Regional output value prediction method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention discloses a regional output value prediction method and device, a storage medium and electronic equipment, and relates to the technical field of output value prediction, prediction is carried out based on a power direct consumption coefficient, and the coefficient of a predicted target year is calculated by using the direct consumption coefficient of each industry in historical years to the power industry, so that the output value of a region is predicted. According to the method, the degree of dependence on the power industry in an industrial production structure is measured, then the coefficient is converted into a unit for measuring the output value in combination with industrial electricity consumption data and regional electricity price, the scale of the increased output value of the industry is obtained, and then the output value is adjusted by using a flat subtraction index to enable the output value to have comparability with an actual output value, and an invariable-price industrial added value is obtained. And the regional output value can be predicted according to the sum of the predicted non-variable-price industry added values of each industry, the rapid and accurate prediction of the regional output value is realized by using the electric power related indexes with accurate data and simple sources, the actual change condition of the output value can be met, and the prediction result is scientific and reasonable.

Description

Technical Field

[0001] The present application relates to the technical field of output value prediction, and in particular to a method, device, storage medium and electronic device for predicting regional output value. Background Art

[0002] Regional output value, namely regional gross domestic product (regional GDP), refers to the final result of the production activities of all permanent units in the region within a certain period of time. Regional GDP is equal to the sum of the added value of each industry. GDP accounting is divided into annual accounting and quarterly accounting. According to the different levels of detail of the basic accounting data obtained, annual accounting and quarterly accounting use different methods. Annual accounting uses direct accounting method, and quarterly accounting uses indirect accounting method. Quarterly GDP is consistent with annual GDP in basic concepts and scope, but due to the availability and timeliness of basic data, quarterly accounting uses indirect accounting method, that is, using the growth rate of relevant indicators to extrapolate the growth rate and total amount of added value of each industry, also known as "speed extrapolation method". What kind of relevant indicators should be selected for prediction to achieve better results has always been a difficult problem in the industry. The prediction method must not only ensure that the data is true and reliable, and the extrapolation process is not too complicated, but also ensure that the predicted data can match the actual data to ensure the scientificity and rationality of the prediction results. Summary of the invention

[0003] The main purpose of this application is to provide a method, device, storage medium and electronic device for predicting regional output value, aiming to solve the problem that the methods for predicting regional output value in the prior art are relatively poor in effect.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for predicting regional output value, comprising the following steps:

[0006] Based on the direct consumption coefficients of various industries on the power industry in historical years, obtain the direct consumption coefficients of various industries on the power industry in the target year;

[0007] The added value of the industry is obtained based on the industry electricity consumption data, regional electricity price data and the direct consumption coefficient of each industry to the power industry in the target year;

[0008] According to the output value deflator and industry value added, the constant price industry value added is obtained;

[0009] Based on the industry value added at constant prices, the regional forecast output value is obtained.

[0010] In a possible implementation of the first aspect, based on the direct consumption coefficients of various industries for the power industry in historical years, obtaining the direct consumption coefficients of various industries for the power industry in the target year includes:

[0011] Based on the linear change trend of the direct consumption coefficient of various industries to the power industry in historical years, the time series model is used to predict the direct consumption coefficient of various industries to the power industry in the target year.

[0012] In a possible implementation of the first aspect, based on the linear change trend of the direct consumption coefficient of each industry to the power industry in historical years, a time series model is used to predict the direct consumption coefficient of each industry to the power industry in the target year, including:

[0013] Based on the linear change trend of the direct consumption coefficient of various industries to the power industry in historical years, the time series model is used and the target lag order is selected according to the Akaike information criterion to predict the direct consumption coefficient of various industries to the power industry in the target year.

[0014] In a possible implementation of the first aspect, before obtaining the direct consumption coefficient of each industry for the power industry in the target year based on the direct consumption coefficient of each industry for the power industry in the historical years, the method further includes:

[0015] The historical direct consumption coefficients are weighted averaged by year to obtain the direct consumption coefficients of various industries to the power industry in historical years.

[0016] In a possible implementation of the first aspect, before obtaining the industry value added at constant prices according to the output value deflator and the industry value added, the method further includes:

[0017] Convert the output value production index during the sample period to the output value index of the base year;

[0018] According to the output value index of the base year, the actual output value with the base year as the constant price is obtained;

[0019] The output deflator is obtained based on the nominal output value and the real output value at constant prices in the base year.

