CN112488590B - Target object classification method and device, storage medium and electronic device - Google Patents
Target object classification method and device, storage medium and electronic device Download PDFInfo
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Abstract
The invention provides a method and a device for classifying target objects, a storage medium and an electronic device, wherein the method comprises the following steps: obtaining operation parameters of household electrical appliance operated by a target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; determining a score value corresponding to the target object according to the operation parameter; and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories. By adopting the technical scheme, the problem that the target object using the household appliance cannot be classified and identified in the related technology is solved.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method and an apparatus for classifying a target object, a storage medium, and an electronic apparatus.
Background
Along with the development of the society, the intelligent application of household appliances is more and more common, more intelligent and convenient service is provided for users, the inevitable trend of the development of the household appliances is, and the problem that manufacturers pay more attention to is also provided. The reason for this problem is the lack of a scheme for classifying and identifying the usage behavior of the user of the home appliance. For example, the air conditioner can only provide operation modes such as refrigeration, heating, dehumidification, automation and sleep for users, and cannot classify the using behaviors of the users, and then more intelligent services can be provided according to different types of users.
Aiming at the problem that the target object using the household appliance cannot be classified and identified in the related art, an effective solution is not provided yet.
Disclosure of Invention
The embodiment of the invention provides a method and a device for classifying a target object, a storage medium and an electronic device, which are used for solving the problem that the related art cannot classify and identify the target object using household appliances.
According to an embodiment of the present invention, there is provided a method of classifying a target object, including: obtaining operation parameters of household electrical appliance operated by a target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; determining a score value corresponding to the target object according to the operation parameter; and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories.
Optionally, determining a score value corresponding to the target object according to the operation parameter includes: acquiring coefficient values corresponding to the operation parameters respectively, wherein the coefficient values are obtained by analyzing the operation parameters of which the number is greater than a preset threshold value; and determining the score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter respectively.
Optionally, determining the score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter respectively includes: determining a score value Z corresponding to the target object according to the following formula: z = a x1+ b x2+ c y, where x1 is the operation time length, x2 is the setting temperature, y is the energy consumption value, and a, b, and c are the operation time length, the setting temperature, and the coefficient value corresponding to the energy consumption value, respectively.
Optionally, obtaining the coefficient values corresponding to the operating parameters respectively includes: acquiring the number of operating parameters larger than a preset threshold value; and processing the operation parameters of which the number is greater than a preset threshold value according to a linear regression mode to obtain coefficient values corresponding to the operation parameters respectively, wherein the linear regression mode is used for indicating that the operation time length and the set temperature are used as independent variables, and the energy consumption value is used as a dependent variable to determine the relation between the dependent variable and the independent variable.
Optionally, the obtaining of the operation parameters of the home appliance operated by the target object includes: determining the operation duration in the operation parameters, wherein the operation duration is determined by the starting time and the ending time of the household appliance; and sending a query instruction to the household appliance, and receiving the set temperature corresponding to the running time of the household appliance between the starting time and the ending time and the energy consumption value of the household appliance in the running time, which are fed back by the household appliance.
Optionally, before determining the category of the target object according to the interval to which the score value belongs, the method further includes: and setting corresponding relations between intervals of different score values and different categories, wherein the intervals and the categories have one-to-one corresponding relations.
According to still another embodiment of the present invention, there is also provided a classification apparatus of a target object, including: the acquisition module is used for acquiring the operation parameters of the household appliance operated by the target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; the first determining module is used for determining a score value corresponding to the target object according to the operation parameter; and the second determining module is used for determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories.
Optionally, the first determining module is further configured to obtain coefficient values corresponding to the operating parameters, respectively, where the coefficient values are obtained by analyzing the operating parameters whose number is greater than a preset threshold; and determining the score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter respectively.
According to yet another embodiment of the invention, there is also provided a computer-readable storage medium comprising a stored program, wherein the program when executed performs the method described in any of the above.
According to yet another embodiment of the present invention, there is also provided an electronic apparatus comprising a memory having a computer program stored therein and a processor arranged to perform the method described in any one of the above by means of the computer program.
