CN113657700A - Mine safety production real-time monitoring method and system based on big data - Google Patents
Mine safety production real-time monitoring method and system based on big data Download PDFInfo
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Abstract
The invention relates to a mine safety production real-time monitoring method and a system based on big data, wherein the method comprises the following steps: collecting and analyzing information; judging whether the acquired information is safe or not; if the security is ensured, the information is continuously received and processed; if not, stopping receiving the information and uploading an alarm. According to the method, potential safety hazards can be found and problems can be solved in time in a big data sharing and pushing mode, and the potential safety hazards are prevented from being enlarged; the loss is reduced, and the real-time performance of safe production of the mine is guaranteed.
Description
Technical Field
The invention belongs to the field of mine safety production, and particularly relates to a mine safety production real-time monitoring method and system based on big data.
Background
Big data is a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is massive, high-growth-rate and diversified information assets which can have stronger decision-making power, insight discovery power and flow optimization capability only by a new processing mode.
The mine mainly comprises one or more mining workshops (or pittings, mines, open stopes and the like) and auxiliary workshops, and most mines also comprise a beneficiation plant (coal washing plant). The mine safety comprises mine safety management, safe production technology, occupational health and the like.
At present, mine safety production monitoring has defects, safety accidents are in a passive state in response to the safety accidents, emergency handling capacity is poor, the safety accidents are easy to occur, and great loss is caused to enterprise staff and production, so the invention provides a mine safety production real-time monitoring and management method based on big data to solve the problems.
Disclosure of Invention
In order to solve the problems, the invention provides a mine safety production real-time monitoring method and a mine safety production real-time monitoring system based on big data, so as to solve the problem that when the existing mine safety production proposed in the background technology has potential safety hazards, the mine safety production situation is difficult to remind by using data information, so that the safety risk is enlarged.
The technical scheme of the invention is as follows:
a mine safety production real-time monitoring method based on big data comprises the following steps:
collecting and analyzing information;
judging whether the acquired information is safe or not; if the security is ensured, the information is continuously received and processed; if not, stopping receiving the information and uploading an alarm.
Further, the analysis information includes a collection data information keyword.
Further, whether the acquired information is safe or not is judged according to the following steps:
the information platform also comprises a data mining log library, wherein a mine safety production situation index system is recorded, qualitative and quantitative indexes in the system are quantified and risk grades are classified, and a machine learning algorithm is applied by utilizing data in historical mine safety production accidents:
(P (yk | x) ═ P (x | yk) P (x)) to obtain a bayesian network model, the specific formula is:
P(yk)＝Nyk+αN+kαP(yk)＝Nyk+αN+kα，(1)
n is the total number of samples, k is the total number of classes, NykNyk is the number of samples whose class is ykyk, and α α is a smoothed value;
P(xi|yk)＝Nyk,xi+αNyk+nαP(xi|yk)＝Nyk,xi+αNyk+nα，(2)
NykNyk is the number of samples with the category of ykyk, n is the dimension of the feature, Nyk, xiNyk, xi is the number of samples with the category of ykyk, the value of the ith dimension feature is the number of samples with xixi, and alpha is a smooth value;
monitoring data acquired by a data acquisition end through a data receiving and analyzing end, preprocessing the data input into a Bayesian network model, predicting time sequence data of the data acquisition end through a long-short memory neural network (LSTM) model, and inputting the predicted data into the Bayesian network model to obtain the change trend of the safety state in a period of time in the future;
determining an early warning grade according to the change trend result and the determined risk grade threshold interval for early warning;
and giving out an alarm handling suggestion according to the inferred causative node condition attributed by the Bayesian network model.
Further, the establishment of the mine safety production situation index system specifically comprises:
extracting situation characteristic elements from historical case data in mine safety production accidents and an underground mine equipment acquisition system, and establishing a mine safety production situation index system by combining expert experience of related fields;
the mine safety production situation index system comprises a mine gas accident safety situation index system, a mine electromechanical accident safety situation index system, a mine water seepage accident safety situation index system and a mine landslide accident safety situation index system.
