CN112541528A - Power transmission and transformation project cost prediction index optimization method based on fuzzy clustering - Google Patents

Power transmission and transformation project cost prediction index optimization method based on fuzzy clustering Download PDF

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CN112541528A
CN112541528A CN202011398768.0A CN202011398768A CN112541528A CN 112541528 A CN112541528 A CN 112541528A CN 202011398768 A CN202011398768 A CN 202011398768A CN 112541528 A CN112541528 A CN 112541528A
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明月
熊一
文习山
陈小月
李猎
廖晓红
陈然
高晓晶
唐学军
柯方超
周秋鹏
孙利平
马莉
王巍
熊川羽
张赵阳
王琪鑫
段志强
周蠡
李智威
姜山
张雪霏
贺兰菲
董弘川
郭婷
廖爽
邹雨馨
任羽纶
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State Grid Corp of China SGCC
Wuhan University WHU
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Wuhan University WHU
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

A power transmission and transformation project cost prediction index optimization method based on fuzzy clustering is characterized in that n indexes are extracted from a technical-warp calculation system, the initial number of clustering centers is set to be 2, all indexes are classified by adopting a fuzzy clustering algorithm, a membership degree evaluation index K is calculated according to a fuzzy clustering result, then the number of the clustering centers is increased by one, the fuzzy clustering algorithm is adopted for continuous iteration until the calculated K value reaches the maximum value, and the clustering center corresponding to the K value at the moment is used as the optimized index. The design not only can effectively reduce the calculation difficulty of prediction, but also can improve the prediction precision.

Description

Power transmission and transformation project cost prediction index optimization method based on fuzzy clustering
Technical Field
The invention belongs to the field of power transmission and transformation engineering design, and particularly relates to a power transmission and transformation engineering cost prediction index optimization method based on fuzzy clustering.
Background
The cost of the power transmission and transformation project is an important component of the investment of power grid enterprises, and accurate and reasonable estimation of the power transmission and transformation project is needed to make the best investment strategy. The existing cost compiling depends on manual work, the automation degree of the 'calculation amount' step which consumes most time, wastes most labor and is most prone to error in the compiling process is low, the engineering amount calculation mainly depends on manual work, the efficiency is low, the accuracy is poor, and the balance rate level of the engineering is always high. Meanwhile, the current power grid engineering cost data are stored in various different application systems in a scattered manner, a uniform service data collection platform is lacked, data association is not tight, a data island phenomenon is serious, a large amount of time and energy of basic engineering cost professionals are consumed for collection, filling and checking of the basic engineering data, the basic engineering data are influenced by factors such as professional quality and responsibility of the data collection and filling professionals, and authenticity and quality of the basic engineering data are lacked in cost analysis.
In order to solve the problems, intelligent algorithms appear in succession and show wide application value in the field of cost prediction. The traditional prediction methods are divided into qualitative prediction methods and quantitative prediction methods. The qualitative prediction method mainly comprises the following steps: the qualitative forecasting method is characterized in that the opinions and expectations of experts on projects are collected, the process is complicated, and the forecasting result inevitably introduces subjective factors of the experts. The quantitative prediction method comprises the following steps: the quantitative prediction method is not influenced by subjective factors and is widely applied to prediction of actual engineering, however, for complex construction projects, original data are complex and diverse, data regularity is poor, and the method has poor processing capability on interaction effects and nonlinear relations among data.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provide a fuzzy clustering-based power transmission and transformation project cost prediction index optimization method which can effectively reduce the prediction calculation difficulty and improve the prediction precision.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a power transmission and transformation project cost prediction index optimization method based on fuzzy clustering sequentially comprises the following steps:
step A, extracting n indexes from a technical and economic quantity system to form a vector X of each index1，X2，…Xn；
Step B, setting the initial number of the clustering centers to be 2, classifying each index by adopting a fuzzy clustering algorithm, and calculating a membership degree evaluation index K according to a fuzzy clustering result;
step C, adding one to the number of the clustering centers, classifying each index again by adopting a fuzzy clustering algorithm, and recalculating a membership degree evaluation index K;
and D, circularly repeating the step C until the calculated K value reaches the maximum value, and taking the clustering center corresponding to the K value at the moment as an optimized index.
