Data asset similarity comparison method for data asset registration

Abstract

The invention discloses a data asset similarity comparison method for data asset registration, and particularly relates to the technical field of cloud storage, and the method comprises a data asset input step, a monitoring time region division step, a data asset data acquisition step, a data asset data analysis step, a comprehensive data processing step, a comprehensive data discrimination step and an early warning step. According to the method, data assets are input into a database, data can be structured, processed and stored in the database, and data collection is greatly facilitated; deep analysis is carried out through collected data, and potential problems occurring in the process of analyzing data assets to carry out similarity comparison are solved to a great extent; the corresponding data in each time period can be more effectively and more accurately monitored through data acquisition, so that the overall efficiency is improved; meanwhile, signals can be sent to a terminal for processing in real time, decision support is provided by management personnel, and a more scientific and reasonable coping scheme is formulated to ensure the stability and safety of data assets.

Description

Technical Field

[0001] The present invention relates to the field of cloud storage technology, and more specifically, to a data asset similarity comparison method for data asset registration. Background Art

[0002] With the development and growth of the digital era, cloud storage technology uses distributed systems and virtualization technology to store data on multiple servers in the cloud, achieving high reliability and scalability of data; with the acceleration of digital transformation, enterprises have a growing demand for cloud storage services. On the one hand, cloud storage services can break the limitations of physical storage devices and enable data to be accessed and shared anytime and anywhere; on the other hand, cloud storage service providers have huge data centers and advanced storage technologies, which can ensure high availability, scalability and security of data. These advantages make cloud storage the preferred solution for enterprises to store and manage data.

[0003] A data asset similarity comparison method for data asset registration has gradually emerged in the public eye, which is conducive to improving efficiency and reducing costs, and has become an important hot spot in the industry. The existing data asset similarity comparison method mainly includes data collection steps, data analysis steps, and video computing network discrimination steps; the data collection step is mainly based on the fact that the data in the database in the existing technology is completely collected by computers, resulting in high data asset efficiency, while ensuring the reliability of data assets in the bank database; the data analysis step converts non-digital parameters into digitized parameters, making the analysis of data assets more convenient; the video computing network discrimination step discriminates the data counted by the existing method through the data counted in previous years by the database statistics.

[0004] However, it still has some shortcomings in actual use. In the existing methods, data collection is carried out by collecting data of some data assets, which easily leads to incomplete data collection; in data analysis, the collected data is analyzed and processed, but the existing technology analyzes and processes huge data, which will cause data errors and data missing, greatly reducing the data accuracy; the existing methods lack a comprehensive mathematical model to determine the similarity of data assets, which causes inconvenience to managers and high costs for enterprises.

[0005] There is an urgent need for a data asset similarity comparison method for data asset registration. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a data asset similarity comparison method for data asset registration, which solves the problems raised in the above-mentioned background technology through the field of cloud storage technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a data asset similarity comparison method for data asset registration, comprising:

[0008] Step 1: Data asset entry: used to structure the data assets of the target enterprise's related products and store them in the database;

[0009] Step 2: Monitoring time zone division: It is used to divide the structured data assets stored in the database into target time zones with equal intervals, divide the target time zone into sub-time zones by means of equal intervals, and number the sub-time zones in the target time zone as i=1, 2, ..., n in sequence;

[0010] Step 3: Data asset data collection: for collecting data of each time sub-area using a business-oriented data collection unit and an operation resource data collection unit, and uploading the collected data to the data asset data analysis step;

[0011] Step 4: Data asset data analysis: including a business data calculation unit and an operation resource data calculation unit, used to analyze the data transmitted by the business data collection unit and the operation resource data collection unit, and transmit the analysis results to the comprehensive data processing step;

[0012] Step 5: Comprehensive data processing: used to establish a comprehensive data processing model, import the data transmitted in the data asset data analysis step into the comprehensive data processing model, calculate the data asset similarity index of the target time zone, and transmit it to the comprehensive data identification step;

[0013] Step 6: Comprehensive data identification: used to accurately compare the data asset similarity index of the target time zone with the data asset similarity index of the preset target time zone during the actual operation process, and transmit the identification result to the early warning step;

[0014] Step 7: Early warning: It is used to receive the data transmitted in the comprehensive data identification step and send a corresponding early warning signal to the terminal for processing.

