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Cross-industry data resource integration system based on big data

A technology of data resources and big data, which is applied in the field of cross-industry data resource integration system to achieve the effects of high comprehensive competitiveness, cost reduction and operational efficiency improvement

Inactive Publication Date: 2019-08-20
QI AUTOMOTIVE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the other hand, there are tens of thousands of parts suppliers in the aftermarket, and it is impossible to match parts suppliers with parts only by relying on the accumulation of historical experience.

Method used

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  • Cross-industry data resource integration system based on big data
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  • Cross-industry data resource integration system based on big data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] Example 1 Multi-source heterogeneous data acquisition and storage process

[0051] According to the present invention, refer to the appended figure 1 a and 1b, taking the rack and pinion steering assembly as an example, the data source can choose Taobao, 4S store and sales order feedback.

[0052] first see attached figure 1 a, attached figure 1 a shows the method of collecting and processing multi-source heterogeneous data by using the collection sub-module. For 4S stores and sales record collection paths, structured data is generally obtained, and the fields and attributes in the data table can be customized through the Sqoop tool, and the structured data table can be extracted in batches to a distributed data warehouse based on the Hadoop architecture Hive.

[0053] For the search path of Taobao, the obtained data is generally unstructured. In this embodiment, search for the rack and pinion steering gear assembly to obtain a total number of merchants, choose a m...

Embodiment 2

[0055] Embodiment 2 Multi-source heterogeneous data fusion calculation embodiment

[0056] According to the present invention, refer to the appended figure 2 , also take the rack and pinion steering gear as an example.

[0057] First, in the BP neural network model, the calculation method is as follows:

[0058] (1) Choose a two-layer BP model without limiting the number of hidden layer nodes;

[0059] (2) Determine the input data indicators and select 6 important influencing factors as indicators;

[0060] (3) Determine the number of nodes in the input layer and the output layer to be 6 and 1 respectively;

[0061] (4) Using the Sigmoid function as the transfer function, the formula of the Sigmoid function is: f(x)=(1+e-x)-1;

[0062] (5) Input the input data index into the input layer, the input layer receives the input signal, calculates the weight sum, and then transmits the signal to the middle layer according to the transfer function of the unit, and sends the outpu...

Embodiment 3

[0073] Embodiment 3 The overall flow of the cross-industry data resource integration system based on big data

[0074] Such as image 3 As shown, the cross-industry data resource integration system based on big data includes multi-source heterogeneous data acquisition module, multi-source heterogeneous data fusion calculation module, product data management module, procurement management module and sales management module.

[0075] The multi-source heterogeneous data collection module is used to collect different types of data from at least one data source. At least one data source can be an e-commerce platform, 4S store, customs, and sales orders. Different types of data include numerical data, text data, Transaction records, historical order information, logistics tracking information, etc. Refer to Example 1 for the data collection method.

[0076] After the collection is completed, the multi-source heterogeneous data fusion calculation module is used to process the multi...

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Abstract

The invention provides a cross-industry data resource integration system based on the big data, which comprises a multi-source heterogeneous data acquisition module, a multi-source heterogeneous datafusion calculation module, a product data management module, a purchase management module and a sales management module, and can further comprise an inventory prediction early warning module, a logistics management module and a production module. According to the cross-industry data resource integration system based on the big data, accessory information can be comprehensively and accurately collected and processed, potential requirements of non-vulnerable parts can be mined, and meanwhile transparency of matching of the accessory information and suppliers can be achieved through label matching. By means of the system, an enterprise can accurately grasp the product period and carry out research and development layout in advance, so that rapid and accurate investment is achieved when the market has requirements, continuous and stable supply is guaranteed, and the production cost is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of data resource integration, and in particular relates to a cross-industry data resource integration system based on big data. Background technique [0002] As a relatively large means of transportation, automobiles involve tens of thousands of spare parts, and the categories and models are complex. Traditional auto parts include common maintenance parts, wearing parts, non-wearing parts that are rarely replaced, and accident parts that need to be replaced after an accident. Among the four types of accessories mentioned above, although the replacement frequency of non-wearing parts is low, due to the high unit price of accessories, the value of accessories in the aftermarket accounts for the highest proportion, and the circulation is the most difficult. For companies that focus on the non-consumable parts market, the main bottleneck and challenge at present lies in the wide variety of non-consumable parts,...

Claims

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Application Information

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IPC IPC(8): G06F16/25G06F16/951G06Q10/04G06Q10/06G06Q10/08
CPCG06F16/258G06F16/951G06Q10/04G06Q10/06315G06Q10/0833G06Q10/087
Inventor 尹江华钟永铎鞠晓凤
Owner QI AUTOMOTIVE CO LTD
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