CN118365150B - Construction drawing full-caliber budget verification method based on full-element matching - Google Patents

Abstract

The present invention discloses a full-caliber budget verification method for construction drawings based on full-element matching, which relates to the technical field of full-caliber budget verification, and comprises: before the budget verification process, performing a risk assessment on cost data to obtain first data, wherein the first data comprises equipment update data; converting the equipment update data into image data through a three-dimensional coordinate axis, and drawing the image data into a curve, and extracting features of the curve through cluster analysis; performing cluster analysis according to the extracted features, and dividing the curve into different cluster clusters; calculating the equipment update impact coefficient by multiplying different cluster clusters; establishing a cost risk assessment model based on the equipment update impact coefficient, and calculating a cost verification risk index; performing budget verification risk analysis based on the cost verification risk index, and judging whether there is a risk of insufficient verification accuracy in the budget verification to be performed, and if so, performing risk management and control, thereby effectively improving the accuracy and stability of the budget verification.

Description

A full-caliber budget verification method for construction drawings based on full-factor matching

Technical Field

The present invention relates to the technical field of full-caliber budget verification, and more specifically, to a full-caliber budget verification method for construction drawings based on full-element matching.

Background Art

Construction drawings refer to detailed design drawings of engineering projects, which contain design details of various aspects such as architecture, electrical, and mechanical, and are used to guide actual construction work;

A full-scope budget is a budget that comprehensively considers and estimates the costs of all aspects of the entire project; this includes all possible expenses, such as building materials, labor costs, equipment rental fees, management costs, indirect costs, etc., to ensure that no problems will arise during the implementation of the project due to omissions of costs;

Full factor matching means that when conducting budget verification, we must ensure that all elements of the project are included in the budget and that these elements match the indicators in the actual construction drawings. This includes matching in terms of quantity, quality, specifications, standards, etc.

Budget verification methods refer to a series of techniques and procedures used to verify the accuracy and completeness of a project budget. These methods may involve detailed analysis of construction drawings, estimation and comparison of various costs, and comparison with historical data or market conditions.

For example, the patent application with publication number: CN112116339A discloses a method and device for verifying the full-link control of funds. The method receives payment data uploaded by an application and obtains a scenario number based on the payment data analysis. The scenario number matches the pre-stored fund flow control table and scenario control table in the database to obtain the payment rule data and the participant order limit data corresponding to the scenario number. The payment rule data and the participant order limit data are used to verify the fund amount and fund flow logic between the participants in the payment data respectively. When the verification is passed, the payment verification result is fed back, and the rationality of the participants such as the payment and collection of orders in multiple scenarios, the rationality and accuracy of the payment and collection paths of order-level payments, the rationality and accuracy of payment paths in multiple scenarios, and the rationality of order-level ledger accounting are effectively verified.

For example, the invention patent with publication number: CN110428316B discloses an information verification method and device, which obtains budget surplus information, difference adjustment item information and financial surplus information; determines a first financial surplus value based on the budget surplus information and the difference adjustment item information; determines the financial surplus based on the financial surplus information, and determines the financial surplus as a second financial surplus value; verifies whether the first financial surplus value is consistent with the second financial surplus value; if the first financial surplus value is inconsistent with the second financial surplus value, prompts to adjust the difference adjustment item; through the embodiments of the present invention, the accuracy of accounting can be improved and the efficiency of recording difference adjustment information can be improved.

However, the inventors of this application found that the above technology has at least the following technical problems in the process of implementing the technical solution of the invention in the embodiment of this application:

(1) The prices of electrical equipment and materials are affected by many factors, such as market supply and demand, raw material prices, and policy changes. Large price fluctuations may lead to budget deviations.

(2) Technology in the electrical field is updated rapidly. The application of new technologies may bring cost uncertainty, especially in long-term projects. Technology updates may make the original budget insufficient.

