CN113447820A - Electric quantity monitoring method and device, intelligent ammeter and processor - Google Patents
Electric quantity monitoring method and device, intelligent ammeter and processor Download PDFInfo
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Abstract
The invention discloses an electric quantity monitoring method and device, an intelligent ammeter and a processor. Wherein, the method comprises the following steps: acquiring account electricity consumption data; inputting account electricity utilization data into an electricity quantity monitoring model, and predicting to obtain expected electricity utilization days, wherein the electricity quantity monitoring model is obtained by pre-training a machine learning model according to account historical electricity utilization data; and sending electric quantity warning information according to the expected electric quantity using days. The invention solves the technical problem of untimely charging fee reminding of the users due to larger difference of electricity utilization habits of different users.
Description
Technical Field
The invention relates to the field of electric power operation, in particular to an electric quantity monitoring method and device, an intelligent ammeter and a processor.
Background
When the user pays the electricity fee, the electricity fee is prestored into the corresponding account in a mode of pre-storing and paying according to the user, the electricity fee is directly deducted from the account balance after the electric appliance of the user consumes the electric quantity, and the user is powered off when the electricity fee of the account is used up. In the past, only unified power charge account residual amount threshold reminding can be set for all users, however, due to different specific conditions of different families, a mode of unified threshold reminding is adopted because the daily average power consumption of some user groups is larger and the daily average power consumption of some user groups is smaller, and the power failure is caused because some user groups are not available to pay the power charge because the reminding is too late; the remaining amount of money in some user accounts is enough for the user to use for a long time, and the reminding to the user is too early, so that the purpose of helping the user to judge when the user should pay the electricity fee is not provided.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides an electric quantity monitoring method and device, an intelligent ammeter and a processor, and at least solves the technical problem that charging fees of different users are not timely reminded due to large difference of electricity utilization habits of the different users.
According to an aspect of an embodiment of the present invention, there is provided a power monitoring method, including: acquiring account electricity consumption data; inputting the account power consumption data into a power consumption monitoring model, and predicting to obtain expected power consumption days, wherein the power consumption monitoring model is obtained by pre-training a machine learning model according to historical account power consumption data; and sending electric quantity warning information according to the expected electric quantity using days.
Optionally, the obtaining account electricity consumption data includes: receiving account power consumption original data; and processing the account electricity utilization original data according to a preset data processing script to obtain the account electricity utilization data.
Optionally, the receiving of the account power consumption raw data includes: receiving the account electricity consumption raw data through an HPLC communication network interface, wherein the account electricity consumption raw data is transmitted through an HPLC communication network.
Optionally, according to the expected number of days of power usage, sending power warning information, including: analyzing the historical account power consumption data and determining a threshold value of warning days in advance; and sending the electric quantity warning information according to the expected electric quantity use days and the threshold value of the number of the early warning days.
Optionally, the machine learning model comprises: and (5) linear regression model.
Optionally, the account electricity consumption data includes at least one of: voltage, current, amount of remaining electricity charged to the account.
According to another aspect of the embodiments of the present invention, there is also provided an electric quantity monitoring device, including: the acquisition module is used for acquiring account electricity consumption data; the prediction module is used for inputting the account electricity utilization data into an electricity quantity monitoring model and predicting to obtain expected electricity utilization days, wherein the electricity quantity monitoring model is obtained by pre-training a machine learning model according to the account historical electricity utilization data; and the sending module is used for sending electric quantity warning information according to the expected electric quantity use days.
According to another aspect of the embodiments of the present invention, there is also provided a smart meter including: the system comprises a data acquisition unit, a data processing unit and a display unit, wherein the data acquisition unit is used for acquiring account electricity utilization data; the data processing unit is connected with the data acquisition unit and used for operating an electric quantity monitoring model to process the account electricity utilization data and predicting to obtain expected electricity utilization days, wherein the electric quantity monitoring model trains a machine learning model in advance according to the account historical electricity utilization data to obtain and store the electric quantity monitoring model in the data processing unit; the display unit is connected with the data processing unit and used for displaying the expected electricity usage days and/or displaying the electricity warning information.
According to still another aspect of the embodiments of the present invention, there is further provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above power monitoring methods.
According to still another aspect of the embodiments of the present invention, there is provided a processor, where the program executes the power monitoring method described in any one of the above.
