CN117010696A - Miner individual safety instant navigation early warning system and method thereof - Google Patents
Miner individual safety instant navigation early warning system and method thereof Download PDFInfo
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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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
- G06Q50/02—Agriculture; Fishing; Mining
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
Abstract
The invention discloses a miner individual safety instant navigation early warning system and a method thereof, belonging to the technical field of miner safety early warning, comprising the following steps: the data acquisition layer is used for acquiring human body characteristic data and operation environment data; the data processing layer is used for treating the human body characteristic data and the operation environment data by a data processing method, setting a corresponding treatment library and an escape library according to data classification and application scenes to form a human body sign monitoring index database, simultaneously taking the human body sign monitoring index database as the input of a machine learning algorithm, learning the data by the machine learning algorithm, and forming a human body safety risk early warning model according to a learning rule; the data transmission layer is used for realizing data transmission of the data acquisition layer and the data processing layer based on a network communication protocol; the miner individual safety instant navigation early warning system and the method thereof overcome the defect that only sign information is singly collected in the prior art, and the analysis method of data fusion is not combined to deeply evaluate the risk relationship between the environment and the person.
Description
Technical Field
The invention belongs to the technical field of miner safety early warning, and particularly relates to a miner individual safety instant voyage early warning system and a method thereof.
Background
At present, the basis of intrinsic safety management in the field of coal exploitation in China is hazard source identification and risk analysis in the safety production process, and the core is application of technical means to ensure personnel safety and reduce safety accident risks. The known technical means for controlling the risk of safety accidents and guaranteeing the safety of personnel in the coal mine are to apply informatization to solve key factors affecting safety in four factors of 'people, machines, rings and pipes', such as a production automation control system for equipment safety; the environment risk online monitoring system is used for environment safety; a security management information system for managing security; a personnel positioning management system for personnel safety, etc. The essence of ignoring security management is "people". A large number of production accidents show that the accidents and the behaviors of people have very close relation, the people are key factors in the coal mine safety production process, and the physiological index anomalies such as fatigue, mood fluctuation, distraction and the like of physiological characteristics of the people have very important influence on the safety production. The existing personnel positioning system directly related to personnel safety only solves the problem that the intrinsic safety of people is not fundamentally solved by grasping the position information of 'people' in real time in the production process.
In the energy industry in China, coal accounts for about 70% of the primary energy production and consumption structures in China, and more than 50% of coal is expected to be consumed in 2050. Meanwhile, 95% of coal mining is underground operation, and coal mine accidents account for 72.8-89.6% of major accidents of more than 10 deaths at one time in industrial and mining enterprises; in the case of more than 10 accidents of coal mine enterprises, gas accidents account for 71% of the dead people, wherein a large number of accidents show that the accidents and the behaviors of operators have very close relations, the operators are main factors in the production process, and the method is an optimal technical means for preventing and predicting the unsafe behaviors caused by abnormal physiological indexes such as fatigue, mood fluctuation, distraction, blood pressure rise, heart rate acceleration and the like of the human body in the coal mine production process and the injuries to the human body in the multi-disaster accident environment.
The technology for monitoring vital signs of rescue teams in coal mines is disclosed in the literature [ Wang Peng, weak and Wu Xianli ] the technology for monitoring vital signs of rescue teams in coal mines [ J ]. Coal mine safety 2013, 44 (11): 98-99 ], wherein the technology uses pyroelectric infrared sensors to collect skin temperature of the rescue teams, piezoelectric sensors to monitor heart rate, a human motion model is established to monitor human body posture parameters, and the technology is used for designing a device for monitoring underground disaster relief vital sign parameters of the emergency rescue teams after the disasters of the coal mines, so as to help ground commanders to master the physical condition of each rescue teams in real time, and provide data support for accurate judgment and coordination management of command centers in a short time.
In the technology, the physiological state change of rescue workers after disasters is mainly monitored, and the technology is basically different from the working scene of the workers in the multi-disaster environment, only the acquisition and the monitoring of human body characteristics are realized, and the analysis and the evaluation of data fusion are not carried out by combining disaster environment data and human body sign data, so that a miner individual safety instant navigation early warning system and a miner individual safety instant navigation early warning method are required to be developed to solve the existing problems.
Disclosure of Invention
The invention aims to provide a miner personal safety instant navigation early warning system and a miner personal safety instant navigation early warning method, which are used for solving the problem that single acquired sign information cannot deeply evaluate the relationship between environment and personal risk.