(3) Power construction requires highly skilled human resources, and labor costs are greatly affected by factors such as region and skills. Changes in human resources and cost fluctuations are also important factors in budget verification.

(4) Delays and quality issues in the supply chain may lead to increased costs and also affect the progress of the entire project, which requires timely response.

Therefore, it is necessary to establish a cost risk analysis model, identify potential cost risk factors, and propose corresponding response strategies to improve verification accuracy.

Summary of the invention

In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a full-caliber budget verification method for construction drawings based on full-factor matching. By establishing a cost risk analysis model, potential cost risk factors are identified, and corresponding response strategies are proposed to solve the problems raised in the above-mentioned background technology.

To achieve the above object, the present invention provides the following technical solutions:

The full-caliber budget verification method for construction drawings based on full-factor matching includes the following steps:

Before the budget checking process, a risk assessment of cost data is performed to obtain first data, wherein the first data includes equipment update data;

The device update data is converted into image data through a three-dimensional coordinate axis, and the image data is plotted into a curve, and features are extracted from the curve through cluster analysis;

Cluster analysis is performed based on the extracted features to divide the curves into different clusters;

Calculate the device update impact coefficient by multiplying different clusters;

Establishing a cost risk assessment model based on the equipment update impact coefficient and calculating a cost verification risk index;

Conduct budget verification risk analysis based on the cost verification risk index to determine whether the upcoming budget verification has a risk of insufficient verification accuracy. If so, perform risk management.

The technical effects and advantages of the full-caliber budget verification method for construction drawings based on full-factor matching of the present invention are as follows:

1. The present invention uses data-driven methods, historical data and statistical analysis methods, such as Monte Carlo simulation, multivariate linear regression model, cluster analysis, etc., to accurately identify and quantify cost fluctuation risks, technical risks and supply chain problem risks, and provide project managers with practical budget verification accuracy assessment and risk management strategies, thereby effectively improving the accuracy and stability of budget verification, and providing strong support for the smooth implementation of projects and cost control.

2. The present invention comprehensively considers various cost risk factors by utilizing preset risk coefficient ratios, including supply chain issues, equipment update impacts, human resource fluctuations, and abnormal fluctuations in equipment and material prices. By calculating the cost verification risk index, the risk level of budget verification can be quantitatively evaluated, thereby effectively determining whether there is a risk of insufficient accuracy in budget verification. The quantitative evaluation feature makes the grasp of the risk level more accurate, and the risk marking and management strategy based on the preset fluctuation threshold ensures the timeliness and flexibility of risk management, providing strong support for effective cost verification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of a full-caliber budget verification method for construction drawings based on full-factor matching according to the present invention.

DETAILED DESCRIPTION

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.

Embodiment 1, FIG1 shows a method for verifying the full-caliber budget of a construction drawing based on full-factor matching of the present invention, comprising the following steps:

S10, before the budget verification process, perform a risk assessment on the cost data to obtain first data, wherein the first data includes equipment update data; S11, convert the equipment update data into image data through a three-dimensional coordinate axis, plot the image data into a curve, and extract features from the curve through cluster analysis; S12, perform cluster analysis based on the extracted features, and divide the curve into different clusters; S13, calculate the equipment update impact coefficient by multiplying different clusters; S14, establish a cost risk assessment model based on the equipment update impact coefficient, and calculate the cost verification risk index; S15, perform a budget verification risk analysis based on the cost verification risk index to determine whether the budget verification to be performed has a risk of insufficient verification accuracy, and if so, perform risk management.

The calculation formula of the cost verification risk index is as follows:

In the formula, is the cost verification risk index, is the preset proportional coefficient of the supply chain risk factor, The preset scaling factor for the device update impact factor, is the preset proportional coefficient of the human resource fluctuation coefficient, It is the preset proportional coefficient of the abnormal coefficient of equipment and material price fluctuation. is the cost verification risk index, is the supply chain risk factor, is the equipment update impact coefficient, is the human resource fluctuation coefficient, It is the abnormal coefficient of equipment and material price fluctuation.

