Electric power long-span line project high tower construction cost prediction method and system

Abstract

The invention discloses an electric power long-span line project high tower construction cost prediction method, which comprises the steps of obtaining construction data of a to-be-predicted power transmission line project tower group, and identifying construction cost influence factors in the construction data of the power transmission line project tower group; constructing a construction cost prediction model; and inputting the obtained construction cost influence factors into the construction cost prediction model as input items to obtain a construction cost prediction value of the to-be-predicted power transmission line tower construction project. The method for effectively and efficiently predicting the tower construction cost of the power transmission line is realized, and thoughts and suggestions are provided for cost prediction of power enterprises.

Description

Technical Field

[0001] The present invention relates to a power engineering cost prediction technology, and in particular to a method and system for predicting the construction cost of a high tower of a large-span power line project. Background Art

[0002] Transmission line construction projects are geographically wide, with large differences in design conditions and many uncertain factors. These characteristics have brought great difficulties to the design, construction and operation of transmission lines, and put forward high requirements for the cost management of transmission line projects. Cost forecasting is a key link in cost management, especially with transmission line construction cost data as one of the main forecasting data. How to establish an effective and efficient method for predicting transmission line construction costs and provide ideas and suggestions for cost forecasting of power companies is a major difficulty. Summary of the invention

[0003] Purpose of the invention: In view of the above problems, the purpose of the present invention is to provide a method and system for predicting the construction cost of high towers in large-span power line projects, to achieve an effective and efficient method for predicting the construction costs of transmission lines, and to provide ideas and suggestions for cost prediction for power companies.

[0004] Technical solution: On one hand, the present invention provides a method for predicting the construction cost of a high tower of a large-span power line project, comprising:

[0005] Acquire the construction data of the tower assembly of the transmission line project to be predicted, and identify the factors affecting the construction cost in the tower assembly construction data of the transmission line project; wherein the factors affecting the construction cost include tower assembly type data, tower height data, and tower weight data;

[0006] Build a construction cost prediction model;

[0007] The obtained construction cost influencing factors are input into the construction cost prediction model as input items to obtain the construction cost prediction value of the transmission line tower assembly project to be predicted.

[0008] Furthermore, the construction cost prediction model includes:

[0009] Y i =β0+β1X i1 +β2X i2 +…+β k X ik +ε i ,i=1,2,…,n

[0010] Where Y i represents the construction cost forecast value, X ik represents the factors affecting construction cost, k represents the number of factors affecting construction cost, β k is the coefficient, εi represents random error, i represents the i-th group of samples, and n is the number of samples.

[0011] Furthermore, the construction cost prediction model also includes:

[0012]

[0013] In the formula, E(ε i ) represents the mean of random errors, σ 2 represents variance;

[0014] The random error follows a normal distribution, that is, ε i ~N(0,σ 2 ),i=1,2,…,n, where N represents the n-dimensional normal distribution.

[0015] Another aspect of the present invention provides a system for predicting the construction cost of a high tower of a large-span power line project, which is used to implement the method for predicting the construction cost of a high tower of a large-span power line project, comprising:

[0016] A data acquisition module is used to acquire the construction data of the transmission line tower project to be predicted and identify the factors affecting the construction cost in the transmission line tower construction data;

[0017] The prediction module is used to input the acquired construction cost influencing factors as input items into the preset construction cost prediction model to obtain the construction cost prediction value of the transmission line project to be predicted, thereby realizing the prediction of the construction cost.

[0018] On the other hand, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for predicting the construction cost of high towers for large-span power line projects when executing the computer program.

[0019] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for predicting the construction cost of high towers for large-span power line projects are implemented.

[0020] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0021] The present invention takes the transmission line tower construction cost data as the influencing factor, applies the multivariate regression analysis theory to analyze it, establishes a reasonable multivariate linear regression equation by selecting research indicators, and conducts a statistical test on it. The results show that the multivariate linear regression equation is in line with reality and can be used to predict the construction cost of transmission line tower projects, providing ideas and suggestions for cost prediction of power companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flowchart of a method for predicting the construction cost of high towers for large-span power transmission line projects;

[0023] Figure 2 The block diagram of a high tower construction cost prediction system for large-span power line projects is shown in FIG. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0025] The method for predicting the construction cost of a high tower of a large-span power line project described in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0026] Step 1, obtaining the construction data of the tower assembly of the transmission line project to be predicted, and identifying the construction cost influencing factors in the tower assembly construction data of the transmission line project; wherein the construction cost influencing factors include tower assembly type data, tower height data and tower weight data;

[0027] Step 2: Build a construction cost prediction model;

[0028] Step 3: Input the acquired construction cost influencing factors as input items into the construction cost prediction model to obtain the construction cost prediction value of the transmission line tower assembly project to be predicted.

