Integrated intelligent monitoring equipment and method for dust, poison gas and noise

Abstract

The invention discloses integrated intelligent monitoring equipment and method for dust, poison gas and noise. The equipment is characterized in that a mass flow controller, a flow pump and a flow limiting assembly are sequentially connected to a gas inlet pipeline in series; the first laser and the light trap are oppositely mounted inside the particle measurement chamber; the lens and the first laser are coaxially distributed, and the focus of the lens is located at the particle detection channel; the photoelectric sensing unit is mounted on the inner wall of the rear side of the particle measuring chamber; the filter and the sensing surface of the photoelectric sensing unit are coaxially distributed; the communicating pipeline is respectively connected with the particle detection outlet and the gas detection inlet; the filter membrane is connected in series to the communicating pipeline; the second laser and the multi-channel narrow-band detector are oppositely mounted inside the gas measuring chamber; the noise measuring unit comprises an MEMS microphone array; the method comprises the following steps: wearing a plurality of dust, poison gas and noise integrated intelligent monitoring devices on different individuals, and synchronously monitoring the levels of dust, poison gas and noise by using the dust, poison gas and noise integrated intelligent monitoring devices. The equipment and the method can be used for synchronously monitoring various pollutants.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring, and specifically relates to an integrated intelligent monitoring device and method for dust, poisonous gas and noise. Background Art

[0002] In some environments with poor production conditions, the effective treatment of dust, poisonous gas and noise cannot be achieved, which in turn makes the workers in such environments face multiple threats from various pollutants such as dust, poisonous gas and noise. Long-term exposure to such a working environment may cause various health problems for the workers, such as respiratory diseases, cardiovascular diseases and noise-induced deafness. For some production environments, monitoring devices for detecting dust concentration, monitoring devices for detecting poisonous gas concentration, and monitoring devices for detecting noise decibels are provided. Thus, it can play a warning role for relevant workers. However, the functions of traditional monitoring devices are often relatively single, usually only capable of monitoring a specific pollutant, and unable to simultaneously monitor multiple different pollutants. In this way, a variety of independent environmental monitoring devices usually need to be set up in the production site, which not only occupies a large amount of limited space, but also greatly increases the production input cost. In addition, the existing environmental monitoring devices are usually bulky and difficult to move or carry, and thus cannot meet the real-time detection requirements in harsh environments such as construction sites. In order to effectively solve the above problems, there is an urgent need to provide a monitoring system and method that is easy to move or portable and can simultaneously monitor multiple environmental pollutants such as dust, poisonous gas and noise. Summary of the Invention

[0003] Aiming at the problems existing in the above-mentioned prior art, the present invention provides an integrated intelligent monitoring device and method for dust, poisonous gas and noise. The device has high integration, good reliability, diverse monitoring functions, high monitoring accuracy, high automation degree, and is easy to move and carry. It can simultaneously monitor the dust concentration data in the environment, the types of harmful gases and the concentration data of harmful gases, and the pollution level of noise, which helps to provide a safe health guarantee for users; the implementation process of the method is simple and has a good degree of intelligence. By wearing multiple integrated intelligent monitoring devices for dust, poisonous gas and noise on multiple individuals in the same working environment, it can centrally and effectively monitor the dust concentration data, the types of harmful gases and the concentration data of harmful gases, and the noise level at different positions in the working environment in real time. At the same time, it can conduct risk level assessment and feasible health opinions, and can effectively ensure the personal safety of the wearers.

[0004] In order to achieve the above object, the present invention provides an integrated intelligent monitoring device for dust, poisonous gas and noise, including a housing, a particle monitoring unit, a gas monitoring unit, a noise measurement unit and a data processing unit;

[0005] The housing is provided with a main air inlet and a main air outlet; the particle monitoring unit, the gas monitoring unit, the noise measurement unit and the data processing unit are all arranged inside the housing;

[0006] The particle monitoring unit includes a particle measurement chamber, an intake pipeline, a mass flow controller, a flow pump, a current limiting component, a first laser, an optical trap, a lens, a photoelectric induction unit, a filter, a first driving module and a first signal amplifier; the particle measurement chamber is provided with a particle detection inlet and a particle detection outlet, and a particle detection channel is formed between the inlet and the outlet; the intake pipeline is respectively connected to the main air inlet and the particle detection inlet; the mass flow controller, the flow pump and the current limiting component are connected in series on the intake pipeline in sequence; the first laser and the optical trap are oppositely installed on the left and right inner walls of the particle measurement chamber; the lens is coaxially distributed with the first laser, and its focus is located at the particle detection channel; the photoelectric induction unit is installed on the rear inner wall of the particle measurement chamber, and its induction direction is distributed at 90° to the optical path direction of the first laser; the filter is coaxially distributed with the induction surface of the photoelectric induction unit; the first driving module is connected to the first laser; the first signal amplifier is connected to the photoelectric induction unit;

[0007] The gas monitoring unit includes a gas measurement chamber, a connecting pipeline, an outlet pipeline, a filter membrane, a second laser, a multi-channel narrow-band detector, a second driving module and a second signal amplifier; the gas measurement chamber is provided with a gas detection inlet and a gas detection outlet, and a gas detection channel is formed between the inlet and the outlet; the connecting pipeline is respectively connected to the particle detection outlet and the gas detection inlet; the outlet pipeline is respectively connected to the gas detection outlet and the main air outlet; the filter membrane is connected in series on the connecting pipeline; the second laser and the multi-channel narrow-band detector are oppositely installed on the left and right inner walls of the gas measurement chamber; the second driving module is connected to the second laser; the second signal amplifier is connected to the multi-channel narrow-band detector;

[0008] The noise measurement unit installation includes a MEMS microphone array, and the MEMS microphone array is composed of a plurality of MEMS microphones;

[0009] The data processing unit includes a data acquisition module, a communication module, a power supply module and a controller. The data acquisition module is respectively connected to the first signal amplifier, the second signal amplifier and the MEMS microphone array, and the controller is respectively connected to the power supply module, the first driving module, the second driving module, the mass flow controller, the flow pump and the communication module.

[0010] Furthermore, in order to facilitate warning and reminding the wearer in case of an abnormality to effectively ensure the health and safety of the wearer, an acoustic-optic alarm device is further included, and the acoustic-optic alarm device is installed on the surface of the housing; the controller is connected to the acoustic-optic alarm device.

[0011] Further, for the convenience of quickly replacing different filter membranes, the filter membranes are connected in series to the connecting pipeline in a detachable manner.

[0012] Further, to improve the accuracy of noise detection, the MEMS microphone array communicates with the outside through sound pickup holes formed in the housing.

[0013] Further, to facilitate the perception of temperature data and humidity data in the environment, and at the same time, to facilitate the calibration of the monitored data, a temperature and humidity monitoring unit is further included. The temperature and humidity monitoring unit includes a temperature sensor and a humidity sensor, and both the temperature sensor and the humidity sensor are installed on the outer surface of the housing; the data acquisition module is respectively connected to the temperature sensor and the humidity sensor.

