CN103761439B - A kind of Difference of Occupational Stress assessment system based on Internet of Things - Google Patents
A kind of Difference of Occupational Stress assessment system based on Internet of Things Download PDFInfo
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- 208000007684 Occupational Stress Diseases 0.000 title claims abstract description 50
- 238000011835 investigation Methods 0.000 claims abstract description 18
- 238000004891 communication Methods 0.000 claims abstract description 5
- 230000035487 diastolic blood pressure Effects 0.000 claims abstract description 4
- 230000035488 systolic blood pressure Effects 0.000 claims abstract description 4
- 238000012417 linear regression Methods 0.000 claims description 34
- 238000000034 method Methods 0.000 claims description 17
- 238000012360 testing method Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 7
- 208000019901 Anxiety disease Diseases 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 6
- 210000000265 leukocyte Anatomy 0.000 claims description 6
- 230000000116 mitigating effect Effects 0.000 claims description 6
- 238000004820 blood count Methods 0.000 claims description 5
- 230000006399 behavior Effects 0.000 claims description 4
- 230000010485 coping Effects 0.000 claims description 4
- 238000006467 substitution reaction Methods 0.000 claims description 4
- 206010054089 Depressive symptom Diseases 0.000 claims description 3
- 102000001554 Hemoglobins Human genes 0.000 claims description 3
- 108010054147 Hemoglobins Proteins 0.000 claims description 3
- 230000036506 anxiety Effects 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 230000036772 blood pressure Effects 0.000 claims description 3
- 230000003455 independent Effects 0.000 claims description 3
- 230000003340 mental effect Effects 0.000 claims description 3
- 230000001373 regressive effect Effects 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000011017 operating method Methods 0.000 claims 1
- 230000035479 physiological effects, processes and functions Effects 0.000 claims 1
- 238000011160 research Methods 0.000 abstract description 5
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- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 3
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- 238000005516 engineering process Methods 0.000 description 2
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- 230000004048 modification Effects 0.000 description 2
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- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
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Abstract
The invention discloses a kind of Difference of Occupational Stress assessment system based on Internet of Things, including client, server admin platform, data exchange is carried out by communication network between client and server admin platform；Server admin platform includes survey information load-on module, questionnaire assignment module, Difference of Occupational Stress survey information database and statistical module, and the physical signs information that statistical module is calculated includes systolic pressure, diastolic pressure, HDL.In such scheme, by gathering user's Difference of Occupational Stress information in real time, survey data is reliable, can enter Line Continuity, the investigation of large sample amount, improves Difference of Occupational Stress investigation rate, saves research cost；And can Real-time Feedback investigation result so that user understands own situation, so that user takes corresponding measure, even prevention the Job burnout generation of disease.
Description
Technical field
The present invention relates to data statistics field, and in particular to a kind of Difference of Occupational Stress assessment system based on Internet of Things.
Background technology
From in the 1970s, occupational stress is since the developed countries such as America and Europe begin one's study, in the 1990s, north
Denmark, Norway, the Sweden in Europe successively reduce the occupational stress degree in professional population by legislation demands.Since nineteen ninety, I
State scholar starts to study occupational stress problem.At present, the research both at home and abroad to pressure is concentrated mainly on searching generation pressure
The factor of power and the stress reaction of body, wherein research occupational stress psychological impact factor and evaluation tensional level are relied primarily on and asked
The method filled in is rolled up, researcher is relied primarily on by filling in papery questionnaire face to face and reclaiming to reach occupational stress information gathering
Purpose, but fill substance subjectivity tendency is stronger, thereby results in that researcher is not enough, expends a large amount of manpower and materials, questionnaire statistics
The problems such as inefficiency, questionnaire quality are difficult to ensure that.
The content of the invention
To solve the above problems, the invention provides a kind of Difference of Occupational Stress assessment system based on Internet of Things, its can accurately,
Efficiently Difference of Occupational Stress is estimated based on Internet of Things.
