CN113947273A - Multi-path distribution network cost accounting system based on big data - Google Patents
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Abstract
The invention discloses a cost accounting system of a multi-path distribution network based on big data, which comprises the cost accounting system, wherein the cost accounting system comprises a central processing unit, the output end of the central processing unit is electrically connected with the input end of a communication module, the input end of the central processing unit is electrically connected with the output end of the communication module, the output end of the communication module is in signal connection with the input end of a big data analysis platform, and the input end of the communication module is in signal connection with the output end of the big data analysis platform. According to the multi-path distribution network cost accounting system based on the big data, through the arrangement of the big data analysis platform and the data analysis module, project cost data of the same type of distribution network can be obtained through the big data analysis platform, then the project cost data is compared and analyzed with the project cost data, and finally cost accounting is carried out through the cost accounting module, so that timely and effective management and decision making can be conveniently carried out on the cost accounting process.
Description
Technical Field
The invention relates to the technical field of distribution network cost accounting, in particular to a multi-path distribution network cost accounting system based on big data.
Background
Big data, a term that refers to large or complex data sets for which traditional data processing applications are inadequate to handle; given the same total data size, the subsequent analysis of each small data set yields much additional information and data relationships than if the individual small data sets were analyzed individually.
At present, the cost accounting of the construction of the distribution network is carried out by manually controlling by using electronic forms, the cost control in the real sense cannot be realized by the mode, because many bugs exist in the link of manual operation, and the cost accounting of the distribution network needs more data support.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a big data-based multi-path distribution network cost accounting system.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: a cost accounting system of a multi-path distribution network based on big data comprises a cost accounting system, wherein the cost accounting system comprises a central processing unit, the output end of the central processing unit is electrically connected with the input end of a communication module, the input end of the central processing unit is electrically connected with the output end of the communication module, the output end of the communication module is in signal connection with the input end of a big data analysis platform, the input end of the communication module is in signal connection with the output end of the big data analysis platform, the input end of the big data analysis platform is in signal connection with the output end of a local area similar distribution network project database, the input end of the big data analysis platform is in signal connection with the output end of a domestic and foreign similar distribution network project database, the output end of the central processing unit is electrically connected with the input end of a display module, and the output end of the central processing unit is electrically connected with the input end of a storage module, the input end of the central processing unit is electrically connected with the output end of the storage module. .
Preferably, the input end of the central processing unit is electrically connected with the output end of the parameter information input module.
Preferably, the output end of the central processing unit is electrically connected with the input end of the cost accounting module, and the input end of the central processing unit is electrically connected with the output end of the cost accounting module.
Preferably, the input end of the central processing unit is electrically connected with the output end of the data analysis module, and the output end of the central processing unit is electrically connected with the input end of the data analysis module.
Preferably, the input end of the data analysis module is electrically connected with the output end of the data acquisition unit, and the input end of the cost accounting system is electrically connected with the output end of the power module.
Preferably, the data acquisition unit comprises an investment period cost statistic module, an operation and maintenance cost statistic module and a scrapping period cost statistic module.
Preferably, the investment period cost statistic module is used for carrying out statistic calculation on one-time investment expenses in each early period of design, equipment purchase, construction and the like.
Preferably, the operation and maintenance and overhaul cost statistic module is used for calculating material cost, construction cost, labor cost and the like involved in distribution network inspection, overhaul, technical improvement and overhaul and equipment fault maintenance work, and meanwhile, the operation and maintenance and overhaul cost statistic module also comprises the power selling profit loss caused by power failure and indirect loss brought to social production life.
Preferably, the scrapping period cost statistic module is used for carrying out statistic calculation on the net value and the disassembly cost of the scrapped asset.
Preferably, the algorithm in the cost accounting module is
Wherein CI is the cost of the input period, CM is the cost of operation and maintenance, and CD is the cost of the abandonment period.
