Big data analysis system for innovation and entrepreneurship

Abstract

The invention provides a big data analysis system for innovation and entrepreneurship, and relates to the technical field of big data processing, and the system comprises a data obtaining unit which obtains the total hot industries of innovation and entrepreneurship under a preset constraint condition and the industry profit data of all the total hot industries; the regional data processing unit is used for determining regional hot industries under preset constraint conditions according to the total hot industries and the industry profit data of the total hot industries; and the regional difficulty analysis unit is used for analyzing the market saturation condition of the regional hot industry to obtain the entrepreneurial difficulty distribution of the corresponding regional hot industry under the preset constraint condition. According to the method, the total hot industry of the whole market and the regional hot industry can be combined, and the total hot industry capable of meeting the requirements of the regional market is obtained, so that the comprehensiveness and applicability of the total hot industry in innovation and entrepreneurship are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing, and in particular to a big data analysis system for innovation and entrepreneurship. Background Art

[0002] Innovation and entrepreneurship refers to entrepreneurial activities aimed at innovation in various aspects, including technological innovation, product innovation, brand innovation, service innovation, business model innovation, management innovation, organizational innovation, market innovation, and channel innovation.

[0003] The existing data analysis on innovation and entrepreneurship can only analyze whether an industry is a popular industry from the perspective of the entire market, which results in the industry being unable to adapt to the development needs of the local or regional area, thereby increasing the risks in implementing innovation and entrepreneurship. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to provide a big data analysis system for innovation and entrepreneurship, which is used to solve the problem that in the prior art, when analyzing innovation and entrepreneurship data, it is only possible to analyze whether an industry is a generally popular industry from the perspective of the entire market, which leads to the fact that the analyzed industry cannot adapt to the development needs of the local or local area, thereby increasing the risk of implementing innovation and entrepreneurship.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a big data analysis system for innovation and entrepreneurship, including: a data acquisition unit, which acquires the total popular industries for innovation and entrepreneurship under preset constraints and the industry profit data of each of the total popular industries; a regional data processing unit, which determines the regional hot industries under the preset constraints based on the total hot industries and the industry profit data of the total hot industries; a regional difficulty analysis unit, which analyzes the market saturation of regional hot industries and obtains the entrepreneurial difficulty distribution of the corresponding regional hot industries under the preset constraints; an analysis confirmation unit, which searches for the remaining hot industries in the regional hot industries that do not exist in the total hot industries in the entire market, determines the entrepreneurial difficulty distribution of the remaining hot industries, and determines and outputs the appropriate total hot industries in combination with the entrepreneurial difficulty distribution of the regional hot industries.

[0006] In one embodiment of the present invention, the data acquisition unit includes: a constraint setting module, which controls the setting of the preset constraints when acquiring the data of the total hot industries and the industry profit data corresponding to the total hot industries; and an acquisition control module, which screens out the corresponding total hot industries and the industry profit data of each of the total hot industries according to the preset constraints.

[0007] In one embodiment of the present invention, the preset constraints include a time period corresponding to data acquisition, a geographical scope for data acquisition, a basic industrial chain, and a secondary industrial chain.

[0008] In one embodiment of the present invention, the regional data processing unit includes: a profit arrangement module, which arranges the industry profit data corresponding to the total popular industries in order of high to low; a regional mainstream determination module, which obtains the total popular industries corresponding to a preset number of industry profit data arranged in order of high to low, and then screens them to obtain the regional hot industries.

[0009] In one embodiment of the present invention, the regional mainstream determination module includes: a continuous period query module, which controls the time for continuous growth of a preset number of total popular industries before querying to obtain corresponding industry profit data as a continuous period; a period screening module, which controls the screening of industries whose continuous period reaches a preset value among the preset number of total popular industries before screening as the regional hot industries.

[0010] In one embodiment of the present invention, the regional difficulty analysis unit includes: a saturation calculation module, which determines the corresponding market saturation value according to the store distribution of each of the popular industries in the region within the entire geographical scope of data acquisition; a regional mainstream arrangement module, which controls and arranges the market saturation values ​​in order from high to low in the order of entrepreneurial difficulty.

[0011] In one embodiment of the present invention, the saturation calculation module includes: a dispersion query module, which queries the dispersion data of the stores in the popular industries in each region within the entire geographical range of data acquisition; and a data conversion module, which calculates the market saturation value of each popular industry in the region based on the product of the dispersion data and the proportional conversion factor.

