CN112053543A - Production safety monitoring and early warning system and method based on Internet of things - Google Patents
Production safety monitoring and early warning system and method based on Internet of things Download PDFInfo
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Abstract
The invention discloses a production safety monitoring and early warning system and a method based on the Internet of things, the system comprises a data acquisition terminal module, an early warning management module and a monitoring module, the data acquisition terminal can judge the danger level and calculate the danger level through the working content of each post, the early warning management module can warn the staff exceeding the early warning value and predict whether the staff can exceed the early warning value again, the monitoring module can monitor each dangerous corner and predict whether the steps of the staff can make relevant errors again for prompt, the invention is scientific and reasonable, is safe and convenient to use, is provided with an early prediction unit, utilizes monitoring equipment to detect the operation steps of the staff who make errors, speculates whether the next step of the staff can make errors through the operation steps, and is provided with a data comparison unit, the maximum value of the data was used to analyze the condition of the next month.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a production safety monitoring and early warning system and method based on the Internet of things.
Background
With the rapid development of the modern internet of things, more and more factories pay attention to the safety in the production process, generally, in the production process, safety protection can be performed according to different operating environments, the close connection between a camera and the internet of things can be utilized to monitor the on-site environment in real time to prevent dangerous accidents, various existing hidden dangers can be remarked and detected in real time in the form of the internet of things, but in the process, the safety is not analyzed from the perspective of a system, so that related vulnerabilities can exist in the aspect of safety;
1. after a safety accident occurs, comprehensive risk assessment is not carried out on future situations;
2. operators who are bad or have accidents are not supervised, and whether danger exists or not is predicted for each production step of the operators;
therefore, there is a need for a family-reading resource recommendation service system and method to solve the above problems.
Disclosure of Invention
The invention aims to provide a production safety monitoring and early warning system and method based on the Internet of things, and aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a production safety monitoring early warning system based on thing networking which characterized in that: the monitoring and early warning system comprises a data acquisition terminal module for acquiring and calculating the danger levels of the working types of each department, an early warning management module for finding out the working personnel with the danger levels exceeding the early warning value and a monitoring module for monitoring the working personnel with the danger levels exceeding the early warning value in real time, the data acquisition terminal module can be used for calculating the danger levels of the working types, the working environment condition can be known through the danger levels, the early warning management module can be used for predicting the future danger levels after knowing the danger levels to make early prevention, the monitoring module can be used for monitoring each corner of the working environment, the next operation steps of the working personnel can be predicted in advance, and the operation safety of the working personnel can be protected;
the data acquisition terminal module is connected with the early warning management module, the monitoring module is connected with the data acquisition terminal module, and the early warning management module is connected with the monitoring module.
The data acquisition terminal module comprises a service feedback unit, a danger judging progression unit, a data calculating unit and a data feedback unit, wherein the service feedback unit judges danger levels of different working types, the service feedback unit can comprehensively know working environment conditions and make corresponding danger coefficients, the danger judging progression unit is used for carrying out progression judgment on possible caused danger conditions, the data calculating unit is used for calculating the danger numbers of different types of work, the data feedback unit is used for feeding back calculation results to a system and leaders of departments for clearly knowing the danger progression conditions of each department every month, the output end of the service feedback unit is connected with the input end of the danger judging progression unit, and the output end of the danger judging progression unit is connected with the input end of the data calculating unit, danger of monitoring working personnel at any time by utilizing data acquisition terminal
The early warning management module comprises a data storage unit, a classification early warning unit and a hidden danger investigation unit, wherein the data storage unit is used for storing different types of danger levels and selecting a maximum value, the classification early warning unit is used for comparing the maximum value in data of each door with an early warning value and warning a worker with the maximum value generated by the danger levels, the condition of the monthly danger levels is estimated through related data, the hidden danger investigation unit is used for overhauling an abnormal machine and reporting the overhauling process in real time, the output end of the data storage unit is connected with the input end of the danger judgment level unit, the output end of the classification early warning unit is connected with the input end of the data storage unit, the early warning management module can be used for storing the past data of the worker and estimating the conditions of several months later according to the data condition, and feeding the estimation result back to the system and each department leader to attach importance to the data.
