CN117273456A - Project cost intelligent management system based on big data technology - Google Patents
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Abstract
The invention discloses a project cost intelligent management system based on big data technology, which relates to the technical field of big data, wherein the system is carried in a cloud, the cloud acquires cost parameter information through a management data module, a risk evaluation module and a preprocessing module are arranged in the cloud, the risk evaluation module is used for extracting risk evaluation parameters and generating a risk evaluation value Fxgz, and corresponding strategies are executed according to comparison results after comparison; the technical key points are as follows: and acquiring a plurality of risk assessment parameters to obtain a risk assessment value Fxgz of the same engineering project, acquiring an overall cost benefit assessment value Xygz, and making a ranking table for a plurality of engineering projects in the same area by comprehensively considering the cost benefit assessment value Xygz and the risk assessment value Fxgz.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an intelligent project cost management system based on big data technology.
Background
Big data technology refers to a series of technologies and methods for processing, storing and analyzing large-scale data sets, and the big data technology can help people extract valuable information from huge data, so that decision making and innovation are supported, and the concrete content of the big data technology comprises: in summary, big data technology is a series of technology and method developed to cope with massive, diversified and fast growing data, and through effective data processing and analysis, insight and value can be obtained from big data.
In the process of managing project cost, large data technology is needed to be used for processing, comprehensive management is carried out on the aspects of business, labor and materials in engineering projects, and early warning analysis is carried out by using large data.
The prior art has the following defects:
when all engineering projects are managed in the same area, for example, n engineering projects are arranged in an XX area of an XX city, and when the n engineering projects are managed, professional quality inspection teams are required to be dispatched for quality inspection operation in the final inspection quality inspection link, however, the number of engineering projects in the same area is more, and is far more than that of the professional quality inspection teams, so that sorting quality inspection is required for each engineering.
The traditional management rules are ordered according to English letters of the first letters of the distance or project names, if the risk of a certain project is large, and the unqualified positions are more, the quality inspection work time can be greatly prolonged, the efficiency of the whole management work is reduced, and for some project with high benefit and small risk, if the project cannot be subjected to quality inspection and acceptance in the first time, the follow-up work cannot be carried out, and then the economic benefit of the corresponding project is affected.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an intelligent project cost management system based on a big data technology, which is used for acquiring a plurality of risk assessment parameters, obtaining a risk assessment value Fxgz of the same project according to the risk assessment parameters after dimensionless processing, obtaining an overall cost benefit assessment value Xygz according to the cost parameter information after dimensionless processing, and making a ranking table for a plurality of projects in the same area by comprehensively considering the cost benefit assessment value Xygz and the risk assessment value Fxgz.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the project cost intelligent management system based on the big data technology is carried in a cloud, the cloud acquires cost parameter information through a management data module, a risk assessment module and a preprocessing module are arranged in the cloud, the risk assessment module is used for extracting risk assessment parameters and generating a risk assessment value Fxgz, and corresponding strategies are executed according to comparison results after comparison; the preprocessing module preprocesses the cost parameter information and the risk assessment parameter, and the data in the cloud end are stored through a cloud end database;
the cloud transmits the preprocessed cost parameter information to the management terminal, generates a cost benefit evaluation value Xygz through a cost evaluation module built in the management terminal, and feeds back the cost benefit evaluation value Xygz to the cloud;
the cloud end sends the risk evaluation value Fxgz and the cost benefit evaluation value Xygz of the same project to the quality inspection end, an evaluation and sorting module arranged in the quality inspection end generates a project sorting value Pxj, sorts the project sorting values Pxj of a plurality of project projects in the same area according to the order from small to large, and completes quality inspection operation according to the sorting order.
Further, the management data module comprises a business unit, a labor unit and a material unit;
business unit: collecting bidding budgets, predicting the variation trend of the bidding budgets by utilizing a big data technology, comparing the bidding budgets with actual bidding settlement, and carrying out budget planning according to the comparison result;
labor unit: the method is used for recording and processing labor information and labor cost in the same project;
the labor information at least comprises labor contracts, personal files, card-punching attendance records and labor expenses of each labor, wherein the labor expenses comprise actual labor application expenses and actual labor expense expenses;
material unit: for storing and managing material information in a cloud database, and the material information includes: a material contract, a bill of materials, and a material cost including a purchase price and a market guidance price for the corresponding material, a material purchase quantity, and a quantity of specifications on the material contract.
