Underground coal mine CO sensor early warning diagnosis method and device

Abstract

The invention discloses a coal mine underground CO sensor early warning diagnosis method and device. The coal mine underground CO sensor early warning diagnosis method comprises the following steps: step 1, data acquisition; a second step of acquiring a feature input vector; 3, designing a topological structure of the back propagation neural network; 4, creating a neural network model; and 5, training the neural network model. By installing the device beside the CO sensor, the state recognition of the fuel vehicle passing through the CO sensor can be realized, so that early warning can be performed in advance, and the carbon monoxide alarm can be diagnosed.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring devices, and in particular to an early warning diagnosis method and device for a CO sensor in an underground coal mine. Background Art

[0002] At present, large equipment and consumables in mines are usually transported by fuel trucks. However, fuel trucks will produce a large amount of carbon monoxide in closed tunnels. When passing through the tunnels with CO sensors mounted, the CO sensors will sound an alarm. Currently, only manual marking can be done on the host computer for advance filing, and no early warning is possible. In addition, the host computer cannot intelligently diagnose carbon monoxide alarms caused by the passage of fuel trucks. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0004] To this end, the present invention proposes a coal mine underground CO sensor early warning diagnosis method and device. By installing the coal mine underground CO sensor early warning diagnosis device next to the CO sensor, the various states of the fuel vehicle passing through the CO sensor can be identified, thereby giving an early warning and diagnosing the carbon monoxide alarm.

[0005] The method for early warning diagnosis of CO2 sensor in underground coal mine according to an embodiment of the present invention comprises the following steps:

[0006] Step 1, data collection: using a sound sensor, an acceleration sensor, a photoelectric sensor, a NO sensor, and a NO2 sensor to detect sound characteristics, acceleration characteristics, light intensity, nitric oxide, and nitrogen dioxide respectively, the data collection frequency of the sound sensor, the acceleration sensor, the photoelectric sensor, the NO sensor, and the NO2 sensor is f;

[0007] Step 2: Get the feature input vector: Select the time slice window t; First, perform FFT fast Fourier transform operation on the sound sensor and acceleration sensor to obtain the frequency domain characteristics of the sound and acceleration, and select the frequency value with the largest amplitude [X k1 ,X k2 ] as feature labels; where X k1 Indicates the frequency value corresponding to the maximum amplitude after the sound signal is fast Fourier transformed; X k2 represents the frequency value corresponding to the maximum amplitude after the fast Fourier transform of the acceleration signal; secondly, the time slice window selects the maximum value of the sound sensor, acceleration sensor, photoelectric sensor, NO sensor, NO2 sensor, and CO sensor [X k3 , X k4 , X k5 , X k6 , X k7 , X k8] as feature labels; where X k3 It represents the maximum value obtained by the sound sensor within t time; X k4 Indicates the maximum value obtained by the acceleration sensor within time t; X k5 Indicates the maximum value obtained by the photoelectric sensor within t time; X k6 It represents the maximum value obtained by the nitric oxide sensor within t time; X k7 Indicates the maximum value obtained by the nitrogen dioxide sensor within t time; X k8 represents the maximum value obtained by the carbon monoxide sensor within t time; finally, set the input vector to [X k1 , X k2 , X k3 , X k4 , X k5 , X k6 , X k7 , X k8 ];

[0008] Step 3: Design the topological structure of the back propagation neural network: the topological structure includes the number of network layers and the number of nodes in each layer, wherein the number of network layers includes an input layer, a hidden layer and an output layer; the number of nodes in the input layer is m=8, and the eigenvector is [X k 1, X k2 , X k3 , X k4 , X k5 , X k6 , X k7 , X k8 ], the number of nodes in the output layer is n=3, and the output layer vector is represented by [0, 1, 2], where 0 means no fuel vehicle is detected; 1 means a fuel vehicle is detected approaching, which is used as a warning mark; 2 means a fuel vehicle is detected passing by, which is used as a carbon monoxide alarm diagnosis;

[0009] Step 4: Create a neural network model;

[0010] Step 5: Train the neural network model.

