Port iron ore import quantity prediction method and device based on real material quantity

Abstract

The invention provides a port iron ore import quantity prediction method and device based on the real material quantity, and relates to the technical field of port iron ore import quantity prediction.The method utilizes key real material quantity data of the iron ore downstream industry to predict the domestic iron ore actual demand quantity, the key physical quantity data comprises a traffic route length comprehensive coefficient, a real estate enterprise house construction area and an automobile yield. In view of high correlation between the key real material quantity data and the actual demand quantity of the iron ore, the influence of price factors can be automatically eliminated by predicting the actual demand quantity of the iron ore in China by using the key real material quantity data, so that the prediction model is more objective, and the output prediction result is more accurate. After the actual demand quantity of the domestic iron ore is obtained, the port iron ore import quantity can be obtained by combining the port import iron ore stock increment and the domestic iron ore raw ore yield in the prediction time period, and the technical problem that an existing method is low in prediction precision is solved.

Description

Technical Field

[0001] The invention relates to the technical field of port iron ore import volume prediction, and in particular to a port iron ore import volume prediction method and device based on physical quantity. Background Art

[0002] With the growth of global iron ore trade, ports are important nodes for iron ore import and export, and their operational efficiency is crucial to the stability of the steel industry's supply chain. Accurately predicting the port's iron ore import volume is of great significance for port planning, warehousing management, and logistics scheduling. However, traditional prediction methods mainly rely on statistical analysis of historical data and fail to fully consider the various complex factors that affect the iron ore import volume. Therefore, the existing methods have the technical problem of low prediction accuracy of port iron ore import volume. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for predicting the import volume of iron ore at a port based on the physical quantity, so as to alleviate the technical problem of low prediction accuracy in the existing methods.

[0004] In a first aspect, the present invention provides a method for predicting the import volume of iron ore at a port based on physical quantity, comprising: obtaining the increase in the inventory of imported iron ore at the port, the domestic raw iron ore output and the key physical quantity data of the downstream iron ore industry within a prediction time period; wherein the key physical quantity data include: a comprehensive coefficient of the length of a transportation line, the construction area of ​​houses of real estate enterprises and the output of automobiles; the comprehensive coefficient of the length of a transportation line is used to quantify the iron ore demand for the length of a newly built transportation line; the key physical quantity data of the downstream iron ore industry is input into a preset iron ore demand prediction model to obtain the actual domestic iron ore demand within the prediction time period; wherein the preset iron ore demand prediction model is a function equation constructed by comprehensively considering the data sequence characteristics of each key physical quantity data; based on the actual domestic iron ore demand, the increase in the inventory of imported iron ore at the port and the domestic raw iron ore output, the import volume of iron ore at the port within the prediction time period is calculated.

[0005] In an optional embodiment, the newly built transportation routes include: newly built railways, newly built highways and newly built pipelines; the method also includes: obtaining the length of each newly built transportation route and the corresponding iron ore demand in a specified historical time period to determine the iron ore demand per unit length of each newly built transportation route, and obtain the iron ore demand coefficient of each newly built transportation route; obtaining the length of each newly built transportation route in a predicted time period; and calculating the comprehensive coefficient of transportation route length based on the length of each newly built transportation route and the corresponding iron ore demand coefficient.

[0006] In an optional implementation, it also includes: obtaining key physical quantity data and corresponding actual domestic iron ore demand in multiple historical time periods; constructing a variety of alternative function equations corresponding to the actual domestic iron ore demand for each key physical quantity data; calculating the goodness of fit and mean square error of the multiple alternative function equations corresponding to each key physical quantity data; based on the goodness of fit and mean square error, selecting a target function equation corresponding to the actual domestic iron ore demand from the multiple alternative function equations for each key physical quantity data; and constructing a preset iron ore demand prediction model based on the target function equations corresponding to all key physical quantity data.

[0007] In an optional embodiment, the preset iron ore demand forecasting model is expressed as: ;in, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. , , are weight coefficients, and , The actual domestic demand for iron ore output from the objective function equation corresponding to the comprehensive coefficient of the transportation line length is represented by: The actual domestic demand for iron ore outputted by the objective function equation corresponding to the housing construction area of ​​real estate enterprises, It represents the actual domestic demand for iron ore output as output of the objective function equation corresponding to automobile production.