In the embodiment of the invention, the electric quantity monitoring model is adopted for prediction, the account electricity consumption data is input into the electric quantity monitoring model by acquiring the account electricity consumption data, the expected electricity consumption days are obtained through prediction, and then the electric quantity warning information is sent according to the expected electricity consumption days, so that the purpose of timely reminding a user of paying the electric charge according to the characteristics of the user is achieved, the technical effect of customizing the personalized electric charge reminding message according to the electricity consumption habits of the user is realized, and the technical problem of reminding the user of untimely charging charge caused by large difference of the electricity consumption habits of different users is solved.
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 schematic flow chart of a power monitoring method according to an embodiment of the present invention;
fig. 2 is a block diagram of an embodiment of a power monitoring device according to the present invention;
fig. 3 is a block diagram of a smart meter according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
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. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or 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.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment of power monitoring, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a schematic flow chart of a power monitoring method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
and step S102, acquiring account electricity consumption data. The account power consumption data may be data reflecting a current power consumption condition of an account, and one account may correspond to one family or one house.
As an alternative embodiment, the account electricity consumption data may include at least one of: voltage, current, amount of remaining electricity charged to the account. The voltage, the current and the account remaining electricity fee amount all include the current electricity utilization state of the family, and the subsequent electricity consumption condition of the family can be predicted.
And step S104, inputting the account electricity consumption data into an electricity consumption monitoring model, and predicting to obtain the expected electricity consumption days, wherein the electricity consumption monitoring model is obtained by pre-training a machine learning model according to the account historical electricity consumption data.
The machine learning model is trained by adopting the historical account power consumption data to obtain the power monitoring model, so that the power monitoring model can learn to obtain the power consumption habits of the families corresponding to the account, the information of the ordinary day power consumption condition is extracted through machine learning and is integrated into the power monitoring model, and the result of the residual power consumption days corresponding to the families is more accurate and more targeted by adopting the power monitoring model to predict the account.
Optionally, after the machine learning model is trained, the parameter related to the daily power consumption of the family corresponding to the account and the account remaining power charge amount of the account may be input into the power monitoring model, and the power monitoring model predicts, according to the data, how long the account remaining power charge amount can support the family corresponding to the account to use, that is, the predicted expected number of power usage days.
It should be noted that, the historical electricity consumption data of the account to be predicted may be the historical data of the account to be predicted, so that the obtained electricity consumption monitoring model predicts the electricity consumption condition of the account to be predicted more accurately.
And step S106, sending electric quantity warning information according to the expected electric quantity using days.
Through the steps, the mode that the electric quantity monitoring model is used for predicting is adopted, the account electric quantity data is input into the electric quantity monitoring model by acquiring the account electric quantity data, the expected electric quantity use days are obtained through prediction, then the electric quantity warning information is sent according to the expected electric quantity use days, the purpose of timely reminding the user of paying the electric charge according to the characteristics of the user is achieved, the technical effect of customizing the personalized electric charge reminding message according to the electric consumption habits of the user is achieved, and the technical problem that the user is reminded of untimely charging charge due to the fact that the difference of the electric consumption habits of different users is large is solved.
As an alternative embodiment, the account electricity consumption data may be acquired by: receiving account power consumption original data; and processing the original account power consumption data according to a preset data processing script to obtain the account power consumption data.
The data volume of the account electricity consumption raw data is very large and may not be all valid data, so that the raw data can be subjected to preliminary processing by adopting the method of the embodiment to obtain the account electricity consumption data containing valuable electricity consumption information of the user.
Optionally, processing of the account electricity consumption raw data, such as data extraction, cleaning, integration and the like, may be implemented through a Python programming language, and data change may also be monitored in real time to ensure real-time performance and validity of the data. Through cleaning and integrating the extracted data and simply setting script parameters, invalid data can be removed, the data can be screened and cleaned, manual intervention is not needed after initial setting, the functions are automatically executed, simplicity and high efficiency are realized, and manpower is saved.
