CN115994784A - Price determination model and construction method thereof - Google Patents
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Abstract
The invention discloses a price determination model construction method, which comprises the following steps: s1, selecting influencing factors based on an index calculation process and actual application scene factors, respectively extracting factors influencing equipment material pricing from three aspects of production cost, market factors and policy factors, and analyzing a price forming mechanism; s2, data collection and data processing: collecting influence factor data and actual price data, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, and preprocessing data to form a data set; and S3, constructing a neural network electric power engineering material pricing model based on genetic algorithm optimization, optimizing the neural network model by utilizing the weight and the threshold value of the genetic algorithm optimization BPNN, and further obtaining a model for determining the price of the electric transmission and transformation engineering iron tower material, so that the price is intelligently determined.
Description
Technical Field
The invention particularly relates to a price determining model and a construction method thereof, which are particularly applied to determining the price of iron tower materials in power transmission and transformation engineering, and belong to the technical field of data processing.
Background
In the face of continuous development of social economy and increasingly complex external construction environment, the method provides greater challenges for the management of power grid infrastructure engineering. In the project review process, the equipment material price is generally counted according to contract price, market information price and power grid project equipment material information price in sequence; if new materials and new equipment appear, the necessary pricing basis is lacking, and the price in the similar equipment of recent engineering or market price inquiry and listing is generally required to be referred, the phenomenon that the price is greatly deviated from the actual cost of the engineering often occurs, and the fund optimizing configuration and the use efficiency of the company are affected. Under the market economic condition, the price of new equipment and new materials is influenced by factors such as regional economic development level, industrial policy, industry monopolization and the like besides the cost of the new equipment and new materials, so that the pricing mode is difficult, the traditional pricing mode depends on more manual experience or a simple statistical mode, and the pricing scientificity is to be improved.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a price determination model construction method, which explores the influence factors of equipment material price from market, macroscopic economy, policy and other aspects, utilizes influence factor mining and analysis technology to carry out deep analysis on the factors influencing the equipment material price, then combines data processing technology to construct a neural network prediction analysis model optimized based on genetic algorithm, evaluates model prediction effect, realizes correct grasp on the change trend of the equipment material price, strengthens the equipment material price analysis and pricing work, is beneficial to reasonably estimating the power engineering cost, optimizes investment strategies and reduces engineering investment risks.
The technical scheme of the invention is as follows:
the invention provides a price determination model construction method, which comprises the following steps:
s1, a processing system receives influence factors which are obtained after price influence factor identification and can influence the price of iron tower materials of power transmission and transformation engineering;
s2, collecting influence factor data corresponding to the influence factors and actual price data of the power transmission and transformation engineering iron tower material, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, finding out and deleting the influence factor data and abnormal values and repeated values in the actual price data, and forming a data set;
s3, inputting the data in the data set into a genetic algorithm, and optimizing the weight and the threshold value of the BP neural network model by using the genetic algorithm to obtain a price determining model for determining the price of the power transmission and transformation engineering iron tower material.
Further, in the step S2, a variable mean vector and a variance-covariance matrix are used as prior information to construct a markov chain, and the markov chain is repeatedly simulated by sampling to obtain stable posterior distribution, so as to generate an estimate of missing data, which specifically includes the following steps:
s21, receiving continuous data vector set Y c ＝[Y 1 ,Y 2 ,....,Y n ]Wherein the ith data vector is Y (i) = [ Y ] i (1),y i (2),.....,y i (D)]I=1, 2,) N, wherein the data in the data vector set is a price influencing factor obtained after the identification and analysis in step S1, Y c Including observed data Y wz And missing data Y qs ；
S22, setting a Gaussian model according to the ith item data, wherein a parameter space of the Gaussian model is theta, and estimating a value theta according to the parameter space theta g Calculating the occurrence probability p (Y) qs Y (Y) wz ，θ g )；
S23, according to the occurrence probability of the current complete data and the missing data,calculating the occurrence probability of the parameter space theta
And updating the estimated value of the parameter space theta of the Gaussian model until the obtained Markov chain
And during convergence, estimating missing data, wherein a calculation formula of the missing data is shown as a formula (I):
wherein N is sample N is the total number of samples Burn-in In order to determine the number of samples to be deleted,
is missing data;
s24, deleting the abnormal value and the repeated value to finally obtain the processed data set.
