CN104023064B - Statistical method of working hours of field personnel based on intelligent mobile terminal and activity characteristic analysis - Google Patents
Statistical method of working hours of field personnel based on intelligent mobile terminal and activity characteristic analysis Download PDFInfo
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Abstract
The invention discloses a statistical method of working hours of field personnel based on an intelligent mobile terminal and activity characteristic analysis. The statistical method comprises the following steps of: firstly, constructing a system including the intelligent mobile terminal, a background system and a map application; secondly, combining a gyro and a GPS (Global positioning System) to carry out arrival detection, departure detection and activity detection; and lastly, carrying out checking-in and statistics of the working hours according to a credible arrival event, a credible departure event and a credible activity which are obtained according to the arrival detection, the departure detection and the activity detection. According to the statistical method, the more accurate arrival, departure and site activity detection can be achieved, and the anti-cheating detection of arrival, departure and activity can be realized.
Description
Technical field
The present invention relates to locating and tracking and the production automation, more particularly, to one kind are based on intelligent mobile terminal and active characteristics
Field Force's working time statistical method of analysis.
Background technology
Working time management for field personnel is difficult to accurately accomplish at present, and the work of field personnel
Time of making but is closely related with its job performance, disbursement and sattlement.Under some scenes can by install at the scene attendance recorder
Realize.But in most cases, do not have such condition.Simultaneously attendance recorder mode be also not suitable for outdoor, be not powered on or
Duration shorter building-site.There are some systems to follow the tracks of field personnel by GPS location at present, but GPS has letter
Number blind spot, cannot detect to indoor activity, can not solve the scene of some cheatings simultaneously, such as wear for people.
Content of the invention
For the problems referred to above, applicant's empirical studies are improved, and provide one kind to divide with active characteristics based on intelligent mobile terminal
Field Force's working time statistical method of analysis, realizes more accurately reaching, leaves and site activity detection, and realize to arriving
Reach, leave and activity anti-cheating detection.
Technical scheme is as follows：
A kind of Field Force's working time statistical method based on intelligent mobile terminal and analysis on active characteristics, including as follows
Step：
(1) constructing system；
System is made up of intelligent mobile terminal, background system, map application；
Intelligent mobile terminal refer to be equipped with GPS module and gyroscope including smart mobile phone, wearable device
Movably computing device, can run Android or iOS operating system, be provided with client software, call client in the following text；
Described client software is the client software based on Android or IOS exploitation, is capable of identify that user geographical position, detecting are used
Family activity, is communicated with background server end and realizes the record of all kinds of events；
Background system includes：Web service interface：Client and the communication interface of service end；User interface：Pass through for user
Its maintenance work content, checks work statistic；Event logging module：For recording all kinds of times sent by client；Work
Content managing module：For maintenance work type, job site and border；Analysis on active characteristics module：For default confidence
Interval is calculated, and judges the confidence level of work activities；Working time statistical module：For being counted according to believable work activities
Working time；Background system calls server end in the following text；
Map application, is publicly available Internet map application；
(2) reach detection, comprise the following steps：
Step 1-1, markers work place and border on map；
Step 1-2, the position that client provides according to GPS, field personnel is detected and reach job site；
Step 1-3, Client-Prompt user confirms to reach, and after user confirms to reach, client is sent out from trend server end
Send arrival event；
Step 1-4, the arrival event that received server-side client sends automatically, or receive user pass through client hand
The dynamic arrival event sending；
The data structure of described arrival event includes：Personnel ID, Time To Event, geographical location information, action；
The data structure of described action includes：Work item ID, job category, action description, default arrival time, director
ID；
Step 1-5, server end analyzes the legitimacy of arrival event：Judge the geographical location information whether position of arrival event
In the border of operating position；If it is judged that for being to proceed to step 1-6, if it is judged that proceed to step 1-7 for no；
Step 1-6, points out client to have confirmed that arrival, then proceeds to step 1-8；
Step 1-7, points out client arrival unconfirmed；
Step 1-8, follow-up activities detect：Judge whether meet this work in the follow-up active characteristics left before event
The active characteristics of type；If it is judged that for being to proceed to step 1-9, if it is judged that proceed to step 1-10 for no；