By the invention, the operation parameters of the household appliance operated by the target object are obtained, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; determining a score value corresponding to the target object according to the operation parameter; and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories. That is, the score value corresponding to the target object is determined according to the running time length, the set temperature and the energy consumption value of the household appliance; and determining the category of the target object according to the section to which the score value belongs. By adopting the technical scheme, the problem that the target object using the household appliance cannot be classified and identified in the related technology is solved, so that the using behaviors of the users are classified, and then more intelligent service can be provided according to different types of users.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a computer terminal of a classification method of a target object according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of classifying a target object according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a user classification method for air conditioner heating according to an embodiment of the present invention;
fig. 4 is a diagram illustrating a result of linear regression of data of an operation parameter of air-conditioning heating according to an embodiment of the present invention;
FIG. 5 is a schematic illustration of a process of calculating a score value according to an embodiment of the invention;
fig. 6 is a block diagram of a classification apparatus for target data according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method provided by the embodiment of the application can be executed in a computer terminal or a similar operation device. Taking the example of the method running on a computer terminal, fig. 1 is a block diagram of a hardware structure of the computer terminal of the method for classifying a target object according to the embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more processors 102 (only one is shown in fig. 1), wherein the processors 102 may include, but are not limited to, a Microprocessor (MPU) or a Programmable Logic Device (PLD), and a memory 104 for storing data, and in an exemplary embodiment, the computer terminal may further include a transmission device 106 for communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, a computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or more functionality than that shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program of an application software and a module, such as a computer program corresponding to the determination method of the classification method of the target object in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 can further include memory located remotely from the processor 102, which can be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In this embodiment, a method for classifying a target object is provided, which is applied to the above-mentioned computer terminal, and fig. 2 is a flowchart of the method for classifying a target object according to the embodiment of the present invention, where the flowchart includes the following steps:
step S202: the method comprises the steps of obtaining operation parameters of household electrical appliance operated by a target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time;
step S204: determining a score value corresponding to the target object according to the operation parameter;
step S206: and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories.
By the invention, the operation parameters of the household appliance operated by the target object are obtained, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; determining a score value corresponding to the target object according to the operation parameter; and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories. That is, the score value corresponding to the target object is determined according to the running time length, the set temperature and the energy consumption value of the household appliance; and determining the category of the target object according to the section to which the score value belongs. By adopting the technical scheme, the problem that the target object using the household appliance cannot be classified and identified in the related technology is solved, so that the using behaviors of the users are classified, and then more intelligent service can be provided according to different types of users.
After step S202, determining a score value corresponding to the target object according to the operation parameter includes: acquiring coefficient values corresponding to the operation parameters respectively, wherein the coefficient values are obtained by analyzing the operation parameters of which the number is greater than a preset threshold value; and determining the score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter respectively.
It should be noted that the coefficient values corresponding to the operating parameters are determined according to a linear regression equation Y = a × x1+ b × x2+ d. Wherein Y is the energy consumption value, x1 is the operation duration, x2 is the operation duration, a, b are the operation duration, respectively, the coefficient value corresponding to the set temperature, and d is a constant. Multiple sets Y, x1, x2 of values are obtained, where each set Y, x1, x2 of values corresponds, i.e., each set Y, x, x2 is linearly related. After obtaining multiple groups of Y, x1 and x2 numerical values, normalizing the obtained multiple groups of Y, x1 and x2 numerical values respectively, substituting the normalized Y, x and x2 into a linear regression equation Y = a x1+ b x2+ d, and calculating coefficients a and b of the operation duration and a constant d. After the coefficients a and b are calculated, the fractional value corresponding to the target object is determined according to the operation parameter and the coefficient value corresponding to the operation parameter, wherein the coefficient of the energy consumption value may be 1.