[0011] According to the coal mine underground CO sensor early warning and diagnosis device of the embodiment of the present invention, the device has a shell, an aerial plug module and a detection module, and the aerial plug module and the detection module are integrated on the shell; the detection module includes a sound sensor, an acceleration sensor, a photoelectric sensor, a NO sensor and a NO2 sensor; the aerial plug module is used to upload the data collected by the detection module to the host computer in real time, and obtain the CO sensor data; the sound sensor is used to detect the sound characteristics, and the sound characteristics include the loudness and frequency of the roar; the acceleration sensor is used to detect the acceleration characteristics, and the acceleration characteristics include the vibration amplitude and frequency; the photoelectric sensor is used to monitor the light intensity; the NO sensor is used to detect the concentration of nitric oxide; the NO2 sensor is used to detect the concentration of nitrogen dioxide.

[0012] The beneficial effect of the present invention is that the coal mine underground CO sensor early warning diagnosis method and device of the present invention can intelligently diagnose whether there is a fuel vehicle passing through the coal mine, thereby giving early warning to the CO sensor and realizing the alarm diagnosis of carbon monoxide; selecting feature detection parameters according to the travel characteristics of the fuel vehicle in the coal mine, and performing data fusion, and using the back propagation neural network algorithm for identification, which greatly improves the reliability of identification; it can be applied not only to the typical scenario of carbon monoxide alarm caused by the passage of fuel vehicles in the coal mine, but also to the scenario of carbon monoxide alarm caused by blasting in the coal mine.

[0013] According to one embodiment of the present invention, in step 3, the number of nodes in the hidden layer is l, and the empirical formula is:

[0014]

[0015] The symbols in formula (1) mean:

[0016] m represents the number of nodes in the input layer;

[0017] n represents the number of nodes in the output layer;

[0018] l represents the number of nodes in the hidden layer, l=6.

[0019] According to one embodiment of the present invention, in step 3, the feature vector [X k1 , X k2 , X k3 , X k4 , X k5 , X k6 , X k7 , X k8 ]The corresponding weight vector is w=[w1,w2,w3 ... w l], the initial value of the weight is initialized using Xavier, the activation function of the hidden layer uses the sigmoid function, and the activation function of the output layer uses the softmax function.

[0020] According to one embodiment of the present invention, in step 4, the mean square error function is selected as the loss function, and the calculation formula is as follows:

[0021]

[0022] The symbols in formula (2) mean:

[0023] MSE stands for mean square error;

[0024] n represents the number of nodes in the output layer;

[0025] p represents the total number of training sample sets;

[0026] Indicates the expected output value;

[0027] y pj Indicates the actual output value;

[0028] j represents the index number of the output unit.

[0029] According to one embodiment of the present invention, in step 5, the neural network model is trained, and the specific process is:

[0030] Step ①: Given the input feature quantity and expected output, initialize the weights;

[0031] Step ②: Input the training sample set;

[0032] Step ③, calculate the output of hidden layer and output layer units;

[0033] Step ④: Calculate the deviation between the expected value and the actual output;

[0034] Step 5: Determine whether the deviation meets the requirements:

[0035] When the deviation is judged to meet the requirements, the total error is judged to meet the requirements. If the total error meets the requirements, the neural network model training process is terminated; if the total error does not meet the requirements, the network parameters are optimized and the process returns to step ②.

[0036] When the judgment deviation does not meet the requirements, the network parameters are optimized and the process returns to step ②.

[0037] According to an embodiment of the present invention, after the data acquisition frequency f and the time slice window t are determined, the first timing time 1 / f of the timer and the second timing time t of the timer are set.

[0038] According to one embodiment of the present invention, when the first timing time 1 / f of the judgment timer is reached, each sensor collects data signals.

[0039] According to one embodiment of the present invention, when the second timing time t of the judgment timer is reached, the data sequence [X1, X2, X3, X4, X5, X6, X7, X8, ..., X ft ] and then perform data filtering.