To achieve the above object, the technical scheme taken of the present invention is：
Difference of Occupational Stress assessment system based on Internet of Things, including：
The client logged in for user, fill in questionnaire, checked investigation result；
And for loading questionnaire to client, storage, survey questionnaire information, Difference of Occupational Stress score and to visitor
The server admin platform of family end feedback survey object information；
Data exchange is carried out by communication network between client and server admin platform；
The information of questionnaire investigation includes objective indicator and subjective index；Objective indicator include blood pressure, white blood cell count(WBC),
Red blood cell count(RBC), content of hemoglobin, the Physiological and biochemical index of hdl concentration；Subjective index includes tension-causing factor
Packet, the packet of personal characteristics factor, mitigation factors packet, psychoreaction packet；Tension-causing factor packet includes job control flow
Table, work requirements scale, work risk scale, work monotonicity scale, work prospect scale, lifting and participation opportunity scale
Index；Personal characteristics factor is grouped into special including type A behavior scale, work locus of control scale, sense of self-respect scale, anxiety
The index of the loyal meter of quality table, tissue；Mitigation factors packet includes the index of coping strategy scale, Social Support Rating Scale；
Psychoreaction packet includes job satisfaction scale, mental healthy education course, depressive symptom scale, anxiety state scale, body and embraced
Blame the index of scale；
Server admin platform includes：
For the survey information load-on module being loaded into questionnaire content on client related web page；
Assignment is carried out for each entry in the questionnaire information to client feedback, calculate each Job Stress score
Questionnaire assignment module；
Difference of Occupational Stress survey information database for storing data；
And the Difference of Occupational Stress score for being obtained according to questionnaire assignment module, analyzed, drawn back using statistical method
Return equation, the statistical module of corresponding physical signs information is drawn according to regression equation calculation；What statistical module was calculated
Physical signs information includes systolic pressure, diastolic pressure, HDL；
Specifically scheme is：
Server admin platform also includes being used for counting user log-on message, limits repeated registration in user's short time, fills out
Write the survey information acquisition module of questionnaire.
Statistical module adopts analyze data with the following method, including step：
S10：An objective indicator X is chosen in Difference of Occupational Stress survey information database, point in a subjective index is chosen
Group α, index related between the objective indicator X in the packet α is chosen with multi-element linear regression method；
S20：Judge whether to complete the multiple linear regression analysis between all objective indicators and all packets, if completing
Index related to objective indicator X in all packets screened is collected, α is through multivariate regressive analysis, with most
Small square law formation objective indicator X and the index screened in packet α, carry out entering step after multiple linear regression equations
S30, otherwise returns to step S10；
S30：By index related to objective indicator X in all packets screened, substitute into what the step S20 was drawn
Objective indicator X and packet α middle finger target multiple linear regression equations, calculate the every objective indicator for obtaining user.
Finger related between objective indicator X in packet α is chosen in step S10 with following multi-element linear regression method
Mark, including step：
S11：The objective indicator X of all samples and packet α in Difference of Occupational Stress survey information database is extracted, least square is used
Method obtains index X and the multiple linear regression equations of each index in packet α；
S12：Calculating multiple linear regression equations Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b
(m) * X (m) degree of correlation b, its value illustrates that system is better to the fitting degree of data closer to 1；
S13：The index in each packet α is verified with progressively back-and-forth method, to select which index to enter regression model, root
According to the sum of squares of partial regression U of the packet each indexs of α(k) Pi, filter out index related to objective indicator X in packet α.
Least square method in step S11 is concretely comprised the following steps：
S111：Set up each index and objective indicator X initial multiple linear regression equations in packet α：
Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X (m)；
S112：The initial multiple linear regression equations set up in α in each index substitution step S111, meter will be grouped
Calculation system predicts the outcome index Y；
S113：Calculate objective indicator X and system prediction result index Y sum of sguares of deviation from mean φ；
S114：The coefficient b and constant term b0 of each index in change system, when sum of sguares of deviation from mean φ is minimum, draw
Equation of linear regression, otherwise returns to step S111.