It should be noted that before determining the coefficient values corresponding to the operating parameters according to the linear regression equation Y = a × x1+ b × x2+ d, correlation analysis needs to be performed on the energy consumption values with the operating duration and the set temperature, respectively. Taking air conditioner heating as an example, as shown in fig. 5, wherein avg _ energy, avg _ setup, and avg _ daytime respectively represent the average value of energy consumption, the average value of operation time length, and the average value of set temperature, and the correlation coefficient between the average value of energy consumption and the average value of operation time length, and the correlation coefficient between the average value of energy consumption and the average value of set temperature are calculated according to the values of avg _ energy, avg _ setup, and avg _ daytime. The calculation result is that the correlation coefficient of the energy consumption average value and the running time length average value is a positive number, and the correlation coefficient of the energy consumption average value and the set temperature average value is a positive number. Finally, a significant positive correlation of the energy consumption value with the set temperature can be obtained, and the energy consumption value is significantly positively correlated with the running time. If the air conditioner is used for refrigeration, the calculation result is that the correlation coefficient of the energy consumption average value and the running time length average value is a positive number, and the correlation coefficient of the energy consumption average value and the set temperature average value is a negative number. It is possible to obtain a significantly negative correlation of the energy consumption value with the set temperature and a significantly positive correlation of the energy consumption value with the operating time duration. It follows that the energy consumption values, which are dependent variables, and the operating time length and the set temperature, which are independent variables, may be significantly correlated with the operating time length and the set temperature, respectively, and linear regression calculation may be performed on the energy consumption values, the operating time length and the set temperature.
It should be noted that, after determining the coefficient values corresponding to the operating parameters, an error of the linear regression equation needs to be checked, if the error of the linear regression equation is smaller than a preset threshold, the coefficient value obtained may be used, and if the error of the linear regression equation is larger than the preset threshold, the coefficient values corresponding to the operating parameters need to be recalculated.
After step S202, determining a score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter, further comprising: determining a score value Z corresponding to the target object according to the following formula: z = a x1+ b x2+ c y, where x1 is the operation duration, x2 is the setting temperature, y is the energy consumption value, and a, b, and c are the coefficient values corresponding to the operation duration, the setting temperature, and the energy consumption value, respectively.
It should be noted that, determining the score value Z corresponding to the target object: before Z = a x1+ b x2+ c y, it is necessary to perform a correlation analysis of the energy consumption value with the operating duration and the set temperature. After determining that the correlation of the energy consumption values with the operating time period and the set temperature, respectively, is significant, coefficient values corresponding to the operating parameters, respectively, are determined according to a linear regression equation Y = a x1+ b x2+ d. After determining the coefficient values corresponding to the operating parameters, the error of the linear regression equation needs to be checked, and if the error of the linear regression equation is smaller than the target threshold, the target threshold may be set by itself, and then the coefficients a and b are substituted into Z = a × x1+ b × x2+ c × y, where c may be 1.
It should be noted that c may be determined from the linear regression equation Y = a × x1+ b × x2+ d, and if the linear regression equation is in the form of Y = a × x1+ b × x2+ d, c may take 1, if the linear regression equation transforms into another form: c x Y = a x1+ b x2+ d, then c may take the value after the transformation of the linear regression equation, and c may be adjusted in size at will, but a and b may change accordingly. C may be adjusted to an appropriate value according to the usage environment of the home appliance. By the technical means, score calculation can be performed on the using behaviors of the household appliance users, so that the users can be classified and identified, and corresponding services can be provided for different users.
Before determining the score value corresponding to the target object according to the operation parameter, it is required to obtain coefficient values corresponding to the operation parameter, including: acquiring operation parameters of which the number is larger than a preset threshold value; and processing the operation parameters of which the number is greater than a preset threshold value according to a linear regression mode to obtain coefficient values corresponding to the operation parameters respectively, wherein the linear regression mode is used for indicating that the operation duration and the set temperature are used as independent variables, and the energy consumption value is used as a dependent variable to determine the relation between the dependent variable and the independent variable.
It should be noted that, acquiring the operation parameters of which the number is greater than the preset threshold refers to acquiring multiple sets of data of the energy consumption value, the operation duration and the set temperature, where the number of the multiple sets of data is greater than the preset threshold, and each set of data has a correlation. And processing the operation parameters with the number larger than a preset threshold value in a linear regression mode with the operation duration and the set temperature as independent variables and the energy consumption value as dependent variables, namely determining the coefficient values respectively corresponding to the operation parameters according to a linear regression equation Y = a x1+ b x2+ d.
The method for acquiring the operating parameters of the household appliance operated by the target object comprises the following steps: determining the operation duration in the operation parameters, wherein the operation duration is determined by the starting time and the ending time of the household appliance; and sending a query instruction to the household appliance, and receiving a set temperature and an energy consumption value corresponding to the running time of the household appliance between the starting time and the ending time, which are fed back by the household appliance.
