CN115936765A - Engineering data analysis-based engineering cost prediction method and system - Google Patents
Engineering data analysis-based engineering cost prediction method and system Download PDFInfo
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Abstract
The invention relates to the technical field of engineering cost, in particular to an engineering cost prediction method and an engineering cost prediction system based on engineering data analysis, wherein the method comprises the following steps: s1: reading data of engineering data and field cost data of the engineering project to obtain quantity information materials and unit price data materials; s2: performing characteristic matching on the quantity information material and the unit price data material to obtain labor cost and material cost of the engineering project; s3: and predicting the transportation fee according to the labor fee and the material fee, and predicting the construction cost based on the labor fee, the material fee and the transportation fee. The problems that the prediction result of the construction cost is possibly poor in practicality due to the fact that only manpower estimation is used for estimation and the like, and the data related to the construction cost is lost due to the fact that the construction cost storage mode is not reliable are solved.
Description
Technical Field
The invention relates to the technical field of engineering cost, in particular to an engineering cost prediction method and system based on engineering data analysis.
Background
With the development of science and technology, many cost estimation methods have appeared in the domestic engineering cost field. However, although these methods can solve the fast estimation of the construction cost in the process of some engineering projects, there are disadvantages that the most active factors in competition are set to be fixed values, which are difficult to adapt to the market requirements, resulting in separation of technology and economy, resulting in too large estimation cost error, and still difficult to meet the actual requirements of engineering construction in the market economy development.
In the actual production engineering execution process, the engineering cost estimation is an important link of engineering supervision and project implementation, the current engineering cost total estimation is basically manually estimated by workers through experience, and the mode not only has no unified standard for the engineering cost estimation due to different personal experiences, but also has the problems of labor consumption and lower estimation accuracy. And the comparison with similar projects is lacked, so that the prediction result of the project cost is possibly poor in practicality and the project cost storage mode is unreliable, and the problem of loss of data related to the project cost is caused.
Disclosure of Invention
The invention relates to a project cost prediction method and a project cost prediction system based on project data analysis, which are used for solving the problems that the project cost related data is lost and the like because the project cost is difficult to adapt to the market requirement only by manpower estimation, the technical and economic separation is caused, the comparison with similar projects is lacked, and the prediction result of the project cost is possibly poor in practicability and the storage mode of the project cost is unreliable.
A project cost prediction method based on project data analysis comprises the following steps:
s1: reading data of engineering data and field cost data of the engineering project to obtain quantity information materials and unit price data materials;
s2: performing characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost of the engineering project;
s3: predicting a transportation fee according to the labor fee and the material fee, and predicting a project cost based on the labor fee, the material fee and the transportation fee;
s4: matching the engineering project with other engineering projects according to the engineering information;
s5: if the matching degree is greater than a preset matching degree threshold value, acquiring the project cost records corresponding to the other project items;
s6: and correcting the construction cost based on the construction cost record.
Further, the S1: data reading is carried out on engineering data and field cost data of the engineering project to obtain quantity information materials and unit price data materials, and the method comprises the following steps:
s101: inputting quantity information materials and unit price data materials uploaded by workers of the engineering project, and preprocessing the quantity information materials and the unit price data materials to obtain engineering data text data; the pretreatment comprises the following steps: storing the text in a uniform text format;
s102: scanning the engineering data text data to obtain engineering time nodes corresponding to quantity information and unit price data respectively;
s103: packaging and storing the engineering time nodes respectively corresponding to the obtained quantity information and the unit price data to obtain a quantity storage packet and a unit price storage packet;
s104: and acquiring the field materials and the personnel information through the monitoring equipment, comparing the acquired field materials and the personnel information with the uploaded materials and the personnel information, and checking the similarity degree of the acquired field materials and the personnel information and the uploaded materials and the personnel information of the monitoring equipment.
Further, the step S2: and performing characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost of the engineering project, wherein the method comprises the following steps:
s201: monitoring the quantity storage package and the unit price storage package in real time, and acquiring newly uploaded information of project workers and corresponding uploading time;
s202: decompressing the newly uploaded information to obtain data related to labor cost and material cost;
s203: automatically matching the related data of the labor cost and the material cost to obtain the labor cost and the material cost in the implementation process of the engineering project;
and S204, evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time, and judging whether to use the uploaded data or not according to the importance degree and the reliability.
Further, S3: predicting a transportation cost from said labor cost and said material cost, predicting a construction cost based on said labor cost, material cost and said transportation cost, and:
according to the correlation between the labor cost and the material cost and the transportation cost, substituting the labor cost and the material cost into a transportation cost calculation formula, and calculating to obtain the transportation cost;
and adding the labor cost, the material cost and the transportation cost to obtain the predicted construction cost.
Further, the engineering project is matched with other engineering projects according to the engineering information:
s401: acquiring a cost prediction scheme of other similar projects of the same type as the predicted project from a big data platform to serve as a comparison project;
s402: carrying out characteristic extraction on the data of the manual quantity, the material quantity and the material type number of the predicted project and the comparison project to obtain data characteristic parameters;
s403: and calculating the similarity according to the extracted data characteristic parameters, and if the similarity is greater than or equal to a preset similarity threshold, adopting the predicted engineering cost.
