CN110619414A

CN110619414A - Time prediction method, device and computer storage medium

Info

Publication number: CN110619414A
Application number: CN201810638679.5A
Authority: CN
Inventors: 梁德锋; 肖群虎; 范志荣; 孟德龙; 曾成鑫; 尹二强; 唐润敏; 杨应彬; 陈炽锵; 周宗旭
Original assignee: Foshan Shunde Midea Electrical Heating Appliances Manufacturing Co Ltd
Current assignee: Foshan Shunde Midea Electrical Heating Appliances Manufacturing Co Ltd
Priority date: 2018-06-20
Filing date: 2018-06-20
Publication date: 2019-12-27
Anticipated expiration: 2038-06-20
Also published as: CN110619414B

Abstract

The invention provides a time prediction method, a time prediction device and a computer storage medium. The method comprises the following steps: acquiring a data value of at least one time influence factor; and obtaining the working time consumption of the household appliance according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

Description

Time prediction method, device and computer storage medium

Technical Field

The present invention relates to the field of home appliance technologies, and in particular, to a time prediction method, apparatus, and computer storage medium.

Background

Due to a variety of factors affecting cooking time, currently, the cooking time of a cooking appliance (e.g., a rice cooker) cannot be accurately estimated, which results in that a user cannot accurately obtain the time required for cooking (e.g., cooking rice), which prevents the user from making various schedules better.

Disclosure of Invention

In order to solve the existing technical problem, embodiments of the present invention provide a time prediction method, a time prediction apparatus, and a computer storage medium.

In order to achieve the above purpose, the technical solution of the embodiment of the present invention is realized as follows:

the embodiment of the invention provides a time prediction method, which comprises the following steps:

acquiring a data value of at least one time influence factor;

and obtaining the working time consumption of the household appliance according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

In the foregoing solution, obtaining the working time consumption of the home appliance according to the data value of the time-affecting factor and the correlation coefficient corresponding to the time-affecting factor includes:

obtaining estimated time corresponding to each time influence factor according to the data value of each time influence factor;

and obtaining the working time consumption of the household appliance according to the estimated time and the correlation coefficient corresponding to the time influence factor.

In the foregoing solution, obtaining the working time consumption of the household appliance according to the estimated time and the correlation coefficient corresponding to the time-affecting factor includes:

calculating the product of the estimated time and a correlation coefficient corresponding to the time influence factor;

and determining the sum of the products as the work time of the household appliance.

In the foregoing solution, the process of determining the correlation coefficient corresponding to the time-affecting factor includes:

acquiring at least one history record meeting the screening condition, wherein the history record records the data values of the time consumption and the time influence factors;

calculating a correlation coefficient of the time-influencing factor and the recorded work elapsed time based on the data value of the time-influencing factor.

In the above scheme, the data values of the time-affecting factors recorded in the history are the data values of the candidate time-affecting factors;

before the obtaining of the data value of the at least one time influencing factor, the method further includes: and determining the candidate time influence factors meeting the set conditions from the candidate time influence factors as time influence factors according to the correlation coefficient.

In the foregoing solution, the calculating a correlation coefficient between the time-affecting factor and the recorded work time includes:

extracting the work time consumption in the historical records to form a work time consumption data set;

extracting the data values of the time influence factors in the historical records to form a time influence factor data set corresponding to the time influence factors;

and obtaining a correlation coefficient between the time influence factor and the recorded work consumed time according to the time influence factor data set and the work consumed time data set.

In the foregoing solution, the determining, according to the correlation coefficient, a candidate time influencing factor that satisfies a set condition from among the candidate time influencing factors as a time influencing factor includes:

sorting the corresponding candidate time influence factors according to the magnitude of the correlation coefficient;

the set number of candidate time-affecting factors having a large correlation coefficient is determined as the time-affecting factor.

The embodiment of the invention also provides a time prediction method, which comprises the following steps:

receiving cooking function setting information for a cooking apparatus;

receiving a target weighing request sent by a mobile terminal, and acquiring the position information of the mobile terminal based on the target weighing request;

sending a weighing instruction to an intelligent scale based on the target weighing request, and receiving the weight of a target nominal weight sent by the intelligent scale based on the weighing instruction;

determining a data value of at least one time-affecting factor of the cooking appliance based on the cooking function setting information, the position information, and the weight of the target nominal weight;

and obtaining the working time consumption of the cooking equipment according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

The embodiment of the invention also provides a data acquisition method, which comprises the following steps:

receiving a weighing instruction sent by a server;

sending out a weight placing prompt according to a set prompt sequence to obtain the weight of each weight;

calculating the weight of at least one target weight according to the weight of each weighing object;

sending the weight of the target nominal weight to the server;

receiving a recommended weight of a specified target weight sent by the server based on a user recommendation request;

when the absolute value of the difference value between the weight of the specified target nominal weight and the recommended weight is determined to be smaller than a set weight threshold, a matching completion prompt is sent out;

and sending the matched weight of the specified target nominal weight to the server.

In the above scheme, when the difference between the weight of the specified target nominal weight and the recommended weight is greater than a set weight threshold, a decrement prompt is sent out;

and when the difference value between the recommended weight and the weight of the specified target weight is greater than a set weight threshold value, sending an increment prompt.