Degree of correlation b specific calculation procedure is in step S12：
S121：Set up each index and index X initial multiple linear regression equations in packet α：
Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X (m)；
S122：The True Data of each index in packet α is substituted into, system prediction result index Y is calculated；
S123：Parameter X and index Y sum of sguares of deviation from mean φ；
S124：The coefficient b and constant term b0 of each index in change system, when sum of sguares of deviation from mean φ is minimum, draw
The coefficient that each index in α is grouped in equation of linear regression is degree of correlation b, otherwise returns to step S121.
Step S13 concrete operation step is：
S131：Calculate the sum of squares of partial regression U of each index not yet selected in each packet α(k) Pi, wherein k=1,
2 ..., represent that kth step is returned, UpiFor sum of squares of partial regressions of the objective indicator Xi to each index in packet α；I=1,2 ...,
M, m are independent variable number or number；
S132：Compare each U(k) Pi, find out the U of maximum(k) PiIt is designated as maxU(k) Pi；
S133：To maxU(k) PiMake the F tests of partial regression, by the independent variable if notable (statistical significance P ＜ 0.05)
Regression equation is selected into kth step；
S134：The F tests for partial regression conspicuousness that all independents variable selected before kth is walked are tried again, if having
It is changed into inapparent (P ＞ 0.05), that is, gives and picking out；
S135：To maxU(k) PiThe F tests of partial regression are done, if not significantly (P ＞ 0.05), successive Regression terminates.
In such scheme, by gathering user's Difference of Occupational Stress information in real time, survey data is reliable, can enter Line Continuity, big
The investigation of sample size, improves Difference of Occupational Stress investigation rate, saves research cost；And can Real-time Feedback investigation result so that Yong Huliao
Own situation is solved, so that user takes corresponding measure, even prevention the Job burnout generation of disease.
Brief description of the drawings
Fig. 1 is the Difference of Occupational Stress appraisal procedure flow chart of the invention based on Internet of Things；
Fig. 2 is schematic structural view of the invention.
Embodiment
In order that objects and advantages of the present invention are more clearly understood, the present invention is carried out with reference to embodiments further
Describe in detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to limit this hair
It is bright.
Difference of Occupational Stress assessment system provided by the present invention based on Internet of Things as shown in figure 1, including：Stepped on for user
Land, the client 10 filled in questionnaire, check investigation result；And for loading questionnaire to client 10, storage, system
Count questionnaire information, Difference of Occupational Stress score and the server admin platform 20 to the feedback survey object information of client 10；Visitor
Data exchange is carried out by communication network 30 between family end 10 and server admin platform 20；Communication network 30 includes wired network
Network, wireless network；Client includes PC or mobile phone.
The information of questionnaire investigation includes objective indicator and subjective index；Objective indicator include blood pressure, white blood cell count(WBC),
Red blood cell count(RBC), content of hemoglobin, the Physiological and biochemical index of hdl concentration；Subjective index includes tension-causing factor
Packet, the packet of personal characteristics factor, mitigation factors packet, psychoreaction packet；Tension-causing factor packet includes job control flow
Table, work requirements scale, work risk scale, work monotonicity scale, work prospect scale, lifting and participation opportunity scale
Index；Personal characteristics factor is grouped into special including type A behavior scale, work locus of control scale, sense of self-respect scale, anxiety
The index of the loyal meter of quality table, tissue；Mitigation factors packet includes the index of coping strategy scale, Social Support Rating Scale；
Psychoreaction packet includes job satisfaction scale, mental healthy education course, depressive symptom scale, anxiety state scale, body and embraced
Blame the index of scale；Job control scale includes task control, Decision Control, environmental Kuznets Curves and resources control, and the work is needed
Seeking scale includes fixed quantitative load, load variations, technology producing level, the lifting and participation opportunity scale include elevator can and
Participative decision making, the type A behavior scale includes patience and competitiveness, and the coping strategy scale includes control strategy and support
Strategy, the Social Support Rating Scale includes higher level's support, Peer support and Family Functions.
