CN113657699A - Electric power material management and control system and method based on risk index - Google Patents
Electric power material management and control system and method based on risk index Download PDFInfo
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Abstract
The invention provides a risk index-based electric power material management and control system and method. The management and control method comprises the steps of firstly collecting material data of an electric power project, preprocessing the material data, establishing a material data set, then establishing a hierarchical structure model, calculating material risk values corresponding to each type of material in the electric power project, and then obtaining a material supply risk index of the electric power project according to a calculation result; and finally, formulating a material allocation scheme according to the material supply risk index to allocate the materials. According to the invention, a specific material allocation scheme is formulated through the material risk value and the material risk index, so that the stability of the power material supply chain is ensured, the power project is prevented from being shut down due to insufficient material supply, and unnecessary loss is reduced.
Description
Technical Field
The invention relates to the technical field of material management and control, in particular to an electric power material management and control method based on risk indexes.
Background
In recent years, with the continuous improvement of the economic and social level of China, the speed of building, upgrading and reconstructing the power grid of China is increased, and the demand for power materials is increased. However, the increasingly large power supply chain also exposes some of the supply risks.
Because the supply efficiency of the materials is influenced by the production work efficiency of the material suppliers, the fluctuation is easy to occur, and if the materials meet emergencies such as natural disasters, the material supply has larger supply loopholes. If a plurality of power projects require a large amount of the same material to supply, an imbalance between material inventory and material production supply and material consumption may occur, thereby affecting the stability of the power material supply chain. The instability of the material supply chain can cause the problems of unnecessary suspension construction and the like of the power project, thereby influencing the implementation process of the power project and causing unnecessary economic cost loss.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a risk index-based electric power material management and control system and method.
The purpose of the invention is realized by the following technical scheme:
a risk index-based electric power material management and control method comprises the following steps:
the method comprises the following steps that firstly, a data acquisition module acquires material data of an electric power project, and the data acquisition module transmits the material data to a data processing module;
secondly, the data processing module preprocesses the material data, and the data processing module establishes a material data set according to the preprocessed material data;
thirdly, the data analysis module establishes a hierarchical structure model according to the material data set, calculates the material risk value corresponding to each type of material in the electric power project through the hierarchical structure model, and acquires the material supply risk index of the electric power project according to the material risk values of all the materials in the electric power project;
and step four, the data analysis module formulates a material allocation scheme according to the material supply risk index, and allocates and supplements materials in the power project with high material risk index according to the material allocation scheme.
Calculate through the goods and materials risk value to every kind of goods and materials in the electric power project to obtain the goods and materials risk index of electric power project, when carrying out goods and materials allotment management, the priority is considered and is carried out the goods and materials allotment to the electric power project that goods and materials risk index is high, and also the priority is carried out the allotment replenishment to the goods and materials that goods and materials risk value is high to the electric power project that goods and materials risk index is high, guarantee that electric power project can not be because of the not enough construction of goods and materials supply, guaranteed the stability of electric power goods and materials supply chain, avoid causing huge loss.
Further, the specific process of establishing the hierarchical structure model by the data analysis module in the step two is as follows: the data analysis module selects index factors of the hierarchical structure model based on an empirical formula, performs weight calculation on the index factors according to the importance of the index factors, and sets a material risk value calculation formula corresponding to each index factor.
The method has the advantages that after the hierarchical structure model is built, the material supply risk can be reasonably and accurately evaluated, the material risk value corresponding to each type of material can be accurately obtained, the material can be formulated according to the calculated material risk value in the subsequent material allocation scheme, the material with a high material risk value is preferentially allocated, and the material supply can meet the material requirement when the power project is implemented.
Further, the index factors include material demand, material adjustability, material supply efficiency and environmental factors.
And factors influencing material supply are used as index factors of the hierarchical structure model, so that the output result of the hierarchical structure model can accurately reflect the risk of material supply.