[0008] In an optional embodiment, the least square method is used to solve the weight coefficient in the preset iron ore demand prediction model.

[0009] In an optional embodiment, based on the actual domestic iron ore demand, the increase in port imported iron ore inventory and the domestic iron ore raw ore output, the port iron ore import volume within the forecast period is calculated, including: using the formula Calculate the port's iron ore import volume during the forecast period; where: Indicates the port's iron ore import volume, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. Indicates the increase in the port's imported iron ore inventory. Indicates the preset discount factor, Indicates the domestic iron ore production.

[0010] In an optional embodiment, it also includes: determining a first iron content per unit mass of domestic iron ore and a second iron content per unit mass of port imported iron ore; calculating the ratio of the first iron content to the second iron content, and using the ratio result as a preset discount coefficient.

[0011] In a second aspect, the present invention provides a device for predicting the import volume of iron ore at a port based on physical quantity, comprising: a first acquisition module, used to obtain the increase in the inventory of imported iron ore at the port, the domestic raw iron ore output and the key physical quantity data of the downstream iron ore industry within the prediction time period; wherein the key physical quantity data include: a comprehensive coefficient of the length of transportation lines, the construction area of ​​houses of real estate enterprises and the automobile output; the comprehensive coefficient of the length of transportation lines is used to quantify the iron ore demand for the length of newly built transportation lines; a prediction module, used to input the key physical quantity data of the downstream iron ore industry into a preset iron ore demand prediction model to obtain the actual domestic iron ore demand within the prediction time period; wherein the preset iron ore demand prediction model is a function equation constructed by comprehensively considering the data sequence characteristics of each key physical quantity data; a first calculation module, used to calculate the import volume of iron ore at the port within the prediction time period based on the actual domestic iron ore demand, the increase in the inventory of imported iron ore at the port and the domestic raw iron ore output.

[0012] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method for predicting the port iron ore import volume based on physical quantity according to any one of the aforementioned embodiments is implemented.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the method for predicting port iron ore import volume based on physical quantity according to any one of the aforementioned embodiments.

[0014] The present invention provides a method for predicting the import volume of iron ore at a port based on physical quantity. The method first uses the key physical quantity data of the downstream iron ore industry to input a preset iron ore demand prediction model to predict the actual domestic demand for iron ore, wherein the key physical quantity data include: comprehensive coefficient of traffic line length, housing construction area of ​​real estate enterprises and automobile production. Since the above-mentioned key physical quantity data are highly correlated with the actual demand for iron ore, the use of key physical quantity data to predict the actual domestic demand for iron ore can automatically eliminate the influence of price factors, making the prediction model more objective and the output prediction result more accurate. In addition, since the preset iron ore demand prediction model is a function equation constructed by integrating the data sequence characteristics of each key physical quantity data, the prediction model can capture multiple data characteristics of each key physical quantity data, thereby ensuring the prediction accuracy. After obtaining the actual domestic demand for iron ore, the port iron ore import volume during the forecast period can be solved by combining the increase in port imported iron ore inventory and domestic iron ore raw ore output during the forecast period. This method comprehensively considers a variety of data that affect the port iron ore import volume during the forecast period, thereby effectively alleviating the technical problem of low prediction accuracy in existing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flowchart of a method for predicting port iron ore import volume based on physical quantity provided by an embodiment of the present invention; Figure 2 This is the fitting result diagram of the actual domestic iron ore demand and the comprehensive coefficient of transportation line length determined based on the data in Table 1; Figure 3 It is a comparison chart of the predicted fitting values ​​output by the objective function equation of the actual domestic iron ore demand and the comprehensive coefficient of the transportation line length; Figure 4 This is the fitting result diagram of the actual domestic iron ore demand and the housing construction area of ​​real estate enterprises determined based on the data in Table 1; Figure 5 It is a comparison chart of the predicted fitting values ​​output by the objective function equation of the actual demand for domestic iron ore and the housing construction area of ​​real estate enterprises; Figure 6 This is the fitting result diagram of the actual domestic iron ore demand and automobile production determined based on the data in Table 1; Figure 7 A comparison chart of the predicted fitting values ​​output by the objective function equation for the actual domestic iron ore demand and automobile production; Figure 8 A comparison chart of the prediction results of the prediction model and the actual domestic iron ore demand when the weight coefficients in the preset iron ore demand prediction model are all 1 / 3; Fig. 9 A functional module diagram of a port iron ore import volume prediction device based on physical quantity provided by an embodiment of the present invention; Fig.10 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] Embodiment 1 Figure 1 A flowchart of a method for predicting port iron ore import volume based on physical quantity provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically comprises the following steps: Step S102, obtaining the key physical quantity data of the port's imported iron ore inventory increase, domestic iron ore raw ore production and the iron ore downstream industry within the forecast time period.

