotion activities for the tableware of the intelligent rental cabinet comprise tableware preferential resource distribution activities and/or promotion activities for pushing the tableware mainly.
Therefore, the more the content of the target data corresponding to the intelligent rental cabinet is, the more the analysis accuracy of the tableware user loss prediction result is favorably improved, the determination accuracy and the reliability of the early warning strategy corresponding to the tableware user loss prediction result are further improved, the tableware release optimization is further improved, the tableware meeting the user requirements is released, and the occurrence of the tableware user loss is further reduced.
In the embodiment of the present invention, it is further optional that the tableware data corresponding to the intelligent rental cabinet includes tableware data of at least one tableware type, at this time, the tableware usage data of the intelligent rental cabinet specifically includes tableware usage data of at least one tableware type, and the tableware circulation data of the intelligent rental cabinet specifically includes tableware circulation data of at least one tableware type. The tableware types are classified into different types according to different classification bases, wherein the classification bases can include but are not limited to at least one of shape classification bases, material classification bases, color classification bases, size classification bases, function classification bases, use occasion classification bases and rental mode classification bases, wherein the rental mode classification bases include package rental mode classification bases and/or independent rental mode classification bases, for example: the lunch box and the soup box need to be rented simultaneously, and the lunch box and the soup box need to be rented separately; the function classification basis comprises at least one of a heat preservation classification basis, a refrigeration classification basis, a heat insulation classification basis and a heating classification basis. Therefore, the more the classification basis of the tableware types is, the more the classification accuracy and the reliability of the tableware types are improved, the analysis accuracy and the reliability of the tableware user loss prediction result corresponding to the tableware types are further improved, the determination accuracy and the reliability of the early warning strategy corresponding to the tableware types are further improved, the tableware throwing accuracy of the tableware types is optimized in a targeted mode, and the occurrence situation of the tableware user loss of the tableware types is reduced. Further optionally, the cutlery usage data of the intelligent rental locker may further comprise cutlery user data of at least one cutlery type, wherein the cutlery user data of each cutlery type comprises a cutlery user type of each cutlery type and/or a cutlery user average height of each cutlery type. Still further optionally, before calculating the average height of the tableware users of each tableware type, the average height of the tableware users of the tableware type is calculated after the maximum height and the minimum height of the tableware users of the tableware type are removed, so that the calculation accuracy and reliability of the average height of the tableware users of the tableware type can be improved.
In this embodiment of the present invention, optionally, the target time period may be any one time period, may also be a certain time period specified by the user, and may also be a time period according to a time period matched with the current time, for example: if the current time is 12:00 am, the duration matched with 12:00 am is 144 hours before the current time, and may be a duration corresponding to a certain type of food that the user wants to know, for example: the type of the heat-preservation functional tableware corresponds to a 288-hour time period. It should be noted that the target duration may be a duration formed by an uninterrupted duration, or may be a duration formed by combining a plurality of discontinuous sub-durations.
In the embodiment of the present invention, optionally, the number of the intelligent rental cabinets may be greater than or equal to 1, and the intelligent rental cabinets may be intelligent rental cabinets in a target area, for example: intelligent rental cabinet in university city district. The intelligent rental cabinet is provided with a plurality of storage spaces, and optionally, each storage space of the intelligent rental cabinet has a corresponding storage attribute, wherein the storage attribute corresponding to each storage space comprises one or more combinations of storage attributes such as storage type, storage size, storage grade, storage material and storage shape. It should be noted that all storage spaces included in the intelligent rental cabinet include at least one type of storage space, wherein the type includes a tableware type and/or a non-tableware type, wherein the non-tableware type includes but is not limited to at least one of a mobile power source type, a book type, an umbrella type, and a medicine type. Through setting up the parking space of different types like this for different types of article can be deposited to intelligence lease cabinet, can enrich the function of depositing of intelligence lease cabinet, improve the parking space's of intelligence lease cabinet resource utilization. Further, in special cases, the categories of the storage spaces may be interchanged, i.e., the categories of the storage spaces may be converted from a tableware category to a non-tableware category or from a non-tableware category to a tableware category, wherein the special cases include at least one of a case where the current time period is a tableware return peak time period or a case where the current time period is a rainy time period, and the like. For example, the category of the storage space a is a tableware category, and if the current time period is a rainy time period, the category of the storage space a is converted from the tableware category to an umbrella category. Therefore, the storage function of the intelligent rental cabinet can be further enriched by converting the type of the storage space of the intelligent rental cabinet according to special conditions, and the resources of the storage space are further fully utilized.
102. And analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
In the embodiment of the invention, optionally, target data corresponding to the intelligent rental cabinet is analyzed to obtain a tableware user loss prediction result of the intelligent rental cabinet, including;
inputting target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis;
and obtaining an analysis result output by the user loss prediction model, and using the analysis result as a tableware user loss prediction result of the intelligent rental cabinet.
Therefore, in the optional implementation mode, the target data corresponding to the intelligent rental cabinet is input into the user loss prediction model for analysis, so that the analysis accuracy and efficiency of the target data corresponding to the intelligent rental cabinet can be improved, the acquisition accuracy and efficiency of the tableware user loss prediction result can be improved, and the determination accuracy and efficiency of the early warning strategy corresponding to the tableware user loss prediction result can be improved.