Further, when the weight is calculated, the data analysis module constructs a judgment matrix of the importance of the index factors through an empirical formula, and the data analysis module calculates a weight vector of the judgment matrix so as to obtain the weight of each index factor.
The material risk value is calculated by setting the weight, and because different indexes have different influence degrees on the material risk value of material supply, the weight is set according to the influence degree of the index factors on the material supply risk, so that the finally calculated material risk value is ensured to be in accordance with the reality.
Further, after the data analysis module constructs the judgment matrix, the data analysis module also performs consistency check on the judgment matrix, if the consistency check is passed, the feature vector of the judgment matrix is the weight vector, and if the consistency check is not passed, the data analysis module reconstructs the judgment matrix.
The consistency check is to check the harmony between the importance degrees of each element, prevent the scoring matrix from generating logic contradiction and improve the calculation accuracy of the hierarchical structure model.
Further, the preprocessing of the data processing module on the material data in the second step is specifically data cleaning of the material data, and the specific steps of the data cleaning are as follows:
2.1, screening missing and repeated data in the material data through a regular expression;
2.2, extracting historical data of the same material data with missing data by the data processing module, and filling the missing data by using the average value of the historical data;
and 2.3, the data processing module carries out clearing processing on the data with the repetition.
Due to the fact that statistics omission may occur during material data statistics, situations such as data missing or data repeating exist inevitably, the data missing and data repeating need to be cleaned under the situations, influences of the data missing or the data repeating on subsequent material risk value calculation are reduced, and accuracy of material risk value calculation is improved.
Further, after the data analysis module calculates the material risk value corresponding to each type of material in the power project through the hierarchical structure model in the third step, the data analysis module ranks the supply risk of each type of material through the material risk value corresponding to each type of material, the higher the material risk value is, the higher the risk level corresponding to the material is, and the data analysis module also distinguishes and marks the materials with different risk levels through colors.
The goods and materials with different risk levels are marked through different colors, so that different risk levels are shown concisely and clearly, and the formulation efficiency of the goods and materials allocation scheme is further improved.
Furthermore, after the data analysis module formulates a material allocation scheme, the data analysis module predicts an implementation result of the material allocation scheme, and the data analysis module transmits the material allocation scheme and the predicted implementation result to the display module for display.
The utility model provides an electric power material management and control system based on risk index, includes data acquisition module, data processing module and data analysis module, data acquisition module is connected with data processing module, data acquisition module is used for gathering the material data of electric power project, data processing module is used for carrying out the preliminary treatment to the material data, data analysis module and data processing module are connected, data analysis module is used for calculating the material risk index of electric power project and formulates material allotment scheme.
Furthermore, the electric power material management and control system based on the risk index further comprises a display module, wherein the display module is connected with the data analysis module, and the display module is used for displaying the material allocation scheme and the prediction implementation result of the material allocation scheme.
The invention has the beneficial effects that:
the material risk value is calculated through the material data of all kinds in the electric power project, so that the material supply risk index of the electric power project is obtained, a material allocation scheme is formulated according to the material supply risk index, materials in the electric power project with high material risk index are allocated preferentially, the material supply is guaranteed to meet the requirements of all the electric power projects, the condition that a certain electric power project is forced to pause construction due to insufficient material supply can not occur, and unnecessary loss is reduced. The goods and materials are reasonably allocated through the goods and materials allocation scheme, so that accidents can be met in time or the goods and materials supply source is in trouble, and the stability of the goods and materials supply chain can be ensured.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic view of a hierarchical structure model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of the present invention;
wherein: 1. the system comprises a data acquisition module 2, a data processing module 3, a data analysis module 4 and a display module.
Detailed Description
The invention is further described below with reference to the figures and examples.
