CN102890754A - Danger source monitoring system for mine - Google Patents
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Abstract
The invention provides a danger source monitoring system for mines. The system comprises sensor acquisition equipment, data fusion equipment and sensor network routing equipment, wherein the sensor acquisition equipment comprises one or more sensors and is used for acquiring data of the sensors; the data fusion equipment is used for performing stepwise assessment on risk assessment models (with different levels) subjected to sensor data application pre-storage from a high-danger level to a low-danger level, and when one assessment result is equal to or higher than a preset danger level, the assessment result is uploaded corresponding to the sensor data related to the danger level; and the sensor network routing equipment is used for receiving the sensor data uploaded by a plurality of data fusion equipment and routing the sensor data to an upper computer according to a sequence from a high danger level to a low danger level. According to the system, results are rapidly calculated during major danger source monitoring by using a plurality of key factor data, so that urgent data can be timely transmitted to the upper computer.
Description
Technical field
Mine of the present invention relates to the safety production technique field, relates in particular to a kind of mining dangerous matter sources monitoring system.
Background technology
The single-sensor monitoring reliability is poor in the dangerous matter sources monitoring and warning of colliery, to become those skilled in the art's common recognition.The multisensor fuzzy data fusion is applied in the dangerous matter sources monitor and early warning system of colliery, select a plurality of sensors that the abnormal information of down-hole is carried out overall monitor, can remedy the deficiency that adopts single type, single-sensor, the monitoring range on the expansion time and the space.According to supervisory system each Sensor monitoring real time data and historical data, every sub-systems is analyzed according to different models and is judged, provides respectively advanced warning grade and corresponding employing engineering or organization and administration measure, reduces or control danger.
Existing mining production safety monitoring and controlling system belongs to multiple independently Monitor And Control Subsystem substantially.Some monitoring and controlling system by radio sensing network with data transmission to control center, by host computer carry out data message merge analyze, early warning and support management decision etc.But a large amount of data transmission easily causes network blockage, because radio communication channel is limited, in the situation that node is seized channel capacity is the same, probably cause significant data can not be sent to the earliest host computer, therefore cause data results and actual there is any discrepancy.
List of references 1 (structure of various dimensions perception mine system and application design, P:82～85,2011/11, technology of Internet of things [J]) provides a kind of perception Mine Safety in Production supervisory system, and its structural representation as shown in Figure 1.This system comprises: perception key-course, information set stratification and management decision layer.Wherein, perception and key-course are comprised of a plurality of sensing control subsystem, realize collection and the use of various sense and control technique information in mine production and the security process.The information set stratification is integrated into control center by Industrial Ethernet with information and carries out various information processings (such as information fusion, information excavating etc.), to be used for the terminal of safety production monitoring, they can realize monitoring and control function to subsystems in the Safety of Coal Mine Production.The management decision layer is that each function part of mine can realize higher level application by network, such as Mine Safety in Production evaluation and supervision, Coal Mine Disasters early warning and control, coalmine resource ﹠ environment control and evaluation etc.In above-mentioned perception Mine Safety in Production supervisory system, information set stratification and management decision layer all are positioned at the upstream of mine, and a plurality of sensing control subsystem of perception key-course need in real time to information set stratification reported data.
In realizing process of the present invention; the applicant finds that there is following technological deficiency in prior art: the data between a plurality of sensors of sensing control subsystem have correlativity; thereby its data that report to the information set stratification have a large amount of data redundancies, often can cause emergency data can't in time be passed to the host computer of information set stratification.
Summary of the invention
The technical matters that (one) will solve
For solving above-mentioned one or more problems, the invention provides a kind of mining dangerous matter sources monitoring system, so that emergency data in time is passed to the host computer of information set stratification.
(2) technical scheme
According to an aspect of the present invention, provide a kind of mining dangerous matter sources monitoring system.This system comprises: the sensor collecting device, comprise one or more sensors, and be used for the pick-up transducers data; Data fusion equipment, the risk evaluation model that is used for the different brackets that application prestores to sensing data carries out by the step by step assessment of high-risk grade to low danger classes, when the assessment result of one of them is equal to or higher than default danger classes, the relevant sensing data of the corresponding danger classes of this assessment result is uploaded; And the sensing network routing device, be used for receiving the sensing data that a plurality of data fusion equipment are uploaded, according to danger classes order from high to low, sensing data is routed to host computer.