(III) advantageous effects
Compared with the prior art, the invention provides a multi-path distribution network cost accounting system based on big data, which has the following beneficial effects:
according to the multi-path distribution network cost accounting system based on the big data, through the arrangement of the big data analysis platform and the data analysis module, project cost data of the same type of distribution network can be obtained through the big data analysis platform, then the project cost data is compared and analyzed with the project cost data, and finally cost accounting is carried out through the cost accounting module, so that timely and effective management and decision making can be conveniently carried out on the cost accounting process.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a cost accounting system of a multi-path distribution network based on big data comprises a cost accounting system, the cost accounting system comprises a central processing unit, an output end of the central processing unit is electrically connected with an input end of a communication module, an input end of the central processing unit is electrically connected with an output end of the communication module, an output end of the communication module is in signal connection with an input end of a big data analysis platform, an input end of the communication module is in signal connection with an output end of a big data analysis platform, an input end of the big data analysis platform is in signal connection with an output end of a local area homogeneous distribution network project database, an input end of the big data analysis platform is in signal connection with an output end of a domestic and foreign homogeneous distribution network project database, an output end of the central processing unit is electrically connected with an input end of a display module, an output end of the central processing unit is electrically connected with an input end of a storage module, an input end of the central processing unit is electrically connected with an output end of the storage module, the input end of the central processing unit is electrically connected with the output end of the parameter information input module, the output end of the central processing unit is electrically connected with the input end of the cost accounting module, the input end of the central processing unit is electrically connected with the output end of the cost accounting module, and the algorithm in the cost accounting module is
Wherein CI is the cost of input period, CM is the maintenance cost of operation, CD is the cost of abandonment period, central processing unit's input is connected with data analysis module's output electricity, central processing unit's output is connected with data analysis module's input electricity, data analysis module's input is connected with data acquisition unit's output electricity, data acquisition unit includes input period cost statistics module, operation maintenance cost statistics module and abandonment period cost statistics module, input period cost statistics module is for designing, each prophase disposable input class expense such as equipment purchase and construction is counted and calculated, operation maintenance cost statistics module is for joining in marriage net tour, overhaul, technical improvement overhaul, the material cost that involves in the equipment trouble repair work, overhaul work is carried out to the netThe system comprises a power supply module, a scrapping period cost counting module, a cost accounting system and a power supply module, wherein the power supply module is used for carrying out statistics and calculation on net value and disassembly cost of scrapped assets;
in the cost accounting, personnel need to predict the electricity sales, so as to make reference for the cost accounting of the personnel, the electricity sales prediction analysis is an important market prediction technical means for managing the electricity demand side, the accurate electricity sales prediction is beneficial to a company to make an effective operation plan, whether the electricity sales can be accurately predicted or not is greatly influenced on the annual operation management and the annual economic benefit of the electric power company, the electricity sales data is influenced by factors such as temperature and GDP (general data processing), especially high-temperature weather, in order to analyze the normal trend change rule of the electricity sales, the influence of high-temperature factors needs to be eliminated, the influence analysis method of the high-temperature on the electricity sales is eliminated, the influence of the high-temperature on the electricity sales can be calculated, and the trend of the electricity sales after the high-temperature influence is eliminated, and the basic idea is divided into four steps: firstly, selecting quarterly GDP acceleration data of each province, and obtaining monthly GDP data of each province by using a linear interpolation method; secondly, selecting monthly average highest temperature and GDP acceleration data of each province, calculating upper and lower confidence intervals of the monthly average highest temperature and the GDP data by using a box type graph, thirdly, giving out a confidence interval of the electricity selling quantity acceleration based on a polynomial regression algorithm, judging whether target monthly electricity quantity homonymy acceleration of each province is in the confidence interval, if not, considering that the electricity selling quantity of the province is influenced by high temperature, and fourthly, for province companies influenced by the high temperature, obtaining an influence change value of the electricity selling quantity by subtracting the upper limit value of the confidence interval from the actual electricity quantity of a target month, wherein the influence change value of the high temperature on the electricity selling quantity is caused by the abnormity of the GDP and the temperature, and the influence change value of the GDP and the high temperature is calculated, wherein the highest temperature: generally means the average maximum monthly temperature of each province, and is obtained by the maximum temperature balance of local cities of each province, wherein a box type chart is shown: the box chart, also called box chart, is a statistical chart for displaying a set of data dispersion data, named by the shape of box, is also commonly used in various fields, commonly used in quality management, and utilizes five statistics in data: the data are described by the minimum value, the first quartile, the median, the third quartile and the maximum value, so that whether the data have information such as symmetry, distribution dispersion and the like can be seen, and abnormal values in the data can be visually and clearly identified, and polynomial fitting is performed: polynomial fitting refers to approximately describing or comparing the functional relationship between coordinates represented by a group of discrete points on a plane by using a continuous curve, and is a method for approximating discrete data by using an analytical expression, and a confidence interval: the confidence interval is an estimation interval of a general parameter constructed by the sample statistic, statistically, the confidence interval of a probability sample is an interval estimation of a certain general parameter of the sample, and the confidence curve shows the degree that the true value of the parameter has a certain probability to fall around the measurement result;
the specific process is as follows:
1. data range description:
the data used by the method provided by the invention comprises the electricity selling quantity data of residents in the first, second and third industries of each province and urban and rural areas as well as GDP acceleration and highest temperature data.