It should be noted that the operation parameters of the household electrical appliance operated by the target object include the energy consumption value, the operation time length and the set temperature. And sending a query instruction to the household appliance, so that the running time of the household appliance, the set temperature of the household appliance in the running time and the energy consumption value of the household appliance in the running time can be received.
It should be noted that, the acquiring of the operation parameter of the home appliance operated by the target object may be sending an inquiry instruction to the home appliance, and then sending the operation parameter by the home appliance. The operation parameters of the household appliance operated by the target object can be obtained through active reporting of the household appliance.
Before determining the category of the target object according to the section to which the score value belongs, the method further includes: and setting corresponding relations between intervals of different score values and different categories, wherein the intervals and the categories have one-to-one corresponding relations.
It should be noted that the correspondence between the sections with different score values and different categories may be used to classify the target object according to the quartile point. For example, in an alternative embodiment, the air conditioner heating is of an energy-saving emission-reducing type with users of Z < -0.08; -users of 0.08 straw z < =0.18 are of the natural equalization type; users with Z >0.18 are experience-optimized; the air-conditioning refrigeration takes users with Z <0.37 as an energy-saving emission-reducing type; users with 0.37 and z < =0.70 are of a natural equalization type; users with Z >0.70 are the best experience. Wherein Z is the score value corresponding to the target object. It should be noted that the user type may be set by itself, and the classification of the target object may also be set by itself.
In order to better understand the above technical solution, the following optional schematic diagrams are used to explain the implementation process of user classification.
Fig. 3 is a schematic diagram of a user classification method for air conditioning heating according to an embodiment of the present invention, as shown in fig. 3:
air conditioner users are classified into three categories: energy conservation and emission reduction, natural balance and optimal experience. The air conditioner heats and uses users with Z < -0.08 as energy-saving emission-reducing type; -users of 0.08 straw z < =0.18 are of the natural equalization type; users with Z >0.18 are the best experience.
Fig. 4 is a diagram illustrating a result of linear regression of data of an operation parameter of air-conditioning heating according to an embodiment of the present invention, and fig. 5 is a diagram illustrating a process of calculating a score value according to an embodiment of the present invention, as shown in fig. 4 and 5:
avg _ energy, avg _ settmp, avg _ daytime represent the average value of energy consumption, the average value of operating time length, and the average value of set temperature, respectively. avg _ energy _ std, avg _ settmp _ std, avg _ daytimestd are the normalized average value of the energy consumption and the average value of the operating time length and the average value of the set temperature. The normalized formula is:
x＝(x-min)/(max-min)
and performing linear regression on the normalized energy consumption average value, the running time length average value and the set temperature average value according to a linear regression equation Y = a x1+ b x2+ d, so as to determine coefficient values respectively corresponding to the running parameters. Wherein x1 is the operation time length, x2 is the set temperature, and y is the energy consumption value. In fig. 4, the error of the non-standard coefficient is used to check the error of the linear regression equation. If the error of the linear regression equation is smaller than the target threshold, the coefficient value may be used, and if the error of the linear regression equation is larger than the target threshold, the coefficient value corresponding to each of the operating parameters may need to be recalculated. t and Sig are parameters for measuring whether the correlation between different parameters is significant, wherein a Sig value less than 0.01 represents that the correlation between two parameters is significant, for example, a Sig value of a set temperature is 0 and is less than 0.01, so that the correlation between the set temperature and energy consumption is significant.
And Z = a x1+ b x2+ c y, wherein a, b and c are coefficient values corresponding to the operation duration, the set temperature and the energy consumption value respectively. Energy, setmp, and daytime in fig. 5 represent values of a × x1, b × x2, and c × y, respectively. Finally, Z was calculated from Z = a x1+ b x2+ c y, where Score in fig. 5 represents the value of Z.
By the invention, the operation parameters of the household appliance operated by the target object are obtained, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; determining a score value corresponding to the target object according to the operation parameter; and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories. That is, the score value corresponding to the target object is determined according to the running time length, the set temperature and the energy consumption value of the household appliance; and determining the category of the target object according to the section to which the score value belongs. By adopting the technical scheme, the problem that the target object using the household appliance cannot be classified and identified in the related technology is solved, so that the using behaviors of the users are classified, and then more intelligent service can be provided according to different types of users.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a device for classifying a target object is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and the description of the device that has been already made is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware or a combination of software and hardware is also possible and contemplated.
