CN116663964A - Engineering unit price rapid calculation method and system based on characteristic values of list items - Google Patents
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Abstract
The invention discloses a method and a system for rapidly calculating engineering unit price based on characteristic values of inventory items, wherein the method comprises the following steps: constructing characteristic data of each construction project in a three-dimensional model of the history project as a first characteristic; the index data in the history project engineering is stored in a structuring mode to form a manufacturing cost result database; estimating characteristic indexes and characteristic data of class projects to which the target project projects belong as second characteristics; calculating the difference condition of various project projects of the target project unit price in the cost result database by using an Euclidean distance calculation method; project projects with the smallest difference in the cost result database are screened out, an influence coefficient with the highest relativity with the target project unit price is calculated as an optimal coefficient to generate a target project unit price analysis table of the target project, and the project unit price of the target project, namely, the target project unit price, is calculated through the target project unit price analysis table. By rapidly calculating engineering unit price, the working rate of project engineering cost is improved.
Description
Technical Field
The invention relates to engineering cost technology, in particular to a method and a system for rapidly calculating engineering unit price based on characteristic values of inventory items.
Background
In the current stage, engineering cost personnel need to update huge quota compiling data issued by countries or industries regularly when estimating engineering unit price, and according to the steps of quota matching, adjustment and various parameter compiling, calculation of one engineering unit price can be completed, but the calculation process is complicated, and due to improvement of a construction process and input of advanced mechanical equipment, deviation of estimated data and measured data can be increased, so that the whole result is greatly influenced by error filling in order to reduce deviation, and the price estimating speed and data accuracy under a main unit price rapid price estimating scene are urgently required to be improved.
Disclosure of Invention
The invention provides a method and a system for rapidly calculating engineering unit price based on characteristic values of inventory characteristic items, which are used for solving the problems in the prior art.
The engineering unit price rapid calculation method based on the characteristic values of the list items is characterized by comprising the following steps of:
s100: acquiring technical economic index data in a historical project, establishing a characteristic system index according to the professional category of the industry in which the historical project is located, constructing a three-dimensional model of the historical project, and acquiring characteristic data of each construction project in the three-dimensional model in a keyword extraction mode to serve as a first characteristic;
S200: setting construction organizations and index system specifications according to the first characteristic values to conduct construction result data arrangement, and conducting structural storage on index data in historical project engineering to form a construction result database;
s300: and obtaining the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the category project to which the target project belongs as the second characteristic.
Preferably, the engineering unit price rapid calculation method based on the characteristic value of the list item further comprises the following steps:
s400: setting an influence coefficient of target engineering unit price in target project engineering by each economic index, acquiring feature data in the second feature to construct Euclidean space, and calculating the difference condition of the target engineering unit price in various project projects in the cost result database by using an Euclidean distance calculation method;
s500: screening project projects with the smallest difference in a cost result database, constructing an index calculation model, performing repeated training, and calculating an influence coefficient with the highest correlation with the target project unit price as an optimal coefficient;
s600: and obtaining similar project projects of the target project, generating a target project price analysis table of the target project according to the optimal coefficient, and calculating the project price of the target project, namely the target project price, through the target project price analysis table.
Preferably, in step S200, the cost result database includes:
s201: collecting different types of case projects in historical project projects, and extracting project information of the case projects as cost index data;
s202: preprocessing the cost index data, including uncompetitive cost processing and missing value processing in historical project engineering; the processed cost index data is incorporated into a cost result database, and the cost result database comprises characteristic descriptions of historical project projects and is used for selectively searching target project projects and simulating characteristic matching with the historical project projects;
s203: and carrying out normalization processing on the cost index data, and carrying out precision measurement and calculation on the cost index data so as to enable the cost result database to reach a preset data standardization threshold value.
Preferably, in step S600, the generating a target project unit price analysis table of the target project according to the optimal coefficient includes:
s601: constructing an engineering unit price measuring and calculating model based on a neural network, selecting factors influencing a manufacturing cost result as key characteristic indexes, and obtaining a preset prefabrication rate by inputting corresponding key characteristic index values of a target project in the engineering unit price measuring and calculating model;
S602: setting an implicit layer in the neural network, processing and transforming key characteristic index data, and mapping out a calculation function of the prefabrication rate;
s603: setting neuron nodes of an hidden layer, wherein each neuron node contains a plurality of weights, and calculating the accuracy of an engineering unit price measuring and calculating model by determining the network structure of a neural network.