Further, the process of comparing the actual labor application cost and the actual labor expenditure cost in the labor unit is as follows:
if the actual labor application cost is far greater than the actual attendance cost and the risk of the virtual report engineering quantity is indicated, early warning display is carried out through a visual tool; if the actual labor application cost is far less than the actual attendance cost and the risk of poor management exists, performing early warning display through a visual tool;
the process of managing material costs in the material unit is:
continuously comparing the material purchase quantity with the quantity specified on the material contract while comparing the purchase price with the market guiding price;
if the purchasing price is lower than or higher than the market guiding price, alarming is carried out through a visualization tool, and if the purchasing quantity of the materials is lower than or higher than the quantity of the specifications on the contract, alarming is carried out through the visualization tool.
Further, the risk assessment module comprises an acquisition unit and an assessment unit;
the collection unit extracts risk assessment parameters, including: the cost deviation index, the environment index and the labor attendance index are generated according to dimensionless processed risk assessment parameters by building a data analysis model in an assessment unit, and the risk assessment value Fxgz is generated according to the following formula:
in the method, in the process of the invention,for the cost deviation index>Mqz is labor attendance index, alpha, beta and gamma are respectively the cost deviation index, the environment index and the preset proportionality coefficient of labor attendance index, and alpha+beta+gamma= 5.721, alpha>γ>β>0；
Wherein, the cost deviation indexXcz the difference between the bidding budget and the actual bidding settlement in the business unit, lfz the difference between the actual labor application cost and the actual labor expenditure cost in the labor unit, cpc the difference between the actual material purchase total cost and the material purchase contract total cost in the material unit, material purchase total actual cost = purchase unit price times material purchase quantity, material purchase contract total cost = contract unit price times quantity of specifications on the material contract;
environmental index Representing the average temperature of the location where the same item is located,the average humidity of the position of the same item is represented, the average temperature is obtained by weather forecast or the average value of the highest temperature and the lowest temperature in the weather of the current day displayed on the mobile phone is obtained and calculated, and the average humidity is obtained in the same way as the average temperature;
labor attendance index Mqz: the ratio of the full work workers to the number of workers in the same engineering project is represented, and the full work workers can directly obtain the attendance record through punching cards.
Further, the risk assessment module further includes a threshold unit, which sets a first assessment threshold Mgz and a second assessment threshold Mgz2, and Mgz2 > Mgz1, and compares the two sets of assessment thresholds with a risk assessment value Fxgz;
if the risk evaluation value Fxgz is smaller than the first evaluation threshold Mgz1, the risk is not found in the corresponding engineering project, the management system does not send out an early warning signal, and a related strategy is not made;
if the first evaluation threshold Mgz1 is less than or equal to the risk evaluation value Fxgz is less than or equal to the second evaluation threshold Mgz2, the corresponding engineering project is at a low risk level, the management system sends out a first-level early warning signal, and maintenance and management are carried out through the management system on the premise of continuing the engineering project;
if the risk evaluation value Fxgz is greater than the second evaluation threshold Mgz, the corresponding engineering project is at a high risk level, the management system sends out a secondary early warning signal, immediately stops the corresponding engineering project, and performs maintenance management through the management system.
Further, the cost evaluation module extracts cost parameter information, wherein the cost parameter information comprises actual labor expense, material purchase total actual expense and actual bidding settlement, a data processing model is built in the cost evaluation module, and an overall cost benefit evaluation value Xygz is generated according to the dimensionless processed cost parameter information, and the following formula is adopted:
wherein Lr is the actual labor expense, cr is the total actual cost of material purchase, sr is the actual bidding settlement, delta, theta and sigma are the preset proportionality coefficients of the expense deviation index, the environment index and the labor attendance index respectively, and delta+theta+sigma=9.257, sigma>θ>δ>0，G 1 Is a constant correction coefficient.