[0020] In a possible implementation of the first aspect, obtaining the regional forecast output value based on the industry value added at constant prices includes:

[0021] The regional forecast output value is obtained by summing up the added value of the industry at constant prices.

[0022] In a possible implementation of the first aspect, after obtaining the regional forecast output value based on the industry value added at constant prices, the method further includes:

[0023] Compare the actual output value of a region with the predicted output value of the region to obtain the forecast evaluation results.

[0024] In a second aspect, an embodiment of the present application provides a regional output value prediction device, comprising:

[0025] A first acquisition module, the first acquisition module is used to obtain the direct consumption coefficient of each industry to the power industry in the target year based on the direct consumption coefficient of each industry to the power industry in the historical years;

[0026] The second acquisition module is used to obtain the industry added value based on the industry electricity consumption data, regional electricity price data and the direct consumption coefficient of each industry to the power industry in the target year;

[0027] The third acquisition module is used to obtain the constant price industry added value according to the output value deflator and the industry added value;

[0028] The target acquisition module is used to obtain the regional predicted output value based on the industry added value at constant prices.

[0029] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the method for predicting regional output value provided in any one of the first aspects above is implemented.

[0030] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein:

[0031] The memory is used to store computer programs;

[0032] The processor is used to load and execute a computer program so that the electronic device executes the regional output value prediction method provided in any one of the first aspects above.

[0033] Compared with the prior art, the beneficial effects of this application are:

[0034] The embodiments of the present application propose a method, device, storage medium and electronic device for predicting regional output value. The method includes: obtaining the direct consumption coefficient of each industry for the power industry in the target year based on the direct consumption coefficient of each industry for the power industry in historical years; obtaining the industry added value according to the industry electricity consumption data, regional electricity price data and the direct consumption coefficient of each industry for the power industry in the target year; obtaining the constant price industry added value according to the output value deflator and the industry added value; and obtaining the regional predicted output value according to the constant price industry added value. This application makes predictions based on the direct consumption coefficient of electricity. By using the direct consumption coefficient of each industry for the electricity industry in historical years, the coefficient of the predicted target year is calculated to measure the degree of dependence on the electricity industry in the industry production structure. Then, the coefficient is converted into a unit of measurement of output value in combination with the industry electricity consumption data and the regional electricity price to obtain the scale of output value increased by the industry. Then, the output value is adjusted using the deflator to make it comparable with the actual output value to obtain the constant price industry added value. The regional output value can be predicted by adding up the constant price industry added value of each industry predicted. By using electricity-related indicators with accurate data and simple sources, a rapid and accurate prediction of regional output value can be achieved, which can meet the actual changes in output value, and the prediction results are scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of a flow chart of a method for predicting regional output value provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram showing the comparison between the predicted regional quarterly added value and the actual quarterly GDP in the regional output value prediction method provided in the embodiment of the present application;

[0038] Figure 4 A schematic diagram showing a comparison between the predicted regional quarterly added value and the actual quarterly GDP growth rate in the regional output value prediction method provided in an embodiment of the present application;

[0039] Figure 5 A schematic diagram of a module of a regional output value prediction device provided in an embodiment of the present application;

[0040] Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] See attached Figure 1 , attached Figure 1 This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage; the processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0043] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0044] As attached Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and a regional output value prediction device.

[0045] In the attached Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the regional output value prediction device stored in the memory 105 through the processor 101, and executes the regional output value prediction method provided in the embodiment of the present application.

[0046] See attached Figure 2 Based on the hardware device of the aforementioned embodiment, the embodiment of the present application provides a method for predicting regional output value, comprising the following steps:

[0047] S10: Based on the direct consumption coefficients of various industries for the power industry in historical years, obtain the direct consumption coefficients of various industries for the power industry in the target year.

[0048] In the specific implementation process, the direct consumption coefficient of each industry for the power industry is calculated by referring to the data provided in the "China Regional Input-Output Table", and the input-output tables of Fujian Province in 2002, 2007, 2012 and 2017 provided in the table are used as examples for explanation. Through the historical change trend of the direct consumption coefficient of each industry for the power industry in historical years, the forecast for subsequent years is realized based on the forecast model, and the direct consumption coefficient of each industry for the power industry from 2018 to 2024 is selected for forecast. These years have actual GDP data, and the forecast results can be verified to ensure the effectiveness of the method, so that the method of this application can be used for forecasts in future years. Specifically: Based on the direct consumption coefficient of each industry for the power industry in historical years, the direct consumption coefficient of each industry for the power industry in the target year is obtained, including:

[0049] Based on the linear change trend of the direct consumption coefficient of various industries to the power industry in historical years, the time series model is used to predict the direct consumption coefficient of various industries to the power industry in the target year.

