CN103810389A - Occupational stress evaluation system based on internet of things - Google Patents
Occupational stress evaluation system based on internet of things Download PDFInfo
- Publication number
- CN103810389A CN103810389A CN201410057010.9A CN201410057010A CN103810389A CN 103810389 A CN103810389 A CN 103810389A CN 201410057010 A CN201410057010 A CN 201410057010A CN 103810389 A CN103810389 A CN 103810389A
- Authority
- CN
- China
- Prior art keywords
- index
- grouping
- scale
- occupational stress
- difference
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Abstract
The invention discloses an occupational stress evaluation system based on the internet of things. The occupational stress evaluation system comprises a client side and a server management platform. The client side and the server management platform exchange data through a communication network. The server management platform comprises a survey information loading module, a questionnaire assignment module, an occupational stress survey information database and a statistic module. Physical index information calculated through the statistic module comprises systolic pressure, diastolic pressure and high-density lipoprotein. By the adoption of the scheme, occupational stress information of users is acquired in real time, survey data are reliable, continuous and large-sample-size survey can be conducted, the occupational stress survey rate is increased, and survey cost is saved; survey results can be fed back in real time, and therefore the users can know their own situations and take corresponding measures to prevent occupation burnout and even diseases.
Description
Technical field
The present invention relates to data statistics field, be specifically related to a kind of Difference of Occupational Stress evaluating system based on Internet of Things.
Background technology
From 20 century 70s, occupational stress is since the developed countries such as America and Europe begin one's study, and to the nineties in 20th century, the Denmark in Northern Europe, Norway, Sweden successively require to reduce the occupational stress degree in professional population by legislation.Since nineteen ninety, Chinese scholar starts occupational stress problem to study.At present, both at home and abroad the research of pressure is mainly concentrated on to find and produce the factor of pressure and the stress reaction of body, wherein study occupational stress psychological impact factor and evaluate the method that nervous level mainly relies on questionnaire to fill in, the main researchist of dependence is by filling in papery questionnaire face to face and reclaiming to reach the object of occupational stress information acquisition, but the problems such as the subjective tendency of fill substance is stronger, causes thus researchist's deficiency, expend a large amount of manpower and materials, questionnaire statistical efficiency is low, questionnaire difficult quality guarantee.
Summary of the invention
For addressing the above problem, the invention provides a kind of Difference of Occupational Stress evaluating system based on Internet of Things, it can be assessed Difference of Occupational Stress based on Internet of Things accurately and fast.
For achieving the above object, the technical scheme that the present invention takes is:
Difference of Occupational Stress evaluating system based on Internet of Things, comprising:
Be used for the client that user logged in, filled in questionnaire, checks investigation result;
And for loading questionnaire to client, storage, survey questionnaire information, Difference of Occupational Stress score and the server admin platform to client feedback survey results information;
Between client and server admin platform, carry out exchanges data by communication network;
The information of questionnaire investigation comprises objective indicator and subjective index; Objective indicator comprises the Physiological and biochemical index of blood pressure, white blood cell count(WBC), red blood cell count(RBC), content of hemoglobin, hdl concentration; Subjective index comprises tension-causing factor grouping, the grouping of personal characteristics factor, mitigation factors grouping, psychoreaction grouping; Tension-causing factor grouping comprises job control flowmeter, work requirements scale, the index of the dangerous scale of working, shop order tonality scale, work prospect scale, lifting and participation opportunity scale; Personal characteristics factor is grouped into the index that comprises type A behavior scale, work locus of control scale, sense of self-respect scale, anxiety speciality scale, organizes loyalty scale; Mitigation factors grouping comprises the index of coping strategy scale, social support scale; Psychoreaction grouping comprises the index of job satisfaction scale, mental health scale, depressive symptom scale, anxiety state scale, body complaint scale;
Server admin platform comprises:
For questionnaire content is loaded into the survey information load-on module on client related web page;
For each entry in the questionnaire information of client feedback being carried out to assignment, calculating the questionnaire assignment module of each Job Stress score;
For the Difference of Occupational Stress survey information database of storage data;
And Difference of Occupational Stress score for obtaining according to questionnaire assignment module, adopt statistical method analysis, draw regression equation, draw the statistical module of corresponding physical signs information according to regression equation calculation; The physical signs information that statistical module calculates comprises systolic pressure, diastolic pressure, high-density lipoprotein (HDL).