Further, the method also comprises the following steps: s7: encrypting the construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain; wherein, the S7: encrypting the predicted construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain, wherein the method comprises the following steps:
carrying out data extraction on the predicted construction cost to obtain cost data;
encrypting the manufacturing cost data according to a threshold encryption algorithm to obtain manufacturing cost encrypted data and a corresponding secret key share;
uploading the cost data to a block chain network;
and distributing the key share corresponding to the threshold encryption algorithm to all the clients having the authority of inquiring the predicted engineering cost under the block chain intelligent contract.
Further, the method also comprises the following steps: s8: when an accident happens in the project site of the predicted project, splitting information of the accident, performing accident grade evaluation on the accident on the basis of a preset accident grade judgment standard, and correcting the predicted project cost according to an accident grade evaluation result; wherein, the S8: when an accident happens in the project site of the predicted project, splitting information of the accident, performing accident rating on the accident based on a preset accident rating judgment standard, and correcting the predicted project cost according to an accident rating result, wherein the method comprises the following steps:
splitting information of the unexpected situation of the predicted project to obtain a plurality of unexpected information items of the same type;
acquiring a first accident type corresponding to the accident information items;
acquiring two types of accident information items used for evaluating the accident grade in the accident grade evaluation standard;
matching the first-class accident information item with the second-class accident information item, taking an information item matched and matched with the first-class accident information item and the second-class accident information item as a third-class accident information item, judging a sub-information item which is corresponding to the third accident information item and belongs to a specific accident grade in the accident rating standard, and determining that the first accident type is the accident type of the specific accident grade;
and after the accident grade of the first accident type is determined, multiplying the predicted construction cost by a preset correction coefficient of the accident grade corresponding to the first accident type to obtain the corrected predicted construction cost.
A project cost prediction system based on project data analysis, the project cost prediction system comprising:
the reading module is used for reading the engineering data and the field cost data to obtain a quantity information material and a unit price data material;
the matching module is used for carrying out characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost in the project;
the proportion module is used for acquiring transportation cost according to labor cost and material cost to obtain predicted construction cost;
the similarity module is used for matching the similarity of the project information of the predicted project and the similar project, acquiring the cost scheme of the similar project if the matching degree is greater than a preset matching threshold, and correcting the predicted project cost based on the cost scheme;
a storage module: encrypting the construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain;
an accident assessment module: when an accident happens in the project site of the predicted project, information of the accident is split, the accident is rated based on a preset accident rating judgment standard, and the predicted project cost is corrected according to an accident rating result.
Further, the reading module includes:
the system comprises an input module, a storage module and a display module, wherein the input module is used for inputting quantity information materials and unit price data materials uploaded by project workers, preprocessing the quantity information materials and the unit price data materials, storing the quantity information materials and the unit price data materials into a uniform text format and acquiring engineering data text data;
the corresponding module is used for scanning the engineering data text data to obtain engineering time nodes corresponding to the quantity information and the unit price data respectively;
the storage module is used for packaging and storing the engineering time nodes respectively corresponding to the acquired quantity information and the unit price data to acquire a quantity storage packet and a unit price storage packet;
and the monitoring comparison module is used for acquiring the field materials and the personnel information through the monitoring equipment, comparing the acquired field materials and the personnel information with the uploaded materials and the personnel information, and checking the similarity degree of the acquired field materials and the personnel information and the uploaded materials and the personnel information of the monitoring equipment.
Further, the matching module comprises:
the marking module is used for monitoring the quantity storage packet in real time and acquiring information newly uploaded by project workers as a temporary quantity information storage data packet;
the temporary storage module is used for monitoring the quantity storage package and the unit price storage package in real time and acquiring newly uploaded information of project workers and corresponding uploading time;
the decompression module is used for decompressing the newly uploaded information to obtain the data related to the labor cost and the material cost;
the automatic matching module is used for automatically matching the related data of the labor cost and the material cost to obtain the labor cost and the material cost in the implementation process of the engineering project;
and the primary examination module is used for evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time, and judging whether to use the uploaded data or not according to the importance degree and the reliability.
Further, the proportion module comprises:
the labor cost and the material cost are brought into a transportation cost calculation formula, and the transportation cost is calculated;
and adding the labor cost, the material cost and the transportation cost to obtain the predicted construction cost.
Further, the similarity module includes:
the benchmarking module is used for acquiring a cost prediction scheme of other similar projects of the same type as the predicted project from the big data platform as a comparison project;
the characteristic extraction module is used for carrying out characteristic extraction on the data of the manual quantity, the material quantity and the material type number of the predicted project and the comparison project to obtain data characteristic parameters;
and the similarity analysis module is used for calculating the similarity according to the extracted data characteristic parameters, and if the similarity is greater than or equal to a preset similarity threshold, adopting the predicted engineering cost.
The invention has the beneficial effects that:
according to the engineering cost prediction method and system based on engineering data analysis, actual data in the engineering project construction process are obtained through engineering data and field cost data which embody engineering construction field data, corresponding labor cost and material cost are obtained through automatic matching of the actual data, meanwhile, proportion distribution is carried out through actual conditions of the labor cost and the material cost, and then the total cost is predicted. The engineering cost prediction method and the engineering cost prediction system based on engineering data analysis can effectively improve the accuracy of total cost estimation, can further improve the accuracy of total cost estimation according to the proportional relation between labor cost and material cost, effectively reduce the participation of a manual accounting part, effectively reduce labor cost consumption, and maximally reduce the time spent on total cost prediction. Further, by extracting characteristic parameters of a project site, the cost prediction scheme is compared with similar projects in a weighting mode; according to the comparison result, the feasibility of the construction cost scheme is confirmed, the reliability of the project construction cost prediction scheme is ensured, and the application range of the method is widened.
