CN110096532A - A kind of safety in production big data analysis method for digging and system - Google Patents
A kind of safety in production big data analysis method for digging and system Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of safety in production big data analysis method for digging and system, this method comprises: the hidden danger type based on the hidden troubles removing data acquisition difference type of business is distributed, the type of business of different hidden danger types is distributed, and the Rectification of hidden dangers rate of the different types of business；Based on the distribution of the examination of law enforcement data acquisition check item frequency and hidden danger discovery frequency distribution；Based on the incidence relation between industrial accident data acquisition traffic injury time, accident pattern and incident classification；Incidence relation based on the hidden danger type and the accident pattern, and it regard hidden danger type distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger discovery frequency distribution as accident weight, the traffic injury time of the different types of business, accident pattern and incident classification are predicted.The probability that reduction accident occurs, discovery accident potential disclose accident rule, and then reduce the generation of work safety accident.
Description
Technical field
The present embodiments relate to safety in production data analysis technique field more particularly to a kind of safety in production big datas point
Analyse method for digging and system.
Background technique
Safety in production is related to the fundamental interests of country, the people, is related to social development reform, stablizes harmonious overall situation,
It is China market economic stability, lasting, quick, sound development basic assurance, and the most fundamental requirement of developing the productivity.Closely
Nian Lai, situation of production overall stability, accident total amount and death toll continue to keep decline, but especially great compared with major break down
Accident is not contained that situation of production is still severe effectively yet.
Enterprise necessarily has mass data to need to handle in actual production and operating activities.But data and the information content
Utilization rate is not high.Currently, situation of production analysis only resides within simple statistics level, shortage is dug from magnanimity basic data
The method for excavating valuable information knowledge lacks effective security risk early warning technology means.
Summary of the invention
The embodiment of the present invention provides a kind of safety in production big data analysis method for digging and system, has fully considered existing peace
Effective application of full production mass data has carried out stage construction by the method for science, comprehensive excavation is handled, promotion safety
The science and accuracy of production management.
In a first aspect, the embodiment of the present invention provides a kind of safety in production big data analysis method for digging, comprising:
Hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, the type of business of different hidden danger types
Distribution, and the Rectification of hidden dangers rate of the different types of business；Based on the distribution of the examination of law enforcement data acquisition check item frequency and hidden danger hair
Existing frequency distribution；
It is closed based on the association between industrial accident data acquisition traffic injury time, accident pattern and incident classification
System；
Based on the incidence relation of the hidden danger type and the accident pattern that are previously obtained, with the hidden danger type point
Cloth, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and the hidden danger discovery frequency are distributed conduct
Accident weight predicts the accident pattern of the different types of business, and based on traffic injury time, accident pattern and accident etc.
Incidence relation between grade, the traffic injury time and incident classification of the accident pattern predicted
Second aspect, the embodiment of the present invention provide a kind of safety in production big data analysis digging system, which is characterized in that packet
Include data extraction module, data preprocessing module and data analysis module:
The data extraction module, for extracting the enterprise-class in hidden troubles removing data based on the field information that need to be analyzed
Type and hidden danger type, the check item frequency and hidden danger extracted in examination of law enforcement data find the frequency, extract industrial accident number
Traffic injury time, accident pattern and incident classification in；
The data preprocessing module, for the hidden danger type based on the hidden troubles removing data acquisition difference type of business point
Cloth, the type of business distribution of different hidden danger types, and the Rectification of hidden dangers rate of the different types of business；It is obtained based on examination of law enforcement data
Take the distribution of the check item frequency and hidden danger discovery frequency distribution；Based on industrial accident data acquisition traffic injury time, accident
Incidence relation between type and incident classification；
The data analysis module is closed for the association based on the hidden danger type and the accident pattern being previously obtained
System, with hidden danger type distribution, type of business distribution, the Rectification of hidden dangers rate of the different type of business, the distribution of the check item frequency and
Hidden danger finds that frequency distribution is used as accident weight, predicts the accident pattern of the different types of business, and be based on accident
Incidence relation between time, accident pattern and incident classification, the traffic injury time and accident of the accident pattern predicted
Grade.
