CN109345128A - Gas hidden danger big data analysis method and system - Google Patents
Gas hidden danger big data analysis method and system Download PDFInfo
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- CN109345128A CN109345128A CN201811188640.4A CN201811188640A CN109345128A CN 109345128 A CN109345128 A CN 109345128A CN 201811188640 A CN201811188640 A CN 201811188640A CN 109345128 A CN109345128 A CN 109345128A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Abstract
The present invention provides a kind of gas hidden danger big data analysis and systems, this method comprises: by obtaining the gas safety incipient fault data in big data platform；According to batch system MapReduce, the gas safety incipient fault data is handled, obtains gas safety Analysis of Potential result.Big data technology and behavior safety theory are combined to realize, gas behavior safety hidden danger big data is stored using HDFS platform, and it analyzes, find out the unsafe acts and states of matter that repeated, the purpose of guidance is provided for coal mining enterprise's safety management, convenient for formulating targeted security risk Solve Problem, improve the safety culture of enterprise, the final generation for reducing or remitting accident.
Description
Technical field
The present invention relates to the safety management technology fields of coal mining enterprise, and in particular, to a kind of gas hidden danger big data point
Analyse method and system.
Background technique
Safety in production is to promote the primary condition of social progress and economic sustainable and healthy development, be a Civilization with into
The important symbol of step.There is different size of accident in annual every profession and trade and field, cause huge casualties, property damage
It becomes estranged environmental disruption.And the reason of causing accident, is broadly divided into three classes factor, the respectively uneasiness of Unsafe behavior, object
Total state, unpredictable element.
Currently, coal mining enterprise has accumulated the data of magnanimity in gas hidden danger safety inspection, these multi-parameters, various dimensions,
As the time, exponentially type increased, urgent need is effectively analyzed and is utilized for structuring and partly-structured data.
But traditional data management scheme can not be coped with that these scale of constructions are big, type is more, value density is low, relationship is complicated
Incipient fault data.Moreover, a unsafe condition for object is only focused in traditional incipient fault data analysis, the dangerous of wherein people is but had ignored
Behavior.
Summary of the invention
For the defects in the prior art, the object of the present invention is to provide a kind of gas hidden danger big data analysis method and it is
System.
In a first aspect, the present invention provides a kind of gas hidden danger big data analysis method, comprising:
Obtain the gas safety incipient fault data in big data platform；
According to batch system MapReduce (Map Reduce, map reduction), to the gas safety incipient fault data into
Row processing, obtains gas safety Analysis of Potential result.
Optionally, before obtaining the gas safety incipient fault data in big data platform, further includes:
According to security system theory, gas hidden danger classification is divided into artificial hidden danger and environmental factor；Wherein, artificial hidden danger packet
It includes: not checking that Workplace Safety situation, fatigue are on duty, unsafe operation equipment, inhale before not dressing protective articles, operation
Cigarette；Wherein, environmental factor includes: gas density, oxygen content, ventilation quantity, temperature；
According to the behavioral data of operating personnel, judge whether the behavioral data meets the artificial hidden danger；If meeting,
The behavioral data is stored to the big data platform；
According to the environmental factor index that monitoring device acquires, judge whether the environmental factor index meets preset condition；
If meeting, the environmental factor index is stored to the big data platform.
Optionally, according to batch system MapReduce, the gas safety incipient fault data is handled, acquisition watt
This security even analysis result, comprising:
According to the gas hidden danger classification, the corresponding security risk grade of the gas hidden danger classification is determined；
According to the gas hidden danger classification, the gas safety incipient fault data is carried out to count the gas hidden danger classification pair
The hidden danger frequency of occurrence answered；
According to the security risk grade and hidden danger frequency of occurrence, the security risk classification is ranked up, acquisition watt
This security even analysis result.
Optionally, according to batch system MapReduce, the gas safety incipient fault data is handled, is obtained
After gas safety Analysis of Potential result, further includes:
According to the gas safety Analysis of Potential as a result, generating security risk Solve Problem.