[0021] The embodiment of the present invention has previously subdivided and screened the factors affecting the port's iron ore import volume. After analysis, it can be seen that the impact of the port's iron ore inventory on the port's iron ore import volume is direct and obvious. For example, in a certain year, if the port's foreign trade import iron ore inventory increases, the corresponding foreign trade iron ore import volume will increase, regardless of whether the downstream industry demand increases. The impact of domestic iron ore raw ore production on port iron ore foreign trade imports is also very direct. For example, in a certain year, if the domestic iron ore raw ore production increases significantly, the import of foreign trade iron ore will inevitably decrease accordingly, regardless of whether the downstream industry demand increases. In addition, changes in indicators such as infrastructure construction investment, real estate investment, and manufacturing investment will increase or decrease the demand for steel, and then affect the demand for foreign trade imports of iron ore. After correlation analysis, it can be seen that the physical quantity data of the downstream related industries of iron ore has a relatively high correlation coefficient with the foreign trade import volume of iron ore.

[0022] Therefore, in order to determine the port's iron ore import volume during the forecast period, the embodiment of the present invention needs to predetermine the planned increase in the port's imported iron ore inventory, the planned production of domestic iron ore raw ore, and the key physical quantity data of the downstream iron ore industry during the forecast period. Among them, considering factors such as relevance, representativeness, and convenience, in the embodiment of the present invention, the key physical quantity data include: a comprehensive coefficient of transportation line length, housing construction area of ​​real estate enterprises, and automobile production; the comprehensive coefficient of transportation line length is used to quantify the iron ore demand for the length of the newly built transportation line.

[0023] Step S104, inputting key physical quantity data of the downstream iron ore industry into a preset iron ore demand forecasting model to obtain the actual domestic iron ore demand within the forecasting time period.

[0024] Through data analysis, it can be known that the key physical quantity data of the downstream iron ore industry will directly affect the actual domestic demand for iron ore. Therefore, the embodiment of the present invention pre-constructs a model for predicting the actual domestic demand for iron ore based on the key physical quantity data (that is, a preset iron ore demand prediction model). After obtaining the key physical quantity data within the preset time period, it is input into the prediction model to obtain the actual domestic demand for iron ore within the prediction time period. Among them, the preset iron ore demand prediction model is a function equation constructed by integrating the data sequence characteristics of each key physical quantity data. Therefore, the prediction model can capture the time dependence, seasonality, trend and periodicity characteristics of each key physical quantity data, thereby improving the accuracy and reliability of the model.

[0025] Step S106, calculating the port iron ore import volume within the forecast period based on the actual domestic iron ore demand, the increase in port imported iron ore inventory and the domestic iron ore raw ore output.

[0026] If the iron content of domestic iron ore and imported iron ore is not much different, then in any forecast period, the port iron ore import volume plus the domestic iron ore raw ore production, minus the increase in port imported iron ore inventory, is equal to the actual domestic iron ore demand. Therefore, based on the above data correlation, after predicting the actual domestic iron ore demand through the model, further combined with the increase in port imported iron ore inventory and domestic iron ore raw ore production, the port iron ore import volume in the forecast period can be calculated.

