CN115864384A - Capacity expansion detection method, device, equipment and medium based on daily generated energy data - Google Patents
Capacity expansion detection method, device, equipment and medium based on daily generated energy data Download PDFInfo
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- CN115864384A CN115864384A CN202211559348.5A CN202211559348A CN115864384A CN 115864384 A CN115864384 A CN 115864384A CN 202211559348 A CN202211559348 A CN 202211559348A CN 115864384 A CN115864384 A CN 115864384A
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Abstract
The invention discloses a daily generated energy data-based capacity expansion detection method, device, equipment and medium. The method comprises the following steps: acquiring daily generated energy data of a target user within a preset time length and a target quarter to which the daily generated energy data belong; wherein the daily power generation data includes a date and corresponding power generation data; determining a maximum power generation threshold in the target quarter; and determining whether the target user is an expansion user or not according to the daily generated energy data and the corresponding maximum generated energy threshold, calculating the maximum generated energy threshold of each user and recording the daily generated energy data of each user, and judging whether the expansion behavior exists in the user in time based on the analysis of the maximum generated energy threshold and the daily generated energy so as to maintain legal rights and interests, realize the monitoring of the daily generated energy of the user and improve the accuracy of a detection result.
Description
Technical Field
The invention relates to the technical field of new energy power generation, in particular to a daily generated energy data-based capacity expansion detection method, device, equipment and medium.
Background
Through years of development, the application range of photovoltaic power generation in China is gradually expanded, relevant subsidy policies are issued in various places, but the phenomenon that users fraudulently obtain subsidies and expand capacity privately often occurs. Therefore, the detection of the capacity expansion behavior of the user has important significance for maintaining the legal rights and interests of the power grid.
The traditional capacity expansion detection method is used for judging whether a user has capacity expansion behavior according to monthly generated energy data of the user.
According to the method, the situation that the user may have capacity expansion behavior but keep the monthly total power generation amount normal exists, so that the detection result is inaccurate, and the detection result has certain time delay.
Disclosure of Invention
The invention provides a daily generated energy data-based capacity expansion detection method, a daily generated energy data-based capacity expansion detection device, a daily generated energy data-based capacity expansion detection equipment and a daily generated energy data-based capacity expansion detection medium, so that the daily generated energy of a user is monitored, whether capacity expansion behaviors exist or not is judged, and the accuracy of detection results is improved.
In a first aspect, an embodiment of the present invention provides a daily power generation data-based capacity expansion detection method, where the method includes:
acquiring daily generated energy data of a target user within a preset time length and a target quarter to which the daily generated energy data belong; wherein the daily power generation data comprises a date and corresponding power generation data;
determining a maximum power generation threshold in a target quarter;
and determining whether the target user is an expansion user or not according to the daily generated energy data and the corresponding maximum generated energy threshold value.
In a second aspect, an embodiment of the present invention provides an expansion detection device based on daily power generation amount data, which is applied to expansion detection based on the daily power generation amount data, and the expansion detection device based on the daily power generation amount data includes:
the data acquisition module is used for acquiring daily generated energy data of a target user within a preset time length and a target quarter to which the daily generated energy data belongs; wherein the daily power generation data comprises a date and corresponding power generation data;
a maximum threshold determination module to determine a maximum power generation threshold in a target quarter;
and the user identification module is used for determining whether the target user is an expansion user according to the daily generated energy data and the corresponding maximum generated energy threshold value.