Preferably, in step S601, the selecting the factor affecting the cost result as the key feature index includes:
s6011: acquiring key characteristic indexes in an engineering unit price measuring and calculating model, wherein the target project engineering comprises a plurality of key characteristic indexes, and optimizing the numerical values of the key characteristic indexes by using a genetic algorithm;
s6012: coding each item of numerical value in the key characteristic index, determining an initialization group, scoring the optimized numerical value in the key characteristic index by using an fitness function, and continuously improving the scoring score through a plurality of iterative processes;
s6013: and acquiring key characteristic indexes in the historical project, training the optimized numerical values in the target project, and continuously reducing errors of the key characteristic indexes in the historical project until the errors reach a preset range.
An engineering unit price rapid computing system based on characteristic values of inventory items, which is characterized by comprising:
and a model building module: acquiring technical economic index data in a historical project, establishing a characteristic system index according to the professional category of the industry in which the historical project is located, constructing a three-dimensional model of the historical project, and acquiring characteristic data of each construction project in the three-dimensional model in a keyword extraction mode to serve as a first characteristic;
and the structure storage module is as follows: setting construction organizations and index system specifications according to the first characteristic values to conduct construction result data arrangement, and conducting structural storage on index data in historical project engineering to form a construction result database;
category feature module: and obtaining the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the category project to which the target project belongs as the second characteristic.
Preferably, the engineering unit price rapid computing system based on the characteristic value of the inventory item further comprises:
the difference calculation module: setting an influence coefficient of target engineering unit price in target project engineering by each economic index, acquiring feature data in the second feature to construct Euclidean space, and calculating the difference condition of the target engineering unit price in various project projects in the cost result database by using an Euclidean distance calculation method;
Model construction module: screening project projects with the smallest difference in a cost result database, constructing an index calculation model, and performing repeated training to calculate an influence coefficient with the highest correlation with the target project unit price as an optimal coefficient;
project analysis module: and obtaining similar project projects of the target project, generating a target project unit price analysis table of the target project according to the optimal coefficient, and calculating the project unit price of the target project, namely the target project unit price, through the target project unit price analysis table.
Preferably, the cost result database includes:
the manufacturing cost index submodule comprises: collecting different types of case projects in historical project projects, and extracting project information of the case projects as cost index data;
pretreatment submodule: preprocessing the cost index data, including uncompetitive cost processing and missing value processing in historical project engineering; the processed cost index data is incorporated into a cost result database, and the cost result database comprises characteristic descriptions of historical project projects and is used for selectively searching target project projects and simulating characteristic matching with the historical project projects;
Normalization processing submodule: and carrying out normalization processing on the cost index data, and carrying out precision measurement and calculation on the cost index data so as to enable the cost result database to reach a preset data standardization threshold value.
Preferably, the generating the target project unit price analysis table of the target project according to the optimal coefficient includes:
the index calculation sub-module: constructing an engineering unit price measuring and calculating model based on a neural network, selecting factors influencing a manufacturing cost result as key characteristic indexes, and obtaining a preset prefabrication rate by inputting corresponding key characteristic index values of a target project in the engineering unit price measuring and calculating model;
hidden layer processing sub-module: setting an implicit layer in the neural network, processing and transforming key characteristic index data, and mapping out a calculation function of the prefabrication rate;
and the precision calculation sub-module: setting neuron nodes of an hidden layer, wherein each neuron node contains a plurality of weights, and calculating the accuracy of an engineering unit price measuring and calculating model by determining the network structure of a neural network.
Preferably, the selecting the factor affecting the cost result as the key feature index includes:
and (3) an optimization sub-module: acquiring key characteristic indexes in an engineering unit price measuring and calculating model, wherein the target project engineering comprises a plurality of key characteristic indexes, and optimizing the numerical values of the key characteristic indexes by using a genetic algorithm;
And an evaluation sub-module: coding each item of numerical value in the key characteristic index, determining an initialization group, scoring the optimized numerical value in the key characteristic index by using an fitness function, and continuously improving the scoring score through a plurality of iterative processes;
training submodule: and acquiring key characteristic indexes in the historical project, training the optimized numerical values in the target project, and continuously reducing errors of the key characteristic indexes in the historical project until the errors reach a preset range.
Compared with the prior art, the invention has the following advantages:
the invention provides a method and a system for rapidly calculating engineering unit price based on characteristic values of inventory items, which are characterized in that a similar item unit price analysis table is obtained according to a Euclidean distance calculation method through accumulation of historical data, so that engineering unit price estimation is enabled to be circulated, and accurate valuation is realized through optimization of a genetic algorithm; the traditional valuation operation steps are simplified, so that the concentrated workload in the valuation process is greatly reduced, meanwhile, the probability of error in complex operation steps and programming of a plurality of coefficients is reduced, and the quick valuation is truly realized; the simplification of complicated steps in the traditional valuation process can also reduce the technical threshold of engineering unit price valuation, and the rapid valuation can be performed according to the method as long as the important characteristics of engineering construction projects are familiar.
