CN112163709B - Method and device for electricity utilization promotion, storage medium, and electronic device - Google Patents

Method and device for electricity utilization promotion, storage medium, and electronic device Download PDF

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CN112163709B
CN112163709B CN202011074257.3A CN202011074257A CN112163709B CN 112163709 B CN112163709 B CN 112163709B CN 202011074257 A CN202011074257 A CN 202011074257A CN 112163709 B CN112163709 B CN 112163709B
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data
electricity utilization
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electricity
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CN112163709A (en
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李雨铭
宋德超
陈翀
岳冬
李少鹏
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Gree Electric Appliances Inc of Zhuhai
Zhuhai Lianyun Technology Co Ltd
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Zhuhai Lianyun Technology Co Ltd
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Abstract

The application discloses an electricity utilization promotion method and device, a storage medium and an electronic device. Wherein the method comprises the following steps: acquiring historical electricity utilization data and historical environment information of a user; and generating a power utilization recommendation according to the historical environment information and the historical power utilization data. The power consumption control method and device solve the technical problem that power consumption of electric equipment in the related technology is large.

Description

Method and device for electricity utilization promotion, storage medium, and electronic device
Technical Field
The application relates to the field of smart home, in particular to a power consumption prompting method and device, a storage medium and an electronic device.
Background
Various electric equipment becomes an indispensable part of life, brings great convenience to the life of people, promotes the development of scientific society, and brings about side effects, wherein the most direct consumption of electricity is brought about by the use of the electric equipment.
The population is increased, the electric equipment is multiplied, and the electric load of the city is increased. While current appliances generally have energy-saving modes, the greatest reason is the involuntary power consumption behavior of the user, which results in a large power consumption, and the general products cannot effectively suppress the behavior due to the initiation of the user.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides an electricity utilization promoting method and device, a storage medium and an electronic device, which are used for at least solving the technical problem of high power consumption of electric equipment in the related technology.
According to an aspect of an embodiment of the present application, there is provided an electricity usage recommendation method, including: acquiring historical electricity utilization data and historical environment information of a user; and generating a power utilization recommendation according to the historical environment information and the historical power utilization data.
Optionally, generating the electricity usage recommendation according to the historical environmental information and the historical electricity usage data includes: generating an association relation according to the historical environment information and the historical electricity consumption data, wherein the association relation is used for representing the corresponding relation between the environment information and the electricity consumption data; and generating the electricity utilization promotion according to the current environment information of the user and the association relation.
Optionally, generating the association relation according to the historical environment information and the historical electricity consumption data includes: and processing the historical environment information and the historical electricity consumption data by adopting an FFM algorithm to generate an electricity consumption curve representing the association relation.
Optionally, processing the historical environmental information and the historical electricity consumption data by adopting an FFM algorithm, and generating the electricity consumption curve representing the association relationship includes: performing data integration and normalization processing on the historical environment information and the historical electricity consumption data; acquiring a loss function value of a calculation model; updating parameters of the calculation model according to the selected algorithm parameter strategy; and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
Optionally, generating the electricity utilization recommendation according to the current environmental information of the user and the association relation includes: searching a power utilization mode matched with the current environment information from a power utilization curve; and generating the electricity utilization prompt corresponding to the electricity utilization mode.
Optionally, before generating the electricity utilization suggestion according to the current environmental information of the user and the association relationship, the method further includes: and acquiring the current environment information of the user.
Optionally, acquiring the current environmental information of the user includes: and acquiring the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment of the user, wherein the current environment information comprises the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment.
According to another aspect of the embodiments of the present application, there is also provided an electricity consumption reminding device, including: the acquisition unit is used for acquiring historical electricity utilization data and historical environment information of the user; and the prompting unit is used for generating electricity utilization prompts according to the historical environment information and the historical electricity utilization data.
Optionally, the prompting unit is further configured to: generating an association relation according to the historical environment information and the historical electricity consumption data, wherein the association relation is used for representing the corresponding relation between the environment information and the electricity consumption data; and generating the electricity utilization promotion according to the current environment information of the user and the association relation.
Optionally, the prompting unit is further configured to: and processing the historical environment information and the historical electricity consumption data by adopting an FFM algorithm to generate an electricity consumption curve representing the association relation.
Optionally, the prompting unit is further configured to: performing data integration and normalization processing on the historical environment information and the historical electricity consumption data; acquiring a loss function value of a calculation model; updating parameters of the calculation model according to the selected algorithm parameter strategy; and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
Optionally, the prompting unit is further configured to: searching a power utilization mode matched with the current environment information from a power utilization curve; and generating the electricity utilization prompt corresponding to the electricity utilization mode.