In order to achieve the above purpose, the present invention provides the following technical solutions: a miner personal safety instant pilot early warning system comprising:
data acquisition layer: the system is used for collecting human body characteristic data and operation environment data;
data processing layer: the method is used for managing the human body characteristic data and the operation environment data through a data processing method; setting a corresponding disposal library and an escape library according to data classification and application scenes to form a human body sign monitoring index database, simultaneously taking the human body sign monitoring index database as input of a machine learning algorithm, learning data through the machine learning algorithm, forming a human body safety risk early warning model according to a learning rule, and obtaining an early warning threshold value of human body sign;
data transmission layer: the data transmission device is used for realizing data transmission of the data acquisition layer and the data processing layer based on a network communication protocol;
data application layer: the method is used for comprehensively managing the data and applying the scenes.
Preferably, the human body characteristic data includes: heart rate, blood pressure, body temperature, or a combination of more than one of them.
Preferably, the job environment data includes: one or more of environment monitoring video, gas concentration, temperature, humidity, dust and air quantity are combined;
preferably, the data application layer includes:
the vital sign real-time monitoring module is used for monitoring heart rate, respiratory rate, temperature and blood pressure information of underground operators in real time and displaying a change trend in a text and curve form;
the abnormal condition alarm module is used for automatically alarming and prompting according to a set early warning threshold value and judging whether a set early warning level conveys an instruction to start an emergency treatment plan when abnormal conditions such as tachycardia or stopping, tachypnea or stopping and hyperthermia or hypothermia occur to personnel;
the instruction issuing and displaying module is used for issuing rescue command in a text mode, and the information acquisition terminal receives and displays the rescue command in real time and plays the information in a voice mode;
the statistical information module is used for displaying the communication state, the continuous working time, the current rescue task number, the abnormal number and the times of vital signs and/or the manual alarm times of all underground operators in real time;
the collaborative rescue module is used for collecting the position information of the existing personnel in the mine, the wind speed and direction information of the field environment data and the human body characteristic data according to the underground disaster type, uniformly deploying personnel rescue schemes according to a data analysis list, and notifying members of a group to develop collaborative rescue in a form of issuing a remote command;
the GIS map display module is used for displaying the position information of all networking personnel on a map in real time according to the map function provided by the monitoring command center;
preferably, the data application layer further includes:
the information maintenance module is used for editing basic information, vital sign threshold information and database configuration information of all operators;
and the historical data query analysis module is used for querying vital sign historical data of operators according to conditions and displaying and analyzing in a curve mode.
Preferably, the method comprises the steps of,
the machine learning algorithm adopts BP neural network human vital signs to analyze and predict, and specifically comprises the following steps:
in the machine learning process of the BP neural network, the following formula is used for calculating the node number:
；
in the above formula, a is expressed as an arbitrary constant between (1, 10); n is represented as an output; m is represented as an input; l is represented as a node;
the nodes of the input layer comprise 8 attributes of personnel numbers, sign indexes, sign values, post types, disaster types, influence environment parameters, operation duration and scenes; the input layer m=8 of the network; according to the prediction and analysis requirements, taking the analysis result as a network output result, so that an output layer node n=1; the three-layer BP network is selected because the three-layer BP network can be close to any continuous function. Meanwhile, according to the number of the input layer, the output layer and the hidden layers, the number of nodes of each hidden layer is 5;
the BP network learning process is designed as follows: the nodes are neurons in the neural network, and finally participate in the learning process of the neural network, namely, the input value is multiplied by the weight to carry out weighted summation, and then the value of the weighted summation is subjected to activation function calculation;
input vector is，/>Setting a threshold value for the hidden layer nerve unit; the hidden layer output vector is +.>，/>Setting a threshold value for the output layer neural unit; the output layer output vector is +.>，/>Representing the output layer->The output vectors; the desired output vector is +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein,i、m、j、l、k、nnatural numbers of various quantities are input and output in the expression;
the weight matrix from the input layer to the hidden layer isWherein->To hide layer->Weight vectors corresponding to the neurons; the weight matrix from the hidden layer to the output layer is +.>Wherein->For the output layer->Weight vectors corresponding to the neurons; the execution steps are as follows:
step 1, weight matrixAnd->Random number is given, and learning rate is selected>Setting a target error rate;
step 2, inputting training samplesAnd->Learning and training the model;
the layer outputs were calculated using the following formula:
，/>the output of the ith node of the hidden layer;
connecting weights from the input layer to the hidden layer;
，/>for the output layer->The output of the individual nodes;
is the connection weight of the hidden layer to the output layer.
Wherein:，/>transformation function->The method comprises the steps of carrying out a first treatment on the surface of the When the calculated error is not an ideal error, the error of the weight value needs to be updated to recalculate the error to reach the ideal value; />Representing an error in updating the weights;
the weights are adjusted using the following formula:
representing the gradient of the output layer;
，
wherein:，/>，/>is a weight matrix->An adjustment amount of the corresponding component;
representing the gradient of the hidden layer;
，
wherein:，/>，/>is a weight matrix->An adjustment amount of the corresponding component;
and step 3, training the sample data repeatedly until reaching the target error rate allowable range, and establishing a BP neural network learning process.
