CN116128256B - Power business data processing method - Google Patents
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Abstract
The invention discloses a power service data processing method, which comprises the steps of obtaining a daily electricity standard time period diagram of each unit in a power grid database in a networking way; sequentially acquiring a period P when the power consumption ratio in each graph reaches a threshold value in the current statistical region 0 The method comprises the steps of carrying out a first treatment on the surface of the Removing the time periods which are not included in the statistics definition in the daily electricity quantity standard time period diagram of each unit, and obtaining the daily electricity quantity standard time period diagram of each round; according to the time period P in the standard time period diagram of the daily electric quantity of each two rounds 0 Respectively carrying out sequential energy storage according to the time sequence of the energy storage period P 0 And (3) obtaining the proportion of the total electricity consumption of the current statistical region according to the corresponding proportion, and completing time-sharing sequential allocation of the upper energy storage resource pools of all units. The distribution of the second-stage period of the electric power is completed by screening the key data in the second-round processing of the electricity utilization data graph, the calculation power of the processor is saved on the basis that the unified accuracy of the data is not affected, the intelligent dispatching performance is improved, and the electricity utilization stability of the current unit is ensured.
Description
Technical Field
The invention relates to the technical field of power, in particular to a power business data processing method.
Background
At present, according to the planning requirement of the national power grid 'thirteen five', the power industry greatly develops the intelligent power grid, and promotes the development of the automation of the national power distribution network.
In the field of power distribution networks, the power supply requirements of different areas and different areas in the same area are inconsistent, the total power supply in the local area is basically mature, the total power supplied every day is basically stable, the power grid company can perform pre-allocation according to the previous actual power supply requirements of different areas in the specific power distribution process, the optimal allocation of the power is achieved, the situation that the power consumption of the areas is too tension or too loose is prevented, and the allocation of the power transmission lines is performed according to the corresponding allocation strategy. When a corresponding power resource allocation strategy is formulated, the power service data is required to be analyzed and processed, the energy storage of the upper resource pool is formulated according to the analysis and processing result, the existing analysis and processing method is mainly used for carrying out integral allocation of the power resources by simply overlapping the normalized daily electricity consumption to form regional electricity consumption proportion, the consideration of the relevant time period in the secondary allocation process is not involved, and the resource allocation strategy formed by the power service data processing method is too general and has insufficient intelligent allocation.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-mentioned problems occurring in the conventional power service data processing method.
Therefore, the technical problems solved by the invention are as follows: the method solves the problems that the existing power business data processing method does not relate to the consideration of the related time period in the secondary distribution process, the formed resource allocation strategy is too general, and the intelligent mobility is insufficient.
In order to solve the technical problems, the invention provides the following technical scheme: the power business data processing method comprises the following steps: s1: acquiring a daily electricity standard time period diagram of each unit in a power grid database in a networking way; s2: sequentially obtaining time periods P when the power consumption in each graph reaches a threshold value compared with the total power consumption in the current statistical region according to the daily power consumption standard time period graph of each unit 0 The method comprises the steps of carrying out a first treatment on the surface of the S3: the cloud processor respectively counts each unit period P 0 Removing the time periods which are not included in the standard time period diagram of the daily electricity quantity of each unit and are not statistically defined, and obtaining the standard time period diagram of the daily electricity quantity of each two rounds; s4: according to the time period P in each of the two-wheel daily electricity standard time period diagrams 0 Respectively carrying out sequential energy storage according to the time sequence of the energy storage period P 0 The corresponding proportion is used for obtaining the proportion of the total electricity consumption of the current statistical region, and time-sharing sequential allocation of the upper energy storage resource pools of all units is completed; wherein electricity is obtained through networkingThe daily electricity standard time period diagram of each unit in the network database specifically comprises the following steps: s1: networking to obtain all daily electricity consumption time period diagrams of each unit in the power grid database within a selected time; s2: counting the electricity consumption time periods and the corresponding