[0015] Preferably, the structured processing requires organizing and analyzing the original or unstructured data assets to determine their data structure and mode to meet the requirements of the database management system (DBMS), and then converting these data assets into a structured format and storing them securely and efficiently in the database for subsequent query, collection, management and utilization.

[0016] Preferably, the sales information data includes the sales order amount, the number of purchased products and the sales profit rate, which are respectively recorded as Xj, Xm and Xt; the user information data includes the number of new user registrations, the number of user churn and the user purchase rate, which are respectively recorded as Yx, Yl and Yg; the enterprise product information data includes the product loss amount, product input cost and inventory turnover rate, which are respectively recorded as Ck, Ct and Cz.

[0017] Preferably, the production information data includes the frequency of production equipment failures, the number of qualified products and the amount of production and transportation losses, which are respectively denoted as St, Sh and Sw; the system technical performance data includes the frequency of data storage data updates, the amount of invalid data checked and the frequency of shared data, which are respectively denoted as Jg, Jp and Jx.

[0018] Preferably, the business-oriented data calculation unit is used to analyze various types of data transmitted by the business-oriented data collection unit, and transmit the analysis results to the comprehensive data processing step; the business-oriented data calculation unit includes a sales information data calculation subunit, a user information data calculation subunit and an enterprise product information data calculation subunit.

[0019] Preferably, the sales information data calculation subunit is used to establish a sales information data mathematical model, import the sales information data transmitted by the business data collection unit into the sales information data mathematical model, and calculate the sales information coefficient value of each time sub-area, which is specifically expressed as:

[0020]

[0021] XA i represents the sales information coefficient value of the i-th time sub-region, Xj i Indicates the sales order amount in the i-th time sub-region, Xm i represents the number of products purchased in the i-th time sub-region, Xt i represents the sales profit margin of the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0022] Preferably, the user information data calculation subunit is used to establish a user information data mathematical model, import the user information data transmitted by the service data collection unit into the user information data mathematical model, and calculate the user information coefficient value of each time sub-area, which is specifically expressed as:

[0023]

[0024] YA i represents the user information coefficient value of the i-th time sub-region, Yx i represents the number of new user registrations in the i-th time sub-area, Yli represents the number of users who have churned in the i-th time sub-region, Yg i represents the user purchase rate of the i-th time sub-region, and n represents the total number of time divisions of the target region; the enterprise product information data calculation subunit is used to establish an enterprise product information data mathematical model, import the enterprise product information data transmitted by the business data collection unit into the enterprise product information data mathematical model, and calculate the enterprise product information coefficient value of each time sub-region, which is specifically expressed as:

[0025]

[0026] CA i represents the enterprise product information coefficient value of the i-th time sub-region, Ck i-1 Indicates the product loss amount in the i-1th time sub-region, Ck i Indicates the product loss amount in the i-th time sub-region, Ct i represents the product input cost of the i-th time sub-region, Ct max represents the product input cost in the target time zone, Cz i represents the inventory turnover rate of the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0027] Preferably, the operation resource data unit is used to analyze various types of data transmitted by the operation resource data acquisition unit, and transmit the analysis results to the comprehensive data processing step; the operation resource data calculation unit includes a production information data calculation subunit and a system technical performance data calculation subunit.

