CN115391985B - Metal resource metabolic process prediction method based on system dynamics - Google Patents

Metal resource metabolic process prediction method based on system dynamics Download PDF

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CN115391985B
CN115391985B CN202210866430.6A CN202210866430A CN115391985B CN 115391985 B CN115391985 B CN 115391985B CN 202210866430 A CN202210866430 A CN 202210866430A CN 115391985 B CN115391985 B CN 115391985B
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石岩
邵珊珊
杨学习
邓敏
王达
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Central South University
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Abstract

The application belongs to the field of space-time big data mining, and particularly relates to a metal resource metabolic process prediction method based on system dynamics. The prediction method comprises the following steps: classifying the state quantity of the metal resources according to the metabolism stage of the metal resources; constructing a metal resource metabolic dynamics model according to the conversion relation of the state quantity of the metal resource in different stages; calculating a social accumulation of metal resources based on the statistical data; solving model parameters by adopting a simulated annealing algorithm; predicting the amount of metal resource materials and the net import amount of the metal materials extracted from natural mineral products; and predicting the state quantity of the metal resource based on the metal resource metabolic dynamics model according to the predicted values and parameters. The method greatly reduces the dependence of statistical investigation data of the predicted result, avoids introducing subjective experience parameters, has stronger reality and interpretability, and improves the reliability and practicability of the predicted result of the metal resource metabolic process.

Description

Metal resource metabolic process prediction method based on system dynamics
Technical Field
The application belongs to the field of space-time big data mining, and particularly relates to a metal resource metabolic process prediction method based on system dynamics.
Background
With the continuous improvement of the science and technology level and the modernization degree of China, the urban production and construction level and the human living consumption level are gradually improved, and the macroscopic demands on metal resources such as iron, copper, aluminum and the like and the production volume are continuously increased. However, the blind expansion of metal resource production strength is extremely easy to cause the structural contradiction between excessive metal productivity and supply and demand, and excessive scrapped products and waste metals are enriched, so that the current metal recovery system with low recovery efficiency, few recovery nodes and narrow recovery coverage is finally brought with huge impact and serious threat, thereby inducing a series of social problems such as heavy metal pollution, land resource occupation, metal resource waste and the like.
The government and industry organizations scientifically regulate and reasonably guide the quantitative cognition and reliable prediction of the metabolic processes of the metal resources. The metal resource metabolism process comprises three stages of production and processing, use and scrapping and recycling. Wherein, the process that natural mineral products undergo industrial links such as exploitation, processing, purification and the like to become metal products is called a production processing stage; when the metal product produced in the stage provides service for human beings in the form of social products, the metal product enters a use stage until the metal product cannot continuously meet the production and life use requirements of the human beings; finally, various scrap metal products belong to the scrap recovery stage from the beginning of entering the recovery station until being re-refined or directly discarded, and the successfully recovered metal is re-entered as raw material into the production stage to continue to participate in the metal resource metabolism cycle. At present, the problems of unclear cognition of the metal resource metabolic process and difficult accurate grasp of the future trend of the metal resource in each stage still exist in China, thereby seriously obstructing the fine formulation and adaptive adjustment of the metal resource production and recovery strategy in China and finally restricting the green low-carbon sustainable development way in China. Therefore, scientifically and reliably modeling metal resource metabolic processes and predicting future development situations are key problems to be solved urgently.
Currently, three main types of modeling and predicting methods for metal resource metabolism are substance flow analysis, average life method and regression analysis. The material flow analysis method estimates the metal resource use stock in the use stage in a bottom-up or top-down mode based on multi-source statistical data such as industry enterprises, social consumption and the like. However, this method can only estimate the availability of metal resources and cannot predict their future situation. The average life method is used for estimating the scrapped and recovered stock of metal resources by utilizing the in-use stock and life probability distribution of various metal products from the service life perspective of the metal products, and the method needs to finely distinguish the production year and service time of different metal products and relies on subjective experience and expert knowledge to determine a plurality of complex model parameters. However, the variety of metal products in the current society is large and the update is fast, which inevitably leads to a large uncertain deviation between the prediction result of the average life method and the actual situation. Regression analysis methods generally utilize regression modeling modes such as autoregressive mode or multiple regression mode to quantitatively fit interpretation variables such as socioeconomic mode and the like to the metabolic inventory relationship of metal resources. Because of the remarkable space-time heterogeneity and state dependence of the metal resource stock in each stage, the simple linear regression mode is difficult to characterize the metal resource stock and the real metabolic process in different stages, the prediction effect is poor, and meanwhile, how to scientifically select the interpretation variable and reduce the uncertainty introduced by the interpretation variable is a great challenge for the application of the method.