The monitoring module comprises a data acquisition unit, a data comparison unit, a data feedback unit and an early warning unit, wherein the data acquisition unit is used for acquiring the condition of workers exceeding an early warning value, the data comparison unit is used for comparing the operation steps of the workers with the correct operation steps, the early prediction unit is used for predicting whether errors or deviations occur in the next step of the workers according to the operation steps of the workers, the data feedback unit is used for feeding the errors which possibly occur back to the workers and the leaders of relevant departments, the hidden danger troubleshooting unit is connected with the monitoring module, the output end of the data acquisition unit is connected with the input end of the data comparison unit, the output end of the early warning unit is connected with the input end of the data feedback unit, and the monitoring module is utilized, the method and the system realize monitoring of the workers with the largest danger value in each department and judge whether the operation steps of the workers are correct or not according to the database, and guarantee is realized in the operation process of the workers.
A production safety monitoring and early warning system and method based on the Internet of things comprises the following steps:
s1: the service feedback unit is used for judging dangers caused by different working types, and the danger level unit is used for marking the level of the caused danger;
s2: calculating the danger numbers of different posts at the bottom of the month, and feeding the results back to the leaders and the systems of the relevant departments;
s3: selecting a maximum value from the calculated results by using an early warning management module, comparing the maximum value with an early warning value, carrying out hidden danger investigation on machines which are abnormal once, reporting the progress in real time, and predicting the next danger number condition through the danger number of the previous months;
s4: predicting the next step of the staff by using the monitoring and early warning unit, and judging whether errors occur again;
in the steps S1-S2, the risks incurred in different jobs are classified into four grades of light injury, heavy injury, disability, and death, and the risk factors of the four grades are expressed as: 0.1 minor injury, 0.3 major injury, 0.6 disability and 0.9 death, and in step S2, the set of the number of minor injuries per department is Q ═ Q1,q2,q3,q4,…qm-1,qmThe number of the people with serious injury of each department is Z ═ Z1,z2,z3,z4,…zm-1,zmThe set of the disabled people of each department is C ═ C1,c2,c3,c4,…cm-1,cmThe number of dead people in each department is S ═ S1,s2,s3,s4,…sm-1,sm}；
According to the formula:
when in use
Then Q is obtainedmIs the maximum value;
wherein: w is the danger level of a certain door;
in the step S3, the risk level of a department in the first month is W1The number of dangerous stages occurring in the second month is W2；
According to the formula:
wherein:
is a moving average of the t-th period, Wt-1Is the observed value in the t-1 th stage (t ═ 1,2,3 … N), MAD is the mean absolute error, | etAnd | is the absolute value of the deviation.
In the step S4, the product is cut according to the acute angle θ formed clockwise from the certain entry point e by the correct operation step of the certain staff in the databaseMoving F according to the acute angle theta to produce a certain product, and measuring the actual working step theta of a certain operator according to the data1Further moving D in a certain direction;
according to the formula:
wherein: p is the distance traveled when θ1When not equal to θ or D not equal to F, it is determined that the operation procedure of the operator is incorrect, and there is a risk that the corresponding coefficient is generated.
And in the data comparison unit, the correct operation steps are stored in the database, the existing steps and the steps in the database are compared and analyzed, and whether the operation steps are correct or not is judged according to the result, so that the safety of the working personnel in the operation process is guaranteed.
The hidden danger troubleshooting unit is used for overhauling the abnormal machine and troubleshooting hidden dangers of the processed machine in real time, the life cycle of the machine can be known through real-time troubleshooting, and related measures are taken to protect the machine.
Compared with the prior art, the invention has the beneficial effects that:
1. the early warning management module is arranged, the maximum value can be selected from the dangerous series and compared with the set early warning value, so that the dangerous situation in each department can be better known, the classification early warning unit is arranged, the dangerous series situation in the next month can be predicted according to the past dangerous series, and the condition which possibly occurs can be prevented in advance.