(3) beneficial effect
Can find out from technique scheme, the present invention is mining, and the dangerous matter sources monitoring system has following beneficial effect:
(1) be conducive to use minimum key factor data to calculate rapidly the result in the major hazard source monitoring, thereby emergency data can in time be passed to host computer;
(2) carry out Data Fusion at data fusion equipment, greatly reduced the volume of transmitted data in the network, thereby alleviated the contradiction of information set stratification and sensing control subsystem bandwidth anxiety;
(3) set up security risk level evaluation pattern, the level evaluation of different brackets uses different data sources, different algorithms, and higher grade, and the data that need are more, calculate more complicatedly, and the result who obtains is the truest.According to final assessment result, can directly control relevant equipment, for the processing of accident is raced against time.
Description of drawings
Fig. 1 is a kind of schematic diagram of perception Mine Safety in Production supervisory system;
Fig. 2 is a kind of schematic diagram of Intellisense network structure of mining major hazard source monitoring;
Fig. 3 is the multistage evaluation work flow process of data fusion equipment;
The data fusion equipment risk evaluation model M odle that Fig. 4 is
iThe detailed operation process flow diagram.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.
Need to prove, in accompanying drawing or instructions description, similar or identical part is all used identical figure number.The implementation that does not illustrate in the accompanying drawing or describe is form known to a person of ordinary skill in the art in the affiliated technical field.In addition, although this paper can provide the demonstration of the parameter that comprises particular value, should be appreciated that, parameter need not definitely to equal corresponding value, but can be similar to corresponding value in acceptable error margin or design constraint.
In one exemplary embodiment of the present invention, provide a kind of Intellisense network of mining major hazard source monitoring.Fig. 2 is the Intellisense schematic network structure according to a kind of mining major hazard source monitoring of the embodiment of the invention.As shown in Figure 2, the present embodiment comprises: sensor collecting device, data fusion equipment, sensing network routing device and Central Control Room form.Wherein, under each data fusion equipment the multiple sensors collecting device is arranged, and many number of units can be connected to Central Control Room by router according to fusion device.Below respectively each several part is elaborated.
The sensor collecting device comprises one or more sensors, is used for the pick-up transducers data, and sensing data is uploaded to data fusion equipment by wireless mode.As shown in Figure 2, mining dangerous matter sources monitoring system is responsible for K major hazard source monitored area, A
1～A
N1, B
1～B
N2..., K
1～K
NkRepresent the sensor node in K the monitored area, wherein N
kBe positive integer.
In Minepit environment, with different mining areas a large amount of dissimilar sensors are installed according to different layers, the dissimilar sensors such as gas, CO, ventilation, smog, water level, flow, flow velocity, roof pressure, rock burst, geology, temperature, humidity are arranged.For two different monitored areas, the type of the sensor that it comprises can be identical with number, also can be different.
The general purpose transducer acquisition interface adopts the pattern of PSOC in the sensor collecting device, realizes different simulated modulation with software mode on a standard device; Its sensing data communication interface adopts programmable way, can support different data communication protocols.Sensing data is encoded according to unified mode and is transmitted, and solves each subsystem data protocol difference in the existing monitoring system, the Data Fusion hard problem.Can effectively increase the network ductility, increase or reduce the different sensor type of use in the support project.
The sensing network routing device is such as R among Fig. 2
1, R
2..., R
mShown in, for the receiving sensor data and according to the system evaluation system monitoring situation is assessed, according to Data Fusion situation and assessment result actuator is processed simultaneously; Simultaneously data communication device is crossed the sensing network routing device and be transferred to Central Control Room.
The sensing network routing device is as the special-purpose routing device in the network, can be according to communication protocol, importance rate according to the Context resolution data of data, realize controllable QoS service, guarantee under network bandwidth situation of change, the high-risk level data can access preferential and reliably transmission, solves original system channel contention ability.
Wireless, wire transmission function that data fusion equipment has has the uplink and downlink informational function.Has network communicating function.Can be connected to IP-based trunk communication network; Can represent mode based on unified Monitoring Data.The non-immediate data function of active request as required; The risk evaluation model of preserving according to inside carries out level evaluation; Can directly report to the police or the control relevant device according to the control result.This equipment is to realize that mining major hazard source monitoring intelligent sensing network carries out the key of data fusion in transmission course.