GDP data interpolation:
GDP acceleration rate original data are quarterly data, influence characteristics of monthly high temperature on electricity sales are to be drawn, and therefore GDP data are processed through a linear interpolation method.
The specific algorithm process is as follows:
note the book
Is data to be processed, wherein
Is a date. Such as
Is the spontaneous self-power consumption in 2016 (10 months).
For the treatment to beData of
Removing data which is to be judged whether to be an abnormal value, performing box separation, and determining the upper boundary and the lower boundary thereof, namely:
wherein
The mean value is represented by the average value,
the upper quartile is represented as the number of quartiles,
the lower quartile is indicated.
And judging whether the data to be processed is in the limit, and if the data to be processed is not in the limit, judging that the data to be processed is an abnormal value. After the outliers are identified, the average values to be processed for the previous months are used for the replacement.
2. And (3) curve fitting:
selecting a main network load characteristic curve and a photovoltaic power generation power characteristic curve of an electric power company under different weather types, wherein the different weather types mainly comprise sunny days and rainy days. Firstly, an expression of a main grid load characteristic curve and a photovoltaic power generation characteristic curve is obtained based on a curve fitting method. The method comprises the following specific steps:
2.1 Curve fitting method
Data point recording
Wherein
Means one day
The time of each measurement point can be taken as
；
Is referred to as
The value of the time of day. Using fitting of data points to a polynomial expression of
Estimating coefficients in a polynomial model by a least squares algorithm
For a linear model, the least squares formulation is:
whereinnFor the number of data pieces, the formula (4) is simplified to obtain:
respectively record
Then there is
Then coefficient matrix
2.2 fitting Main network load characteristic Curve
If the main network characteristic load data point is obtained
Wherein
Means one day
The time of each measurement point can be taken as
；
Is referred to as
The main network load value at the time is obtained as follows
Data for curve fitting:
and (3) solving curve parameters by using a least square algorithm, wherein the function relation between the main network load characteristic curve and time under a typical weather type is as follows:
here, the
For the main network load value under typical weather types,
the time of day values of the points are measured.
Are parameters of the curve model.
2.3 fitting photovoltaic power generation power characteristic curve
If the photovoltaic power generation power data point is obtained
Wherein
Means one day
The time of each measurement point can be taken as
；
Is referred to as
The photovoltaic power generation power value at the moment is obtained as follows
Data for curve fitting:
and (3) solving curve parameters by using a least square algorithm, wherein the function relation between the main network load characteristic curve and time under a typical weather type is as follows:
here, the
Is the photovoltaic power generation power value under the typical weather type,
the time of day values of the points are measured.
Are parameters of the curve model.
3. Area integration method based on solving theoretical value of electricity sold and theoretical value of electricity generated
Processing the main network load characteristic curve and the photovoltaic power generation characteristic curve under different weather types and different time periods by using an area integration method to respectively obtain the power selling theoretical value and the power generating capacity theoretical value under different weather types and different time periods, wherein the process is as follows:
3.1 calculating the theoretical value of the electricity sold in different weather types and different time periods
If main network load curve data points on a certain sunny day are obtained
Wherein
Means one day
The time of the individual measuring points is,
is a positive integer multiple of 24, and can be taken as
；
Is referred to as
The value of the mains network load at the moment. Through the step 2, the expression of the major network characteristic load under the typical weather type is obtained as the formula (7), and the theoretical value of the power sold in sunny days
Can be calculated by the following formula:
similarly, based on the main network load characteristic curve in rainy days, the theoretical value of the electricity selling quantity in rainy days can be calculated to be
。
The time range of the peak of the fine day is 11 am to 13 pm, then
Then, the theoretical value of the electricity sold in the peak two hours in a sunny day is calculated by the following area integral formula:
the ratio of the theoretical value of the electricity sold in cloudy days to the theoretical value of the electricity sold in sunny days is recorded as
The ratio of the theoretical value of the electricity sold in two hours at the peak in sunny days to the theoretical value of the electricity sold in sunny days is
。
3.2 calculating theoretical values of power generation amount in different weather types and different time periods
If the photovoltaic power generation power data point of a certain sunny day is obtained
Wherein
Means one day
The time of each measurement point can be taken as
；
Is referred to as
The photovoltaic power value at the moment. Through the step 2, the expression of the photovoltaic power generation power under the typical weather type is obtained and is shown as the formula (8), and the theoretical value of the power generation amount in sunny days
Can be calculated by the following formula:
similarly, based on the photovoltaic power generation power data in rainy days, the theoretical value of the generated energy in rainy days can be calculated to be
。
The time range of the peak of the fine day is 11 am to 13 pm, then
Then, the theoretical value of the power generation amount at two hours of the peak in a sunny day is calculated by the following area formula:
the ratio of the theoretical value of the generated energy in cloudy days to the theoretical value of the generated energy in sunny days is recorded as
The ratio of the theoretical value of the power generation capacity in two hours at the peak in a sunny day to the theoretical value of the power generation capacity in the sunny day is
。
4. Calculating the load permeability in the sunny day and the peak two hours in the sunny day
The load permeability on a sunny day and two hours on the sunny day is calculated, and the method comprises the following specific steps:
4.1, monthly electricity sales data and spontaneous electricity consumption of the power company after data preprocessing are obtained, and load permeability of the monthly distributed photovoltaic power supply is calculated by using a formula (1).