[0014] As a preference, the controller is a PLC controller.

[0015] Further, to facilitate the relevant personnel to observe the monitored dust concentration data in real time, a display screen is further included. The display screen is embedded in the surface of the housing and is connected to the controller.

[0016] Further, to enable the synchronous detection operation of multiple different gases, six detection channels are provided on the multi-channel narrowband detector.

[0017] In the present invention, a mass flow controller, a flow pump, and a flow limiting component are connected in series in sequence on the intake pipeline, which can facilitate the use of the flow pump to provide negative pressure to divert the air flow in the environment into the detection channel for detection. At the same time, it can facilitate the use of the mass flow controller to detect the gas flow data in real time, and at the same time, it can also accurately control the passing flow rate of the gas, thereby effectively ensuring the acquisition accuracy of the signal during the monitoring process, which is conducive to accurately calculating the dust concentration data and gas concentration data subsequently. The flow limiting component can facilitate limiting the cross-sectional area of the air flow passing through the intake pipeline, thereby ensuring the stability of the detection air flow. In the particle measurement chamber, the first laser and the optical trap are oppositely installed at both ends of the chamber, preferably on the same central axis. The optical trap can effectively absorb the optical signal irradiated on the right side wall, and can avoid the loss of the measurement result caused by the reflected optical signal on the right side wall. An opto-electronic induction unit is installed on the rear side wall of the particle measurement chamber, which can facilitate detecting the scattered optical signal generated when the laser beam irradiates the particulate matter in the dusty air flow and converting it into a particle detection electrical signal; a filter is arranged on the front side of the induction surface of the opto-electronic induction unit, which can use the filter to filter out irrelevant wavelength signals, which is conducive to ensuring the accuracy of the particle detection electrical signal. By connecting the opto-electronic induction unit through a first signal amplifier, it can facilitate using the first signal amplifier to amplify the obtained particle detection electrical signal, and then more accurate dust concentration data can be obtained based on the amplified particle detection signal. A filter membrane is connected in series on the communication pipeline, which can facilitate effectively filtering the solid particles and pollutants in the dusty air flow through the filter membrane during the operation of the flow pump, and then a clean air flow can be formed, which can ensure the accurate detection of the subsequent gas types and concentrations. In the gas measurement chamber, the second laser and the multi-channel narrowband detector are oppositely arranged at both ends of the chamber, preferably on the same central axis, which can facilitate using multiple channels in the multi-channel narrowband detector to receive multiple specific wavelength optical signals transmitted through the air flow, and then obtaining a gas detection electrical signal; since the characteristics of multiple channels absorbing multiple specific wavelengths of light can be associated with multiple different types of harmful gases in advance, in this way, as long as the output channel of the optical signal is matched, the type of the identified harmful gas can be conveniently and accurately judged. At the same time, by connecting the multi-channel narrowband detector through a second signal amplifier, it can facilitate amplifying the obtained gas detection electrical signal, and then more accurate gas concentration data can be obtained based on the amplified gas detection electrical signal. Through the setting of the data acquisition module, it can facilitate receiving the amplified particle detection electrical signal, gas detection electrical signal, temperature signal, and humidity signal, and converting them from analog electrical signals into electrical signals and sending them to the controller. Then, it can facilitate the controller to obtain dust concentration data based on the particle detection electrical signal, gas concentration data based on the gas detection electrical signal, temperature data and humidity data based on the temperature signal and humidity signal. Through the setting of the communication module, it can facilitate establishing a communication link between the monitoring device and an external terminal.Through the setting of the power supply module, the device can be made to have the ability to work offline, making it easy to move and carry.

[0018] The device has a high degree of integration, good reliability, diverse monitoring functions, high monitoring accuracy, high automation, and is easy to move and carry. It can simultaneously monitor the dust concentration data in the environment, the types of harmful gases and the concentration data of harmful gases, and the pollution level of noise, which helps to provide users with safe health protection. At the same time, it has a wide range of applications and can be used in various harsh environments, with good economic and social benefits.

[0019] The present invention also provides an integrated intelligent monitoring method for dust, poisonous gas and noise. Using an integrated intelligent monitoring device for dust, poisonous gas and noise, it includes the following steps;

[0020] Step 1: First, set the working parameters of the integrated intelligent monitoring device for dust, poisonous gas and noise. Then, wear multiple integrated intelligent monitoring devices for dust, poisonous gas and noise on different individuals located in the same working environment to be measured, and establish a communication connection between multiple integrated intelligent monitoring devices for dust, poisonous gas and noise and a remote processing terminal through wireless communication. Establish a communication connection between the remote processing terminal and a personal communication terminal through wireless communication to form an integrated intelligent monitoring system for dust, poisonous gas and noise;

[0021] Step 2: Send a start signal to each integrated intelligent monitoring device for dust, poisonous gas and noise through the remote processing terminal. After the controller receives the start signal, it controls the flow pump to start working, so that the dust-containing air flow in the environment enters the particle detection channel of the particle detection chamber through the intake pipeline. At the same time, use a mass flow controller to collect gas flow data and send it to the controller;

[0022] At the same time, control the first laser to emit a laser beam through the first drive module, and use a lens to focus the laser beam at the particle detection channel. Synchronously, use a photoelectric detection unit to collect the scattered light signal on the surface of the particulate matter and convert it into a particulate matter detection electrical signal, and then send the particulate matter detection electrical signal to the first signal amplifier for amplification. The first signal amplifier sends the amplified particulate matter detection electrical signal to the data acquisition module;

[0023] Meanwhile, the dust-containing air flow that has passed through particulate matter detection is diverted towards the gas detection chamber through a connecting pipeline. At the same time, the particulate matter in the dust-containing air flow is filtered out by a filter membrane, so that the air flow without particulate matter enters the gas detection channel. Synchronously, the second laser is driven by the second driving module to emit a laser beam, and multiple channels in the multi-channel narrowband detector respectively receive optical signals of multiple specific wavelengths passing through the gas detection channel, and convert the received multiple optical signals into gas detection electrical signals, and then send the gas detection electrical signals to the second signal amplifier for amplification. The second signal amplifier sends the amplified gas detection electrical signals to the data acquisition module;

[0024] Meanwhile, multiple MEMS microphones in the MEMS microphone array are used to collect noise signals in the environment, and the obtained multi-channel noise signals are sent to the data acquisition module; meanwhile, a temperature sensor and a humidity sensor are used to collect temperature signals and humidity signals in the environment respectively and send them to the data acquisition module;

