CN113780625B - User electricity charge prediction method, system, terminal and storage medium - Google Patents
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Abstract
The invention provides a method, a system, a terminal and a storage medium for predicting the electricity charge of a user, comprising the following steps: collecting the latest version of charging scheme from the cloud; collecting a historical daily electricity consumption record of a user, and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption record; and predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side. The invention can automatically predict the monthly electricity charge of the user, lead the user to have visual data, further plan the production time period scheme for the user and guide the user to use electricity by staggering peaks.
Description
Technical Field
The invention relates to the technical field of power supply service, in particular to a user electricity fee prediction method, a system, a terminal and a storage medium.
Background
At present, optimizing the commercial environment is a great importance in providing high-quality service for customers, and customers pay high attention to the electricity cost of the customers. Meanwhile, the user is guided to stagger peak power utilization, and the stability of the power grid is also facilitated. However, at present, most of the electric charge charging standards are pushed to customers in time, and the customers adjust the production time, so that the problems that the customers cannot plan production schemes and pay no attention to the electric charge charging standards, and the like, so that peak staggering electricity utilization pushing is slow exist.
Disclosure of Invention
The invention provides a user electricity charge prediction method, a system, a terminal and a storage medium for solving the technical problems.
In a first aspect, the present invention provides a method for predicting an electric charge of a user, including:
collecting the latest version of charging scheme from the cloud;
collecting a historical daily electricity consumption record of a user, and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption record;
and predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side.
Further, collecting the latest version of the charging scheme from the cloud comprises the following steps:
sending a request to a cloud, wherein the request is provided with a region number of a local power supply region;
and receiving the latest version charging scheme matched with the area number and returned by the cloud.
Further, collecting a historical daily electricity consumption record of the user, and analyzing a user electricity consumption period and corresponding electricity consumption amount from the historical daily electricity consumption record, wherein the method comprises the following steps:
collecting historical daily electricity records of a user in one month from a power supply metering system;
the average power consumption of the user in each hour in one day is counted by utilizing big data analysis technology.
Further, predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side, including:
analyzing charging standards of each period in the charging scheme;
calculating the electricity charge of each hour according to the average electricity consumption of each hour and the charging standard of the period;
accumulating the electricity charges of each hour to obtain the daily average electricity charge of the user;
and predicting the monthly electric charge of the user according to the daily average electric charge of the user, and pushing the monthly electric charge to the user side.
Further, the method further comprises:
dividing the actual peak period of daily electricity consumption of the user according to the average electricity consumption of each hour of the user, and calculating the actual peak period;
analyzing the limited peak time duration in the charging scheme;
according to the user operation field, the corresponding pre-stored optional working time period and the production cost of the optional working time period are called from the database;
constructing a user total cost function, wherein the total cost function=user electricity consumption and production cost, and the user electricity consumption and the production cost are related to the actual peak time of the user;
and selecting the actual peak time of the user when the total cost function value is minimum as an ideal production time planning scheme of the user, and pushing the planning scheme to the user side.
In a second aspect, the present invention provides a user electricity fee prediction system, including:
the scheme acquisition unit is used for acquiring the latest version of charging scheme from the cloud;
the electricity consumption analysis unit is used for collecting historical daily electricity consumption records of the user and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption records;
and the electricity charge prediction unit is used for predicting the monthly electricity charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount and pushing the monthly electricity charge to the user side.
Further, the electricity analysis unit includes:
the record acquisition module is used for acquiring historical daily electricity utilization records of a user in one month from the power supply metering system;
and the electricity consumption statistics module is used for counting the average electricity consumption of the user in each hour in one day by utilizing a big data analysis technology.
Further, the system further comprises:
the peak analysis unit is used for dividing the actual peak time of daily electricity consumption of the user according to the average electricity consumption of the user in each hour and calculating the actual peak time duration;
the scheme analysis unit is used for analyzing the limited peak time in the charging scheme;
the data calling unit is used for calling the corresponding pre-stored optional working time period and the production cost of the optional working time period from the database according to the user operation field;
the function construction unit is used for constructing a user total cost function, wherein the total cost function=user electricity consumption and production cost, and the user electricity consumption and the production cost are related to the actual peak time of the user;
and the planning pushing unit is used for selecting the actual peak time of the user when the total cost function value is minimum as an ideal production time planning scheme of the user and pushing the planning scheme to the user side.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program,
the processor is configured to call and run the computer program from the memory, so that the terminal performs the method of the terminal as described above.