Fig. 6 is a block diagram showing the structure of a classification apparatus for a target object according to an embodiment of the present invention; as shown in fig. 6, includes:
the first receiving module 60 is configured to obtain an operation parameter of the home appliance operated by the target object, where the operation parameter includes: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time;
a first determining module 62, configured to determine, according to the operating parameter, a score value corresponding to the target object;
a second determining module 64, configured to determine the category of the target object according to the interval to which the score value belongs, where different intervals correspond to different categories.
By the invention, the operation parameters of the household appliance operated by the target object are obtained, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time; determining a score value corresponding to the target object according to the operation parameter; and determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories. That is to say, the score value corresponding to the target object is determined according to the running time length, the set temperature and the energy consumption value of the household appliance; and determining the category of the target object according to the section to which the score value belongs. By adopting the technical scheme, the problem that the target object using the household appliance cannot be classified and identified in the related technology is solved, so that the using behaviors of the users are classified, and then more intelligent service can be provided according to different types of users.
Optionally, the first determining module 62 is further configured to obtain coefficient values corresponding to the operating parameters, respectively, where the coefficient values are obtained by analyzing the operating parameters whose number is greater than a preset threshold; and determining the score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter respectively.
It should be noted that the coefficient values corresponding to the operating parameters are determined according to a linear regression equation Y = a × x1+ b × x2+ d. Wherein Y is the energy consumption value, x1 is the operation duration, x2 is the operation duration, a, b are the operation duration, respectively, the coefficient value corresponding to the set temperature, and d is a constant. Multiple sets Y, x1, x2 of values are obtained, where each set Y, x1, x2 of values corresponds, i.e., each set Y, x, x2 is linearly related. After acquiring multiple groups of numerical values Y, x, x2, normalizing the acquired multiple groups of numerical values Y, x, x2, respectively, substituting a linear regression equation Y = a x1+ b x2+ d into the normalized Y, x, x2, and calculating coefficients a and b of the operation duration and a constant d. After the coefficients a and b are calculated, the fractional value corresponding to the target object is determined according to the operation parameter and the coefficient value corresponding to the operation parameter, wherein the coefficient of the energy consumption value may be 1.
It should be noted that before determining the coefficient values corresponding to the operating parameters according to the linear regression equation Y = a × x1+ b × x2+ d, correlation analysis needs to be performed on the energy consumption values with the operating duration and the set temperature, respectively. Taking air conditioner heating as an example, as shown in fig. 5, wherein avg _ energy, avg _ setup, and avg _ daytime respectively represent the average value of energy consumption, the average value of operation time length, and the average value of set temperature, and the correlation coefficient between the average value of energy consumption and the average value of operation time length, and the correlation coefficient between the average value of energy consumption and the average value of set temperature are calculated according to the values of avg _ energy, avg _ setup, and avg _ daytime. The calculation result is that the correlation coefficient of the energy consumption average value and the running time length average value is a positive number, and the correlation coefficient of the energy consumption average value and the set temperature average value is a positive number. Finally, a significant positive correlation of the energy consumption value with the set temperature can be obtained, and the energy consumption value is significantly positively correlated with the running time. If the air conditioner is used for refrigeration, the calculation result is that the correlation coefficient of the energy consumption average value and the running time length average value is a positive number, and the correlation coefficient of the energy consumption average value and the set temperature average value is a negative number. It can be obtained that the energy consumption value is significantly negatively correlated with the set temperature and the energy consumption value is significantly positively correlated with the length of operation. It follows that the energy consumption values, which are dependent variables and independent variables, are significantly correlated with the operating time length and the set temperature, respectively, and linear regression calculation can be performed on the energy consumption values, which are dependent variables, and the operating time length and the set temperature.
It should be noted that, after determining the coefficient values corresponding to the operating parameters, an error of the linear regression equation needs to be checked, if the error of the linear regression equation is smaller than a preset threshold, the coefficient value obtained may be used, and if the error of the linear regression equation is larger than the preset threshold, the coefficient values corresponding to the operating parameters need to be recalculated.
