CN115469626A - Industrial production monitoring system and method based on Internet of things - Google Patents
Industrial production monitoring system and method based on Internet of things Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 96
- 238000009776 industrial production Methods 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000011156 evaluation Methods 0.000 claims abstract description 65
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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Abstract
The invention discloses an industrial production monitoring system and method based on the Internet of things, belonging to the technical field of industrial production; the method comprises the steps of simultaneously acquiring the risk coefficient of historical damage data of different aspects of each area, simultaneously acquiring the division degree of the risk coefficient and data of various aspects of equipment industrial production, and dividing each area of the industrial production based on the division degree to realize modular processing of different areas so as to provide data support for targeted monitoring, early warning and dynamic regulation and control of subsequent industrial production; performing matching evaluation based on different working time periods of the personnel and the duration of different staying risk areas, and judging whether the areas where the corresponding personnel are located meet the operation requirements and whether potential safety hazards exist; the invention is used for solving the technical problem that the prior scheme can not implement differential monitoring on industrial production and can adaptively implement different intervention modes to improve the overall effect of industrial production monitoring.
Description
Technical Field
The invention relates to the technical field of industrial production, in particular to an industrial production monitoring system and method based on the Internet of things.
Background
Industrial production monitoring can acquire the condition of industrial production in real time; most of the existing industrial production monitoring schemes still stay in the one-way display monitoring condition during implementation, and more wisdom is to carry out active early warning prompt, but the operation risk of the region position where the personnel are located in the industrial production cannot be evaluated and measures are actively taken to eliminate potential safety hazards, so that the overall effect of industrial production monitoring is poor.
Disclosure of Invention
The invention aims to provide an industrial production monitoring system and method based on the Internet of things, which are used for solving the technical problem that the overall effect of industrial production monitoring cannot be improved by implementing differentiated monitoring and adaptively implementing different intervention modes on industrial production in the conventional scheme.
The purpose of the invention can be realized by the following technical scheme:
the industrial production monitoring system based on the Internet of things comprises a region division module, a region monitoring module, a camera processing module and an early warning regulation and control module;
the region division module is used for carrying out risk assessment and division on each region of industrial production to obtain region division data; the method comprises the following steps:
numbering each device in industrial production and marking the device as i, i belongs to {1,2,3,. And n }, wherein n is a positive integer, obtaining the coordinate of each device in industrial production, expanding the length and width of the occupied area of the device according to the preset expansion length and the preset expansion width to obtain a rectangular expansion area, and marking the area of each expansion area as TMi;
matching the name of the equipment with a pre-constructed equipment-weight table to obtain corresponding equipment weight and marking the equipment weight as SQi;
acquiring danger coefficients corresponding to the expansion areas and marking the danger coefficients as Wxi; extracting the numerical value of each item of marked data and calculating and acquiring a graduation index HFi through a formula HFi = h1 × WXi + h2 × TMi + h3 × SQi; in the formula, h1, h2 and h3 are different proportionality coefficients, and h2 is more than 0 and less than h3 and less than h1;
dividing each region of industrial production by using the dividing degree to obtain region dividing data comprising a plurality of first-class regions, second-class regions and third-class regions;
the region monitoring module is used for shooting each region divided in the region division data to obtain a monitoring shooting set;
the camera processing module is used for monitoring and evaluating the industrial production safety of each area according to the monitoring camera set to obtain an area evaluation set containing a second evaluation signal, a first marking area, a third evaluation signal and a second marking area;
and the early warning regulation and control module is used for early warning and dynamically regulating and controlling the industrial production of each region according to the region evaluation set.
Preferably, the step of obtaining the risk coefficient corresponding to each expansion area includes:
acquiring historical danger data of each area in industrial production, wherein the historical danger data comprises historical injury times, historical injury number and historical injury grade;
respectively marking the historical injury times and the historical injury number as SCi and SRi;
setting different damage grades to correspond to different damage weights, matching the obtained historical damage grades with all the damage grades to obtain corresponding damage weights, and marking the damage weights as SHi;
extracting the numerical values of the marked data items in parallel and obtaining the numerical values through a formula
Calculating and obtaining a danger coefficient Wxi corresponding to the expansion area; in the formula, w1 and w2 are different proportionality coefficients, and w2 is more than 0 and less than w1.
