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Channel allocation method and system with autonomous learning characteristic

A channel allocation and self-learning technology, applied in the field of multi-channel retrieval, can solve the problems of data transmission delay between the server and the client, the channel cannot meet the needs of large data volume, and the performance of the server is disadvantageous, so as to reduce the data processing volume and improve the performance of the server. Retrieval efficiency and the effect of improving user experience

Pending Publication Date: 2021-02-09
SHANDONG LUNENG SOFTWARE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In this way, because the number of channels is fixed, if the amount of retrieved data is too small, it will cause waste of resources in some channels of the server, which is not conducive to the performance of other applications on the server; and if the amount of retrieved data is huge, fixed The number of channels may not be able to meet the demand for large amounts of data, and there will be a long data delay during data transmission between the server and the client, resulting in slow retrieval efficiency and waste of resources

Method used

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  • Channel allocation method and system with autonomous learning characteristic

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Effect test

Embodiment 1

[0038] In one or more implementations, a channel allocation method with self-learning characteristics is disclosed, refer to figure 1 , including the following procedures:

[0039] Step S101: Extract keywords of the original text to be processed, segment keywords at the finest granularity, and perform data indexing based on the index keywords after segmentation;

[0040] Specifically, the process of performing data indexing includes the following processes:

[0041] Step 1011: extracting the keywords of the original text to be processed, and performing the most fine-grained segmentation of the keywords to form several individual candidate differential words;

[0042] Step 1012: The single candidate differential word is matched with the full-service data center, and the index keyword group is obtained through screening;

[0043] Step 1013: sort the index keywords in the index keyword group according to the order of priority, and determine the weight of the vocabulary category...

Embodiment 2

[0060] In one or more embodiments, a channel allocation system with self-learning characteristics is disclosed, including:

[0061] The data index module is used to extract the keywords of the original text to be processed, perform the most fine-grained segmentation of keywords, and perform data indexing based on the index keywords after segmentation;

[0062] The channel allocation module is configured to input the data level corresponding to the index result corresponding to the index keyword with the highest weight into the channel number selection model, and output the number of channels to be allocated.

[0063] It should be noted that, the specific working modes of the above modules are realized by the method disclosed in the first embodiment, which will not be repeated here.

Embodiment 3

[0065] In one or more embodiments, a terminal device is disclosed, including a server, the server includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the The program realizes the channel allocation method with the self-learning feature in the first embodiment. For the sake of brevity, details are not repeated here.

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PUM

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Abstract

The invention discloses a channel allocation method and system with an autonomous learning characteristic. The method comprises the steps of extracting a keyword of a to-be-processed original text, carrying out the finest-grained segmentation of the keyword, and carrying out the data indexing based on the segmented index keyword, and inputting the data magnitude corresponding to the index result corresponding to the index keyword with the highest weight into the channel number selection model, and outputting the number of channels needing to be allocated. The method for dynamically allocatingthe number of the channels based on the data magnitude has the beneficial effects that excessive channels are not occupied, resource waste can be reduced, the occupied number of system responses is not increased, the retrieval efficiency is improved, and the user experience is improved.

Description

technical field [0001] The invention belongs to the technical field of multi-channel retrieval, and in particular relates to a channel allocation method and system with self-learning characteristics. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] When performing data indexing in the existing technology, word segmentation is often used for word segmentation, and data retrieval is realized by matching based on words stored in the dictionary; when processing retrieved data, the server directly retrieves the data by calling a fixed number of channels and connecting to the data pool. result set. [0004] In this way, because the number of channels is fixed, if the amount of retrieved data is too small, it will cause waste of resources in some channels of the server, which is not conducive to the performance of other applications on the server...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06F16/31G06F16/33G06F16/332G06F16/383G06F40/289
CPCG06F9/5038G06F9/5011G06F16/316G06F16/3325G06F16/3346G06F16/383G06F40/289G06F2209/5021Y02D10/00
Inventor 孔平杨军虎柳明辉靳占新朱郯博李玉华董斌徐晓蕊王莉莉魏荣久
Owner SHANDONG LUNENG SOFTWARE TECH
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