Example (b):
an electric power material management and control method based on risk index is shown in fig. 1, and includes the following steps:
the method comprises the following steps that firstly, a data acquisition module 1 acquires material data of an electric power project, and the data acquisition module 1 transmits the material data to a data processing module 2;
secondly, the data processing module 2 preprocesses the material data, and the data processing module 2 establishes a material data set according to the preprocessed material data;
step three, the data analysis module 3 establishes a hierarchical structure model according to the material data set, calculates the material risk value corresponding to each type of material in the electric power project through the hierarchical structure model, and the data analysis module 3 acquires the material supply risk index of the electric power project according to the material risk values of all the materials in the electric power project;
and step four, the data analysis module 3 formulates a material allocation scheme according to the material supply risk index, and allocates and supplements materials in the power project with high material risk index according to the material allocation scheme.
For the power project with lower material risk index, normal scheduled construction can be carried out according to the power project requirement, and the stored materials can meet the normal construction requirement. For the power project with higher material risk index, the construction is arranged according to the change of the inventory and the supply efficiency of the supplier, and because the power project with higher material risk index is not particularly urgent to the material requirement, the material supply can be matched through the work arrangement of the construction, and unnecessary construction pause can be avoided. For the power project with high material risk index, the material supply is urgent, and the material supply requirement needs to be met firstly, so that a material allocation scheme is formulated, the required materials are allocated preferentially for use, and the work can be carried out smoothly.
The specific process of establishing the hierarchical structure model by the data analysis module 3 in the second step is as follows: the data analysis module 3 selects index factors of the hierarchical structure model based on an empirical formula, and the data analysis module 3 performs weight calculation of the index factors according to the importance of the index factors and sets a material risk value calculation formula corresponding to each index factor.
The index factors include material demand, material adjustability, material supply efficiency and environmental factors.
As shown in FIG. 2, the higher level of the hierarchical model is the target level and the lower level is the factor level. The high-level (target level) display shows the risk index of material supply, namely the output result of the model, and the low-level (factor level) corresponds to four primary indexes of material demand, material supply efficiency, material adjustability and environmental factors, wherein the index of the material supply efficiency is divided into three secondary indexes of repeated work condition, quality sampling inspection and delay condition.
When calculating the weight, the data analysis module 3 constructs a judgment matrix of the importance of the index factors through an empirical formula, and the data analysis module 3 calculates the weight vector of the judgment matrix so as to obtain the weight of each index factor.
Starting from the 2 nd layer of the hierarchical structure model, for each factor of the same layer or each factor of the same layer subordinate to the same factor of the previous layer, a pair comparison matrix is constructed by using a pair comparison method and a comparison scale of 1-9 until the lowest layer of the hierarchical structure model. The judgment matrix result constructed by the first-level index and the second-level index of the hierarchical structure model is as follows:
(1) the first layer of the judgment matrix is:
in matrix C1、C2、C3Respectively the material demand, the material supply efficiency, the material adjustability and the environmental factors.
(2) The second layer judgment matrix is:
the judgment matrix under the material supply efficiency index is as follows:
wherein A is21、A22、A23The conditions of rework, quality sampling inspection and time delay are respectively.
After the data analysis module 3 constructs the judgment matrix, the data analysis module 3 also performs consistency check on the judgment matrix, if the consistency check is passed, the feature vector of the judgment matrix is the weight vector, and if the consistency check is not passed, the data analysis module 3 reconstructs the judgment matrix.
The steps for checking and judging the consistency of the matrix are as follows:
calculating an index CI for measuring the inconsistency degree of a judgment matrix A (n is greater than 1 order square matrix):
where λ (a) is the maximum eigenvalue of matrix a. The consistency of the check judgment matrix A is passed or not, and the judgment is carried out by judging the value of standard RI (average random consistency index), wherein RI is only related to the order number n of the matrix, and the comparison table of RI and the order number n of the matrix is shown in Table 1.
TABLE 1 RI vs. matrix order n
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| RI | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 |
Then, the random consistency ratio CR of the judgment matrix A is calculated:
when CR <0.1, matrix a is judged to pass the consistency check.
