CN115544336A

CN115544336A - Industrial chain generation method and device, computer equipment and storage medium

Info

Publication number: CN115544336A
Application number: CN202211529390.2A
Authority: CN
Inventors: 龚起航; 周尚礼; 郑楷洪; 李胜; 刘玉仙; 曾璐琨
Original assignee: Southern Power Grid Digital Grid Research Institute Co Ltd
Current assignee: Southern Power Grid Digital Grid Research Institute Co Ltd
Priority date: 2022-12-01
Filing date: 2022-12-01
Publication date: 2022-12-30

Abstract

The application relates to an industrial chain generation method, an industrial chain generation device, computer equipment and a storage medium. The method comprises the following steps: determining data association degree between each target industry according to industrial electric quantity data and industrial production data of each target industry; determining the name association degree among the target industries according to the distance among the name information of the target industries; determining the relationship association degree among the target industries according to the entity relationship information in the industry association information of the target industries; and generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relation association degree among the target industries. By adopting the method, the timeliness of the construction of the industrial chain can be improved.

Description

Industrial chain generation method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for generating an industrial chain.

Background

The upstream and downstream of the industrial chain can reflect the incidence relation among different industries, and a research basis is provided for guiding macroscopic economic production, guiding the development of the industries and the like.

At present, the existing industrial chain is mainly established by manually establishing a visual industrial chain, however, the manual industrial chain establishing method needs to establish the industrial chain according to expert experience, and often needs to spend a lot of time, so that the method has the problem of poor timeliness.

Disclosure of Invention

In view of the above, it is desirable to provide an industrial chain generation method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product, which can improve timeliness of industrial chain construction.

In a first aspect, the present application provides a method for generating an industry chain. The method comprises the following steps:

determining data association degree between each target industry according to industrial electric quantity data and industrial production data of each target industry;

determining the name association degree among the target industries according to the distance among the name information of the target industries;

determining the relationship association degree among the target industries according to the entity relationship information in the industry association information of the target industries;

and generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relationship association degree among the target industries.

In one embodiment, determining a data association degree between each target industry according to industry power data and industry production data of each target industry comprises:

obtaining historical electric quantity growth rate of each target industry according to the industrial electric quantity data of each target industry;

obtaining historical production growth rate of each target industry according to the industrial production data of each target industry;

clustering each target industry according to the historical electric quantity growth rate and the historical production growth rate to obtain an industry classification result of each target industry;

and obtaining the data association degree among the target industries according to the industry classification result of each target industry.

In one embodiment, determining the name association degree between the target industries according to the distance between the name information of the target industries includes:

under the condition that the name information of a first target industry is detected to be different from the name information of a second target industry, acquiring the number of times of text updating required for updating the name information of the first target industry into the name information of the second target industry; the first target industry is any one of the target industries; the second target industry is any one of the target industries except the first target industry;

obtaining the distance between the name information of the first target industry and the name information of the second target industry according to the text updating times;

and obtaining the name association degree between the first target industry and the second target industry according to the distance, wherein the name association degree is used as the name association degree between the target industries.

In one embodiment, determining the relationship association degree between the target industries according to the entity relationship information in the industry association information of the target industries includes:

performing entity relationship extraction processing on the industry associated information of each target industry to obtain entity relationship information in the industry associated information;

according to the upstream and downstream relation among industries in an industry relation dictionary library, performing upstream probability judgment and downstream probability judgment on the target industry in the entity relation information to obtain the upstream probability and the downstream probability of the target industry in the entity relation information;

and obtaining the relationship association degree between the target industries according to the upstream probability and the downstream probability of the target industries in the entity relationship information.

In one embodiment, generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree, and the relationship association degree among the target industries includes:

determining an industry association probability among the target industries according to the data association degree, the name association degree and the relationship association degree among the target industries;

determining an upstream industry and a downstream industry corresponding to each target industry according to the upstream probability, the downstream probability and the industry association probability;

and carrying out visual graphic processing on the upstream industry and the downstream industry corresponding to each target industry to obtain the industry chain map.

In one embodiment, after generating the industry chain map corresponding to each target industry according to the data association degree, the name association degree, and the relationship association degree between each target industry, the method further includes:

acquiring current electric quantity data of each target industry in the industry chain map in the current time period;

obtaining the current electric quantity growth rate of each target industry according to the current electric quantity data of each target industry;

performing a same-ratio evaluation and a ring-ratio evaluation on the current electric quantity growth rate and the historical electric quantity growth rate of each target industry to obtain an evaluation result of each target industry;

and under the condition that the evaluation result is abnormal, performing upstream and downstream updating on the target industry corresponding to the evaluation result in the industry chain map to obtain the industry chain map of the current time period.