Optionally, the first determining module 62 is further configured to determine the score value Z corresponding to the target object according to the following formula: z = a x1+ b x2+ c y, where x1 is the operation duration, x2 is the setting temperature, y is the energy consumption value, and a, b, and c are the coefficient values corresponding to the operation duration, the setting temperature, and the energy consumption value, respectively.
It should be noted that, determining the score value Z corresponding to the target object: before Z = a x1+ b x2+ c y, it is necessary to perform a correlation analysis of the energy consumption value with the operating duration and the set temperature. After determining that the correlation of the energy consumption values with the operating time duration and the set temperature, respectively, is significant, coefficient values corresponding to the operating parameters, respectively, are determined according to a linear regression equation Y = a x1+ b x2+ d. After determining the coefficient values corresponding to the operating parameters, the error of the linear regression equation needs to be checked, and if the error of the linear regression equation is smaller than the target threshold, the target threshold may be set by itself, then the coefficients a and b are substituted into Z = a × x1+ b × x2+ c × y, where c may be 1.
It should be noted that c can be determined from the linear regression equation Y = a x1+ b x2+ d, and if the linear regression equation is of the form Y = a x1+ b x2+ d, c can take 1, if the linear regression equation transforms another form: c x Y = a x1+ b x2+ d, then c may take the value after the transformation of the linear regression equation, and c may be adjusted in size at will, but a and b may change accordingly. C may be adjusted to an appropriate value according to the usage environment of the home appliance. By the technical means, the score of the using behaviors of the household appliance user can be calculated, so that the user can be classified and identified, and corresponding services can be provided for different users.
Optionally, the first determining module 62 is further configured to obtain the number of operating parameters greater than a preset threshold; and processing the operation parameters of which the number is greater than a preset threshold value according to a linear regression mode to obtain coefficient values corresponding to the operation parameters respectively, wherein the linear regression mode is used for indicating that the operation time length and the set temperature are used as independent variables, and the energy consumption value is used as a dependent variable to determine the relation between the dependent variable and the independent variable.
It should be noted that, acquiring the operation parameters of which the number is greater than the preset threshold refers to acquiring multiple sets of data of the energy consumption value, the operation duration and the set temperature, where the number of the multiple sets of data is greater than the preset threshold, and each set of data has a correlation. And processing the operation parameters with the number larger than a preset threshold value in a linear regression mode with the operation duration and the set temperature as independent variables and the energy consumption value as dependent variables, namely determining the coefficient values respectively corresponding to the operation parameters according to a linear regression equation Y = a x1+ b x2+ d.
Optionally, the obtaining module 60 is further configured to determine an operation duration in the operation parameters, where the operation duration is determined by a start time and an end time of the household appliance; and sending a query instruction to the household appliance, and receiving a set temperature and an energy consumption value corresponding to the running time of the household appliance between the starting time and the ending time, which are fed back by the household appliance.
It should be noted that the operation parameters of the household appliance operated by the target object include the energy consumption value, the operation time length and the set temperature. Sending a query instruction to the household electrical appliance, and then receiving the running time of the household electrical appliance, the set temperature of the household electrical appliance in the running time and the energy consumption value of the household electrical appliance in the running time, which are fed back by the household electrical appliance.
It should be noted that, the obtaining of the operation parameter of the home appliance operated by the target object may be sending an inquiry instruction to the home appliance, and then sending the operation parameter by the home appliance. The operation parameters of the household appliance operated by the target object can be obtained through active reporting of the household appliance.
Optionally, the second determining module 64 is further configured to set correspondence between intervals of different score values and different categories, where the intervals and the categories have a one-to-one correspondence.
It should be noted that, the correspondence between the intervals with different score values and different categories may be used to classify the target object according to the quartile bit point. For example, in an alternative embodiment, the air conditioner heating is of an energy-saving emission-reducing type with users of Z < -0.08; -users of 0.08 straw z < =0.18 are of the natural equalization type; users with Z >0.18 are experience-optimized; the air-conditioning refrigeration takes users with Z <0.37 as an energy-saving emission-reducing type; users with 0.37 and z < =0.70 are of a natural equalization type; users with Z >0.70 are experience-optimized. Wherein Z is the score value corresponding to the target object. It should be noted that the user type may be set by itself, and the classification of the target object may also be set by itself.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, obtaining operation parameters of household electrical appliance operated by a target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time;
s2, determining a score value corresponding to the target object according to the operation parameters;
and S3, determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention further provide an electronic device, comprising a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, obtaining operation parameters of household electrical appliance operated by a target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time;
s2, determining a score value corresponding to the target object according to the operation parameters;
and S3, determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories.