System is by M sensing network data transmission fusion device, as shown in Figure 2 A, C, K, B etc., the risk evaluation model of the different brackets of preserving in the data of this equipment receiving sensor, application apparatus (sensing datas that the assessment models of different brackets is corresponding different) is assessed step by step to sensing data.Be preset with H level risk evaluation model, use respectively from low to high Modle
1, Modle
2..., Modle
HRepresent, higher grade, and the data that need are more, and estimating velocity is slower, and the result who draws is more accurate.The service rating risk evaluation model, the factor of high-risk grade is assessed in inferior grade, the assessment in the height assessment of low danger classes, process sensor data is not lost again critical data rapidly.
Host computer carries out more detailed processing to data again in the Central Control Room, and carries out result.In addition, data fusion equipment is in uploading data, and control center sends warning message earthward, perhaps directly controls relevant equipment.
In this system, key is the workflow of data fusion equipment.Fig. 3 is the multistage evaluation work flow process of data fusion equipment, and concrete steps are as follows:
Steps A: then its monitoring range inner sensor data of data fusion equipment periodic reception enter step B;
Step B: data fusion equipment is according to risk evaluation model Modle
iCarry out risk assessment, then enter step C;
Step C: risk assessment assessment Modle
iSafety enters step e as a result, otherwise carries out step D;
Step D: upload Modle
iThe data line correlation of going forward side by side is processed, and then enters step e;
Step e: judge Modle
iWhether, be enter step F, carry out the Modle of subordinate otherwise return step B if being highest assessment
I+1Assessment;
Step F: judge highest risk assessment Modle
HWhether the result safety, safe then return steps A, otherwise enter step H;
Step H: upload Modle
HThe data line correlation of going forward side by side is processed.
Fig. 4 is data fusion device step B risk evaluation model Modle
iThe detailed operation flow process:
Step B1: carry out the risk class assessment and check at first whether the required data of this grade assessment are complete, if the complete step B6 that then enters, the imperfect step B2 that enters of data;
Step B2: data T
i(T
iAt { C
i..., Q
jInterior value) do not exist, step B3 entered;
Step B3: judging provides data T
iEquipment whether in present networks, if then entering step B4, otherwise enter step B5;
Step B4: active request T
iData enter step B1 behind the receive data;
Step B5: search T
iThe place network is by the non-direct T of higher level's network active request
iData enter step B1 behind the receive data, such as step 1 among Fig. 2, shown in 2;
Step B6: carry out level evaluation according to risk evaluation model.
The grade risk evaluation model
Disaster maximum under coal mine is exactly gas accident, floods, roof pressure, therefore must monitor these three major hazard sources.In Minepit environment, gas, CO, ventilation, smog, water level, flow, flow velocity, roof pressure, rock burst, geology, temperature-humidity sensor are installed as example, are may further comprise the steps based on the major hazard source grade Risk Evaluation method of multi-sensor data:
(1) determines risk evaluation model.Modle＝{Modle
1，Modle
2，Modle3，Modle
4，Modle
5}；
One-level is the harmful gas risk evaluation model;
Secondary is hydrology risk evaluation model;
Three grades is the geologic risk assessment models;
Level Four is the environmental aspect assessment models;
Pyatyi is that the information such as historical evaluation result, historical early warning, manual control ability are carried out Integrated Evaluation Model.
Certainly owing to the difference of different environmental monitoring objects, the model grade of risk assessment and content also are different, make concrete analyses of concrete problems.
(2) determine the set of factors of each grade.The evaluation grade of default Contents for Monitoring design is as example in this programme, and different Contents for Monitorings should change to some extent.
One-level X1={x
11, x
12, x
13, x
14, wherein: x
11Represent gas, x
12Represent CO, x
13Representative is ventilated, x
14Represent smog;
Secondary X2={x
21, x
22, x
23, x
24, wherein: x
21Representation level, x
22Represent slight flow, x
23Represent flow velocity, x
24Represent higher level's assessment result;
Three grades of X3={x
31, x
32, x
33, x
34, wherein: x
31Represent roof pressure, x
32Represent rock burst, x
33Represent positional information, x
34Represent higher level's assessment result;
Level Four X4={x
41, x
42, x
43, x
44, wherein: x
41Representation temperature, x
42Represent humidity, x43 represents the density of population, x
44Represent higher level's assessment result.
Pyatyi X5={x
51, x
52, x
53, x
54, wherein: x
51Represent the historical evaluation result, x
52Represent history and predict the outcome, x
53Represent artificial controllability, x
54Represent higher level's assessment result.
Data Source is sensor active upload or according to the calculating needs of fusion device, obtains by the direct and non-immediate data of active request.Except the first order, the result of prime is used in senior computing afterwards always, and highest computing adds last evaluation result and risk profile.