Further, the preprocessing the index data input into the bayesian network model specifically includes: eliminating error values and completing missing values;
inputting the predicted metric data into the bayesian network model comprises: setting a qualitative index which is partially unpredictable to be constant in a time range; and inputting the index data which can be predicted into the Bayesian network model for reasoning.
Further, setting and filtering data information which is considered to have potential safety hazards; the way of filtering the data information includes:
receiving data information to be filtered firstly without continuous transmission, decomposing the received data information to be filtered, randomly disordering the data and deleting the received content in a soft mode;
or, modifying or deleting the content of the part of the data information which needs to be filtered and has the potential safety hazard, and continuously transmitting other parts of the data information.
The invention also relates to a mine safety production real-time monitoring system based on the big data, which comprises a front-end platform, a cloud platform big data center, a server and an information platform;
the front-end platform is used for setting management rules, detecting platform states, monitoring videos and displaying data;
the cloud platform big data center stores data information and receives and sends the data information, and serves as a data sharing center;
the server is connected with the cloud platform big data center;
the information platform is used for carrying out data information transmission with the cloud platform big data center through the server and comprises a data acquisition end, a data filtering end and a data receiving and analyzing end, wherein the output end of the data acquisition end is connected with the input end of the data filtering end, the output end of the data filtering end is connected with the input end of the data receiving and analyzing end, and the data filtering end and the data receiving and analyzing end are used for carrying out data information transmission with the cloud platform big data center through the server;
the data receiving and analyzing end analyzes and identifies the data information and judges whether potential safety hazards exist in the data information;
the information platform also comprises a data mining log library, wherein a mine safety production situation index system is recorded, qualitative and quantitative indexes in the system are quantified and risk grades are classified, and a machine learning algorithm is applied by utilizing data in historical mine safety production accidents:
(P (yk | x) ═ P (x | yk) P (x)) to obtain a bayesian network model, the specific formula is:
P(yk)＝Nyk+αN+kαP(yk)＝Nyk+αN+kα，(1)
n is the total number of samples, k is the total number of classes, NykNyk is the number of samples whose class is ykyk, and α α is a smoothed value;
P(xi|yk)＝Nyk,xi+αNyk+nαP(xi|yk)＝Nyk,xi+αNyk+nα，(2)
NykNyk is the number of samples with the category of ykyk, n is the dimension of the feature, Nyk, xiNyk, xi is the number of samples with the category of ykyk, the value of the ith dimension feature is the number of samples with xixi, and alpha is a smooth value;
monitoring data acquired by a data acquisition end through a data receiving and analyzing end, preprocessing the data input into a Bayesian network model, predicting time sequence data of the data acquisition end through a long-short memory neural network (LSTM) model, and inputting the predicted data into the Bayesian network model to obtain the change trend of the safety state in a period of time in the future;
determining an early warning grade according to the change trend result and the determined risk grade threshold interval for early warning;
and giving out an alarm handling suggestion according to the inferred causative node condition attributed by the Bayesian network model.
Further, the preprocessing the index data input into the bayesian network model specifically includes: and eliminating error values and completing missing values.
Inputting the predicted metric data into the bayesian network model comprises: setting a qualitative index which is partially unpredictable to be constant in a time range; and inputting the index data which can be predicted into the Bayesian network model for reasoning.
Further, the information platform transmits the result of the data information analysis and judgment to the cloud platform big data center through the server, and other information platforms share the result of the data information analysis and judgment through the cloud platform big data center, so that the warning effect is achieved.
Further, the information platform further comprises an early warning module for early warning the data information which is considered to have potential safety hazards.
Compared with the prior art, the invention has the following beneficial effects:
1) the data mining of mine safety information stored in massive original meta logs is realized, when potential safety hazards appear in a mine, early warning is carried out in advance in a large data sharing and pushing mode, the potential safety hazards are prevented from being enlarged, loss caused by major faults is reduced, data servitization, functional modularization and application modularization are realized through large data platform combination, and the processing capacity of massive data is improved;
2) the loss is reduced, and the real-time performance of safe production of the mine is guaranteed.
