CN-122022235-A - Intelligent material allocation method and device suitable for new energy station

CN122022235ACN 122022235 ACN122022235 ACN 122022235ACN-122022235-A

Abstract

The invention discloses an intelligent material allocation method and device suitable for a new energy station, which comprise the steps of obtaining basic parameters required by operation and maintenance of the new energy station, determining comprehensive cost indexes of a supply chain according to material demand parameters, cost parameters, time period parameters and total stock storage capacity in the basic parameters, constructing an objective function aiming at minimizing the comprehensive cost indexes of the supply chain, constructing constraint conditions with balance of total supply and demand, upper storage limit, material demand and upper resource investment limit as constraints according to the material demand parameters, the cost parameters, the time period parameters, boundary constraint parameters and dynamic safety stock coefficients in the basic parameters, constructing a multi-material cooperative allocation model based on the objective function and the constraint conditions, solving the model, determining the amount of various materials in each period, and forming a multi-material allocation scheme of the new energy station. The invention can realize the accurate allocation of multiple materials of the new energy station and provide reliable support for the fine operation and maintenance of the new energy station.

Inventors

LIU ZHEN
WU XUEJIE
HU KUN
LIU JUN
SHEN ZHONGMING

Assignees

中电建新能源集团股份有限公司

Dates

Publication Date: 20260512
Application Date: 20251216

Claims (12)

