Regional industry gravity center analysis method based on electric quantity association

Abstract

At present, a traditional economics method is mostly adopted for industry gravity center analysis, on one hand, economic digits directly related to currency are easily influenced by factors such as expansion, exchange rate fluctuation and statistical calibers, and on the other hand, the economic digits may be tampered and pinched by illegal persons to cause interference on statistical results, however, the economic digits cannot be tampered and pinched by the illegal persons. If only the electricity consumption of the industry is adopted to measure the economic status of the industry in the region, serious overestimation is generated for the high-energy-consumption and low-added-value industry, and serious underestimation is generated for the low-energy-consumption and high-added-value industry, the regional industry gravity center analysis method based on the electricity consumption association has the advantages of electricity consumption objectivity and timeliness, and meanwhile, the regional industry gravity center analysis efficiency is improved. The problem that the electricity consumption is not matched with the output value is avoided.

Description

Technical Field

[0001] The present invention provides a method for analyzing the center of gravity of regional industries based on electricity quantity correlation, which realizes that within a specified area and a specified time period, if the electricity quantity curve of a certain industry has more correlation relationships with the electricity quantity curves of other industries and the stronger the correlation strength is, the more important the position of the industry in the area and the time period will be. Background Art

[0002] The electricity consumption of industry users can directly reflect their production and operation conditions. At the same time, the correlation between industry electricity consumption data is an energy projection of the production and economic correlation between industries. By analyzing the correlation between electricity consumption in the region, parsing the economic correlation characteristics of regional industries, and then obtaining the regional industry center of gravity, it can provide energy reference and effect feedback for the formulation and implementation of government industrial economic strategies. This is also a means to fully tap the social and economic information carried by electricity consumption data and release the production factor value of power and electricity data. Summary of the invention

[0003] The present invention is implemented by adopting the following technical solutions:

[0004] A. Collect and organize industry electricity data;

[0005] B. Calculation of industry electricity correlation;

[0006] C. Draw industry association diagrams and analyze regional industry focus.

[0007] The step A specifically comprises the following steps:

[0008] A1: Determination of electricity consumption charges and industry labels for users in designated areas;

[0009] A2: Sum the power consumption of users in the same industry on a daily basis to obtain the daily power consumption sequence of each industry;

[0010] The step B specifically comprises the following steps:

[0011] B1: Calculate the distance between two industries’ electricity consumption and quantitatively describe the correlation between industry electricity consumption;

[0012] B2: Screen industry correlations based on distance thresholds and cut off weak correlations;

[0013] B3: Using the results of B2, calculate the centrality of each industry based on the industry electricity correlation

[0014] B4: Sort the centrality of each industry obtained in B3 and extract the regional industry center of gravity.

[0015] The step C specifically comprises the following steps:

[0016] C1: Based on the results of B3 and B4, draw a correlation diagram of industry centers of gravity;

[0017] C2: Analyze the C1 graph to obtain the industry association characteristics of the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Flowchart of regional industry center of gravity analysis based on electricity correlation DETAILED DESCRIPTION

[0019] like Figure 1 Step 1: For the designated research area, collect the daily electricity consumption data of users for more than one year. For non-residential users, determine the industry label of each user according to the "2017 National Economic Industry Classification (GB / T 4754-2017)". The industry classification granularity is not limited, but it should be guaranteed to be at the same level (such as category, major category, medium category, etc.).

[0020] Step 2: Sum the power consumption data of users with the same industry label on the same date to form the daily power consumption sequence of each industry [Q i，t ], where Q represents electricity consumption, i represents the industry serial number, and t represents the date.

[0021] Step 3: Calculate the electricity distance between industries i and j where ρ ij For [Q i，t ] and [Q j，t ] is the Pearson correlation coefficient between the two. The higher the distance value, the weaker the correlation. When the distance exceeds a certain threshold d c (The default value is 0.7, which can be adjusted according to the calculation results), and it is believed that there is no significant correlation between the two industries.

[0022] Step 4: Use Pagerank algorithm to calculate the centrality of each industry, R i .

[0023] Step 5: To consider the influencing factors of electricity consumption scale, calculate the electricity weight factor of each industry (i.e. the ratio of the logarithm of the total electricity consumption of the industry to the logarithm of the total electricity consumption of the region) according to the following formula:

[0024]

[0025] Multiply this factor by the industry centrality to obtain the modified centrality of the industry, i.e. R i ＊ =R i *r i .

[0026] Step 6: Sort the corrected industry centralities from large to small to obtain the results of the regional industry electricity center of gravity analysis.

[0027] Step 7: Select the top N industry nodes (default value is 5, which can be adjusted according to the calculation results) in step 6 as the center of gravity industry nodes. In the results of step 3, select the industry nodes directly related to the center of gravity industry (excluding the center of gravity industry node) as the first-order connected industry nodes. Furthermore, in the results of step 3, select the industry nodes directly related to the first-order connected industry nodes (excluding the center of gravity industry node and the first-order connected industry node) as the second-order connected industry nodes.

[0028] Step 8: Draw the industry center of gravity correlation diagram. You can use the python open source toolkit networkx to draw it. First draw the center of gravity industry node, then draw the first-order connection industry node on its periphery according to the node association situation, and finally draw the second-order connection industry node on the periphery of the first-order connection industry node. Different sizes can be used to represent different levels of nodes, and different colors can be used to represent different industry classifications.

[0029] Step 9: Analyze the industry correlation characteristics of the region based on the characteristics of the industry center of gravity correlation diagram.

Claims

1. A regional industry center of gravity analysis method based on electricity correlation, the characteristics of which provide An analysis method based on the correlation between electricity data among industries is used to analyze the dependence of various industries in a specified area and then obtain the industry center of gravity of the area.

2. The method according to claim 1, characterized in that An analysis method based on the correlation between electricity data among industries is provided to analyze the dependence of various industries in a specified area and then obtain the industry center of gravity of the area.

3. The method according to claim 1, characterized in that Measuring the importance of industry electricity by the strength of the industry electricity correlation instead of the industry electricity consumption volume can avoid the problem of overestimating high-energy consumption and low-value-added industries and underestimating low-energy consumption and high-value-added industries. It can relatively objectively reflect the economic status of the industry in the region and provide energy support for the government to formulate industrial development strategies.

4. The method according to claim 1, characterized in that By comparing the topological distribution in different periods, we can reflect the changes in industry associations and provide energy feedback for the implementation effect of the government's industrial policies.

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