[0025] Step 3: The data acquisition module sends the received particulate matter detection electrical signals, gas detection electrical signals, noise electrical signals, temperature signals and humidity signals to the controller. The controller respectively obtains particulate matter detection data, gas detection data, noise data, temperature data and humidity data based on the particulate matter detection electrical signals, gas detection electrical signals, multi-channel noise signals, temperature signals and humidity signals, and then sends the gas flow data, particulate matter detection data, gas detection data, multi-channel noise data, temperature data and humidity data to the remote processing terminal through wireless communication for trial transmission;

[0026] Step 4: The remote processing terminal (24) receives multi-source heterogeneous data sent from multiple integrated intelligent monitoring devices for dust, poisonous gas and noise in the environment to be measured. The multi-source heterogeneous data includes gas flow data, particulate matter detection data, gas detection data, multi-channel noise data, temperature data and humidity data;

[0027] After receiving the multi-channel noise data, the remote processing terminal performs weighted processing on multiple noise data x m (t) from multiple MEMS microphones according to formula (1) to obtain a fused output signal y(t), and by adjusting w m and τ mTo construct a beam pointing in a specific spatial direction, thereby achieving precise positioning of the noise source direction, obtaining the positioning information of the noise source, and reconstructing the entire sound field distribution through an inversion algorithm to obtain the distribution information of the entire special session; at the same time, perform a short-time Fourier transform on the noise data x(t) from each MEMS microphone according to formula (2) to obtain the time-frequency signal X(t,f), and then use the noise classification model built into the remote processing terminal for identification and classification processing to output the noise category and the corresponding noise decibel information; further, draw a noise hazard heat map through a sound source tracking algorithm to quantify the cumulative impact of noise in a specific azimuth on the human body; then, use the built-in health risk model to focus on analyzing the damage characteristics of the 2000-8000Hz high-frequency band to hearing and calculate the probability of the wearer developing noise-induced deafness;

[0028]

[0029] In the formula, M is the number of MEMS microphones; w m is the weight of each MEMS microphone; τ m is the delay calculated according to the desired incident angle;

[0030] At the same time, after the remote processing terminal receives the gas flow data, particulate matter detection data, temperature data, and humidity data, first calculate the particle size distribution data of the particles through the built-in particle size distribution measurement model, and then use the built-in dust concentration calibration model to perform calibration calculations based on the particle size distribution data, temperature data, and humidity data of the particles to obtain the calibrated dust concentration data; further, dynamically compare the calibrated dust concentration data with the time-weighted average allowable concentration and calculate the real-time exposure ratio; then, use the built-in health risk model to integrate the real-time dust concentration data and the exposure duration, and calculate the deposition amount of dust in the wearer's lungs according to the wearer's breathing rate;

[0031] At the same time, after the remote processing terminal receives the gas detection data, determine the type information of the harmful gas based on the output channel corresponding to the gas detection data. At the same time, calculate the concentration distribution data of the gas through the gas diffusion model based on the gas detection data, and then use the gas concentration calibration model based on the physical information neural network to perform calibration calculations based on the gas concentration distribution data, temperature data, and humidity data to obtain the calibrated harmful gas concentration data; further, compare and analyze the type information of the harmful gas and the calibrated gas concentration data with the median lethal concentration and short-term exposure limit of the corresponding harmful gas; then, use the built-in health risk model to simulate the metabolic process of the harmful gas in the human body and calculate the biologically effective dose in combination with the wearer's exposure route;

[0032] Step 5: The built-in health risk model in the remote processing terminal performs risk level assessment based on the calculated result data to obtain a three-level response plan, and gives feasible health opinions on the three-level response plan. Among them, when the primary response plan is obtained, suggestions for adjusting the operation position or shortening the exposure time are given; when the intermediate response plan is obtained, indication information for upgrading personal protective equipment is given; when the advanced response plan is obtained, indication information for linked environmental control is given.

[0033] Furthermore, in order to improve the reminder effect and effectively ensure the personal health and safety of the wearer, in Step 5, after obtaining the risk level assessment result and the corresponding feasible health opinion, the remote processing terminal sends them to the personal communication terminal and the integrated intelligent monitoring device for dust, poisonous gas, and noise through wireless communication. After receiving the risk level assessment result and the corresponding feasible health opinion, the integrated intelligent monitoring device for dust, poisonous gas, and noise controls the sound and light alarm device to give an alarm reminder. At the same time, the information is displayed in real time through the display screen.

[0034] In the present invention, a multi-channel narrowband detector is used to receive multiple specific wavelength optical signals passing through the air flow. The absorption characteristics corresponding to multiple specific wavelengths of multiple channels can be used to correlate with multiple harmful gases in advance, and then the synchronous identification of multiple harmful gases can be achieved quickly, efficiently, and accurately, greatly improving the identification efficiency of harmful gases. Therefore, the wearer can be reminded in a timely manner by means of warning when harmful gases with great harmfulness are detected for the first time, so that the wearer can take effective emergency treatment measures immediately, and thus the health and safety of the staff can be effectively guaranteed. During the calculation process of dust concentration data, the particle size distribution data is first calculated using the particle size distribution measurement model built in the controller, and then the dust concentration calibration model built in the controller is combined with the temperature and humidity data for calibration calculation, greatly improving the calculation accuracy of dust concentration data. During the calculation process of gas concentration data, the gas concentration distribution data is first calculated using the gas diffusion model built in the controller, and then the gas concentration calibration model based on the physics-informed neural network built in the controller is used for calibration calculation, greatly improving the calculation accuracy of gas concentration data. Multiple MEMS microphones are used to receive the sound in the environment simultaneously, which can effectively capture weak acoustic wave signals. At the same time, the signals collected by each MEMS microphone not only contain the amplitude information of the noise, but also contain the phase and time delay information relative to other MEMS microphones. In addition, the spatial differences of the signals collected by multiple MEMS microphones at different positions can be used to analyze the directionality and distribution of the sound source, which is conducive to realizing the localization of the noise source and the reconstruction of the sound field based on the beamforming technology using the collected noise signals, and then drawing a noise hazard heat map through the sound source tracking algorithm to quantify the cumulative impact of noise at a specific azimuth on the human body.

[0035] This method has a simple implementation process and good intelligence. By wearing multiple integrated intelligent monitoring devices for dust, poisonous gas, and noise on multiple individuals in the same working environment, it can centrally and effectively monitor the dust concentration data, types of harmful gases and their concentration data, and noise levels at different positions in the working environment in real time. At the same time, it can conduct risk level assessment and provide feasible health advice, effectively ensuring the personal safety of the wearers. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a schematic structural diagram of the present invention;

[0037] Figure 2 is a principle block diagram of the communication process among the integrated intelligent monitoring device for dust, poisonous gas, and noise, the remote processing terminal, and the personal communication terminal in the present invention.

