CN106651159A - Barrier-free detection system-based user evaluation method - Google Patents
Barrier-free detection system-based user evaluation method Download PDFInfo
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- CN106651159A CN106651159A CN201611120283.9A CN201611120283A CN106651159A CN 106651159 A CN106651159 A CN 106651159A CN 201611120283 A CN201611120283 A CN 201611120283A CN 106651159 A CN106651159 A CN 106651159A
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Abstract
The invention discloses a barrier-free detection system-based user evaluation method. The method comprises the steps of firstly calculating points of a user, wherein a point corresponding to a task is added when the user finishes the task correctly each time, the larger the number of correctly finished tasks is, the larger the number of points is, and the profession degree is also increased along with an increase in number of points; secondly calculating an accuracy rate of task finishing, wherein the profession degree of the user is positively related to the accuracy of task finishing, and a profession degree score of the user takes the accuracy rate as a main element; thirdly calculating an abandonment rate of task finishing, wherein when the proportion of abandoning the task by the user is increased, the evaluation is negatively influenced, so that the profession degree score is corresponding reduced according to the abandonment rate; and finally calculating the profession degree of the user by integrating the above factors. The method has the advantages that a task allocation policy is based on the points of the user and the profession degree score; and the profession degree evaluation of the method can enable the task to be allocated to a more suitable user and increase the accuracy rate of a task result.
Description
Technical field
The present invention relates to the user in mass-rent system evaluates and task allocation technique field, accessible detection is based particularly on
User's evaluation methodology of system.
Background technology
By the end of the year 2014, China 60 years old and above aging population about 2.12 hundred million, people with disability's quantity there are about 85,020,000.With
The continuous rising of the highly popular and the Internet of the Internet importance in daily life, for large number of old people, residual
For the special populations such as disease people, vision and disappearance acoustically directly hinder them normally to obtain the information on the Internet.Therefore
How to make this part population obtain internet information with no obstacle to be particularly important.
In order that having impedient people more effectively to obtain and using the information on the Internet, there is provided the website of information needs symbol
Close the standard of webpage accessibility designs guide (WCAG, Web Content Accessibility Guidelines).Unfortunate
It is that only least a portion of website meets clog-free standard.And in order to whether the design for verifying a website meets accessible mark
It is accurate, it would be desirable to which that the page therein is detected according to the rule of standard.Therefore have devised accessible detecting system.By
In China, current Websites quantity is huge, manual detection it is undermanned, then efficient task allocative decision will be system
Main target.And task distribution is based on user's appraisement system.
At present accessible detecting system in the world cans be counted on one's fingers, corresponding user's appraisement system be not particularly suited for be
System, therefore the user's evaluation methodology for proposing is by with reference to the task application behavior of the feature of the system and user and performance, it is right
Professional degree of the user on each task category is scored, and it is high that each task is distributed to into the specialty degree on this task category
Crowd, so as to effectively improve the accuracy rate of task allocative efficiency and task result.
The content of the invention
The present invention will overcome the above-mentioned determination of prior art, there is provided a kind of user based on accessible detecting system evaluates
Method, to improve the efficiency of accessible detecting system distribution task and improve the accuracy rate of last task result.
The present invention proposes a kind of user's evaluation methodology based on accessible detecting system, comprises the following steps：
1) integration of user is calculated, user often does will increase the corresponding integration of task to a task, completes correct appointing
The quantity of business is more, and integration is more, and professional degree also will increase with number of tasks and increasing for integration；
2) accuracy rate of the task that completes is calculated, the professional degree of user is proportionate with the accuracy rate of the task that completes, accuracy rate
It is the key component of user's specialty degree scoring；
3) abandonment rate of the task that completes is calculated, the ratio that user abandons task is higher, it will have negative effect to evaluation, because
This also will accordingly reduce professional degree scoring according to abandonment rate；
4) amid all these factors the professional degree of user calculated.
Step 1) described in task integration with number of tasks specifically：
11) for Detection task, grade classification will be carried out according to its detection difficulty size, and will distribute corresponding integration, it is difficult
Degree is more big, integrates higher；
12) specialty degree evaluation is calculated mainly for different task categories, and the task quantity performed of certain classification is got over
Many (the correct number of tasks of result), professional degree also will be higher, and last number of tasks will be added to professional degree scoring according to certain weight
In.