Concrete scheme is:
Server admin platform also comprises for counting user log-on message, the survey information acquisition module that limit repeated registration in user's short time, fills in questionnaires.
Statistical module adopts following methods analyst data:
S10: choose an objective indicator X in Difference of Occupational Stress survey information database, choose a grouping α in subjective index, choose index relevant in described grouping α and between described objective indicator X by multi-element linear regression method;
S20: judged whether the multiple linear regression analysis between all objective indicators and all groupings, as completed, the index relevant to objective indicator X in all groupings that screen gathered, after then α forms through multivariate regressive analysis the index screening in objective indicator X and grouping α multiple linear regression equations with least square method, enter step S30, otherwise get back to step S10;
S30: by the index relevant to objective indicator X in all groupings that screen, the objective indicator X that step S103 draws described in substitution and grouping α middle finger target multiple regression equation, calculate every objective indicator of user.
In step S10, choose index relevant in grouping α and between objective indicator X by following multi-element linear regression method:
S11: extract objective indicator X and the grouping α of all samples in Difference of Occupational Stress survey information database, obtain the multiple linear regression equations of each index in index X and grouping α by least square method;
S12: calculate the coefficient of determination R2 of multiple linear regression equations, its value, more close to 1, illustrates that model is better to the fitting degree of data;
S13: use progressively back-and-forth method to verify the index in each grouping α, to select which index can enter regression model, according to the sum of squares of partial regression U of the each index of grouping α
(k) pi, filter out the index relevant to objective indicator X in grouping α.
The detailed process of the least square method in step S11 is:
S111: the initial multiple linear regression equations of each index and objective indicator X in foundation grouping α:
Y＝b0+b(1)*X(1)+b(2)*X(2)+b(3)*X(3)+……+b(m)*X(m)；
S112: by the initial multiple linear regression equations of setting up in step S111 described in each index substitution in grouping α, calculate model prediction result index X ';
S113: the sum of sguares of deviation from mean φ that calculates objective indicator X and model prediction result index X ';
S114: change coefficient b and the constant term b0 of each index in model, when sum of sguares of deviation from mean φ is for hour, draw equation of linear regression, otherwise get back to step S111.
In step S12, the circular of degree of correlation b is:
S121: the linear regression model (LRM) of each index and index X in foundation grouping α:
Y＝b0+b(1)*X(1)+b(2)*X(2)+b(3)*X(3)+……+b(m)*X(m)；
S122: bring the True Data of each index in grouping α into, calculate model prediction result index X ';
The sum of sguares of deviation from mean φ of S123: parameter X and index X ';
S124: change coefficient b and the constant term b0 of each index in model, when sum of sguares of deviation from mean φ is for hour, show that the coefficient of each index in the α that divides into groups in equation of linear regression is degree of correlation b, otherwise get back to step S121.
The concrete operations of step S13 are:
S131: the sum of squares of partial regression U that calculates the each index being not yet selected in each grouping α
(k) pi, wherein k=1,2 ..., represent that k step returns, U
pifor the sum of squares of partial regression of objective indicator Xi to each index in grouping α; I=1,2 ..., m, m is independent variable number or number;
S132: more each U
(k) pi, find out maximum U
(k) pibe designated as maxU
(k) pi;
S133: to maxU
(k) pido the F test of partial regression, if significantly (statistical significance P < 0.05) is just selected into regression equation by this independent variable in k step;
S134: to the try again F test of partial regression conspicuousness of all independent variables selected before k step, become inapparent (P > 0.05) if having, give and picking out;
S135: to maxU
(k) pido the F test of partial regression, if not significantly (P > 0.05), successive Regression finishes.
In such scheme, by Real-time Collection user Difference of Occupational Stress information, enquiry data is reliable, can carry out the investigation of continuity, large sample amount, improves Difference of Occupational Stress investigation rate, saves research cost; And can Real-time Feedback investigation result, make user understand s own situation, so that user takes corresponding measure, the even generation of disease of prevention Job burnout.