As an alternative embodiment, the raw account power data may be received through an HPLC communication network interface, wherein the raw account power data is transmitted through the HPLC communication network. Wherein, the method comprises the following steps. An HPLC (High-speed Power Line Carrier, abbreviated as HPLC) communication network is a High-speed Power Line Carrier, also called a broadband Power Line Carrier, and is a broadband Power Line Carrier technology for data transmission on a low-voltage Power Line. More user data can be collected (for example, once every 15 Min) by depending on new technologies such as marketing measurement HPLC (high performance liquid chromatography) high-frequency information collection, power failure reporting and the like. Through the collection to these user data, can analyze city resident account power consumption remaining amount, prop up and establish electric quantity monitoring model and realize monitoring the long time that the electric energy remaining amount of money can be used in user's house, generate simultaneously and report an emergency and ask for help or increased vigilance information and remind to guarantee user's normal power consumption.
As an alternative embodiment, the machine learning model may employ a linear regression model. The linear regression model conforms to the service types processed by the method, can be efficiently trained and completed, and can find the causal relationship among variables, thereby generating a good prediction effect.
As an alternative embodiment, sending the power warning message according to the expected power usage days may include the following steps: analyzing historical account power consumption data and determining a threshold value of warning days in advance; and sending electric quantity warning information according to the expected electric quantity use days and the threshold value of the early warning days. Preferably, the payment habits of the user of the power account can be obtained by analyzing the historical electricity consumption data of the account, for example, the payment of some people is frequent, and the payment times of some people are few. For people who pay frequently, because the people have the payment habits, a lower threshold value of the number of days of early warning can be set for the people, and the users can usually respond to warning information quickly and complete payment; and for the people who pay the fees infrequently, the paying fee may be inconvenient, so that the higher number of days of warning in advance can be set for the people, the users can be informed as soon as possible, the users can arrange the proper paying fee in the early days, and the technical effect of providing proper service for different customers is achieved.
Example 2
According to an embodiment of the present invention, there is also provided an electric quantity monitoring device for implementing the electric quantity monitoring method, and fig. 2 is a block diagram of a structure of the electric quantity monitoring device according to the embodiment of the present invention, as shown in fig. 2, the electric quantity monitoring device includes: an acquisition module 22, a prediction module 24 and a transmission module 26, which will be described below.
The acquisition module 22 is used for acquiring account electricity utilization data;
the prediction module 24 is connected to the acquisition module 22, and is configured to input the account power consumption data into the power consumption monitoring model, and predict the expected number of days for which the power consumption is expected, where the power consumption monitoring model trains a machine learning model in advance according to the account historical power consumption data;
and a sending module 26, connected to the predicting module 24, for sending the power warning message according to the expected power usage days.
It should be noted here that the acquiring module 22, the predicting module 24 and the sending module 26 correspond to steps S102 to S106 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1.
Example 3
According to an embodiment of the present invention, there is further provided a smart meter, and fig. 3 is a block diagram of a structure of the smart meter according to an embodiment of the present invention, as shown in fig. 2, the smart meter includes: a data acquisition unit 32, a data processing unit 34 and a display unit 36, which are described below.
The data acquisition unit 32 is used for acquiring account electricity utilization data;
the data processing unit 34 is connected to the data acquisition unit 32 and is used for operating the electric quantity monitoring model to process account electricity consumption data and predicting expected electricity consumption days, wherein the electric quantity monitoring model trains a machine learning model in advance according to account historical electricity consumption data to obtain and store the electric quantity monitoring model in the data processing unit;
and the display unit 36 is connected to the data processing unit 34 and is used for displaying expected electricity usage days and/or displaying electricity warning information.
Example 4
An embodiment of the present invention may provide a computer device, and optionally, in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.
The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the power monitoring method and apparatus in the embodiments of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the power monitoring method is implemented. The memory 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 may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through 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 processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring account electricity consumption data; inputting account electricity utilization data into an electricity quantity monitoring model, and predicting to obtain expected electricity utilization days, wherein the electricity quantity monitoring model is obtained by pre-training a machine learning model according to account historical electricity utilization data; and sending electric quantity warning information according to the expected electric quantity using days.
Optionally, the processor may further execute the program code of the following steps: acquiring account electricity consumption data, comprising: receiving account power consumption original data; and processing the original account power consumption data according to a preset data processing script to obtain the account power consumption data.
Optionally, the processor may further execute the program code of the following steps: receiving account power consumption raw data, comprising: and receiving the account electricity utilization raw data through an HPLC communication network interface, wherein the account electricity utilization raw data is transmitted through an HPLC communication network.