[0012] In one embodiment of the present invention, the analysis and confirmation unit includes: a full market data acquisition module, which controls the acquisition of all total popular industries within the current full market as the total hot industries in the full market; a remaining industry query module, which controls the removal of the regional hot industries from the total hot industries in the full market according to the total hot industries in the full market and the regional hot industries, to obtain the remaining hot industries; a remaining mainstream arrangement module, which determines the entrepreneurial difficulty distribution of the remaining hot industries according to the distribution of the remaining hot industries in the entire geographical scope of data acquisition; and a control output module, which controls the output of the remaining hot industries that meet the entrepreneurial difficulty distribution requirements and the remaining hot industries that meet the entrepreneurial difficulty distribution requirements.

[0013] As described above, a big data analysis system for innovation and entrepreneurship of the present invention has the following beneficial effects: by obtaining the preset constraints of the data area, the total popular industries and industry profit data that meet the requirements are obtained. And the corresponding regional popular industry situation can be obtained based on the industry profit data analysis. At the same time, the saturation of the regional popular industries in the region where they are located will be analyzed, and then the corresponding entrepreneurial difficulty distribution data will be obtained. And the total popular industries of the entire market will be further queried to find the remaining hot industries other than the regional hot industries. And the corresponding entrepreneurial difficulty distribution data of the remaining hot industries are analyzed, and then combined with the entrepreneurial difficulty distribution data of the regional hot industries to analyze the appropriate total hot industries. Through the above method, the current total hot industry situation of innovation and entrepreneurship that meets the local characteristics can be determined more accurately, so as to achieve the control of obtaining personalized recommendation data of the total hot industry by combining the entire industry with the local total hot industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Shown is an architecture diagram of the big data analysis system of the present invention. DETAILED DESCRIPTION

[0015] The following is an explanation of the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the features in the following embodiments and the embodiments can be combined with each other without conflict. It should also be understood that the terms used in the embodiments of the present invention are intended to describe specific embodiments, rather than to limit the scope of protection of the present invention. The test methods for which specific conditions are not specified in the following examples are usually carried out under conventional conditions or according to the conditions recommended by the manufacturers.

[0016] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the effects and purposes that can be achieved by the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description, and are not used to limit the scope of the implementation of the present invention. The change or adjustment of their relative relationship should also be regarded as the scope of the implementation of the present invention without substantially changing the technical content.

[0017] See also Figure 1 The present invention provides a big data analysis system for innovation and entrepreneurship, including: a data acquisition unit, the data acquisition unit acquires the total hot industries under preset constraints and the industry profit data of each of the total hot industries; a regional data processing unit, the regional data processing unit determines the regional hot industries under preset constraints according to the total hot industries and the industry profit data of the total hot industries; a regional difficulty analysis unit, the regional difficulty analysis unit analyzes the market saturation of the regional hot industries, and obtains the entrepreneurial difficulty distribution of the corresponding regional hot industries under the preset constraints; an analysis confirmation unit, the analysis confirmation unit searches for the remaining hot industries in the regional hot industries that do not exist in the total hot industries in the entire market, determines the entrepreneurial difficulty distribution of the remaining hot industries, and determines and outputs the appropriate total hot industries in combination with the entrepreneurial difficulty distribution of the regional hot industries.

[0018] It is not difficult to find from the above content that in the process of innovation and entrepreneurship analysis, the data acquisition unit is used to retrieve all the total hot industries under the preset constraints. And after obtaining the total hot industries, further obtain the industry profit data corresponding to each total hot industry. Through the regional data processing unit, according to the total hot industries and the corresponding industry profit data, further determine the regional hot industries of the corresponding data acquisition area. That is, the level of industry profit data can be used as a determining factor. Then, the regional difficulty analysis unit is used to realize the discrete degree corresponding to the data acquisition area of ​​each regional hot industry, and then determine the corresponding market saturation, so as to further realize the distribution processing of entrepreneurial difficulty. Through the analysis confirmation unit, the query of the total hot industries of the whole market that dominate the whole market industry is realized. And according to the aforementioned regional hot industries, the remaining remaining hot industries are confirmed, and the entrepreneurial difficulty distribution of the remaining hot industries is further obtained. By combining the entrepreneurial difficulty distribution of the regional hot industries, the appropriate total hot industries are further determined. Through the above method, the analysis of the total hot industries with development potential that meet the division of each region can be realized more accurately and reasonably.

[0019] Among them, the data acquisition unit includes: a constraint setting module, which controls the setting of the preset constraints when acquiring the data of the total popular industries and the industry profit data corresponding to the total popular industries; an acquisition control module, which screens out the corresponding total popular industries and the industry profit data of each of the total popular industries according to the preset constraints.