electricity consumption of each daily electricity consumption time period graph; s3: sequentially obtaining electricity consumption time periods and corresponding electricity consumption of which the electricity consumption ratio in the daily electricity consumption time period map exceeds a preset proportion according to the counted electricity consumption time periods and corresponding electricity consumption, and synchronously removing the electricity consumption time periods and corresponding electricity consumption of which the electricity consumption ratio in the daily electricity consumption time period map does not reach the preset proportion, and obtaining each reduced daily electricity consumption time period map; s4: the cloud processor collects the electricity consumption time periods and the corresponding electricity consumption of each reduced daily electricity consumption time period graph; s5: classifying the power consumption time periods in sequence according to the power consumption error threshold value in time sequence, acquiring and summarizing the classified power consumption time periods; s6: the standard time period [ P ] of each power consumption under the same classification category is sequentially obtained according to the following formula 1 ，P 2 ]And its standard electricity consumption, wherein P 1 ∈[P i ]max，P 2 ∈[P j ]min; wherein P is i For the starting time of each power utilization period under the same classification category, P j The end time of each power utilization period under the same classification category; wherein, each electricity consumption standard period [ P ] 1 ，P 2 ]The corresponding electricity standard quantity is counted according to the following formula:
wherein A is 1P Representing the current electricity consumption standard period [ P ] in the first reduced electricity consumption period chart 1 ，P 2 ]Corresponding electricity consumption, A 2P Representing the current electricity consumption standard period [ P ] in the reduced second daily electricity consumption period chart 1 ，P 2 ]Corresponding electricity consumption, A NP Representing the current electricity consumption standard period [ P ] in the reduced Nth daily electricity consumption period chart 1 ，P 2 ]The corresponding power consumption amount is used for the power generation,n is the number of pictures of all the daily electricity consumption time periods in the selected time; a represents a standard period of electricity consumption [ P ] 1 ，P 2 ]Corresponding electricity standard quantity; s7: according to the acquired standard time period [ P ] of each power consumption 1 ，P 2 ]And combining the electricity consumption standard quantities to form the daily electricity consumption standard time period diagram.
As a preferable scheme of the power business data processing method of the present invention, wherein: the preset proportion is set to 15%.
As a preferable scheme of the power business data processing method of the present invention, wherein: the error threshold is defined as 200 degrees; when the electricity consumption in the electricity consumption time period does not reach the error threshold value, the compared electricity consumption time periods of different daily electricity consumption time period diagrams belong to the same time category.
As a preferable scheme of the power business data processing method of the present invention, wherein: the threshold is defined as follows: when the total power consumption of the area is less than 3000 degrees, the threshold is defined as 8%; when the total power consumption of the area is more than 3000 degrees and less than 5000 degrees, the threshold is defined as 5%; when the total power consumption of the area is more than 5000 degrees and less than 10000 degrees, the threshold value is defined as 2%; when the total power consumption of the area is more than 10000 degrees, the threshold is defined as 0.6%.
As a preferable scheme of the power business data processing method of the present invention, wherein: period P 0 The statistical definition of (c) is as follows: when the total power consumption of the area is less than 3000 DEG, the period P 0 Is defined as 3h; when the total power consumption of the area is more than 3000 degrees and less than 5000 degrees, the period P 0 Is defined as 2h; when the total power consumption of the area is more than 5000 degrees and less than 10000 degrees, a period P 0 Is defined as 1h; when the total power consumption of the area is more than 10000 DEG, the period P 0 Is defined as 0.5h.
As a preferable scheme of the power business data processing method of the present invention, wherein: after obtaining the standard time interval diagram of the daily electric quantity of each round, the method also comprises the step of comparing each unit time interval P 0 Unifying accounting and concrete packageThe method comprises the following steps: s1: each unit period P is performed according to time sequence 0 Is arranged in the row of (a); s2: when each unit period P 0 When the coincidence phenomenon occurs, the coincidence period P 0 Running a unified accounting rule; wherein, the accounting unified rule is defined as: acquiring the coincident time period P 0 The ratio of each unit to the current statistics area total power consumption is the ratio gamma; comparing the corresponding duty ratio gamma; acquiring an allocation rule; completing unified accounting, and completing the distribution of the corresponding energy storage of each unit according to the distribution rule; wherein, the allocation rule is:
wherein H is the energy storage capacity of the selected unit, gamma z Period P coinciding for the selected cell z 0 X is the period P in which the x-th selected cell coincides 0 Ratio of H γz Period P coinciding for the selected cell z 0 Is used for the power consumption of the battery.