The encrypted construction cost data is stored by using the block chain, so that the reliability of the construction cost data is ensured, and the possible loss condition caused by the construction cost data is prevented; and further introducing an accident grade judgment standard for evaluating the accident condition, carrying out accident grade evaluation on the accident condition, and correcting the predicted construction cost according to a grade evaluation result, thereby further ensuring the flexibility and reliability of the construction cost prediction.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The following goes through the drawings and examples. The technical scheme of the invention is further described in detail.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention in any way:
FIG. 1 is a diagram of the steps of the method of the present invention;
FIG. 2 is a schematic diagram of a system of the present invention;
fig. 3 is a detailed view of the system of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
The embodiment of the invention provides a project cost prediction method based on project data analysis, which comprises the following steps:
s1: reading data of engineering data and field cost data of the engineering project to obtain quantity information materials and unit price data materials;
s2: performing characteristic matching on the quantity information material and the unit price data material to obtain labor cost and material cost of the engineering project;
s3: predicting a transportation fee according to the labor fee and the material fee, and predicting a project cost based on the labor fee, the material fee and the transportation fee;
s4: matching the engineering project with other engineering projects according to the engineering information;
s5: if the matching degree is greater than a preset matching degree threshold value, acquiring the project cost records corresponding to the other project items;
s6: and correcting the construction cost based on the construction cost record.
The working principle of the embodiment is as follows:
firstly, monitoring and reading engineering data uploaded by working personnel in the process of an engineering project in real time, and acquiring quantity information materials recorded in the engineering data, namely the quantity information of the working personnel and the quantity information of materials entering and exiting a field; then, monitoring and reading the field cost data uploaded by a competent department in real time, and acquiring unit price data materials in the field cost data, namely, artificial unit price data and project material unit price data; then, automatically matching the staff quantity information and the entering and exiting field material quantity information with the manual unit price data and the engineering material unit price data in a mode of automatically calling and matching corresponding data to obtain the labor cost and the material cost in the engineering project implementation process; further, according to the proportion principle of the labor cost and the material cost in the construction cost, the total amount range of the construction cost is predicted by utilizing the labor cost and the material cost; and finally, matching the similarity of the project information of the predicted project and the similar project, and judging whether to adopt the predicted project cost according to the similarity.
Based on the project cost record, correcting the project cost comprises:
and presetting a record correction coefficient, and multiplying the project cost by the preset record correction coefficient to realize the correction of the project cost.
Then, encrypting the construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain;
and finally, when an accident happens in the project site of the predicted project, splitting information of the accident, evaluating the accident grade of the accident on the basis of a preset accident grade judgment standard, and correcting the predicted project cost according to the accident grade evaluation result.
The beneficial effect of this embodiment does:
according to the engineering cost prediction method based on engineering data analysis, actual data in the engineering project construction process are obtained through engineering data and field cost data which reflect engineering construction field data, corresponding labor cost and material cost are obtained through automatic matching of the actual data, meanwhile, proportion distribution is conducted through actual conditions of the labor cost and the material cost, and then the total cost is predicted. The engineering cost prediction method based on engineering data analysis can effectively improve the accuracy of total cost estimation, can further improve the accuracy of total cost estimation according to the proportional relation between labor cost and material cost, effectively reduces the participation of a manual accounting part, effectively reduces the consumption of labor cost, and furthest reduces the time spent on total cost prediction. Further, by extracting characteristic parameters of a project site, the cost prediction scheme is compared with similar projects in a weighting mode; according to the comparison result, the feasibility of the construction cost scheme is confirmed, the reliability of the project construction cost prediction scheme is ensured, and the application range of the method is widened.
The encrypted construction cost data is stored by using the block chain, so that the reliability of the construction cost data is ensured, and the possible loss condition caused by the construction cost data is prevented; and an accident grade judgment standard for evaluating the accident condition is further introduced, the accident grade of the occurred accident condition is evaluated, and the predicted construction cost is corrected according to a grade evaluation result, so that the flexibility and the reliability of the construction cost prediction are further ensured.
In one embodiment, the specific method for reading the engineering data and the field cost data to obtain the quantity information and the unit price data includes:
s101: inputting quantity information materials and unit price data materials uploaded by project workers, preprocessing the quantity information materials and the unit price data materials, storing the quantity information materials and the unit price data materials into a uniform text format, and acquiring engineering data text data;
s102: scanning the engineering data text data to obtain engineering time nodes corresponding to quantity information and unit price data respectively;
s103: packing and storing the engineering time nodes respectively corresponding to the obtained quantity information and the unit price data to obtain a quantity storage packet and a unit price storage packet;
s104: and acquiring the field materials and personnel information through the monitoring equipment, comparing the field materials and the personnel information with the uploaded materials and personnel information, and checking the similarity degree of the field materials and the personnel information acquired by the monitoring equipment and the uploaded materials and personnel information.