In a second aspect, the present application further provides an industrial chain generating device. The device comprises:

the first association degree determining module is used for determining the data association degree between each target industry according to the industry electric quantity data and the industry production data of each target industry;

the second association degree determining module is used for determining the name association degree between the target industries according to the distance between the name information of the target industries;

a third association degree determining module, configured to determine, according to entity relationship information in the industry association information of each target industry, a relationship association degree between the target industries;

and the industry chain map generation module is used for generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relation association degree among the target industries.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:

and generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relation association degree among the target industries.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:

According to the industrial chain generation method, the industrial chain generation device, the computer equipment, the storage medium and the computer program product, the data association degree between the target industries is determined according to the industrial electric quantity data and the industrial production data of the target industries; determining the name association degree among the target industries according to the distance among the name information of the target industries; determining the relationship association degree among the target industries according to the entity relationship information in the industry association information of the target industries; and generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relation association degree among the target industries. By adopting the method, the data association degree between the target industries is determined through the industrial electricity quantity data and the industrial production data of the target industries, the relation association degree between the target industries is determined through the industrial association information of the target industries, and the industrial chain map is comprehensively generated by combining the name association degree between the target industries, so that the problem of single data dimension of the industrial chain constructed in the traditional technology is solved, the generated industrial chain map can more comprehensively show the association of the target industries on various factors such as economic factors, production factors and text factors, the industrial chain map is not required to be established by depending on artificial experience, and the timeliness of the generated industrial chain map is effectively improved.

Drawings

FIG. 1 is a schematic flow chart diagram of a method for generating an industry chain in one embodiment;

FIG. 2 is a schematic diagram of an embodiment of an industry chain map;

FIG. 3 is a flowchart illustrating the step of obtaining a spectrum of an industry chain map for a current time period in one embodiment;

FIG. 4 is a schematic flow chart diagram of a method for generating an industry chain in another embodiment;

FIG. 5 is a block diagram of an embodiment of an industrial chain generation apparatus;

FIG. 6 is a diagram of the internal structure of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In an embodiment, as shown in fig. 1, an industrial chain generating method is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented by interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices. In this embodiment, the method includes the steps of:

and step S101, determining the data association degree among the target industries according to the industrial electric quantity data and the industrial production data of the target industries.

The industrial electric quantity data is data describing the electricity utilization condition of a target industry; for example, industry electricity quantity data includes, but is not limited to, real-time electricity usage and past year electricity usage for a target industry; the industrial electricity quantity data is used for reflecting the first-line production factor of the target industry. Industrial production data refers to data describing the production economics of the target industry; for example, industry production data includes, but is not limited to, total production values for the target industry; the industrial production data reflects the economic factors of the target industry.

The data association degree is an index for measuring the association degree between each two target industries in terms of production factors and economic factors. The target industry refers to an industry for constructing an industry chain map; the number of target industries is at least two.

Specifically, if a user needs to analyze the association relationship between different industries and can send an industry chain graph spectrum viewing request to the terminal, the terminal can obtain the industry power consumption data of one or more target industries associated with the industry chain graph spectrum viewing request from the metering automation system of the metering power grid platform. Under the condition that the obtained industrial electricity data is in a structured database table form, the terminal can perform data cleaning and data verification processing on the industrial electricity data to obtain the processed industrial electricity data. The terminal can also perform crawler processing on a statistical website recorded with industrial production data so as to acquire industrial production data of at least two target industries from the statistical website; and under the condition that the obtained industrial production data is in a semi-structured form, converting the semi-structured industrial production data into structured industrial production data, and then carrying out data cleaning and data verification processing on the structured industrial production data to obtain the processed industrial production data. And then the terminal clusters at least two target industries according to the processed industry electricity data and the processed industry production data of each target industry to obtain industry classification results of the at least two target industries, and determines the data association degree between every two target industries according to the distance between the at least two target industries in the industry classification results or within the class.

And step S102, determining the name association degree among the target industries according to the distance among the name information of the target industries.

The name information is an industry name of a target industry. The name association degree is an index for measuring the association degree between two target industries in terms of name.

Specifically, the terminal calculates and obtains the distance between the name information of each target industry according to the name information of each target industry. The smaller the distance between the name information of each target industry is, the greater the name association degree between each target industry is; conversely, the greater the distance between the name information of the respective target industries, the smaller the degree of name association between the respective target industries.

Step S103, determining the relationship association degree among the target industries according to the entity relationship information in the industry association information of the target industries.

The industry related information is text data which may include the upstream and downstream relation of the industry. Entity relationship information refers to information that describes upstream and downstream relationships between entities (e.g., between two industries). The relationship association degree refers to an index of the degree of association between two target industries in terms of upstream and downstream relationships in the industry chain.