[0040] According to an embodiment of the present invention, the device further comprises a digital tube module, and the digital tube module is used to display the detection parameters.

[0041] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a structural schematic diagram of a CO sensor early warning and diagnostic device for underground coal mines of the present invention;

[0045] Figure 2 It is a flowchart of feature input vector acquisition;

[0046] Figure 3 It is a flowchart of neural network model training.

[0047] The numbers in the figure are: 1. Shell; 2. Aviation plug-in module; 3. Digital tube module; 4. Sound sensor; 5. Acceleration sensor; 6. Photoelectric sensor; 7. NO sensor; 8. NO2 sensor. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In the description of the present invention, it is necessary to understand that the orientations or positional relationships indicated by the terms “one side”, “the other side”, “both sides”, “between”, “middle”, “upper end”, “lower end”, etc. are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0050] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection, and can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] The following specifically describes the coal mine underground CO sensor early warning diagnosis method and device of the embodiment of the present invention with reference to the accompanying drawings, mainly targeting the problem that the passage of fuel vehicles in coal mines causes carbon monoxide alarms, but no early warning and intelligent diagnosis can be performed.

[0052] See Figure 1 The CO sensor early warning and diagnosis device for underground coal mines comprises a housing 1, an air plug module 2, a digital tube module 3 and a detection module, wherein the air plug module 2, the digital tube module 3 and the detection module are all integrated on the housing 1; the detection module comprises a sound sensor 4, an acceleration sensor 5, a photoelectric sensor 6, a NO sensor 7 and a NO2 sensor 8. It should be noted that the CO sensor is not integrated on the housing 1, and the interface of the air plug module 2 of the CO sensor early warning and diagnosis device for underground coal mines can be used to interact with the CO sensor next to it to obtain data.

[0053] Among them, the CO sensor is a carbon monoxide sensor; the NO sensor 7 is a nitric oxide sensor; and the NO2 sensor is a nitrogen dioxide sensor.

[0054] The aviation plug module 2 is the power supply interface and communication interface of the device. It can upload the collected data to the host computer in real time, and can obtain the CO sensor data next to it through the communication interface. That is, the aviation plug module 2 is used to upload the data collected by the detection module to the host computer in real time, and obtain the CO sensor data. The sound sensor module 4 is used to detect the sound characteristics, including the loudness and frequency of the roar. The acceleration sensor module 5 is used to detect the acceleration characteristics, including the vibration amplitude and frequency. The photoelectric sensor module 6 is used to monitor the light intensity. The NO sensor 7 is used to detect the concentration of nitric oxide; the NO2 sensor 8 is used to detect the concentration of nitrogen dioxide. The digital tube module 3 is used to realize the display of the detection parameters, and can display the real-time values ​​of the detection module in turn, which is convenient for human-computer interaction.

[0055] When a fuel vehicle is traveling in a lane, it is usually accompanied by the following characteristics: 1. The internal combustion engine of the fuel vehicle will emit a roaring sound during operation. According to the Doppler effect, when the fuel vehicle approaches the CO sensor, it is mainly reflected in the low-frequency signal becoming a high-frequency signal; when the fuel vehicle is far away from the CO sensor, it is mainly reflected in the high-frequency signal becoming a low-frequency signal. The present invention detects the sound characteristics by using a sound sensor 4, wherein the sound characteristics include the loudness and frequency of the roaring sound. 2. When a fuel vehicle passes through a chamber equipped with a CO sensor, the suspended CO sensor may shake. The present invention detects the vibration amplitude and frequency by using an acceleration sensor 5. 3. Mines are mostly dark environments, and fuel vehicles usually travel with lights on. The present invention monitors the light intensity by using a photoelectric sensor 6. 4. In addition to producing incompletely burned carbon monoxide gas during the travel of the fuel vehicle, it also produces obvious nitrogen oxides, such as nitric oxide gas and nitrogen dioxide gas. The present invention detects the above-mentioned characteristic gases (nitric oxide gas and nitrogen dioxide gas) by using NO sensor 7 and NO2 sensor 8.