Step 1-9, mark arrival event is credible；
Step 1-10, mark arrival event is suspicious；
(3) leave detection, comprise the following steps；
Step 2-1, markers work place and border on map；
Step 2-2, the position that client provides according to GPS, field personnel is detected and leave job site；
Step 2-3, Client-Prompt user confirms to leave, and after user confirms to leave, client is sent out from trend server end
It is sent from out event；
Step 2-4, what received server-side client sent automatically leaves event, or receive user passes through client hand
That moves transmission leaves event；
The described data structure leaving event includes：Personnel ID, Time To Event, geographical location information, action；
The data structure of described action includes：Work item ID, job category, action description, default time departure, director
ID；
Step 2-5, the legitimacy of event is left in server end analysis：Judge whether the geographical position leaving event is located at work
Make outside location boundary and whether have corresponding arrival event；If it is judged that for being to proceed to step 2-6, if it is judged that
Proceed to step 2-7 for no；
Step 2-6, points out client to have confirmed that and leaves, then proceed to step 2-8；
Step 2-7, points out that client is unconfirmed leaves；
Step 2-8, follow-up activities detect：Judge whether meet this work class in the described active characteristics left before event
The active characteristics of type；If it is judged that for being to proceed to step 2-9, if it is judged that proceed to step 2-10 for no；
Step 2-9, it is credible that mark leaves event；
Step 2-10, it is suspicious that mark leaves event；
(4) activity detection, comprises the following steps；
Step 3-1, by work including translational speed, displacement, mobile height, direction change for the gyroscope detection
Dynamic data；After gyroscope detection activity data, periodically send activity detection regular reporting event to server end；Server end root
According to described activity detection regular reporting event, calculate same day intensity index and frequency index；
The data structure of described activity detection regular reporting event includes：Personnel ID, record time started/end time,
Report time, translational speed, mobile height, angle change, displacement, action；The data structure of described action
Including：Work item ID, job category, action description, director ID；
Whether step 3-2, judge described intensity index and frequency index within corresponding confidential interval span；As
Fruit judged result is is to proceed to step 3-3, if it is judged that proceeding to step 3-4 for no；
Step 3-3, is labeled as credible activity, then proceeds to step 3-6；
Step 3-4, is labeled as suspicious activity, then proceeds to step 3-5 or step 3-6；
Step 3-5, adminicle；Step 3-3 is proceeded to after the completion of adminicle；
Described adminicle refers to submit to the photo of the inclusion work on the spot for manually can be determined that work authenticity, visitor
The written document at family is in interior evidence；
Step 3-6, updates property data base according to mark result；
(5) according to above-mentioned arrival detection, leave detection, the believable arrival event of activity detection acquisition, believable leave
Event, believable activity, are carried out work attendance and are counted with operating time.
Note：Field personnel of the present invention is different from the personnel being fixed on office work for a long time, and referring to can be by
At the appointed time arrival appointed place is required to be operated, such as sales force, workmen, engineering patrol officer etc..
The method have the benefit that：
The present invention realizes realizing more accurately reaching, leaving and site activity than simple GPS mode with reference to gyroscope and GPS
Detection；Carry out BMAT in conjunction with back-end data, realize to reach, leave and activity anti-cheating detection.
Advantages of the present invention will be given in the description of specific embodiment part below, partly will from the following description
Become obvious, or recognized by the practice of the present invention.
Brief description
Fig. 1 is the system construction drawing of the present invention.
Fig. 2 is to reach overhaul flow chart.
Fig. 3 is arrival event structure chart.
Fig. 4 is to leave overhaul flow chart.
Fig. 5 is to leave event structure figure.
Fig. 6 is activity detection flow chart.
Fig. 7 is activity detection regular reporting event structure figure.
Specific embodiment
Below in conjunction with the accompanying drawings the specific embodiment of the present invention is described further.
As shown in figure 1, being answered by intelligent mobile terminal, background system and map it is necessary first to build for realizing the present invention
With the system being formed.Being described as follows of system components：
1. intelligent mobile terminal：Refer to be equipped with the smart mobile phone of GPS module and gyroscope, wearable device or other can
The computing device (such as embedded in the safety cap of computing device) of movement, can run Android or iOS operating system.With
It is referred to as down client.
1.1. client software：Based on Android or IOS exploitation client software, be capable of identify that user geographical position,
Detecting User Activity, is communicated with background server end and realizes the record of all kinds of events.
2. background system：
2.1.Web service interface：Client and the communication interface of service end.
