Power transmission and transformation project cost rationality examination system

Abstract

The invention discloses a power transmission and transformation project cost rationality review system, and relates to the technical field of project review, and the system comprises the following steps: an obtaining module, which is used for obtaining project management data and review operation data affected by the review frequency when the power transmission and transformation project cost is reviewed; the evaluation module is used for establishing an evaluation model based on a machine learning algorithm according to the project management data and the review operation data influenced by the review frequency, and generating a project cost review evaluation coefficient; and the determination module is used for drawing a curve graph according to the review evaluation coefficient obtained according to the evaluation model, calculating the curve smoothness and determining the optimal review frequency according to the curve smoothness. According to the method, the optimal examination frequency is determined, so that the defect of reasonability analysis of the power transmission and transformation project cost caused by the examination frequency is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering review, and more specifically, to a system for reviewing the rationality of the cost of a power transmission and transformation project. Background Art

[0002] Transmission and transformation projects are a vital part of the power system, involving the transmission and conversion of electricity to ensure that electricity is safely and efficiently delivered from power stations to users. Such projects usually include substations, transmission lines and distribution systems; project cost management aims to control project costs and ensure that the project is successfully completed within the budget. Rationality review is an important part of cost management, and its purpose is to ensure the rationality and compliance of project costs through a systematic review process.

[0003] Deficiencies of existing technologies: In the power transmission and transformation project cost rationality review system, the high and low review frequencies will bring their own shortcomings. High-frequency reviews will increase the cost of the review itself. Too frequent reviews will affect the progress of the project and cause delays in the construction period. The project team may be distracted by constant reviews and unable to focus on actual construction and management; too low a review frequency may lead to early problems and risks not being discovered and corrected in time, thereby having a negative impact on the overall cost and progress of the project. If the review is not frequent enough, cost control may occur, resulting in the final cost exceeding the budget and increasing the economic pressure of the project; therefore, the present invention determines the optimal review frequency through the power transmission and transformation project cost rationality analysis to solve the shortcomings of the high and low review frequency on the power transmission and transformation project cost rationality analysis.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a system for reviewing the rationality of the cost of power transmission and transformation projects, which determines the optimal review frequency by analyzing the rationality of the cost of power transmission and transformation projects to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A system for reviewing the rationality of the cost of a power transmission and transformation project comprises the following steps: an acquisition module for acquiring project management data and review operation data affected by the review frequency when reviewing the cost of a power transmission and transformation project; an evaluation module for establishing an evaluation model based on the project management data and review operation data affected by the review frequency based on a machine learning algorithm, and generating a project cost review evaluation coefficient; a determination module for plotting the review evaluation coefficient obtained according to the evaluation model into a curve graph, calculating the curve smoothness, and determining the optimal review frequency according to the curve smoothness.

[0007] In a preferred embodiment, the project management data includes a project progress impact coefficient and a project additional cost, and the review operation data includes a review resource coefficient.

[0008] In a preferred embodiment, the method for obtaining the engineering progress impact coefficient is as follows: establish an engineering progress impact coefficient model, and input the project review frequency, the length of each review, the completion status of the engineering tasks, and the work efficiency of the project team as feature data into the model, and standardize and normalize the feature data; based on the complex nonlinear relationship between multi-layer networks in deep learning, model the model, and use the back propagation algorithm to train the model, adjust the learning rate and the number of neural network layers; based on the trained model, given the review frequency and other feature data, predict the impact of the review on the progress, and the output prediction result is the engineering progress impact coefficient.

[0009] In a preferred embodiment, the specific process of the normalization processing is as follows: the feature data are arranged in ascending order according to the time field, the mean and standard deviation of each feature data are calculated, combined with the known standardized calculation formula, and the sliding window superposition tomography method is used to make all feature data have the same scale and range of variation.

[0010] In a preferred embodiment, the specific steps for obtaining the additional project costs are as follows: calculate the additional costs caused by design changes, including the designer's working hours and the cost of re-examination; identify the additional costs caused by incorrect use or waste of materials; calculate the repair costs required due to quality problems that were not discovered in time; and combine all calculated costs to obtain the additional project costs.