The method needs a large amount of complete high-quality data support to ensure the reliability and the robustness of an estimation result, has higher data collection and statistical analysis requirements and complex operation, and in addition, the prior art models most of the multi-aspect metal resources in a single stage, relies on subjective experience to perform parameter setting and variable selection, ignores conversion constraint and rule connection among the metal resources in different stages, and leads to poor prediction of the metal resources in each stage and even result conflict.
Disclosure of Invention
Aiming at the technical problems, in order to break through the limitation that the prior art depends on high-quality data and a fracture metabolic process and solve the problems of the prior art that the prediction result is not stable and inaccurate, the modeling and prediction model of the metal resource metabolic process based on system dynamics is constructed by combining the real metabolic conversion of the metal resource in the real world.
Based on the above purpose, the application provides a metal resource metabolic process prediction method based on system dynamics, which specifically comprises the following steps:
classifying the metal resource state quantity according to the metal resource metabolism stage to obtain metal resource material quantity, metal resource social accumulation quantity and metal resource scrapping quantity;
according to the conversion relation of the state quantity of the metal resources in different stages, and considering the influence of the quantity of imported and exported metal materials and the quantity of the metal resource materials extracted from natural minerals, constructing a metal resource metabolic dynamics model;
calculating the social accumulation of the metal resources based on the statistical data, and solving parameters in the metal resource metabolic dynamics model by adopting a simulated annealing algorithm;
constructing a metal resource material quantity prediction model and a metal material net import quantity prediction model extracted from natural mineral products, and acquiring corresponding predicted values;
and predicting the state quantity of the metal resources according to the social accumulation quantity of the metal resources, the predicted value of the quantity of the metal resources materials extracted from natural minerals, the predicted value of the net import quantity of the metal materials and parameters in a metal resource metabolic dynamics model.
Further, the metal resource metabolic dynamics model specifically comprises the following steps:
M(t+1)＝M(t)-M(t)*α+D(t)*γ+I(t)+P(t) (1)
U(t+1)＝U(t)-U(t)*β+M(t)*α (2)
D(t+1)＝D(t)-D(t)*γ+U(t)*β (3)
wherein M (t), U (t), D (t), I (t) and P (t) respectively represent the amount of metal resource materials of a certain type, the social accumulation amount, the scrapping amount, the net import amount and the amount of metal resource materials extracted from natural minerals in a research area at the beginning of a t period; alpha represents the productivity of a certain type of metal resource material which is manufactured by factory processing in unit time and is used as a social product to put into production and living; beta represents the rejection rate of a certain type of metal resource along with the rejection of social products in unit time; gamma represents the recovery rate of some type of scrapped metal resources subjected to recycling by the recovery system in unit time.
Further, the process for calculating the social accumulation of the metal resource based on the statistical data specifically comprises the following steps:
determining the number of the structural units and the metal strength according to the national statistics annual survey and the industrial statistics report;
the mass flow analysis method from bottom to top is adopted to accumulate and calculate the social accumulation amount of metal resources in the research area layer by layer, and the formula is as follows:
wherein U (t) represents the social accumulation of a certain type of metal resource in the t period; n (N) i Representing the number of the ith social products, namely the number of the structural units; k (K) i The use intensity of a certain type of metal resource in the ith social product is represented.