Wherein, actual labor expense Lr: by directly acquiring the labor cost in the labor unit;
material purchase total real cost Cr: obtained by calculation within the material cost in the material unit, the calculation process is as follows: material procurement total actual cost cr=contract unit price multiplied by the number of specifications on the material contract;
actual bid settlement Sr: by being obtained directly in the business unit.
Further, the manner in which the evaluation ranking module obtains the project ranking values Pxj of the plurality of project projects in the same area is as follows:
where a1 and a2 are weight coefficients of the risk assessment value and the overall cost benefit assessment value in the same item, respectively, and a1 > a2 > 0.
(III) beneficial effects
The invention provides a project cost intelligent management system based on big data technology, which has the following beneficial effects:
1. the method comprises the steps that early warning or alarming is carried out on the situation of deviation between each management data module and actual cost, a targeted warning effect is achieved, a plurality of risk assessment parameters are collected, a risk assessment value Fxgz of the same engineering project is obtained according to the risk assessment parameters after dimensionless treatment, the accuracy of the risk assessment value Fxgz can be guaranteed by considering the project cost, the project environment and the multidimensional factors of project personnel, and then the risk assessment value Fxgz is compared with a set assessment threshold value, so that the risk degree of a corresponding engineering project can be judged efficiently, and relevant maintenance strategies are provided, so that the subsequent intelligent targeted management is facilitated;
2. the method comprises the steps of acquiring a plurality of pieces of cost parameter information, preprocessing the cost parameter information, acquiring an overall cost benefit evaluation value Xygz according to the cost parameter information after dimensionless processing, and directly operating according to the ranking table to improve management efficiency to a certain extent when quality inspection operation is performed in order to save time and ensure maximization of economic benefit, wherein the greater the overall cost benefit evaluation value Xygz is, the higher the benefit of the corresponding engineering project is.
Drawings
FIG. 1 is a general block diagram of a project cost intelligent management system based on big data technology of the present invention;
FIG. 2 is a schematic diagram of the project cost intelligent management system based on big data technology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1-2, the invention provides a project cost intelligent management system based on big data technology, the system is carried in a cloud, the cloud acquires cost parameter information through a management data module, a risk evaluation module and a preprocessing module are arranged in the cloud, the risk evaluation module is used for extracting risk evaluation parameters and generating a risk evaluation value Fxgz, and corresponding strategies are executed according to comparison results after comparison; the preprocessing module preprocesses the cost parameter information and the risk assessment parameter, and data in the cloud are stored through a cloud database;
the cloud transmits the preprocessed cost parameter information to the management terminal, generates a cost benefit evaluation value Xygz through a cost evaluation module built in the management terminal, and feeds back the cost benefit evaluation value Xygz to the cloud; the cloud sends the risk evaluation value Fxgz and the cost benefit evaluation value Xygz of the same project to the quality inspection end, an evaluation and sorting module arranged in the quality inspection end generates a project sorting value Pxj, sorts the project sorting values Pxj of a plurality of project projects in the same area according to the order from small to large, and completes quality inspection operation according to the sorting order
The management data module comprises a business unit, a labor unit and a material unit;
business unit: collecting bidding budgets, predicting the variation trend of the bidding budgets by utilizing a big data technology, comparing the bidding budgets with actual bidding settlement, and helping a manager to carry out reasonable budget planning;
comparing the bid budget to the actual bid settlement: the management system automatically records and calculates the cost information of the project, compares the cost information with the actual bid settlement, helps a manager to know the cost control condition of the project, and stores the comparison and information recording processes in a cloud database to avoid data loss;
labor unit: the method is used for recording and processing labor information and labor cost in the same project;
the labor information at least comprises labor contracts, personal files, punching attendance records and labor fees of each labor, wherein the labor fees comprise actual labor application fees and actual labor expenditure fees, and the processing of the labor units on the actual labor application fees and the actual labor expenditure fees is as follows:
comparing the actual labor application cost with the actual labor expenditure cost;
if the actual labor application cost is far greater than the actual labor expenditure cost (i.e. the actual attendance cost), and the risk of the virtual report engineering quantity is indicated, early warning display is carried out through a visual tool; if the actual labor application cost is far less than the actual labor expenditure cost (namely the actual attendance cost), the risk of improper management is indicated, and early warning display is carried out through a visual tool;
the labor contracts and personal files of each labor in the labor information are recorded in real time through a computer, and the other checking-in attendance records are checked-in attendance through a mobile terminal small program or hardware equipment, wherein the mobile terminal small program is as follows: the nails are opened, the hardware equipment comprises a fingerprint machine and a face recognition card punching machine, the working hours and the attendance condition of a labor worker are recorded, and the accuracy and the reliability of the attendance information of the labor worker are ensured; the visualization tool can select a touch display screen or a liquid crystal display screen to display early warning information, for example: red stroboscopic typeface of "risk of false report engineering quantity" is presented; the actual labor application cost is far greater than or far less than the actual labor expenditure cost, which means that the cost difference between the former and the latter exceeds ten thousand RMB.
