CN113919687B - Electric energy metering material stock distribution method - Google Patents
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Abstract
The invention discloses a method and a system for distributing electric energy metering material stock, and relates to distribution of electric energy metering material stock. S1, counting the actual monthly demand quantity of electric energy metering supplies of all storehouses; s2, predicting the demand quantity in a future period according to historical data; and S3, calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to the future demand predicted value and the fluctuation. The method is mainly used for optimizing and distributing the storage capacity of each warehouse when the total number of the electric energy metering materials is fixed. The invention can take the uncertainty of the demand of the electric energy metering materials into account, integrate the uncertainty of the predicted value and the historical data and allocate the demand of each warehouse.
Description
Technical Field
The invention discloses a method for distributing electric energy metering material stock, which relates to distribution of electric energy metering material stock.
Background
The power enterprise is a capital-intensive enterprise, and power material management is a basic work of the power enterprise and directly involves aspects such as power engineering construction, company cost management, safety production and the like. The electric energy metering material is one of the most important materials of the electric energy material, and mainly comprises various types of electric energy meters, devices (comprising a joint junction box and a secondary wire) for metering electric energy, which are formed by connecting metering voltage and current transformers and secondary circuits of the electric energy meters, and acquisition equipment (comprising various types of load control terminals, concentrators and collectors) for acquiring electric energy data. With the rapid increase of the investment scale of the power grid construction, the proportion of electric energy metering materials in the whole power grid investment is larger and larger. With the advance of future electric power marketization, the requirement for electric energy metering will be higher and higher, and the required amount of metering materials will be continuously increased, so that the electric energy metering materials are necessary to be managed in a lean way.
In the actual production management process, many power enterprises still adopt a more traditional and backward management method in the aspect of metering device management, and a great gap exists between the traditional and backward management method and lean management requirements. The metering equipment requirements of many power enterprises are mainly submitted by infrastructure and high-low voltage client managers, and in order to smoothly develop the service, the requirements submitting personnel often store a large amount of stock and increase the requirements, the unrealistic of the demand information easily causes errors of a metering center in equipment management, or causes unreasonable configuration of funds occupation and inventory quantity, and seriously violates the intensive management thought of materials of power enterprises.
Disclosure of Invention
The invention aims at overcoming the defects, and provides a method for distributing the stock quantity of electric energy metering materials, which is mainly used for optimally distributing the storage quantity of each warehouse when the total number of the electric energy metering materials is fixed. The invention can take the uncertainty of the demand of the electric energy metering materials into account, integrate the uncertainty of the predicted value and the historical data and allocate the demand of each warehouse.
The invention is realized by adopting the following technical scheme:
an electric energy metering material stock distribution method comprises the following steps:
s1, counting the actual monthly demand quantity (namely the warehouse-out quantity) of the electric energy metering materials of each warehouse and the fluctuation of the demand quantity;
s2, predicting the demand quantity in a future period according to the historical demand data;
and S3, calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to the future demand predicted value and the fluctuation.
The statistical method in the step S1 is to count the actual monthly demand quantity and the fluctuation of the demand quantity of the warehouse, and record the demand data of the t month in the history of a certain device as X t And the standard deviation of the historical demand of the equipment is counted, and the calculation method is as follows:
in the formula (1), X t The demand data representing the historic T month of a certain device is E (X) the expected value of the demand of the device, T the number of samples of the history record, namely the total month of the inventory statistical data, and sigma the standard deviation of the change amount of the history data of the device.
Step S2, excavating historical warehouse output of each warehouse through step S1, and predicting the demand of future electric energy metering material products, wherein the prediction model is as follows:
in the formula (2), the amino acid sequence of the compound,for the primary smoothed value of the device at time t+1, a is the exponential smoothing coefficient, X t Is the actual value of the t-th period; />For the second smoothed value of the device at time t +1 +.>For a smoothed value of the device at time t, < >>For the second smoothed value of the device at time t, is->For the third smoothed value of the device at time t +1 +.>The tertiary smoothed value of the device at time t.
In the formula (2), X t Is a known value, butWhen the initial point t=0, it cannot be directly calculated according to the recurrence relation in the formula (2), and the initial assignment needs to be performed, and the assignment mode is as follows:
in the formula (2), a is a weight coefficient, and the value is between 0 and 1.
The value of a has a relatively tight fluctuation relation with the original data, can be set according to historical experience, and is generally set by referring to the following modes:
if the fluctuation of the original data sequence is not large, the value interval of a is 0.1-0.3;
if the fluctuation of the original data sequence is larger, the value interval of a is 0.6-0.8, so that the weight of the recent observation value can be increased, and the weight of the observation value in each period is quickly reduced from the near to the far.