In the step B, the membership degree evaluation index K is calculated by adopting the following formula:
Figure BDA0002811765070000021
in the above formula, c is the number of cluster centers, uijAnd the membership degree of the ith index to the jth clustering center.
In the step B, the step of classifying the indexes by adopting the fuzzy clustering algorithm sequentially comprises the following steps:
b1, randomly dividing a membership matrix U in the range of 0-1, and initializing 2 clustering centers;
b2, determining the membership u of each index according to the following formulaij：
Figure BDA0002811765070000022
In the above formula, dijThe distortion degree of the ith index belonging to the jth cluster center, j being 1, 2 … c;
b3, updating the membership clustering center according to the calculated membership degree:
Figure BDA0002811765070000023
in the above formula, XiIs a vector of the i-th index, pjA vector for the jth cluster center;
b4, repeating the steps B2 and B3 circularly until pjThe updated distance satisfies the following condition:
Figure BDA0002811765070000024
in the above formula, pj m-1And epsilon is an allowable error constant for the vector of the jth cluster center after the m-1 th update.
In step B2, d isijThe calculation formula of (2) is as follows:
dij＝‖Xi-pj‖＝(Xi-pj)T(Xi-pj)。
in the step A, the n indexes comprise a peak, a base plane, a chassis, a sleeve, a chuck, a wire drawing disc, a concrete assembled foundation, a foundation cushion layer, a cast-in-place foundation, a foundation connecting beam, a protective cap, a rock anchor rod foundation, a cast-in-place pile foundation, a tree root pile foundation, a prefabricated pile foundation, a steel pipe pile foundation, a hole digging pile foundation, a flood discharge ditch, a retaining wall, a slope protection, a grounding body, a grounding electrode, a grounding module, a foundation permanent cofferdam, a foundation bolt, a plug-in angle steel or steel pipe, a steel ring, a concrete rod, a steel pipe rod, a tower, a wire drawing, a falling prevention device, a lightning conductor, an OPGW, an insulator string, a shockproof hammer, a spacer, a damping wire, an ice resistance ring, a.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a power transmission and transformation project cost prediction index optimization method based on fuzzy clustering, which comprises the steps of firstly extracting n indexes from a technical computation system, then setting the initial number of clustering centers as 2, classifying the indexes by adopting a fuzzy clustering algorithm, then calculating a membership evaluation index K according to a fuzzy clustering result, then adding one to the number of clustering centers, continuously iterating by adopting the fuzzy clustering algorithm until the calculated K value reaches the maximum value, and taking the clustering center corresponding to the K value at the moment as an optimized index, wherein the membership evaluation index K is introduced to represent the clustering effect on the basis of the conventional fuzzy clustering, so that the optimal clustering effect can be finally obtained, the calculation difficulty of subsequent project cost prediction can be effectively reduced, and the project cost prediction error based on the clustering result is smaller, with higher accuracy. Therefore, the invention not only can effectively reduce the calculation difficulty of prediction, but also can improve the prediction precision.
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FIG. 1 is a flow chart of the present invention.
FIG. 2 is a K-value curve obtained by continuous iterative calculation in example 1.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings.
Referring to fig. 1, a power transmission and transformation project cost prediction index optimization method based on fuzzy clustering sequentially comprises the following steps:
step A, extracting n indexes from a technical and economic quantity system to form a vector X of each index1，X2，…Xn；
Step B, setting the initial number of the clustering centers to be 2, classifying each index by adopting a fuzzy clustering algorithm, and calculating a membership degree evaluation index K according to a fuzzy clustering result;
step C, adding one to the number of the clustering centers, classifying each index again by adopting a fuzzy clustering algorithm, and recalculating a membership degree evaluation index K;
and D, circularly repeating the step C until the calculated K value reaches the maximum value, and taking the clustering center corresponding to the K value at the moment as an optimized index.