In a fourth aspect, there is provided a computer storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method of the above aspects.
The invention has the advantages that,
according to the user electricity charge prediction method, the system, the terminal and the storage medium, the charging scheme is updated in time by collecting the latest version of the charging scheme from the cloud, meanwhile, the historical daily electricity consumption record of the user is analyzed to obtain the electricity consumption period and the corresponding electricity consumption amount of the user, the monthly electricity charge of the user can be predicted according to the charging scheme, the electricity consumption period and the corresponding electricity consumption amount of the user, and the monthly electricity charge is pushed to the user side. The invention can automatically predict the monthly electricity charge of the user, lead the user to have visual data, further plan the production time period scheme for the user and guide the user to use electricity by staggering peaks.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
FIG. 1 is a schematic flow chart of a method of one embodiment of the invention. The execution subject of fig. 1 may be a user electricity fee prediction system.
As shown in fig. 1, the method includes:
step 110, collecting the latest version of charging scheme from the cloud;
step 120, collecting a historical daily electricity consumption record of a user, and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption record;
and step 130, predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side.
In order to facilitate understanding of the present invention, the present invention further describes the user electric charge prediction method according to the principles of the user electric charge prediction method according to the present invention, in combination with the process of predicting the user electric charge in the embodiment.
Specifically, the user electricity fee prediction method includes:
s1, collecting the latest version of charging scheme from the cloud.
Sending a request to a cloud, wherein the request is provided with a region number of a local power supply region; and receiving the latest version charging scheme matched with the area number and returned by the cloud.
The cloud end stores a total regional charging scheme, for example, the cloud end stores a provincial charging scheme, and a provincial power company formulates charging schemes of all cities in the provincial, each city has a specific regional number. For example, when the service end of the a city obtains the charging scheme from the cloud end, a request is sent to the cloud end, the request carries the area number of the a city, and after the cloud end receives the request, the latest version of the charging scheme of the a city is returned to the service end of the a city.
S2, acquiring a historical daily electricity consumption record of the user, and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption record.
Collecting historical daily electricity records of a user in one month from a power supply metering system; the average power consumption of the user in each hour in one day is counted by utilizing big data analysis technology.
A historical daily electricity record of the user within one month is retrieved from the local power supply metering system, for example 0:00-1:00, the electricity consumption is w 1 The method comprises the steps of carrying out a first treatment on the surface of the 1:00-2:00, and the electricity consumption is w 2 The method comprises the steps of carrying out a first treatment on the surface of the …; the electricity consumption of 23:00-0:00 is w 24 。
The average power consumption per hour was calculated to be 0:00-1:00 for example, 0 for one month of no day is calculated: 00-1: average value W of 00 electricity consumption 1 . And so on, the average power consumption per hour is calculated.
The electricity consumption of enterprise users in production time period is far greater than that of non-production time period, so that the production time period of the users can be identified according to the average electricity consumption of each hour, and the generation time period is taken as the actual peak electricity consumption time period of the users.
S3, predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side.
Analyzing charging standards of each period in the charging scheme; calculating the electricity charge of each hour according to the average electricity consumption of each hour and the charging standard of the period; accumulating the electricity charges of each hour to obtain the daily average electricity charge of the user; and predicting the monthly electric charge of the user according to the daily average electric charge of the user, and pushing the monthly electric charge to the user side.
Assuming that the charging scheme is 8:00-20:00 charging peak period, each degree charges electricity f 1 Electric charge per degree f of 20:00-8:00 2 Then calculate the daily average electricity charge of the user as f= (W) 9 +W 10 +W 11 +W 12 +W 13 +W 14 +W 15 +W 16 +W 17 +W 18 +W 19 +W 20 )×f 1 +(W 21 +W 22 +W 23 +W 24 +W 1 +W 2 +W 3 +W 4 +W 5 +W 6 +W 7 +W 8 )×f 2
The monthly electricity fee is 30 xf.
Pushing the predicted monthly electric charge to a user side. Because the average electricity consumption is calculated by the daily electricity consumption record of the user in one month, the monthly electricity fee predicted by the application also changes after the user adjusts the production time.
And S4, making an ideal production time planning scheme of the user.