2.2. user interface：User passes through user interface maintenance work content, checks work statistic.
2.3. event logging module：Record all kinds of times (reach, leave, timing activity reports) sent by client.
2.4. action management module：Maintenance work type, job site and border.
2.5. analysis on active characteristics module：Based on job category and active characteristics database, according to default confidential interval
Calculated, judged the confidence level of work activities.
2.6. working time statistical module：Based on the believable work activities statistical work time.
Background system hereinafter referred to as server end.
3. map application：Publicly available Internet map application, such as Baidu map.
Based on said system, the present invention realizes respectively reaching detection, leaves detection, activity detection, and is based on above-mentioned detection
Carry out work attendance to count with operating time.Illustrate individually below.
As shown in Fig. 2 the flow process reaching detection is as follows：
1-1. markers work place and border on map；
1-2. client detects field personnel according to the position that GPS provides and reaches job site；
1-3. Client-Prompt user confirms to reach, and after user confirms to reach, client is sent to from trend server end
Reach event；
The arrival event that 1-4. received server-side client sends automatically, or user is by the manual transmission of client
Arrival event；
1-5. server end analyzes the legitimacy of arrival event, and that is, whether geographical position is located in the border of operating position？If
It is to proceed to step 1-6；If it is not, proceeding to step 1-7；
1-6. prompting client has confirmed that arrival；
1-7. points out client arrival unconfirmed；
1-8. follow-up activities detect, judge whether the active characteristics before leaving event meet the activity of this job category
Feature？This is subsequent check, that is, after event of leaving occurs, system is analyzed, and whether backtracking analysis arrival event is credible.If
It is to proceed to step 1-9；If it is not, proceeding to step 1-10；
1-9. mark arrival event is credible；
1-10. mark arrival event is suspicious.
As shown in figure 3, the structure of above-mentioned arrival event is：Including personnel ID, Time To Event, geographical location information,
Action；The structure of action is：Including in work item ID, job category (marketing activity, construction activities ...), work
Hold description, default arrival time, director ID.
As shown in figure 4, the flow process leaving detection is as follows：
2-1. markers work place and border on map；
2-2. client detects field personnel according to the position that GPS provides and leaves job site；
2-3. Client-Prompt user confirm leave, user confirm leave after, client from trend server end send from
Open event；
What 2-4. received server-side client sent automatically leaves event, or user passes through what client sent manually
Leave event；
The legitimacy of event is left in the analysis of 2-5. server end, that is, judge whether geographical position is located at operating position border
Outward？And whether have corresponding arrival event？If be being, proceed to step 2-6；If at least one is no, proceed to step 2-7；
2-6. prompting client has confirmed that to be left；
Client is unconfirmed leaves for 2-7. prompting；
2-8. follow-up activities detect, judge whether the active characteristics before leaving event meet the activity of this job category
Feature？If so, proceed to step 2-9；If it is not, proceeding to step 2-10；
It is credible that 2-9. mark leaves event；
It is suspicious that 2-10. mark leaves event.
As shown in figure 5, above-mentioned leave event structure be：Including personnel ID, Time To Event, geographical location information,
Action；The structure of action is：Including in work item ID, job category (marketing activity, construction activities ...), work
Hold description, default time departure, director ID.
As shown in fig. 6, the flow process of activity detection is as follows：
3-1. calculates same day intensity index and frequency index；
Gyroscope is able to detect that translational speed, displacement, mobile height, direction change data；Activity detection is main
Periodically send to server end after depending on gyroscope detection activity data；
As shown in fig. 7, the structure of activity detection regular reporting event is：Including personnel ID, record the time started/at the end of
Between, report time, translational speed (average), mobile height (absolute value summation, summation), angle change (absolute value summation, always
With), displacement (absolute value summation), action；The structure of action is：(sell including work item ID, job category
Activity, construction activities ...), action description, director ID.
The calculation of intensity index is as follows：
Taking the displacement (Dt) in the t time as a example,
Dt=summation (in the t time, that samples every time moves horizontally apart from D).
The calculation of frequency index is as follows：
Taking activity ratio (APt) in the t time as a example,
APt=ANt/TNt*100%, ANt are the sample size that in the t time, sampled result is activity, and TNt is in the t time
Total hits.