Optionally, the processor may further execute the program code of the following steps: sending electric quantity warning information according to expected electric quantity using days, comprising: analyzing historical account power consumption data and determining a threshold value of warning days in advance; and sending electric quantity warning information according to the expected electric quantity use days and the threshold value of the early warning days.
Optionally, the processor may further execute the program code of the following steps: the machine learning model includes: and (5) linear regression model.
Optionally, the processor may further execute the program code of the following steps: the account electricity consumption data comprises at least one of the following: voltage, current, amount of remaining electricity charged to the account.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
Example 5
Embodiments of the present invention also provide a computer-readable storage medium. Optionally, in this embodiment, the computer-readable storage medium may be configured to store the program code executed by the power monitoring method provided in embodiment 1.
Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network, or in any one of a group of mobile terminals.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: acquiring account electricity consumption data; inputting account electricity utilization data into an electricity quantity monitoring model, and predicting to obtain expected electricity utilization days, wherein the electricity quantity monitoring model is obtained by pre-training a machine learning model according to account historical electricity utilization data; and sending electric quantity warning information according to the expected electric quantity using days.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: acquiring account electricity consumption data, comprising: receiving account power consumption original data; and processing the original account power consumption data according to a preset data processing script to obtain the account power consumption data.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: receiving account power consumption raw data, comprising: and receiving the account electricity utilization raw data through an HPLC communication network interface, wherein the account electricity utilization raw data is transmitted through an HPLC communication network.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: sending electric quantity warning information according to expected electric quantity using days, comprising: analyzing historical account power consumption data and determining a threshold value of warning days in advance; and sending electric quantity warning information according to the expected electric quantity use days and the threshold value of the early warning days.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: the machine learning model includes: and (5) linear regression model.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: the account electricity consumption data comprises at least one of the following: voltage, current, amount of remaining electricity charged to the account.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for monitoring power, comprising:
acquiring account electricity consumption data;
inputting the account power consumption data into a power consumption monitoring model, and predicting to obtain expected power consumption days, wherein the power consumption monitoring model is obtained by pre-training a machine learning model according to historical account power consumption data;
and sending electric quantity warning information according to the expected electric quantity using days.
2. The method of claim 1, wherein obtaining account electricity usage data comprises:
receiving account power consumption original data;
and processing the account electricity utilization original data according to a preset data processing script to obtain the account electricity utilization data.
3. The method of claim 2, wherein receiving account electricity usage raw data comprises: receiving the account electricity consumption raw data through an HPLC communication network interface, wherein the account electricity consumption raw data is transmitted through an HPLC communication network.
4. The method of claim 1, wherein sending a power alert message based on the expected number of days of power usage comprises:
analyzing the historical account power consumption data and determining a threshold value of warning days in advance;
and sending the electric quantity warning information according to the expected electric quantity use days and the threshold value of the number of the early warning days.
5. The method of claim 1, wherein the machine learning model comprises: and (5) linear regression model.
6. The method of any one of claims 1 to 5, wherein the account electricity usage data comprises at least one of: voltage, current, amount of remaining electricity charged to the account.
7. An electrical quantity monitoring device, comprising:
the acquisition module is used for acquiring account electricity consumption data;
the prediction module is used for inputting the account electricity utilization data into an electricity quantity monitoring model and predicting to obtain expected electricity utilization days, wherein the electricity quantity monitoring model is obtained by pre-training a machine learning model according to the account historical electricity utilization data;
and the sending module is used for sending electric quantity warning information according to the expected electric quantity use days.
8. A smart meter, comprising: a data acquisition unit, a data processing unit and a display unit, wherein,
the data acquisition unit is used for acquiring account electricity utilization data;
the data processing unit is connected with the data acquisition unit and used for operating an electric quantity monitoring model to process the account electricity utilization data and predicting to obtain expected electricity utilization days, wherein the electric quantity monitoring model trains a machine learning model in advance according to the account historical electricity utilization data to obtain and store the electric quantity monitoring model in the data processing unit;
the display unit is connected with the data processing unit and used for displaying the expected electricity usage days and/or displaying the electricity warning information.
9. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the power monitoring method according to any one of claims 1 to 6.
10. A processor, characterized in that the processor is configured to run a program, wherein the program is executed to execute the power monitoring method according to any one of claims 1 to 6.
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