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 thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a method for fast computing a characteristic value based on inventory items according to an embodiment of the present invention;
FIG. 2 is a construction step of a cost result database of a rapid calculation method based on characteristic values of inventory items according to an embodiment of the present invention;
FIG. 3 is a block diagram of a target engineering unit price analysis table generated in a fast computing system based on inventory item feature values according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides a rapid calculation method based on characteristic values of inventory items, which comprises the following steps:
s100: acquiring technical economic index data in a historical project, establishing a characteristic system index according to the professional category of the industry in which the historical project is located, constructing a three-dimensional model of the historical project, and acquiring characteristic data of each construction project in the three-dimensional model in a keyword extraction mode to serve as a first characteristic;
s200: setting construction organizations and index system specifications according to the first characteristic values to conduct construction result data arrangement, and conducting structural storage on index data in historical project engineering to form a construction result database;
s300: and obtaining the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the category project to which the target project belongs as the second characteristic.
The working principle of the technical scheme is as follows: the project with project unit price to be calculated is taken as a target project, technical and economic index data in historical project is obtained, wherein the technical and economic index data comprises cost indexes used in building density, greening coefficient, volume coefficient, consumption index, unit cost, cost occupation ratio and the like in the target project, and the category characteristics of industries and professions where the target project is located are obtained. Establishing a characteristic system index of a history project engineering, constructing a three-dimensional model of the history project, acquiring characteristic values of the history project engineering in a keyword extraction and web technology crawler mode, and extracting characteristic data of each construction project in the three-dimensional model as a first characteristic value. And acquiring the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the same project of the target project as the second characteristic.
In another embodiment, the engineering unit price rapid calculation method based on the characteristic value of the list item further comprises the following steps of
S400: setting an influence coefficient of target engineering unit price in target project engineering by each economic index, acquiring feature data in the second feature to construct Euclidean space, and calculating the difference condition of the target engineering unit price in various project projects in the cost result database by using an Euclidean distance calculation method;
s500: screening project projects with the smallest difference in a cost result database, constructing an index calculation model, and performing repeated training to calculate an influence coefficient with the highest correlation with the target project unit price as an optimal coefficient;
s600: and obtaining similar project projects of the target project, generating a target project unit price analysis table of the target project according to the optimal coefficient, and calculating the project unit price of the target project, namely the target project unit price, through the target project unit price analysis table.
The working principle of the technical scheme is as follows: the solution adopted in this embodiment is that, when the target engineering project is estimated, the euclidean distance calculation method is used to calculate the difference condition of various projects of the target engineering unit price in the cost result database according to the feature data in the second feature, and the formula is as follows:
Wherein n represents the number of all project projects in the cost result database, x represents the characteristic value in the target project, x i Representing similar eigenvalues, p, in historical project engineering i And (5) representing the influence coefficient of each target project, and finally calculating to obtain the historical project with the smallest target project unit price difference with the target project.
And screening out engineering projects with the smallest difference from a cost result database, establishing an index calculation model, repeatedly training the index calculation model according to service requirements, searching an influence coefficient closest to the target cost in the target project according to the characteristic system index in the three-dimensional model, and perfecting the index calculation model. The method comprises the steps of obtaining similar project projects with minimum variability with a target project by using an index calculation model, obtaining structural data such as unit price composition, quantity, calculation coefficient, characteristic value, fee taking method and the like of the similar project projects, generating a target project unit price analysis table, calculating an influence coefficient according to the requirement, adjusting, introducing various basic unit price in the target project into the target project unit price analysis table, and calculating the project unit price of the target project.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the target project unit price analysis table is obtained according to the project similarity algorithm through accumulation of the characteristic data in the historical project, so that project unit price estimation is tracked and is optimized to realize accurate valuation.
In another embodiment, in step S200, the cost result database includes:
s201: collecting different types of case projects in historical project projects, and extracting project information of the case projects as cost index data;
s202: preprocessing the cost index data, including uncompetitive cost processing and missing value processing in historical project engineering; the processed cost index data is incorporated into a cost result database, and the cost result database comprises characteristic descriptions of historical project projects and is used for selectively searching target project projects and simulating characteristic matching with the historical project projects;
s203: and carrying out normalization processing on the cost index data, and carrying out precision measurement and calculation on the cost index data so as to enable the cost result database to reach a preset data standardization threshold value.