Drawings
In order to more clearly illustrate the technical solution of the implementation of the present invention, the drawings used in the implementation description will be briefly introduced below, and it is obvious that the drawings in the following description are only some implementations of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a block diagram of a front-end platform according to the present invention;
FIG. 3 is a block diagram of an information platform according to the present invention;
FIG. 4 is a schematic representation of a model of the present invention;
FIG. 5 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples without making any creative effort, shall fall within the protection scope of the present invention.
Unless otherwise defined, technical or scientific terms used in the embodiments of the present application should have the ordinary meaning as understood by those having ordinary skill in the art. The use of "first," "second," and similar terms in the present embodiments does not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. "mounted," "connected," and "coupled" are to be construed broadly and may, for example, be fixedly coupled, detachably coupled, or integrally coupled; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. "Upper," "lower," "left," "right," "lateral," "vertical," and the like are used solely in relation to the orientation of the components in the figures, and these directional terms are relative terms that are used for descriptive and clarity purposes and that can vary accordingly depending on the orientation in which the components in the figures are placed.
As shown in fig. 1, the mine safety production real-time monitoring system based on big data of the embodiment includes a front-end platform, a cloud platform big data center, a server and an information platform.
As shown in fig. 2, the front-end platform is used for setting management rules, detecting a status platform, monitoring video and displaying data. The corresponding modules are all existing devices or programs.
The cloud platform big data center stores data information and receives and sends the data information, and serves as a data sharing center. And the server is connected with the cloud platform big data center.
The information platform is connected with the server, and the information platform is in data information transmission with the cloud platform big data center through the server.
As shown in fig. 3, the information platform includes a data acquisition end, a data filtering end and a data receiving and analyzing end, an output end of the data acquisition end is connected with an input end of the data filtering end, an output end of the data filtering end is connected with an input end of the data receiving and analyzing end, and the data filtering end and the data receiving and analyzing end transmit data information with the cloud platform big data center through the server.
The data receiving and analyzing end analyzes and identifies the data information and judges whether potential safety hazards exist in the data information.
The information platform also comprises a data mining log library, wherein a mine safety production situation index system is recorded, qualitative and quantitative indexes in the system are quantified and risk grades are classified, and a machine learning algorithm is applied by utilizing data in historical mine safety production accidents:
(P (yk | x) ═ P (x | yk) P (x)) to obtain a bayesian network model, the specific formula is:
(1): p (yk) Nyk + α N + k α p (yk) Nyk + α N + k α, N being the total number of samples, k being the total number of classes, NykNyk being the number of samples of class ykyk, α α being a smoothed value;
(2): p (xi | yk) ═ Nyk, xi + α Nyk + n α P (xi | yk) ═ Nyk, xi + α Nyk + n α, where NykNyk is the number of samples of the class ykyk, n is the dimension of the feature, Nyk, xiNyk, and xi are the number of samples of the class ykyk, the value of the i-th dimension of the feature is xixi, and α is a smoothed value.
Monitoring data acquired by a data acquisition end through a data receiving and analyzing end, preprocessing the data input into a Bayesian network model, predicting time sequence data of the data acquisition end through a long-short memory neural network (LSTM) model, and inputting the predicted data into the Bayesian network model to obtain the change trend of the safety state in a period of time in the future; determining an early warning grade according to the change trend result and the determined risk grade threshold interval for early warning; and giving out an alarm handling suggestion according to the inferred causative node condition attributed by the Bayesian network model.
The establishment of the mine safety production situation index system specifically comprises the following steps: extracting situation characteristic elements from historical case data in mine safety production accidents and an underground mine equipment acquisition system, and establishing a mine safety production situation index system by combining expert experience of related fields.
The mine safety production situation index system comprises a mine gas accident safety situation index system, a mine electromechanical accident safety situation index system, a mine water seepage accident safety situation index system and a mine landslide accident safety situation index system.
The preprocessing of the index data input into the bayesian network model specifically comprises: and eliminating error values and completing missing values.
Inputting the predicted metric data into the bayesian network model comprises: setting a qualitative index which is partially unpredictable to be constant in a time range; and inputting the index data which can be predicted into the Bayesian network model for reasoning.