The effects and impacts of equipment and material price fluctuation coefficient on the accuracy of budget analysis and verification are as follows:

Accuracy assessment: The price fluctuation coefficient reflects the degree of fluctuation of equipment and material prices. A higher fluctuation coefficient means a larger price change, while a lower fluctuation coefficient means a relatively stable price. Based on the size of the fluctuation coefficient, a preliminary assessment of the accuracy of budget verification can be made. A lower fluctuation coefficient usually means a higher budget verification accuracy, while a higher fluctuation coefficient may cause a greater budget deviation.

Risk control: The coefficient of fluctuation is one of the important indicators for measuring cost risk. When the coefficient of fluctuation is high, it means that the risk of price fluctuations also increases accordingly, which may lead to increased uncertainty in budget verification. Therefore, for equipment and materials with high price fluctuation coefficients, it is necessary to strengthen risk control and adopt corresponding response strategies to reduce the uncertainty of budget verification.

Budget adjustment strategy: Different budget adjustment strategies can be formulated according to the price fluctuation coefficient of equipment and materials. For equipment and materials with low fluctuation coefficients, a relatively stable budget management strategy can be adopted; while for equipment and materials with high fluctuation coefficients, a more flexible budget adjustment strategy is required to adjust the budget in time to cope with the risks brought by price fluctuations.

Therefore, analyzing the price fluctuation coefficient of equipment and materials can timely discover the risks of insufficient accuracy and stability of full-caliber budget verification caused by cost fluctuation risks.

The specific method for obtaining the abnormal coefficient of equipment and material price fluctuation is as follows:

Obtain the price of each equipment and material, label and establish a material price set, obtain the average logarithm and logarithmic standard deviation of the equipment and material prices based on statistical analysis of historical price data, make the prices of equipment and materials conform to the normal distribution, and obtain the probability density function of the price; generate multiple random number samples that conform to the distribution based on the probability density function of the price, label and establish a sample set, perform analysis and calculation to obtain the equipment and material price fluctuation coefficient, and compare and analyze with the preset equipment and material price fluctuation expected value to obtain the equipment and material price fluctuation abnormality coefficient;

The specific formula of the probability density function of the price is as follows:

The calculation formula for the abnormal coefficient of equipment and material price fluctuation is as follows:

In the formula, is the price of equipment and materials, is the average logarithm of equipment material prices, is the logarithmic standard deviation, A sample of random numbers, is the total number of samples, is the random number sample label, is the random number sample mean, It is the expected value of equipment and material price fluctuation.

It should be noted that this method is a numerical calculation method based on probability statistics, which simulates the behavior of the system by sampling random variables multiple times; when calculating the price volatility coefficient of equipment materials, Monte Carlo simulation can be used to simulate the random changes in prices, and the volatility coefficient can be calculated based on the simulation results.

From the calculation formula of the equipment and material price fluctuation abnormal coefficient, it can be seen that when the performance value of the equipment and material price fluctuation abnormal coefficient is larger, it means that the price fluctuation of construction materials and equipment is more drastic, the cost risk is greater, and the accuracy and stability of budget verification is worse; when the performance value of the equipment and material price fluctuation abnormal coefficient is smaller, it means that the price fluctuation of construction materials and equipment is more stable, the cost risk is smaller, and the accuracy and stability of budget verification is better.

Human resource costs play a vital role in power construction projects. Affected by multiple factors such as region, skill level, and market supply and demand, fluctuations and changes in human resource costs have become factors that cannot be ignored in budget verification. The level of economic development, skill demand and supply in the project area directly affect the cost level of human resources. With the increase in skill requirements and the intensification of market competition, the cost of highly skilled talents is usually higher. At the same time, changes in market supply and demand will also cause fluctuations in human resource costs. Therefore, accurate prediction and evaluation of changes and fluctuations in human resource costs is an important step to ensure budget accuracy and stability, which helps project managers formulate effective cost control strategies.