2. The monitoring module is arranged, so that the staff with dangerous conditions can be monitored in real time, comparison is carried out between each operation step of the staff and the correct operation step in the database, and whether the dangerous conditions occur in the next step of the staff is predicted according to each step of the staff, so that the staff can have dangerous conditions during production
Drawings
FIG. 1 is a schematic diagram of a module composition of a production safety monitoring and early warning system and method based on the Internet of things;
FIG. 2 is a schematic diagram of steps of a production safety monitoring and early warning system and method based on the Internet of things;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the monitoring and early warning system includes a data acquisition terminal module for acquiring and calculating the risk levels of the work types of each department, an early warning management module for checking that the risk levels exceed the early warning value, and a monitoring module for monitoring the operation steps of the workers exceeding the early warning value in real time, so that the workers can avoid the reoccurrence of the risk condition in the subsequent production process;
the data acquisition terminal module is connected with the early warning management module, the monitoring module is connected with the data acquisition terminal module, and the early warning management module is connected with the monitoring module.
The data acquisition terminal module comprises a service feedback unit, a danger judgment progression unit, a data calculation unit and a data feedback unit, wherein the service feedback unit is used for judging danger levels of different working types, the danger judgment progression unit is used for carrying out progression judgment on possible danger conditions, the data calculation unit is used for calculating the danger numbers of different types of work, the data feedback unit is used for feeding back calculation results to the system and the leaders of all departments, the output end of the service feedback unit is connected with the input end of the danger judgment progression unit, the output end of the danger judgment progression unit is connected with the input end of the data calculation unit, the danger progression obtained by calculation can be fed back to the system and the leaders of all departments by the data acquisition terminal module, and therefore the conditions of all the departments can be clearly known and relevant measures can be taken for prevention.
The early warning management module comprises a data storage unit, a classification early warning unit and a hidden danger investigation unit, the data storage unit is used for storing different types of dangerous progression and selecting the maximum value, the classification early warning unit is used for comparing data of all doors with early warning values, the hidden danger investigation unit is used for overhauling an abnormal machine and reporting an overhauling process in real time, the output end of the data storage unit is connected with the input end of the danger judgment progression unit, the output end of the classification early warning unit is connected with the input end of the data storage unit, the possibility of dangerous progression of the next month can be presumed by using the early warning management module according to the dangerous progression data of the past months, and the module can also be used for overhauling the machine with hidden danger.
The monitoring module comprises a data acquisition unit, a data comparison unit, a data feedback unit and an early warning unit, wherein the data acquisition unit is used for acquiring the condition of workers exceeding the early warning value, the data comparison unit is used for comparing the operation steps of the workers with the correct operation steps, the early prediction unit is used for predicting whether errors or deviations occur in the next step of the workers according to the operation steps of the workers, the data feedback unit is used for feeding the errors which possibly occur back to the workers and the leaders of relevant departments, the hidden danger investigation unit is connected with the monitoring module, the output end of the data acquisition unit is connected with the input end of the data comparison unit, the output end of the early warning unit is connected with the input end of the data feedback unit, and the monitoring module can be used for comparing the operation steps of the workers with the steps in a database, and whether the deviation occurs is presumed according to the next step of the staff, and when the operating step of the staff is not consistent with the step in the database, the short message is sent to inform the relevant staff in time.