Preferably, the division of the various zones of the industrial production is performed using division scales, comprising:
matching the division degree with a preset division range;
if the division degree is greater than the maximum value of the division range, judging that the risk of the corresponding region is high and marking the type label of the corresponding region as a first-class region;
if the division degree is not greater than the maximum value of the division range and not less than the minimum value of the division range, judging that the risk of the corresponding region is medium and marking the type label of the region as a second-class region;
if the division degree is smaller than the minimum value of the division range, judging that the danger of the corresponding region is low and marking the type label of the corresponding region as a three-class region;
the dividing degree and a plurality of first-class areas, second-class areas and third-class areas form area dividing data.
Preferably, the monitoring and evaluation of the industrial production safety of each area according to the monitoring camera set comprises the following steps:
acquiring real-time images of each area in a monitoring camera set, acquiring time points of personnel entering and leaving an extended area according to the real-time images, setting the time points as a first time stamp t1 and a second time stamp t2 respectively, and acquiring monitoring time periods [ t1, t2] according to the first time stamp t1 and the second time stamp t 2;
acquiring a type label corresponding to an extended area where a person is located, and acquiring a corresponding early warning time set and a corresponding regulation time set according to the type label; the early warning time set and the regulation and control time set respectively comprise a plurality of different early warning time periods and different regulation and control time periods;
and evaluating the safety of personnel according to the monitoring time period, the early warning time set and the regulation and control time set to obtain a regional evaluation set.
Preferably, the judgment of the entering and leaving of the personnel in the expansion area according to the real-time image analysis is realized based on a video image character recognition algorithm.
Preferably, the safety of the personnel is evaluated according to the monitoring period, the early warning time set and the regulation and control time set, and the safety evaluation method comprises the following steps:
matching the monitoring time periods with a plurality of early warning time periods in the early warning time set and a plurality of regulation time periods in the regulation time set respectively;
and if the monitoring time period is partially overlapped with one early warning time period of the early warning time set or one regulation and control time period of the regulation and control time set, generating a second evaluation signal and marking the corresponding expansion area to obtain a first marking area.
Preferably, if the monitoring time period is completely overlapped with one early warning time period of the early warning time set or one regulation and control time period of the regulation and control time set, generating a third evaluation signal and marking a corresponding expansion area to obtain a second marking area;
the second evaluation signal and the first mark region, the third evaluation signal and the second mark region form a region evaluation set.
Preferably, according to regional assessment set, carry out early warning and dynamic control to the industrial production in each region, including:
acquiring and analyzing a region evaluation set, if the region evaluation set contains a second evaluation signal, generating a primary early warning prompt for the monitoring platform, and simultaneously displaying the coordinates of the first marking region;
and if the regional evaluation set contains a third evaluation signal, generating a secondary early warning prompt for the monitoring platform, displaying the coordinates of the second marking region, and controlling the industrial production in the second marking region to pause.
In order to solve the problem, the invention also discloses an industrial production monitoring method based on the Internet of things, which comprises the following steps:
carrying out risk assessment and division on each region of industrial production, wherein the region division data comprises a plurality of first-class regions, second-class regions and third-class regions;
shooting each divided region in the region division data to obtain a monitoring shooting set;
monitoring and evaluating the industrial production safety of each area according to the monitoring camera set to obtain an area evaluation set comprising a second evaluation signal, a first marking area, a third evaluation signal and a second marking area;
and carrying out early warning and dynamic regulation and control on industrial production of each region according to the region evaluation set.
Compared with the prior scheme, the invention has the following beneficial effects:
1. according to the method, historical injury data of different aspects of each area are simultaneously acquired to obtain the risk coefficient, the risk coefficient and data of industrial production aspects of equipment are simultaneously acquired to obtain the division degree, and each area of the industrial production is divided based on the division degree to realize modular processing of different areas, so that data support is provided for subsequent targeted monitoring and early warning and dynamic regulation and control of industrial production.
2. According to the method and the system, matching evaluation is carried out on the basis of the working time periods of the personnel and the duration of the stay in different risk areas, and whether the areas where the corresponding personnel are located meet the operation requirements and whether the potential safety hazards exist or not is judged, so that the potential safety hazards can be eliminated by carrying out early warning and automatic intervention in a targeted manner, the occurrence of safety accidents is avoided, and the overall effect of industrial production monitoring is improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a block diagram of an industrial production monitoring system based on the internet of things according to the present invention.