The working principle of the technical scheme is as follows: in the scheme adopted by the embodiment, in the measurement and calculation of the building project engineering, case project engineering of different building types is collected in a mode of using project engineering characteristic indexes through target measurement and calculation, and a matrix of characteristic indexes and cost index samples of the project engineering is established for quantifying the characteristic indexes. Processing the uncompetitive fees and missing values in the data, wherein the uncompetitive fees, such as tax, including labor fees, material fees, construction machine use fees, enterprise management fees, profits and sums of the regulation fees multiplied by comprehensive tax rate are used for calculating the engineering cost index of the sample data on the project engineering. The missing values are generated for a number of reasons, including mechanical reasons, such as imperfections in data storage, and human reasons, such as during collection, because of data missing due to personal errors, and the missing values in the cost result database need to be processed. The processing scheme is mainly used for deleting case project engineering with missing values or processing the missing values by using interpolation methods, including methods of median, mode, average number and the like. The cost result database has the characteristics of integration and sharing,
The process of establishing the characteristic index of project engineering is to store the case project engineering in the cost result database. The project engineering of the case is integrally described in the cost result database, and the project engineering can be divided into a net-shaped database, a relational database and an object-oriented database according to the cost result database, and the structure system of the cost result database is summarized and classified to ensure that enough project engineering exists in the cost result database, so that the project engineering is convenient for searching with optimal selectivity and matching with the proposed project. And finally, carrying out normalization processing on the sample data, and normalizing the sample data and the measuring and calculating data through dimensionless processing to calculate the precision of the sample data.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the manufacturing cost flow system is perfected by carrying out data collection on the sample data, carrying out pretreatment on the sample book, constructing a manufacturing cost result database and carrying out normalization processing.
In another embodiment, in step S600, the generating a target project unit price analysis table of the target project according to the optimal coefficient includes:
s601: constructing an engineering unit price measuring and calculating model based on a neural network, selecting factors influencing a manufacturing cost result as key characteristic indexes, and obtaining a preset prefabrication rate by inputting corresponding key characteristic index values of a target project in the engineering unit price measuring and calculating model;
S602: setting an implicit layer in the neural network, processing and transforming key characteristic index data, and mapping out a calculation function of the prefabrication rate;
s603: setting neuron nodes of an hidden layer, wherein each neuron node contains a plurality of weights, and calculating the accuracy of an engineering unit price calculation model by determining the network structure of a neural network.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that an engineering unit price measuring and calculating model based on a neural network is constructed, the most direct factor affecting the manufacturing cost is selected as a key characteristic index, and stone square hole digging is taken as an example, wherein the key characteristic index comprises a digging section, a transportation distance and a working face length. The prefabrication rate of the prefabricated part is obtained by inputting corresponding key characteristic indexes into the engineering unit price calculation model through the input layer of calculating the prefabrication rate of the prefabricated part and unilateral increment values and the target project engineering through the input of the values of the key characteristic indexes. And setting hidden layers in the neural network, wherein the selection of the number of layers of the neural network directly influences the approximation capability and the adaptation capability of the neural network function. The hidden layer is the middle part of the engineering unit price measuring and calculating model, and has the function of processing and transforming information, and the functions in all the neural networks can be mapped by adopting a single hidden layer structure in the network structure design.
The method comprises the steps of designing hidden layer neuron nodes, wherein the hidden layer neuron nodes are used for extracting hidden internal rules from key characteristic index data, mapping the neuron nodes and each node in a neural network, and each hidden layer neuron node contains a plurality of weights, so that determination of the number of hidden layer neural network neuron nodes is a key of a network structure, and the accuracy of an engineering unit price calculation model is influenced.
Setting an output layer of the engineering unit price measuring and calculating model as a calculation result value of the engineering unit price measuring and calculating model, and finishing prediction of key characteristic indexes of the engineering unit price.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the calculation accuracy of engineering unit price is higher through the construction of the neural network.