Further, the method further comprises: and (2) carrying out correlation analysis on the data in the data set obtained after the processing in the step (S2), analyzing whether the correlation between the influence factors and the material price dependent variables is in a strong-weak relation or not based on a bivariate correlation analysis model, and obtaining main influence factors according to the strong-weak relation to be used as an input object of a price determination model.
Further, the bivariate correlation analysis model is analyzed by using Pearson simple correlation coefficient or hypothesis test.
Further, the specific steps of optimizing the neural network model in the step S3 are as follows:
s31, in a BP neural network model, determining a network topology structure, obtaining an initial weight and an initial threshold length of the BP neural network model, substituting the initial weight and the initial threshold length into a genetic algorithm, and encoding the initial weight and the initial threshold length by using the genetic algorithm;
s32, inputting data in the data set obtained after preprocessing in the step S2 into the genetic algorithm, taking the data in the data set, the coded initial weight and the error obtained after the coded threshold length are trained by the BP neural network model as adaptive values, sequentially carrying out selection operation, crossover operation and mutation operation in the genetic algorithm on the adaptive values, carrying out fitness value calculation, and circularly entering the adaptive values which do not meet the end conditions into the selection operation, crossover operation and mutation operation;
s33, substituting the fitness value which meets the end condition after the genetic algorithm operation in the step S32 into the BP neural network model again to obtain an optimal weight value and an optimal threshold length, and then sequentially carrying out error calculation, updating the weight value and the threshold value until the end condition is met, so as to obtain a final prediction result, namely a final price; and (5) circulating the weight and the threshold length which do not meet the end condition into error calculation and weight and threshold updating until the end condition is met.
The invention also provides a method for determining the price of the iron tower material of the power transmission and transformation project, which comprises the following steps:
acquiring a data set of a power transmission and transformation engineering iron tower material to be priced, wherein the data set is price influence factor data and actual price data;
and inputting the data set into a price determination model constructed according to the price determination model construction method to obtain the price of the power transmission and transformation engineering iron tower material to be priced.
The invention also provides a price determination model construction device, which comprises:
the price influence factor collection module is used for receiving influence factors which are obtained after price influence factor identification and can influence the price of the iron tower material of the power transmission and transformation project;
the data preprocessing model construction module is used for collecting influence factor data corresponding to the influence factors and actual price data of the power transmission and transformation engineering iron tower materials, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, finding and deleting abnormal values and repeated values in the influence factor data and the actual price data to form a data set, inputting data in the data set into a genetic algorithm, and optimizing weights and thresholds of a BP neural network model by utilizing the genetic algorithm to obtain a price determination model for determining the price of the power transmission and transformation engineering iron tower materials.
The present invention also provides a price determining apparatus comprising:
the acquisition module is used for acquiring a data set of the power transmission and transformation project iron tower material to be priced, wherein the data set is price influence factor data and actual price data;
and the input module is used for inputting the data set into a price determination model constructed by the price determination model construction method according to any one of the embodiments, so as to obtain the price of the power transmission and transformation project iron tower material to be priced.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for determining the price of the material of the iron tower of the power transmission and transformation project when executing the program.
The invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the method for determining the price of the material of the iron tower of the power transmission and transformation project.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention combines related research results and sample data to intelligently determine new material prices as research targets, comprehensively considers influencing factors of market, macroscopic economy, policy and other aspects on the material prices of the iron towers of the power transmission and transformation projects, utilizes influencing factor mining and analysis technology to carry out deep analysis on factors influencing the material prices of equipment, then combines data processing technology to construct a neural network prediction analysis model optimized based on genetic algorithm, substitutes the optimized model into influencing factors to finally determine the equipment material prices through analysis and calculation, can accurately grasp the change trend of the equipment material prices, enhances the equipment material price analysis and pricing work, and is favorable for reasonably estimating the power project cost, optimizing investment strategies and reducing the project investment risks.