As a preferable scheme of the power business data processing method of the present invention, wherein: transmitting each graph to a cloud processor according to a transmission rate protocol;
the transmission rate protocol is defined as:
wherein η is the transmission rate; t' is the number of code word segments contained in the transmitted map; t is the number of code word nodes contained in all transmission diagrams; s is the number of transmitted pictures; s (u) is the period P contained in the transmission diagram 0 Is a function of the number of (2); l (S) is a function formula for sharing instructions sent by a transmission diagram, and S is an integer.
The invention has the beneficial effects that: the invention provides a power service data processing method, which is characterized in that a data mode is longitudinally compared through an obtained daily electricity quantity time interval chart of each unit, a round of processing is carried out to obtain a daily electricity quantity standard time interval chart of each unit, then transverse unit comparison is carried out according to the total electricity quantity proportion condition, a round of processing is carried out to obtain a daily electricity quantity standard time interval chart of each round of two rounds of processing, sequential energy storage is respectively carried out according to the time sequence of time intervals in the time interval in the illustration, the proportion of the total electricity quantity in the current statistics area is obtained according to the proportion corresponding to the current energy storage time interval, and the time-sharing sequential allocation of the upper energy storage resource pool of each unit is completed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flowchart of an overall method of the power business data processing method provided by the invention.
Fig. 2 is a flowchart of a method for obtaining a daily electricity consumption standard time period diagram of each unit in a power grid database through networking.
FIG. 3 shows a pattern of the present invention for each unit period P 0 And carrying out a unified accounting method flow chart.
Fig. 4 is a flowchart illustrating the operation of the unified rule according to the present invention.
Fig. 5 is a general practical architecture diagram of the power grid database resource management system expanded by the invention.
Fig. 6 is a partially-displayed view of a daily electricity consumption period chart according to the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill 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.
When a corresponding power resource allocation strategy is formulated, the power service data is required to be analyzed and processed, the energy storage of a superior resource pool is formulated according to an analysis processing result, the existing analysis processing method is mainly used for carrying out integral allocation of power resources by simply overlapping normalized daily electricity consumption to form regional electricity consumption proportion, the consideration of related time periods in the secondary allocation process is not involved, the resource allocation strategy formed by the power service data processing method is too general, the intelligent allocation is insufficient, and when a certain unit electricity consumption in a preamble period has a large surge change, the existing pre-allocation strategy cannot timely guarantee the current unit electricity utilization stability.
Accordingly, referring to fig. 1, the present invention provides a method for processing electric power business data, comprising the following steps:
s1: acquiring a daily electricity standard time period diagram of each unit in a power grid database in a networking way;
it should be noted that the network acquisition process further includes control of the user resource acquisition authority, including:
performing authority control on various users; the method comprises the steps of,
dynamically mapping various user authorities and functions of the system;
wherein, various users comprise newly added users, users needing to be deleted after leaving the job and users with varied job positions.
Specifically, performing authority control on various users includes adding user authority, deleting user authority, changing user authority and inquiring user authority.
The rights management module design table is shown in table 1 below:
table 1: rights management module design table
As shown in table 1 above, the function of the rights management module is mainly divided into two parts, the first part is to perform rights management on various users, and the second part is to dynamically map.
When the system is initialized or new personnel participate in the system, the user authority can be given, and the system can be accessed after the user authority is given; when personnel change occurs, such as personnel rising or department transferring, the authority modification can update the authority information of the user in real time; when a person leaves the office, all information including the authority of the user is deleted correspondingly.
As shown in fig. 5, the present invention additionally provides a resource management architecture diagram of a power grid database, which is essentially the storage and acquisition of network cloud resources.