4.2 calculate the average electricity selling quantity in sunny days and two hours of peak hours in sunny days
Obtaining electricity selling amount of a month
And the ratio of the theoretical value of the electricity sold in rainy days to the theoretical value of the electricity sold in sunny days
And the number of days appearing in a certain sunny day in the area is acquired from the weather website
Days of occurrence in cloudy or rainy days
Day, thereinKFor a certain number of days in a month, the calculation formula of the average electricity sales amount in a sunny day is as follows:
the ratio of the theoretical value of the electricity sold in two hours at the peak of sunny days to the theoretical value of the electricity sold in sunny days is utilized
The average electricity selling amount of two hours at peak on sunny day can be calculated
The following formula:
4.3 calculating average spontaneous self-electricity consumption at sunny days and two hours at peak of sunny days
Acquiring the self-electricity consumption in a month
And the ratio of the theoretical value of the generated energy in cloudy and rainy days to the theoretical value of the generated energy in sunny days
The ratio of the spontaneous self-power consumption in rainy days to the spontaneous self-power consumption in sunny days is also taken as
The number of days of the region appearing in the sunny day is acquired from the weather website
Days of occurrence in cloudy or rainy days
Day, therein
Is the number of days of the month. Then, the calculation formula of the average spontaneous power consumption in a sunny day is as follows:
the ratio of the theoretical value of the generated energy in two hours at the peak of a sunny day to the theoretical value of the generated energy in the sunny day
As the ratio of the spontaneous power consumption in two hours at the peak in a sunny day to the spontaneous power consumption in a sunny day, the average spontaneous power consumption in two hours at the peak in a sunny day can be calculated
The following formula:
4.4 calculating the load permeability of the distributed photovoltaic power supply in sunny days and two hours in sunny days respectively
The average selling electricity quantity and the average spontaneous electricity consumption quantity in a sunny day and two hours in a sunny day are respectively calculated through the steps, and then the formula of the load permeability in the sunny day is as follows:
wherein
Is the average spontaneous self-electricity consumption in fine days,
is the average electricity selling amount in fine days.
The load permeability at the peak of two hours in a sunny day is as follows:
wherein
The average spontaneous self-power consumption in two hours at the peak of a sunny day,
is the average electricity selling amount in two hours in a sunny day.
The specific algorithm involved in the method is as follows:
1) least square algorithm
Two variables were studied in our study
When the data are related, a series of paired data can be obtained
The data are plotted in an x-y rectangular coordinate system, and if the points are found to be near a curve, the equation for the curve can be made as follows:
wherein the content of the first and second substances,
is any real number.
Needs to be determined for establishing the curve equation
Using the principle of least squares, i.e. actual values
And the calculated value obtained by using the formula 1
The square of the difference of (a) and (b) is taken as an optimization basis, and when the difference between the two is the minimum, we consider the curve equation as the best fit equation of the data. I.e. min
. Order:
by substituting formula (12) for formula (13)
If it is at the same time
At a minimum, the function can be matched
Calculating a deviation guide, order
To pair
Is equal to 0, i.e.
Get k equations
…
Will this
The final set of equations is as follows:
(22)
equation (22) can be converted into equation (6), i.e., a parameter matrix can be obtained
As shown in the following formula
We can get the regression equation we need.