As shown in fig. 2, a production safety monitoring and early warning system and method based on the internet of things includes the following steps:
s1: the service feedback unit is used for judging dangers caused by different working types, and the danger level unit is used for marking the level of the caused danger;
s2: calculating the danger numbers of different posts at the bottom of the month, and feeding the results back to the leaders and the systems of the relevant departments;
s3: selecting a maximum value from the calculated results by using an early warning management module, comparing the maximum value with an early warning value, carrying out hidden danger investigation on machines which are abnormal once, reporting the progress in real time, and predicting the next danger number condition through the danger number of the previous months;
s4: predicting the next step of the staff by using the monitoring and early warning unit, and judging whether errors occur again;
in steps S1-S2, the risks incurred in different jobs are classified into four grades of light injury, heavy injury, disability, and death, and the risk factors of the four grades are expressed as: 0.1 minor injury, 0.3 major injury, 0.6 disability and 0.9 death, and in step S2, the set of the number of minor injuries per department is Q ═ Q1,q2,q3,q4,…qm-1,qmThe number of the people with serious injury of each department is Z ═ Z1,z2,z3,z4,…zm-1,zmThe set of the disabled people of each department is C ═ C1,c2,c3,c4,…cm-1,cmThe number of dead people in each department is S ═ S1,s2,s3,s4,…sm-1,sm}；
According to the formula:
when in use
Then Q is obtainedmIs the maximum value;
wherein: w is the danger level of a certain door;
in step S3, the department is in danger of the first month1The number of dangerous stages occurring in the second month is W2；
According to the formula:
wherein:
is a moving average of the t-th period, Wt-1Is the observed value in the t-1 th stage (t ═ 1,2,3 … N), MAD is the mean absolute error, | etAnd | is the absolute value of the deviation.
In step S4, according to the actual operation procedure of a worker in the database, an acute angle theta is formed clockwise from a certain entry point e, and F is moved in the direction of the acute angle theta to cut an article, and the correct operation procedure is to cut the article towards theta1The direction is moved by D so as to cut;
according to the formula:
wherein: p is the distance traveled when θ1When not equal to theta or D not equal to F, judging that the operation steps of the operator existBy mistake, the risk of corresponding coefficients is created.
In the data comparison unit, the correct operation steps are stored in the database, the existing steps and the steps in the database are compared and analyzed, the data comparison unit can be used for detecting the operation abnormity of the working personnel in real time, and the related risks can be avoided.
The hidden danger troubleshooting unit is used for overhauling the abnormal machine and troubleshooting hidden dangers of the processed machine in real time, related risks can be reduced to the lowest by using the hidden danger troubleshooting unit, and the maintenance progress and the maintenance result of the machine are reported to the system in real time to avoid the bad results of the machine.
The first embodiment is as follows: in steps S1-S2, the risks incurred in different jobs are classified into four grades of light injury, heavy injury, disability, and death, and the risk factors of the four grades are expressed as: 0.1 of light injury, 0.3 of heavy injury, 0.6 of disability, and 0.9 of death, in step S2, the set of the number of light injury people per department is Q ═ 10,20,2,5,0, the set of the number of heavy injury people per department is Z ═ 6,4,0,1,0, the set of the number of disability people per department is C ═ 1,0,0,1,0, and the set of the number of death people per department is S ═ 0,1,0,0,0 };
according to the formula:
the maximum value of the number of the light injury people of each department is Q220, the maximum number of serious injuries per department is Z1The maximum number of dead people in each department is S2
Example two: in step S3, a department has a risk progression of 9.1 in the first month, 8.3 in the second month, 5.2 in the third month, 8.92 in the fourth month and 7.6 in the fifth month;
according to the formula:
|e4|＝|8.2-8.92|＝0.72
|e5|＝|7.6-7.88|＝0.28
wherein | e4|＞|e5And finding that the predicted value of the fifth month is more correct than the actual observed value.
Wherein:
is a moving average of the t-th period, Wt-1Is an observed value in the t-1 th stage (t ═ 1,2,3 … N), | etAnd | is the absolute value of the deviation.
