CN106020508A - Self-learning method for rapid and intelligent input of data - Google Patents
Self-learning method for rapid and intelligent input of data Download PDFInfo
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- CN106020508A CN106020508A CN201610563521.7A CN201610563521A CN106020508A CN 106020508 A CN106020508 A CN 106020508A CN 201610563521 A CN201610563521 A CN 201610563521A CN 106020508 A CN106020508 A CN 106020508A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 238000012886 linear function Methods 0.000 claims description 3
- 230000007547 defect Effects 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000013479 data entry Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000037213 diet Effects 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000005457 optimization Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
- G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
- G06F3/0233—Character input methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/90335—Query processing
- G06F16/90348—Query processing by searching ordered data, e.g. alpha-numerically ordered data
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Abstract
The invention discloses aself-learning method for rapid and intelligent input of data. The method comprises the following steps: A) inputting a basic data to a database and setting degree of association between the use frequency of the basic data and the basic data; B) using input equipment forinputting to-be-inputted dataone by one, rearranging the to-be-inputted data by the system according to the degree of association between the to-be-inputted data and the inputted data and the use frequency of the to-be-inputted data, and supplying to a user; C) adjusting the use frequency of the to-be-inputted data and the degree of association by the system according to the selection of the user for the to-be-inputted data. According to the method provided by the invention, the defects of the prior art are overcome and the data inputting speed is increased.
Description
Technical Field
The invention relates to the technical field of data entry, in particular to a self-learning intelligent data rapid input method.
Background
At present, the basic data of the medical institution is input by filtering through query conditions and then selected by an operator, and when more similar basic data exist, the required data need to be searched line by line, so that the hit rate is low. The designated sorting mode is used in daily life, so that the priority sorting which is possibly commonly used is single, the common data required by a plurality of business departments are different, and the rapid input cannot be well solved by the sorting mode.
Disclosure of Invention
The invention aims to provide a self-learning intelligent data rapid input method, which can solve the defects of the prior art and improve the data input speed.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A self-learning intelligent data rapid input method comprises the following steps:
A. inputting the basic data into a database, and setting the association degree between the use frequency of the basic data and the basic data;
B. inputting data to be input one by using input equipment, and reordering the data to be input by the system according to the association degree of the data to be input and the input data and the use frequency of the data to be input, and providing the data to a user;
C. the system adjusts the use frequency and the relevance of the subsequent data to be input according to the selection of the user on the data to be input.
Preferably, in step A, the relationship between the set value and the initial value of the use frequency is as follows,
wherein F is an initial value of the use frequency,in order to use the set value of the frequency,、and β is a scaling factor and r is a degree of correlation.
Preferably, in step B, the order of the data to be input is determined according to the relevance of all the input data and the use frequency of the data to be input,
wherein, f is the sequencing result,in order to use the frequency setting value, r is the degree of correlation, and the larger the value of f, the higher the ranking.
Preferably, in step C, the adjustment method of the frequency used is,
the method for adjusting the degree of association is that,
wherein,in order to adjust the frequency of use after the adjustment,in order to adjust the degree of association after the adjustment,andis a scaling factor.
Preferably, the association degree of the data in the database is corrected twice at the same time as the input of the data, and the second correction is performed by,
wherein,for the correlation degree of the inputted data and the data to be inputted, g (x) is a linear function.
Adopt the beneficial effect that above-mentioned technical scheme brought to lie in: the invention sequences the data by using the use frequency and the mutual correlation degree of the data, and can accurately select the data to be selected with higher hit rate. Compared with the data input association technology in the prior art, the data input association method based on the multi-user interaction optimizes the arrangement sequence of the data to be input in real time by using the association property of the association degree and the use frequency, and effectively improves the hit rate of the data to be selected. The invention can intelligently learn without predefining, automatically display high-heat data according to categories and departments, and improve the input speed.
Drawings
FIG. 1 is a schematic diagram of one embodiment of the present invention.
Detailed Description
Referring to fig. 1, one embodiment of the present invention includes the steps of:
A. inputting the basic data into a database, and setting the association degree between the use frequency of the basic data and the basic data;
B. inputting data to be input one by using input equipment, and reordering the data to be input by the system according to the association degree of the data to be input and the input data and the use frequency of the data to be input, and providing the data to a user;
C. the system adjusts the use frequency and the relevance of the subsequent data to be input according to the selection of the user on the data to be input.
In step A, the relationship between the set value and the initial value of the use frequency is as follows,
wherein F is an initial value of the use frequency,in order to use the set value of the frequency,、and β is a scaling factor and r is a degree of correlation.
In step B, the sequence of the data to be input is determined according to the relevance of all the input data and the use frequency of the data to be input,
wherein, f is the sequencing result,in order to use the frequency setting value, r is the degree of correlation, and the larger the value of f, the higher the ranking.
In step B, the sequence of the data to be input is determined according to the relevance of all the input data and the use frequency of the data to be input,
wherein, f is the sequencing result,in order to use the frequency setting value, r is the degree of correlation, and the larger the value of f, the higher the ranking.