The working principle of the embodiment is as follows:
in the process of engineering project proceeding, inputting quantity information materials and unit price data materials uploaded by workers in each project stage; scanning and reading quantity information materials and unit price data materials uploaded by workers in the process of engineering projects in real time, and identifying text information in the quantity information materials and the unit price data materials; and acquiring the number information of the workers, the number information of the goods and materials in and out of the field and the corresponding time points thereof recorded in the number information material, and the manual unit price data and the engineering goods and materials unit price data recorded in the unit price data material and the corresponding time points thereof in a text scanning and identifying mode. The engineering stage time point comprises stage starting time and stage ending time in an engineering time period corresponding to the current quantity information materials and the unit price data materials, and quantity information material and unit price data material uploading time.
Packaging currently acquired data information corresponding to staff quantity information and material quantity information of an entrance and an exit to form a quantity information storage data packet, and packaging currently acquired artificial unit price data and data information corresponding to project material unit price data information to form a unit price data storage data packet; performing data storage on a quantity information storage data packet and a unit price data storage data packet according to a time period corresponding to a stage starting time and a stage ending time in an engineering time period corresponding to a current quantity information material and a unit price data material, wherein the time period is a quantity storage packet and a unit price storage packet; and after the quantity information storage data packet and the unit price data storage data packet complete data storage, marking the time information corresponding to the current quantity information material and the unit price data material uploading time on the quantity information storage data packet and the unit price data storage data packet.
The working principle of the monitoring benchmarking module is as follows:
recording material and personnel flow through a camera of a project site;
automatically identifying and recording materials and personnel in the monitoring video through video identification software;
comparing the monitored and extracted material and personnel information with the material and personnel information uploaded by project workers, and checking the similarity degree of the monitored information and the uploaded information.
The beneficial effect of this embodiment does:
by the method, the information acquisition efficiency and the information acquisition accuracy of the relevant data of the engineering project can be effectively improved. Meanwhile, the accuracy of data calling in the subsequent automatic matching process can be effectively improved by storing information in a time period unit mode according to the time sequence. Furthermore, the monitoring data is compared with the data uploaded by the workers, and the accuracy of engineering data acquisition is improved.
In one embodiment, the S2: and performing characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost of the engineering project, wherein the method comprises the following steps:
s201: monitoring the quantity storage package and the unit price storage package in real time, and acquiring newly uploaded information of project workers and corresponding uploading time;
s202: decompressing the newly uploaded information to obtain data related to labor cost and material cost;
s203: automatically matching the related data of the labor cost and the material cost to obtain the labor cost and the material cost in the engineering project implementation process;
and S204, evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time, and judging whether to use the uploaded data or not according to the importance degree and the reliability.
The working principle of the embodiment is as follows:
firstly, monitoring quantity information of quantity storage data packets stored in a quantity storage packet and a unit price storage packet in real time; when the newly added and stored quantity information storage data packet appears in the quantity storage packet, acquiring time information marked on the quantity information storage data packet; when the newly-added stored quantity information storage data packet appears in the quantity storage data packet, scanning the time information marked on the unit price data storage data packet, and extracting the unit price data storage data packet which is the nearest to the time information of the mark of the newly-added stored quantity information storage data packet to serve as a unit price data storage data packet; then, monitoring whether a newly-added stored unit price data storage data packet occurs in the unit price storage packet in real time within a preset monitoring time period, and if the newly-added stored unit price data storage data packet occurs in the unit price storage packet in real time within the preset monitoring time period, taking the newly-added stored unit price data storage data packet as a unit price data storage data packet; otherwise, keeping the originally acquired unit price data storage data packet unchanged; decompressing and analyzing the unit price waiting data storage data packet to obtain artificial unit price data and engineering material unit price data stored in the unit price waiting data storage data packet; simultaneously, decompressing and analyzing the project price information storage data packet to acquire the number information of the staff and the number information of the goods and materials entering and leaving the field, which are stored in the number information storage data packet; and finally, automatically matching the staff quantity information and the quantity information of the goods and materials entering and leaving the field with the manual unit price data and the project goods and materials unit price data to obtain the labor cost and the material cost in the implementation process of the project.
The real-time calling comprises the steps of monitoring the quantity storage packet and the unit price storage packet in real time, and acquiring newly uploaded information of project workers and corresponding uploading time
The specific method for evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time and judging whether to use the uploaded data according to the importance degree and the reliability comprises the following steps:
selecting a working post and workers of the post;
acquiring the data uploading times of the workers in unit time;
acquiring the data modification times of the workers in unit time;
substituting the uploading times and the modification times into a formula to obtain an importance degree parameter of the working post and a reliability degree parameter of the worker;
and judging the reliability of the data uploaded by the staff at the working post in unit time according to the importance degree parameter and the reliability degree parameter.
The formula is:
in the formula:
x is an importance degree parameter;
y is a reliability parameter;
k is an importance weight coefficient, and the value range is 0.01,0.03;
w is a reliability weight coefficient, and the value range is 1.5,2.3;
n is the data uploading times of the staff of the post in unit time;
m is the data modification times of the staff at the post in unit time.
The larger the value of X is, the higher the importance degree of the position is; the greater the value of Y, the greater the reliability of the worker.