1. An intelligent material allocation method suitable for a new energy station is characterized by comprising the following steps: The method comprises the steps of acquiring basic parameters required by operation and maintenance of a new energy station, wherein the basic parameters at least comprise material demand parameters, cost parameters, time period parameters and boundary constraint parameters, the boundary constraint parameters at least comprise total stock capacity, and the material demand parameters and the boundary constraint parameters are used for determining dynamic safety stock coefficients; Determining a comprehensive cost index of a supply chain of a whole period according to the material demand parameter, the cost parameter, the time period parameter and the total stock capacity, and constructing an objective function aiming at minimizing the comprehensive cost index of the supply chain; Constructing constraint conditions which take total supply and demand balance, storage upper limit, material demand and resource investment upper limit as constraints according to the material demand parameters, cost parameters, time period parameters, boundary constraint parameters and the dynamic safety inventory coefficient; And constructing a multi-product material collaborative allocation model based on the objective function and the constraint condition, solving the multi-product material collaborative allocation model, and determining the amount of various materials to be prepared in each period to form a new energy station multi-product material allocation scheme.
2. The method of claim 1, wherein the material demand parameters at least comprise a material type number, a material demand amount of each type of material in each period, a material demand standard deviation of each type of material in each period, an initial warehouse storage amount of each type of material and a service level coefficient of each type of material, the cost parameters at least comprise a material amount of each type of material in each period, a transfer material amount of each type of material in each period, a warehouse unit storage cost and a backorder cost coefficient of each type of material, the time period parameters at least comprise a preparation period, a material preparation period of each type of material and a transfer period of each type of material, and the boundary constraint parameters further comprise a total warehouse upper limit, a single material preparation resource investment upper limit, a material threshold coefficient of each type of material and a material unit occupied inventory capacity.
3. The method of claim 1, wherein the material demand parameter and the boundary constraint parameter are used to determine a dynamic safety stock factor, comprising: acquiring a dynamic safety stock coefficient of the previous period and the raising amount of various materials in the raising trigger period; Determining the net warehousing quantity of all materials according to the amount of the materials in the stage triggering period, the occupied stock capacity of the materials, the demand quantity of the materials in each period and the number of the materials; and determining the dynamic safety stock coefficient of each period according to the dynamic safety stock coefficient of the last period and the ratio of the net warehousing quantity of all materials to the total warehousing capacity.
4. The method of claim 1, wherein determining a full-cycle supply chain composite cost indicator based on the supply demand parameter, cost parameter, time period parameter, and the total inventory capacity comprises: determining an effective preparation period according to the difference value of the allocation period, the preparation period of various materials and the transfer period of various materials; In the effective funding period, multiplying and summing the resource quantity of various materials in each period and the funding quantity of various materials in each period to determine the funding cost of the materials; In the allocation period, multiplying and summing the dynamic safety stock coefficient, the warehouse unit storage cost and the total stock storage capacity of each period to determine the material storage cost; In the effective funding period, multiplying and summing the transportation resource quantity of various materials in each period and the funding quantity of various materials in each period to determine the material transportation cost; Determining the stock shortage cost of the materials according to the safety stock demand of the materials in each time period and the residual stock of the materials in each time period, wherein the safety stock demand of the materials in each time period is determined according to the material demand parameter and the time period parameter, and the residual stock of the materials in each time period is determined according to the stock of the materials in the stock triggering time period; And summing the material preparation cost, the material storage cost, the material transportation cost and the material shortage cost, and determining a comprehensive cost index of the supply chain in a full period.
5. The method of claim 4, wherein the safety stock requirement of each type of material in each time period is determined according to the material requirement parameter and the time period parameter, comprising: Determining the safety stock demand of various materials in each time period according to the service level coefficient of various materials, the standard deviation of the demand of various materials in each time period, the preparation period of various materials and the transfer period of various materials; the remaining stock quantity of various materials in each period is determined according to the amount of various materials in the period of the trigger period, and the method comprises the following steps: the method comprises the steps of obtaining the residual stock quantity of various materials in a previous period, and determining the residual stock quantity of various materials in each period according to the residual stock quantity of various materials in the previous period and the prepared quantity of various materials in a prepared triggering period; Correspondingly, the determining the stock-out cost of the materials according to the safety stock demand of various materials in each time period and the residual stock of various materials in each time period comprises the following steps: comparing the residual stock quantity of each period with the safety stock demand quantity of each period; when the safety stock demand of each time period is greater than or equal to the residual stock of each time period, taking the difference value between the safety stock demand of each time period and the residual stock of each time period as the inventory shortage; and in the allocation period, multiplying and summing the backorder quantity of each material and backorder cost coefficients of each material to determine the backorder cost of the material.
6. The method of claim 1, wherein constructing constraints with a total supply and demand balance, an upper warehouse limit, a material demand and an upper fund use limit as constraints based on the material demand parameters, the cost parameters, the time period parameters, the boundary constraint parameters and the dynamic safety stock coefficients comprises: summing the amount of the various materials in each period in the effective period, and determining the total amount of the various materials in the effective period; Determining movable total material amount according to the total amount and initial warehouse storage of various materials; Summing the demand of various materials in each period in the allocation period, and determining the total demand of various materials in the allocation period; The total movable material quantity is larger than or equal to the total demand and is used as a total supply and demand balance constraint; comparing the safety stock demand of various materials in each period with the demand of various materials in each period, and determining the ratio of the larger parameter in the comparison result to the total stock capacity; And taking the dynamic safety stock coefficient of each time period as the upper limit constraint of the stock, wherein the ratio of the larger parameter to the total stock capacity is larger than or equal to the upper limit of the total stock.
7. The method of claim 1, wherein constructing constraint conditions with total supply and demand balance, upper storage limit, upper material demand and resource investment limit as constraints according to the material demand parameter, cost parameter, time period parameter, boundary constraint parameter and dynamic safety stock coefficient, further comprises: the method comprises the steps of taking the residual stock quantity of various materials in each time period as the material demand constraint of various materials, wherein the demand quantity of various materials in each time period is smaller than or equal to the residual stock quantity of various materials in each time period; Multiplying and summing the resource quantity of various materials in each time period and the raised quantity of various materials in each time period to obtain the raised total resource quantity of all materials; and taking the resource investment upper limit of the total resource amount of the fund as the resource investment upper limit constraint, wherein the resource investment upper limit is smaller than or equal to the resource investment upper limit of single material fund.
8. The method of claim 1, wherein said solving the multi-category collaborative deployment model comprises: And solving the multi-product material collaborative allocation model by adopting an intelligent optimization algorithm, wherein the intelligent optimization algorithm comprises any one of a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm and a tabu search algorithm.
9. Intelligent material allocation device suitable for new forms of energy station, its characterized in that includes: The system comprises an acquisition module, a dynamic safety stock coefficient determination module and a dynamic safety stock coefficient determination module, wherein the acquisition module is used for acquiring basic parameters required by operation and maintenance of a new energy station, the basic parameters at least comprise a material demand parameter, a cost parameter, a time period parameter and a boundary constraint parameter, the boundary constraint parameter at least comprises a total stock capacity, and the material demand parameter and the boundary constraint parameter are used for determining the dynamic safety stock coefficient; the objective function construction module is used for determining a full-period supply chain comprehensive cost index according to the material demand parameter, the cost parameter, the time period parameter and the total library storage capacity, and constructing an objective function aiming at minimizing the supply chain comprehensive cost index; the constraint condition construction module is used for constructing constraint conditions which take total supply and demand balance, storage upper limit, material demand and resource investment upper limit as constraints according to the material demand parameters, cost parameters, time period parameters, boundary constraint parameters and the dynamic safety stock coefficients; And the model solving module is used for constructing a multi-grade material cooperative allocation model based on the objective function and the constraint condition, solving the multi-grade material cooperative allocation model, determining the amount of each material in each period and forming a new energy station multi-grade material allocation scheme.
10. An electronic device, comprising: a memory and a processor in communication with each other, the memory having stored therein computer instructions which, upon execution, cause the processor to perform the steps of the method of any of claims 1 to 8.
11. A computer storage medium storing computer program instructions which, when executed, implement the steps of the method of any one of claims 1 to 8.
12. A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