Judge whether sampled result is activity, can be determined with intensity index threshold value TD set in advance by contrasting D, example
As TD is 1 meter, the sampling interval is 30 seconds, if sampled result D<1 is labeled as this sample is inertia.
Whether 3-2. judges intensity index, frequency index within corresponding confidential interval span？If so, proceed to step
Rapid 3-3；If it is not, proceeding to step 3-4；
Based on the activity analysis of job category and active characteristics, the corresponding active characteristics of different job categories are variant
, the active characteristics database that the important principle of the present invention is namely based on backstage is analyzed to active characteristics judging, from
And determine that its activity reasonability is come, and then the work to field personnel confirms.Meanwhile, background data base can pass through
Constantly accumulate related activity data to improve sample size, and then the accuracy analyzed.The distribution of activity intensity/frequency index
Citing such as table 1：
Table 1 activity intensity/frequency index distribution
In practical application, system can arrange arbitrary confidence interval values to realize the judgement to suspicious activity.Typically
95% as confidential interval, effect is more satisfactory in actual applications.
3-3. is labeled as credible activity；
3-4. is labeled as suspicious activity；
After 3-5. is labeled as suspicious activity, can be with adminicle, adminicle refers to submit to for manually can be determined that work
Make the photo of the such as work on the spot of authenticity, written document of client etc..If adminicle success, can re-flag for
Credible activity；
3-6. updates property data base according to mark result.
Finally, carry out work attendance based on above-mentioned detection function (to turn out for work and count/come to work late and leave early statistics/work with operating time statistics
Make duration statistics).Turn out for work statistics：Based on believable arrival, leave event statistics attendance；Come to work late and leave early statistics：Being based on can
Letter arrival, leave event and the default Time transfer receiver of action after can count the situation of comining to work late and leave early；Operating time counts：Base
In believable arrival, arrival time of leaving event and reality, time departure statistical work duration.This part is existing skill
Art, the present invention refuses to explain in detail
Above-described is only the preferred embodiment of the present invention, the invention is not restricted to above example.It is appreciated that this
Skilled person directly derive without departing from the spirit and concept in the present invention or associate other improve and become
Change, be all considered as being included within protection scope of the present invention.
Claims (1)
1. a kind of Field Force's working time statistical method based on intelligent mobile terminal and analysis on active characteristics it is characterised in that
Comprise the steps：
(1) constructing system；
System is made up of intelligent mobile terminal, background system, map application；
Intelligent mobile terminal refers to be equipped with the removable including smart mobile phone, wearable device of GPS module and gyroscope
Dynamic computing device, can run Android or iOS operating system, be provided with client software, call client in the following text；Described
Client software is the client software based on Android or IOS exploitation, is capable of identify that to live in user geographical position, detecting user
Dynamic, communicate with background server end and realize the record of all kinds of events；
Background system includes：Web service interface：Client and the communication interface of service end；User interface：Tieed up by it for user
Shield action, checks work statistic；Event logging module：For recording all kinds of times sent by client；Action
Management module：For maintenance work type, job site and border；Analysis on active characteristics module：For default confidential interval
Calculated, judged the confidence level of work activities；Working time statistical module：For according to believable work activities statistical work
Time；Background system calls server end in the following text；
Map application, is publicly available Internet map application；
(2) reach detection, comprise the following steps：
Step 1-1, markers work place and border on map；
Step 1-2, the position that client provides according to GPS, field personnel is detected and reach job site；
Step 1-3, Client-Prompt user confirms to reach, and after user confirms to reach, client is sent to from trend server end
Reach event；
Step 1-4, the arrival event that received server-side client sends automatically, or receive user are sent out manually by client
The arrival event sent；
The data structure of described arrival event includes：Personnel ID, Time To Event, geographical location information, action；Described
The data structure of action includes：Work item ID, job category, action description, default arrival time, director ID；
Step 1-5, server end analyzes the legitimacy of arrival event：Judge whether the geographical location information of arrival event is located at work