[0028] Preferably, the production information data calculation subunit is used to establish a production information data mathematical model, import the production information data transmitted by the operation resource data acquisition unit into the production information data mathematical model, and calculate the production information coefficient value of each sub-time zone, which is specifically expressed as:

[0029]

[0030] SA i represents the production information coefficient value of the i-th time sub-region, St i-1 represents the failure frequency of production equipment in the i-1th time sub-region, Sh i Sh represents the number of qualified products in the i-th time sub-area. all represents the total production of the i-th time sub-region, Sw i-1 represents the production and transportation loss in the i-1th time sub-region, Sw irepresents the production and transportation loss of the i-th time sub-area, and n represents the total number of time divisions of the target area; the system technical performance data calculation subunit is used to establish a system technical performance data mathematical model, import the system technical performance data transmitted by the operation resource data acquisition unit into the system technical performance data mathematical model, and calculate the system technical performance coefficient value of each time sub-area, which is specifically expressed as:

[0031]

[0032] J A i represents the system technical performance coefficient value of the i-th time sub-area, Jg i Indicates the data storage data update frequency of the i-th time sub-region, Jx i-1 represents the frequency of shared data in the i-1th time sub-region, Jx i represents the frequency of shared data in the i-th time sub-region, Jp i-1 represents the amount of invalid data checked in the i-1th time sub-region, Jp i It represents the amount of invalid data checked in the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0033] Preferably, the comprehensive data processing model is obtained in the following manner:

[0034]

[0035] Where L represents the data asset similarity index of the target time zone, θ i represents the service-oriented evaluation value of the i-th time sub-region, ρ i represents the operational resource evaluation value of the i-th time sub-region, μ1 represents the mean of the business-oriented data of the target time region, μ2 represents the mean of the operational resource data of the target time region, α represents other influencing factors of the data asset similarity index of the target time region, where i = 1, 2, ..., n, and n represents the total number of time divisions of the target region.

[0036] Technical effects and advantages of the present invention:

[0037] 1. The present invention can process and store data in a structured manner in a database through the data asset entry step, greatly facilitating data collection; through the monitoring time area division step, more accurate monitoring can be performed on the corresponding data in each time period more effectively, thereby improving overall efficiency, and at the same time, it can be flexibly adjusted according to actual conditions to adapt to changing needs;

[0038] 2. The present invention can obtain various types of main data of detailed data assets through the data asset data collection step, greatly improving the comprehensiveness of the collected data; through the data asset data analysis step, a mathematical model is established to conduct in-depth analysis of the collected data, which greatly analyzes the potential problems that arise in the process of analyzing the similarity comparison of data assets, providing an important basis for subsequent optimization;

[0039] 3. The present invention establishes a comprehensive data processing model through a comprehensive data processing step, imports the data transmitted by the data asset data analysis step into the comprehensive data processing model, and calculates the data asset similarity index of the target time zone, so that the method is more intuitive and convenient, and improves efficiency; through the early warning step, it can be discovered that after the data assets are compared for similarity, a signal can be sent to the terminal for processing in real time, and the management personnel can provide decision support and formulate a more scientific and reasonable response plan to ensure the stability and security of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] As attached Figure 1 A data asset similarity comparison method for data asset registration is shown in FIG, including:

[0043] Step 1: Data asset entry: used to structure the data assets of the target enterprise's related products and store them in the database.

[0044] In this embodiment, it is specifically necessary to explain that the structured processing requires organizing and analyzing the original or unstructured data assets, determining their data structure and mode to meet the requirements of the database management system (DBMS), and then converting these data assets into a structured format and storing them securely and efficiently in the database for subsequent query, collection, management and utilization.

[0045] Step 2: Monitoring time zone division: It is used to divide the structured data assets stored in the database into target time zones with equal intervals, divide the target time zone into sub-time zones by means of equal intervals, and number the sub-time zones in the target time zone as i=1, 2, ..., n.

[0046] In this embodiment, it is specifically necessary to explain that a time interval is preset by installing a counter in the system hardware device, and a suitable time division precision is selected according to actual needs, including every day, every hour, and every minute.

[0047] Step 3: Data asset data collection: It is used to collect data of each time sub-area using the business-oriented data collection unit and the operation resource data collection unit, and upload the collected data to the data asset data analysis step.