Accompanying drawing explanation
Fig. 1 is the Difference of Occupational Stress appraisal procedure process flow diagram that the present invention is based on Internet of Things;
Fig. 2 is structural representation of the present invention.
Embodiment
In order to make objects and advantages of the present invention clearer, below in conjunction with embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
Difference of Occupational Stress evaluating system based on Internet of Things provided by the present invention as shown in Figure 1, comprising: log in, fill in questionnaire, check the client 10 of investigation result for user; And for loading questionnaire to client 10, storage, survey questionnaire information, Difference of Occupational Stress score and the server admin platform 20 to client 10 feedback survey object informations; Between client 10 and server admin platform 20, carry out exchanges data by communication network 30; Communication network 30 includes spider lines, wireless network; Client comprises PC or mobile phone.
The information of questionnaire investigation comprises objective indicator and subjective index; Objective indicator comprises the Physiological and biochemical index of blood pressure, white blood cell count(WBC), red blood cell count(RBC), content of hemoglobin, hdl concentration; Subjective index comprises tension-causing factor grouping, the grouping of personal characteristics factor, mitigation factors grouping, psychoreaction grouping; Tension-causing factor grouping comprises job control flowmeter, work requirements scale, the index of the dangerous scale of working, shop order tonality scale, work prospect scale, lifting and participation opportunity scale; Personal characteristics factor is grouped into the index that comprises type A behavior scale, work locus of control scale, sense of self-respect scale, anxiety speciality scale, organizes loyalty scale; Mitigation factors grouping comprises the index of coping strategy scale, social support scale; Psychoreaction grouping comprises the index of job satisfaction scale, mental health scale, depressive symptom scale, anxiety state scale, body complaint scale; Job control scale comprises task control, Decision Control, environment control and resource control, described work requirements scale comprises that fixed quantitative load, load variations, technology utilize degree, described lifting and participation opportunity scale comprise hoister meeting and participative decision making, described type A behavior scale comprises patience and competitiveness, described coping strategy scale comprises control strategy and support policy, and described social support scale comprises that higher level's support, Peer support and family support.
Use idiographic flow that native system carries out Difference of Occupational Stress assessment as shown in Figure 1, comprise that statistical module adopts following methods analyst data:
S10: choose an objective indicator X in Difference of Occupational Stress survey information database, choose a grouping α in subjective index, choose index relevant in described grouping α and between described objective indicator X by multi-element linear regression method;
S20: judged whether the multiple linear regression analysis between all objective indicators and all groupings, as completed, the index relevant to objective indicator X in all groupings that screen gathered, after then α forms through multivariate regressive analysis the index screening in objective indicator X and grouping α multiple linear regression equations with least square method, enter step S30, otherwise get back to step S10;
S30: by the index relevant to objective indicator X in all groupings that screen, the objective indicator X that step S103 draws described in substitution and grouping α middle finger target multiple regression equation, calculate every objective indicator of user.
More detailed is operating as, and in step S10, chooses index relevant in grouping α and between objective indicator X by following multi-element linear regression method:
S11: extract objective indicator X and the grouping α of all samples in Difference of Occupational Stress survey information database, obtain the multiple linear regression equations of each index in index X and grouping α by least square method;
S12: calculate the coefficient of determination R2 of multiple linear regression equations, its value, more close to 1, illustrates that model is better to the fitting degree of data;
S13: use progressively back-and-forth method to verify the index in each grouping α, to select which index can enter regression model, according to the sum of squares of partial regression U of the each index of grouping α
(k) pi, filter out the index relevant to objective indicator X in grouping α.
The detailed process of the least square method in step S11 is:
S111: the initial multiple linear regression equations of each index and objective indicator X in foundation grouping α:
Y＝b0+b(1)*X(1)+b(2)*X(2)+b(3)*X(3)+……+b(m)*X(m)；
S112: by the initial multiple linear regression equations of setting up in step S111 described in each index substitution in grouping α, calculate model prediction result index X ';
S113: the sum of sguares of deviation from mean φ that calculates objective indicator X and model prediction result index X ';
S114: change coefficient b and the constant term b0 of each index in model, when sum of sguares of deviation from mean φ is for hour, draw equation of linear regression, otherwise get back to step S111.