For the first layer judgment matrix F, n is 4, and lambda is calculatedmax4.0014, the value of the index CI that can obtain the degree of inconsistency of the first-layer determination matrix F is:
as can be seen from the table lookup, when n is 4, RI is 0.9, so
Similarly, the second layer may have CI of 0.00023, CR of 0.0004;
it can be seen from this that, since the CR values of the first layer and the second layer are both smaller than 0.1 and the determination matrices of both layers pass the consistency check, the weight vector calculation is performed on the first layer and the second layer.
The weight vector W of the first layer matrix F is (0.3912, 0.2474, 0.2008, 0.1606)TThe weight of the four factors, namely the material demand, the material supply efficiency, the material adjustability and the environmental factor, is 0.3912, 0.2474, 0.2008 and 0.1606 respectively.
Second layer judgment matrix S2Has a weight vector of W2＝(0.6072 0.2377 0.1551)TThe weights of the rework condition, the quality sampling inspection condition and the delay condition are 0.6072, 0.2377 and 0.1551 respectively.
The calculation criteria for the material risk values for each factor are as follows:
index factors are as follows: required amount of material
Even for materials not involved in the power project list, a certain amount of demand is required to deal with the construction of a major emergency project, and thus the average monthly demand of the materials in the region of the power project material demand + 20% previous years is defined, and when the materials are not on the project list, the average monthly demand of the materials is defined as 0.
(1) The material demand of the power project-the self stock is less than or equal to 0, and the material risk value is defined to be 0.
(2) The demand quantity of the power project material-the self stock quantity is more than 0, the data is normalized, and the calculated value is mapped to the interval of 0-1.
Therefore, the material risk value is (the required amount of the electric power project material-the self stock quantity)/the average monthly required amount of the material in the area of the last years is 100, and the material risk value is defined as 100 when the required amount of the project material minus the stock quantity is equal to or more than the average required amount in the last year.
Index factor two: material supply efficiency:
the first-level index factor is as follows: and (4) rework condition:
(1) the material demand of the power project-the self stock is less than or equal to 0, and the material risk value is defined to be 0.
(2) When the inventory of the supplier does not meet the demand, defining the material risk value corresponding to the reworking capacity of the supplier outside a province according to the reworking condition of the supplier as follows: when the re-work capacity is less than 30%, more than or equal to 30% and less than 60%, more than or equal to 60% and less than 90% and more than 90%, the corresponding material risk values are 70, 50, 30 and 0, respectively, the capacity and material risk value of a supplier in a certain province is determined by the re-work electric quantity proportion of the supplier, if the re-work electric quantity proportion is less than 0.1, the supplier is regarded as not re-work, the material risk value is (1-re-work electric quantity proportion)/(1-0.1) × 100, and if the calculated material risk value is more than 100, the value is forced to be 100.
(3) And when data corresponding to the supplier reworking condition is lost and the inventory is less than the demand, judging the material risk value according to the material type. If the corresponding material is a supermarket type, the risk influence is small due to the flexibility of supply, and the material risk value is defined to be 0; if the corresponding material is of other types, the high risk is defined, and the material risk value is 80.
Second-level index factor two: quality spot check:
the risk value of the supplies with quality problems is 90 in 3 months, the risk value of the supplies with quality problems is 60 in 6 months, the risk value of the supplies with quality problems is 30 in one year, and the risk value of the supplies with quality problems is 0 in more than one year or no quality problems.
A second-level index factor III: the delay condition is as follows:
the risk value of the delayed problem materials of the suppliers in 3 months is 90, the risk value of the materials in 6 months is 60, the risk value of the materials in one year is 30, and the risk value of the materials in more than one year or without the delayed problem is 0.
