Object data association index system, system construction method and system application method

Abstract

The invention relates to an object data association index system, a system construction method and a system application method, and belongs to the technical field of computers. The system comprises a plurality of objects, an object small data server and an object index data server. Object small data is stored by utilizing the object small data server; an object identifier ID and an operation ID are obtained from the object small data server by utilizing the object index data server; and object index data is generated and stored, so that an object data association index among the object ID, the operation ID matched with the object ID and the object small data generated by operation can be established, convenient data interaction and sharing among different data systems are realized, a threshold of big data utilization is lowered, data association among the different data systems can be determined, an association model is analyzed, and the data utilization value is finally improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to the field of computer network technology, and specifically refers to an object data association index system and a construction and application method of the system. Background technique [0002] Big data, or huge amount of data, refers to the amount of data involved that is so large that it cannot be captured, managed, processed, and organized within a reasonable period of time through current mainstream software tools to help enterprises make more positive business decisions. Information. [0003] Its concept was first proposed by Victor Mayer-Schönberger in "The Era of Big Data". The author believes that if the "big data" data is comprehensive enough, the reuse value of useless data, especially the correlation value, can be rediscovered through analysis and mining technology, so as to obtain data value that cannot be obtained from "small data". Therefore, in terms ...

AI Technical Summary

Problems solved by technology

[0008] 3. The Internet of Things (public service data) or mobile Internet (mobile devices / wearable devices) is becoming one of the main data sources of "big data", but because the amount of data is not large enough, and the way of specific data is limited, It is not yet possible to obtain more effective data through analysis

[0011] 2. Although "big data" is more diverse than "small data", it is only relative

Many data originally have a strong correlation or even a causal relationship, but due to the lack of existing technical means, the correlation between these data cannot be reflected in the "small data", so we have to use the method of big data to "guess"

[0012] 3. Since the method of "big data" is "collection first and then analysis", and the so-called "collection" activity is not in the process of data generation, but in the process of data collection after the event, so diversity cannot be included in the data enhanced during production

[0015] 2. Since the source of big data is many "small data", and the data production process of "small data" does not originate from the analysis and mining purpose that data application intends to achieve, so although the data volume of big data can be very large, However, the causal relationship and association relationship between the data are missing, so the data directly loses a lot of its inherent value

Many data analysis and mining methods of big data occur after the data is collected, and it is impossible to intervene in the data production process and production methods beforehand or during the data production, which also affects the "controllability" of the value of big data

[0017] 4. Barriers to Progressive Utilization

[0018] 1. The technical threshold of big data is very high, which cannot be controlled by ordinary enterprises. Therefore, it is difficult for a data owner to obtain data benefits by relying on its own strength

[0019] 2. One of the core values ​​of big data lies in the size of the data volume, and obtaining a large amount of data requires many data sources. It is difficult for any single enterprise or enterprise cluster to realize the value of big data only through its own data accumulation. Incremental Exploitation Forms a Huge Barrier

[0020] 3. Large numbers require the intervention of external professional companies, making the value chain involve many aspects, including technical conditions, professional knowledge, data sources, application specifications and other factors, and local optimization is difficult to produce results

[0035] From this, it can be considered that as many cooperative enterprises in the supply chain need to develop as many ERP system data interfaces, the development work is quite complicated, and the development and application costs are high

[0036] At the same time, this data interaction mode also has problems with data storage capacity and data security

[0037] The ERP of each enterprise will obtain the necessary data for collaborative work in the data chain through the data interface for localized storage. Often the same data will be stored in the ERP systems of multiple enterprises; commodity manufacturers and e-commerce / stores need Through big data analysis of commodity sales to formulate corresponding production plans and commodity sales market operations, etc., it is also necessary to collect enterprise ERP data from other links in the supply chain, which will cause the same data to be duplicated in multiple ERP systems in the supply chain. repeated storage

[0038] The data security in the entire supply chain is very weak. Usually, as long as there is a security loophole in the ERP system of one enterprise, all the data of the goods in the entire supply chain will often be leaked, causing losses to all enterprises in the supply chain.

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