The role and impact of the human resource fluctuation coefficient on the accuracy of budget analysis and verification are as follows:

Analysis of the human resource fluctuation coefficient can improve the accuracy of cost forecasting. Through comprehensive analysis of historical data and market trends of human resource costs, the trend and range of changes in the human resource fluctuation coefficient can be determined. This enables project managers to more accurately predict future fluctuations in human resource costs, thereby more accurately considering this factor during the budget preparation stage and avoiding budget deviations.

Analysis of the human resource fluctuation coefficient helps to identify and manage potential risks. A high fluctuation coefficient means that the instability of human resource costs is high, which may have a negative impact on the cost control and progress of the project. By timely monitoring and analyzing the changes in the fluctuation coefficient, project managers can better understand the potential risks of human resource cost fluctuations and take corresponding risk management measures to ensure the smooth progress of the project.

The analysis of human resource fluctuation coefficient provides an important basis for the formulation of budget optimization and cost control strategies. According to the size and trend of the fluctuation coefficient, project managers can adjust the allocation and management strategy of human resources to adapt to different degrees of cost fluctuations. For example, in the case of a high fluctuation coefficient, more flexible cost control measures can be taken, such as strengthening budget monitoring, optimizing resource allocation, and improving human resource management efficiency, so as to reduce the negative impact of fluctuations and ensure that project costs are controlled within a reasonable range.

By analyzing the human resource fluctuation coefficient, project managers can more accurately assess the impact of human resource cost fluctuations on projects and provide support for decision-making. Based on the understanding of the fluctuation coefficient, more targeted and effective decision-making measures can be formulated to improve the efficiency of project management and the effect of cost control, thereby achieving the smooth realization of project goals.

Therefore, analyzing the price fluctuation coefficient of equipment and materials can timely discover the risk of insufficient accuracy and stability of full-caliber budget verification caused by cost fluctuation risks.

The specific method for obtaining the human resource fluctuation coefficient is as follows:

Obtain various factors that affect human resource costs, label them and establish a factor matrix; establish a multivariate linear regression model of human resource costs and a human resource matrix; use the least squares method to fit the multivariate linear regression model and obtain the regression model coefficients that minimize the residual sum of squares; use matrix operations to calculate the human resource volatility coefficient;

The multivariate linear regression model of human resource cost is as follows:

The calculation formula of the human resource fluctuation coefficient is:

In the formula, is the human resource cost, which is also a vector of n*1, where n is the number of data points. is the intercept term, is the regression model coefficient, k is the number of factors that affect human resource costs, is the human resource fluctuation coefficient, It is a matrix of n*(k+1) recording influencing factor data.

It should be noted that the purpose of using the least squares method to fit the model is to minimize the sum of squares of the residuals between the observed values and the model predicted values.

It can be seen from the calculation formula of the human resource fluctuation coefficient that when the performance value of the human resource fluctuation coefficient is larger, it means that the human resource cost fluctuation is more drastic and the cost risk is greater, indicating that the accuracy and stability of the budget verification are worse; when the performance value of the human resource fluctuation coefficient is larger, it means that the human resource cost fluctuation is more stable and the cost risk is smaller, indicating that the accuracy and stability of the budget verification are better.