Second aspect, the present invention provide a kind of gas hidden danger big data analysis system, comprising: processor and memory, institute
It states and is stored with program instruction in memory, the processor, which is executed for transferring described program program instruction in first aspect, appoints
Gas hidden danger big data analysis method described in one.
Compared with prior art, the present invention have it is following the utility model has the advantages that
The present invention provides a kind of gas hidden danger big data analysis method and system, by obtaining the gas in big data platform
Security risk data；According to batch system MapReduce, the gas safety incipient fault data is handled, obtains gas
Security even analysis result.Big data technology and behavior safety theory are combined to realize, utilize HDFS (Hadoop
Distributed File System, Hadoop distributed file system) platform storage gas behavior safety hidden danger big data,
Solves the storage problem for the incipient fault data that the scale of construction is big, type is more, value density is low, relationship is complicated, using based on MapReduce
The analysis of parallelization FP-growth (Frequent Pattern Growth, frequent mode increase) algorithm process incipient fault data,
The unsafe acts and states of matter that repeated are found out, the processing time is shortened, reduces processing cost and difficulty, are coal mining enterprise
Safety management provides the purpose of guidance, convenient for formulating targeted security risk Solve Problem, improves the safety culture of enterprise,
The generation of final deduction and exemption accident.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is the schematic illustration of gas hidden danger big data method provided in an embodiment of the present invention；
Fig. 2 is the flow chart of gas hidden danger big data method provided in an embodiment of the present invention；
Fig. 3 is the Data Storage Models of gas hidden danger big data method provided in an embodiment of the present invention；
Fig. 4 is the data processing model of gas hidden danger big data method provided in an embodiment of the present invention.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, several changes and improvements can also be made.These belong to the present invention
Protection scope.
Fig. 1 is the schematic illustration of gas hidden danger big data method provided in an embodiment of the present invention, as shown in Figure 1, will inspection
It looks into record data and environmental factor index that detection device obtains is stored into big data platform HDFS, using being based on
The parallelization FP-growth algorithm of MapReduce is analyzed and is handled to former data, and the unsafe acts that repeated are found out
And states of matter, gas safety Analysis of Potential is obtained as a result, simultaneously further formulating security risk Solve Problem, is coal mining enterprise's bursting tube
Reason provides guidance.
Fig. 2 is the flow chart of gas hidden danger big data method provided in an embodiment of the present invention, as shown in Fig. 2, this method can
To include:
Gas safety incipient fault data in S101, acquisition big data platform.
Fig. 3 is the Data Storage Models of gas hidden danger big data method provided in an embodiment of the present invention, as shown in figure 3, this
In embodiment, the unsafe acts and dangerous states of matter that are checked using big data platform HDFS storage；Wherein people's is dangerous
Behavior is entered into HDFS by hand according to the record of behavior observation person, and the unsafe condition of object is then set by monitorings such as sensors
In standby automatically incoming HDFS, solves the storage problem for the incipient fault data that the scale of construction is big, type is more, value density is low, relationship is complicated.
Before executing S101, further includes: according to security system theory, gas hidden danger classification is divided into artificial hidden danger and ring
Border factor；Wherein, artificial hidden danger includes: not check Workplace Safety situation before not dressing protective articles, operation, in fatigue
Hilllock, unsafe operation equipment, smoking；Wherein, environmental factor includes: gas density, oxygen content, ventilation quantity, temperature；According to
The behavioral data of operating personnel, judges whether behavioral data meets artificial hidden danger；If meeting, behavioral data is stored to big number
According to platform；According to the environmental factor index that monitoring device acquires, judge whether environmental factor index meets preset condition；If symbol
It closes, then stores environmental factor index to big data platform.