In another embodiment, in step S601, the selecting the factor affecting the cost result as the key feature index includes:
s6011: acquiring key characteristic indexes in an engineering unit price measuring and calculating model, wherein the target project engineering comprises a plurality of key characteristic indexes, and optimizing the numerical values of the key characteristic indexes by using a genetic algorithm;
s6012: coding each item of numerical value in the key characteristic index, determining an initialization group, scoring the optimized numerical value in the key characteristic index by using an fitness function, and continuously improving the scoring score through a plurality of iterative processes;
S6013: and acquiring key characteristic indexes in the historical project, training the optimized numerical values in the target project, and continuously reducing errors of the key characteristic indexes in the historical project until the errors reach a preset range.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that the key characteristic indexes of the engineering unit price measuring and calculating model are obtained, a plurality of key characteristic indexes are contained in the target project engineering, and the numerical values of the key characteristic indexes are calculated by using a genetic algorithm. And each item of numerical value in the key characteristic index is encoded, a binary encoding mode is used for obtaining the unique encoding of the numerical value of the key characteristic index, and the binary encoding mode is the simplest and most convenient. And continuously determining an initial group of key characteristic index values, wherein the initial group is used as a set of initial solutions of the genetic algorithm, and the size of the group influences the convergence speed and the feasible accuracy of the genetic algorithm.
The adaptive evolution is carried out on the group through the fitness function, the larger the fitness is, the stronger the adaptability of the group is, and the calculation formula is as follows:
wherein M is sample data in the target project engineering, j represents a sample data individual, For the fitness of the sample data individual, the fitness is selected according to the actual output and the expected output of the neural network, and p are determined j Respectively, the expected output value and the actual output value, N is the number of sample data individuals in the group,/>The larger the value of (c) the stronger the indication of fitness. And H is the probability that a certain sample data individual obtained through calculation is selected as the key characteristic index.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the key characteristic index is optimized through the genetic algorithm, so that the engineering unit price is calculated more accurately.
In another embodiment, an engineering unit price rapid computing system based on inventory feature items includes:
and a model building module: acquiring technical economic index data in a historical project, establishing a characteristic system index according to the professional category of the industry in which the historical project is located, constructing a three-dimensional model of the historical project, and acquiring characteristic data of each construction project in the three-dimensional model in a keyword extraction mode to serve as a first characteristic;
and the structure storage module is as follows: setting construction organizations and index system specifications according to the first characteristic values to conduct construction result data arrangement, and conducting structural storage on index data in historical project engineering to form a construction result database;
Category feature module: and obtaining the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the category project to which the target project belongs as the second characteristic.
The working principle of the technical scheme is as follows: the project engineering with the project unit price to be calculated is used as a target project engineering by a project engineering with the project unit price to be calculated through a system registration mode, and technical economic index data in historical project engineering is obtained through a model building module, wherein the technical economic index data comprises cost indexes used by building density, greening coefficient, volume coefficient, consumption index, unit cost, cost ratio and the like in the target project engineering, and category characteristics of industries and professions where the target project engineering is located are obtained. Establishing a characteristic system index of a history project engineering, constructing a three-dimensional model of the history project, acquiring characteristic values of the history project engineering in a keyword extraction and web technology crawler mode, and extracting characteristic data of each construction project in the three-dimensional model as a first characteristic value. And constructing a structure storage module, namely forming a cost result database and performing structural storage on index data in the history project. And in the category characteristic module, acquiring the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the same project of the target project as second characteristics.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the engineering unit price calculation is more accurate by establishing the first distinguishing characteristic and the second distinguishing characteristic.
In another embodiment, an engineering unit price rapid computing system based on inventory item feature values, further comprising:
the difference calculation module: setting an influence coefficient of target engineering unit price in target project engineering by each economic index, acquiring feature data in the second feature to construct Euclidean space, and calculating the difference condition of the target engineering unit price in various project projects in the cost result database by using an Euclidean distance calculation method;
model construction module: screening project projects with the smallest difference in a cost result database, constructing an index calculation model, and performing repeated training to calculate an influence coefficient with the highest correlation with the target project unit price as an optimal coefficient;
project analysis module: and obtaining similar project projects of the target project, generating a target project unit price analysis table of the target project according to the optimal coefficient, and calculating the project unit price of the target project, namely the target project unit price, through the target project unit price analysis table.
The working principle of the technical scheme is that engineering projects with the smallest difference are screened out from a cost result database, an index calculation model is established through a model construction module, the index calculation model is trained repeatedly according to service requirements, and according to the characteristic system index in the three-dimensional model, the influence coefficient closest to the target cost in the target project is searched, and the index calculation model is perfected. The method comprises the steps of obtaining similar project projects with minimum variability with target project projects by using an index calculation model, obtaining structural data such as unit price composition, quantity, calculation coefficient, characteristic value, fee taking method and the like of the similar project projects, generating a target project unit price analysis table by a project analysis module, calculating influence coefficients according to requirements, adjusting, bringing various basic unit price in the target project into the target project unit price analysis table, and calculating the project unit price of the target project.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the target project unit price analysis table is obtained according to the project similarity algorithm through accumulation of the characteristic data in the historical project, so that project unit price estimation is tracked and is optimized to realize accurate valuation.