The third aspect, the embodiment of the present invention provides a kind of electronic equipment, including memory, processor and is stored in memory
Computer program that is upper and can running on a processor, the processor realize such as first aspect embodiment when executing described program
The step of safety in production big data analysis method for digging.
Fourth aspect, the embodiment of the present invention provide a kind of non-transient computer readable storage medium, are stored thereon with calculating
Machine program realizes that big data analysis of keeping the safety in production as described in first aspect embodiment is dug when the computer program is executed by processor
The step of pick method.
A kind of safety in production big data analysis method for digging provided in an embodiment of the present invention and system, efficiently use and analyze
Hidden troubles removing data, examination of law enforcement data, industrial accident data disclose the emphasis of hidden troubles removing, the weight of examination of law enforcement
Point and accident rule reduce the probability that accident occurs to find accident potential in advance, and discovery accident potential discloses accident rule
Rule, and then the generation of work safety accident is reduced, it realizes the transformation from " subsequent management " to " preventing " in advance, realizes the peace of enterprise
Full production.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the safety in production big data analysis method for digging flow chart according to the embodiment of the present invention；
Fig. 2 is the safety in production big data analysis digging system schematic diagram according to the embodiment of the present invention；
Fig. 3 is the safety in production big data analysis excavating equipment structural schematic diagram according to the embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Enterprise necessarily has mass data to need to handle in actual production and operating activities.But data and the information content
Utilization rate is not high.Currently, situation of production analysis only resides within simple statistics level, shortage is dug from magnanimity basic data
The method for excavating valuable information knowledge lacks effective security risk early warning technology means.Therefore the embodiment of the present invention has
Effect utilize simultaneously analyze hidden troubles removing data, examination of law enforcement data, industrial accident data, disclose hidden troubles removing emphasis,
Emphasis and the accident rule of examination of law enforcement reduce the probability that accident occurs to find accident potential in advance.It below will be by more
A embodiment carries out expansion explanation and introduction.
Fig. 1 shows a kind of safety in production big data analysis method for digging of the embodiment of the present invention, comprising:
S1, the hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, the enterprise of different hidden danger types
Type distribution, and the Rectification of hidden dangers rate of the different types of business；It is distributed based on the examination of law enforcement data acquisition check item frequency and hidden
Suffer from discovery frequency distribution；
S2, based on the association between industrial accident data acquisition traffic injury time, accident pattern and incident classification
Relationship；
S3, the incidence relation based on the hidden danger type and the accident pattern that are previously obtained, with the hidden danger type
Distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger discovery frequency distribution are made
For accident weight, the accident pattern of the different types of business is predicted, and is based on traffic injury time, accident pattern and accident
Incidence relation between grade, the traffic injury time and incident classification of the accident pattern predicted.
In the present embodiment, it by the way that hidden troubles removing data are carried out with the analysis of different dimensions, extracts in hidden troubles removing data
The hidden danger type of different type enterprise is distributed, and can access the hidden danger type of each type enterprise, to some type of business into
When row Analysis of Potential, hidden troubles removing can be carried out for the corresponding hidden danger distribution of the type of business；Meanwhile and extracting hidden troubles removing number
According to the type of business distribution of middle different hidden danger types, when analyzing single hidden danger type, the hidden danger type pair can be directed to
The enterprise object situation answered, to corresponding, enterprise is targetedly checked.
Meanwhile in the present embodiment, the emphasis of examination of law enforcement is obtained by analyzing examination of law enforcement data, is specifically enforced the law
The check item frequency of inspection personnel is distributed and the discovery frequency distribution of corresponding hidden danger；There is two o'clock advantage to the analysis of above-mentioned data,
One can be based on the check item frequency distribution and corresponding hidden danger discovery the frequency be distributed to obtain the emphasis of examination of law enforcement, second is that
It is distributed the probability that the corresponding accident of available hidden danger type is excluded based on the hidden danger discovery frequency, it is right to disclose accident rule
Subsequent accident forecast provides basis.