[0038] In the figure: 1. Housing, 2. Limiting component, 3. Flow mass controller, 4. Flow pump, 5. First driving module, 6. Second driving module, 7. First laser, 8. Second laser, 9. Lens, 10. Optical trap, 11. Multi-channel narrowband detector, 12. First signal amplifier, 13. Second signal amplifier, 14. Main air inlet, 15. Main air outlet, 16. Filter membrane, 17. Particle measurement chamber, 18. Gas measurement chamber, 19. Display screen, 20. Acousto-optic alarm device, 21. Noise detection unit, 22. Data processing unit, 23. Intake pipeline, 24. Remote processing terminal, 25. Personal communication terminal, 26. Exhaust pipeline, 27. Connecting pipeline. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The present invention will be further described below with reference to the accompanying drawings.

[0040] As Figure 1 and Figure 2 shown, the present invention provides an integrated intelligent monitoring device for dust, poisonous gas, and noise, including a housing 1, a particle monitoring unit, a gas monitoring unit, a noise measurement unit, and a data processing unit;

[0041] The upper and lower ends of the housing 1 are provided with a main air inlet 14 and a main air outlet 15, wherein the main air inlet 14 and the main air outlet 15 can be distributed non-oppositely;

[0042] The particle monitoring unit, the gas monitoring unit, and the noise measurement unit are all arranged inside the housing;

[0043] The particle monitoring unit includes a particle measurement chamber 17, an intake pipeline 23, a mass flow controller 3, a flow pump 4, a current limiting component 2, a first laser 7, an optical trap 10, a lens 9, a photoelectric induction unit, a filter, a first driving module 5 and a first signal amplifier 12; the particle measurement chamber 17 is located between the main intake port 14 and the main outlet port 15, and its length direction extends horizontally; particle detection inlets and particle detection outlets are formed at the upper and lower ends of the particle measurement chamber 17, and a particle detection channel is formed in the area between the particle detection inlet and the particle detection outlet; both ends of the intake pipeline 16 are respectively connected to the main intake port 14 and the particle detection inlet on the particle measurement chamber 17; the mass flow controller 3, the flow pump 4 and the current limiting component 2 are connected in series on the intake pipeline 23 in sequence from the intake side to the outlet side; the first laser 7 and the optical trap 10 are oppositely installed on the left inner wall and the right inner wall of the particle measurement chamber 17; the lens 9 is fixedly installed on the left side of the particle detection channel and is coaxially distributed with the first laser 7, and the focal point of the lens 9 is located at the particle detection channel; the photoelectric induction unit is installed on the rear inner wall of the particle measurement chamber 17, and its induction direction is distributed at 90° to the optical path direction of the first laser 7. At the same time, the photoelectric induction unit and the first laser 7 are located on the same plane; the filter is fixedly supported on the rear side of the particle detection channel and is coaxially distributed with the induction surface of the photoelectric induction unit; the first driving module 5 is installed outside the particle measurement chamber 17 and is connected to the first laser 7 for outputting a driving signal to the first laser 7; the first signal amplifier 12 is installed outside the particle measurement chamber 17 and is connected to the photoelectric induction unit for amplifying the analog signal output by the photoelectric induction unit;

[0044] In order to effectively detect a variety of dust particles, it is preferred that the first laser 7 is a wide-band infrared light source;

[0045] The gas monitoring unit includes a gas measurement chamber 18, a connecting pipeline 27, an air outlet pipeline 26, a filter membrane 16, a second laser 8, a multi-channel narrowband detector 11, a second driving module 6, and a second signal amplifier 13; the gas measurement chamber 18 is located between the particle measurement chamber 17 and the main air outlet 15, and its length direction extends horizontally; gas detection inlets and gas detection outlets are provided at the upper and lower ends of the gas measurement chamber 18, and a gas detection channel is formed in the area between the gas detection inlet and the gas detection outlet; both ends of the connecting pipeline 27 are respectively connected to the particle detection outlet of the particle measurement chamber 17 and the gas detection inlet of the gas measurement chamber 18; both ends of the air outlet pipeline 26 are respectively connected to the gas detection outlet of the gas measurement chamber 18 and the main air outlet 15; the filter membrane 16 is connected in series in the middle section of the connecting pipeline 27; the second laser 8 and the multi-channel narrowband detector 11 are relatively installed on the left inner wall and the right inner wall of the gas measurement chamber 18. Further preferably, the second laser 8 and the multi-channel narrowband detector 11 are located on the same plane; the second driving module 6 is installed outside the gas measurement chamber 18 and is connected to the second laser 8 for outputting a driving signal to the second laser 8; the second signal amplifier 13 is installed outside the gas measurement chamber 18 and is connected to the multi-channel narrowband detector 11 for amplifying the analog signal output by the multi-channel narrowband detector 11;

[0046] In order to effectively detect a variety of harmful gases, the second laser 8 is a wide-band infrared light source.

[0047] The noise measurement unit is installed with a MEMS microphone array, and the MEMS microphone array is composed of a plurality of high-sensitivity MEMS microphones; the plurality of MEMS microphones form a sensor array in three-dimensional space, so that weak acoustic signals can be effectively captured; among them, the signals collected by each MEMS microphone not only contain the amplitude information of the noise, but also contain the phase and time delay information relative to other MEMS microphones. Further, the spatial differences of the signals collected by the plurality of MEMS microphones can be used to analyze the directivity and distribution of the sound source, which is conducive to realizing the localization of the noise source and the reconstruction of the sound field based on the beamforming technology using the collected noise signals;

[0048] In order to improve the accuracy of noise detection, the MEMS microphone array communicates with the outside through the sound pickup holes provided on the housing 1;

[0049] The data processing unit 22 includes a data acquisition module, a communication module, a power supply module, and a controller, which are used to uniformly calibrate the received data and obtain accurate dust, poison, and noise data. At the same time, it can conduct real-time analysis in combination with the actual situation of the working position and environment to obtain feasible health opinions. The controller is respectively connected to the first signal amplifier 12, the second signal amplifier 13, and the MEMS microphone array through the data acquisition module. At the same time, it is also respectively connected to the power supply module, the first driving module 5, the second driving module 6, the mass flow controller 3, the flow pump 4, and the communication module.

[0050] In order to facilitate warning and reminding the wearer in case of an abnormality to effectively ensure the health and safety of the wearer, an acoustic-optic alarm device 20 is further included. The acoustic-optic alarm device 20 is installed on the surface of the housing 1. Preferably, the acoustic-optic alarm device 20 is built-in with a sound alarm and a light alarm. The controller is connected to the acoustic-optic alarm device 20.

[0051] In order to facilitate the quick replacement of different filter membranes, the filter membrane 16 is connected in series to the communication pipeline 27 in a detachable manner.

[0052] In order to form an integrated intelligent monitoring of dust, poisonous gas, and noise, a remote processing terminal 24 and a personal communication terminal 25 are further included. The remote processing terminal 24 is connected to the controller in a plurality of integrated intelligent monitoring devices for dust, poisonous gas, and noise through wireless communication. The personal communication terminal 25 is connected to the remote processing terminal 24 through wireless communication. As a preference, the remote processing terminal 24 is an industrial computer, and the personal communication terminal 25 is a mobile phone.