Specifically, the terminal can respectively extract texts from texts such as industry annual reports, news reports, policy guidelines and the like associated with each target industry to obtain industry associated information of each target industry; then the terminal performs entity relation extraction processing on the industry associated information of each target industry to obtain entity relation information in the industry associated information; and obtaining the relationship association degree between the target industries according to the upstream probability and the downstream probability among the entity relationship information.

And step S104, generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relation association degree among the target industries.

The industry chain map refers to graphic data for visually displaying the upstream and downstream of the industry chain.

Specifically, determining the industry association degree among all industries according to the data association degree, the name association degree and the relationship association degree among all target industries of the terminal; the industry association degree refers to an index which integrates the data association degree, the name association degree and the relationship association degree and is used for comprehensively evaluating the association degree among different industries. And the terminal determines the upstream industry and the downstream industry corresponding to each target industry according to the industry association degree between each industry, and then performs visualization processing on the upstream industry and the downstream industry corresponding to each target industry to obtain the industry chain map corresponding to the industry chain map viewing request in the step S101. After the terminal generates the industrial chain map, the industrial chain map can be displayed so that a user can check the industrial chain map in real time.

In the industrial chain generation method, the data association degree between the target industries is determined according to the industrial electric quantity data and the industrial production data of the target industries; determining the name association degree between the target industries according to the distance between the name information of the target industries; determining the relationship association degree among the target industries according to the entity relationship information in the industry association information of the target industries; and generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relation association degree among the target industries. By adopting the method, the data association degree between the target industries is determined through the industrial electricity quantity data and the industrial production data of the target industries, the relation association degree between the target industries is determined through the industrial association information of the target industries, and the industrial chain map is comprehensively generated by combining the name association degree between the target industries, so that the problem of single data dimension of the industrial chain constructed in the traditional technology is solved, the generated industrial chain map can more comprehensively show the association between the target industries on various factors such as economic factors and production factors, the industrial chain map is not required to be established by depending on artificial experience, and the timeliness of the generated industrial chain map is effectively improved.

In an embodiment, in the step S101, determining the data association degree between the target industries according to the industry electric quantity data and the industry production data of the target industries specifically includes the following steps: obtaining historical electric quantity growth rate of each target industry according to the industrial electric quantity data of each target industry; obtaining the historical production growth rate of each target industry according to the industrial production data of each target industry; clustering each target industry according to the historical electric quantity growth rate and the historical production growth rate to obtain an industry classification result of each target industry; and obtaining the data association degree among the target industries according to the industry classification result of each target industry.

The historical electricity quantity growth rate refers to data describing the growth condition of electricity quantity data of a target industry in a historical time period; the historical charge increase rate includes, but is not limited to, a historical charge cycle rate increase rate and a historical charge parity rate increase rate. The historical production growth rate refers to data describing the growth condition of production data of a target industry in a historical time period; the historical production growth rate includes, but is not limited to, a historical production ring ratio growth rate and a historical production peer ratio growth rate.

Specifically, the terminal obtains a historical electric quantity ring ratio growth rate and a historical electric quantity same-ratio growth rate of each target industry according to historical industrial electric quantity data of each target industry; obtaining the historical production ring ratio growth rate and the historical production equal ratio growth rate of each target industry according to the historical industry production data of each target industry; and then the terminal performs clustering processing on the historical electric quantity ring ratio increase rate, the historical electric quantity same-ratio increase rate, the historical production ring ratio increase rate and the historical production same-ratio increase rate of each target industry, wherein the K-means clustering processing can be performed on the historical electric quantity ring ratio increase rate, the historical electric quantity same-ratio increase rate, the historical production ring ratio increase rate and the historical production same-ratio increase rate of each target industry, and then the terminal obtains an industry classification result of each target industry. The terminal obtains the data association degree between each target industry according to the industry classification result of each target industry, and the data association degree can be set for each target industry at first, then the initial data association degree between every two target industries in the same industry classification is increased, the initial data association degree between every two target industries in different industry classifications is reduced, and the updated data association degree between every two target industries is obtained; and then, acquiring the distance between every two target industries in the same industry classification, and increasing the updated data association degree between every two target industries in the same industry classification according to the sequence of the distances from large to small to obtain the data association degree between every two target industries, namely the smaller the distance between every two target industries in the same industry classification is, the larger the data association degree between every two target industries is.

In the embodiment, the historical electric quantity growth rate and the historical production growth rate of each target industry are clustered to obtain the industry classification result of each target industry; and then obtaining data association degrees among the target industries according to the industry classification results of the target industries, analyzing the data association degrees among the target industries by combining the historical electric quantity growth rate and the historical production growth rate of the target industries, and mining rules of industries upstream and downstream of an industry chain in the aspects of electric quantity and production, so that the generated industry chain diagram can show the association among the target industries on various factors such as economic factors and production factors more comprehensively and more finely.