The value of a can be used as reference if no history experience exists, the history sample can be used for estimation, and a smooth estimation result is directly adopted to reversely calculate the reasonable value of a, and the reverse calculation method is as follows: in the interval of 0.1-0.9, a is self-increased according to the step length of 0.1, and the total deviation of the primary smoothing errors under different a parameter conditions is respectively obtained:
equation (4) is a calculation method of total deviation, totalbias is total deviation, and T is historical data point number. And after calculating the total deviation of the a under different parameter conditions, selecting the a value with the lowest total deviation as the final prediction parameter.
And (3) after obtaining the reasonable parameters of the a, carrying out future equipment demand prediction according to the comprehensive formulas (2) and (3).
In the future equipment demand prediction:
for the demand budget at the next time, in equation (2)The predicted value of the equipment requirement at the next moment is obtained;
if equipment demand conditions are to be predicted for a plurality of time intervals in the future, the prediction is performed by using the formula (5),
Y＝a+bT+cT 2 (5)
in the formula (5), Y is a predicted value, T is a time interval, a, b and c are fitting coefficients, and the values are smoothedAnd the weight coefficient a, the detailed calculation method is as follows:
in step S3, because there is uncertainty in the device demand, any prediction method can only predict the future demand approximately, and cannot be completely accurate, so that further consideration is required for the uncertainty of the future demand. In actual production management, the stock quantity of the electric energy metering materials is kept in a reasonable range so as to meet the production construction and fault operation and maintenance requirements as primary tasks, but excessive stock is not reserved at the same time, and excessive capital cost and storage cost are avoided. Therefore, when the total amount of the electric energy metering materials is fixed, the required amount of each warehouse needs to be optimized to improve the utilization efficiency of the assets, and the detailed optimization method is as follows:
in the formula (7), cost is the total objective function, X i To be variable, subscript i represents the ith storehouse, n is the total number of the storehouse, and Cost is divided into three parts dis X is the additional allocation cost when the inventory is insufficient t+1_i Is the predicted value of the t+1 period of the ith warehouse, sigma i Standard deviation of historical demand change for i warehouse, C d_i The allocation Cost constant for the ith warehouse can be set according to the actual transportation Cost, cost rep C, re-checking the cost of the equipment after exceeding the effective period of the warehouse r_i For the cost coefficient of warehouse verification, the cost coefficient of warehouse verification far away from the verification center should be set to a higher value, F adj Allocating adjustment costs for total inventory, f i For the adjustment cost coefficient of the i warehouse, the supply range is large, and the warehouse f is convenient to transport i Shall be set to a smaller value, X t+1_Total The allocated amount is stored for the total pool at time t+1.
X solved by formula (7) i Namely, the distribution value of the stock quantity of each warehouse.
A system for implementing an electrical energy metering material inventory allocation method, comprising:
the actual demand quantity counting module is used for counting the monthly actual demand quantity of the electric energy metering materials of each warehouse;
the future period demand quantity prediction module is used for predicting the demand of future electric energy metering material products by adopting the statistical data of the actual demand quantity statistical module;
and the optimal distribution forming module is used for calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to the future demand predicted value and the fluctuation.
The invention has the advantages that: the invention can fully utilize limited electric energy metering material resources, reduce the allocation cost of various electric energy metering materials among all storehouses and the verification cost after the warehouse duration exceeds the period, effectively improve the utilization rate level of the assets, and further improve the lean level of the material management of the electric company.
Drawings
Fig. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and to specific embodiments.
The invention optimally distributes the storage capacity of each warehouse according to the fixed total number of the electric energy metering materials.
Specific examples:
a system for implementing a method of electric energy metering material inventory allocation, comprising,
the actual demand quantity counting module is used for counting the monthly actual demand quantity of the electric energy metering materials of each warehouse;
the future period demand quantity prediction module is used for predicting the demand of future electric energy metering material products by adopting the statistical data of the actual demand quantity statistical module;
and the optimal distribution forming module is used for calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to the future demand predicted value and the fluctuation.
An electric energy metering material stock distribution method comprises the following steps:
s1, counting the actual monthly demand quantity of the electric energy metering materials of each warehouse, namely the quantity of warehouse-out;
in particular to the monthly actual demand quantity of the storehousesCounting, and recording the requirement data of a certain equipment in the t month historically as X t And the standard deviation of the historical demand of the equipment is counted, and the calculation method is as follows:
in the formula (1), X t The demand data representing the historic T month of a certain device is E (X) the expected value of the demand of the device, T the number of samples of the history record, namely the total month of the inventory statistical data, and sigma the standard deviation of the change amount of the history data of the device.