Description

Intelligent material allocation method and device suitable for new energy station Technical Field The invention relates to the technical field of operation and maintenance of new energy stations, in particular to an intelligent material allocation method and device suitable for new energy stations. Background New energy stations such as wind power, photovoltaic and the like become important components of a novel power system, and safe and stable operation of the new energy stations directly relates to power supply reliability and energy transformation effect. However, the new energy stations generally have remarkable characteristics of remote geographic positions, severe operating environments (such as highland, mountain areas and seas) and the like, so that the material guarantee and inventory management and control face a plurality of outstanding problems, such as complex and changeable material demands, difficult collaborative management of multiple material types, contradiction between high backorder risks and long replenishment periods and the like. In the prior art, the related material allocation scheme focuses on the replenishment or storage scheduling of single-product materials, the multidimensional basic parameters required by the operation and maintenance of the new energy station cannot be fully considered, and a complete allocation system of 'multidimensional basic parameter cooperation-objective function construction-multiple constraint-model solving' is not formed, so that the prospective and full-period linked new energy station multi-product material allocation scheme cannot be accurately and efficiently manufactured. In view of the above problems, no effective solution has been proposed at present. Disclosure of Invention The embodiment of the specification provides an intelligent material allocation method and device suitable for a new energy station, which are used for solving the problem that a prospective and full-period linkage new energy station multi-product material allocation scheme cannot be accurately and efficiently manufactured in the prior art. In a first aspect, an embodiment of the present disclosure provides an intelligent material allocation method applicable to a new energy station, including: The method comprises the steps of acquiring basic parameters required by operation and maintenance of a new energy station, wherein the basic parameters at least comprise material demand parameters, cost parameters, time period parameters and boundary constraint parameters, the boundary constraint parameters at least comprise total stock capacity, and the material demand parameters and the boundary constraint parameters are used for determining dynamic safety stock coefficients; Determining a comprehensive cost index of a supply chain of a whole period according to the material demand parameter, the cost parameter, the time period parameter and the total stock capacity, and constructing an objective function aiming at minimizing the comprehensive cost index of the supply chain; Constructing constraint conditions which take total supply and demand balance, storage upper limit, material demand and resource investment upper limit as constraints according to the material demand parameters, cost parameters, time period parameters, boundary constraint parameters and the dynamic safety inventory coefficient; And constructing a multi-product material collaborative allocation model based on the objective function and the constraint condition, solving the multi-product material collaborative allocation model, and determining the amount of various materials to be prepared in each period to form a new energy station multi-product material allocation scheme. In some embodiments, the material demand parameters at least comprise the material category number, the material demand of each type of material in each period, the standard deviation of the material demand of each type of material in each period, the initial warehouse storage capacity of each type of material and the service level coefficient of each type of material, the cost parameters at least comprise the resource amount of each type of material in each period, the transfer resource amount of each type of material in each period, the warehouse unit storage cost and the backorder cost coefficient of each type of material, the time period parameters at least comprise the allocation period, the preparation period of each type of material and the transfer period of each type of material, and the boundary constraint parameters further comprise the total warehouse upper limit, the single material preparation resource investment upper limit, the threshold coefficient of each type of material and the stock occupation capacity of each type of material unit. In some embodiments, the material demand parameters and the boundary constraint parameters are used to determine dynamic safety inventory coefficients, comprising: acquiring a dynamic safet