An embodiment of the present invention further provides a time prediction apparatus, where the apparatus includes:

a data acquisition unit for acquiring a data value of at least one time-influencing factor;

and the time consumption prediction unit is used for obtaining the work time consumption of the household appliance according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

An embodiment of the present invention further provides a data acquisition apparatus, where the apparatus includes:

the instruction receiving unit is used for receiving a weighing instruction sent by the server;

the target weighing unit is used for sending out a weighing object placing prompt according to a set prompt sequence and acquiring the weight of each weighing object;

a target calculation unit for calculating the weight of at least one target weight according to the weight of each weight;

a data transmitting unit for transmitting the weight of the target nominal weight to the server;

the weight recommending unit is used for receiving the recommended weight of the specified target weight sent by the server based on the user recommendation request; when the absolute value of the difference value between the weight of the specified target nominal weight and the recommended weight is determined to be smaller than a set weight threshold, a matching completion prompt is sent out; and sending the weight of the matched specified target nominal weight to the server.

An embodiment of the present invention further provides a time prediction apparatus, where the apparatus includes: a first processor and a first memory for storing a computer program operable on the first processor;

wherein the first processor is configured to execute the steps of any of the above methods of the time prediction apparatus when running the computer program.

An embodiment of the present invention further provides a data acquisition apparatus, where the apparatus includes: a second processor and a second memory for storing a computer program operable on the second processor;

wherein the second processor is configured to execute the steps of any of the above methods of the data acquisition apparatus when running the computer program.

An embodiment of the present invention further provides a computer storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of any method of the time prediction apparatus or to implement the steps of any method of the data acquisition apparatus.

The embodiment of the invention provides a time prediction method, a time prediction device and a computer storage medium, which are used for acquiring a data value of at least one time influence factor; and obtaining the work consumed time of the household appliance according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor, wherein the correlation coefficient is used for showing the correlation between the time influence factor and the work consumed time, the correlation coefficient is larger, the correlation degree of the time influence factor to the work consumed time is larger, the work consumed time is predicted according to the time influence factor of the household appliance and the correlation coefficient corresponding to the time influence factor, the accurate work consumed time of the household appliance can be obtained, a user can conveniently carry out overall arrangement on the work time of the household appliance, and the household appliance is enabled to be more reasonable in time starting and running.

Drawings

FIG. 1 is a flow chart of a time prediction method according to an embodiment of the present invention;

FIG. 2 is a flow chart of another method for temporal prediction according to an embodiment of the present invention;

FIG. 3 is a flow chart of a data acquisition method according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of a time prediction system of a cooking device according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a cooking apparatus time prediction process according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a time prediction apparatus according to an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of a data acquisition apparatus according to an embodiment of the present invention;

FIG. 8 is a diagram illustrating a hardware structure of a time prediction apparatus according to an embodiment of the present invention;

FIG. 9 is a diagram illustrating a hardware structure of a data acquisition device according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of a time prediction system according to an embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples.

An embodiment of the present invention provides a time prediction method, which is applied to a time prediction apparatus, and as shown in fig. 1, the method includes:

step 101: acquiring a data value of at least one time influence factor;

step 102: and obtaining the working time consumption of the household appliance according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

The correlation coefficient is used for showing the correlation between the time influence factors and the work consumed time, the correlation coefficient is larger, the correlation degree of the time influence factors to the work consumed time is larger, the work consumed time is predicted according to the time influence factors of the household electrical appliance and the correlation coefficient corresponding to the time influence factors, the accurate work consumed time of the household electrical appliance can be obtained, a user can conveniently carry out overall arrangement on the work time of the household electrical appliance, and the household electrical appliance is enabled to be more reasonable in starting and running on time.

In some embodiments, the obtaining the working elapsed time of the home appliance according to the data value of the time-affecting factor and the correlation coefficient corresponding to the time-affecting factor includes:

Obtaining the work time consumption of the household appliance according to the estimated time and the correlation coefficient corresponding to the time influence factor, wherein the work time consumption comprises the following steps:

In this embodiment, when predicting the working time consumption of the home appliance, first, determining a data value of each time influence factor of a current working environment of the home appliance, matching a history record corresponding to the data value of each time influence factor from the history data based on the history data, and determining the working time consumption recorded in the history record as an estimated time, wherein if more than one history record is matched from the history data, an average value of the working time consumption recorded in a plurality of history records can be determined as the estimated time, and the history data includes at least one history record; then, calculating the product of the estimated time and the correlation coefficient corresponding to each time influence factor; and finally, determining the sum value of the products as the work time of the household appliance. Specifically, if the time-affecting factor is A, B, C in order, the data value of A is a and the correlation coefficient is λ₁The data value of B is B and the correlation coefficient is lambda₂The data value of C is C and the correlation coefficient is λ₃When the work time consumption is predicted, firstly, a history record which is the same as or similar to the data value a of the A, the data value B of the B and the data value C of the C is matched from the history data, and the work time consumption recorded in the history record is determined as predicted time T; then, the estimated time T and the correlation coefficient lambda are respectively calculated₁Product of (a) ("lambda")₁T, estimated time T and correlation coefficient lambda₂Product of (a) ("lambda")₂T and estimated time T and correlation coefficient lambda₃Product of (a) ("lambda")₃T; finally, determining the sum value of the products as the work time T^*In particular，T^*＝λ₁T+λ₂T+λ₃T。