In the step C, the adjustment method of the use frequency is that,
the method for adjusting the degree of association is that,
wherein,in order to adjust the frequency of use after the adjustment,in order to adjust the degree of association after the adjustment,andis a scaling factor.
While inputting data, the relevance of the data in the database is corrected for the second time, the method of the second correction is,
wherein,for the correlation degree of the inputted data and the data to be inputted, g (x) is a linear function.
In daily use, after data is selected, the heat of the data is recorded by taking departments as units, the data is automatically analyzed in use, high-heat data is preferentially displayed when the data is called, quick selection can be realized under the condition of few query conditions or no query conditions, and the data hit rate is improved. The method is initiated by a user, intelligently optimizes data, selects the data and then forms a self-learning library for providing basic data for intelligent optimization, and forms a high-efficiency closed loop. In different business departments, the data with high heat degree used by the department is analyzed by taking the department as a unit, the data which is not commonly used by the department is decoupled, and professional rapid entry is realized. Packet analysis is performed for different types of data, using independent heat levels, depending on the type. The data dictionary in the service system comprises categories of examination, inspection, treatment, diet and the like, the categories are classified according to the categories to generate learning libraries of corresponding categories, and the frequency of using items in different clinical departments is inconsistent, for example, the frequency of using related items in gynecological examination is very high, so the related items in gynecological examination need to be displayed preferentially. By automatically learning and optimizing data through categories and professional departments, the data hit rate is improved, the data selection time is reduced, an operator has more time to perform own business operation, and the productivity is improved.
The results of comparative tests using the input method of the present invention and the commonly used dog search input method (with medical vocabulary thesaurus) of the prior art are as follows:
| hit rate of the third character | Hit rate for the fifth character | Hit rate for seventh character | Hit rate for the ninth character | |
| Prior Art | 76.3% | 81.4% | 77.9% | 65.8% |
| The invention | 79.4% | 85.3% | 89.2% | 91.5% |
The above table shows that the input method of the present invention can effectively improve the selection hit rate of the data to be input, and the effect is better under the condition of larger data volume.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A self-learning intelligent data rapid input method is characterized in that: the method comprises the following steps:
A. inputting the basic data into a database, and setting the association degree between the use frequency of the basic data and the basic data;
B. inputting data to be input one by using input equipment, and reordering the data to be input by the system according to the association degree of the data to be input and the input data and the use frequency of the data to be input, and providing the data to a user;
C. the system adjusts the use frequency and the relevance of the subsequent data to be input according to the selection of the user on the data to be input.
2. The self-learning intelligent data rapid input method according to claim 1, characterized in that: in step A, the relationship between the set value and the initial value of the use frequency is as follows,
wherein F is an initial value of the use frequency,in order to use the set value of the frequency,、and β is a scaling factor and r is a degree of correlation.
3. The self-learning intelligent data rapid input method according to claim 2, characterized in that: in step B, the sequence of the data to be input is determined according to the relevance of all the input data and the use frequency of the data to be input,
wherein, f is the sequencing result,for setting the frequency of use, r is the degree of correlation, fThe larger the value the more advanced the ranking.
4. The self-learning intelligent data rapid input method according to claim 3, characterized in that: in the step C, the adjustment method of the use frequency is that,
the method for adjusting the degree of association is that,
wherein,in order to adjust the frequency of use after the adjustment,in order to adjust the degree of association after the adjustment,andis a scaling factor.
5. The self-learning intelligent data rapid input method according to claim 4, characterized in that: while inputting data, the relevance of the data in the database is corrected for the second time, the method of the second correction is,
wherein,for the correlation degree of the inputted data and the data to be inputted, g (x) is a linear function.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108240978A (en) * | 2016-12-26 | 2018-07-03 | 同方威视技术股份有限公司 | Self-learning type method for qualitative analysis based on Raman spectrum |
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| CN1825837A (en) * | 2005-02-23 | 2006-08-30 | 朗迅科技公司 | Personal information subscribing for and transmitting by instant message transmission |
| CN101004738A (en) * | 2006-01-16 | 2007-07-25 | 夏普株式会社 | Character input device, device for possessing same and input method |
| CN101923795A (en) * | 2010-03-03 | 2010-12-22 | 陈小芳 | Synchronous word reading method of word and sentence |
| CN103218447A (en) * | 2013-04-24 | 2013-07-24 | 东莞宇龙通信科技有限公司 | Associating input method and device |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1825837A (en) * | 2005-02-23 | 2006-08-30 | 朗迅科技公司 | Personal information subscribing for and transmitting by instant message transmission |
| CN101004738A (en) * | 2006-01-16 | 2007-07-25 | 夏普株式会社 | Character input device, device for possessing same and input method |
| CN101923795A (en) * | 2010-03-03 | 2010-12-22 | 陈小芳 | Synchronous word reading method of word and sentence |
| CN103218447A (en) * | 2013-04-24 | 2013-07-24 | 东莞宇龙通信科技有限公司 | Associating input method and device |
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| CN108240978A (en) * | 2016-12-26 | 2018-07-03 | 同方威视技术股份有限公司 | Self-learning type method for qualitative analysis based on Raman spectrum |
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