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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- 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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- Human Computer Interaction (AREA)
- Computational Linguistics (AREA)
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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 present invention relates to data entry techniques field, a kind of method that self learning type intelligent data rapidly inputs.
Background technology
At present, the typing of medical institutions' basic data is to be filtered by querying condition, is then selected by operator, when similar
After basic data is many, needing to find line by line the data of needs, hit rate is low.Daily middle use specifies sortord, and allowing can
The priority ordering that can commonly use, mode is single, and the frequently-used data that multiple business section office need is not quite similar, and sortord can not be fine
Solution Rapid input.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of method that self learning type intelligent data rapidly inputs, it is possible to solve existing
There is the deficiency of technology, improve the speed of data input.
For solving above-mentioned technical problem, the technical solution used in the present invention is as follows.
A kind of method that self learning type intelligent data rapidly inputs, comprises the following steps:
A, by basic data input database, set the degree of association between use frequency and the basic data of basic data；
B, use input equipment to input data to be entered one by one, system according to data to be entered with inputted data the degree of association and
Data to be entered are resequenced by the use frequency of data to be entered, it is provided that to user；
C, system are according to user for the selection of data to be entered, and use frequency and the degree of association to follow-up data to be entered are entered
Row sum-equal matrix.
As preferably, in step A, the setting value using frequency is as follows with the relation of initial value,
Wherein, F is the initial value using frequency,For using the setting value of frequency,、Being proportionality coefficient with β, r is association
Degree.
As preferably, in step B, the sequence of data to be entered is according to the degree of association of the total data inputted and to be entered
The use frequency of data is determined,
Wherein, f is ranking results,For using the setting value of frequency, r is the degree of association, and the biggest sequence of numerical value of f is the most forward.
As preferably, in step C, the method for adjustment of use frequency is,
The method of adjustment of the degree of association is,
Wherein,For adjust after use frequency,For adjust after the degree of association,WithFor proportionality coefficient.
As preferably, while input data, the degree of association of the data in data base being carried out second-order correction, secondary is repaiied
Positive method is,
Wherein,For inputting the degree of association of data and data to be entered, g(x) it is linear function.
Use having the beneficial effects that of being brought of technique scheme: the present invention utilizes the use frequency of data and mutual
Data are ranked up by the degree of association, can select the data to be selected that hit rate is higher exactly.Simultaneously compared to prior art
In data input association technique, the present invention utilize the degree of association and use frequency association character, the arrangement to data to be entered
Order carries out real-time optimization, is effectively improved the hit rate of data to be selected.On the premise of the present invention need not predefine, intelligence
Study, category, section office's automatic Display high temperature data, improve input speed.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of one detailed description of the invention of the present invention.
Detailed description of the invention
With reference to Fig. 1, a specific embodiment of the present invention comprises the following steps:
A, by basic data input database, set the degree of association between use frequency and the basic data of basic data；
B, use input equipment to input data to be entered one by one, system according to data to be entered with inputted data the degree of association and
Data to be entered are resequenced by the use frequency of data to be entered, it is provided that to user；
C, system are according to user for the selection of data to be entered, and use frequency and the degree of association to follow-up data to be entered are entered
Row sum-equal matrix.
In step A, the setting value using frequency is as follows with the relation of initial value,
Wherein, F is the initial value using frequency,For using the setting value of frequency,、Being proportionality coefficient with β, r is association
Degree.
In step B, the sequence of data to be entered is according to the degree of association of total data inputted and making of data to be entered
It is determined by frequency,
Wherein, f is ranking results,For using the setting value of frequency, r is the degree of association, and the biggest sequence of numerical value of f is the most forward.
In step B, the sequence of data to be entered is according to the degree of association of total data inputted and making of data to be entered
It is determined by frequency,
Wherein, f is ranking results,For using the setting value of frequency, r is the degree of association, and the biggest sequence of numerical value of f is the most forward.
In step C, the method for adjustment of use frequency is,
The method of adjustment of the degree of association is,
Wherein,For adjust after use frequency,For adjust after the degree of association,WithFor proportionality coefficient.