Need to prove, the serve as reasons scope of a certain sensor values that obtains of sensing data is herein carried out classification and is calculated and obtain, and the progression at place is sensing data herein.
(3) determine the weights collection of each factor in each grade.
W
i＝{w
i1，w
i2，......，w
ij}。W
IjRepresent j the corresponding weights that assessment factor is shared in the assessment of i level.According to different location, different colliery, the weight of different evaluation grades also is different.Closely related with the geologic condition of locality, geographic position, historical situation etc.
Wherein: w
I1+ w
I2+ ...+w
Ij=1, i 1,5} value, j is the influence factor number in the i level assessment;
The weights of each factor in assessment algorithm adopt the self study mode to generate according to typical sample.The factor weights are along with the change of time, position, geologic condition.At first, a given Sample Storehouse is determined the proportion of each factor in valuation functions, and the real time data collection that collects of fusion device inside then keeps L optimum data and carries out weights and calculate (L is for presetting number of samples).。
(4) determine to pass judgment on collection V
i={ v
I1, v
I2, v
I3, v
I4, v
I5.v
I1Represent i level assessment result safety, v
I2Represent i level assessment result slightly dangerous, v
I3Represent i level assessment result poor risk, v
I4Represent i level assessment result R4, v
I5Represent i level assessment result grave danger.
The many factors such as geographical conditions of the density of population of the possibility that evaluation rank occurs according to accident, the extent of injury of accident, personal casualty loss that accident causes, accident spot, the economic loss that accident causes, accident spot are comprehensively passed judgment on and are drawn.
(5) assess according to model, wherein value-at-risk corresponding to this risk evaluation model:
Modle wherein
i(x) expression i level risk class assessment result; w
IjThe weights of corresponding j factor in the expression i level risk evaluation model; X
IjThe sensing data of j the factor of correspondence in the risk assessment of expression i level.
By following formula, assess according to the assessment models of every one-level.With value-at-risk Modle
i(x) contrast with passing judgment on collection Vi, draw assessment result, and carry out relevant treatment according to final assessment result.
Exemplary application
Certain colliery installation gas, CO, ventilation, smog, water level, flow, flow velocity, roof pressure, rock burst, geology, temperature-humidity sensor are example.
(1) classification
Evaluation grade is divided into 5 grades.The risk assessment of evil gas; Hydrology risk assessment; Pressure, geologic risk assessment; The environmental aspect assessment; The information such as historical evaluation result, historical early warning, manual control ability are carried out comprehensive assessment.
(2) determine the set of factors of each grade, as shown in table 2:
Table 2 evaluation grade
(3) determine the assessment weights collection W of each grade
i, the appraisal right collection is that a series of representative values calculate, and does not enumerate herein.As shown in table 3:
Table 3 assessment weights collection W
(4) assessment result of determining each grade is passed judgment on collection V
i, in conjunction with each factor of colliery.As shown in table 4:
Table 4 assessment result is passed judgment on collection V
i
Assessment result is safety 0～10, and 10～30 is slight dangerous, 30～50 R4s, 50～100 grave dangers.
(5) calculate:
According to
Model is assessed:
Example 1: measurement somewhere gas density transfinites, CO is normal, ventilates normal, and smog is normal.Draw Modle
1=22, illustrate and monitor dangerous;
Measure that this place's water level is normal, flow is normal, flow velocity is normal, Modle
1Unusually.Draw Modle
2=16, illustrate and monitor dangerous;
Measure that this place's roof pressure is normal, rock burst is normal, is positioned at return airway, Modle
2Unusually.Draw Modle
3=8, illustrate that this place is normal;
Modle
4, Modle
5Normally.Illustrate that this place is normal, possible this place is positioned at the gas higher positions such as return airway.
Example 2: measurement somewhere gas density transfinites, CO is normal, ventilates normal, and smog is normal.Draw Modle
1=22, illustrate and monitor dangerous;
Measure that this place's water level is normal, flow is normal, flow velocity is normal, Modle
1Unusually.Draw Modle
2=26, illustrate and monitor dangerous;
Measure that this place's roof pressure transfinites, rock burst transfinites, and is positioned at fully mechanized coal face, Modle
2Unusually.Draw Modle
3=28, this place's poor risk is described, gas density is higher, and gas burst accident may occur;
Measure that this place's temperature is high, humidity is low, the density of population is large, Modle
3Unusually.Draw Modle
3=39, this place's R4 is described;
Measure this place's historical results danger, prediction danger, manual control power is poor, and Modle4 is unusual.Draw Modle
5=58, this place's grave danger is described, gas density is higher, and gas burst accident may occur, and should carry out rapidly and withdraw and other rescue measure.