In an embodiment of the present invention, optionally, when the tableware data corresponding to the intelligent rental cabinet includes tableware data of at least one tableware type, the tableware user loss prediction result of the intelligent rental cabinet includes tableware user loss prediction result of at least one tableware type. Further optionally, a corresponding identifier is set for the prediction result of the dishware user loss of each dishware type, for example: the number identification, the symbol identification and the image-text identification are used for clearly distinguishing the tableware user loss prediction results by setting the corresponding identifications for the tableware user loss prediction results of each tableware type, so that the efficiency and the accuracy of determining the early warning strategies corresponding to each tableware type are improved.
In this optional implementation manner, before inputting target data corresponding to the intelligent rental cabinet into the determined user churn prediction model for analysis, the method may further include the following steps:
judging whether the data characteristics of the target data corresponding to the intelligent rental cabinet are matched with the data characteristics matched with the determined user loss prediction model;
and when the intelligent rental cabinets are matched with the user loss prediction models, triggering and executing the operation of inputting the target data corresponding to the intelligent rental cabinets into the determined user loss prediction models for analysis.
In this alternative embodiment, the data characteristics matched by the determined user churn prediction model may optionally include, but are not limited to, data length and/or data type. Optionally, when only one data feature of the target data corresponding to the intelligent rental cabinet is present, it is determined that the data feature is not matched with the data feature matched with the user churn prediction model.
Therefore, according to the optional implementation mode, after the target data corresponding to the intelligent rental cabinet is obtained, whether the data features of the target data are matched with the data features matched with the user loss prediction model or not can be further judged, if the data features of the target data are matched with the data features matched with the user loss prediction model, the subsequent data analysis operation is executed, and the situations that the obtaining of the low-accuracy tableware user loss prediction result is caused due to the fact that the data features of the target data corresponding to the intelligent rental cabinet are not matched with the data features of the user loss prediction model and the obtaining efficiency is low can be reduced.
This optional embodiment, further optional, may further comprise the steps of:
and when the data characteristics of the target data corresponding to the intelligent rental cabinet are judged not to be matched with the data characteristics matched with the determined user loss prediction model, the target data corresponding to the intelligent rental cabinet are obtained again, and/or the target data corresponding to the intelligent rental cabinet are processed according to the data characteristics matched with the user loss prediction model, so that the target data matched with the data characteristics matched with the user loss prediction model are obtained.
In this alternative embodiment, specifically, if the data length of the tableware use duration is within the determined data length range, for example: and 20-50, matching the data length representing the service time of the tableware with the data length corresponding to the tableware user loss prediction model.
Therefore, when the optional embodiment judges that the data characteristics of the target data corresponding to the intelligent rental cabinet are not matched with the data characteristics matched with the user loss prediction model, the required target data is obtained by re-acquiring the data and/or processing the target data corresponding to the intelligent rental cabinet, the occurrence of low accuracy and waste of communication resources caused by the fact that the target data corresponding to the intelligent rental cabinet does not meet the requirements but continues to perform subsequent operations for analyzing the tableware user loss prediction result can be reduced, the accurate tableware user loss prediction result can be ensured to be rapidly acquired, and the function of the early warning device for the tableware user loss can be enriched.
103. And determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
In this embodiment of the present invention, after the step 103 is executed, the method further includes: and sending the early warning strategy to an authorization management device. The early warning strategy comprises at least one of a position identification uniquely corresponding to the intelligent rental cabinet, target data corresponding to the intelligent rental cabinet and the emergency degree of the early warning strategy. The unique corresponding position identification of the intelligent rental cabinet comprises an actual geographic position identification of the intelligent rental cabinet and/or a serial number identification of the intelligent rental cabinet.
In an optional embodiment, the method further comprises the steps of:
determining the geographical position of the intelligent rental cabinet, and judging whether a corresponding tableware storage point exists in a preset area range corresponding to the geographical position of the intelligent rental cabinet;
and, after the step 103 is performed, the method further comprises the steps of:
and when judging that the corresponding tableware storage point exists, sending the early warning strategy to storage management equipment corresponding to the tableware storage point.
Therefore, in the optional embodiment, the early warning strategy matched with the tableware user loss prediction result is sent to the storage management device closer to the geographical position of the intelligent rental cabinet, so that managers can execute the early warning strategy in time, and the throwing of the tableware in the intelligent rental cabinet is optimized in time, for example: the tableware is placed in the storage space matched with the height of the corresponding user, the cleaning and disinfecting storage space is formed, the interaction timeliness of the management platform is improved, and the occurrence of tableware user loss is further reduced.
In this optional embodiment, optionally, when it is determined that there is no corresponding tableware storage point, the foregoing early warning policy is generated to the management device corresponding to the tableware emergency point. Wherein the cutlery emergency point has an integrated cutlery point with the ability to handle emergency events. Therefore, even if no storage point exists near the intelligent rental cabinet, the early warning strategy can be timely executed, and the occurrence condition that tableware users of the intelligent rental cabinet run off is further guaranteed to be reduced. Further, tableware is stored in the tableware emergency point.
In another optional embodiment, the method further comprises the steps of:
obtaining a sample target data set of the intelligent sample rental cabinet and a sample tableware user loss prediction result corresponding to each sample target data in the sample target data set;
and respectively training the determined prediction models according to the sample target data and the sample tableware user loss prediction results corresponding to the sample target data to obtain the trained prediction models, and determining the trained prediction models as the user loss prediction models.
In this alternative embodiment, the determined predictive model may include, but is not limited to, an LM neural network model and/or a CART decision tree model.