In step S12, the circular of degree of correlation b is:
S121: the linear regression model (LRM) of each index and index X in foundation grouping α:
Y＝b0+b(1)*X(1)+b(2)*X(2)+b(3)*X(3)+……+b(m)*X(m)；
S122: bring the True Data of each index in grouping α into, calculate model prediction result index X ';
The sum of sguares of deviation from mean φ of S123: parameter X and index X ';
S124: change coefficient b and the constant term b0 of each index in model, when sum of sguares of deviation from mean φ is for hour, show that the coefficient of each index in the α that divides into groups in equation of linear regression is degree of correlation b, otherwise get back to step S121.
The concrete operations of step S13 are:
S131: the sum of squares of partial regression U that calculates the each index being not yet selected in each grouping α
(k) pi, wherein k=1,2 ..., represent that k step returns, U
pifor the sum of squares of partial regression of objective indicator Xi to each index in grouping α; I=1,2 ..., m, m is independent variable number or number;
S132: more each U
(k) pi, find out maximum U
(k) pibe designated as maxU
(k) pi;
S133: to maxU
(k) pido the F test of partial regression, if significantly (statistical significance P < 0.05) is just selected into regression equation by this independent variable in k step;
S134: to the try again F test of partial regression conspicuousness of all independent variables selected before k step, become inapparent (P > 0.05) if having, give and picking out;
S135: to maxU
(k) pido the F test of partial regression, if not significantly (P > 0.05), successive Regression finishes.
Generally speaking, using the present invention to carry out the flat assessment of professional tensioning comprises the steps:
1, investigator, at server end typing Difference of Occupational Stress questionnaire, sets up Difference of Occupational Stress database, for storing registered user's information, questionnaire information, Difference of Occupational Stress score and feedback result; Registered user's information, questionnaire information, Difference of Occupational Stress score and feedback result is word form or exl form or epidata form.
2, user, by computer or mobile phone login, fills in questionnaires, and submits to by network;
3, the Difference of Occupational Stress investigation that storage counting user are submitted to, according to statistics, sets up user's Difference of Occupational Stress feedback form;
4, user submits questionnaire to, checks investigation result;
5, investigator's login system, checks, management survey result.
By Real-time Collection user Difference of Occupational Stress information, enquiry data is reliable, can carry out the investigation of continuity, large sample amount, improves Difference of Occupational Stress investigation rate, saves research cost; And can Real-time Feedback investigation result, make user understand s own situation, so that user takes corresponding measure, the even generation of disease of prevention Job burnout.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (7)
1. the Difference of Occupational Stress evaluating system based on Internet of Things, comprising:
Be used for the client that user logged in, filled in questionnaire, checks investigation result;
And for loading questionnaire to client, storage, survey questionnaire information, Difference of Occupational Stress score and the server admin platform to client feedback survey results information;
Between client and server admin platform, carry out exchanges data by communication network;
The information of questionnaire investigation comprises objective indicator and subjective index; Objective indicator comprises the Physiological and biochemical index of blood pressure, white blood cell count(WBC), red blood cell count(RBC), content of hemoglobin, hdl concentration; Subjective index comprises tension-causing factor grouping, the grouping of personal characteristics factor, mitigation factors grouping, psychoreaction grouping; Tension-causing factor grouping comprises job control flowmeter, work requirements scale, the index of the dangerous scale of working, shop order tonality scale, work prospect scale, lifting and participation opportunity scale; Personal characteristics factor is grouped into the index that comprises type A behavior scale, work locus of control scale, sense of self-respect scale, anxiety speciality scale, organizes loyalty scale; Mitigation factors grouping comprises the index of coping strategy scale, social support scale; Psychoreaction grouping comprises the index of job satisfaction scale, mental health scale, depressive symptom scale, anxiety state scale, body complaint scale;
Server admin platform comprises:
For questionnaire content is loaded into the survey information load-on module on client related web page;
For each entry in the questionnaire information of client feedback being carried out to assignment, calculating the questionnaire assignment module of each Job Stress score;
For the Difference of Occupational Stress survey information database of storage data;
And Difference of Occupational Stress score for obtaining according to questionnaire assignment module, adopt statistical method analysis, draw regression equation, draw the statistical module of corresponding physical signs information according to regression equation calculation; The physical signs information that statistical module calculates comprises systolic pressure, diastolic pressure, high-density lipoprotein (HDL).
