CN114139770A - Metal industry economic estimation system and method based on Solo growth and stock recursion - Google Patents
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Abstract
The invention provides a metal industry economic estimation system and method based on Solo growth and stock recursion, wherein the economic estimation system comprises: an economic database of the metal industry for storing economic data; the data preprocessing module is used for preprocessing data of an economic database of the metal industry; the model building module builds an economic prediction model through variable selection and model optimization; and an economic estimation module for obtaining the macroscopic economic estimation result and the microscopic economic estimation result through the economic estimation model. The invention can predict the economic development trend of the metal industry, is beneficial to government departments to make scientific economic decisions on the future metal industry in advance, prevents the occurrence of excess output and maximally meets the market demand and economic benefit.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of economic estimation of industrial capacity, in particular to a metal industry economic estimation system and method based on Solo growth and stock recursion.
[ background of the invention ]
At present, the capacity of the nonferrous metal industry in China is excessive, and a large number of enterprises reduce production and stop production. The nonferrous metal industry is in a low-lost period, and the falling capacity is eliminated and the industry upgrading is promoted at a good time from the positive side. Therefore, adjusting the happy program cannot be done without any puzzlement, and it is necessary to have a hold. The vibrant planning of the nonferrous metal industry develops a plurality of prescriptions aiming at various difficult problems existing in the current nonferrous metal industry in China. On one hand, the method helps the nonferrous metal industry to overcome the difficulty before the leap, ensures the stable development of the industry, and on the other hand, the method focuses on long-term structure adjustment and industry upgrading. Wherein, the total amount requirement is a dynamic variable quantity, and the actual requirement is closely related to the economic state of the metal industry.
The economic prediction is to apply various scientific methods to analyze and research the grasped economic information and predict the development condition and the change trend of future economic activities on the basis of deep investigation of objective economic activity processes in a certain period. A qualitative prediction method, also called judgment prediction method and experience prediction method, is a prediction method which obtains the history and actual data of various factors related to a prediction object according to the experience and analysis judgment ability of the expert under the condition of lacking available historical data, and judges the future condition of the prediction object on the basis of processing, sorting, analyzing and researching the data.
Common economic prediction methods comprise a qualitative prediction method and a quantitative prediction method, wherein the qualitative prediction method generally comprises an investigation and research method, an expert opinion method and a business personnel estimation method, which have more subjective intentions and are difficult to give accurate economic prediction from a scientific angle; a quantitative prediction method, which is also called a numerical prediction method, a statistical analysis method, or the like, is a quantitative analysis of a prediction object. The method is a prediction method for predicting future economic development prospect and change trend by scientifically analyzing and predicting quantity by using a certain mathematical method according to mastered relatively complete historical statistical data to reveal the regularity relation among whole variables.
The invention is one of quantitative prediction methods, and more specifically, the invention predicts the future economy of the domestic metal industry based on the current situation of the domestic metal industry in China.
Accordingly, there is a need to develop a metal industry economic estimation system and method based on the growth of the Soro and the recursion of the inventory to address the deficiencies of the prior art and to solve or mitigate one or more of the problems set forth above.
[ summary of the invention ]
In view of the above, the invention provides a metal industry economic estimation system and method based on the growth of the Solo and the stock recursion, which can predict the economic development trend of the metal industry, facilitate government departments to make scientific economic decisions for the future metal industry in advance, prevent the occurrence of excess yield, and maximally meet the market demand and economic benefit.
In one aspect, the present invention provides a metal industry economic estimation system based on solilolo growth and stock recursion, wherein the economic estimation system comprises:
an economic database of the metal industry for storing economic data;
the data preprocessing module is used for preprocessing data of an economic database of the metal industry;
the model building module builds an economic prediction model through variable selection and model optimization;
a economic estimation module, which obtains a macroscopic economic estimation result and a microscopic economic estimation result through an economic estimation model;
and the interaction module is used for inputting economic data and outputting economic estimation results.
The above-described aspects and any possible implementation manners further provide an implementation manner, where the data preprocessing module includes a data cleaning unit, a data information extraction unit, a data information conversion unit, and a data re-planning unit;
the data cleaning unit is used for removing the economic data of the metal industry or the non-metal industry which has the deficiency so as to unify the repeated data;
the data information extraction unit is used for extracting a time domain, establishing a first economic data index corresponding to the time domain, and marking the outstanding data in the first economic data index of the time domain;
the data information conversion unit is used for converting the first economic data index of the time domain and calculating to obtain a second economic data index of the same time domain;
the first economic data metric comprises: the method comprises the following steps of (1) obtaining a total production value Y of the metal industry in a certain area, a metal industry capital stock K in the certain area and a labor input L in the certain area;
the second economic data index includes: the full factor productivity TEP in a certain area and the metal industry capital yield elastic coefficient α in a certain area.