Server admin platform 20 includes：For the investigation being loaded into questionnaire content on the related web page of client 10
Signal load unit 21；For each entry in the questionnaire information fed back to client 10 to carry out assignment, to calculate each occupation tight
The questionnaire assignment module 22 of Zhang Yinsu scores；Difference of Occupational Stress survey information database 23 for storing data；And for root
The Difference of Occupational Stress score obtained according to questionnaire assignment module, is analyzed using statistical method, draws regression equation, according to regression equation
Calculate the statistical module 24 of corresponding physical signs information；The physical signs information that statistical module 24 is calculated includes
Systolic pressure, diastolic pressure, HDL.For counting user log-on message, limit repeated registration in user's short time, fill in
The survey information acquisition module 25 of questionnaire.Survey information acquisition module 25, for the user computer according to record IP address or
Subscriber phone number, limits repeated registration in user's short time, fills in questionnaires, influence investigation accuracy.
The idiographic flow of Difference of Occupational Stress assessment is carried out using the system as shown in figure 1, using such as lower section including statistical module
Method analyze data, including step：
S10：An objective indicator X is chosen in Difference of Occupational Stress survey information database, point in a subjective index is chosen
Group α, index related between the objective indicator X in the packet α is chosen with multi-element linear regression method；
S20：Judge whether to complete the multiple linear regression analysis between all objective indicators and all packets, if completing
Index related to objective indicator X in all packets screened is collected, α is through multivariate regressive analysis, with most
Small square law formation objective indicator X and the index screened in packet α, carry out entering step after multiple linear regression equations
S30, otherwise returns to step S10；
S30：By index related to objective indicator X in all packets screened, substitute into what the step S20 was drawn
Objective indicator X and packet α middle finger target multiple linear regression equations, calculate the every objective indicator for obtaining user.
More detailed operation be chosen in step S10 with following multi-element linear regression method in packet α with it is objective
Related index between index X, including step：
S11：The objective indicator X of all samples and packet α in Difference of Occupational Stress survey information database is extracted, least square is used
Method obtains index X and the multiple linear regression equations of each index in packet α；
S12：Calculating multiple linear regression equations Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b
(m) * X (m) degree of correlation b, its value illustrates that system is better to the fitting degree of data closer to 1；
S13：The index in each packet α is verified with progressively back-and-forth method, to select which index to enter regression model, root
According to the sum of squares of partial regression U of the packet each indexs of α(k) Pi, filter out index related to objective indicator X in packet α.
Least square method in step S11 is concretely comprised the following steps：
S111：Set up each index and objective indicator X initial multiple linear regression equations in packet α：
Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X (m)；
S112：The initial multiple linear regression equations set up in α in each index substitution step S111, meter will be grouped
Calculation system predicts the outcome index Y；
S113：Calculate objective indicator X and system prediction result index Y sum of sguares of deviation from mean φ；
S114：The coefficient b and constant term b0 of each index in change system, when sum of sguares of deviation from mean φ is minimum, draw
Equation of linear regression, otherwise returns to step S111.
Degree of correlation b specific calculation procedure is in step S12：
S121：Set up each index and index X initial multiple linear regression equations in packet α：
Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X (m)；
S122：The True Data of each index in packet α is substituted into, system prediction result index Y is calculated；
S123：Parameter X and index Y sum of sguares of deviation from mean φ；
S124：The coefficient b and constant term b0 of each index in change system, when sum of sguares of deviation from mean φ is minimum, draw
The coefficient that each index in α is grouped in equation of linear regression is degree of correlation b, otherwise returns to step S121.