Specifically, according to behavior safety theory, gas hidden danger is classified；Gas incipient fault data is divided into the dangerous row of people
For the unsafe condition with object, wherein Unsafe behavior can be divided into again do not dress protective articles, operation before do not check work
Place safety situation, fatigue are on duty, unsafe operation equipment, smoke.And the unsafe condition of object is divided into gas density uneasiness
Entirely, oxygen content is low, ventilation quantity is low, temperature is high.The unsafe acts of operating personnel, behavior observation are recorded by behavior observation person
Member observes table not timing observation whithin a period of time according to behavior safety, finds potential unsafe acts, and records observation knot
Fruit.The unsafe condition that object is recorded by monitoring device records object safe condition, sensing using the sensor being arranged in mine
Device includes gas concentration sensor, oxygen concentration sensor, air velocity transducer, temperature sensor.For example, working as gas gas density
Sensor detects that gas density is higher than the first preset threshold, then is judged as that gas density is dangerous, by gas density at this time
Index is stored to big data platform；When oxygen concentration sensor detects that oxygen concentration higher than the second preset threshold, is then judged as
Oxygen content is low, and oxygen content index at this time is stored to big data platform.
S102, according to batch system MapReduce, gas safety incipient fault data is handled, obtain gas safety
Analysis of Potential result.
Fig. 4 is the data processing model of gas hidden danger big data method provided in an embodiment of the present invention, as shown in figure 4, this
In embodiment, according to gas hidden danger classification, the corresponding security risk grade of gas hidden danger classification is determined；According to gas hidden danger class
Not, the corresponding hidden danger frequency of occurrence of statistics gas hidden danger classification is carried out to gas safety incipient fault data；According to security risk grade
With hidden danger frequency of occurrence, security risk classification is ranked up, obtains gas safety Analysis of Potential result.
Specifically, pass through the hidden danger big data of the parallel frame processing storage of batch system MapReduce；By FP-
Growth algorithm is compiled into java language, and with the parallel frame processing of MapReduce, which can find out hidden danger big data
Frequent item set.Frequent simultaneous security risk is found out.By analyzing these often simultaneous security risks,
It was found that potential security risk, and deficiency existing for current safety management aspect is found out with this.
After executing S102, further includes: according to gas safety Analysis of Potential as a result, generating security risk Solve Problem.
Specifically, processing result is fed back into safety manager, safety manager is targeted according to processing result
Carry out the education of gas behavior safety in ground.To the Safety Management System of perfect enterprise, the generation of accident is prevented.
The present embodiment, by obtaining the gas safety incipient fault data in big data platform；According to batch system
MapReduce handles gas safety incipient fault data, obtains gas safety Analysis of Potential result.It will be big to realize
Data technique and behavior safety theory combine, and store gas behavior safety hidden danger big data using HDFS platform, and analyze, look for
The unsafe acts and states of matter that repeated out provide the purpose of guidance for coal mining enterprise's safety management, are directed to convenient for formulating
Property security risk Solve Problem, improve the safety culture of enterprise, the final generation for reducing or remitting accident.
It should be noted that the step in gas hidden danger big data analysis method provided by the invention, can use gas
Corresponding module, device, unit etc. are achieved in hidden danger big data analysis system, and those skilled in the art are referred to system
Technical solution implementation method step process, that is, the embodiment in system can be regarded as the preference of implementation method, herein not
It gives and repeating.
One skilled in the art will appreciate that in addition to realizing system provided by the invention in a manner of pure computer readable program code
And its other than each device, completely can by by method and step carry out programming in logic come so that system provided by the invention and its
Each device is in the form of logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and embedded microcontroller etc.
To realize identical function.So system provided by the invention and its every device are considered a kind of hardware component, and it is right
The device for realizing various functions for including in it can also be considered as the structure in hardware component；It can also will be for realizing each
The device of kind function is considered as either the software module of implementation method can be the structure in hardware component again.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make a variety of changes or modify within the scope of the claims, this not shadow
Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase
Mutually combination.