Further, the objective function in the process of solving the parameters in the metal resource metabolic dynamics model by adopting the simulated annealing algorithm is as follows:
wherein M is t 、D t And U t Respectively representing the actual values of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period; eM (eM) t 、eD t And eU t Respectively representing the predicted value of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period.
Further, the construction of the metal resource material quantity prediction model extracted from natural mineral products specifically comprises the following steps:
fitting the historical material quantity of a certain type of metal resource extracted from natural mineral products based on a Gompertz model, and fitting the following model by using a least square method:
wherein K represents an upper limit approximation of P (t); a and b are parameters to be estimated, respectively.
Further, the constructing the metal material net import quantity prediction model to obtain the metal material net import quantity specifically includes:
a time series autoregressive ARIMA model is used to fit a historical net import amount of a metal resource of a certain type for predicting the net import amount I (t) of the metal resource of the certain type within a future period t.
The beneficial effects are that:
the application provides the metal resource metabolic process prediction method based on system dynamics, which considers the characteristic of interconversion dependence of the metal resource real metabolic process in the real world, greatly reduces the statistical investigation data dependence of the prediction result, completely avoids the introduction of subjective experience parameters, has stronger reality and interpretability, and improves the reliability and practicability of the metal resource metabolic process prediction result.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a metal resource metabolic process prediction method based on system dynamics provided by an embodiment of the application;
FIG. 2 is a schematic diagram of a metal resource metabolic dynamics model according to an embodiment of the present application;
FIG. 3 is a line graph of actual material quantity and scrap quantity of steel resources provided by the embodiment of the application; wherein (A) is a steel resource actual material quantity line graph, and (B) is a steel resource scrapping quantity line graph;
FIG. 4 is a diagram of state quantity lines of each stage of steel predicted by a metal resource metabolic dynamics model according to an embodiment of the application; wherein (A) is a steel resource material quantity prediction line diagram; (B) is a steel resource social accumulation prediction line diagram; and (C) a steel resource scrapping amount prediction line diagram.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, the embodiment of the application provides a flow chart of a metal resource metabolic process prediction method based on system dynamics.
Step S101, classifying the state quantity of the metal resource, specifically: and classifying the metal resource state quantity according to the metal resource metabolism stage to obtain metal resource material quantity, metal resource social accumulation quantity and metal resource scrapping quantity.
In the embodiment of the application, the metal resource metabolic process in the real world is considered to comprise three stages of metal mineral production and processing, metal product use and scrap metal recycling. Therefore, the scheme divides the state quantity of the metal resource into: (1) The metal resource material amount M is imported from the outside of the research area, is extracted and processed from natural mineral products or waste metal mineral products, such as steel bars, steel pipes and the like, is not used, and can be further processed into metal materials of social products; (2) The social accumulation amount U of metal resources is the amount of metal products which are used for processing and manufacturing metal materials and putting the metal materials into social production and life, such as social products containing metal resources, such as mechanical equipment, motor vehicles, metal furniture and the like; (3) The metal resource scrapped amount D cannot be used continuously and is out of the amount of the waste metal products used in social production and life due to damage or service expiration and the like, such as various waste articles with waste metal in a garbage station, and the waste metal products are also called waste metal minerals.
Step S102, modeling metal resource metabolism dynamics, specifically: according to the conversion relation of the state quantity of the metal resources in different stages, the influence of the quantity of imported and exported metal materials and the quantity of the metal resource materials extracted from natural minerals is considered, and a metal resource metabolic dynamics model is constructed.