[0053] In order to facilitate the perception of temperature data and humidity data in the environment, and at the same time, in order to facilitate the calibration of the monitored data, a temperature and humidity monitoring unit is further included. The temperature sensor and the humidity sensor are both installed on the surface of the housing 1. The data acquisition module is respectively connected to the temperature sensor and the humidity sensor.

[0054] As a preference, the controller is a PLC controller.

[0055] In order to facilitate relevant personnel to observe the monitored dust concentration data in real time, a display screen 19 is further included. The display screen 19 is embedded on the surface of the housing 1 and is connected to the controller.

[0056] In order to realize the synchronous detection operation of multiple different gases, six detection channels are provided on the multi-channel narrowband detector 11. In this way, the complexity of the system and potential failure points can be effectively reduced, which is beneficial to improving the overall reliability.

[0057] Specifically, the multi-channel narrow-band detector uses the direct integration of the super-surface micro-nano structure and the silicon-based pyroelectric film to achieve selective filtering and efficient light absorption. The super-surface micro-nano structure can form a resonance effect at a specific wavelength, enhance the absorption and selective filtering effect of a specific wavelength, effectively reduce the signal loss, and thus improve the sensitivity and response speed of the detector. The silicon-based pyroelectric film can quickly respond to tiny temperature changes and generate electrical signals, which is suitable for gas monitoring. The direct integration of the super-surface micro-nano structure and the silicon-based pyroelectric film does not require discrete filters, which is conducive to reducing the combined cost of multiple filters and detectors.

[0058] In the present invention, a mass flow controller, a flow pump, and a flow limiting component are connected in series in sequence on the intake pipeline, which can facilitate the use of the flow pump to provide negative pressure to divert the airflow in the environment into the detection channel for detection. At the same time, it can facilitate the use of the mass flow controller to detect the gas flow data in real time, and at the same time, it can also accurately control the passing flow rate of the gas, thereby effectively ensuring the acquisition accuracy of signals during the monitoring process, which is conducive to accurately calculating the dust concentration data and gas concentration data subsequently. The flow limiting component can facilitate limiting the cross-sectional area of the airflow passing through the intake pipeline, thereby ensuring the stability of the detection airflow. In the particle measurement chamber, the first laser and the optical trap are oppositely installed at both ends of the chamber, preferably on the same central axis. The optical trap can effectively absorb the optical signal irradiated on the right side wall, and can avoid the loss of the measurement result caused by the reflected optical signal on the right side wall. An optoelectronic induction unit is installed on the rear side wall of the particle measurement chamber, which can facilitate detecting the scattered optical signal generated when the laser beam irradiates the particulate matter in the dust-containing airflow and converting it into a particle detection electrical signal; a filter is arranged on the front side of the induction surface of the optoelectronic induction unit, which can use the filter to filter out irrelevant wavelength signals, which is conducive to ensuring the accuracy of the particle detection electrical signal. By connecting the optoelectronic induction unit through a first signal amplifier, it can facilitate using the first signal amplifier to amplify the obtained particle detection electrical signal, and then more accurate dust concentration data can be obtained based on the amplified particle detection signal. A filter membrane is connected in series on the communication pipeline, which can facilitate effectively filtering the solid particles and pollutants in the dust-containing airflow through the filter membrane during the operation of the flow pump, and then a clean airflow can be formed, which can ensure the accurate detection of the subsequent gas types and concentrations. In the gas measurement chamber, the second laser and the multi-channel narrowband detector are oppositely arranged at both ends of the chamber, preferably on the same central axis, which can facilitate using multiple channels in the multi-channel narrowband detector to receive multiple specific wavelength optical signals transmitted through the airflow, and then obtaining a gas detection electrical signal; since the characteristics of multiple channels absorbing multiple specific wavelengths of light can be associated with multiple different types of harmful gases in advance, in this way, as long as the output channel of the optical signal is matched, the type of the identified harmful gas can be conveniently and accurately judged. At the same time, by connecting the multi-channel narrowband detector through a second signal amplifier, it can facilitate amplifying the obtained gas detection electrical signal, and then more accurate gas concentration data can be obtained based on the amplified gas detection electrical signal. Through the setting of the data acquisition module, it can facilitate receiving the amplified particle detection electrical signal, gas detection electrical signal, temperature signal, and humidity signal, and converting them from analog electrical signals into electrical signals and sending them to the controller. Then, it can facilitate the controller to obtain dust concentration data based on the particle detection electrical signal, gas concentration data based on the gas detection electrical signal, temperature data and humidity data based on the temperature signal and humidity signal. Through the setting of the communication module, it can facilitate establishing a communication link between the monitoring device and the external terminal.Through the setting of the power supply module, the device can have the ability to work offline, making it easy to move and carry.

[0059] The device has a high degree of integration, good reliability, diverse monitoring functions, high monitoring accuracy, high automation, and is easy to move and carry. It can simultaneously monitor the dust concentration data, the types of harmful gases and the concentration data of harmful gases, and the noise pollution level in the environment, which helps to provide users with safe health protection. At the same time, it has a wide range of applications and can be used in various harsh environments, with good economic and social benefits.

[0060] The present invention also provides an integrated intelligent monitoring method for dust, poisonous gas and noise, which adopts an integrated intelligent monitoring device for dust, poisonous gas and noise, and includes the following steps;

[0061] Step 1: First, set the working parameters of the integrated intelligent monitoring device for dust, poisonous gas and noise. Then, wear multiple integrated intelligent monitoring devices for dust, poisonous gas and noise on different individuals located in the same working environment to be measured, and establish a communication connection between multiple integrated intelligent monitoring devices for dust, poisonous gas and noise and the remote processing terminal 24 through wireless communication. Establish a communication connection between the remote processing terminal 24 and the personal communication terminal 25 through wireless communication to form an integrated intelligent monitoring system for dust, poisonous gas and noise;

[0062] Step 2: Send a start signal to each integrated intelligent monitoring device for dust, poisonous gas and noise through the remote processing terminal 24. After the controller receives the start signal, it controls the flow pump 4 to start working, so that the dust-containing air flow in the environment enters the particle detection channel of the particle detection chamber 17 through the air inlet pipeline 23. At the same time, use the mass flow controller 3 to collect gas flow data and send it to the controller;

[0063] At the same time, control the first laser 7 to emit a laser beam through the first driving module 5, and use the lens 9 to focus the laser beam at the particle detection channel. Synchronously, use the photoelectric detection unit to collect the scattered light signal on the surface of the particulate matter and convert it into a particulate matter detection electrical signal, and then send the particulate matter detection electrical signal to the first signal amplifier 12 for amplification. The first signal amplifier 12 sends the amplified particulate matter detection electrical signal to the data acquisition module;