Technical risk refers to the fact that power projects involve complex technologies and equipment, such as generators, transformers, transmission lines, etc., so the occurrence of technical risks may have a serious impact on the project; for example, during the execution of the project, technical problems or equipment failures may occur, resulting in the project being unable to proceed as planned or outputting substandard products. In order to deal with these problems, additional costs may be required to repair or replace the affected equipment, redesign the project, or take other emergency measures. These additional costs may not only lead to project cost overruns, but may also extend the project's duration, increase the project's operating costs, and may affect the quality and reliability of the project. Therefore, the identification and management of technical risks are crucial to ensuring the successful completion of the project and controlling costs;

The effect and influence of the equipment update impact coefficient on the accuracy of the analysis budget verification are as follows:

Improve accuracy: Equipment updates usually mean more efficient and advanced technologies and equipment, which can improve production efficiency and reduce costs in power engineering projects. Therefore, if the impact coefficient of equipment updates is positive, it means that the adoption of new technologies can improve project execution efficiency and reduce costs, thereby improving the accuracy of budget verification.

Reduce risk: The application of new technology may reduce the technical risk of the project and the possibility of equipment failure, because new technology is usually more reliable and stable. Therefore, the positive value of the equipment update impact coefficient indicates that the introduction of new technology can reduce the probability of technical risks during project execution, thereby improving the accuracy of budget verification;

Increased uncertainty: Although equipment updates may bring many benefits, they may also introduce new uncertainties. The application of new technologies may require additional training and debugging time, or face challenges in technology integration; therefore, the size of the equipment update impact coefficient also reflects the degree of uncertainty in the project execution process; a higher impact coefficient may mean that the accuracy of the project budget verification is affected by greater uncertainty;

Demand adjustment: Equipment updates may change project requirements. For example, the application of new technologies may require redesigning engineering processes or adjusting project scope. Therefore, the equipment update impact coefficient also reflects the degree of change in project requirements, which may affect the accuracy of project costs and budget verification.

Therefore, analyzing the impact coefficient of equipment updates can timely identify the risks of insufficient accuracy and stability of full-caliber budget verification caused by cost fluctuation risks.

The method for obtaining the equipment update impact coefficient is as follows:

Collect learning data of different power technologies, the learning data including learning performance, evaluation score, and error rate; convert the learning data into technical learning image data through three-dimensional coordinate axes, draw each learning data into a learning curve, and extract features of the learning curve through cluster analysis; perform cluster analysis based on the extracted features and divide the learning curve into different clusters; calculate the equipment update impact coefficient based on the clusters;

The method for updating the influence coefficient according to the clustering cluster computing device is as follows: calculating the standard deviation of the learning effect of each sample at a time point relative to the average learning effect of the clustering cluster, and then averaging to obtain the overall fluctuation term; calculating the average value of the standard deviation of the learning effect of all samples at each time point relative to the average learning effect of the clustering cluster, to obtain the learning curve fluctuation term; calculating the average value of the ratio of the variance of the learning effect of each sample at each time point to the variance of the learning effect of all samples in the clustering cluster at that time point, to obtain the local fluctuation term;

The calculation formula of the overall fluctuation term is as follows:

The calculation formula of the learning curve fluctuation term is as follows:

The calculation formula of the learning local fluctuation term is as follows:

The calculation formula of the equipment update impact coefficient is:

In the formula, Update the impact coefficient for the device, is the overall fluctuation term, is the fluctuation term of the learning curve, To learn the local fluctuation term, is the number of samples in the cluster, is the number of time points, is the time point number, is the learning effect of the jth sample in the vth cluster at time point t, is the time point of all samples in the vth cluster The average learning effect of is the average learning effect of all samples in the vth cluster at all times.

It should be noted that the equipment update impact coefficient is the contribution of the cluster to the project risk, which is calculated based on the shape, volatility, and slope characteristics of the learning curve; the overall volatility term is used to measure the volatility of each sample relative to the cluster as a whole; the learning curve volatility term is used to measure the volatility of the overall learning curve; the local volatility term is used to measure the ratio of the volatility at each time point to the overall volatility.

It can be seen from the calculation formula of the equipment update impact coefficient that when the performance value of the equipment update impact coefficient is larger, it means that the impact of the equipment update is faster, the cost risk is greater, and the accuracy and stability of the budget verification are worse; when the performance value of the equipment update impact coefficient is smaller, it means that the impact of the equipment update is more stable, the cost risk is smaller, and the accuracy and stability of the budget verification are better.

