CN114320469B - Cloud-edge intelligence-based underground hazard source detection method - Google Patents
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Abstract
The invention discloses an underground hazard source detection method based on cloud-edge intelligence, which comprises the following steps: step 1, cloud-side parameter optimization: selecting an SSD network model as a detection model, replacing a VGG-16 network for extracting the primary characteristics of a target with a MobileNet network, and reducing the network depth of the characteristic part of the model extraction depth by using a MobileNet-SSD target detection model consisting of the SSD network model and the MobileNet network, turning and cutting a data set picture, removing redundant objects and partially amplifying the target; step 2, establishing a cloud-edge detection system of the coal mine: the camera and the sensor are deployed on the edge equipment, real-time detection is realized on the edge side, and the sensor comprises a temperature sensor, a smoke sensor and a CO sensor; step 3, sensing of a danger source of edge side multi-sensor fusion: performing dynamic weighting fusion processing on the information detected by the camera and the sensor in the step 2, and judging whether flame is generated; step 4, multi-network convergence network: and (4) the information detected by the multiple devices in the step (2) and the step (3) is fused and wirelessly transmitted to an upper computer.
Description
Technical Field
The invention relates to the technical field of mine safety monitoring, in particular to an underground hazard source detection method based on cloud-edge intelligence.
Background
Mine fire is one of the major prevention and control disasters in the field of coal mine safety, in recent years, underground fire accidents are high and constant, property loss is brought, life safety of workers is threatened, coal mine safety accidents are divided into 8 categories of gas, roofs, transportation and the like, and from the statistical analysis of accidents in 2007-2017, the average number of deaths of the fire/gas accidents at each time is up to 21/5.
Therefore, effective monitoring measures are taken, the fire prevention is very important, and at the present stage, the method for monitoring the external cause fire in the underground coal mine mainly adopts sensors such as gas, temperature, smoke and the like, and a large number of sensors are deployed above a mine tunnel to monitor the operation condition of equipment. However, this method has many disadvantages, firstly, the deployment of a large number of sensors is not only time-consuming, labor-consuming and expensive, but also needs to be regularly checked and maintained at a later stage, when the deployed sensors are few, the fire source far away from the sensors cannot be detected in time, which is the most important problem facing the monitoring of the fire under the mine by using sensing currently.
The development of Artificial Intelligence (AI) brings a new idea for the design of a safety monitoring system, and the AI-based safety monitoring system mainly has two deployment methods: the cloud-based method is characterized in that a large amount of collected data are transmitted to the cloud through the Internet, a series of data processing is carried out in the cloud, if the data are attacked, privacy is leaked, and in addition, as the data volume collected by terminal equipment is increased continuously, the cloud-based method brings huge data transmission pressure to a network and cannot meet the real-time requirement of a safety monitoring system; the security monitoring system based on the edge architecture can process data on the edge device without uploading the data to the cloud by using edge computing, so that network attacks can be effectively resisted, privacy leakage of the data can be prevented, and the real-time requirement of the system can be responded, however, the computing resource of the edge device is limited, and when the computing intensive monitoring tasks (such as video processing and image recognition) are processed, the performance bottleneck is usually met.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a cloud-edge intelligent underground hazard source detection method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a cloud-edge intelligence-based underground hazard source detection method comprises the following steps:
step 1, cloud-edge parameter optimization
Selecting an SSD network model as a detection model, replacing a VGG-16 network for extracting the primary characteristics of a target with a MobileNet network, and reducing the network depth of the characteristic part of the model extraction depth by using a MobileNet-SSD target detection model consisting of the SSD network model and the MobileNet network, turning and cutting a data set picture, removing redundant objects and partially amplifying the target;
step 2, cloud-edge detection system of coal mine
Arranging a camera and a multi-sensor on edge equipment, and realizing real-time detection on an edge side, wherein the multi-sensor comprises a temperature sensor, a smoke sensor and a CO sensor;
the method is implemented according to the following steps:
s21, training the MobileNet-SSD target detection model in the step 1, and transplanting the trained model on an edge camera;
s22, the video processing module calls a camera to obtain a video frame, and then transmits the obtained picture to the image analysis and detection module;
s23, transmitting the picture obtained in the S22 to a dynamic random access memory of the Cambricon1H8 edge intelligent device, calling a neural network model embedded in the Cambricon1H8 edge intelligent device by a program to process the picture and identify smoke and fire in the picture, and then transmitting the position of the smoke and fire to an OSD detection target superposition module;
s24, recognizing the image every 0.5 second by the program, and only when fireworks are recognized on three continuous pictures, determining that flame or smoke is generated currently;
s25, detecting by using a temperature sensor, a smoke sensor and a CO sensor to assist a camera, and detecting abnormal conditions in real time;
step 3, dangerous source perception of edge side multi-sensor fusion
Performing dynamic weighting fusion processing on the information detected by the cameras and the multiple sensors in the step 2, and judging whether flame is generated;
step 4, multi-network convergence network
And (4) the information detected by the multiple devices in the step (2) and the step (3) is fused and wirelessly transmitted to an upper computer.