In the regression process, the regression equation may not pass through exactly all of the data points
To determine the quality of the regression equation, the correlation coefficient can be usedRMaking a judgment on the correlation coefficientRCloser to 1 indicates better fit of the regression equation, where:
wherein the content of the first and second substances,
is the sample volume.
2) Linear interpolation method
Linear interpolation refers to a method of determining an unknown quantity between two known quantities using a straight line connecting the two known quantities.
Let us assume that two known quantities of coordinates are known to us
,
To obtain
At a certain position in the interval
The value on the straight line. Firstly, determining a straight line equation according to two points as follows:
simplifying to obtain:
wherein
。
Then, to
Any point of the interval
Is the interpolation of
Into equation (24):
to sum up, the multi-path distribution network cost accounting system based on the big data can acquire the project cost data of the similar distribution network through the big data analysis platform by setting the big data analysis platform and the data analysis module, then carries out contrastive analysis with the project cost data, and finally carries out cost accounting through the cost accounting module, thereby facilitating the implementation of timely and effective management and decision-making on the cost accounting process.
The related modules involved in the system are all hardware system modules or functional modules combining computer software programs or protocols with hardware in the prior art, and the computer software programs or the protocols involved in the functional modules are all known in the technology of persons skilled in the art, and are not improvements of the system; the improvement of the system is the interaction relation or the connection relation among all the modules, namely the integral structure of the system is improved, so as to solve the corresponding technical problems to be solved by the system.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A big data-based multi-path distribution network cost accounting system comprises a cost accounting system and is characterized in that: the cost accounting system comprises a central processing unit, the output end of the central processing unit is electrically connected with the input end of the communication module, the input end of the central processing unit is electrically connected with the output end of the communication module, the output end of the communication module is in signal connection with the input end of the big data analysis platform, the input end of the communication module is in signal connection with the output end of a big data analysis platform, the input end of the big data analysis platform is in signal connection with the output end of a local homogeneous distribution network project database, the input end of the big data analysis platform is in signal connection with the output end of the domestic and foreign similar power distribution network project database, the output end of the central processing unit is electrically connected with the input end of the display module, the output end of the central processing unit is electrically connected with the input end of the storage module, and the input end of the central processing unit is electrically connected with the output end of the storage module.
2. The big-data-based multi-path distribution network cost accounting system according to claim 1, wherein: the input end of the central processing unit is electrically connected with the output end of the parameter information input module.
3. The big-data-based multi-path distribution network cost accounting system according to claim 1, wherein: the output end of the central processing unit is electrically connected with the input end of the cost accounting module, and the input end of the central processing unit is electrically connected with the output end of the cost accounting module.
4. The big-data-based multi-path distribution network cost accounting system according to claim 1, wherein: the input end of the central processing unit is electrically connected with the output end of the data analysis module, and the output end of the central processing unit is electrically connected with the input end of the data analysis module.
5. The big-data-based multi-path distribution network cost accounting system according to claim 1, wherein: the input end of the data analysis module is electrically connected with the output end of the data acquisition unit, and the input end of the cost accounting system is electrically connected with the output end of the power supply module.
6. The big-data-based multi-path distribution network cost accounting system according to claim 1, wherein: the data acquisition unit comprises an investment period cost statistic module, an operation maintenance cost statistic module and a scrapping period cost statistic module.
7. The big-data-based multi-path distribution network cost accounting system according to claim 6, wherein: the investment period cost statistic module is used for carrying out statistic calculation on one-time investment type expenses in each early period of design, equipment purchase, construction and the like.
8. The big-data-based multi-path distribution network cost accounting system according to claim 6, wherein: the operation and maintenance cost statistic module is used for calculating material cost, construction cost, labor cost and the like related to distribution network inspection, maintenance, technical improvement and overhaul and equipment fault maintenance work, and meanwhile, the operation and maintenance cost statistic module also comprises the loss of electricity selling profits caused by power failure and indirect loss brought to social production life.
9. The big-data-based multi-path distribution network cost accounting system according to claim 6, wherein: and the scrapping period cost counting module is used for counting and calculating the net value and the disassembly cost of the scrapped assets.
10. The big-data-based multi-path distribution network cost accounting system according to claim 1, wherein: the algorithm in the cost accounting module is
Wherein CI is the cost of the input period, CM is the cost of operation and maintenance, and CD is the cost of the abandonment period.
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