[0048] The business-oriented data collection unit is used to collect sales information data, user information data and enterprise product information data in each time sub-area.

[0049] In this embodiment, it should be specifically explained that the sales information data includes the sales order amount, the number of purchased products and the sales profit rate, which are respectively recorded as Xj, Xm and Xt; the user information data includes the number of new user registrations, the number of user churn and the user purchase rate, which are respectively recorded as Yx, Yl and Yg; the enterprise product information data includes the product loss amount, product input cost and inventory turnover rate, which are respectively recorded as Ck, Ct and Cz.

[0050] The data asset data in the database is uploaded through the cloud server to include sales order amount, quantity of purchased products, sales profit margin, sales order amount, quantity of purchased products, sales profit margin, product loss amount, product input cost and inventory turnover rate.

[0051] The operation resource data collection unit is used to collect production information data and system technical performance data of each time sub-area.

[0052] In this embodiment, it should be specifically noted that the production information data includes the frequency of production equipment failures, the number of qualified products, and the amount of production and transportation losses, which are respectively recorded as St, Sh, and Sw; the system technical performance data includes the frequency of data storage data update, the amount of invalid data detection, and the frequency of shared data, which are respectively recorded as Jg, Jp, and Jx;

[0053] The data asset data in the database is uploaded through the cloud server to the frequency of production equipment failures, the number of qualified products, the amount of production and transportation losses, the frequency of data storage data updates, the amount of invalid data checked, and the frequency of shared data.

[0054] Step 4: Data asset data analysis: including the business data calculation unit and the operation resource data calculation unit, which are used to analyze the data transmitted by the business data collection unit and the operation resource data collection unit, and transmit the analysis results to the comprehensive data processing step.

[0055] The business-oriented data calculation unit is used to analyze various types of data transmitted by the business-oriented data collection unit, and transmit the analysis results to the comprehensive data processing step; the business-oriented data calculation unit includes a sales information data calculation subunit, a user information data calculation subunit and an enterprise product information data calculation subunit.

[0056] In this embodiment, it is specifically required to explain that the sales information data calculation subunit is used to establish a sales information data mathematical model, import the sales information data transmitted by the business data collection unit into the sales information data mathematical model, and calculate the sales information coefficient value of each time sub-area, which is specifically expressed as:

[0057]

[0058] XA i represents the sales information coefficient value of the i-th time sub-region, Xj i Indicates the sales order amount in the i-th time sub-region, Xm i represents the number of products purchased in the i-th time sub-region, Xt i represents the sales profit margin of the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0059] In this embodiment, it should be specifically explained that the user information data calculation subunit is used to establish a user information data mathematical model, import the user information data transmitted by the service data collection unit into the user information data mathematical model, and calculate the user information coefficient value of each time sub-area, which is specifically expressed as:

[0060]

[0061] YA i represents the user information coefficient value of the i-th time sub-region, Yx i represents the number of new user registrations in the i-th time sub-area, Yl i represents the number of users who have churned in the i-th time sub-region, Yg i represents the user purchase rate of the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0062] In this embodiment, it should be specifically explained that the enterprise product information data calculation subunit is used to establish an enterprise product information data mathematical model, import the enterprise product information data transmitted by the business data collection unit into the enterprise product information data mathematical model, and calculate the enterprise product information coefficient value of each time sub-region, which is specifically expressed as:

[0063]

[0064] CAi represents the enterprise product information coefficient value of the i-th time sub-region, Ck i-1 Indicates the product loss amount in the i-1th time sub-region, Ck i Indicates the product loss amount in the i-th time sub-region, Ct i represents the product input cost of the i-th time sub-region, Ct max represents the product input cost in the target time zone, Cz i represents the inventory turnover rate of the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0065] The business data calculation unit calculates the sales information coefficient value of the i-th time sub-region, the user information coefficient value of the i-th time sub-region, and CA i The business-oriented evaluation value of the i-th time sub-region is calculated by the enterprise product information coefficient value of the i-th time sub-region, which is specifically expressed as:

[0066]

[0067] θ i represents the service-oriented evaluation value of the i-th time sub-area, XA i represents the sales information coefficient value of the i-th time sub-region, YA i represents the user information coefficient value of the i-th time sub-region, CA i represents the enterprise product information coefficient value of the i-th time sub-region, and δ1 represents the fitting coefficient of the business evaluation value.