Supply chain risk refers to various uncertainties and emergencies that may occur in the process of supply chain management, which may affect the normal operation of the supply chain and the realization of business goals. Among them, the problem based on the accuracy of budget verification refers to the fact that in the supply chain, the accuracy of budget verification is affected by many factors, such as the accuracy of cost estimates, changes in cargo transportation and warehousing costs, supplier price fluctuations, exchange rate risks, etc. These factors may cause actual expenditures to be inconsistent with the budget, which in turn affects the profitability and financial status of the company. Managers need to respond to the challenges brought by supply chain risks through effective risk management and cost control measures, as well as improving the accuracy of budget verification, to ensure the stable operation of the supply chain and the sustainable development of the business;

The role and impact of the supply chain risk coefficient on the accuracy of budget analysis and verification are as follows:

Identify potential risks: The process of calculating the supply chain risk factor involves identifying and quantifying various potential problems and risks in the supply chain, such as supplier delays, raw material price fluctuations, transportation losses, etc. By analyzing the risk factor, managers can better understand which aspects may cause actual expenditures to exceed budgets.

Quantify the impact of risk: The value of the risk factor reflects the impact of various risks on the supply chain. A higher risk factor means that the risk has a greater impact on the supply chain operation and may lead to a greater degree of budget deviation. Therefore, the supply chain risk factor can help managers quantify the impact of different risk factors on the accuracy of budget verification.

Monitor supply chain stability: The changing trend and level of the risk factor can reflect the stability and reliability of the supply chain. Managers can monitor the changes in the risk factor to promptly identify possible problems and risks in the supply chain and take appropriate measures to solve them, so as to ensure the stable operation of the supply chain and the realization of business goals.

Adjust budget verification strategy: Based on the analysis results of the supply chain risk coefficient, managers can timely adjust the budget verification strategy and methods to improve its accuracy and effectiveness. For example, they can increase the attention to high-risk factors and strengthen communication and cooperation with suppliers to reduce the impact of risks, thereby reducing the occurrence of budget deviations.

Timely detection and response to potential budget deviations: By analyzing the supply chain risk factor, managers can promptly detect potential problems that may lead to budget deviations and take appropriate measures to respond to them, so as to ensure the accuracy and controllability of the budget and ensure the realization of the company's financial goals.

Therefore, analyzing the supply chain risk coefficient can timely discover the risks of insufficient accuracy and stability of full-caliber budget verification caused by cost fluctuation risks.

The method for obtaining the supply chain risk coefficient is as follows:

The average number of days of supplier delivery delays is calculated by statistical calculation based on the historical delivery delays of each supplier; the inventory turnover rate is calculated by analyzing the average inventory and average sales; the standard deviation of product demand is calculated by statistical calculation based on the historical data of product demand; the average loss rate in transportation is obtained by monitoring the loss rate during transportation; the supply chain risk coefficient is calculated by combining the average number of days of delivery delays, inventory turnover rate, standard deviation of product demand, and average loss rate;

The calculation formula of the supply chain risk coefficient is as follows:

In the formula, is the number of suppliers, is the number of days the z-th supplier is delayed, is the average inventory, is the average sales volume, is the standard deviation of product demand, is the average loss rate;

It can be seen from the calculation formula of the supply chain risk coefficient that when the performance value of the supply chain risk coefficient is larger, it indicates that the risk of supply chain problems is greater, the cost risk is higher, and the accuracy and stability of budget verification are worse; when the performance value of the supply chain risk coefficient is smaller, it indicates that the risk of supply chain problems is smaller, the cost risk is lower, and the accuracy and stability of budget verification are lower.