Converting the second economic data index and the first economic data index by applying a Solo growth model and an inventory recursion formula to the economic data; wherein the content of the first and second substances,
according to the model of the Solo growth, the following can be obtained:
according to the stock recursive formula, the following can be obtained:
wherein Y is the total production value of the metal industry in a certain area, L is the labor input amount in a certain area, K is the metal industry capital stock in a certain area, TFP is the full element productivity in a certain area, TFP belongs to (0,0.1), alpha is the metal industry capital production elasticity coefficient in a certain area, alpha belongs to (0,1), t is the current year, and t-1 is the previous year of the current year.
And the data replanning unit replans the data in the economic database of the metal industry according to the first economic data index and the second economic data index, and establishes a metal industry hierarchical economic database.
The above-mentioned aspect and any possible implementation manner further provide an implementation manner, wherein the marking of the outstanding data in the economic data of the time domain metal industry in the data information extraction is specifically:
the method comprises the steps of obtaining a mapping relation between a time domain and economic data of the metal industry corresponding to the time domain by establishing a linear regression equation, determining the linear regression equation in a discrete and weighted average mode, and marking data which do not accord with the mapping relation under the linear regression equation in a first economic data index in the time domain.
The above-mentioned aspects and any possible implementation manners further provide an implementation manner, and the data replanning module further includes processing the marked data, where the processing manner is a replacement manner and/or a deletion manner.
The above-described aspect and any possible implementation manner further provide an implementation manner, when the metal industry hierarchical economic database is built in the data replanning module, data is layered in a neural tree manner, layered contents include a first economic data index, a second economic data index and a time domain, and a layering sequence is not fixed.
The above-mentioned aspects and any possible implementation manners further provide an implementation manner, where the data in the economic database of the metal industry obtained in the interaction module is microscopic economic data, and specifically is a historical economic data index related to metal industry demand.
The above-described aspects and any possible implementation further provide an implementation, and the data source selected by the variables in the model building module is a metal industry hierarchical economic database.
The above-described aspects and any possible implementations further provide an implementation, wherein the model optimizing includes: model selection and evaluation optimization, wherein;
the model is selected as a Kobub-Douglas production function model, and the constructed metal industry economic prediction model is as follows:
Y＝TFP×Kα×L(1-α)
wherein Y is the total production value of the metal industry in a certain area, K is the capital stock of the metal industry in a certain area, L is the labor input amount in a certain area, TFP is the full-element productivity in a certain area, and alpha is the metal industry capital output elastic coefficient in a certain area;
and the evaluation optimization is to adopt a multiple regression model as an evaluation model of the metal industry economic prediction model, select a least square method in a parameter optimization mode, introduce a likelihood ratio for post-dimensional selection, and finish the multi-dimensional evaluation optimization of the evaluation model of the metal industry economic prediction model.
The above-mentioned aspects and any possible implementation manners further provide a metal industry economic prediction method based on the growth of the sorbite and the stock recursion, the economic prediction is realized through the economic prediction system, and the economic prediction method comprises the following steps:
s1: inputting economic data;
s2: data preprocessing, namely preprocessing data of an economic database of the metal industry;
s3: establishing and determining a model, and establishing an economic prediction model through variable selection and model optimization;
s4: performing economic prediction, namely obtaining a macroscopic economic prediction result and a microscopic economic result through an economic prediction model;
s5: and outputting an economic estimation result.
Compared with the prior art, the invention can obtain the following technical effects:
1): the method is beneficial to making scientific economic decisions by government departments and avoiding the problem of excess energy;
2): the method of the invention is beneficial to strengthening management functions;
3): the method of the invention is beneficial to enterprises to develop markets, expand operation and enhance market competitiveness;
4): the method of the invention is beneficial to improving the management level.
Of course, it is not necessary for any one product in which the invention is practiced to achieve all of the above-described technical effects simultaneously.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a block diagram of an economic forecasting system provided by one embodiment of the present invention;
FIG. 2 is a flow chart of a method for economic estimation according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The invention provides a metal industry economic estimation system and a method based on Solo growth and stock recursion, wherein the economic estimation system shown in figure 1 comprises: economic databases, algorithmic programs (modules), and interactive modules for the metal industry.