In a third aspect, the present invention also provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the daily power generation amount data-based expansion detection method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, a computer-readable storage medium is provided, where computer instructions are stored, and the computer instructions are configured to, when executed by a processor, implement the daily power generation amount data-based expansion detection method according to any embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, the daily generated energy data of a target user in a preset time length and the target quarter to which the daily generated energy data belong are obtained; wherein the daily power generation data comprises a date and corresponding power generation data; determining a maximum power generation threshold in a target quarter; according to the daily generated energy data and the corresponding maximum generated energy threshold value, whether the target user is an expansion user is determined, the problems that the detection result of the expansion of the user is inaccurate and the detection result has certain time delay are solved, the daily generated energy of the user is monitored, whether expansion action exists is judged, and the accuracy of the detection result is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a daily power generation data-based capacity expansion detection method according to an embodiment of the present invention;
fig. 2 is a flowchart of a capacity expansion detection method based on daily power generation data according to a second embodiment of the present invention;
fig. 3 is a flowchart of a capacity expansion detection method based on daily power generation data according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an expansion detection device based on daily power generation data according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the daily power generation amount data-based capacity expansion detection method according to the 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, 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 one
Fig. 1 is a flowchart of a daily power generation data-based capacity expansion detection method according to an embodiment of the present invention, where this embodiment is applicable to detecting whether a capacity expansion behavior exists in a user, the method may be executed by a daily power generation data-based capacity expansion detection device, the daily power generation data-based capacity expansion detection device may be implemented in a hardware and/or software manner, and the daily power generation data-based capacity expansion detection device may be configured in a computer.
As shown in fig. 1, the method includes:
s110, acquiring daily power generation data of a target user in a preset time length and a target quarter to which the daily power generation data belong; wherein the daily power generation data includes a date and corresponding power generation data.
The target user refers to a user who uses the photovoltaic power generation device to generate power for production and life, and may include but is not limited to: residential users, enterprise users, and the like. The preset duration refers to a preset time range within which the user is considered not to perform capacity expansion, and further, the preset duration is set according to actual conditions and experience, which is not limited in this embodiment. The daily generated energy refers to the total energy value of the solar energy converted into the electric energy by the photovoltaic device and output outwards. The daily power generation amount data is data including a date and a corresponding power generation amount. The target quarter refers to a quarter corresponding to a day in the daily power generation data.
Specifically, the daily power generation data of a target user in a certain day in a preset time length is obtained from the data system, a target quarter corresponding to the day in the daily power generation data of the day is determined, and further, the weather condition of the day corresponding to the obtained daily power generation data of the day must be a fine day.
For example, if the preset time duration is 30 days from grid connection and the grid connection date is 2022 years, 3 months and 20 days, the power generation amount data of the target user in 2022 years, 3 months and 29 days is stored in the photovoltaic power generation metering system: 2022.3.29-20 deg., and the target quarter corresponding to 3.29 is determined to be quarter 2 according to the common quarterly division.
Optionally, the daily generated energy data of the target user within the preset time length is obtained based on the timing task; and determining the target quarter to which the daily power generation data belong according to the date in the daily power generation data.
The timing task refers to a task for periodically detecting the capacity expansion behavior of the target user.
Illustratively, the timing task is to perform detection on the capacity expansion behavior of the target user once in 12 months every year, the preset duration is 30 days, the daily power generation amount data of the target user within 30 days from the last grid connection is obtained from the photovoltaic power generation metering system, and the target season corresponding to the day is determined.
It should be noted that grid connection means that direct current generated by a solar module is converted into alternating current meeting requirements through a grid connection inverter and then directly connected to a public power grid, and users can earn subsidies through grid connection, so some users can expand capacity by increasing the solar module and increasing installed capacity.
And S120, determining a maximum power generation threshold in the target quarter.
The maximum power generation threshold refers to the maximum power generation of the target user in the target quarter per day.
Specifically, the theoretical maximum daily generated energy of each quarter is determined through the illumination duration of the target quarter, the number of solar modules and the installed capacity of a user, and further, the installed capacity refers to the generated power of the photovoltaic power generation device.
For example, for target users in the area a, the daily illumination duration in the area a is less than or equal to 3 hours in the 1 st quarter of 2022, and according to the installed capacity of the target users, the number of solar modules and the maximum illumination duration in the area a, the maximum power generation threshold of the target users in the 1 st quarter can be determined to be 15 °; in quarter 2 of 2022, the daily length of illumination in region a is less than or equal to 4 hours, and the maximum power generation threshold for the target user in quarter 1 may be determined to be 25 °.