S2, predicting the demand quantity in a future period according to historical data;
through the excavation of historical warehouse output of each warehouse in the step S1, the demand of future electric energy metering material products is predicted, and the adopted prediction model is as follows:
in the formula (2), the amino acid sequence of the compound,for the primary smoothed value of the device at time t+1, a is the exponential smoothing coefficient, X t Is the actual value of the t-th period; />For the second smoothed value of the device at time t +1 +.>For a smoothed value of the device at time t, < >>For the second smoothed value of the device at time t, is->At time t+1Three smoothed values of the device, +.>The tertiary smoothed value of the device at time t.
In the formula (2), X t Is a known value, butWhen the initial point t=0, it cannot be directly calculated according to the recurrence relation in the formula (2), and the initial assignment needs to be performed, and the assignment mode is as follows:
in the formula (2), a is a weight coefficient, and the value is between 0 and 1.
The value of a has a relatively tight fluctuation relation with the original data, can be set according to historical experience, and is generally set by referring to the following modes:
if the fluctuation of the original data sequence is not large, the value interval of a is 0.1-0.3;
if the fluctuation of the original data sequence is larger, the value interval of a is 0.6-0.8, so that the weight of the recent observation value can be increased, and the weight of the observation value in each period is quickly reduced from the near to the far.
The value of a can be used as reference if no history experience exists, the history sample can be used for estimation, and a smooth estimation result is directly adopted to reversely calculate the reasonable value of a, and the reverse calculation method is as follows: in the interval of 0.1-0.9, a is self-increased according to the step length of 0.1, and the total deviation of the primary smoothing errors under different a parameter conditions is respectively obtained:
equation (4) is a calculation method of total deviation, totalbias is total deviation, and T is historical data point number. And after calculating the total deviation of the a under different parameter conditions, selecting the a value with the lowest total deviation as the final prediction parameter.
And (3) after obtaining the reasonable parameters of the a, carrying out future equipment demand prediction according to the comprehensive formulas (2) and (3).
In the future equipment demand prediction:
for the demand budget at the next time, in equation (2)The predicted value of the equipment requirement at the next moment is obtained;
if equipment demand conditions are to be predicted for a plurality of time intervals in the future, the prediction is performed by using the formula (5),
Y＝a+bT+cT 2 (5)
in the formula (5), Y is a predicted value, T is a time interval, a, b and c are fitting coefficients, and the values are smoothedAnd the weight coefficient a, the detailed calculation method is as follows:
s3, calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to future demand predicted values and fluctuation;
because of the uncertainty of the equipment requirement, any prediction method can only predict the future requirement approximately and cannot be completely accurate, so that the uncertainty of the future requirement needs to be further considered. In actual production management, the stock quantity of the electric energy metering materials is kept in a reasonable range so as to meet the production construction and fault operation and maintenance requirements as primary tasks, but excessive stock is not reserved at the same time, and excessive capital cost and storage cost are avoided. Therefore, when the total amount of the electric energy metering materials is fixed, the required amount of each warehouse needs to be optimized to improve the utilization efficiency of the assets, and the detailed optimization method is as follows:
in the formula (7), cost is the total objective function, X i To be variable, subscript i represents the ith storehouse, n is the total number of the storehouse, and Cost is divided into three parts dis X is the additional allocation cost when the inventory is insufficient t+1_i Is the predicted value of the t+1 period of the ith warehouse, sigma i Standard deviation of historical demand change for i warehouse, C d_i The allocation Cost constant for the ith warehouse can be set according to the actual transportation Cost, cost rep C, re-checking the cost of the equipment after exceeding the effective period of the warehouse r_i For the cost coefficient of warehouse verification, the cost coefficient of warehouse verification far away from the verification center should be set to a higher value, F adj Allocating adjustment costs for total inventory, f i For the adjustment cost coefficient of the i warehouse, the supply range is large, and the warehouse f is convenient to transport i Shall be set to a smaller value, X t+1_Total The allocated amount is stored for the total pool at time t+1.
X solved by formula (7) i Namely, the distribution value of the stock quantity of each warehouse.
According to the embodiment, the uncertainty of the demand of the electric energy metering materials can be considered in prediction, the uncertainty of the predicted value and the historical data is integrated, and the demand of each warehouse is allocated. The utilization rate level of the assets is improved, and the lean level of the material management of the electric company is further improved.