This embodiment uses a data-driven approach, historical data and statistical analysis methods, such as Monte Carlo simulation, multivariate linear regression model, cluster analysis, etc., to accurately identify and quantify cost fluctuation risks, technical risks and supply chain problem risks, and provide project managers with practical budget verification accuracy assessment and risk management strategies, thereby effectively improving the accuracy and stability of budget verification, and providing strong support for the smooth implementation of projects and cost control.

In the above scheme, the specific steps for budget verification risk analysis based on cost verification risk index are as follows:

Set a severe fluctuation threshold, and mark the budget verification corresponding to the cost verification risk index according to the set severe fluctuation threshold; mark the budget verification corresponding to the cost verification risk index that is higher than the preset severe fluctuation threshold as advanced risk verification and perform advanced risk management; mark the budget verification corresponding to the cost verification risk index that is lower than the preset stable fluctuation threshold as risk-free verification; and continuously monitor the budgets corresponding to the cost verification risk index in other situations.

This embodiment takes into account various cost risk factors by using preset risk coefficient ratios, including supply chain issues, equipment update impact, human resource fluctuations, and abnormal equipment and material price fluctuations. By calculating the cost verification risk index, the risk level of budget verification can be quantitatively evaluated, thereby effectively judging whether there is a risk of insufficient accuracy in budget verification. The quantitative evaluation feature makes the grasp of the risk level more accurate, and the risk marking and control strategy based on the preset fluctuation threshold ensures the timeliness and flexibility of risk management, providing strong support for effective cost verification.

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.

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.

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.

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.

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.

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 (8)