The beneficial effect of this embodiment does:
by the method, the matching accuracy of the actual field data corresponding to the engineering data and the corresponding data in the field cost data can be effectively improved, and further the problem of error in calculation of labor cost and material cost caused by data matching errors is effectively reduced; meanwhile, the automatic matching efficiency of the data can be effectively improved through the information calling and automatic matching mode, and the overall speed and efficiency of total cost prediction are further improved to the maximum extent.
According to the method and the device, the importance degree of the uploaded data of the working post and the reliability of the uploaded data in unit time are evaluated by selecting one working post and the working personnel of the post, the number of times of uploading the data of the working personnel and the number of times of modifying the data of the working personnel in unit time are obtained, whether the uploaded data are used or not is judged according to the importance degree and the reliability, the accuracy of engineering data collection is guaranteed, meanwhile, the calculation time is saved by depending on an importance degree formula and a reliability formula, the cost is saved, and the production efficiency is improved.
Meanwhile, the unit time is selected within the working hours of the normal legal working days, working days caused by network faults, power failure and various accidents are eliminated, and abnormal conditions of engineering data acquisition caused by field facility faults and artificial accidents are avoided, so that the accuracy of engineering data acquisition is ensured.
In one embodiment, S3: predicting a transportation fee from said labor cost and said material cost, predicting a construction cost based on said labor cost, material cost and said transportation fee:
according to the correlation between the labor cost and the material cost and the transportation cost, substituting the labor cost and the material cost into a transportation cost calculation formula, and calculating to obtain the transportation cost;
and adding the labor cost, the material cost and the transportation cost to obtain the predicted construction cost.
The working principle of the embodiment is as follows:
in the actual engineering construction process, the transportation cost is determined by the type and the frequency of the transported materials and the transportation mileage, so that the transportation cost and the material cost have a direct correlation;
meanwhile, the transported materials are inseparable from the construction of personnel, so that the transportation cost and the labor cost are also related;
therefore, transportation fees can be associated with material fees and labor fees in the form of a bulletin, and the formula is as follows:
and substituting labor cost L and material cost E into a transportation calculation formula to obtain transportation cost K, wherein the transportation calculation formula is as follows:
K＝P·L+Q·E
in the formula, P is a labor cost coefficient and takes the value of (0.025,0.031); q is a material cost coefficient, and the value range is 0.031,0.037;
and adding the labor cost L, the material cost L and the transportation cost K to obtain the predicted engineering cost R.
R＝(1+P)·L+(1+Q)·E
The beneficial effect of this embodiment does:
the proportion of the labor cost and the material cost in the total engineering amount is obtained according to the proportion principle of the labor cost and the manufacturing cost, so that the standard uniformity of proportion estimation can be effectively improved, and the problem of overlarge uncertainty of total amount prediction caused by different experience standards from person to person is avoided. Meanwhile, the proportion principle of the labor cost and the manufacturing cost can effectively combine the proportion distribution with the actual fund amount relation of the labor cost and the manufacturing cost, and further the matching between the proportion setting and the actual fund consumption condition is improved to the maximum extent.
On the other hand, the forecasting model can carry out concrete forecasting analysis according to different proportion conditions, and a mode of acquiring the total amount range by adopting different construction cost floating ranges is adopted, so that the reasonability and the accuracy of engineering construction cost total amount forecasting can be improved to the maximum extent, and the problem that the inaccurate total amount forecasting causes adverse effects on engineering implementation is effectively prevented.
In one embodiment, the engineering project is project information matched with other engineering projects:
s401: acquiring a cost prediction scheme of other similar projects of the same type as the predicted project from the big data platform as a comparison project;
s402: carrying out characteristic extraction on the data of the manual quantity, the material quantity and the material type number of the predicted project and the comparison project to obtain data characteristic parameters;
s403: and calculating the similarity according to the extracted data characteristic parameters, and if the similarity is greater than or equal to a preset similarity threshold, adopting the predicted engineering cost.
The working principle of the embodiment is as follows:
finding out the actually existing projects with the same type as the predicted projects from large data platforms such as professional websites generally recognized in the industry as comparison projects, and respectively extracting the manual quantity, the material quantity and the material type number of the predicted projects and the comparison projects to extract data characteristics so as to obtain data characteristic parameters of the total cost of labor, the total cost of material and the total length of construction time; and carrying out weighted comparison on the data characteristic parameters of the public engineering and the comparison engineering to obtain the similarity between the predicted engineering and the comparison engineering: when the similarity is higher than 80%, adopting a cost scheme; when the similarity is lower than 80%, the cost scheme needs to be redesigned. The similarity calculation formula of the characteristic parameter weighted comparison is as follows:
1＝A+B+C
in the above formula:
eta is similarity;
a is the weighting coefficient of the total cost parameter of the artificial fee, and the value range is (0.1,0.5);
b is a weighting coefficient of the total price parameter of the material charge, and the value range is (0.2,0.7);
c is a weighting coefficient of the total length parameter of the construction time, and the value range is (0.1,0.6);
v1 is a total price parameter of the predicted engineering labor cost;
f1 is the total price parameter of the engineering material cost;
h1 is the total length parameter of the predicted engineering construction time;
v2 is a total price parameter of the comparative engineering labor cost;
f2 is a total price parameter of the comparative engineering material cost;
h2 is the total length parameter of the construction time of the comparative engineering.