In another embodiment, the cost result database includes:
the manufacturing cost index submodule comprises: collecting different types of case projects in historical project projects, and extracting project information of the case projects as cost index data;
pretreatment submodule: preprocessing the cost index data, including uncompetitive cost processing and missing value processing in historical project engineering; the processed cost index data is incorporated into a cost result database, and the cost result database comprises characteristic descriptions of historical project projects and is used for selectively searching target project projects and simulating characteristic matching with the historical project projects;
normalization processing submodule: and carrying out normalization processing on the cost index data, and carrying out precision measurement and calculation on the cost index data so as to enable the cost result database to reach a preset data standardization threshold value.
The working principle of the technical scheme is as follows: in the scheme adopted by the embodiment, in the measurement and calculation of the building project engineering, case project engineering of different building types is collected in a mode of using project engineering characteristic indexes through target measurement and calculation, and a matrix of characteristic indexes and cost index samples of the project engineering is established for quantifying the characteristic indexes. Processing the uncompetitive fees and missing values in the data, wherein the uncompetitive fees, such as tax, including labor fees, material fees, construction machine use fees, enterprise management fees, profits and sums of the regulation fees multiplied by comprehensive tax rate are used for calculating the engineering cost index of the sample data on the project engineering. The missing values are generated for a number of reasons, including mechanical reasons, such as imperfections in data storage, and human reasons, such as during collection, because of data missing due to personal errors, and the missing values in the cost result database need to be processed. The processing scheme is mainly used for deleting case project engineering with missing values or processing the missing values by using interpolation methods, including methods of median, mode, average number and the like. The cost result database has the characteristics of integration and sharing,
The process of establishing the characteristic index of project engineering is to store the case project engineering in the cost result database. The project engineering of the case is integrally described in the cost result database, and the project engineering can be divided into a net-shaped database, a relational database and an object-oriented database according to the cost result database, and the structure system of the cost result database is summarized and classified to ensure that enough project engineering exists in the cost result database, so that the project engineering is convenient for searching with optimal selectivity and matching with the proposed project. And finally, carrying out normalization processing on the sample data, and normalizing the sample data and the measuring and calculating data through dimensionless processing to calculate the precision of the sample data.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the manufacturing cost flow system is perfected by carrying out data collection on the sample data, carrying out pretreatment on the sample book, constructing a manufacturing cost result database and carrying out normalization processing.
In another embodiment, the generating the target project unit price analysis table of the target project according to the optimal coefficient includes:
the index calculation sub-module: constructing an engineering unit price measuring and calculating model based on a neural network, selecting factors influencing a manufacturing cost result as key characteristic indexes, and obtaining a preset prefabrication rate by inputting corresponding key characteristic index values of a target project in the engineering unit price measuring and calculating model;
Hidden layer processing sub-module: setting an implicit layer in the neural network, processing and transforming key characteristic index data, and mapping out a calculation function of the prefabrication rate;
and the precision calculation sub-module: setting neuron nodes of an hidden layer, wherein each neuron node contains a plurality of weights, and calculating the accuracy of an engineering unit price measuring and calculating model by determining the network structure of a neural network.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that an engineering unit price measuring and calculating model based on a neural network is constructed, the most direct factor affecting the manufacturing cost is selected as a key characteristic index, and stone square hole digging is taken as an example, wherein the key characteristic index comprises a digging section, a transportation distance and a working face length. The prefabrication rate of the prefabricated part is obtained by inputting corresponding key characteristic indexes into the engineering unit price calculation model through the input layer of calculating the prefabrication rate of the prefabricated part and unilateral increment values and the target project engineering through the input of the values of the key characteristic indexes. And setting hidden layers in the neural network, wherein the selection of the number of layers of the neural network directly influences the approximation capability and the adaptation capability of the neural network function. The hidden layer is the middle part of the engineering unit price measuring and calculating model, and has the function of processing and transforming information, and the functions in all the neural networks can be mapped by adopting a single hidden layer structure in the network structure design.
The method comprises the steps of designing hidden layer neuron nodes, wherein the hidden layer neuron nodes are used for extracting hidden internal rules from key characteristic index data, mapping the neuron nodes and each node in a neural network, and each hidden layer neuron node contains a plurality of weights, so that determination of the number of hidden layer neural network neuron nodes is a key of a network structure, and the accuracy of an engineering unit price measuring and calculating model is influenced.