And S130, determining whether the target user is an expansion user or not according to the daily power generation data and the corresponding maximum power generation threshold.
The capacity expansion user is a user for increasing the generated energy by expanding the installed capacity and additionally arranging the solar module.
Optionally, if the daily generated energy data is greater than the maximum generated energy threshold of the target quarter to which the daily generated energy data belongs, the target user is determined to be an expansion user.
For example, since the same manner of processing the power generation amount on each day is used, the description will be given with respect to the data of the power generation amount on one day among them: acquiring daily power generation data corresponding to 12 months and 20 days in 2022 of a target user: 2022.12.20-25 °, it is determined that the target quarter is the 1 st quarter, the installed capacity and the number of solar modules of the target user can be obtained through information registered by the photovoltaic equipment of the target user, it is determined that the maximum power generation threshold of the target user in the 1 st quarter is 15 ° according to the maximum illumination duration of the 1 st quarter and the installed capacity and the number of solar modules of the target user, it is determined that the daily power generation of the target user is greater than the maximum power generation threshold by comparing the daily power generation with the maximum power generation threshold, and it is determined that the target user is an expansion user.
According to the technical scheme of the embodiment of the invention, the daily generated energy data of a target user in a preset time length and the target quarter to which the daily generated energy data belong are obtained; wherein the daily power generation data comprises a date and corresponding power generation data; determining a maximum power generation threshold in a target quarter; according to the daily generated energy data and the corresponding maximum generated energy threshold value, whether the target user is an expansion user is determined, the problems that the detection result of the expansion of the user is inaccurate and the detection result has certain time delay are solved, the daily generated energy of the user is monitored, whether expansion action exists is judged, and the accuracy of the detection result is improved.
Example two
Fig. 2 is a flowchart of an expansion detection method based on daily power generation data according to a second embodiment of the present invention, and based on the foregoing embodiment, a maximum power generation threshold in a target quarter may be further optimized, and a specific implementation manner of the method may refer to detailed descriptions of the embodiment of the present invention, where the same or corresponding technical terms as those in the foregoing embodiment are not repeated herein.
As shown in fig. 2, the method includes:
s210, acquiring daily power generation data of a target user in a preset time length and a target quarter to which the daily power generation data belongs; wherein the daily power generation data includes a date and corresponding power generation data.
S220, determining the month corresponding to the target quarter based on a preset quarter dividing principle.
The quarterly dividing principle refers to a principle of dividing the quarterly according to the regional illumination condition rule, and the quarterly dividing principle can be divided according to actual conditions according to differences of the south and north hemispheres, and the embodiment is not limited herein.
Specifically, each quarter is divided into the following quarters according to a quarter division principle: 3-5 months in quarter 1, 6-8 months in quarter 2, 9-11 months in quarter 3, and 12-2 months in quarter 4.
S230, determining a reference date according to a preset reference date determination condition; wherein the reference date determination condition corresponds to a weather parameter.
The reference date is a date on which the default user does not generate power exceeding a prescribed threshold value on the day. The reference date determination conditions are specifically: the time range of the reference date is within 30 days from the grid-connected date and the weather condition of the day is: in a sunny day, little/no cloud. The weather parameters refer to weather conditions, such as sunny, cloudy, heavy rain, light snow, and the like.
Illustratively, the grid connection date is 2021, 6 months and 3 days, and the reference date determination conditions are as follows: and in sunny days, cloudiness/cloudiness is less, the rainfall is 0, the reference date is determined to be 2021 year 6 month 20 days within 30 days from 2021 year 6 month 3 days, wherein the weather condition in the 20 days of 2021 year 6 month is sunny days, cloudiness/cloudiness is less.
And S240, determining the maximum power generation threshold of the target quarter to which the reference date belongs according to the standard power generation data corresponding to the reference date.
Wherein, the standard power generation refers to the average daily power generation of the target quarter of the reference date.