Claims (6)
1. An electric energy metering material stock distribution method is characterized by comprising the following steps:
s1, counting the actual monthly demand quantity of electric energy metering materials of all storehouses and the fluctuation of demand quantity, wherein the actual demand quantity is the monthly actual ex-warehouse quantity of all storehouses;
s2, predicting the demand quantity in a future period according to the historical demand data;
s3, calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to the future demand predicted value and the standard deviation of the demand;
statistical method of step S1The method is to count the actual monthly demand quantity and the fluctuation of demand quantity of a warehouse, and record the historical demand data of a certain equipment in the t month as X t And the standard deviation of the historical demand of the equipment is counted, and the calculation method is as follows:
in the formula (1), E (X) is an expected value of the demand of the equipment, T is the number of samples of the historical record, namely the total month number of the inventory statistical data, and sigma is the standard deviation of the historical data change of the equipment;
step S2, excavating historical warehouse output of each warehouse through step S1, and predicting the demand of future electric energy metering material products, wherein the prediction model is as follows:
in the formula (2), the amino acid sequence of the compound,for t+1 month, the first smoothed value of the device, a is the smoothing coefficient, X t Is the actual value of month t; />For t+1 month the second smoothed value of the device,/->For t months a smoothed value of the device, < >>For t months the second smoothed value of the device, < >>Three levels of the device for t+1 monthsSlip value->Three smoothed values for the device for t months;
in the formula (2), X t Is a known value, butWhen the initial point t=0, it cannot be directly calculated according to the recurrence relation in the formula (2), and the initial assignment needs to be performed, and the assignment mode is as follows:
in step S3, the demand of each warehouse is further optimized, and the detailed optimization method is as follows:
in the formula (7), cost is the total objective function, X i For the variables to be solved, the optimal allocation amount of the i warehouse in t+1 month is represented, the index i represents the i warehouse, n is the total number of the warehouses, and the Cost is divided into three parts dis X is the additional allocation cost when the inventory is insufficient t+1_i Is the predicted value of t+1month of the ith warehouse, sigma i Standard deviation of historical demand change for i warehouse, C d_i The allocation Cost constant for the ith warehouse can be set according to the actual transportation Cost, cost rep C, re-checking the cost of the equipment after exceeding the effective period of the warehouse r_i For the cost coefficient of warehouse verification, the cost coefficient of warehouse verification far away from the verification center should be set to a higher value, F adj Allocating adjustment costs for total inventory, f i For the adjustment cost coefficient of the i warehouse, the supply range is large, and the warehouse f is convenient to transport i Shall be set to a smaller value, X t+1_Total Allocation amount for total pool memory of t+1th month;
x solved by formula (7) i Namely, the distribution value of the stock quantity of each warehouse.
2. The method for distributing an electric energy metering material stock according to claim 1, wherein a in the formula (2) has a value of 0 to 1.
3. The method for distributing the electric energy metering material stock according to claim 2, wherein the value of a is as follows:
if the fluctuation of the original data sequence is not large, the value interval of a is 0.1-0.3;
if the fluctuation of the original data sequence is large, the value interval of a is 0.6-0.8;
if the original data is not referenced, a reasonable value of a is directly reversely deduced by adopting a smooth estimation result.
4. The method for distributing the electric energy metering material stock according to claim 3, wherein the reasonable numerical method of the reverse-push a is that a is self-increased within a range of 0.1 to 0.9 according to a step length of 0.1, and the total deviation of one smoothing error under different parameter conditions of a is respectively obtained:
the formula (4) is a calculation method of total deviation, and Totalbias is total deviation; and after calculating the total deviation of the a under different parameter conditions, selecting the a value with the lowest total deviation as the final prediction parameter.
5. The electric energy metering material stock allocation method according to claim 1, wherein for the demand budget of the next month, the method in the formula (2)The predicted value of the equipment requirement in the next month is obtained.
6. A system for implementing the electrical energy metering material inventory allocation method of claim 1, the system comprising:
the actual demand quantity counting module is used for counting the monthly actual demand quantity of the electric energy metering materials of each warehouse;
the future period demand quantity prediction module is used for predicting the demand of future electric energy metering material products by adopting the statistical data of the actual demand quantity statistical module;
and the optimal distribution forming module is used for calculating the optimal distribution quantity of the electric energy metering materials of each warehouse according to the future demand predicted value and the standard deviation of the demand.
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