[0064] Meanwhile, the dust-containing airflow that has passed through particulate matter detection is diverted towards the gas detection chamber 18 using the connecting pipeline 27. At the same time, the particulate matter in the dust-containing airflow is filtered out using the filter membrane 16, allowing the airflow without particulate matter to enter the gas detection channel. Synchronously, the second laser 8 is driven by the second driving module 6 to emit a laser beam, and multiple channels in the multi-channel narrowband detector 11 respectively receive optical signals of multiple specific wavelengths passing through the gas detection channel, convert the received multiple optical signals into gas detection electrical signals, and then send the gas detection electrical signals to the second signal amplifier 13 for amplification. The second signal amplifier 13 sends the amplified gas detection electrical signals to the data acquisition module;

[0065] Meanwhile, multiple MEMS microphones in the MEMS microphone array are used to collect noise signals in the environment, and the obtained multi-channel noise signals are sent to the data acquisition module; meanwhile, a temperature sensor and a humidity sensor are used to collect temperature signals and humidity signals in the environment respectively and send them to the data acquisition module;

[0066] Step 3: The data acquisition module sends the received particulate matter detection electrical signals, gas detection electrical signals, noise electrical signals, temperature signals, and humidity signals to the controller. The controller respectively obtains particulate matter detection data, gas detection data, noise data, temperature data, and humidity data based on the particulate matter detection electrical signals, gas detection electrical signals, multi-channel noise signals, temperature signals, and humidity signals, and then sends the gas flow data, particulate matter detection data, gas detection data, multi-channel noise data, temperature data, and humidity data to the remote processing terminal 24 through wireless communication for trial transmission;

[0067] Step 4: The remote processing terminal (24) receives multi-source heterogeneous data sent from multiple integrated intelligent monitoring devices for dust, poisonous gas, and noise in the environment to be measured. The multi-source heterogeneous data includes gas flow data, particulate matter detection data, gas detection data, multi-channel noise data, temperature data, and humidity data;

[0068] After receiving the multi-channel noise data, the remote processing terminal 24 performs weighted processing on multiple noise data x m (t) from multiple MEMS microphones according to formula (1) to obtain the fused output signal y(t), and by adjusting w m and τ mTo construct a beam pointing to a specific spatial direction, thereby achieving precise positioning of the noise source direction, obtaining the positioning information of the noise source, and reconstructing the entire sound field distribution through an inversion algorithm to obtain the distribution information of the entire sound field, so as to provide basic data for subsequent noise characteristic analysis; at the same time, to perform real-time time-frequency analysis on the multi-channel collected noise signals, perform short-time Fourier transform on the noise data x(t) from each MEMS microphone according to formula (2) to obtain the time-frequency signal X(t,f). Through time-frequency analysis, the obtained time-frequency spectrogram can effectively display the energy distribution of the signal at different times and frequencies. Further, the obtained time-frequency representation can be input into the distilled deep learning model (noise classification model) deployed in the remote processing terminal 24. The deep learning model (noise classification model) can automatically extract the time-frequency features of the noise signal through network learning and classify the noise types, such as mechanical noise, crowd noise, traffic noise, etc.; specifically, use the noise classification model built in the remote processing terminal 24 to perform identification and classification processing, identify various types of noise and the corresponding noise decibel information, and then output the noise category and the corresponding noise decibel information; thus, the discrimination and quantitative evaluation of noise pollution in a complex working environment are realized; further, draw a noise hazard heat map through a sound source tracking algorithm to quantify the cumulative impact of noise in a specific direction on the human body; then, break through the traditional decibel accumulation mode, use the built-in health risk model to focus on analyzing the damage characteristics of the 2000-8000Hz high-frequency band to hearing, and can calculate the probability of the wearer suffering from noise-induced deafness according to the ISO 1999 standard; where x m (t) contains the superposition of the original noise signal and the interference signal;

[0069]

[0070] In the formula, M is the number of MEMS microphones; w m is the weight of each MEMS microphone; τ m is the delay calculated according to the expected incident angle;

[0071] Meanwhile, after receiving the gas flow data, particulate matter detection data, temperature data, and humidity data, the remote processing terminal 24 first calculates the particle size distribution data of the particles through the built-in particle size distribution measurement model, and then uses the built-in dust concentration calibration model to perform calibration calculations based on the particle size distribution data, temperature data, and humidity data of the particles to obtain the calibrated dust concentration data; further, the calibrated dust concentration data is dynamically compared with the time-weighted average allowable concentration, and the real-time exposure ratio is calculated. Preferably, the time-weighted average allowable concentration is the time-weighted average allowable concentration (TWA) in the national standard; then, the built-in health risk model integrates the real-time dust concentration data with the exposure duration and calculates the deposition amount of dust in the lungs of the wearer according to the breathing rate of the wearer; as a further preference, a lung deposition amount prediction model can be established to facilitate the rapid and accurate calculation of the deposition amount of dust in the lungs of the wearer.

[0072] Meanwhile, after receiving the gas detection data, the remote processing terminal 24 determines the type information of the harmful gas based on the output channel corresponding to the gas detection data. At the same time, it calculates the gas concentration distribution data through the gas diffusion model based on the gas detection data, and then uses the gas concentration calibration model based on the physical information neural network to perform calibration calculations based on the gas concentration distribution data, temperature data, and humidity data to obtain the calibrated harmful gas concentration data; further, the type information of the harmful gas and the calibrated gas concentration data are compared and analyzed with the median lethal concentration (LC50) and short-term exposure limit (STEL) of the corresponding harmful gas; then, the built-in health risk model simulates the metabolic process of the harmful gas in the human body and calculates the biologically effective dose in combination with the exposure route (inhalation / skin contact) of the wearer; as a preference, a toxicokinetics model can be constructed to facilitate the quick, efficient, and accurate calculation of the biologically effective dose.

[0073] Among them, the harmful gases include volatile organic compounds (VOCs) and other toxic gases (such as CO, H2S).

[0074] Step 5: The health risk model built into the remote processing terminal 24 conducts a risk level assessment based on the calculated result data, obtains a three-level response plan, and gives a feasible health opinion on the three-level response plan. Among them, when the primary response plan is obtained, suggestions for adjusting the operation position or shortening the exposure time are given. When the intermediate response plan is obtained, indication information for upgrading personal protective equipment is given, such as switching from a common mask to a full-face respirator, recommending a suitable earplug noise reduction level (NRR value), guiding personnel to adjust the operation orientation based on the sound source localization information, and maximizing the use of the head shadow effect to reduce the sound pressure received by the ears. When the advanced response plan is obtained, indication information for linkage environmental control is given, and further, a strong exhaust device set in the local area can be automatically started or the full-plant evacuation alarm action can be triggered.