Specifically, the maximum power generation threshold value of the quarter where the reference date is located is calculated according to the average daily power generation of the target quarter where the reference date is located.
Optionally, determining a first conversion coefficient according to the average daily photovoltaic power generation amount corresponding to the target area to which the target user belongs in a sunny day in the historical year and the average daily photovoltaic power generation amount corresponding to the target area in a sunny day in the historical year in a quarterly; determining standard power generation data of a reference date based on the first conversion coefficient and the daily power generation data corresponding to the reference date; and determining the maximum power generation threshold of the target quarter to which the reference date belongs according to the standard power generation data.
The target area refers to an area to which the target user belongs. The historical year refers to the year prior to the current year. The average daily photovoltaic power generation amount refers to the ratio of the total photovoltaic power generation amount of all the clear days in a specific time range to the days of the clear days in the specific time range in the region. The first conversion coefficient is the ratio of the photovoltaic daily average power generation amount of all sunny days in a certain historical year in the target area to the photovoltaic daily average power generation amount of all sunny days in the ith quarter.
Specifically, since the processing manner is the same for each target quarter, the processing for one of the target quarters will now be described: the target quarter is quarter 3, the total photovoltaic power generation amount of all the sunny days corresponding to the historical year of the target area is obtained by performing accumulation processing on the power generation amounts of all the sunny days of the target area in one year of the historical year, and the daily average photovoltaic power generation amount = the total photovoltaic power generation amount of all the sunny days of the historical year of the target area/the number of days of the historical sunny days of the target area; the first conversion coefficient = the daily average photovoltaic power generation amount corresponding to the historical year of the region/the daily average photovoltaic power generation amount corresponding to the 3 rd quarter of the region; and (3) standard electric energy generation on a reference day = a first conversion coefficient x the daily electric energy generation on the reference day, and then the standard electric energy generation on the reference day is processed according to the fluctuation condition of the weather to obtain the maximum electric energy generation threshold of the target quarter to which the reference day belongs.
Illustratively, a target area of a target user A is an area A, the daily power generation data of a reference day is 2022.5.6-20 degrees, r represents the daily power generation of the reference day, r =20, the target quarter is a quarter 2, the daily average photovoltaic power generation of the area A in 2021 year is calculated to be M, the daily average photovoltaic power generation of the area A in quarter 2 in 2021 year is n, a first conversion coefficient a = M/n, the standard power generation of the reference day is M = a r, and a correlation coefficient is set according to the power fluctuation condition to process the standard daily power generation to obtain the maximum power generation threshold of the quarter 2.
Further, determining standard generated energy data of each quarter on a standard quarter day according to the standard generated energy data of the reference date and the first conversion coefficient; and determining the maximum power generation threshold value of each quarter according to the standard quarterly daily power generation data and the corresponding change coefficient.
Wherein the coefficient of variation is determined based on weather factors.
The standard quarterly daily power generation amount refers to a theoretical average daily power generation amount corresponding to each quarterly. The variation coefficient is a coefficient set in consideration of daily power generation amount fluctuation caused by regional weather condition variation, and is generally 1.05.
Specifically, according to a first refraction coefficient =1,2,3,4, a first refraction coefficient corresponding to each quarter is calculated, and according to a ratio of the standard power generation of the reference day to the first refraction coefficient of each quarter, the standard quarterly power generation of each quarter is obtained. And obtaining the maximum power generation threshold value of each quarter according to the product of the standard quarter daily power generation of each quarter and the change coefficient.
It should be noted that, for the reference day, the standard daily power generation amount on the reference day = the standard quarterly daily power generation amount on the quarterly to which the reference day belongs.