[0050] In the specific implementation process, artificial intelligence models are used for prediction. Specifically, the coefficients of the corresponding time can be predicted according to the year sequence through the time series model, and the AR model can be constructed through the direct consumption coefficients of various industries. The optimal lag order p can be selected according to the AIC criterion, that is, based on the linear change trend of the direct consumption coefficients of various industries to the power industry in historical years, the direct consumption coefficients of various industries to the power industry in the target year can be predicted using the time series model, including:

[0051] Based on the linear change trend of the direct consumption coefficient of various industries to the power industry in historical years, the time series model is used and the target lag order is selected according to the Akaike information criterion to predict the direct consumption coefficient of various industries to the power industry in the target year.

[0052] In the specific implementation process, the AIC criterion is the Akaike information criterion, and the target lag order is the optimal lag order p, and the formula is as follows:

[0053]

[0054] Among them, t is the year, j is the industry, and p j is the optimal lag order for industry j, is the direct consumption coefficient of industry j in year t, is the autoregressive coefficient of lag i, After estimating the coefficients in the model through the data of industry j from 2002 to 2017, the direct consumption coefficients of industry j from 2018 to 2024 can be predicted through the time series model.

[0055] In one embodiment, before obtaining the direct consumption coefficient of each industry for the power industry in the target year based on the direct consumption coefficient of each industry for the power industry in the historical years, the method further includes:

[0056] The historical direct consumption coefficients are weighted averaged by year to obtain the direct consumption coefficients of various industries to the power industry in historical years.

[0057] In the specific implementation process, since the regional input-output is compiled every five years, the direct consumption coefficient of each year is a linear function of the year, that is, the linear change trend in the historical years. In the years without input-output table, the direct consumption coefficient of the industry to the power sector can be obtained according to the weighted average of the years. The calculation formula is as follows:

[0058]

[0059] in:

[0060] t=(2003,2004,…2016)

[0061] t l =(2002,2007,2012)

[0062] t r =(2007,2012,2017)

[0063] like:

[0064]

[0065] It means: the direct consumption coefficient in 2005 is "direct consumption coefficient in 2002 × (1-(2005-2002) / 5) + direct consumption coefficient in 2007 × (2005-2002) / 5". Based on the above method, the direct consumption coefficients of various industries from 2002 to 2017 can be predicted, which can reflect the historical trend of direct consumption coefficients of various industries in Fujian Province to the power industry. In other words, the direct consumption coefficient can be directly calculated or predicted based on the availability of data. For example, the data years provided in the "China Regional Input-Output Table" are calculated according to the following formula:

[0066]

[0067] Among them, X j is the direct consumption of the electricity sector by industry j; Value_add jis the added value of industry j, which is equal to the sum of the initial inputs of the industry; a j is the direct consumption coefficient of industry j to the power industry, reflecting the direct consumption of the unit value added of industry j to the power industry. Since the regional input-output is compiled every five years, there is no direct data for some years, so the prediction can be made according to the above method.

[0068] S20: Obtain industry added value based on industry electricity consumption data, regional electricity price data, and the direct consumption coefficient of each industry to the power industry in the target year.

[0069] In the specific implementation process, since the total industry output and the intermediate input of the power industry used in calculating the direct consumption coefficient are both measured in monetary terms, and the unit of industry electricity consumption is kilowatt-hour, it is necessary to multiply it by the electricity price (yuan / kilowatt-hour) to obtain the intermediate input of the power industry measured in monetary form. The intermediate input of the power industry measured in monetary form is divided by the direct consumption coefficient to obtain the scale of industry added value. The calculation formula is as follows:

[0070]