[0029] Furthermore, the construction cost prediction model includes:

[0030] Y i =β0+β1X i1 +β2X i2 +…+β k X ik +ε i ,i=1,2,…,n

[0031] Where Y i represents the construction cost forecast value, X ik represents the factors affecting construction cost, k represents the number of factors affecting construction cost, β k is the coefficient, ε i represents random error, i represents the i-th group of samples, and n is the number of samples.

[0032] In the example, a large amount of cost basic data will be generated during the actual construction of the transmission line tower. The change in cost is affected by many factors. Based on the existing research results, some of the influencing factors with relevant information are merged, and the three influencing factors of tower type, tower height and tower weight are selected for regression analysis, which are recorded as X1, X2, and X3 respectively as explanatory variables of the multiple regression model, and the construction cost is recorded as Y. Multiple linear regression studies whether there is a mutual dependence or linear relationship between two or more independent variables and a dependent variable. This linear relationship can usually be expressed by a multiple regression equation, and the relationship between a dependent variable and multiple independent variables is characterized by a multiple equation. A linear regression model with two or more independent variables in the equation is called a multiple linear regression model. In this multiple linear regression model, the dependent variable Y is multiple independent variables X1, X2,…, X k The linear function of the error term is as follows:

[0033] Y=β0+β1X1+β2X2+…+β k X k

[0034] For the random error term ε, it is often assumed that E(ε)=0, Var(ε)=σ 2 , and said:

[0035] E(Y)=β0+β1X1+β2X2+…+β k X k

[0036] is the theoretical regression equation. In practical applications, if n sets of observation data are obtained: (X i1 ,X i2 ,…,X ik ; Y i ),i=1,2,…,n, then:

[0037] Y i =β0+β1X i1 +β2X i2 +…+β k X ik +ε i

[0038] The corresponding matrix expression is:

[0039] Y=Xβ+ε

[0040] In order to facilitate the estimation of model parameters, the above equations have the following basic assumptions:

[0041] Explanatory variables X1, X2, …, X k is a deterministic variable, X is a full rank matrix;

[0042] The random error term has zero mean and equal variance, that is, it satisfies the Gauss--Markov condition:

[0043]

[0044] In the formula, E(ε i ) represents the mean of random errors, σ 2 represents variance;

[0045] The random error follows a normal distribution, that is, ε i ~N(0,σ 2 ),i=1,2,…,n, where N represents the n-dimensional normal distribution.

[0046] For the matrix form of multiple linear regression, it also satisfies: ε~N(0,σ 2 I n ), where I n is the matrix of the nth sample.

[0047] From the above assumptions and the properties of multivariate normal distribution, we can see that Y obeys n-dimensional normal distribution, that is:

[0048] Y~N(Xβ,σ 2 I n )

[0049] In one embodiment, the actual cost data of a power grid company in 2021 is taken. Various irrelevant information that can easily affect the prediction results has been removed from these data. The construction cost of transmission line tower assembly corresponding to the above three influencing factors is studied. The actual data is shown in Table 1 below.

[0050] Table 1

[0051]

[0052]

[0053] The statistical analysis tool EViews is used to fit the regression relationship between the independent variables and the dependent variables, and the fitting of the cost prediction model and the reasonableness and reliability of the results are evaluated based on the results.

[0054] In order to ensure the rationality of the linear model, the correlation analysis between the various factors was first performed on the obtained data, and the results are shown in Table 2. According to the analysis results, it can be seen that the tower type, tower height and tower weight have a strong correlation with the cost, and the significance test with a confidence level of α = 0.01 has been passed, indicating that the eternal linear model is more suitable to explain their relationship.