2. According to the invention, the calculation of the equipment material price is carried out by constructing the model, so that the problems of unclear price-based basis, unreasonable price adjustment, large deviation of price and actual engineering cost and the like caused by manual price calculation or simple statistical mode in the prior art can be overcome, the price scientificity and rationality are improved, a scientific and reasonable new material price determination model is formed, the precision degree of cost control can be further improved, and further, the reasonable allocation of resources and the sustainable development of enterprises are realized.
Drawings
FIG. 1 is a schematic diagram of a genetic algorithm optimized neural network according to embodiment 1 of the present invention;
FIG. 2 is a graph showing the fitness curve in example 1 of the present invention;
FIG. 3 is a graph comparing the results of pricing the optimized GA-BPNN model and the BPNN model in example 1 of the present invention;
FIG. 4 is a graph showing the relative error of pricing the optimized GA-BPNN model and the BPNN model in example 1 of the present invention.
Detailed Description
The invention is further described in connection with the accompanying drawings and the preferred embodiments, which are given solely for the purpose of illustration and are not intended to limit the scope of the invention.
Example 1
A price determination model construction method comprises the following steps:
s1, a processing system receives influence factors which are obtained after price influence factor identification and can influence the price of iron tower materials of power transmission and transformation engineering; the price influencing factor identification process comprises the following steps: based on the index calculation process and the actual application scene factors, the influence factors are selected, the factors influencing the pricing of the equipment materials are extracted from three aspects of production cost, market factors and policy factors, and the price forming mechanism is analyzed;
s2, data collection and data processing: collecting influence factor data and actual price data, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, preprocessing the data, and finding out and deleting abnormal values and repeated values in the data to form a data set;
the data set formed after pretreatment comprises factors influencing equipment material pricing, such as labor cost, raw material price, product life cycle, supply and demand relationship, currency expansion degree, industry monopoly degree, regional economy development level, industry policy, currency policy, population quantity, air temperature, topography and the like, (the specific data mainly comprise data published by selected bidding manufacturers, statistical bureaus and the like)
S3, constructing a neural network electric power engineering material pricing model optimized based on a genetic algorithm, inputting data in the data set obtained in the step S2 into the genetic algorithm, optimizing the weight and the threshold of the BPNN by utilizing the genetic algorithm, optimizing the neural network model, and further obtaining a model for determining the price of the electric transmission and transformation engineering iron tower material, so that the price is intelligently determined.
In this embodiment, a correlation coefficient verification step may be added in step S2 and step S3, correlation analysis may be performed on data in the data set obtained after the processing in step S2, and based on a bivariate correlation analysis model, whether the correlation between the influencing factor and the material price dependent variable is strong or weak may be analyzed to obtain a main influencing factor, where the main influencing factor is factors such as population number, air temperature, topography and the like, which have weak correlation with the prediction structure, are removed, and finally the main influencing factor is used as an input object of the intelligent determination model.
In this embodiment, in step S1, a fishbone map method is used to identify and analyze influencing factors, and the influencing factors are distinguished according to major factors, major factors and minor factors, including labor cost, raw material price, product life cycle, supply-demand relationship, inflation degree, industry monopoly degree, regional economy development level, industry policy, currency policy, population number, air temperature and topography, and total 12 influencing factors.