[0068] The operation resource data unit is used to analyze various types of data transmitted by the operation resource data acquisition unit, and transmit the analysis results to the comprehensive data processing step; the operation resource data calculation unit includes a production information data calculation subunit and a system technical performance data calculation subunit.

[0069] In this embodiment, it should be specifically explained that the production information data calculation subunit is used to establish a production information data mathematical model, import the production information data transmitted by the operation resource data acquisition unit into the production information data mathematical model, and calculate the production information coefficient value of each sub-time zone, which is specifically expressed as:

[0070]

[0071] SA i represents the production information coefficient value of the i-th time sub-region, St i-1 represents the failure frequency of production equipment in the i-1th time sub-region, Sh i Sh represents the number of qualified products in the i-th time sub-area. allrepresents the total production of the i-th time sub-region, Sw i-1 represents the production and transportation loss in the i-1th time sub-region, Sw i It represents the production and transportation loss of the i-th time sub-area, and n represents the total number of time divisions of the target area.

[0072] In this embodiment, it should be specifically explained that the system technical performance data calculation subunit is used to establish a system technical performance data mathematical model, import the system technical performance data transmitted by the operation resource data acquisition unit into the system technical performance data mathematical model, and calculate the system technical performance coefficient value of each time sub-area, which is specifically expressed as:

[0073]

[0074] J A i represents the system technical performance coefficient value of the i-th time sub-area, Jg i Indicates the data storage data update frequency of the i-th time sub-region, Jx i-1 represents the frequency of shared data in the i-1th time sub-region, Jx i represents the frequency of shared data in the i-th time sub-region, Jp i-1 represents the amount of invalid data checked in the i-1th time sub-region, Jp i It represents the amount of invalid data checked in the i-th time sub-region, and n represents the total number of time divisions of the target region.

[0075] The operation resource data calculation unit calculates the operation resource evaluation value of the i-th time sub-region through the production information coefficient value of the i-th time sub-region and the system technical performance coefficient value of the i-th time sub-region, which is specifically expressed as:

[0076] ρ i =(SA i +JA i )×δ2,

[0077] ρ i represents the operational resource assessment value of the i-th time sub-region, SA i represents the production information coefficient value of the i-th time sub-region, JA i represents the system technical performance coefficient value of the i-th time sub-area, and δ2 represents the fitting coefficient of the operation resource evaluation value.

[0078] Step 5: Comprehensive data processing: used to establish a comprehensive data processing model, import the data transmitted in the data asset data analysis step into the comprehensive data processing model, calculate the data asset similarity index of the target time zone, and transmit it to the comprehensive data discrimination step.

[0079] In this embodiment, it should be specifically explained that the method for obtaining the comprehensive data processing model is:

[0080]

[0081] Where L represents the data asset similarity index of the target time zone, θ i represents the service-oriented evaluation value of the i-th time sub-region, ρ i represents the operational resource evaluation value of the i-th time sub-region, μ1 represents the mean of the business-oriented data of the target time region, μ2 represents the mean of the operational resource data of the target time region, α represents other influencing factors of the data asset similarity index of the target time region, where i = 1, 2, ..., n, and n represents the total number of time divisions of the target region.

[0082] Step 6: Comprehensive data judgment: used to accurately compare the data asset similarity index of the target time zone with the data asset similarity index of the preset target time zone during the actual operation process, and transmit the judgment result to the early warning step.

[0083] In this embodiment, it should be specifically noted that the data asset similarity index of the preset target time zone is recorded as L def , when L <L def When L>L def When , it means that the data asset similarity index of the target time zone is greater than the data asset similarity index of the preset target time zone, indicating that the data similarity of the data assets in the target time zone is low.