On the basis of the above embodiments, the hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business
Before, further includes:
Measurement period is set, based on the field information that need to be analyzed, extracts the type of business and hidden danger in hidden troubles removing data
Type, the check item frequency and hidden danger extracted in examination of law enforcement data find the frequency, extract the thing in industrial accident data
Therefore time of origin, accident pattern and incident classification.
In the present embodiment, time type, time setting, time range is selected to determine measurement period, according to needed for analysis
Field information extracts data, including extracting the type of business and hidden danger type in hidden troubles removing data, extracts examination of law enforcement data
In the check item frequency and hidden danger find the frequency, extract industrial accident data in traffic injury time, accident pattern and
Incident classification.
On the basis of the various embodiments described above, it is based on industrial accident data acquisition traffic injury time, accident pattern
Incidence relation between incident classification, specifically includes:
Extract traffic injury time distribution, accident pattern distribution and the incident classification point in the industrial accident data
Cloth；
Analysis is associated to traffic injury time distribution, accident pattern distribution and incident classification distribution, obtains thing
Therefore the incidence relation between time of origin and accident pattern, the incidence relation between traffic injury time and incident classification, and
Incidence relation between accident pattern and incident classification.
It in the present embodiment, can be by neural metwork training or the method for decision tree training, to industrial accident number
Traffic injury time distribution, accident pattern distribution and incident classification distribution in are trained analysis, finally obtain the thing
Therefore time of origin distribution, accident pattern distribution and incident classification distribution are associated analysis, obtain traffic injury time and accident
Incidence relation between type, incidence relation and accident pattern and accident etc. between traffic injury time and incident classification
Incidence relation between grade.
On the basis of the various embodiments described above, to the traffic injury time of the different types of business, accident pattern and accident etc.
Before grade is predicted, further includes:
The hidden danger incidence of each hidden danger type of the hidden danger type distributed acquisition based on each type of business is based on institute
The hidden danger discovery rate for stating the distribution of the check item frequency and the hidden danger discovery each hidden danger type of frequency distributed acquisition, the hidden danger is occurred
Training obtains the hidden danger incidence, hidden danger discovery rate as accident weight, and in advance for rate, hidden danger discovery rate and Rectification of hidden dangers rate
With the weighted value of Rectification of hidden dangers rate.
In the present embodiment, the corresponding relationship based on hidden danger type and accident pattern, incident classification is carrying out accident class
When type, incident classification prediction, it can also pass through using the incidence of hidden danger type, discovery rate and rectification rate as a weight parameter
Training obtains the weighted value of hidden danger incidence, hidden danger discovery rate and Rectification of hidden dangers rate, to final accident pattern, accident etc.
Grade, traffic injury time are accurately predicted.
On the basis of the various embodiments described above, further includes:
Respectively to hidden danger type distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, inspection
The distribution of the item frequency and hidden danger discovery frequency distribution progress visualized graphs displaying.
A kind of safety in production big data analysis digging system is also shown in the embodiment of the present invention, is based on the various embodiments described above
In safety in production big data analysis method for digging, as shown in Figure 2, including data extraction module 10, data preprocessing module
20 and data analysis module 30, in which:
The data extraction module 10 obtains critical data information in database, based on the field information that need to be analyzed, extracts
The type of business and hidden danger type in hidden troubles removing data extract the check item frequency and hidden danger discovery frequency in examination of law enforcement data
It is secondary, extract traffic injury time, accident pattern and incident classification in industrial accident data；
The data preprocessing module pre-processes the data information obtained from database, is based on hidden troubles removing number
According to the hidden danger type distribution for obtaining the different types of business, the type of business distribution of different hidden danger types, and the different types of business
Rectification of hidden dangers rate；Based on the distribution of the examination of law enforcement data acquisition check item frequency and hidden danger discovery frequency distribution；Based on safe life
Produce the incidence relation between casualty data acquisition traffic injury time, accident pattern and incident classification；
The data analysis module 30 carries out operation to data and then realizes mining analysis, described hidden based on what is be previously obtained
Suffer from the incidence relation of type and the accident pattern, with hidden danger type distribution, type of business distribution, the different type of business
Rectification of hidden dangers rate, the distribution of the check item frequency and hidden danger discovery frequency distribution are used as accident weight, to the accident of the different types of business
Type predicted, and based on the incidence relation between traffic injury time, accident pattern and incident classification, the thing predicted
Therefore the traffic injury time of type and incident classification.