Step 2) described in accuracy rate specifically：
21) number of tasks of user's application is apply_num, and correct number of tasks is correct_ in last these tasks
Num (distributes to many people to complete, end product is obtained by certain rule arbitration, with end product identical people's explanation with a task
As a result correctly otherwise it is considered as error result), then corresponding accuracy rate is
22) accuracy rate is higher, illustrates that user has higher authority, therefore accuracy rate as its specialty on the generic task
The key component of degree scoring.
Step 3) described in abandonment rate specifically：
31) number of tasks of user's application is apply_num, and the number of tasks that user's selection is abandoned in last these tasks is
(abandoning for task distributes to the task of user to giveup_num originally, and user selects to abandon completing the task, and these tasks will
Other users are reallocated to by reclaiming again), then corresponding abandonment rate is
32) abandon task and increase the efficiency decline that system task will be caused to distribute, therefore abandonment rate is that professional degree is evaluated
Important indicator, then can accordingly reduce as abandonment rate increases user's specialty degree.
Step 4) described in professional degree scoring, obtain after being calculated using different weights the above-mentioned factor.
It is an advantage of the current invention that：The scoring of user integral and specialty degree is the basis of Task Assigned Policy, of the invention
Professional degree evaluation can make task distribute to more suitably user and improve the accuracy rate of task result.
Description of the drawings
Fig. 1 is method of the present invention flow chart.
Specific embodiment
Referring to the drawings, the present invention is further illustrated：
A kind of user's evaluation methodology based on accessible detecting system, the method is comprised the following steps：
1) integration of user is calculated, user often does will increase the corresponding integration of task to a task, completes correct appointing
The quantity of business is more, and integration is more, and professional degree also will increase with number of tasks and increasing for integration；
2) accuracy rate of the task that completes is calculated, the professional degree of user is proportionate with the accuracy rate of the task that completes, accuracy rate
It is the key component of user's specialty degree scoring；
3) abandonment rate of the task that completes is calculated, the ratio that user abandons task is higher, it will have negative effect to evaluation, because
This also will accordingly reduce professional degree scoring according to abandonment rate；
4) amid all these factors the professional degree of user calculated.
Step 1) described in task integration with number of tasks specifically：
11) for Detection task, grade classification will be carried out according to its detection difficulty size, and will distribute corresponding integration, it is difficult
Degree is more big, integrates higher；
12) specialty degree evaluation is calculated mainly for different task categories, and the task quantity performed of certain classification is got over
Many (the correct number of tasks of result), professional degree also will be higher, and last number of tasks will be added to professional degree scoring according to certain weight
In.
Step 2) described in accuracy rate specifically：
21) number of tasks of user's application is apply_num, and correct number of tasks is correct_ in last these tasks
Num (distributes to many people to complete, end product is obtained by certain rule arbitration, with end product identical people's explanation with a task
As a result correctly otherwise it is considered as error result), then corresponding accuracy rate is
22) accuracy rate is higher, illustrates that user has a higher authority on the generic task, therefore when calculating specialty and spending, it is accurate
Weight really shared by rate is larger.
Step 3) described in abandonment rate specifically：
31) number of tasks of user's application is apply_num, and the number of tasks that user's selection is abandoned in last these tasks is
(abandoning for task distributes to the task of user to giveup_num originally, and user selects to abandon completing the task, and these tasks will
Other users are reallocated to by reclaiming again), then corresponding abandonment rate is
32) abandon task and increase the efficiency decline that system task will be caused to distribute, therefore abandonment rate is that professional degree is evaluated
Important indicator, then can accordingly reduce as abandonment rate increases user's specialty degree.
Step 4) described in professional degree scoring, obtain after being calculated using different weights the above-mentioned factor.Often
It once to these data can be updated for using when task is distributed.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, the protection of the present invention
Being not construed as of scope is only limitted to the concrete form that embodiment is stated, protection scope of the present invention is also and in this area skill
Art personnel according to present inventive concept it is conceivable that equivalent technologies mean.
Claims (5)
1. a kind of user's evaluation methodology based on accessible detecting system, comprises the steps：
1) integration of user is calculated, user often does will increase the corresponding integration of task to a task, complete correct task
Quantity is more, and integration is more, and professional degree also will increase with number of tasks and increasing for integration；
2) accuracy rate of the task that completes is calculated, the professional degree of user is proportionate with the accuracy rate of the task that completes, accuracy rate is to use
The key component of family specialty degree scoring；
3) abandonment rate of the task that completes is calculated, the ratio that user abandons task is higher, it will have negative effect to evaluation, therefore
Professional degree scoring will accordingly be reduced according to abandonment rate；
4) amid all these factors the professional degree of user calculated.