In some embodiments, the determining of the correlation coefficient corresponding to the temporal influence factor includes: acquiring at least one history record meeting the screening condition, wherein the history record records the data values of the time consumption and the time influence factors;

Here, the determining of the correlation coefficient of the time influence factor is to determine the correlation coefficient of the designated time influence factor, and when the correlation coefficient of the designated time influence factor is determined, to screen out a history that satisfies a screening condition from the history data, wherein the screening condition may be that a data value of each of the other time influence factors other than the designated time influence factor is equal to a set value corresponding to the other time influence factor. In this embodiment, in the screened history that satisfies the screening condition, the data values of each of the other time influencing factors except the designated time influencing factor are fixed, and only the data value of the designated time influencing factor is changed, so that the correlation between the designated time influencing factor and the work time consumption can be accurately determined, that is, an accurate correlation coefficient corresponding to the designated time influencing factor can be obtained. The setting values corresponding to other time influence factors can be set according to the current working environment, specifically, the setting values of other time influence factors can be set as data values of other time influence factors in the current working environment, so that the obtained correlation coefficient of the specified time influence factors can accurately depict the correlation between the specified time influence factors and the working time consumption in the current working environment, and the method is favorable for accurately predicting the working time consumption of the household appliance. For example, if the time-affecting factor is A, B, C in turn, the data value of a in the current working environment is a, the data value of B is B, and the data value of C is C, then when determining the correlation coefficient of a, a history record satisfying the screening condition "the data value of B is B and the data value of C is C" can be screened from the history data, where the set value corresponding to B is B and the set value corresponding to C is C. At this time, in the history records screened from the history data, the data values of B and C are both set values, and only the data value of A and the recorded work time consumption are changed, so that the correlation between A and the work time consumption can be accurately determined, and the accurate correlation coefficient corresponding to A is obtained.

After obtaining at least one history record meeting the screening condition, the calculating a correlation coefficient between the time influence factor and the recorded work time consumption includes:

Wherein the obtaining of the correlation coefficient between the time influence factor and the recorded work consumption time according to the time influence factor data set and the work consumption time data set includes:

and determining the covariance of the time influence factor data set and the working time consumption data set as a correlation coefficient of the time influence factor and the working time consumption.

Here, since the data value of each of the other time-affecting factors in the screened history that satisfies the screening condition is equal to the set value corresponding to the other time-affecting factor, that is, the data value of each of the other time-affecting factors is fixed, it is possible to obtain an accurate correlation coefficient corresponding to the time-affecting factor by calculating the time-affecting factor data set and the work-time consuming data set. Specifically, the correlation coefficient between the time-influencing factor and the work elapsed time may be determined by calculating the covariance between the time-influencing factor data set and the work elapsed time data set, and the specific calculation process of the correlation coefficient may be thatWherein X ═ { X ═ X₁,X₂,…,X_i,…,X_nA time influence factor data set corresponding to a time influence factor, where T is { T ═ T }₁,T₂,…,T_i,…,T_nAnd the lambda is a correlation coefficient of the time influence factor and the work time consumption. For example, if one time-affecting factor is altitude, the altitude data set corresponding to altitude is H ═ H { (H ═ H)₁,H₂,…,H_i,…,H_nThe correlation coefficient between the altitude and the work time is }

It should be noted that, in this embodiment, the correlation coefficient between the time-influencing factor and the work consumed time may be determined by calculating the covariance of the time-influencing factor data set and the work consumed time data set, or may be determined by calculating the variance, multiple regression, standard deviation, unitary regression, and the like of the time-influencing factor data set and the work consumed time data set.

In some embodiments, the data values of the time-affecting factors recorded in the history are data values of candidate time-affecting factors;

Here, since there may be a large number of time-affecting factors affecting the working time consumption of the home appliance, the time-affecting factors have different effects on the working time consumption, some time-affecting factors have a large effect on the working time consumption, and some time-affecting factors have a small effect on the working time consumption, even a small effect can be ignored, it is often necessary to determine a more effective time-affecting factor from a plurality of candidate time-affecting factors. In this embodiment, a more effective time-affecting factor is determined according to the magnitude of the correlation coefficient of each time-affecting factor.

Specifically, the determining, according to the correlation coefficient, a candidate time influencing factor satisfying a set condition from among the candidate time influencing factors as a time influencing factor includes: sorting the corresponding candidate time influence factors according to the magnitude of the correlation coefficient; the set number of candidate time-affecting factors having a large correlation coefficient is determined as the time-affecting factor.

Here, after obtaining the correlation coefficient corresponding to each candidate time influencing factor, each candidate time influencing factor is ranked according to the magnitude of each correlation coefficient, wherein a set number (for example, 7) of candidate time influencing factors having a large correspondence correlation coefficient may be selected as the final time influencing factor, or a candidate time influencing factor having a correspondence correlation coefficient larger than a set correlation threshold may be selected as the final time influencing factor.

The above steps may be performed by a time prediction apparatus, which may be a terminal or a server. The terminal can be a terminal device such as a smart phone, a PDA, a PAD and the like.