Material unit: for storing and managing material information in a cloud database, and the material information includes: a material contract, a bill of materials, and a material cost including a purchase price and a market guiding price of the corresponding material, a material purchase quantity, and a quantity of specifications on the material contract, and a contract price as above;
when managing the material contracts and the bill of materials, the functions of inquiring and increasing or decreasing the contract or bill pages are provided;
the process of managing the material cost is as follows:
continuously comparing the material purchase quantity with the quantity specified on the material contract while comparing the purchase price with the market guiding price;
if the purchasing price is lower than or higher than the market guiding price, alarming is carried out through a visualization tool, and if the purchasing quantity of the materials is lower than or higher than the quantity of the specifications on the contract, alarming is carried out through the visualization tool.
The risk assessment module comprises an acquisition unit and an assessment unit;
extracting risk assessment parameters by an acquisition unit, comprising: cost deviation index, environment index, labor attendance index; by building a data analysis model in the evaluation unit, generating a risk evaluation value Fxgz according to the risk evaluation parameters after dimensionless processing, wherein the basis formula is as follows:
in the method, in the process of the invention,for the cost deviation index>Mqz is labor attendance index, alpha, beta and gamma are respectively the cost deviation index, the environment index and the preset proportionality coefficient of labor attendance index, and alpha+beta+gamma= 5.721, alpha>γ>β>0。
Cost deviation indexXcz the difference between the bidding budget and the actual bidding settlement in the business unit, lfz the difference between the actual labor application cost and the actual labor expenditure cost in the labor unit, cpc the difference between the actual material purchase total cost and the material purchase contract total cost in the material unit, material purchase total actual cost = purchase unit price times material purchase quantity, material purchase contract total cost = contract unit price times quantity of specifications on the material contract;
environmental index Representing the average temperature of the location where the same item is located,the average humidity of the position of the same item is represented, the average temperature is obtained by weather forecast or the average value of the highest temperature and the lowest temperature in the weather of the current day displayed on the mobile phone is obtained and calculated, and the average humidity is obtained in the same way as the average temperature;
labor attendance index Mqz: the ratio of the full work workers to the number of workers in the same engineering project is represented, and the full work workers can directly obtain the attendance record through punching cards.
The risk evaluation module further comprises a threshold unit, a first evaluation threshold Mgz1 and a second evaluation threshold Mgz2 are set, mgz is larger than Mgz1, and two groups of evaluation thresholds are compared with the risk evaluation value Fxgz, wherein the set basis of the evaluation thresholds is that historical storage data in a cloud database are combined with engineering projects without risks in practice;
if the risk evaluation value Fxgz is smaller than the first evaluation threshold Mgz1, the risk is not found in the corresponding engineering project, the management system does not send out an early warning signal, and a related strategy is not made;
if the first evaluation threshold Mgz1 is less than or equal to the risk evaluation value Fxgz is less than or equal to the second evaluation threshold Mgz2, the corresponding engineering project is at a low risk level, the management system sends out a first-level early warning signal, and maintenance and management are carried out through the management system on the premise of continuing the engineering project;
if the risk evaluation value Fxgz is greater than the second evaluation threshold Mgz, the corresponding engineering project is at a high risk level, the management system sends out a secondary early warning signal, immediately stops the corresponding engineering project, and performs maintenance management through the management system.