Preferably, the data application layer further comprises an evaluation module for evaluating the health condition of the operator, ensuring that the body of the underground operator is not abnormal under the environment without sudden accident or the body of the underground operator is not abnormal before the underground operator is in the process, wherein the evaluation module is based on the comprehensive evaluation result of vital signs of the underground operator monitored by heart rate, oxygen pulse, anaerobic threshold and respiratory entropy; the method for comprehensively evaluating the vital signs of the underground operators comprises the following steps:
using the heart beating frequency of the human bodyHR) Representing the maximum life survival period of a human body, representing the age and the trapped time of trapped personnel in a disaster environment by using the maximum heart rate, and evaluating the vital signs of the human body and the heart load parameters by monitoring the heart rate of the trapped personnel in the disaster environment; the calculation formula is as follows:
HRmax = 220－Age
wherein:HRmaxexpressed as heart rate maximum of trapped people in the disaster environment, age is Age of trapped people in the disaster environment;
pulse with oxygen) The oxygen content of each pulse output of the heart is represented, the myocardial function damage degree and cardiopulmonary function burden condition of trapped personnel in a disaster environment are reflected, and a mathematical model is established by taking oxygen pulse as a parameter:
wherein:expressed as the oxygen uptake of trapped personnel in the disaster environment,HRa heart rate expressed as a trapped person in a disaster environment;
the pulse condition and the respiration condition of an individual are represented by an anaerobic threshold AT, the larger the anaerobic threshold is, the closer the vital sign metabolism of the human body is to the critical point, and the vital sign taking the anaerobic threshold as a constraint parameter in a disaster environment is evaluated by combining the anaerobic threshold boundary condition;
the respiration and oxygen exchange rate of trapped personnel are represented by respiratory entropy RQ, and the environmental parameter monitoring result is utilized to combine respiratory rate, respiratory volume, oxygen uptake volume and heart rate vital sign parameters to establish the evaluation of vital sign respiratory entropy in disaster environment.
The invention also provides an early warning method of the miner individual safety instant navigation early warning system, which comprises the following steps:
the data acquisition layer transmits information of mine pressure monitoring, gas monitoring, rock burst, roof dynamic and personnel positioning to the data processing layer through a safety monitoring private network; transmitting information of the coal mining system, the tunneling system, the electromechanical system, the transportation system, the ventilation system and the drainage system to the data processing layer through an automatic industrial network; transmitting the data of the inspection video, the operation video, the overhaul video and the early warning video to a data processing layer through a video monitoring private network; the data of water wave geology, hidden danger management, emergency resources and double pre-control management are sent to a data processing layer through an office management private network;
the data processing layer is used for treating human body characteristic data and operation environment data through data storage, data screening, data modeling and data classification, setting a corresponding disposal library and an escape library according to data classification and application scenes to form a human body sign monitoring index database, and simultaneously, as input of a machine learning algorithm, learning the data through the machine learning algorithm, and forming a human body safety risk early warning model according to a learning rule;
the human safety risk early warning model processes and outputs the information to the data application layer, when the vital sign is monitored by real-time operation personnel, the alarm prompt is automatically given according to a set early warning threshold value, the emergency treatment plan is started according to whether the set early warning level is transmitted or not, the event early warning is realized, the rescue command is issued in a text mode, the information acquisition terminal can receive and display the information in real time, the information can be played in a voice mode to realize the early warning treatment, the personnel rescue plan is uniformly deployed according to the underground disaster type, the position information, the field environment data, the wind speed and wind direction information and the human body characteristic data of the existing personnel of the mine are acquired, and the team is informed of the development cooperative rescue by the mode issued by the remote command to realize the emergency rescue.
The invention has the technical effects and advantages that: the miner individual safety instant navigation early warning system and the method thereof take data acquisition and processing as support, namely, a human physiological characteristic monitoring and instant communication integrated system architecture based on wearable equipment is constructed; the comprehensive analysis and early warning of data are used as important points, namely, a human body safety risk early warning model under a mine special operation environment is constructed by adopting a machine learning algorithm, the change of personnel sign parameters of underground operators when different disasters occur is analyzed, escape opportunities are judged, intervention means for reducing injuries are searched, individual safety accidents caused by the conditions of fatigue operation, sickness operation, emotion fluctuation and the like during the operation of the personnel under a multi-disaster coupling environment are avoided, and meanwhile, the online intelligent guidance of safety early warning, disposal, danger avoidance and escape is realized by combining with the human body safety risk early warning model; the method overcomes the defect that only single acquisition of physical sign information is carried out in the prior art, and the analysis method of data fusion is not combined to deeply evaluate the risk relation between the environment and the person, so that the three-layer system structure which is convenient for the implementation of the physical sign acquisition and monitoring analysis method of the human body is formed by layering the original independent architecture system of the complex multi-network and multi-heterogeneous system in the underground coal mine through an adaptable protocol for judging the physiological change fluctuation of the person and assisting rescue personnel and operation personnel to automatically escape and emergency rescue; the method comprises the steps of forming a human body safety risk early warning model under a mine multi-disaster coupling environment, establishing a set of body function comprehensive analysis association rule functions capable of reflecting vital sign changes by applying a BP neural network linear expression formula, and carrying out personnel safety risk early warning by using an association rule prediction method.