Therefore, the optional embodiment obtains the user loss prediction model by pre-training the prediction model, can be convenient for analyzing the tableware user loss prediction result of the intelligent rental cabinet directly on the basis of the user loss prediction model, and is beneficial to improving the analysis efficiency and the accuracy of the tableware user loss prediction result.
In this optional embodiment, optionally, the user churn prediction model continues to be trained based on the target data corresponding to the intelligent rental cabinet and the corresponding user churn prediction result. Therefore, the accuracy and the reliability of the user loss prediction model can be improved by adaptively training the user loss prediction model, so that a more accurate user loss prediction result can be obtained.
In yet another alternative embodiment, the cutlery usage data for the intelligent rental bin further comprises cutlery user data for at least one cutlery type and cutlery storage space for each cutlery type, wherein the cutlery user data for each cutlery type comprises average height of the cutlery user for that cutlery type, and optionally, determining an alert policy that matches the cutlery user churn prediction based on the cutlery user churn prediction for the intelligent rental bin may comprise:
when the prediction result of the tableware user loss of the intelligent rental cabinet is used for indicating that the tableware storage space of the tableware type corresponding to the intelligent rental cabinet is not matched with the average height of the tableware user of the tableware type, screening the storage space matched with the average height of the tableware user of the tableware type from the storage space of the intelligent rental cabinet according to the average height of the tableware user of each tableware type;
and acquiring a space identifier which is uniquely corresponding to the storage space matched with the average height of the tableware users of the tableware types, and generating a tableware storage instruction according to the space identifier which is uniquely corresponding to the storage space matched with the average height of the tableware users of each tableware type, wherein the tableware storage instruction comprises the space identifier which is uniquely corresponding to the storage space matched with the average height of the tableware users of each tableware type. The utensil storage indication is used to indicate that each utensil type is stored in a storage space that matches the average height of the user of the utensil of that utensil type.
In this alternative embodiment, the cutlery user data for each cutlery type further comprises a cutlery user type for that cutlery type, wherein the cutlery user type for each cutlery type comprises at least one of a gender of the user, an age of the user, an occupation of the user, and a preference of the user.
Therefore, when the prediction result of the loss of the tableware users is used for representing that the storage space of the tableware type is not matched with the average height of the tableware users of the tableware type, the optional embodiment facilitates the users to obtain the tableware by matching the storage space of the average height of the tableware users of the tableware type for the tableware type, improves the efficiency and accuracy of obtaining the tableware, and reduces the loss of the tableware users.
In this alternative embodiment, the cutlery usage data for the intelligent rental bin may optionally further include the height of the cutlery holding space for the remaining cutlery of at least one cutlery type in the intelligent rental bin. Wherein the rest tableware of the tableware types in the intelligent rental cabinet is the tableware which is currently in the intelligent rental cabinet and can be rented.
In this optional embodiment, optionally, screening the storage space of the intelligent rental cabinet according to the average height of the tableware users of each tableware type, the screening including:
determining the storage space of the rest tableware of each tableware type in the intelligent rental cabinet, and acquiring target rest tableware, of which the height of the storage space of the rest tableware of each tableware type is matched with the average height of a tableware user of the tableware type;
the storage space corresponding to the target remaining cutlery for each cutlery type is determined as the storage space matching the average height of the cutlery user for that cutlery type.
In this alternative embodiment, the utensil storage instructions are specifically used to instruct that the utensils of each utensil type be stored in the storage space corresponding to the target remaining utensils for that utensil type. The storage space corresponding to the target remaining tableware of each tableware type includes a storage space of the target remaining tableware of the tableware type and/or a storage space around the storage space, and the storage space around the storage space of the target remaining tableware is a storage space which is located from near to far away from the storage space of the target remaining tableware by taking the location of the storage space of the target remaining tableware as a center point, for example: the storage space around the storage space A is the upper, lower, left and right storage space of the storage space A.
Therefore, the optional embodiment can also directly take the storage space where the tableware is currently stored in the intelligent rental cabinet and the storage space and/or the surrounding storage space are/is matched with the height of the user of the type of the tableware as the storage space of the subsequent tableware, the existing tableware in the intelligent rental cabinet does not need to be moved, and the tableware storage efficiency is improved.
In yet another alternative embodiment, the method may further comprise the steps of:
determining the storage space of the rest tableware of each tableware type in the intelligent rental cabinet, and acquiring first rest tableware, the height of the storage space of which is not matched with the average height of a tableware user of the tableware type, and second rest tableware, the height of the storage space of which is matched with the average height of the tableware user of the tableware type, in the rest tableware of each tableware type;
determining a space identifier uniquely corresponding to the storage space of the first remaining tableware of each tableware type and a space identifier uniquely corresponding to the storage space of the second remaining tableware of the tableware type, and sending a tableware moving indication to the intelligent rental cabinet, wherein the tableware moving indication comprises the space identifier uniquely corresponding to the storage space of the first remaining tableware of each tableware type and the space identifier uniquely corresponding to the storage space of the second remaining tableware of the tableware type, and the tableware moving indication is used for triggering the intelligent rental cabinet to perform the following operations:
the intelligent rental cabinet controls a driving mechanism thereof to move first remaining tableware of each tableware type from a storage space of the first remaining tableware to a storage space of second remaining tableware of the tableware type and/or a storage space beside the storage space, wherein the storage space beside the storage space is a storage space which is from near to far away from the storage space and takes the position of the target storage space of the remaining tableware as a central point.