[0020] In one embodiment of the present invention, a constraint setting module is used to establish constraint conditions for data acquisition before acquiring the total popular industries. Furthermore, an acquisition control module is used to select the total popular industries and corresponding industry profit data that meet the requirements according to the established preset constraint conditions.

[0021] Preferably, the preset constraint conditions include the time period corresponding to the acquisition of data, the geographical scope of data acquisition, the basic industrial chain and the secondary industrial chain. In this embodiment, the preset constraint conditions can be the time period corresponding to the acquisition of data, the geographical scope of data acquisition, the basic industrial chain and the secondary industrial chain. Through the control of the above conditions, the screening of the core total hot industries can be achieved.

[0022] Furthermore, the regional data processing unit includes: a profit arrangement module, which arranges the industry profit data corresponding to the total popular industries in order of high to low; a regional mainstream determination module, which obtains the total popular industries corresponding to a preset number of industry profit data arranged in order of high to low, and then screens them to obtain the regional hot industries.

[0023] In one embodiment of the present invention, the profit arrangement module is used to arrange the industry profit data of the total hot industries in order from high to low. The regional mainstream determination module is used to select the total hot industries of the total hot industries arranged from high to low, so as to further obtain the regional hot industries.

[0024] Specifically, the regional mainstream determination module includes: a continuous cycle query module, which controls the time for continuous growth of a preset number of total popular industries before querying to obtain corresponding industry profit data as the continuous cycle; a cycle screening module, which controls the screening of industries whose continuous cycle reaches a preset value among the preset number of total popular industries before screening as the regional hot industries.

[0025] In one embodiment of the present invention, a continuous period query module is used to query the continuous period used to reach the current industry profit data. And a period screening module is used to screen out a preset number of total hot industries with relatively short continuous periods as regional hot industries.

[0026] Preferably, the regional difficulty analysis unit includes: a saturation calculation module, which determines the corresponding market saturation value according to the store distribution of each of the hot industries in the region within the entire geographical scope of data acquisition; and a regional mainstream arrangement module, which controls and arranges the market saturation values ​​in order of numerical value from high to low in order of entrepreneurial difficulty from high to low.

[0027] In one embodiment of the present invention, the saturation calculation module is used to realize the store distribution of the current regional popular industry within the entire data acquisition region, so as to determine whether it is relatively sparse, and further determine the market saturation. The regional mainstream arrangement module is used to realize the control arrangement of the order of entrepreneurship difficulty from high to low according to the market saturation value.

[0028] Next, the saturation calculation module includes: a dispersion query module, which queries the dispersion data of the stores in the popular industries in each region within the entire geographical range of data acquisition; a data conversion module, which calculates the market saturation value of the popular industries in each region according to the product of the dispersion data and the proportional conversion factor.

[0029] In one embodiment of the present invention, the discreteness query module is used to query the discreteness data of the stores in the regional hot industry within the entire geographical range of data acquisition. The discreteness data can be obtained by calculation. And the data conversion module is used to multiply the discreteness data and the proportional conversion factor to obtain the market saturation value of the regional hot industry. It is worth noting that the discreteness data and the market saturation value are usually negatively correlated.

[0030] Furthermore, the analysis and confirmation unit includes: a full market data acquisition module, which controls the acquisition of all the total popular industries in the current full market as the total hot industries in the full market; a remaining industry query module, which controls the removal of the regional hot industries in the total hot industries in the full market according to the total hot industries in the full market and the regional hot industries, to obtain the remaining hot industries; a remaining mainstream arrangement module, which determines the entrepreneurial difficulty distribution of the remaining hot industries according to the distribution of the remaining hot industries in the entire geographical scope of data acquisition; and a control output module, which controls the output of the remaining hot industries that meet the entrepreneurial difficulty distribution requirements and the remaining hot industries that meet the entrepreneurial difficulty distribution requirements.

[0031] In one embodiment of the present invention, the total hot industries in the current entire market are used as the total hot industries in the entire market through the full market data acquisition module. And the remaining hot industries after removing the regional hot industries from the total hot industries in the entire market are queried through the remaining industry query module. The remaining mainstream arrangement module is used to control the distribution of the entrepreneurial difficulty of the remaining hot industries. The control output module is used to control the output of the remaining hot industries that meet the entrepreneurial difficulty distribution requirements and the remaining hot industries that meet the entrepreneurial difficulty distribution requirements.