In an embodiment, in step S102, the name association degree between the target industries is determined according to the distance between the name information of each target industry, and the method specifically includes the following steps: under the condition that the name information of the first target industry is detected to be different from the name information of the second target industry, acquiring the text updating times required for updating the name information of the first target industry into the name information of the second target industry; the first target industry is any one of the target industries; the second target industry is any one of the target industries except the first target industry; obtaining the distance between the name information of the first target industry and the name information of the second target industry according to the text updating times; and obtaining the name association degree between the first target industry and the second target industry according to the distance, wherein the name association degree is used as the name association degree between the target industries.

The text updating times are the times required for updating the name information of the first target industry according to the name information of the second target industry, and the updating times are the minimum times.

Specifically, the terminal takes any one of all target industries as a first target industry, and simultaneously takes any one of all target industries except the first target industry as a second target industry; further detecting whether the obtained industry name of the first target industry is the same as the obtained industry name of the second target industry; when detecting that the name information of the first target industry is different from the name information of the second target industry, the terminal performs character conversion on characters, which are different from the name information of the second target industry, in the name information of the first target industry according to each character in the name information of the second target industry, wherein characters, which are the same as the name information of the second target industry, in the name information of the first target industry do not need to be subjected to character conversion, the number of character conversion times required for completely updating the name information of the first target industry to the minimum name information of the second target industry is obtained, and the number of character conversion times is used as the number of text updating times.

Further, the terminal increases the initial distance between the name information of the first target industry and the name information of the second target industry in the order of the number of times of text update from small to large to obtain the distance between the name information of the first target industry and the name information of the second target industry, that is, the smaller the number of times of text update between the name information of the first target industry and the name information of the second target industry, the closer the distance between the name information of the first target industry and the name information of the second target industry. The terminal increases the initial name association degree between the first target industry and the second target industry in the order from large to small according to the distance between the name information of the first target industry and the name information of the second target industry to obtain the name association degree between the first target industry and the second target industry, namely, the smaller the distance between the name information of the first target industry and the name information of the second target industry is, the larger the name association degree between the first target industry and the second target industry is, and the name association degree between the target industries is.

For example, suppose that the name information of the target industry a is the ferrous metal ore mining and sorting industry, the name information of the target industry B is the ferrous metal smelting and rolling processing industry, and the name information of the target industry C is the thermal power production and supply industry; if the name information of the target industry A is converted into the name information of the target industry B, the 'mining and mining' of the target industry A needs to be converted into 'smelting and calendaring', and at least 7 characters need to be converted, so that the distance between the name information of the target industry B and the name information of the target industry A is 7; if the name information of the destination industry C is converted into the name information of the destination industry B, it is necessary to convert "thermal production and supply" in the destination industry C into "ferrous metal smelting and rolling process", and it is necessary to perform character conversion on at least 11 characters, and therefore, the distance between the name information of the destination industry B and the name information of the destination industry C is 11, and the name association degree between the destination industry B and the destination industry a is greater than the name association degree between the destination industry B and the destination industry C.

In this embodiment, in a case where it is detected that name information of a first target industry is different from name information of a second target industry, acquiring a minimum number of text update times required to update the name information of the first target industry to the name information of the second target industry; further, according to the number of times of text updating, the distance between the name information of the first target industry and the name information of the second target industry is obtained; according to the distance, the name association degree between the first target industry and the second target industry is obtained and is used as the name association degree between the target industries, and the name association degree between the target industries is obtained according to the characteristic that similar industries are similar in name, so that the name association degree can be used for determining the industry association degree between the target industries subsequently, and the accuracy of the generated industry chain map is improved.

In an embodiment, in step S103, determining a relationship association degree between the target industries according to entity relationship information in the industry association information of each target industry, specifically including the following contents: extracting entity relationship from the industry associated information of each target industry to obtain entity relationship information in the industry associated information; according to the upstream and downstream relation between industries in the industry relation dictionary base, performing upstream probability judgment and downstream probability judgment on a target industry in the entity relation information to obtain the upstream probability and the downstream probability of the target industry in the entity relation information; and obtaining the relationship association degree between the target industries according to the upstream probability and the downstream probability of the target industries in the entity relationship information.

The industry relation dictionary base is a dictionary base constructed according to the upstream and downstream relations among conventional industries. Upstream probability refers to the probability that an industry is located upstream in the industry chain. Downstream probability refers to the probability that an industry is located downstream in the industry chain.