Therefore, the optional embodiment can enable the intelligent rental cabinet to move the residual tableware in the intelligent rental cabinet to the storage space matched with the height of the user by sending the tableware moving instruction to the intelligent rental cabinet, so that the user can conveniently obtain the tableware, the tableware obtaining experience is favorably improved, the occurrence of tableware user loss is reduced, and the intelligent function of the intelligent rental cabinet is enriched.
In yet another alternative embodiment, the tableware data corresponding to the intelligent rental cabinet optionally comprises tableware data of at least one tableware type. Optionally, before analyzing the target data corresponding to the intelligent rental cabinet and obtaining the tableware user loss prediction result of the intelligent rental cabinet, the method may further include the following steps:
judging whether the promotion requirement of the tableware aiming at the target tableware type exists or not, wherein all the tableware types comprise the target tableware type;
and when judging that the demand of the promotion demand of the tableware of the target tableware type does not exist, triggering and executing the operation of analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
In this alternative embodiment, optionally, the number of target utensil types is greater than or equal to 1. Further optionally, the target dinnerware type can be a plurality of top dinnerware types or dinnerware types ready for popularization, which are ranked according to popularity in all dinnerware types corresponding to the intelligent rental cabinet.
In this optional embodiment, optionally, when promotion content (e.g., preferential resource (e.g., coupon) distribution data, etc.) for the tableware of the target tableware type exists, it is determined that promotion demand exists for the tableware of the target tableware type; when a promotion request for tableware of a target tableware type is received, there is a promotion demand for the tableware of the target tableware type. Wherein the promotion content aiming at the dishware of the target dishware type comprises preferential resource (such as coupon) distribution data and/or propaganda advertisement.
It can be seen that, after obtaining the target data corresponding to the intelligent rental cabinet, the alternative embodiment first determines whether there is any dishware of the target dishware type, for example: the popularization requirement of tableware with high popularity is that if the popularization requirement does not exist, the follow-up operation is continuously triggered and executed, and the data analysis accuracy and reliability of target data corresponding to the intelligent rental cabinet can be improved.
In yet another alternative embodiment, the method may further comprise the steps of:
when the popularization requirement of the tableware of the target tableware type is judged to exist, screening target data corresponding to the target tableware type from the target data corresponding to the intelligent rental cabinet;
optionally, analyzing target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet may include:
and analyzing the target data corresponding to the target tableware type to obtain the tableware user loss prediction result of the target tableware type.
In this optional embodiment, please refer to the above description for the target data corresponding to the intelligent rental cabinet for the target data corresponding to the target tableware type, which is not described herein again.
Therefore, when the optional embodiment judges that the popularization content of the tableware aiming at the target tableware type exists, the target data corresponding to the target tableware type can be automatically selected, the target data corresponding to the target tableware type can be intelligently and directly analyzed, the tableware user loss prediction result of the required tableware type can be obtained in a targeted mode, the early warning strategy of the tableware is designated in a targeted mode, the tableware required by the user is released, and therefore the occurrence situation of the tableware user loss is reduced.
It can be seen that, the implementation of the method for warning the loss of the tableware user described in fig. 1 can be implemented by acquiring data of the intelligent rental cabinet, for example: the tableware use data, the tableware circulation data and the like are automatically analyzed, the prediction of potential tableware user loss risks is facilitated, a corresponding early warning strategy is automatically determined according to analyzed tableware user loss prediction results, the occurrence situation of tableware user loss can be reduced, the popularization of tableware and the environment protection are facilitated, the throwing of tableware is optimized according to the early warning strategy, the thrown tableware is the tableware required by a user, and the use viscosity of the tableware is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating another early warning method for tableware user loss according to an embodiment of the present invention. The early warning method for tableware user loss described in fig. 2 may be applied to a server of an intelligent rental cabinet, where the server of the intelligent rental cabinet includes a local server or a cloud server, and the embodiment of the present invention is not limited thereto. It should be noted that the intelligent rental cabinet can be understood as any one of intelligent rental devices in any shape, such as an intelligent rental bucket and an intelligent rental cabinet, the embodiment of the present invention is not limited, and "rental" can be understood as meaning of sharing, using, acquiring, renting, borrowing, and the like. As shown in fig. 2, the method for warning the loss of the tableware user may include the following operations:
201. and acquiring target data corresponding to the intelligent rental cabinet in the long section at the target, wherein the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet.
202. And analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
203. Judging whether the intelligent rental cabinet has a corresponding strategy to be executed or not, and triggering to execute step 204 when judging that the strategy to be executed does not exist; when it is determined that there is a policy to be executed, step 205 is triggered.
In the embodiment of the invention, the strategy to be executed comprises a strategy related to at least one of tableware storage of the intelligent rental cabinet, cleaning of a storage space of the intelligent rental cabinet, promotion content of tableware of the intelligent rental cabinet, equipment maintenance (maintenance and/or nursing) of the intelligent rental cabinet and tableware movement of the intelligent rental cabinet. The tableware movement of the intelligent rental cabinet is used for indicating that the tableware needing to be removed is taken out from the storage space corresponding to the intelligent rental cabinet and/or is placed in other storage spaces after being taken out.
In the embodiment of the invention, optionally, when the to-be-executed identifier for the intelligent rental cabinet is detected, the intelligent rental cabinet is determined to have a corresponding to-be-executed policy; or when a to-be-executed strategy of a certain tableware type for the intelligent rental cabinet is detected, determining that the intelligent rental cabinet has a corresponding to-be-executed strategy.