[0027] The embodiment of the present invention provides a method for predicting the import volume of iron ore at a port based on physical quantity. The method first uses the key physical quantity data of the downstream iron ore industry to input a preset iron ore demand prediction model to predict the actual domestic demand for iron ore, wherein the key physical quantity data include: comprehensive coefficient of traffic line length, housing construction area of ​​real estate enterprises and automobile production. Since the above-mentioned key physical quantity data are highly correlated with the actual demand for iron ore, the use of key physical quantity data to predict the actual domestic demand for iron ore can automatically eliminate the influence of price factors, making the prediction model more objective and the output prediction results more accurate. In addition, since the preset iron ore demand prediction model is a function equation constructed by integrating the data sequence characteristics of each key physical quantity data, the prediction model can capture multiple data characteristics of each key physical quantity data, thereby ensuring the prediction accuracy. After obtaining the actual domestic demand for iron ore, the port iron ore import volume during the forecast period can be solved by combining the increase in port imported iron ore inventory and domestic iron ore raw ore output during the forecast period. This method comprehensively considers a variety of data that affect the port iron ore import volume during the forecast period, thereby effectively alleviating the technical problem of low prediction accuracy in existing methods.

[0028] In an optional implementation manner, the newly built transportation route includes: a newly built railway, a newly built highway and a newly built pipeline; the method of the present invention further includes the following steps: Step S201, obtaining the length of each newly built transportation route and the corresponding iron ore demand in a specified historical time period to determine the iron ore demand per unit length of each newly built transportation route, and obtaining the iron ore demand coefficient of each newly built transportation route.

[0029] Step S202, obtaining the length of each newly built traffic route within the predicted time period.

[0030] Step S203, calculating the comprehensive coefficient of the length of each newly built transportation route based on the length of each newly built transportation route and the corresponding iron ore demand coefficient.

[0031] Specifically, the embodiment of the present invention selects newly built railways, roads and pipelines as the main factors for iron ore consumption in infrastructure construction. Considering different modes of transportation, the demand for iron ore per kilometer of newly built lines varies greatly. Therefore, the embodiment of the present invention first obtains the length of various newly built transportation lines and the corresponding iron ore demand (that is, the actual consumption) within a specified historical time period. Based on the above data, the iron ore demand per unit length of each newly built transportation line can be calculated. The unit length can be selected as 1 kilometer, 10 kilometers, or other lengths, and the embodiment of the present invention does not specifically limit it. Assuming that the unit length is 1 kilometer, and the construction of 1 kilometer of railway requires M tons of iron ore, the construction of 1 kilometer of road requires N tons of iron ore, and the construction of 1 kilometer of pipeline requires L tons of iron ore, then the iron ore demand coefficients of the newly built railway, newly built road and newly built pipeline are: M, N, L respectively.

[0032] Next, after obtaining the length of each newly built transportation route within the forecast time period, the amount of iron ore required for all newly built transportation routes can be calculated based on the iron ore demand coefficients of the newly built railways, newly built roads and newly built pipelines, as well as the corresponding unit lengths. Next, the calculated amount of iron ore required for all newly built transportation routes is converted into a comprehensive coefficient of transportation route length through a specified conversion algorithm. The embodiment of the present invention does not specifically limit the above conversion algorithm, for example, it can be divided by a specified value, taken logarithm, etc.

[0033] In an optional embodiment, the method of the present invention further comprises the following steps: Step S301, obtaining key physical quantity data and corresponding actual domestic iron ore demand in multiple historical time periods.

[0034] Step S302 , constructing a plurality of alternative function equations corresponding to the actual domestic iron ore demand for each key physical quantity data.

[0035] Specifically, after collecting key physical quantity data and domestic iron ore demand in multiple historical time periods, a variety of functional equations for predicting domestic iron ore demand were constructed for the comprehensive coefficient of transportation line length, housing construction area of ​​real estate enterprises, and automobile production.

[0036] For example, with domestic iron ore demand as output and comprehensive coefficient of traffic line length as input, linear, logarithmic, exponential, and quadratic polynomial function equations are constructed respectively. The above four function equations are alternative function equations corresponding to comprehensive coefficient of traffic line length. The embodiment of the present invention does not specifically limit the form of the above alternative function equations, and users can set multiple function relationships according to actual needs. By analogy, multiple alternative function equations corresponding to the construction area of ​​houses of real estate enterprises and multiple alternative function equations corresponding to automobile production are constructed.