[0032] In summary, the present invention obtains the total popular industries and industry profit data that meet the requirements according to the preset constraints of the data acquisition area. And the corresponding regional popular industry situation can be obtained based on the industry profit data analysis. At the same time, the saturation of the regional popular industries in the region where they are located will be analyzed, and then the corresponding entrepreneurial difficulty distribution data will be obtained. And the total popular industries of the entire market will be further queried to find the remaining hot industries other than the regional hot industries. And the corresponding entrepreneurial difficulty distribution data of the remaining hot industries are analyzed, and then combined with the entrepreneurial difficulty distribution data of the regional hot industries to analyze the appropriate total hot industries. Through the above method, the current total hot industry situation of innovation and entrepreneurship that meets local characteristics can be determined more accurately, thereby realizing the control of obtaining personalized recommended data of the total hot industry by combining the entire industry with the local total hot industry. Therefore, the present invention effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0033] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A big data analysis system for innovation and entrepreneurship, characterized in that: include: A data acquisition unit, wherein the data acquisition unit acquires industry profit data of the total popular industries for innovation and entrepreneurship under preset constraints and each of the total popular industries; A regional data processing unit, which determines regional hot industries for innovation and entrepreneurship within a regional scope under preset constraints based on total hot industries and industry profit data of the total hot industries; A regional difficulty analysis unit, which analyzes the market saturation of regional hot industries and obtains the entrepreneurial difficulty distribution of the corresponding regional hot industries under the preset constraints; An analysis confirmation unit is provided, wherein the analysis confirmation unit searches for remaining hot industries in the regional hot industries that do not exist in the total hot industries in the entire market, determines the entrepreneurial difficulty distribution of the remaining hot industries, and determines and outputs appropriate total hot industries in combination with the entrepreneurial difficulty distribution of the regional hot industries.

2. The big data analysis system for innovation and entrepreneurship according to claim 1 is characterized by: The data acquisition unit comprises: A constraint setting module, which controls the setting of the preset constraint conditions when acquiring the data of the total popular industries and the industry profit data corresponding to the total popular industries; An acquisition control module is provided, wherein the acquisition control module selects the corresponding total hot industries and the industry profit data of each of the total hot industries according to the preset constraint conditions.

3. The big data analysis system for innovation and entrepreneurship according to claim 2 is characterized by: The preset constraints include the time period corresponding to the data acquisition, the geographical scope of data acquisition, the basic industrial chain and the secondary industrial chain.

4. The big data analysis system for innovation and entrepreneurship according to claim 1 is characterized by: The regional data processing unit comprises: A profit arrangement module, wherein the profit arrangement module arranges the industry profit data corresponding to the total popular industries in order of high to low; A regional mainstream determination module obtains the total popular industries corresponding to a preset number of industry profit data arranged in order of high and low, and then screens them to obtain the regional popular industries.

5. The big data analysis system for innovation and entrepreneurship according to claim 4 is characterized by: The regional mainstream determination module includes: A continuous cycle query module, which controls the time for the continuous growth of a preset number of total popular industries before querying to obtain the corresponding industry profit data as a continuous cycle; A cycle screening module controls the screening of industries whose continuous cycles reach a preset value among a preset number of total popular industries as the regional popular industries.

6. The big data analysis system for innovation and entrepreneurship according to claim 1 is characterized by: The regional difficulty analysis unit comprises: A saturation calculation module, which determines a corresponding market saturation value according to the store distribution of each of the hot industries in the region within the entire geographical range of data acquisition; The regional mainstream arrangement module obtains the market saturation values ​​and controls the arrangement of the entrepreneurial difficulties in descending order according to the numerical values.

7. The big data analysis system for innovation and entrepreneurship according to claim 1 is characterized by: The saturation degree calculation module comprises: A dispersion query module, wherein the dispersion query module queries the dispersion data of stores in the hot industries in each region within the entire geographical range of data acquisition; A data conversion module is used to calculate the market saturation value of each of the hot industries in the region according to the product of the discreteness data and the proportional conversion factor.

8. The big data analysis system for innovation and entrepreneurship according to claim 1 is characterized by: The analysis and confirmation unit comprises: A full market data acquisition module, wherein the full market data acquisition module controls the acquisition of all the total popular industries within the current full market as the total popular industries of the full market; A remaining industry query module, wherein the remaining industry query module controls the removal of the regional hot industries from the total hot industries of the entire market according to the total hot industries of the entire market and the regional hot industries, to obtain the remaining hot industries; A remaining mainstream arrangement module, which determines the entrepreneurial difficulty distribution of the remaining popular industries according to the distribution of the remaining popular industries in the entire geographical range of data acquisition; A control output module is provided, wherein the control output module controls and outputs the remaining popular industries that meet the requirements of the distribution of entrepreneurial difficulty and the remaining popular industries that meet the requirements of the distribution of entrepreneurial difficulty.

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