Example three: in step S4, the operation step of a worker in the database is to cut an object by moving 20 clockwise from a certain entry point e (20, 80) according to the angle direction, and the coordinates of the tool in the hand of the worker are (15,60), and the step of finding out the correct operation of the worker from the database is: when the worker produces a product, the worker cuts the product at an angle of 30 degrees, and then moves 35 degrees in a certain direction to cut the product;
according to the formula:
from the results calculations it follows that: d20, F35, θ1＝41,θ＝30；
From the results of the calculations, D ≠ F and θ1Not equal to θ, it can be determined that the operation step is erroneous.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. The utility model provides a production safety monitoring early warning system based on thing networking which characterized in that: the monitoring and early warning system comprises a data acquisition terminal module for calculating the working risk level of each department, an early warning management module for predicting the risk level and a monitoring module for monitoring the operation steps of workers in real time;
the data acquisition terminal module is connected with the early warning management module, the monitoring module is connected with the data acquisition terminal module, and the early warning management module is connected with the monitoring module.
2. The production safety monitoring and early warning system based on the Internet of things as claimed in claim 1, wherein: the data acquisition terminal module comprises a service feedback unit, a risk judgment progression unit, a data calculation unit and a data feedback unit, wherein the service feedback unit is used for judging risk grades of different working types, the risk judgment progression unit is used for carrying out progression judgment on possible caused risk conditions, the data calculation unit is used for calculating the risk number of different types of work, the data feedback unit is used for feeding back calculation results to a system and leaders of all departments, the output end of the service feedback unit is connected with the input end of the risk judgment progression unit, and the output end of the risk judgment progression unit is connected with the input end of the data calculation unit.
3. The production safety monitoring and early warning system based on the Internet of things as claimed in claim 1, wherein: the early warning management module comprises a data storage unit, a classification early warning unit and a hidden danger troubleshooting unit, wherein the data storage unit is used for storing different types of dangerous series and selecting the maximum value, the classification early warning unit is used for comparing the maximum value and the early warning value in data of each door, the hidden danger troubleshooting unit is used for overhauling an abnormal machine and reporting an overhauling process in real time, the output end of the data storage unit is connected with the input end of the danger judgment series unit, and the output end of the classification early warning unit is connected with the input end of the data storage unit.
4. The production safety monitoring and early warning system based on the Internet of things as claimed in claim 1, wherein: the monitoring module comprises a data acquisition unit, a data comparison unit, a data feedback unit and an early warning unit, wherein the data acquisition unit is used for acquiring the condition of workers exceeding an early warning value, the data comparison unit is used for comparing the operation steps of the workers with the correct operation steps, the early prediction unit is used for predicting whether errors or deviations occur in the next step of the workers according to the operation steps of the workers, the data feedback unit is used for feeding the errors which possibly occur back to the workers and the leaders of relevant departments, the hidden danger troubleshooting unit is connected with the monitoring module, the output end of the data acquisition unit is connected with the input end of the data comparison unit, and the output end of the early warning unit is connected with the input end of the data feedback unit.
5. A production safety monitoring and early warning method based on the Internet of things is characterized by comprising the following steps: the method comprises the following steps:
s1: the service feedback unit is used for judging dangers caused by different working types, and the danger level unit is used for marking the level of the caused danger;
s2: calculating the danger numbers of different posts at the bottom of the month, and feeding the results back to the leaders and the systems of the relevant departments;
s3: selecting a maximum value from the calculated results by using an early warning management module, comparing the maximum value with an early warning value, carrying out hidden danger investigation on machines which are abnormal once, reporting the progress in real time, and predicting the next danger number condition through the danger number of the previous months;
s4: and predicting the next step of the staff by using the monitoring and early warning unit, and judging whether errors occur again.
6. The production safety monitoring and early warning method based on the Internet of things as claimed in claim 5, wherein: in the steps S1-S2, the risks incurred in different jobs are classified into four grades of light injury, heavy injury, disability, and death, and the risk factors of the four grades are expressed as: 0.1 minor injury, 0.3 major injury, 0.6 disability and 0.9 death, and in step S2, the set of the number of minor injuries per department is Q ═ Q1,q2,q3,q4,…qm-1,qmThe number of the people with serious injury of each department is Z ═ Z1,z2,z3,z4,…zm-1,zmThe set of the disabled people of each department is C ═ C1,c2,c3,c4,…cm-1,cm}, each of saidThe set of department deaths is S ═ S1,s2,s3,s4,…sm-1,sm}；
According to the formula:
when in use
Then Q is obtainedmIs the maximum value;
wherein: w is the number of dangerous stages occurring in a certain door, qiNumber of mild injuries ziThe number of severe injuries ciThe number of disabled persons, siIs the number of deaths.