Correspondingly, as a specific application of the method, an embodiment of the present invention further provides a time prediction method applied to a time prediction apparatus, where the time prediction apparatus is disposed in a server for controlling a cooking device, and the time prediction apparatus is used to implement the work consumption time prediction of the cooking device, as shown in fig. 2, the method includes:

step 201: receiving cooking function setting information for a cooking apparatus;

step 202: receiving a target weighing request sent by a mobile terminal, and acquiring the position information of the mobile terminal based on the target weighing request;

step 203: sending a weighing instruction to an intelligent scale based on the target weighing request, and receiving the weight of a target nominal weight sent by the intelligent scale based on the weighing instruction;

step 204: determining a data value of at least one time-affecting factor of the cooking appliance based on the cooking function setting information, the position information, and the weight of the target nominal weight;

step 205: and obtaining the working time consumption of the cooking equipment according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

In the embodiment, the data value of at least one time influence factor influencing the time consumption of the cooking equipment is obtained, then the time consumption of the cooking equipment is predicted according to the data value of each time influence factor of the cooking equipment and the correlation coefficient corresponding to the time influence factor, the accurate time consumption of the cooking equipment can be obtained, a user can conveniently conduct overall arrangement on the working time of the cooking equipment, and the cooking equipment is enabled to be more reasonable in starting and running in time.

In this embodiment, the cooking device generates cooking function setting information for the cooking device based on the cooking function setting input by the user, and sends the cooking function setting information to the time prediction device; the mobile terminal generates a target weighing request based on the cooking material setting for the cooking function input by the user, and sends the target weighing request to the time prediction device.

After receiving cooking function setting information aiming at the cooking equipment and a target weighing request sent by a mobile terminal, a time prediction device firstly acquires position information of the mobile terminal based on the target weighing request, wherein the position information of the mobile terminal can be determined as the position information of the cooking equipment as the position of the mobile terminal is the same as or close to the position of the cooking equipment; then, based on the target weighing request, sending a weighing instruction to an intelligent scale, and receiving the weight of a target weighing object sent by the intelligent scale based on the weighing instruction, wherein the target weighing object is a cooking material, and the cooking material can comprise water, rice and the like; then, determining a data value of at least one time influence factor of the cooking device based on the cooking function setting information, the position information and the weight of the target weight, wherein the time influence factor of the cooking device may include a cooking function, a weight of each cooking material, an altitude, an air pressure, an ambient temperature, an ambient humidity and the like, and the data values respectively corresponding to the altitude, the air pressure, the ambient temperature and the ambient humidity may be obtained by querying a relevant server through the position information; and finally, predicting the accurate work time consumption of the cooking equipment according to the time influence factors and the correlation coefficients corresponding to the time influence factors, so that the user can schedule the events at hand based on the accurate work time consumption, and the user experience is improved. Specifically, in this embodiment, the cooking device may be an electric cooker, and the cooking function may be cooking rice, cooking porridge, or the like. When the cooking apparatus is an electric cooker, the time-affecting factors may include altitude, air pressure, ambient temperature, ambient humidity, rice weight, water weight, cooking function.

In some embodiments, the obtaining of the operation elapsed time of the cooking apparatus according to the data value of the time-affecting factor and the correlation coefficient corresponding to the time-affecting factor includes:

and obtaining the working time consumption of the cooking equipment according to the estimated time and the correlation coefficient corresponding to the time influence factor.

Wherein obtaining the working time consumption of the cooking equipment according to the estimated time and the correlation coefficient corresponding to the time influence factor comprises:

determining a sum value of the summed products as the operation time of the cooking apparatus.

In this embodiment, when predicting the operation time consumption of the cooking apparatus, first, the data value of each time influence factor of the current operating environment of the cooking apparatus is determined, and history data is used as a basis to match the history data with the data value of each time influence factorRecording, namely determining the working time consumption recorded in the historical records as estimated time, and determining the average value of the working time consumption recorded in a plurality of historical records as the estimated time if more than one historical record is matched from the historical data, wherein the historical data comprises at least one historical record; then, calculating the product of the estimated time and the correlation coefficient corresponding to each time influence factor; finally, the sum of the products is determined as the time consumed by the operation of the cooking apparatus. Specifically, if the time-affecting factor is A, B, C in order, the data value of A is a and the correlation coefficient is λ₁The data value of B is B and the correlation coefficient is lambda₂The data value of C is C and the correlation coefficient is λ₃When the work time consumption is predicted, firstly, a history record which is the same as or similar to the data value a of the A, the data value B of the B and the data value C of the C is matched from the history data, and the work time consumption recorded in the history record is determined as predicted time T; then, the estimated time T and the correlation coefficient lambda are respectively calculated₁Product of (a) ("lambda")₁T, estimated time T and correlation coefficient lambda₂Product of (a) ("lambda")₂T and estimated time T and correlation coefficient lambda₃Product of (a) ("lambda")₃T; finally, determining the sum value of the products as the work time T^*In particular, T^*＝λ₁T+λ₂T+λ₃T。

In some embodiments, the determining of the correlation coefficient corresponding to the temporal influence factor includes:

Here, since there may be a large number of time-affecting factors affecting the operation time of the cooking apparatus, the time-affecting factors have different effects on the operation time, some of the time-affecting factors have a large effect on the operation time, and some of the time-affecting factors have a small effect on the operation time, even the small effect is negligible, it is often necessary to determine a more effective time-affecting factor from a plurality of candidate time-affecting factors. In this embodiment, a more effective time-affecting factor is determined according to the magnitude of the correlation coefficient of each time-affecting factor.