S2: sequentially obtaining time periods P when the power consumption ratio in each graph reaches a threshold value in the current statistical region according to the standard time period graph of the daily power consumption of each unit 0 ；
S3: the cloud processor respectively counts each unit period P 0 Removing the time periods which are not included in the statistics definition in the daily electricity quantity standard time period diagram of each unit, and obtaining the daily electricity quantity standard time period diagram of each two rounds;
s4: according to the time period P in the standard time period diagram of the daily electric quantity of each two rounds 0 Respectively carrying out sequential energy storage according to the time sequence of the energy storage period P 0 And (3) obtaining the proportion of the total electricity consumption of the current statistical region according to the corresponding proportion, and completing time-sharing sequential allocation of the upper energy storage resource pools of all units.
Further, referring to fig. 2, the obtaining a daily electricity standard time period chart of each unit in the grid database by networking specifically includes:
s1: networking to obtain all daily electricity consumption time period diagrams of each unit in the power grid database within a selected time;
it should be noted that, the selected time of each unit in the invention can be 1 month, 3 months, 6 months, etc., and a plurality of daily electricity consumption time period diagrams with corresponding quantity are synchronously obtained, so long as the calculation force allows, the step does not do redundant limitation on the selected time.
As shown in fig. 6, the present invention provides a partially developed view of a conventional daily electricity consumption time period diagram.
S2: counting the electricity consumption time period and the corresponding electricity consumption thereof in each daily electricity consumption time period diagram;
s3: according to the counted electricity consumption time periods and the corresponding electricity consumption, sequentially acquiring electricity consumption time periods and corresponding electricity consumption periods, wherein the electricity consumption ratio of the electricity consumption time period diagrams in each day exceeds a preset proportion, and synchronizing the electricity consumption time periods and corresponding electricity consumption, the electricity consumption ratio of the electricity consumption time periods in each day is removed from the electricity consumption time period diagrams in each day, the total electricity consumption in each day is not up to the preset proportion, and the reduced electricity consumption time period diagrams in each day are acquired;
the standard time interval diagram of the daily electricity consumption of each unit is obtained through one round of reduction of the electricity consumption time interval diagram, and a core main program algorithm for reducing the database is as follows:
Spring.datasource.url＝jdbc:mysql://localhost:3307/springboot-crud-mysql-vuejsserverTimezone＝UTC&useSSL＝false
Spring.datasource.username＝root
Spring.datasource.password＝(δ,δ＇)
Spring.datasource.driver-class-name＝com.mysql.jdbc.Driver
Spring.jpa.hibernate.ddl-auto＝create
Spring.jpa.database-platform＝org.hiberate.dialect.MySQL1.2Dialect
Spring.jpa.database-platform＝org.hiberate.dialect.MySQL0.9Dialect
Spring.jpa.generate-ddl＝true
Spring.jpa.show-sql＝true
Spring.freemarker.suffix＝.html
s4: the cloud processor collects the electricity consumption time period and the corresponding electricity consumption of each reduced daily electricity consumption time period chart;
s5: classifying the power consumption time periods in sequence according to the power consumption error threshold value in time sequence, acquiring and summarizing the classified power consumption time periods;
s6: the standard time period [ P ] of each power consumption under the same classification category is sequentially obtained according to the following formula 1 ，P 2 ]And its standard electricity consumption, wherein P 1 ∈[P i ]max，P 2 ∈[P j ]min；
Wherein P is i For the starting time of each power utilization period under the same classification category, P j The end time of each power utilization period under the same classification category;
wherein, each electricity consumption standard period [ P ] 1 ，P 2 ]The corresponding electricity standard quantity is counted according to the following formula:
wherein A is 1P Representing the current electricity consumption standard period [ P ] in the reduced first daily electricity consumption period chart 1 ，P 2 ]Corresponding electricity consumption, A 2P Representing the current electricity consumption standard period [ P ] in the reduced second daily electricity consumption period chart 1 ，P 2 ]Corresponding electricity consumption, A NP Representing the current electricity consumption standard period [ P ] in the reduced Nth daily electricity consumption period chart 1 ，P 2 ]The corresponding electricity consumption, N is the number of pictures of all daily electricity consumption time periods in the selected time; a represents a standard period of electricity consumption [ P ] 1 ，P 2 ]Corresponding electricity standard quantity;
s7: according to the acquired standard time period [ P ] of each power consumption 1 ，P 2 ]And the electricity consumption standard quantities are combined to form a daily electricity consumption standard time period diagram.