Be noted that above-mentioned definition to each element is not limited in various concrete structures or the shape of mentioning in the embodiment, those of ordinary skill in the art can replace simply to it with knowing, for example:
(1) the installation of sensors mode can also be used the various ways such as wired, wireless, fixing or portable, and sensor collecting device can only have a kind of sensor also can integrated multiple sensors;
The sensor of the types such as these CO that (2) mention in the installation of sensors, pressure transducer can be needed such as H with other monitoring system
2S, O
2, CO
2Replace etc. various dangerous matter sources monitoring sensors;
(3) classification of different brackets, influence factor and the weight thereof of each grade are to change according to the change of different environmental baselines, monitored content in the assessment of system's risk.
(4) parameter in the such scheme (weighted value) and the drawn result of example are and illustrate, and actual conditions are please decided according to test environment.
Dangerous matter sources monitoring system that the present invention is mining is processed the correlation fusion of sensing data and is progressively finished in the data transfer process, the data analysis that can directly carry out by data fusion equipment when accident occurs, the front early warning of realization accident.
Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of making, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a mining dangerous matter sources monitoring system is characterized in that, comprising:
The sensor collecting device comprises one or more sensors, is used for the pick-up transducers data;
Data fusion equipment, the risk evaluation model that is used for the different brackets that application prestores to sensing data carries out by the step by step assessment of high-risk grade to low danger classes, when the assessment result of one of them is equal to or higher than default danger classes, the relevant sensing data of the corresponding danger classes of this assessment result is uploaded; And
The sensing network routing device is used for receiving the sensing data that a plurality of data fusion equipment are uploaded, and according to danger classes order from high to low, sensing data is routed to host computer.
2. mining dangerous matter sources monitoring system according to claim 1 is characterized in that, in the risk evaluation model that described data fusion equipment prestores: the risk evaluation model that danger classes is higher, its sensing data that needs is fewer, and computing velocity is faster.
3. mining dangerous matter sources monitoring system according to claim 1 is characterized in that, the risk evaluation model risk class that described data fusion equipment prestores comprises from high to low at least:
The harmful gas risk evaluation model, its sensing data that relates to comprises: gas density, CO concentration, smokescope and ventilation condition;
Hydrology risk evaluation model, its sensing data that relates to comprises: water level, slight flow, flow velocity and harmful gas risk evaluation result; And
The geologic risk assessment models, its sensing data that relates to comprises: roof pressure, rock burst, positional information and hydrology risk evaluation result.
4. mining dangerous matter sources monitoring system according to claim 3 is characterized in that, the risk evaluation model risk class that described data fusion equipment prestores also comprises from high to low:
The environmental aspect assessment models, its sensing data that relates to comprises: temperature, humidity, the density of population and geologic risk assessment result; With
Integrated Evaluation Model, its sensing data that relates to comprises: historical evaluation result, history predicts the outcome, artificial controllability and environmental aspect assessment result.
5. mining dangerous matter sources monitoring system according to claim 1 is characterized in that, described data fusion equipment carries out being comprised by the step by step assessment of high-risk grade to low danger classes:
Steps A: periodically receive its monitoring range inner sensor data;
Step B: according to sensing data by risk evaluation model Modle
iCarry out risk assessment;
Step C: judge risk assessment assessment models Modle
iWhether the result safety, if safety then enters step e, otherwise carries out step D;
Step D: upload risk assessment assessment models Modle
iThe data line correlation of going forward side by side is processed, and then enters step e;
Step e: judge risk assessment assessment models Modle
iWhether, be enter step F, carry out the risk evaluation model Modle of subordinate otherwise return step B if being highest assessment
(i+1)Assessment;
Step F: judge highest risk evaluation model Modle
HWhether the result safety, safe then return steps A, otherwise enter step H;
Step H: upload risk assessment assessment models Modle
HThe data line correlation of going forward side by side is processed.
6. mining dangerous matter sources monitoring system according to claim 5 is characterized in that, described data fusion equipment according to sensing data by risk evaluation model Modle
iCarrying out risk assessment comprises:
Collection is by risk evaluation model Modle
iCarry out the needed parameter of risk assessment;
Calculate the value-at-risk of i level risk evaluation model:
Wherein, w
IjWeights for corresponding j factor in the i level risk evaluation model; X
IjBe the sensing data of j the factor of correspondence in the i level risk evaluation model, n
jThe number that relates to factor for this risk evaluation model;
With value-at-risk Modle
i(x) with this risk evaluation model Modle
iEach harmful grade is compared among the corresponding judge collection Vi, determines the assessment result of risk class.