In this embodiment, in step S2, a markov chain is constructed by using a variable mean vector and a variance-covariance matrix as prior information, so as to ensure that the distribution of elements thereof can converge to a stable distribution, the markov chain is repeatedly simulated by sampling to obtain a stable posterior distribution, an estimate of missing data is generated, the missing data is estimated to be a missing data estimated value (missing data which cannot be directly statistically collected or cannot provide complete data, for example, data of several years or months before and after knowing, and then current year or month data is estimated), and the method specifically includes the following steps: s21, receiving continuous data vector set Y c ＝[Y 1 ,Y 2 ,....,Y n ]Wherein the ith data vector is Y (i) = [ Y ] i (1),y i (2),.....,y i (D)]I=1, 2,) N, wherein the data in the data vector set is a price influencing factor obtained after the identification and analysis in step S1, Y c Including observed data Y wz And missing data Y qs ；
S22, setting a Gaussian model according to the ith item data, wherein a parameter space of the Gaussian model is theta, and estimating a value theta according to the parameter space theta g Calculating the probability of occurrence of missing data p (Y qs Y (Y) wz ，θ g )；
S23, according to the occurrence probability of the current complete data (the data which can be directly collected and can be completely obtained is the complete data) and the missing data, for example: knowing the data of years or months before and after, and further estimating the current year or month data) calculates the probability of occurrence of the parameter space θ
And updating the estimated value of the parameter space θ of the gaussian model until the resulting markov chain +.>
Upon convergence, missing data is estimated to satisfy the phaseThe data integrity of the influence factors is related, and the calculation formula of the missing data is shown as formula (I):
wherein N is sample N is the total number of samples Burn-in In order to determine the number of samples to be deleted,
is missing data;
s24, deleting abnormal values and repeated values, wherein the abnormal values are abrupt values in the data, the repeated values are two data with nested relation, and finally the processed data set is obtained.
In this embodiment, the bivariate correlation analysis model may be analyzed using Pearson simple correlation coefficients or hypothesis testing;
if the Pearson simple correlation coefficient is used for measuring the linear correlation relationship of the distance variable, the calculation formula is shown as (II):
where n is the number of samples, x i And y i The two variables are respectively valued in different samples, and the calculation formula of the Pearson simple correlation coefficient is exactly in the form of a matrix product, so the calculation formula is also called a product distance correlation coefficient. The correlation coefficient can be expressed as x as found by changing the formula i And y i Respectively carrying out normalized multiplication and then solving the average of n products;
after the correlation coefficient is obtained according to the variable characteristics, the correlation coefficient can be analyzed; when r=0, it means that there is no linear correlation between the two variables; when 0< |r| is less than or equal to 0.3, the two are weakly related; when 0.3< |r| is less than or equal to 0.5, the two are in low correlation; when 0.5< |r| is less than or equal to 0.8, the two are obviously related; when 0.8< |r| <1, the two are highly correlated; when r=1, the two are completely linearly related;
in the analysis of the influence factors of the power transmission and transformation project cost, a foundation can be laid for subsequent multi-factor dimension reduction, key factor screening and the like by judging whether the factors have obvious linear relations or not.
When the correlation analysis is performed by the addition test, the joint distribution of the two variables is preset to be a two-dimensional normal distribution: when X takes any value, the conditional distribution of Y is normal distribution; when Y takes any value, the condition distribution of X is normal distribution, and the result obtained based on sampling cannot be directly used for explaining the whole because of the randomness of sampling, less sample capacity and the like, and the condition distribution is deduced through a hypothesis testing method, which comprises the following steps:
(1) Providing an original assumption that there is no obvious linear correlation between two variables;
constructing a test statistic; the test statistic of the Pearson correlation coefficient is T statistic, T-T (n-2);
calculating an observation value of the test statistic, looking up a table to obtain a corresponding significance (Sig) of the observation value, and comparing the significance with a significance level; if the significance level is smaller than the significance level, rejecting the original assumption, and considering that a significant linear correlation exists between the two variables; if the current value is positive, the original assumption is accepted;
(2) Gray correlation cluster analysis
N observation objects are arranged, each object observes m pieces of characteristic data, and the obtained sequence is as follows:
X i ＝(x i (1),x i (2),…,x i (n)) and is formed by X i 、X j Generated initial point nulling image
The following are listed below
Wherein (1)>
Let->
Then X is i And X is j The gray absolute correlation of (2) is:
thereby obtaining an upper triangular matrix A:
wherein ε ij ＝1，i＝1,2,…,m；