Make in location boundary；If it is judged that for being to proceed to step 1-6, if it is judged that proceed to step 1-7 for no；
Step 1-6, points out client to have confirmed that arrival, then proceeds to step 1-8；
Step 1-7, points out client arrival unconfirmed；
Step 1-8, follow-up activities detect：Judge whether meet this job category in the follow-up active characteristics left before event
Active characteristics；If it is judged that for being to proceed to step 1-9, if it is judged that proceed to step 1-10 for no；
Step 1-9, mark arrival event is credible；
Step 1-10, mark arrival event is suspicious；
(3) leave detection, comprise the following steps；
Step 2-1, markers work place and border on map；
Step 2-2, the position that client provides according to GPS, field personnel is detected and leave job site；
Step 2-3, Client-Prompt user confirm leave, user confirm leave after, client from trend server end send from
Open event；
Step 2-4, what received server-side client sent automatically leaves event, or receive user is sent out manually by client
That send leaves event；
The described data structure leaving event includes：Personnel ID, Time To Event, geographical location information, action；Described
The data structure of action includes：Work item ID, job category, action description, default time departure, director ID；
Step 2-5, the legitimacy of event is left in server end analysis：Judge whether the geographical position leaving event is located at working position
Put outside border and whether have corresponding arrival event；If it is judged that for being to proceed to step 2-6, if it is judged that being no
Proceed to step 2-7；
Step 2-6, points out client to have confirmed that and leaves, then proceed to step 2-8；
Step 2-7, points out that client is unconfirmed leaves；
Step 2-8, follow-up activities detect：Judge whether meet this job category in the described active characteristics left before event
Active characteristics；If it is judged that for being to proceed to step 2-9, if it is judged that proceed to step 2-10 for no；
Step 2-9, it is credible that mark leaves event；
Step 2-10, it is suspicious that mark leaves event；
(4) activity detection, comprises the following steps；
Step 3-1, by movable number including translational speed, displacement, mobile height, direction change for the gyroscope detection
According to；After gyroscope detection activity data, periodically send activity detection regular reporting event to server end；Server end is according to institute
State activity detection regular reporting event, calculate same day intensity index and frequency index；
The data structure of described activity detection regular reporting event includes：Personnel ID, record time started/end time, report
Time, translational speed, mobile height, angle change, displacement, action；The data structure of described action includes：
Work item ID, job category, action description, director ID；
Whether step 3-2, judge described intensity index and frequency index within corresponding confidential interval span；If sentenced
Disconnected result is is to proceed to step 3-3, if it is judged that proceeding to step 3-4 for no；
Step 3-3, is labeled as credible activity, then proceeds to step 3-6；
Step 3-4, is labeled as suspicious activity, then proceeds to step 3-5 or step 3-6；
Step 3-5, adminicle；Step 3-3 is proceeded to after the completion of adminicle；
Described adminicle refers to submit to the photo of the inclusion work on the spot for manually can be determined that work authenticity, client
Written document is in interior evidence；
Step 3-6, updates property data base according to mark result；
(5) according to above-mentioned arrival detection, leave detection, activity detection acquisition believable arrival event, believable leave event,
Believable activity, is carried out work attendance and is counted with operating time.
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| CN105989017A (en) * | 2015-01-28 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Information matching method, device and platform server |
| CN104636872A (en) * | 2015-02-11 | 2015-05-20 | 厦门雅迅网络股份有限公司 | Method and device for recording action and visit duration of field operation worker |
| CN104700228A (en) * | 2015-03-25 | 2015-06-10 | 上海化学工业区公共管廊有限公司 | Public pipe gallery inspection management system based on Android |
| CN105225283A (en) * | 2015-10-08 | 2016-01-06 | 杨川林 | Based on the signing in method of wireless router and mobile phone |
| DE102015226194A1 (en) * | 2015-12-21 | 2017-06-22 | Robert Bosch Gmbh | Mobile functional device |
| CN107765281A (en) * | 2016-08-17 | 2018-03-06 | 深圳市图元科技有限公司 | Outdoor study work attendance positioning terminal is afforested based on the environmental sanitation that network distribution type calculates |
| CN110111082B (en) * | 2019-05-14 | 2023-04-28 | 青岛奥利普奇智智能工业技术有限公司 | Random shooting method for field checking work |
| CN114223189B (en) * | 2019-10-08 | 2024-06-04 | 深圳市欢太科技有限公司 | Time length statistics method, device, electronic equipment and computer readable medium |
| CN111784113B (en) * | 2020-06-08 | 2024-07-23 | 珠海格力电器股份有限公司 | Method and device for determining working efficiency of field staff |
| CN111666510A (en) * | 2020-06-30 | 2020-09-15 | 成都新潮传媒集团有限公司 | Statistical method and device for customer visit data |
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