The primary early warning signal is lower than the secondary early warning signal, and the specific content of maintenance management through the management system is as follows: the bid cost information in the business unit, the labor cost in the labor unit, and the material cost in the material unit are checked.
The method has the advantages that early warning or alarming is carried out on the situation of deviation between each management data module and actual cost, a targeted warning effect is achieved, the risk assessment value Fxgz of the same engineering project is obtained according to the risk assessment parameters after dimensionless treatment by collecting a plurality of risk assessment parameters, the accuracy of the risk assessment value Fxgz can be guaranteed by considering the project cost, the project environment and the multidimensional factors of project personnel, then the risk assessment value Fxgz is compared with a set assessment threshold value, the risk degree of the corresponding engineering project can be judged efficiently, and relevant maintenance strategies are provided, so that targeted management can be carried out conveniently.
The specific process for preprocessing the cost parameter information and the risk assessment parameter is as follows: and data cleaning, data conversion and normalization ensure the accuracy and stability of the follow-up cost parameter information and risk assessment parameters in use.
Data cleaning: the method comprises the steps of performing data cleaning on cost parameter information and risk assessment parameters, including removing repeated values, processing missing values and abnormal values, wherein the missing values can be filled by an interpolation method, and the abnormal values can be identified and processed by a statistical method or rules;
conversion and normalization: some cost parameters and risk assessment parameters may exist in different units of measure, and for unified comparison and analysis, data conversion and normalization techniques may be used, and common methods include normalization, max-min normalization, etc., to convert data to a unified scale.
Example 2: based on embodiment 1, the cloud transmits the preprocessed cost parameter information to the management terminal, generates a cost benefit evaluation value Xygz through a cost evaluation module built in the management terminal, and feeds back the cost benefit evaluation value Xygz to the cloud; the cost evaluation module extracts cost parameter information, wherein the cost parameter information comprises actual labor expense cost, material purchase total actual cost and actual bidding settlement, a data processing model is built in the cost evaluation module, and an overall cost benefit evaluation value Xygz is generated according to the dimensionless processed cost parameter information, and the following formula is adopted:
wherein Lr is the actual labor expense, cr is the total actual cost of material purchase, Sr is the actual bid settlement, delta, theta and sigma are the preset proportionality coefficients of the expense deviation index, the environment index and the labor attendance index respectively, and delta+theta+sigma=9.257 and sigma>θ>δ>0，G 1 The constant correction coefficient is specifically 1.735.
It should be noted that: a person skilled in the art collects a plurality of groups of sample data and sets a corresponding preset scaling factor for each group of sample data; substituting the preset proportionality coefficient, which can be the preset proportionality coefficient and the collected sample data, into a formula, forming a binary primary equation set by any two formulas, screening the calculated coefficient and taking an average value to obtain the values of delta, theta and sigma; the magnitude of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, the magnitude of the coefficient depends on the number of sample data and the corresponding preset proportional coefficient preliminarily set by a person skilled in the art for each group of sample data, that is, the coefficient is preset according to the actual practice, so long as the proportional relation between the parameter and the quantized numerical value is not influenced, and the above description is also adopted for the preset proportional coefficient and the constant correction coefficient described in other formulas.
Actual labor expense Lr: by directly acquiring the labor cost in the labor unit;
material purchase total real cost Cr: obtained by calculation within the material cost in the material unit, the calculation process is as follows: material procurement total actual cost cr=contract unit price multiplied by the number of specifications on the material contract;
actual bid settlement Sr: by being obtained directly in the business unit.
The greater the overall cost-benefit assessment value Xygz, the higher the benefit of the corresponding project.