7. mining dangerous matter sources monitoring system according to claim 6 is characterized in that, the weights of described factor adopt the self study mode to generate according to typical sample.
8. mining dangerous matter sources monitoring system according to claim 6 is characterized in that, described judge collection:
V
i＝{v
i1，v
i2，v
i3，v
i4，v
i5}，
Wherein, v
I1Represent i level assessment result safety, v
I2Represent i level assessment result slightly dangerous, v
I3Represent i level assessment result poor risk, v
I4Represent i level assessment result R4, v
I5Represent i level assessment result grave danger.
9. each described mining dangerous matter sources monitoring system in 8 according to claim 1 is characterized in that, the data communication interface of each sensor is encoded according to unified mode and transmitted in the described sensor collecting device.
10. each described mining dangerous matter sources monitoring system in 8 according to claim 1 is characterized in that, each sensor transfers to data fusion equipment by wired or wireless mode with sensing data in the described sensor collecting device.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103775129A (en) * | 2013-12-26 | 2014-05-07 | 中国矿业大学 | Method for early warning of dangerous sources of coal mine |
| CN104731074A (en) * | 2015-03-18 | 2015-06-24 | 安徽惠洲地质安全研究院股份有限公司 | Geophysical prospecting instrument remote control system based on mine Internet of things technology and control method thereof |
| CN107067657A (en) * | 2017-03-29 | 2017-08-18 | 甘肃有色冶金职业技术学院 | A kind of Mine Geological Hazard monitoring system based on Internet of Things |
| CN107301312A (en) * | 2017-04-28 | 2017-10-27 | 青岛高智高新科技有限公司 | A kind of city efficient context supervisory systems and its monitoring and managing method |
| CN109167786A (en) * | 2018-09-03 | 2019-01-08 | 罗杰雄 | A kind of Information Security Management System |
| CN103927692B (en) * | 2014-03-19 | 2019-06-04 | 国网安徽省电力公司铜陵供电公司 | A kind of transmission line safety risk status appraisal procedure |
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| CN104731074A (en) * | 2015-03-18 | 2015-06-24 | 安徽惠洲地质安全研究院股份有限公司 | Geophysical prospecting instrument remote control system based on mine Internet of things technology and control method thereof |
| CN104731074B (en) * | 2015-03-18 | 2017-10-10 | 安徽惠洲地质安全研究院股份有限公司 | A kind of geophysical prospecting equipment tele-control system and its control method based on mine technology of Internet of things |
| CN107067657A (en) * | 2017-03-29 | 2017-08-18 | 甘肃有色冶金职业技术学院 | A kind of Mine Geological Hazard monitoring system based on Internet of Things |
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| CN109167786B (en) * | 2018-09-03 | 2021-07-27 | 深圳供电局有限公司 | Information security risk management system |
| CN109167786A (en) * | 2018-09-03 | 2019-01-08 | 罗杰雄 | A kind of Information Security Management System |
| CN110046834A (en) * | 2019-05-13 | 2019-07-23 | 苏州大学 | Workplace pernicious gas health risk quantitative evaluating system and its appraisal procedure |
| CN110046834B (en) * | 2019-05-13 | 2022-06-24 | 苏州大学 | Workplace harmful gas health risk quantitative evaluation system and evaluation method thereof |
| CN112632755A (en) * | 2020-12-03 | 2021-04-09 | 中煤科工集团重庆研究院有限公司 | Comprehensive judgment and identification method for risk of hydrogen sulfide disaster of abandoned oil well in coal mining area |
| CN112632755B (en) * | 2020-12-03 | 2024-04-30 | 中煤科工集团重庆研究院有限公司 | Comprehensive judgment method for hydrogen sulfide disaster risk of abandoned oil well in coal mining area |
| CN113398521A (en) * | 2021-03-22 | 2021-09-17 | 东莞市三航军民融合创新研究院 | Fire fighting monitoring system and method based on Internet of things |
| CN114320469A (en) * | 2021-12-27 | 2022-04-12 | 中国矿业大学 | Cloud-edge intelligence-based underground hazard source detection method |
| CN114320469B (en) * | 2021-12-27 | 2023-04-07 | 中国矿业大学 | Cloud-edge intelligence-based underground hazard source detection method |
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