Power transmission and transformation project cost dynamic control target calculating system and calculating method

Abstract

The invention relates to a power transmission and transformation project cost dynamic control target calculating system and a calculating method. The method comprises the steps that a power transmission and transformation project cost data set is established; data preprocessing is carried out, and a project feature vector set is defined; similar project identification is carried out by inputting a similar project identification condition; a fuzzy identification technology is used to select similar projects; before a power transmission and transformation project cost dynamic control target is calculated, similar projects are selected in historical data samples according to the attribute of a cost control object; a weighted Hamming distance proximity formula is used to calculate the proximity of a target project of cost control and historical data; according to the number of the samples and the sample proximity, a proximity threshold is determined; according to the proximity threshold, whether the number of similar projects meets a calculation requirement is determined; according to the proximity threshold and the number of similar projects, a similar project cost sequence is generated; identified power transmission and transformation similar project cost is decomposed to determine the power transmission and transformation project cost control target. According to the invention, power transmission and transformation project cost pre-intervention and in-process control are realized.

Description

Technical field [0001] The invention relates to a power transmission and transformation project cost calculation system and method, in particular to a power transmission and transformation project cost dynamic control target calculation system and calculation method. Background technique [0002] The cost control of power transmission and transformation projects is mostly based on the "four calculations" of estimation, budget, budget, and settlement, and is based on cost management experience and historical data of project cost to carry out ex post control. At present, the commonly used methods for cost control of power transmission and transformation projects include Gantt chart method and curve method. The Gantt chart shows the sequence and duration of a specific project's activities in the form of graphs or tables through an activity list and a time scale, which is intuitive and easy to understand. The curve method is relatively easy to draw standard cost control targets, and...

AI Technical Summary

Problems solved by technology

[0003] However, the above methods have the following deficiencies: (1) The method only reflects the triple constraints of cost control, namely time, cost and scope, lacks scientific correlation between schedule and cost, and cannot effectively and scientifically assess schedule and cost. Integrated control

(2) The cost analysis of traditional power transmission and transformation projects takes the voltage level as the main classification basis, and the difference or similarity between projects depends entirely on the experience of cost control personnel

Cost control based on this, lacks objectivity and accuracy, and is prone to large errors

(3) Project cost control is a dynamic process, which needs to analyze and judge the progress and cost of the project in a specific period of time. Using the above method, it is necessary to summarize a lot of basic data in order to obtain an approximate progress and cost occurrence. Huge time consumption and lack of accuracy

(4) The above-mentioned methods are relatively passive in the control of the project cost. They are all based on historical data and carry out "post-event" analysis and control. They lack the actual guiding significance for the project itself, the prior intervention in the project cost and the cost adjustment in the process of project implementation. Relatively lack of dynamic control

Although the autoregressive moving average model has fast calculation speed, convenient method and high prediction accuracy, it is only suitable for dealing with regular time series with large batches of data.

Images