204. And determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
205. And determining a target early warning strategy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result and the strategy to be executed of the intelligent rental cabinet.
Therefore, after the tableware user loss prediction result of the intelligent rental cabinet is obtained, whether a strategy to be executed for the intelligent rental cabinet exists or not is further judged, if not, subsequent operation is directly executed, if yes, an early warning strategy for tableware putting can be determined by combining the strategy to be executed and the tableware user loss prediction result, the determination accuracy of the early warning strategy for tableware putting is improved, an accurate early warning strategy is rapidly obtained, the tableware putting accuracy is improved, the occurrence of tableware user loss is further reduced, tableware meeting the user requirements is put, the experience of the user for using the tableware is improved, and therefore popularization of the tableware and environmental protection are facilitated.
In the embodiment of the present invention, please refer to the related detailed description of steps 101 to 103 in the first embodiment for the other descriptions of steps 201, 202, and 204, which is not repeated herein.
In an optional embodiment, after determining that the intelligent rental cabinet has the corresponding policy to be executed, and before determining the target early warning policy corresponding to the intelligent rental cabinet based on the predicted result of the loss of the tableware users of the intelligent rental cabinet and the policy to be executed, the method may further include the following steps:
acquiring implementation time of a strategy to be executed, and judging whether a time interval between the current time and the implementation time of the strategy to be executed is less than or equal to a determined time interval threshold value or not;
and when the judgment result is less than or equal to the time interval threshold, triggering and executing the tableware user loss prediction result and the strategy to be executed based on the intelligent rental cabinet, and determining the operation of the target early warning strategy corresponding to the intelligent rental cabinet.
In this optional embodiment, optionally, when it is determined that the time interval is greater than the time interval threshold, ending the process or triggering execution of the operation of the early warning policy that is matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
It can be seen that, in the optional embodiment, after determining that the intelligent rental cabinet has the corresponding policy to be executed, it is further determined whether the execution time of the policy to be executed is further away from the current time, and if not, the subsequent operation of determining the early warning policy related to the tableware dispensing based on the tableware user loss prediction result and the policy to be executed is triggered to be executed, so that the accuracy and reliability of determining the early warning policy related to the tableware dispensing can be improved, the accuracy of the tableware dispensing can be improved, and the condition of the tableware dispensing can be improved, for example: the interaction timeliness and accuracy of the user and the management platform are improved, the sanitation of the storage space is improved, the experience of the user in using the tableware is improved, and therefore the possibility of the tableware user loss is reduced.
In another optional embodiment, after determining that the corresponding policy to be executed exists in the intelligent rental cabinet, or after determining that a time interval between the current time and the time for implementing the policy to be executed is less than or equal to the determined time interval threshold, and before determining the target early warning policy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result of the intelligent rental cabinet and the policy to be executed, the method may further include the following steps:
judging whether the strategy to be executed has a sub strategy to be executed corresponding to a certain target tableware type;
and when the judgment result does not exist, triggering and executing the tableware user loss prediction result and the strategy to be executed based on the intelligent rental cabinet, and determining the operation of the target early warning strategy corresponding to the intelligent rental cabinet.
And when the judgment result is that the strategy exists, deleting a sub-strategy to be executed corresponding to a certain target tableware type from the strategies to be executed to obtain a strategy to be executed after the execution data is deleted, and determining an early warning strategy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result of the intelligent rental cabinet and the strategy to be executed after the execution data is deleted.
In this alternative embodiment, the target dishware type includes dishware type with a storage time length greater than or equal to a storage time length threshold value in the intelligent rental cabinet and/or dishware type with a popularity degree lower than or equal to a popularity degree threshold value in the intelligent rental cabinet.
Therefore, in the optional embodiment, before determining the early warning policy related to the tableware putting based on the tableware user loss prediction result and the strategy to be executed, it is determined whether the to-be-executed policy includes the tableware with a long storage time or the sub-to-be-executed policy corresponding to the tableware type with a low popularity, if not, the subsequent operation is directly executed, if so, the corresponding sub-to-be-executed policy is deleted, and the early warning policy is determined based on the to-be-executed policy after data deletion and the user loss prediction result, so that the accuracy and reliability of determining the early warning policy related to the tableware putting can be further improved, the experience of the user using the tableware can be improved, and the possibility of the tableware user loss can be reduced.
It can be seen that, the implementation of the method for warning the loss of the tableware user described in fig. 2 can be implemented by acquiring data of the intelligent rental cabinet, for example: the tableware use data, the tableware circulation data and the like are automatically analyzed, the prediction of potential tableware user loss risks is facilitated, a corresponding early warning strategy is automatically determined according to analyzed tableware user loss prediction results, the occurrence situation of tableware user loss can be reduced, the popularization of tableware and the environment protection are facilitated, the throwing of tableware is optimized according to the early warning strategy, the thrown tableware is the tableware required by a user, and the use viscosity of the tableware is improved. The tableware early warning system can also improve the accuracy of determining the early warning strategy for tableware putting, quickly acquire the accurate early warning strategy, is favorable for improving the accuracy of tableware putting, further reduces the occurrence of tableware user loss, puts in tableware meeting the user demand, is favorable for improving the experience of the user for using the tableware, and is favorable for popularization of the tableware and environment protection.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of an early warning device for tableware user loss according to an embodiment of the present invention. The early warning device for tableware user loss described in fig. 3 may be applied to a server of an intelligent rental cabinet, where the server of the intelligent rental cabinet includes a local server or a cloud server, and the embodiment of the present invention is not limited thereto. It should be noted that the intelligent rental cabinet can be understood as any one of intelligent rental devices in any shape, such as an intelligent rental bucket and an intelligent rental cabinet, the embodiment of the present invention is not limited, and "rental" can be understood as meaning of sharing, using, acquiring, renting, borrowing, and the like. As shown in fig. 3, the warning device for the loss of tableware users may include an obtaining module 301, an analyzing module 302 and a determining module 303, wherein:
the acquisition module 301 is used for acquiring target data corresponding to the intelligent rental cabinet in the long section at the target, the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet, and the tableware data corresponding to the intelligent rental cabinet comprises tableware use data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet.