The beneficial effect of this embodiment does:
the invention provides a project cost prediction method based on project data analysis, which ensures the reliability of the predicted project cost prediction by comparing the similarity with similar projects; meanwhile, the artificial quantity, the material quantity and the material model are subjected to feature extraction, so that the reliability of the analysis foundation of the project cost prediction scheme is ensured; and finally, calculating by a formula, and simultaneously adjusting the weighting coefficient A of the total labor cost parameter, the weighting coefficient B of the total material cost parameter and the weighting coefficient C of the total construction time length parameter according to specific conditions and requirements, thereby improving the application range of the invention.
The method comprises the steps that actually existing projects with the same type as a predicted project are found from large data platforms such as professional websites and the like generally accepted in the industry as comparison projects, the manual quantity, the material quantity and the material type number of the predicted project and the comparison projects are respectively extracted for data feature extraction, and data feature parameters V1 and V2 of total labor cost, data feature parameters F1 and F2 of total material cost and data feature parameters H1 and H2 of total construction time are obtained; meanwhile, the similarity of the predicted project and the comparison project can be calculated more accurately by combining the weighting coefficient A of the total cost parameter of the labor cost, the weighting coefficient B of the total cost parameter of the material cost and the weighting coefficient C of the total length parameter of the construction time, the calculation efficiency is higher, the accuracy and the similarity are better, and the problems of single characteristic parameter caused unreliable evaluation one-sided and evaluation results are avoided.
After the similarity between the predicted project and the comparison project is calculated, the similarity can be compared with a preset similarity, for example, the preset similarity is 80%, and if the calculated similarity exceeds 80%, the predicted construction cost scheme is high in reliability and good in reliability and can be adopted; if the calculated similarity is lower than 80%, the reliability of the predicted cost scheme is questioned, the reliability is required to be improved, and the cost scheme needs to be redesigned.
In one embodiment, further comprising: s7: encrypting the construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain; wherein, the S7: encrypting the predicted construction cost to obtain cost encrypted data, and storing the cost encrypted data into a block chain, comprising:
carrying out data extraction on the predicted construction cost to obtain cost data;
encrypting the manufacturing cost data according to a threshold encryption algorithm to obtain manufacturing cost encrypted data and a corresponding secret key share;
uploading the cost data to a block chain network;
and distributing the key share corresponding to the threshold encryption algorithm to all the clients having the authority of inquiring the predicted engineering cost under the block chain intelligent contract.
The working principle of the embodiment is as follows:
the threshold encryption algorithm is designed based on a threshold encryption constitution, in the threshold encryption constitution, n participants jointly generate a public key, and a decryption key is jointly held by the n participants; the public key can directly encrypt information, but decryption requires at least t persons in the n persons to participate in the decryption at the same time, and less than t persons cannot acquire any information.
In this embodiment, the two participating users, the person who generates the public key, and the person who participates in decryption are all owners who have the user end who inquires the predicted engineering cost right under the intelligent contract of the block chain, that is, t = n.
The beneficial effect of this embodiment does:
in the embodiment, the encrypted construction cost data is stored by using the block chain, so that the reliability of the construction cost data is ensured, and the possible loss condition caused by the construction cost data is prevented; meanwhile, successful decryption of the encrypted construction cost data can be realized only when all persons who participate in encrypting the predicted construction cost data and have the authority to inquire the predicted construction cost participate in decrypting the encrypted construction cost data at the same time, and if one person does not participate, any predicted construction cost data cannot be obtained; therefore, perfect and careful confidential treatment is realized on the predicted project cost data, and the information safety of the project cost data of the predicted project is ensured.
In one embodiment, further comprising: s8: when an accident happens in the project site of the predicted project, splitting information of the accident, performing accident grade evaluation on the accident on the basis of a preset accident grade judgment standard, and correcting the predicted project cost according to an accident grade evaluation result; wherein, the S8: when an accident occurs in the project site of the predicted project, splitting information of the accident, performing accident grade evaluation on the occurred accident based on a preset accident grade judgment standard, and correcting the predicted project cost according to an accident grade evaluation result, wherein the method comprises the following steps:
splitting information of unexpected situations occurring in a predicted project to obtain a plurality of unexpected information items of the same type;
acquiring a first accident type corresponding to the accident information items;
acquiring two types of accident information items used for evaluating the accident grade in the accident grade evaluation standard;
matching the first-class accident information item with the second-class accident information item, taking an information item matched and matched with the first-class accident information item and the second-class accident information item as a third-class accident information item, judging a sub-information item which is corresponding to the third accident information item and belongs to a specific accident grade in the accident rating standard, and determining that the first accident type is the accident type of the specific accident grade;
and after the accident grade of the first accident type is determined, multiplying the predicted construction cost by a preset correction coefficient of the accident grade corresponding to the first accident type to obtain the corrected predicted construction cost.
The working principle of the embodiment is as follows:
the accident situation of the project site usually has the condition that the project site can not be normally constructed due to weather, accident, policy influence and the like, and the grade evaluation standard of the accident situation can be made according to the number of days which can not be constructed due to the accident situation, for example, the grade evaluation standard is one grade in one week, two grades in one week to one month, three grades in one month to three months, and four grades in more than three months.