As shown in a metal resource metabolic dynamics model schematic diagram in fig. 2, there is continuous state quantity conversion between metal resources in each stage, and the influence of import and export processes on the metal material quantity is considered, and the metal resource metabolic process is modeled as the following kinetic equation set:
M(t+1)＝M(t)-M(t)*α+D(t)*γ+I(t)+P(t) (1)
U(t+1)＝U(t)-U(t)*β+M(t)*α (2)
D(t+1)＝D(t)-D(t)*γ+U(t)*β (3)
wherein M (t), U (t), D (t), I (t) and P (t) respectively represent the amount of metal resource materials of a certain type, the social accumulation amount, the scrapping amount, the net import amount and the amount of metal resource materials extracted from natural minerals in a research area at the beginning of a t period; alpha represents the productivity of a certain type of metal resource material which is processed and manufactured by factories in unit time and is used as a social product put into production and life, such as the ratio of the steel amount in the social product processed and manufactured in unit time to the total amount of all steel materials; beta represents the rejection rate of a certain type of metal resource scrapped along with social products in unit time, such as the ratio of the steel quantity in the scrapped social products in unit time to the steel quantity in all existing social products; gamma represents the recovery rate of some type of scrap metal resources subjected to recycling by the recovery system in a unit time, such as the ratio of the amount of steel successfully extracted from the scrap social products in a unit time to all scrap steel.
Step S103, estimating a priori state quantity of metal resource metabolism, which specifically comprises the following steps: and calculating the social accumulation of the metal resource based on the statistical data.
In the embodiment of the application, the number of structural units and the metal strength are determined through national statistics annual survey and industrial statistics report, and then the social accumulation of metal resources in a research area is calculated layer by using a 'bottom-up' material flow analysis method, wherein the formula is as follows:
wherein U (t) represents the social accumulation of a certain type of metal resource in the t period; n (N) i Representing the number of the ith social products, namely the number of the structural units; k (K) i The use intensity of a certain type of metal resource in the ith social product is represented.
Step S104, solving parameters of a metal resource metabolic dynamics model, specifically: and solving parameters in the metal resource metabolic dynamics model by adopting a simulated annealing algorithm.
In the embodiment of the application, the productivity alpha, the rejection rate beta and the recovery rate gamma are all unknown dynamics model parameters, the parameters are solved by adopting a simulated annealing algorithm based on the metal resource material quantity M, the net import quantity I, the rejection quantity D, the metal resource social accumulation quantity U estimated in the step S103 and the metal resource material quantity P extracted from natural minerals, and the objective function used in the solving process is as follows:
wherein M is t 、D t And U t Respectively representing the actual values of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period; eM (eM) t 、eD t And eU t Respectively representing the predicted value of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period.
Step S105, predicting the metabolic process of the metal resource, specifically: constructing a metal resource material quantity prediction model and a metal material net import quantity prediction model extracted from natural mineral products, and acquiring corresponding predicted values; and predicting the state quantity of the metal resources according to the social accumulation quantity of the metal resources, the predicted value of the quantity of the metal resources materials extracted from natural minerals, the predicted value of the net import quantity of the metal materials and parameters in a metal resource metabolic dynamics model.
In the embodiment of the application, the historical material quantity of a certain type of metal resource extracted from natural mineral products is fitted based on a Gompertz model, and the following model is fitted by using a least square method:
wherein K represents an upper limit approximation of P (t); a and b are parameters to be estimated, respectively. Modeling predictions are made of the amount of some type of metal resource material P (t) that is being extracted from natural minerals over a future period t. A time series autoregressive ARIMA model is used for fitting the historical net import quantity of the metal resources of a certain type, so that the net import quantity I (t) of the metal resources of the certain type in a future period t is predicted.
Substituting the kinetic model parameters alpha, beta, gamma and the P, I predicted values obtained in the step S104 into formulas (1) - (3), and predicting the storage of the metal resource material quantity, the social accumulation quantity, the scrapping quantity and other stages in the future time in the research area.
Specific examples are described below.
The concrete implementation process of the application is illustrated by adopting national statistical annual-service and industrial annual-service data and selecting steel metal resources:
(1) Study data is acquired. In the embodiment, china is selected as a research area, and required raw data is obtained through national statistics annual survey and steel industry annual survey from 1990 to 2020, and specifically comprises steel output, waste steel consumption, steel stock, steel import and export quantities, and steel content data in various products and various types of products contained in buildings, durable goods, mechanical equipment, infrastructure and transportation facility systems required for estimating the steel social accumulation quantity a priori.