Further, the step 3 is specifically implemented according to the following steps:
s31, defining a dynamic weighting judgment algorithm as follows:
F＝[a 1, a 2, a 3, a 4 ][b 1, b 2, b 3, b 4 ] T
wherein a is 1 、a 2 、a 3 、a 4 Respectively detected by a camera, a temperature sensor, a smoke sensor and a CO sensor, a 1 、a 2 、a 3 、a 4 Is 1 or 0, wherein when a 1 When the value is not less than 0, the camera does not detect flame, and when a is detected 1 When =1, the camera detects the flame; when a is 2 =0 indicates that the downhole temperature is within the normal operating range, when a 2 Time =1 indicates that the downhole temperature has exceeded normal; when a is 3 When the value is not less than 0, the smoke is not in the underground or the smoke concentration is ignored, when a 3 1-hour represents the concentration difference of the smoke in the wellFrequently; when a is 4 That the concentration of CO downhole is in the normal range when =0, when a 4 The time of =1 represents that the concentration of downhole CO is abnormal; b 1 、b 2 、b 3 、b 4 For the experimentally derived weight parameters, b 1 、b 2 、b 3 、b 4 The sum of the values of (a) and (b) is 1;
s32, setting a parameter b when the camera detects that the flame exists 1 、b 2 、b 3 、b 4 Respectively 0.75, 0.12, 0.05, 0.08, a threshold value F is set 8 Is 0.80;
s33, multiplying the results detected in the step 2 by the weight respectively to obtain a value F and a threshold value F 8 Comparing the sizes, when F is more than or equal to F 8 When a hazard source fire is detected, when F<F 8 When, it indicates that no source of danger is detected;
s34, setting a parameter b when the camera does not detect flame but the sensor detects abnormal conditions 2 、b 3 、b 4 Respectively 0.4, 0.2, 0.4, a threshold value F is set 8 Is 0.6;
s35, multiplying the results detected in the step 2 by the weight respectively to obtain a value F and a threshold value F 8 Comparing the sizes, when F is more than or equal to F 8 When a hazard source fire is detected, when F<F 8 When, it indicates that no source of danger is detected;
and S36, judging whether to send out a danger alarm or not according to the judgment result obtained in the step S33 or the step S35.
Further, the step 4 is specifically implemented according to the following steps:
s41, a ZigBee network is used, and a plurality of sensor terminal nodes, camera terminal nodes and communication nodes of the inspection robot are wirelessly connected to a ZigBee coordinator;
s42, wirelessly connecting the ZigBee coordinator in the S41 to an embedded intelligent gateway, wherein the embedded intelligent gateway is connected with a router;
s43, the upper computer receives the information detected by the sensor and the camera and the patrol inspection fed back by the patrol inspection robot through WiFi generated by the router.
The invention has the beneficial effects that:
(1) According to the cloud-edge intelligent underground hazard source detection method, the detection method is deployed at the edge side, and the target is directly detected at the edge side, so that the step of uploading data to the cloud is omitted, a hazard source can be quickly detected compared with the traditional detection method, and the accident rate is greatly reduced.
(2) The invention combines the edge intelligent technology, compared with the cloud computing, the edge computing has the excellent performances of reducing communication delay, lightening transmission load and preventing privacy leakage of users.
(3) The invention also combines the technologies of robots and mine internet of things, and really realizes underground intelligent detection of dangerous sources.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments 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 creative efforts.