In the step B, the membership degree evaluation index K is calculated by adopting the following formula:
Figure BDA0002811765070000041
in the above formula, c is the number of cluster centers, uijAnd the membership degree of the ith index to the jth clustering center.
In the step B, the step of classifying the indexes by adopting the fuzzy clustering algorithm sequentially comprises the following steps:
b1, randomly dividing a membership matrix U in the range of 0-1, and initializing 2 clustering centers;
b2, determining the membership u of each index according to the following formulaij：
Figure BDA0002811765070000042
In the above formula, dijThe distortion degree of the ith index belonging to the jth cluster center, j being 1, 2 … c;
b3, updating the membership clustering center according to the calculated membership degree:
Figure BDA0002811765070000043
in the above formula, XiIs a vector of the i-th index, pjA vector for the jth cluster center;
b4, repeating the steps B2 and B3 circularly until pjThe updated distance satisfies the following condition:
Figure BDA0002811765070000044
in the above formula, pj m-1And epsilon is an allowable error constant for the vector of the jth cluster center after the m-1 th update.
In step B2, d isijThe calculation formula of (2) is as follows:
dij＝‖Xi-pj‖＝(Xi-pj)T(Xi-pj)。
in the step A, the n indexes comprise a peak, a base plane, a chassis, a sleeve, a chuck, a wire drawing disc, a concrete assembled foundation, a foundation cushion layer, a cast-in-place foundation, a foundation connecting beam, a protective cap, a rock anchor rod foundation, a cast-in-place pile foundation, a tree root pile foundation, a prefabricated pile foundation, a steel pipe pile foundation, a hole digging pile foundation, a flood discharge ditch, a retaining wall, a slope protection, a grounding body, a grounding electrode, a grounding module, a foundation permanent cofferdam, a foundation bolt, a plug-in angle steel or steel pipe, a steel ring, a concrete rod, a steel pipe rod, a tower, a wire drawing, a falling prevention device, a lightning conductor, an OPGW, an insulator string, a shockproof hammer, a spacer, a damping wire, an ice resistance ring, a.
The principle of the invention is illustrated as follows:
the invention provides a power transmission and transformation project cost prediction index optimization method based on fuzzy clustering, which adopts a membership degree evaluation index K in the process of fuzzy clustering. Because the sum of the membership degrees in the fuzzy clustering is 1, the greater the maximum value in the known membership degrees is, the better the clustering effect is, so that the K value can be increased along with the improvement of the clustering effect, and the c value corresponding to the K value reaching the maximum value is taken as the final clustering center number, so that the best clustering effect can be achieved.
Example 1:
referring to fig. 1, a power transmission and transformation project cost prediction index optimization method based on fuzzy clustering is sequentially performed according to the following steps:
1. extracting 43 indexes from the technical calculation system to form a vector X of each index1，X2，…X43The indexes comprise peaks, base planes, chassis, sleeves, chucks, anchor plates, concrete assembled foundations, foundation cushions, cast-in-place foundations, foundation connecting beams, protective caps, rock bolt foundations, cast-in-place pile foundations, tree root pile foundations, prefabricated pile foundations, steel pipe pile foundations, bored pile foundations, flood discharge ditches, retaining walls, slope protection, grounding bodies, grounding electrodes, grounding modules, foundation permanent cofferdams, foundation bolts, inserted angle steel or steel pipes, steel rings, concrete poles, steel pipe poles, towers, pull lines, anti-falling devices, lightning protection lines, leads, OPGWs, insulator chains, vibration dampers, spacing bars, heavy hammers, damping lines, ice resistance rings, jumper wires and spans;
2. firstly, setting the initial number of the clustering centers to be 2, then randomly dividing a membership matrix U with the range of 0-1, and initializing 2 clustering centers;
3. determining the membership degree u of each index according to the following formulaij：
Figure BDA0002811765070000051
dij＝‖Xi-pj‖＝(Xi-pj)T(Xi-pj)
In the above formula, dijFor the distortion degree of the ith index belonging to the jth cluster center, the distance between two vectors is used for representing j is 1, 2 … c, and dikThe same process is carried out;
4. updating the membership clustering center according to the calculated membership:
Figure BDA0002811765070000052