Drawings
FIG. 1 is a schematic diagram of a frame of a system of the present invention;
FIG. 2 is a schematic diagram of a training process of the machine learning model according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a miner individual safety instant navigation early warning system as shown in fig. 1, which is based on a human physiological characteristic monitoring and instant communication integrated system framework of wearable equipment, and the framework is used as a platform support of a personnel vital sign collecting and analyzing method, and is divided into four layers of logic structures according to the capabilities of human vital sign and environmental data collecting, transmitting, summarizing, analyzing, information feedback and the like, wherein the four layers of logic structures are as follows from low to high in sequence: the specific structure of the data acquisition layer, the data transmission layer, the data processing layer and the data application layer is shown in fig. 2.
The system architecture is designed for integrating a data acquisition interface of multi-protocol adaptation aiming at a multi-source heterogeneous system, a data channel can be established between the data acquisition layer and a sensing unit for acquiring the operation environment data and the human body characteristic data quickly through the protocol, and a data source is acquired, wherein the data collected by the data acquisition layer mainly comprises heart rate, blood pressure, body temperature and other data for human body characteristic monitoring and video, gas concentration, temperature, humidity, dust, air quantity and other data for monitoring the environment where an underground operator is located;
the data transmission layer is connected with the data acquisition layer and the data processing layer up and down, performs data transmission through a network communication protocol, is composed of a plurality of heterogeneous network protocols and can be adapted to stable transmission of various network guarantee data;
the virtual data network is integrated in the system architecture at the upper layer of each physical network, which can ensure the quality of various data and the data loss caused by the interruption or short delay of the physical network to the data transmission;
the data processing layer is mainly used for treating human body characteristic data and operation environment data through related methods of data processing, such as data storage, data screening, data modeling, data classification and the like, and a corresponding treatment library, escape library, early warning library and the like can be arranged according to data classification and application scenes according to data treatment results, and meanwhile, the data processing layer is used as input of a machine learning algorithm, learns the data through the machine learning algorithm and forms a human body safety risk early warning model according to learning rules; in the embodiment, an ETL data processing tool is arranged in the data management system, and the source data is loaded into a data warehouse after being extracted, cleaned and converted;
the data application layer is mainly used for comprehensively managing and applying the data to the scene, and the main functions of the data application layer include:
the vital sign real-time monitoring module is used for monitoring information such as heart rate, respiratory rate, temperature, blood pressure and the like of underground operators in real time and displaying a change trend in a text and curve mode. Particularly, the human body sign fluctuation under the daily operation condition and disaster condition is monitored in real time;
the abnormal condition alarm module is used for automatically giving an alarm prompt according to a set early warning threshold when abnormal conditions such as tachycardia or stopping, tachypnea or stopping, hyperthermia or hypothermia occur to a worker, and upwards conveying an instruction according to a set early warning level to start an emergency treatment plan;
the instruction issuing and displaying module is used for issuing rescue command in a text mode, the information acquisition terminal can receive and display the command in real time, and the information can be played in a voice mode;
the statistical information module is used for displaying the communication state, the continuous working time, the current rescue task number, the abnormal number and times of vital signs, the manual alarm times and the like of all underground operators in real time;
the collaborative rescue module is used for collecting the position information, the field environment data, the wind speed and direction information and the human body characteristic data of the existing personnel of the mine according to the underground disaster type, uniformly deploying personnel rescue schemes according to a data analysis list, and notifying members of a group to develop collaborative rescue in a form of issuing a remote command;
the GIS map display module is used for monitoring map functions provided by the command center, and the position information of all networking personnel can be displayed on the map in real time;
the information maintenance module is used for editing basic information, vital sign threshold information, database configuration information and the like of all operators by the monitoring command center;