Specifically, the terminal performs entity extraction processing on the industry associated information of each target industry to obtain entity industry information in each industry associated information; and then the terminal performs entity relationship extraction processing on the entity industry information, wherein the entity relationship extraction processing can be performed on the entity industry information based on rules, and then the terminal obtains the entity relationship information. The terminal judges the upstream probability of the target industry in the entity relation information according to the upstream and downstream relation between the industries in the industry relation dictionary library to obtain the upstream probability of the target industry in the entity relation information, and judges the downstream probability of the target industry in the entity relation information to obtain the downstream probability of the target industry in the entity relation information; according to the upstream probability and the downstream probability of the target industry in the entity relationship information, the upstream probability and the downstream probability of two target industries in the entity relationship information can be integrated to obtain the relationship association degree between every two target industries.

For example, suppose that the obtained industry-related information "news report indonesia 2021 exports 4 hundred million tons of coal all year round, of which more than 1.8 million tons are sold to china, accounting for more than 61% of the total export amount and accounting for 75% of the total imported coal in china. Events mainly relate to the correlated coal mining and washing industries, so that the energy consumption index is increased by 234 MW/ten thousand yuan during 1-3 months, which is 3% of the same percentage; and performing entity extraction processing on the industry associated information to obtain entity industry information of coal mining and washing industry and industry power and heating power production and supply industry, wherein the entity relationship exists between the coal mining and washing industry and the industry power and heating power production and supply industry, and performing upstream probability judgment and downstream probability judgment on the coal mining and washing industry and the industry power and heating power production and supply industry according to the upstream and downstream relationship between the industries in the industry relationship dictionary library to obtain that the downstream probability of the industry power and heating power production and supply industry is higher than that of the coal mining and washing industry, and simultaneously the upstream probability of the coal mining and washing industry is higher than that of the industry power and heating power production and supply industry, namely the industry power and heating power production and supply industry is considered to have higher probability as the downstream of the coal mining and washing industry.

In this embodiment, according to the upstream-downstream relationship between industries in the industry relationship dictionary library, the upstream probability and the downstream probability of a target industry in entity relationship information extracted from the industry association information of each target industry are determined by performing upstream probability determination and downstream probability determination on the target industry in the entity relationship information; and furthermore, the relationship association degree between each target industry is obtained according to the upstream probability and the downstream probability of the target industry in the entity relationship information, and the upstream and downstream relationship of each target industry is judged without the need of personnel such as experts by means of manual experience, so that the relationship association degree between each industry can be scientifically extracted from the industry association information, and the generation efficiency of an industry chain map is effectively improved.

In an embodiment, in step S104, an industry chain map corresponding to each target industry is generated according to the data association degree, the name association degree, and the relationship association degree between each target industry, and specifically includes the following contents: determining the industry association probability among the target industries according to the data association degree, the name association degree and the relationship association degree among the target industries; determining upstream and downstream industries corresponding to each target industry according to the upstream probability, the downstream probability and the industry association probability; and carrying out visual graphic processing on upstream and downstream industries corresponding to each target industry to obtain an industry chain map.

Wherein, the industry association probability refers to the probability that the association exists between two target industries.

Specifically, the terminal integrates the data association degree, the name association degree and the relationship association degree among the target industries, may weight the data association degree, the name association degree and the relationship association degree among the target industries, and may determine the industry association probability among the target industries of the terminal only according to any one unilateral association degree among the data association degree, the name association degree and the relationship association degree; the terminal carries out matrix processing on the industry association probability, the upstream probability and the downstream probability among all target industries to obtain an association degree matrix; determining upstream and downstream industries corresponding to each target industry according to the relevancy matrix; performing visual graph processing on upstream and downstream industries corresponding to each target industry, wherein a graph database is used for constructing visual nodes of each target industry, and the visual nodes are connected according to the upstream and downstream industries corresponding to each target industry to obtain an industry chain map; for example, a neo4j (a kind of NOSQL graph database) is used to construct a visualization node of each target industry, and the visualization nodes are connected according to upstream and downstream industries corresponding to each target industry to obtain an industry chain graph, where a schematic diagram of the industry chain graph is shown in fig. 2.

In the embodiment, the industry association probability among the target industries is determined through the data association degree, the name association degree and the relationship association degree among the target industries; determining an upstream industry and a downstream industry corresponding to each target industry according to the upstream probability, the downstream probability and the industry association probability; the upstream and downstream industries corresponding to each target industry are subjected to visual graphic processing to obtain the industry chain map, so that the obtained industry chain map can reflect the data association degree, the name association degree and the relation association degree among the target industries, the relation surface of the obtained industry chain map is improved, the industry chain map is not required to be established by virtue of manual experience, and the generation efficiency of the industry chain map is effectively improved.

In one embodiment, as shown in fig. 3, after generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree, and the relationship association degree between each target industry, the method further includes:

step S301, obtaining current electric quantity data of each target industry in the industry chain map in the current time period.