Fig. 2 is a flow chart of the industrial production monitoring 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.
Example one
As shown in fig. 1, the invention relates to an industrial production monitoring system based on the internet of things, which comprises a region division module, a region monitoring module, a camera processing module and an early warning regulation and control module;
the region division module is used for carrying out risk assessment and division on each region of industrial production to obtain region division data; the method comprises the following steps:
numbering and marking each device of the industrial production as i, i belongs to {1,2,3,. Eta., n }, wherein n is a positive integer and represents the total number;
obtaining coordinates of each equipment in industrial production, expanding the length and width of the occupied area of the equipment according to preset expansion length and expansion width to obtain rectangular expansion areas, and marking the area of each expansion area as TMi; the purpose of expansion is to realize the modularized monitoring of industrial production so as to improve the overall monitoring effect, and the expansion length and the expansion width can be expanded according to the floor area of each device and the preset expansion proportion; the statistics of the coordinates and the occupied area of the equipment can be obtained based on the existing Internet of things equipment;
matching the name of the equipment with a pre-constructed equipment-weight table to obtain corresponding equipment weight and marking the equipment weight as SQi; the device-weight table comprises a plurality of different devices and corresponding device weights thereof, and the device weights corresponding to the different devices can be preset according to risks;
acquiring danger coefficients corresponding to the expansion areas and marking the danger coefficients as Wxi; extracting the numerical value of each item of marked data and calculating and acquiring a graduation index HFi through a formula HFi = h1 × WXi + h2 × TMi + h3 × SQi; in the formula, h1, h2 and h3 are different proportionality coefficients, h2 is more than 0 and less than h3 and less than h1, h1 can be 3.6857, h2 can be 1.7315, and h3 can be 2.3674;
it should be noted that the division degree is a numerical value used for evaluating and dividing each area by simultaneously connecting data of various aspects of the industrial production of the equipment, and each area of the industrial production is divided based on the division degree to realize differentiated monitoring, so that the monitoring effect of different dangerous areas can be improved.
The step of obtaining the risk coefficient corresponding to each expansion area comprises the following steps:
acquiring historical danger data of each area in industrial production, wherein the historical danger data comprises historical injury times, historical injury number and historical injury grade; considering that the number of accidents of industrial production is small and effective data support is lacked, the historical threat data can be data of the same industrial production in the same industry;
respectively marking the historical injury times and the historical injury number as SCi and SRi;
setting different damage grades to correspond to different damage weights, matching the obtained historical damage grades with all the damage grades to obtain corresponding damage weights, and marking the damage weights as SHi; the injury grade can be set or customized according to the injury grade of the personnel;
extracting the numerical values of the marked data items in parallel and obtaining the numerical values through a formula
Calculating and obtaining a danger coefficient Wxi corresponding to the expansion area; in the formula, w1 and w2 are different proportionality coefficients, w2 is more than 0 and less than w1, w1 can be 1.6433, and w2 can be 0.8217;
the risk coefficient is a numerical value used for integrally evaluating the safety risk of each region by combining historical injury data of different aspects of each region, and the accuracy of the division calculation and evaluation can be improved through the risk coefficient;
each area of industrial production is divided by using a dividing degree, and the specific steps comprise:
matching the division degree with a preset division range;
if the division degree is larger than the maximum value of the division range, judging that the danger of the corresponding region is high and marking the type label of the corresponding region as a first-class region;
if the division degree is not greater than the maximum value of the division range and not less than the minimum value of the division range, judging that the risk of the corresponding region is medium and marking the type label of the region as a second-class region;
if the division degree is smaller than the minimum value of the division range, judging that the danger of the corresponding region is low and marking the type label of the corresponding region as a three-class region;
the dividing degree and a plurality of first-class areas, second-class areas and third-class areas form area dividing data; the type labeling processing is carried out on each area of the industrial production, and differential monitoring and control are realized on the basis of risk so as to improve the monitoring effect and the safety effect.
By dividing and classifying different regions of industrial production, differential monitoring can be implemented, so that different monitoring schemes can be efficiently implemented on key regions, for example, safety accidents caused by illegal operation of workers in the existing industrial production process.