[0075] As an optimization, the health risk model can be established based on the dynamic mapping relationship of the exposure dose-effect relationship;

[0076] To improve the reminder effect and effectively ensure the personal health and safety of the wearer, in Step 5, after obtaining the risk level assessment result and the corresponding feasible health opinion, the remote processing terminal 24 sends them to the personal communication terminal 25 and the integrated intelligent monitoring device for dust, poisonous gas, and noise through wireless communication. After receiving the risk level assessment result and the corresponding feasible health opinion, the integrated intelligent monitoring device for dust, poisonous gas, and noise controls the sound and light alarm device 20 to give an alarm reminder. At the same time, the information is displayed in real time through the display screen 19.

[0077] In the present invention, a multi-channel narrowband detector is adopted to receive multiple specific wavelength optical signals passing through the airflow. The absorption characteristics corresponding to multiple specific wavelengths of multiple channels can be used to correlate with multiple harmful gases in advance, so as to quickly, efficiently and accurately realize the synchronous identification of multiple harmful gases, greatly improving the identification efficiency of harmful gases. Therefore, when a highly harmful gas is detected for the first time, the wearer can be reminded in time through a warning method, enabling the wearer to take effective emergency treatment measures immediately, thereby effectively protecting the health and safety of the staff. During the calculation process of dust concentration data, the particle size distribution data is first calculated by using the particle size distribution measurement model built in the controller, and then the dust concentration calibration model built in the controller is used to perform calibration calculation in combination with temperature and humidity data, greatly improving the calculation accuracy of dust concentration data. During the calculation process of gas concentration data, the gas concentration distribution data is first calculated by using the gas diffusion model built in the controller, and then the gas concentration calibration model based on the physics-informed neural network built in the controller is used to perform calibration calculation, greatly improving the calculation accuracy of gas concentration data. Multiple MEMS microphones are used to simultaneously receive the sounds in the environment, which can effectively capture weak acoustic wave signals. At the same time, the signals collected by each MEMS microphone not only contain the amplitude information of the noise, but also contain the phase and time delay information relative to other MEMS microphones. In addition, the spatial differences of the signals collected by multiple MEMS microphones at different positions can be used to analyze the directivity and distribution of the sound source, which is beneficial to realizing the localization of the noise source and the reconstruction of the sound field based on the beamforming technology by using the collected noise signals. Furthermore, a noise hazard heat map is drawn through a sound source tracking algorithm to quantify the cumulative impact of noise at a specific azimuth on the human body.

[0078] The implementation process of this method is simple and has a good degree of intelligence. By wearing multiple integrated intelligent monitoring devices for dust, poisonous gas and noise on multiple individuals in the same working environment, it can centrally and effectively monitor the dust concentration data, types of harmful gases and harmful gas concentration data, and noise levels at different positions in the working environment. At the same time, it can conduct risk level assessment and feasible health opinions, effectively ensuring the personal safety of the wearer.

Claims

1. An integrated intelligent monitoring device for dust, toxic gas and noise, comprising a housing (1), characterized in that: It also includes a particle monitoring unit, a gas monitoring unit, a noise measurement unit and a data processing unit (22); the housing (1) is provided with a main air inlet (14) and a main air outlet (15); the particle monitoring unit, the gas monitoring unit, the noise measurement unit and the data processing unit (22) are all arranged inside the housing (1); The particle monitoring unit comprises a particle measurement chamber (17), an air intake pipeline (23), a mass flow controller (3), a flow pump (4), a current limiting component (2), a first laser (7), a light trap (10), a lens (9), a photoelectric sensing unit, a filter, a first driving module (5) and a first signal amplifier (12); the particle measurement chamber (17) is provided with a particle detection inlet and a particle detection outlet, and a particle detection channel is formed between the inlet and the outlet; the air intake pipeline (16) is respectively connected to the main air intake (14) and the particle detection inlet; the mass flow controller (3), the flow pump (4), the current limiting component ( 2) are sequentially connected in series on the air intake pipeline (23); the first laser (7) and the light trap (10) are relatively installed on the left and right inner walls of the particle measurement chamber (17); the lens (9) is coaxially distributed with the first laser (7), and its focus is located at the particle detection channel; the photoelectric sensing unit is installed on the rear inner wall of the particle measurement chamber (17), and its sensing direction is distributed at 90 degrees with the light path direction of the first laser (7); the filter is coaxially distributed with the sensing surface of the photoelectric sensing unit; the first driving module (5) is connected to the first laser (7); the first signal amplifier (12) is connected to the photoelectric sensing unit; The gas monitoring unit comprises a gas measurement chamber (18), a connecting pipeline (27), an air outlet pipeline (26), a filter membrane (16), a second laser (8), a multi-channel narrow-band detector (11), a second driving module (6) and a second signal amplifier (13); the gas measurement chamber (18) is provided with a gas detection inlet and a gas detection outlet, and a gas detection channel is formed between the inlet and the outlet; the connecting pipeline (27) is respectively connected to the particle detection outlet and the gas detection inlet; the air outlet pipeline (26) is respectively connected to the gas detection outlet and the main air outlet (15); the filter membrane (16) is serially connected to the connecting pipeline (27); the second laser (8) and the multi-channel narrow-band detector (11) are relatively mounted on the left and right inner walls of the gas measurement chamber (18); the second driving module (6) is connected to the second laser (8); and the second signal amplifier (13) is connected to the multi-channel narrow-band detector (11); The noise measurement unit is installed to include a MEMS microphone array, and the MEMS microphone array is composed of multiple MEMS microphones; the data processing unit (22) includes a data acquisition module, a communication module, a power module and a controller, the data acquisition module is respectively connected to the first signal amplifier (12), the second signal amplifier (13) and the MEMS microphone array, and the controller is respectively connected to the power module, the first drive module (5), the second drive module (6), the mass flow controller (3), the flow pump (4) and the communication module.

2. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 1 is characterized in that: It also comprises an audible and visual alarm device (20), which is mounted on the surface of the housing (1); and the controller is connected to the audible and visual alarm device (20).

3. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 1 is characterized in that: The filter membrane (16) is connected in series to the connecting pipeline (27) in a detachable manner.

4. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 2 is characterized in that: The MEMS microphone array is connected to the outside world via a sound pickup hole provided on the housing (1).

5. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 1 is characterized in that: It also comprises a temperature and humidity monitoring unit, which comprises a temperature sensor and a humidity sensor, both of which are mounted on the outer surface of the housing (1); and the data acquisition module is connected to the temperature sensor and the humidity sensor respectively.

6. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 1 is characterized in that: The controller is a PLC controller.

7. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 1 is characterized in that: It also comprises a display screen (19), wherein the display screen (19) is embedded in the surface of the housing (1) and connected to the controller.

8. The integrated intelligent monitoring device for dust, toxic gas and noise according to claim 4 is characterized in that: The multi-channel narrow-band detector (11) is provided with six detection channels.