In the present embodiment, it by the way that hidden troubles removing data are carried out with the analysis of different dimensions, extracts in hidden troubles removing data
The hidden danger type of different type enterprise is distributed, and can access the hidden danger type of each type enterprise, to some type of business into
When row Analysis of Potential, hidden troubles removing can be carried out for the corresponding hidden danger distribution of the type of business；Meanwhile and extracting hidden troubles removing number
According to the type of business distribution of middle different hidden danger types, when analyzing single hidden danger type, the hidden danger type pair can be directed to
The enterprise object situation answered, to corresponding, enterprise is targetedly checked.
Meanwhile in the present embodiment, the emphasis of examination of law enforcement is obtained by analyzing examination of law enforcement data, is specifically enforced the law
The check item frequency of inspection personnel is distributed and the discovery frequency distribution of corresponding hidden danger；There is two o'clock advantage to the analysis of above-mentioned data,
One can be based on the check item frequency distribution and corresponding hidden danger discovery the frequency be distributed to obtain the emphasis of examination of law enforcement, second is that
It is distributed the probability that the corresponding accident of available hidden danger type is excluded based on the hidden danger discovery frequency, it is right to disclose accident rule
Subsequent accident forecast provides basis.
It on the basis of the above embodiments, further include data outputting module 40 and early warning analysis module 50；
The data outputting module 40 carries out display output for the mining analysis result to data analysis module 30；
The early warning analysis module 50, for respectively to hidden danger type distribution, type of business distribution, different enterprises
Rectification of hidden dangers rate, the distribution of the check item frequency and the hidden danger discovery frequency distribution of industry type carry out visualized graphs displaying, and are based on
The prediction result of data analysis module 30 carries out early warning analysis, specifically, color also can be used to open up different incident classifications
Show, such as red, orange, yellow, blue.
It further include analyzing object module 60, comprehensive report generation module 90, information to deposit on the basis of the various embodiments described above
Store up module 70 and report template management module 80；
The report template management module 80, for managing report template；
The analysis object module 60, data, the result of early warning analysis for being extracted based on data extraction module 10 and
The report template pre-established automatically generates report；Report content includes analysis result and conclusive suggestion.
The comprehensive report generation module 90, for carrying out two based on each report template in report template management module 80
Secondary combination generates comprehensive report；In the present embodiment, comprehensive report generation module 90 is also based on preset report template,
Carrying out chart or figure etc. to the processing result of data extraction module 10, data preprocessing module 20 and data analysis module 30 can
It is shown depending on changing.
The information storage module 70, for storing the data and edited information extracted.
Fig. 3 is the structural block diagram for showing the safety in production big data analysis excavating equipment of the present embodiment.
Referring to Fig. 3, big data analysis excavating equipment of keeping the safety in production, including processor (processor) 810, memory
(memory) 830, communication interface (Communications Interface) 820 and bus 840；
Wherein,
The processor 810, memory 830, communication interface 820 complete mutual communication by the bus 840；
The processor 810 is used to call the program instruction in the memory 830, to execute above-mentioned each method embodiment
Provided safety in production big data analysis method for digging, for example,
S1, the hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, the enterprise of different hidden danger types
Type distribution, and the Rectification of hidden dangers rate of the different types of business；It is distributed based on the examination of law enforcement data acquisition check item frequency and hidden
Suffer from discovery frequency distribution；
S2, based on the association between industrial accident data acquisition traffic injury time, accident pattern and incident classification
Relationship；
S3, the incidence relation based on the hidden danger type and the accident pattern that are previously obtained, with the hidden danger type
Distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger discovery frequency distribution are made
For accident weight, the accident pattern of the different types of business is predicted, and is based on traffic injury time, accident pattern and accident
Incidence relation between grade, the traffic injury time and incident classification of the accident pattern predicted.