[0084] Step 7: Early warning: It is used to receive the data transmitted in the comprehensive data identification step and send a corresponding early warning signal to the terminal for processing.

[0085] The present invention can process and store data in a database in a structured manner through the data asset entry step, which greatly facilitates data collection; through the monitoring time area division step, the corresponding data in each time period can be more effectively monitored more accurately, thereby improving overall efficiency, and can be flexibly adjusted according to actual conditions to adapt to changing needs; through the data asset data collection step, various main data of detailed data assets can be obtained, greatly improving the comprehensiveness of the collected data; through the data asset data analysis step, a mathematical model is established to conduct in-depth analysis of the collected data, greatly analyzing the potential problems that arise in the process of similarity comparison of data assets, and providing an important basis for subsequent optimization; through the comprehensive data processing step, a comprehensive data processing model is established, the data transmitted by the data asset data analysis step is imported into the comprehensive data processing model, and the data asset similarity index of the target time area is calculated, making the method more intuitive and convenient, and improving efficiency; through the early warning step, it can be found that after the data assets are compared for similarity, a signal can be sent to the terminal for processing in real time, and the management personnel can provide decision support and formulate a more scientific and reasonable response plan to ensure the stability and security of data assets.

[0086] Secondly, in the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data asset similarity comparison method for data asset registration, characterized in that: include: Step 1: Data asset entry: used to structure the data assets of the target enterprise's related products and store them in the database; Step 2: Monitoring time zone division: It is used to divide the structured data assets stored in the database into target time zones with equal intervals, divide the target time zone into sub-time zones by means of equal intervals, and number the sub-time zones in the target time zone as i=1, 2, ..., n in sequence; Step 3: Data asset data collection: for collecting data of each time sub-area using a business-oriented data collection unit and an operation resource data collection unit, and uploading the collected data to the data asset data analysis step; Step 4: Data asset data analysis: including a business data calculation unit and an operation resource data calculation unit, used to analyze the data transmitted by the business data collection unit and the operation resource data collection unit, and transmit the analysis results to the comprehensive data processing step; Step 5: Comprehensive data processing: used to establish a comprehensive data processing model, import the data transmitted in the data asset data analysis step into the comprehensive data processing model, calculate the data asset similarity index of the target time zone, and transmit it to the comprehensive data identification step; Step 6: Comprehensive data identification: used to accurately compare the data asset similarity index of the target time zone with the data asset similarity index of the preset target time zone during the actual operation process, and transmit the identification result to the early warning step; Step 7: Early warning: It is used to receive the data transmitted in the comprehensive data identification step and send a corresponding early warning signal to the terminal for processing.

2. A data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The structured processing requires organizing and analyzing the original or unstructured data assets to determine their data structure and mode to meet the requirements of the database management system (DBMS), and then converting these data assets into a structured format and storing them securely and efficiently in the database for subsequent query, collection, management and utilization.

3. A data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The sales information data includes the sales order amount, the number of purchased products and the sales profit rate, which are respectively recorded as Xj, Xm and Xt; the user information data includes the number of new user registrations, the number of user churn and the user purchase rate, which are respectively recorded as Yx, Yl and Yg; the enterprise product information data includes the product loss amount, product input cost and inventory turnover rate, which are respectively recorded as Ck, Ct and Cz.

4. The data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The production information data includes the frequency of production equipment failure, the number of qualified products and the amount of production and transportation losses, which are respectively denoted as St, Sh and Sw; the system technical performance data includes the frequency of data storage data update, the amount of invalid data checked and the frequency of shared data, which are respectively denoted as Jg, Jp and Jx.

5. The data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The business-oriented data calculation unit is used to analyze various types of data transmitted by the business-oriented data collection unit, and transmit the analysis results to the comprehensive data processing step; The business-oriented data calculation unit includes a sales information data calculation subunit, a user information data calculation subunit and an enterprise product information data calculation subunit.