Setting an output layer of the engineering unit price measuring and calculating model as a calculation result value of the engineering unit price measuring and calculating model, and finishing prediction of key characteristic indexes of the engineering unit price.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the engineering unit price calculation accuracy is higher through the construction of the neural network.
In another embodiment, the selecting the factor affecting the cost result as the key feature index includes:
and (3) an optimization sub-module: acquiring key characteristic indexes in an engineering unit price measuring and calculating model, wherein the target project engineering comprises a plurality of key characteristic indexes, and optimizing the numerical values of the key characteristic indexes by using a genetic algorithm;
And an evaluation sub-module: coding each item of numerical value in the key characteristic index, determining an initialization group, scoring the optimized numerical value in the key characteristic index by using an fitness function, and continuously improving the scoring score through a plurality of iterative processes;
training submodule: and acquiring key characteristic indexes in the historical project, training the optimized numerical values in the target project, and continuously reducing errors of the key characteristic indexes in the historical project until the errors reach a preset range.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that in an optimization submodule, an initial key characteristic index of an engineering unit price measuring and calculating model is obtained, a plurality of key characteristic indexes are contained in a target project engineering, and the numerical value of the key characteristic indexes is calculated by using a genetic algorithm. Each item of numerical value in the key characteristic index is coded through the evaluation sub-module, the unique coding of the numerical value of the key characteristic index is obtained by using a binary coding mode, and the binary coding mode is the simplest and most convenient. And continuously determining an initial group of key characteristic index values in the training submodule, wherein the initial group is used as a set of initial solutions of the genetic algorithm, and the size of the group influences the convergence speed and the feasible accuracy of the genetic algorithm.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the key characteristic index is optimized through the genetic algorithm, so that the engineering unit price is calculated more accurately.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The engineering unit price rapid calculation method based on the characteristic values of the list items is characterized by comprising the following steps of:
s100: acquiring technical economic index data in a historical project, establishing a characteristic system index according to the professional category of the industry in which the historical project is located, constructing a three-dimensional model of the historical project, and acquiring characteristic data of each construction project in the three-dimensional model in a keyword extraction mode to serve as a first characteristic;
s200: setting construction organizations and index system specifications according to the first characteristic values to conduct construction result data arrangement, and conducting structural storage on index data in historical project engineering to form a construction result database;
S300: and obtaining the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the category project to which the target project belongs as the second characteristic.
2. The method for rapidly calculating engineering unit price based on characteristic values of inventory items according to claim 1, further comprising:
s400: setting an influence coefficient of target engineering unit price in target project engineering by each economic index, acquiring feature data in the second feature to construct Euclidean space, and calculating the difference condition of the target engineering unit price in various project projects in the cost result database by using an Euclidean distance calculation method;
s500: screening project projects with the smallest difference in a cost result database, constructing an index calculation model, performing repeated training, and calculating an influence coefficient with the highest correlation with the target project unit price as an optimal coefficient;
s600: and obtaining similar project projects of the target project, generating a target project price analysis table of the target project according to the optimal coefficient, and calculating the project price of the target project, namely the target project price, through the target project price analysis table.
3. The method according to claim 1, wherein in step S200, the cost result database includes:
S201: collecting different types of case projects in historical project projects, and extracting project information of the case projects as cost index data;
s202: preprocessing the cost index data, including uncompetitive cost processing and missing value processing in historical project engineering; the processed cost index data is incorporated into a cost result database, and the cost result database comprises characteristic descriptions of historical project projects and is used for selectively searching target project projects and simulating characteristic matching with the historical project projects;
s203: and carrying out normalization processing on the cost index data, and carrying out precision measurement and calculation on the cost index data so as to enable the cost result database to reach a preset data standardization threshold value.
4. The rapid project unit price calculating method based on the characteristic values of the inventory project according to claim 2, wherein in step S600, the generating the target project unit price analysis table of the target project according to the optimal coefficient comprises:
s601: constructing an engineering unit price measuring and calculating model based on a neural network, selecting factors influencing a manufacturing cost result as key characteristic indexes, and obtaining a preset prefabrication rate by inputting corresponding key characteristic index values of a target project in the engineering unit price measuring and calculating model;
S602: setting an implicit layer in the neural network, processing and transforming key characteristic index data, and mapping out a calculation function of the prefabrication rate;
s603: setting neuron nodes of an hidden layer, wherein each neuron node contains a plurality of weights, and calculating the accuracy of an engineering unit price measuring and calculating model by determining the network structure of a neural network.