Step S302, obtaining the current electric quantity growth rate of each target industry according to the current electric quantity data of each target industry.

Step S303, carrying out same-ratio evaluation and ring-ratio evaluation on the current electric quantity growth rate and the historical electric quantity growth rate of each target industry to obtain the evaluation result of each target industry.

And step S304, under the condition that the evaluation result is abnormal, updating the upstream and downstream of the target industry corresponding to the evaluation result in the industry chain map to obtain the industry chain map of the current time period.

Wherein, the current electricity quantity growth rate refers to data describing the growth condition of the electricity quantity data of the target industry in the historical time period. The current charge rate of increase includes, but is not limited to, a current charge-to-loop ratio increase rate and a current charge-to-loop ratio increase rate.

Specifically, after the terminal generates and obtains the industry chain map in step S104, the terminal obtains current electric quantity data of each target industry in the industry chain map in the current time period, and then calculates and obtains a current electric quantity ring ratio increase rate and a current electric quantity ring ratio increase rate of each target industry in the current time period according to the current electric quantity data of each target industry and the industry electric quantity data in the historical time period. The terminal carries out ring ratio evaluation on the current electric quantity ring ratio growth rate and the historical electric quantity ring ratio growth rate of each target industry to obtain electric quantity ring ratio evaluation results of each target industry; carrying out same-ratio evaluation on the current electric quantity same-ratio growth rate and the historical electric quantity same-ratio growth rate of each target industry to obtain an electric quantity same-ratio evaluation result of each target industry; the terminal synthesizes the electric quantity ring ratio evaluation result and the electric quantity ring ratio evaluation result to obtain the evaluation result of each target industry, or determines the evaluation result of each target industry according to any one evaluation result of the electric quantity ring ratio evaluation result or the electric quantity ring ratio evaluation result; and under the condition that the evaluation result is abnormal, performing upstream and downstream updating on the target industry of which the evaluation result is abnormal in the industry chain map to obtain the updated industry chain map in the current time period.

In this embodiment, the current electric quantity growth rate of each target industry is obtained according to the current electric quantity data of each target industry in the current time period in the industry chain map; carrying out the same-ratio evaluation and the ring-ratio evaluation on the current electric quantity growth rate and the historical electric quantity growth rate of each target industry to obtain the evaluation result of each target industry; and when the evaluation result is abnormal, the target industry corresponding to the evaluation result is updated in the industry chain map in the upstream and downstream manner to obtain the industry chain map in the current time period, so that the industry with abnormal electric quantity fluctuation in the industry chain map can be found in time, and the real-time screening and automatic updating of the industry chain map are realized, thereby further improving the timeliness of the industry chain map and improving the accuracy of the industry chain map.

In an embodiment, as shown in fig. 4, another industry chain generation method is provided, which is described by taking an example that the method is applied to a terminal, and includes the following steps:

step S401, obtaining historical electric quantity growth rate of each target industry according to the industry electric quantity data of each target industry; and obtaining the historical production growth rate of each target industry according to the industrial production data of each target industry.

Step S402, clustering each target industry according to the historical electric quantity growth rate and the historical production growth rate to obtain an industry classification result of each target industry; and obtaining the data association degree among the target industries according to the industry classification result of each target industry.

In step S403, when it is detected that the name information of the first target industry is different from the name information of the second target industry, a number of times of text update required to update the name information of the first target industry to the name information of the second target industry is acquired.

The first target industry is any one of the target industries; the second target industry is any one of the target industries other than the first target industry.

Step S404, obtaining the distance between the name information of the first target industry and the name information of the second target industry according to the text updating times; and obtaining the name association degree between the first target industry and the second target industry according to the distance, wherein the name association degree is used as the name association degree between the target industries.

Step S405, entity relation extraction processing is carried out on the industry related information of each target industry, and entity relation information in each industry related information is obtained.

Step S406, according to the upstream and downstream relations between industries in the industry relation dictionary base, performing upstream probability judgment and downstream probability judgment on the target industry in the entity relation information to obtain the upstream probability and the downstream probability of the target industry in the entity relation information.

Step S407, obtaining the relationship association degree between the target industries according to the upstream probability and the downstream probability of the target industries in the entity relationship information.

Step S408, determining the industry association probability among the target industries according to the data association degree, the name association degree and the relationship association degree among the target industries; and determining the upstream and downstream industries corresponding to each target industry according to the upstream probability, the downstream probability and the industry association probability.

And step S409, performing visual graphic processing on upstream and downstream industries corresponding to each target industry to obtain an industry chain map.