the historical data query analysis module is used for a monitoring command center to query vital sign historical data of operators according to conditions and display the historical data in a curve form so as to facilitate comparison analysis after the operation;
constructing an integrated regression model of each life index by using a single factor regression model and a data mining method according to the analysis results of the influences of various unsafe factors in the coal mine disasters; aiming at the regression model of each life index, one of the environmental factors is taken as a unique independent variable in the model, and other factors are assigned according to the influence parameters to obtain the comparison between the life index and the two models of single factor and multiple factor;
human health and safety risk early warning model construction based on machine learning algorithm:
based on the standardized processing result of the original data, determining the influence weight of each factor on the dependent variable through principal component analysis, sequencing the weight proportion of each factor affecting the vital index to obtain the data fluctuation and scientific expression of the vital sign of the constructor in daily operation and multi-disaster environment by the coal mine disaster, and establishing a human body safety risk early warning model in the multi-disaster environment; the risk early warning model is based on a BP neural network machine learning algorithm, and comprehensively trains and learns a physical sign data base, an individual history base and environmental data through the design of the machine learning algorithm to obtain a target function aiming at classification or regression of human body characteristic data in daily environment and disaster environment, so that a physical sign index early warning setting model aiming at miners and individuals in different working scenes and underground environments is established;
the machine learning algorithm adopts a BP neural network to analyze and predict vital signs of a human body, and the following formula is used for calculating the node number in the machine learning process of the BP neural network:
；
in the above formula, a is expressed as an arbitrary constant between (1, 10); n is represented as an output; m is represented as an input; l is represented as a node;
the nodes of the input layer comprise 8 attributes of personnel numbers, sign indexes, sign values, post types, disaster types, influence environment parameters, operation duration and scenes, so that the input layer m=8 of the network; according to the prediction and analysis requirements, taking the analysis result as a network output result, so that an output layer node n=1; the three-layer BP network can be close to any continuous function, so that the BP three-layer network is selected; meanwhile, according to the number of the input layer, the output layer and the hidden layers, the number of nodes of each hidden layer is 5;
the BP network learning process is designed as follows: the nodes are neurons in the neural network, and finally participate in the learning process of the neural network, namely, the input value is multiplied by the weight to carry out weighted summation, and then the value of the weighted summation is subjected to activation function calculation;
input vector is，/>Setting a threshold value for the hidden layer nerve unit; the hidden layer output vector is +.>，/>Setting a threshold value for the output layer neural unit; the output layer output vector is +.>The method comprises the steps of carrying out a first treatment on the surface of the The desired output vector is +.>；i、m、j、l、k、nThe natural number of each input and output quantity in the expression can be any positive integer except 0;
the weight matrix from the input layer to the hidden layer isWherein->To hide layer->Weight vectors corresponding to the neurons; the weight matrix from the hidden layer to the output layer is +.>Wherein->For the output layer->Weight vectors corresponding to the neurons; the execution steps are as follows:
step 1, weight matrixAnd->Random number is given, and learning rate is selected>Setting a target error rate;
step 2, inputting training samplesAnd->Learning and training the model;
the layer outputs were calculated using the following formula:
，/>the output of the ith node of the hidden layer;
connecting weights from the input layer to the hidden layer;
，/>for the output layer->The output of the individual nodes;
connecting weights from the hidden layer to the output layer;
wherein:，/>transformation function->The method comprises the steps of carrying out a first treatment on the surface of the When the calculated error is not an ideal error, the error of the weight value needs to be updated to recalculate the error to reach the ideal value; />Representing an error in updating the weights;
the weights are adjusted using the following formula:
representing the gradient of the output layer;
，
wherein:，/>，/>is a weight matrix->An adjustment amount of the corresponding component;
representing the gradient of the hidden layer;
，
wherein:，/>，/>is a weight matrix->An adjustment amount of the corresponding component;
and step 3, training the sample data repeatedly until reaching the target error rate allowable range, and establishing a BP neural network learning process.