Critical value τ (0)<The size of tau.ltoreq.1) can be determined according to the requirements of practical problems, and tau is generally>0.5; the closer τ is to 1, the finer the classification, the fewer features in each class; the smaller the τ value, the coarser the classification, and then the more features in each class; when epsilon ij When τ is not less than, then consider X i And X is i Features of the same kind at level τ, thus obtaining feature X 1 ,X 2 ,…,X n A classification at level τ;
due to when X i And X is i In positive correlation, the corresponding S values are the same as the numbers (both positive or both negative), |s i -s j Smaller, X i And X is i The association degree of the (2) is larger; when X is i And X is i In the negative correlation, the corresponding S value is different in sign, |s i -s j Larger, X i And X is i Is less correlated, therefore X i And X is i And can be considered to be positively correlated with the same class of features at level τ;
after analyzing two variables of the influence factors and the price according to any correlation analysis method, selecting corresponding main influence factors, wherein the influence factors are used as input data of a subsequent genetic algorithm, and the following table shows:
in this embodiment, the specific steps for optimizing the neural network model in the step S3 are shown in fig. 1:
s31, in a BP neural network model, determining a network topology structure, obtaining an initial weight and an initial threshold length of the BP neural network model, substituting the initial weight and the initial threshold length into a genetic algorithm, and encoding the initial weight and the initial threshold length by using the genetic algorithm;
s32, inputting data in the data set obtained after preprocessing in the step S2 into the genetic algorithm, taking the data in the data set, the coded initial weight and the error obtained after the coded threshold length are trained by the BP neural network model as adaptive values, sequentially carrying out selection operation, crossover operation and mutation operation in the genetic algorithm on the adaptive values, carrying out fitness value calculation, and circularly entering the adaptive values which do not meet the end conditions into the selection operation, crossover operation and mutation operation;
the end condition is that when the fitness value tends to be stable, the weight and the threshold value of the optimal BP neural network are selected and can be brought into the BP neural network of the next step; for example, the weight and the threshold of the BP neural network are optimized by using a genetic algorithm with parameters set, the change of moderate values of the genetic algorithm in the optimizing process is shown in fig. 2, and as can be seen from fig. 2, when the genetic algorithm evolves to 19 generations, the average fitness tends to be stable, the value of the average fitness approaches to 1.3, and the optimized weight and threshold of the BP neural network are obtained, and the result is as follows:
input layer to hidden layer connection weights:
connection weights of hidden layer to output layer: w (W) jk ＝[-1.1520 -0.3231…-0.2231]；
Input layer to hidden layer threshold: b 1 ＝[1.1935 -2.7450…-2.6296]；
Threshold of hidden layer to output layer: b 2 ＝2.6825；
S33, substituting the fitness value which meets the end condition after the genetic algorithm operation in the step S32 into the BP neural network model again to obtain an optimal weight value and an optimal threshold length, and then sequentially carrying out error calculation, updating the weight value and the threshold value until the end condition is met, so as to obtain a final prediction result, namely a final price; the weight and the threshold length which do not meet the end condition circularly enter error calculation and weight and threshold updating until the end condition is met;
s33, substituting the fitness value which meets the end condition after the genetic algorithm operation in the step S32 into the BP neural network operation again to obtain an optimal weight value and a threshold length, and then sequentially carrying out error calculation, updating the weight value and the threshold until the end condition is met, so as to obtain a final prediction result, namely a final price; the weight and the threshold length which do not meet the end condition circularly enter error calculation and weight and threshold updating until the end condition is met, wherein the end condition is an error target value set before calculation;
specifically, in step S4, a genetic algorithm is applied to optimize the BP neural network to price the electric power engineering material; firstly, setting parameters of a genetic algorithm and a BP neural network, and taking the mean square error between an output value and an actual value of the BP neural network as an adaptability value of the genetic algorithm; setting the number of nodes of an input layer of the BP neural network as 14, the number of nodes of an hidden layer as 10, and the number of nodes of an output layer as 1, and constructing a BP neural network structure with the BP neural network structure of 14-10-1; other parameter settings are shown in table 1;
TABLE 1 remaining parameter settings
In this embodiment, in order to verify the effectiveness of the model, 35 sets of iron tower prices are selected as sample data, the first 25 sets are selected as training data of the pricing model, the second 10 sets are selected as test data of the model, and 14 influence factors are used as inputs to verify the effectiveness of the model;
substituting the optimized parameters into a BPNN pricing model to price the power engineering materials. To show the effectiveness of the optimized GA-BPNN model, BPNN model pricing results were introduced as a comparison, and the results are shown in Table 2 and FIG. 3, from which the average absolute percentage error of GA-BPNN results was 7.55% for 10 test sample projects, which is 14.63% better than the average absolute percentage error of BPNN results.