[0037] Step S303, calculating the goodness of fit and mean square error of multiple candidate function equations corresponding to each key physical quantity data.

[0038] Step S304, based on the goodness of fit and the mean square error, for each key physical quantity data, a target function equation corresponding to the actual domestic iron ore demand is selected from a plurality of alternative function equations.

[0039] Next, the goodness of fit and mean square error of each alternative function equation are calculated, and factors such as the actual development law of the curve and its own variables are considered. For each key physical quantity data, a function equation with the best performance is selected from the multiple alternative function equations as the objective function equation.

[0040] The following Table 1 is the historical data used to construct the prediction model. According to the data provided in Table 1 and referring to the above method for determining the objective function equation, it can be obtained that the objective function equation of the comprehensive coefficient of transportation line length and the actual domestic iron ore demand is a logarithmic function equation: y=112420ln(x)-212483; y is the output and x is the input. Figure 2 This is the fitting result diagram of the actual domestic iron ore demand and the comprehensive coefficient of transportation line length determined based on the data in Table 1. Figure 3 This is a comparison chart of the predicted fitting values ​​output by the objective function equation of the actual domestic iron ore demand and the comprehensive coefficient of the transportation line length. The average error of the predicted fitting values ​​is 1.5%, and the goodness of fit is good.

[0041] According to the data provided in Table 1 and referring to the above method for determining the objective function equation, the objective function equation of the housing construction area of ​​real estate enterprises and the actual demand for domestic iron ore is a quadratic polynomial function equation: y=-8E-08x 2 +0.2015x+22883; y is the output and x is the input. Figure 4 This is a fitting result chart of the actual domestic demand for iron ore and the housing construction area of ​​real estate enterprises based on the data in Table 1. Figure 5 This is a comparison chart of the predicted fitting values ​​output by the objective function equation of the actual domestic iron ore demand and the housing construction area of ​​real estate enterprises. The average error of the predicted fitting values ​​is 0.8%, and the goodness of fit is good.

[0042] According to the data provided in Table 1 and referring to the above method for determining the objective function equation, the objective function equation of automobile output and actual domestic iron ore demand is a straight line linear function equation: y=38.579x+29577; y is the output and x is the input. Figure 6 This is the fitting result diagram of the actual domestic iron ore demand and automobile production based on the data in Table 1. Figure 7This is a comparison chart of the predicted fitting values ​​output by the objective function equation of the actual domestic iron ore demand and automobile production. The average error of the predicted fitting values ​​is -0.4%, and the goodness of fit is good.

[0043] Table 1 Historical data used to build the prediction model

[0044] Step S305: construct a preset iron ore demand forecasting model based on the objective function equation corresponding to all key physical quantity data.

[0045] Based on the above description, it can be known that each key physical quantity data can predict the domestic iron ore demand. Therefore, by integrating the above three objective function equations, an iron ore demand prediction model can be constructed. The integration method can be to obtain the arithmetic average of the three objective function equations, or to configure corresponding weights for each objective function equation, and then perform weighted summation. The embodiment of the present invention does not specifically limit it, and the user can select according to actual needs.

[0046] In an optional implementation, the preset iron ore demand forecasting model is expressed as: ;in, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. , , are weight coefficients, and , The actual domestic demand for iron ore output from the objective function equation corresponding to the comprehensive coefficient of the transportation line length is represented by: The actual domestic demand for iron ore outputted by the objective function equation corresponding to the housing construction area of ​​real estate enterprises, It represents the actual domestic demand for iron ore output as output of the objective function equation corresponding to automobile production.

[0047] Obviously, if the arithmetic average of the three objective function equations is used as the preset iron ore demand forecast model, then , , The value of is 1 / 3. However, if the arithmetic mean method is not used, the prediction accuracy of the prediction model can be improved by optimizing the above three weight coefficients.

[0048] Optionally, the least square method is used to solve the weight coefficient in the preset iron ore demand forecasting model. That is, the input data are constructed according to the historical data. , , The vector and output data The solution of the weight coefficient is then transformed into the solution of the weight coefficient vector, and the weight coefficient vector is calculated by the least squares method. Figure 8 When the weight coefficients in the preset iron ore demand forecasting model are all 1 / 3, the comparison chart between the forecasting results of the forecasting model and the actual domestic iron ore demand shows that the average error of the forecast fitting is 0.6%.