The early warning threshold of the human body sign is combined with the comprehensive evaluation result of the vital sign of the underground operation personnel based on heart rate, oxygen pulse, anaerobic threshold and respiration entropy monitoring, and the comprehensive evaluation result of the vital sign of the underground operation personnel is that the system acquires key indexes affecting the health condition of the vital sign of the personnel, such as pulse, respiration, heart rate, blood pressure, body temperature, blood oxygen and the like through wearable equipment. The current physical condition of the vital signs of the person is obtained through fluctuation analysis of the data by taking the health reference value of the eight vital sign standard of the person as a standard; the method aims to ensure that the physical health degree of each logging person reaches a normal state before logging, and avoid safety accidents caused by unsafe behaviors of the logging person due to the abnormality of individual physical signs; providing a health evaluation guide for personnel through periodical physical sign index analysis; the comprehensive evaluation method for the vital signs of the underground operators comprises the following steps:
using the heart beating frequency of the human bodyHR) Representing the maximum life survival period of a human body, representing the age and the trapped time of trapped personnel in a disaster environment by using the maximum heart rate, and evaluating the vital signs of the human body and the heart load parameters by monitoring the heart rate of the trapped personnel in the disaster environment; the calculation formula is as follows:
HRmax = 220－Age
wherein:HRmaxexpressed as heart rate maximum of trapped people in the disaster environment, age is Age of trapped people in the disaster environment;
pulse with oxygenBeating) The oxygen content of each pulse output of the heart is represented, the myocardial function damage degree and cardiopulmonary function burden condition of trapped personnel in a disaster environment are reflected, and a mathematical model is established by taking oxygen pulse as a parameter:
wherein:expressed as the oxygen uptake of trapped personnel in the disaster environment,HRa heart rate expressed as a trapped person in a disaster environment;
the pulse condition and the respiratory condition of an individual are represented by an Anaerobic Threshold (AT), the larger the anaerobic threshold is, the closer the vital sign metabolism of the human body is to the critical point, and the vital sign taking the anaerobic threshold as a constraint parameter in the disaster environment is evaluated by combining the anaerobic threshold boundary condition;
characterizing respiration and oxygen exchange rate of trapped people by using respiratory entropy (RQ), and establishing evaluation of vital sign respiratory entropy in disaster environment by using environmental parameter monitoring results and combining respiratory rate, respiratory quantity, oxygen uptake quantity and heart rate vital sign parameters;
normalizing and standardizing the pre-monitored human vital sign indexes, forming a human vital sign monitoring index database which comprises index IDs, index categories, index names, index thresholds, normal range of indexes, weights, health evaluation values and the like, realizing early warning by the system, judging the acquired values and the early warning threshold of the human vital signs, sending early warning information if the acquired values are at the lower limit of the early warning threshold, and sending alarm information if the acquired values are up to the upper limit of the early warning threshold; the technical problem of normalization of the data is solved, and the acquired data can be formatted to achieve more accurate early warning of the system;
according to the change rule of the influence parameters of the mine multi-disaster environment and the individual differences of operators in different disaster environments, a human body sign threshold monitoring and alarming method is adopted to express the daily operation state and the unsafe state of the personnel sign characteristics in the disaster environment state, and a health risk early warning model such as fatigue, abnormal breathing, abnormal blood pressure and the like of the underground operators is established; through the combined feature vectors of the autoregressive coefficient, the signal amplitude and the inclination angle, a BP neural network machine learning linear expression is adopted, and human body safety risk early warning models such as personnel poisoning, drowning, burial pressure, falling and the like are established;
the invention also provides an early warning method of the miner individual safety instant navigation early warning system, which comprises the following steps:
the data acquisition layer transmits information of mine pressure monitoring, gas monitoring, rock burst, roof dynamics and personnel positioning to the data processing layer through the safety monitoring private network; transmitting information of the coal mining system, the tunneling system, the electromechanical system, the transportation system, the ventilation system and the drainage system to the data processing layer through an automatic industrial network; transmitting the data of the inspection video, the operation video, the overhaul video and the early warning video to a data processing layer through a video monitoring private network; the data of water wave geology, hidden danger management, emergency resources and double pre-control management are sent to a data processing layer through an office management private network;
the data processing layer is used for treating human body characteristic data and operation environment data through data storage, data screening, data modeling and data classification, setting a corresponding disposal library and an escape library according to data classification and application scenes to form a human body sign monitoring index database, and simultaneously, as input of a machine learning algorithm, learning the data through the machine learning algorithm, and forming a human body safety risk early warning model according to a learning rule;
the human safety risk early warning model processes and outputs the information to the data application layer, monitors vital signs of operators in real time, automatically gives an alarm prompt according to a set early warning threshold value when abnormal conditions occur, starts an emergency treatment plan according to whether a set early warning level conveys an instruction or not, achieves event early warning, gives a rescue command in a text mode, can receive and display the information in real time, plays the information in a voice mode to achieve early warning treatment, collects position information, field environment data, wind speed and wind direction information and human body characteristic data of the existing operators of a mine according to underground disaster types, uniformly deploys the personnel rescue plan, and informs members of a group to develop cooperative rescue in a mode of remote command delivery to achieve emergency rescue.