Data in an economic database of the metal industry are acquired through an interaction module, the data in the economic database of the metal industry in the interaction module are acquirable microscopic economic data, specifically historical economic data indexes related to metal industry requirements, and a data acquisition mode can be acquired from an economic data official website by a crawler.
The algorithm program comprises an economic estimation algorithm, and the algorithm program specifically comprises the following steps:
the data preprocessing module is used for preprocessing data of an economic database of the metal industry;
the data preprocessing module comprises a data cleaning unit, a data information extracting unit, a data information converting unit and a data replanning unit;
the data cleaning unit is used for removing the economic data completion of the metal industry or the economic data of the nonmetal industry with the missing data so as to unify the repeated data.
The economic data generally comprises macroscopic economic data and microscopic economic data, the microscopic economic data can be generally obtained through direct statistics, the macroscopic economic data needs to be obtained through calculation or other methods, and the actual effect of the data cleaning unit is to clean the microscopic economic data which can be directly obtained through statistics and part of macroscopic economic data which can be directly obtained;
the completion mode mainly comprises self completion through a conversion result in the data information conversion unit and replacement of the marked data through the data replanning unit;
the removal mode mainly comprises the steps of deleting economic data of the nonmetal industry and deleting marking data.
The data information extraction unit is used for extracting a time domain, establishing a first economic data index corresponding to the time domain, and marking the salient data in the first economic data index of the time domain in a specific way:
the method comprises the steps of obtaining a mapping relation between a time domain and economic data of the metal industry corresponding to the time domain by establishing a linear regression equation, determining the linear regression equation in a discrete and weighted average mode, and marking data which do not accord with the mapping relation under the linear regression equation in the economic data of the metal industry in the time domain.
For example, the relevant economic data of 1990 and 2020, with 5 years as a time domain, with 1990-1995 as the first time domain, 1995-2000 as the second time domain, 2000-2005 as the third time domain, and so on, a total of 6 time domains are obtained, establishing a linear regression equation by taking the time domain as an independent variable and the related economic data in the corresponding time domain as a dependent variable, the specific time limit of the time domain is not fixed, and is selected according to the actual situation, such as 2008, as the node, because of the global economic crisis in this year, the economic fluctuation is large before and after the year, for convenience of explanation, the node is not considered in the embodiment, and through a calculation mode of discrete and weighted average, the linear regression equation is adjusted by gradual approximation and points deviating from the regression line are not considered (the reasons for this point are many, and the global economic crisis is one of the reasons for this point). And finally, obtaining a linear regression equation which is the closest to an actual result, verifying the input data in the economic database of the metal industry one by one, and marking the economic data which does not conform to or deviate from the linear regression equation and has a larger result.
The data information conversion unit is used for converting the first economic data index of the time domain and calculating to obtain a second economic data index of the same time domain;
the first economic data index is derived from directly-crawled economic data, the data is mainly micro economic data, and in the invention, the first economic data index comprises the following components: the total production value of the metal industry in a certain area, the capital stock of the metal industry in a certain area and the labor input in a certain area; and calculating and acquiring a second economic data index through the first economic data and the following conversion mode, wherein the second economic data index comprises the full element productivity in a certain area and the metal industry capital output elastic coefficient in a certain area.
The conversion mode between the second economic data index and the first economic data index is converted and calculated through a simultaneous recursion formula and a Solo economic growth model formula, and the specific conversion calculation mode is as follows:
the economic growth model of the Solo:
GY＝αGK+(1-α)GL+TFP(α+β＝1) (1)
in the formula (1), GY is the economic growth rate, GL is the labor input growth rate, GK is the capital input growth rate, beta is the convergence parameter, and alpha is the capital output elastic coefficient.
From the above formula (1), the following formula (2) can be derived:
capital stock recursion formula:
in the formula (3), L is the labor input amount in a certain area, T is the full factor productivity in a certain area, alpha is the capital output elastic coefficient, and T is the current year.