(2) And classifying the state quantity of the steel resources. The iron and steel resource metabolism process in the real world comprises three stages of iron ore production and processing, iron and steel product use and scrap iron and steel recycling. Therefore, the scheme divides the state quantity of steel resources into: (1) the steel resource material quantity M is imported from the outside of the research area, is extracted and processed from natural mineral products or waste metal mineral products, such as steel bars, steel pipes and other metal materials which are not put into use and can be further processed into social products; (2) the social accumulation amount U of steel resources is the amount of steel products which are used for processing and manufacturing steel materials and putting the steel materials into social production and life, such as social products containing steel resources, such as mechanical equipment, motor vehicles, metal furniture and the like; (3) the steel resource scrapped amount D cannot be used continuously and is out of the amount of waste steel products used in social production and life due to damage or service expiration and the like, such as various waste articles with waste steel in a garbage station, and the waste steel products are also called waste steel mineral products. The change line diagram of the steel resource material quantity and the scrapped quantity is drawn from 1990 to 2020 and is shown in figure 3.
(3) And modeling the steel resource metabolism dynamics. The continuous state quantity conversion exists among the steel resources at each stage, and the influence of the import and export process on the steel material quantity is considered to model the steel resource metabolic process into the following kinetic equation set:
M(t+1)＝M(t)-M(t)*α+D(t)*γ+I(t)+P(t) (7)
U(t+1)＝U(t)-U(t)*β+M(t)*a (8)
D(t+1)＝D(t)-D(t)*γ+U(t)*β (9)
wherein M (t), U (t), D (t), I (t) and P (t) respectively represent the amount of steel resource materials in the research area, the social accumulation amount, the scrapping amount, the net import amount and the amount of steel resource materials extracted from natural minerals at the beginning of the t period; alpha represents the ratio of the steel amount in the social product manufactured in unit time to the total amount of all steel materials; beta represents the ratio of the steel quantity in the scrapped social products in unit time to the steel quantity in all the existing social products; gamma represents the ratio of the amount of steel successfully extracted from the rejected social products to all the rejected steel per unit time.
(4) And estimating the prior quantity of the social accumulation of the steel resources. And obtaining the quantity of 5 types of products of the building, the durable goods, the mechanical equipment, the infrastructure and the transportation facility system and the steel content data in various products according to the national statistical annual survey and the annual survey of the steel industry. And then accumulating and calculating the social accumulation amount of the metal resources in the research area layer by using a 'bottom-up' material flow analysis method, wherein the formula is as follows:
wherein U (t) represents the social accumulation of steel resources in the t period; n (N) i Representing the number of i-th social products; k (K) i The use strength of steel resources in the ith social product is represented.
(5) And (5) solving parameters of the steel resource metabolic dynamics model. In the steel resource metabolic dynamics model, the productivity alpha, the rejection rate beta and the recovery rate gamma are all unknown dynamics model parameters, and based on the steel resource material quantity M, the net import quantity I, the rejection quantity D, the steel resource social accumulation quantity U estimated in the last step and the steel resource material quantity P extracted from natural minerals, the parameters are solved by adopting a simulated annealing algorithm, and the objective functions used in the solving process are as follows:
wherein M is t 、D t And U t Respectively representing the actual values of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period; eM (eM) t 、eD t And eU t Respectively representing the predicted value of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period.
(6) Predicting the amount of steel resource materials extracted from future natural minerals. Fitting the historical material quantity of steel resources extracted from natural minerals based on a Gompertz model, and fitting the following model by using a least square method:
wherein K represents an upper limit approximation of P (t); a and b are parameters to be estimated, respectively. Modeling prediction is performed on the amount of steel resource material P (t) extracted from natural minerals within a future period t.
(7) And predicting the net import quantity of the steel resources in the future. And constructing an ARIMA model to fit the historical net import quantity of the steel resources, and further predicting the net import quantity of the steel resources in the future period t.
(8) Predicting future process of steel resource metabolism. And (3) substituting P, I values predicted by the kinetic model parameters alpha, beta, gamma and (6) - (7) obtained in the step (5) into the formulas (1) - (3), predicting the stock change trend and conversion relation of each stage such as steel resource material quantity, social accumulation quantity and scrappage quantity at the future moment in China, and comparing with the actual metabolic process change trend, for example, as shown in figure 4.