The present embodiment discloses a kind of computer program product, and the computer program product includes being stored in non-transient calculating
Computer program on machine readable storage medium storing program for executing, the computer program include program instruction, when described program instruction is calculated
When machine executes, computer is able to carry out such as above-mentioned safety in production big data analysis method for digging, for example,
S1, the hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, the enterprise of different hidden danger types
Type distribution, and the Rectification of hidden dangers rate of the different types of business；It is distributed based on the examination of law enforcement data acquisition check item frequency and hidden
Suffer from discovery frequency distribution；
S2, based on the association between industrial accident data acquisition traffic injury time, accident pattern and incident classification
Relationship；
S3, the incidence relation based on the hidden danger type and the accident pattern that are previously obtained, with the hidden danger type
Distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger discovery frequency distribution are made
For accident weight, the accident pattern of the different types of business is predicted, and is based on traffic injury time, accident pattern and accident
Incidence relation between grade, the traffic injury time and incident classification of the accident pattern predicted.
A kind of non-transient computer readable storage medium is additionally provided in the present embodiment, the non-transient computer is readable to deposit
Storage media stores computer instruction, and the computer instruction makes the computer execute such as above-mentioned safety in production big data analysis
Method for digging, for example,
S1, the hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, the enterprise of different hidden danger types
Type distribution, and the Rectification of hidden dangers rate of the different types of business；It is distributed based on the examination of law enforcement data acquisition check item frequency and hidden
Suffer from discovery frequency distribution；
S2, based on the association between industrial accident data acquisition traffic injury time, accident pattern and incident classification
Relationship；
S3, the incidence relation based on the hidden danger type and the accident pattern that are previously obtained, with the hidden danger type
Distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger discovery frequency distribution are made
For accident weight, the accident pattern of the different types of business is predicted, and is based on traffic injury time, accident pattern and accident
Incidence relation between grade, the traffic injury time and incident classification of the accident pattern predicted.
Shown in sum up, a kind of safety in production big data analysis method for digging provided in an embodiment of the present invention and system, effectively
Using and analyze hidden troubles removing data, examination of law enforcement data, industrial accident data, disclose the emphasis of hidden troubles removing, hold
Emphasis and the accident rule of method inspection reduce the probability that accident occurs to find accident potential in advance, discovery accident potential,
Announcement accident rule, and then the generation of work safety accident is reduced, realize the transformation from " subsequent management " to " preventing " in advance, it is real
The safety in production of existing enterprise.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of safety in production big data analysis method for digging characterized by comprising
Hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, the type of business point of different hidden danger types
Cloth, and the Rectification of hidden dangers rate of the different types of business；Based on the distribution of the examination of law enforcement data acquisition check item frequency and hidden danger discovery
Frequency distribution；
Based on the incidence relation between industrial accident data acquisition traffic injury time, accident pattern and incident classification；
Based on the incidence relation of the hidden danger type and the accident pattern that are previously obtained, with hidden danger type distribution, enterprise
The distribution of industry type, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger discovery frequency distribution are used as accident
Weight predicts the accident pattern of the different types of business, and based on traffic injury time, accident pattern and incident classification it
Between incidence relation, the traffic injury time and incident classification of the accident pattern predicted.
2. safety in production big data analysis method for digging according to claim 1, which is characterized in that be based on hidden troubles removing number
Before the hidden danger type distribution for obtaining the different types of business, further includes:
Measurement period is set, based on the field information that need to be analyzed, extracts the type of business and hidden danger type in hidden troubles removing data,
The check item frequency and hidden danger extracted in examination of law enforcement data find the frequency, and the accident extracted in industrial accident data occurs
Time, accident pattern and incident classification.
3. safety in production big data analysis method for digging according to claim 1, which is characterized in that based on safety in production thing
Therefore the incidence relation between data acquisition traffic injury time, accident pattern and incident classification, it specifically includes:
Extract traffic injury time distribution, accident pattern distribution and the incident classification distribution in the industrial accident data；
Analysis is associated to traffic injury time distribution, accident pattern distribution and incident classification distribution, obtains accident hair
Incidence relation between raw time and accident pattern, incidence relation and accident between traffic injury time and incident classification
Incidence relation between type and incident classification.