7. The production safety monitoring and early warning method based on the Internet of things as claimed in claim 5, wherein: in the step S3, the risk level of a department in the first month is W1The number of dangerous stages occurring in the second month is W2；
According to the formula:
wherein:
is a moving average of the t-th period, Wt-1Is the t-1 th stageIs 1,2,3 … N, MAD is the mean absolute error, | etAnd | is the absolute value of the deviation.
8. The production safety monitoring and early warning method based on the Internet of things as claimed in claim 5, wherein: in step S4, according to the fact that the correct operation procedure of a worker in the database is to cut a product by performing cutting production from an acute angle θ formed clockwise from a certain entry point e and moving F in the direction of the acute angle θ, the actual operation procedure of a worker is measured by data that θ is1Further moving D in a certain direction;
according to the formula:
wherein: p is the distance traveled when θ1When not equal to θ or D not equal to F, it is determined that the operation procedure of the operator is incorrect, and there is a risk that the corresponding coefficient is generated.
9. The production safety monitoring and early warning system based on the internet of things as claimed in claim 7, wherein: and in the data comparison unit, storing the correct operation steps into a database, and performing comparison analysis on the existing steps and the steps in the database.
10. The production safety monitoring and early warning system based on the internet of things as claimed in claim 9, wherein: the hidden danger troubleshooting unit is used for overhauling the abnormal machine and troubleshooting hidden dangers of the processed machine in real time.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103456136A (en) * | 2013-09-18 | 2013-12-18 | 戴会超 | Internet of Things framework type system for monitoring and early warning of major accident potential safety hazards of water conservancy and hydropower project |
| CN106781581A (en) * | 2016-11-29 | 2017-05-31 | 深圳职业技术学院 | Safe driving behavior monitoring early warning system and method based on the coupling of people's car |
| CN107451732A (en) * | 2017-07-31 | 2017-12-08 | 华腾软科（北京）信息技术有限公司 | A kind of enterprise safety operation total management system and method |
| CN107564228A (en) * | 2017-09-04 | 2018-01-09 | 安徽网网络科技有限公司 | Forest fire protection grade early warning system and its application method |
| US20190001885A1 (en) * | 2013-09-28 | 2019-01-03 | Crh Americas Materials, Inc. | Advanced warning and risk evasion system and method |
| CN110044409A (en) * | 2019-03-26 | 2019-07-23 | 河海大学常州校区 | A kind of big transport intellectual monitoring early warning system of dangerous material based on Internet of Things and method |
-
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- 2020-07-29 CN CN202010744464.9A patent/CN112053543B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103456136A (en) * | 2013-09-18 | 2013-12-18 | 戴会超 | Internet of Things framework type system for monitoring and early warning of major accident potential safety hazards of water conservancy and hydropower project |
| US20190001885A1 (en) * | 2013-09-28 | 2019-01-03 | Crh Americas Materials, Inc. | Advanced warning and risk evasion system and method |
| CN106781581A (en) * | 2016-11-29 | 2017-05-31 | 深圳职业技术学院 | Safe driving behavior monitoring early warning system and method based on the coupling of people's car |
| CN107451732A (en) * | 2017-07-31 | 2017-12-08 | 华腾软科（北京）信息技术有限公司 | A kind of enterprise safety operation total management system and method |
| CN107564228A (en) * | 2017-09-04 | 2018-01-09 | 安徽网网络科技有限公司 | Forest fire protection grade early warning system and its application method |
| CN110044409A (en) * | 2019-03-26 | 2019-07-23 | 河海大学常州校区 | A kind of big transport intellectual monitoring early warning system of dangerous material based on Internet of Things and method |
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