The above examples illustrate only one embodiment of the application, which is described in more detail and is not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.

Claims (4)

1. The metal resource metabolic process prediction method based on system dynamics is characterized by comprising the following steps of:
classifying the metal resource state quantity according to the metal resource metabolism stage to obtain metal resource material quantity, metal resource social accumulation quantity and metal resource scrapping quantity;
according to the conversion relation of the state quantity of the metal resources in different stages, and considering the influence of the quantity of imported and exported metal materials and the quantity of the metal resource materials extracted from natural minerals, constructing a metal resource metabolic dynamics model;
calculating a social accumulation of metal resources based on the statistical data; and adopting a simulated annealing algorithm to solve parameters in the metal resource metabolic dynamics model, wherein an objective function in the process of adopting the simulated annealing algorithm to solve the parameters in the metal resource metabolic dynamics model is as follows:
wherein M is t 、D t And U t Respectively representing the actual values of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period; eM (eM) t 、eD t And eU t Respectively representing the predicted value of the material quantity, the scrapping quantity and the social accumulation quantity of a certain type of metal resource in the t period;
constructing a metal resource material quantity prediction model and a metal material net import quantity prediction model extracted from natural mineral products, and acquiring corresponding predicted values;
the construction of the metal resource material quantity prediction model extracted from natural mineral products specifically comprises the following steps:
fitting the historical material quantity of a certain type of metal resource extracted from natural mineral products based on a Gompertz model, and fitting the following model by using a least square method:
wherein K represents an upper limit approximation of P (t); a and b are parameters to be estimated, respectively;
and predicting the state quantity of the metal resources according to the social accumulation quantity of the metal resources, the predicted value of the metal resource material quantity extracted from natural minerals, the predicted value of the import and export metal material quantity and parameters in a metal resource metabolic dynamics model.
2. The method for predicting a metabolic process of a metal resource based on system dynamics according to claim 1, wherein the metal resource metabolic dynamics model is specifically:
M(t+1)＝M(t)-M(t)*α+D(t)*γ+I(t)+P(t)
U(t+1)＝U(t)-U(t)*β+M(t)*α
D(t+1)＝D(t)-D(t)*γ+U(t)*β
wherein M (t), U (t), D (t), I (t) and P (t) respectively represent the amount of metal resource materials of a certain type, the social accumulation amount, the scrapping amount, the net import amount and the amount of metal resource materials extracted from natural minerals in a research area at the beginning of a t period; alpha represents the productivity of a certain type of metal resource material which is manufactured by factory processing in unit time and is used as a social product to put into production and living; beta represents the rejection rate of a certain type of metal resource along with the rejection of social products in unit time; gamma represents the recovery rate of some type of scrapped metal resources subjected to recycling by the recovery system in unit time.
3. The method for predicting metal resource metabolic process based on system dynamics according to claim 2, wherein the calculating the metal resource social accumulation process based on statistical data specifically comprises:
determining the number of the structural units and the metal strength according to the national statistics annual survey and the industrial statistics report;
the mass flow analysis method from bottom to top is adopted to accumulate and calculate the social accumulation amount of metal resources in the research area layer by layer, and the formula is as follows:
wherein U (t) represents the social accumulation of a certain type of metal resource in the t period; n (N) i Representing the number of the ith social products, namely the number of the structural units; k (K) i The use intensity of a certain type of metal resource in the ith social product is represented.
4. The method for predicting the metabolic process of metal resources based on system dynamics according to claim 2, wherein the constructing the import and export metal material quantity prediction model to obtain the import and export metal material quantity specifically comprises:
a time series autoregressive ARIMA model is used to fit a historical net import amount of a metal resource of a certain type for predicting the net import amount I (t) of the metal resource of the certain type within a future period t.
CN202210866430.6A 2022-07-22 2022-07-22 Metal resource metabolic process prediction method based on system dynamics Active CN115391985B (en)

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