The method comprises the steps of acquiring a plurality of pieces of cost parameter information, preprocessing the cost parameter information, acquiring an overall cost benefit evaluation value Xygz according to the cost parameter information after dimensionless processing, and directly operating according to the ranking table to improve management efficiency to a certain extent when quality inspection operation is performed in order to save time and ensure maximization of economic benefit, wherein the greater the overall cost benefit evaluation value Xygz is, the higher the benefit of the corresponding engineering project is.
The cloud sends the risk evaluation value Fxgz and the cost benefit evaluation value Xygz of the same project to the quality inspection end, an evaluation and sorting module arranged in the quality inspection end generates a project sorting value Pxj, and the project sorting values Pxj of a plurality of engineering projects in the same area are sorted according to the order from small to large, so that quality inspection operation is completed according to the sorting order.
The manner in which the assessment ordering module obtains the project ordering values Pxj for a plurality of project projects within the same area is as follows:
wherein a1 and a2 are weight coefficients of the risk evaluation value and the overall cost benefit evaluation value in the same item respectively, and a1 is more than a2 is more than 0; for example: item 1 within the same region has a rank value Pxj of 7.325 and item 2 has a rank value Pxj of 3.357, then the quality acceptance operation of item 2 precedes item 1.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described 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 various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (9)
1. The project cost intelligent management system based on big data technology is characterized in that: the system is carried in a cloud, the cloud acquires cost parameter information through a management data module, a risk assessment module and a preprocessing module are arranged in the cloud, the risk assessment module is used for extracting risk assessment parameters and generating a risk assessment value Fxgz, and corresponding strategies are executed according to comparison results after comparison; the preprocessing module preprocesses the cost parameter information and the risk assessment parameter, and the data in the cloud end are stored through a cloud end database;
the cloud transmits the preprocessed cost parameter information to the management terminal, generates a cost benefit evaluation value Xygz through a cost evaluation module built in the management terminal, and feeds back the cost benefit evaluation value Xygz to the cloud;
the cloud end sends the risk evaluation value Fxgz and the cost benefit evaluation value Xygz of the same project to the quality inspection end, an evaluation and sorting module arranged in the quality inspection end generates a project sorting value Pxj, sorts the project sorting values Pxj of a plurality of project projects in the same area according to the order from small to large, and completes quality inspection operation according to the sorting order.
2. The project cost intelligent management system based on big data technology according to claim 1, wherein: the management data module comprises a business unit, a labor unit and a material unit;
business unit: collecting bidding budgets, predicting the variation trend of the bidding budgets by utilizing a big data technology, comparing the bidding budgets with actual bidding settlement, and carrying out budget planning according to the comparison result;
labor unit: the method is used for recording and processing labor information and labor cost in the same project;
the labor information at least comprises labor contracts, personal files, card-punching attendance records and labor expenses of each labor, wherein the labor expenses comprise actual labor application expenses and actual labor expense expenses;
material unit: for storing and managing material information in a cloud database, and the material information includes: a material contract, a bill of materials, and a material cost including a purchase price and a market guidance price for the corresponding material, a material purchase quantity, and a quantity of specifications on the material contract.
3. The project cost intelligent management system based on big data technology according to claim 2, wherein: the process of comparing the actual labor application cost and the actual labor expenditure cost in the labor unit is as follows:
if the actual labor application cost is far greater than the actual attendance cost and the risk of the virtual report engineering quantity is indicated, early warning display is carried out through a visual tool; if the actual labor application cost is far less than the actual attendance cost and the risk of poor management exists, performing early warning display through a visual tool;
the process of managing material costs in the material unit is:
continuously comparing the material purchase quantity with the quantity specified on the material contract while comparing the purchase price with the market guiding price;
if the purchasing price is lower than or higher than the market guiding price, alarming is carried out through a visualization tool, and if the purchasing quantity of the materials is lower than or higher than the quantity of the specifications on the contract, alarming is carried out through the visualization tool.