While input data, the degree of association of the data in data base is carried out second-order correction, the method for second-order correction
For,
Wherein,For inputting the degree of association of data and data to be entered, g(x) it is linear function.
The present invention is in routine use, and after have selected data, in units of section office, record uses the temperature of data, makes
Automatically analyze data in, preferentially show high temperature data when calling data, reach few querying condition or without querying condition
In the case of, can quickly select, improve data hit rate.Initiated by user, intelligent optimization data, formed certainly after selecting data
Learning database, provides basic data for intelligent optimization, forms efficient closed loop.In different business section office, have and oneself use
The data of high temperature, with section office for unit analytical data, decouple the data that these section office are of little use, it is achieved the Rapid input of specialty.
Fractional analysis is carried out for different types of data, different according to type, use independent temperature.Data dictionary in operation system
Comprise inspection, check, treat, the classification such as meals, category is classified, and generates the learning database of respective classes, faces different
Bed section office use project frequency inconsistent, as gynecologial examination relevant item uses frequency the highest in gynecological, therefore need to preferentially show
Gynecologial examination relevant item.Automatically learn to optimize data by classification, specialty section office, improve data hit rate, reduce data choosing
Select the time, allow operator have the more time to carry out the business operation of oneself, improve the productivity.
The search dog input method (with medical vocabulary dictionary) that the input method of the use present invention and prior art are commonly used is carried out
Contrast test, result is as follows:
| The hit rate of the 3rd character | The hit rate of the 5th character | The hit rate of the 7th character | The hit rate of the 9th character | |
| Prior art | 76.3% | 81.4% | 77.9% | 65.8% |
| The present invention | 79.4% | 85.3% | 89.2% | 91.5% |
As can be seen from the above table, the input method of the present invention can be effectively improved the selection hit rate of data to be entered, Er Qie
In the case of larger data amount better.
The ultimate principle of the present invention and principal character and advantages of the present invention have more than been shown and described.The technology of the industry
Personnel, it should be appreciated that the present invention is not restricted to the described embodiments, simply illustrating this described in above-described embodiment and description
The principle of invention, without departing from the spirit and scope of the present invention, the present invention also has various changes and modifications, and these become
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and
Equivalent defines.
Claims (5)
1. the method that a self learning type intelligent data rapidly inputs, it is characterised in that: comprise the following steps:
A, by basic data input database, set the degree of association between use frequency and the basic data of basic data；
B, use input equipment to input data to be entered one by one, system according to data to be entered with inputted data the degree of association and
Data to be entered are resequenced by the use frequency of data to be entered, it is provided that to user；
C, system are according to user for the selection of data to be entered, and use frequency and the degree of association to follow-up data to be entered are entered
Row sum-equal matrix.
The method that self learning type intelligent data the most according to claim 1 rapidly inputs, it is characterised in that: in step A, make
Relation by the setting value of frequency Yu initial value is as follows,
Wherein, F is the initial value using frequency,For using the setting value of frequency,、Being proportionality coefficient with β, r is association
Degree.
The method that self learning type intelligent data the most according to claim 2 rapidly inputs, it is characterised in that: in step B, treat
The sequence of input data is determined according to the degree of association of total data inputted and the use frequency of data to be entered,
Wherein, f is ranking results,For using the setting value of frequency, r is the degree of association, and the biggest sequence of numerical value of f is the most forward.
The method that self learning type intelligent data the most according to claim 3 rapidly inputs, it is characterised in that: in step C, make
By the method for adjustment of frequency it is,
The method of adjustment of the degree of association is,
Wherein,For adjust after use frequency,For adjust after the degree of association,WithFor proportionality coefficient.
The method that self learning type intelligent data the most according to claim 4 rapidly inputs, it is characterised in that: in input data
While, the degree of association of the data in data base is carried out second-order correction, the method for second-order correction is,
Wherein,For inputting the degree of association of data and data to be entered, g(x) it is 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 |
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 |
-
2016
- 2016-07-18 CN CN201610563521.7A patent/CN106020508A/en active Pending
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 |
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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Application publication date: 20161012 |