[0055] Table 2

[0056]

[0057] In order to eliminate the influence of multicollinearity between independent variables that may distort model estimation, a multicollinearity test was performed on the data. The results are shown in Table 3. The variance ratios obtained in Table 3 do not have a number close to 1, and there is no multicollinearity between the data.

[0058] Table 3

[0059] X1 X2 X3 X1 1 0 0 X2 0 1 0.558 X3 0 0.558 1

[0060] There is a strong linear relationship between the independent variable and the dependent variable. The transmission line tower construction cost prediction model obtained by running EViews10 software is:

[0061] Y=66350.05X1+8217.803X2+1555.010X3-969057.7

[0062] When using the multivariate linear regression model to predict the construction cost of transmission line towers, it is necessary to test the fit and significance of the regression equation and to study whether the model is valuable. The steps of the statistical test of the model are as follows:

[0063] (1) Goodness of fit test:

[0064] The goodness of fit is used to test the degree of fit of the regression equation to the independent variable value. The general determination coefficient R is in the range of 0.8-1. 2 The closer it is to 1, the higher the degree of fit of the regression plane. It can be determined that the independent variable and the dependent variable have a strong correlation. It represents the degree of correlation between the sample data and the predicted data. The R of the transmission line tower construction cost prediction model in this example is 0.985, and the correlation coefficient is close to 1, indicating that the actual value and predicted value of the transmission line tower construction cost have a strong fit.

[0065] (2) F test:

[0066] The significance test of the multivariate linear regression equation is to see whether the independent variable has a significant impact on the dependent variable as a whole. Given a significance level of 0.01, the F value test Sig=0.00 is much smaller than 0.01, that is, the overall effect of the regression equation is significant, the overall linear relationship of the final model is significantly established, and the regression model has significant significance.

[0067] (3) DW test:

[0068] A method commonly used in statistical analysis to test sequence autocorrelation. The explanatory variable is uncorrelated with the random term, that is, there is no heteroskedasticity. In this example, the DW value d=0.97 for the test of heteroskedasticity is output.

[0069] (4) Comparison between the model's prediction results and the true values:

[0070] In order to test the validity of the model, the following Table 4 gives the relationship between the predicted value and the true value and the difference obtained by substituting 38 data samples. From the analysis of the nearly 38 samples, it can be seen that the total cost of tower assembly is on an upward trend, and the relative error between the true value and the predicted value varies by about 0.082. Further analysis shows that this is consistent with the actual situation and meets the economic significance test. In summary, the multivariate linear regression model works well in predicting the construction cost of transmission line tower assembly.

[0071] Table 4

[0072]

[0073]

[0074] In this example, by using the theory of multiple linear regression analysis and the EViews statistical analysis tool, the relevant indicators that affect the construction cost of transmission line tower assembly are analyzed, and some theoretical parameters that have no practical contribution to the calculation accuracy of the model are discarded. As a result, a good construction cost prediction model is obtained. Among them, the total height of the tower and the total cost show a very significant correlation, which is an important indicator for predicting the construction cost of transmission lines to a large extent. As one of the many methods for predicting the construction cost of transmission line tower assembly, the cost prediction method based on the multiple linear regression mathematical model has obvious characteristics. This method has clear theory, simple structure, simple calculation, strong practicality and good fitting. To a certain extent, it can be seen that the construction cost of transmission line tower assembly can help enterprises formulate effective measures to control costs, so as to reduce the subjectivity and blindness of the cost decision-making process, and has certain practical significance.

[0075] like Figure 2 As shown, in one example, a high tower construction cost prediction system for a large-span power line project includes:

[0076] A data acquisition module is used to acquire the construction data of the transmission line tower project to be predicted, and identify the construction cost influencing factors in the transmission line tower assembly construction data; wherein the construction cost influencing factors at least include tower assembly type data, tower height data and tower weight data;

[0077] The prediction module is used to input the acquired construction cost influencing factors as input items into the preset construction cost prediction model to obtain the construction cost prediction value of the transmission line project to be predicted, thereby realizing the prediction of the construction cost.

[0078] The specific implementation process of the transmission line tower construction cost prediction system can refer to the specific process of the high tower construction cost prediction method for large-span power projects described in the above embodiment, which will not be repeated here.