Specifically, the determining, according to the correlation coefficient, a candidate time influencing factor satisfying a set condition from among the candidate time influencing factors as a time influencing factor includes:

Correspondingly, an embodiment of the present invention further provides a data obtaining method, which is applied to a data obtaining device, where the data obtaining device is arranged in an intelligent scale, and as shown in fig. 3, the method includes:

step 301: receiving a weighing instruction sent by a server;

step 302: sending out a weight placing prompt according to a set prompt sequence to obtain the weight of each weight;

step 303: calculating the weight of at least one target weight according to the weight of each weighing object;

step 304: sending the weight of the target nominal weight to the server;

step 305: receiving a recommended weight of a specified target weight sent by the server based on a user recommendation request;

step 306: when the absolute value of the difference value between the weight of the specified target nominal weight and the recommended weight is determined to be smaller than a set weight threshold, a matching completion prompt is sent out;

step 307: and sending the matched weight of the specified target nominal weight to the server.

Here, after receiving a weighing instruction from the server, the intelligent scale enters a weighing state and sends out a weight placing prompt according to a set prompt sequence, wherein the sequence of the weight placing prompt is the weighing sequence of the cooking materials corresponding to the cooking function. For example, if the cooking function is rice cooking, the order of the weight placement cue may be, in order, placing a container-placing a container and rice-placing a container, rice and water, so that the weight of rice and the weight of water, which are the target weights, can be obtained by calculation.

In addition, when the user grasps the amount of the cooking material (for example, the amount of water for cooking), a user recommendation request may be transmitted to the server based on the mobile terminal; then, the server determines the specified target nominal weight and the corresponding recommended weight based on the user recommendation request, and sends the recommended weight of the specified target nominal weight to the intelligent scale; and then, the intelligent scale weighs the specified target nominal weight according to the recommended weight of the specified target nominal weight, wherein in the weighing process, the intelligent scale gives a weight placing prompt to prompt a user to place the specified target weight on the intelligent scale.

Specifically, when the absolute value of the difference between the weight of the specified target nominal weight and the recommended weight is determined to be smaller than a set weight threshold, a matching completion prompt is sent out;

when the difference value between the weight of the specified target nominal weight and the recommended weight is larger than a set weight threshold value, a decrement prompt is sent out;

When the intelligent scale detects that the weight of the weighing object is larger than the recommended weight and the difference value between the weight of the weighing object and the recommended weight is larger than the weight threshold, a decrement prompt is sent, when the weight of the weighing object is smaller than the recommended weight and the absolute value of the difference value between the weight of the weighing object and the recommended weight is larger than the weight threshold, an increment prompt is sent, when the absolute value of the difference value between the weight of the weighing object and the recommended weight is smaller than the set weight threshold, a matching completion prompt is sent, and in addition, the weight of the matched specified target nominal weight is sent to.

The processing of the above steps can be specifically completed by a data acquisition device, and the data acquisition device is an intelligent scale.

As shown in fig. 4, in this embodiment, the intelligent scale may include the following modules: a weight sensor: for measuring the weight of an object; a communication module: the data interaction is carried out with the server; a prompt module: the weighing device is used for prompting the weighing state, wherein the prompting module can be a lamp, a loudspeaker, a motor and the like, and in addition, different weighing states can be prompted through the colors of the lamp, the light flicker frequency, the sound of the loudspeaker, the vibration of the motor and the like.

The present invention will be described in detail with reference to the following application examples.

In the embodiment of the application, the process for predicting the working time consumption of the cooking equipment comprises the following steps:

1) the mobile terminal generates a target weighing request based on a rice measuring and water quantity calculating option aiming at a rice cooking function clicked by a user, and sends the target weighing request to the server so that the server informs the intelligent scale to enter a rice measuring and water quantity calculating state.

2) The server receives a target weighing request sent by the mobile terminal, and acquires the position information of the mobile terminal based on the target weighing request, wherein the position information of the mobile terminal can be determined as the position information of the cooking equipment because the position of the mobile terminal is the same as or close to the position of the cooking equipment. In addition, based on the target weighing request, the server sends a weighing instruction to the intelligent scale so that the intelligent scale enters a weighing state.

3) The intelligent scale receives a weighing instruction sent by a server, enters a weighing state, sends out a weighed object placing prompt according to a set prompt sequence, and firstly prompts a user to place an empty container (such as a rice cooker) on a gravity sensor of the intelligent scale so as to measure the weight _ empty of the container; then, prompting the user that rice can be added into the container to measure the weight _ full of 'rice + container', wherein if the weight of the container and the weight of 'rice + container' are measured, the intelligent scale can calculate the weight _ rice in the current container in real time, and the calculation formula of the weight of rice is as follows: weight _ rice is weight _ full-weight _ empty; finally, prompting the user to place the washed rice and the added water on an intelligent scale for measurement, and measuring weight _ current of 'rice + container + water', so that the intelligent scale can calculate the weight _ water of the water in the current container in real time, and the calculation formula of the weight of the water is as follows: weight _ water ═ weight _ current-weight _ empty-weight _ rice.