[0011] In a preferred embodiment, the steps for obtaining the review resource coefficient are as follows: define various types of resources involved in the review process, including human resources, material resources, and financial resources; obtain the total number of review items, and combine human resources, material resources, and financial resources to obtain the review resource coefficient in the evaluation review process, and construct a resource constraint model to minimize the amount of resource waste, ensure that the review resource coefficient is within a reasonable range, and the total amount of resources used does not exceed the total amount of available resources, and obtain the constrained review resource coefficient.

[0012] In a preferred embodiment, the specific process of determining the optimal review frequency based on the smoothness of the curve is as follows: a coordinate system is established with the review frequency as the X-axis and the review evaluation coefficient as the Y-axis, and a two-dimensional curve graph is drawn according to the review evaluation coefficients corresponding to different review frequencies, the peak value in the curve graph is found, and the smoothness of the curve is calculated by quadratic derivation, and the review frequency corresponding to the peak value of the smoothest curve is determined to be the optimal review frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a schematic diagram of the structure of a power transmission and transformation project cost rationality review system. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Embodiment 1, Figure 1 The invention provides a system for reviewing the rationality of the cost of a power transmission and transformation project.

[0016] An acquisition module is used to acquire project management data and review operation data affected by the review frequency when reviewing the cost of power transmission and transformation projects; The project management data includes the engineering progress impact coefficient and engineering additional cost, and the review operation data includes the review resource coefficient; The method for obtaining the engineering progress impact coefficient is as follows: First, a model is established to predict the relationship between review frequency and project schedule delay. The review frequency, the length of each review, the completion of engineering tasks (such as the difference between planned time and actual time), and the work efficiency of the project team (such as the amount of work completed per hour) are input into the model as feature data. The feature data is standardized and normalized to ensure that the scale of each feature is consistent. The specific process of the normalization process is as follows: Arrange the characteristic data in ascending order according to the time field, calculate the mean and standard deviation of each characteristic data, combine the standardized calculation formula, and use the sliding window stacking tomography method to make all characteristic data have the same scale and variation range; The sliding window stacking chromatography method is specifically: Select a window function JH with a fixed length L, divide the procurement time series data set with a fixed step length l, and obtain data segments of fixed length. The specific calculation formula of the window function is:

[0017] Among them, JH is the total set of the sliced ​​procurement time series data set, is a dataset of time series fragments, is the first data in the data set. For each sample, the signal difference is the jth time series segment after slicing, and N is the total amount of data in the purchased data set; Based on neural network learning, the model is modeled through the complex nonlinear relationship between multi-layer network learning, and the model is trained using the back propagation algorithm to adjust the learning rate and the number of neural network layers; According to the trained model, given the review frequency and other feature data, the impact of the review on the progress is predicted, and the output prediction result is the project progress impact coefficient T; By constructing the project progress impact coefficient, project managers can quantify the impact of review frequency on progress and efficiency; use appropriate features and models to handle complex nonlinear relationships, thereby optimizing review strategies, improving the overall progress and efficiency of the project, and clarifying the specific impact of review frequency on project progress and efficiency; reduce unnecessary reviews, and ensure that the review frequency can both guarantee project quality and not delay project progress; by understanding the resources (time, personnel, etc.) required for each review, resources can be allocated more reasonably to avoid excessive time and manpower consumption of reviews; Additional costs refer to all related costs incurred due to the failure to detect problems in a timely manner due to low-frequency audits, including: Design change costs: costs incurred due to design modifications that must be made after problems are discovered; Material waste costs: additional material costs due to repeated use or incorrect selection of materials; Quality defect repair costs: repair costs caused by quality problems that are not discovered in time due to low-frequency reviews; The specific steps for obtaining the additional cost of the project are as follows: Calculate the additional costs incurred due to design changes, including the designer's time costs and re-examination costs; identify and calculate the additional costs incurred due to incorrect use or waste of materials; calculate the cost of repairs required due to quality problems that were not discovered in time; combine all calculated costs to obtain the additional cost of the project; The specific calculation formula for the additional cost of the project is as follows:

[0018] In the formula, It is an additional engineering cost. It is the additional cost caused by design changes. It is the additional cost caused by the wrong use or waste of materials. It is the cost of repairs required due to quality problems that were not discovered in time; The steps for obtaining the review resource coefficient are as follows: Define the various resources involved in the review process, including human resources, material resources, and financial resources; Obtain the total number of review items, and combine human resources, material resources, and financial resources to obtain the review resource coefficient in the evaluation and review process. The method for obtaining the review resource coefficient is as follows:

[0019] In the formula, R is the review resource coefficient in the review process, Q is the number of review items, H is human resources, M is material resources, F is financial resources, a represents the impact factor of material resources in the review, and b represents the impact factor of financial resources in the review; During the review process, the amount of resource waste is described by the waste prediction model, and an optimization goal is set to minimize the resource waste W while ensuring that the review resource coefficient R is within a reasonable range and the total amount of resources used does not exceed the total amount of available resources. This problem can be described as the following constrained optimization problem:

[0020] Where W is the amount of resource waste, H is human resources, M is material resources, F is financial resources, z is the efficiency coefficient of human resources, d is the efficiency coefficient of material resources, v is the efficiency coefficient of financial resources, and R is the review resource coefficient in the evaluation and review process. is the maximum threshold of the review resource coefficient, C is the total amount of available resources; High-frequency reviews can identify design defects, material problems or construction quality problems earlier, so that the plan can be adjusted in time to avoid additional costs caused by large-scale repairs at a later stage, which helps reduce additional costs caused by design or construction problems during the project process; regular reviews help identify potential construction and design problems in the early stages of the project, ensure that the project proceeds in an orderly manner as planned, reduce progress delays caused by problems, and thus avoid increased costs due to delays; the project team can adjust the construction plan in a timely manner based on the review results to ensure that the project is completed on time and reduce additional costs caused by delays; However, high-frequency audits will increase cost items and the cost of the audit itself, such as auditors, inspection tools, time, etc., but these expenses are relatively small and can often be offset by reducing unexpected problems.

[0021] Infrequent reviews may result in potential problems not being discovered in time, leading to design changes, material waste, or a surge in costs when repairing quality defects, increasing the overall cost; if the review is not frequent enough, many unnecessary errors and rework may occur during the construction process, causing the project cost to be far higher than the budget. Infrequent reviews may increase a large amount of repair costs in the later stages of the project, especially when serious design defects are discovered, which not only increases the cost of repair, but may also cause work stoppages and affect the progress of the project; A low review frequency may lead to the accumulation of problems, which may affect the project progress by the time they are discovered, resulting in additional costs due to delays. Under low-frequency reviews, large-scale adjustments are required when the project encounters major problems, which may extend the construction period, leading to an increase in equipment rental, personnel costs, etc., which directly affects the overall cost of the project. Therefore, by analyzing the project management data and review operation data affected by the high and low review frequencies during the review of the power transmission and transformation project cost, the optimal review frequency can be found. An evaluation module is used to establish an evaluation model based on a machine learning algorithm according to project management data and review operation data affected by the review frequency, and generate a review evaluation coefficient for the construction cost; The specific calculation formula of the engineering cost review and evaluation coefficient is as follows:

[0022] Where G is the engineering cost review and evaluation coefficient, , , They are the project progress impact coefficient, the additional cost of the project, and the weight factor of the review resource coefficient, and , T is the project progress impact coefficient, Engineering additional cost, R is the review resource factor; Cost review assessment factors have multiple benefits in determining the optimal review frequency. These benefits not only improve the efficiency of the review process, but also reduce project costs and risks. Here are some of the main benefits: 1. Improve review efficiency Data-driven decision-making: The evaluation coefficient is based on historical data and project characteristics, which can help managers quickly identify high-risk projects, so as to prioritize their review and avoid wasting resources; Optimize review resources: Through accurate evaluation coefficients, review personnel and time can be reasonably arranged to ensure that important projects receive sufficient attention while unnecessary repeated reviews are reduced; 2. Reduce costs Reduce unnecessary reviews: By setting a reasonable review frequency, we can avoid excessive reviews of low-risk projects, saving manpower and time costs; Prevent budget overruns: Timely identification and review of potential problems can help avoid significant financial losses later in the project, thereby keeping the budget reasonable; 3. Risk Management Timely identification of risks: Through the evaluation coefficient, the review frequency can be dynamically adjusted to timely identify and respond to possible risks and reduce the probability of project failure; Improve decision-making confidence: The evaluation coefficient provides quantitative data support, making project managers more confident when making decisions on review frequency; 4. Promote transparency and traceability Enhanced transparency of review: The evaluation coefficient provides a quantitative basis for the review process, enhancing the understanding and trust of all parties in the review process; Data recording and analysis: Recording of historical review data and evaluation coefficients can provide valuable reference for subsequent projects and promote the accumulation and sharing of experience; 5. Improve project quality Ensure quality control: A reasonable review frequency can ensure that the project is properly supervised and guided at all stages, thereby improving the quality of the final delivery; Promote compliance: Through effective review arrangements, we can ensure that projects comply with relevant laws, regulations and industry standards, thereby reducing compliance risks; 6. Dynamic adjustment and optimization Flexible response to changes: The evaluation coefficient can be dynamically adjusted according to project progress and market changes to ensure that the review frequency always adapts to current project needs; Long-term improvement: Based on the feedback from the evaluation coefficients, the review strategy can be continuously optimized to improve review efficiency and effectiveness; 7. Support strategic decision making Develop long-term plans: By analyzing the review and evaluation coefficients of different projects, management can better formulate long-term review and management strategies and improve the overall project management level; A determination module, used to plot the review evaluation coefficient obtained according to the evaluation model into a curve graph, calculate the smoothness of the curve, and determine the optimal review frequency according to the smoothness of the curve; A coordinate system is established with the review frequency as the X-axis and the review evaluation coefficient as the Y-axis, and a two-dimensional curve graph is drawn according to the review evaluation coefficients corresponding to different review frequencies. The peak value in the curve graph is found, and the smoothness of the curve is calculated by quadratic derivation. The review frequency corresponding to the peak value of the smoothest curve is determined to be the optimal review frequency. The review assessment coefficient obtained from the assessment model is plotted into a curve and the curve smoothness is calculated to determine the optimal review frequency, which has the following benefits: 1. Visually display data Visual analysis: The curve chart can intuitively display the changing trend of the review and evaluation coefficient, which helps managers quickly understand the relationship and fluctuation of the data; Identify patterns: Through visualization, managers can easily identify periodic changes, trends, and outliers in the data, providing a basis for decision-making; 2. Quantitative analysis and comparison Smoothness calculation: By calculating the smoothness of the curve, the stability and consistency of the data can be quantified, so as to judge the rationality of the review frequency. This quantitative analysis is helpful for comparison between different projects or stages. Simplify decision-making: A curve with high smoothness indicates that the data changes less, which helps to determine the appropriateness of the review frequency and reduce the frequency of review, thereby saving resources; 3. Optimize review frequency Dynamic adjustment: By analyzing the smoothness of the curve, the optimal review frequency can be identified to ensure that reviews are conducted when necessary, while reducing reviews during the stable phase to improve review efficiency; Improve resource utilization: A reasonable review frequency can reduce review costs, improve the utilization efficiency of human resources, and make the review work more targeted; 4. Risk control and early warning Identify risks in advance: Abnormal fluctuations that may appear in the curve chart can serve as risk warnings, helping managers to adjust the review frequency in a timely manner and conduct targeted analysis and intervention; Enhanced project control capabilities: Through smoothing changes, financial and schedule risks of projects can be better predicted and controlled; 5. Support continuous improvement Feedback mechanism: By drawing curves and calculating smoothness, a dynamic feedback mechanism can be established to help the team continuously evaluate and improve the review process; Data-driven optimization: Continuously monitoring the changes in the review evaluation coefficient and the smoothness of the curve can provide data basis for future review strategies and achieve long-term optimization; 6. Promote communication and collaboration Share visual information: Graphs can be used as a communication tool to share important findings during the review process with team members or stakeholders, promoting cross-departmental collaboration; Enhance transparency: Improve the transparency of the review process through visualization, so that all parties have a clear understanding of the basis for review decisions and enhance trust; 7. Improve decision-making quality Data-based decision making: Using curve graphs and their smoothness for analysis can make decisions based on data rather than experience, thereby improving the accuracy and effectiveness of decisions; Long-term planning: By evaluating the historical trends of review frequency, we can better plan for future project review strategies and improve the foresight of project management.

[0023] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0024] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0025] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0026] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0027] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0028] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for reviewing the rationality of the cost of power transmission and transformation projects, characterized in that: The following steps are involved: An acquisition module is used to acquire project management data and review operation data affected by the review frequency when reviewing the cost of power transmission and transformation projects; An evaluation module is used to establish an evaluation model based on a machine learning algorithm according to project management data and review operation data affected by the review frequency, and generate a review evaluation coefficient for the construction cost; The determination module is used to plot the review evaluation coefficient obtained according to the evaluation model into a curve graph, calculate the curve smoothness, and determine the optimal review frequency according to the curve smoothness.