It should be noted that, the process of merging to form the daily electricity standard time period chart is conventional data integration, and redundant description is not made here.
Wherein, the preset proportion is set to 15%.
Wherein the error threshold is defined as 200 degrees;
when the electricity consumption in the electricity consumption time period does not reach the error threshold value, the compared electricity consumption time periods of different daily electricity consumption time period diagrams belong to the same time category.
It is clear that the electricity consumption per day in different time periods after the same unit is standardized is stable, for example, different units can be identified as schools, residential areas, factory parks and the like, the electricity consumption per day in different time periods is not different, and whether the electricity consumption is in the same class of time period P can be preliminarily determined according to the peak size and the consumption 0 。
Specifically, the threshold is defined as follows:
when the total power consumption of the area is less than 3000 degrees, the threshold value is defined as 8%;
when the total power consumption of the area is more than 3000 degrees and less than 5000 degrees, the threshold value is defined as 5%;
when the total power consumption of the area is more than 5000 degrees and less than 10000 degrees, the threshold value is defined as 2%;
when the total power consumption of the area is 10000 degrees or more, the threshold is defined as 0.6%.
Specifically, period P 0 The statistical definition of (c) is as follows:
when the total power consumption of the area is less than 3000 DEG, the period P 0 Is defined as 3h;
when the total power consumption of the area is more than 3000 degrees and less than 5000 degrees, the period P 0 Is defined as 2h;
when the total power consumption of the area is more than 5000 degrees and less than 10000 degrees, the period P 0 Is defined as 1h;
when the total power consumption of the area is more than 10000 DEG, the period P 0 Is defined as 0.5h.
Wherein, based on the RTOS simulation platform, the prediction test of calculation power and accuracy is carried out, and the statistical results are shown in the following tables 2 and 3:
table 2 shows the calculated force rate (rad/s) and accuracy (%) of the operation performed after the standard time period map of the daily electricity consumption is obtained by one round of processing of the electricity consumption map;
table 3 shows the calculated force rate (rad/s) and accuracy (%) of the operation performed after the two-round daily electricity consumption standard time period map is obtained by the two-round electricity consumption map processing;
table 2: one-round processing performance statistics
| One round of treatment | Not being treated in one round | Conventional method | |
| Calculation force Rate (rad/s) | 312 | 1089 | 103 |
| Accuracy (%) | 92.45 | 77.16 | 40.88 |
Wherein, the convention in table 2 is: and selecting the electricity consumption time period diagram of any day as a daily electricity consumption standard time period diagram.
Table 3: two-round processing performance statistics
| Two-round treatment | Not treated in two rounds | |
| Calculation force Rate (rad/s) | 461 | 1537 |
| Accuracy (%) | 87.64 | 44.92 |
Further, after obtaining the standard time interval diagram of the daily electricity of each two rounds, the method further comprises the step of comparing each unit time interval P 0 With reference to fig. 3, the method specifically includes the following steps:
s1: each unit period P is performed according to time sequence 0 Is arranged in the row of (a);
s2: when each unit period P 0 When the coincidence phenomenon occurs, the coincidence period P 0 Running a unified accounting rule;
referring to fig. 4, the accounting unification rule is defined as:
acquiring the coincident time period P 0 The ratio of each unit to the current statistics area total power consumption is the ratio gamma;
comparing the corresponding duty ratio gamma;
acquiring an allocation rule;
completing unified accounting, and completing the distribution of the corresponding energy storage of each unit according to the distribution rule;
wherein, the allocation rule is:
wherein H is the energy storage capacity of the selected unit, gamma z Period P coinciding for the selected cell z 0 X is the period P in which the x-th selected cell coincides 0 Ratio of H γz Period P coinciding for the selected cell z 0 Is used for the power consumption of the battery.