FIG. 1 is a flow chart of a cloud-edge intelligence based downhole hazard detection method of the present invention;
FIG. 2 is a flow chart of a dynamic weighting decision module in the cloud-edge intelligence-based downhole hazard detection method of the present invention;
FIG. 3 is a flow chart of a weighting judgment module for camera missing detection conditions in the cloud-edge intelligence based underground hazard source detection method of the present invention;
FIG. 4 is a flow chart of a multi-network fusion network in the cloud-edge intelligence-based downhole hazard detection method of the present invention.
Detailed Description
The invention is illustrated by the following specific examples, which are not intended to be limiting.
Aiming at the problems that the computing resources of the edge equipment are limited, and the performance bottleneck can be usually met when the monitoring tasks with intensive computing, such as video processing and image recognition, are processed, the method optimizes the parameters of the detection model, aims at the problem of shooting angle transformation in the running process of the camera, and simultaneously enhances the trained data set, thereby improving the generalization capability of the model.
Referring to fig. 1-4, the invention relates to a cloud-edge intelligence-based downhole hazard source detection method, which comprises the following steps:
step 1, cloud-edge parameter optimization
Selecting an SSD network model as a detection model, replacing a VGG-16 network for extracting the primary characteristics of a target with a MobileNet network, and reducing the network depth of the characteristic part of the model extraction depth by using a MobileNet-SSD target detection model consisting of the SSD network model and the MobileNet network, turning and cutting a data set picture, removing redundant objects and partially amplifying the target;
step 2, establishing a cloud-edge detection system of the coal mine
The coal mine cloud-edge system comprises an inspection track system, an inspection robot, a camera and a sensor;
for the design of the inspection track system, according to the actual underground condition, an annular inspection track is arranged above a roadway, the track adopts a track system with multiple degrees of freedom consisting of a flexible guide rail and a rigid guide rail, and an inspection robot is hung on the track to walk.
The robot is mainly designed to comprise a power module, a wireless communication module, a control module, a driving module and a position acquisition module; the power module comprises a lithium ion power battery pack and a power supply protection board, different power supplies are provided for the whole inspection robot according to the explosion-proof design requirement of the inspection robot, the power module comprises an intrinsic safety power supply and an explosion-proof power supply, the control module is used as an information traffic junction of the robot and is responsible for allocation, use and control of other functional modules of the system, the control module uses an STM32 single chip microcomputer as a robot control core device, and functions of walking, data acquisition, data transmission and the like of the inspection robot are achieved.
According to the intelligent inspection robot, the inspection robot is installed on an inspection track system, the camera and the sensor are deployed on the intelligent inspection robot, underground safety conditions are inspected back and forth through the inspection robot, collected data are wirelessly transmitted to the terminal, and when an onboard detection system detects abnormal conditions, the robot can feed back the inspection position to the terminal.
Step 2 is carried out in the following specific steps
Arranging a camera and a multi-sensor on edge equipment, and realizing real-time detection on an edge side, wherein the multi-sensor comprises a temperature sensor, a smoke sensor and a CO sensor;
the step 2 is specifically implemented according to the following steps:
s21, training the MobileNet-SSD target detection model in the step 1, and transplanting the trained model on an edge camera;
s22, the video processing module calls a camera to obtain a video frame, and then transmits the obtained picture to the image analysis and detection module;
s23, transmitting the picture obtained in the S22 to a dynamic random access memory of the Cambricon1H8 edge intelligent device, calling a neural network model embedded in the Cambricon1H8 edge intelligent device by a program to process the picture and identify smoke and fire in the picture, and then transmitting the position of the smoke and fire to an OSD detection target superposition module;
s24, recognizing the image every 0.5 second by the program, and only when fireworks are recognized on three continuous pictures, determining that flame or smoke is generated currently;
s25, detecting by the aid of the temperature sensor, the smoke sensor and the CO sensor, and detecting abnormal conditions in real time.
Step 3, dangerous source perception of edge side multi-sensor fusion
Performing dynamic weighting fusion processing on the information detected by the cameras and the multiple sensors in the step 2, and judging whether flame is generated;
step 4, multi-network convergence network
And (3) the information detected by the multiple devices in the steps 2 and 3 is transmitted to an upper computer (upper computer) in a converged wireless manner.