Illustratively, the variation coefficient is γ, the reference daily power generation data is 2022.5.6-20 °, r indicates the daily power generation of the reference day and r =20, the target quarter is quarter 2, and the first conversion coefficient corresponding to quarter 2 is a 2 And then the standard generating capacity M = a of the reference day 2 * r, obtaining first refraction coefficients corresponding to the 1 st quarter, the 3 rd quarter and the 4 th quarter respectively as a through calculation 1 ,a 3 ,a 4 By Q i The standard quarterly daily generated energy corresponding to each quarterly is represented, then
The maximum power generation amount threshold value L corresponding to each quarter i ＝γ*Q i . And then the capacity-expanding user can be identified through the relation between the maximum power generation threshold value of each season and the daily power generation.
And S250, determining whether the target user is an expansion user or not according to the daily generated energy data and the corresponding maximum generated energy threshold value.
According to the technical scheme of the embodiment of the invention, the daily generated energy data of a target user in a preset time length and the target quarter to which the daily generated energy data belong are obtained; wherein the daily power generation data comprises a date and corresponding power generation data; determining months corresponding to target quarters based on a preset quarter dividing principle; for each target quarter, determining a reference date according to a preset reference date determination condition and a corresponding month; wherein the reference date determination condition corresponds to a weather parameter; determining a maximum power generation threshold value of a target quarter to which a reference date belongs according to standard power generation data corresponding to the reference date; whether a target user is an expansion user is determined according to daily power generation data and the corresponding maximum power generation threshold, the maximum power generation threshold corresponding to each quarter is calculated by considering weather and quarter factors, and the maximum power generation threshold is compared with the daily power generation of the user to judge whether the expansion action exists in the user, so that the accuracy of a judgment result is improved.
EXAMPLE III
Fig. 3 is a flowchart of a capacity expansion detection method based on daily power generation data according to a third embodiment of the present invention, and based on the foregoing embodiment, the capacity expansion detection method based on daily power generation data may be further optimized, and specific implementation manners thereof may refer to detailed descriptions of the embodiments of the present invention, where technical terms the same as or corresponding to the foregoing embodiments are not repeated herein.
And S310, acquiring daily power generation data of the target user.
Illustratively, daily photovoltaic power generation amount data corresponding to each target user is obtained through a photovoltaic power generation metering system.
And S320, determining the reference date and the quarter of the reference date, and taking the daily power generation of the reference date as the daily power generation of the target user.
Wherein the time range of the reference day is within 30 days from the grid-connected date; the weather condition of the day is that the weather type is required to be sunny, the cloud amount condition is less cloud/no cloud, and the rainfall is 0.
Illustratively, the grid connection date is 2021, 6 months and 3 days, and the reference date determination conditions are as follows: and in sunny days, cloudiness/cloudiness is less, the rainfall is 0, the reference date is determined to be 2021 year 6 month 20 days within 30 days from 2021 year 6 month 3 days, wherein the weather condition in the 20 days of 2021 year 6 month is sunny days, cloudiness/cloudiness is less, and the rainfall is 0. Acquiring power generation amount data corresponding to 20 days at 6 months in 2021: 2022.5.6-20 deg., the reference daily power generation amount of the target user is 20 deg..
And S330, determining a first conversion coefficient, and determining standard electric energy generation corresponding to a reference date according to the first conversion coefficient and the reference daily electric energy generation.
On the basis of the above example, the daily average photovoltaic power generation amount M of the a region 2021 year, the daily average photovoltaic power generation amount n of the a region of the 2 nd quarter 2021 year are obtained, then the first conversion coefficient a = M/n, and then the standard power generation amount M = a × r corresponding to the reference date
And S340, determining standard quarterly daily generated energy of each quarter according to the standard generated energy corresponding to the reference date.
Illustratively, the data of the daily power generation amount on the reference day is 2022.5.6-20 °, r represents the daily power generation amount on the reference day, and then r =20, the target quarter is quarter 2, and the first conversion factor corresponding to the second quarter is a 2 Then the standard generating capacity of the reference day M = a 2 * r, calculating to obtain first refraction coefficients a corresponding to the 1 st quarter, the 3 rd quarter and the 4 th quarter 1 ,a 3 ,a 4 By Q i The standard quarterly daily generated energy corresponding to each quarterly is represented, then
And S350, determining the maximum power generation threshold of each quarter according to the standard quarter daily power generation number corresponding to each quarter and the corresponding change coefficient.