Claims (5)
1. a kind of gas hidden danger big data analysis method characterized by comprising
Obtain the gas safety incipient fault data in big data platform；
According to batch system MapReduce, the gas safety incipient fault data is handled, obtains gas safety hidden danger point
Analyse result.
2. the method according to claim 1, wherein obtaining the gas safety incipient fault data in big data platform
Before, further includes:
According to security system theory, gas hidden danger classification is divided into artificial hidden danger and environmental factor；Wherein, artificial hidden danger includes:
Do not check that Workplace Safety situation, fatigue are on duty, unsafe operation equipment, smoke before not dressing protective articles, operation；Its
In, environmental factor includes: gas density, oxygen content, ventilation quantity, temperature；
According to the behavioral data of operating personnel, judge whether the behavioral data meets the artificial hidden danger；If meeting, by institute
Behavioral data is stated to store to the big data platform；
According to the environmental factor index that monitoring device acquires, judge whether the environmental factor index meets preset condition；If symbol
It closes, then stores the environmental factor index to the big data platform.
3. according to the method described in claim 2, it is characterized in that, being pacified according to batch system MapReduce to the gas
Full incipient fault data is handled, and gas safety Analysis of Potential result is obtained, comprising:
According to the gas hidden danger classification, the corresponding security risk grade of the gas hidden danger classification is determined；
According to the gas hidden danger classification, carry out counting the gas hidden danger classification corresponding to the gas safety incipient fault data
Hidden danger frequency of occurrence；
According to the security risk grade and hidden danger frequency of occurrence, the security risk classification is ranked up, obtains gas peace
Full Analysis of Potential result.
4. the method according to claim 1, wherein according to batch system MapReduce, to the gas
Security risk data are handled, after acquisition gas safety Analysis of Potential result, further includes:
According to the gas safety Analysis of Potential as a result, generating security risk Solve Problem.
5. a kind of gas hidden danger big data analysis system characterized by comprising processor and memory, in the memory
It is stored with program instruction, the processor requires any one of 1-4 institute for transferring described program program instruction with perform claim
The gas hidden danger big data analysis method stated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201811188640.4A CN109345128A (en) | 2018-10-11 | 2018-10-11 | Gas hidden danger big data analysis method and system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811188640.4A CN109345128A (en) | 2018-10-11 | 2018-10-11 | Gas hidden danger big data analysis method and system |
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ID=65309085
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102722775A (en) * | 2012-05-16 | 2012-10-10 | 山西潞安环保能源开发股份有限公司 | Security crisis management system and method for coal mine |
| CN106194263A (en) * | 2016-08-29 | 2016-12-07 | 中煤科工集团重庆研究院有限公司 | Coal mine gas disaster monitoring early-warning system and method for early warning |
| CN106872663A (en) * | 2017-03-06 | 2017-06-20 | 河南理工大学 | A kind of gas outbursts Prediction method for early warning based on big data platform |
| CN107956513A (en) * | 2017-12-01 | 2018-04-24 | 太原科技大学 | One kind is used for safety of coal mines intelligence real-time early warning system |
-
2018
- 2018-10-11 CN CN201811188640.4A patent/CN109345128A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102722775A (en) * | 2012-05-16 | 2012-10-10 | 山西潞安环保能源开发股份有限公司 | Security crisis management system and method for coal mine |
| CN106194263A (en) * | 2016-08-29 | 2016-12-07 | 中煤科工集团重庆研究院有限公司 | Coal mine gas disaster monitoring early-warning system and method for early warning |
| CN106872663A (en) * | 2017-03-06 | 2017-06-20 | 河南理工大学 | A kind of gas outbursts Prediction method for early warning based on big data platform |
| CN107956513A (en) * | 2017-12-01 | 2018-04-24 | 太原科技大学 | One kind is used for safety of coal mines intelligence real-time early warning system |
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Application publication date: 20190215 |
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