Optionally, the prompting unit is further configured to: and acquiring the current environment information of the user before generating the electricity utilization suggestion according to the current environment information of the user and the association relation.
Optionally, the prompting unit is further configured to: and acquiring the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment of the user, wherein the current environment information comprises the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program that when executed performs the above-described method.
According to another aspect of the embodiments of the present application, there is also provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the method described above by the computer program.
In the embodiment of the application, the power consumption condition of the user is obtained to obtain the optimal power consumption curve meeting the condition of the user, the power consumption advice is provided for the user to improve the power consumption behavior of the user, and the technical problem of high power consumption of the electric equipment in the related technology can be solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of an alternative power utilization recommendation method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative electrical lift device according to an embodiment of the present application;
and
Fig. 3 is a block diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of the embodiments of the present application, a method embodiment of an electricity utilization recommendation method is provided. FIG. 1 is a flow chart of an alternative power utilization recommendation method, as shown in FIG. 1, according to an embodiment of the present application, which may include the steps of:
step S1, historical electricity utilization data and historical environment information of a user are obtained.
And S2, generating a power utilization suggestion according to the historical environment information and the historical power utilization data.
Optionally, generating the electricity usage recommendation according to the historical environmental information and the historical electricity usage data includes: generating an association relation according to the historical environment information and the historical electricity consumption data, wherein the association relation is used for representing the corresponding relation between the environment information and the electricity consumption data; and generating the electricity utilization promotion according to the current environment information of the user and the association relation.
Optionally, generating the association relation according to the historical environment information and the historical electricity consumption data includes: and processing the historical environment information and the historical electricity consumption data by adopting an FFM algorithm to generate an electricity consumption curve representing the association relation.
Optionally, processing the historical environmental information and the historical electricity consumption data by adopting an FFM algorithm, and generating the electricity consumption curve representing the association relationship includes: performing data integration and normalization processing on the historical environment information and the historical electricity consumption data; acquiring a loss function value of a calculation model; updating parameters of the calculation model according to the selected algorithm parameter strategy; and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
Optionally, generating the electricity utilization recommendation according to the current environmental information of the user and the association relation includes: searching a power utilization mode matched with the current environment information from a power utilization curve; and generating the electricity utilization prompt corresponding to the electricity utilization mode.
Optionally, before generating the electricity utilization suggestion according to the current environmental information of the user and the association relationship, the method further includes: and acquiring the current environment information of the user.
Optionally, acquiring the current environmental information of the user includes: and acquiring the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment of the user, wherein the current environment information comprises the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment.
Through the steps, the optimal electricity consumption curve meeting the situation of the user is obtained by acquiring the electricity consumption situation of the user, the electricity consumption advice is provided for the user to improve the electricity consumption behavior of the user, and the technical problem of high electricity consumption of the electric equipment in the related technology can be solved.
As an alternative example, the technical solution of the present application is further described in detail below in connection with specific embodiments.
Considering that the electricity demand of a user does not have larger fluctuation in an effective time, wherein the effective time mainly refers to a time period in which factors such as the temperature and humidity of the surrounding environment, the activities of the user and the like remain stable; in a period of effective time, the user and the electric equipment thereof form a closed system, i.e. the user and the electric equipment are not greatly increased or reduced; the effective times are independent, namely the effective times are selected independently and are not influenced.
Under the conditions described above, an inference can be made that: in a period of effective time, there must be a most energy-saving electricity distribution, so the problem to be solved finally is to fit the optimal electricity utilization curve of the user.
In an ideal case, the optimal electricity consumption of the user in the effective time is 0. In actual situations, the electricity distribution is not 0 under the influence of the user demand, the equipment energy consumption and the like, so in actual situations, the optimal user curve needs to be obtained under the influence of the influence. These effects are relatively fixed to the user, with the preconditions satisfied, and do not substantially change significantly over the effective time. These effects are herein set as I. In view of the complexity of the real environment, the environmental impact in constraint I is here set to the temperature, humidity and brightness of the surrounding environment.
In terms of algorithm, FFM models are chosen here. The model introduces fields to make full use of information with the same properties so that the target value can be derived more accurately. Meanwhile, the algorithm still maintains excellent performance under the conditions of large data volume and sparse data characteristics.