From the above formula (3), the following formula (4) can be derived:
in the formulas (2) and (4), Y is a total production value of the metal industry in a certain area, L is labor input amount in a certain area, K is a metal industry capital stock in a certain area, TFP is full-element productivity in a certain area, TFP belongs to (0,0.1), alpha is a metal industry capital production elasticity coefficient in a certain area, and alpha belongs to (0,1) t is the current year, and t-1 is the previous year of the current year;
simultaneous type (2) and formula (4), i.e. simultaneous:
and substituting the year data in a corresponding time domain [ t to (t + z) ], wherein z is a positive integer, performing conversion calculation to obtain the full factor productivity in a certain area and the metal industry capital output elastic coefficient alpha in a certain area of the TFP in the second economic data index.
The definition and the related calculation mode of each index in the invention are as follows:
1): labor input in certain area (L)
The labor input amount in a certain area refers to the total production value of the labor force participating in the metal industry in the certain area;
2): metal industry capital stock (K) in a region;
the metal industry capital inventory in a certain area refers to the sum of various capital invested in the metal industry;
3): a Total Factor Productivity (TFP) in a certain area;
the total element productivity in a certain area refers to the ratio of the metal yield to the total element input, i.e. the technological progress in a broad sense.
4) Metal industry capital yield elastic coefficient (alpha) in a certain area
The metal industry capital yield elastic coefficient in a region refers to the percent change in production resulting from a given percent change in one input element while all other input elements remain unchanged. The method can be used for evaluating the conversion effect of resource input, and can be used for evaluating the sensitivity of the change of the product yield to the change of the input amount of production elements in production. In short, the metal industry yields increase by as much as one percent when a certain production factor increases.
The labor input amount calculation formula in a certain area is as follows:
L＝∑qH×LFPR(q)×WAP(q) (5)
in the formula (5), q is an age group of working age population, two groups are 15-64 years old and 65 years old and above, H is education level, WAP is the working age population, and LFPR is labor participation rate of each age group.
Wherein, the calculation formula of the education degree (H) is as follows:
in the formula (6), MSY is the average education year, the statistics of the education years are the total number of years from study to stop of the academic, such as employment after graduations of researchers (2 years), and MSY is 18; university's employment after graduation, MSY is 16; after the high school is graduated, namely MSY is 12.
The data are provided by enterprises and demographic bureaus related to the metal industry, belong to the microscopic population data indexes, and also belong to the microscopic economic indexes in the microscopic economic data because the data comprise information related to the metal industry.
The calculation of the full element productivity in a certain area in the future needs to refer to the full element productivity increase level of the developed country, and the calculation formula is as follows:
in formulae (7) and (8), TFPLTFP, the productivity of the whole element of developed countriesL(0) For the full-factor productivity of the developed countries of the benchmark year,
increase rate of productivity of all elements in developed countries, gLRate of increase of full factor productivity for developing countriesBeta is the convergence parameter, t is the current year, and tau is the convergence age.
The metal industry capital stock (K) in a certain area is measured and calculated by a perpetual inventory method, and the initial year calculation formula is as follows:
K(t+1)＝(1-d)K(t)+I(t) (9)
in the formula (9), I is the sum of fixed capital formation of the metal industry, d is the metal industry depreciation rate, and according to the existing research, the depreciation rate takes a fixed value of 6.3%.
The metal industry capital yield factor of elasticity (α) in a certain area is a measure of the relative change in the impact of capital on yield, i.e., when the production capital increases by 1%, the yield increases by an average of α%, and the initial year calculation formula is as follows:
in the formula (10), alpha belongs to (0,1), the normal output condition of the metal industry exists, the condition that a is more than 1 hardly exists, and P iskFor return on investment of capital, P is taken according to existing researchkAnd K is the metal industry capital stock in a certain region, and Y is the total metal industry production value in a certain region.
And the data replanning unit replans the data in the economic database of the metal industry according to the first economic data index and the second economic data index, and establishes a metal industry hierarchical economic database.
When the economic data are layered, the neural tree model is referred to in a layered mode, and the top end of the neural tree model is a collection of all economic data;
specifically, in a possible implementation, the second layer includes all index names in the first economic data index and all index names in the second economic data index, the third layer includes corresponding different time domains, and each layer is provided with specific data corresponding to statistics;
specifically, in a possible implementation, the second layer includes different time domains, the third layer includes all index names in the corresponding first economic data index and all index names in the corresponding second economic data index, and each layer has specific data corresponding to statistics.
The data replanning unit also processes the marked data in a replacement and/or deletion mode after the second repeated verification, and the second repeated verification mode can be used for rechecking data sources except the data channel acquired by the interaction module, for example, a computer crawls and checks data through other websites, checks corresponding archive data searched from a national library, and the like.