[0049] In an optional implementation manner, the above step S106, based on the actual domestic iron ore demand, the increase in the port's imported iron ore inventory and the domestic iron ore raw ore output, calculates the port's iron ore import volume within the forecast period, specifically including the following contents: Using the formula Calculate the port's iron ore import volume during the forecast period; where: Indicates the port's iron ore import volume, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. Indicates the increase in the port's imported iron ore inventory. Indicates the preset discount factor, Indicates the domestic iron ore production.

[0050] Considering that the iron content of domestic iron ore is quite different from that of imported iron ore, when predicting the import volume of port iron ore, it is necessary to reduce the domestic iron ore output by a certain percentage, and the preset discount coefficient is 0< <1. In order to accurately obtain the preset discount coefficient, in an optional implementation manner, the method of the present invention further includes the following steps: Step S401, determining a first iron content per unit mass of domestic iron ore and a second iron content per unit mass of port imported iron ore.

[0051] Step S402, calculating the ratio of the first iron content to the second iron content, and using the ratio result as a preset discount coefficient.

[0052] For example, if the unit mass is 1 ton, and the first iron content of 1 ton of domestic iron ore is determined to be The second iron content of 1 ton of port imported iron ore is , then the preset discount factor is .

[0053] In summary, the embodiment of the present invention provides a method for predicting the port iron ore import volume based on physical quantity. The method uses a preset iron ore demand prediction model. The input data of the model is the key physical quantity data of the downstream iron ore industry that is highly correlated with the port iron ore import volume, and the model is a function equation constructed by integrating the data sequence characteristics of each key physical quantity data. Therefore, it is possible to objectively and accurately predict the actual domestic iron ore demand. The port iron ore import volume can be obtained by performing simple arithmetic operations with the increase in port imported iron ore inventory and the domestic iron ore raw ore output. The above method plays an important role in improving the resilience of port ore transportation, efficient allocation of ore resources, and steady development of the steel industry.

[0054] Embodiment 2 An embodiment of the present invention also provides a port iron ore import volume prediction device based on physical quantity, which is mainly used to execute the port iron ore import volume prediction method based on physical quantity provided in the above-mentioned embodiment 1. The following is a specific introduction to the port iron ore import volume prediction device based on physical quantity provided in the embodiment of the present invention.

[0055] Fig. 9 A functional module diagram of a port iron ore import volume prediction device based on physical quantity provided by an embodiment of the present invention, such as Fig. 9 As shown, the device mainly includes: a first acquisition module 10, a prediction module 20, and a first calculation module 30, wherein: The first acquisition module 10 is used to obtain the increase in port imported iron ore inventory, domestic iron ore raw ore production and key physical quantity data of iron ore downstream industries within the forecast time period; wherein the key physical quantity data include: comprehensive coefficient of transportation line length, housing construction area of ​​real estate enterprises and automobile production; the comprehensive coefficient of transportation line length is used to quantify the iron ore demand for the length of newly built transportation lines.

[0056] The prediction module 20 is used to input the key physical quantity data of the downstream iron ore industry into the preset iron ore demand prediction model to obtain the actual domestic iron ore demand within the prediction time period; wherein the preset iron ore demand prediction model is a function equation constructed by comprehensively considering the data sequence characteristics of each key physical quantity data.

[0057] The first calculation module 30 is used to calculate the port iron ore import volume within the forecast period based on the actual domestic iron ore demand, the increase in the port imported iron ore inventory and the domestic iron ore raw ore output.