And the analysis module 302 is used for analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
And the determining module 303 is configured to determine an early warning policy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
In the embodiment of the invention, optionally, the tableware data corresponding to the intelligent rental cabinet comprises tableware use data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet;
the tableware use data of the intelligent rental cabinet comprises at least one of the current residual quantity of tableware of the intelligent rental cabinet, the tableware use quantity of the intelligent rental cabinet, the tableware use duration of the intelligent rental cabinet and the tableware use evaluation data of the intelligent rental cabinet.
The tableware circulation data of the intelligent rental cabinet comprises at least one of tableware obtaining duration of the intelligent rental cabinet, sanitary data of a storage space of the intelligent rental cabinet and tableware distribution data of the intelligent rental cabinet.
It can be seen that the early warning device for the tableware user loss described in fig. 3 can obtain the data of the intelligent rental cabinet by: the tableware use data, the tableware circulation data and the like are automatically analyzed, the prediction of potential tableware user loss risks is facilitated, a corresponding early warning strategy is automatically determined according to analyzed tableware user loss prediction results, the occurrence situation of tableware user loss can be reduced, the popularization of tableware and the environment protection are facilitated, the throwing of tableware is optimized according to the early warning strategy, the thrown tableware is the tableware required by a user, and the use viscosity of the tableware is improved.
In an alternative embodiment, the tableware data corresponding to the intelligent rental cabinet comprises tableware data of at least one tableware type. And as shown in fig. 4, the apparatus further comprises: a first determining module 304, wherein:
a first determining module 304, configured to determine whether there is a promotion demand for a target tableware type before the analyzing module 302 analyzes target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet, where all tableware types include the target tableware type; when it is determined that there is no demand for promotion of tableware of the target tableware type, the analysis module 302 is triggered to execute the above-mentioned operation of analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
Therefore, when the device described in fig. 4 is implemented, when it is determined that the promotion content for the target tableware type exists, the target data corresponding to the target tableware type can be automatically selected, the target data corresponding to the target tableware type can be intelligently and directly analyzed, the tableware user loss prediction result of the required tableware type can be obtained in a targeted manner, the early warning strategy for the tableware can be specified in a targeted manner, and the tableware required by the user can be released, so that the occurrence situation of the tableware user loss can be reduced.
In another alternative embodiment, as shown in fig. 4, the apparatus further comprises: a screening module 305, wherein:
the screening module 305 is used for screening target data corresponding to the target tableware type from the target data corresponding to the intelligent rental cabinet when the first judging module 304 judges that the promotion requirement for the tableware of the target tableware type exists;
the method for analyzing the target data corresponding to the intelligent rental cabinet by the analysis module 302 to obtain the tableware user loss prediction result of the intelligent rental cabinet specifically comprises the following steps:
and analyzing the target data corresponding to the target tableware type to obtain the tableware user loss prediction result of the target tableware type.
Therefore, when the device described in fig. 4 is implemented, when it is determined that the promotion content for the target tableware type exists, the target data corresponding to the target tableware type can be automatically selected, the target data corresponding to the target tableware type can be intelligently and directly analyzed, the tableware user loss prediction result of the required tableware type can be obtained in a targeted manner, the early warning strategy for the tableware can be specified in a targeted manner, and the tableware required by the user can be released, so that the occurrence situation of the tableware user loss can be reduced.
In yet another alternative embodiment, as shown in fig. 4, the analysis module 302 includes an analysis sub-module 3021 and an acquisition sub-module 3022, wherein:
and the analysis submodule 3021 is configured to input target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis.
The obtaining submodule 3022 is configured to obtain an analysis result output by the user loss prediction model, and use the analysis result as a tableware user loss prediction result of the intelligent rental cabinet.
Therefore, by implementing the device described in fig. 4, the target data corresponding to the intelligent rental cabinet can be input into the user loss prediction model for analysis, and the analysis accuracy and efficiency of the target data corresponding to the intelligent rental cabinet can be improved, so that the acquisition accuracy and efficiency of the tableware user loss prediction result can be improved, and the determination accuracy and efficiency of the early warning strategy corresponding to the tableware user loss prediction result can be improved.
In yet another alternative embodiment, as shown in fig. 4, the analysis module 302 may further include a judgment sub-module 3023, wherein:
the judging submodule 3023 is configured to, before the analyzing submodule 3021 inputs the target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis, judge whether the data characteristics of the target data corresponding to the intelligent rental cabinet match the data characteristics matched with the determined user loss prediction model.
Analysis submodule 3021, in particular for:
when the judgment sub-module 3023 judges that the target data are matched, the target data corresponding to the intelligent rental cabinet are input into the determined user loss prediction model for analysis.