Table 2 results comparison table
As can be seen from table 3 and fig. 4, the relative error of the results of the GA-BPNN model at sample 4, sample 7, sample 9 is inferior to the BPNN model; the relative errors of the results of the other 7 projects are better than those of the BPNN, and in sum, the accuracy of the pricing results of the GA-BPNN model in the power engineering materials is obviously better than that of the BPNN model, so that the method has certain practicability.
Example 2
The embodiment provides a method for determining the price of a material of an iron tower of a power transmission and transformation project, which comprises the following steps:
acquiring a data set of a power transmission and transformation engineering iron tower material to be priced, wherein the data set is price influence factor data and actual price data;
and inputting the data set into a price determination model constructed by the price determination model construction method in any embodiment to obtain the price of the power transmission and transformation engineering iron tower material to be priced.
Example 3
The present embodiment provides a price determination model construction apparatus including:
the price influence factor collection module is used for receiving influence factors which are obtained after price influence factor identification and can influence the price of the iron tower material of the power transmission and transformation project;
the data preprocessing model construction module is used for collecting influence factor data corresponding to the influence factors and actual price data of the power transmission and transformation engineering iron tower materials, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, finding and deleting abnormal values and repeated values in the influence factor data and the actual price data to form a data set, inputting data in the data set into a genetic algorithm, and optimizing weights and thresholds of a BP neural network model by utilizing the genetic algorithm to obtain a price determination model for determining the price of the power transmission and transformation engineering iron tower materials.
Example 4
The present embodiment provides a price determining apparatus including:
the acquisition module is used for acquiring a data set of the power transmission and transformation project iron tower material to be priced, wherein the data set is price influence factor data and actual price data;
and the input module is used for inputting the data set into a price determination model constructed according to the price determination model construction method described in the embodiment 1 to obtain the price of the power transmission and transformation engineering iron tower material to be priced.
Example 5
The embodiment also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for determining the price of the material of the iron tower of the power transmission and transformation project according to the embodiment 1 when executing the program.
Example 6
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the power transmission and transformation project iron tower material price determining method as described in embodiment 1.
Those skilled in the art will appreciate that all or part of the processes implementing the methods of the embodiments described above may be implemented by a computer program for instructing the relevant hardware, where the program may be stored in a computer readable storage medium; wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related arts are included in the scope of the present invention.
Claims (10)
1. The price determining model construction method is characterized by comprising the following steps:
s1, a processing system receives influence factors which are obtained after price influence factor identification and can influence the price of iron tower materials of power transmission and transformation engineering;
s2, collecting influence factor data corresponding to the influence factors and actual price data of the power transmission and transformation engineering iron tower material, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, finding out and deleting the influence factor data and abnormal values and repeated values in the actual price data, and forming a data set;
s3, inputting the data in the data set into a genetic algorithm, and optimizing the weight and the threshold value of the BP neural network model by using the genetic algorithm to obtain a price determining model for determining the price of the power transmission and transformation engineering iron tower material.