The region monitoring module is used for shooting each region divided in the region division data to obtain a monitoring shooting set; monitoring can be performed based on the existing camera shooting scheme;
the camera processing module is used for monitoring and evaluating the industrial production safety of each area according to the monitoring camera set to obtain an area evaluation set; the method comprises the following specific steps:
acquiring real-time images of each area in a monitoring camera set, acquiring time points of personnel entering and leaving an extended area according to the real-time images, setting the time points as a first time stamp t1 and a second time stamp t2 respectively, and acquiring monitoring time periods [ t1, t2] according to the first time stamp t1 and the second time stamp t 2;
the method comprises the steps that whether a person enters or leaves an expansion area is judged according to real-time image analysis, and the judgment is achieved based on a video image person identification algorithm;
acquiring a type label corresponding to an extended area where a person is located, and acquiring a corresponding early warning time set and a corresponding regulation time set according to the type label; the early warning time set and the regulation and control time set respectively comprise a plurality of different early warning time periods and different regulation and control time periods; setting an early warning time period and a regulation time period based on big data of industrial production;
evaluating the safety of personnel according to the monitoring time period, the early warning time set and the regulation and control time set, and respectively matching the monitoring time period with a plurality of early warning time periods in the early warning time set and a plurality of regulation and control time periods in the regulation and control time set;
if the monitoring time period is different from any one of the early warning time set and the regulation time set in an overlapping mode, judging that the behavior of the personnel meets the operation requirement and generating a first evaluation signal;
if the monitoring time period is partially overlapped with one early warning time period of the early warning time set or one regulation and control time period of the regulation and control time set, judging that the behavior of personnel is slightly illegal, generating a second evaluation signal, and marking a corresponding expansion area according to the second evaluation signal to obtain a first marking area;
if the monitoring time period is completely overlapped with one early warning time period of the early warning time set or one regulation and control time period of the regulation and control time set, judging that the behaviors of the personnel are seriously violated, generating a third evaluation signal, and marking a corresponding expansion area according to the third evaluation signal to obtain a second marking area;
the first evaluation signal, the second evaluation signal and the first marking region, the third evaluation signal and the second marking region form a region evaluation set.
In the embodiment of the invention, matching evaluation is carried out based on different working time periods of personnel and the duration of different staying risk areas, and whether the area where the corresponding personnel is located meets the operation requirement and whether the potential safety hazard exists is judged, so that the potential safety hazard can be eliminated by carrying out early warning and automatic intervention in a targeted manner, and the occurrence of safety accidents is avoided.
The early warning regulation and control module is used for carrying out early warning and dynamic regulation and control on industrial production of each region according to the region assessment set, and the specific steps comprise:
acquiring and analyzing a region evaluation set, if the region evaluation set contains a second evaluation signal, generating a primary early warning prompt for the monitoring platform, and simultaneously displaying the coordinates of the first marking region;
if the regional evaluation set contains a third evaluation signal, generating a secondary early warning prompt for the monitoring platform, displaying the coordinates of the second marking region, and controlling the industrial production in the second marking region to pause; the second marked area can be a first-type area and a second-type area, wherein industrial production pause can be implemented on one type of the second marked area, or both the first-type area and the second-type area of the second marked area according to actual situations.
In the embodiment of the invention, differential early warning prompt and regulation and control are carried out on different risk areas according to the type labels, potential safety hazards caused by illegal operation of personnel are eliminated, and early warning countermeasures are made in a self-adaptive manner, so that the overall effect of industrial production monitoring is improved.
It should be noted that the above formulas are all calculated by removing dimensions and taking values thereof, and are one formula that is closest to the real situation and obtained by collecting a large amount of data and performing software simulation, and the proportionality coefficient in the formula and each preset threshold value in the analysis process are set by a person skilled in the art according to the actual situation or obtained by simulating a large amount of data.
Example two
As shown in fig. 2, the invention relates to an industrial production monitoring method based on the internet of things, which comprises the following specific steps:
carrying out risk assessment and division on each region of industrial production, wherein the region division data comprises a plurality of first-class regions, second-class regions and third-class regions;
shooting each divided region in the region division data to obtain a monitoring shooting set;
monitoring and evaluating the industrial production safety of each area according to the monitoring camera set to obtain an area evaluation set comprising a second evaluation signal and a first marking area, and a third evaluation signal and a second marking area;
and carrying out early warning and dynamic regulation and control on industrial production of each region according to the region evaluation set.