Additionally, each graph is transmitted to the cloud processor according to the transmission rate protocol;
the transmission rate protocol is defined as:
wherein η is the transmission rate; t' is the number of code word segments contained in the transmitted map; t is the number of code word nodes contained in all transmission diagrams; s is the number of transmitted pictures; s (u) is the period P contained in the transmission diagram 0 Is a function of the number of (2); l (S) is a function formula for sharing instructions sent by a transmission diagram, and S is an integer.
As shown in table 4 below, when 20 electricity consumption charts are transmitted simultaneously, the overall efficiency for the transmission limiting method of the present invention is compared with that for the non-limiting transmission:
table 4: transmission efficiency integral comparison table
| Number of times of overall delay (times)) | Overall delay time(s) | Overall efficiency | |
| The invention is that | 0 | 0.44 | 0.981 |
| Without transmission restrictions | 11 | 16.87 | 0.353 |
The invention provides a power service data processing method, which is characterized in that a data mode is longitudinally compared through an obtained daily electricity quantity time interval chart of each unit, a round of processing is carried out to obtain a daily electricity quantity standard time interval chart of each unit, then transverse unit comparison is carried out according to the total electricity quantity proportion condition, a round of processing is carried out to obtain a daily electricity quantity standard time interval chart of each round of two rounds of processing, sequential energy storage is respectively carried out according to the time sequence of time intervals in the time interval in the illustration, the proportion of the total electricity quantity in the current statistics area is obtained according to the proportion corresponding to the current energy storage time interval, and the time-sharing sequential allocation of the upper energy storage resource pool of each unit is completed.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (7)
1. The power business data processing method is characterized by comprising the following steps of:
s1: acquiring a daily electricity standard time period diagram of each unit in a power grid database in a networking way;
s2: sequentially obtaining time periods P when the power consumption in each graph reaches a threshold value compared with the total power consumption in the current statistical region according to the daily power consumption standard time period graph of each unit 0 ；
S3: the cloud processor respectively counts each unit period P 0 Removing the time periods which are not included in the standard time period diagram of the daily electricity quantity of each unit and are not statistically defined, and obtaining the standard time period diagram of the daily electricity quantity of each two rounds;
s4: according to the time period P in each of the two-wheel daily electricity standard time period diagrams 0 Respectively carrying out sequential energy storage according to the time sequence of the energy storage period P 0 The corresponding proportion is used for obtaining the proportion of the total electricity consumption of the current statistical region, and time-sharing sequential allocation of the upper energy storage resource pools of all units is completed;
the method for acquiring the daily electricity standard time period diagram of each unit in the power grid database by networking specifically comprises the following steps:
s1: networking to obtain all daily electricity consumption time period diagrams of each unit in the power grid database within a selected time;
s2: counting the electricity consumption time periods and the corresponding electricity consumption of each daily electricity consumption time period graph;
s3: sequentially obtaining electricity consumption time periods and corresponding electricity consumption of which the electricity consumption ratio in the daily electricity consumption time period map exceeds a preset proportion according to the counted electricity consumption time periods and corresponding electricity consumption, and synchronously removing the electricity consumption time periods and corresponding electricity consumption of which the electricity consumption ratio in the daily electricity consumption time period map does not reach the preset proportion, and obtaining each reduced daily electricity consumption time period map;
s4: the cloud processor collects the electricity consumption time periods and the corresponding electricity consumption of each reduced daily electricity consumption time period graph;
s5: classifying the power consumption time periods in sequence according to the power consumption error threshold value in time sequence, acquiring and summarizing the classified power consumption time periods;
s6: the standard time period [ P ] of each power consumption under the same classification category is sequentially obtained according to the following formula 1 ，P 2 ]And its standard electricity consumption, wherein P 1 ∈[P i ]max，P 2 ∈[P j ]min；
Wherein P is i For the starting time of each power utilization period under the same classification category, P j The end time of each power utilization period under the same classification category;
wherein, each electricity consumption standard period [ P ] 1 ，P 2 ]The corresponding electricity standard quantity is counted according to the following formula:
wherein A is 1P Representing the current electricity consumption standard period [ P ] in the first reduced electricity consumption period chart 1 ，P 2 ]Corresponding electricity consumption, A 2P Representing the current electricity consumption standard period [ P ] in the reduced second daily electricity consumption period chart 1 ，P 2 ]Corresponding electricity consumption, A NP Representing the current electricity consumption standard period [ P ] in the reduced Nth daily electricity consumption period chart 1 ，P 2 ]The corresponding electricity consumption, N is the number of all the daily electricity consumption time period pictures in the selected time; a represents a standard period of electricity consumption [ P ] 1 ，P 2 ]Corresponding electricity standard quantity;
s7: according to the acquired standard time period [ P ] of each power consumption 1 ，P 2 ]And combining the electricity consumption standard quantities to form the daily electricity consumption standard time period diagram.