In addition, if the user does not know whether the weight of the water is proper or not, the user recommendation request can be sent to the server through the mobile terminal, and thus, the server can send the recommended water weight _ enable to the intelligent scale based on the user recommendation request. The intelligent scale receives the recommended water weight sent by the server, compares the current water weight with the recommended water weight, and if the current water weight is greater than the recommended water weight and the difference value between the current water weight and the recommended water weight is greater than a weight threshold, considers that weight _ water > weight _ enable, and as shown in fig. 4, the intelligent scale is specifically a prompt module in the intelligent scale to prompt a user to reduce the water amount; if the current water weight is smaller than the recommended water weight and the absolute value of the difference value between the current water weight and the recommended water weight is larger than the weight threshold, it is considered that weight _ water < weight _ suitable, and the intelligent scale is specifically a prompting module in the intelligent scale for prompting the user to increase the water amount. In addition, if the intelligent scale detects that the container is taken away, a communication module in the intelligent scale sends the weight of rice _ rice and the weight of water _ water in the current container to a server.

4) The cooking device receives a cooking function (for example, cooking porridge, cooking soup and the like) setting input by a user, generates cooking function setting information for the cooking device based on the cooking function setting input by the user, and the communication module in the cooking device sends the cooking function setting information to the server, wherein the working start time _ start is contained in the cooking function setting information. Here, as shown in fig. 4, the cooking apparatus is provided with the following modules: a communication module: the data interaction is carried out with the server; an input module: the system comprises a cooking function setting module, a control module and a display module, wherein the cooking function setting module is used for acquiring cooking function settings input by a user, and the input module can be a key and a function panel; a heating module: the heating device is used for heating according to cooking functions, wherein different cooking functions correspond to different heating powers; a display module: and the display unit is used for displaying the completion time of the current cooking task, wherein the completion time is the current time plus the work consumed time, and the completion time is used for identifying the specific moment for completing the current cooking task.

5) The server obtains the altitude, the air pressure, the ambient temperature, the ambient humidity and the like of the current area of the position information based on the position information of the mobile terminal, and can specifically obtain the information from the server to other application servers; determining a power adjustment strategy based on the cooking function setting information of the cooking equipment; and finally, inputting the data values of the time influence factors influencing the working time consumption of the cooking equipment into a big data analysis model, and calculating the working time consumption matched with the current working environment of the cooking equipment. The server transmits the work time to the cooking appliance and the mobile terminal. Here, as shown in fig. 5, the big data analysis model is used to obtain a data value of at least one time-affecting factor; and obtaining the working time consumption of the cooking equipment according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor. Specifically, in this embodiment, when the big data analysis model of the server predicts the working time consumption of the cooking device, first, the data value of each time influence factor of the current working environment of the cooking device is determined, a history record corresponding to the data value of each time influence factor is matched from the history data based on the history data, the working time consumption recorded in the history record is determined as the estimated time, and in addition, if more than one history record is matched from the history data, the average value of the working time consumption recorded in the plurality of history records can be determined as the estimated time, wherein the history data includes at least one history record; then, calculating the product of the estimated time and the correlation coefficient corresponding to each time influence factor; finally, the sum of the products is determined as the time consumed by the operation of the cooking apparatus.

6) After cooking is finished, the cooking equipment, specifically a communication module in the cooking equipment, sends the working end time _ end to the server.

7) The server calculates the real working time _ work based on the working end time and the working start time sent by the cooking equipment, wherein the working time has a calculation formula as follows: time word is time end-time start. Therefore, the server can record the data values of the time influence factors of the cooking and the real working time consumption, so that the big data analysis model can be corrected through the newly recorded data.

In order to implement the time prediction method according to the embodiment of the present invention, an embodiment of the present invention further provides a time prediction apparatus, as shown in fig. 6, where the time prediction apparatus includes: a data acquisition unit 601 and a time consumption prediction unit 602; wherein the content of the first and second substances,

a data acquisition unit 601 for acquiring data values of at least one time-affecting factor;

and a time consumption prediction unit 602, configured to obtain the working time consumption of the home appliance according to the data value of the time-affecting factor and the correlation coefficient corresponding to the time-affecting factor.

In some embodiments, the time-consuming prediction unit 602 is specifically configured to:

In some embodiments, the obtaining the working time of the home appliance according to the estimated time and the correlation coefficient corresponding to the time-affecting factor includes:

In some embodiments, the method further includes determining, according to the correlation coefficient, a candidate time influencing factor satisfying a set condition as a time influencing factor from among the candidate time influencing factors.

In some embodiments, said calculating a correlation coefficient of said temporal influencing factor with said recorded work hours comprises:

In some embodiments, the determining, according to the correlation coefficient, a candidate time influencing factor satisfying a set condition from among the candidate time influencing factors as a time influencing factor includes:

In practical applications, the data obtaining unit 601 may be implemented by a first processor of a time prediction apparatus in combination with a first communication interface, and the time-consuming prediction unit 602 may be implemented by the first processor of the time prediction apparatus.

It should be noted that: in the time prediction apparatus provided in the above embodiment, when performing time prediction, only the division of each program module is illustrated, and in practical applications, the processing of each program module may be distributed to different program modules according to needs, that is, the internal structure of the time prediction apparatus may be divided into different program modules to complete all or part of the processing described above. In addition, the time prediction apparatus and the time prediction method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.