[0058] The embodiment of the present invention provides a port iron ore import volume prediction device based on physical quantity, which first uses the key physical quantity data of the downstream iron ore industry to input the preset iron ore demand prediction model to predict the actual domestic iron ore demand, wherein the key physical quantity data include: comprehensive coefficient of traffic line length, housing construction area of ​​real estate enterprises and automobile production. Since the above key physical quantity data are highly correlated with the actual iron ore demand, the use of key physical quantity data to predict the actual domestic iron ore demand can automatically eliminate the influence of price factors, making the prediction model more objective and the output prediction result more accurate. In addition, since the preset iron ore demand prediction model is a function equation constructed by integrating the data sequence characteristics of each key physical quantity data, the prediction model can capture multiple data characteristics of each key physical quantity data, thereby ensuring the prediction accuracy. After obtaining the actual domestic demand for iron ore, the port's iron ore import volume within the forecast period can be solved by combining the increase in port imported iron ore inventory and domestic iron ore raw ore output within the forecast period. The device comprehensively considers a variety of data that affect the port's iron ore import volume within the forecast period, thereby effectively alleviating the technical problem of low prediction accuracy in existing methods.

[0059] Optionally, the newly built transportation line includes: a newly built railway, a newly built highway and a newly built pipeline; the device also includes: The second acquisition module is used to obtain the length of each newly built transportation line and the corresponding iron ore demand in a specified historical time period to determine the iron ore demand per unit length of each newly built transportation line and obtain the iron ore demand coefficient of each newly built transportation line.

[0060] The third acquisition module is used to obtain the length of each newly built traffic route within the prediction time period.

[0061] The second calculation module is used to calculate the comprehensive coefficient of transportation line length based on the length of each newly built transportation line and the corresponding iron ore demand coefficient.

[0062] Optionally, the device is also used for: Obtain key physical quantity data and corresponding domestic iron ore actual demand over multiple historical time periods.

[0063] Multiple alternative function equations related to the actual domestic iron ore demand are constructed for each key physical quantity data.

[0064] Calculate the goodness of fit and mean square error of multiple alternative function equations corresponding to each key physical quantity data.

[0065] Based on the goodness of fit and mean square error, the objective function equation corresponding to the actual domestic iron ore demand is selected from a variety of alternative function equations for each key physical quantity data.

[0066] Based on the objective function equation corresponding to all key physical quantity data, a preset iron ore demand forecasting model is constructed.

[0067] Optionally, the preset iron ore demand forecasting model is expressed as: ;in, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. , , are weight coefficients, and , The actual domestic demand for iron ore output from the objective function equation corresponding to the comprehensive coefficient of the transportation line length is represented by: The actual domestic demand for iron ore outputted by the objective function equation corresponding to the housing construction area of ​​real estate enterprises, It represents the actual domestic demand for iron ore output as output of the objective function equation corresponding to automobile production.

[0068] Optionally, the least square method is used to solve the weight coefficient in the preset iron ore demand prediction model.

[0069] Optionally, the first calculation module 30 is specifically used for: Using the formula Calculate the port's iron ore import volume during the forecast period; where: Indicates the port's iron ore import volume, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. Indicates the increase in the port's imported iron ore inventory. Indicates the preset discount factor, Indicates the domestic iron ore production.

[0070] Optionally, the device is also used for: Determine the first iron content per unit mass of domestic iron ore and the second iron content per unit mass of port imported iron ore.

[0071] The ratio of the first iron content to the second iron content is calculated, and the ratio result is used as a preset discount coefficient.

[0072] Embodiment 3 See also Fig.10 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0073] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0074] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.10 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0075] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0076] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0077] A computer program product of a method and apparatus for predicting port iron ore import volume based on physical quantity provided in an embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor, wherein the instructions included in the program code can be used to execute the method described in the previous method embodiment. For specific implementation, please refer to the method embodiment, which will not be repeated here.

[0078] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0080] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0081] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0082] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0083] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting port iron ore import volume based on physical quantity, characterized in that: include: Obtain the key physical quantity data of the increase in port imported iron ore inventory, domestic iron ore raw ore production and downstream iron ore industries within the forecast period; wherein, the key physical quantity data include: comprehensive coefficient of transportation line length, housing construction area of ​​real estate enterprises and automobile production; the comprehensive coefficient of transportation line length is used to quantify the iron ore demand for the length of newly built transportation lines; Inputting the key physical quantity data of the downstream iron ore industry into a preset iron ore demand forecasting model to obtain the actual domestic iron ore demand within the forecasting time period; wherein the preset iron ore demand forecasting model is a function equation constructed by comprehensively considering the data sequence characteristics of each key physical quantity data; The port iron ore import volume during the forecast period is calculated based on the actual domestic iron ore demand, the increase in the port's imported iron ore inventory and the domestic iron ore raw ore output.