Further, the step 3 is specifically implemented according to the following steps:
s31, defining a dynamic weighting judgment algorithm as follows:
F＝[a 1, a 2, a 3, a 4 ][b 1, b 2, b 3, b 4 ] T
wherein a is 1 、a 2 、a 3 、a 4 Respectively detected by a camera, a temperature sensor, a smoke sensor and a CO sensor, a 1 、a 2 、a 3 、a 4 Is 1 or 0, wherein when a 1 =0 indicating that the camera has not detected a flame, when a 1 When =1, the camera detects the flame; when a is 2 When =0, the downhole temperature is in the normal working range, when a 2 Time =1 indicates that the downhole temperature has exceeded normal; when a is 3 When the mark is 0, the underground smoke is not generated or the smoke concentration is ignored, when a 3 1, indicating that the underground smoke concentration is abnormal; when a is 4 When =0, it indicates that the concentration of downhole CO is within the normal range, when a 4 The time of =1 represents that the concentration of downhole CO is abnormal; b is a mixture of 1 、b 2 、b 3 、b 4 For the experimentally derived weight parameters, b 1 、b 2 、b 3 、b 4 The sum of the values of (a) and (b) is 1;
s32, setting a parameter b when the camera detects that the flame exists 1 、b 2 、b 3 、b 4 Respectively 0.75, 0.12, 0.05, 0.08, a threshold value F is set 8 Is 0.80;
s33, multiplying the results detected in the step 2 by weight weighted summation respectively to obtain a value F and a threshold value F 8 Comparing the sizes, when F is more than or equal to F 8 When a hazard source fire is detected, when F<F 8 When, it indicates that no source of danger is detected;
s34, setting a parameter b when the camera does not detect flame but the sensor detects abnormal conditions 2 、b 3 、b 4 Respectively 0.4, 0.2, 0.4, a threshold value F is set 8 Is 0.6;
s35, multiplying the results detected in the step 2 by the weights respectively to obtain the weighted sumTo a value F and a threshold value F 8 Comparing the sizes, when F is more than or equal to F 8 When a hazard source fire is detected, when F<F 8 When, it indicates that no source of danger is detected;
and S36, judging whether to send out a danger alarm or not according to the judgment result obtained in the step S33 or the step S35.
Further, the step 4 is specifically implemented according to the following steps:
s41, a ZigBee network is used, and a plurality of sensor terminal nodes, camera terminal nodes and communication nodes of the inspection robot are wirelessly connected to a ZigBee coordinator;
s42, wirelessly connecting the ZigBee coordinator in the S41 to an embedded intelligent gateway, wherein the embedded intelligent gateway is connected with a router;
and S43, the upper computer receives the information detected by the sensor and the camera and the patrol inspection fed back by the patrol inspection robot through WiFi generated by the router.
The cloud-edge intelligent-based underground hazard source detection method is deployed at the edge side, and the target is directly detected at the edge side, so that the step of uploading data to the cloud is omitted, and compared with the traditional detection method, the underground hazard source detection method can quickly detect a hazard source, so that the accident rate is greatly reduced; in combination with the edge intelligent technology, compared with cloud computing, edge computing shows excellent performance of reducing communication delay, reducing transmission load and preventing privacy leakage of users; meanwhile, the technology of the Internet of things of robots and mines is combined, and underground intelligent detection of dangerous sources is really achieved.
Finally, it should be noted that the above embodiments are only used for illustrating and not limiting the technical solutions of the present invention, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the present invention without departing from the spirit and scope of the present invention, and all modifications or partial substitutions should be covered by the scope of the claims of the present invention.