Illustratively, the coefficient of variation is γ, which typically takes a value of 1.05. The maximum power generation amount threshold value L corresponding to each quarter i ＝γ*Q i 。
And S360, comparing the daily generated energy data of the target user with the maximum generated energy threshold value of the corresponding quarter, and if the daily generated energy is larger than the maximum generated energy threshold value of the corresponding quarter, determining that the user is an expansion user.
Illustratively, the power generation amount of the target user a in 6 months and 4 days is 30 °, and the corresponding maximum power generation amount threshold value in the 2 nd quarter is 25 °, it is determined that the target user a is an expansion user.
According to the technical scheme of the embodiment of the invention, the daily generated energy data of a target user in a preset time length and the target quarter to which the daily generated energy data belong are obtained; wherein the daily power generation data comprises a date and corresponding power generation data; determining a maximum power generation threshold in a target quarter; according to the daily generated energy data and the corresponding maximum generated energy threshold value, whether the target user is an expansion user is determined, the problems that the detection result of the expansion of the user is inaccurate and the detection result has certain time delay are solved, the daily generated energy of the user is monitored, whether expansion action exists is judged, and the accuracy of the detection result is improved.
Example four
Fig. 4 is a schematic structural diagram of an expansion detection device based on daily power generation data according to a fourth embodiment of the present invention.
As shown in fig. 4, the apparatus includes:
the data acquisition module 410 is used for acquiring daily power generation data of a target user within a preset time length and a target quarter to which the daily power generation data belongs; wherein the daily power generation data comprises a date and corresponding power generation data; a maximum threshold determination module 420 for determining a maximum power generation threshold in a target quarter; and the user identification module 430 is configured to determine whether the target user is an expansion user according to the daily power generation data and the corresponding maximum power generation threshold.
On the basis of the above technical solutions, the data acquisition module is specifically configured to:
acquiring daily generated energy data of a target user within a preset time length based on the timing task; and determining the target quarter to which the daily power generation data belong according to the date in the daily power generation data.
On the basis of the above technical solutions, the maximum threshold determining module specifically includes:
the month determining unit is used for determining the month corresponding to each target quarter based on a preset quarter dividing principle; a reference date determination unit that determines a reference date based on a preset reference date determination condition; wherein the reference date determination condition corresponds to a weather parameter; and the maximum power generation threshold determining unit is used for determining the maximum power generation threshold of the target quarter to which the reference date belongs according to the standard power generation data corresponding to the reference date.
On the basis of the above technical solutions, the maximum power generation amount threshold determining unit includes:
the first conversion coefficient determining subunit is used for determining a first conversion coefficient according to the daily average photovoltaic power generation amount corresponding to the target area to which the target user belongs in the historical year on a sunny day and the daily average photovoltaic power generation amount corresponding to the target area in the historical year on a sunny day; a standard power generation amount determining subunit configured to determine standard power generation amount data for a reference date based on the first conversion coefficient and daily power generation amount data corresponding to the reference date; and the maximum power generation threshold determining subunit is used for determining the maximum power generation threshold of the target quarter to which the reference date belongs according to the standard power generation data.
On the basis of the above technical solutions, the maximum power generation amount threshold determining subunit is further configured to:
determining standard generated energy data of each quarter day according to the standard generated energy data of the reference date and the first conversion coefficient; and determining the maximum power generation threshold value of each quarter according to the standard quarterly daily power generation data and the corresponding change coefficient.
Optionally, the coefficient of variation is determined based on weather factors.
On the basis of the above technical solutions, the subscriber identity module is specifically configured to:
and if the daily generated energy data is larger than the maximum generated energy threshold value of the target quarter to which the daily generated energy data belongs, determining the target user as an expansion user.