Step S13 concrete operation step is：
S131：Calculate the sum of squares of partial regression U of each index not yet selected in each packet α(k) Pi, wherein k=1,
2 ..., represent that kth step is returned, UpiFor sum of squares of partial regressions of the objective indicator Xi to each index in packet α；I=1,2 ...,
M, m are independent variable number or number；
S132：Compare each U(k) Pi, find out the U of maximum(k) PiIt is designated as maxU(k) Pi；
S133：To maxU(k) PiThe F tests of partial regression are done, by the independent variable if notable (statistical significance P ＜ 0.05)
Regression equation is selected into kth step；
S134：The F tests for partial regression conspicuousness that all independents variable selected before kth is walked are tried again, if having
It is changed into inapparent (P ＞ 0.05), that is, gives and picking out；
S135：To maxU(k) PiMake the F tests of partial regression, if not significantly (P ＞ 0.05), successive Regression terminates.
Generally speaking, the flat assessment of occupation tensioning is carried out using the present invention to comprise the following steps：
1st, investigator sets up Difference of Occupational Stress database in server end typing Difference of Occupational Stress questionnaire, for storing note
Information, questionnaire information, Difference of Occupational Stress score and the feedback result of volume user；The information of registered user, questionnaire information,
Difference of Occupational Stress score and feedback result are word forms or exl forms or epidata forms.
2nd, user is logged in by computer or mobile phone, is filled in questionnaires, is submitted by network；
3rd, the Difference of Occupational Stress investigation that storage and counting user are submitted, according to statistical result, sets up user's Difference of Occupational Stress feedback
Form；
4th, user submits questionnaire, checks investigation result；
5th, investigator's login system, is checked, management survey result.
By gathering user's Difference of Occupational Stress information in real time, survey data is reliable, can enter Line Continuity, the tune of large sample amount
Look into, improve Difference of Occupational Stress investigation rate, save research cost；And can Real-time Feedback investigation result so that user understands own situation,
So that user takes corresponding measure, even prevention the Job burnout generation of disease.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (5)
1. the Difference of Occupational Stress assessment system based on Internet of Things, including：
The client logged in for user, fill in questionnaire, checked investigation result；
And for loading questionnaire to client, storage, survey questionnaire information, Difference of Occupational Stress score and to client
The server admin platform of feedback survey object information；
Data exchange is carried out by communication network between client and server admin platform；
The information of questionnaire investigation includes objective indicator and subjective index；Objective indicator includes blood pressure, white blood cell count(WBC), red thin
Born of the same parents' counting, content of hemoglobin, the Physiological and biochemical index of hdl concentration；Subjective index includes tension-causing factor point
Group, the packet of personal characteristics factor, mitigation factors packet, psychoreaction packet；Tension-causing factor packet include job control flowmeter,
Work requirements scale, work risk scale, work monotonicity scale, work prospect scale, lifting and participation opportunity scale
Index；Personal characteristics factor is grouped into including type A behavior scale, work locus of control scale, sense of self-respect scale, anxiety speciality
The index of the loyal meter of scale, tissue；Mitigation factors packet includes the index of coping strategy scale, Social Support Rating Scale；The heart
Reason reaction packet includes job satisfaction scale, mental healthy education course, depressive symptom scale, anxiety state scale, body complaint
The index of scale；
Server admin platform includes：
For the survey information load-on module being loaded into questionnaire content on client related web page；
Assignment is carried out for each entry in the questionnaire information to client feedback, calculate asking for each Job Stress score
Roll up assignment module；
Difference of Occupational Stress survey information database for storing data；
And the Difference of Occupational Stress score for being obtained according to questionnaire assignment module, analyzed using statistical method, draw recurrence side
Journey, the statistical module of corresponding physical signs information is drawn according to regression equation calculation；The physiology that statistical module is calculated
Indication information includes systolic pressure, diastolic pressure, HDL；
Statistical module adopts analyze data with the following method, including step：
S10：An objective indicator X is chosen in Difference of Occupational Stress survey information database, the packet α in a subjective index is chosen,