5. The method for rapidly calculating engineering price based on characteristic values of inventory items according to claim 4, wherein in step S601, the selecting factors affecting the cost result as key characteristic indexes comprises:
s6011: acquiring key characteristic indexes in an engineering unit price measuring and calculating model, wherein the target project engineering comprises a plurality of key characteristic indexes, and optimizing the numerical values of the key characteristic indexes by using a genetic algorithm;
s6012: coding each item of numerical value in the key characteristic index, determining an initialization group, scoring the optimized numerical value in the key characteristic index by using an fitness function, and continuously improving the scoring score through a plurality of iterative processes;
s6013: and acquiring key characteristic indexes in the historical project, training the optimized numerical values in the target project, and continuously reducing errors of the key characteristic indexes in the historical project until the errors reach a preset range.
6. An engineering unit price rapid computing system based on characteristic values of inventory items, which is characterized by comprising:
and a model building module: acquiring technical economic index data in a historical project, establishing a characteristic system index according to the professional category of the industry in which the historical project is located, constructing a three-dimensional model of the historical project, and acquiring characteristic data of each construction project in the three-dimensional model in a keyword extraction mode to serve as a first characteristic;
and the structure storage module is as follows: setting construction organizations and index system specifications according to the first characteristic values to conduct construction result data arrangement, and conducting structural storage on index data in historical project engineering to form a construction result database;
category feature module: and obtaining the construction condition and the professional category of the target project, and predicting the characteristic index and the characteristic data of the category project to which the target project belongs as the second characteristic.
7. The engineering price computing system based on inventory item feature values of claim 6, further comprising:
the difference calculation module: setting an influence coefficient of target engineering unit price in target project engineering by each economic index, acquiring feature data in the second feature to construct Euclidean space, and calculating the difference condition of the target engineering unit price in various project projects in the cost result database by using an Euclidean distance calculation method;
Model construction module: screening project projects with the smallest difference in a cost result database, constructing an index calculation model, and performing repeated training to calculate an influence coefficient with the highest correlation with the target project unit price as an optimal coefficient;
project analysis module: and obtaining similar project projects of the target project, generating a target project unit price analysis table of the target project according to the optimal coefficient, and calculating the project unit price of the target project, namely the target project unit price, through the target project unit price analysis table.
8. The rapid engineering price computing system based on inventory item feature values of claim 6, wherein the cost result database comprises:
the manufacturing cost index submodule comprises: collecting different types of case projects in historical project projects, and extracting project information of the case projects as cost index data;
pretreatment submodule: preprocessing the cost index data, including uncompetitive cost processing and missing value processing in historical project engineering; the processed cost index data is incorporated into a cost result database, and the cost result database comprises characteristic descriptions of historical project projects and is used for selectively searching target project projects and simulating characteristic matching with the historical project projects;
Normalization processing submodule: and carrying out normalization processing on the cost index data, and carrying out precision measurement and calculation on the cost index data so as to enable the cost result database to reach a preset data standardization threshold value.
9. The rapid project unit price computing system based on the characteristic values of the inventory items according to claim 7, wherein the generating the target project unit price analysis table of the target project according to the optimal coefficient comprises:
the index calculation sub-module: constructing an engineering unit price measuring and calculating model based on a neural network, selecting factors influencing a manufacturing cost result as key characteristic indexes, and obtaining a preset prefabrication rate by inputting corresponding key characteristic index values of a target project in the engineering unit price measuring and calculating model;
hidden layer processing sub-module: setting an implicit layer in the neural network, processing and transforming key characteristic index data, and mapping out a calculation function of the prefabrication rate;
and the precision calculation sub-module: setting neuron nodes of an hidden layer, wherein each neuron node contains a plurality of weights, and calculating the accuracy of an engineering unit price measuring and calculating model by determining the network structure of a neural network.
10. The rapid engineering price computing system based on characteristic values of inventory items according to claim 9, wherein the selecting factors affecting the cost result as key characteristic indexes comprises:
And (3) an optimization sub-module: acquiring key characteristic indexes in an engineering unit price measuring and calculating model, wherein the target project engineering comprises a plurality of key characteristic indexes, and optimizing the numerical values of the key characteristic indexes by using a genetic algorithm;
and an evaluation sub-module: coding each item of numerical value in the key characteristic index, determining an initialization group, scoring the optimized numerical value in the key characteristic index by using an fitness function, and continuously improving the scoring score through a plurality of iterative processes;
training submodule: and acquiring key characteristic indexes in the historical project, training the optimized numerical values in the target project, and continuously reducing errors of the key characteristic indexes in the historical project until the errors reach a preset range.
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