Index factor three: material adjustability:
this factor is mainly considered for the adjustability and replaceability of the materials. The method can be divided into four types of customized fixed length type, storage and check distribution type, adjustable type and supermarket type according to the material characteristics. The adjustable material mainly comprises a wire, a low-voltage cable, hardware fittings, a porcelain insulator, a lightning arrester, a pipeline and the like, and the material is easy to allocate and has relatively small supply risk. The supermarket purchase goods and materials such as wiring terminals, fuses and the like are generally used as auxiliary materials, the universality is strong, engineering construction teams have replaceable products, the flexibility of the supermarket purchase is strong, and suppliers are not fixed, so that the risk of supplying the goods and materials is relatively small. Secondly, materials are distributed by adopting a storage and inspection formula, and the existing transformer and cement poles are directly influenced by the storage of a central warehouse and the logistics distribution. The customized fixed-length materials are manufactured according to drawings and cannot be used universally, such as iron towers, high-voltage cables, ring main units and the like, and the risk index of the materials is highest. The index is thus defined as follows:
(1) when the stock of the customized fixed-length materials is sufficient, the material risk value is 50, otherwise, the material risk value is 100;
(2) when the storage, the inspection and the distribution of the materials are sufficient, the risk value of the materials is 0, otherwise, the risk value of the materials is 50;
(3) when the inventory of the adjustable materials is sufficient, the material risk value is 0, otherwise, the material risk value is 35;
(4) the risk value of the supermarket type material is 0 when the stock is sufficient, otherwise, the risk value of the material is 35.
Index factor four: environmental factors:
counting the weather of 15 days in the future of a certain city, if extreme weather such as typhoon, snow disaster, earthquake, strong convection and the like accounts for 90 of the material risk value above 2/3, 60 of the material risk value above 1/3, 30 of the material risk value above 1 day but less than 1/3 and 0 of the material risk value of the non-extreme weather.
The material risk index of the electric power project reflects the whole material risk level of the project, and the calculation formula of the material risk index of the electric power project is as follows:
in the second step, the data processing module 2 is used for preprocessing the material data, specifically, cleaning the material data, and the data cleaning comprises the following specific steps:
2.1, screening missing and repeated data in the material data through a regular expression;
2.2, extracting historical data of the same material data with missing data by the data processing module, and filling the missing data by using the average value of the historical data;
and 2.3, the data processing module carries out clearing processing on the data with the repetition.
After the data analysis module 3 calculates the material risk value corresponding to each type of material in the power project through the hierarchical structure model, the data analysis module 3 ranks the supply risk of each type of material through the material risk value corresponding to each type of material, the higher the material risk value is, the higher the risk level corresponding to the material is, and the data analysis module 3 also marks the materials with different risk levels through color differentiation.
Calculating the material risk index of the electric power project by calculating the material risk value of each material in the electric power project, and if the value range of the material supply risk index is [0,33.3 ], grading the material supply risk index to be green; when the output value range of the material risk index is [33.3,66.6), the grade is yellow; and when the output value range of the material risk index is [66.6,100], the output value range is graded as red.
After the data analysis module 3 formulates a material allocation scheme, the data analysis module 3 predicts an implementation result of the material allocation scheme, and the data analysis module 3 transmits the material allocation scheme and the predicted implementation result to the display module 4 for display.
The utility model provides an electric power material management and control system based on risk index, as shown in fig. 3, includes data acquisition module 1, data processing module 2, data analysis module 3 and display module 4, data acquisition module 1 is connected with data processing module 2, data acquisition module 1 is used for gathering the material data of electric power project, data processing module 2 is used for carrying out the preliminary treatment to the material data, data analysis module 3 is connected with data processing module 2, data analysis module 3 is used for calculating the material risk index of electric power project and formulates the material allotment scheme.