[0056] See Figure 2 , the coal mine underground CO sensor early warning diagnosis method includes the following steps:

[0057] Step 1, data collection: Use various sensors to collect data in real time, with a collection frequency of f, preferably 1000 Hz; specifically, use a sound sensor, an acceleration sensor, a photoelectric sensor, a NO sensor, and a NO2 sensor to detect sound characteristics, acceleration characteristics, light intensity, nitric oxide, and nitrogen dioxide, respectively, and the data collection frequency of the sound sensor, the acceleration sensor, the photoelectric sensor, the NO sensor, and the NO2 sensor is f;

[0058] Step 2: Get the feature input vector:

[0059] Select a time slice window t of t=1s (t represents time, s represents seconds), determine the data acquisition frequency f and the time slice window t, set the timer first timing time 1 / f and the timer second timing time t, set the timer interrupt and enable it. The timer is set in the MCU module inside the CO sensor early warning diagnosis device in the coal mine.

[0060] When it is determined that the first timing time 1 / f of the timer has arrived (ie, the first timing time 1 / f of the timer is interrupted), each sensor collects data signals.

[0061] When it is determined that the second timing time t of the timer has arrived (i.e., the timer interrupts the processing at the second timing time t), the data sequence of each sensor is obtained]X1,X2,X3,X4,X5,X6,X7,X8,...,X ft ], and then the processor inside the underground coal mine CO sensor early warning diagnosis device performs data filtering, sets the data collection completion flag flag = 1, and then exits the interrupt.

[0062] The main function main() first determines whether the data collection completion flag is 1: when it is judged that the flag is not 1, the process ends; when it is judged that the flag is 1, the data collection completion flag is cleared first, and then the following operations are performed;

[0063] The sound sensor data sequence and the acceleration sensor data sequence are subjected to FFT fast Fourier transform operation to obtain the sound frequency and acceleration frequency corresponding to the maximum amplitude: First, the sound sensor and the acceleration sensor are subjected to FFT fast Fourier transform operation to obtain the frequency domain characteristics of the sound and acceleration, and the frequency value with the largest amplitude [X k1 ,X k2 ] as feature labels; where X k1 Indicates the frequency value corresponding to the maximum amplitude after the sound signal is fast Fourier transformed; X k2 Indicates the frequency value corresponding to the maximum amplitude of the acceleration signal after fast Fourier transform;

[0064] Find the maximum value of each sensor data sequence: Secondly, select the maximum value of the sound sensor, acceleration sensor, photoelectric sensor, NO sensor, NO2 sensor, and CO sensor in the time slice window [X k3 , X k4 , X k5 , X k6 , X k7 , X k8 ] as feature labels; where X k3 It represents the maximum value obtained by the sound sensor within t time; X k4 Indicates the maximum value obtained by the acceleration sensor within time t; Xk5 Indicates the maximum value obtained by the photoelectric sensor within t time; X k6 It represents the maximum value obtained by the nitric oxide sensor within t time; X k7 Indicates the maximum value obtained by the nitrogen dioxide sensor within t time; X k8 It indicates the maximum value obtained by the carbon monoxide sensor within time t;

[0065] Composition of feature input vector: Finally, set the input vector to [X k1 , X k2 , X k3 , X k4 , X k5 , X k6 , X k7 , X k8 ];

[0066] Step 3: Design the topology of the back propagation neural network: The topology includes the number of network layers and the number of nodes in each layer, where the network layers include an input layer, a hidden layer, and an output layer;

[0067] The number of nodes in the input layer is m = 8, and the feature vector is [X k1 , X k2 , X k3 , X k4 , X k5 , X k6 , X k7 , X k8 ], the number of nodes in the output layer is n=3, and the output layer vector is represented by [0, 1, 2], where 0 means no fuel vehicle is detected; 1 means a fuel vehicle is detected approaching, which is used as a warning mark; 2 means a fuel vehicle is detected passing by, which is used as a carbon monoxide alarm diagnosis;

[0068] In step 3, the number of nodes in the hidden layer is l, and the empirical formula is:

[0069]

[0070] The symbols in formula (1) mean:

[0071] m represents the number of nodes in the input layer;

[0072] n represents the number of nodes in the output layer;

[0073] l represents the number of nodes in the hidden layer, l=6.