The miner individual safety instant navigation early warning system and the method thereof take data acquisition and processing as support, namely, a human physiological characteristic monitoring and instant communication integrated system architecture based on wearable equipment is constructed; the comprehensive analysis and early warning of data are used as important points, namely, a human body safety risk early warning model under a mine special operation environment is constructed by adopting a machine learning algorithm, the change of personnel sign parameters of underground operators when different disasters occur is analyzed, escape opportunities are judged, intervention means for reducing injuries are searched, individual safety accidents caused by the conditions of fatigue operation, sickness operation, emotion fluctuation and the like during the operation of the personnel under a multi-disaster coupling environment are avoided, and meanwhile, the online intelligent guidance of safety early warning, disposal, danger avoidance and escape is realized by combining with the human body safety risk early warning model; the method overcomes the defect that only single acquisition of sign information is carried out in the prior art, and an analysis method of data fusion is not combined to deeply evaluate the risk relation between the environment and the person, and for judging physiological change fluctuation of the person and assisting rescue personnel and operation personnel in autonomous escape and emergency rescue when disasters occur, an original independent architecture system of a multi-network and multi-heterogeneous system in a coal mine is subjected to layering organization through an adaptable protocol to form a three-layer architecture which is convenient for implementation of a human sign acquisition and monitoring analysis method, a human safety risk early warning model is formed in a mine multi-disaster coupling environment, a set of physical function comprehensive analysis association rule functions capable of reflecting vital sign changes is established by applying a BP neural network linear expression formula, and personnel safety risk early warning is carried out by utilizing an association rule prediction method.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (8)
1. An ore worker individual safety instant voyage early warning system which is characterized in that: comprising the following steps:
the data acquisition layer is used for acquiring human body characteristic data and operation environment data;
the data processing layer is used for treating the human body characteristic data and the operation environment data through a data processing method; the treatment results form a human body sign monitoring index database according to the data classification and the application scene, and are used as the input of a machine learning algorithm to learn so as to form a human body safety risk early warning model, so as to obtain an early warning threshold value of human body sign;
the data transmission layer is used for realizing data transmission of the data acquisition layer and the data processing layer;
and the data application layer is used for comprehensively managing the data and applying the scene.
2. The mining individual safety instant pilot early warning system according to claim 1, wherein: the human body characteristic data includes: heart rate, blood pressure, body temperature, or a combination of more than one of them.
3. The mining individual safety instant pilot early warning system according to claim 1, wherein: the job environment data includes: one or more of environment monitoring video, gas concentration, temperature, humidity, dust and air quantity.
4. The mining individual safety instant pilot early warning system according to claim 1, wherein: the data application layer includes:
the vital sign real-time monitoring module is used for monitoring heart rate, respiratory rate, temperature and blood pressure information of underground operators in real time and displaying a change trend in a text and curve form;
the abnormal condition alarm module is used for automatically alarming and prompting according to a set early warning threshold value and judging whether a set early warning level conveys an instruction to start an emergency treatment plan when abnormal conditions such as tachycardia or stopping, tachypnea or stopping and hyperthermia or hypothermia occur to personnel;
the instruction issuing and displaying module is used for issuing rescue command in a text mode, and the information acquisition terminal receives and displays the command in real time and plays the command in a voice mode;
the statistical information module is used for displaying the communication state, the continuous working time, the current rescue task number, the abnormal number and the times of vital signs and/or the manual alarm times of all underground operators in real time;
the collaborative rescue module is used for collecting the position information, the field environment data, the wind speed and direction information and the human body characteristic data of the existing personnel of the mine according to the underground disaster type, uniformly deploying personnel rescue schemes according to a data analysis list, and notifying members of a group to develop collaborative rescue in a form of issuing a remote command;
and the GIS map display module is used for displaying the position information of all networking personnel on the map in real time according to the map function provided by the monitoring command center.
5. The mining individual safety instant pilot early warning system according to claim 1, wherein: the data application layer further comprises:
the information maintenance module is used for editing basic information, vital sign threshold information and database configuration information of all operators;
and the historical data query analysis module is used for querying vital sign historical data of operators according to conditions and displaying and analyzing in a curve mode.