2. the Difference of Occupational Stress evaluating system based on Internet of Things as claimed in claim 1, is characterized in that: server admin platform also comprises for counting user log-on message, the survey information acquisition module that limit repeated registration in user's short time, fills in questionnaires.
3. the Difference of Occupational Stress evaluating system based on Internet of Things as claimed in claim 1, is characterized in that, statistical module adopts following methods analyst data:
S10: choose an objective indicator X in Difference of Occupational Stress survey information database, choose a grouping α in subjective index, choose index relevant in described grouping α and between described objective indicator X by multi-element linear regression method;
S20: judged whether the multiple linear regression analysis between all objective indicators and all groupings, as completed, the index relevant to objective indicator X in all groupings that screen gathered, after then α forms through multivariate regressive analysis the index screening in objective indicator X and grouping α multiple linear regression equations with least square method, enter step S30, otherwise get back to step S10;
S30: by the index relevant to objective indicator X in all groupings that screen, the objective indicator X that step S103 draws described in substitution and grouping α middle finger target multiple regression equation, calculate every objective indicator of user.
4. the Difference of Occupational Stress evaluating system based on Internet of Things as claimed in claim 3, is characterized in that, in step S10, chooses index relevant in grouping α and between objective indicator X by following multi-element linear regression method:
S11: extract objective indicator X and the grouping α of all samples in Difference of Occupational Stress survey information database, obtain the multiple linear regression equations of each index in index X and grouping α by least square method;
S12: calculate the coefficient of determination R2 of multiple linear regression equations, its value, more close to 1, illustrates that model is better to the fitting degree of data;
S13: use progressively back-and-forth method to verify the index in each grouping α, to select which index can enter regression model, according to the sum of squares of partial regression U of the each index of grouping α
(k) pi, filter out the index relevant to objective indicator X in grouping α.
5. the Difference of Occupational Stress evaluating system based on Internet of Things as claimed in claim 4, is characterized in that, the detailed process of the least square method in described step S11 is:
S111: the initial multiple linear regression equations of each index and objective indicator X in foundation grouping α:
Y＝b0+b(1)*X(1)+b(2)*X(2)+b(3)*X(3)+……+b(m)*X(m)；
S112: by the initial multiple linear regression equations of setting up in step S111 described in each index substitution in grouping α, calculate model prediction result index X ';
S113: the sum of sguares of deviation from mean φ that calculates objective indicator X and model prediction result index X ';
S114: change coefficient b and the constant term b0 of each index in model, when sum of sguares of deviation from mean φ is for hour, draw equation of linear regression, otherwise get back to step S111.
6. the Difference of Occupational Stress evaluating system based on Internet of Things as claimed in claim 4, is characterized in that, in described step S12, the circular of degree of correlation b is:
S121: the linear regression model (LRM) of each index and index X in foundation grouping α:
Y＝b0+b(1)*X(1)+b(2)*X(2)+b(3)*X(3)+……+b(m)*X(m)；
S122: bring the True Data of each index in grouping α into, calculate model prediction result index X ';
The sum of sguares of deviation from mean φ of S123: parameter X and index X ';
S124: change coefficient b and the constant term b0 of each index in model, when sum of sguares of deviation from mean φ is for hour, show that the coefficient of each index in the α that divides into groups in equation of linear regression is degree of correlation b, otherwise get back to step S121.