in the above formula, XiIs a vector of the i-th index, pjA vector for the jth cluster center;
5. repeating the steps 3 and 4 circularly until pjThe updated distance satisfies the following condition:
Figure BDA0002811765070000053
in the above formula, pj m-1The vector is the vector of the jth clustering center after the m-1 th update, epsilon is an allowable error constant, and the value of epsilon is 0.05;
6. calculating a membership degree evaluation index K to be 0.824 by adopting the following formula;
Figure BDA0002811765070000061
in the above formula, c is the number of cluster centers, and n isTotal number of indices, uijThe membership degree of the ith index to the jth clustering center;
7. adding one to the number of the clustering centers, and repeating the steps 3-6;
8. and (3) repeating the step (7) in a circulating manner until the calculated K value reaches the maximum value (see fig. 2), wherein the clustering center c corresponding to the K value is 18, and indexes (respectively: loop number, lead unit price, tower material unit price, terrain, geology, wind speed, ice coating, lead section, strain proportion, earthwork amount, basic concrete amount, basic steel amount, tower material amount, lead amount, synthetic insulator amount, disc insulator amount, hardware amount and tower amount) corresponding to the 18 clustering centers are used as optimized indexes.
In order to verify the effectiveness of the method, the clustering results of c 17, c 18 and c 19 are respectively selected to predict the construction cost of different power transmission and transformation projects, and the specific prediction steps are as follows:
the method comprises the following steps of taking optimized indexes as input, taking power transmission and transformation project cost as output, and solving a regression equation of a function by using a support vector machine to obtain a prediction model, wherein the method specifically comprises the following steps:
(1) three descriptive factors of loop number, terrain and geology are quantized:
the single loop, the double loop, the three loop and the four loops of the loop number of the power transmission line are respectively expressed by 1, 2, 3 and 4, if a section of line contains various loop numbers, the contained loop numbers are required to be subjected to weighted average processing according to the proportion of the length of the line with different loop numbers in the length of the total line;
the power transmission line approach terrain comprises flat ground, hills, a river network, a mud and marsh, mountain land, mountains and deserts, the flat ground is divided into 5 grades, the flat ground is a first grade, the hills are a second grade, the river network, the mud and marsh are combined into a third grade, the mountain land and the deserts are respectively a fourth grade and a fifth grade, the 5 terrain grades are respectively represented by 1, 2, 3, 4 and 5, and if one section of line comprises multiple terrains, weighted average processing needs to be carried out on the contained terrains according to the proportion of the different terrains.
The power transmission line approach geology comprises a common soil pit, a hard soil pit, loose sand stones, dry sand, a water pit, a muddy water pit, quicksand and a rock pit, wherein the common soil pit is of a first grade, the hard soil pit is of a second grade, the loose sand stones and the dry sand are of a third grade, the water pit, the muddy water pit, the quicksand and the rock pit are respectively of a fourth grade, a fifth grade, a sixth grade and a seventh grade, the geological grades are represented by 1-7, and if the geological conditions of a section of line are complex and various, the contained geology is subjected to weighted average treatment according to the proportion occupied by different geology;
(2) determining a sample matrix according to the sample data of each index value, and processing the sample data;
(3) determining a kernel function K (x)i，x)＝exp(-||xi-x||/(2σ2))dSetting an error limit epsilon to 0.05, and obtaining optimized parameters (C, sigma) through cross validation, specifically, respectively substituting different parameters (C, sigma) into the prediction model
Figure BDA0002811765070000071
Verifying the model, and taking the (C, sigma) corresponding to the predicted value of the power transmission and transformation project cost obtained by calculating the model as an optimized (C, sigma) value;
(4) obtaining a power transmission and transformation project cost prediction model:
Figure BDA0002811765070000072
and secondly, predicting the construction cost of the power transmission and transformation project according to the obtained prediction model.