[0071] Among them, Elec j is the electricity consumption of the industry, which is predicted based on industry expansion data and other data; a j is the direct consumption coefficient of industry j for the power industry; P is the regional electricity price, which is calculated based on the actual electricity charges and electricity consumption of the month. Given that the regional input-output table only contains 42 major industries, it is assumed that the direct consumption coefficients of the subdivided industries are consistent with those of the major industries. According to the model prediction, the electricity consumption data of 72 industries are obtained. The GDP scale of the region can be obtained by summing up the added value predicted based on the industry electricity consumption. If it is assumed that the direct consumption coefficients of agriculture, forestry, animal husbandry and other industries in agriculture, forestry, animal husbandry and fishery are consistent with those of the power industry, a comparison table of the industry classification in the input-output table and the industry classification predicted for electricity consumption can be obtained, as shown in Table 1-1:

[0072] Table 1-1 Comparison table of input-output industry and electricity consumption forecast industry classification

[0073]

[0074]

[0075] S30: Obtain the industry value added at constant price based on the output value deflator and industry value added.

[0076] In the specific implementation process, in order to ensure that the estimated industry added value is comparable to the actual GDP output value, the actual GDP and the estimated industry added value are deflated to obtain constant price added value and constant price GDP. Specifically: Based on the output value deflation index and industry added value, before obtaining the constant price industry added value, the method also includes:

[0077] Convert the output value production index during the sample period to the output value index of the base year;

[0078] According to the output value index of the base year, the actual output value with the base year as the constant price is obtained;

[0079] The output deflator is obtained based on the nominal output value and the real output value at constant prices in the base year.

[0080] In the specific implementation process, taking the sample period of 2016-2024 as an example, with 2016 as the base year, first calculate the GDP index with 2016 as the constant price, set the GDP index of each quarter in 2016 (the same period of the previous year = 100) as 100, and convert the GDP production index of the remaining quarters in the sample period into the GDP index (constant price) with 2016 as the base year, that is, convert it into the output value index of the base year. The calculation formula is as follows:

[0081]

[0082] Among them, t is year, q is quarter, PI qt represents the GDP index in the qth quarter of the tth year at constant prices in 2016, m is the year from 2016 to t, LPI qm It is the GDP index in the q quarter of year m with the previous year as 100. For example, the GDP index in the third quarter of 2018 at constant prices in 2016 is:

[0083] PI 3,2018 =LPI 3,2016 ×LPI 3,2017 ×LPI 3,2018

[0084] Furthermore, the real GDP at constant prices in 2016 is calculated as follows:

[0085] Real_GDP q,t =Nominal_GDP q,2016 ×PI q,t

[0086] Among them, t is the year, q is the quarter, Real_GDP represents the real GDP at constant prices in 2016, and Nominal_GDP is the nominal GDP, also known as monetary GDP, which refers to the market value of all final products calculated at the sales prices of the goods and services produced in the current year. Then, the GDP deflator is calculated as follows:

[0087] Inflation qt =Nominal_GDP qt / Real_GDP qt

[0088] Finally, considering that the industrial producer price index is mainly the relative change of the producer's price cost and does not reflect the change of the inflation rate, the GDP deflator is used to calculate the constant price industry added value. The calculation formula is as follows:

[0089]

[0090] Among them, Value_add j In order to estimate the scale of industry added value, taking into account the changes in purchase prices and ex-factory prices in the industrial production process, this application adjusts the electricity price to the electricity price based on 2016 as the base period based on the industrial producer price index of "Electricity, heat production and supply industry (previous year price = 100)", and calculates the industry added value with 2016 as the unchanged electricity price.

[0091] S40: Obtain regional forecast output value based on industry value added at constant prices.

[0092] In the specific implementation process, based on the above steps, the constant price industry added value of each industry is obtained, that is, the adjusted predicted real GDP. The constant price industry added value of all the predicted regions is summed up to obtain the regional added value, that is, the regional predicted output value, to achieve the regional output value prediction based on the direct power consumption coefficient. That is: according to the constant price industry added value, the regional predicted output value is obtained, including:

[0093] The regional forecast output value is obtained by summing up the added value of the industry at constant prices.

[0094] In this embodiment, prediction is made based on the direct consumption coefficient of electricity. By using the direct consumption coefficient of each industry for the power industry in historical years, the coefficient of the predicted target year is calculated to measure the degree of dependence on the power industry in the industry production structure. Then, the coefficient is converted into a unit for measuring output value in combination with the industry electricity consumption data and the regional electricity price to obtain the scale of output value increased by the industry. Then, the output value is adjusted using the deflator to make it comparable with the actual output value to obtain the constant price industry added value. The regional output value is predicted by adding up the constant price industry added value of each industry. By using electricity-related indicators with accurate data and simple sources, a rapid and accurate prediction of the regional output value can be achieved, which can meet the actual changes in the output value, and the prediction result is scientific and reasonable.