Index related between the objective indicator X in the packet α is chosen with multi-element linear regression method；
S20：Judge whether to complete the multiple linear regression analysis between all objective indicators and all packets, will sieve if completing
The index related to objective indicator X collects in all packets elected, and α is through multivariate regressive analysis, with a most young waiter in a wineshop or an inn
Multiplication formation objective indicator X and the index screened in packet α, are carried out into step S30 after multiple linear regression equations, no
Then return to step S10；
S30：By index related to objective indicator X in all packets screened, it is objective that the substitution step S20 is drawn
Index X and packet α middle finger target multiple linear regression equations, calculate the every objective indicator for obtaining user；
Index related between objective indicator X in packet α is chosen in step S10 with following multi-element linear regression method,
Including step：
S11：The objective indicator X of all samples and packet α in Difference of Occupational Stress survey information database is extracted, is obtained with least square method
To index X and the multiple linear regression equations of each index in packet α；
S12：Calculating multiple linear regression equations Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X
(m) degree of correlation b, its value illustrates that system is better to the fitting degree of data closer to 1；
S13：Verify the index in each packet α with progressively back-and-forth method, to select which index to enter regression model, according to point
The sum of squares of partial regression U of the group each indexs of α(k) Pi, wherein k=1,2 ..., represent that kth step is returned, filter out and be grouped in α and visitor
See the related indexs of index X.
2. the Difference of Occupational Stress assessment system as claimed in claim 1 based on Internet of Things, it is characterised in that：Server admin platform is also
Including for counting user log-on message, limiting repeated registration in user's short time, the survey information acquisition module filled in questionnaires.
3. the Difference of Occupational Stress assessment system as claimed in claim 1 based on Internet of Things, it is characterised in that in the step S11
Least square method is concretely comprised the following steps：
S111：Set up each index and objective indicator X initial multiple linear regression equations in packet α：
Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X (m)；
S112：The initial multiple linear regression equations set up in α in each index substitution step S111 will be grouped, calculated
System prediction result index Y；
S113：Calculate objective indicator X and system prediction result index Y sum of sguares of deviation from mean φ；
S114：The coefficient b and constant term b0 of each index in change system, when sum of sguares of deviation from mean φ is minimum, obtain cutting edge aligned
Regression equation, otherwise returns to step S111.
4. the Difference of Occupational Stress assessment system as claimed in claim 1 based on Internet of Things, it is characterised in that phase in the step S12
Pass degree b specific calculation procedure is：
S121：Set up each index and index X initial multiple linear regression equations in packet α：
Y=b0+b (1) * X (1)+b (2) * X (2)+b (3) * X (3)+...+b (m) * X (m)；
S122：The True Data of each index in packet α is substituted into, system prediction result index Y is calculated；
S123：Parameter X and index Y sum of sguares of deviation from mean φ；
S124：The coefficient b and constant term b0 of each index in change system, when sum of sguares of deviation from mean φ is minimum, obtain cutting edge aligned
The coefficient that each index in α is grouped in regression equation is degree of correlation b, otherwise returns to step S121.
5. the Difference of Occupational Stress assessment system as claimed in claim 4 based on Internet of Things, it is characterised in that the tool of the step S13
Body operating procedure is：
S131：Calculate the sum of squares of partial regression U of each index not yet selected in each packet α(k) Pi, wherein k=1,2 ..., table
Show kth step recurrence；UpiFor sum of squares of partial regressions of the objective indicator Xi to each index in packet α；I=1,2 ..., m, m are from change
Measure number or number；
S132：Compare each U(k) Pi, find out the U of maximum(k) PiIt is designated as maxU(k) Pi；
S133：To maxU(k) PiThe F tests of partial regression are done, if significantly, i.e. statistical significance P ＜ 0.05 just exist the independent variable
Kth step is selected into regression equation；
S134：The F tests for the partial regression conspicuousness that tried again to all independents variable selected before kth is walked, are changed into if having
Inapparent, i.e. P ＞ 0.05 give and picking out；
S135：To maxU(k) PiThe F tests of partial regression are done, if not significantly, i.e. P ＞ 0.05, successive Regression terminates.
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