Therefore, by implementing the device described in fig. 4, after the target data corresponding to the intelligent rental cabinet is obtained, whether the data features of the target data are matched with the data features matched with the user loss prediction model or not can be further judged, and if the data features of the target data are matched with the data features matched with the user loss prediction model, subsequent data analysis operation is executed, so that the situations that the obtaining of the low-accuracy tableware user loss prediction result is caused due to the fact that the data features of the target data corresponding to the intelligent rental cabinet are not matched with the data of the user loss prediction model and the obtaining efficiency is low can be reduced.
In yet another alternative embodiment, as shown in fig. 4, the apparatus may further include a second determining module 306, where:
a second judging module 306, configured to judge whether a corresponding policy to be executed exists in the intelligent rental cabinet before the determining module 303 determines the early warning policy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet, where the policy to be executed is a policy related to at least one of tableware storage in the intelligent rental cabinet, cleaning of a storage space in the intelligent rental cabinet, and promotion content of tableware in the intelligent rental cabinet; when the strategy to be executed does not exist, the determining module 303 is triggered to execute the above-mentioned operation of determining the early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
The determining module 303 is further configured to determine a target early warning policy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result of the intelligent rental cabinet and the policy to be executed when the second determining module 306 determines that the policy to be executed exists.
It can be seen that, after the device described in fig. 4 is implemented to obtain the prediction result of the loss of the tableware user in the intelligent rental cabinet, it is further determined whether the current strategy to be executed for the intelligent rental cabinet exists, if not, the subsequent operation is directly executed, if so, the early warning strategy for putting the tableware can be determined by combining the strategy to be executed and the prediction result of the loss of the tableware user, the determination accuracy of the early warning strategy for putting the tableware is improved, the accurate early warning strategy is rapidly obtained, the accuracy of putting the tableware is improved, the occurrence of the loss of the tableware user is further reduced, the tableware meeting the user demand is put in, the experience of using the tableware by the user is improved, and therefore, the popularization of the tableware and the protection of the environment are facilitated.
In yet another alternative embodiment, as shown in fig. 4, the cutlery usage data for the intelligent rental locker further comprises cutlery user data for at least one cutlery type and a cutlery storage space for each cutlery type, wherein the cutlery user data for each cutlery type comprises the average height of the cutlery user for that cutlery type; the mode of determining the early warning strategy matched with the tableware user loss prediction result by the determining module 303 according to the tableware user loss prediction result of the intelligent rental cabinet is specifically as follows:
when the prediction result of the tableware user loss of the intelligent rental cabinet is used for indicating that the tableware storage space of the tableware type corresponding to the intelligent rental cabinet is not matched with the average height of the tableware user of the tableware type, screening the storage space matched with the average height of the tableware user of the tableware type from the storage space of the intelligent rental cabinet according to the average height of the tableware user of each tableware type;
the method comprises the steps of obtaining a space identifier uniquely corresponding to a storage space matched with the average height of each tableware type of tableware user, and generating a tableware storage instruction according to the space identifier uniquely corresponding to the storage space matched with the average height of each tableware type of tableware user, wherein the tableware storage instruction comprises the space identifier uniquely corresponding to the storage space matched with the average height of each tableware type of tableware user, and the tableware storage instruction is used for instructing that tableware of each tableware type is stored in the storage space matched with the average height of the tableware user of the tableware type.
Therefore, the device described in the embodiment of fig. 4 can also be used for matching the storage space of the average height of the tableware users with the tableware types when the tableware user loss prediction result is used for indicating that the storage space of the tableware types is not matched with the average height of the tableware users with the tableware types, so that the users can conveniently obtain the tableware, the efficiency and the accuracy of obtaining the tableware are improved, and the occurrence of tableware user loss is reduced.
Example four
Referring to fig. 5, fig. 5 is a schematic diagram illustrating another warning device for tableware user loss according to an embodiment of the present invention. As shown in fig. 5, the early warning device for the loss of the tableware user may include:
a storage 501 in which executable program code is stored;
a processor 502 coupled to a store 501;
further, an input interface 503 and an output interface 504 coupled to the processor 502 may be included;
the processor 502 calls the executable program code stored in the storage 501 for executing the steps of the method for warning the loss of the tableware user described in the first embodiment or the second embodiment.
EXAMPLE five
The embodiment of the invention discloses a computer storage medium which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps in the early warning method for the loss of tableware users described in the first embodiment or the second embodiment.
EXAMPLE six
The embodiment of the invention discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, wherein the computer program is operable to make a computer execute the steps of the tableware user loss early warning method described in the first embodiment or the second embodiment.
The above-described embodiments of the apparatus are merely illustrative, and the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement the present invention without inventive effort.
Through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, wherein the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EPROM-on-ROM), an optical Disc (EEPROM), a Read-Only optical Disc (CD-on-ROM), or other magnetic Disc, or a ROM, a magnetic disk, or a combination thereof, A tape storage, or any other medium readable by a computer that can be used to carry or store data.
Finally, it should be noted that: the early warning method and device for the loss of the tableware users disclosed in the embodiment of the invention are only preferred embodiments of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A tableware user loss early warning method is characterized by comprising the following steps:
acquiring target data corresponding to an intelligent rental cabinet in a long section during target, wherein the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet;
analyzing target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet;
and determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
2. The early warning method for tableware user loss according to claim 1, wherein the tableware data corresponding to the intelligent rental cabinet comprises tableware use data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet;
wherein the tableware usage data of the intelligent rental cabinet comprises at least one of the current residual quantity of tableware of the intelligent rental cabinet, the tableware usage quantity of the intelligent rental cabinet, the tableware usage duration of the intelligent rental cabinet and the tableware usage evaluation data of the intelligent rental cabinet;
the tableware circulation data of the intelligent rental cabinet comprises at least one of tableware obtaining duration of the intelligent rental cabinet, sanitary data of a storage space of the intelligent rental cabinet and tableware distribution data of the intelligent rental cabinet.