6. A data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The sales information data calculation subunit is used to establish a sales information data mathematical model, import the sales information data transmitted by the business data collection unit into the sales information data mathematical model, and calculate the sales information coefficient value of each time sub-area, which is specifically expressed as: XA i represents the sales information coefficient value of the i-th time sub-region, Xj i Indicates the sales order amount in the i-th time sub-region, Xm i represents the number of products purchased in the i-th time sub-region, Xt i represents the sales profit margin of the i-th time sub-region, and n represents the total number of time divisions of the target region.

7. A data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The user information data calculation subunit is used to establish a user information data mathematical model, import the user information data transmitted by the service data collection unit into the user information data mathematical model, and calculate the user information coefficient value of each time sub-area, which is specifically expressed as: YA i represents the user information coefficient value of the i-th time sub-region, Yx i represents the number of new user registrations in the i-th time sub-area, Yl i represents the number of users who have churned in the i-th time sub-region, Yg i represents the user purchase rate of the i-th time sub-region, and n represents the total number of time divisions of the target region; the enterprise product information data calculation subunit is used to establish an enterprise product information data mathematical model, import the enterprise product information data transmitted by the business data collection unit into the enterprise product information data mathematical model, and calculate the enterprise product information coefficient value of each time sub-region, which is specifically expressed as: CA i represents the enterprise product information coefficient value of the i-th time sub-region, Ck i-1 Indicates the product loss amount in the i-1th time sub-region, Ck i Indicates the product loss amount in the i-th time sub-region, Ct i represents the product input cost of the i-th time sub-region, Ct max represents the product input cost in the target time zone, Cz i represents the inventory turnover rate of the i-th time sub-region, and n represents the total number of time divisions of the target region.

8. The data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The operation resource data unit is used to analyze various types of data transmitted by the operation resource data acquisition unit and transmit the analysis results to the comprehensive data processing step; The operation resource data calculation unit includes a production information data calculation subunit and a system technical performance data calculation subunit.

9. A data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The production information data calculation subunit is used to establish a production information data mathematical model, import the production information data transmitted by the operation resource data acquisition unit into the production information data mathematical model, and calculate the production information coefficient value of each sub-time zone, which is specifically expressed as: SA i St represents the production information coefficient value of the i-th time sub-region, i-1 represents the failure frequency of production equipment in the i-1th time sub-region, Sh i Sh represents the number of qualified products in the i-th time sub-area, all represents the total production of the i-th time sub-region, Sw i-1 represents the production and transportation loss in the i-1th time sub-region, Sw i represents the production and transportation loss of the i-th time sub-area, and n represents the total number of time divisions of the target area; the system technical performance data calculation subunit is used to establish a system technical performance data mathematical model, import the system technical performance data transmitted by the operation resource data acquisition unit into the system technical performance data mathematical model, and calculate the system technical performance coefficient value of each time sub-area, which is specifically expressed as: J A i represents the system technical performance coefficient value of the i-th time sub-area, Jg i Indicates the data storage data update frequency of the i-th time sub-region, Jx i-1 represents the frequency of shared data in the i-1th time sub-region, Jx i represents the frequency of shared data in the i-th time sub-region, Jp i-1 represents the amount of invalid data checked in the i-1th time sub-region, Jp i It represents the amount of invalid data checked in the i-th time sub-region, and n represents the total number of time divisions of the target region.

10. A data asset similarity comparison method for data asset registration according to claim 1, characterized in that: The comprehensive data processing model is obtained in the following manner: Where L represents the data asset similarity index of the target time zone, θ i represents the service-oriented evaluation value of the i-th time sub-region, ρ i represents the operational resource evaluation value of the i-th time sub-region, μ1 represents the mean of the business-oriented data of the target time region, μ2 represents the mean of the operational resource data of the target time region, α represents other influencing factors of the data asset similarity index of the target time region, where i = 1, 2, ..., n, and n represents the total number of time divisions of the target region.

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