Dividing the actual peak period of daily electricity consumption of the user according to the average electricity consumption of each hour of the user, and calculating the actual peak period; analyzing the limited peak time duration in the charging scheme; according to the user operation field, the corresponding pre-stored optional working time period and the production cost of the optional working time period are called from the database; constructing a user total cost function, wherein the total cost function=user electricity consumption and production cost, and the user electricity consumption and the production cost are related to the actual peak time of the user; and selecting the actual peak time of the user when the total cost function value is minimum as an ideal production time planning scheme of the user, and pushing the planning scheme to the user side.
By interfacing the local database to the internet, big data technology is utilized to collect production costs, such as labor costs, for different periods of each industry. Let the production costs for the different time periods be G (G1, G2, …, G24) respectively.
Taking the actual peak period of the user obtained in the step S2 as 8h as an example, assume that the production period of the user is t i -t i+8 Then
Total cost h=user electricity consumption + production cost
And (3) carrying out polling to assign a value to i, calculating the total cost H of each value of i at the whole point time of which the assignment range is 1:00-24:00 (0:00), and selecting the value of i when the total cost H is minimum, thus obtaining the ideal production time planning scheme of the user. And pushing the obtained planning scheme to the user side.
The embodiment can plan ideal production time for users, guide the users to stagger peak power utilization, and has extremely high reference value.
As shown in fig. 2, the system 200 includes:
the scheme collecting unit 210 is configured to collect the latest version of the charging scheme from the cloud;
the electricity analysis unit 220 is configured to collect a historical daily electricity record of a user, and analyze a electricity consumption period and a corresponding electricity consumption amount of the user from the historical daily electricity record;
and the electricity charge prediction unit 230 is configured to predict a monthly electricity charge of the user according to the charging scheme, the electricity consumption period of the user, and the corresponding electricity consumption amount, and push the monthly electricity charge to the user side.
Alternatively, as an embodiment of the present invention, the electricity consumption analysis unit includes:
the record acquisition module is used for acquiring historical daily electricity utilization records of a user in one month from the power supply metering system;
and the electricity consumption statistics module is used for counting the average electricity consumption of the user in each hour in one day by utilizing a big data analysis technology.
Optionally, as an embodiment of the present invention, the system further includes:
the peak analysis unit is used for dividing the actual peak time of daily electricity consumption of the user according to the average electricity consumption of the user in each hour and calculating the actual peak time duration;
the scheme analysis unit is used for analyzing the limited peak time in the charging scheme;
the data calling unit is used for calling the corresponding pre-stored optional working time period and the production cost of the optional working time period from the database according to the user operation field;
the function construction unit is used for constructing a user total cost function, wherein the total cost function=user electricity consumption and production cost, and the user electricity consumption and the production cost are related to the actual peak time of the user;
and the planning pushing unit is used for selecting the actual peak time of the user when the total cost function value is minimum as an ideal production time planning scheme of the user and pushing the planning scheme to the user side.
Fig. 3 is a schematic structural diagram of a terminal 300 according to an embodiment of the present invention, where the terminal 300 may be used to execute the method for predicting the electricity charge of a user according to the embodiment of the present invention.
The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. The components may communicate via one or more buses, and it will be appreciated by those skilled in the art that the configuration of the server as shown in the drawings is not limiting of the invention, as it may be a bus-like structure, a star-like structure, or include more or fewer components than shown, or may be a combination of certain components or a different arrangement of components.
The memory 320 may be used to store instructions for execution by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile memory terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. The execution of the instructions in memory 320, when executed by processor 310, enables terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by running or executing software programs and/or modules stored in the memory 320, and invoking data stored in the memory. The processor may be comprised of an integrated circuit (Integrated Circuit, simply referred to as an IC), for example, a single packaged IC, or may be comprised of a plurality of packaged ICs connected to the same function or different functions. For example, the processor 310 may include only a central processing unit (Central Processing Unit, simply CPU). In the embodiment of the invention, the CPU can be a single operation core or can comprise multiple operation cores.
And a communication unit 330 for establishing a communication channel so that the storage terminal can communicate with other terminals. Receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium in which a program may be stored, which program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.