According to the technical scheme of the embodiment of the invention, the daily generated energy data of a target user in a preset time length and the target quarter to which the daily generated energy data belong are obtained; wherein the daily power generation data comprises a date and corresponding power generation data; determining a maximum power generation threshold in a target quarter; according to the daily generated energy data and the corresponding maximum generated energy threshold value, whether the target user is the capacity expansion user or not is determined, the problems that the detection result of the capacity expansion of the user is inaccurate and the detection result has certain time delay are solved, the daily generated energy of the user is monitored, whether capacity expansion behaviors exist or not is judged, and the accuracy of the detection result is improved.
The capacity expansion detection device based on the daily generated energy data provided by the embodiment of the invention can execute the capacity expansion detection method based on the daily generated energy data provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
FIG. 5 illustrates a block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the daily power generation data-based capacity expansion detection method may be implemented as a computer program that is tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the daily power generation amount data-based expansion detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the daily power generation capacity data-based capacity expansion detection method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An expansion detection method based on daily generated energy data is characterized by comprising the following steps:
acquiring daily generated energy data of a target user within a preset time length and a target quarter to which the daily generated energy data belong; wherein the daily power generation data includes a date and corresponding power generation data;
determining a maximum power generation threshold in the target quarter;
and determining whether the target user is an expansion user or not according to the daily generated energy data and the corresponding maximum generated energy threshold value.
2. The method of claim 1, wherein the obtaining of the daily power generation data of the target user within a preset time period and the target quarter to which the daily power generation data belongs comprises:
acquiring daily generated energy data of a target user within a preset time length based on the timing task;
and determining a target quarter to which the daily power generation data belong according to the date in the daily power generation data.
3. The method of claim 1, wherein determining the maximum power generation threshold in the target quarter comprises:
determining months corresponding to target quarters based on a preset quarter division principle;
determining a reference date according to a preset reference date determination condition; wherein the reference date determination condition corresponds to a weather parameter;
and determining the maximum power generation threshold of the target quarter to which the reference date belongs according to the standard power generation data corresponding to the reference date.
4. The method according to claim 3, wherein the determining the maximum power generation threshold value of the target quarter to which the reference date belongs based on the standard power generation data corresponding to the reference date includes:
determining a first conversion coefficient according to the average daily photovoltaic power generation amount corresponding to the target area of the target user in a sunny day in the historical year and the average daily photovoltaic power generation amount corresponding to the target area in a sunny day in the historical year;
determining standard power generation amount data of the reference date based on the first calculating coefficient and the daily power generation amount data corresponding to the reference date;
and determining the maximum power generation threshold of the target quarter to which the reference date belongs according to the standard power generation data.
5. The method according to claim 4, wherein the determining the maximum power generation threshold value for the target quarter to which the reference date belongs based on the standard power generation data includes:
determining standard quarterly daily power generation data of each quarter according to the standard power generation data of the reference date and the first conversion coefficient;
and determining the maximum power generation threshold value of each quarter according to the standard quarterly daily power generation data and the corresponding change coefficient.
6. The method of claim 5, wherein the coefficient of variation is determined based on weather factors.
7. The method of claim 1, wherein determining whether the target user is an expansion user based on the daily power generation data and a corresponding maximum power generation threshold comprises:
and if the daily generated energy data are larger than the maximum generated energy threshold value of the target quarter to which the daily generated energy data belong, determining that the target user is an expansion user.
8. The utility model provides an dilatation detection device based on day generated energy data which characterized in that includes:
the data acquisition module is used for acquiring daily generated energy data of a target user within a preset time length and a target quarter to which the daily generated energy data belongs; wherein the daily power generation data includes a date and corresponding power generation data;
a maximum threshold determination module to determine a maximum power generation threshold in the target quarter;
and the user identification module is used for determining whether the target user is an expansion user or not according to the daily generated energy data and the corresponding maximum generated energy threshold value.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the daily power generation amount data-based expansion detection method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for causing a processor to execute the daily power generation amount data-based capacity expansion detection method according to any one of claims 1 to 7.
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