The industrial chain generation method can achieve the following beneficial effects: the data association degree between each target industry is determined through the industrial electricity quantity data and the industrial production data of each target industry, the relationship association degree between each target industry is determined through the industrial association information of each target industry, and the industrial chain map is generated comprehensively by combining the name association degree between each target industry, so that the problem of single data dimension of the industrial chain constructed in the traditional technology is solved, the generated industrial chain map can more comprehensively show the association of each target industry on various factors such as economic factors, production factors and text factors, the industrial chain map is not required to be established by means of artificial experience, and the timeliness of the generated industrial chain map is effectively improved.

In order to more clearly illustrate the industrial chain generation method provided by the embodiment of the present disclosure, the industrial chain generation method is specifically described below with a specific embodiment. The utility model provides another industrial chain generation method, which can be applied to the terminal, and specifically comprises the following contents:

the first step is a data acquisition link: acquiring real-time industrial electricity quantity data from a metering automation system, wherein the real-time industrial electricity quantity data comprises electricity consumption of 133 types of typical industries, re-work and re-production conditions and the like; acquiring industrial production data of a 133-class typical industry from a statistical bureau website through a crawler; and acquiring the industry associated information of the 133 type typical industry from the texts of industry annual reports, news reports, policy guides and the like.

The second is a data processing link: 1) The industrial electric quantity data obtained from the metering automation system is structured base table data, and data cleaning and data verification are carried out on the industrial electric quantity data to obtain processed industrial electric quantity data so as to ensure the quality of the processed industrial electric quantity data; and converting the processed industrial electric quantity data into an entity in an industrial chain map through a neo4j-import module in the neo4j graphic database. 2) The method comprises the steps that industrial production data obtained from a statistical bureau website are semi-structured data, the industrial production data are converted into industrial production data in a structured form, then data cleaning and data verification are carried out on the industrial production data, and processed industrial production data are obtained to ensure the quality of the processed industrial production data; and converting the processed industrial production data into an entity in an industrial chain map through a neo4j-import module in the neo4j graphic database. 3) Carrying out industry feature extraction processing on the industry associated information to obtain industry feature information in the industry associated information; and according to the industry relation dictionary library, carrying out entity industry matching on the industry characteristic information to obtain entity industry information corresponding to the industry characteristic information.

Thirdly, a comprehensive industry association degree calculation link: 1) The terminal obtains the historical electric quantity ring ratio growth rate and the historical electric quantity same-ratio growth rate of each target industry according to the historical industry electric quantity data of each target industry; obtaining the historical production ring ratio growth rate and the historical production equal ratio growth rate of each target industry according to the historical industry production data of each target industry; performing K-means clustering on the historical electric quantity ring ratio increase rate, the historical electric quantity same-ratio increase rate, the historical production ring ratio increase rate and the historical production same-ratio increase rate of each target industry, and enabling the terminal to obtain an industry classification result of each target industry; and the terminal obtains the data association degree among the target industries according to the industry classification result of each target industry. 2) And determining the minimum text updating times required for converting the name information of one target industry into the name information of another target industry according to the acquired name information of each target industry, and calculating to obtain the distance between the name information of each target industry according to the required minimum text updating times. And calculating the name association degree between the two industries according to the distance. 3) Extracting entity relationship from the industry associated information of each target industry to obtain entity relationship information in the industry associated information; and obtaining the relationship association degree between the target industries according to the upstream probability and the downstream probability of the target industries in the entity relationship information.

And fourthly, an industrial chain generation link: determining the industry association probability among the target industries according to the data association degree, the name association degree and the relationship association degree among the target industries; performing matrix processing on the industry association probability, the upstream probability and the downstream probability among all target industries to obtain an association degree matrix as a comprehensive industry association degree; determining upstream and downstream industries corresponding to each target industry according to the comprehensive industry association degree; and (3) constructing visual nodes of each target industry by using neo4j (an NOSQL (structured query language) graphic database), and connecting the visual nodes according to upstream and downstream industries corresponding to each target industry to obtain an industry chain map.

In the embodiment, the data association degree between the target industries is determined through the industrial electricity quantity data and the industrial production data of the target industries, the relationship association degree between the target industries is determined through the industrial association information of the target industries, and the industrial chain map is generated comprehensively by combining the name association degree between the target industries, so that the problem of single data dimension of the industrial chain constructed in the traditional technology is solved, the generated industrial chain map can more comprehensively show the association of the target industries on various factors such as economic factors, production factors and text factors, the industrial chain map is not required to be established by means of artificial experience, and the timeliness of the generated industrial chain map is effectively improved.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application further provides an industrial chain generating device for implementing the industrial chain generating method. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the industrial chain generation apparatus provided below can be referred to the above limitations on the industrial chain generation method, and details are not described here.