In order to implement the data obtaining method according to the embodiment of the present invention, an embodiment of the present invention further provides a data obtaining apparatus, as shown in fig. 7, where the data obtaining apparatus includes: an instruction receiving unit 701, a target weighing unit 702, a target calculating unit 703, a data transmitting unit 704, and a weight recommending unit 705; wherein the content of the first and second substances,

an instruction receiving unit 701, configured to receive a weighing instruction sent by a server;

a target weighing unit 702, configured to send weighing object placement prompts according to a set prompt sequence, and obtain weights of the weighing objects;

a target calculation unit 703 for calculating the weight of at least one target nominal weight based on the weight of each weighted object;

a data transmitting unit 704, configured to transmit the weight of the target nominal weight to the server;

a weight recommending unit 705, configured to receive a recommended weight of a specified target weight sent by the server based on a user recommendation request; when the absolute value of the difference value between the weight of the specified target nominal weight and the recommended weight is determined to be smaller than a set weight threshold, a matching completion prompt is sent out; and sending the weight of the matched specified target nominal weight to the server.

When the data acquisition device is positioned in the intelligent scale, a weighing instruction sent by the server is received through a communication module of the intelligent scale; sending the weight of the target nominal weight to the server through a communication module of the intelligent scale; receiving the recommended weight of the specified target weight sent by the server based on the user recommendation request through a communication module of the intelligent scale; and sending the matched weight of the specified target nominal weight to the server through a communication module of the intelligent scale. Sending weighing object placing prompts sent according to a set prompting sequence to a prompting module of the intelligent scale, presenting the weighing object placing prompts to a user through the prompting module of the intelligent scale, and prompting the user to place a weighing object according to the prompts; and sending the matching completion prompt to a prompt module of the intelligent scale so as to present the matching completion prompt to the user through the prompt module of the intelligent scale and prompt the user that the matching is completed. And acquiring the weight of each weighing object through a weight sensor of the intelligent scale.

In practical applications, the instruction receiving unit 701, the data sending unit 704 and the weight recommending unit 705 may be implemented by a second processor of the data acquiring apparatus in combination with a second communication interface, and the target weighing unit 702 and the target calculating unit 703 may be implemented by the second processor of the data acquiring apparatus.

It should be noted that: in the data acquisition apparatus provided in the above embodiment, when data is acquired, only the division of each program module is taken as an example, and in practical applications, the processing of each program module may be distributed to different program modules according to needs, that is, the internal structure of the data acquisition apparatus may be divided into different program modules to complete all or part of the processing described above. In addition, the data acquisition device and the data acquisition method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.

In order to implement the time prediction method at the time prediction apparatus side in the embodiment of the present invention, an embodiment of the present invention further provides a time prediction apparatus implemented based on hardware, specifically a server, and as shown in fig. 8, the time prediction apparatus 810 includes: a first processor 801 and a first memory 802 for storing a computer program operable on the processor, wherein the first processor 801 is configured to perform, when running the computer program:

acquiring a data value of at least one time influence factor;

In some embodiments, the first processor 801 is further configured to, when running the computer program, perform:

and determining the candidate time influence factors meeting the set conditions from the candidate time influence factors as time influence factors according to the correlation coefficient.

The embodiments of the time prediction apparatus and the time prediction method at the time prediction apparatus side provided in the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments, and are not described herein again.

Of course, in practical applications, as shown in fig. 8, the time prediction apparatus may further include at least one first communication interface 803. The various components in the temporal prediction unit are coupled together by a first bus system 804. It is understood that the first bus system 804 is used to enable connection communications between these components. The first bus system 804 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as the first bus system 804 in fig. 8.

Wherein, the first communication interface 803 is used for interacting with other devices.

In particular, the first processor 801 may obtain data values of at least one time influencing factor via the first communication interface 803.

It will be appreciated that the first memory 802 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The first memory 802 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The first memory 802 in the embodiment of the present invention is used to store various types of data to support the operation of the time prediction apparatus.

The method disclosed in the above embodiments of the present invention may be applied to the first processor 801 or implemented by the first processor 801. The first processor 801 may be an integrated circuit chip having signal processing capability, and more specifically, having a time prediction algorithm built therein, i.e., having time prediction capability. In implementation, the steps of the above method may be implemented by integrated logic circuits of hardware or instructions in the form of software in the first processor 801. The first Processor 801 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The first processor 801 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a computer storage medium located in the first memory 802, and the first processor 801 reads the information in the first memory 802 and performs the steps of the aforementioned method in conjunction with its hardware.

In an exemplary embodiment, the time prediction apparatus may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field-Programmable Gate arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the foregoing methods.

In order to implement the data acquisition method at the data acquisition device side in the embodiment of the present invention, an embodiment of the present invention further provides a data acquisition device implemented based on hardware, specifically an intelligent scale, and as shown in fig. 9, the data acquisition device 910 includes: a second processor 901 and a second memory 902 for storing a computer program operable on the processor, wherein the second processor 901 is configured to perform, when running the computer program:

receiving a weighing instruction sent by a server;

sending the weight of the target nominal weight to the server;

It should be noted that: the data acquisition device and the data acquisition method at the data acquisition device side provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.

Of course, in practical applications, as shown in fig. 9, the communication device may further include at least one second communication interface 903. The various components in the communication device are coupled together by a second bus system 904. It will be appreciated that the second bus system 904 is used to enable communications among the components. The second bus system 904 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as the second bus system 904 in figure 9.

The second communication interface 903 is used for interacting with other devices.

Specifically, the second processor 901 may receive a weighing instruction sent by a server through the second communication interface 903, send the weight of the target nominal weight to the server through the second communication interface 903, receive a recommended weight of a specified target nominal weight sent by the server based on a user recommendation request through the second communication interface 903, and send the matched weight of the specified target nominal weight to the server through the second communication interface 903.