The general flow is as follows: collecting electricity consumption curves of users, namely electricity consumption curves in a period of effective time; classifying the power utilization curves of which the I changes within the allowable range into one type, obtaining one curve in each type by utilizing an FFM algorithm, and obtaining the optimal curve under the constraint condition I by integrating each type, wherein the allowable range is defined by determining a reasonable value through multiple tests; and under similar constraint conditions, the optimal electricity consumption of the user can be deduced according to the obtained curve. I.e. the optimal power consumption of the user under the environmental factors within the allowable range. The specific implementation mode is as follows:
and (3) data acquisition: the algorithm requires the collection of a certain amount of user data to form a model tailored to the particular user, so the device will collect operational data and ambient parameters of the user for a significant period of time during the pre-use of the product. The user operation data may approximately represent user demand information, the surrounding environment information being mainly represented by temperature, humidity, and brightness. The data form effective time and electricity consumption data table.
Data integration and normalization: the collected data are relatively messy and inconsistent, wherein the collected data comprise numerical data and category data, and the FFM algorithm adopts a unified data format (LIBSVM or FFMs format) for data input, so that the data types need to be processed uniformly. Firstly, for the category type characteristics, the value of a certain attribute in the data table can be correspondingly formed into attribute value pairs as new attribute to replace the original attribute, and the value of the corresponding attribute value pair on a certain piece of data is 1, otherwise, the value of the corresponding attribute value pair is 0. For numeric features, we discretize the data before format conversion as a class type feature.
Loss function: and calculating a loss function value of the model according to the data. The FFM algorithm uses a different loss function than the conventional CTR prediction, i.e., advertisement click rate prediction algorithm. The invention can be analogized to advertisement click prediction, namely, a threshold value of illumination, temperature and the like meeting the user requirements is estimated according to the environment data and the prior user operation data, and electric equipment is controlled according to the threshold value and the brightness and the temperature of the current surrounding environment, so that the invention is compared with CTR prediction. The usual loss function in CRT prediction is as follows:
for the FFM model, the loss function is:
wherein x is a sample, n is the total number of features, fi and fj are domains of the ith feature and the jth feature respectively, w (i, fj) represents a vector corresponding to the ith feature in the domain fj, and x (i) is a value of the sample x corresponding to the ith feature.
The loss function of the FFM algorithm contains the information of a domain, namely, the class to which the data features belong, and features with the same properties or attributes are divided into the same domain. Thus, larger information quantity can be obtained, and an algorithm model closer to the user can be learned.
Parameter optimization: and carrying out parameter updating optimization according to the selected algorithm parameter strategy. The method for updating the parameters with the strategy SG+AdaGrad adopted by the FFM algorithm comprises the specific steps of firstly calculating the gradient, and then accumulating the square of the gradient by means of the AdaGrad algorithm and updating the related parameters.
The method can continuously learn the user, and continuously improve the optimization algorithm parameters so as to be more suitable for the requirements of the user.
Curve fitting: the input of the algorithm is user operation data and environment parameters in a period of time, the user electricity consumption can be obtained through the user operation data, namely, a data table of effective time and electricity consumption can be generated, and an electricity consumption curve can be obtained according to the data table. Therefore, in order to obtain the optimal electricity utilization curve, the operation with the least electricity consumption on the premise of meeting the requirement of the user needs to be obtained, and the operation is based on the output of the algorithm. By the method, the optimal curve of the user can be obtained.
And (3) effective time selection: the effective time mainly refers to a period of time when factors such as the temperature and humidity of the surrounding environment and the activities of a user remain stable. Typically, the length of this period of time is not fixed and varies from user to user and from environment to environment, and a specific length of time is required to be assessed experimentally for a specific application.
The optimal electricity utilization curve of the user is obtained by combining the self requirements and conditions of the user through a related algorithm, and the suggestions are pushed, so that the electricity utilization behavior of the user is further improved, and the purposes of saving energy and reducing the urban electricity utilization load are achieved.
The invention realizes that the optimal electricity consumption curve of the user is obtained through the electricity consumption history and personal condition of the user and provides relevant suggestions for the user to restrain the electricity consumption behavior of the user.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.
According to another aspect of the embodiments of the present application, there is also provided an electricity usage recommendation device for implementing the electricity usage recommendation method described above. FIG. 2 is a schematic illustration of an alternative electrical lift device, as shown in FIG. 2, according to an embodiment of the present application, which may include:
an acquisition unit 21 for acquiring historical electricity consumption data and historical environmental information of a user;
and a prompting unit 23, configured to generate an electricity utilization recommendation according to the historical environmental information and the historical electricity utilization data.