Optionally, in this option, the specific examples in this embodiment may refer to the examples described in the foregoing embodiment and optional implementation, and this embodiment is not described again here.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention shall be included in the protection scope of the present invention.
Claims (8)
1. A method of classifying a target object, comprising:
obtaining operation parameters of household electrical appliance operated by a target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time;
determining a score value corresponding to the target object according to the operation parameter;
determining the category of the target object according to the section to which the score value belongs, wherein different sections correspond to different categories, and determining the score value corresponding to the target object according to the operation parameter comprises the following steps:
acquiring coefficient values corresponding to the operation parameters respectively, wherein the coefficient values are obtained by analyzing the operation parameters of which the number is greater than a preset threshold value;
determining score values corresponding to the target object according to the operation parameters and coefficient values corresponding to the operation parameters respectively, wherein before the coefficient values corresponding to the operation parameters respectively are obtained, the method further comprises: and carrying out correlation analysis on the energy consumption value and the running time length and the set temperature respectively to determine that the energy consumption value is significantly positively or negatively correlated with the set temperature and the energy consumption value is significantly positively correlated with the running time length.
2. The method of claim 1, wherein determining the score value corresponding to the target object according to the operation parameter and the coefficient value corresponding to the operation parameter comprises:
determining a score value Z corresponding to the target object according to the following formula:
z = a x1+ b x2+ c y, where x1 is the operation time length, x2 is the setting temperature, y is the energy consumption value, and a, b, and c are the operation time length, the setting temperature, and the coefficient value corresponding to the energy consumption value, respectively.
3. The method of claim 1, wherein obtaining the respective coefficient values corresponding to the operating parameters comprises:
acquiring operation parameters of which the number is larger than a preset threshold value;
and processing the operation parameters of which the number is greater than a preset threshold value according to a linear regression mode to obtain coefficient values corresponding to the operation parameters respectively, wherein the linear regression mode is used for indicating that the operation time length and the set temperature are used as independent variables, and the energy consumption value is used as a dependent variable to determine the relation between the dependent variable and the independent variable.
4. The method of claim 1, wherein obtaining operational parameters of the home device operated by the target object comprises:
determining the operation duration in the operation parameters, wherein the operation duration is determined by the starting time and the ending time of the household appliance;
and sending a query instruction to the household appliance, and receiving a set temperature and an energy consumption value corresponding to the running time of the household appliance between the starting time and the ending time, which are fed back by the household appliance.
5. The method according to any one of claims 1 to 4, wherein before determining the category of the target object according to the section to which the score value belongs, the method further comprises:
and setting corresponding relations between intervals of different score values and different categories, wherein the intervals and the categories have one-to-one corresponding relations.
6. An apparatus for classifying a target object, comprising:
the acquisition module is used for acquiring the operation parameters of the household appliance operated by the target object, wherein the operation parameters comprise: the running time of the household appliance, the set temperature of the household appliance in the running time, and the energy consumption value of the household appliance in the running time;
the first determining module is used for determining a score value corresponding to the target object according to the operation parameter;
the second determining module is configured to determine the category of the target object according to the interval to which the score value belongs, where different intervals correspond to different categories, and the first determining module is further configured to obtain coefficient values corresponding to the operating parameters, respectively, where the coefficient values are obtained by analyzing the operating parameters whose number is greater than a preset threshold; and determining the score value corresponding to the target object according to the coefficient values corresponding to the operation parameters and the operation parameters respectively, wherein the first determining module is further configured to perform correlation analysis on the energy consumption value and the operation duration and the set temperature respectively to determine that the energy consumption value is significantly positively or negatively correlated with the set temperature and the energy consumption value is significantly positively correlated with the operation duration.
7. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 5.
8. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 5 by means of the computer program.
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