2. user's evaluation methodology as claimed in claim 1 based on accessible detecting system, it is characterised in that：Described step
1) the task integration described in is with number of tasks specifically：
11) for Detection task, grade classification will be carried out according to its detection difficulty size, and will distribute corresponding integration, difficulty will be got over
It is big then integrate higher；
12) specialty degree evaluation is calculated mainly for different task categories, and the task quantity performed of certain classification is more, specially
Industry degree also will be higher, and last number of tasks will be added in professional degree scoring according to certain weight.
3. user's evaluation methodology as claimed in claim 1 based on accessible detecting system, it is characterised in that：Step 2) in institute
The accuracy rate stated is specifically：
21) number of tasks of user's application is apply_num, and correct number of tasks is correct_num in last these tasks, together
Individual task is distributed to many people and is completed, and end product is obtained by certain rule arbitration, with end product identical people explanation result just
Really otherwise it is considered as error result, then corresponding accuracy rate is
22) accuracy rate is higher, illustrates that user has higher authority on the generic task, therefore accuracy rate is commented as its professional degree
The key component for dividing.
4. user's evaluation methodology as claimed in claim 1 based on accessible detecting system, it is characterised in that：Step 3) in institute
The abandonment rate stated is specifically：
31) number of tasks of user's application is apply_num, and the number of tasks that user's selection is abandoned in last these tasks is
Giveup_num, abandoning for task distributes to the task of user originally, and user selects to abandon completing the task, and these tasks will
Other users are reallocated to by reclaiming again, then corresponding abandonment rate is
32) abandon task and increase the efficiency decline that system task will be caused to distribute, therefore abandonment rate is the important of professional degree evaluation
Index, then can accordingly reduce as abandonment rate increases user's specialty degree.
5. user's evaluation methodology as claimed in claim 1 based on accessible detecting system, it is characterised in that：Step 4) in institute
The professional degree scoring stated, is obtained after being calculated using different weights the above-mentioned factor.
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| CN201611120283.9A CN106651159A (en) | 2016-12-08 | 2016-12-08 | Barrier-free detection system-based user evaluation method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107316156A (en) * | 2017-06-30 | 2017-11-03 | 北京金山安全软件有限公司 | Data processing method, device, server and storage medium |
| CN108595562A (en) * | 2018-04-12 | 2018-09-28 | 西安邮电大学 | User's evaluation data analysing method based on accurate sex determination |
Citations (4)
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| CN101989303A (en) * | 2010-11-02 | 2011-03-23 | 浙江大学 | Automatic barrier-free network detection method |
| CN103279548A (en) * | 2013-06-06 | 2013-09-04 | 浙江大学 | Method for performing barrier-free detection on websites |
| CN103955782A (en) * | 2014-03-27 | 2014-07-30 | 广州市集智信息科技有限公司 | Operational capability measuring and evaluating system |
| US20140244572A1 (en) * | 2006-11-27 | 2014-08-28 | Alex T. Hill | Qualification of website data and analysis using anomalies relative to historic patterns |
-
2016
- 2016-12-08 CN CN201611120283.9A patent/CN106651159A/en not_active Withdrawn
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140244572A1 (en) * | 2006-11-27 | 2014-08-28 | Alex T. Hill | Qualification of website data and analysis using anomalies relative to historic patterns |
| CN101989303A (en) * | 2010-11-02 | 2011-03-23 | 浙江大学 | Automatic barrier-free network detection method |
| CN103279548A (en) * | 2013-06-06 | 2013-09-04 | 浙江大学 | Method for performing barrier-free detection on websites |
| CN103955782A (en) * | 2014-03-27 | 2014-07-30 | 广州市集智信息科技有限公司 | Operational capability measuring and evaluating system |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107316156A (en) * | 2017-06-30 | 2017-11-03 | 北京金山安全软件有限公司 | Data processing method, device, server and storage medium |
| CN107316156B (en) * | 2017-06-30 | 2020-10-09 | 北京金山安全软件有限公司 | Data processing method, device, server and storage medium |
| CN108595562A (en) * | 2018-04-12 | 2018-09-28 | 西安邮电大学 | User's evaluation data analysing method based on accurate sex determination |
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