[0095] In one embodiment, after obtaining the regional predicted output value based on the industry added value at constant price, the method further includes:

[0096] Compare the actual output value of a region with the predicted output value of the region to obtain the forecast evaluation results.

[0097] In the specific implementation process, in order to verify the scientificity and rationality of the forecasting method, the predicted regional output value is compared with the actual GDP. Taking quarterly accounting as an example, the predicted regional quarterly added value is compared with the actual quarterly GDP. Based on the predicted electricity consumption from January 2020 to December 2023 and the forecasting method of this application, the regional quarterly added value is predicted. In order to illustrate the accuracy of the forecasting method and verify whether the predicted year-on-year growth rate of the regional added value has the same trend as the actual GDP growth rate, the predicted regional quarterly added value and the actual quarterly GDP, as well as the year-on-year growth rate and the actual GDP growth rate trend are compared and analyzed.

[0098] As attached Figure 3 The figure shows the comparison between the predicted regional quarterly value added and the actual quarterly GDP. It can be seen that the actual GDP has a certain periodicity, which is manifested in a slight increase in GDP from the first quarter to the third quarter, and the highest fluctuation trend of GDP in the fourth quarter. The predicted regional value added in the same period also shows the same trend of change.

[0099] As attached Figure 4 Shown is a schematic diagram comparing the predicted regional quarterly value added and the actual quarterly GDP growth rate. It can be seen that the estimated year-on-year growth rate of the regional quarterly value added has the same trend as the actual GDP growth rate, and the estimated year-on-year growth rate of the regional quarterly value added in 2021 is basically close to the actual GDP year-on-year growth rate, which verifies the feasibility of this application to predict regional industry added value based on industry electricity consumption coefficient, electricity consumption and electricity price data, and at the same time shows that electricity consumption data can effectively predict economic trends.

[0100] The predicted regional quarterly added value is summed up to obtain the regional annual added value, which is then compared with the annual actual GDP for analysis, as shown in Table 1-2:

[0101] Table 1-2 Comparison of predicted regional annual value added and actual GDP

[0102]

[0103] It can be seen that the annual regional added value obtained based on the forecasting method of this application is slightly lower than the annual actual GDP scale, but is relatively close to the annual actual GDP. The average difference between the estimated annual regional added value and the annual actual GDP from 2020 to 2023 is less than 2%. The forecasted regional added value in 2023 is slightly higher than the actual GDP, but the difference is less than 3%. The above results further illustrate the scientificity and rationality of the forecasting method of this application.

[0104] See attached Figure 5 Based on the same inventive concept as in the above-mentioned embodiment, the embodiment of the present application further provides a regional output value prediction device, comprising:

[0105] A first acquisition module, the first acquisition module is used to obtain the direct consumption coefficient of each industry to the power industry in the target year based on the direct consumption coefficient of each industry to the power industry in the historical years;

[0106] The second acquisition module is used to obtain the industry added value based on the industry electricity consumption data, regional electricity price data and the direct consumption coefficient of each industry to the power industry in the target year;

[0107] The third acquisition module is used to obtain the constant price industry added value according to the output value deflator and the industry added value;

[0108] The target acquisition module is used to obtain the regional predicted output value based on the industry added value at constant prices.

[0109] Those skilled in the art should understand that the division of the various modules in the embodiment is merely a division of logical functions, and in actual application, all or part of them can be integrated into one or more actual carriers, and these modules can be implemented entirely in the form of software called by a processing unit, or entirely in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in the regional output value prediction device in this embodiment corresponds one-to-one to each step in the regional output value prediction method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned regional output value prediction method, which will not be repeated here.

[0110] Based on the same inventive concept as in the aforementioned embodiment, the embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the regional output value prediction method provided in the embodiment of the present application is implemented.

[0111] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein:

[0112] The memory is used to store computer programs;

[0113] The processor is used to load and execute a computer program so that the electronic device executes the regional output value prediction method provided in the embodiment of the present application.