It should be noted that, the acquiring unit 21 in this embodiment may be used to perform step S1 in the embodiment of the present application, and the presenting unit 23 in this embodiment may be used to perform step S2 in the embodiment of the present application.
Through the module, the optimal electricity consumption curve meeting the situation of the user is obtained by acquiring the electricity consumption situation of the user, the electricity consumption advice is provided for the user to improve the electricity consumption behavior of the user, and the technical problem of larger electricity consumption of the electric equipment in the related technology can be solved.
Optionally, the prompting unit is further configured to: generating an association relation according to the historical environment information and the historical electricity consumption data, wherein the association relation is used for representing the corresponding relation between the environment information and the electricity consumption data; and generating the electricity utilization promotion according to the current environment information of the user and the association relation.
Optionally, the prompting unit is further configured to: and processing the historical environment information and the historical electricity consumption data by adopting an FFM algorithm to generate an electricity consumption curve representing the association relation.
Optionally, the prompting unit is further configured to: performing data integration and normalization processing on the historical environment information and the historical electricity consumption data; acquiring a loss function value of a calculation model; updating parameters of the calculation model according to the selected algorithm parameter strategy; and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
Optionally, the prompting unit is further configured to: searching a power utilization mode matched with the current environment information from a power utilization curve; and generating the electricity utilization prompt corresponding to the electricity utilization mode.
Optionally, the prompting unit is further configured to: and acquiring the current environment information of the user before generating the electricity utilization suggestion according to the current environment information of the user and the association relation.
Optionally, the prompting unit is further configured to: and acquiring the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment of the user, wherein the current environment information comprises the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above embodiments. It should be noted that, the above modules may be implemented in a corresponding hardware environment as part of the apparatus, and may be implemented in software, or may be implemented in hardware, where the hardware environment includes a network environment.
According to another aspect of the embodiments of the present application, there is also provided a server or a terminal for implementing the above power consumption recommendation method.
Fig. 3 is a block diagram of a terminal according to an embodiment of the present application, and as shown in fig. 3, the terminal may include: one or more (only one is shown) processors 201, memory 203, and transmission means 205, as shown in fig. 3, the terminal may also include input output devices 207.
The memory 203 may be used to store software programs and modules, such as program instructions/modules corresponding to the power utilization recommendation method and apparatus in the embodiments of the present application, and the processor 201 executes the software programs and modules stored in the memory 203, thereby performing various functional applications and data processing, that is, implementing the power utilization recommendation method described above. Memory 203 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 203 may further comprise memory remotely located relative to the processor 201, which may be connected to the terminal via 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 205 is used for receiving or transmitting data via a network, and may also be used for data transmission between the processor and the memory. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 205 includes a network adapter (Network Interface Controller, NIC) that may be connected to other network devices and routers via a network cable to communicate with the internet or a local area network. In one example, the transmission device 205 is a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
Wherein in particular the memory 203 is used for storing application programs.
The processor 201 may call the application program stored in the memory 203 through the transmission means 205 to perform the following steps:
acquiring historical electricity utilization data and historical environment information of a user;
and generating a power utilization recommendation according to the historical environment information and the historical power utilization data.
The processor 201 is further configured to perform the steps of:
performing data integration and normalization processing on the historical environment information and the historical electricity consumption data;
acquiring a loss function value of a calculation model;
updating parameters of the calculation model according to the selected algorithm parameter strategy;
and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is only illustrative, and the terminal may be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 3 is not limited to the structure of the electronic device. For example, the terminal may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 3, or have a different configuration than shown in fig. 3.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
Embodiments of the present application also provide a storage medium. Alternatively, in the present embodiment, the above-described storage medium may be used to execute the program code of the electricity utilization recommendation method.
Alternatively, in this embodiment, the storage medium may be located on at least one network device of the plurality of network devices in the network shown in the above embodiment.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of:
acquiring historical electricity utilization data and historical environment information of a user;
and generating a power utilization recommendation according to the historical environment information and the historical power utilization data.
Optionally, the storage medium is further arranged to store program code for performing the steps of:
performing data integration and normalization processing on the historical environment information and the historical electricity consumption data;
acquiring a loss function value of a calculation model;
updating parameters of the calculation model according to the selected algorithm parameter strategy;
and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.