3. The early warning method for tableware user loss according to claim 1 or 2, wherein the tableware data corresponding to the intelligent rental cabinet comprises tableware data of at least one tableware type;
and before analyzing the target data corresponding to the intelligent rental cabinet and obtaining the tableware user loss prediction result of the intelligent rental cabinet, the method further comprises the following steps:
determining whether there is a promotional demand for a target cutlery type, all of the cutlery types including the target cutlery type;
and when judging that the demand of the promotion demand of the tableware of the target tableware type does not exist, triggering and executing the operation of analyzing the target data corresponding to the intelligent rental cabinet to obtain the tableware user loss prediction result of the intelligent rental cabinet.
4. The method for providing early warning of loss of eating utensil users according to claim 3, further comprising:
when the promotion requirement for the tableware of the target tableware type is judged to exist, screening target data corresponding to the target tableware type from the target data corresponding to the intelligent rental cabinet;
the analyzing of the target data corresponding to the intelligent rental cabinet to obtain the prediction result of the tableware user loss of the intelligent rental cabinet comprises:
and analyzing the target data corresponding to the target tableware type to obtain the tableware user loss prediction result of the target tableware type.
5. The early warning method for tableware user loss according to claim 1 or 2, wherein the target data corresponding to the intelligent rental cabinet is analyzed to obtain a tableware user loss prediction result of the intelligent rental cabinet, including;
inputting target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis;
and obtaining an analysis result output by the user loss prediction model as a tableware user loss prediction result of the intelligent rental cabinet.
6. The method for warning the loss of tableware users according to claim 5, wherein before inputting the target data corresponding to the intelligent rental cabinet into the determined user loss prediction model for analysis, the method further comprises:
judging whether the data characteristics of the target data corresponding to the intelligent rental cabinet are matched with the data characteristics matched with the determined user loss prediction model;
and when the intelligent rental cabinets are matched with the intelligent rental cabinets, triggering and executing the operation of inputting the target data corresponding to the intelligent rental cabinets into the determined user loss prediction model for analysis.
7. The method for warning the loss of tableware users according to claim 1, 2, 4 or 6, wherein before determining the warning strategy matching the predicted loss of tableware users according to the predicted loss of tableware users of the intelligent rental cabinet, the method further comprises:
judging whether the intelligent rental cabinet has a corresponding strategy to be executed, wherein the strategy to be executed is a strategy related to at least one of tableware storage of the intelligent rental cabinet, cleaning of a storage space of the intelligent rental cabinet and promotion content of the tableware of the intelligent rental cabinet;
when the strategy to be executed does not exist, triggering and executing the operation of determining the early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet;
and when the strategy to be executed is judged to exist, determining a target early warning strategy corresponding to the intelligent rental cabinet based on the tableware user loss prediction result of the intelligent rental cabinet and the strategy to be executed.
8. The method for warning the loss of tableware users according to claim 2, 4 or 6, wherein the tableware usage data of the intelligent rental cabinet further comprises tableware user data of at least one tableware type and tableware storage space of each tableware type, wherein the tableware user data of each tableware type comprises the average height of the tableware users of the tableware type;
wherein, according to the tableware user loss prediction result of the intelligent rental cabinet, determining an early warning strategy matched with the tableware user loss prediction result, comprises:
when the prediction result of the tableware user loss of the intelligent rental cabinet is used for indicating that the tableware storage space of the tableware type corresponding to the intelligent rental cabinet is not matched with the average height of the tableware user of the tableware type, screening the storage space matched with the average height of the tableware user of the tableware type from the storage space of the intelligent rental cabinet according to the average height of the tableware user of each tableware type;
the method comprises the steps of obtaining a space identifier uniquely corresponding to a storage space matched with the average height of each tableware user of each tableware type, and generating a tableware storage indication according to the space identifier uniquely corresponding to the storage space matched with the average height of each tableware user of each tableware type, wherein the tableware storage indication comprises the space identifier uniquely corresponding to the storage space matched with the average height of each tableware user of each tableware type, and the tableware storage indication is used for indicating that each tableware of the tableware types is stored in the storage space matched with the average height of the tableware user of the tableware type.
9. An early warning device for loss of a dinnerware user, the device comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring target data corresponding to an intelligent rental cabinet in a long section when a target is reached, the target data corresponding to the intelligent rental cabinet comprises tableware data corresponding to the intelligent rental cabinet, and the tableware data corresponding to the intelligent rental cabinet comprises tableware use data of the intelligent rental cabinet and/or tableware circulation data of the intelligent rental cabinet;
the analysis module is used for analyzing target data corresponding to the intelligent rental cabinet to obtain a tableware user loss prediction result of the intelligent rental cabinet;
and the determining module is used for determining an early warning strategy matched with the tableware user loss prediction result according to the tableware user loss prediction result of the intelligent rental cabinet.
10. An early warning device for loss of a dinnerware user, the device comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the method for warning of the loss of a dishware user according to any of claims 1-8.
CN202110270890.8A 2021-03-12 2021-03-12 Early warning method and device for tableware user loss Pending CN113112052A (en)

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Application publication date: 20210713