6. The mining individual safety instant pilot early warning system according to claim 1, wherein:
the machine learning algorithm adopts a BP neural network to analyze and predict vital signs of a human body, and the following formula is used for calculating the node number in the machine learning process of the BP neural network:
；
in the above formula, a is expressed as an arbitrary constant between (1, 10); n is represented as an output; m is represented as an input; l is represented as a node;
the nodes of the input layer comprise 8 attributes of personnel numbers, sign indexes, sign values, post types, disaster types, influence environment parameters, operation duration and scenes, so that the input layer m=8 of the network; according to the prediction and analysis requirements, taking the analysis result as a network output result, so that an output layer node n=1; the three-layer BP network can be close to any continuous function, so that the BP three-layer network is selected; meanwhile, according to the number of the input layer, the output layer and the hidden layers, the number of nodes of each hidden layer is 5;
the BP network learning process is designed as follows:
input vector is，/>Setting a threshold value for the hidden layer nerve unit; the hidden layer output vector is +.>，/>Setting a threshold value for the output layer neural unit; the output layer output vector is +.>，/>Representing the output layer->Outputs ofVector; the desired output vector is +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein,i、m、j、l、k、nnatural numbers of various quantities are input and output in the expression;
the weight matrix from the input layer to the hidden layer isWherein->To hide layer->Weight vectors corresponding to the neurons; the weight matrix from the hidden layer to the output layer is +.>Wherein->For the output layer->Weight vectors corresponding to the neurons; the execution steps are as follows:
weight matrixAnd->Random number is given, and learning rate is selected>Setting a target error rate;
inputting training samplesAnd->Learning and training the model;
the layer outputs were calculated using the following formula:
，/>the output of the ith node of the hidden layer;
connecting weights from the input layer to the hidden layer;
，/>for the output layer->The output of the individual nodes;
connecting weights from the hidden layer to the output layer;
wherein:，/>transformation function->；
The weights are adjusted using the following formula:
representing the gradient of the output layer;
，
wherein:，/>，/>is a weight matrix->An adjustment amount of the corresponding component;
representing the gradient of the hidden layer;
，
wherein:，/>，/>is a weight matrix->An adjustment amount of the corresponding component;
and (5) training the sample data repeatedly until reaching the target error rate allowable range, and establishing a BP neural network learning process.
7. The mining individual safety instant pilot early warning system according to claim 1, wherein: the data application layer further comprises an evaluation module for evaluating the health condition of the operators, and the evaluation module monitors the vital sign comprehensive evaluation result of the underground operators based on heart rate, oxygen pulse, anaerobic threshold and respiration entropy; the method for comprehensively evaluating the vital signs of the underground operators comprises the following steps:
the method comprises the steps of representing the maximum life survival period of a human body by using the heart beat frequency of the human body, representing the age and the trapped time of trapped personnel in a disaster environment by using the maximum heart rate, and evaluating the vital signs of the human body and the heart load parameters by monitoring the heart rate of the trapped personnel in the disaster environment; the calculation formula is as follows:
HRmax = 220－Age
wherein:HRmaxexpressed as heart rate maximum of trapped people in the disaster environment, age is Age of trapped people in the disaster environment;
characterizing the oxygen content of the heart per pulse by oxygen pulse, usingA representation; reflecting the myocardial function damage degree and cardiopulmonary function burden condition of trapped personnel in a disaster environment, and adopting oxygen pulse as parameters to establish a mathematical model:
;
wherein:expressed as the oxygen uptake of trapped personnel in the disaster environment,HRa heart rate expressed as a trapped person in a disaster environment;
the pulse condition and the respiration condition of the individual are represented by the anaerobic threshold, the greater the anaerobic threshold is, the closer the metabolism of vital signs of the human body is to the critical point, and the vital signs taking the anaerobic threshold as a constraint parameter in the disaster environment are evaluated by combining the anaerobic threshold boundary condition;
the breathing entropy is used for representing the breathing and oxygen exchange rate of trapped people, and the environmental parameter monitoring result is used for combining the breathing frequency, the breathing quantity, the oxygen uptake quantity and the heart rate vital sign parameters to establish the evaluation of vital sign breathing entropy in the disaster environment.
8. The early warning method of the miner individual safety instant pilot early warning system according to claim 1, wherein the early warning method is characterized in that: the method comprises the following steps:
the data acquisition layer transmits information of mine pressure monitoring, gas monitoring, rock burst, roof dynamics and personnel positioning to the data processing layer through the safety monitoring private network; transmitting information of the coal mining system, the tunneling system, the electromechanical system, the transportation system, the ventilation system and the drainage system to the data processing layer through an automatic industrial network; transmitting the data of the inspection video, the operation video, the overhaul video and the early warning video to a data processing layer through a video monitoring private network; the data of water wave geology, hidden danger management, emergency resources and double pre-control management are sent to a data processing layer through an office management private network;
the data processing layer is used for treating human body characteristic data and operation environment data through data storage, data screening, data modeling and data classification, setting a corresponding disposal library and an escape library according to data classification and application scenes to form a human body sign monitoring index database, and simultaneously, as input of a machine learning algorithm, learning the data through the machine learning algorithm, and forming a human body safety risk early warning model according to a learning rule;
the human safety risk early warning model processes and outputs the vital signs of operators to the data application layer, automatically gives an alarm prompt according to a set early warning threshold value when the operators are abnormal, starts an emergency treatment plan according to whether the set early warning level conveys an instruction or not, achieves event early warning, gives rescue command in a text mode, receives and displays information in real time, plays the information in a voice mode to achieve early warning treatment, collects the position information, the field environment data, the wind speed and wind direction information and the human characteristic data of the existing personnel of a mine according to the underground disaster type, uniformly deploys the personnel rescue plan, and informs the members of a group to develop cooperative rescue in a remote command issuing mode to achieve emergency rescue.
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