2. The method for predicting port iron ore import volume based on physical quantity according to claim 1, characterized in that: The newly built transportation lines include: newly built railways, newly built roads and newly built pipelines; the method also includes: Obtain the length of each newly built transportation line and the corresponding iron ore demand in a specified historical time period to determine the iron ore demand per unit length of each newly built transportation line, and obtain the iron ore demand coefficient of each newly built transportation line; Obtaining the length of each newly built traffic route within the predicted time period; Based on the length of each of the newly built transportation routes and the corresponding iron ore demand coefficient, the comprehensive coefficient of the transportation route length is calculated.

3. The method for predicting port iron ore import volume based on physical quantity according to claim 1, characterized in that: Also includes: Obtain key physical quantity data and corresponding domestic iron ore actual demand in multiple historical time periods; Constructing multiple alternative function equations for each key physical quantity data and the actual domestic iron ore demand; Calculate the goodness of fit and mean square error of multiple alternative function equations corresponding to each key physical quantity data; Based on goodness of fit and mean square error, for each key physical quantity data, select a target function equation corresponding to the actual domestic iron ore demand from a plurality of alternative function equations; Based on the objective function equation corresponding to all key physical quantity data, the preset iron ore demand forecasting model is constructed.

4. The method for predicting port iron ore import volume based on physical quantity according to claim 3, characterized in that: The preset iron ore demand forecasting model is expressed as: ; in, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. , , are weight coefficients, and , The actual domestic demand for iron ore output from the objective function equation corresponding to the comprehensive coefficient of the transportation line length is represented by: The actual domestic demand for iron ore outputted by the objective function equation corresponding to the housing construction area of ​​real estate enterprises, It represents the actual domestic demand for iron ore output as output of the objective function equation corresponding to automobile production.

5. The method for predicting port iron ore import volume based on physical quantity according to claim 1, characterized in that: The least square method is used to solve the weight coefficient in the preset iron ore demand prediction model.

6. The method for predicting port iron ore import volume based on physical quantity according to claim 1, characterized in that: Based on the actual domestic demand for iron ore, the increase in the port's imported iron ore inventory and the domestic iron ore raw ore output, the port's iron ore import volume within the forecast period is calculated, including: Using the formula Calculate the port's iron ore import volume during the forecast period; where: represents the iron ore import volume of the port, It represents the actual domestic iron ore demand output by the preset iron ore demand forecast model. Indicates the increase in the inventory of imported iron ore at the port. Indicates the preset discount factor, Indicates the domestic iron ore production.

7. The method for predicting port iron ore import volume based on physical quantity according to claim 6, characterized in that: Also includes: Determine the first iron content per unit mass of domestic iron ore and the second iron content per unit mass of port imported iron ore; The ratio of the first iron content to the second iron content is calculated, and the ratio result is used as the preset discount coefficient.

8. A port iron ore import volume prediction device based on physical quantity, characterized in that: include: The first acquisition module is used to obtain the increase in the port's imported iron ore inventory, the domestic iron ore raw ore output and the key physical quantity data of the downstream iron ore industry within the forecast period; wherein the key physical quantity data include: the comprehensive coefficient of the length of the transportation line, the construction area of ​​the real estate enterprise's house and the automobile output; the comprehensive coefficient of the length of the transportation line is used to quantify the iron ore demand for the length of the newly built transportation line; A prediction module, used for inputting the key physical quantity data of the downstream iron ore industry into a preset iron ore demand prediction model to obtain the actual domestic iron ore demand within the prediction time period; wherein the preset iron ore demand prediction model is a function equation constructed by comprehensively considering the data sequence characteristics of each key physical quantity data; The first calculation module is used to calculate the port iron ore import volume within the forecast time period based on the actual domestic iron ore demand, the increase in the port imported iron ore inventory and the domestic iron ore raw ore output.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method for predicting the port iron ore import volume based on physical quantity according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the method for predicting the port iron ore import volume based on physical quantity according to any one of claims 1 to 7.

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