In one embodiment, as shown in fig. 5, there is provided an industry chain generation apparatus 500 comprising: a first relevance determining module 501, a second relevance determining module 502, a third relevance determining module 503 and an industry chain map spectrum generating module 504, wherein:

the first association determining module 501 is configured to determine data association between target industries according to industry electric quantity data and industry production data of the target industries.

A second association degree determining module 502, configured to determine name association degrees between the target industries according to distances between the name information of the target industries.

A third association degree determining module 503, configured to determine, according to the entity relationship information in the industry association information of each target industry, a relationship association degree between each target industry.

An industry chain map generation module 504, configured to generate an industry chain map corresponding to each target industry according to the data association degree, the name association degree, and the relationship association degree between each target industry.

In an embodiment, the first association degree determining module 501 is further configured to obtain a historical electric quantity growth rate of each target industry according to the industry electric quantity data of each target industry; obtaining the historical production growth rate of each target industry according to the industrial production data of each target industry; clustering each target industry according to the historical electric quantity growth rate and the historical production growth rate to obtain an industry classification result of each target industry; and obtaining the data association degree among the target industries according to the industry classification result of each target industry.

In one embodiment, the second association degree determining module 502 is further configured to, in a case that it is detected that the name information of the first target industry is different from the name information of the second target industry, obtain a number of text updates required to update the name information of the first target industry to the name information of the second target industry; the first target industry is any one of the target industries; the second target industry is any one of the target industries except the first target industry; obtaining the distance between the name information of the first target industry and the name information of the second target industry according to the text updating times; and obtaining the name association degree between the first target industry and the second target industry according to the distance, wherein the name association degree is used as the name association degree between the target industries.

In an embodiment, the third association determining module 503 is further configured to perform entity relationship extraction processing on the industry association information of each target industry to obtain entity relationship information in each industry association information; according to the upstream and downstream relation among industries in the industry relation dictionary library, performing upstream probability judgment and downstream probability judgment on a target industry in the entity relation information to obtain the upstream probability and the downstream probability of the target industry in the entity relation information; and obtaining the relationship association degree between each target industry according to the upstream probability and the downstream probability of the target industry in the entity relationship information.

In one embodiment, the industry chain graph spectrum generating module 504 is further configured to determine an industry association probability between the target industries according to the data association degree, the name association degree, and the relationship association degree between the target industries; determining upstream and downstream industries corresponding to each target industry according to the upstream probability, the downstream probability and the industry association probability; and carrying out visual graphic processing on upstream and downstream industries corresponding to each target industry to obtain an industry chain map.

In one embodiment, the industry chain generating apparatus 500 further includes an industry chain updating module, configured to obtain current electric quantity data of each target industry in the industry chain map in the current time period; obtaining the current electric quantity growth rate of each target industry according to the current electric quantity data of each target industry; carrying out same-ratio evaluation and ring-ratio evaluation on the current electric quantity growth rate and the historical electric quantity growth rate of each target industry to obtain an evaluation result of each target industry; and under the condition that the evaluation result is abnormal, performing upstream and downstream updating on the target industry corresponding to the evaluation result in the industry chain map to obtain the industry chain map of the current time period.

The modules in the industrial chain generating device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 6. The computer apparatus includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input device. The processor, the memory and the input/output interface are connected by a system bus, and the communication interface, the display unit and the input device are connected by the input/output interface to the system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement an industry chain generation method. The display unit of the computer device is used for forming a visual picture and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An industrial chain generation method, comprising:

2. The method of claim 1, wherein the determining the data association degree between the target industries according to the industry power data and the industry production data of the target industries comprises:

3. The method according to claim 1, wherein the determining the name association degree between the target industries according to the distance between the name information of the target industries comprises:

4. The method according to claim 1, wherein the determining the relationship association degree between the target industries according to entity relationship information in the industry association information of the target industries comprises:

5. The method according to claim 4, wherein the generating an industry chain graph corresponding to each target industry according to the data association degree, the name association degree and the relationship association degree among the target industries comprises:

6. The method according to any one of claims 1 to 5, further comprising, after generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree, and the relationship association degree between the target industries:

7. An industrial chain generation apparatus, characterized in that the apparatus comprises:

and the industry chain map generation module is used for generating an industry chain map corresponding to each target industry according to the data association degree, the name association degree and the relationship association degree among all target industries.

8. The apparatus according to claim 7, wherein the first association degree determining module is further configured to obtain a historical electricity quantity growth rate of each target industry according to the industry electricity quantity data of each target industry; obtaining the historical production growth rate of each target industry according to the industrial production data of each target industry; clustering each target industry according to the historical electric quantity growth rate and the historical production growth rate to obtain an industry classification result of each target industry; and obtaining the data association degree among the target industries according to the industry classification result of each target industry.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.