Taking weather as an example, a stormy day may contain a category of unexpected information items: the method comprises the steps of judging the level of an accident condition of a rainstorm day by using a specific accident level sub-information item in the accident assessment standard, and judging the level of the accident condition of the rainstorm day by using the specific accident level sub-information item in the accident assessment standard.
And after the grade of the accident situation is judged, multiplying the predicted construction cost by the correction coefficient corresponding to each grade according to the specific situation of the site.
The correction coefficient is a parameter with a value more than or equal to 1 preset according to the actual situation of the engineering site.
The beneficial effect of this embodiment does:
accidents are inevitable in the construction process, so that the construction date is prolonged, the construction cost is increased, and the increased cost is uncertain; the embodiment can accurately position the accident situation, improves the prejudgment of the increase of the engineering cost, is biased to further make a corresponding scheme or recalculate the engineering cost, and further ensures the flexibility and the reliability of the engineering cost prediction.
The embodiment of the invention provides a project cost prediction system based on project data analysis, which comprises:
the reading module is used for reading the engineering data and the field cost data to obtain a quantity information material and a unit price data material;
the matching module is used for carrying out characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost in the project;
the proportion module is used for acquiring transportation cost according to labor cost and material cost to obtain predicted construction cost;
and the similarity module is used for matching the similarity of the project information of the predicted project and the similar project and judging whether the predicted project cost is adopted or not according to the similarity.
In one embodiment, the reading module includes:
the system comprises an input module, a storage module and a display module, wherein the input module is used for inputting quantity information materials and unit price data materials uploaded by project workers, preprocessing the quantity information materials and the unit price data materials, storing the quantity information materials and the unit price data materials into a uniform text format and acquiring engineering data text data;
the corresponding module is used for scanning the engineering data text data to obtain engineering time nodes corresponding to the quantity information and the unit price data respectively;
the storage module is used for packaging and storing the engineering time nodes respectively corresponding to the acquired quantity information and the unit price data to acquire a quantity storage packet and a unit price storage packet;
and the monitoring comparison module is used for acquiring the field materials and the personnel information through the monitoring equipment, comparing the acquired field materials and the personnel information with the uploaded materials and the personnel information, and checking the similarity degree of the acquired field materials and the personnel information and the uploaded materials and the personnel information of the monitoring equipment.
In one embodiment, the matching module comprises:
the temporary storage module is used for monitoring the quantity storage package and the unit price storage package in real time and acquiring newly uploaded information of project workers and corresponding uploading time;
the decompression module is used for decompressing the newly uploaded information to obtain the relevant data of labor cost and material cost;
the automatic matching module is used for automatically matching the related data of the labor cost and the material cost to obtain the labor cost and the material cost in the implementation process of the engineering project;
and the primary examination module is used for evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time, and judging whether to use the uploaded data or not according to the importance degree and the reliability.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent techniques, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A project cost prediction method based on project data analysis is characterized by comprising the following steps:
s1: reading the engineering data and the site cost data of the engineering project to obtain a quantity information material and a unit price data material;
s2: performing characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost of the engineering project;
s3: predicting a transportation fee according to the labor fee and the material fee, and predicting a project cost based on the labor fee, the material fee and the transportation fee;
s4: matching the engineering project with other engineering projects according to the engineering information;
s5: if the matching degree is greater than a preset matching degree threshold value, acquiring the project cost records corresponding to the other project items;
s6: and correcting the construction cost based on the construction cost record.
2. The project cost prediction method based on project data analysis according to claim 1, wherein the S1: data reading is carried out on engineering data and field cost data of the engineering project to obtain quantity information materials and unit price data materials, and the method comprises the following steps:
s101: inputting quantity information materials and unit price data materials uploaded by workers of the engineering project, and preprocessing the quantity information materials and the unit price data materials to obtain engineering data text data; the pretreatment comprises the following steps: storing the text in a uniform text format;
s102: scanning the engineering data text data to obtain engineering time nodes corresponding to quantity information and unit price data respectively;
s103: packing and storing the engineering time nodes respectively corresponding to the obtained quantity information and the unit price data to obtain a quantity storage packet and a unit price storage packet;
s104: and acquiring the field materials and the personnel information through the monitoring equipment, comparing the acquired field materials and the personnel information with the uploaded materials and the personnel information, and checking the similarity degree of the acquired field materials and the personnel information and the uploaded materials and the personnel information of the monitoring equipment.
3. The project cost prediction method based on project data analysis according to claim 1, wherein the S2: and performing characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost of the engineering project, wherein the method comprises the following steps:
s201: monitoring the quantity storage package and the unit price storage package in real time, and acquiring newly uploaded information of project workers and corresponding uploading time;
s202: decompressing the newly uploaded information to obtain data related to labor cost and material cost;
s203: automatically matching the related data of the labor cost and the material cost to obtain the labor cost and the material cost in the implementation process of the engineering project;
and S204, evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time, and judging whether to use the uploaded data or not according to the importance degree and the reliability.
4. The project cost prediction method based on project data analysis according to claim 1, characterized in that said S3: predicting a transportation fee according to the labor fee and the material fee, predicting a project cost based on the labor fee, the material fee and the transportation fee, comprising:
according to the correlation between the labor cost and the material cost and the transportation cost, the labor cost and the material cost are brought into a transportation cost calculation formula, and the transportation cost is calculated;
and adding the labor cost, the material cost and the transportation cost to obtain the predicted construction cost.