2. The power transmission and transformation project cost rationality review system according to claim 1 is characterized in that: The project management data includes the engineering progress impact coefficient and the engineering additional cost, and the review operation data includes the review resource coefficient.

3. The power transmission and transformation project cost rationality review system according to claim 1 is characterized in that: The method for obtaining the engineering progress impact coefficient is as follows: Establish a project progress impact coefficient model, and input the project review frequency, the duration of each review, the completion of the project tasks, and the work efficiency of the project team as feature data into the model, and standardize and normalize the feature data; Based on the complex nonlinear relationship between multi-layer networks in deep learning, the model is modeled and trained using the back-propagation algorithm to adjust the learning rate and the number of neural network layers; Based on the trained model, given the review frequency and other feature data, the impact of the review on the progress is predicted, and the output prediction result is the project progress impact coefficient.

4. The power transmission and transformation project cost rationality review system according to claim 3 is characterized in that: The specific process of the normalization process is as follows: The characteristic data are arranged in ascending order according to the time field, the mean and standard deviation of each characteristic data are calculated, and the known standardized calculation formula is combined, and the sliding window superposition tomography method is used to make all the characteristic data have the same scale and variation range.

5. The power transmission and transformation project cost rationality review system according to claim 4 is characterized in that: The sliding window stacking chromatography method is specifically: Select a window function JH with a fixed length L, divide the procurement time series data set with a fixed step length l, and obtain data segments of fixed length. The specific calculation formula of the window function is: Among them, JH is the total set of the sliced ​​procurement time series data set, is a dataset of time series fragments, is the first data in the data set. For each sample, the signal difference is the jth time series segment after slicing, and N is the total amount of data in the purchased data set.

6. The power transmission and transformation project cost rationality review system according to claim 1 is characterized in that: The specific steps for obtaining the additional cost of the project are as follows: Calculate the additional costs caused by design changes, including the designer's time costs and re-examination costs; identify the additional costs caused by incorrect use or waste of materials; calculate the costs of repairs required due to quality problems that were not discovered in time; combine all calculated costs to obtain the additional cost of the project.

7. The power transmission and transformation project cost rationality review system according to claim 1 is characterized in that: The steps for obtaining the review resource coefficient are as follows: Define the various resources involved in the review process, including human resources, material resources, and financial resources; Obtain the total number of review projects, and combine human resources, material resources, and financial resources to obtain the review resource coefficient during the review process. A resource constraint model is constructed to minimize the amount of resource waste, ensure that the review resource coefficient is within a reasonable range, and that the total amount of resources used does not exceed the total amount of available resources, and obtain the constrained review resource coefficient.

8. The power transmission and transformation project cost rationality review system according to claim 1 is characterized in that: The specific calculation formula of the review resource coefficient is as follows: In the formula, R is the review resource coefficient in the review process, Q is the number of review items, H is human resources, M is material resources, F is financial resources, a represents the impact factor of material resources in the review, and b represents the impact factor of financial resources in the review; The specific calculation formula of the resource constraint model is as follows: Where W is the amount of resource waste, H is human resources, M is material resources, F is financial resources, z is the efficiency coefficient of human resources, d is the efficiency coefficient of material resources, v is the efficiency coefficient of financial resources, and R is the review resource coefficient in the evaluation and review process. is the maximum threshold of the review resource coefficient, and C is the total amount of available resources.

9. The power transmission and transformation project cost rationality review system according to claim 1 is characterized in that: The specific calculation formula of the engineering cost review and evaluation coefficient is as follows: Where G is the engineering cost review and evaluation coefficient, , , They are the project progress impact coefficient, the additional cost of the project, and the weight factor of the review resource coefficient, and , T is the project progress impact coefficient, Engineering additional cost, R is the review resource coefficient.

10. The power transmission and transformation project cost rationality review system according to claim 1, characterized in that: The specific process of determining the optimal review frequency according to the curve smoothness is as follows: A coordinate system is established with the review frequency as the X-axis and the review evaluation coefficient as the Y-axis, and a two-dimensional curve graph is drawn according to the review evaluation coefficients corresponding to different review frequencies. The peak value in the curve graph is found, and the smoothness of the curve is calculated through quadratic derivation. The review frequency corresponding to the peak value of the smoothest curve is determined to be the optimal review frequency.

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