The prediction error calculation was performed on the obtained prediction data, and the results are shown in table 1:
TABLE 1 prediction error analysis Table
Figure BDA0002811765070000073
According to the results shown in table 1, when c is 18, that is, when the K value reaches the maximum, the cost of the power transmission and transformation project has the minimum error, that is, the method of the present invention can optimize the cost prediction index of the power transmission and transformation project, and improve the final prediction accuracy.

Claims (5)

1. A power transmission and transformation project cost prediction index optimization method based on fuzzy clustering is characterized by comprising the following steps:
the optimization method sequentially comprises the following steps:
step A, extracting n indexes from a technical and economic quantity system to form a vector X of each index1，X2，…Xn；
Step B, setting the initial number of the clustering centers to be 2, classifying each index by adopting a fuzzy clustering algorithm, and calculating a membership degree evaluation index K according to a fuzzy clustering result;
step C, adding one to the number of the clustering centers, classifying each index again by adopting a fuzzy clustering algorithm, and recalculating a membership degree evaluation index K;
and D, circularly repeating the step C until the calculated K value reaches the maximum value, and taking the clustering center corresponding to the K value at the moment as an optimized index.
2. The power transmission and transformation project cost prediction index optimization method based on fuzzy clustering of claim 1, characterized in that:
in the step B, the membership degree evaluation index K is calculated by adopting the following formula:
Figure FDA0002811765060000011
in the above formula, c is the number of cluster centers, uijAnd the membership degree of the ith index to the jth clustering center.
3. The power transmission and transformation project cost prediction index optimization method based on fuzzy clustering according to claim 1 or 2, characterized in that:
in the step B, the step of classifying the indexes by adopting the fuzzy clustering algorithm sequentially comprises the following steps:
b1, randomly dividing a membership matrix U in the range of 0-1, and initializing 2 clustering centers;
b2, determining the membership u of each index according to the following formulaij：
Figure FDA0002811765060000012
In the above formula, dijThe distortion degree of the ith index belonging to the jth cluster center, j being 1, 2 … c;
b3, updating the membership clustering center according to the calculated membership degree:
Figure FDA0002811765060000021
in the above formula, XiIs a vector of the i-th index, pjA vector for the jth cluster center;
b4, repeating the steps B2 and B3 circularly until pjThe updated distance satisfies the following condition:
Figure FDA0002811765060000022
in the above formula, pj m-1And epsilon is an allowable error constant for the vector of the jth cluster center after the m-1 th update.
4. The power transmission and transformation project cost prediction index optimization method based on fuzzy clustering of claim 3, wherein:
in step B2, d isijThe calculation formula of (2) is as follows:
dij＝‖Xi-pj‖＝(Xi-pj)T(Xi-pj)。
5. the power transmission and transformation project cost prediction index optimization method based on fuzzy clustering according to claim 1 or 2, characterized in that: in the step A, the n indexes comprise a peak, a base plane, a chassis, a sleeve, a chuck, a wire drawing disc, a concrete assembled foundation, a foundation cushion layer, a cast-in-place foundation, a foundation connecting beam, a protective cap, a rock anchor rod foundation, a cast-in-place pile foundation, a tree root pile foundation, a prefabricated pile foundation, a steel pipe pile foundation, a hole digging pile foundation, a flood discharge ditch, a retaining wall, a slope protection, a grounding body, a grounding electrode, a grounding module, a foundation permanent cofferdam, a foundation bolt, a plug-in angle steel or steel pipe, a steel ring, a concrete rod, a steel pipe rod, a tower, a wire drawing, a falling prevention device, a lightning conductor, an OPGW, an insulator string, a shockproof hammer, a spacer, a damping wire, an ice resistance ring, a.
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