Claims (8)

1. An electricity consumption recommendation method, comprising:
acquiring historical electricity utilization data and historical environment information of a user, including: acquiring environment information in effective time and operation data of a user on electric equipment in the effective time, and acquiring electricity utilization data of the user according to the operation data, wherein the effective time is a period of time for maintaining stable environment conditions;
generating an electricity utilization curve representing the corresponding relation between the environment information and the electricity utilization data according to the historical environment information and the historical electricity utilization data;
searching an optimal electricity utilization curve matched with the current environment information of the user from the electricity utilization curve, and determining an electricity utilization mode according to user operation data corresponding to the searched optimal electricity utilization curve; the optimal electricity utilization curve refers to a relation curve between environment information and lowest electricity utilization data;
and generating the electricity utilization prompt corresponding to the electricity utilization mode.
2. The method of claim 1, wherein generating a power usage curve representing a correspondence between environmental information and power usage data from the historical environmental information and the historical power usage data comprises:
and processing the historical environment information and the historical electricity utilization data by adopting an FFM algorithm to generate the electricity utilization curve.
3. The method of claim 2, wherein processing the historical environmental information and the historical electricity usage data using an FFM algorithm, generating the electricity usage curve comprises:
performing data integration and normalization processing on the historical environment information and the historical electricity consumption data;
acquiring a loss function value of a calculation model;
carrying out parameter updating on the calculation model according to a selected algorithm parameter strategy, wherein the algorithm parameter strategy is a SG+AdaGrad method;
and inputting the historical environment information and the historical electricity utilization data subjected to data integration and normalization processing into the calculation model to generate the electricity utilization curve.
4. The method of claim 1, wherein prior to finding a best electricity usage profile from the electricity usage profiles that matches the current environmental information of the user, the method further comprises:
and acquiring the current environment information of the user.
5. The method of claim 4, wherein obtaining current context information for the user comprises:
and acquiring the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment of the user, wherein the current environment information comprises the temperature of the surrounding environment, the humidity of the surrounding environment and the brightness of the surrounding environment.
6. An electricity consumption display device, comprising:
an obtaining unit, configured to obtain historical electricity consumption data and historical environmental information of a user, including: acquiring environment information in effective time and operation data of a user on electric equipment in the effective time, and acquiring electricity utilization data of the user according to the operation data, wherein the effective time is a period of time for maintaining stable environment conditions;
the prompting unit is used for generating a power utilization curve representing the corresponding relation between the environment information and the power utilization data according to the historical environment information and the historical power utilization data; the method is also used for searching an optimal electricity utilization curve matched with the current environment information of the user from the electricity utilization curves, and determining an electricity utilization mode according to the user operation data corresponding to the searched optimal electricity utilization curve; the optimal electricity utilization curve refers to a relation curve between environment information and lowest electricity utilization data; and generating the electricity utilization prompt corresponding to the electricity utilization mode.
7. A storage medium comprising a stored program, wherein the program when run performs the method of any one of the preceding claims 1 to 5.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor performs the method according to any of the preceding claims 1 to 5 by means of the computer program.
CN202011074257.3A 2020-10-09 2020-10-09 Method and device for electricity utilization promotion, storage medium, and electronic device Active CN112163709B (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102930354A (en) * 2012-11-06 2013-02-13 北京国电通网络技术有限公司 Method and device for predicating electricity consumption of residential area
CN106600129A (en) * 2016-12-07 2017-04-26 杭州清源电力设计有限公司 Power grid planning method and system
CN110060176A (en) * 2019-04-03 2019-07-26 南京邮电大学 Electric power Internet of Things intelligent power distribution method, readable storage medium storing program for executing and terminal
CN110414724A (en) * 2019-07-10 2019-11-05 东软集团股份有限公司 For predicting method, apparatus, readable storage medium storing program for executing and the electronic equipment of power consumption
CN111539563A (en) * 2020-04-13 2020-08-14 珠海格力电器股份有限公司 Energy consumption safety state prediction method, device, server and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102930354A (en) * 2012-11-06 2013-02-13 北京国电通网络技术有限公司 Method and device for predicating electricity consumption of residential area
CN106600129A (en) * 2016-12-07 2017-04-26 杭州清源电力设计有限公司 Power grid planning method and system
CN110060176A (en) * 2019-04-03 2019-07-26 南京邮电大学 Electric power Internet of Things intelligent power distribution method, readable storage medium storing program for executing and terminal
CN110414724A (en) * 2019-07-10 2019-11-05 东软集团股份有限公司 For predicting method, apparatus, readable storage medium storing program for executing and the electronic equipment of power consumption
CN111539563A (en) * 2020-04-13 2020-08-14 珠海格力电器股份有限公司 Energy consumption safety state prediction method, device, server and storage medium

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