[0114] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0115] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0116] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0117] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0118] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0119] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0121] In summary, the present application provides a method, device, storage medium and electronic device for predicting regional output value, which method includes: based on the direct consumption coefficient of each industry for the power industry in historical years, obtaining the direct consumption coefficient of each industry for the power industry in the target year; obtaining the industry added value according to the industry electricity consumption data, regional electricity price data and the direct consumption coefficient of each industry for the power industry in the target year; obtaining the constant price industry added value according to the output value deflator and the industry added value; obtaining the regional predicted output value according to the constant price industry added value. This application makes predictions based on the direct consumption coefficient of electricity. By using the direct consumption coefficient of each industry for the electricity industry in historical years, the coefficient of the predicted target year is calculated to measure the degree of dependence on the electricity industry in the industry production structure. Then, the coefficient is converted into a unit of measurement of output value in combination with the industry electricity consumption data and the regional electricity price to obtain the scale of output value increased by the industry. Then, the output value is adjusted using the deflator to make it comparable with the actual output value to obtain the constant price industry added value. The regional output value can be predicted by adding up the constant price industry added value of each industry predicted. By using electricity-related indicators with accurate data and simple sources, a rapid and accurate prediction of regional output value can be achieved, which can meet the actual changes in output value, and the prediction results are scientific and reasonable.

[0122] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for predicting regional output value, characterized in that: The following steps are involved: Based on the direct consumption coefficients of various industries on the power industry in historical years, obtain the direct consumption coefficients of various industries on the power industry in the target year; Obtain industry added value based on industry electricity consumption data, regional electricity price data, and the direct consumption coefficient of each industry to the power industry in the target year; According to the output deflator and the added value of the industry, the added value of the industry at constant price is obtained; Based on the industry value added at constant prices, the regional forecast output value is obtained.

2. The method for predicting regional output value according to claim 1, characterized in that: The direct consumption coefficient of each industry on the power industry based on the historical years is used to obtain the direct consumption coefficient of each industry on the power industry in the target year, including: Based on the linear change trend of the direct consumption coefficient of various industries to the power industry in historical years, the time series model is used to predict the direct consumption coefficient of various industries to the power industry in the target year.

3. The method for predicting regional output value according to claim 2, characterized in that: The linear change trend of the direct consumption coefficient of each industry to the power industry in the historical years is used to predict the direct consumption coefficient of each industry to the power industry in the target year using a time series model, including: Based on the linear change trend of the direct consumption coefficient of various industries to the power industry in historical years, the time series model is used and the target lag order is selected according to the Akaike information criterion to predict the direct consumption coefficient of various industries to the power industry in the target year.

4. The method for predicting regional output value according to claim 1, characterized in that: Before obtaining the direct consumption coefficients of the electric power industry by various industries in the target year based on the direct consumption coefficients of the electric power industry by various industries in the historical years, the method further comprises: The historical direct consumption coefficients are weighted averaged by year to obtain the direct consumption coefficients of various industries to the power industry in the historical years.

5. The method for predicting regional output value according to claim 1, characterized in that: Before obtaining the constant price industry value added according to the output value deflator and the industry value added, the method further comprises: Convert the output value production index during the sample period to the output value index of the base year; According to the output value index of the base year, the actual output value at the base year as constant price is obtained; The output deflator is obtained based on the nominal output value and the real output value at constant prices in the base year.

6. The method for predicting regional output value according to claim 1, characterized in that: The regional forecast output value is obtained based on the constant price industry added value, including: The regional forecast output value is obtained based on the sum of the added value of the industries at constant prices.

7. The method for predicting regional output value according to claim 1, characterized in that: After obtaining the regional predicted output value according to the constant price industry added value, the method further includes: Compare the actual output value of a region with the predicted output value of the region to obtain a forecast evaluation result.

8. A regional output value prediction device, characterized in that: include: A first obtaining module, the first obtaining module is used to obtain the direct consumption coefficient of each industry to the power industry in the target year based on the direct consumption coefficient of each industry to the power industry in historical years; A second acquisition module, the second acquisition module is used to obtain industry added value based on industry electricity consumption data, regional electricity price data and the direct consumption coefficient of each industry to the power industry in the target year; A third obtaining module, the third obtaining module is used to obtain constant price industry added value according to the output value deflator and the industry added value; A target acquisition module is used to obtain the regional predicted output value based on the constant price industry added value.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by the processor, the method for predicting regional output value according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device executes the regional output value prediction method according to any one of claims 1 to 7.

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