Claims (3)
1. A cloud-edge intelligence-based underground hazard source detection method is characterized by comprising the following steps:
step 1, cloud-edge parameter optimization
Selecting an SSD network model as a detection model, replacing a VGG-16 network for extracting the primary characteristics of a target with a MobileNet network, and reducing the network depth of the characteristic part of the model extraction depth by using a MobileNet-SSD target detection model consisting of the SSD network model and the MobileNet network, turning and cutting a data set picture, removing redundant objects and partially amplifying the target;
step 2, cloud-edge detection system of coal mine
Arranging a camera and a plurality of sensors on edge equipment, and realizing real-time detection on an edge side, wherein the plurality of sensors comprise a temperature sensor, a smoke sensor and a CO sensor;
the method is implemented according to the following steps:
s21, training the MobileNet-SSD target detection model in the step 1, and transplanting the trained model on an edge camera;
s22, the video processing module calls a camera to obtain a video frame, and then transmits the obtained picture to the image analysis and detection module;
s23, transmitting the picture obtained in the S22 to a dynamic random access memory of the Cambricon1H8 edge intelligent device, calling a neural network model embedded in the Cambricon1H8 edge intelligent device by a program to process the picture and identify smoke and fire in the picture, and then transmitting the position of the smoke and fire to an OSD detection target superposition module;
s24, identifying an image every 0.5 second by the program, and only when fireworks are identified on three continuous pictures, determining that flame or smoke is generated currently;
s25, detecting by using a temperature sensor, a smoke sensor and a CO sensor to assist a camera, and detecting abnormal conditions in real time;
step 3, dangerous source perception of edge side multi-sensor fusion
Performing dynamic weighting fusion processing on the information detected by the cameras and the multiple sensors in the step 2, and judging whether flame is generated;
step 4, multi-network convergence network
And (3) the information detected by the multiple devices in the steps 2 and 3 is wirelessly transmitted to an upper computer in a converged manner.
2. The cloud-edge intelligence-based downhole hazard source detection method according to claim 1, wherein the step 3 is specifically implemented according to the following steps:
s31, defining a dynamic weighting judgment algorithm, as follows:
F＝[a 1 ,a 2 ,a 3 ,a 4 ][b 1 ,b 2 ,b 3 ,b 4 ] T
wherein a is 1 、a 2 、a 3 、a 4 Respectively detected by a camera, a temperature sensor, a smoke sensor and a CO sensor, a 1 、a 2 、a 3 、a 4 Is 1 or 0, wherein when a 1 When the value is not less than 0, the camera does not detect flame, and when a is detected 1 When =1, the camera detects the flame; when a is 2 When =0, the downhole temperature is in the normal working range, when a 2 Time =1 indicates that the downhole temperature has exceeded normal; when a is 3 When the mark is 0, the underground smoke is not generated or the smoke concentration is ignored, when a 3 1, indicating that the underground smoke concentration is abnormal; when a is 4 When =0, it indicates that the concentration of downhole CO is within the normal range, when a 4 1 indicates that the concentration of CO in the well is abnormal; b is a mixture of 1 、b 2 、b 3 、b 4 For the experimentally derived weight parameters, b 1 、b 2 、b 3 、b 4 The sum of the values of (a) and (b) is 1;
s32, setting a parameter b when the camera detects that the flame exists 1 、b 2 、b 3 、b 4 Respectively 0.75, 0.12, 0.05, 0.08, a threshold value F is set e Is 0.80;
s33, multiplying the results detected in the step 2 by the weight respectively to obtain a value F and a threshold value F e Comparing the sizes when F>F e When a hazard source fire is detected, when F<F e When, it indicates that no source of danger is detected;
s34, setting a parameter b when the camera does not detect flame but the sensor detects abnormal conditions 2 、b 3 、b 4 Respectively 0.4, 0.2, 0.4, a threshold value F is set e Is 0.6;
s35, multiplying the results detected in the step 2 by weight weighted summation respectively to obtain a value F and a threshold value F e Comparing the sizes, when F is more than or equal to F e When a hazard source fire is detected, when F<F e When, it indicates that no source of danger is detected;
and S36, judging whether to send out a danger alarm or not according to the judgment result obtained in the step S33 or the step S35.
3. The cloud-edge intelligence-based downhole hazard source detection method according to claim 1, wherein the step 4 is specifically implemented according to the following steps:
s41, a ZigBee network is used, and a plurality of sensor terminal nodes, camera terminal nodes and communication nodes of the inspection robot are wirelessly connected to a ZigBee coordinator;
s42, wirelessly connecting the ZigBee coordinator in the S41 to an embedded intelligent gateway, wherein the embedded intelligent gateway is connected with a router;
s43, the upper computer receives the information detected by the sensor and the camera and the patrol inspection fed back by the patrol inspection robot through WiFi generated by the router.
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