The display module 4 is connected with the data analysis module 3, and the display module 4 is used for displaying the material allocation scheme and the prediction implementation result of the material allocation scheme.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (10)
1. A risk index-based electric power material management and control method is characterized by comprising the following steps:
the method comprises the following steps that firstly, a data acquisition module (1) acquires material data of an electric power project, and the data acquisition module (1) transmits the material data to a data processing module (2);
secondly, the data processing module (2) preprocesses the material data, and the data processing module (2) establishes a material data set according to the preprocessed material data;
step three, the data analysis module (3) establishes a hierarchical structure model according to the material data set, calculates material risk values corresponding to each type of materials in the electric power project through the hierarchical structure model, and the data analysis module (3) obtains material supply risk indexes of the electric power project according to the material risk values of all the materials in the electric power project;
and step four, the data analysis module (3) formulates a material allocation scheme according to the material supply risk index, and performs preferential allocation and supplement on the materials with high material risk values in the power project with high material risk index according to the material allocation scheme.
2. The electric power material management and control method based on risk index as claimed in claim 1, wherein the specific process of establishing the hierarchical structure model by the data analysis module (3) in the second step is as follows: the data analysis module (3) selects index factors of the hierarchical structure model based on an empirical formula, the data analysis module (3) performs weight calculation of the index factors according to the importance of the index factors, and sets a material risk value calculation formula corresponding to each index factor.
3. The method as claimed in claim 2, wherein the index factors include material demand, material adjustability, material supply efficiency, and environmental factors.
4. The electric power material management and control method based on the risk index as claimed in claim 3, wherein when calculating the weight, the data analysis module (3) constructs a judgment matrix of the importance of the index factors by an expert analysis method, and the data analysis module (3) calculates the weight vector of the judgment matrix so as to obtain the weight of each index factor.
5. The electric power material management and control method based on the risk index as claimed in claim 4, wherein after the data analysis module (3) constructs the judgment matrix, the data analysis module (3) further performs consistency check on the judgment matrix, if the consistency check is passed, the eigenvector of the judgment matrix is the weight vector, and if the consistency check is not passed, the data analysis module (3) reconstructs the judgment matrix.
6. The electric power material management and control method based on the risk index as claimed in claim 1, wherein in the second step, the data processing module (2) pre-processes the material data, specifically, performs data cleaning on the material data, and the data cleaning specifically comprises the following steps:
2.1, screening missing and repeated data in the material data through a regular expression;
2.2, extracting historical data of the same-class material data with missing data by the data processing module (2), and filling the missing data by using the average value of the historical data;
2.3, the data processing module (2) carries out clearing processing on the data with the duplication.
7. The electric power material management and control method based on the risk index as claimed in claim 1, wherein after the data analysis module (3) calculates the material risk value corresponding to each type of material in the electric power project through the hierarchical structure model in the third step, the data analysis module (3) ranks the supply risk of each type of material through the material risk value corresponding to each type of material, and the material with different risk levels is further labeled by the data analysis module (3) through color differentiation when the risk level corresponding to the material with higher material risk value is higher.
8. The electric power material management and control method based on the risk index as claimed in claim 1, wherein after the data analysis module (3) formulates the material allocation scheme, the data analysis module (3) predicts the implementation result of the material allocation scheme, and the data analysis module (3) transmits the material allocation scheme and the predicted implementation result to the display module (4) for display.
9. The utility model provides an electric power material management and control system based on risk index, its characterized in that, includes data acquisition module (1), data processing module (2) and data analysis module (3), data acquisition module (1) is connected with data processing module (2), data acquisition module (1) is used for gathering the material data of electric power project, data processing module (2) are used for carrying out the preliminary treatment to the material data, data analysis module (3) are connected with data processing module (2), data analysis module (3) are used for calculating the material risk index of electric power project and formulate the material scheme of allotment.
10. The risk index-based electric power material management and control system according to claim 9, further comprising a display module (4), wherein the display module (4) is connected to the data analysis module (3), and the display module (4) is configured to display the material allocation scheme and the predicted implementation result of the material allocation scheme.
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