1. A method for verifying the full-caliber budget of construction drawings based on full-factor matching, characterized in that it comprises the following steps: Before the budget checking process, a risk assessment of cost data is performed to obtain first data, wherein the first data includes equipment update data; The device update data is converted into image data through a three-dimensional coordinate axis, and the image data is plotted into a curve, and features are extracted from the curve through cluster analysis; Cluster analysis is performed based on the extracted features to divide the curves into different clusters; Update the impact coefficient based on different clustering calculation devices; The specific method for obtaining the equipment update impact coefficient is: Collecting learning data of different power technologies, wherein the learning data includes learning performance, evaluation scores, and error rates; Calculate the standard deviation of the learning effect of each sample at a time point relative to the average learning effect of the cluster corresponding to the curve division, and then average them to obtain the overall fluctuation term; Calculate the average value of the standard deviation of the learning effect of all samples at each time point relative to the average learning effect of the cluster cluster to obtain the fluctuation term of the learning curve; Calculate the average value of the ratio of the variance of the learning effect of each sample at each time point to the variance of the learning effect of all samples in the cluster at that time point to obtain the local fluctuation term; The overall fluctuation term, the learning curve fluctuation term, and the local fluctuation term are multiplied together to obtain the equipment update impact coefficient; Establishing a cost risk assessment model based on the equipment update impact coefficient and calculating a cost verification risk index; The calculation formula of the cost verification risk index is as follows: In the formula, is the cost verification risk index, is the preset proportional coefficient of the supply chain risk factor, The preset scaling factor for the device update impact factor, is the preset proportional coefficient of the human resource fluctuation coefficient, It is the preset proportional coefficient of the abnormal coefficient of equipment and material price fluctuation. is the cost verification risk index, is the supply chain risk factor, Update the impact coefficient for the device, is the human resource fluctuation coefficient, is the abnormal coefficient of equipment and material price fluctuation; Conduct budget verification risk analysis based on the cost verification risk index to determine whether the upcoming budget verification has a risk of insufficient verification accuracy. If so, perform risk management. 2. According to the method for full-caliber budget verification of construction drawings based on full-factor matching in claim 1, it is characterized in that the budget verification risk analysis based on the cost verification risk index is specifically: Set a severe fluctuation threshold, and mark the budget verification corresponding to the cost verification risk index according to the set severe fluctuation threshold; The budget check corresponding to the cost check risk index that is higher than the preset severe fluctuation threshold is marked as a high-level risk check and high-level risk management and control is performed; The budget check corresponding to the cost check risk index below the preset stable fluctuation threshold is marked as a risk-free check; The budget corresponding to the cost verification risk index for other situations is continuously monitored. 3. The method for verifying the full-caliber budget of construction drawings based on full-factor matching according to claim 2 is characterized in that the abnormal coefficient of price fluctuation of equipment and materials is obtained by the following specific steps: Obtain the prices of various equipment and materials, and label them to create a material price collection; The average logarithm and logarithmic standard deviation of equipment and material prices are obtained based on statistical analysis of historical price data; the prices of equipment and materials are made to conform to normal distribution, and the probability density function of prices is obtained; Generate multiple random number samples that conform to the normal distribution according to the probability density function of the price, establish a sample set, and calculate and analyze the equipment and material price fluctuation coefficient in combination with the average logarithm and logarithmic standard deviation of the equipment and material prices; Compare and analyze the equipment and material price fluctuation coefficient with the preset equipment and material price fluctuation expected value to obtain the equipment and material price fluctuation abnormal coefficient. 4. The method for verifying the full-caliber budget of construction drawings based on full-factor matching according to claim 2 is characterized in that the specific calculation method of the human resource fluctuation coefficient is: Obtain the factors that affect human resource costs and establish a factor matrix; According to the combined factor matrix and the multivariate linear regression model combined with human resource cost, a human resource matrix is established; Use the least squares method to fit the human resource matrix and obtain the regression model coefficients that minimize the residual sum of squares; The coefficient of human resource volatility is calculated using matrix operations based on the coefficients of the regression model that minimizes the residual sum of squares. 5. The method for verifying the full-caliber budget of construction drawings based on full-factor matching according to claim 2 is characterized in that the specific calculation method of the supply chain risk coefficient is as follows: The average number of days of supplier delivery delay is obtained by statistically calculating the historical delivery delays of each supplier; Inventory turnover ratio is calculated by analyzing average inventory and average sales; Calculate the standard deviation of product demand by statistically analyzing the historical data of product demand; By monitoring the loss rate during transportation, the average loss rate during transportation is obtained; The supply chain risk coefficient is calculated by combining the average delivery delay days, inventory turnover rate, standard deviation of product demand, and average loss rate. 6. The method for verifying the full-caliber budget of construction drawings based on full-factor matching according to claim 3 is characterized in that the probability density function of the price The specific formula is as follows: The abnormal coefficient of price fluctuation of equipment and materials The calculation formula is as follows: In the formula, is the price of equipment and materials, is the average logarithm of equipment material prices, is the logarithmic standard deviation, A sample of random numbers, is the total number of samples, is the random number sample label, is the random number sample mean, It is the expected value of equipment and material price fluctuation. 7. The method for verifying the full-caliber budget of construction drawings based on full-factor matching according to claim 4 is characterized in that the multivariate linear regression model of the human resource cost is as follows: The calculation formula of the human resource fluctuation coefficient is: In the formula, is the human resource cost, which is also a vector of n*1, where n is the number of data points. is the intercept term, is the regression model coefficient, k is the number of factors that affect human resource costs, is the human resource fluctuation coefficient, It is a matrix of n*(k+1) recording influencing factor data. 8. The method for verifying the full-caliber budget of construction drawings based on full-factor matching according to claim 5 is characterized in that the calculation formula of the overall fluctuation term is as follows: The calculation formula of the learning curve fluctuation term is as follows: The calculation formula of the local fluctuation term is as follows: In the formula, is the overall fluctuation term, is the fluctuation term of the learning curve, To learn the local fluctuation term, is the number of samples in the cluster, is the number of time points, is the time point number, is the learning effect of the jth sample in the vth cluster at time point t, is the time point of all samples in the vth cluster The average learning effect of is the average learning effect of all samples in the vth cluster at all times.