9. An integrated intelligent monitoring method for dust, toxic gas and noise, using an integrated intelligent monitoring device for dust, toxic gas and noise as claimed in claim 8, characterized in that: The steps include: Step 1: first set the working parameters of the integrated intelligent monitoring equipment for dust, toxic gas and noise, then respectively wear a plurality of integrated intelligent monitoring equipment for dust, toxic gas and noise on different individuals in the same working environment to be tested, and establish a communication connection between the plurality of integrated intelligent monitoring equipment for dust, toxic gas and noise and a remote processing terminal (24) by wireless communication, and establish a communication connection between the remote processing terminal (24) and a personal communication terminal (25) by wireless communication, so as to form an integrated intelligent monitoring system for dust, toxic gas and noise; Step 2: Sending a start signal to each integrated intelligent monitoring device for dust, toxic gas and noise through the remote processing terminal (24); after receiving the start signal, the controller controls the flow pump (4) to start working, so that the dust-laden airflow in the environment enters the particle detection channel of the particle detection chamber (17) through the air intake pipeline (23); at the same time, using the mass flow controller (3) to collect gas flow data and send it to the controller; At the same time, the first laser (7) is controlled to emit a laser beam through the first driving module (5), and the laser beam is focused on the particle detection channel by using the lens (9). Synchronously, the scattered light signal on the surface of the particle is collected by using the photoelectric detection unit and converted into a particle detection electrical signal. The particle detection electrical signal is then sent to the first signal amplifier (12) for amplification. The first signal amplifier (12) sends the amplified particle detection electrical signal to the data acquisition module. At the same time, the dust-containing airflow that has passed the particle detection is guided toward the gas detection chamber (18) by means of a connecting pipe (27), and at the same time, the particle in the dust-containing airflow is filtered out by means of a filter membrane (16), so that the airflow without particle enters the gas detection channel. Synchronously, the second laser (8) is driven to emit a laser beam by means of a second driving module (6), and a plurality of channels in a multi-channel narrow-band detector (11) are used to receive a plurality of optical signals of specific wavelengths passing through the gas detection channel, and the received plurality of optical signals are converted into gas detection electrical signals, and the gas detection electrical signals are then sent to a second signal amplifier (13) for amplification, and the second signal amplifier (13) sends the amplified gas detection electrical signals to a data acquisition module; At the same time, multiple MEMS microphones in the MEMS microphone array are used to collect noise signals in the environment, and the obtained multi-channel noise signals are sent to the data acquisition module; at the same time, the temperature sensor and the humidity sensor are used to collect the temperature signal and the humidity signal in the environment respectively and send them to the data acquisition module; Step 3: The data acquisition module sends the received particle detection electrical signal, gas detection electrical signal, noise electrical signal, temperature signal and humidity signal to the controller, and the controller obtains particle detection data, gas detection data, noise data, temperature data and humidity data based on the particle detection electrical signal, gas detection electrical signal, multi-channel noise signal, temperature signal and humidity signal, and then sends the gas flow data, particle detection data, gas detection data, multi-channel noise data, temperature data and humidity data to the remote processing terminal (24) through wireless communication. Step 4: The remote processing terminal (24) receives multi-source heterogeneous data from a plurality of integrated intelligent monitoring devices for dust, toxic gas and noise in the environment to be tested, wherein the multi-source heterogeneous data includes gas flow data, particle detection data, gas detection data, multi-channel noise data, temperature data and humidity data; After receiving the multi-channel noise data, the remote processing terminal (24) processes the multiple noise data x from the multiple MEMS microphones according to formula (1): m (t) is weighted to obtain the fused output signal y(t), and by adjusting w m and τ m To construct a beam pointing to a specific spatial direction, thereby accurately locating the direction of the noise source, obtaining the location information of the noise source, and reconstructing the entire sound field distribution through an inversion algorithm to obtain the distribution information of the entire special field; at the same time, according to formula (2), the noise data x(t) from each MEMS microphone is short-time Fourier transformed to obtain a time-frequency signal X(t,f), and then the noise classification model built into the remote processing terminal (24) is used to perform identification and classification processing, and the noise category and corresponding noise decibel information are output; further, a noise hazard heat map is drawn through a sound source tracking algorithm to quantify the cumulative impact of noise in a specific direction on the human body; then, the built-in health risk model is used to focus on analyzing the damage characteristics of the high frequency band of 2000-8000Hz to hearing, and the probability of noise-induced hearing loss in the wearer is calculated; Where M is the number of MEMS microphones; w m is the weight of each MEMS microphone; τ m is the delay calculated based on the expected incident angle; At the same time, after receiving the gas flow data, the particle detection data, the temperature data and the humidity data, the remote processing terminal (24) first calculates the particle size distribution data of the particles through the built-in particle size distribution measurement model, and then uses the built-in dust concentration calibration model to perform calibration calculation based on the particle size distribution data, the temperature data and the humidity data of the particles to obtain the calibrated dust concentration data; further, the calibrated dust concentration data is dynamically compared with the time-weighted average permissible concentration, and the real-time exposure ratio is calculated; then, the real-time dust concentration data and the exposure time are integrated using the built-in health risk model, and the deposition amount of dust in the wearer's lungs is calculated according to the wearer's breathing rate; At the same time, after receiving the gas detection data, the remote processing terminal (24) determines the type information of the harmful gas based on the output channel corresponding to the gas detection data, and at the same time, calculates the gas concentration distribution data based on the gas detection data through the gas diffusion model, and then uses the gas concentration calibration model based on the physical information neural network to perform calibration calculation based on the gas concentration distribution data, temperature data and humidity data to obtain the calibrated harmful gas concentration data; further, the type information of the harmful gas and the calibrated gas concentration data are compared and analyzed with the median lethal concentration and short-term exposure limit of the corresponding harmful gas; then, the built-in health risk model is used to simulate the metabolic process of the harmful gas in the human body, and the biological effective dose is calculated in combination with the wearer's exposure pathway; Step 5: The health risk model built into the remote processing terminal (24) performs a risk level assessment based on the calculated result data, obtains a three-level response plan, and gives a feasibility health opinion on the three-level response plan. When a primary response plan is obtained, a suggestion for adjusting the work position or shortening the exposure time is given; when an intermediate response plan is obtained, an instruction for upgrading personal protective equipment is given; and when an advanced response plan is obtained, an instruction for linkage environmental control is given.

10. The integrated intelligent monitoring method for dust, toxic gas and noise according to claim 9 is characterized in that: In step five, after obtaining the risk level assessment result and the corresponding feasibility health opinion, the remote processing terminal (24) sends them to the personal communication terminal (25) and the integrated intelligent monitoring device for dust, toxic gas and noise through wireless communication. After receiving the risk level assessment result and the corresponding feasibility health opinion, the integrated intelligent monitoring device for dust, toxic gas and noise controls the sound and light alarm device (20) to issue an alarm reminder, and at the same time, displays the information in real time through the display screen (19).

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