If the association exists, the data information is considered to be the data information considered to have the potential safety hazard; if the association does not exist, the data information is considered to be the data information without potential safety hazard; the information platform transmits the result of the data information analysis and judgment to the cloud platform big data center through the server, and other information platforms can share the result of the analysis and judgment through the cloud platform big data center to achieve the warning effect.
The information platform comprises an early warning module, the data information is analyzed by the data receiving and analyzing end and is divided into data information m1 and data information n1, and the data information m1 and the data information n1 are data information which is considered to have potential safety hazards and data information which is considered to have no potential safety hazards respectively. The data information m1 and the data information n1 are sent to a cloud platform big data center through a server, the cloud platform big data center shares the data information m1 and the data information n1 with other information platforms, and the other information platforms can identify the data information m1 and the data information n1 and set and filter the data information considered to be risky.
The data filtering end filters data information in the following two ways:
the first method comprises the following steps: receiving data information to be filtered firstly without continuous transmission, decomposing the received data information to be filtered, scrambling the data and deleting the received content;
and the second method comprises the following steps: and modifying the content of the part of the data information which is in risk and needs to be filtered or deleting the part of the content, and continuously transmitting other parts of the data information.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware.
As shown in fig. 5, based on the above apparatus, the present embodiment also relates to a real-time monitoring method for mine safety production, which is performed as follows:
collecting and analyzing information; judging whether the acquired information is safe or not; if the security is ensured, the information is continuously received and processed; if not, stopping receiving the information and uploading an alarm.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a readable storage medium or transmitted from one readable storage medium to another readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Optionally, an embodiment of the present application further provides a storage medium, where instructions are stored, and when the storage medium is run on a computer, the storage medium causes the computer to execute the method according to the embodiment described above.
Optionally, an embodiment of the present application further provides a chip for executing the instruction, where the chip is configured to execute the method in the foregoing illustrated embodiment.
The embodiments of the present application also provide a program product, where the program product includes a computer program, where the computer program is stored in a storage medium, and at least one processor can read the computer program from the storage medium, and when the at least one processor executes the computer program, the at least one processor can implement the method of the above-mentioned embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A mine safety production real-time monitoring method based on big data is characterized in that: the method comprises the following steps:
collecting and analyzing information;
judging whether the acquired information is safe or not; if the security is ensured, the information is continuously received and processed; if not, stopping receiving the information and uploading an alarm.
2. The mine safety production real-time monitoring method according to claim 1, characterized in that: the analysis information includes collected data information keywords.
3. The mine safety production real-time monitoring method according to claim 1, characterized in that: judging whether the collected information is safe according to the following steps:
the information platform also comprises a data mining log library, wherein a mine safety production situation index system is recorded, qualitative and quantitative indexes in the system are quantified and risk grades are classified, and a machine learning algorithm is applied by utilizing data in historical mine safety production accidents:
(P (yk | x) ═ P (x | yk) P (x)) to obtain a bayesian network model, the specific formula is:
P(yk)＝Nyk+αN+kαP(yk)＝Nyk+αN+kα，(1)
n is the total number of samples, k is the total number of classes, NykNyk is the number of samples whose class is ykyk, and α α is a smoothed value;
P(xi|yk)＝Nyk,xi+αNyk+nαP(xi|yk)＝Nyk,xi+αNyk+nα，(2)
NykNyk is the number of samples with the category of ykyk, n is the dimension of the feature, Nyk, xiNyk, xi is the number of samples with the category of ykyk, the value of the ith dimension feature is the number of samples with xixi, and alpha is a smooth value;
monitoring data acquired by a data acquisition end through a data receiving and analyzing end, preprocessing the data input into a Bayesian network model, predicting time sequence data of the data acquisition end through a long-short memory neural network (LSTM) model, and inputting the predicted data into the Bayesian network model to obtain the change trend of the safety state in a period of time in the future;
determining an early warning grade according to the change trend result and the determined risk grade threshold interval for early warning;
and giving out an alarm handling suggestion according to the inferred causative node condition attributed by the Bayesian network model.
4. The mine safety production real-time monitoring method according to claim 3, characterized in that: the establishment of the mine safety production situation index system specifically comprises the following steps:
extracting situation characteristic elements from historical case data in mine safety production accidents and an underground mine equipment acquisition system, and establishing a mine safety production situation index system by combining expert experience of related fields;
the mine safety production situation index system comprises a mine gas accident safety situation index system, a mine electromechanical accident safety situation index system, a mine water seepage accident safety situation index system and a mine landslide accident safety situation index system.