4. safety in production big data analysis method for digging according to claim 1, which is characterized in that the different types of business
Traffic injury time, accident pattern and incident classification predicted before, further includes:
The hidden danger incidence of each hidden danger type of the hidden danger type distributed acquisition based on each type of business is based on the inspection
The hidden danger discovery rate for looking into the distribution of frequency and the hidden danger discovery each hidden danger type of frequency distributed acquisition, by the hidden danger incidence,
Hidden danger discovery rate and Rectification of hidden dangers rate as accident weight, and in advance training obtain the hidden danger incidence, hidden danger discovery rate and
The weighted value of Rectification of hidden dangers rate.
5. safety in production big data analysis method for digging according to claim 1, which is characterized in that further include:
Respectively to hidden danger type distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, check item frequency
Secondary distribution and hidden danger discovery frequency distribution carry out visualized graphs displaying.
6. a kind of safety in production big data analysis digging system, which is characterized in that including data extraction module, data prediction mould
Block and data analysis module:
The data extraction module, for based on the field information that need to be analyzed, extract the type of business in hidden troubles removing data and
Hidden danger type, the check item frequency and hidden danger extracted in examination of law enforcement data find the frequency, extract in industrial accident data
Traffic injury time, accident pattern and incident classification；
The data preprocessing module, for the hidden danger type distribution based on the hidden troubles removing data acquisition difference type of business, no
The type of business with hidden danger type is distributed, and the Rectification of hidden dangers rate of the different types of business；It is examined based on examination of law enforcement data acquisition
Look into a frequency distribution and hidden danger discovery frequency distribution；Based on industrial accident data acquisition traffic injury time, accident pattern
Incidence relation between incident classification；
The data analysis module, for the incidence relation based on the hidden danger type and the accident pattern that are previously obtained,
With hidden danger type distribution, type of business distribution, the Rectification of hidden dangers rate of the different types of business, the distribution of the check item frequency and hidden danger
It was found that the frequency distribution be used as accident weight, the accident pattern of the different types of business is predicted, and based on traffic injury time,
Incidence relation between accident pattern and incident classification, the traffic injury time and incident classification of the accident pattern predicted.
7. safety in production big data analysis digging system according to claim 6, which is characterized in that further include data output
Module and early warning analysis module；
The data outputting module carries out display output for the analysis result to data analysis module；
The early warning analysis module, for respectively to hidden danger type distribution, type of business distribution, the different types of business
Rectification of hidden dangers rate, the distribution of the check item frequency and hidden danger discovery frequency distribution carry out visualized graphs displaying, and based on data point
The prediction result for analysing module carries out early warning analysis.
8. safety in production big data analysis digging system according to claim 7, which is characterized in that further include analysis result
Module, comprehensive report generation module, information storage module and report template management module；
The report template management module, for managing report template；
The analysis object module, the result of data, early warning analysis for being extracted based on data extraction module and is pre-established
Report template, automatically generate report；
The comprehensive report generation module, for carrying out secondary combination based on each report template in report template management module,
Generate comprehensive report；
The information storage module, for storing the data extracted.
9. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that realize when the processor executes described program and given birth to safely as described in any one of claim 1 to 5
The step of producing big data analysis method for digging.
10. a kind of non-transient computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer
The step for big data analysis method for digging of keeping the safety in production as described in any one of claim 1 to 5 is realized when program is executed by processor
Suddenly.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116501780A (en) * | 2023-06-27 | 2023-07-28 | 中交二公局东萌工程有限公司 | Enterprise audit data analysis processing system and method |
| CN118014375A (en) * | 2024-04-09 | 2024-05-10 | 宁德时代新能源科技股份有限公司 | Data-driven secure production management method, system, device and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116501780A (en) * | 2023-06-27 | 2023-07-28 | 中交二公局东萌工程有限公司 | Enterprise audit data analysis processing system and method |
| CN116501780B (en) * | 2023-06-27 | 2023-09-01 | 中交二公局东萌工程有限公司 | Enterprise audit data analysis processing system and method |
| CN118014375A (en) * | 2024-04-09 | 2024-05-10 | 宁德时代新能源科技股份有限公司 | Data-driven secure production management method, system, device and storage medium |
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