2. The method for constructing a price determining model according to claim 1, wherein in the step S2, a markov chain is constructed by using a variable mean vector and a variance-covariance matrix as prior information, and the markov chain is repeatedly simulated by sampling to obtain a stable posterior distribution, and the method for constructing the estimate of missing data comprises the steps of:
s21, receiving continuous data vector set Y c ＝[Y 1 ,Y 2 ,....,Y n ]Wherein the ith data vector is Y (i) = [ Y ] i (1),y i (2),.....,y i (D)]I=1, 2,) N, wherein the data in the data vector set is the influencing factor in step S1, Y c Including observed data Y wz And absence ofData Y qs ；
S22, setting a Gaussian model according to the ith item data, wherein a parameter space of the Gaussian model is theta, and estimating a value theta according to the parameter space theta g Calculating the occurrence probability p (Y) qs Y (Y) wz ，θ g )；
S23, calculating the occurrence probability of the parameter space theta according to the occurrence probability of the current complete data and the missing data
And updating the estimated value of the parameter space theta of the Gaussian model until the obtained Markov chain
And during convergence, estimating missing data, wherein a calculation formula of the missing data is shown as a formula (I):
wherein N is sample N is the total number of samples Burn-in In order to determine the number of samples to be deleted,
is missing data.
S24, deleting the abnormal value and the repeated value to finally obtain the processed data set.
3. A price determination model construction method as claimed in claim 1, characterized in that the method further comprises: and (2) carrying out correlation analysis on the data in the data set obtained after the processing in the step (S2), analyzing whether the correlation between the influence factors and the material price dependent variables is in a strong-weak relation or not based on a bivariate correlation analysis model, and obtaining main influence factors according to the strong-weak relation to be used as an input object of a price determination model.
4. A price determining model construction method according to claim 3, characterized in that the bivariate correlation analysis model is analyzed by Pearson's simple correlation coefficient or hypothesis test.
5. The method for constructing a price determining model according to claim 1, wherein optimizing the BP neural network model in step S3 comprises:
s31, in a BP neural network model, determining a network topology structure, obtaining an initial weight and an initial threshold length of the BP neural network model, substituting the initial weight and the initial threshold length into a genetic algorithm, and encoding the initial weight and the initial threshold length by using the genetic algorithm;
s32, inputting data in the data set obtained after preprocessing in the step S2 into the genetic algorithm, taking the data in the data set, the coded initial weight and the error obtained after the coded threshold length are trained by the BP neural network model as adaptive values, sequentially carrying out selection operation, crossover operation and mutation operation in the genetic algorithm on the adaptive values, carrying out fitness value calculation, and circularly entering the adaptive values which do not meet the end conditions into the selection operation, crossover operation and mutation operation;
s33, substituting the fitness value which meets the end condition after the genetic algorithm operation in the step S32 into the BP neural network model again to obtain an optimal weight value and an optimal threshold length, and then sequentially carrying out error calculation, updating the weight value and the threshold value until the end condition is met, so as to obtain a final prediction result, namely a final price; and (5) circulating the weight and the threshold length which do not meet the end condition into error calculation and weight and threshold updating until the end condition is met.
6. The method for determining the price of the material of the iron tower of the power transmission and transformation project is characterized by comprising the following steps:
acquiring a data set of a power transmission and transformation engineering iron tower material to be priced, wherein the data set is price influence factor data and actual price data;
inputting the data set into a price determination model constructed according to the price determination model construction method of any one of claims 1-5, so as to obtain the price of the power transmission and transformation engineering iron tower material to be priced.
7. A price determining model construction apparatus, comprising:
the price influence factor collection module is used for receiving influence factors which are obtained after price influence factor identification and can influence the price of the iron tower material of the power transmission and transformation project;
the data preprocessing model construction module is used for collecting influence factor data corresponding to the influence factors and actual price data of the power transmission and transformation engineering iron tower materials, constructing a data preprocessing model based on an improved particle swarm optimization clustering model, finding and deleting abnormal values and repeated values in the influence factor data and the actual price data to form a data set, inputting data in the data set into a genetic algorithm, and optimizing weights and thresholds of a BP neural network model by utilizing the genetic algorithm to obtain a price determination model for determining the price of the power transmission and transformation engineering iron tower materials.
8. A price determining device, comprising:
the acquisition module is used for acquiring a data set of the power transmission and transformation project iron tower material to be priced, wherein the data set is price influence factor data and actual price data;
the input module is used for inputting the data set into a price determination model constructed according to the price determination model construction method according to any one of claims 1-5, so as to obtain the price of the power transmission and transformation engineering iron tower material to be priced.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 6 when the program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 6.
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