[0074] The feature vector [X k1 , X k2 , X k3 , X k4 X k5 , Xk6 , X k7 , X k8 ]The corresponding weight vector is w=[w1,w2,w3 ...w l ], the initial value of the weight is initialized using Xavier, the activation function of the hidden layer uses the sigmoid function, and the activation function of the output layer uses the softmax function.

[0075] Step 4: Create a neural network model;

[0076] In step 4, the mean square error function is selected as the loss function, and the calculation formula is as follows:

[0077]

[0078] The symbols in formula (2) mean:

[0079] MSE stands for mean square error;

[0080] n represents the number of nodes in the output layer;

[0081] p represents the total number of training sample sets;

[0082] Indicates the expected output value;

[0083] y pj Indicates the actual output value;

[0084] j represents the index number of the output unit.

[0085] The sample set refers to randomly selecting 30% of the samples in the complete data set as the test sample set, and the remaining 70% of the samples as the training sample set.

[0086] Step 5: Train the neural network model:

[0087] In step 5, the ADAM algorithm is selected to update the network parameters to improve the accuracy and generalization ability of the model.

[0088] See Figure 3 , train the neural network model, the specific process is:

[0089] Step ①: Given the input feature quantity and expected output, initialize the weights;

[0090] Step ②: Input the training sample set;

[0091] Step ③, calculate the output of hidden layer and output layer units;

[0092] Step ④: Calculate the deviation between the expected value and the actual output;

[0093] Step 5: Determine whether the deviation meets the requirements:

[0094] When the deviation is judged to meet the requirements, the total error is judged to meet the requirements. If the total error meets the requirements, the neural network model training process is terminated; if the total error does not meet the requirements, the network parameters are optimized and the process returns to step ②.

[0095] When the judgment deviation does not meet the requirements, the network parameters are optimized and the process returns to step ②.

[0096] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for early warning diagnosis of CO sensor in underground coal mines, characterized in that: The following steps are involved: Step 1, data collection: using a sound sensor, an acceleration sensor, a photoelectric sensor, a NO sensor, and a NO2 sensor to detect sound characteristics, acceleration characteristics, light intensity, nitric oxide, and nitrogen dioxide respectively, the data collection frequency of the sound sensor, the acceleration sensor, the photoelectric sensor, the NO sensor, and the NO2 sensor is f; Step 2: Get the feature input vector: Select the time slice window t; First, perform FFT fast Fourier transform operation on the sound sensor and acceleration sensor to obtain the frequency domain characteristics of the sound and acceleration, and select the frequency value with the largest amplitude [X k1 ,X k2 ] as feature labels; where X k1 Indicates the frequency value corresponding to the maximum amplitude after the sound signal is fast Fourier transformed; X k2 represents the frequency value corresponding to the maximum amplitude after the fast Fourier transform of the acceleration signal; secondly, the time slice window selects the maximum value of the sound sensor, acceleration sensor, photoelectric sensor, NO sensor, NO2 sensor, and CO sensor [X k3 ,X k4 ,X k5 ,X k6 ,X k7 ,X k8 ] as feature labels; where X k3 It represents the maximum value obtained by the sound sensor within t time; X k4 Indicates the maximum value obtained by the acceleration sensor within time t; X k5 Indicates the maximum value obtained by the photoelectric sensor within t time; X k6 It represents the maximum value obtained by the nitric oxide sensor within t time; X k7 Indicates the maximum value obtained by the nitrogen dioxide sensor within t time; X k8 represents the maximum value obtained by the carbon monoxide sensor within t time; finally, set the input vector to [X k1 ,X k2 ,X k3 ,X k4 ,X k5 ,X k6 ,X k7 ,X k8 ]; Step 3: Design the topological structure of the back propagation neural network: the topological structure includes the number of network layers and the number of nodes in each layer, wherein the number of network layers includes an input layer, a hidden layer and an output layer; the number of nodes in the input layer is m=8, and the eigenvector is [X k1 ,X k2 ,X k3 ,X k4 ,X k5 ,X k6 ,X k7 ,X k8 ], the number of nodes in the output layer is n=3, and the output layer vector is represented by [0,1,2], where 0 means no fuel vehicle is detected; 1 means a fuel vehicle is detected approaching, which is used as a warning mark; 2 means a fuel vehicle is detected passing by, which is used as a carbon monoxide alarm diagnosis; Step 4: Create a neural network model; Step 5: Train the neural network model.

2. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 1, characterized in that: In step 3, the number of nodes in the hidden layer is l, and the empirical formula is: The symbols in formula (1) mean: m represents the number of nodes in the input layer; n represents the number of nodes in the output layer; l represents the number of nodes in the hidden layer, l=6.

3. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 2, characterized in that: In step 3, the feature vector [X k1 ,X k2 ,X k3 ,X k4 ,X k5 ,X k6 ,X k7 ,X k8 ]The corresponding weight vector is w=[w1,w2,w3…w l ], the initial value of the weight is initialized using Xavier, the activation function of the hidden layer uses the sigmoid function, and the activation function of the output layer uses the softmax function.

4. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 1, characterized in that: In step 4, the mean square error function is selected as the loss function, and the calculation formula is as follows: The symbols in formula (2) mean: MSE stands for mean square error; n represents the number of nodes in the output layer; p represents the total number of training sample sets; Indicates the expected output value; y pj Indicates the actual output value; j represents the index number of the output unit.

5. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 1, characterized in that: In step 5, the neural network model is trained. The specific process is: Step ①: Given the input feature quantity and expected output, initialize the weights; Step ②: Input the training sample set; Step ③: Calculate the output of hidden layer and output layer units; Step ④: Calculate the deviation between the expected value and the actual output; Step 5: Determine whether the deviation meets the requirements: When the deviation is judged to meet the requirements, the total error is judged to meet the requirements. If the total error meets the requirements, the neural network model training process is terminated. If the total error does not meet the requirements, the network parameters are optimized and the process returns to step ②. When the judgment deviation does not meet the requirements, the network parameters are optimized and the process returns to step ②.

6. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 1, characterized in that: After determining the data acquisition frequency f and the time slice window t, set the first timing time of the timer 1 / f and the second timing time of the timer t.

7. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 1, characterized in that: When the first timing time 1 / f of the judgment timer is reached, each sensor collects data signals.

8. The method for early warning diagnosis of CO sensor in underground coal mines according to claim 1, characterized in that: When the second timing time t of the judgment timer arrives, the data sequence of each sensor [X1, X2, X3, X4, X5, X6, X7, X8, ..., X ft [, and then perform data filtering.

9. A CO sensor early warning diagnosis device for an underground coal mine that implements the CO sensor early warning diagnosis method for an underground coal mine as claimed in claim 1, characterized in that: The device comprises a housing (1), an air plug module (2) and a detection module, wherein the air plug module (2) and the detection module are both integrated on the housing (1); the detection module comprises a sound sensor (4), an acceleration sensor (5), a photoelectric sensor (6), an NO sensor (7) and an NO2 sensor (8); The aerial plug-in module (2) is used to upload the data collected by the detection module to the host computer in real time and obtain the CO sensor data; The sound sensor (4) is used to detect sound characteristics, wherein the sound characteristics include the loudness and frequency of the roaring sound; The acceleration sensor (5) is used to detect acceleration characteristics, wherein the acceleration characteristics include vibration amplitude and frequency; The photoelectric sensor (6) is used to monitor light intensity; The NO sensor (7) is used to detect the concentration of nitric oxide; The NO2 sensor (8) is used to detect the concentration of nitrogen dioxide.

10. The CO sensor early warning and diagnosis device for underground coal mines according to claim 9, characterized in that: The device also has a digital tube module (3), and the digital tube module (3) is used to display the detection parameters.

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