5. The mine safety production real-time monitoring method according to claim 3, characterized in that: the preprocessing of the index data input into the bayesian network model specifically comprises: eliminating error values and completing missing values;
inputting the predicted metric data into the bayesian network model comprises: setting a qualitative index which is partially unpredictable to be constant in a time range; and inputting the index data which can be predicted into the Bayesian network model for reasoning.
6. The mine safety production real-time monitoring method according to claim 3, characterized in that: setting and filtering data information considered to have potential safety hazards; the way of filtering the data information includes:
receiving data information to be filtered firstly without continuous transmission, decomposing the received data information to be filtered, randomly disordering the data and deleting the received content in a soft mode;
or, modifying or deleting the content of the part of the data information which needs to be filtered and has the potential safety hazard, and continuously transmitting other parts of the data information.
7. The utility model provides a mine safety production real-time monitoring system based on big data which characterized in that: the system comprises a front-end platform, a cloud platform big data center, a server and an information platform;
the front-end platform is used for setting management rules, detecting platform states, monitoring videos and displaying data;
the cloud platform big data center stores data information and receives and sends the data information, and serves as a data sharing center;
the server is connected with the cloud platform big data center;
the information platform is used for carrying out data information transmission with the cloud platform big data center through the server and comprises a data acquisition end, a data filtering end and a data receiving and analyzing end, wherein the output end of the data acquisition end is connected with the input end of the data filtering end, the output end of the data filtering end is connected with the input end of the data receiving and analyzing end, and the data filtering end and the data receiving and analyzing end are used for carrying out data information transmission with the cloud platform big data center through the server;
the data receiving and analyzing end analyzes and identifies the data information and judges whether potential safety hazards exist in the data information;
the information platform also comprises a data mining log library, wherein a mine safety production situation index system is recorded, qualitative and quantitative indexes in the system are quantified and risk grades are classified, and a machine learning algorithm is applied by utilizing data in historical mine safety production accidents:
(P (yk | x) ═ P (x | yk) P (x)) to obtain a bayesian network model, the specific formula is:
P(yk)＝Nyk+αN+kαP(yk)＝Nyk+αN+kα，(1)
n is the total number of samples, k is the total number of classes, NykNyk is the number of samples whose class is ykyk, and α α is a smoothed value;
P(xi|yk)＝Nyk,xi+αNyk+nαP(xi|yk)＝Nyk,xi+αNyk+nα，(2)
NykNyk is the number of samples with the category of ykyk, n is the dimension of the feature, Nyk, xiNyk, xi is the number of samples with the category of ykyk, the value of the ith dimension feature is the number of samples with xixi, and alpha is a smooth value;
monitoring data acquired by a data acquisition end through a data receiving and analyzing end, preprocessing the data input into a Bayesian network model, predicting time sequence data of the data acquisition end through a long-short memory neural network (LSTM) model, and inputting the predicted data into the Bayesian network model to obtain the change trend of the safety state in a period of time in the future;
determining an early warning grade according to the change trend result and the determined risk grade threshold interval for early warning;
and giving out an alarm handling suggestion according to the inferred causative node condition attributed by the Bayesian network model.
8. The mine safety production real-time monitoring system of claim 7, wherein: the preprocessing of the index data input into the bayesian network model specifically comprises: eliminating error values and completing missing values;
inputting the predicted metric data into the bayesian network model comprises: setting a qualitative index which is partially unpredictable to be constant in a time range; and inputting the index data which can be predicted into the Bayesian network model for reasoning.
9. The mine safety production real-time monitoring system of claim 7, wherein: the information platform transmits the result of the data information analysis and judgment to the cloud platform big data center through the server, and other information platforms share the result of the analysis and judgment through the cloud platform big data center, so that the warning effect is achieved.
10. The mine safety production real-time monitoring system of claim 7, wherein: the information platform further comprises an early warning module for early warning the data information which is considered to have potential safety hazards.
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