5. The project cost prediction method based on project data analysis as claimed in claim 1, characterized in that the project item is matched with other project items for project information:
s401: acquiring a cost prediction scheme of other similar projects of the same type as the predicted project from a big data platform to serve as a comparison project;
s402: carrying out feature extraction on the manual quantity, the material quantity and the material model data of the predicted project and the comparison project to obtain data feature parameters;
s403: and calculating similarity according to the extracted data characteristic parameters, if the similarity is greater than a preset matching threshold, acquiring a construction cost scheme of the similar project, and correcting the predicted construction cost based on the construction cost scheme.
6. The project cost prediction method based on project data analysis as claimed in claim 1, further comprising: s7: encrypting the construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain; wherein, the S7: : encrypting the predicted construction cost to obtain cost encrypted data, and storing the cost encrypted data into a block chain, comprising:
carrying out data extraction on the predicted construction cost to obtain cost data;
encrypting the manufacturing cost data according to a threshold encryption algorithm to obtain manufacturing cost encrypted data and a corresponding secret key share;
uploading the cost data to a block chain network;
and distributing the key share corresponding to the threshold encryption algorithm to all the clients having the authority of inquiring the predicted engineering cost under the block chain intelligent contract.
7. The project cost prediction method based on project data analysis as claimed in claim 1, further comprising: s8: when an accident happens in the project site of the predicted project, splitting information of the accident, performing accident grade evaluation on the accident on the basis of a preset accident grade judgment standard, and correcting the predicted project cost according to an accident grade evaluation result; wherein, the S8: when an accident occurs in the project site of the predicted project, splitting information of the accident, performing accident grade evaluation on the occurred accident based on a preset accident grade judgment standard, and correcting the predicted project cost according to an accident grade evaluation result, wherein the method comprises the following steps:
splitting information of the unexpected situation of the predicted project to obtain a plurality of unexpected information items of the same type;
acquiring a first accident type corresponding to the accident information item;
acquiring two types of accident information items used for evaluating the accident grade in the accident grade evaluation standard;
matching the first-class accident information item with the second-class accident information item, taking an information item matched and matched with the first-class accident information item and the second-class accident information item as a third-class accident information item, judging a sub-information item which is corresponding to the third accident information item and belongs to a specific accident grade in the accident rating standard, and determining that the first accident type is the accident type of the specific accident grade;
and after the accident grade of the first accident type is determined, multiplying the predicted construction cost by a preset correction coefficient of the accident grade corresponding to the first accident type to obtain the corrected predicted construction cost.
8. A project cost prediction system based on project data analysis, the project cost prediction system comprising:
the reading module is used for reading the engineering data and the field cost data to obtain a quantity information material and a unit price data material;
the matching module is used for carrying out characteristic matching on the quantity information material and the unit price data material to obtain the labor cost and the material cost in the project;
the proportion module is used for acquiring transportation cost according to labor cost and material cost to obtain predicted engineering cost;
the similarity module is used for matching the similarity of the project information of the predicted project and the similar project, acquiring a cost scheme of the similar project if the matching degree is greater than a preset matching threshold, and correcting the predicted project cost based on the cost scheme;
a storage module: encrypting the construction cost to obtain construction cost encrypted data, and storing the construction cost encrypted data into a block chain;
an accident assessment module: when an accident happens in the project site of the predicted project, information of the accident is split, the accident is rated based on a preset accident rating judgment standard, and the predicted project cost is corrected according to an accident rating result.
9. The project cost prediction system based on project data analysis of claim 8, wherein the reading module comprises:
the system comprises an input module, a storage module and a display module, wherein the input module is used for inputting quantity information materials and unit price data materials uploaded by project workers, preprocessing the quantity information materials and the unit price data materials, storing the quantity information materials and the unit price data materials into a uniform text format and acquiring engineering data text data;
the corresponding module is used for scanning the text data of the engineering data to obtain engineering time nodes corresponding to the quantity information and the unit price data respectively;
the storage module is used for packaging and storing the engineering time nodes respectively corresponding to the acquired quantity information and the unit price data to acquire a quantity storage packet and a unit price storage packet;
and the monitoring comparison module is used for acquiring the field materials and the personnel information through the monitoring equipment, comparing the field materials and the personnel information with the uploaded materials and the personnel information, and checking the similarity degree of the field materials and the personnel information acquired by the monitoring equipment and the uploaded materials and the personnel information.
10. The project cost prediction system based on project data analysis of claim 8, wherein the matching module comprises:
the temporary storage module is used for monitoring the quantity storage package and the unit price storage package in real time and acquiring newly uploaded information of project workers and corresponding uploading time;
the decompression module is used for decompressing the newly uploaded information to obtain the data related to the labor cost and the material cost;
the automatic matching module is used for automatically matching the related data of the labor cost and the material cost to obtain the labor cost and the material cost in the implementation process of the engineering project;
and the primary examination module is used for evaluating the importance degree of the uploaded data and the reliability of the uploaded data according to the uploading times and the modification times of the uploading personnel in unit time, and judging whether to use the uploaded data or not according to the importance degree and the reliability.
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