4. The project cost intelligent management system based on big data technology according to claim 1, wherein: the risk assessment module comprises an acquisition unit and an assessment unit;
the collection unit extracts risk assessment parameters, including: the cost deviation index, the environment index and the labor attendance index are generated according to dimensionless processed risk assessment parameters by building a data analysis model in an assessment unit, and the risk assessment value Fxgz is generated according to the following formula:
in the method, in the process of the invention,for the cost deviation index>Mqz is labor attendance index, alpha, beta and gamma are respectively the cost deviation index, the environment index and the preset proportionality coefficient of labor attendance index, and alpha+beta+gamma= 5.721, alpha>γ>β>0。
5. The intelligent project cost management system based on big data technology according to claim 4, wherein: cost deviation indexXcz the difference between the bidding budget and the actual bidding settlement in the business unit, lfz the difference between the actual labor application cost and the actual labor expenditure cost in the labor unit, cpc the difference between the actual material purchase total cost and the total material purchase contract cost in the material unit, the total material purchase total actual cost = purchase price multiplied by the material purchase quantity, the total material purchase contract totalCost = contract price times the number of specifications on the material contract;
environmental index The average temperature of the position of the same item is represented, sd represents the average humidity of the position of the same item, the average value of the highest temperature and the lowest temperature in the weather of the same day displayed on the mobile phone is obtained and calculated through weather forecast, namely the average temperature, and the obtaining mode of the average humidity is the same as the obtaining mode of the average temperature;
labor attendance index Mqz: the ratio of the full work workers to the number of workers in the same engineering project is represented, and the full work workers can directly obtain the attendance record through punching cards.
6. The intelligent project cost management system based on big data technology according to claim 5, wherein: the risk assessment module further comprises a threshold unit, a first assessment threshold Mgz and a second assessment threshold Mgz are set, mgz is larger than Mgz1, and the two sets of assessment thresholds are compared with a risk assessment value Fxgz;
if the risk evaluation value Fxgz is smaller than the first evaluation threshold Mgz1, the risk is not found in the corresponding engineering project, the management system does not send out an early warning signal, and a related strategy is not made;
if the first evaluation threshold Mgz1 is less than or equal to the risk evaluation value Fxgz is less than or equal to the second evaluation threshold Mgz2, the corresponding engineering project is at a low risk level, the management system sends out a first-level early warning signal, and maintenance and management are carried out through the management system on the premise of continuing the engineering project;
if the risk evaluation value Fxgz is greater than the second evaluation threshold Mgz, the corresponding engineering project is at a high risk level, the management system sends out a secondary early warning signal, immediately stops the corresponding engineering project, and performs maintenance management through the management system.
7. The intelligent project cost management system based on big data technology according to claim 6, wherein: the cost evaluation module extracts cost parameter information, wherein the cost parameter information comprises actual labor expense cost, material purchase total actual cost and actual bidding settlement, a data processing model is built in the cost evaluation module, and an overall cost benefit evaluation value Xygz is generated according to the dimensionless processed cost parameter information, and the following formula is adopted:
wherein Lr is the actual labor expense, cr is the total actual cost of material purchase, sr is the actual bidding settlement, delta, theta and sigma are the preset proportionality coefficients of the expense deviation index, the environment index and the labor attendance index respectively, and delta+theta+sigma=9.257, sigma>θ>δ>0，G 1 Is a constant correction coefficient.
8. The project cost intelligent management system based on big data technology according to claim 7, wherein: actual labor expense Lr: by directly acquiring the labor cost in the labor unit;
material purchase total real cost Cr: obtained by calculation within the material cost in the material unit, the calculation process is as follows: material procurement total actual cost cr=contract unit price multiplied by the number of specifications on the material contract;
actual bid settlement Sr: by being obtained directly in the business unit.
9. The project cost intelligent management system based on big data technology according to claim 8, wherein: the manner in which the evaluation ranking module obtains the project ranking values Pxj of the plurality of project projects in the same area is as follows:
where a1 and a2 are weight coefficients of the risk assessment value and the overall cost benefit assessment value in the same item, respectively, and a1 > a2 > 0.
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