It will be appreciated that the second memory 902 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Wherein the nonvolatile Memory can be ROM, PROM, EPROM, EEPROM, FRAM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM; the magnetic surface storage may be disk storage or tape storage. Volatile memory can be RAM, which acts as external cache memory. By way of example but not limitation, many forms of RAM are available, such as SRAM, SSRAM, DRAM, SDRAM, DDRSDRAM, ESDRAM, SLDRAM, DRRAM. The described second memory 902 of embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The second memory 902 in the present embodiment is used to store various types of data to support the operation of the communication device.

The method disclosed in the above embodiments of the present invention may be applied to the second processor 901, or implemented by the second processor 901. The second processor 901 may be an integrated circuit chip having signal processing capabilities, and more specifically, having a data acquisition algorithm built therein, i.e., having data acquisition capabilities. In implementation, the steps of the above method may be implemented by an integrated logic circuit of hardware or an instruction in the form of software in the second processor 901. The second processor 901 described above may be a general purpose processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The second processor 901 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a computer storage medium located in the second memory 902, and the second processor 901 reads the information in the second memory 902, and in combination with its hardware, performs the steps of the foregoing method.

In an exemplary embodiment, the communication device may be implemented by one or more ASICs, DSPs, PLDs, CPLDs, FPGAs, general-purpose processors, controllers, MCUs, microprocessors, or other electronic components for performing the foregoing methods.

An embodiment of the present invention further provides a time prediction system, as shown in fig. 10, the system includes:

a server 1010 for receiving cooking function setting information for a cooking apparatus; receiving a target weighing request sent by a mobile terminal, and acquiring the position information of the mobile terminal based on the target weighing request; sending a weighing instruction to the intelligent scale based on the target weighing request;

the intelligent scale 1020 is used for receiving a weighing instruction sent by the server; sending out a weight placing prompt according to a set prompt sequence to obtain the weight of each weight; calculating the weight of at least one target weight according to the weight of each weighing object; sending the weight of the target nominal weight to the server; receiving a recommended weight of a specified target weight sent by the server based on a user recommendation request; when the absolute value of the difference value between the weight of the specified target nominal weight and the recommended weight is determined to be smaller than a set weight threshold, a matching completion prompt is sent out; sending the matched weight of the specified target nominal weight to the server;

the server 1010 is further used for receiving the weight of the target nominal weight sent by the intelligent scale based on the weighing instruction; determining a data value of at least one time-affecting factor of the cooking appliance based on the cooking function setting information, the position information, and the weight of the target nominal weight; and obtaining the working time consumption of the cooking equipment according to the data value of the time influence factor and the correlation coefficient corresponding to the time influence factor.

It should be noted that: the specific processing procedures of the server 1010 and the intelligent scale 1020 are described in detail above, and are not described herein again.

In an exemplary embodiment, the embodiment of the present invention further provides a computer storage medium, specifically a computer readable storage medium, for example, including the first memory 802 storing a computer program, which is executable by the first processor 801 of the time prediction apparatus 810 to perform the steps of the foregoing method. For example, the second memory 902 may store a computer program, which may be executed by the second processor 901 of the data acquisition apparatus 910 to perform the steps of the method. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.

It should be noted that: the technical schemes described in the embodiments of the present invention can be combined arbitrarily without conflict.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims

1. A method of temporal prediction, the method comprising:

acquiring a data value of at least one time influence factor;

2. The method of claim 1, wherein obtaining the working time of the home appliance according to the data value of the time-influencing factor and the correlation coefficient corresponding to the time-influencing factor comprises:

3. The method of claim 2, wherein obtaining the working time of the home appliance according to the estimated time and the correlation coefficient corresponding to the time-influencing factor comprises:

4. The method according to claim 1, wherein the determining of the correlation coefficient corresponding to the temporal influencing factor comprises:

5. The method of claim 4,

the data values of the time influencing factors recorded in the historical records are the data values of the candidate time influencing factors;

6. The method of claim 4, wherein said calculating a correlation coefficient of said temporal influencing factor with said recorded work hours comprises:

7. The method according to claim 5, wherein the determining, from the candidate time influencing factors, the candidate time influencing factor satisfying a set condition as a time influencing factor according to the correlation coefficient comprises:

8. A time prediction method is applied to a server for controlling a cooking device, and comprises the following steps:

receiving cooking function setting information for a cooking apparatus;

9. A data acquisition method is characterized by being applied to an intelligent scale, and comprises the following steps:

receiving a weighing instruction sent by a server;

sending the weight of the target nominal weight to the server;

10. The method of claim 9,

11. A temporal prediction apparatus, the apparatus comprising:

12. A data acquisition device is characterized in that the data acquisition device is applied to an intelligent scale, and the device comprises:

13. A temporal prediction apparatus, the apparatus comprising: a first processor and a first memory for storing a computer program operable on the first processor;

wherein the first processor is adapted to perform the steps of the method of any one of claims 1 to 7 or to perform the steps of the method of claim 8 when running the computer program.

14. A data acquisition apparatus, characterized in that the apparatus comprises: a second processor and a second memory for storing a computer program operable on the second processor;

wherein the second processor is adapted to perform the steps of the method of claim 9 or 10 when running the computer program.

15. A computer storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 7, or implements the steps of the method of claim 8, or implements the steps of the method of claim 9 or 10.