In the embodiments provided by the present invention, it should be understood that the disclosed system may be implemented in other manners. For example, the above-described embodiments of the invention are merely illustrative, and for example, a module may be divided into only one logic function, and another division may be implemented in practice.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
It is obvious to a person skilled in the art that the invention is not restricted to details of the above-described exemplary embodiments, but that it can be implemented in other specific forms without departing from the essential characteristics of the invention.
Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the same, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. The industrial production monitoring system based on the Internet of things is characterized by comprising a region division module, a region monitoring module, a camera processing module and an early warning regulation and control module;
the region division module is used for carrying out risk assessment and division on each region of industrial production to obtain region division data; the method comprises the following steps:
numbering each device in industrial production and marking the device as i, i belongs to {1,2,3,. Eta., n }, wherein n is a positive integer, obtaining a coordinate of each device in industrial production, expanding the length and width of the occupied area of the device according to a preset expansion length and expansion width to obtain a rectangular expansion area, and marking the area of each expansion area as TMi;
matching the name of the equipment with a pre-constructed equipment-weight table to obtain corresponding equipment weight and marking the equipment weight as SQi;
acquiring danger coefficients corresponding to the expansion areas and marking the danger coefficients as Wxi; extracting the numerical value of each item of marked data and calculating and acquiring a graduation index HFi through a formula HFi = h1 × WXi + h2 × TMi + h3 × SQi; in the formula, h1, h2 and h3 are different proportionality coefficients, and h2 is more than 0 and less than h3 and less than h1;
dividing each region of industrial production by using the dividing degree to obtain region dividing data comprising a plurality of first-class regions, second-class regions and third-class regions;
the region monitoring module is used for shooting each region divided in the region division data to obtain a monitoring shooting set;
the camera processing module is used for monitoring and evaluating the industrial production safety of each area according to the monitoring camera set to obtain an area evaluation set containing a second evaluation signal, a first marking area, a third evaluation signal and a second marking area;
and the early warning regulation and control module is used for early warning and dynamically regulating and controlling the industrial production of each region according to the region evaluation set.
2. The industrial production monitoring system based on the internet of things according to claim 1, wherein the step of obtaining the risk coefficients corresponding to the expansion areas comprises:
acquiring historical danger data of each area in industrial production, wherein the historical danger data comprises historical damage times, historical damage number and historical damage grade;
respectively marking the historical damage times and the historical damage number as SCi and SRi;
setting different damage grades to correspond to different damage weights, matching the obtained historical damage grades with all the damage grades to obtain corresponding damage weights, and marking the damage weights as SHi;
extracting the numerical values of the marked data items in parallel and obtaining the numerical values through a formula
Calculating and obtaining a danger coefficient Wxi corresponding to the expansion area; in the formula, w1 and w2 are different proportionality coefficients, and w2 is more than 0 and less than w1.
3. The internet of things-based industrial production monitoring system according to claim 1, wherein the division of the areas of the industrial production by the division scale comprises:
matching the division degree with a preset division range;
if the division degree is greater than the maximum value of the division range, judging that the risk of the corresponding region is high and marking the type label of the corresponding region as a first-class region;
if the division degree is not greater than the maximum value of the division range and not less than the minimum value of the division range, judging that the risk of the corresponding region is medium and marking the type label of the region as a second-class region;
if the division degree is smaller than the minimum value of the division range, judging that the risk of the corresponding region is low and marking the type label of the corresponding region as a three-class region;
the dividing degree and a plurality of first-class areas, second-class areas and third-class areas form area dividing data.
4. The industrial production monitoring system based on the internet of things according to claim 1, wherein the monitoring and evaluation of the industrial production safety of each area according to the monitoring camera set comprises:
acquiring real-time images of each area in a monitoring camera set, acquiring time points of personnel entering and leaving an extended area according to the real-time images, setting the time points as a first time stamp t1 and a second time stamp t2 respectively, and acquiring monitoring time periods [ t1, t2] according to the first time stamp t1 and the second time stamp t 2;
acquiring a type label corresponding to an extended area where a person is located, and acquiring a corresponding early warning time set and a corresponding regulation time set according to the type label; the early warning time set and the regulation and control time set respectively comprise a plurality of different early warning time periods and different regulation and control time periods;
and evaluating the safety of personnel according to the monitoring time period, the early warning time set and the regulation and control time set to obtain a regional evaluation set.