The model building module is used for building an economic prediction model through variable selection and model optimization;
the economic prediction module is used for obtaining a macroscopic economic prediction result and a microscopic economic prediction result through an economic prediction model;
the interaction module is used for inputting economic data and outputting economic estimation results.
The model optimizing and establishing comprises the following steps: model selection and evaluation optimization, wherein;
the model is selected as a Kobub-Douglas production function model, and the constructed metal industry economic prediction model is as follows:
Y＝TFP×Kα×L(1-α) (11)
in the formula (11), Y is the total production value of the metal industry in a certain area, K is the metal industry capital stock in a certain area, L is the labor input amount in a certain area, TFP is the full-element production rate in a certain area, TFP < economic growth rate, TFP belongs to (0,0.1), alpha is the metal industry capital production elastic coefficient in a certain area, and alpha belongs to (0, 1);
the evaluation optimization is that a multivariate regression model is adopted as an evaluation model of the metal industry economic prediction model, a least square method is selected in a parameter optimization mode, and the multivariate regression model is selected as the evaluation model for the evaluation optimization based on the accuracy of data, wherein the evaluation model is as follows:
y is beta X + epsilon, Y is a dependent variable matrix (corresponding to a total metal industry production value Y in a certain area), X (corresponding to a metal industry capital stock K in a certain area, a labor input amount L in a certain area, a full factor productivity TFP in a certain area and a metal industry capital output elastic coefficient alpha in a certain area) is an independent variable matrix, beta is a coefficient matrix, and epsilon is a residual error matrix.
Parameter calculation, β ═ X ' 1(X ' Y), X ' is the transpose of the X matrix.
And introducing a likelihood ratio to carry out post-dimensional selection, and completing multi-dimensional evaluation optimization of an evaluation model of the metal industry economic prediction model.
The method comprises the following specific steps:
introducing likelihood ratio values to carry out dimension selection afterwards, and defining:
where LR1 is the likelihood function of the unconstrained equation, T is the number of points,
variance estimation for unconstrained equations; LR2 is a likelihood function of a constraint equation,
for variance estimation of constrained equations, LR1, LR2 obey χ2Distribution, each time trying to eliminate the influence of an independent variable on the original equation, if the influence exceeds x2And (3) a critical value proves that the elimination of the independent variable has overlarge influence on the equation, if two independent variables with strong correlation exist in the equation, when one independent variable is eliminated, the other independent variable does not have great influence on the equation, and the effect of carrying out dimension selection on the original multiple regression model is achieved.
As shown in fig. 2, the present invention further provides a metal industry economic estimation method based on the growth of the sorbite and the stock recursion, wherein the economic estimation method comprises the following steps:
s1: inputting economic data;
s2: data preprocessing, namely preprocessing data of an economic database of the metal industry;
s3: establishing and determining a model, and establishing an economic prediction model through variable selection and model optimization;
s4: performing economic prediction, namely obtaining a macroscopic economic prediction result and a microscopic economic prediction result through an economic prediction model;
s5: and outputting an economic estimation result.
The invention provides a metal industry economic prediction system and a metal industry economic prediction method based on Soro growth and stock recursion, wherein the system and the method are realized based on a shared socioeconomic development path, the existing shared socioeconomic development path (SSPs) is constructed in the field of international climate change research for carrying out comprehensive analysis on the influence, adaptation and slowing of future climate change, and is used for describing five different development modes of the future socioeconomic system (SSP1 (Sustainability): sustainable development path, SSP2(Middle of the Road): Middle path) and SSP3(Regional Rival): region competition path, SSP4 (Inequality): unbalanced path, SSP5 (Fossil-full Development): fossil fuel is the main development path), reflects the correlation between economic society development and coping with climate change, and is the basis for the evaluation of climate change by the inter-government climate change specialized committee (IPCC). In the invention, based on a shared social economic path (SSPs) parameter basic framework, a future Chinese economy estimation localization parameter system is shown in Table 1.
TABLE 1SSP1-5 Chinese economic forecast localization parameter system
And selecting corresponding shared social economic path (SSPs) based parameters of the counted years by using the economic estimation system in the table, and adjusting the estimation system based on the SSPs parameters to estimate the economy of the metal industry.
By the economic estimation method, multiple factors such as data availability and data reliability are comprehensively considered, prediction accuracy is improved, and decision basis is provided for government departments to make metal industry development targets.
Meanwhile, the economic estimation method can be applied to the metal industry, and can also be applied to macroscopic economic prediction or a certain specific industry field.