7. the Difference of Occupational Stress evaluating system based on Internet of Things as claimed in claim 4, is characterized in that, the concrete operations of described step S13 are:
S131: the sum of squares of partial regression U that calculates the each index being not yet selected in each grouping α
(k) pi, wherein k=1,2 ..., represent that k step returns; U
pifor the sum of squares of partial regression of objective indicator Xi to each index in grouping α; I=1,2 ..., m, m is independent variable number or number;
S132: more each U
(k) pi, find out maximum U
(k) pibe designated as maxU
(k) pi;
S133: to maxU
(k) pido the F test of partial regression, if significantly (statistical significance P < 0.05) is just selected into regression equation by this independent variable in k step;
S134: to the try again F test of partial regression conspicuousness of all independent variables selected before k step, become inapparent (P > 0.05) if having, give and picking out;
S135: to maxU
(k) pido the F test of partial regression, if not significantly (P > 0.05), successive Regression finishes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410057010.9A CN103810389A (en) | 2014-02-15 | 2014-02-15 | Occupational stress evaluation system based on internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410057010.9A CN103810389A (en) | 2014-02-15 | 2014-02-15 | Occupational stress evaluation system based on internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103810389A true CN103810389A (en) | 2014-05-21 |
Family
ID=50707149
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410057010.9A Pending CN103810389A (en) | 2014-02-15 | 2014-02-15 | Occupational stress evaluation system based on internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103810389A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017533805A (en) * | 2014-11-11 | 2017-11-16 | グローバル ストレス インデックス プロプライエタリー リミテッド | System and method for generating stress level and stress tolerance level profiles within a population |
-
2014
- 2014-02-15 CN CN201410057010.9A patent/CN103810389A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017533805A (en) * | 2014-11-11 | 2017-11-16 | グローバル ストレス インデックス プロプライエタリー リミテッド | System and method for generating stress level and stress tolerance level profiles within a population |
| EP3218835A4 (en) * | 2014-11-11 | 2018-11-14 | Global Stress Index Pty Ltd | A system and a method for generating a profile of stress levels and stress resilience levels in a population |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107146160B (en) | Health condition analysis method for insurance client and server | |
| CN109523188B (en) | Man-machine interface display-oriented method and system for evaluating man-machine cognitive characteristic work efficiency | |
| CN101110699A (en) | System with network satisfaction degree estimation and early warning function and implementing method thereof | |
| CN104318497A (en) | Method and system for automatic communitization learning | |
| KR102175451B1 (en) | Apparatus and system for performing adaptive test based item response theory | |
| CN101763590A (en) | Bank counter service system | |
| Benfenati et al. | Results of a round-robin exercise on read-across | |
| CN114219242A (en) | Efficiency evaluation method and efficiency evaluation system construction system for vehicle equipment | |
| JP2013012120A (en) | Questionnaire tabulation system | |
| CN114782123A (en) | Credit assessment method and system | |
| CN112598184B (en) | Method and device for predicting repeated air suction risk of drug addict | |
| CN110377828A (en) | Information recommendation method, device, server and storage medium | |
| CN103810389A (en) | Occupational stress evaluation system based on internet of things | |
| CN103761439A (en) | Occupational stress evaluation system based on internet of things | |
| CN110233864B (en) | Crowdsourcing-based information acquisition method, device, equipment and storage medium | |
| Pourahmad et al. | Service quality assessment in the academic library: Use of hybrid fuzzy expert system | |
| CN106656943B (en) | A kind of matching process and device of network user's attribute | |
| CN111899823A (en) | Method and system for processing gauge information, terminal equipment and storage medium | |
| Az-zahra et al. | Usability evaluation of user interface in badan narkotika nasional east java province website | |
| CN116151460A (en) | Optimization method and device for intelligent vehicle product, server and storage medium | |
| CN115274113A (en) | Evaluation system based on psychological assessment scale scoring algorithm | |
| CN114944219A (en) | Mental scale recommendation method and device based on artificial intelligence and storage medium | |
| CN113610383A (en) | Method and system for evaluating quality and sharing effect of atmospheric combined pollution observation data | |
| JP7231186B2 (en) | Technology judgment support system | |
| CN106682182A (en) | Method and system for testing job hunting readiness degree |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20140521 |
|
| WD01 | Invention patent application deemed withdrawn after publication |