5. The internet of things-based industrial production monitoring system of claim 4, wherein the judgment of the entrance and exit of people into and out of the expansion area based on real-time image analysis is implemented based on a video image person recognition algorithm.
6. The internet of things-based industrial production monitoring system according to claim 4, wherein the evaluation of personnel safety according to the monitoring period and the early warning time set and the regulation and control time set comprises:
matching the monitoring time periods with a plurality of early warning time periods in the early warning time set and a plurality of regulation time periods in the regulation time set respectively;
and if the monitoring time period is partially overlapped with one early warning time period of the early warning time set or one regulation and control time period of the regulation and control time set, generating a second evaluation signal and marking the corresponding expansion area to obtain a first marking area.
7. The industrial production monitoring system based on the internet of things according to claim 6, wherein if the monitoring time period is completely overlapped with one early warning time period of the early warning time set or one regulation time period of the regulation time set, a third evaluation signal is generated and a corresponding expansion area is marked to obtain a second marking area;
the second evaluation signal and the first mark region, the third evaluation signal and the second mark region constitute a region evaluation set.
8. The industrial production monitoring system based on the internet of things according to claim 1, wherein the early warning and dynamic regulation and control of the industrial production of each region according to the region assessment set comprises:
acquiring and analyzing a region evaluation set, if the region evaluation set contains a second evaluation signal, generating a primary early warning prompt for the monitoring platform, and simultaneously displaying the coordinates of the first marking region;
and if the regional evaluation set contains a third evaluation signal, generating a secondary early warning prompt for the monitoring platform, displaying the coordinates of the second marking region, and controlling the industrial production in the second marking region to pause.
9. The industrial production monitoring method based on the Internet of things is characterized by comprising the following steps:
carrying out risk assessment and division on each region of industrial production, wherein the region division data comprises a plurality of first-class regions, second-class regions and third-class regions;
shooting each divided region in the region division data to acquire a monitoring shooting set;
monitoring and evaluating the industrial production safety of each area according to the monitoring camera set to obtain an area evaluation set comprising a second evaluation signal and a first marking area, and a third evaluation signal and a second marking area;
and carrying out early warning and dynamic regulation and control on industrial production of each region according to the region evaluation set.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116345693A (en) * | 2023-04-21 | 2023-06-27 | 沈阳工程学院 | Safety monitoring management method and system for transformer substation |
| CN116634367A (en) * | 2023-07-21 | 2023-08-22 | 三峡高科信息技术有限责任公司 | Intelligent construction monitoring management system based on Internet of things |
| CN117620324A (en) * | 2023-12-21 | 2024-03-01 | 杭州新世宝电动转向系统有限公司 | Worm gear running-in device of steering gear |
| CN118071144A (en) * | 2024-02-26 | 2024-05-24 | 青岛诚晟达精密机械有限公司 | Intelligent factory production on-line monitoring system based on big data |
| CN118135475A (en) * | 2023-09-25 | 2024-06-04 | 海南海长星消防工程有限公司 | Fire-fighting hidden danger detection and early warning system and method based on Internet of things |
-
2022
- 2022-10-03 CN CN202211217377.3A patent/CN115469626A/en not_active Withdrawn
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116345693A (en) * | 2023-04-21 | 2023-06-27 | 沈阳工程学院 | Safety monitoring management method and system for transformer substation |
| CN116345693B (en) * | 2023-04-21 | 2024-08-13 | 沈阳工程学院 | Safety monitoring management method and system for transformer substation |
| CN116634367A (en) * | 2023-07-21 | 2023-08-22 | 三峡高科信息技术有限责任公司 | Intelligent construction monitoring management system based on Internet of things |
| CN116634367B (en) * | 2023-07-21 | 2023-10-03 | 三峡高科信息技术有限责任公司 | Intelligent construction monitoring management system based on Internet of things |
| CN118135475A (en) * | 2023-09-25 | 2024-06-04 | 海南海长星消防工程有限公司 | Fire-fighting hidden danger detection and early warning system and method based on Internet of things |
| CN117620324A (en) * | 2023-12-21 | 2024-03-01 | 杭州新世宝电动转向系统有限公司 | Worm gear running-in device of steering gear |
| CN118071144A (en) * | 2024-02-26 | 2024-05-24 | 青岛诚晟达精密机械有限公司 | Intelligent factory production on-line monitoring system based on big data |
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