The above provides a metal industry economic estimation system and method based on the increase of the lolo and the stock recursion, which are described in detail. The above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core ideas; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
As used in the specification and claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect. The description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The protection scope of the present application shall be subject to the definitions of the appended claims.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The foregoing description shows and describes several preferred embodiments of the present application, but as aforementioned, it is to be understood that the application is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the application as described herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the application, which is to be protected by the claims appended hereto.
Claims (10)
1. A metal industry economic forecast system based on the growth of a sorbite and the recursion of stocks, characterized in that the economic forecast system comprises:
an economic database of the metal industry for storing economic data;
the data preprocessing module is used for preprocessing data of an economic database of the metal industry;
the model building module builds an economic prediction model through variable selection and model optimization;
a economic estimation module, which obtains a macroscopic economic estimation result and a microscopic economic estimation result through an economic estimation model;
and the interaction module is used for inputting economic data and outputting economic estimation results.
2. The economic forecast system of claim 1, wherein the data preprocessing module comprises a data cleaning unit, a data information extraction unit, a data information conversion unit and a data re-planning unit;
the data cleaning unit is used for removing the economic data of the metal industry or the non-metal industry which has the deficiency so as to unify the repeated data;
the data information extraction unit is used for extracting a time domain, establishing a first economic data index corresponding to the time domain, and marking the outstanding data in the first economic data index of the time domain;
the data information conversion unit is used for converting the first economic data index of the time domain and calculating to obtain a second economic data index of the same time domain;
and the data replanning unit replans the data in the economic database of the metal industry according to the first economic data index and the second economic data index, and establishes a metal industry hierarchical economic database.
3. The economic forecast system of claim 2, wherein the data information extraction is used for marking the outburst data in the economic data of the metal industry in the time domain, and specifically comprises:
the method comprises the steps of obtaining a mapping relation between a time domain and economic data of the metal industry corresponding to the time domain by establishing a linear regression equation, determining the linear regression equation in a discrete and weighted average mode, and marking data which do not accord with the mapping relation under the linear regression equation in a first economic data index in the time domain.
4. The economic forecasting system of claim 3, wherein the data replanning module further comprises processing the marked data in a replacement mode and/or a deletion mode.
5. The economic forecasting system of claim 4, wherein when the metal industry hierarchical economic database is built in the data replanning module, data is layered in a neural tree mode, layered contents comprise a first economic data index, a second economic data index and a time domain, and the layering sequence is not fixed.
6. The economic estimation system according to claim 1, wherein the data in the economic database of the metal industry obtained in the interaction module is microscopic economic data, specifically historical economic data indexes related to metal industry requirements.
7. The economic forecasting system of claim 3, wherein the data source selected by the variables in the model building module is a metal industry hierarchical economic database.
8. The economic forecasting system of claim 3, wherein the model optimizing setup comprises: model selection and evaluation optimization, wherein;
the model is selected as a Kobub-Douglas production function model, and the constructed metal industry economic prediction model is as follows:
Y＝TFP×Kα×L(1-α)
wherein Y is the total production value of the metal industry in a certain area, K is the capital stock of the metal industry in a certain area, L is the labor input amount in a certain area, TFP is the full-element productivity in a certain area, and alpha is the metal industry capital output elastic coefficient in a certain area;
and the evaluation optimization is to adopt a multiple regression model as an evaluation model of the metal industry economic prediction model, select a least square method in a parameter optimization mode, introduce a likelihood ratio for post-dimensional selection, and finish the multi-dimensional evaluation optimization of the evaluation model of the metal industry economic prediction model.
9. The economic forecast system of claim 2, wherein the first economic indicator comprises: the total production value of the metal industry in a certain area, the capital stock of the metal industry in a certain area and the labor input in a certain area;
the second economic data index includes a full factor productivity in a region and a metal industry capital output yield factor in a region.
10. A metal industry economic prediction method based on the growth of the sorbite and the stock recursion, which realizes economic prediction by the economic prediction system of one of the above claims 1 to 9, characterized in that the economic prediction method comprises the following steps:
s1: inputting economic data;
s2: data preprocessing, namely preprocessing data of an economic database of the metal industry;
s3: establishing and determining a model, and establishing an economic prediction model through variable selection and model optimization;
s4: performing economic prediction, namely obtaining a macroscopic economic prediction result and a microscopic economic result through an economic prediction model;
s5: and outputting an economic estimation result.
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