[0079] Accordingly, another aspect of the present invention further provides a computer device, which includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities, and the memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the construction cost of a transmission line is implemented.

[0080] It will be understood by those skilled in the art that the structure of the above-mentioned computer device is only a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than those in the above-mentioned case, or combine certain components, or have a different arrangement of components.

[0081] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0082] Acquire transmission line construction data of the transmission line tower assembly project to be predicted, and identify construction cost influencing factors in the transmission line tower assembly construction data, wherein the construction cost influencing factors at least include tower assembly type data, tower height data, and tower weight data;

[0083] The obtained construction cost influencing factors are input as input items into a preset construction cost prediction model to obtain a construction cost prediction value of the transmission line tower assembly project to be predicted, thereby realizing construction cost prediction.

[0084] Accordingly, another aspect of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the following steps when executed by a processor;

[0085] Acquire transmission line construction data of the transmission line tower assembly project to be predicted, and identify construction cost influencing factors in the transmission line tower assembly construction data, wherein the construction cost influencing factors at least include tower assembly type data, tower height data, and tower weight data;

[0086] The obtained construction cost influencing factors are input as input items into a preset construction cost prediction model to obtain a construction cost prediction value of the transmission line tower assembly project to be predicted, thereby realizing construction cost prediction.

[0087] It is understandable that more details of the various steps involved in the above-mentioned computer device and computer-readable storage medium can refer to the aforementioned definition of the method for predicting the construction cost of transmission line tower assembly, and will not be repeated here.

[0088] Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0089] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] It should be noted that the system described in the above embodiment corresponds to the method described in the above embodiment. Therefore, the undetailed parts of the system described in the above embodiment can be obtained by referring to the contents of the method described in the above embodiment, and will not be repeated here.

[0091] Furthermore, if the transmission line tower assembly construction cost prediction system described in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0092] In summary, the implementation of the embodiments of the present invention has the following beneficial effects:

[0093] The method, system, equipment and medium for predicting the construction cost of high towers for large-span power line projects provided by the present invention take the construction cost data of transmission line tower assembly as the influencing factors, apply the multivariate regression analysis theory to analyze it, establish a reasonable multivariate linear regression equation by selecting research indicators, and perform statistical tests on it. The results show that the equation is in line with reality and can be used to predict the construction cost of transmission line tower assembly projects, providing ideas and suggestions for cost prediction of power companies.

[0094] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for predicting the construction cost of a high tower in a large-span power line project, characterized in that: include: Acquire the construction data of the tower assembly of the transmission line project to be predicted, and identify the factors affecting the construction cost in the tower assembly construction data of the transmission line project; wherein the factors affecting the construction cost include tower assembly type data, tower height data, and tower weight data; Build a construction cost prediction model; The obtained construction cost influencing factors are input into the construction cost prediction model as input items to obtain the construction cost prediction value of the transmission line tower assembly project to be predicted.

2. A method for predicting the construction cost of a high tower of a large-span power line project according to claim 1, characterized in that: The construction cost prediction model includes: Y i =β0+β1X i1 +β2X i2 +…+b k X ik +e i ,i=1,2,…,n Where Y i represents the construction cost forecast value, X ik represents the factors affecting construction cost, k represents the number of factors affecting construction cost, β k is the coefficient, ε i represents random error, i represents the i-th group of samples, and n is the number of samples.

3. A method for predicting the construction cost of a high tower of a large-span power line project according to claim 2, characterized in that: The construction cost prediction model also includes: In the formula, E(ε i ) represents the mean of random errors, σ 2 represents variance; The random error follows a normal distribution, that is, ε i ~N(0,σ 2 ),i=1,2, … ,n,N represents n-dimensional normal distribution.

4. A system for predicting the construction cost of high towers for large-span power line projects, characterized in that: Used to implement the method according to any one of claims 1 to 3, characterized in that it includes: A data acquisition module is used to acquire the construction data of the transmission line tower project to be predicted and identify the factors affecting the construction cost in the transmission line tower construction data; The prediction module is used to input the acquired construction cost influencing factors as input items into the preset construction cost prediction model to obtain the construction cost prediction value of the transmission line project to be predicted, thereby realizing the prediction of the construction cost.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

Images