Therefore, the charging scheme is updated in time by collecting the latest version of the charging scheme from the cloud, meanwhile, the historical daily electricity consumption record of the user is analyzed to obtain the electricity consumption period and the corresponding electricity consumption quantity of the user, the monthly electricity charge of the user can be predicted according to the charging scheme, the electricity consumption period and the corresponding electricity consumption quantity of the user, and the monthly electricity charge is pushed to the user side. The invention can automatically predict the monthly electricity charge of the user, so that the user has visual data, further plans a production period scheme for the user, guides the user to stagger peak electricity consumption, and the technical effects achieved by the embodiment can be seen from the description above and are not repeated here.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solution in the embodiments of the present invention 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 a U-disc, a mobile hard disc, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc. various media capable of storing program codes, including several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in the embodiments of the present invention.
The same or similar parts between the various embodiments in this specification are referred to each other. In particular, for the terminal embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference should be made to the description in the method embodiment for relevant points.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or 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 interface, system or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
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 the embodiments of the present invention 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.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A user electricity rate prediction method, comprising:
collecting the latest version of charging scheme from the cloud;
collecting a historical daily electricity consumption record of a user, and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption record;
predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side;
collecting the latest version of the charging scheme from the cloud, wherein the charging scheme comprises the following steps of:
sending a request to a cloud, wherein the request is provided with a region number of a local power supply region;
receiving a latest version charging scheme which is returned by the cloud and is matched with the area number;
collecting a historical daily electricity consumption record of a user, analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption record, and comprising the following steps:
collecting historical daily electricity records of a user in one month from a power supply metering system;
using big data analysis technology to count average electricity consumption of users in each hour in one day;
predicting the monthly electric charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electric charge to the user side, wherein the method comprises the following steps:
analyzing charging standards of each period in the charging scheme;
calculating the electricity charge of each hour according to the average electricity consumption of each hour and the charging standard of the period;
accumulating the electricity charges of each hour to obtain the daily average electricity charge of the user;
predicting the monthly electric charge of the user according to the daily average electric charge of the user, and pushing the monthly electric charge to the user side;
the method further comprises the steps of:
dividing the actual peak period of daily electricity consumption of the user according to the average electricity consumption of each hour of the user, and calculating the actual peak period;
analyzing the limited peak time duration in the charging scheme;
according to the user operation field, the corresponding pre-stored optional working time period and the production cost of the optional working time period are called from the database;
constructing a user total cost function, wherein the total cost function=user electricity consumption and production cost, and the user electricity consumption and the production cost are related to the actual peak time of the user;
and selecting the actual peak time of the user when the total cost function value is minimum as an ideal production time planning scheme of the user, and pushing the planning scheme to the user side.
2. A system configured to perform the electric charge prediction method according to claim 1, characterized by comprising:
the scheme acquisition unit is used for acquiring the latest version of charging scheme from the cloud;
the electricity consumption analysis unit is used for collecting historical daily electricity consumption records of the user and analyzing the electricity consumption period and the corresponding electricity consumption amount of the user from the historical daily electricity consumption records;
the electricity charge prediction unit is used for predicting the monthly electricity charge of the user according to the charging scheme, the electricity consumption period of the user and the corresponding electricity consumption amount, and pushing the monthly electricity charge to the user side;
the electricity analysis unit includes:
the record acquisition module is used for acquiring historical daily electricity utilization records of a user in one month from the power supply metering system;
the electricity consumption statistics module is used for counting the average electricity consumption of a user in each hour in one day by utilizing a big data analysis technology;
the system further comprises:
the peak analysis unit is used for dividing the actual peak time of daily electricity consumption of the user according to the average electricity consumption of the user in each hour and calculating the actual peak time duration;
the scheme analysis unit is used for analyzing the limited peak time in the charging scheme;
the data calling unit is used for calling the corresponding pre-stored optional working time period and the production cost of the optional working time period from the database according to the user operation field;
the function construction unit is used for constructing a user total cost function, wherein the total cost function=user electricity consumption and production cost, and the user electricity consumption and the production cost are related to the actual peak time of the user;
and the planning pushing unit is used for selecting the actual peak time of the user when the total cost function value is minimum as an ideal production time planning scheme of the user and pushing the planning scheme to the user side.
3. A terminal, comprising:
a processor;
a memory for storing execution instructions of the processor;
wherein the processor is configured to perform the method of claim 1.
4. A computer readable storage medium storing a computer program, which when executed by a processor implements the method of claim 1.
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