2. The power service data processing method according to claim 1, wherein: the preset proportion is set to 15%.
3. The power service data processing method according to claim 2, characterized in that: the error threshold is defined as 200 degrees;
when the electricity consumption in the electricity consumption time period does not reach the error threshold value, the compared electricity consumption time periods of different daily electricity consumption time period diagrams belong to the same time category.
4. A power service data processing method according to claim 3, wherein the threshold is defined as follows:
when the total power consumption of the area is less than 3000 degrees, the threshold is defined as 8%;
when the total power consumption of the area is more than 3000 degrees and less than 5000 degrees, the threshold is defined as 5%;
when the total power consumption of the area is more than 5000 degrees and less than 10000 degrees, the threshold value is defined as 2%;
when the total power consumption of the area is more than 10000 degrees, the threshold is defined as 0.6%.
5. The method for processing power service data according to claim 4, wherein the period P 0 The statistical definition of (c) is as follows:
when the total power consumption of the area is less than 3000 DEG, the period P 0 Is defined as 3h;
when the total power consumption of the area is more than 3000 degrees and less than 5000 degrees, the period P 0 Is defined as 2h;
when the total power consumption of the area is more than 5000 degrees and less than 10000 degrees, a period P 0 Is defined as 1h;
when the total power consumption of the area is more than 10000 DEG, the period P 0 Is defined as 0.5h.
6. The power business data processing method of claim 5, characterized in thatThe method is characterized in that: after obtaining the standard time interval diagram of the daily electric quantity of each round, the method also comprises the step of comparing each unit time interval P 0 The method for performing accounting unification specifically comprises the following steps:
s1: each unit period P is performed according to time sequence 0 Is arranged in the row of (a);
s2: when each unit period P 0 When the coincidence phenomenon occurs, the coincidence period P 0 Running a unified accounting rule;
wherein, the accounting unified rule is defined as:
acquiring the coincident time period P 0 The ratio of each unit to the current statistics area total power consumption is the ratio gamma;
comparing the corresponding duty ratio gamma;
acquiring an allocation rule;
completing unified accounting, and completing the distribution of the corresponding energy storage of each unit according to the distribution rule;
wherein, the allocation rule is:
wherein H is the energy storage capacity of the selected unit, gamma z Period P coinciding for the selected cell z 0 X is the period P in which the x-th selected cell coincides 0 Ratio of H γz Period P coinciding for the selected cell z 0 Is used for the power consumption of the battery.
7. The method for processing power business data according to claim 6, wherein each graph is transmitted to the cloud processor according to a transmission rate protocol;
the transmission rate protocol is defined as:
0.86，0＜S≤3
wherein η is the transmission rate; t' is the number of code word segments contained in the transmitted map; t is the number of code word nodes contained in all transmission diagrams; s is the number of transmitted pictures; s (u) is the period P contained in the transmission diagram 0 Is a function of the number of (2); l (S) is a function formula for sharing instructions sent by a transmission diagram, and S is an integer.
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